PhD, Harbin Medical University, Basic Medical Science (2015)
PURPOSE: To identify immune subtypes and investigate the immune landscape of squamous cell carcinomas (SCCs), which share common etiology and histological features.EXPERIMENTAL DESIGN: Based on the immune gene expression profiles of 1,368 SCC patients in the Cancer Genome Atlas (TCGA), we used consensus clustering to identify robust clusters of patients, and assessed their reproducibility in an independent pan-SCC cohort of 938 patients. We further applied graph structure learning-based dimensionality reduction to the immune profiles to visualize the distribution of individual patients.RESULTS: We identified and independently validated 6 reproducible immune subtypes associated with distinct molecular characteristics and clinical outcomes. An immune-cold subtype had the least amount of lymphocyte infiltration and a high level of aneuploidy, and these patients had the worst prognosis. By contrast, an immune-hot subtype demonstrated the highest infiltration of CD8+ T cells, activated NK cells, and elevated IFN-gamma response. Accordingly, these patients had the best prognosis. A third subtype was dominated by M2-polarized macrophages with potent immune-suppressive factors such as TGF-bsignaling and reactive stroma, and these patients had relatively inferior prognosis. Other subtypes showed more diverse immunological features with intermediate prognoses. Finally, our analysis revealed a complex immune landscape consisting of both discrete clusters and continuous spectrum.CONCLUSION: This study provides a conceptual framework to understand the tumor immune microenvironment of SCCs. Future work is needed to evaluate its relevance in the design of combination treatment strategies and guiding optimal selection of patients for immunotherapy.
View details for PubMedID 30833271
PURPOSE: Breast cancer is a heterogeneous disease and not all patients respond equally to adjuvant radiotherapy. Predictive biomarkers are needed to select patients who will benefit from the treatment and spare others the toxicity and burden of radiation.EXPERIMENTAL DESIGN: We first trained and tested an intrinsic radiosensitivity gene signature to predict local recurrence after radiotherapy in three cohorts of 948 patients. Next, we developed an antigen processing and presentation-based immune signature by maximizing the treatment interaction effect in 129 patients. To test their predictive value, we matched patients treated with or without radiotherapy in an independent validation cohort for clinicopathologic factors including age, ER status, HER2 status, stage, hormone-therapy, chemotherapy, and surgery. Disease specific survival (DSS) was the primary endpoint.RESULTS: Our validation cohort consisted of 1,439 patients. After matching and stratification by the radiosensitivity signature, patients who received radiotherapy had better DSS than patients who did not in the radiation-sensitive group (hazard ratio [HR]=0.68, P=0.059, n=322), while a reverse trend was observed in the radiation-resistant group (HR=1.53, P=0.059, n=202). Similarly, patients treated with radiotherapy had significantly better DSS in the immuneeffective group (HR=0.46, P=0.0076, n=180), with no difference in DSS in the immunedefective group (HR=1.27, P=0.16, n=348). Both signatures were predictive of radiotherapy benefit (Pinteraction=0.007 and 0.005). Integration of radiosensitivity and immune signatures further stratified patients into three groups with differential outcomes for those treated with or without radiotherapy (Pinteraction=0.003).CONCLUSIONS: The proposed signatures have the potential to select patients who are most likely to benefit from radiotherapy.
View details for PubMedID 29921729
Prognostic biomarkers are needed to guide the management of early-stage non-small cell lung cancer (NSCLC). This work aims to develop an image-based prognostic signature and assess its complementary value to existing biomarkers.We retrospectively analyzed data of stage I NSCLC in 8 cohorts. On the basis of an analysis of 39 computed tomography (CT) features characterizing tumor and its relation to neighboring pleura, we developed a prognostic signature in an institutional cohort (n = 117) and tested it in an external cohort (n = 88). A third cohort of 89 patients with CT and gene expression data was used to create a surrogate genomic signature of the imaging signature. We conducted further validation using data from 5 gene expression cohorts (n = 639) and built a composite signature by integrating with the cell-cycle progression (CCP) score and clinical variables.An imaging signature consisting of a pleural contact index and normalized inverse difference was significantly associated with overall survival in both imaging cohorts (P = .0005 and P = .0009). Functional enrichment analysis revealed that genes highly correlated with the imaging signature were related to immune response, such as lymphocyte activation and chemotaxis (false discovery rate < 0.05). A genomic surrogate of the imaging signature remained a significant predictor of survival when we adjusted for known prognostic factors (hazard ratio, 1.81; 95% confidence interval, 1.34-2.44; P < .0001) and stratified patients within subgroups as defined by stage, histology, or CCP score. A composite signature outperformed the genomic surrogate, CCP score, and clinical model alone (P < .01) regarding concordance index (0.70 vs 0.62-0.63).The proposed CT imaging signature reflects fundamental biological differences in tumors and predicts overall survival in patients with stage I NSCLC. When combined with established prognosticators, the imaging signature improves survival prediction.
View details for PubMedID 29439884
To identify novel breast cancer subtypes by extracting quantitative imaging phenotypes of the tumor and surrounding parenchyma, and to elucidate the underlying biological underpinnings and evaluate the prognostic capacity for predicting recurrence-free survival (RFS).We retrospectively analyzed dynamic contrast-enhanced magnetic resonance imaging data of patients from a single-center discovery cohort (n=60) and an independent multi-center validation cohort (n=96). Quantitative image features were extracted to characterize tumor morphology, intra-tumor heterogeneity of contrast agent wash-in/wash-out patterns, and tumor-surrounding parenchyma enhancement. Based on these image features, we used unsupervised consensus clustering to identify robust imaging subtypes, and evaluated their clinical and biological relevance. We built a gene expression-based classifier of imaging subtypes and tested their prognostic significance in five additional cohorts with publically available gene expression data but without imaging data (n=1160).Three distinct imaging subtypes, i.e., homogeneous intratumoral enhancing, minimal parenchymal enhancing, and prominent parenchymal enhancing, were identified and validated. In the discovery cohort, imaging subtypes stratified patients with significantly different 5-year RFS rates of 79.6%, 65.2%, 52.5% (logrank P=0.025), and remained as an independent predictor after adjusting for clinicopathological factors (hazard ratio=2.79, P=0.016). The prognostic value of imaging subtypes was further validated in five independent gene expression cohorts, with average 5-year RFS rates of 88.1%, 74.0%, 59.5% (logrank P from <0.0001 to 0.008). Each imaging subtype was associated with specific dysregulated molecular pathways that can be therapeutically targeted.Imaging subtypes provide complimentary value to established histopathological or molecular subtypes, and may help stratify breast cancer patients.
