Massive data analysis advances the understanding of how immunotherapy works

What factors influence the success or failure of immunotherapy treatment in patients with advanced bladder cancer? Why do only 20% of advanced bladder cancer cases respond to immunotherapy? These are the questions addressed by a recent study led by the Biomedical Informatics Research Programme (GRIB) and the Cancer Programme at the Hospital del Mar Research Institute and with the collaboration of Pompeu Fabra University. The study, published in Nature Communications, was coordinated by Mar Albà, Júlia Perera, and Joaquim Bellmunt. Robert Castelo, from the Department of Medicine and Life Sciences (MELIS) at UPF, has also participated. The authors analyzed public data from over 700 individuals with this type of cancer across six different cohorts to determine what distinguishes responders from non-responders to the treatment. The results of this study, the largest conducted to date on this cancer type, could also be applied to other cancers. 

The findings indicate that among the five tumor subtypes in this cancer, the rarest one, the neuronal subtype, responds best to this therapeutic approach. The other subtypes exhibit lower response rates. As GRIB researcher Lilian Marie Boll explains, "We found that in a subset of patients with advanced bladder cancer, the markers identified so far work well to predict treatment response. For the others, we believe response depends on other biological factors, a field that requires further investigation." 

Key Types of Markers 

 
The study developed a machine-learning algorithm to predict treatment response in patients with various tumor subtypes. The most reliable markers for predicting treatment success include the tumor mutational burden (the number of mutations in the tumor), mutations introduced by APOBEC enzymes, wich are associated with tumor heterogeneity, and the abundance of proinflamatory macrophages. Additionally, the researchers identified markers in the tumor microenvironment that hinder treatment efficacy. Beyond these known markers, the large sample size enabled the identification of rare mutations that might present new protein fragments on the tumor surface, making them visible to the immune system. 

"As we already knew, immune cell infiltration in the tumor is important. But it is not the only indicator of treatment response, nor does it apply to all patients," notes Dr. Júlia Perera Bel, also a GRIB researcher. In fact, the researchers observed that dividing patients into those with and without immune infiltration made the algorithm more precise, prioritizing different markers for each group. "The key to our study is understanding the response mechanisms within these subgroups, rather than treating all bladder cancer as a single entity," says Lilian Marie Boll. 

Dr. Joaquim Bellmunt, coordinator of the Urologic Cancer Research Group at the Hospital del Mar Research Institute and the Dana-Farber Cancer Institute, states, "This study expands our knowledge of tumor heterogeneity, a limiting factor in immunotherapy efficacy. It highlights the importance of identifying immune cell populations that facilitate immunotherapy response, while others have inhibitory effects." 

The results underscore the critical relationship between tumor biology and the surrounding immunological microenvironment in determining treatment response, as well as the need to consider tumor subtype-specific factors when selecting the most appropriate approach. This is why, according to the research team, it is so important to have large datasets to be able to develop predictive computational models that integrate substantial amounts of data to differentiate patient subgroups, advancing the field toward precision medicine. 

Reference Article 

Boll, L.M., Vázquez Montes de Oca, S., Camarena, M.E. et al. Predicting immunotherapy response of advanced bladder cancer through a meta-analysis of six independent cohorts. Nat Commun 16, 1213 (2025). https://www.nature.com/articles/s41467-025-56462-0