PREDICT: PRoviding Effective longitudinal models of RAS DIrect inhibition to Combat Tumor resistance

Projektinformationen

PREDICT

ID Finanzhilfevereinbarung: 101189458

DOI

10.3030/101189458

Projekt abgeschlossen

EK-Unterschriftsdatum 7 Oktober 2024

Startdatum 1 Januar 2025

Enddatum 30 Juni 2026

Finanziert unter

• European Research Council (ERC)

Gesamtkosten

Keine Daten

EU-Beitrag

€ 150 000,00

Koordiniert durch

UNIVERSITA DEGLI STUDI DI TORINO
Italy

Periodic Reporting for period 1 - PREDICT (PREDICT: PRoviding Effective longitudinal models of RAS DIrect inhibition to Combat Tumor resistance)

Berichtszeitraum: 2025-01-01 bis 2026-06-30

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

PREDICT addresses the urgent need to anticipate and overcome resistance to KRAS-targeted therapies in KRAS-mutant NSCLC. Although KRAS inhibitors such as sotorasib and adagrasib have created new treatment opportunities, resistance often limits their long-term clinical benefit. The project aimed to develop a longitudinal experimental platform combining engineered KRAS cell-line models, drug-response profiling, multi-omics data and AI-based prediction. Its objective was to model how resistance evolves across sequential therapies and identify more rational second- and third-line treatment strategies. By generating functional biological evidence rather than only interpreting existing clinical or genomic data, PREDICT offers a differentiated precision oncology approach.
The expected impact is to support drug development, clinical trial design and, in the longer term, more personalised treatment decisions for patients with KRAS-mutant lung cancer.To accomplish this, we approached the project from both Technical and Business perspectives, with a perfect synergy between the research and business teams, and a close contact with clinical collaborators and stakeholders.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

The PREDICT project involved research teams working on the generation, validation and longitudinal assessment of KRAS-driven models in non-small cell lung cancer. The aim of the project was to validate the feasibility of a biologically grounded platform able to model resistance to KRAS-targeted therapies and support the identification of more effective treatment sequences. From a technical and scientific perspective, the project generated a panel of clinically relevant KRAS resistance mutants, identified through the SPARK clinical study, and introduced them into genetically controlled cellular systems. These models were functionally validated and tested against different classes of KRAS-targeted therapies, including KRAS(OFF), KRAS(ON) and pan-RAS inhibitors. In parallel, PREDICT established longitudinal drug-response models by exposing KRAS-mutant lung cancer cells to sequential treatments, including cisplatin, sotorasib, adagrasib and daraxonrasib. This work produced a unique collection of resistance-node samples across successive therapeutic trajectories, enabling the study of how resistance evolves over time. Overall, the action delivered a validated experimental platform and biobank for KRAS inhibitor resistance, providing the basis for future molecular profiling, AI-based prediction and identification of genotype-specific therapeutic vulnerabilities.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

The PREDICT project resulted in a new translational platform that goes beyond the current state of the art in KRAS-mutant NSCLC resistance modelling. While most existing solutions mainly interpret already available data, such as genomic reports, EHRs or real-world datasets, PREDICT generates functional biological evidence from engineered KRAS-driven cell models and longitudinal drug-response experiments. This allows the platform to model how resistance evolves under sequential therapeutic pressure and to identify genotype-specific vulnerabilities that may support more rational second- and third-line treatment strategies.

The potential impact of PREDICT lies in its ability to support drug development, clinical trial design and future treatment sequencing for KRAS-targeted therapies. At the end of the project, PREDICT delivered a validated panel of clinically relevant KRAS resistance mutants, longitudinal resistance models to cisplatin, sotorasib, adagrasib and daraxonrasib, and a unique biobank of resistance-node samples for downstream molecular analyses. To ensure further uptake and success, the next key needs are access to high-quality real-world and clinical trial data, completion of genomic and transcriptomic profiling, algorithm validation, workflow standardisation, IPR/FTO clarification and partnerships with pharma, biotech or CROs for pilot validation and commercial development.