iiinova — where your digital self keeps evolving

Reposted by @pbousquets.bsky.social

Diego Mallo @dmallo-phylo.bsky.social · 11/05/2026

The culmination of ~4 years of work with a wonderful team

pbousquets.bsky.social @pbousquets.bsky.social · 11/05/2026

In our patient data, we found that budding is strongly rejected accross all tissues. Cloning (both daughter crypts end up with the same set of stem cells) is often the preferred model. This is new evidence of symmetric gland fission as possibly the norm in healthy human tissue 🎯

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pbousquets.bsky.social @pbousquets.bsky.social · 11/05/2026

We then thought what else we could learn with PHYFUM. We implemented four different crypt division models in PHYFUM, and found we have signal in simulated data to detect them.

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pbousquets.bsky.social @pbousquets.bsky.social · 11/05/2026

We applied PHYFUM to 22 patients (colon, small intestine, endometrium). Gut glands diverged early, with most branching happened during childhood/puberty. Endometrial glands showed much more recent divergence, consistent with cyclical glandular renewal 🔄

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pbousquets.bsky.social @pbousquets.bsky.social · 11/05/2026

We carried out simulations under a number of conditions, and found that the median parameter error: 5.6%. Tree reconstruction error (wRF) is also low, indicating that we can successfully reconstruct our simulated phylogenies

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pbousquets.bsky.social @pbousquets.bsky.social · 11/05/2026

Building on previous work, we developed a mathematical model using methylation in CpGs that stochastically switch over time (fCpGs). Because this is happening in a large number of sites and rapidly enough, we have resolution to see evolution in normal tissue

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pbousquets.bsky.social @pbousquets.bsky.social · 11/05/2026

Studying how normal tissues evolve in living humans is incredibly hard. Most phylogenetic methods rely on DNA mutations, but mutation rates are too low to get fine-grained resolution of tissue history. We needed a faster molecular clock ⏱️

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pbousquets.bsky.social @pbousquets.bsky.social · 11/05/2026

How do cells lining our epithelium evolve over a lifetime? 🧬 We built PHYFUM, a new Bayesian phylogenetic method to reconstruct the evolutionary history of normal tissue using Fluctuating Methylation. Here's what we found 👇 #CancerEvolution doi.org/10.64898/202...

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Reposted by @pbousquets.bsky.social

Jeffrey Townsend @jeffreytownsend.bsky.social · 09/03/2025

"Many, and perhaps most, members of our academic community do not understand [the benefits of publishing] in society-based journals ... compared with for-profit journals from companies like Springer Nature, Frontiers, Wiley, and Elsevier." 🧪 academic.oup.com/mbe/article/...

academic.oup.com

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Reposted by @pbousquets.bsky.social

Trevor Graham @trevorgraham.bsky.social · 07/03/2025

Our #Cancer #Evolution summer school runs again 30 June-3 July at the beautiful Genome Campus in Hinxton, UK sponsored by @wellcometrust.bsky.social Ideal for starters in the field; this year's focus is on using genomic data for cancer evo research. Brilliant & inspiring faculty! bit.ly/40nLdDD

bit.ly

Evolutionary Biology and Ecology of Cancer — 20250630

Evolutionary Biology and Ecology of Cancer

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pbousquets.bsky.social @pbousquets.bsky.social · 28/12/2024

None of this work would have been possible without the incredible collaborators, mentors, and institutions I’ve been privileged to work with. A heartfelt thank-you to all of you for the guidance and support! 🙏✨ More works from our lab will be coming out soon. Stay tuned for more!

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pbousquets.bsky.social @pbousquets.bsky.social · 28/12/2024

With DNA from cord blood, demonstrated the neonatal origin of the disease, with t(7;12) appearing early in HSPCs in this sample, while a chr19 trisomy was demonstrated to have appeared after birth. 📰 Full details: www.nature.com/articles/s41...

nature.com

Backtracking NOM1::ETV6 fusion to neonatal pathogenesis of t(7;12) (q36;p13) infant AML - Leukemia

Leukemia - Backtracking NOM1::ETV6 fusion to neonatal pathogenesis of t(7;12) (q36;p13) infant AML

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pbousquets.bsky.social @pbousquets.bsky.social · 28/12/2024

t(7;12) translocations in iAML frequently involve MNX1::ETV6. However, in this case we found that NOM1, a gene physically close to MNX1 was the gene rearranged with ETV6. We demonstrated that, still, this alteration lead to MNX1 overexpression, probably by enhancer hijacking.

