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How Big Data Can Help Fight Cancer

cancer.nautil.us

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Re: How Big Data Can Help Fight Cancer

#11
post #9
post #8

Earlier quoted context omitted.

I would add to that that because most cancer causing (single mutation) variants are in the coding space, it ultimately seems reasonable to be able to revert the mutations back to wild type with genome editing tools such as Cas9 in the (decade-ish future?). Using small molecules to fight against cancer as the article speaks to really seems like the last vestiges of a 20th century technology, while designing novel gene…

>"it ultimately seems reasonable to be able to revert the mutations back to wild type with genome editing tools such as Cas9 in the (decade-ish future?)" What are you basing this on? Have you seen a study where they report "modification" in a living organism using this tech? I highly doubt it, due to toxicity. Also see my post above regarding the presence of aneuploidy and chromosomal instability in cancer cells. How…

>What are you basing this on?

Logical extension of how the strategies for technologies like Car-T therapies are being developed and delivered. By first targeting an ex-vivo tissue (like T-cells that can be harvested and then re-implanted), you first develop the tools to use genome editing techniques effectively, without significant off-target effects. It's essentially practice for much more useful therapies that can be done in-vivo. Further, the design of the biological 'sensors' in the T-cells are precisely the kinds of sensors you'd need to deliver a payload to and only to a mutation-containing cell. So you would neither need to target an entire organism (rather a tissue, or even a particular cell type), and off target effects from genomic insertion would be curtailed by advances in the technologies surrounding Cas9 itself. For many cancers you wouldn't need to repair the mutation in the entire organism as the cancer is tissue- or cell-specific.

You are correct that you would, however, likely need to repair the prior to significant metastasis or loss of stability of the chromosome. At that point fixing a point mutation is not going to help much. All the more reason screening and baselines should start being established now - so that we can even detect a mutation prior to it becoming 'cancerous'.

If you know you have a stop codon mutation in your Her2 gene when you're born, it will not be too far off when that stop codon, in particularly sensitive tissues, could be reverted with reasonable safety prior to the development of an (inevitable) cancer. And ultimately that actually cures the the cancer in a way a small molecule can never, from first principles, hope to do.

Re: How Big Data Can Help Fight Cancer

#12

Earlier quoted context omitted.

Cancer genomics researcher here. I agree wholeheartedly about getting sequencing done if you have cancer - it's what I would do for myself or my family. Two minor quibbles about your thoughts: 1) Exome sequencing is below $1000, but analyzing that data adds a non-negligible cost. Still, even 2 or 3 grand is way cheaper than wasting time on treatments that won't work. Whole-genome sequencing is even better (for a litt…

> 1) Exome sequencing is below $1000, but analyzing that data adds a non-negligible cost. That is by and large the most significant factor that we've seen which slows adoption of more widespread whole-exome or whole-genome sequencing. It takes less than a day and costs less than $1000 to sequence your exome (and even your whole genome), but the backlog for analysis of the sequencing results in labs can be 9 months or…

> It takes less than a day and costs less than $1000 to sequence your exome (and even your whole genome), but the backlog for analysis of the sequencing results in labs can be 9 months or more.

Layman qs. what is stopping the labs to quickly analyze the genome? Computational power or few labs doing this kind of work?

Re: How Big Data Can Help Fight Cancer

#13

Earlier quoted context omitted.

> 1) Exome sequencing is below $1000, but analyzing that data adds a non-negligible cost. That is by and large the most significant factor that we've seen which slows adoption of more widespread whole-exome or whole-genome sequencing. It takes less than a day and costs less than $1000 to sequence your exome (and even your whole genome), but the backlog for analysis of the sequencing results in labs can be 9 months or…

> It takes less than a day and costs less than $1000 to sequence your exome (and even your whole genome), but the backlog for analysis of the sequencing results in labs can be 9 months or more. Layman qs. what is stopping the labs to quickly analyze the genome? Computational power or few labs doing this kind of work?

In general it's a (computationally) hard problem to restitch a genome together. Even today, when you 'get your genome sequenced' you are not getting a full read-through of your entire genome's data.

Imagine you want to reconstruct the data on two RAIDs that are mostly, but importantly not exactly, mirrors of each other. Each RAID has 23 drives. Each drive has ~1Gb or so of data. And much of the data is not only mirrored between the two RAIDs, but is also mirrored between the 23 drives - and many of that mirrored data is 'off by 1' in very important ways (both 'must', and 'must not' scenarios). Further some of the data contains very long sections of highly repetitive data. And some of the data is mechanically biased to be harder to read than others.

You must now reconstruct those two RAIDs with single-bit accuracy - as a single bit-flip in certain sections determines whether or not you get cancer. The data you are given to do the reconstruction is a 200Gb single column CSV file with each row being 12 bytes of data.

Go.

Re: How Big Data Can Help Fight Cancer

#14

Earlier quoted context omitted.

> 1) Exome sequencing is below $1000, but analyzing that data adds a non-negligible cost. That is by and large the most significant factor that we've seen which slows adoption of more widespread whole-exome or whole-genome sequencing. It takes less than a day and costs less than $1000 to sequence your exome (and even your whole genome), but the backlog for analysis of the sequencing results in labs can be 9 months or…

> It takes less than a day and costs less than $1000 to sequence your exome (and even your whole genome), but the backlog for analysis of the sequencing results in labs can be 9 months or more. Layman qs. what is stopping the labs to quickly analyze the genome? Computational power or few labs doing this kind of work?

