Blas Chaves-Urbano

PhD student at @CNIOStopCancer | La Caixa INPhINIT Retaining Fellow @BecariosFLC

[Barbara Hernando @BHFHeptathlon]

📢 New preprint out!

We are thrilled to introduce our framework to track #ongoingCIN by leveraging single-cell genomics and CIN signatures:

https://t.co/hpILZIv4PK

[Blas Chaves-Urbano @blaschaves_]

Very excited to share my PhD journey (and those of my colleagues) supported by @BecariosFLC ! 🚀🚀 Check out the videos to learn more about our research 👇

[Becas Fundación "la Caixa" @BecariosFLC]

1/ 🌟 Curious about today’s most cutting-edge research?

Today, 1⃣6⃣ INPhINIT doctoral #laCaixaFoundFellows are taking on a challenge: explaining their PhD projects in a way that’s engaging and easy to understand. 

Listen to their “Shooting your PhD”: 👇
https://t.co/LqGdlulhfC

[Hereditary Endocrine Cancer Group @CNIOStopCancer @Endocrine_CNIO]

Another year, another European Researchers Night #NIGHTMADRID at @CNIOStopCancer. @Endocrine_CNIO and friends ready to rock this exciting event!!

[EL PAÍS @el_pais]

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
https://t.co/VDLRqMAE44

[CNIO Stop Cancer @CNIOStopCancer]

#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.

https://t.co/omw2d1JYIG

[Geoff Macintyre @gjmacintyre]

🚨Chemo treatment upgrade!🚨

Check out our approach to modernise chemotherapy treatment published today in @NatureGenet. From @CNIOStopCancer @TailorBio @CR_UK https://t.co/IgUxlRYBgm More details 👇

[Blas Chaves-Urbano @blaschaves_]

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 @EACRnews @CNIOStopCancer @BecariosFLC @gjmacintyre

[Barbara Hernando @BHFHeptathlon]

💥New preprint from the @gjmacintyre lab!

Thrilled to share this major team effort, with @Sanroman_Fern as co-first author, now live on bioRxiv!

Check out our threat to learn more about our work👇

[Geoff Macintyre @gjmacintyre]

@MaxGalder @blaschaves_ @m_escobarrey @patricgsant @alicecadiz_ @LuisPaz_Ares @JuanMaCoya @dagsbio

[Geoff Macintyre @gjmacintyre]

…and to the patients and funders @CNIOStopCancer @SaludISCIII @CienciaGob @BecariosFLC @H12Octubre

[Geoff Macintyre @gjmacintyre]

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/

[Geoff Macintyre @gjmacintyre]

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/

[Geoff Macintyre @gjmacintyre]

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/

[Geoff Macintyre @gjmacintyre]

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/

[Geoff Macintyre @gjmacintyre]

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/

[Geoff Macintyre @gjmacintyre]

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/

[Geoff Macintyre @gjmacintyre]

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

[Geoff Macintyre @gjmacintyre]

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/

[Geoff Macintyre @gjmacintyre]

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/

[Geoff Macintyre @gjmacintyre]

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 https://t.co/hCUjXQWTzl) for this 4/