Publications

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Selected Publications

Maximilian Diehn, MD, PhD
Jack, Lulu, and Sam Willson Professor and Professor of Radiation Oncology (Radiation Therapy)

Publications

  • Integrating genomic features for non-invasive early lung cancer detection NATURE Chabon, J. J., Hamilton, E. G., Kurtz, D. M., Esfahani, M. S., Moding, E. J., Stehr, H., Schroers-Martin, J., Nabet, B. Y., Chen, B., Chaudhuri, A. A., Liu, C., Hui, A. B., Jin, M. C., Azad, T. D., Almanza, D., Jeon, Y., Nesselbush, M. C., Keh, L., Bonilla, R. F., Yoo, C. H., Ko, R. B., Chen, E. L., Merriott, D. J., Massion, P. P., Mansfield, A. S., Jen, J., Ren, H. Z., Lin, S. H., Costantino, C. L., Burr, R., Tibshirani, R., Gambhir, S. S., Berry, G. J., Jensen, K. C., West, R. B., Neal, J. W., Wakelee, H. A., Loo, B. W., Kunder, C. A., Leung, A. N., Lui, N. S., Berry, M. F., Shrager, J. B., Nair, V. S., Haber, D. A., Sequist, L. V., Alizadeh, A. A., Diehn, M. 2020

    View details for DOI 10.1038/s41586-020-2140-0

    View details for Web of Science ID 000521531000011

  • Noninvasive Early Identification of Therapeutic Benefit from Immune Checkpoint Inhibition. Cell Nabet, B. Y., Esfahani, M. S., Moding, E. J., Hamilton, E. G., Chabon, J. J., Rizvi, H. n., Steen, C. B., Chaudhuri, A. A., Liu, C. L., Hui, A. B., Almanza, D. n., Stehr, H. n., Gojenola, L. n., Bonilla, R. F., Jin, M. C., Jeon, Y. J., Tseng, D. n., Liu, C. n., Merghoub, T. n., Neal, J. W., Wakelee, H. A., Padda, S. K., Ramchandran, K. J., Das, M. n., Plodkowski, A. J., Yoo, C. n., Chen, E. L., Ko, R. B., Newman, A. M., Hellmann, M. D., Alizadeh, A. A., Diehn, M. n. 2020

    Abstract

    Although treatment of non-small cell lung cancer (NSCLC) with immune checkpoint inhibitors (ICIs) can produce remarkably durable responses, most patients develop early disease progression. Furthermore, initial response assessment by conventional imaging is often unable to identify which patients will achieve durable clinical benefit (DCB). Here, we demonstrate that pre-treatment circulating tumor DNA (ctDNA) and peripheral CD8 T cell levels are independently associated with DCB. We further show that ctDNA dynamics after a single infusion can aid in identification of patients who will achieve DCB. Integrating these determinants, we developed and validated an entirely noninvasive multiparameter assay (DIREct-On, Durable Immunotherapy Response Estimation by immune profiling and ctDNA-On-treatment) that robustly predicts which patients will achieve DCB with higher accuracy than any individual feature. Taken together, these results demonstrate that integrated ctDNA and circulating immune cell profiling can provide accurate, noninvasive, and early forecasting of ultimate outcomes for NSCLC patients receiving ICIs.

    View details for DOI 10.1016/j.cell.2020.09.001

    View details for PubMedID 33007267

  • Circulating tumor DNA dynamics predict benefit from consolidation immunotherapy in locally advanced non-small-cell lung cancer Nature Cancer Moding, E. J., Liu, Y., Nabet, B. Y., Chabon, J. J., Chaudhuri, A. A., Hui, A. B., Bonilla, R. F., Ko, R. B., Yoo, C. H., He, J., Qiao, Y., Xu, T., Heymach, J. V., Tsao, A., Liao, Z., Gomez, D. R., Das, M., Padda, S. K., Ramchandran, K. J., Neal, J. W., Wakelee, H. A., Loo, B. W., Lin, S. H., Alizadeh, A. A., Diehn, M. 2020; 1

