Please do reach out if you would like to discuss about (non exhaustive list) the project, anything related to complex rearrangements in cancer and beyond, or the lab tech and postdoc positions we will advertise soon..
all that is *no small feat*! And, as always, we are immensely grateful to the patients and families who donate samples for us to do research!
This is a particularly good opportunity to thank all my team members, mentors, collaborators and funders over the years for their work and support, as well as the admin, IT and grants teams helping with all the paperwork and countless meetings related to this and other projects..
Ultimately, we aim to generate the necessary knowledge to inform the design of novel immunotherapy approaches to treat the many cancers driven by rearranged chromosomes, which unfortunately remain largely incurable.
The project will be strongly focused on aggressive solid tumours in children, although the concepts and methods we will develop will also be applicable to study fundamental aspects of tumour evolution relevant to diverse paediatric and adult cancer types.
To this aim, we will use a combination of multidisciplinary technologies applied to clinical samples to dissect the mechanisms underpinning cancer genome evolution and how highly rearranged chromosomes shape cancer cell phenotypes and immune evasion.
Most honoured and thankful for receiving funding from the
@erc.europa.eu for the #ERCCoG project BrokenChromosomes! We will investigate how scrambled chromosomes in cancer cells arise to drive aggressive disease
@embl.org @ebi.embl.org @sangerinstitute.bsky.social @cancerresearchuk.org
📢 The 2nd European Cancer Dependency Map Symposium is coming!
🗓️ 20 Nov 2025
📍 Human Technopole, Milan
Join us for a one-day dive into:
🔹 Cancer genomics
🔹 CRISPR screening
🔹 AI-driven target discovery
With the patronage of AIRC
🔗 humantechnopole.it/en/trainings...
open access publication here www.nature.com/articles/s41... @nanoporetech.com @pacbio.bsky.social
Kudos to Sonia Zumalave in my lab for working out how to flag and remove such fold-back-like artifacts, with key contributions from @hbelrick.bsky.social @carolinmsa.bsky.social and @jevalleinclan.bsky.social. Thread on our algorithm bsky.app/profile/isid... and ..
Very glad to see this preprint by @lh3lh3.bsky.social and Meyerson labs www.biorxiv.org/content/10.1... confirming our finding of artifactual fold-back inv in long reads (Fig S1 in our @natmethods.nature.com paper presenting SAVANA, which filters such artifacts to improve SV calling 👇
Work by researchers in the group of @isidrolauscher.bsky.social at EMBL-EBI, the R&D lab of @genomicsengland.bsky.social, in collaboration with clinical partners at @ucl.ac.uk, Royal National Orthopaedic Hospital, Instituto de Medicina Molecular João Lobo Antunes, and Boston Children’s Hospital.
Very well done indeed @hbelrick.bsky.social ! 😀
SAVANA is out in the wild 🦁! #SAVANA detects haplotype-resolved somatic structural variants (SVs), copy number aberrations, and calculates tumour purity and ploidy using long-read data. Together with it, a robust, data-driven benchmarking effort! Below is a thread with all the advantages 👇
and huge thanks to our funders 🙏 @curesarcoma.bsky.social CTOS, @embl.org and others!
HT @jevalleinclan.bsky.social nclan.bsky.social, and other lab members at @ebi.embl.org bl.org, our great collaborators at @bostonchildrens.bsky.social s.bsky.social Melanie Tanguy and Greg Elgar @genomicsengland.bsky.social sengland.bsky.social IMM Lisbon...
SAVANA was developed by two superstars in the lab @hbelrick.bsky.social & Carolin Sauer in close collaboration once again with Prof. Flanagan and team at @ucl.ac.uk with key contributions from...
In sum, we establish best practices for benchmarking SV detection methods for somatic (eg cancer) genome analysis, and show that SAVANA enables the application of long-read sequencing to detect SVs and SCNAs reliably in clinical samples.
Finally, SAVANA predictive modelling framework incorporates Conformal Prediction, a mathematically sound method to control the error rate of predictions (hence the ‘reliable’ in the title). Conformal prediction is a robust method we've used in other contexts as well eg pubs.acs.org/doi/10.1021/...
Importantly, #SAVANA harnesses read-phasing information during model training & provides haplotype-resolved SV calls, facilitating the assembly of complex SVs at single-haplotype resolution, eg our work on LTA chromothripsis in osteosarcoma @cellpress.bsky.social
www.cell.com/cell/fulltex...
In practice, this means that we can now study, reliably, complex genomic rearrangements (e.g. #chromothripsis) and clinically relevant events causing tumour suppressor gene loss using long reads (left) with comparable accuracy to Illumina (right):
Moreover, using #SAVANA, we can estimate tumour purity and ploidy with comparable accuracy to illumina data (using the fantastic pipeline developed by the Hartwig Medical Foundation @ecuppen.bsky.social @danielisskeptical.bsky.social for clinical reports) even WITHOUT a germline control!
Using SAVANA, we recover most of the SVs detected in short-read data (note the higher than two-fold diff in coverage between long and short reads here!!), and most of the SVs detected using long reads are detected in illumina data (note that we are not using ultra-long reads)
Now that we have a robustly-validated algorithm we can address the question you are all waiting for (and which many colleagues have asked us many times): what is the relative performance of long & short reads to analyze human cancer genomes?
What underpins the higher performance of SAVANA? A key innovation of SAVANA is the use of machine learning to distinguish true somatic signal from artefacts. The key challenge here was to curate a large training set (see details in the paper).
In sum, these data indicate that SAVANA delivers SV results consistent with tumour biology, and the differences in SV rates across algorithms are caused by variable algorithmic performance, rather than true biological signal (see other analysis in support of this conclusion in the paper)
For example, existing methods detect 100s to 1000s of SVs in each sample mapping to microsatellite regions (#SAVANA doesn’t). The tumour types we analysed (sarcomas and glioblastomas) rarely show such levels of repeat instability, which we confirmed for our sample using illumina
We found the same when using simulated sequencing replicates of the blood samples we use as germline controls. So, what are the false positive SVs called by some algorithms and not by others? What drives such strong differences in performance?
Using sequencing replicates of the normal cell line COLO829BL, we found that SAVANA shows 13- and 82-times higher specificity than the second and third-best performing algorithms (391x higher than the worse performing one). In practice, this means 10s-1000s less false positives..