View details for DOI 10.1158/1078-0432.CCR-16-2415
View details for PubMedID 28073839
The prevalence of early-stage non-small cell lung cancer (NSCLC) is expected to increase with recent implementation of annual screening programs. Reliable prognostic biomarkers are needed to identify patients at a high risk for recurrence to guide adjuvant therapy.To develop a robust, individualized immune signature that can estimate prognosis in patients with early-stage nonsquamous NSCLC.This retrospective study analyzed the gene expression profiles of frozen tumor tissue samples from 19 public NSCLC cohorts, including 18 microarray data sets and 1 RNA-Seq data set for The Cancer Genome Atlas (TCGA) lung adenocarcinoma cohort. Only patients with nonsquamous NSCLC with clinical annotation were included. Samples were from 2414 patients with nonsquamous NSCLC, divided into a meta-training cohort (729 patients), meta-testing cohort (716 patients), and 3 independent validation cohorts (439, 323, and 207 patients). All patients underwent surgery with a negative surgical margin, received no adjuvant or neoadjuvant therapy, and had publicly available gene expression data and survival information. Data were collected from July 22 through September 8, 2016.Overall survival.Of 2414 patients (1205 men [50%], 1111 women [46%], and 98 of unknown sex [4%]; median age [range], 64 [15-90] years), a prognostic immune signature of 25 gene pairs consisting of 40 unique genes was constructed using the meta-training data set. In the meta-testing and validation cohorts, the immune signature significantly stratified patients into high- vs low-risk groups in terms of overall survival across and within subpopulations with stage I, IA, IB, or II disease and remained as an independent prognostic factor in multivariate analyses (hazard ratio range, 1.72 [95% CI, 1.26-2.33; P?.001] to 2.36 [95% CI, 1.47-3.79; P?.001]) after adjusting for clinical and pathologic factors. Several biological processes, including chemotaxis, were enriched among genes in the immune signature. The percentage of neutrophil infiltration (5.6% vs 1.8%) and necrosis (4.6% vs 1.5%) was significantly higher in the high-risk immune group compared with the low-risk groups in TCGA data set (P?.003). The immune signature achieved a higher accuracy (mean concordance index [C-index], 0.64) than 2 commercialized multigene signatures (mean C-index, 0.53 and 0.61) for estimation of survival in comparable validation cohorts. When integrated with clinical characteristics such as age and stage, the composite clinical and immune signature showed improved prognostic accuracy in all validation data sets relative to molecular signatures alone (mean C-index, 0.70 vs 0.63) and another commercialized clinical-molecular signature (mean C-index, 0.68 vs 0.65).The proposed clinical-immune signature is a promising biomarker for estimating overall survival in nonsquamous NSCLC, including early-stage disease. Prospective studies are needed to test the clinical utility of the biomarker in individualized management of nonsquamous NSCLC.
View details for PubMedID 28687838
Purpose To identify the molecular basis of quantitative imaging characteristics of tumor-adjacent parenchyma at dynamic contrast material-enhanced magnetic resonance (MR) imaging and to evaluate their prognostic value in breast cancer. Materials and Methods In this institutional review board-approved, HIPAA-compliant study, 10 quantitative imaging features depicting tumor-adjacent parenchymal enhancement patterns were extracted and screened for prognostic features in a discovery cohort of 60 patients. By using data from The Cancer Genome Atlas (TCGA), a radiogenomic map for the tumor-adjacent parenchymal tissue was created and molecular pathways associated with prognostic parenchymal imaging features were identified. Furthermore, a multigene signature of the parenchymal imaging feature was built in a training cohort (n = 126), and its prognostic relevance was evaluated in two independent cohorts (n = 879 and 159). Results One image feature measuring heterogeneity (ie, information measure of correlation) was significantly associated with prognosis (false-discovery rate < 0.1), and at a cutoff of 0.57 stratified patients into two groups with different recurrence-free survival rates (log-rank P = .024). The tumor necrosis factor signaling pathway was identified as the top enriched pathway (hypergeometric P < .0001) among genes associated with the image feature. A 73-gene signature based on the tumor profiles in TCGA achieved good association with the tumor-adjacent parenchymal image feature (R(2) = 0.873), which stratified patients into groups regarding recurrence-free survival (log-rank P = .029) and overall survival (log-rank P = .042) in an independent TCGA cohort. The prognostic value was confirmed in another independent cohort (Gene Expression Omnibus GSE 1456), with log-rank P = .00058 for recurrence-free survival and log-rank P = .0026 for overall survival. Conclusion Heterogeneous enhancement patterns of tumor-adjacent parenchyma at MR imaging are associated with the tumor necrosis signaling pathway and poor survival in breast cancer. (©) RSNA, 2017 Online supplemental material is available for this article.
View details for PubMedID 28708462
To evaluate the prognostic value and molecular basis of a CT-derived pleural contact index (PCI) in early stage non-small cell lung cancer (NSCLC).We retrospectively analysed seven NSCLC cohorts. A quantitative PCI was defined on CT as the length of tumour-pleura interface normalised by tumour diameter. We evaluated the prognostic value of PCI in a discovery cohort (n?=?117) and tested in an external cohort (n?=?88) of stage I NSCLC. Additionally, we identified the molecular correlates and built a gene expression-based surrogate of PCI using another cohort of 89 patients. To further evaluate the prognostic relevance, we used four datasets totalling 775 stage I patients with publically available gene expression data and linked survival information.At a cutoff of 0.8, PCI stratified patients for overall survival in both imaging cohorts (log-rank p?=?0.0076, 0.0304). Extracellular matrix (ECM) remodelling was enriched among genes associated with PCI (p?=?0.0003). The genomic surrogate of PCI remained an independent predictor of overall survival in the gene expression cohorts (hazard ratio: 1.46, p?=?0.0007) adjusting for age, gender, and tumour stage.CT-derived pleural contact index is associated with ECM remodelling and may serve as a noninvasive prognostic marker in early stage NSCLC.? A quantitative pleural contact index (PCI) predicts survival in early stage NSCLC. ? PCI is associated with extracellular matrix organisation and collagen catabolic process. ? A multi-gene surrogate of PCI is an independent predictor of survival. ? PCI can be used to noninvasively identify patients with poor prognosis.
View details for PubMedID 28786009
Salmonella arizonae (also called Salmonella subgroup IIIa) is a Gram-negative, non-spore-forming, motile, rod-shaped, facultatively anaerobic bacterium. S. arizonae strain RKS2983 was isolated from a human in California, USA. S. arizonae lies somewhere between Salmonella subgroups I (human pathogens) and V (also called S. bongori; usually non-pathogenic to humans) and so is an ideal model organism for studies of bacterial evolution from non-human pathogen to human pathogens. We hence sequenced the genome of RKS2983 for clues of genomic events that might have led to the divergence and speciation of Salmonella into distinct lineages with diverse host ranges and pathogenic features. The 4,574,836 bp complete genome contains 4,203 protein-coding genes, 82 tRNA genes and 7 rRNA operons. This genome contains several characteristics not reported to date in Salmonella subgroup I or V and may provide information about the genetic divergence of Salmonella pathogens.
View details for DOI 10.1186/s40793-015-0015-z
View details for Web of Science ID 000367987500001
View details for PubMedID 26203341
View details for PubMedCentralID PMC4511000
Factors within the tissue of breast cancer (BC) may shift the polarization of CD4+ T cells towards Th2 direction. This tendency can promote tumor development and be enhanced by the use of tamoxifen during the treatment. Thus, the patients with low levels of tumor-induced Th2 polarization prior to tamoxifen treatment may better endure the immune-polarizing side effects (IPSE) of tamoxifen and have better prognoses. Estimation of Th2 polarization status should help predict the IPSE among tamoxifen-treated patients and guide the use of tamoxifen among all BC patients before the tamoxifen therapy. Here, we report profiling of differentially expressed (DE) intratumoral cytokines as a signature to evaluate the IPSE of tamoxifen. The DE genes of intratumoral CD4+ T cells (CD4 DEGs) were identified by gene expression profiles of purified CD4+ T cells from BC patients and validated by profiling of cultured intratumoral CD4+ T cells. Functional enrichment analyses showed a directed Th2 polarization of intratumoral CD4+ T cells. To find the factors inducing the Th2 polarization of CD4+ T cells, we identified 995 common DE genes of bulk BC tissues (BC DEGs) by integrating five independent datasets. Five DE cytokines observed in bulk BC tissues with dysregulated receptors in the intratumoral CD4+ T cells were selected as the predictor of the IPSE of tamoxifen. The patients predicted to suffer low IPSE (low Th2 polarization) had a significantly lower distant relapse risk than the patients predicted to suffer high IPSE in independent datasets (n = 608; HR = 4.326, P = 0.000897; HR = 2.014, P = 0.0173; HR = 2.72, P = 0.04077). Patients predicted to suffer low IPSE would benefit from tamoxifen treatment (HR = 2.908, P = 0.03905). The DE intratumoral cytokines identified in this study may help predict the IPSE of tamoxifen and justify the use of tamoxifen in BC treatment.
