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!

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​.

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

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​.

I'd love to be there! Thanks!