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      The Promises and Challenges of Tumor Mutation Burden as an Immunotherapy Biomarker: A Perspective from the International Association for the Study of Lung Cancer Pathology Committee

      , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
      Journal of Thoracic Oncology
      Elsevier BV

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          Abstract

          <p class="first" id="d1865971e619">Immune checkpoint inhibitor (ICI) therapies have revolutionized the management of patients with NSCLC and have led to unprecedented improvements in response rates and survival in a subset of patients with this fatal disease. However, the available therapies work only for a minority of patients, are associated with substantial societal cost, and may lead to considerable immune-related adverse events. Therefore, patient selection must be optimized through the use of relevant biomarkers. Programmed death-ligand 1 protein expression by immunohistochemistry is widely used today for the selection of programmed cell death protein 1 inhibitor therapy in patients with NSCLC; however, this approach lacks robust sensitivity and specificity for predicting response. Tumor mutation burden (TMB), or the number of somatic mutations derived from next-generation sequencing techniques, has been widely explored as an alternative or complementary biomarker for response to ICIs. In theory, a higher TMB increases the probability of tumor neoantigen production and therefore, the likelihood of immune recognition and tumor cell killing. Although TMB alone is a simplistic surrogate of this complex interplay, it is a quantitative variable that can be relatively readily measured using currently available sequencing techniques. A large number of clinical trials and retrospective analyses, employing both tumor and blood-based sequencing tools, have evaluated the performance of TMB as a predictive biomarker, and in many cases reveal a correlation between high TMB and ICI response rates and progression-free survival. Many challenges remain before the implementation of TMB as a biomarker in clinical practice. These include the following: (1) identification of therapies whose response is best informed by TMB status; (2) robust definition of a predictive TMB cut point; (3) acceptable sequencing panel size and design; and (4) the need for robust technical and informatic rigor to generate precise and accurate TMB measurements across different laboratories. Finally, effective prediction of response to ICI therapy will likely require integration of TMB with a host of other potential biomarkers, including tumor genomic driver alterations, tumor-immune milieu, and other features of the host immune system. This perspective piece will review the current clinical evidence for TMB as a biomarker and address the technical sequencing considerations and ongoing challenges in the use of TMB in routine practice. </p>

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          Signatures of mutational processes in human cancer

          All cancers are caused by somatic mutations. However, understanding of the biological processes generating these mutations is limited. The catalogue of somatic mutations from a cancer genome bears the signatures of the mutational processes that have been operative. Here, we analysed 4,938,362 mutations from 7,042 cancers and extracted more than 20 distinct mutational signatures. Some are present in many cancer types, notably a signature attributed to the APOBEC family of cytidine deaminases, whereas others are confined to a single class. Certain signatures are associated with age of the patient at cancer diagnosis, known mutagenic exposures or defects in DNA maintenance, but many are of cryptic origin. In addition to these genome-wide mutational signatures, hypermutation localized to small genomic regions, kataegis, is found in many cancer types. The results reveal the diversity of mutational processes underlying the development of cancer with potential implications for understanding of cancer etiology, prevention and therapy.
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            Pembrolizumab plus Chemotherapy in Metastatic Non–Small-Cell Lung Cancer

            First-line therapy for advanced non-small-cell lung cancer (NSCLC) that lacks targetable mutations is platinum-based chemotherapy. Among patients with a tumor proportion score for programmed death ligand 1 (PD-L1) of 50% or greater, pembrolizumab has replaced cytotoxic chemotherapy as the first-line treatment of choice. The addition of pembrolizumab to chemotherapy resulted in significantly higher rates of response and longer progression-free survival than chemotherapy alone in a phase 2 trial.
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              Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer.

              Immune checkpoint inhibitors, which unleash a patient's own T cells to kill tumors, are revolutionizing cancer treatment. To unravel the genomic determinants of response to this therapy, we used whole-exome sequencing of non-small cell lung cancers treated with pembrolizumab, an antibody targeting programmed cell death-1 (PD-1). In two independent cohorts, higher nonsynonymous mutation burden in tumors was associated with improved objective response, durable clinical benefit, and progression-free survival. Efficacy also correlated with the molecular smoking signature, higher neoantigen burden, and DNA repair pathway mutations; each factor was also associated with mutation burden. In one responder, neoantigen-specific CD8+ T cell responses paralleled tumor regression, suggesting that anti-PD-1 therapy enhances neoantigen-specific T cell reactivity. Our results suggest that the genomic landscape of lung cancers shapes response to anti-PD-1 therapy. Copyright © 2015, American Association for the Advancement of Science.
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                Author and article information

                Journal
                Journal of Thoracic Oncology
                Journal of Thoracic Oncology
                Elsevier BV
                15560864
                September 2020
                September 2020
                : 15
                : 9
                : 1409-1424
                Article
                10.1016/j.jtho.2020.05.019
                32522712
                2b97c521-2e51-40e7-be73-6a6e597d0419
                © 2020

                https://www.elsevier.com/tdm/userlicense/1.0/

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