Unique Neoantigens Arise from Somatic Mutations in Patients with Gastrointestinal Cancers

Parkhurst, Maria R.; Robbins, Paul F.; Tran, Eric; Prickett, Todd D.; Gartner, Jared J.; Li, Jia; Ivey, Gabriel D.; Li, Yong F.; El‐Gamil, Mona; Lalani, Almin; Crystal, Jessica S.; Sachs, Abraham; Groh, Eric; Ray, Satyajit; Ngo, Lien T.; Kivitz, Scott; Pasetto, Anna; Yossef, Rami; Lowery, Frank J.; Goff, Stephanie L.; Lo, Winifred; Cafri, Gal; Deniger, Drew C.; Malekzadeh, Parisa; Ahmadzadeh, Mojgan; Wunderlich, John R.; Somerville, Robert; Rosenberg, Steven A.

doi:10.1158/2159-8290.cd-18-1494

Cited by 208 publications

(226 citation statements)

References 34 publications

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“…Of the 1071 genes harboring predicted IMMs in 2 or more patients, the ones containing the most included TP53 (68), CTNNB1 (18), PIK3CA (16), HRAS (8), KRAS (7), PTEN (7), FBXW7 (6), EGFR (5), MDN1 (5), POLE (5), TRRAP (5), and VPS13C (5) ( Table S9c). Six missense mutations harbored by patients in the TCGA cohorts were previously validated by CD8+ T cell response assays (53) (54) (55). Of the six missense mutations, TP53 R248Q , TP53 Y220C , TP53 R175H , TP53 R248W and KRAS G12D were predicted to be IMMs by our MHCnuggets pipeline and were shared by three or more of the TCGA patients.…”

Section: Candidate Mhc Class I Immunogenic Missense Mutations In Tcgamentioning

confidence: 97%

High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets

Shao

Bhattacharya

Huang

et al. 2019

Preprint

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Running title: High-throughput prediction of neoantigens with MHCnuggets Potential Conflicts of Interest: V.A receives research funding from Bristol-Myers Abstract Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins is an emerging biomarker for predicting patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHC binding affinity and are limited by low positive predictive value for actual peptide presentation, inadequate support for rare MHC alleles and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method to predict peptide-MHC binding. MHCnuggets is the only method to handle binding prediction for common or rare alleles of MHC Class I or II, with a single neural network architecture. Using a long shortterm memory network (LSTM), MHCnuggets accepts peptides of variable length and is capable of faster performance than other methods. When compared to methods that integrate binding affinity and HLAp data from mass spectrometry, MHCnuggets yields a fourfold increase in positive predictive value on independent MHC-bound peptide (HLAp) data. We applied MHCnuggets to 26 cancer types in TCGA, processing 52.6 million allele-peptide comparisons in under 2.3 hours, yielding 103,587 candidate immunogenic missense mutations (IMMs). IMM hotspots occurred in 36 genes, including 22 driver genes. Predicted IMM load was significantly associated with increased immune cell infiltration (p<2e-16) including CD8+ T cells. Notably, only 0.15% of predicted immunogenic missense mutations were observed in >2 patients, with 65% of these derived from driver mutations. Our results provide a new method for neoantigen prediction with high performance characteristics and demonstrate its utility in large data sets across human cancers. SynopsisWe developed a new in silico predictor of Major Histocompatibility Complex (MHC) ligand binding and demonstrated its utility to assess potential neoantigens and immunogenic missense mutations (IMMs) in 6613 TCGA patients.

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Section: Candidate Mhc Class I Immunogenic Missense Mutations In Tcgamentioning

confidence: 97%

High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets

Shao

Bhattacharya

Huang

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…Neoantigen burden is the total number of neoantigens produced by somatic mutations of a tumor genome. A higher neoantigen burden is associated with improved immune therapy response [90][91][92][93]. The predictive value of neoantigen burden is still being evaluated and additional validation via clinical trials is needed.…”

Section: Neoantigen Burdenmentioning

confidence: 99%

Immunotherapy in Metastatic Colorectal Cancer: Could the Latest Developments Hold the Key to Improving Patient Survival?