    View details for DOI 10.1038/s43018-019-0011-0

  • KEAP1/NFE2L2 mutations predict lung cancer radiation resistance that can be targeted by glutaminase inhibition. Cancer discovery Binkley, M. S., Jeon, Y. J., Nesselbush, M. n., Moding, E. J., Nabet, B. Y., Almanza, D. n., Kunder, C. n., Stehr, H. n., Yoo, C. H., Rhee, S. n., Xiang, M. n., Chabon, J. J., Hamilton, E. n., Kurtz, D. M., Gojenola, L. n., Owen, S. G., Ko, R. B., Shin, J. H., Maxim, P. G., Lui, N. S., Backhus, L. M., Berry, M. F., Shrager, J. B., Ramchandran, K. J., Padda, S. K., Das, M. n., Neal, J. W., Wakelee, H. A., Alizadeh, A. A., Loo, B. W., Diehn, M. n. 2020

    Abstract

    Tumor genotyping is not routinely performed in localized non-small cell lung cancer (NSCLC) due to lack of associations of mutations with outcome. Here, we analyze 232 consecutive patients with localized NSCLC and demonstrate that KEAP1 and NFE2L2 mutations are predictive of high rates of local recurrence (LR) after radiotherapy but not surgery. Half of LRs occurred in KEAP1/NFE2L2 mutation tumors, indicating they are major molecular drivers of clinical radioresistance. Next, we functionally evaluate KEAP1/NFE2L2 mutations in our radiotherapy cohort and demonstrate that only pathogenic mutations are associated with radioresistance. Furthermore, expression of NFE2L2 target genes does not predict LR, underscoring the utility of tumor genotyping. Finally, we show that glutaminase inhibition preferentially radiosensitizes KEAP1 mutant cells via depletion of glutathione and increased radiation-induced DNA damage. Our findings suggest that genotyping for KEAP1/NFE2L2 mutations could facilitate treatment personalization and provide a potential strategy for overcoming radioresistance conferred by these mutations.

    View details for DOI 10.1158/2159-8290.CD-20-0282

    View details for PubMedID 33071215

  • Detection and Surveillance of Bladder Cancer Using Urine Tumor DNA CANCER DISCOVERY Dudley, J. C., Schroers-Martin, J., Lazzareschi, D., Shi, W., Chen, S. B., Esfahani, M. S., Trivedi, D., Chabon, J. J., Chaudhuri, A. A., Stehr, H., Liu, C., Lim, H., Costa, H. A., Nabet, B. Y., Sin, M. Y., Liao, J. C., Alizadeh, A. A., Diehn, M. 2019; 9 (4): 500–509

    View details for DOI 10.1158/2159-8290.CD-18-0825

    View details for Web of Science ID 000462990400024

  • Early detection of molecular residual disease in localized lung cancer by circulating tumor DNA profiling. Cancer discovery Chaudhuri, A. A., Chabon, J. J., Lovejoy, A. F., Newman, A. M., Stehr, H. n., Azad, T. D., Khodadoust, M. S., Esfahani, M. S., Liu, C. L., Zhou, L. n., Scherer, F. n., Kurtz, D. M., Say, C. n., Carter, J. N., Merriott, D. J., Dudley, J. C., Binkley, M. S., Modlin, L. n., Padda, S. K., Gensheimer, M. F., West, R. B., Shrager, J. B., Neal, J. W., Wakelee, H. A., Loo, B. W., Alizadeh, A. A., Diehn, M. n. 2017

    Abstract

    Identifying molecular residual disease (MRD) after treatment of localized lung cancer could facilitate early intervention and personalization of adjuvant therapies. Here we apply Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) circulating tumor DNA (ctDNA) analysis to 255 samples from 40 patients treated with curative intent for stage I-III lung cancer and 54 healthy adults. In 94% of evaluable patients experiencing recurrence, ctDNA was detectable in the first post-treatment blood sample, indicating reliable identification of MRD. Post-treatment ctDNA detection preceded radiographic progression in 72% of patients by a median of 5.2 months and 53% of patients harbored ctDNA mutation profiles associated with favorable responses to tyrosine kinase inhibitors or immune checkpoint blockade. Collectively, these results indicate that ctDNA MRD in lung cancer patients can be accurately detected using CAPP-Seq and may allow personalized adjuvant treatment while disease burden is lowest.

