Society for Immunotherapy of Cancer clinical and biomarkers data sharing resource document: Volume II—practical challenges

Abstract

The development of strongly predictive validated biomarkers is essential for the field of immuno-oncology (IO) to advance. The highly complex, multifactorial data sets required to develop these biomarkers necessitate effective, responsible data-sharing efforts in order to maximize the scientific knowledge and utility gained from their collection. While the sharing of clinical- and safety-related trial data has already been streamlined to a large extent, the sharing of biomarker-aimed clinical trial derived data and data sets has been met with a number of hurdles that have impaired the progression of biomarkers from hypothesis to clinical use. These hurdles include technical challenges associated with the infrastructure, technology, workforce, and sustainability required for clinical biomarker data sharing. To provide guidance and assist in the navigation of these challenges, the Society for Immunotherapy of Cancer (SITC) Biomarkers Committee convened to outline the challenges that researchers currently face, both at the conceptual level (Volume I) and at the technical level (Volume II). The committee also suggests possible solutions to these problems in the form of professional standards and harmonized requirements for data sharing, assisting in continued progress toward effective, clinically relevant biomarkers in the IO setting.

Society for Immunotherapy of Cancer (SITC) checkpoint inhibitor resistance definitions: efforts to harmonize terminology and accelerate immuno-oncology drug development

“…Work by the SITC Biomarkers Committee outlined both conceptual and practical challenges to data sharing. 8 Solutions for addressing these barriers include striving for realistic goals and culture shifts as they relate to data sharing. Engaging with key stakeholders using the recommendations from the SITC Biomarkers Committee in concert with SITC resistance definitions may help in gaining access to data supporting both efforts, which is drastically needed for future IO drug development.…”

Stocking the toolbox—Using preclinical models to understand the development and treatment of immune checkpoint inhibitor‐induced immune‐related adverse events

“…With increasing availability of high‐throughput data of the immune composition for cancer patients treated with ICIs, this also represents an opportunity to build more robust cohorts to ascertain predictive information relating to ICI‐initiated irAEs. Concerted efforts to improve data‐sharing and annotation of cancer patients from individual datasets have been growing, although there remains a number of technical, ethical, and intellectual concerns to the implementation of this process 16,17 . To date, in the setting of examining ICI treatment efficacy, there has been an inability to distill mechanistic insight from clinical trials or real‐world cohorts to redefine biomarkers for therapeutic response that provide additive value to the presence of PD‐L1 in the tumor or the tumor mutational burden (TMB).…”

“…Concerted efforts to improve data-sharing and annotation of cancer patients from individual datasets have been growing, although there remains a number of technical, ethical, and intellectual concerns to the implementation of this process. 16,17 To date, in the setting of examining ICI treatment F I G U R E 1 Immune checkpoint inhibitors enhance immune activation. Clinically approved immune checkpoint inhibitors targeting either (A) PD-1, PD-L1, (B) CTLA-4, and (C) LAG-3 are highlighted.…”