OncoTraj: a public benchmark for longitudinal resistance prediction in EGFR-mutant non-small-cell lung cancer on osimertinib
arXiv:2606.11144v1 Announce Type: new Abstract: Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computational models on the corresponding longitudinal patient trajectories. We introduce OncoTraj, a public benchmark of 813 EGFR-mutant NSCLC patients receiving first-line osimertinib, harmonized from three real-world clinical-genomic sources: MSK-CHORD (672 patients), AACR Project GENIE BPC NSCLC (34 patients), and th
The increasing availability of real-world clinical data combined with advancements in machine learning allows for the creation of specialized benchmarks like OncoTraj.
This benchmark provides a crucial standardized tool for developing and evaluating AI models that can predict cancer treatment resistance, accelerating personalized medicine.
The existence of a public, harmonized dataset specifically for predicting osimertinib resistance will significantly improve the accuracy and reliability of computational models in this domain.
- · AI-driven drug discovery platforms
- · Oncology research institutions
- · Pharmaceutical companies developing cancer therapies
- · Patients with EGFR-mutant NSCLC
- · Traditional, less data-driven oncology research methods
Improved predictive models for drug resistance in lung cancer will emerge.
Faster development and deployment of personalized treatment strategies, leading to better patient outcomes.
The success of OncoTraj could catalyze similar public benchmarks for other complex diseases and treatments, accelerating AI adoption in clinical research broadly.
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Read at arXiv cs.LG