Unraveling immunotherapy response: modeling tumor microenvironment and biomarkers
Óscar Lapuente Santana defended his thesis at the Department of Biomedical Engineering on June 26th.
In the past decade, cancer care has seen a major breakthrough with the development of immunotherapy using immune checkpoint blockers (ICBs). These innovative treatments use the body's immune system to fight cancer. However, only a minority of patients actually respond to this therapy, making it necessary to identify predictive biomarkers for effective treatment planning. The challenges lie in the high heterogeneity of tumors in patients and within individuals themselves. Existing FDA-approved biomarkers have shown limited accuracy in predicting patient response to ICBs. To address this, Óscar Lapuente Santana, researcher in the Computational Biology group, conducted a comprehensive study to explore the multifaceted nature of the tumor microenvironment (TME) and identify potential biomarkers for immunotherapy response.
Lapuente Santana's research delved into the complex interactions within the TME and used systems biology approaches to gain a comprehensive understanding of its behavior. He constructed a mechanistic representation of the TME, incorporating various factors such as cell type abundance, pathways, transcription factors, cytokine activity scores and ligand-receptor interactions. Using multi-task machine learning, associations between these characteristics and features of the immune response were established, leading to the identification of predictive biomarkers for immune therapy response in different cancer types.
Tumor "foreignness”
Building on this foundation, the research then focused on integrating biomarker-based predictions of immune response with information on tumor "foreignness," quantified by tumor mutation burden (TMB). This novel approach resulted in a best-performing predictor of progressive disease in clinical trials of non-small cell lung cancer (NSCLC). Moreover, the inclusion of imaging-derived fibroblast data helped decipher the mechanisms of immune response and infiltration, significantly improving the accuracy of ICB response prediction.
Further in the study, Lapuente Santana examined the spatial distribution of cells within the TME by integrating pathology images with cell type-specific expression signatures. This allowed the identification of different immune/fibrotic subtypes of the microenvironment, providing valuable insights into tumor immune and local structure infiltration. Finally, he examined the intracellular signaling mechanisms that cancer cells use to evade immune attacks. Understanding these mechanisms could open avenues for targeting intracellular signaling with standard anticancer drugs to overcome immune resistance.
Personalized immunotherapy
Lapuente Santana's research demonstrates the value of studying the multifaceted nature of the tumor microenvironment in predicting patients' response to immunotherapy. By quantifying various factors and interactions within the TME, new biomarkers have been identified that lead to more accurate predictions of treatment outcomes. This research is an important step toward personalized immunotherapy and offers hope for improved patient care and better clinical decision-making. Future studies building on Lapuente Santana's work may further refine the understanding of the TME and potentially uncover even more opportunities to improve the effectiveness of immunotherapy in the fight against cancer.
Title of PhD thesis: “Systems Biology Meets Immuno-Oncology: Investigation of Biomarkers of Immunotherapy Response through Modeling of the Tumor and its Micro-Environment”
Supervisors: Peter Hilbers and Federica Eduati