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Updated: Dec 24, 2025

Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Progress Toward Identifying Exact Proxies for Predicting Response to Immunotherapies
Aleksandra Filipovic1, George Miller2, Joseph Bolen1
1PureTech Health PLC, Boston, MA, United States.
Abstract:
Clinical value and utility of checkpoint inhibitors, a drug class targeting adaptive immune suppression pathways (PD-1, PDL-1, and CTLA-4), is growing rapidly and maintains status of a landmark achievement in oncology. Their efficacy has transformed life expectancy in multiple deadly cancer types (melanoma, lung cancer, renal/urothelial carcinoma, certain colorectal cancers, lymphomas, etc.). Despite significant clinical development efforts, therapeutic indication of approved checkpoint inhibitors are not as wide as the oncology community and patients would like them to be, potentially bringing into question their universal efficacy across tumor histologies. With the main goal of expanding immunotherapy applications, identifying of biomarkers to accurately predict therapeutic response and treatment related side-effects are a paramount need in the field. Specificities surrounding checkpoint inhibitors in clinic, such as unexpected tumor response patterns (pseudo- and hyper-progression), late responders, as well as specific immune mediated toxicities, complicate the management of patients. They stem from the complexities and dynamics of the tumor/host immune interactions, as well as baseline tumor biology. Search for clinically effective biomarkers therefore calls for a holistic approach, rather than implementation of a single analyte. The goal is to achieve dynamic and comprehensive acquisition, analyses and interpretation of immunological and biologic information about the tumor and the immune system, and to compute these parameters into an actionable, maximally predictive value at the individual patient level. Limitation delaying swift incorporation of validated immuno-oncology biomarkers span from standardized biospecimens acquisition and processing, selection of proficient biomarker discovery and validation methods, to establishing multidisciplinary consortiums and data sharing platforms. Multi-disciplinary efforts have already yielded some approved (PDL-1 and MSI-status) and other advanced tests (TMB, neoantigen pattern, and TIL infiltration rate). Importantly, clinical trial taskforces now recognize the imperative of the biomarker-driven trial design and execution, to enable translating biomarker discoveries into the clinical setting. This will ensure we utilize the "conspiracy" between the peripheral and intra-tumoral dynamic markers in shaping responses to checkpoint blockade, for the ultimate patient benefit.
Insights
Checkpoint inhibitors are revolutionizing cancer treatment, but wider application requires better biomarkers. A holistic approach to analyzing tumor and immune data is crucial for predicting patient response and improving outcomes.
Area of Science:
- Oncology
- Immunology
- Biomarker Discovery
Background:
- Checkpoint inhibitors targeting PD-1, PDL-1, and CTLA-4 pathways represent a major advance in cancer therapy, significantly improving survival in various malignancies.
- Despite successes, their efficacy varies across tumor types, necessitating broader applications and improved predictive tools.
Purpose of the Study:
- To address the need for expanded immunotherapy applications by identifying biomarkers for predicting therapeutic response and side effects of checkpoint inhibitors.
- To explore a holistic approach for analyzing complex tumor-host immune interactions and tumor biology to develop predictive biomarkers.
Main Methods:
- Review of current challenges in checkpoint inhibitor therapy, including response variability and immune-related toxicities.
- Discussion of the need for comprehensive acquisition, analysis, and interpretation of immunological and biological data.
- Emphasis on multidisciplinary efforts and data sharing for biomarker validation and clinical integration.
Main Results:
- Current approved biomarkers include PD-L1 expression and MSI status, with others like TMB, neoantigen patterns, and TIL infiltration under investigation.
- The complexity of tumor-immune dynamics requires a multi-analyte, holistic approach rather than single markers.
- Biomarker-driven clinical trial design is essential for translating discoveries into practice.
Conclusions:
- Accurate prediction of checkpoint inhibitor response and toxicity necessitates a dynamic, comprehensive biomarker strategy.
- Overcoming limitations in biospecimen handling, validation methods, and data sharing is key to incorporating new biomarkers.
- Multidisciplinary collaboration and biomarker-driven trials are critical for maximizing patient benefit from immunotherapy.
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