Geoff Macintyre

nature.comIntegrated signatures define mutational processes in prostate cancer - NatureEight integrated mutational footprints collectively explain the mutational processes in 85% of primary prostate cancer genomes.

12 Sept

Checkout our latest in @nature.com Integrated Mutational Footprints (SBS+Indels+SV+CIN signatures) define the mutational processes shaping 85% of prostate cancer genomes and outcomes. Explainer 👇

Barbara Hernando@bhernando.bsky.social

1/ NEW PAPER 🎉 🧵 We mapped the mutational processes shaping 959 prostate cancer genomes from the Pan Prostate Cancer Group (PPCG), integrating SBS, ID, CX and complex SV signatures. Eight footprints account for the processes in 85% of cases www.nature.com/articles/s41... @nature.com

16 Apr

I’m looking forward to connecting with colleagues at #AACR26 next week. The Computational Oncology group from @cniostopcancer.bsky.social will be presenting three posters that bridge the gap between chromosomal instability and clinical implementation 👇

👆in collaboration with Tailor Bio. 🚀 Tailor Bio is currently fundraising to launch a Phase II clinical trial using this biomarker to select patients. If you're interested in the intersection of genomics and drug development, let’s talk!

16 Apr

Safe travels to everyone heading to California—reach out if you’d like to grab a coffee or talk science!

📍 Poster #4698 | Tuesday, 9:00 AM – 12:00 PM Quantifying ongoing CIN using scWGS Static snapshots of genomes don't tell the whole story. We’re using single-cell whole-genome sequencing to measure ongoing instability. 🔗 Preprint: www.biorxiv.org/content/10.1...

biorxiv.orgbiorxiv.org

16 Apr

📍Poster #7712 | Wednesday, 9:00 AM – 12:00 PM WNT-driven CIN as a biomarker for PORCN inhibition We’ve identified a specific CIN-based biomarker that identifies patients most likely to respond to PORCN inhibitors. Preprint: www.biorxiv.org/content/10.6...

biorxiv.orgbiorxiv.org

📍 Poster #3534 | Monday, 2:00 PM – 5:00 PM Forecasting oncogene amplification We’ve developed a framework to shift from describing chaotic genomes to predicting their future🔗 Preprint: www.biorxiv.org/content/10.1...

biorxiv.orgbiorxiv.org

biorxiv.orgTracking ongoing chromosomal instability using single-cell whole-genome sequencingChromosomal instability (CIN) generates aneuploid genomes that are characteristic of most cancers. While bulk genome sequencing reveals historical CIN, it lacks the resolution to identify ongoing CIN ...

28 Nov 2025

Checkout the latest preprint from the lab - single-cell CIN signatures unlocked! Ongoing HRD improves PARPi sensitivity detection and ongoing NHEJ associates with subclonal diversification in TNBC

Barbara Hernando@bhernando.bsky.social

📢 New preprint out! We are thrilled to introduce our framework to track #ongoingCIN by leveraging single-cell genomics and CIN signatures: www.biorxiv.org/content/10.1...

Geoff Macintyre reposted

Calum Gabbutt

@calumgabbutt.bsky.social

10 Sept 2025

Cancer is an evolutionary disease, but does knowing a cancer’s evolutionary past help predict its future? Out today in @nature, we learnt the evolution of 2000 lymphoid cancers and found it was highly correlated with clinical outcomes! (1/7) rdcu.be/eFrrc

rdcu.beFluctuating DNA methylation tracks cancer evolution at clinical scaleNature - Cancer evolutionary dynamics are quantitatively inferred using a method, EVOFLUx, applied to fluctuating DNA methylation.

🚨Chemo treatment upgrade!🚨 Check out our approach to modernise chemotherapy treatment published today in @natgenet.nature.com. From @cniostopcancer.bsky.social #TailorBio @cruk-ci.bsky.social www.nature.com/articles/s41... More details 👇

12/ Thanks to patients and funders for their support @cniostopcancer.bsky.social @isciiisalud.bsky.social isciii.bsky.social @cienciagob.bsky.social #BecariosFLC #illumina @innovateuk.bsky.social @cuh.nhs.uk #TailorBio @cruk-ci.bsky.social

communities.springernature.comResearch Communities by Springer NatureResearch Communities by Springer Nature provide a forum for all those interested in research to share the latest discoveries, news, and opinions.

13/ Want to know the genesis story of this research? Check out the “behind the paper” post communities.springernature.com/posts/toward...

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!

11/ Kudos to @jsneaththompson.bsky.social, Laura Madrid, @bhernando.bsky.social & co-authors for driving this work!

8/ It worked! We emulated trials to validate resistance predictions to platins, taxanes & anthracyclines across ovarian, breast, prostate & sarcoma

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

6/ Our results looked great!

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!

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

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

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)

nature.comA pan-cancer compendium of chromosomal instability - NatureCopy number signatures characterize different types of chromosomal instability and predict drug response.

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

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

bit.lyForecasting oncogene amplification and tumour suppressor deletionOncogene amplification and tumour suppressor deletion can drive tumour initiation, progression and treatment resistance. Detection at diagnosis often signals poor prognosis, but it can also enable opp...

If tumour genome evolution is deterministic, can we forecast genomic changes? Yes we can! At least for oncogene amps and tumour suppressor dels. @bhernando.bsky.social Angel Fernandez-Sanroman present our method for forecasting driver gene copy number change. Preprint: bit.ly/4kxF14y ⬇️1/

…and to the patients and funders @cniostopcancer.bsky.social ‪@isciiisalud.bsky.social‬ @cienciagob.bsky.social #BecariosFLC #H12O

@blaschaves.bsky.social @mescobarrey.bsky.social @torresmarina.bsky.social

ey.bsky.socialBluesky

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/

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/

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/

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/

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/

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/

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/

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/

27 Apr 2025

If you are at AACR and want to find out how we are turning chemotherapies into precision medicines, come to my talk on Tuesday! www.abstractsonline.com/pp8/#!/20273...