Jimin Moon

Although genetic sequencing is routine in cancer care, translating a tumor’s complex mutation profile into actionable treatment decisions remains a central challenge. MutationProjector is pretrained on a large corpus of genomic alterations across more than 30,000 tumors, integrated with extensive molecular knowledge. The resulting projection reveals a tumor’s altered molecular pathways, facilitating model interpretation, and it accurately reconstructs held-out mutations, demonstrating model generalization. When applied to predict immunotherapy or chemotherapy resistance across multiple cancer types and cohorts, MutationProjector achieves or exceeds state-of-the-art performance in all contexts. It identifies unexpected biomarkers, including KMT2D mutation in immunotherapy sensitivity and joint alteration of SMARCA4 and STK11 in immunotherapy resistance. These results establish a unifying framework for connecting tumor genotypes to biological mechanisms and therapeutic outcomes. Significance:This work describes MutationProjector, a tumor genomic foundation model pretrained on more than 30,000 tumors. It maps mutations to biological states for diagnosis and treatment, achieving state-of-the-art performance in predicting therapy resistance and identifying biomarkers such as KMT2D (sensitivity) and SMARCA4/STK11 coalterations (resistance).

Figure S1 shows mutational heterogeneity of the pre-training dataset. Figure S2 shows detailed model architecture. Figure S3 shows inferring the status of tumor infiltrating lymphocytes. Figure S4 shows the analysis of latent representations. Figure S5 shows the prediction of drug response using clinical biomarkers. Figure S6 shows the prediction of metastasis-related tasks. Figure S7 shows the method for computing feature importance. Figure S8 shows the analysis of high model attention.

Table S1 contains the similarities of interaction pairs between networks. Table S2 contains the predictive performance in pretraining tasks. Table S3 contains the feature importance scores for anti-cancer drug response tasks. Table S4 contains the gene alteration frequencies in predicted responders and non-responders in immunotherapy treated lung cancer patients. Table S5 contains gene co-alteration frequencies. Table S6 contains number of patients with tumor infiltrating lymphocyte status information. Table S7 contains the prediction performance of anti-cancer drug response tasks.

While genetic sequencing is routine in cancer care, translating a tumor’s complex mutation profile into actionable treatment decisions remains a central challenge. MutationProjector is pre-trained from a large corpus of genomic alterations across 30,000+ tumors, integrated with extensive molecular knowledge. The resulting projection reveals a tumor’s altered molecular pathways, facilitating model interpretation, and it accurately reconstructs held-out mutations, demonstrating model generalization. When applied to predict immunotherapy or chemotherapy resistance across multiple cancer types and cohorts, MutationProjector achieves or exceeds state-of-the-art performance in all contexts. It identifies unexpected biomarkers, including KMT2D mutation in immunotherapy sensitivity and joint alteration of SMARCA4 and STK11 in immunotherapy resistance. These results establish a unifying framework for connecting tumor genotypes to biological mechanisms and therapeutic outcomes.

While immune checkpoint inhibitors have revolutionized cancer therapy, many patients exhibit poor outcomes. Here, we show immunotherapy responses in bladder and non–small cell lung cancers are effectively predicted by factoring tumor mutation burden (TMB) into burdens on specific protein assemblies. This approach identifies 13 protein assemblies for which the assembly-level mutation burden (AMB) predicts treatment outcomes, which can be combined to powerfully separate responders from nonresponders in multiple cohorts (e.g., 76% versus 37% bladder cancer 1-year survival). These results are corroborated by (i) engineered disruptions in the predictive assemblies, which modulate immunotherapy response in mice, and (ii) histochemistry showing that predicted responders have elevated inflammation. The 13 assemblies have diverse roles in DNA damage checkpoints, oxidative stress, or Janus kinase/signal transducers and activators of transcription signaling and include unexpected genes (e.g., PIK3CG and FOXP1) for which mutation affects treatment response. This study provides a roadmap for using tumor cell biology to factor mutational effects on immune response.