    View details for PubMedID 28899864

  • Role of KEAP1/NRF2 and TP53 Mutations in Lung Squamous Cell Carcinoma Development and Radiation Resistance. Cancer discovery Jeong, Y., Hoang, N. T., Lovejoy, A., Stehr, H., Newman, A. M., Gentles, A. J., Kong, W., Truong, D., Martin, S., Chaudhuri, A., Heiser, D., Zhou, L., Say, C., Carter, J. N., Hiniker, S. M., Loo, B. W., West, R. B., Beachy, P., Alizadeh, A. A., Diehn, M. 2016

    Abstract

    Lung squamous cell carcinoma (LSCC) pathogenesis remains incompletely understood, and biomarkers predicting treatment response remain lacking. Here, we describe novel murine LSCC models driven by loss of Trp53 and Keap1, both of which are frequently mutated in human LSCCs. Homozygous inactivation of Keap1 or Trp53 promoted airway basal stem cell (ABSC) self-renewal, suggesting that mutations in these genes lead to expansion of mutant stem cell clones. Deletion of Trp53 and Keap1 in ABSCs, but not more differentiated tracheal cells, produced tumors recapitulating histologic and molecular features of human LSCCs, indicating that they represent the likely cell of origin in this model. Deletion of Keap1 promoted tumor aggressiveness, metastasis, and resistance to oxidative stress and radiotherapy (RT). KEAP1/NRF2 mutation status predicted risk of local recurrence after RT in patients with non-small lung cancer (NSCLC) and could be noninvasively identified in circulating tumor DNA. Thus, KEAP1/NRF2 mutations could serve as predictive biomarkers for personalization of therapeutic strategies for NSCLCs.We developed an LSCC mouse model involving Trp53 and Keap1, which are frequently mutated in human LSCCs. In this model, ABSCs are the cell of origin of these tumors. KEAP1/NRF2 mutations increase radioresistance and predict local tumor recurrence in radiotherapy patients. Our findings are of potential clinical relevance and could lead to personalized treatment strategies for tumors with KEAP1/NRF2 mutations. Cancer Discov; 7(1); 86-101. ©2016 AACR.This article is highlighted in the In This Issue feature, p. 1.

    View details for PubMedID 27663899

  • Circulating tumour DNA profiling reveals heterogeneity of EGFR inhibitor resistance mechanisms in lung cancer patients NATURE COMMUNICATIONS Chabon, J. J., Simmons, A. D., Lovejoy, A. F., Esfahani, M. S., Newman, A. M., Haringsma, H. J., Kurtz, D. M., Stehr, H., Scherer, F., Karlovich, C. A., Harding, T. C., Durkin, K. A., Otterson, G. A., Purcell, W. T., Camidge, D. R., Goldman, J. W., Sequist, L. V., Piotrowska, Z., Wakelee, H. A., Neal, J. W., Alizadeh, A. A., Diehn, M. 2016; 7

    Abstract

    Circulating tumour DNA (ctDNA) analysis facilitates studies of tumour heterogeneity. Here we employ CAPP-Seq ctDNA analysis to study resistance mechanisms in 43 non-small cell lung cancer (NSCLC) patients treated with the third-generation epidermal growth factor receptor (EGFR) inhibitor rociletinib. We observe multiple resistance mechanisms in 46% of patients after treatment with first-line inhibitors, indicating frequent intra-patient heterogeneity. Rociletinib resistance recurrently involves MET, EGFR, PIK3CA, ERRB2, KRAS and RB1. We describe a novel EGFR L798I mutation and find that EGFR C797S, which arises in ∼33% of patients after osimertinib treatment, occurs in <3% after rociletinib. Increased MET copy number is the most frequent rociletinib resistance mechanism in this cohort and patients with multiple pre-existing mechanisms (T790M and MET) experience inferior responses. Similarly, rociletinib-resistant xenografts develop MET amplification that can be overcome with the MET inhibitor crizotinib. These results underscore the importance of tumour heterogeneity in NSCLC and the utility of ctDNA-based resistance mechanism assessment.

