The Quest for Universal Biomarkers in Cancer Immunotherapy
Imagine a revolutionary cancer treatment that doesn't attack cancer cells directly but instead empowers the body's own immune system to recognize and destroy tumors.
This is the promise of immunotherapy, particularly drugs known as immune checkpoint inhibitors that target molecules like CTLA-4 and PD-1. These treatments have transformed cancer care, producing remarkable, long-lasting responses in some patients with advanced cancers that were once considered untreatable 6 .
Yet, this breakthrough comes with a significant challenge: these powerful drugs don't work for everyone. In fact, they only benefit a subset of patients, while others endure potentially serious side effects with no therapeutic benefit 9 . This hit-or-miss approach has sparked an urgent scientific quest to find better ways to predict who will respond—a search for biological clues known as biomarkers. The ultimate goal? To find reliable, universal biomarkers that can guide treatment decisions across different cancer types, moving beyond the current "one-size-fits-all" approach to truly personalized cancer care.
Biomarkers in immunotherapy fall into two main categories: prognostic biomarkers that provide information about a patient's overall cancer outcome regardless of treatment, and predictive biomarkers that specifically identify patients likely to respond to a particular therapy 4 . Several biomarkers have already entered clinical practice, though each has limitations.
Measures the total number of mutations in a tumor's DNA. More mutations create more targets for the immune system 2 .
The core problem limiting current biomarkers is their lack of generalizability—what works well as a predictor in one cancer type often fails in another. This variability stems from several fundamental biological and technical challenges:
Tumors contain diverse cell populations with different characteristics. A small biopsy from one part may not represent the entire tumor's biology 5 .
Differences in assay platforms, scoring systems, and cutoff thresholds create consistency problems 7 .
The immune response involves multiple overlapping pathways. A 2025 study found high PD-1 RNA expression correlated with other immune checkpoints 7 .
A 2025 study focusing on an Egyptian population found that specific genetic variants in the CTLA-4 gene were associated with increased risk of pancreatic ductal adenocarcinoma (PDAC) and higher levels of soluble CTLA-4 1 .
| Biomarker | Key Strength | Generalizability Challenge |
|---|---|---|
| PD-L1 Expression | Clinically validated in multiple cancers | Heterogeneous expression; different scoring systems across cancer types |
| Tumor Mutational Burden (TMB) | Tissue-agnostic approval | Cut-off values not uniform across cancers; affected by patient ancestry |
| Mismatch Repair Deficiency | Strong predictor when present | Only present in small subset of most cancer types |
| CTLA-4 Polymorphisms | Identified in specific populations | Variants may not predict response in other ethnic groups or cancer types 1 |
A landmark study published in 2025 in npj Genomic Medicine provides valuable insights into the generalizability challenge by systematically mapping PD-1 expression across different cancer types 7 . This research represents precisely the kind of comprehensive analysis needed to identify more universal biomarkers.
| Cancer Type | High PD-1 Expression | Moderate PD-1 Expression | Low PD-1 Expression |
|---|---|---|---|
| Pancreatic Cancer | 21.82% | 49.09% | 29.09% |
| Liver/Bile Duct | 21.05% | 47.37% | 31.58% |
| Colorectal Cancer | 17.14% | 47.86% | 35.00% |
| Breast Cancer | 16.33% | 53.06% | 30.61% |
| Lung Cancer | 15.00% | 50.00% | 35.00% |
| Occult Primary | 7.59% | 43.04% | 49.37% |
The search for generalizable biomarkers relies on sophisticated laboratory tools and reagents. Here are some essential components of the biomarker discovery toolkit:
| Research Tool | Function in Biomarker Research | Application Example |
|---|---|---|
| TaqMan™ SNP Genotyping Assays | Detects specific genetic variations | Analyzing CTLA-4 polymorphisms in pancreatic cancer risk 1 |
| ELISA Kits | Measures protein concentrations in solutions | Quantifying soluble CTLA-4 levels in patient serum 1 |
| OmniSeq Immune Panel | Comprehensive immune gene expression profiling | Mapping PD-1 expression across 31 cancer types 7 |
| TaqMan™ Genotyping Master Mix | Essential component for genetic amplification | Genotyping CTLA-4 variants in PCR experiments 1 |
| Multiplex Immunofluorescence | Simultaneously visualizes multiple protein markers | Analyzing spatial relationships between immune cells in tumors 5 |
| RNA Extraction Kits | Isolates high-quality RNA from patient samples | Preparing samples for transcriptomic analysis of PD-1 3 |
As the limitations of single biomarkers become increasingly apparent, the field is shifting toward integrated approaches that combine multiple data types. The future lies in composite biomarkers that simultaneously consider tumor genetics, immune environment, and patient factors 5 9 .
Instead of relying solely on invasive tumor biopsies, researchers are developing blood-based biomarkers that provide a "liquid biopsy" of the cancer immune environment.
These include circulating immune cells, soluble checkpoint proteins, and other inflammatory markers 5 . For instance, the relative eosinophil count (REC) in peripheral blood has shown promise as a predictor of response to CTLA-4 blockade in melanoma patients 2 .
The integration of multiple data layers—genomics, transcriptomics, proteomics—coupled with advanced computational analysis represents the cutting edge of biomarker discovery.
One study demonstrated approximately 15% improvement in predictive accuracy using multi-omics with machine learning models 2 . These approaches can analyze the complex interplay between tumors and the immune system in ways that were previously impossible.
The quest for generalizable biomarkers of response to CTLA-4 and PD-1 blockade represents one of the most important frontiers in cancer research. While significant challenges remain—tumor heterogeneity, technical variability, and biological complexity—the scientific community is making steady progress.
The transition from single, inflexible biomarkers to adaptive, multi-parameter models will ultimately allow clinicians to tailor immunotherapy with unprecedented precision. As research continues to unravel the complex dialogue between tumors and the immune system, the vision of predicting immunotherapy response for each individual patient, regardless of their cancer type, moves closer to reality.
Future success will depend on continuing to map the immune landscape across diverse cancers and populations, standardizing assessment methods, and embracing integrated analytical approaches. With these advances, the revolutionary potential of immunotherapy may finally be accessible to all patients who can benefit, ushering in a new era of truly personalized cancer care.
The future for ICIs is undeniably bright, with promising recent results in the neoadjuvant setting and for inhibitors of targets beyond PD-1–PD-L1 and CTLA-4. Intensifying efforts to enhance data standardization, sharing of existing trial datasets, and prospective validation of candidate biomarkers in diverse populations will be crucial for the development of more-effective biomarkers. 9