Damilakis

Mavroudis

Sfakianaki

et al. 2020

Cancers

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Immunotherapy has considerably increased the number of anticancer agents in many tumor types including metastatic colorectal cancer (mCRC). Anti-PD-1 (programmed death 1) and cytotoxic T-lymphocyte–associated antigen 4 (CTLA-4) immune checkpoint inhibitors (ICI) have been shown to benefit the mCRC patients with mismatch repair deficiency (dMMR) or high microsatellite instability (MSI-H). However, ICI is not effective in mismatch repair proficient (pMMR) colorectal tumors, which constitute a large population of patients. Several clinical trials evaluating the efficacy of immunotherapy combined with chemotherapy, radiation therapy, or other agents are currently ongoing to extend the benefit of immunotherapy to pMMR mCRC cases. In dMMR patients, MSI testing through immunohistochemistry and/or polymerase chain reaction can be used to identify patients that will benefit from immunotherapy. Next-generation sequencing has the ability to detect MSI-H using a low amount of nucleic acids and its application in clinical practice is currently being explored. Preliminary data suggest that radiomics is capable of discriminating MSI from microsatellite stable mCRC and may play a role as an imaging biomarker in the future. Tumor mutational burden, neoantigen burden, tumor-infiltrating lymphocytes, immunoscore, and gastrointestinal microbiome are promising biomarkers that require further investigation and validation.

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“…126,129,134 Steven Rosenberg, MD (National Cancer Institute, Bethesda, Maryland), pioneered a tandem minigene approach to identify the precise neoantigens recognized by T cells from patients treated with adoptive cell transfer. 135,136 In melanoma, neoantigens were identified in 29 of 31 patients (94%), with each of the identified neoantigens being unique to the autologous patient. 135 In common gastrointestinal cancers, neoantigen-reactive T cells were identified in 62 of 75 patients (83%), with 99% of the neoantigenic determinants unique to each autologous patient.…”

Section: Cancer Neoantigens (Status: Early Emerging)mentioning

confidence: 99%

“…135 In common gastrointestinal cancers, neoantigen-reactive T cells were identified in 62 of 75 patients (83%), with 99% of the neoantigenic determinants unique to each autologous patient. 136 Immunogenic tumor antigens associated with adoptive cell transfer complete regressions have been identified in metastatic breast cancer 137 and human papillomavirus-associated metastatic cervical cancer. 138 Although this area is promising, several challenges would need to be overcome before neoantigen prediction or neoantigen load could become part of routine cancer immunotherapy testing.…”

Section: Cancer Neoantigens (Status: Early Emerging)mentioning

confidence: 99%

The Cancer Immunotherapy Biomarker Testing Landscape

Walk

Yohe

Beckman

et al. 2019

Archives of Pathology &Amp; Laboratory Medicine

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Context.— Cancer immunotherapy provides unprecedented rates of durable clinical benefit to late-stage cancer patients across many tumor types, but there remains a critical need for biomarkers to accurately predict clinical response. Although some cancer immunotherapy tests are associated with approved therapies and considered validated, other biomarkers are still emerging and at various states of clinical and translational exploration. Objective.— To provide pathologists with a current and practical update on the evolving field of cancer immunotherapy testing. The scientific background, clinical data, and testing methodology for the following cancer immunotherapy biomarkers are reviewed: programmed death ligand-1 (PD-L1), mismatch repair, microsatellite instability, tumor mutational burden, polymerase δ and ε mutations, cancer neoantigens, tumor-infiltrating lymphocytes, transcriptional signatures of immune responsiveness, cancer immunotherapy resistance biomarkers, and the microbiome. Data Sources.— Selected scientific publications and clinical trial data representing the current field of cancer immunotherapy. Conclusions.— The cancer immunotherapy field, including the use of biomarker testing to predict patient response, is still in evolution. PD-L1, mismatch repair, and microsatellite instability testing are helping to guide the use of US Food and Drug Administration–approved therapies, but there remains a need for better predictors of response and resistance. Several categories of tumor and patient characteristics underlying immune responsiveness are emerging and may represent the next generation of cancer immunotherapy predictive biomarkers. Pathologists have important roles and responsibilities as the field of cancer immunotherapy continues to develop, including leadership of translational studies, exploration of novel biomarkers, and the accurate and timely implementation of newly approved and validated companion diagnostics.

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Unique Neoantigens Arise from Somatic Mutations in Patients with Gastrointestinal Cancers

Cited by 208 publications

References 34 publications

High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets

High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets

Immunotherapy in Metastatic Colorectal Cancer: Could the Latest Developments Hold the Key to Improving Patient Survival?

The Cancer Immunotherapy Biomarker Testing Landscape

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