What is lead-time bias in #cancer screening?

Imagine that a meteor was hurtling through space towards the Earth. Its speed and trajectory indicate that it will destroy the planet in approximately 10 years.

Now, let's say that our best sensors are only ...
... capable of seeing said meteor 1 year in advance. So, 9 years go by and we are blissfully unaware of our impending doom. Then, at the 9-year mark, we detect the meteor and measure our remaining survival time to be just 1 year.
What if I gave you a better sensor? What if this sensor could see the meteor from 10 years away instead of just 1?

How long would our survival time be? While we may have a 10-year lead time instead of a 1-year lead time, the meteor still strikes us on the same day.
The 10X improvement in 'survival time' is illusory because our planet was destroyed at precisely the same time.

Now, of course, you could argue that the longer lead-time widens our set of options, and that is true!
We could scramble to figure out a way to divert the meteor or blow it up or embark on another fantastical idea.

However, we've got no way to know if we'll be successful. We do know that in 100% of attempts to save the planet, we will accrue significant costs.
Secondly, what happens if our sensor is wrong?

At 10 years in the future, a meteor that looks to be coming right at us may narrowly miss us.

If we scrambled to find a solution to that eventual non-problem, what are we left with besides unnecessary costs?
This last point emphasizes how lead-time bias can create overtreatment ~ unneeded procedures performed on tumors (meteors) that were never destined to kill.

Still, I bet if you were asked, you still might tell me you want the sensor anyway.
Even mired by all these risks, it still FEELS good to know ahead of time ~ to feel like we're more in control when we aren't. It's only human and I won't lie that I feel the same way.

However, my (and any of our feelings) shouldn't dictate policy.
As promising and powerful as this wave of new sensors (liquid biopsies) are, without carefully controlled studies, we're likely going to be spending a lot of time trying to avoid what may be inescapable on the hope that by Year 10, we've got it all figured out.

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More from @sbarnettARK

16 Sep
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.

Please see our general disclosure: ark-invest.com/terms/#twitter
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:
Read 28 tweets
9 Jun
We often discuss how more comprehensive and sensitive techniques improve the diagnostic yield for patients affected by rare genetic diseases. Indeed, yields have improved as we've gone from microarrays to whole genome #sequencing.

However, there's another critical component.
Case-Level Reanalysis (CLR)

By reanalyzing genomic data, as our global knowledge-base grows, we improve diagnostic yields.

We believe the broadest tests should be done first to avoid the need to re-contact and re-accession patient samples.

The economics for both the lab (and patient) change dramatically as well in a 'generate-once-reassess-often' framework. As more is known, variant interpretation may shift from being more manual to more automated.

Still, this is a really hard technological problem.
Read 5 tweets
14 Apr
The widespread adoption of liquid biopsy seems to be 'un-commoditizing' DNA synthesis in the molecular diagnostics industry.

Recall that synthetic DNA probes, molecules that bind and pull a DNA out of solution, are a critical input for liquid biopsy.
Diagnostics companies buy probes to use in their clinical tests, oftentimes in bulk, from a synthetic DNA provider. There's been a prevailing notion recently that DNA providers only can differentiate on the basis of cost or turnaround time.

I think liquid biopsy changes this.
Firstly, a huge technical constraint in liquid biopsy is the availability of cancerous DNA in a tube of blood, which decreases exponentially with tumor size.

Remember that smaller tumors don't leak as much DNA into the bloodstream.

Sequencing can be a lossy process.
Read 10 tweets
11 Mar
@NatHarooni @snicobio I’m watching Jeopardy—will come back later tonight. Short answer—no, not competitive to PacBio. Likely friends down the road.
@NatHarooni @snicobio Alright, so in theory the QSI platform can enable DNA (or RNA) sequencing on chip. However, I think of it more like a call option and less of a near-term goal. Proteomics is the killer app enabled by the QSI platform. But, as OP alluded to, multi-omics (inc. proteins) on one ...
@NatHarooni @snicobio ... instrument could be an attractive value prop. from a capital outlay point of view, especially for $50K which is achievable for many labs w/o needing to seek a major grant (so speedy sales cycles). Now, back to the main point about sequencing. If you read the patents ...
Read 10 tweets
22 Feb
@NatHarooni @AlbertVilella In my opinion — HiFi reads are the most accurate/complete, but currently are more expensive and lower throughput. Nanopore reads are cheap, fast, and high-throughput, but have a weaker error profile. Both of these descriptors are changing and may not be the case in a few years.
@NatHarooni @AlbertVilella As far as QSI is concerned, it’s a little too early for me to calculate operating costs per run. I’ll update when I know more.
@NatHarooni @AlbertVilella Regardless of how you consider the remaining engineering obstacles, necessary R&D spend, or computational issues—I feel that long-read sequencing (as a class of tech), will outperform short reads on virtually every relevant metric by 2024-2025
Read 4 tweets
21 Feb
@nhawk45 @AlbertVilella Hey, @nhawk45 -- Sure, I think I can take some of these. Let's start from the beginning to help explain why certain features of QSI's approach/IP are needed and interesting.

First: Why are proteins the hardest molecules to sequence of the 'big three'? (DNA/RNA/Proteins) ...
@nhawk45 @AlbertVilella Here are some of my notes on reasons I could think of, but I'll take a moment to elaborate on some of them.

The proteome is estimated to have the largest 'unit diversity' for lack of a better term. DNA = 20K genes, RNA = 10^5 isoforms, proteins = 10^6 proteofroms ...
@nhawk45 @AlbertVilella Proteomic diversity is driven by post-translational modifications (PTM), which is a set of chemical alterations to peptides not dissimilar to how DNA can have #epigenetic modifications like methylation. Combine this w/ the fact that peptides have a 20-letter alphabet ...
Read 7 tweets

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