Christina Curtis

All Publications

• Looking backward in time to define the chronology of metastasis. Nature communications Hu, Z., Curtis, C. 2020; 11 (1): 3213

  View details for DOI 10.1038/s41467-020-16995-y

  View details for PubMedID 32587245

• Translating Basic Cancer Discoveries to the Clinic CANCER CELL Mardis, E. R., Dawson, M. A., Curtis, C., Xu, R., Long, G. V., Scolyer, R. A., Bakhoum, S. F., Nam, D., Garnett, M., Huang, A. 2020; 37 (6): 735?37

  View details for Web of Science ID 000540245900001

  View details for PubMedID 32516583

• Multi-cancer analysis of clonality and the timing of systemic spread in paired primary tumors and metastases. Nature genetics Hu, Z., Li, Z., Ma, Z., Curtis, C. 2020

  Abstract

  Metastasis is the primary cause of cancer-related deaths, but the natural history, clonal evolution and impact of treatment are poorly understood. We analyzed whole-exome sequencing (WES) data from 457 paired primary tumor and metastatic samples from 136 patients with breast, colorectal and lung cancer, including untreated (n=99) and treated (n=100) metastases. Treated metastases often harbored private 'driver' mutations, whereas untreated metastases did not, suggesting that treatment promotes clonal evolution. Polyclonal seeding was common in untreated lymph node metastases (n=17 out of 29, 59%) and distant metastases (n=20 out of 70, 29%), but less frequent in treated distant metastases (n=9 out of 94, 10%). The low number of metastasis-private clonal mutations is consistent with early metastatic seeding, which we estimated occurred 2-4 years before diagnosis across these cancers. Furthermore, these data suggest that the natural course of metastasis is selectively relaxed relative to early tumorigenesis and that metastasis-private mutations are not drivers of cancer spread but instead associated with drug resistance.

  View details for DOI 10.1038/s41588-020-0628-z

  View details for PubMedID 32424352

• CRISPR screens in cancer spheroids identify 3D growth-specific vulnerabilities NATURE Han, K., Pierce, S. E., Li, A., Spees, K., Anderson, G. R., Seoane, J. A., Lo, Y., Dubreuil, M., Olivas, M., Kamber, R. A., Wainberg, M., Kostyrko, K., Kelly, M. R., Yousefi, M., Simpkins, S. W., Yao, D., Lee, K., Kuo, C. J., Jackson, P. K., Sweet-Cordero, A., Kundaje, A., Gentles, A. J., Curtis, C., Winslow, M. M., Bassik, M. C. 2020

  View details for DOI 10.1038/s41586-020-2099-x

  View details for Web of Science ID 000519162500002

• Characterizing the ecological and evolutionary dynamics of cancer. Nature genetics Zahir, N., Sun, R., Gallahan, D., Gatenby, R. A., Curtis, C. 2020

  Abstract

  Tumor initiation and progression are somatic evolutionary processes driven by the accumulation of genetic alterations, some of which confer selective fitness advantages to the host cell. This gene-centric model has shaped the field of cancer biology and advanced understanding of cancer pathophysiology. Importantly, however, each genotype encodes diverse phenotypic traits that permit acclimation to varied microenvironmental conditions. Epigenetic and transcriptional changes also contribute to the heritable phenotypic variation required for evolution. Additionally, interactions between cancer cells and surrounding stromal and immune cells through autonomous and non-autonomous signaling can influence competition for survival. Therefore, a mechanistic understanding of tumor progression must account for evolutionary and ecological dynamics. In this Perspective, we outline technological advances and model systems to characterize tumor progression through space and time. We discuss the importance of unifying experimentation with computational modeling and opportunities to inform cancer control.

  View details for DOI 10.1038/s41588-020-0668-4

  View details for PubMedID 32719518

• CHRISTINA CURTIS COMPUTING CANCER NATURE Curtis, C. 2020; 577 (7791): 586

  View details for Web of Science ID 000508976700029

• CRISPR screens in cancer spheroids identify 3D growth-specific vulnerabilities. Nature Han, K., Pierce, S. E., Li, A., Spees, K., Anderson, G. R., Seoane, J. A., Lo, Y. H., Dubreuil, M., Olivas, M., Kamber, R. A., Wainberg, M., Kostyrko, K., Kelly, M. R., Yousefi, M., Simpkins, S. W., Yao, D., Lee, K., Kuo, C. J., Jackson, P. K., Sweet-Cordero, A., Kundaje, A., Gentles, A. J., Curtis, C., Winslow, M. M., Bassik, M. C. 2020; 580 (7801): 136?41

  Abstract

  Cancer genomics studies have identified thousands of putative cancer driver genes1. Development of high-throughput and accurate models to define the functions of these genes is a major challenge. Here we devised a scalable cancer-spheroid model and performed genome-wide CRISPR screens in 2D monolayers and 3D lung-cancer spheroids. CRISPR phenotypes in 3D more accurately recapitulated those of in vivo tumours, and genes with differential sensitivities between 2D and 3D conditions were highly enriched for genes that are mutated in lung cancers. These analyses also revealed drivers that are essential for cancer growth in 3D and in vivo, but not in 2D. Notably, we found that carboxypeptidase D is responsible for removal of a C-terminal RKRR motif2 from the ?-chain of the insulin-like growth factor 1 receptor that is critical for receptor activity. Carboxypeptidase D expression correlates with patient outcomes in patients with lung cancer, and loss of carboxypeptidase D reduced tumour growth. Our results reveal key differences between 2D and 3D cancer models, and establish a generalizable strategy for performing CRISPR screens in spheroids to reveal cancer vulnerabilities.

  View details for DOI 10.1038/s41586-020-2099-x

  View details for PubMedID 32238925

• Quantifying mutations in healthy blood. Science (New York, N.Y.) Curtis, C. 2020; 367 (6485): 1426?27

  View details for DOI 10.1126/science.aba9891

  View details for PubMedID 32217714

• Quantitative evidence for early metastatic seeding in colorectal cancer. Nature genetics Hu, Z., Ding, J., Ma, Z., Sun, R., Seoane, J. A., Scott Shaffer, J., Suarez, C. J., Berghoff, A. S., Cremolini, C., Falcone, A., Loupakis, F., Birner, P., Preusser, M., Lenz, H., Curtis, C. 2019

  Abstract

  Both the timing and molecular determinants of metastasis are unknown, hindering treatment and prevention efforts. Here we characterize the evolutionary dynamics of this lethal process by analyzing exome-sequencing data from 118biopsies from 23patients with colorectal cancer with metastases to the liver or brain. The data show that the genomic divergence between the primary tumor and metastasis is low and that canonical driver genes were acquired early. Analysis within a spatial tumor growth model and statistical inference framework indicates that early disseminated cells commonly (81%, 17 out of 21evaluable patients) seed metastases while the carcinoma is clinically undetectable (typically, less than 0.01cm3). We validated the association between early drivers and metastasis in an independent cohort of 2,751colorectal cancers, demonstrating their utility as biomarkers of metastasis. This conceptual and analytical framework provides quantitative in vivo evidence that systemic spread can occur early in colorectal cancer and illuminates strategies for patient stratification and therapeutic targeting of the canonical drivers of tumorigenesis.

  View details for DOI 10.1038/s41588-019-0423-x

  View details for PubMedID 31209394

• Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups. Nature Rueda, O. M., Sammut, S., Seoane, J. A., Chin, S., Caswell-Jin, J. L., Callari, M., Batra, R., Pereira, B., Bruna, A., Ali, H. R., Provenzano, E., Liu, B., Parisien, M., Gillett, C., McKinney, S., Green, A. R., Murphy, L., Purushotham, A., Ellis, I. O., Pharoah, P. D., Rueda, C., Aparicio, S., Caldas, C., Curtis, C. 2019

  Abstract

  The rates and routes of lethal systemic spread in breast cancer are poorly understood owing to a lack of molecularly characterized patient cohorts with long-term, detailed follow-up data. Long-term follow-up is especially important for those with oestrogen-receptor (ER)-positive breast cancers, which can recur up to two decades after initial diagnosis1-6. It is therefore essential to identify patients who have a high risk of late relapse7-9. Here we present a statistical framework that models distinct disease stages (locoregional recurrence, distant recurrence, breast-cancer-related deathand death from other causes) and competing risks of mortality from breast cancer, while yielding individual risk-of-recurrence predictions. We apply this model to 3,240 patients with breast cancer, including 1,980 for whom molecular data are available, and delineate spatiotemporal patterns of relapse across different categories of molecular information (namely immunohistochemical subtypes; PAM50 subtypes, which are based on gene-expression patterns10,11; and integrative or IntClust subtypes, which are based on patterns of genomic copy-number alterations and gene expression12,13). We identify four late-recurring integrative subtypes, comprisingabout one quarter (26%) of tumours that are both positive for ER and negative for human epidermal growth factor receptor 2, each with characteristic tumour-driving alterations in genomic copy number and a high risk of recurrence (mean 47-62%) up to 20 years after diagnosis. We also define a subgroup of triple-negative breast cancers in which cancer rarely recurs after five years, and a separate subgroup in which patients remain at risk. Use of the integrative subtypes improves the prediction of late, distant relapse beyond what is possible with clinical covariates (nodal status, tumour size, tumour grade and immunohistochemical subtype). These findings highlight opportunities for improved patient stratification and biomarker-driven clinical trials.

  View details for PubMedID 30867590

• Clonal replacement and heterogeneity in breast tumors treated with neoadjuvant HER2-targeted therapy. Nature communications Caswell-Jin, J. L., McNamara, K., Reiter, J. G., Sun, R., Hu, Z., Ma, Z., Ding, J., Suarez, C. J., Tilk, S., Raghavendra, A., Forte, V., Chin, S. F., Bardwell, H., Provenzano, E., Caldas, C., Lang, J., West, R., Tripathy, D., Press, M. F., Curtis, C. 2019; 10 (1): 657

  Abstract

  Genomic changes observed across treatment may result from either clonal evolution or geographically disparate sampling of heterogeneous tumors. Here we use computational modeling based on analysis of fifteen primary breast tumors and find that apparent clonal change between two tumor samples can frequently be explained by pre-treatment heterogeneity, such that at least two regions are necessary to detect treatment-induced clonal shifts. To assess for clonal replacement, we devise a summary statistic based on whole-exome sequencing of a pre-treatment biopsy and multi-region sampling of the post-treatment surgical specimen and apply this measure to five breast tumors treated with neoadjuvant HER2-targeted therapy. Two tumors underwent clonal replacement with treatment, and mathematical modeling indicates these two tumors had resistant subclones prior to treatment and rates of resistance-related genomic changes that were substantially larger than previous estimates. Our results provide a needed framework to incorporate primary tumor heterogeneity in investigating the evolution of resistance.

  View details for PubMedID 30737380

• Chromatin regulators mediate anthracycline sensitivity in breast cancer. Nature medicine Seoane, J. A., Kirkland, J. G., Caswell-Jin, J. L., Crabtree, G. R., Curtis, C. 2019

  Abstract

  Anthracyclines are a highly effective component of curative breast cancer chemotherapy but are associated with substantial morbidity1,2. Because anthracyclines work in part by inhibiting topoisomerase-II (TOP2) on accessible DNA3,4, we hypothesized that chromatin regulatory genes (CRGs) that mediate DNA accessibility might predict anthracycline response. We studied the role of CRGs in anthracycline sensitivity in breast cancer through integrative analysis of patient and cell line data. We identified a consensus set of 38 CRGs associated with anthracycline response across ten cell line datasets. By evaluating the interaction between expression and treatment in predicting survival in a metacohort of 1006 patients with early-stage breast cancer, we identified 54 CRGs whose expression levels dictate anthracycline benefit across the clinical subgroups; of these CRGs, 12 overlapped with those identified in vitro. CRGs that promote DNA accessibility, including Trithorax complex members, were associated with anthracycline sensitivity when highly expressed, whereas CRGs that reduce accessibility, such as Polycomb complex proteins, were associated with decreased anthracycline sensitivity. We show that KDM4B modulates TOP2 accessibility to chromatin, elucidating a mechanism of TOP2 inhibitor sensitivity. These findings indicate that CRGs mediate anthracycline benefit by altering DNA accessibility, with implications for the stratification of patients with breast cancer and treatment decision making.

  View details for DOI 10.1038/s41591-019-0638-5

  View details for PubMedID 31700186

• Promoter of lncRNA Gene PVT1 Is a Tumor-Suppressor DNA Boundary Element. Cell Cho, S. W., Xu, J., Sun, R., Mumbach, M. R., Carter, A. C., Chen, Y. G., Yost, K. E., Kim, J., He, J., Nevins, S. A., Chin, S., Caldas, C., Liu, S. J., Horlbeck, M. A., Lim, D. A., Weissman, J. S., Curtis, C., Chang, H. Y. 2018; 173 (6): 1398

  Abstract

  Noncoding mutations in cancer genomes are frequentbut challenging to interpret. PVT1 encodes an oncogenic lncRNA, but recurrent translocations and deletions in human cancers suggest alternative mechanisms. Here, we show that the PVT1 promoter has a tumor-suppressor function that is independent of PVT1 lncRNA. CRISPR interference of PVT1 promoter enhances breast cancer cell competition and growth invivo. The promoters of the PVT1 and the MYC oncogenes, located 55 kb apart on chromosome 8q24, compete for engagement with four intragenic enhancers in the PVT1 locus, thereby allowing the PVT1 promoter to regulate pause release of MYC transcription. PVT1 undergoes developmentally regulated monoallelic expression, and the PVT1 promoter inhibits MYC expression only from the same chromosome via promoter competition. Cancer genome sequencing identifies recurrent mutations encompassing the human PVT1 promoter, and genome editing verified that PVT1 promoter mutation promotes cancer cell growth. These results highlight regulatory sequences of lncRNA genes as potential disease-associated DNA elements.

  View details for PubMedID 29731168

• The chromatin accessibility landscape of primary human cancers. Science (New York, N.Y.) Corces, M. R., Granja, J. M., Shams, S., Louie, B. H., Seoane, J. A., Zhou, W., Silva, T. C., Groeneveld, C., Wong, C. K., Cho, S. W., Satpathy, A. T., Mumbach, M. R., Hoadley, K. A., Robertson, A. G., Sheffield, N. C., Felau, I., Castro, M. A., Berman, B. P., Staudt, L. M., Zenklusen, J. C., Laird, P. W., Curtis, C., Greenleaf, W. J., Chang, H. Y. 2018; 362 (6413)

  Abstract

  We present the genome-wide chromatin accessibility profiles of 410 tumor samples spanning 23 cancer types from The Cancer Genome Atlas (TCGA). We identify 562,709 transposase-accessible DNA elements that substantially extend the compendium of known cis-regulatory elements. Integration of ATAC-seq (the assay for transposase-accessible chromatin using sequencing) with TCGA multi-omic data identifies a large number of putative distal enhancers that distinguish molecular subtypes of cancers, uncovers specific driving transcription factors via protein-DNA footprints, and nominates long-range gene-regulatory interactions in cancer. These data reveal genetic risk loci of cancer predisposition as active DNA regulatory elements in cancer, identify gene-regulatory interactions underlying cancer immune evasion, and pinpoint noncoding mutations that drive enhancer activation and may affect patient survival. These results suggest a systematic approach to understanding the noncoding genome in cancer to advance diagnosis and therapy.

  View details for DOI 10.1126/science.aav1898

  View details for PubMedID 30361341

• Between-region genetic divergence reflects the mode and tempo of tumor evolution. Nature genetics Sun, R., Hu, Z., Sottoriva, A., Graham, T. A., Harpak, A., Ma, Z., Fischer, J. M., Shibata, D., Curtis, C. 2017

  Abstract

  Given the implications of tumor dynamics for precision medicine, there is a need to systematically characterize the mode of evolution across diverse solid tumor types. In particular, methods to infer the role of natural selection within established human tumors are lacking. By simulating spatial tumor growth under different evolutionary modes and examining patterns of between-region subclonal genetic divergence from multiregion sequencing (MRS) data, we demonstrate that it is feasible to distinguish tumors driven by strong positive subclonal selection from those evolving neutrally or under weak selection, as the latter fail to dramatically alter subclonal composition. We developed a classifier based on measures of between-region subclonal genetic divergence and projected patient data into model space, finding different modes of evolution both within and between solid tumor types. Our findings have broad implications for how human tumors progress, how they accumulate intratumoral heterogeneity, and ultimately how they may be more effectively treated.

