Resources

Explore Zephyr-led research at the intersection of AI, real-world data, and precision medicine—advancing discovery across oncology and chronic disease.

Multimodal AI predicts immune checkpoint inhibitor response from clinically available inputs and whole-slide images with explainable tumor biology and combination therapy insights

AACR 2026 Annual Meeting

April 17-22, 2026

San Diego, CA

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

Real world prediction and biological characterization of sotorasib sensitivity using multimodal AI and liquid biopsy genomic inputs

AACR 2026 Annual Meeting

April 17-22, 2026

San Diego, CA

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

Identifying Novel Drivers of Drug Sensitivity Using an AI-Enabled Multi-Modal Biomarker – Osimertinib Sensitivity Beyond EGFR

AACR 2025 Annual Meeting

April 25–30, 2025

Chicago, IL

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.

Evaluation of a Novel Machine Learning Method for PARP Inhibitor Sensitivity Prediction Using Real-World Data

ASCO 2024 Annual Meeting

May 31 - June 4, 2024

Chicago, IL

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™

Generative Bayesian Networks for Augmentation of Molecular Data from Commercial Genetic Panels

AACR 2024 Annual Meeting

April 5–10, 2024

San Diego, CA

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.

Reconstructing a Latent Representation of Gene Expression from Genomic Alterations to Improve Clinical Utility of Real-World Clinicogenomics Data

AACR 2024 Annual Meeting

April 5–10, 2024

San Diego, CA

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.

Reconstructing Gene Expression from Clinical and Genetic Panel Data for Predictions of Tumor Microenvironment Features and Response to Immune Checkpoint Inhibitor Therapy

SITC 2023 Annual Meeting

November 1–5, 2023

San Diego, CA

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.

Integrating Drug Structure and Target Binding Affinity for Improved Prediction of Survival in Cancer Patients Treated with Immune Checkpoint Inhibitors

SITC 2023 Annual Meeting

November 1–5, 2023

San Diego, CA

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.