Inspired by previous work showing that cancer genome evolution is deterministic bit.ly/44sAYBG bit.ly/4d3MBBb we built a model that uses mutation rate and selection estimates to predict the probability of future driver amp/dels, given a tumour genome as input 2/

Simple model? Seemingly so. But estimating mutation rate and selection coefficients from tumour DNA alone, especially for DNA copy number, is tough! We faced a number of challenges: 3/

Challenge 1: amp/del rates cannot be readily measured from an input genome. Solution: approximate using a steady-state probability of locus-specific copy number change over tumour lifetime. We adapted our previous CIN signatures (CX bit.ly/42SVA3i) for this 4/

Challenge 2: growth rates of mutant vs non-mutant cells (and thus selection) cannot be easily determined in a clinical context. Solution: approximate selection coeffs using driver amp/del frequency at a population-level (supported by recent work showing s≈fβ) 5/

Challenge 3: forecasting in a clinical setting. Solution: binarise predictions and only use standard genomic test data as input. We designed guidelines to apply and (if needed) train the model + optimize thresholds for binary risk classification (high vs low) 6/

We trained this model for 241 drivers using 7,880 TCGA samples across 33 tumour types. But do these models work? 7/

First, we tested performance on two independent cohorts: PCAWG: 2,114 primaries; HMF: 4,784 metastases. 147 of the 241 models showed AUC > 0.7 across both datasets 8/

Next we tested longitudinal pairs, forecasting at the early time point (before driver amp) and testing at the latter. In prostate, we predicted AR amp (linked to ADT resistance) in pretreatment samples. In NSCLC, we predicted HIST1H3B amp (exclusive to metastases) in primaries 9/

Encouraging right? We then applied our approach to two clinical scenarios where forecasting specific genetic changes might unlock new clinical opportunities: risk stratification of low-grade glioma (LGG) and anticipation of osimertinib resistance in lung cancer 10/

Currently, LGGs are classified into 4 WHO risk groups. CDK4/PDGFRA amps and CDKN2A dels are linked with poor prognosis but under utilised. Forecasting these facilitates a risk upgrade of 9% of IDHmut-non-codel cases while maintaining median survival times across WHO groups 11/

MET amps cause EGFRi resistance in ~25% of NSCLCs. Forecasting MET amp in 33 EGFR-mutant NSCLC tumours treated with osimertinib showed high-risk patients had shorter PFS & OS. This can be used to flag candidates for upfront EGFR+MET inhibition (eg MARIPOSA trial) 12/

TL;DR Forecasting oncogene amps & tumour suppressor dels is feasible! This can refine risk stratification and anticipate treatment resistance, paving the way for earlier, smarter and more personalised cancer care. There is much more in the preprint so check it out! 13/

…and to the patients and funders CNIO Stop Cancer Instituto de Salud Carlos III (ISCIII) Ministerio de Ciencia, Innovación y Universidades Becas Fundación "la Caixa" Hospital Universitario 12 de Octubre

Maxime Tarabichi Blas Chaves-Urbano María Escobar Rey patricia Alice Cadiz Luis Paz-Ares Juan Manuel Coya David Gómez-Sánchez

💥New preprint from the Geoff Macintyre lab! Thrilled to share this major team effort, with Ángel Fernández Sanromán as co-first author, now live on bioRxiv! Check out our threat to learn more about our work👇

A pleasure to be at the fantastic #EACR2025 and present my research project! Looking forward for cool scientific discussions with amazing cancer researchers and friends EACR CNIO Stop Cancer Becas Fundación "la Caixa" Geoff Macintyre

🚨Chemo treatment upgrade!🚨 Check out our approach to modernise chemotherapy treatment published today in Nature Genetics. From CNIO Stop Cancer Tailor Bio Cancer Research UK nature.com/articles/s4158… More details 👇

#CNIOStopCancer develop test that predicts which patients will not respond to cancer chemotherapy. They have identified biomarkers which, in clinical practice, would allow for more effective treatments and the avoidance of side effects. bit.ly/465QqnR

Descubierto un marcador que indica qué pacientes no responderán a la quimioterapia. La genética de sus tumores puede desvelar qué fármaco no funcionará, lo que ayudaría a los oncólogos a aplicar otro tipo de quimio que sí podría tener efecto social.elpais.com/7rew21

Another year, another European Researchers Night #NIGHTMADRID at CNIO Stop Cancer. Hereditary Endocrine Cancer Group @CNIOStopCancer and friends ready to rock this exciting event!!