Geoff Macintyre
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gmaci.bsky.social
Geoff Macintyre
@gmaci.bsky.social
Group leader in computational oncology at CNIO, Madrid. CSO at Tailor Bio. Chromosomal instability and tumour evolution. www.macintyrelab.org
13/ Want to know the genesis story of this research? Check out the “behind the paper” post communities.springernature.com/posts/toward...
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June 23, 2025 at 9:03 AM
11/ Kudos to @jsneaththompson.bsky.social, Laura Madrid, @bhernando.bsky.social & co-authors for driving this work!
June 23, 2025 at 9:03 AM
10/ What’s next? We’re funded by @mintradigital.bsky.social #NextGenerationEU for analytical validation and will be ready to run prospective trials in 2026. From organoids to algorithms to patients: precision chemo is possible!
June 23, 2025 at 9:03 AM
9/ To ensure a flexible pathway to the clinic, we also tested biomarker reproducibility in ctDNA samples and TSO500 panel data, bringing us closer to real-world implementation
June 23, 2025 at 9:03 AM
8/ It worked! We emulated trials to validate resistance predictions to platins, taxanes & anthracyclines across ovarian, breast, prostate & sarcoma
June 23, 2025 at 9:03 AM
7/ Next step? A prospective trial? We tried but couldn’t! No one wanted to run/fund a trial using “old” chemos. So we had to get creative. Luckily, as chemos are widely used, there was a wealth of real-world data eg TCGA & @HartwigMedical to emulate biomarker trials - even RCTs!
June 23, 2025 at 9:03 AM
6/ Our results looked great!
June 23, 2025 at 9:03 AM
5/ Next step? Proof-of-concept using retrospective data from 50 ovarian cancer samples. Ovarian cancers were ideal as all 3 chemotherapies are routinely used. We focused on predicting resistance. Why resistance? Because it allows patients to avoid toxic side-effects
June 23, 2025 at 9:03 AM
4/ We focused on optimising 3 CIN signature-based biomarkers to classify patients as resistant or sensitive to 3 commonly used chemotherapies: platins, taxanes or anthracyclines. Our goal: to optimise biomarker thresholds to use pan-cancer
June 23, 2025 at 9:03 AM
3/ These prelim data showed correlations between CIN signatures and chemotherapy response. As the full spectrum of CINsigs can be quantified in a tumour using a single genomic test, we hypothesised that CINsigs could predict resistance to multiple chemotherapies at diagnosis
June 23, 2025 at 9:03 AM
2/ Back then, we already had preliminary data suggesting these CIN signatures may be useful as therapy response biomarkers, mainly via synthetic lethality with the mechanism of action of the drug (CIN signature➡️defective pathway➡️dependency, which the drug exploits)
June 23, 2025 at 9:03 AM
1/ 3 yrs ago we developed a computational framework to decode chromosomal instability www.nature.com/articles/s41...: input a tumour genome➡️output CIN signatures. As these CIN signatures represent different causes of DNA damage, they provide a read out of defective pathways in a tumour
A pan-cancer compendium of chromosomal instability - Nature
Copy number signatures characterize different types of chromosomal instability and predict drug response.
www.nature.com
June 23, 2025 at 9:03 AM
…and to the patients and funders @cniostopcancer.bsky.social ‪@isciiisalud.bsky.social‬ @cienciagob.bsky.social #BecariosFLC #H12O
June 10, 2025 at 2:15 PM
We hope this study will inspire further forecasting efforts across other molecular alterations. Thanks to all members of the @gmaci.bsky.social Lab & collaborators Paz Ares Lab #PPCG 14/
June 10, 2025 at 2:15 PM
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/
June 10, 2025 at 2:15 PM
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/
June 10, 2025 at 2:15 PM
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/
June 10, 2025 at 2:15 PM
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/
June 10, 2025 at 2:15 PM
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/
June 10, 2025 at 2:15 PM
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/
June 10, 2025 at 2:15 PM
We trained this model for 241 drivers using 7,880 TCGA samples across 33 tumour types. But do these models work? 7/
June 10, 2025 at 2:15 PM