A little column A, a little column B.

Something worth thinking about: Processing on HIPAA-compliant resources is expensive, and as with any medical procedure, that financing/insurance is complicated to bill for (or at least, takes a long time... which makes it more expensive to run, etc).

Re: How Big Data Can Help Fight Cancer

#16
post #11
post #9

Earlier quoted context omitted.

>"it ultimately seems reasonable to be able to revert the mutations back to wild type with genome editing tools such as Cas9 in the (decade-ish future?)" What are you basing this on? Have you seen a study where they report "modification" in a living organism using this tech? I highly doubt it, due to toxicity. Also see my post above regarding the presence of aneuploidy and chromosomal instability in cancer cells. How…

>What are you basing this on? Logical extension of how the strategies for technologies like Car-T therapies are being developed and delivered. By first targeting an ex-vivo tissue (like T-cells that can be harvested and then re-implanted), you first develop the tools to use genome editing techniques effectively, without significant off-target effects. It's essentially practice for much more useful therapies that can…

This editing procedure is extremely toxic, and I do not believe this is all due to off-target effects. I found this one quickly, where the paper claimed 80% viability when their data showed something more than half of them "go missing" (impossible to tell more from the chart): https://news.ycombinator.com/item?id=12971533

Here is actually one where they injected into Drosophila embryos. It looks like to get 10% success rate, they had to kill 50% of the "subjects" (table 1): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3714591/

Here is another where you see the more cas9 you inject, the more animals (C. elegans) die (table S1): https://www.ncbi.nlm.nih.gov/pubmed/23979586

Re: How Big Data Can Help Fight Cancer

#17
post #6

Well, they need to make sure they are looking at the right type of data. I know it is blasphemous, but why not include the aneuploidy/chromosomal data as well? These error rates appear to be much higher than point mutations, etc: "Nevertheless, the rate of chromosome missegregation in untreated RPE-1 and HCT116 cells is  0.025% per chromosome and increases to 0.6 – 0.8% per chromosome upon the induction of merotely…

[deleted]

Re: How Big Data Can Help Fight Cancer

#18
post #16
post #11

Earlier quoted context omitted.

>What are you basing this on? Logical extension of how the strategies for technologies like Car-T therapies are being developed and delivered. By first targeting an ex-vivo tissue (like T-cells that can be harvested and then re-implanted), you first develop the tools to use genome editing techniques effectively, without significant off-target effects. It's essentially practice for much more useful therapies that can…

This editing procedure is extremely toxic, and I do not believe this is all due to off-target effects. I found this one quickly, where the paper claimed 80% viability when their data showed something more than half of them "go missing" (impossible to tell more from the chart): https://news.ycombinator.com/item?id=12971533 Here is actually one where they injected into Drosophila embryos. It looks like to get 10% succe…

Certainly. That is with unaided, wild-type Cas9, 4 years ago. The understanding (and mitigation) of what causes those side-effects is under some of the most comprehensive and intense research as we speak. Searching PubMed for 'Cas9' shows that 2000 papers (of a total of 3200 results) have been written in the last 16 months. And there are other ways to edit genomes - Cas9 is great for research because it's fast, but there are likely less toxic ways to edit a live genome once you have used Cas9 in the lab to figure out what to actually edit.

Further, the body is pretty resilient. You don't need viability to be 100% before a therapy could be successful. Chemotherapy kills a whole lot of cells and people survive it.

Re: How Big Data Can Help Fight Cancer

#19
The biggest bottleneck to realizing this vision, unbelievably, is lack of funding to do large-scale, high quality whole genome cancer sequencing + high-quality treatment, family, phenotypic etc. data to go along with it.

As nice as Foundation's data is, there are few/no phenotypes to go along with it, representing a bottleneck to extracting any useful information out of it. Keep in mind that genomics is only one half of genetics (the other half being the phenotype) and is meaningless without knowing more about the patient.

On the phenotype side, EMR/EHRs are practically useless as scientific tools (doesn't prevent academics from publishing how they extracted meaningful data from it.. which has little correlation with if something works or is reproducible; unfortunately) and I don't foresee this being fixed in the current healthcare ecosystem in the US. The UK & other European governments with good data + national healthcare systems represent a better chance.

If you don't believe me, I challenge you to point me to a single study containing a comparison of 1000 metastatic sites vs primary tumors in one cancer type, with WGS. Given that metastasis causes > 90% of deaths (ref: Weinberg cancer textbook), you'd think we'd have done this study by now.

A dx company has no hope of reimbursement for doing cancer whole genomes, and does not receive patient data in sufficient detail (how did they fare after treatment? what drugs were they given?) to undertake a study.

Re: How Big Data Can Help Fight Cancer

#20
post #13

Earlier quoted context omitted.

> It takes less than a day and costs less than $1000 to sequence your exome (and even your whole genome), but the backlog for analysis of the sequencing results in labs can be 9 months or more. Layman qs. what is stopping the labs to quickly analyze the genome? Computational power or few labs doing this kind of work?

In general it's a (computationally) hard problem to restitch a genome together. Even today, when you 'get your genome sequenced' you are not getting a full read-through of your entire genome's data. Imagine you want to reconstruct the data on two RAIDs that are mostly, but importantly not exactly, mirrors of each other. Each RAID has 23 drives. Each drive has ~1Gb or so of data. And much of the data is not only mirro…

hmm I don`t think so I fathom the complete complexity of the process but with so many powerful GPU`s out there, is there a possibility of reconstruction in a matter of days if not hours?
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