View details for Web of Science ID 000352204800020
View details for PubMedID 25973310
View details for PubMedCentralID PMC4396041
Products of the SOX gene family play important roles in the life process. One of the members, SOX7, is associated with the development of a variety of cancers as a tumor suppression factor, but its relevance with ovarian cancer was unclear. In this study, we investigated the involvement of SOX7 in the progression and prognosis of epithelial ovarian cancer (EOC) and the involved mechanisms.Expression profiles in two independent microarray data sets were analyzed for SOX7 between malignant and normal tissues. The expression levels of SOX7 in EOC, borderline ovarian tumors and normal ovarian tissues were measured by immunohistochemistry. We also measured levels of COX2 and cyclin-D1 to examine their possible involvement in the same signal transduction pathway as SOX7.The expression of SOX7 was significantly reduced in ovarian cancer tissues compared with normal controls, strongly indicating that SOX7 might be a negative regulator in the Wnt/?-catenin pathway in ovarian cancer. By immunohistochemistry staining, the protein expression of SOX7 showed a consistent trend with that of the gene expression microarray analysis. By contrast, the protein expression level of COX2 and cyclin-D1 increased as the tumor malignancy progressed, suggesting that SOX7 may function through the Wnt/?-catenin signaling pathway as a tumor suppressor. In comparison between the protein expression levels of SOX7 with pathological features of the cancer, we found that SOX7 was down-regulated mainly in serous cystadenocarcinoma and advanced stages of the cancers.The expression of SOX7 correlates with tumor progression as a tumor suppressor, possibly through the Wnt/?-catenin signaling pathway in ovarian cancers, suggesting that SOX7 may be a promising prognostic marker.
View details for DOI 10.1186/s13048-014-0087-1
View details for Web of Science ID 000342045700001
View details for PubMedID 25297608
View details for PubMedCentralID PMC4172779
The altered composition of immune cells in peripheral blood has been reported to be associated with cancer patient survival. However, analysis of the composition of peripheral immune cells are often limited in retrospective survival studies employing banked blood specimens with long-term follow-up because the application of flow cytometry to such specimens is problematic. The aim of this study was to demonstrate the feasibility of deconvolving blood-based gene expression profiles (GEPs) to estimate the proportions of immune cells and determine their prognostic values for cancer patients.Here, using GEPs from peripheral blood mononuclear cells (PBMC) of 108 non-small cell lung cancer (NSCLC) patients, we deconvolved the immune cell proportions and analyzed their association with patient survival. Univariate Kaplan-Meier analysis showed that a low proportion of T cells was significantly associated with poor patient survival, as was the proportion of T helper cells; however, only the proportion of T cells was independently prognostic for patients by a multivariate Cox regression analysis (hazard ratio?=?2.23; 95% CI, 1.01-4.92; p?=?.048). Considering that altered peripheral blood compositions can reflect altered immune responses within the tumor microenvironment, based on a tissue-based GEPs of NSCLC patients, we demonstrated a significant association between poor patient survival and the low level of antigen presentation, which play a critical role in T cell proliferation.These results demonstrate that it is feasible to deconvolve GEPs from banked blood specimens for retrospective survival analysis of alterations of immune cell composition, and suggest the proportion of T cells in PBMC which might reflect the antigen presentation level within the tumor microenvironment can be a prognostic marker for NSCLC patients.
View details for DOI 10.1371/journal.pone.0100934
View details for Web of Science ID 000338633900101
View details for PubMedID 24959668
View details for PubMedCentralID PMC4069164
Current predictors for estrogen receptor-positive (ER-positive) breast cancer patients receiving tamoxifen are often invalid in inter-laboratory validation. We aim to develop a robust predictor based on the relative ordering of expression measurement (ROE) in gene pairs. Using a large integrated dataset of 420 normal controls and 1,129 ER-positive breast tumor samples, we identified the gene pairs with stable ROEs in normal control and significantly reversed ROEs in ER-positive tumor. Using these gene pairs, we characterized each sample of a cohort of 292 ER-positive patients who received tamoxifen monotherapy for 5 years and then identified relapse risk-associated gene pairs. We extracted a gene pair subset that resulted in the largest positive and negative predictive values for predicting 10-year relapse-free survival (RFS) using a genetic algorithm. A predictor was developed based on the gene pair subset and was validated in 2 large multi-laboratory cohorts (N = 250 and 248, respectively) of ER-positive patients who received 5-year tamoxifen alone. In the first validation cohort, the patients predicted to be tamoxifen sensitive had a 10-year RFS of 91 % (95 % confidence interval [CI] 85-97 %) with an absolute risk reduction of 34 % (95 % CI 17-51 %). The patients predicted to be tamoxifen insensitive had a significantly higher relapse risk than the patients predicted to be tamoxifen sensitive (hazard ratio = 4.99, 95 % CI 2.45-10.17, P = 9.13 × 10(-7)). Similar performance was achieved for the second validation cohort. The predictor performed well in both node-negative and node-positive subsets and added significant predictive power to the clinical parameters. In contrast, 2 previously proposed predictors did not achieve significantly better performances than the baselines of the validation cohorts. In summary, the proposed predictor can accurately and robustly predict tamoxifen sensitivity of ER-positive breast cancer patients and identified patients with a high probability of 10-year RFS following tamoxifen monotherapy.
View details for DOI 10.1007/s10549-013-2767-8
View details for Web of Science ID 000328218500004
View details for PubMedID 24253811
Directly comparing gene expression profiles of estrogen receptor-positive (ER+) and estrogen receptor-negative (ER-) breast cancers cannot determine whether differentially expressed genes between these two subtypes result from dysregulated expression in ER+ cancer or ER- cancer versus normal controls, and thus would miss critical information for elucidating the transcriptomic difference between the two subtypes.Using microarray datasets from TCGA, we classified the genes dysregulated in both ER+ and ER- cancers versus normal controls into two classes: (i) genes dysregulated in the same direction but to a different extent, and (ii) genes dysregulated to opposite directions, and then validated the two classes in RNA-sequencing datasets of independent cohorts. We showed that the genes dysregulated to a larger extent in ER+ cancers than in ER- cancers enriched in glycerophospholipid and polysaccharide metabolic processes, while the genes dysregulated to a larger extent in ER- cancers than in ER+ cancers enriched in cell proliferation. Phosphorylase kinase and enzymes of glycosylphosphatidylinositol (GPI) anchor biosynthesis were upregulated to a larger extent in ER+ cancers than in ER- cancers, whereas glycogen synthase and phospholipase A2 were downregulated to a larger extent in ER+ cancers than in ER- cancers. We also found that the genes oppositely dysregulated in the two subtypes significantly enriched with known cancer genes and tended to closely collaborate with the cancer genes. Furthermore, we showed the possibility that these oppositely dysregulated genes could contribute to carcinogenesis of ER+ and ER- cancers through rewiring different subpathways.GPI-anchor biosynthesis and glycogenolysis were elevated and hydrolysis of phospholipids was depleted to a larger extent in ER+ cancers than in ER- cancers. Our findings indicate that the genes oppositely dysregulated in the two subtypes are potential cancer genes which could contribute to carcinogenesis of both ER+ and ER- cancers through rewiring different subpathways.
View details for DOI 10.1371/journal.pone.0070017
View details for Web of Science ID 000324146200088
View details for PubMedID 23875016
View details for PubMedCentralID PMC3715479