    View details for DOI 10.1038/ncomms11815

    View details for PubMedID 27283993

  • An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage NATURE MEDICINE Newman, A. M., Bratman, S. V., To, J., Wynne, J. F., Eclov, N. C., Modlin, L. A., Liu, C. L., Neal, J. W., Wakelee, H. A., Merritt, R. E., Shrager, J. B., Loo, B. W., Alizadeh, A. A., Diehn, M. 2014; 20 (5): 552-558

    Abstract

    Circulating tumor DNA (ctDNA) is a promising biomarker for noninvasive assessment of cancer burden, but existing ctDNA detection methods have insufficient sensitivity or patient coverage for broad clinical applicability. Here we introduce cancer personalized profiling by deep sequencing (CAPP-Seq), an economical and ultrasensitive method for quantifying ctDNA. We implemented CAPP-Seq for non-small-cell lung cancer (NSCLC) with a design covering multiple classes of somatic alterations that identified mutations in >95% of tumors. We detected ctDNA in 100% of patients with stage II-IV NSCLC and in 50% of patients with stage I, with 96% specificity for mutant allele fractions down to ∼0.02%. Levels of ctDNA were highly correlated with tumor volume and distinguished between residual disease and treatment-related imaging changes, and measurement of ctDNA levels allowed for earlier response assessment than radiographic approaches. Finally, we evaluated biopsy-free tumor screening and genotyping with CAPP-Seq. We envision that CAPP-Seq could be routinely applied clinically to detect and monitor diverse malignancies, thus facilitating personalized cancer therapy.

    View details for DOI 10.1038/nm.3519

    View details for Web of Science ID 000335710700028

  • Integrating ctDNA Analysis and Radiomics for Dynamic Risk Assessment in Localized Lung Cancer. Cancer discovery Moding, E. J., Shahrokh Esfahani, M., Jin, C., Hui, A. B., Nabet, B. Y., Liu, Y., Chabon, J. J., Binkley, M. S., Kurtz, D. M., Hamilton, E. G., Chaudhuri, A. A., Liu, C. L., Li, Z., Bonilla, R. F., Jiang, A. L., Lau, B. C., Lopez, P., He, J., Qiao, Y., Xu, T., Yao, L., Gandhi, S., Liao, Z., Das, M., Ramchandran, K. J., Padda, S. K., Neal, J. W., Wakelee, H. A., Gensheimer, M. F., Loo, B. W., Li, R., Lin, S. H., Alizadeh, A. A., Diehn, M. 2025: OF1-OF21

    Abstract

    This study demonstrates that combining tumor features, radiomics, and ctDNA analysis improves outcome prediction in NSCLC treated with CRT therapy. Our integrated model could enable personalized and response-adapted therapies to reduce toxicity and improve outcomes in patients.

    View details for DOI 10.1158/2159-8290.CD-24-1704

    View details for PubMedID 40299851

  • An ultrasensitive method for detection of cell-free RNA. Nature Nesselbush, M. C., Luca, B. A., Jeon, Y. J., Jabara, I., Meador, C. B., Garofalo, A., Binkley, M. S., Hui, A. B., van 't Erve, I., Xu, N., Shi, W. Y., Liu, K. J., Sugio, T., Kastelowitz, N., Hamilton, E. G., Liu, C. L., Olsen, M., Bonilla, R. F., Wang, Y. P., Jiang, A., Lau, B., Eichholz, J., Banwait, M., Schroers-Martin, J., Boegeholz, J., King, D. A., Luikart, H., Esfahani, M. S., Mehrmohamadi, M., Stehr, H., Raclin, T., Tibshirani, R., Khush, K., Srinivas, S., Yu, H., Rogers, A. J., Nair, V. S., Isbell, J. M., Li, B. T., Piotrowska, Z., Sequist, L. V., Hata, A. N., Neal, J. W., Wakelee, H. A., Gentles, A. J., Alizadeh, A. A., Diehn, M. 2025

    Abstract

    Sensitive methods for detection of cell-free RNA (cfRNA) could facilitate non-invasive gene expression profiling and monitoring of diseases1-6. Here we describe RARE-seq (random priming and affinity capture of cfRNA fragments for enrichment analysis by sequencing), a method optimized for cfRNA analysis. We demonstrate that platelet contamination can substantially confound cfRNA analyses and develop an approach to overcome it. In analytical validations, we find RARE-seq to be approximately 50-fold more sensitive for detecting tumour-derived cfRNA than whole-transcriptome RNA sequencing (RNA-seq), with a limit of detection of 0.05%. To explore clinical utility, we profiled 437 plasma samples from 369 individuals with cancer or non-malignant conditions and controls. Detection of non-small-cell lung cancer expression signatures in cfRNA increased with stage (6 out of 20 (30%) in stage I; 5 out of 8 (63%) in stage II; 10 out of 15 (67%) in stage III; 80 out of 96 (83% sensitivity) in stage IV at 95% specificity) and RARE-seq was more sensitive than tumour-naive circulating tumour DNA (ctDNA) analysis. In patients with EGFR-mutant non-small-cell lung cancer who developed resistance to tyrosine kinase inhibitors, we detected both histological transformation and mutation-based resistance mechanisms. Finally, we demonstrate the potential utility of RARE-seq for determination of tissue of origin, assessing benign pulmonary conditions and tracking response to mRNA vaccines. These results highlight the potential value of ultrasensitive cfRNA analysis and provide proof of concept for diverse clinical applications.