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pbousquets.bsky.social @pbousquets.bsky.social · 28/12/2024

4/ Backtracking NOM1::ETV6 fusion to neonatal pathogenesis of t(7;12) (q36;p13) infant AML Infant acute myeloid leukemia is devastating and rare, with cases linked to specific genetic alterations. We studied an iAML case with a rare t(7;12) translocation, associated with aggressive disease.

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pbousquets.bsky.social @pbousquets.bsky.social · 28/12/2024

We demonstrated that RFcaller has a comparable performance to the PCAWG consensus strategy, but much more efficiently. In fact, we recently improved even more the speed (~ 1-2h for a WGS analysis) 💻Test the tool here: github.com/xa-lab/RFcal... 📰 Learn more here: academic.oup.com/nargab/artic...

academic.oup.com

Validate User

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pbousquets.bsky.social @pbousquets.bsky.social · 28/12/2024

3/ RFcaller: a machine learning approach combined with read-level features to detect somatic mutations Accurate detection of somatic mutations is critical for both research and clinical practice. We developed RFcaller, a machine-learning tool that excels in sensitivity, specificity, and speed.

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pbousquets.bsky.social @pbousquets.bsky.social · 28/12/2024

Surprisingly, U2-2P, long thought to be a pseudogene, harboured recurrently this mutation, as well. We characterized it and found out that it was functional gene! 📰 Read more: www.nature.com/articles/s41...

nature.com

PanCancer analysis of somatic mutations in repetitive regions reveals recurrent mutations in snRNA U2 - npj Genomic Medicine

npj Genomic Medicine - PanCancer analysis of somatic mutations in repetitive regions reveals recurrent mutations in snRNA U2

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pbousquets.bsky.social @pbousquets.bsky.social · 28/12/2024

Using Armadillo, we confirmed previously reported U1 snRNA mutations. But our biggest discovery? A recurrent mutation in U2 snRNA, found across multiple cancers (B-cell tumors, prostate, pancreatic cancers) and linked to worse outcomes in some cases.

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pbousquets.bsky.social @pbousquets.bsky.social · 28/12/2024

2/ PanCancer analysis of somatic mutations in repetitive regions reveals recurrent mutations in snRNA U2 Repetitive genomic regions are often overlooked in cancer genomics because they're tricky to analyze. We designed Armadillo, a somatic variant caller for repetitive regions in WGS data

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pbousquets.bsky.social @pbousquets.bsky.social · 28/12/2024

This massive dataset of over 1,100 patients brought together multi-omic data. It allowed us to: - Identify 109 new driver genes - Reveal distinct leukemogenic pathways in subtypes based on IGHV mutation status. - Develop better prognostic models 📰 Learn more: www.nature.com/articles/s41...

nature.com

Molecular map of chronic lymphocytic leukemia and its impact on outcome - Nature Genetics

A genomic, transcriptomic and epigenomic analysis of chronic lymphocytic leukemia identifies genetic drivers and molecular subtypes associated with clinical outcomes.

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pbousquets.bsky.social @pbousquets.bsky.social · 28/12/2024

1/ Molecular map of chronic lymphocytic leukemia and its impact on outcome Chronic lymphocytic leukemia (CLL) is incredibly complex, with outcomes varying widely among patients. To tackle this, we created the largest integrated map of CLL to date, setting a new standard in the field.

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pbousquets.bsky.social @pbousquets.bsky.social · 28/12/2024

After six amazing years at #IUOPA, I'll be moving on to the next chapter soon. Let's take a moment to reflect on the highlights of my research journey—from molecular maps to novel tools in cancer genomics. 🧵👇

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