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      Durvalumab ± Tremelimumab + Platinum-Etoposide in Extensive-Stage Small Cell Lung Cancer (CASPIAN): Outcomes by PD-L1 Expression and Tissue Tumor Mutational Burden

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          Abstract

          Purpose:

          In the CASPIAN trial, first-line durvalumab plus platinum-etoposide (EP) significantly improved overall survival (OS) versus EP alone in extensive-stage small cell lung cancer (ES-SCLC). We report exploratory analyses of CASPIAN outcomes by programmed cell death ligand-1 (PD-L1) expression and tissue tumor mutational burden (tTMB).

          Experimental Design:

          Patients were randomized (1:1:1) to durvalumab (1,500 mg) plus EP, durvalumab plus tremelimumab (75 mg) plus EP, or EP alone. Treatment effects in PD-L1 and tTMB subgroups were estimated using an unstratified Cox proportional hazards model.

          Results:

          The PD-L1 and tTMB biomarker-evaluable populations (BEP) comprised 54.4% (438/805) and 35.2% (283/805) of the intention-to-treat population, respectively. PD-L1 prevalence was low: 5.7%, 25.8%, and 28.3% had PD-L1 expression on ≥1% tumor cells (TC), ≥1% immune cells (IC), and ≥1% TCs or ICs, respectively. OS benefit with durvalumab plus EP versus EP was similar across PD-L1 subgroups, with HRs all falling within the 95% confidence interval (CI) for the PD-L1 BEP (0.47‒0.79). OS benefit with durvalumab plus tremelimumab plus EP versus EP was greater in PD-L1 ≥1% versus <1% subgroups, although CIs overlapped. There was no evidence of an interaction between tTMB and treatment effect on OS (durvalumab plus EP vs. EP, P = 0.916; durvalumab plus tremelimumab plus EP vs. EP, P = 0.672).

          Conclusions:

          OS benefit with first-line durvalumab plus EP in patients with ES-SCLC was observed regardless of PD-L1 or tTMB status. PD-L1 expression may prove to be a useful biomarker for combined treatment with PD-(L)1 and CTLA-4 inhibition, although this requires confirmation with an independent dataset.

          See related commentary by Rolfo and Russo, p. 652

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          New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1).

          E A Eisenhauer, P Therasse, J. Bogaerts … (2009)
          Assessment of the change in tumour burden is an important feature of the clinical evaluation of cancer therapeutics: both tumour shrinkage (objective response) and disease progression are useful endpoints in clinical trials. Since RECIST was published in 2000, many investigators, cooperative groups, industry and government authorities have adopted these criteria in the assessment of treatment outcomes. However, a number of questions and issues have arisen which have led to the development of a revised RECIST guideline (version 1.1). Evidence for changes, summarised in separate papers in this special issue, has come from assessment of a large data warehouse (>6500 patients), simulation studies and literature reviews. HIGHLIGHTS OF REVISED RECIST 1.1: Major changes include: Number of lesions to be assessed: based on evidence from numerous trial databases merged into a data warehouse for analysis purposes, the number of lesions required to assess tumour burden for response determination has been reduced from a maximum of 10 to a maximum of five total (and from five to two per organ, maximum). Assessment of pathological lymph nodes is now incorporated: nodes with a short axis of 15 mm are considered measurable and assessable as target lesions. The short axis measurement should be included in the sum of lesions in calculation of tumour response. Nodes that shrink to <10mm short axis are considered normal. Confirmation of response is required for trials with response primary endpoint but is no longer required in randomised studies since the control arm serves as appropriate means of interpretation of data. Disease progression is clarified in several aspects: in addition to the previous definition of progression in target disease of 20% increase in sum, a 5mm absolute increase is now required as well to guard against over calling PD when the total sum is very small. Furthermore, there is guidance offered on what constitutes 'unequivocal progression' of non-measurable/non-target disease, a source of confusion in the original RECIST guideline. Finally, a section on detection of new lesions, including the interpretation of FDG-PET scan assessment is included. Imaging guidance: the revised RECIST includes a new imaging appendix with updated recommendations on the optimal anatomical assessment of lesions. A key question considered by the RECIST Working Group in developing RECIST 1.1 was whether it was appropriate to move from anatomic unidimensional assessment of tumour burden to either volumetric anatomical assessment or to functional assessment with PET or MRI. It was concluded that, at present, there is not sufficient standardisation or evidence to abandon anatomical assessment of tumour burden. The only exception to this is in the use of FDG-PET imaging as an adjunct to determination of progression. As is detailed in the final paper in this special issue, the use of these promising newer approaches requires appropriate clinical validation studies.
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            Signatures of mutational processes in human cancer

            Ludmil Alexandrov, Serena Nik-Zainal, David Wedge … (2015)
            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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              Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden

              Zachary Chalmers, Caitlin Connelly, David Fabrizio … (2017)
              Background High tumor mutational burden (TMB) is an emerging biomarker of sensitivity to immune checkpoint inhibitors and has been shown to be more significantly associated with response to PD-1 and PD-L1 blockade immunotherapy than PD-1 or PD-L1 expression, as measured by immunohistochemistry (IHC). The distribution of TMB and the subset of patients with high TMB has not been well characterized in the majority of cancer types. Methods In this study, we compare TMB measured by a targeted comprehensive genomic profiling (CGP) assay to TMB measured by exome sequencing and simulate the expected variance in TMB when sequencing less than the whole exome. We then describe the distribution of TMB across a diverse cohort of 100,000 cancer cases and test for association between somatic alterations and TMB in over 100 tumor types. Results We demonstrate that measurements of TMB from comprehensive genomic profiling are strongly reflective of measurements from whole exome sequencing and model that below 0.5 Mb the variance in measurement increases significantly. We find that a subset of patients exhibits high TMB across almost all types of cancer, including many rare tumor types, and characterize the relationship between high TMB and microsatellite instability status. We find that TMB increases significantly with age, showing a 2.4-fold difference between age 10 and age 90 years. Finally, we investigate the molecular basis of TMB and identify genes and mutations associated with TMB level. We identify a cluster of somatic mutations in the promoter of the gene PMS2, which occur in 10% of skin cancers and are highly associated with increased TMB. Conclusions These results show that a CGP assay targeting ~1.1 Mb of coding genome can accurately assess TMB compared with sequencing the whole exome. Using this method, we find that many disease types have a substantial portion of patients with high TMB who might benefit from immunotherapy. Finally, we identify novel, recurrent promoter mutations in PMS2, which may be another example of regulatory mutations contributing to tumorigenesis. Electronic supplementary material The online version of this article (doi:10.1186/s13073-017-0424-2) contains supplementary material, which is available to authorized users.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Clin Cancer Res
                Journal ID (iso-abbrev): Clin Cancer Res
                Title: Clinical Cancer Research
                Publisher: American Association for Cancer Research
                ISSN (Print): 1078-0432
                ISSN (Electronic): 1557-3265
                Publication date (Print): 16 February 2024
                Publication date (Electronic): 06 October 2023
                Volume: 30
                Issue: 4
                Pages: 824-835
                Affiliations
                [1 ]Department of Medical Oncology, Hospital Universitario 12 de Octubre, Lung Cancer Unit CNIO-H120, Complutense University and Ciberonc, Madrid, Spain.
                [2 ]Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
                [3 ]Department of Medicine, Section of Hematology/Oncology, Thoracic Oncology Unit, University of Chicago, Chicago, Illinois.
                [4 ]Cancer and Hematology Centers of Western Michigan, Grand Rapids, Michigan.
                [5 ]Asklepios Lung Clinic, Member of the German Center for Lung Research (DZL), Munich-Gauting, Germany.
                [6 ]Okayama University Hospital, Okayama, Japan.
                [7 ]Petrov Research Institute of Oncology, St. Petersburg, Russian Federation.
                [8 ]Odessa Regional Oncological Dispensary, Odessa, Ukraine.
                [9 ]Karl Landsteiner Institute of Lung Research and Pulmonary Oncology, Klinik Floridsdorf, Vienna, Austria.
                [10 ]Istanbul University−Cerrahpaşa, Cerrahpaşa School of Medicine, Istanbul, Turkey.
                [11 ]Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Republic of South Korea.
                [12 ]Omsk Regional Cancer Center, Omsk, Russian Federation.
                [13 ]Clinic of Medical Oncology, UMHAT St Marina, Varna, Bulgaria.
                [14 ]Dnipropetrovsk Medical Academy, Dnipro, Ukraine.
                [15 ]Thomayer Hospital, First Faculty of Medicine, Charles University, Prague, Czech Republic.
                [16 ]Semmelweis University, Budapest, Hungary.
                [17 ]AstraZeneca, Waltham, Massachusetts.
                [18 ]AstraZeneca, Gaithersburg, Maryland.
                [19 ]AstraZeneca, Cambridge, United Kingdom.
                [20 ]David Geffen School of Medicine at UCLA, Los Angeles, California.
                Author notes
                [* ] Corresponding Authors: Luis Paz-Ares, Hospital Universitario 12 de Octubre, Av de Córdoba s/n, Madrid 28041, Spain. E-mail: lpazaresr@ 123456seom.org ; and Yashaswi Shrestha, 1 Medimmune Way, AstraZeneca, Gaithersburg, MD 20853. Email: yashaswi.shrestha@ 123456astrazeneca.com

                Clin Cancer Res 2024;30:824–35

                Author information
                https://orcid.org/0000-0002-1947-3364
                https://orcid.org/0000-0002-3821-5598
                https://orcid.org/0009-0006-4175-8055
                https://orcid.org/0000-0002-7369-4512
                https://orcid.org/0000-0002-0112-0843
                https://orcid.org/0000-0003-3259-2242
                https://orcid.org/0009-0006-3412-0758
                https://orcid.org/0000-0002-8673-2450
                https://orcid.org/0000-0002-8417-8628
                https://orcid.org/0000-0003-1730-2606
                https://orcid.org/0000-0001-7123-8639
                https://orcid.org/0000-0001-5267-0354
                https://orcid.org/0000-0002-7071-2471
                https://orcid.org/0009-0007-9131-226X
                https://orcid.org/0000-0002-0211-5237
                https://orcid.org/0009-0006-7061-5483
                https://orcid.org/0000-0002-1506-8222
                https://orcid.org/0000-0003-3260-0302
                https://orcid.org/0000-0001-9426-2332
                https://orcid.org/0009-0002-8677-6821
                https://orcid.org/0000-0002-5567-0277
                https://orcid.org/0000-0002-4925-8243
                Article
                Publisher ID: CCR-23-1689
                DOI: 10.1158/1078-0432.CCR-23-1689
                PMC ID: 10870117
                PubMed ID: 37801329
                SO-VID: 3127dc5c-a22d-49f1-8579-1424760a91bf
                Copyright © ©2023 The Authors; Published by the American Association for Cancer Research
                License:

                This open access article is distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.

                History
                Date received : 27 June 2023
                Date revision received : 17 August 2023
                Date accepted : 03 October 2023
                Page count
                Pages: 12
                Categories
                Subject: Biomarkers
                Subject: Immunotherapy
                Subject: Biomarkers for Immunotherapy
                Subject: Checkpoint Blockade
                Subject: Immunotherapy
                Subject: Lung Cancer
                Subject: Precision Medicine and Imaging

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