    View details for DOI 10.1038/s41586-025-08834-1

    View details for PubMedID 40240612

    View details for PubMedCentralID 8060291

  • The effect of ibrutinib on the myeloid cell compartment in CNS lymphoma. Leukemia Kuehn, J. C., Neidert, N. N., Zhang, J., Mutter, J., Alig, S., Klingler, C., Hummel, F., Ranganathan, L., Bleul, S., Beck, J., Prinz, M., Diehn, M., Alizadeh, A., Duyster, J., Sankowski, R., Heiland, D. H., Scherer, F. 2025

    View details for DOI 10.1038/s41375-025-02600-y

    View details for PubMedID 40210767

    View details for PubMedCentralID 3795457

  • Guiding clinical management of patients with CNS lymphomas by minimal-invasive detection of ctDNA in cerebrospinal fluid. Leukemia Weinschenk, S., Philipp, U., Kuehn, J. C., Mueller, K., Fauser, J., Boeckle, D., Gebhard, I., Hinz, M., Neidert, N., Bleul, S., Lauer, E. M., Mutter, J. A., Alig, S. K., Kurtz, D. M., Finke, J., Marks, R., Diehn, M., Alizadeh, A. A., Reinacher, P. C., Wehrle, J., Keller, U., Wolf, D., Kocher, F., Chapuy, B., Beck, J., Prinz, M., von Baumgarten, L., Schorb, E., Duyster, J., Scherer, F. 2025

    View details for DOI 10.1038/s41375-025-02583-w

    View details for PubMedID 40204895

    View details for PubMedCentralID 9214472

  • A vision-language foundation model for precision oncology. Nature Xiang, J., Wang, X., Zhang, X., Xi, Y., Eweje, F., Chen, Y., Li, Y., Bergstrom, C., Gopaulchan, M., Kim, T., Yu, K. H., Willens, S., Olguin, F. M., Nirschl, J. J., Neal, J., Diehn, M., Yang, S., Li, R. 2025

    Abstract

    Clinical decision-making is driven by multimodal data, including clinical notes and pathological characteristics. Artificial intelligence approaches that can effectively integrate multimodal data hold significant promise in advancing clinical care1,2. However, the scarcity of well-annotated multimodal datasets in clinical settings has hindered the development of useful models. In this study, we developed the Multimodal transformer with Unified maSKed modeling (MUSK), a vision-language foundation model designed to leverage large-scale, unlabelled, unpaired image and text data. MUSK was pretrained on 50 million pathology images from 11,577 patients and one billion pathology-related text tokens using unified masked modelling. It was further pretrained on one million pathology image-text pairs to efficiently align the vision and language features. With minimal or no further training, MUSK was tested in a wide range of applications and demonstrated superior performance across 23 patch-level and slide-level benchmarks, including image-to-text and text-to-image retrieval, visual question answering, image classification and molecular biomarker prediction. Furthermore, MUSK showed strong performance in outcome prediction, including melanoma relapse prediction, pan-cancer prognosis prediction and immunotherapy response prediction in lung and gastro-oesophageal cancers. MUSK effectively combined complementary information from pathology images and clinical reports and could potentially improve diagnosis and precision in cancer therapy.

    View details for DOI 10.1038/s41586-024-08378-w

    View details for PubMedID 39779851

    View details for PubMedCentralID 9586871

  • New Approvals Advancing Blood Cancer Medicine BLOOD CANCER DISCOVERY LoRusso, P. M., Bradley, C. J., Bunn Jr, P. A., Cleveland, J. L., Diehn, M., Figueiredo, J. C., Flowers, C., Foti, M., Hricak, H., Llanos, A. M., Pignone, M., Stegmaier, K., Williams, T., Turaga, K., van den Brink, M. M., AACR Canc Progress Report 2024 Steering Comm 2025; 6 (1): 5-9

    Abstract

    Recent advancements in clinical tools for blood cancers are highlighted in this article, adapted from the 14th edition of the annual AACR Cancer Progress Report (https://cancerprogressreport.aacr.org/progress/) to the US Congress and the public.

