One of the greatest challenges in modern oncology is not only developing new cancer treatments but understanding something even more complex: which patients will truly benefit from immunotherapy. Although immunotherapy has transformed cancer treatment across multiple tumor types, clinical reality shows that responses vary widely. Some patients experience long-lasting and remarkable remission, while others gain little or no benefit. This variability has driven intense scientific efforts to identify biomarkers capable of predicting treatment success before therapy even begins.
Immunotherapy works by stimulating the immune system to recognize and eliminate cancer cells. However, the relationship between tumors and the immune system is highly dynamic. Biomarkers used to predict therapeutic response are not fixed; they change depending on tumor biology, the tumor microenvironment, and even previous treatments. As a result, measurements taken at a single moment may fail to accurately reflect a patient’s real potential to respond to immunotherapy.
A clear example of this limitation is the biomarker PD-L1. Currently, the expression of this protein on tumor cells is commonly used to estimate the likelihood of response to immune checkpoint inhibitors targeting PD-1 or PD-L1. Yet interpreting this marker presents significant challenges. PD-L1 expression can vary greatly depending on where the biopsy sample is taken within the tumor. Peripheral tumor regions—where immune cells actively interact with cancer cells—often display higher PD-L1 expression than the tumor core, which is frequently the site sampled during clinical biopsies.
Recent research has also revealed that tumors may not only express PD-L1 on their surface but can actively release it into circulation through extracellular vesicles known as exosomes. These lipid-covered particles transport PD-L1 to distant sites, suppressing immune responses far from the original tumor. Consequently, a tumor may appear PD-L1 negative in tissue analysis while still using this mechanism extensively to evade immune attack. Measuring circulating PD-L1 in blood may therefore become a more accurate predictive tool in the future.
These limitations carry important clinical implications. In many healthcare systems, biomarker results determine whether insurance providers approve immunotherapy coverage. This means some patients may be denied potentially beneficial treatments due to imperfect or incomplete testing methods. Overreliance on evolving diagnostic tools may unintentionally restrict access to innovative therapies.
Currently, two of the most commonly used predictors of immunotherapy response are Microsatellite Instability (MSI) and PD-L1 expression. MSI testing evaluates defects in DNA mismatch repair mechanisms responsible for correcting replication errors. When these repair systems fail, mutations accumulate over time. From an immunological perspective, tumors with numerous mutations appear more foreign to the immune system, making them easier targets for immune recognition.
In colorectal cancer, tumors with high MSI often demonstrate improved prognosis and stronger responses to checkpoint inhibition therapy. However, this relationship is not universal. Certain cancers, such as renal cell carcinoma, may contain relatively few mutations yet still respond well to immunotherapy. This highlights that immune response depends on multiple biological factors beyond mutation burden alone.
An important concept emerges from this observation: even when the immune system fails to eliminate cancer completely, it may still be actively fighting it. This ongoing immune pressure can slow tumor growth and create conditions that make therapeutic immune stimulation more effective. In essence, when the immune system is already attempting to combat the tumor, immunotherapy may simply amplify an existing battle.
Checkpoint inhibitors such as nivolumab or pembrolizumab function by blocking inhibitory molecular interactions that normally restrain immune activity. By removing these immune “brakes,” T cells regain their ability to attack cancer. Increasing evidence now suggests that timing of administration is as critical as drug selection itself.
Immune activation follows a biological sequence. First, an initial immune response must be triggered; only afterward can checkpoint inhibitors effectively enhance that response. When this sequence is disrupted, treatment efficacy may decline. A helpful analogy is starting a car engine—fuel, compression, and ignition must occur in proper synchronization for the system to function.
This understanding has fueled growing interest in intratumoral immunotherapy, in which immune-activating agents are injected directly into tumors. Such approaches can locally stimulate immune recognition even in patients whose biomarkers initially predict poor response. Agents targeting Toll-like receptors (TLR) or the STING pathway may help initiate immune activation before checkpoint inhibitors amplify the response.
Figure 1. Microsatellite instability (MSI) indicates defective DNA mismatch repair, while tumor mutational burden (TMB) reflects the accumulation of mutations and potential neoantigen formation. Circulating PD-L1 illustrates immune checkpoint–mediated tumor escape, and Immunoscore evaluates T-cell infiltration within the tumor microenvironment. Together, these biomarkers contribute to therapeutic stratification and precision targeting.
Another promising advance in predicting treatment outcomes is the Immunoscore, a system designed to measure immune cell infiltration within tumors. Unlike traditional cancer staging—which relies primarily on tumor size or metastatic spread—the Immunoscore evaluates immune presence both at the tumor center and invasive margins.
Tumors heavily infiltrated by immune cells, known as “inflamed tumors”, are generally associated with better prognosis and improved responsiveness to immunotherapy. Conversely, “immune deserts”, characterized by minimal immune infiltration, tend to resist treatment. This represents an important conceptual shift: cancer prognosis may depend more on immune-tumor interaction than on tumor size alone.
From this perspective, some experts propose that future cancer staging systems should integrate immune parameters alongside total tumor volume. Larger tumor burdens produce greater amounts of immunosuppressive factors capable of weakening immune responses. Interestingly, a patient with early-stage disease but large tumor volume could theoretically have worse immunologic prognosis than a patient with advanced disease but smaller tumor burden.
Encouragingly, the tumor microenvironment is not fixed. Emerging evidence suggests that immune deserts can be converted into inflamed tumors through targeted interventions, including intratumoral immunotherapy. This strategy aims to level the therapeutic playing field, allowing patients initially considered poor candidates to develop effective immune responses.
Ultimately, predicting immunotherapy response remains an evolving scientific frontier. No single biomarker currently provides definitive answers. The future likely lies in integrated models combining genetic, metabolic, immunologic, and clinical data to better understand each tumor’s unique biology.
Immunotherapy has revealed the extraordinary power of the immune system in combating cancer. Fully harnessing this potential requires understanding not only the tumor itself but also timing, biological context, and the conditions necessary for effective immune activation. As research progresses, oncology moves closer to truly personalized medicine—where treatment decisions are guided not solely by cancer type, but by the complex interaction between each patient and their disease.
Main reference:
Williams, J. R. (2019). The immunotherapy revolution the immunotherapy revolution: The best New Hope for saving cancer patients’ lives. Independently Published.
