Calum Gabbutt
@calumgabbutt.bsky.social
400 followers 61 following 21 posts
Computational biologist interested in cancer evolution, maths modelling and Bayesian stats.
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Reposted by Calum Gabbutt
trevorgraham.bsky.social
Studying cancer evolution needs multi-region or single cell seq for phylogenetics, right? Amazingly (I think!) we found single-sample bulk methylation suffices, via analysis of "fluctuating methylation". In @nature.com today led by brilliant @calumgabbutt.bsky.social www.nature.com/articles/s41...
Fluctuating DNA methylation tracks cancer evolution at clinical scale - Nature
Cancer evolutionary dynamics are quantitatively inferred using a method, EVOFLUx, applied to fluctuating DNA methylation.
www.nature.com
calumgabbutt.bsky.social
This was a massively collaborative project and it was a pleasure to work with such amazing researchers, but particular thanks to Inaki Martin-Subero, Martí Duran-Ferrer @idibaps.bsky.social and @trevorgraham.bsky.social
calumgabbutt.bsky.social
Finally, fCpGs record clonal dynamics over time. In two patients with Richter transformation (RT), the emergence of an altered phenotype with dismal outcomes, we inferred that the RT clone diverged from the non-RT lineage over 30 years prior to its clinical manifestation! (6/7)
calumgabbutt.bsky.social
In chronic lymphocytic leukaemia (CLL) the high-risk U-CLL subtype had much higher growth rates than the low-risk M-CLL subtype. Stratifying by growth rate within these groups was highly prognostic of the time to first treatment. (5/7)
calumgabbutt.bsky.social
Across 2000 lymphoid cancer samples, we found staggering heterogeneity between different diseases and molecular subtypes! Paediatric (ALL) grew much more rapidly than adult cancers, and the aggressive 11q23/MLL subtype grew even faster than the other subtypes. (4/7)
calumgabbutt.bsky.social
We designed a computational method, EVOFLUx, to infer the early evolutionary history of a cancer from these data. For instance: when did the most recent common ancestor emerge and how rapidly was the cancer growing at that time? Is the cancer undergoing a subclonal expansion? (3/7)
calumgabbutt.bsky.social
We used DNA methylation “evolving barcodes” to record the lineage of cells, which we termed fCpGs. These could be measured using low-cost, bulk methylation arrays. The clonal dynamics of cells are recorded in the patterns of these fCpGs (2/7)
calumgabbutt.bsky.social
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
Fluctuating DNA methylation tracks cancer evolution at clinical scale
Nature - Cancer evolutionary dynamics are quantitatively inferred using a method, EVOFLUx, applied to fluctuating DNA methylation.
rdcu.be
Reposted by Calum Gabbutt
imperial-ix.bsky.social
🚀 AI in Science Fellowships – Applications Now Open!

The I-X Centre for AI in Science is recruiting up to 19 fellows to join their prestigious programme and accelerate artificial intelligence research in Engineering, Natural and Mathematical Sciences.

1/5
LinkedIn
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calumgabbutt.bsky.social
Imperial's Schmidt AI in Science fellowships are now open! As a current fellow, I can highly recommend applying for these prestigious and enriching awards. No prior AI experience needed, just a scientific problem suited to AI. For cancer researchers there are dedicated joint positions with the ICR.
Apply | Research groups | Imperial College London
www.imperial.ac.uk
Reposted by Calum Gabbutt
trevorgraham.bsky.social
Schmidt Fellowships for #AI in #cancer research 2025 round is now open. These are pathway to independence fellowships between @icr.ac.uk & @imperialcollegeldn.bsky.social that support fellows to be competitive for faculty jobs at the end of the 2 year funding. www.imperial.ac.uk/jobs/search-...
Search jobs for external candidates | Jobs | Imperial College London
www.imperial.ac.uk
Reposted by Calum Gabbutt
robjohnnoble.bsky.social
New preprint! Let me tell you a story about trees, caterpillars, brooms, entropy, and getting scooped by 50 years.

Rooted trees of all shapes and sizes crop up all over biology, computing and elsewhere. How can we best compare the shapes of these myriad trees? 1/14

arxiv.org/abs/2507.08615
Darwin's famous sketch of a rooted tree, representing speciation
Reposted by Calum Gabbutt
alexsteinresearch.bsky.social
Interested in population genetics of cancer?

If so, have a look at our manuscript accepted by @genetics-gsa.bsky.social, in which we provide a theoretical assessment of the genetic makeup of cancers before and after treatment!
Reposted by Calum Gabbutt
trevorgraham.bsky.social
Calling data scientists: we have opening in our Data Science core @icr.ac.uk to lead work around cancer spatial biology and single cell analysis. These are staff scientist type positions, ideal for someone who wants to help drive computational research. Apply here: jobs.icr.ac.uk/vacancies/12...
Data Scientist in Sutton | The Institute of Cancer Research
View details and apply for this Data Scientist vacancy in Sutton. Salary : Salary range £39,805 to £49,023 (depending on the experience) Reporting to: Professor Trevor Graham ...
jobs.icr.ac.uk
calumgabbutt.bsky.social
If you're a PhD student interested in using maths to understand cancer, this HCEMM summer school in Szeged, Hungary, is an excellent opportunity.

