Here's a short (?) thread on a recent #PCAWG paper focused on #cancer, long-reads, tumor mutational burden, and a very hard-to-pronounce word called chromothripsis.
'chromo-' = chromosome ; '-thripsis' = shattering into pieces
The researchers' used short-read (~40X) whole-genome #sequencing on ~2,700 tumors across 38 cancer types (most tumors were advanced).
The goal was to study the frequency of a mutational phenomenon (chromothripsis), which previously was though to exist in only 2-3% of tumors.
The prevailing view on tumor formation is that somatic mutations gradually accumulate over time, eventually overwhelming a cell's DNA repair machinery. Conversely, chromothripsis (above) is a single, catastrophic event defined by hundreds of structural rearrangements all at once.
Many previous studies of chromothripsis used arrays, which are not as sensitive to small copy-number variants (a defining feature of chromothripsis). My guess is that arrays systematically underrepresented the presence of chromothripsis in many tumors.
Using short-read #NGS, the researchers discovered a much greater prevalence of chromothripsis in a majority of human cancers (y-axis = % of tumors w/ chromothripsis).
This may suggest that using long-read sequencing (which is even more sensitive to copy-number variation than short-read), will allow for even more precise detection of chromothripsis. Why is this potentially important in clinical #oncology?
Conceptually, chromothripsis falls under tumor mutational burden (TMB) - a well-studied biomarker used to indicate the use of targeted immunotherapies. If long-reads are even more sensitive to chromothripsis, we stand to boost the TMB detection and identify subgroups of patients-
-that may benefit from targeted therapies. It's likely that chromothripsis also may be used as a prognostic metric.
TL;DR:
Researchers sequenced tumors using sequencing, a more accurate technology, and discovered that a nasty mutational event (chromothripsis) was way more ...
... common than previously thought. My guess is that using an even more accurate technology (long-reads) will find even more chromothripsis. This is important because chromothripsis is one reason doctors can prescribe immunotherapies.
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>90% of Americans over 45 have seen a Cologuard ad this week.
Fewer know of the test's parent company, Exact Sciences (EXAS), whose tests also guide care for the majority of early-stage breast cancer patients in the US.
Today, we released a 5-year model + article on Exact.
Our (base case) 2027 price target is $140 ($49 today). To get there, here's what we believe has to happen:
1. Exact's core business (Cologuard + Oncotype DX) grows on avg. >15% per year thru 2027, reaching $4B.
2. Exact achieves EBITDA positivity in the '23/'24 timeframe.
What we believe must happen (Cont.):
3. Exact uses its earnings to reinvest in its burgeoning pipeline, service its outstanding debt, and maintain its capital equipment.
4. Exact's pipeline, in aggregate, hits >$1B revenue by the end of 2027.
Now that @Quantum_Si has given us a peek under the hood of its protein #sequencing platform (Platinum), we can begin comparing actual results to theory.
A few months ago, I shared this paper that gave a theoretical framework for protein sequencing: pubs.acs.org/doi/10.1021/ac…
The author simulated how different factors, such as the # of readable amino acids (AAs) and the read length, would affect a protein sequencer's ability to unambiguously detect the 20,000 canonical human proteins in our bodies.
That chart is attached below.
I've marked in green where QSI currently stacks up. Based on its recent pre-print (linked below), Platinum can directly read seven (7) amino acids (F, Y, W, L, I, V, and R) with peptide reads that seem to max out around 20 AAs.
A recent publication by Dennis Lo et al applied long-read sequencing (LRS) in the prenatal screening (#NIPT) setting. It's a rather unorthodox technology/application pairing, and it's got me scratching my head a bit.
For context, earlier this year, Lo et al published a convolutional neural network ("the HK model") that enabled PacBio LRS devices to read methylation (5mC) across the entire genome with very high fidelity. This is important later.
@MJLBio@Sanctuary_Bio@Biohazard3737 Sure! I realize I was being a little vague with those statements. Generally, I think you're correct in your interpretation of the importance of P2 (great $/GB, but at a smaller scale) as well as duplex sequencing.
Something that is important to recognize, though ...
@MJLBio@Sanctuary_Bio@Biohazard3737 ... is how product deployment works differently between PacBio and Nanopore, which is partly an artefact of culture and of time in the public markets, in the public markets. I'm not advocating for one over the other with my next statements.
@MJLBio@Sanctuary_Bio@Biohazard3737 PacBio has been a public company for a long time. While the management has changed much since the failed Illumina merger, the familiarity with how to operate as a public company has not.
PacBio is more secretive and only unveils fully built-out commercial products.
I'd like to share my initial reaction to today's Berkeley Lights report. But first, I need to do some housekeeping. I can't comment on stock movements, share financial projections, or debate fair value.
Generally, I respect anyone who's put this much work into a topic. I won't pretend to have a clean rebuttal to every point. In my experience, beyond the hyperbole and hasty generalizations, there is some truth in these types of reports.
I want to soberly appraise those truths.
Also, I'd invite the subject-matter experts waiting in the wings to build off of this thread, add detail, or share their experiences. Ultimately, we're all after the same thing.
I will start with a few concessions and end with a few counterpoints to today's report: