Memorial Sloan Kettering researchers showed that data from routine diagnostic workups (imaging, pathology, genomics) could be used to predict how patients with non-small cell lung cancer (NSCLC) will respond to immunotherapy, potentially allowing more precise and effective treatment decisions.
Immunotherapy can significantly improve outcomes for patients with advanced NSCLC, and it has already “rapidly altered” the treatment landscape.
- However, only ~25% of advanced NSCLC patients respond to immunotherapy, and current biomarkers used to predict response have proved to be “only modestly helpful.”
The researchers collected baseline diagnostic data from 247 patients with advanced NSCLC, including CTs, histopathology slides, and genomic sequencing.
- They then had domain experts curate and annotate this data, and leveraged a computational workflow to extract patient-level features (e.g. CT radiomics), before using their DyAM model to integrate the data and predict therapy response.
Using diagnostic data from the same 247 patients, the multimodal DyAM system predicted immunotherapy response with an 0.80 AUC.
- That’s far higher than the current FDA-cleared predictive biomarkers – tumor mutational burden and PD-L1 immunohistochemistry score (AUCs: 0.61 & 0.73) – and all imaging approaches examined in the study (AUCs: 0.62 to 0.64).
Although MSK’s multimodal immunotherapy response research is still in its very early stages and would be difficult to clinically implement, this study “represents a proof of principle” that integrating diagnostic data that is already being captured could improve treatment predictions – and treatment outcomes.
This study also adds to the recent momentum we’re seeing with multi-modal diagnostics and treatment guidance, driven by efforts from academia and highly-funded AI startups like SOPHiA GENETICS and Owkin.