  View details for DOI 10.1038/ng.3891

  View details for PubMedID 28581503

• A Big Bang model of human colorectal tumor growth. Nature genetics Sottoriva, A., Kang, H., Ma, Z., Graham, T. A., Salomon, M. P., Zhao, J., Marjoram, P., Siegmund, K., Press, M. F., Shibata, D., Curtis, C. 2015

  Abstract

  What happens in early, still undetectable human malignancies is unknown because direct observations are impractical. Here we present and validate a 'Big Bang' model, whereby tumors grow predominantly as a single expansion producing numerous intermixed subclones that are not subject to stringent selection and where both public (clonal) and most detectable private (subclonal) alterations arise early during growth. Genomic profiling of 349 individual glands from 15 colorectal tumors showed an absence of selective sweeps, uniformly high intratumoral heterogeneity (ITH) and subclone mixing in distant regions, as postulated by our model. We also verified the prediction that most detectable ITH originates from early private alterations and not from later clonal expansions, thus exposing the profile of the primordial tumor. Moreover, some tumors appear 'born to be bad', with subclone mixing indicative of early malignant potential. This new model provides a quantitative framework to interpret tumor growth dynamics and the origins of ITH, with important clinical implications.

  View details for DOI 10.1038/ng.3214

  View details for PubMedID 25665006

• The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups NATURE Curtis, C., Shah, S. P., Chin, S., Turashvili, G., Rueda, O. M., Dunning, M. J., Speed, D., Lynch, A. G., Samarajiwa, S., Yuan, Y., Graef, S., Ha, G., Haffari, G., Bashashati, A., Russell, R., McKinney, S., Langerod, A., Green, A., Provenzano, E., Wishart, G., Pinder, S., Watson, P., Markowetz, F., Murphy, L., Ellis, I., Purushotham, A., Borresen-Dale, A., Brenton, J. D., Tavare, S., Caldas, C., Aparicio, S. 2012; 486 (7403): 346-352

  Abstract

  The elucidation of breast cancer subgroups and their molecular drivers requires integrated views of the genome and transcriptome from representative numbers of patients. We present an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (copy number variants and single nucleotide polymorphisms) and acquired somatic copy number aberrations (CNAs) were associated with expression in ~40% of genes, with the landscape dominated by cis- and trans-acting CNAs. By delineating expression outlier genes driven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP and MAP2K4. Unsupervised analysis of paired DNA?RNA profiles revealed novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort. These include a high-risk, oestrogen-receptor-positive 11q13/14 cis-acting subgroup and a favourable prognosis subgroup devoid of CNAs. Trans-acting aberration hotspots were found to modulate subgroup-specific gene networks, including a TCR deletion-mediated adaptive immune response in the ?CNA-devoid? subgroup and a basal-specific chromosome 5 deletion-associated mitotic network. Our results provide a novel molecular stratification of the breast cancer population, derived from the impact of somatic CNAs on the transcriptome.

  View details for DOI 10.1038/nature10983

  View details for Web of Science ID 000305466800033

  View details for PubMedID 22522925

  View details for PubMedCentralID PMC3440846

• Deconstructing the origins of PDAC development. Flowers, B., Xu, H., Hanson, K., Curtis, C., Vogel, H., Wood, L., Attardi., L. D. AMER ASSOC CANCER RESEARCH. 2020: 19

  View details for Web of Science ID 000537844900012

• The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution. Cell Rozenblatt-Rosen, O., Regev, A., Oberdoerffer, P., Nawy, T., Hupalowska, A., Rood, J. E., Ashenberg, O., Cerami, E., Coffey, R. J., Demir, E., Ding, L., Esplin, E. D., Ford, J. M., Goecks, J., Ghosh, S., Gray, J. W., Guinney, J., Hanlon, S. E., Hughes, S. K., Hwang, E. S., Iacobuzio-Donahue, C. A., Jane-Valbuena, J., Johnson, B. E., Lau, K. S., Lively, T., Mazzilli, S. A., Pe'er, D., Santagata, S., Shalek, A. K., Schapiro, D., Snyder, M. P., Sorger, P. K., Spira, A. E., Srivastava, S., Tan, K., West, R. B., Williams, E. H., Human Tumor Atlas Network, Aberle, D., Achilefu, S. I., Ademuyiwa, F. O., Adey, A. C., Aft, R. L., Agarwal, R., Aguilar, R. A., Alikarami, F., Allaj, V., Amos, C., Anders, R. A., Angelo, M. R., Anton, K., Ashenberg, O., Aster, J. C., Babur, O., Bahmani, A., Balsubramani, A., Barrett, D., Beane, J., Bender, D. E., Bernt, K., Berry, L., Betts, C. B., Bletz, J., Blise, K., Boire, A., Boland, G., Borowsky, A., Bosse, K., Bott, M., Boyden, E., Brooks, J., Bueno, R., Burlingame, E. A., Cai, Q., Campbell, J., Caravan, W., Cerami, E., Chaib, H., Chan, J. M., Chang, Y. H., Chatterjee, D., Chaudhary, O., Chen, A. A., Chen, B., Chen, C., Chen, C., Chen, F., Chen, Y., Chheda, M. G., Chin, K., Chiu, R., Chu, S., Chuaqui, R., Chun, J., Cisneros, L., Coffey, R. J., Colditz, G. A., Cole, K., Collins, N., Contrepois, K., Coussens, L. M., Creason, A. L., Crichton, D., Curtis, C., Davidsen, T., Davies, S. R., de Bruijn, I., Dellostritto, L., De Marzo, A., Demir, E., DeNardo, D. G., Diep, D., Ding, L., Diskin, S., Doan, X., Drewes, J., Dubinett, S., Dyer, M., Egger, J., Eng, J., Engelhardt, B., Erwin, G., Esplin, E. D., Esserman, L., Felmeister, A., Feiler, H. S., Fields, R. C., Fisher, S., Flaherty, K., Flournoy, J., Ford, J. M., Fortunato, A., Frangieh, A., Frye, J. L., Fulton, R. S., Galipeau, D., Gan, S., Gao, J., Gao, L., Gao, P., Gao, V. R., Geiger, T., George, A., Getz, G., Ghosh, S., Giannakis, M., Gibbs, D. L., Gillanders, W. E., Goecks, J., Goedegebuure, S. P., Gould, A., Gowers, K., Gray, J. W., Greenleaf, W., Gresham, J., Guerriero, J. L., Guha, T. K., Guimaraes, A. R., Guinney, J., Gutman, D., Hacohen, N., Hanlon, S., Hansen, C. R., Harismendy, O., Harris, K. A., Hata, A., Hayashi, A., Heiser, C., Helvie, K., Herndon, J. M., Hirst, G., Hodi, F., Hollmann, T., Horning, A., Hsieh, J. J., Hughes, S., Huh, W. J., Hunger, S., Hwang, S. E., Iacobuzio-Donahue, C. A., Ijaz, H., Izar, B., Jacobson, C. A., Janes, S., Jane-Valbuena, J., Jayasinghe, R. G., Jiang, L., Johnson, B. E., Johnson, B., Ju, T., Kadara, H., Kaestner, K., Kagan, J., Kalinke, L., Keith, R., Khan, A., Kibbe, W., Kim, A. H., Kim, E., Kim, J., Kolodzie, A., Kopytra, M., Kotler, E., Krueger, R., Krysan, K., Kundaje, A., Ladabaum, U., Lake, B. B., Lam, H., Laquindanum, R., Lau, K. S., Laughney, A. M., Lee, H., Lenburg, M., Leonard, C., Leshchiner, I., Levy, R., Li, J., Lian, C. G., Lim, K., Lin, J., Lin, Y., Liu, Q., Liu, R., Lively, T., Longabaugh, W. J., Longacre, T., Ma, C. X., Macedonia, M. C., Madison, T., Maher, C. A., Maitra, A., Makinen, N., Makowski, D., Maley, C., Maliga, Z., Mallo, D., Maris, J., Markham, N., Marks, J., Martinez, D., Mashl, R. J., Masilionais, I., Mason, J., Massague, J., Massion, P., Mattar, M., Mazurchuk, R., Mazutis, L., Mazzilli, S. A., McKinley, E. T., McMichael, J. F., Merrick, D., Meyerson, M., Miessner, J. R., Mills, G. B., Mills, M., Mondal, S. B., Mori, M., Mori, Y., Moses, E., Mosse, Y., Muhlich, J. L., Murphy, G. F., Navin, N. E., Nawy, T., Nederlof, M., Ness, R., Nevins, S., Nikolov, M., Nirmal, A. J., Nolan, G., Novikov, E., Oberdoerffer, P., O'Connell, B., Offin, M., Oh, S. T., Olson, A., Ooms, A., Ossandon, M., Owzar, K., Parmar, S., Patel, T., Patti, G. J., Pe'er, D., Pe'er, I., Peng, T., Persson, D., Petty, M., Pfister, H., Polyak, K., Pourfarhangi, K., Puram, S. V., Qiu, Q., Quintanal-Villalonga, A., Raj, A., Ramirez-Solano, M., Rashid, R., Reeb, A. N., Regev, A., Reid, M., Resnick, A., Reynolds, S. M., Riesterer, J. L., Rodig, S., Roland, J. T., Rosenfield, S., Rotem, A., Roy, S., Rozenblatt-Rosen, O., Rudin, C. M., Ryser, M. D., Santagata, S., Santi-Vicini, M., Sato, K., Schapiro, D., Schrag, D., Schultz, N., Sears, C. L., Sears, R. C., Sen, S., Sen, T., Shalek, A., Sheng, J., Sheng, Q., Shoghi, K. I., Shrubsole, M. J., Shyr, Y., Sibley, A. B., Siex, K., Simmons, A. J., Singer, D. S., Sivagnanam, S., Slyper, M., Snyder, M. P., Sokolov, A., Song, S., Sorger, P. K., Southard-Smith, A., Spira, A., Srivastava, S., Stein, J., Storm, P., Stover, E., Strand, S. H., Su, T., Sudar, D., Sullivan, R., Surrey, L., Suva, M., Tan, K., Terekhanova, N. V., Ternes, L., Thammavong, L., Thibault, G., Thomas, G. V., Thorsson, V., Todres, E., Tran, L., Tyler, M., Uzun, Y., Vachani, A., Van Allen, E., Vandekar, S., Veis, D. J., Vigneau, S., Vossough, A., Waanders, A., Wagle, N., Wang, L., Wendl, M. C., West, R., Williams, E. H., Wu, C., Wu, H., Wu, H., Wyczalkowski, M. A., Xie, Y., Yang, X., Yapp, C., Yu, W., Yuan, Y., Zhang, D., Zhang, K., Zhang, M., Zhang, N., Zhang, Y., Zhao, Y., Zhou, D. C., Zhou, Z., Zhu, H., Zhu, Q., Zhu, X., Zhu, Y., Zhuang, X. 2020; 181 (2): 236?49

  Abstract

  Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.

  View details for DOI 10.1016/j.cell.2020.03.053

  View details for PubMedID 32302568

• Characterizing the tumor and immune microenvironment through treatment to predict response to neoadjuvant HER2-targeted therapy using the Digital Spatial Profiler McNamara, K., Caswell-Jin, J. L., Ma, Z., Zoeller, J. J., Kriner, M., Zhou, Z., Reeves, J., Hoang, M., Beechem, J., Slamon, D. J., Press, M. F., Brugge, J., Hurvitz, S. A., Curtis, C. AMER ASSOC CANCER RESEARCH. 2020

  View details for DOI 10.1158/1538-7445.SABCS19-P4-10-12

  View details for Web of Science ID 000527012502151

• Tumor expression and microenvironment in HER2-positive breast cancer before and on HER2-targeted therapy: Analysis of microarray expression data from the TRIO-US B07 trial Caswell-Jin, J. L., McNamara, K. L., Dering, J., Chen, H., Dichmann, R., Perez, A., Patel, R., Kotler, E., Zoeller, J. J., Brugge, J. S., Press, M. F., Slamon, D. J., Curtis, C., Hurvitz, S. A. AMER ASSOC CANCER RESEARCH. 2020

  View details for DOI 10.1158/1538-7445.SABCS19-P4-07-01

  View details for Web of Science ID 000527012502113

• Novel insights into breast cancer copy number genetic heterogeneity revealed by single-cell genome sequencing. eLife Baslan, T., Kendall, J., Volyanskyy, K., McNamara, K., Cox, H., D'Italia, S., Ambrosio, F., Riggs, M., Rodgers, L., Leotta, A., Song, J., Mao, Y., Wu, J., Shah, R., Gularte-Mérida, R., Chadalavada, K., Nanjangud, G., Varadan, V., Gordon, A., Curtis, C., Krasnitz, A., Dimitrova, N., Harris, L., Wigler, M., Hicks, J. 2020; 9

  Abstract

  Copy number alterations (CNAs) play an important role in molding the genomes of breast cancers and have been shown to be clinically useful for prognostic and therapeutic purposes. However, our knowledge of intra-tumoral genetic heterogeneity of this important class of somatic alterations is limited. Here, using single-cell sequencing, we comprehensively map out the facets of copy number alteration heterogeneity in a cohort of breast cancer tumors. Ou/var/www/html/elife/12-05-2020/backup/r analyses reveal: genetic heterogeneity of non-tumor cells (i.e. stroma) within the tumor mass; the extent to which copy number heterogeneity impacts breast cancer genomes and the importance of both the genomic location and dosage of sub-clonal events; the pervasive nature of genetic heterogeneity of chromosomal amplifications; and the association of copy number heterogeneity with clinical and biological parameters such as polyploidy and estrogen receptor negative status. Our data highlight the power of single-cell genomics in dissecting, in its many forms, intra-tumoral genetic heterogeneity of CNAs, the magnitude with which CNA heterogeneity affects the genomes of breast cancers, and the potential importance of CNA heterogeneity in phenomena such as therapeutic resistance and disease relapse.

  View details for DOI 10.7554/eLife.51480

  View details for PubMedID 32401198

  View details for PubMedCentralID PMC7220379

• The m6A RNA demethylase FTO is a HIF-independent synthetic lethal partner with the VHL tumor suppressor. Proceedings of the National Academy of Sciences of the United States of America Xiao, Y., Thakkar, K. N., Zhao, H., Broughton, J., Li, Y., Seoane, J. A., Diep, A. N., Metzner, T. J., von Eyben, R., Dill, D. L., Brooks, J. D., Curtis, C., Leppert, J. T., Ye, J., Peehl, D. M., Giaccia, A. J., Sinha, S., Rankin, E. B. 2020

  Abstract

  Loss of the von Hippel-Lindau (VHL) tumor suppressor is a hallmark feature of renal clear cell carcinoma. VHL inactivation results in the constitutive activation of the hypoxia-inducible factors (HIFs) HIF-1 and HIF-2 and their downstream targets, including the proangiogenic factors VEGF and PDGF. However, antiangiogenic agents and HIF-2 inhibitors have limited efficacy in cancer therapy due to the development of resistance. Here we employed an innovative computational platform, Mining of Synthetic Lethals (MiSL), to identify synthetic lethal interactions with the loss of VHL through analysis of primary tumor genomic and transcriptomic data. Using this approach, we identified a synthetic lethal interaction between VHL and the m6A RNA demethylase FTO in renal cell carcinoma. MiSL identified FTO as a synthetic lethal partner of VHL because deletions of FTO are mutually exclusive with VHL loss in pan cancer datasets. Moreover, FTO expression is increased in VHL-deficient ccRCC tumors compared to normal adjacent tissue. Genetic inactivation of FTO using multiple orthogonal approaches revealed that FTO inhibition selectively reduces the growth and survival of VHL-deficient cells in vitro and in vivo. Notably, FTO inhibition reduced the survival of both HIF wild type and HIF-deficient tumors, identifying FTO as an HIF-independent vulnerability of VHL-deficient cancers. Integrated analysis of transcriptome-wide m6A-seq and mRNA-seq analysis identified the glutamine transporter SLC1A5 as an FTO target that promotes metabolic reprogramming and survival of VHL-deficient ccRCC cells. These findings identify FTO as a potential HIF-independent therapeutic target for the treatment of VHL-deficient renal cell carcinoma.

