Resources

This research presents Zephyr AI’s AIM-io model, a multimodal machine learning approach that predicts response to immune checkpoint inhibitors using routine clinical inputs, across 3 independent datasets: tissue-derived data, liquid biopsy, and whole-slide images. Across multiple real-world cohorts, AIM-io improves prediction of survival outcomes compared to conventional biomarkers and identifies biologically meaningful tumor

This study demonstrates Zephyr AI’s multimodal AIM-Bx platform for predicting response to KRAS inhibitors in non-small cell lung cancer using clinically available data, including ctDNA. Applied to a real-world cohort, the model identifies patients deriving meaningful benefit from sotorasib beyond KRAS mutation status alone. AIM captures underlying tumor states and pathway dependencies associated

This research highlights Zephyr AI’s AIM-Bx platform—a multi-modal, AI-enabled biomarker that predicts response to osimertinib in NSCLC beyond traditional EGFR mutation status. Trained on routinely available clinical and genomic data from tissue and liquid biopsy, AIM-Bx identified EGFR+ non-responders and EGFR– responders, uncovering transcriptomic programs and tumor dependencies not captured by DNA-level biomarkers.

This study demonstrates Zephyr AI’s multi-modal machine learning method for identifying late-stage ovarian cancer patients likely to respond to olaparib, independent of HRD status. Our model significantly outperformed conventional biomarker approaches, improving real-world survival outcomes and offering biological insights through our Vulnerability Networks™

Zephyr AI’s generative Bayesian network approach synthesizes comprehensive molecular profiles from limited NGS panel data in lung and breast cancer. This method enhances sparse tumor data to enable downstream analysis, biomarker discovery, and advanced ML model development without additional testing, bridging a major gap in real-world precision oncology.

Zephyr AI’s Mut2Ex model reconstructs tumor gene expression profiles using only genetic data from commercial NGS panels combined with minimal clinical information. Trained on ~1,200 DepMap cell lines, Mut2Ex generated expression profiles for ~10,000 TCGA and ~180,000 GENIE tumors, substantially enhancing the clinical utility of real-world clinicogenomics data for precision medicine applications.

Zephyr AI developed a machine learning model that reconstructs tumor gene expression from NGS panel data to predict tumor microenvironment (TME) features critical for immune checkpoint inhibitor (ICI) response. Trained on over 8,000 tumors across 32 cancer types, the model delivers high reconstruction accuracy and supports personalized immunotherapy strategies using real-world clinical data.

This research introduces ImmunoBERT, Zephyr AI’s machine learning model that predicts survival in cancer patients receiving immune checkpoint inhibitors. Integrating drug structure, target binding profiles, and NGS panel data from 1,700 patients, ImmunoBERT outperformed top DREAM challenge submissions and reconstructs key tumor microenvironment features, enabling personalized immunotherapy selection without specialized testing.