Back in 2020 we introduced the concept of plasma whole genome sequencing (WGS) for MRD detection. We showed that genome wide mutational integration severs the limiting dependency between the number of DNA fragments in the sample and sensitivity. nature.com/articles/s4159…
At the time, it was a bit nuts to use WGS bc 💸, but we anticipated that sequencing costs will just keep going down...
We even made an in silico prediction of how we can have yet greater sensitivity if we can sequence deeper
Something changed (let's not go into the why...🫠), and the cost decline stalled...:(
Then we heard 👂about @UltimaGenomics , still deep in stealth mode 🕵️♂️ and got excited...:)
We undertook a comprehensive evaluation comparing short read platforms for plasma WGS and found that @UltimaGenomics delivers clean data for SNV & CNV detection
But just as we predicted back then, higher depth allowed us to increase tumor-informed MRD sensitivity to the parts-per-million range!
And it gets wilder...
Duplex error correction has transformed liquid biopsy detection as it allows for single-molecule mutation calling, but is very greedy in sequencing. So we thought, what if we can harness low cost seq. to do plasma Duplex at the scale of the entire genome? 🤯
@AlexandrePCheng and friends figured out how to Duplex on the single-end Ultima reads and showed extra extra clean plasma WGS
Clean mutational profiles allowed us to match up melanoma plasma samples to a UV signature, and control plasma to a clonal hematopoiesis signature
We then leveraged the ability to match genome-wide mutational profiles with mutational signature to compute the relative contribution of ctDNA vs. clonal hematopoiesis, allowing for NON tumor-informed ctDNA detection in the challenging context of low burden melanoma
Super excited about all the possibilities this work opens up in ctDNA and somatic mosaicism. Super excited for the world of declining sequencing costs to be back with us!
High throughput single-cell multi-omics platform to jointly capture genotype and chromatin accessibility; charting the differentiation of clonal outgrowths
If you have a moment, would love to tell you all about it 👇
Clonal outgrowths are everywhere in us humans. In malignant and non-malignant tissue. But we know little about the phenotypes of somatic driver mutations in primary human samples. To address this key challenge we need the ability to connect genotype and phenotype in single cells
🥳Max-level joy sharing new work with @MarioSuva 🥳
Single-cell multi-omics defines epigenetic encoding, heritability and plasticity of cancer transcriptional cell states @NatureGenet nature.com/articles/s4158…
Short 🪡 /n
Dozens (hundreds?) of studies applied scRNAseq to human cancers and shows that they contain vast transcriptional cell state diversity. Often along well-recognized axes like EMT or stem cell differentiation.
Incl. pioneering work by @MarioSuva & @TiroshLab in glioma cell states
Therapy monitoring is a central pillar of modern medicine. And yet, in many areas of oncology, we lack sensitive monitoring tools for residual disease detection. ctDNA carries the potential to change this, but is scarce in low-burden disease.
The central paradigm in the field aims to overcome ctDNA scarcity through deep targeted sequencing of informative sites. However, we show that limited number of fragments imposes a hard ceiling on cfDNA deep targeted sequencing, with sensitivity limited ~1:1,000 per site.