  View details for DOI 10.1073/pnas.2000516117

  View details for PubMedID 32817424

• Metabolic Profiling Reveals a Dependency of Human Metastatic Breast Cancer on Mitochondrial Serine and One-Carbon Unit Metabolism. Molecular cancer research : MCR Li, A. M., Ducker, G. S., Li, Y., Seoane, J. A., Xiao, Y., Melemenidis, S., Zhou, Y., Liu, L., Vanharanta, S., Graves, E. E., Rankin, E. B., Curtis, C., Massague, J., Rabinowitz, J. D., Thompson, C. B., Ye, J. 2020

  Abstract

  Breast cancer is the most common cancer among American women and a major cause of mortality. To identify metabolic pathways as potential targets to treat metastatic breast cancer, we performed metabolomics profiling on breast cancer cell line MDA-MB-231 and its tissue-tropic metastatic subclones. Here, we report that these subclones with increased metastatic potential display an altered metabolic profile compared to the parental population. In particular, the mitochondrial serine and one-carbon (1C) unit pathway is upregulated in metastatic subclones. Mechanistically, the mitochondrial serine and 1C unit pathway drives the faster proliferation of subclones through enhanced de novo purine biosynthesis. Inhibition of the first rate-limiting enzyme of the mitochondrial serine and 1C unit pathway, serine hydroxymethyltransferase (SHMT2), potently suppresses proliferation of metastatic subclones in culture and impairs growth of lung metastatic subclones at both primary and metastatic sites in mice. Some human breast cancers exhibit a significant association between the expression of genes in the mitochondrial serine and 1C unit pathway with disease outcome and higher expression of SHMT2 in metastatic tumor tissue compared to primary tumors. In addition to breast cancer, a few other cancer types, such as adrenocortical carcinoma (ACC) and kidney chromophobe cell carcinoma (KICH), also display increased SHMT2 expression during disease progression. Together, these results suggest that mitochondrial serine and 1C unit plays an important role in promoting cancer progression, particularly in late stage cancer. Implications: This study identifies mitochondrial serine and 1C unit metabolism as an important pathway during the progression of a subset of human breast cancers.

  View details for DOI 10.1158/1541-7786.MCR-19-0606

  View details for PubMedID 31941752

• Deciphering the origins of PDAC development Flowers, B., Xu, H., Hanson, K., Curtis, C., Vogel, H., Wood, L. D., Attardi, L. D. AMER ASSOC CANCER RESEARCH. 2019

  View details for DOI 10.1158/1538-7445.PANCA19-I15

  View details for Web of Science ID 000526416300153

• Elucidating the role of p53 in the cellular origins of pancreatic cancer development Flowers, B. M., Xu, H., Hanson, K., Curtis, C., Vogel, H., Wood, L. D., Attardi, L. D. AMER ASSOC CANCER RESEARCH. 2019

  View details for DOI 10.1158/1538-7445.PANCA19-A15

  View details for Web of Science ID 000526416300013

• Chromatin state as a mechanism of anthracycline response in breast cancer Seoane, J. A., Kirkland, J. G., Caswell-Jin, J. L., Crabtree, G. R., Curtis, C. AMER ASSOC CANCER RESEARCH. 2019

  View details for DOI 10.1158/1538-7445.SABCS18-2108

  View details for Web of Science ID 000488279400105

• Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen NATURE COMMUNICATIONS Menden, M. P., Wang, D., Mason, M. J., Szalai, B., Bulusu, K. C., Guan, Y., Yu, T., Kang, J., Jeon, M., Wolfinger, R., Nguyen, T., Zaslavskiy, M., Jang, I., Ghazoui, Z., Ahsen, M., Vogel, R., Neto, E., Norman, T., Tang, E. Y., Garnett, M. J., Di Veroli, G. Y., Fawell, S., Stolovitzky, G., Guinney, J., Dry, J. R., Saez-Rodriguez, J., Abante, J., Abecassis, B., Aben, N., Aghamirzaie, D., Aittokallio, T., Akhtari, F. S., Al-lazikani, B., Alam, T., Allam, A., Allen, C., de Almeida, M., Altarawy, D., Alves, V., Amadoz, A., Anchang, B., Antolin, A. A., Ash, J. R., Romeo Aznar, V., Ba-alawi, W., Bagheri, M., Bajic, V., Ball, G., Ballester, P. J., Baptista, D., Bare, C., Bateson, M., Bender, A., Bertrand, D., Wijayawardena, B., Boroevich, K. A., Bosdriesz, E., Bougouffa, S., Bounova, G., Brouwer, T., Bryant, B., Calaza, M., Calderone, A., Calza, S., Capuzzi, S., Carbonell-Caballero, J., Carlin, D., Carter, H., Castagnoli, L., Celebi, R., Cesareni, G., Chang, H., Chen, G., Chen, H., Chen, H., Cheng, L., Chernomoretz, A., Chicco, D., Cho, K., Cho, S., Choi, D., Choi, J., Choi, K., Choi, M., De Cock, M., Coker, E., Cortes-Ciriano, I., Cserzo, M., Cubuk, C., Curtis, C., Van Daele, D., Dang, C. C., Dijkstra, T., Dopazo, J., Draghici, S., Drosou, A., Dumontier, M., Ehrhart, F., Eid, F., ElHefnawi, M., Elmarakeby, H., van Engelen, B., Engin, H., de Esch, I., Evelo, C., Falcao, A. O., Farag, S., Fernandez-Lozano, C., Fisch, K., Flobak, A., Fornari, C., Foroushani, A. K., Fotso, D., Fourches, D., Friend, S., Frigessi, A., Gao, F., Gao, X., Gerold, J. M., Gestraud, P., Ghosh, S., Gillberg, J., Godoy-Lorite, A., Godynyuk, L., Godzik, A., Goldenberg, A., Gomez-Cabrero, D., Gonen, M., de Graaf, C., Gray, H., Grechkin, M., Guimera, R., Guney, E., Haibe-Kains, B., Han, Y., Hase, T., He, D., He, L., Heath, L. S., Hellton, K. H., Helmer-Citterich, M., Hidalgo, M. R., Hidru, D., Hill, S. M., Hochreiter, S., Hong, S., Hovig, E., Hsueh, Y., Hu, Z., Huang, J. K., Huang, R., Hunyady, L., Hwang, J., Hwang, T., Hwang, W., Hwang, Y., Isayev, O., Walk, O., Jack, J., Jahandideh, S., Ji, J., Jo, Y., Kamola, P. J., Kanev, G. K., Karacosta, L., Karimi, M., Kaski, S., Kazanov, M., Khamis, A. M., Khan, S., Kiani, N. A., Kim, A., Kim, J., Kim, J., Kim, K., Kim, K., Kim, S., Kim, Y., Kim, Y., Kirk, P. W., Kitano, H., Klambauer, G., Knowles, D., Ko, M., Kohn-Luque, A., Kooistra, A. J., Kuenemann, M. A., Kuiper, M., Kurz, C., Kwon, M., van Laarhoven, T., Laegreid, A., Lederer, S., Lee, H., Lee, J., Lee, Y., Leppaho, E., Lewis, R., Li, J., Li, L., Liley, J., Lim, W., Lin, C., Liu, Y., Lopez, Y., Low, J., Lysenko, A., Machado, D., Madhukar, N., De Maeyer, D., Malpartida, A., Mamitsuka, H., Marabita, F., Marchal, K., Marttinen, P., Mason, D., Mazaheri, A., Mehmood, A., Mehreen, A., Michaut, M., Miller, R. A., Mitsopoulos, C., Modos, D., Van Moerbeke, M., Moo, K., Motsinger-Reif, A., Movva, R., Muraru, S., Muratov, E., Mushthofa, M., Nagarajan, N., Nakken, S., Nath, A., Neuvial, P., Newton, R., Ning, Z., De Niz, C., Oliva, B., Olsen, C., Palmeri, A., Panesar, B., Papadopoulos, S., Park, J., Park, S., Park, S., Pawitan, Y., Peluso, D., Pendyala, S., Peng, J., Perfetto, L., Pirro, S., Plevritis, S., Politi, R., Poon, H., Porta, E., Prellner, I., Preuer, K., Angel Pujana, M., Ramnarine, R., Reid, J. E., Reyal, F., Richardson, S., Ricketts, C., Rieswijk, L., Rocha, M., Rodriguez-Gonzalvez, C., Roell, K., Rotroff, D., de Ruiter, J. R., Rukawa, P., Sadacca, B., Safikhani, Z., Safitri, F., Sales-Pardo, M., Sauer, S., Schlichting, M., Seoane, J. A., Serra, J., Shang, M., Sharma, A., Sharma, H., Shen, Y., Shiga, M., Shin, M., Shkedy, Z., Shopsowitz, K., Sinai, S., Skola, D., Smirnov, P., Soerensen, I., Soerensen, P., Song, J., Song, S., Soufan, O., Spitzmueller, A., Steipe, B., Suphavilai, C., Tamayo, S., Tamborero, D., Tang, J., Tanoli, Z., Tarres-Deulofeu, M., Tegner, J., Thommesen, L., Tonekaboni, S., Tran, H., De Troyer, E., Truong, A., Tsunoda, T., Turu, G., Tzeng, G., Verbeke, L., Videla, S., Vis, D., Voronkov, A., Votis, K., Wang, A., Wang, H., Wang, P., Wang, S., Wang, W., Wang, X., Wang, X., Wennerberg, K., Wernisch, L., Wessels, L., van Westen, G. P., Westerman, B. A., White, S., Willighagen, E., Wurdinger, T., Xie, L., Xie, S., Xu, H., Yadav, B., Yau, C., Yeerna, H., Yin, J., Yu, M., Yu, M., Yun, S., Zakharov, A., Zamichos, A., Zanin, M., Zeng, L., Zenil, H., Zhang, F., Zhang, P., Zhang, W., Zhao, H., Zhao, L., Zheng, W., Zoufir, A., Zucknick, M., AstraZeneca-Sanger Drug Combinatio 2019; 10: 2674

  Abstract

  The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

  View details for DOI 10.1038/s41467-019-09799-2

  View details for Web of Science ID 000471758500010

  View details for PubMedID 31209238

  View details for PubMedCentralID PMC6572829

• Clonal replacement of tumor-specific T cells following PD-1 blockade. Nature medicine Yost, K. E., Satpathy, A. T., Wells, D. K., Qi, Y., Wang, C., Kageyama, R., McNamara, K. L., Granja, J. M., Sarin, K. Y., Brown, R. A., Gupta, R. K., Curtis, C., Bucktrout, S. L., Davis, M. M., Chang, A. L., Chang, H. Y. 2019

  Abstract

  Immunotherapies that block inhibitory checkpoint receptors on T cells have transformed the clinical care of patients with cancer1. However, whether the T cell response to checkpoint blockade relies on reinvigoration of pre-existing tumor-infiltrating lymphocytes or on recruitment of novel T cells remains unclear2-4. Here we performed paired single-cell RNA and T cell receptor sequencing on 79,046 cells from site-matched tumors from patients with basal or squamous cell carcinoma before and after anti-PD-1 therapy. Tracking T cell receptor clones and transcriptional phenotypes revealed coupling of tumor recognition, clonal expansion and T cell dysfunction marked by clonal expansion of CD8+CD39+ T cells, which co-expressed markers of chronic T cell activation and exhaustion. However, the expansion of T cell clones did not derive from pre-existing tumor-infiltrating T lymphocytes; instead, the expanded clones consisted of novel clonotypes that had not previously been observed in the same tumor. Clonal replacement of T cells was preferentially observed in exhausted CD8+ T cells and evident in patients with basal or squamous cell carcinoma. These results demonstrate that pre-existing tumor-specific T cells may have limited reinvigoration capacity, and that the T cell response to checkpoint blockade derives from a distinct repertoire of T cell clones that may have just recently entered the tumor.

  View details for DOI 10.1038/s41591-019-0522-3

  View details for PubMedID 31359002

• Publisher Correction: Clonal replacement and heterogeneity in breast tumors treated with neoadjuvant HER2-targeted therapy. Nature communications Caswell-Jin, J. L., McNamara, K., Reiter, J. G., Sun, R., Hu, Z., Ma, Z., Ding, J., Suarez, C. J., Tilk, S., Raghavendra, A., Forte, V., Chin, S. F., Bardwell, H., Provenzano, E., Caldas, C., Lang, J., West, R., Tripathy, D., Press, M. F., Curtis, C. 2019; 10 (1): 2433

  Abstract

  The original version of this Article omitted from the Author Contributions statement that 'R.S. and J.G.R contributed equally to this work.' This has been corrected in both the PDF and HTML versions of the Article.

  View details for DOI 10.1038/s41467-019-10456-x

  View details for PubMedID 31147552

• Assessment of ERBB2/HER2 Status in HER2-Equivocal Breast Cancers by FISH and 2013/2014 ASCO-CAP Guidelines. JAMA oncology Press, M. F., Seoane, J. A., Curtis, C., Quinaux, E., Guzman, R., Sauter, G., Eiermann, W., Mackey, J. R., Robert, N., Pienkowski, T., Crown, J., Martin, M., Valero, V., Bee, V., Ma, Y., Villalobos, I., Slamon, D. J. 2018

  Abstract

  Importance: The 2013/2014 American Society of Clinical Oncology and College of American Pathologists (ASCO-CAP) guidelines for HER2 testing by fluorescence in situ hybridization (FISH) designated an "equivocal" category (average HER2 copies per tumor cell ?4-6 with HER2/CEP17 ratio <2.0) to be resolved as negative or positive by assessments with alternative control probes. Approximately 4% to 12% of all invasive breast cancers are characterized as HER2-equivocal based on FISH.Objective: To evaluate the following hypotheses: (1) genetic loci used as alternative controls are heterozygously deleted in a substantial proportion of breast cancers; (2) use of these loci for assessment of HER2 by FISH leads to false-positive assessments; and (3) these HER2 false-positive breast cancer patients have outcomes that do not differ from clinical outcomes for patients with HER2-negative breast cancer.Design, Setting, and Participants: We retrospectively assessed the use of chromosome 17 p-arm and q-arm alternative control genomic sites (TP53, D17S122, SMS, RARA, TOP2A), as recommended by the 2013/2014 ASCO-CAP guidelines for HER2 testing, in patients whose data were available through Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and whose tissues were available through the Breast Cancer International Research Group clinical trials. We used data from an international cohort database of invasive breast cancers (1980 participants) and international clinical trial of adjuvant chemotherapy in invasive, node-positive breast cancer patients.Main Outcomes and Measures: The primary objectives were to (1) assess frequency of heterozygous deletions in chromosome 17 genomic sites used as FISH internal controls for evaluation of HER2 status among HER2-equivocal cancers; (2) characterize impact of using deleted sites for determination of HER2-to-internal-control-gene ratios; (3) assess HER2 protein expression in each subgroup; and (4) compare clinical outcomes for each subgroup.Results: Of the 1980 patients in METABRIC,1915 patients were fully evaluated. In addition, 100 HER2-equivocal breast cancers by FISH and 100 comparator FISH-negative breast cancers from the BCIRG-005 trial were analyzed. Heterozygous deletions, particularly in specific p-arm sites, were common in both HER2-amplified and HER2-not-amplified breast cancers. Use of alternative control probes from these regions to assess HER2 by FISH in HER2-equivocal as well as HER2-not-amplified breast cancers resulted in high rates of false-positive ratios (HER2-to-alternative control ratio ?2.0) owing to heterozygous deletions of control p-arm genomic sites used in ratio denominators. Misclassification of HER2 status was observed not only in breast cancers with ASCO-CAP equivocal status but also in breast cancers with an average of fewer than 4.0 HER2 copies per tumor cell when using alternative control probes.Conclusions and Relevance: The indiscriminate use of alternative control probes to calculate HER2 FISH ratios in HER2-equivocal breast cancers may lead to false-positive interpretations of HER2 status resulting from unrecognized heterozygous deletions in 1 or more of these alternative control genomic sites and incorrect HER2 ratio determinations.