    View details for DOI 10.1158/2643-3230.BCD-24-0300

    View details for Web of Science ID 001394363800004

    View details for PubMedID 39601608

    View details for PubMedCentralID PMC11707506

  • Automated Deep Learning-Based Detection and Segmentation of Lung Tumors at CT. Radiology Kashyap, M., Wang, X., Panjwani, N., Hasan, M., Zhang, Q., Huang, C., Bush, K., Chin, A., Vitzthum, L. K., Dong, P., Zaky, S., Loo, B. W., Diehn, M., Xing, L., Li, R., Gensheimer, M. F. 2025; 314 (1): e233029

    Abstract

    "Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer progression, evaluating treatment responses, and planning radiation therapy; however, manual delineation is labor-intensive and subject to physician variability. Purpose To develop and evaluate an ensemble deep learning model for automating identification and segmentation of lung tumors on CT scans. Materials and Methods A retrospective study was conducted between July 2019 and November 2024 using a large dataset of CT simulation scans and clinical lung tumor segmentations from radiotherapy plans. This dataset was used to train a 3D U-Net-based, image-multiresolution ensemble model to detect and segment lung tumors on CT scans. Model performance was evaluated on internal and external test sets composed of CT simulation scans and lung tumor segmentations from two affiliated medical centers, including single primary and metastatic lung tumors. Performance metrics included sensitivity, specificity, false positive rate, and Dice similarity coefficient (DSC). Model-predicted tumor volumes were compared with physician-delineated volumes. Group comparisons were made with Wilcoxon signed-rank test or one-way ANOVA. P < 0.05 indicated statistical significance. Results The model, trained on 1,504 CT scans with clinical lung tumor segmentations, achieved 92% sensitivity (92/100) and 82% specificity (41/50) in detecting lung tumors on the combined 150-CT scan test set. For a subset of 100 CT scans with a single lung tumor each, the model achieved a median model-physician DSC of 0.77 (IQR: 0.65-0.83) and an interphysician DSC of 0.80 (IQR: 0.72-0.86). Segmentation time was shorter for the model than for physicians (mean 76.6 vs. 166.1-187.7 seconds; p<0.001). Conclusion Routinely collected radiotherapy data were useful for model training. The key strengths of the model include a 3D U-Net ensemble approach for balancing volumetric context with resolution, robust tumor detection and segmentation performance, and the ability to generalize to an external site.

    View details for DOI 10.1148/radiol.233029

    View details for PubMedID 39835976

  • Optimization of leptomeningeal carcinomatosis treatment in lung cancer using cerebrospinal fluid tumor-derived DNA Nanjo, S., Tej, A., Jin, M., Yano, S., Hui, A., Nagpal, S., Gephart, M., Alizadeh, A., Diehn, M. WILEY. 2025: 1891

    View details for Web of Science ID 001401044104344

  • Association between Locoregional Failure and NFE2L2/KEAP1/CUL3 Mutations in NRG/RTOG 9512: A Randomized Trial of Radiation Fractionation in T2N0 Glottic Cancer. Clinical cancer research : an official journal of the American Association for Cancer Research Guan, L., Torres-Saavedra, P. A., Zhao, X., Major, M. B., Holmes, B. J., Nguyen, N. K., Kumaravelu, P., Hodge, T., Diehn, M., Zevallos, J. P., Holsinger, F. C., Emami, B., Jordan, R. C., Hayward, M. C., Sagar, S. M., Morrison, W., Schultz, C., Caudell, J. J., Jones, C. U., Bratman, S. V., Galloway, T. J., Ma, D. J., Yom, S. S., Kudrimoti, M., Kim, H. E., Harris, J., Le, Q. T., Hayes, D. N. 2024