Registration is free and local costs are covered, so apply before 15th April.
calumgabbutt.bsky.social
This week-long summer school looks set to be an excellent opportunity for to learn about cancer evolution!

Bursaries are available for those who require financial assistance, deadline 1st April.
eventswcs.bsky.social
Working in #CancerBiology?

Join us at our expert-led #EBECancer25 course, to discover how insights on the #evolutionary and ecological aspects of #cancer are shaping the future for improved clinical treatments.💊

📅 30 June-3 July 2025

⏰Apply by 1 April!
📎bit.ly/40nLdDD

@sysbiocurie.bsky.social
Wellcome Connecting Science discussion course
Evolutionary Biology and Ecology of Cancer
Course dates: 30 June-3 July 2025
Location: Wellcome Genome Campus, UK

Application and bursary deadline: 1 April 2025
calumgabbutt.bsky.social
Darryl never even made it to Twitter, so Bluesky may be a stretch! Really glad you liked the pre-print, if you've got any questions or would like to chat, please feel free to email me at [email protected]
calumgabbutt.bsky.social
And of course, thanks to all our institutes: the ICR, BCI, IDIBAPS, ASU, CIBERONC, Hospital Clínic de Barcelona, Uppsala University, USZ, Universitat de Barcelona, UCL, USC and ICREA. (9/9)
calumgabbutt.bsky.social
A big thanks to Martí Duran Ferrer, Heather Grant, Diego Mallo, Ferran Nadeu, Jacob Househam, Neus Villamor, Olga Krali, Jessica Nordlund, Thorsten Zenz, Elias Campo, Armando Lopez-Guillermo, Jude Fitzgibbon, Chris P Barnes, Darryl Shibata, José I Martin-Subero and @trevorgraham.bsky.social! (8/9)
calumgabbutt.bsky.social
To conclude, we present a cheap and general new technique to measure evolution in cancer from single-timepoint, bulk samples. This links basic cancer evolution research directly to translational medicine and may allow us to focus treatment on just those who need it. (7/9)
calumgabbutt.bsky.social
In B-ALL, MCL and CLL, different clinical subtypes had markedly different growth rates and effective pop sizes. In CLL, the growth rate was highly prognostic of time to first treatment, whilst the pop size was a better predictor of over survival. (6/9)
The inferred growth rate of the typically more aggressive U-CLL cancers is greater than that of the often indolent M-CLL cancers. A Kaplan Meier curve showing the inferred growth rate stratifies time to first treatment in CLL patients, controlling for IGHV mutational status.
calumgabbutt.bsky.social
We also able to infer the phylogenetic relationship between longitudinal samples. In CLL, some patients undergo Richter transformation (RT), the emergence of an aggressive phenotype – this lineage diverged >30 years prior to clinical detection in 2 CLL patients. (5/9)
A: Timeline of two CLL patients with samples collected longitudinally, annotated with treatment received. Circles represent methylation array (blue: 450K, orange EPIC), squares represent whole genome sequencing, treatment is represented with a green vertical line and Richter transformation (RT) is represented with a vertical pink line. B: (left) The reconstructed phylogenies of the relationship between samples, annotated with the clinical classification of each sample. The black triangles represent the time that occurred since the most recent common ancestor, taken as the posterior median of T-τ from the single-sample EVOFLUx inferences. (right) A heatmap representing the 978 fCpG loci, with the colour a representing the fraction methylated (0% blue, 100% red).
calumgabbutt.bsky.social
Applying EVOFLUx to quantify the evolution of 1,976 lymphoid malignancies, we found widespread heterogeneity between and within cancer types. Our subclonal inference was validated with matched deep whole exome sequencing. (4/9)
A scatterplot showing the inferred evolutionary parameters of 1,976 lymphoid malignancies. Boxplots comparing the distribution of subclonal weightings inferred by EVOFLUx in samples called as neutral vs under subclonal selection via the WES data.
calumgabbutt.bsky.social
We developed a new computational method (EVOFLUx) to infer a cancer’s evolutionary history from methylation data. We can learn how quickly a cancer is growing, when its most recent common ancestor existed, its effective pop size and the presence of subclonal expansions. (3/9)
An example of our model's fit (posterior predictive) to real patient fCpG methylation data. A pairs plot of the posterior of the inference run on simulated data, with the ground truth values used to generate the simulated data highlighted in red.