  View details for PubMedID 30520947

• Tumor Molecular Profiling Aids in Determining Tissue of Origin and Therapy for Metastatic Adenocarcinoma in a Patient With Multiple Primary Malignancies JCO PRECISION ONCOLOGY Costa, H. A., Reyes, R., Mills, M., Zehnder, J. L., Sledge, G., Curtis, C., Ford, J. M., Suarez, C. J. 2018; 2

  View details for DOI 10.1200/PO.18.00177

  View details for Web of Science ID 000462187500001

• A role for chromatin regulatory dynamics in breast cancer evolution. Nature medicine Probert, C., Curtis, C. 2018

  View details for PubMedID 30177822

• Quantification of subclonal selection in cancer from bulk sequencing data (vol 50, pg 895, 2018) NATURE GENETICS Williams, M. J., Werner, B., Heide, T., Curtis, C., Barnes, C. P., Sottoriva, A., Graham, T. A. 2018; 50 (9): 1342

  Abstract

  In the version of this article originally published, in the "Theoretical framework of subclonal selection" section of the main text, ref. 11 instead of ref. 19 should have been cited at the end of the phrase "Our previously presented frequentist approach to detect subclonal selection from bulk sequencing data involves an R2 test statistic." The error has been corrected in the HTML and PDF versions of the article.

  View details for PubMedID 30022114

• Development of plasma cell-free DNA (cfDNA) assays for early cancer detection: first insights from the Circulating Cell-Free Genome Atlas Study (CCGA) Aravanis, A. A., Oxnard, G. R., Maddala, T., Hubbell, E., Venn, O., Jamshidi, A., Shen, L., Amini, H., Beausang, J. A., Betts, C., Civello, D., Davydov, K., Fazullina, S., Filippova, D., Gnerre, S., Gross, S., Hou, C., Jiang, R., Jung, B., Kurtzman, K., Melton, C., Nautiyal, S., Newman, J., Newman, J., Nicolaou, C., Rava, R., Sakarya, O., Satya, R., Shojaee, S., Steffen, K., Valouev, A., Xu, H., Yue, J., Zhang, N., Baselga, J., Lapham, R., Davis, D. G., Smith, D., Richards, D., Seiden, M. V., Swanton, C., Yeatman, T. J., Tibshirani, R., Curtis, C., Plevritis, S. K., Williams, R., Klein, E., Hartman, A., Liu, M. C. AMER ASSOC CANCER RESEARCH. 2018

  View details for DOI 10.1158/1538-7445.AM2018-LB-343

  View details for Web of Science ID 000468818900480

• Harnessing Tumor Evolution to Circumvent Resistance. Trends in genetics : TIG Pogrebniak, K. L., Curtis, C. 2018

  Abstract

  High-throughput sequencing can be used to measure changes in tumor composition across space and time. Specifically, comparisons of pre- and post-treatment samples can reveal the underlying clonal dynamics and resistance mechanisms. Here, we discuss evidence for distinct modes of tumor evolution and their implications for therapeutic strategies. In addition, we consider the utility of spatial tissue sampling schemes, single-cell analysis, and circulating tumor DNA to track tumor evolution and the emergence of resistance, as well as approaches that seek to forestall resistance by targeting tumor evolution. Ultimately, characterization of the (epi)genomic, transcriptomic, and phenotypic changes that occur during tumor progression coupled with computational and mathematical modeling of tumor evolutionary dynamics may inform personalized treatment strategies.

  View details for PubMedID 29903534

• Quantification of subclonal selection in cancer from bulk sequencing data NATURE GENETICS Williams, M. J., Werner, B., Heide, T., Curtis, C., Barnes, C. P., Sottoriva, A., Graham, T. A. 2018; 50 (6): 895-+

  Abstract

  Subclonal architectures are prevalent across cancer types. However, the temporal evolutionary dynamics that produce tumor subclones remain unknown. Here we measure clone dynamics in human cancers by using computational modeling of subclonal selection and theoretical population genetics applied to high-throughput sequencing data. Our method determined the detectable subclonal architecture of tumor samples and simultaneously measured the selective advantage and time of appearance of each subclone. We demonstrate the accuracy of our approach and the extent to which evolutionary dynamics are recorded in the genome. Application of our method to high-depth sequencing data from breast, gastric, blood, colon and lung cancer samples, as well as metastatic deposits, showed that detectable subclones under selection, when present, consistently emerged early during tumor growth and had a large fitness advantage (>20%). Our quantitative framework provides new insight into the evolutionary trajectories of human cancers and facilitates predictive measurements in individual tumors from widely available sequencing data.

  View details for PubMedID 29808029

• AGBT meeting report GENOME BIOLOGY Bhatt, A. S., Curtis, C. 2018; 19: 60

  Abstract

  The Annual Advances in Genome Biology and Technology (AGBT) General Meeting was held in Orlando, Florida, USA, on the 12-15 February 2018. Professors Ami S. Bhatt and Christina Curtis from Stanford University, USA, report advances and applications in the field that were discussed at the meeting.

  View details for PubMedID 29784033

• Big Bang Tumor Growth and Clonal Evolution. Cold Spring Harbor perspectives in medicine Sun, R., Hu, Z., Curtis, C. 2018; 8 (5)

  Abstract

  The advent and application of next-generation sequencing (NGS) technologies to tumor genomes has reinvigorated efforts to understand clonal evolution. Although tumor progression has traditionally been viewed as a gradual stepwise process, recent studies suggest that evolutionary rates in tumors can be variable with periods of punctuated mutational bursts and relative stasis. For example, Big Bang dynamics have been reported, wherein after transformation, growth occurs in the absence of stringent selection, consistent with effectively neutral evolution. Although first noted in colorectal tumors, effective neutrality may be relatively common. Additionally, punctuated evolution resulting from mutational bursts and cataclysmic genomic alterations have been described. In this review, we contrast these findings with the conventional gradualist view of clonal evolution and describe potential clinical and therapeutic implications of different evolutionary modes and tempos.

  View details for PubMedID 28710260

• Organoids reveal cancer dynamics NATURE Kuo, C. J., Curtis, C. 2018; 556 (7702): 441?42

  View details for Web of Science ID 000430793000032

  View details for PubMedID 29686366

• Mapping the in vivo fitness landscape of lung adenocarcinoma tumor suppression in mice NATURE GENETICS Rogers, Z. N., McFarland, C. D., Winters, I. P., Seoane, J. A., Brady, J. J., Yoon, S., Curtis, C., Petrov, D. A., Winslow, M. M. 2018; 50 (4): 483-+

  Abstract

  The functional impact of most genomic alterations found in cancer, alone or in combination, remains largely unknown. Here we integrate tumor barcoding, CRISPR/Cas9-mediated genome editing and ultra-deep barcode sequencing to interrogate pairwise combinations of tumor suppressor alterations in autochthonous mouse models of human lung adenocarcinoma. We map the tumor suppressive effects of 31 common lung adenocarcinoma genotypes and identify a landscape of context dependence and differential effect strengths.

  View details for PubMedID 29610476

• Higher Absolute Lymphocyte Counts Predict Lower Mortality from Early-Stage Triple-Negative Breast Cancer. Clinical cancer research : an official journal of the American Association for Cancer Research Afghahi, A., Purington, N., Han, S. S., Desai, M., Pierson, E., Mathur, M. B., Seto, T., Thompson, C. A., Rigdon, J., Telli, M. L., Badve, S. S., Curtis, C., West, R. B., Horst, K., Gomez, S. L., Ford, J. M., Sledge, G. W., Kurian, A. W. 2018

  Abstract

  Tumor-infiltrating lymphocytes (TILs) in pre-treatment biopsies are associated with improved survival in triple-negative breast cancer (TNBC). We investigated whether higher peripheral lymphocyte counts are associated with lower breast cancer-specific mortality (BCM) and overall mortality (OM) in TNBC.Data on treatments and diagnostic tests from electronic medical records of two healthcare systems were linked with demographic, clinical, pathologic, and mortality data from the California Cancer Registry. Multivariable regression models adjusted for age, race/ethnicity, socioeconomic status, cancer stage, grade, neoadjuvant/adjuvant chemotherapy use, radiotherapy use, and germline BRCA1/2 mutations were used to evaluate associations between absolute lymphocyte count (ALC), BCM and OM. For a subgroup with TILs data available, we explored the relationship between TILs and peripheral lymphocyte counts.1,463 Stage I-III TNBC patients were diagnosed from 2000-2014; 1113 (76%) received neoadjuvant/adjuvant chemotherapy within one year of diagnosis. Of 759 patients with available ALC data, 481 (63.4%) were ever lymphopenic (minimum ALC <1.0 K/?L). On multivariable analysis, higher minimum ALC, but not absolute neutrophil count, predicted lower OM (hazard ratio [HR]: 0.23, 95% confidence interval [CI]: 0.16-0.35) and BCM (HR: 0.19, CI: 0.11-0.34). Five-year probability of BCM was 15% for patients who were ever lymphopenic versus 4% for those who were not. An exploratory analysis (N=70) showed a significant association between TILs and higher peripheral lymphocyte counts during neoadjuvant chemotherapy.Higher peripheral lymphocyte counts predicted lower mortality from early-stage, potentially curable TNBC, suggesting that immune function may enhance the effectiveness of early TNBC treatment.

  View details for PubMedID 29581131

• A population genetics perspective on the determinants of intra-tumor heterogeneity. Biochimica et biophysica acta Hu, Z., Sun, R., Curtis, C. 2017; 1867 (2): 109-126

  Abstract

  Cancer results from the acquisition of somatic alterations in a microevolutionary process that typically occurs over many years, much of which is occult. Understanding the evolutionary dynamics that are operative at different stages of progression in individual tumors might inform the earlier detection, diagnosis, and treatment of cancer. Although these processes cannot be directly observed, the resultant spatiotemporal patterns of genetic variation amongst tumor cells encode their evolutionary histories. Such intra-tumor heterogeneity is pervasive not only at the genomic level, but also at the transcriptomic, phenotypic, and cellular levels. Given the implications for precision medicine, the accurate quantification of heterogeneity within and between tumors has become a major focus of current research. In this review, we provide a population genetics perspective on the determinants of intra-tumor heterogeneity and approaches to quantify genetic diversity. We summarize evidence for different modes of evolution based on recent cancer genome sequencing studies and discuss emerging evolutionary strategies to therapeutically exploit tumor heterogeneity. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

  View details for DOI 10.1016/j.bbcan.2017.03.001

  View details for PubMedID 28274726

• Bayesian Network Inference Modeling Identifies TRIB1 as a Novel Regulator of Cell-Cycle Progression and Survival in Cancer Cells CANCER RESEARCH Gendelman, R., Xing, H., Mirzoeva, O. K., Sarde, P., Curtis, C., Feiler, H. S., McDonagh, P., Gray, J. W., Khalil, I., Korn, W. M. 2017; 77 (7): 1575-1585

  Abstract

  Molecular networks governing responses to targeted therapies in cancer cells are complex dynamic systems that demonstrate nonintuitive behaviors. We applied a novel computational strategy to infer probabilistic causal relationships between network components based on gene expression. We constructed a model comprised of an ensemble of networks using multidimensional data from cell line models of cell-cycle arrest caused by inhibition of MEK1/2. Through simulation of a reverse-engineered Bayesian network model, we generated predictions of G1-S transition. The model identified known components of the cell-cycle machinery, such as CCND1, CCNE2, and CDC25A, as well as revealed novel regulators of G1-S transition, IER2, TRIB1, TRIM27. Experimental validation of model predictions confirmed 10 of 12 predicted genes to have a role in G1-S progression. Further analysis showed that TRIB1 regulated the cyclin D1 promoter via NF?B and AP-1 sites and sensitized cells to TRAIL-induced apoptosis. In clinical specimens of breast cancer, TRIB1 levels correlated with expression of NF?B and its target genes (IL8, CSF2), and TRIB1 copy number and expression were predictive of clinical outcome. Together, our results establish a critical role of TRIB1 in cell cycle and survival that is mediated via the modulation of NF?B signaling. Cancer Res; 77(7); 1575-85. ©2017 AACR.

  View details for DOI 10.1158/0008-5472.CAN-16-0512

  View details for Web of Science ID 000398262400006

  View details for PubMedID 28087598

• Genome co-amplification upregulates a mitotic gene network activity that predicts outcome and response to mitotic protein inhibitors in breast cancer (vol 18, pg 70, 2016) BREAST CANCER RESEARCH Hu, Z., Mao, J., Curtis, C., Huang, G., Gu, S., Heiser, L., Lenburg, M. E., Korkola, J. E., Bayani, N., Samarajiwa, S., Seoane, J. A., Dane, M. A., Esch, A., Feiler, H. S., Wang, N. J., Hardwicke, M., Laquerre, S., Jackson, J., Wood, K. W., Weber, B., Spellman, P. T., Aparicio, S., Wooster, R., Caldas, C., Gray, J. W. 2017; 19: 17

