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Although immunotherapy can offer profound clinical benefit for patients with a variety of difficult-to-treat cancers, many tumors either do not respond to upfront treatment with immune checkpoint inhibitors (ICIs) or progressive/recurrent disease occurs after an interval of initial control. Improved response rates have been demonstrated with the addition of ICIs to cytotoxic therapies, leading to approvals from the US Food and Drug Administration and regulatory agencies in other countries for ICI−chemotherapy combinations in a number of solid tumor indications, including breast, head and neck, gastric, and lung cancer. Designing trials for patients with tumors that do not respond or stop responding to treatment with immunotherapy combinations, however, is challenging without uniform definitions of resistance. Previously, the Society for Immunotherapy of Cancer (SITC) published consensus definitions for resistance to single-agent anti-programmed cell death protein 1 (PD-1). To provide guidance for clinical trial design and to support analyses of emerging molecular and cellular data surrounding mechanisms of resistance to ICI-based combinations, SITC convened a follow-up workshop in 2021 to develop consensus definitions for resistance to multiagent ICI combinations. This manuscript reports the consensus clinical definitions for combinations of ICIs and chemotherapies. Definitions for resistance to ICIs in combination with targeted therapies and with other ICIs will be published in companion volumes to this paper.

Various approaches are being explored to address the unmet medical need among patients with advanced cancer who do not respond to immune checkpoint inhibitors. Interleukin-2 has become a prominent focus of preclinical and clinical investigation, because of its known clinical activity, the important role of this cytokine in immune biology, and the ability to engineer variant proteins with potentially improved antitumor immunomodulatory activity and reduced toxicity. Bempegaldesleukin, the first of the modified IL-2 agents to reach phase 3 evaluation in combination with an anti-PD-1, did not improve outcome for patients with metastatic melanoma and renal carcinoma. The disappointing data raise important questions about the potential efficacy of other interleukin-2 variants, however, several of the other variants appear to be sufficiently differentiated in anticipated pharmacokinetic properties and immune modulatory effects to warrant continued clinical development.

Background Tumor mutational burden (TMB), defined as the number of somatic mutations per megabase of interrogated genomic sequence, demonstrates predictive biomarker potential for the identification of patients with cancer most likely to respond to immune checkpoint inhibitors. TMB is optimally calculated by whole exome sequencing (WES), but next-generation sequencing targeted panels provide TMB estimates in a time-effective and cost-effective manner. However, differences in panel size and gene coverage, in addition to the underlying bioinformatics pipelines, are known drivers of variability in TMB estimates across laboratories. By directly comparing panel-based TMB estimates from participating laboratories, this study aims to characterize the theoretical variability of panel-based TMB estimates, and provides guidelines on TMB reporting, analytic validation requirements and reference standard alignment in order to maintain consistency of TMB estimation across platforms.Methods Eleven laboratories used WES data from The Cancer Genome Atlas Multi-Center Mutation calling in Multiple Cancers (MC3) samples and calculated TMB from the subset of the exome restricted to the genes covered by their targeted panel using their own bioinformatics pipeline (panel TMB). A reference TMB value was calculated from the entire exome using a uniform bioinformatics pipeline all members agreed on (WES TMB). Linear regression analyses were performed to investigate the relationship between WES and panel TMB for all 32 cancer types combined and separately. Variability in panel TMB values at various WES TMB values was also quantified using 95% prediction limits.Results Study results demonstrated that variability within and between panel TMB values increases as the WES TMB values increase. For each panel, prediction limits based on linear regression analyses that modeled panel TMB as a function of WES TMB were calculated and found to approximately capture the intended 95% of observed panel TMB values. Certain cancer types, such as uterine, bladder and colon cancers exhibited greater variability in panel TMB values, compared with lung and head and neck cancers.Conclusions Increasing uptake of TMB as a predictive biomarker in the clinic creates an urgent need to bring stakeholders together to agree on the harmonization of key aspects of panel-based TMB estimation, such as the standardization of TMB reporting, standardization of analytical validation studies and the alignment of panel-based TMB values with a reference standard. These harmonization efforts should improve consistency and reliability of panel TMB estimates and aid in clinical decision-making.