BostonGene announced the publication of a collaborative study in Cell Reports Medicine that introduces a multimodal AI algorithm capable of predicting immunotherapy and targeted therapy outcomes in clear cell renal cell carcinoma (ccRCC). The research, conducted with Washington University in St. Louis and Memorial Sloan Kettering Cancer Center, represents the largest harmonized transcriptomic and clinical dataset in kidney cancer to date.
Addressing Treatment Challenges in Kidney Cancer
Treatment for metastatic clear cell renal cell carcinoma remains challenged by variable responses, frequent progression and therapy-related toxicities, despite the transformative impact of immune checkpoint inhibitors (ICIs) and VEGF inhibitor specific tyrosine kinase inhibitors (TKIs). Current clinical practice lacks guidance on when to apply combination versus single agent therapy, while previous predictive models showed promise but lacked reproducibility and biological interpretability across independent cohorts.
Novel AI Foundation Model Development
Leveraging more than 3,600 patient samples and harmonized clinical data, researchers developed a multimodal foundation model trained on extensive real-world datasets that integrates genomics, transcriptomics and tumor microenvironment (TME) profiling. The digital twin-like model generated representations of patient biology and uncovered five novel Harmonized Immune Tumor Microenvironment (HiTME) subtypes—distinct categories of ccRCC defined by unique immune infiltration patterns, genomic alterations and prognostic outcomes.
The subtypes were validated with spatial proteomics, ensuring predictions mapped directly to tumor biology rather than functioning as a "black box." This validation approach addresses a critical limitation of previous AI models in oncology by providing biological interpretability.
Clinical Decision Support Tool
Using the HiTME subtypes, researchers generated clinically interpretable responder scores that correlated with survival outcomes for both ICIs and TKIs across independent cohorts. This led to the development of a decision-tree tool that stratified patients into ICI-preferred, TKI-preferred or non-responder categories, establishing a data-driven methodology to stratify patients by underlying immune and tumor biology.
The framework also revealed a therapy-resistant subgroup characterized by immune-desert phenotypes and angiogenic signaling, highlighting urgent unmet needs and new therapeutic avenues for drug development.
Clinical Implications
"This study demonstrates how multimodal foundation models can reshape oncology," said Nathan Fowler MD, Chief Medical Officer at BostonGene. "By grounding predictions in tumor biology rather than opaque algorithms, we enable clinicians to understand why patients may or may not respond to treatment—making AI both clinically actionable and scientifically trustworthy."
The research establishes AI-powered foundational models as transformative tools for therapy selection, efficient trial design and drug development, potentially enabling more personalized treatment approaches for patients with metastatic kidney cancer.
