Postdoctoral Training Fellow - Evolution & Translational Genomics in Sutton | The Institute of Cancer Research View details and apply for this Postdoctoral Training Fellow - Evolution & Translational Genomics vacancy in Sutton. Salary: Dependent on relevant Postdoctoral experience. Future progre...

Ben O'Leary & I are looking for a #postdoc to join us in @cec-icr.bsky.social to work on collaborative @cancerresearchuk.org RadNet project about the interrelationship between genome & epigenome instability and radiotherapy response.

Details & apply here: jobs.icr.ac.uk/vacancies/11...

@icr.ac.uk

"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/...

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

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!

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

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...

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.

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​.

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...

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​.

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

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...

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.

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

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.

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...

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​.

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. 🧵👇

I'd love to be there! Thanks!