  View details for PubMedID 28183333

  View details for PubMedCentralID PMC5301377

• Integrated genomic characterization of oesophageal carcinoma NATURE Kim, J., Bowlby, R., Mungall, A. J., Robertson, A. G., Odze, R. D., Cherniack, A. D., Shih, J., Pedamallu, C. S., Cibulskis, C., Dunford, A., Meier, S. R., Kim, J., Raphael, B. J., Wu, H., Wong, A. M., Willis, J. E., Bass, A. J., Derks, S., Garman, K., McCall, S. J., Wiznerowicz, M., Pantazi, A., Parfenov, M., Thorsson, V., Shmulevich, I., Dhankani, V., Miller, M., Sakai, R., Wang, K., Schultz, N., Shen, R., Arora, A., Weinhold, N., Sanchez-Vega, F., Kelsen, D. P., Zhang, J., Felau, I., Demchok, J., Rabkin, C. S., Camargo, M. C., Zenklusen, J. C., Bowen, J., Leraas, K., Lichtenberg, T. M., Curtis, C., Seoane, J. A., Ojesina, A. I., Beer, D. G., Gulley, M. L., Pennathur, A., Luketich, J. D., Zhou, Z., Weisenberger, D. J., Akbani, R., Lee, J., Liu, W., Mills, G. B., Zhang, W., Reid, B. J., Hinoue, T., Laird, P. W., Shen, H., Piazuelo, M. B., Schneider, B. G., McLellan, M., Taylor-Weiner, A., Cibulskis, C., Lawrence, M., Cibulskis, K., Stewart, C., Getz, G., Lander, E., Gabriel, S. B., Ding, L., McLellan, M. D., Miller, C. A., Appelbaum, E. L., Cordes, M. G., Fronick, C. C., Fulton, L. A., Mardis, E. R., Wilson, R. K., Schmidt, H. K., Fulton, R. S., Ally, A., Balasundaram, M., Bowlby, R., Carlsen, R., Chuah, E., Dhalla, N., Holt, R. A., Jones, S. J., Kasaian, K., Brooks, D., Li, H. I., Ma, Y., Marra, M. A., Mayo, M., Moore, R. A., Mungall, A. J., Mungall, K. L., Robertson, A. G., Schein, J. E., Sipahimalani, P., Tam, A., Thiessen, N., Wong, T., Cherniack, A. D., Shih, J., Pedamallu, C. S., Beroukhim, R., Bullman, S., Cibulskis, C., Murray, B. A., Saksena, G., Schumacher, S. E., Gabriel, S., Meyerson, M., Hadjipanayis, A., Kucherlapati, R., Pantazi, A., Parfenov, M., Ren, X., Park, P. J., Lee, S., Kucherlapati, M., Yang, L., Baylin, S. B., Hoadley, K. A., Weisenberger, D. J., Bootwalla, M. S., Lai, P. H., Van den Berg, D. J., Berrios, M., Holbrook, A., Akbani, R., Hwang, J., Jang, H., Liu, W., Weinstein, J. N., Lee, J., Lu, Y., Sohn, B. H., Mills, G., Seth, S., Protopopov, A., Bristow, C. A., Mahadeshwar, H. S., Tang, J., Song, X., Zhang, J., Laird, P. W., Hinoue, T., Shen, H., Cho, J., Defrietas, T., Frazer, S., Gehlenborg, N., Heiman, D. I., Lawrence, M. S., Lin, P., Meier, S. R., Noble, M. S., Doug Voet, D., Zhang, H., Kim, J., Polak, P., Saksena, G., Chin, L., Getz, G., Wong, A. M., Raphael, B. J., Wu, H., Lee, S., Park, P. J., Yang, L., Thorsson, V., Bernard, B., Iype, L., Miller, M., Reynolds, S. M., Shmulevich, I., Dhankani, V., Abeshouse, A., Arora, A., Armenia, J., Kundra, R., Ladanyi, M., Kjong-Van Lehmann, Gao, J., Sander, C., Schultz, N., Sanchez-Vega, F., Shen, R., Weinhold, N., Chakravarty, D., Zhang, H., Radenbaugh, A., Hegde, A., Akbani, R., Liu, W., Weinstein, J. N., Chin, L., Bristow, C. A., Lu, Y., Penny, R., Crain, D., Gardner, J., Curley, E., Mallery, D., Morris, S., Paulauskis, J., Shelton, T., Shelton, C., Bowen, J., Frick, J., Gastier-Foster, J. M., Gerken, M., Leraas, K. M., Lichtenberg, T. M., Ramirez, N. C., Wise, L., Zmuda, E., Tarvin, K., Saller, C., Park, Y. S., Button, M., Carvalho, A. L., Reis, R. M., Matsushita, M. M., Lucchesi, F., de Oliveira, A. T., Le, X., Paklina, O., Setdikova, G., Lee, J., Bennett, J., Iacocca, M., Huelsenbeck-Dill, L., Potapova, C. O., Voronina, O., Liu, O., Fulidou, V., Cates, C., Sharp, A., Behera, M., Force, S., Khuri, F., Owonikoko, T., Pickens, A., Ramalingam, S., Sica, G., Dinjens, W., van Nistelrooij, A., Wijnhoven, B., Sandusky, G., Stepa, S., Crain, D., Paulauskis, J., Penny, R., Gardner, J., Mallery, D., Morris, S., Shelton, T., Shelton, C., Curley, E., Juhl, I. H., Zornig, C., Kwon, S. Y., Kelsen, D., Kim, G. H., Bartlett, J., Parfitt, J., Chetty, R., Darling, G., Knox, J., Wong, R., El-Zimaity, H., Liu, G., Boussioutas, A., Park, D. Y., Kemp, R., Carlotti, C. G., da Cunha Tirapelli, D. P., Saggioro, F. P., Sankarankutty, A. K., Noushmehr, H., dos Santos, J. S., Trevisan, F. A., Eschbacher, J., Eschbacher, J., Dubina, M., Mozgovoy, E., Carey, F., Chalmers, S., Forgie, I., Godwin, A., Reilly, C., Madan, R., Naima, Z., Ferrer-Torres, D., Rathmell, W. K., Dhir, R., Luketich, J., Pennathur, A., Ajani, J. A., McCall, S. J., Janjigian, Y., Kelsen, D., Ladanyi, M., Tang, L., Camargo, M. C., Ajani, J. A., Cheong, J., Chudamani, S., Liu, J., Lolla, L., Naresh, R., Pihl, T., Sun, Q., Wan, Y., Wu, Y., Demchok, J. A., Felau, I., Ferguson, M. L., Shaw, K. R., Sheth, M., Tarnuzzer, R., Wang, Z., Yang, L., Zenklusen, J. C., Hutter, C. M., Sofia, H. J., Zhang, J. 2017; 541 (7636): 169-?

  Abstract

  Oesophageal cancers are prominent worldwide; however, there are few targeted therapies and survival rates for these cancers remain dismal. Here we performed a comprehensive molecular analysis of 164 carcinomas of the oesophagus derived from Western and Eastern populations. Beyond known histopathological and epidemiologic distinctions, molecular features differentiated oesophageal squamous cell carcinomas from oesophageal adenocarcinomas. Oesophageal squamous cell carcinomas resembled squamous carcinomas of other organs more than they did oesophageal adenocarcinomas. Our analyses identified three molecular subclasses of oesophageal squamous cell carcinomas, but none showed evidence for an aetiological role of human papillomavirus. Squamous cell carcinomas showed frequent genomic amplifications of CCND1 and SOX2 and/or TP63, whereas ERBB2, VEGFA and GATA4 and GATA6 were more commonly amplified in adenocarcinomas. Oesophageal adenocarcinomas strongly resembled the chromosomally unstable variant of gastric adenocarcinoma, suggesting that these cancers could be considered a single disease entity. However, some molecular features, including DNA hypermethylation, occurred disproportionally in oesophageal adenocarcinomas. These data provide a framework to facilitate more rational categorization of these tumours and a foundation for new therapies.

  View details for DOI 10.1038/nature20805

  View details for Web of Science ID 000396125500030

  View details for PubMedID 28052061

• Early mutation bursts in colorectal tumors. PloS one Zhao, J., Salomon, M. P., Shibata, D., Curtis, C., Siegmund, K., Marjoram, P. 2017; 12 (3)

  Abstract

  Tumor growth is an evolutionary process involving accumulation of mutations, copy number alterations, and cancer stem cell (CSC) division and differentiation. As direct observation of this process is impossible, inference regarding when mutations occur and how stem cells divide is difficult. However, this ancestral information is encoded within the tumor itself, in the form of intratumoral heterogeneity of the tumor cell genomes. Here we present a framework that allows simulation of these processes and estimation of mutation rates at the various stages of tumor development and CSC division patterns for single-gland sequencing data from colorectal tumors. We parameterize the mutation rate and the CSC division pattern, and successfully retrieve their posterior distributions based on DNA sequence level data. Our approach exploits Approximate Bayesian Computation (ABC), a method that is becoming widely-used for problems of ancestral inference.

  View details for DOI 10.1371/journal.pone.0172516

  View details for PubMedID 28257429

  View details for PubMedCentralID PMC5336211

• Intestinal Enteroendocrine Lineage Cells Possess Homeostatic and Injury-Inducible Stem Cell Activity Cell Stem Cell Yan, K., Gevaert, O., Zheng, G., Anchang, B., Probert, C., et al 2017; 21 (1): 78 - 90.e6

  Abstract

  Several cell populations have been reported to possess intestinal stem cell (ISC) activity during homeostasis and injury-induced regeneration. Here, we explored inter-relationships between putative mouse ISC populations by comparative RNA-sequencing (RNA-seq). The transcriptomes of multiple cycling ISC populations closely resembled Lgr5+ISCs, the most well-defined ISC pool, but Bmi1-GFP+cells were distinct and enriched for enteroendocrine (EE) markers, including Prox1. Prox1-GFP+cells exhibited sustained clonogenic growth in vitro, and lineage-tracing of Prox1+cells revealed long-lived clones during homeostasis and after radiation-induced injury in vivo. Single-cell mRNA-seq revealed two subsets of Prox1-GFP+cells, one of which resembled mature EE cells while the other displayed low-level EE gene expression but co-expressed tuft cell markers, Lgr5 and Ascl2, reminiscent of label-retaining secretory progenitors. Our data suggest that the EE lineage, including mature EE cells, comprises a reservoir of homeostatic and injury-inducible ISCs, extending our understanding of cellular plasticity and stemness.

  View details for DOI 10.1016/j.stem.2017.06.014

  View details for PubMedCentralID PMC5642297

• Intestinal Enteroendocrine Lineage Cells Possess Homeostatic and Injury-Inducible Stem Cell Activity. Cell stem cell Yan, K. S., Gevaert, O., Zheng, G. X., Anchang, B., Probert, C. S., Larkin, K. A., Davies, P. S., Cheng, Z. F., Kaddis, J. S., Han, A., Roelf, K., Calderon, R. I., Cynn, E., Hu, X., Mandleywala, K., Wilhelmy, J., Grimes, S. M., Corney, D. C., Boutet, S. C., Terry, J. M., Belgrader, P., Ziraldo, S. B., Mikkelsen, T. S., Wang, F., von Furstenberg, R. J., Smith, N. R., Chandrakesan, P., May, R., Chrissy, M. A., Jain, R., Cartwright, C. A., Niland, J. C., Hong, Y. K., Carrington, J., Breault, D. T., Epstein, J., Houchen, C. W., Lynch, J. P., Martin, M. G., Plevritis, S. K., Curtis, C., Ji, H. P., Li, L., Henning, S. J., Wong, M. H., Kuo, C. J. 2017; 21 (1): 78?90.e6

  Abstract

  Several cell populations have been reported to possess intestinal stem cell (ISC) activity during homeostasis and injury-induced regeneration. Here, we explored inter-relationships between putative mouse ISC populations by comparative RNA-sequencing (RNA-seq). The transcriptomes of multiple cycling ISC populations closely resembled Lgr5+ISCs, the most well-defined ISC pool, but Bmi1-GFP+cells were distinct and enriched for enteroendocrine (EE) markers, including Prox1. Prox1-GFP+cells exhibited sustained clonogenic growth in vitro, and lineage-tracing of Prox1+cells revealed long-lived clones during homeostasis and after radiation-induced injury in vivo. Single-cell mRNA-seq revealed two subsets of Prox1-GFP+cells, one of which resembled mature EE cells while the other displayed low-level EE gene expression but co-expressed tuft cell markers, Lgr5 and Ascl2, reminiscent of label-retaining secretory progenitors. Our data suggest that the EE lineage, including mature EE cells, comprises a reservoir of homeostatic and injury-inducible ISCs, extending our understanding of cellular plasticity and stemness.

  View details for PubMedID 28686870

• A p53 Super-tumor Suppressor Reveals a Tumor Suppressive p53-Ptpn14-Yap Axis in Pancreatic Cancer. Cancer cell Mello, S. S., Valente, L. J., Raj, N., Seoane, J. A., Flowers, B. M., McClendon, J., Bieging-Rolett, K. T., Lee, J., Ivanochko, D., Kozak, M. M., Chang, D. T., Longacre, T. A., Koong, A. C., Arrowsmith, C. H., Kim, S. K., Vogel, H., Wood, L. D., Hruban, R. H., Curtis, C., Attardi, L. D. 2017; 32 (4): 460?73.e6

  Abstract

  The p53 transcription factor is a critical barrier to pancreatic cancer progression. To unravel mechanisms of p53-mediated tumor suppression, which have remained elusive, we analyzed pancreatic cancer development in mice expressing p53 transcriptional activation domain (TAD) mutants. Surprisingly, the p5353,54 TAD2 mutant behaves as a "super-tumor suppressor," with an enhanced capacity to both suppress pancreatic cancer and transactivate select p53 target genes, including Ptpn14. Ptpn14 encodes a negative regulator of the Yap oncoprotein and is necessary and sufficient for pancreatic cancer suppression, like p53. We show that p53 deficiency promotes Yap signaling and that PTPN14 and TP53 mutations are mutually exclusive in human cancers. These studies uncover a p53-Ptpn14-Yap pathway that is integral to p53-mediated tumor suppression.

  View details for PubMedID 29017057

• Inferring Tumor Phylogenies from Multi-region Sequencing. Cell systems Hu, Z., Curtis, C. 2016; 3 (1): 12-14

  Abstract

  A new computational method illuminates the heterogeneity and evolutionary histories of cells within a tumor.

  View details for DOI 10.1016/j.cels.2016.07.007

  View details for PubMedID 27467243

• Genome co-amplification upregulates a mitotic gene network activity that predicts outcome and response to mitotic protein inhibitors in breast cancer BREAST CANCER RESEARCH Hu, Z., Mao, J., Curtis, C., Huang, G., Gu, S., Heiser, L., Lenburg, M. E., Korkola, J. E., Bayani, N., Samarajiwa, S., Seoane, J. A., Dane, M. A., Esch, A., Feiler, H. S., Wang, N. J., Hardwicke, M. A., Laquerre, S., Jackson, J., Wood, K. W., Weber, B., Spellman, P. T., Aparicio, S., Wooster, R., Caldas, C., Gray, J. W. 2016; 18

  Abstract

  High mitotic activity is associated with the genesis and progression of many cancers. Small molecule inhibitors of mitotic apparatus proteins are now being developed and evaluated clinically as anticancer agents. With clinical trials of several of these experimental compounds underway, it is important to understand the molecular mechanisms that determine high mitotic activity, identify tumor subtypes that carry molecular aberrations that confer high mitotic activity, and to develop molecular markers that distinguish which tumors will be most responsive to mitotic apparatus inhibitors.We identified a coordinately regulated mitotic apparatus network by analyzing gene expression profiles for 53 malignant and non-malignant human breast cancer cell lines and two separate primary breast tumor datasets. We defined the mitotic network activity index (MNAI) as the sum of the transcriptional levels of the 54 coordinately regulated mitotic apparatus genes. The effect of those genes on cell growth was evaluated by small interfering RNA (siRNA).High MNAI was enriched in basal-like breast tumors and was associated with reduced survival duration and preferential sensitivity to inhibitors of the mitotic apparatus proteins, polo-like kinase, centromere associated protein E and aurora kinase designated GSK462364, GSK923295 and GSK1070916, respectively. Co-amplification of regions of chromosomes 8q24, 10p15-p12, 12p13, and 17q24-q25 was associated with the transcriptional upregulation of this network of 54 mitotic apparatus genes, and we identify transcription factors that localize to these regions and putatively regulate mitotic activity. Knockdown of the mitotic network by siRNA identified 22 genes that might be considered as additional therapeutic targets for this clinically relevant patient subgroup.We define a molecular signature which may guide therapeutic approaches for tumors with high mitotic network activity.

  View details for DOI 10.1186/s13058-016-0728-y

  View details for Web of Science ID 000378898900001

  View details for PubMedCentralID PMC4930593

• Genome co-amplification upregulates a mitotic gene network activity that predicts outcome and response to mitotic protein inhibitors in breast cancer. Breast cancer research Hu, Z., Mao, J., Curtis, C., Huang, G., Gu, S., Heiser, L., Lenburg, M. E., Korkola, J. E., Bayani, N., Samarajiwa, S., Seoane, J. A., A Dane, M., Esch, A., Feiler, H. S., Wang, N. J., Hardwicke, M. A., Laquerre, S., Jackson, J., W Wood, K., Weber, B., Spellman, P. T., Aparicio, S., Wooster, R., Caldas, C., Gray, J. W. 2016; 18 (1): 70-?

