My Authors
Read all threads
Excited to share our work @NatureMedicine offering a novel framework [MRDetect] for ultra-sensitive cancer monitoring through whole genome mutational integration of cfDNA
#LiquidBiopsy #WGS #cfDNA #MachineLearning
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.
We propose that genome-wide integration, across many thousands of mutations, can radically change the detection landscape.
This required rethinking the mutation detection process, given that only a single supporting mutated read is expected per locus, at best. We developed a read-centric (instead of locus-centric) caller, enabling machine learning on millions of reads for 10-100X noise reduction
Through mixing studies, we show that this perspective enables detection of tumor fractions of 1:100K (even 1:1M with a bit more sequencing), with high correlation with true tumor fractions 👉ultra-sensitive & quantitive monitoring, *without* deep targeted sequencing.
Similar methodology applied to copy number alterations expands this framework to more tumors with lower mutation rates.
MRDetect applied to pre-surgery CRC as ground truth showed high performance (with specificity to an individual patient). This allows to monitor treatment response to immunotherapy (melanoma), as well as detect post-operative residual disease, closely associated with outcome.
Applied to the challenging setting of lung adenocarcinoma (19% pre-operative detection with patient specific panels), we show maintained performance and post-operative residual disease detection.
Congrats to Asaf Zviran, @RafaelSchulman, @mjs2225 for leading this work! Huge thanks to many smart people who gave so generously to this labor of love. Special thanks to @wolchokj, @gmboland. To @notSoJunkDNA & @AltorkiNasser for believing in this work from day 1.
This work would not have been possible without the commitment of @TheMarkFdn, @LungAssociation, @WCM_MeyerCancer @nygenome for supporting high-risk work to impact patient care.
Finally, as always, we are standing on the shoulders of giants!

@CharlesSwanton @AshAlizadeh @velculescu Nitzan Rosendfeld @ldiaz1 @max_diehn @JShendure @MurtazaMDPhD and many more
access also at
Missing some Tweet in this thread? You can try to force a refresh.

Enjoying this thread?

Keep Current with Dan Landau

Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

Twitter may remove this content at anytime, convert it as a PDF, save and print for later use!

Try unrolling a thread yourself!

how to unroll video

1) Follow Thread Reader App on Twitter so you can easily mention us!

2) Go to a Twitter thread (series of Tweets by the same owner) and mention us with a keyword "unroll" @threadreaderapp unroll

You can practice here first or read more on our help page!

Follow Us on Twitter!

Did Thread Reader help you today?

Support us! We are indie developers!

This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3.00/month or $30.00/year) and get exclusive features!

Become Premium

Too expensive? Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal Become our Patreon

Thank you for your support!