    Abstract

    NFE2L2/KEAP1/CUL3 mutations have been validated for radiation resistance in cell-based assays and animal models. However, clinical validation of these biomarkers has been challenging due to multimodality treatment regimens. This study aims to investigate the association between NFE2L2/KEAP1/CUL3 mutations and patient outcomes, including local failure (LF), locoregional failures (LRF), disease-free survival (DFS) and overall survival (OS), using samples from a phase III trial in which patients were treated with radiation monotherapy at 2 controlled doses.We investigated NFE2L2/KEAP1/CUL3 mutations in 250 randomized patients with T2N0 glottic SCC receiving definitive RT in NRG/RTOG 9512 trial, 119 had available biospecimens that were subjected to amplicon-based next-generation sequencing to assess for presence of NFE2L2/KEAP1/CUL3 mutations without regard to outcomes. Mutations in NFE2L2/KEAP1/CUL3 were assessed blinded to clinical outcomes. Cox models (2-sided alpha = 0.05) were used to evaluate the association with clinical outcomes, performed by an independent statistical team.Nineteen of 119 patients (16.0%) had NFE2L2/KEAP1/CUL3 mutations. Patient, treatment, and tumor characteristics were similar between those with and without mutations. Patients with mutation compared to those without had significantly more LF [HR 3.50 (95% CI 1.56, 7.89), p=0.0025] and LRF [HR 3.80 (95% CI 1.80, 8.03), p=0.0005]. DFS was significantly worse for the mutated compared to the non-mutated group in the first two years [HR 2.88 (95% CI 1.46, 5.66), p=0.0022]. Median DFS was shorter in the mutation group (10.3 months) versus those with intact NFE2L2/KEAP1/CUL3 (4.2 years).NFE2L2/KEAP1/CUL3 mutations may predict radiation treatment failure in T2N0 glottic cancer.

    View details for DOI 10.1158/1078-0432.CCR-24-2334

    View details for PubMedID 39656603

  • Cancer in 2024 CANCER DISCOVERY LoRusso, P. M., Bradley, C. J., Bunn Jr, P. A., Cleveland, J. L., Diehn, M., Lulu, J., Willson, S., Figueiredo, J. C., Flowers, C., Foti, M., Hricak, H., Llanos, A. M., Pignone, M., Stegmaier, K., Turaga, K., van den Brink, M. M., Winn, R. A., Haring, R. C., Gomez, S., Mesa, R. A., Ragin, C. R., Bell, R., Casillas, J. N., Cruz-Correa, M. R., Davis, M. B., Hazard-Jenkins, H. W., Johnson, W. Y., Le Marchand, L., Li, C. I., Nodora, J. N., Odunsi, A. O., Rodriguez, A., Schabath, M. B., Springfield, S. A., Studts, J. L., Terry, M., Thomas, C. R., Tossas, K. Y., Wages, N. A., Weekes, C. D., Winkfield, K. M., Davani, B., Davis, B. C., McCarthy, J., Wallace, T. A., AACR Canc Disparities Progress Report 2024 Steering Comm, AACR Canc Progress Report 2024 Steering Comm 2024; 14 (12): 2324-2331

    Abstract

    Excerpts from the 14th edition of the annual American Association for Cancer Research Cancer Progress Report (https://cancerprogressreport.aacr.org/progress/) and the third edition of the American Association for Cancer Research Cancer Disparities Progress Report (https://cancerprogressreport.aacr.org/disparities/) to US Congress and the public, both released in 2024, highlight significant strides made possible through medical research, much of which is supported by federal investments in the NIH, NCI, FDA, and Centers for Disease Control and Prevention, as well as recent progress in understanding the overlapping and intersecting causes of cancer disparities and in addressing health inequities through evidence-based public policies.

    View details for DOI 10.1158/2159-8290.CD-24-1451

    View details for Web of Science ID 001368753200017

    View details for PubMedID 39618280

  • Phase II Trial of Regorafenib and Oral Methotrexate in Previously Treated Advanced <i>KRAS</i>-Mutant NSCLC JTO CLINICAL AND RESEARCH REPORTS Aredo, J., Wakelee, H. A., Ramchandran, K. J., Neal, J. W., Diehn, M., Hui, A., Salahudeen, A., Kwong, B., Berry, G. J., Guo, H., Cunanan, K., Vali, S., Pancirer, D., Tsang, V., Hwang, G., Loza, M., Johnson, B., Blanchard, I., Padda, S. K. 2024; 5 (12)

    View details for DOI 10.1016/j.jtocrr.2024.100741

    View details for Web of Science ID 001358515100001

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