  Abstract

  High mitotic activity is associated with the genesis and progression of many cancers. Small molecule inhibitors of mitotic apparatus proteins are now being developed and evaluated clinically as anticancer agents. With clinical trials of several of these experimental compounds underway, it is important to understand the molecular mechanisms that determine high mitotic activity, identify tumor subtypes that carry molecular aberrations that confer high mitotic activity, and to develop molecular markers that distinguish which tumors will be most responsive to mitotic apparatus inhibitors.We identified a coordinately regulated mitotic apparatus network by analyzing gene expression profiles for 53 malignant and non-malignant human breast cancer cell lines and two separate primary breast tumor datasets. We defined the mitotic network activity index (MNAI) as the sum of the transcriptional levels of the 54 coordinately regulated mitotic apparatus genes. The effect of those genes on cell growth was evaluated by small interfering RNA (siRNA).High MNAI was enriched in basal-like breast tumors and was associated with reduced survival duration and preferential sensitivity to inhibitors of the mitotic apparatus proteins, polo-like kinase, centromere associated protein E and aurora kinase designated GSK462364, GSK923295 and GSK1070916, respectively. Co-amplification of regions of chromosomes 8q24, 10p15-p12, 12p13, and 17q24-q25 was associated with the transcriptional upregulation of this network of 54 mitotic apparatus genes, and we identify transcription factors that localize to these regions and putatively regulate mitotic activity. Knockdown of the mitotic network by siRNA identified 22 genes that might be considered as additional therapeutic targets for this clinically relevant patient subgroup.We define a molecular signature which may guide therapeutic approaches for tumors with high mitotic network activity.

  View details for DOI 10.1186/s13058-016-0728-y

  View details for PubMedID 27368372

  View details for PubMedCentralID PMC4930593

• Many private mutations originate from the first few divisions of a human colorectal adenoma JOURNAL OF PATHOLOGY Kang, H., Salomon, M. P., Sottoriva, A., Zhao, J., Toy, M., Press, M. F., Curtis, C., Marjoram, P., Siegmund, K., Shibata, D. 2015; 237 (3): 355-362

  View details for DOI 10.1002/path.4581

  View details for PubMedID 26119426

• Genomic profiling of breast cancers. Current opinion in obstetrics & gynecology Curtis, C. 2015; 27 (1): 34-39

  Abstract

  To describe recent advances in the application of advanced genomic technologies towards the identification of biomarkers of prognosis and treatment response in breast cancer.Advances in high-throughput genomic profiling such as massively parallel sequencing have enabled researchers to catalogue the spectrum of somatic alterations in breast cancers. These tools also hold promise for precision medicine through accurate patient prognostication, stratification, and the dynamic monitoring of treatment response. For example, recent efforts have defined robust molecular subgroups of breast cancer and novel subtype-specific oncogenes. In addition, previously unappreciated activating mutations in human epidermal growth factor receptor 2 have been reported, suggesting new therapeutic opportunities. Genomic profiling of cell-free tumor DNA and circulating tumor cells has been used to monitor disease burden and the emergence of resistance, and such 'liquid biopsy' approaches may facilitate the early, noninvasive detection of aggressive disease. Finally, single-cell genomics is coming of age and will contribute to an understanding of breast cancer evolutionary dynamics.Here, we highlight recent studies that employ high-throughput genomic technologies in an effort to elucidate breast cancer biology, discover new therapeutic targets, improve prognostication and stratification, and discuss the implications for precision cancer medicine.

  View details for DOI 10.1097/GCO.0000000000000145

  View details for PubMedID 25502431

• Contributions to Drug Resistance in Glioblastoma Derived from Malignant Cells in the Sub-Ependymal Zone CANCER RESEARCH Piccirillo, S. G., Spiteri, I., Sottoriva, A., Touloumis, A., Ber, S., Price, S. J., Heywood, R., Francis, N., Howarth, K. D., Collins, V. P., Venkitaraman, A. R., Curtis, C., Marioni, J. C., Tavare, S., Watts, C. 2015; 75 (1): 194-202

  Abstract

  Glioblastoma, the most common and aggressive adult brain tumor, is characterized by extreme phenotypic diversity and treatment failure. Through fluorescence-guided resection, we identified fluorescent tissue in the sub-ependymal zone (SEZ) of patients with glioblastoma. Histologic analysis and genomic characterization revealed that the SEZ harbors malignant cells with tumor-initiating capacity, analogous to cells isolated from the fluorescent tumor mass (T). We observed resistance to supramaximal chemotherapy doses along with differential patterns of drug response between T and SEZ in the same tumor. Our results reveal novel insights into glioblastoma growth dynamics, with implications for understanding and limiting treatment resistance. Cancer Res; 75(1); 194-202. ©2014 AACR.

  View details for DOI 10.1158/0008-5472.CAN-13-3131

  View details for Web of Science ID 000347383000020

  View details for PubMedID 25406193

  View details for PubMedCentralID PMC4286248

• Comprehensive molecular characterization of gastric adenocarcinoma NATURE Bass, A. J., Thorsson, V., Shmulevich, I., Reynolds, S. M., Miller, M., Bernard, B., Hinoue, T., Laird, P. W., Curtis, C., Shen, H., Weisenberger, D. J., Schultz, N., Shen, R., Weinhold, N., Keiser, D. P., Bowlby, R., Sipahimalani, P., Cherniack, A. D., Getz, G., Liu, Y., Noble, M. S., Pedamallu, C., Sougnez, C., Taylor-Weiner, A., Akbani, R., Lee, J., Liu, W., Mills, G. B., Yang, D., Zhang, W., Pantazi, A., Parfenov, M., Gulley, M., Piazuelo, M. B., Schneider, B. G., Kim, J., Boussioutas, A., Sheth, M., Demchok, J. A., Rabkin, C. S., Willis, J. E., Ng, S., Garman, K., Beer, D. G., Pennathur, A., Raphael, B. J., Wu, H., Odze, R., Kim, H. K., Bowen, J., Leraas, K. M., Lichtenberg, T. M., Weaver, L., McLellan, M., Wiznerowicz, M., Sakai, R., Getz, G., Sougnez, C., Lawrence, M. S., Cibulskis, K., Lichtenstein, L., Fisher, S., Gabriel, S. B., Lander, E. S., Ding, L., Niu, B., Ally, A., Balasundaram, M., Birol, I., Bowlby, R., Brooks, D., Butterfield, Y. S., Carlsen, R., Chu, A., Chu, J., Chuah, E., Chun, H. E., Clarke, A., Dhalla, N., Guin, R., Holt, R. A., Jones, S. J., Kasaian, K., Lee, D., Li, H. A., Lim, E., Ma, Y., Marra, M. A., Mayo, M., Moore, R. A., Mungall, A. J., Mungall, K. L., Nip, K. M., Robertson, A. G., Schein, J. E., Sipahimalani, P., Tam, A., Thiessen, N., Beroukhim, R., Carter, S. L., Cherniack, A. D., Cho, J., Cibulskis, K., DiCara, D., Frazer, S., Fisher, S., Gabriel, S. B., Gehlenborg, N., Heiman, D. I., Jung, J., Kim, J., Lander, E. S., Lawrence, M. S., Lichtenstein, L., Lin, P., Meyerson, M., Ojesina, A. I., Pedamallu, C. S., Saksena, G., Schumacher, S. E., Sougnez, C., Stojanov, P., Tabak, B., Taylor-Weiner, A., Voet, D., Rosenberg, M., Zack, T. I., Zhang, H., Zou, L., Protopopov, A., Santoso, N., Parfenov, M., Lee, S., Zhang, J., Mahadeshwar, H. S., Tang, J., Ren, X., Seth, S., Yang, L., Xu, A. W., Song, X., Pantazi, A., Xi, R., Bristow, C. A., Hadjipanayis, A., Seidman, J., Chin, L., Park, P. J., Kucherlapati, R., Akbani, R., Ling, S., Liu, W., Rao, A., Weinstein, J. N., Kim, S., Lee, J., Lu, Y., Mills, G., Hinoue, T., Weisenberger, D. J., Bootwalla, M. S., Lai, P. H., Shen, H., Triche, T., Van den Berg, D. J., Baylin, S. B., Herman, J. G., Getz, G., Chin, L., Liu, Y., Murray, B. A., Noble, M. S., Askoy, B. A., Ciriello, G., Dresdner, G., Gao, J., Gross, B., Jacobsen, A., Lee, W., Ramirez, R., Sander, C., Schultz, N., Senbabaoglu, Y., Sinha, R., Sumer, S. O., Sun, Y., Weinhold, N., Thorsson, V., Bernard, B., Iype, L., Kramer, R. W., Kreisberg, R., Miller, M., Reynolds, S. M., Rovira, H., Tasman, N., Shmulevich, I., Ng, S., Haussler, D., Stuart, J. M., Akbani, R., Ling, S., Liu, W., Rao, A., Weinstein, J. N., Verhaak, R. G., Mills, G. B., Leiserson, M. D., Raphael, B. J., Wu, H., Taylor, B. S., Black, A. D., Bowen, J., Carney, J. A., Gastier-Foster, J. M., Helsel, C., Leraas, K. M., Lichtenberg, T. M., McAllister, C., Ramirez, N. C., Tabler, T. R., Wise, L., Zmuda, E., Penny, R., Crain, D., Gardner, J., Lau, K., Curely, E., Mallery, D., Morris, S., Paulauskis, J., Shelton, T., Shelton, C., Sherman, M., Benz, C., Lee, J., Fedosenko, K., Manikhas, G., Voronina, O., Belyaev, D., Dolzhansky, O., Rathmell, W. K., Brzezinski, J., Ibbs, M., Korski, K., Kycler, W., Lazniak, R., Leporowska, E., Mackiewicz, A., Murawa, D., Murawa, P., Spychala, A., Suchorska, W. M., Tatka, H., Teresiak, M., Wiznerowicz, M., Abdel-Misih, R., Bennett, J., Brown, J., Iacocca, M., Rabeno, B., Kwon, S., Penny, R., Gardner, J., Kemkes, A., Mallery, D., Morris, S., Shelton, T., Shelton, C., Curley, E., Alexopoulou, I., Engel, J., Bartlett, J., Albert, M., Park, D., Dhir, R., Luketich, J., Landreneau, R., Janjigian, Y. Y., Kelsen, D. P., Cho, E., Ladanyi, M., Tang, L., McCall, S. J., Park, Y. S., Cheong, J., Ajani, J., Camargo, M. C., Alonso, S., Ayala, B., Jensen, M. A., Pihl, T., Raman, R., Walton, J., Wan, Y., Demchok, J. A., Eley, G., Shaw, K. R., Sheth, M., Tarnuzzer, R., Wang, Z., Yang, L., Zenklusen, J. C., Davidsen, T., Hutter, C. M., Sofia, H. J., Burton, R., Chudamani, S., Liu, J. 2014; 513 (7517): 202-209

  Abstract

  Gastric cancer is a leading cause of cancer deaths, but analysis of its molecular and clinical characteristics has been complicated by histological and aetiological heterogeneity. Here we describe a comprehensive molecular evaluation of 295 primary gastric adenocarcinomas as part of The Cancer Genome Atlas (TCGA) project. We propose a molecular classification dividing gastric cancer into four subtypes: tumours positive for Epstein-Barr virus, which display recurrent PIK3CA mutations, extreme DNA hypermethylation, and amplification of JAK2, CD274 (also known as PD-L1) and PDCD1LG2 (also known as PD-L2); microsatellite unstable tumours, which show elevated mutation rates, including mutations of genes encoding targetable oncogenic signalling proteins; genomically stable tumours, which are enriched for the diffuse histological variant and mutations of RHOA or fusions involving RHO-family GTPase-activating proteins; and tumours with chromosomal instability, which show marked aneuploidy and focal amplification of receptor tyrosine kinases. Identification of these subtypes provides a roadmap for patient stratification and trials of targeted therapies.

  View details for DOI 10.1038/nature13480

  View details for Web of Science ID 000341362800044

  View details for PubMedID 25079317

• The Breast Cancer Oncogene EMSY Represses Transcription of Antimetastatic microRNA miR-31 (vol 53, pg 806, 2014) MOLECULAR CELL Vire, E., Curtis, C., Davalos, V., Git, A., Robson, S., Villanueva, A., Vidal, A., Barbieri, I., Aparicio, S., Esteller, M., Caldas, C., Kouzarides, T. 2014; 54 (1): 203-203

  View details for DOI 10.1016/j.molcel.2014.03.041

  View details for Web of Science ID 000334315900019

• Genome-driven integrated classification of breast cancer validated in over 7,500 samples Genome Biology Ali, R., Rueda, O. M., Chin, S., Curtis, C., Dunning, M. J., Aparicio, S., Caldas, C. 2014; 15 (8): 431
• A tumor DNA complex aberration index is an independent predictor of survival in breast and ovarian cancer. Molecular oncology Vollan, H. K., Rueda, O. M., Chin, S. F., Curtis, C., Turashvili, G., Shah, S., Lingjærde, O. C., Yuan, Y., Ng, C. K., Dunning, M. J., Dicks, E., Provenzano, E., Sammut, S., McKinney, S., Ellis, I. O., Pinder, S., Purushotham, A., Murphy, L. C., Kristensen, V. N., Brenton, J. D., Pharoah, P. D., Børresen-Dale, A. L., Aparicio, S., Caldas, C. 2014

  Abstract

  Complex focal chromosomal rearrangements in cancer genomes, also called "firestorms", can be scored from DNA copy number data. The complex arm-wise aberration index (CAAI) is a score that captures DNA copy number alterations that appear as focal complex events in tumors, and has potential prognostic value in breast cancer. This study aimed to validate this DNA-based prognostic index in breast cancer and test for the first time its potential prognostic value in ovarian cancer. Copy number alteration (CNA) data from 1950 breast carcinomas (METABRIC cohort) and 508 high-grade serous ovarian carcinomas (TCGA dataset) were analyzed. Cases were classified as CAAI positive if at least one complex focal event was scored. Complex alterations were frequently localized on chromosome 8p (n = 159), 17q (n = 176) and 11q (n = 251). CAAI events on 11q were most frequent in estrogen receptor positive (ER+) cases and on 17q in estrogen receptor negative (ER-) cases. We found only a modest correlation between CAAI and the overall rate of genomic instability (GII) and number of breakpoints (r = 0.27 and r = 0.42, p < 0.001). Breast cancer specific survival (BCSS), overall survival (OS) and ovarian cancer progression free survival (PFS) were used as clinical end points in Cox proportional hazard model survival analyses. CAAI positive breast cancers (43%) had higher mortality: hazard ratio (HR) of 1.94 (95%CI, 1.62-2.32) for BCSS, and of 1.49 (95%CI, 1.30-1.71) for OS. Representations of the 70-gene and the 21-gene predictors were compared with CAAI in multivariable models and CAAI was independently significant with a Cox adjusted HR of 1.56 (95%CI, 1.23-1.99) for ER+ and 1.55 (95%CI, 1.11-2.18) for ER- disease. None of the expression-based predictors were prognostic in the ER- subset. We found that a model including CAAI and the two expression-based prognostic signatures outperformed a model including the 21-gene and 70-gene signatures but excluding CAAI. Inclusion of CAAI in the clinical prognostication tool PREDICT significantly improved its performance. CAAI positive ovarian cancers (52%) also had worse prognosis: HRs of 1.3 (95%CI, 1.1-1.7) for PFS and 1.3 (95%CI, 1.1-1.6) for OS. This study validates CAAI as an independent predictor of survival in both ER+ and ER- breast cancer and reveals a significant prognostic value for CAAI in high-grade serous ovarian cancer.

  View details for DOI 10.1016/j.molonc.2014.07.019

  View details for PubMedID 25169931

• Precise inference of copy number alterations in tumor samples from SNP arrays BIOINFORMATICS Chen, G. K., Chang, X., Curtis, C., Wang, K. 2013; 29 (23): 2964-2970

  Abstract

  The accurate detection of copy number alterations (CNAs) in human genomes is important for understanding susceptibility to cancer and mechanisms of tumor progression. CNA detection in tumors from single nucleotide polymorphism (SNP) genotyping arrays is a challenging problem due to phenomena such as aneuploidy, stromal contamination, genomic waves and intra-tumor heterogeneity, issues that leading methods do not optimally address.Here we introduce methods and software (PennCNV-tumor) for fast and accurate CNA detection using signal intensity data from SNP genotyping arrays. We estimate stromal contamination by applying a maximum likelihood approach over multiple discrete genomic intervals. By conditioning on signal intensity across the genome, our method accounts for both aneuploidy and genomic waves. Finally, our method uses a hidden Markov model to integrate multiple sources of information, including total and allele-specific signal intensity at each SNP, as well as physical maps to make posterior inferences of CNAs. Using real data from cancer cell-lines and patient tumors, we demonstrate substantial improvements in accuracy and computational efficiency compared with existing methods.

