Harness the synergy between targeted therapy and immunotherapy: what have we learned and where are we headed?

Society for Immunotherapy of Cancer (SITC) consensus definitions for resistance to combinations of immune checkpoint inhibitors with targeted therapies

Immunotherapy offers deep and durable disease control to some patients, but many tumors do not respond to treatment with single-agent immune checkpoint inhibitors (ICIs). One strategy to enhance responses to immunotherapy is via combinations with signal transduction inhibitors, such as antiangiogenic therapies, which not only directly target cancer cells but also could potentially favorably modulate the tumor immune microenvironment. Combination strategies with ICIs have demonstrated enhanced antitumor activity compared with tumor-targeted or antiangiogenic therapy alone in randomized trials in a variety of solid tumor settings, leading to regulatory approval from the US Food and Drug Administration and agencies in other countries for the treatment of endometrial cancer, kidney cancer, melanoma, and hepatocellular carcinoma. Despite improved survival and response rates for some patients when antiangiogenic or targeted therapies are administered with ICIs, many patients continue to progress after combination treatment and urgently need new strategies to address this manifestation of resistance to immunotherapy. Previously, the Society for Immunotherapy of Cancer (SITC) published consensus definitions for resistance to single-agent anti-PD-(L)1. To provide guidance for clinical trial design and to support analyses of emerging molecular and immune profiling 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 anti-PD-(L)1 ICIs and targeted therapies. Definitions for resistance to ICIs in combination with chemotherapy and with other ICIs will be published in companion volumes to this paper.

Society for Immunotherapy of Cancer (SITC) consensus definitions for resistance to combinations of immune checkpoint inhibitors with chemotherapy

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.

The landscape of novel and complementary targets for immunotherapy: an analysis of gene expression in the tumor microenvironment

Background: Immunotherapies targeting immune checkpoint proteins CTLA-4, PD-1, and PD-L1 have saved lives, but these therapies have only been effective in some patients. Patients positive for expression of immune checkpoint proteins in the tumor microenvironment show better response to immune checkpoint inhibitors. Consequently, knowledge of which genes are consistently expressed in lymphocytes within the tumor microenvironment can convey potentially effective and complementary new immunotherapy targets.

Results: We identified 54 genes that have higher co-expression with the pan T-cell marker CD3E than CTLA4 or PDCD1. In a dataset of 26 patients who received anti-PD-1 therapy, we observed that co-expression between CD3E and PDCD1 was higher among responders than non-responders, supporting our correlation-based approach.

Conclusions: The genes highlighted in these analyses, which include CD6, TIGIT, CD96, and SLAMF6, warrant further investigation of their therapeutic potential.

Methods: We analyzed and ranked genes that were co-expressed with the pan T-cell marker CD3E in 9,601 human tumors, spanning 31 cancer types. To further identify targets that may be complementary to existing PD-1 therapy, we examined and ranked genes with high CD3E co-expression and relatively low PDCD1 co-expression.

A Scalable Pipeline for High-Throughput Flow Cytometry

Flow cytometry (FC) provides high-content data for a variety of applications, including phenotypic analysis of cell surface and intracellular markers, characterization of cell supernatant or lysates, and gene expression analysis. Historically, sample preparation, acquisition, and analysis have presented as a bottleneck for running such types of assays at scale. This article will outline the solutions that have been implemented at Novartis which have allowed high-throughput FC to be successfully conducted and analyzed for a variety of cell-based assays. While these experiments were generally conducted to measure phenotypic responses from a well-characterized and information-rich small molecular probe library known as the Mechanism-of-Action (MoA) Box, they are broadly applicable to any type of test sample. The article focuses on application of automated methods for FC sample preparation in 384-well assay plates. It also highlights a pipeline for analyzing large volumes of FC data, covering a visualization approach that facilitates review of screen-level data by dynamically embedding FlowJo (FJ) workspace images for each sample into a Spotfire file, directly linking them to the metric being observed. Finally, an application of these methods to a screen for MHC-I expression upregulators is discussed.