iiinova — where your digital self keeps evolving

Simone Zaccaria @zaccasimo.bsky.social · 05/10/2026

Why ecDNA? 🔄 Unlike chromosomes, these oncogene-carrying circles split randomly when cells divide. Cancers can crank oncogene copies up or down within a few generations and outpace treatment. How this evolves through metastasis & therapy is still a mystery.

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Simone Zaccaria @zaccasimo.bsky.social · 20/05/2026

On a personal note, it was incredibly special to finally wear the gown from my own graduating university, @unimib. Thanks @ucl.ac.uk for this! 🇮🇹🎓 A wonderful reminder of where my pathway started at @unimib Informatica, and of @paolabonizzoni who, in turn, supported me at the time 🙏

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Simone Zaccaria @zaccasimo.bsky.social · 20/05/2026

🎓ABSOLUTELY BURSTING WITH PRIDE! Huge congrats to our @ccg-ucl.bsky.social first doctoral graduates: Dr @rijazaidi.bsky.social, Dr @abibunkum.bsky.social & Dr @ojlucas.bsky.social!🎉 Whatever pathways they choose next, I have no doubt these brilliant minds will lead critical advances in our society!

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Simone Zaccaria @zaccasimo.bsky.social · 29/11/2024

This is also the first step and output of my @cancerresearchuk.org Career Development Fellowship, and you can read its overall vision and goal in a recent @cancerresearchuk.org 's blog post 👇 news.cancerresearchuk.org/2024/02/27/u...

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Simone Zaccaria @zaccasimo.bsky.social · 29/11/2024

📰Read full story 👉 rdcu.be/d1S6m 🖥️SPRINTER is fully available in GitHub 👉 github.com/zaccaria-lab... 🚆Distributed through Bioconda (with also related container) 👉 bioconda.github.io/recipes/spri... 💾also with a reproducible capsule in CodeOcean👉 doi.org/10.24433/CO.... pic.x.com/zpL2rhbRhl

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Simone Zaccaria @zaccasimo.bsky.social · 29/11/2024

Finally, we proved SPRINTER’s broad applicability on prev datasets of 61,914 TNBC and HGSC cells and found other key insights for high prolif clones: ❗️Increased single-cell rates of genomic variants ‼️Enrichment of prolif-related gene amplifications ❔-> Evolutionary advantage?

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Simone Zaccaria @zaccasimo.bsky.social · 29/11/2024

When integrating serial ctDNA samples across 4 timepoints, we found differential ctDNA shedding between clones, with high prolif clones shedding more ctDNA 🚨It shows that previous similar observations obtained across different tumors also hold for distinct clones within a tumor

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Simone Zaccaria @zaccasimo.bsky.social · 29/11/2024

What's their role? We reconstructed met dissemination patterns and found that: 1. high prolif clones seeded most mets disseminating from other mets 2. met-seeding clones had high proliferation ❔-> association between high prolif and met seeding potential of individual clones

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Simone Zaccaria @zaccasimo.bsky.social · 29/11/2024

Maybe non-genetic? Leveraging SPRINTER, we developed an approach to identify clone-specific altered replication timing (ART) ‼️ We found ART unique to the high prolif and metastatic clones affecting key genes like KRAS, and with associated expression changes from matched RNA-seq

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Simone Zaccaria @zaccasimo.bsky.social · 29/11/2024

To find potential causes of these proliferation differences, we performed a detailed phylogenetic analysis of both SNVs and CNAs, as SPRINTER now enables the simultaneous analysis of clones' evolution and their proliferation. ⛔️ No clearly associated genetic driver was found

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Simone Zaccaria @zaccasimo.bsky.social · 29/11/2024

SPRINTER revealed widespread proliferation heterogeneity both in primary tumor and mets, and both between and within samples. We validated these with orthogonal analyses, including Ki-67 staining, nuclei imaging and clinical imaging. ❓-> Yes, clones can proliferate differently

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Simone Zaccaria @zaccasimo.bsky.social · 29/11/2024

To answer the initial questions ❓ and ❔, we generated a unique scDNA, longitudinal, primary-metastasis-matched dataset of 14,994 non-small cell lung cancer cells from a patient enrolled in the #TRACERx and #PEACEautopsy studies, and we applied SPRINTER to it

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Simone Zaccaria @zaccasimo.bsky.social · 29/11/2024

Using this ground truth dataset, we demonstrated that SPRINTER exhibits high accuracy and sensitivity, improving the power of S-phase identification compared to previous approaches and, especially, enabling accurate assignment of replicating cells to corresponding clones

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Simone Zaccaria @zaccasimo.bsky.social · 29/11/2024

To evaluate SPRINTER’s performance, we used a recent and accurate FACS approach incorporating EdU to generate a ground truth scDNA dataset of 8,844 diploid and tetraploid cancer cells separated into 5 known cell cycle phases

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Simone Zaccaria @zaccasimo.bsky.social · 29/11/2024

💡 To overcome this, we introduced SPRINTER, an algorithm that uses scDNA data to enable accurate identification and clone assignment of S- and G2-phase cells, providing a proxy to estimate clone-specific proliferation rates

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Simone Zaccaria @zaccasimo.bsky.social · 29/11/2024

🚨 With 💪 single-cell whole-genome DNA sequencing (scDNA) like DLP+, we jointly identify clones and replicating cells ⚠️ But estimating clone proliferation remains challenging due to lack of formal methods for clone assignment of replicating cells and low identification power

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Simone Zaccaria @zaccasimo.bsky.social · 29/11/2024

⚠️High proliferation is a key hallmark of cancer and is linked to worse clinical outcomes ❓Can it also differ between distinct clones co-existing within a tumor? ❔If so, do proliferation differences associate with more aggressive phenotypes or other properties of these clones?

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