  View details for DOI 10.1093/bioinformatics/btt521

  View details for Web of Science ID 000327508300002

  View details for PubMedID 24021380

• The shaping and functional consequences of the microRNA landscape in breast cancer NATURE Dvinge, H., Git, A., Graef, S., Salmon-Divon, M., Curtis, C., Sottoriva, A., Zhao, Y., Hirst, M., Armisen, J., Miska, E. A., Chin, S., Provenzano, E., Turashvili, G., Green, A., Ellis, I., Aparicio, S., Caldas, C. 2013; 497 (7449): 378-382

  Abstract

  MicroRNAs (miRNAs) show differential expression across breast cancer subtypes, and have both oncogenic and tumour-suppressive roles. Here we report the miRNA expression profiles of 1,302 breast tumours with matching detailed clinical annotation, long-term follow-up and genomic and messenger RNA expression data. This provides a comprehensive overview of the quantity, distribution and variation of the miRNA population and provides information on the extent to which genomic, transcriptional and post-transcriptional events contribute to miRNA expression architecture, suggesting an important role for post-transcriptional regulation. The key clinical parameters and cellular pathways related to the miRNA landscape are characterized, revealing context-dependent interactions, for example with regards to cell adhesion and Wnt signalling. Notably, only prognostic miRNA signatures derived from breast tumours devoid of somatic copy-number aberrations (CNA-devoid) are consistently prognostic across several other subtypes and can be validated in external cohorts. We then use a data-driven approach to seek the effects of miRNAs associated with differential co-expression of mRNAs, and find that miRNAs act as modulators of mRNA-mRNA interactions rather than as on-off molecular switches. We demonstrate such an important modulatory role for miRNAs in the biology of CNA-devoid breast cancers, a common subtype in which the immune response is prominent. These findings represent a new framework for studying the biology of miRNAs in human breast cancer.

  View details for DOI 10.1038/nature12108

  View details for Web of Science ID 000318952000040

  View details for PubMedID 23644459

• Improving Breast Cancer Survival Analysis through Competition-Based Multidimensional Modeling PLOS COMPUTATIONAL BIOLOGY Bilal, E., Dutkowski, J., Guinney, J., Jang, I. S., Logsdon, B. A., Pandey, G., Sauerwine, B. A., Shimoni, Y., Vollan, H. K., Mecham, B. H., Rueda, O. M., Tost, J., Curtis, C., Alvarez, M. J., Kristensen, V. N., Aparicio, S., Borresen-Dale, A., Caldas, C., Califano, A., Friend, S. H., Ideker, T., Schadt, E. E., Stolovitzky, G. A., Margolin, A. A. 2013; 9 (5)

  Abstract

  Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models.

  View details for DOI 10.1371/journal.pcbi.1003047

  View details for Web of Science ID 000320032100009

  View details for PubMedID 23671412

• Systematic Analysis of Challenge-Driven Improvements in Molecular Prognostic Models for Breast Cancer SCIENCE TRANSLATIONAL MEDICINE Margolin, A. A., Bilal, E., Huang, E., Norman, T. C., Ottestad, L., Mecham, B. H., Sauerwine, B., Kellen, M. R., Mangravite, L. M., Furia, M. D., Vollan, H. K., Rueda, O. M., Guinney, J., Deflaux, N. A., Hoff, B., Schildwachter, X., Russnes, H. G., Park, D., Vang, V. O., Pirtle, T., Youseff, L., Citro, C., Curtis, C., Kristensen, V. N., Hellerstein, J., Friend, S. H., Stolovitzky, G., Aparicio, S., Caldas, C., Borresen-Dale, A. 2013; 5 (181)

  Abstract

  Although molecular prognostics in breast cancer are among the most successful examples of translating genomic analysis to clinical applications, optimal approaches to breast cancer clinical risk prediction remain controversial. The Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge (BCC) is a crowdsourced research study for breast cancer prognostic modeling using genome-scale data. The BCC provided a community of data analysts with a common platform for data access and blinded evaluation of model accuracy in predicting breast cancer survival on the basis of gene expression data, copy number data, and clinical covariates. This approach offered the opportunity to assess whether a crowdsourced community Challenge would generate models of breast cancer prognosis commensurate with or exceeding current best-in-class approaches. The BCC comprised multiple rounds of blinded evaluations on held-out portions of data on 1981 patients, resulting in more than 1400 models submitted as open source code. Participants then retrained their models on the full data set of 1981 samples and submitted up to five models for validation in a newly generated data set of 184 breast cancer patients. Analysis of the BCC results suggests that the best-performing modeling strategy outperformed previously reported methods in blinded evaluations; model performance was consistent across several independent evaluations; and aggregating community-developed models achieved performance on par with the best-performing individual models.

  View details for DOI 10.1126/scitranslmed.3006112

  View details for Web of Science ID 000317720300005

  View details for PubMedID 23596205

• Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Sottoriva, A., Spiteri, I., Piccirillo, S. G., Touloumis, A., Collins, V. P., Marioni, J. C., Curtis, C., Watts, C., Tavare, S. 2013; 110 (10): 4009-4014

  Abstract

  Glioblastoma (GB) is the most common and aggressive primary brain malignancy, with poor prognosis and a lack of effective therapeutic options. Accumulating evidence suggests that intratumor heterogeneity likely is the key to understanding treatment failure. However, the extent of intratumor heterogeneity as a result of tumor evolution is still poorly understood. To address this, we developed a unique surgical multisampling scheme to collect spatially distinct tumor fragments from 11 GB patients. We present an integrated genomic analysis that uncovers extensive intratumor heterogeneity, with most patients displaying different GB subtypes within the same tumor. Moreover, we reconstructed the phylogeny of the fragments for each patient, identifying copy number alterations in EGFR and CDKN2A/B/p14ARF as early events, and aberrations in PDGFRA and PTEN as later events during cancer progression. We also characterized the clonal organization of each tumor fragment at the single-molecule level, detecting multiple coexisting cell lineages. Our results reveal the genome-wide architecture of intratumor variability in GB across multiple spatial scales and patient-specific patterns of cancer evolution, with consequences for treatment design.

  View details for DOI 10.1073/pnas.1219747110

  View details for Web of Science ID 000316377400072

  View details for PubMedID 23412337

  View details for PubMedCentralID PMC3593922

• Single-Molecule Genomic Data Delineate Patient-Specific Tumor Profiles and Cancer Stem Cell Organization CANCER RESEARCH Sottoriva, A., Spiteri, I., Shibata, D., Curtis, C., Tavare, S. 2013; 73 (1): 41-49

  Abstract

  Substantial evidence supports the concept that cancers are organized in a cellular hierarchy with cancer stem cells (CSC) at the apex. To date, the primary evidence for CSCs derives from transplantation assays, which have known limitations. In particular, they are unable to report on the fate of cells within the original human tumor. Because of the difficulty in measuring tumor characteristics in patients, cellular organization and other aspects of cancer dynamics have not been quantified directly, although they likely play a fundamental role in tumor progression and therapy response. As such, new approaches to study CSCs in patient-derived tumor specimens are needed. In this study, we exploited ultradeep single-molecule genomic data derived from multiple microdissected colorectal cancer glands per tumor, along with a novel quantitative approach to measure tumor characteristics, define patient-specific tumor profiles, and infer tumor ancestral trees. We show that each cancer is unique in terms of its cellular organization, molecular heterogeneity, time from malignant transformation, and rate of mutation and apoptosis. Importantly, we estimate CSC fractions between 0.5% and 4%, indicative of a hierarchical organization responsible for long-lived CSC lineages, with variable rates of symmetric cell division. We also observed extensive molecular heterogeneity, both between and within individual cancer glands, suggesting a complex hierarchy of mitotic clones. Our framework enables the measurement of clinically relevant patient-specific characteristics in vivo, providing insight into the cellular organization and dynamics of tumor growth, with implications for personalized patient care.

  View details for DOI 10.1158/0008-5472.CAN-12-2273

  View details for Web of Science ID 000313019800006

  View details for PubMedID 23090114

  View details for PubMedCentralID PMC3544316

• Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling (vol 4, 161er6, 2012) SCIENCE TRANSLATIONAL MEDICINE Yuan, Y., Failmezger, H., Rueda, O. M., Ali, H. R., Graef, S., Chin, S., SCHWARZ, R. F., Curtis, C., DUNNING, M. J., Bardwell, H., Johnson, N., Doyle, S., Turashvili, G., Provenzano, E., Aparicio, S., Caldas, C., Markowetz, F. 2012; 4 (161)

  View details for DOI 10.1126/scitranslmed.3005298

  View details for Web of Science ID 000311426900001

• Calling Sample Mix-Ups in Cancer Population Studies PLOS ONE Lynch, A. G., Chin, S., Dunning, M. J., Caldas, C., Tavare, S., Curtis, C. 2012; 7 (8)

  Abstract

  Sample tracking errors have been and always will be a part of the practical implementation of large experiments. It has recently been proposed that expression quantitative trait loci (eQTLs) and their associated effects could be used to identify sample mix-ups and this approach has been applied to a number of large population genomics studies to illustrate the prevalence of the problem. We had adopted a similar approach, termed 'BADGER', in the METABRIC project. METABRIC is a large breast cancer study that may have been the first in which eQTL-based detection of mismatches was used during the study, rather than after the event, to aid quality assurance. We report here on the particular issues associated with large cancer studies performed using historical samples, which complicate the interpretation of such approaches. In particular we identify the complications of using tumour samples, of considering cellularity and RNA quality, of distinct subgroups existing in the study population (including family structures), and of choosing eQTLs to use. We also present some results regarding the design of experiments given consideration of these matters. The eQTL-based approach to identifying sample tracking errors is seen to be of value to these studies, but requiring care in its implementation.

  View details for DOI 10.1371/journal.pone.0041815

  View details for Web of Science ID 000307378500009

  View details for PubMedID 22912679

  View details for PubMedCentralID PMC3415393

• A Sparse Regulatory Network of Copy-Number Driven Gene Expression Reveals Putative Breast Cancer Oncogenes IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS Yuan, Y., Curtis, C., Caldas, C., Markowetz, F. 2012; 9 (4): 947-954

  Abstract

  Copy number aberrations are recognized to be important in cancer as they may localize to regions harboring oncogenes or tumor suppressors. Such genomic alterations mediate phenotypic changes through their impact on expression. Both cis- and transacting alterations are important since they may help to elucidate putative cancer genes. However, amidst numerous passenger genes, trans-effects are less well studied due to the computational difficulty in detecting weak and sparse signals in the data, and yet may influence multiple genes on a global scale. We propose an integrative approach to learn a sparse interaction network of DNA copy-number regions with their downstream transcriptional targets in breast cancer. With respect to goodness of fit on both simulated and real data, the performance of sparse network inference is no worse than other state-of-the-art models but with the advantage of simultaneous feature selection and efficiency. The DNA-RNA interaction network helps to distinguish copy-number driven expression alterations from those that are copy-number independent. Further, our approach yields a quantitative copy-number dependency score, which distinguishes cis- versus trans-effects. When applied to a breast cancer data set, numerous expression profiles were impacted by cis-acting copy-number alterations, including several known oncogenes such as GRB7, ERBB2, and LSM1. Several trans-acting alterations were also identified, impacting genes such as ADAM2 and BAGE, which warrant further investigation.An R package named lol is available from www.markowetzlab.org/software/lol.html.

  View details for DOI 10.1109/TCBB.2011.105

  View details for Web of Science ID 000304147000002

  View details for PubMedID 21788678

• The clonal and mutational evolution spectrum of primary triple-negative breast cancers NATURE Shah, S. P., Roth, A., Goya, R., Oloumi, A., Ha, G., Zhao, Y., Turashvili, G., Ding, J., Tse, K., Haffari, G., Bashashati, A., Prentice, L. M., Khattra, J., Burleigh, A., Yap, D., Bernard, V., McPherson, A., Shumansky, K., Crisan, A., Giuliany, R., Heravi-Moussavi, A., Rosner, J., Lai, D., Birol, I., Varhol, R., Tam, A., Dhalla, N., Zeng, T., Ma, K., Chan, S. K., Griffith, M., Moradian, A., Cheng, S. G., Morin, G. B., Watson, P., Gelmon, K., Chia, S., Chin, S., Curtis, C., Rueda, O. M., Pharoah, P. D., Damaraju, S., Mackey, J., Hoon, K., Harkins, T., Tadigotla, V., Sigaroudinia, M., Gascard, P., Tlsty, T., Costello, J. F., Meyer, I. M., Eaves, C. J., Wasserman, W. W., Jones, S., Huntsman, D., Hirst, M., Caldas, C., Marra, M. A., Aparicio, S. 2012; 486 (7403): 395-399

  Abstract

  Primary triple-negative breast cancers (TNBCs), a tumour type defined by lack of oestrogen receptor, progesterone receptor and ERBB2 gene amplification, represent approximately 16% of all breast cancers. Here we show in 104 TNBC cases that at the time of diagnosis these cancers exhibit a wide and continuous spectrum of genomic evolution, with some having only a handful of coding somatic aberrations in a few pathways, whereas others contain hundreds of coding somatic mutations. High-throughput RNA sequencing (RNA-seq) revealed that only approximately 36% of mutations are expressed. Using deep re-sequencing measurements of allelic abundance for 2,414 somatic mutations, we determine for the first time-to our knowledge-in an epithelial tumour subtype, the relative abundance of clonal frequencies among cases representative of the population. We show that TNBCs vary widely in their clonal frequencies at the time of diagnosis, with the basal subtype of TNBC showing more variation than non-basal TNBC. Although p53 (also known as TP53), PIK3CA and PTEN somatic mutations seem to be clonally dominant compared to other genes, in some tumours their clonal frequencies are incompatible with founder status. Mutations in cytoskeletal, cell shape and motility proteins occurred at lower clonal frequencies, suggesting that they occurred later during tumour progression. Taken together, our results show that understanding the biology and therapeutic responses of patients with TNBC will require the determination of individual tumour clonal genotypes.

  View details for DOI 10.1038/nature10933

  View details for Web of Science ID 000305466800042

  View details for PubMedID 22495314

• Effects of BRCA2 cis-regulation in normal breast and cancer risk amongst BRCA2 mutation carriers BREAST CANCER RESEARCH Maia, A., Antoniou, A. C., O'Reilly, M., Samarajiwa, S., Dunning, M., Kartsonaki, C., Chin, S., Curtis, C. N., McGuffog, L., Domchek, S. M., Easton, D. F., Peock, S., Frost, D., Evans, D. G., Eeles, R., Izatt, L., Adlard, J., Eccles, D., Sinilnikova, O. M., Mazoyer, S., Stoppa-Lyonnet, D., Gauthier-Villars, M., Faivre, L., Venat-Bouvet, L., Delnatte, C., Nevanlinna, H., Couch, F. J., Godwin, A. K., Caligo, M. A., Barkardottir, R. B., Chen, X., Beesley, J., Healey, S., Caldas, C., Chenevix-Trench, G., Ponder, B. A. 2012; 14 (2)

  Abstract

  Cis-acting regulatory single nucleotide polymorphisms (SNPs) at specific loci may modulate penetrance of germline mutations at the same loci by introducing different levels of expression of the wild-type allele. We have previously reported that BRCA2 shows differential allelic expression and we hypothesize that the known variable penetrance of BRCA2 mutations might be associated with this mechanism.We combined haplotype analysis and differential allelic expression of BRCA2 in breast tissue to identify expression haplotypes and candidate cis-regulatory variants. These candidate variants underwent selection based on in silico predictions for regulatory potential and disruption of transcription factor binding, and were functionally analyzed in vitro and in vivo in normal and breast cancer cell lines. SNPs tagging the expression haplotypes were correlated with the total expression of several genes in breast tissue measured by Taqman and microarray technologies. The effect of the expression haplotypes on breast cancer risk in BRCA2 mutation carriers was investigated in 2,754 carriers.We identified common haplotypes associated with differences in the levels of BRCA2 expression in human breast cells. We characterized three cis-regulatory SNPs located at the promoter and two intronic regulatory elements which affect the binding of the transcription factors C/EBP?, HMGA1, D-binding protein (DBP) and ZF5. We showed that the expression haplotypes also correlated with changes in the expression of other genes in normal breast. Furthermore, there was suggestive evidence that the minor allele of SNP rs4942440, which is associated with higher BRCA2 expression, is also associated with a reduced risk of breast cancer (per-allele hazard ratio (HR) = 0.85, 95% confidence interval (CI) = 0.72 to 1.00, P-trend = 0.048).Our work provides further insights into the role of cis-regulatory variation in the penetrance of disease-causing mutations. We identified small-effect genetic variants associated with allelic expression differences in BRCA2 which could possibly affect the risk in mutation carriers through altering expression levels of the wild-type allele.

