I agree with a lot of what you've laid out above. However, I think I should clarify some parts of my thread and offer counterpoints to a few of yours. I'm always game to trade notes.
I disagree that the DNA sequencing market is worth $10 billion. Today, it’s less than that. Should Illumina (a) drive unit prices lower (w/ super resolution, see below) & (b) help customers up the platform upgrade cycle to realize bleeding-edge OpE...
…that the market could be worth much more. I’ll cede that this position isn’t ideal because, as you point out, the vastest TAM is within clinical genomics. Still, investors could be ‘headed for the exits’ because their time horizons may not be long enough.
As much as I’ve written lovingly about long-reads, I think saying the market will converge to them isn’t quite right. Should all relevant hardware metrics (throughput, F-1 scores, $/GB, etc.) match/exceed today’s standard for short-read, then yes, big swaths may port over.
Which clinical NGS apps. go to long-read is arguable, and in many cases has biological constraints. FFPE cancer tissue, the most common sample, has fragmented (~200 bp) DNA—I’m unsure how that input would work in a Sequel II HiFi workflow. ctDNA presents a similar challenge.
We believe there’s plenty of opportunity for Illumina to expand upon its core franchise. We believe they are going about it the wrong way. I totally agree with you that leveraging its distributed install base is a great idea, hence why we like Invitae’s acquisition of ArcherDx.
In our view, Illumina should empower its customers, such as ArcherDx and FMI, to build IVDs for local use, helping to capture 80% of cancer patients. By competing with Dx customers, they may choose to leave Illumina. I’ll give one example:
Veracyte purchased NanoString’s FDA-cleared FLEX system to vertically-integrate the hardware component of its distributed Dx franchise. Is FLEX as robust as a NextSeq? No, but it gets the job done and allows Veracyte to play out price-elasticity-of-demand its own way.
While gaining access to methylation data isn’t unique, the (a) size of current+future training data and (b) existing ML classifier, I think, gives GRAIL an advantage over GH in the near-term, especially for a pan-cancer test. Bluntly, GRAIL (and others) have published data…
…whereas GH hasn’t initiated a pan-cancer trial. In the end, their commercial strategies are different (pan versus single-indication) and I think GH is strong, generally, as I stated in my original thread. Alright, so some more thoughts on methylation…
…What is it if not a novel biomarker for the *presence*, not *treatment* of cancer? Does it matter what’s feeding the machine if the clinical question is accurately answered? Fragmentomics hasn’t been clinically validated either, yet it’s also pulled into screening ML stacks.
The truth is, we think Illumina has great bones and that there’s a lot it can do to reinvigorate itself. In fact, we’ve written about it and praised it very recently. I’d also argue synthetic biology could’ve been an attractive option that would empower, not stifle, competition.
I’m not sure liquid biopsies would go to nanopore for numerous reasons, though I’d welcome input because I’m not as sure here. Firstly, the short (~200 bp) length of ctDNA could inhibit nanopore sample prep and hamstring $/GB, F-1, and other metrics.
I’m not aware of any well-studied ctDNA assays (from a 3rd party, not ONT) running on nanopore. You’re right ONT doesn’t need amplification, but amp isn’t as much of the enemy here as it is with CG-rich DNA. You can error-correct sequence artifacts, albeit w/ a depth penalty.
Third, ONT (historically) has required frequent software/hardware upgrades. If I were a clinical provider (especially in an IVD setting), I would NOT want to keep revalidating my protocol. I’ll agree that SNV F-1 is improving to ILMN-levels, but questionable at such low VAF.
I don’t think there’s any reason to believe ILMN is playing defense for liquid biopsy. Altogether, we saw this situation as an avoidable one with several path towards reaccelerating growth that did not involve squaring up directly against a crowded Dx market.
Thanks for reading, welcoming feedback/critique/etc.
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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: