A team of researchers at IRB Barcelona has identified five independent factors that predict cancer patients’ response to checkpoint inhibitors (CPIs). The study, which has been published in Geneticsvalidates these factors in more than 1,400 patients and diverse types of cancer. These findings provide a framework to interpret biomarkers of response to CPIs and suggest a future pathway to improve personalized cancer medicine.

Immunotherapy has transformed cancer treatment in recent years by enabling the immune system to attack tumor cells. However, only 20–40% of patients respond positively to immunotherapy, and these rates vary across different types of cancer.

Predicting which patients will respond to immunotherapy and which will not is currently a highly active area of ​​research. Numerous studies conducted so far have focused on the specific characteristics of tumors, their microenvironment, or the patient’s immune system. As a result, which of the proposed biomarkers represent the same underlying factors or how many independent factors influence the effectiveness of this therapy remains unclear.

Researchers at IRB Barcelona have identified five key, independent factors that determine patients’ response and survival after receiving checkpoint inhibitors (CPIs), a type of immunotherapy widely used in cancer treatment. These findings provide a reference framework for current and future biomarkers of immunotherapy response.

They could also, in the future, entail a pathway to a significant advancement in the personalization of cancer treatments, helping to more accurately identify those patients who are likely to benefit from immunotherapy. The results suggest that patients with certain types of tumors who are currently not considered candidates for immunotherapy (such as those with liver or kidney carcinomas) might benefit from this type of treatment.

The team led by Dr. Núria López-Bigas and Dr. Abel González-Perez, from the Biomedical Genomics laboratory at IRB Barcelona, ​​in collaboration with researchers from several international centers, has addressed this issue through a comprehensive analysis of genomic, transcriptomic, and clinical data from 479 patients with metastatic tumors, who received CPI treatment. . This data is from a public database generated by the Dutch Hartwig Medical Foundation.

“We used an unbiased approach to analyze thousands of molecular and clinical features and identified five independent factors that influence response to immunotherapy and patient survival,” explains Dr. López-Bigas, ICREA researcher at IRB Barcelona.

Five factors, five keys to immunotherapy

The five factors identified are tumor mutational burden; effective T cell infiltration; the activity of transforming growth factor beta (TGF-β) in the tumor microenvironment; previous treatment received by the patient; and tumor proliferative potential. These factors in different types of cancer are associated with the response to CPIs and have been validated by the authors in six independent cohorts, covering a total of 1,491 patients.

• Tumor mutational burden (TMB): Tumors with a high number of mutations tend to produce more neoantigens, making it easier for the immune system to recognize and attack them. TMB has been one of the most studied biomarkers for predicting response to CPIs.

• Effective T-cell infiltration: The presence of cytotoxic T-cells in the tumor is essential for the effectiveness of CPIs. This study has confirmed that a higher infiltration of these cells is directly related to a better response to the therapy.

• TGF-β activity in the tumor microenvironment: This factor influences the behavior of some cells in the tumor microenvironment. High TGF-β activity can suppress the immune response, which is reflected in a tendency for patients to have poorer survival after immunotherapy treatment.

• Previous treatment: Patients who have received previous treatments tend to show a poorer response to immunotherapy.

• Tumor proliferative potential: Patients with tumors that have a high proliferative index, which tend to be more aggressive, generally show poorer survival after treatment.

Towards personalized cancer treatment

These five factors provide a framework for organizing the vast current knowledge about biomarkers of immunotherapy response.

“So far, many studies have focused on identifying and reporting individual biomarkers, but our results suggest that many of these biomarkers might be different versions of the same underlying factors,” says Dr. Gonzalez-Pérez.

Furthermore, the researchers demonstrated that a multivariate model combining these five factors allows for more accurate patient classification than using tumor mutational burden alone (as is frequently done in clinical practice), predicting the probability of patients to respond to immunotherapy.

This advance could have significant clinical implications in the future, as it could prevent patients with a low likelihood of response from experiencing the side effects of CPIs, which can lead to autoimmune diseases, and could also help reduce the cost of treatments.

Validation in international cohorts

One of the highlights of this study is the validation of these five factors in six independent cohorts of patients with cancers such as lung, colon and melanoma.

“We have confirmed that these factors are relevant in different types of cancer and different patient populations, thus reinforcing their clinical value. As research continues, new latent factors may be discovered in other types of cancer or larger cohorts,” explains Dr. Joseph Usset, a former postdoc at IRB Barcelona who is now at the Vall d’Hebrón Institute of Oncology.

The team hopes to have a larger volume of patient data in the future to create more accurate models. The accuracy of these models for potential future clinical application should be validated through prospective clinical trials. However, this advancement faces a significant challenge, namely the difficulty accessing data as comprehensive and detailed as that used in this study.

“This study represents an important step in understanding how different tumor characteristics influence treatment response. In the future, we hope that these five factors will be integrated into clinical practice to guide therapeutic decisions,” concludes Dr. Lopez-Bigas.