  View details for DOI 10.1186/bcr3169

  View details for Web of Science ID 000304771800038

  View details for PubMedID 22513257

• Penalized regression elucidates aberration hotspots mediating subtype-specific transcriptional responses in breast cancer BIOINFORMATICS Yuan, Y., Rueda, O. M., Curtis, C., Markowetz, F. 2011; 27 (19): 2679-2685

  Abstract

  Copy number alterations (CNAs) associated with cancer are known to contribute to genomic instability and gene deregulation. Integrating CNAs with gene expression helps to elucidate the mechanisms by which CNAs act and to identify the transcriptional downstream targets of CNAs. Such analyses can help to sort functional driver events from the many accompanying passenger alterations. However, the way CNAs affect gene expression can vary in different cellular contexts, for example between different subtypes of the same cancer. Thus, it is important to develop computational approaches capable of inferring differential connectivity of regulatory networks in different cellular contexts.We propose a statistical deregulation model that integrates copy number and expression data of different disease subtypes to jointly model common and differential regulatory relationships. Our model not only identifies CNAs driving gene expression changes, but at the same time also predicts differences in regulation that distinguish one cancer subtype from the other. We implement our model in a penalized regression framework and demonstrate in a simulation study the feasibility and accuracy of our approach. Subsequently, we show that this model can identify both known and novel aspects of cross-talk between the ER and NOTCH pathways in ER-negative-specific deregulations, when compared with ER-positive breast cancer. This flexible model can be applied on other modalities such as methylation or microRNA and expression to disentangle cancer signaling pathways.The Bioconductor-compliant R package DANCE is available from www.markowetzlab.org/software/yinyin.yuan@cancer.org.uk; florian.markowetz@cancer.org.uk.

  View details for DOI 10.1093/bioinformatics/btr450

  View details for Web of Science ID 000295412200009

  View details for PubMedID 21804112

• ZNF703 is a common Luminal B breast cancer oncogene that differentially regulates luminal and basal progenitors in human mammary epithelium EMBO MOLECULAR MEDICINE Holland, D. G., Burleigh, A., Git, A., Goldgraben, M. A., Perez-Mancera, P. A., Chin, S., Hurtado, A., Bruna, A., Ali, H. R., Greenwood, W., Dunning, M. J., Samarajiwa, S., Menon, S., Rueda, O. M., Lynch, A. G., McKinney, S., Ellis, I. O., Eaves, C. J., Carroll, J. S., Curtis, C., Aparicio, S., Caldas, C. 2011; 3 (3): 167-180

  Abstract

  The telomeric amplicon at 8p12 is common in oestrogen receptor-positive (ER+) breast cancers. Array-CGH and expression analyses of 1172 primary breast tumours revealed that ZNF703 was the single gene within the minimal amplicon and was amplified predominantly in the Luminal B subtype. Amplification was shown to correlate with increased gene and protein expression and was associated with a distinct expression signature and poor clinical outcome. ZNF703 transformed NIH 3T3 fibroblasts, behaving as a classical oncogene, and regulated proliferation in human luminal breast cancer cell lines and immortalized human mammary epithelial cells. Manipulation of ZNF703 expression in the luminal MCF7 cell line modified the effects of TGF? on proliferation. Overexpression of ZNF703 in normal human breast epithelial cells enhanced the frequency of in vitro colony-forming cells from luminal progenitors. Taken together, these data strongly point to ZNF703 as a novel oncogene in Luminal B breast cancer.

  View details for DOI 10.1002/emmm.201100122

  View details for Web of Science ID 000288727200006

  View details for PubMedID 21337521

• The importance of platform annotation in interpreting microarray data LANCET ONCOLOGY Dunning, M. J., Curtis, C., Barbosa-Morais, N. L., Caldas, C., Tavare, S., Lynch, A. G. 2010; 11 (8): 717-717

  View details for Web of Science ID 000281009500013

  View details for PubMedID 20688273

• The pitfalls of platform comparison: DNA copy number array technologies assessed BMC GENOMICS Curtis, C., Lynch, A. G., Dunning, M. J., Spiteri, I., Marioni, J. C., Hadfield, J., Chin, S., Brenton, J. D., Tavare, S., Caldas, C. 2009; 10

  Abstract

  The accurate and high resolution mapping of DNA copy number aberrations has become an important tool by which to gain insight into the mechanisms of tumourigenesis. There are various commercially available platforms for such studies, but there remains no general consensus as to the optimal platform. There have been several previous platform comparison studies, but they have either described older technologies, used less-complex samples, or have not addressed the issue of the inherent biases in such comparisons. Here we describe a systematic comparison of data from four leading microarray technologies (the Affymetrix Genome-wide SNP 5.0 array, Agilent High-Density CGH Human 244A array, Illumina HumanCNV370-Duo DNA Analysis BeadChip, and the Nimblegen 385 K oligonucleotide array). We compare samples derived from primary breast tumours and their corresponding matched normals, well-established cancer cell lines, and HapMap individuals. By careful consideration and avoidance of potential sources of bias, we aim to provide a fair assessment of platform performance.By performing a theoretical assessment of the reproducibility, noise, and sensitivity of each platform, notable differences were revealed. Nimblegen exhibited between-replicate array variances an order of magnitude greater than the other three platforms, with Agilent slightly outperforming the others, and a comparison of self-self hybridizations revealed similar patterns. An assessment of the single probe power revealed that Agilent exhibits the highest sensitivity. Additionally, we performed an in-depth visual assessment of the ability of each platform to detect aberrations of varying sizes. As expected, all platforms were able to identify large aberrations in a robust manner. However, some focal amplifications and deletions were only detected in a subset of the platforms.Although there are substantial differences in the design, density, and number of replicate probes, the comparison indicates a generally high level of concordance between platforms, despite differences in the reproducibility, noise, and sensitivity. In general, Agilent tended to be the best aCGH platform and Affymetrix, the superior SNP-CGH platform, but for specific decisions the results described herein provide a guide for platform selection and study design, and the dataset a resource for more tailored comparisons.

  View details for DOI 10.1186/1471-2164-10-588

  View details for Web of Science ID 000273075400002

  View details for PubMedID 19995423

• Drosophila melanogaster p53 has developmental stage-specific and sex-specific effects on adult life span indicative of sexual antagonistic pleiotropy AGING-US Waskar, M., Landis, G. N., Shen, J., Curtis, C., Tozer, K., Abdueva, D., Skvortsov, D., Tavare, S., Tower, J. 2009; 1 (11): 903-936

  Abstract

  Truncated and mutant forms ofp53 affect life span in Drosophila, nematodes and mice, however the role of wild-type p53 in aging remains unclear. Here conditional over-expression of both wild-type and mutant p53 transgenes indicated that, in adult flies, p53 limits life span in females but favors life span in males. In contrast, during larval development, moderate over-expression of p53 produced both male and female adults with increased life span. Mutations of the endogenous p53 gene also had sex-specific effects on life span under control and stress conditions: null mutation of p53 increased life span in females, and had smaller, more variable effects in males. These developmental stage-specific and sex-specific effects of p53 on adult life span are consistent with a sexual antagonistic pleiotropy model.

  View details for Web of Science ID 000276402700004

  View details for PubMedID 20157574

• Swift: primary data analysis for the Illumina Solexa sequencing platform BIOINFORMATICS Whiteford, N., Skelly, T., Curtis, C., Ritchie, M. E., Loehr, A., Zaranek, A. W., Abnizova, I., Brown, C. 2009; 25 (17): 2194-2199

  Abstract

  Primary data analysis methods are of critical importance in second generation DNA sequencing. Improved methods have the potential to increase yield and reduce the error rates. Openly documented analysis tools enable the user to understand the primary data, this is important for the optimization and validity of their scientific work.In this article, we describe Swift, a new tool for performing primary data analysis on the Illumina Solexa Sequencing Platform. Swift is the first tool, outside of the vendors own software, which completes the full analysis process, from raw images through to base calls. As such it provides an alternative to, and independent validation of, the vendor supplied tool. Our results show that Swift is able to increase yield by 13.8%, at comparable error rate.

  View details for DOI 10.1093/bioinformatics/btp383

  View details for Web of Science ID 000269196000008

  View details for PubMedID 19549630

• Product Length, Dye Choice, and Detection Chemistry in the Bead-Emulsion Amplification of Millions of Single DNA Molecules in Parallel ANALYTICAL CHEMISTRY Tiemann-Boege, I., Curtis, C., Shinde, D. N., Goodman, D. B., Tavare, S., Arnheim, N. 2009; 81 (14): 5770-5776

  Abstract

  The amplification of millions of single molecules in parallel can be performed on microscopic magnetic beads that are contained in aqueous compartments of an oil-buffer emulsion. These bead-emulsion amplification (BEA) reactions result in beads that are covered by almost-identical copies derived from a single template. The post-amplification analysis is performed using different fluorophore-labeled probes. We have identified BEA reaction conditions that efficiently produce longer amplicons of up to 450 base pairs. These conditions include the use of a Titanium Taq amplification system. Second, we explored alternate fluorophores coupled to probes for post-PCR DNA analysis. We demonstrate that four different Alexa fluorophores can be used simultaneously with extremely low crosstalk. Finally, we developed an allele-specific extension chemistry that is based on Alexa dyes to query individual nucleotides of the amplified material that is both highly efficient and specific.

  View details for DOI 10.1021/ac900633y

  View details for Web of Science ID 000268135000025

  View details for PubMedID 19601653

• A screen of apoptosis and senescence regulatory genes for life span effects when over-expressed in Drosophila AGING-US Shen, J., Curtis, C., Tavare, S., Tower, J. 2009; 1 (2): 191-211

  Abstract

  Conditional expression of transgenes in Drosophila was produced using the Geneswitch system, wherein feeding the drug RU486/Mifepristone activates the artificial transcription factor Geneswitch. Geneswitch was expressed using the Actin5C promoter and this was found to yield conditional, tissue-general expression of a target transgene (UAS-GFP) in both larvae and adult flies. Nervous system-specific (Elav-GS) and fat body-specific Geneswitch drivers were also characterized using UAS-GFP. Fourteen genes implicated in growth, apoptosis and senescence regulatory pathways were over-expressed in adult flies or during larval development, and assayed for effects on adult fly life span. Over-expression of a dominant p53 allele (p53-259H) in adult flies using the ubiquitous driver produced increased life span in females but not males, consistent with previous studies. Both wingless and Ras activated form transgenes were lethal when expressed in larvae, and reduced life span when expressed in adults, consistent with results from other model systems indicating that the wingless and Ras pathways can promote senescence. Over-expression of the caspase inhibitor baculovirus p35 during larval development reduced the mean life span of male and female adults, and also produced a subset of females with increased life span. These experiments suggest that baculovirus p35 and the wingless and Ras pathways can have sex-specific and developmental stage-specific effects on adult Drosophila life span, and these reagents should be useful for the further analysis of the role of these conserved pathways in aging.

  View details for Web of Science ID 000276400900005

  View details for PubMedID 20157509

• Explaining differences in saturation levels for Affymetrix GeneChip (R) arrays NUCLEIC ACIDS RESEARCH Skvortsov, D., Abdueva, D., Curtis, C., Schaub, B., Tavare, S. 2007; 35 (12): 4154-4163

  Abstract

  The experimental spike-in studies of microarray hybridization conducted by Affymetrix demonstrate a nonlinear response of fluorescence intensity signal to target concentration. Several theoretical models have been put forward to explain these data. It was shown that the Langmuir adsorption isotherm recapitulates a general trend of signal response to concentration. However, this model fails to explain some key properties of the observed signal. In particular, according to the simple Langmuir isotherm, all probes should saturate at the same intensity level. However, this effect was not observed in the publicly available Affymetrix spike-in data sets. On the contrary, it was found that the saturation intensities vary greatly and can be predicted based on the probe sequence composition. In our experimental study, we attempt to account for the unexplained variation in the observed probe intensities using customized fluidics scripts. We explore experimentally the effect of the stringent wash, target concentration and hybridization time on the final microarray signal. The washing effect is assessed by scanning chips both prior to and after the stringent wash. Selective labeling of both specific and non-specific targets allows the visualization and investigation of the washing effect for both specific and non-specific signal components. We propose a new qualitative model of the probe-target hybridization mechanism that is in agreement with observed hybridization and washing properties of short oligonucleotide microarrays. This study demonstrates that desorption of incompletely bound targets during the washing cycle contributes to the observed difference in saturation levels.

  View details for DOI 10.1093/nar/gkm348

  View details for Web of Science ID 000247817700026

  View details for PubMedID 17567617

• Transcriptional profiling of MnSOD-mediated lifespan extension in Drosophila reveals a species-general network of aging and metabolic genes GENOME BIOLOGY Curtis, C., Landis, G. N., Folk, D., Wehr, N. B., Hoe, N., Waskar, M., Abdueva, D., Skvortsov, D., Ford, D., Luu, A., Badrinath, A., Levine, R. L., Bradley, T. J., Tavare, S., Tower, J. 2007; 8 (12)

  Abstract

  Several interventions increase lifespan in model organisms, including reduced insulin/insulin-like growth factor-like signaling (IIS), FOXO transcription factor activation, dietary restriction, and superoxide dismutase (SOD) over-expression. One question is whether these manipulations function through different mechanisms, or whether they intersect on common processes affecting aging.A doxycycline-regulated system was used to over-express manganese-SOD (MnSOD) in adult Drosophila, yielding increases in mean and maximal lifespan of 20%. Increased lifespan resulted from lowered initial mortality rate and required MnSOD over-expression in the adult. Transcriptional profiling indicated that the expression of specific genes was altered by MnSOD in a manner opposite to their pattern during normal aging, revealing a set of candidate biomarkers of aging enriched for carbohydrate metabolism and electron transport genes and suggesting a true delay in physiological aging, rather than a novel phenotype. Strikingly, cross-dataset comparisons indicated that the pattern of gene expression caused by MnSOD was similar to that observed in long-lived Caenorhabditis elegans insulin-like signaling mutants and to the xenobiotic stress response, thus exposing potential conserved longevity promoting genes and implicating detoxification in Drosophila longevity.The data suggest that MnSOD up-regulation and a retrograde signal of reactive oxygen species from the mitochondria normally function as an intermediate step in the extension of lifespan caused by reduced insulin-like signaling in various species. The results implicate a species-conserved net of coordinated genes that affect the rate of senescence by modulating energetic efficiency, purine biosynthesis, apoptotic pathways, endocrine signals, and the detoxification and excretion of metabolites.

  View details for DOI 10.1186/gb-2007-8-12-r262

  View details for Web of Science ID 000253451800010

  View details for PubMedID 18067683

• Scambio, a novel guanine nucleotide exchange factor for Rho MOLECULAR CANCER Curtis, C., Hemmeryckx, B., Haataja, L., Senadheera, D., Groffen, J., Heisterkamp, N. 2004; 3

  View details for Web of Science ID 000208438800010