How Big Data Can Help Fight Cancer
cancer.nautil.us
How Big Data Can Help Fight Cancer
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Re: How Big Data Can Help Fight Cancer
#2It’s crazy how many ailments are now being heavily data mined.
Re: How Big Data Can Help Fight Cancer
#3As whole-exome sequencing has now dipped below $1,000 [1], this really should become a diagnostic assay of first resort. That said, further improvements are required as it appears the majority of cancer causing sequence variants are found in non-coding regions of the genome [2], suggesting that greater sequencing coverage is tremendously valuable.
[1] https://www.genome.gov/sequencingcosts/
[2] http://www.nature.com/nrg/journal/v17/n2/abs/nrg.2015.17.htm...
Re: How Big Data Can Help Fight Cancer
#4As whole-exome sequencing has now dipped below $1,000 [1], this really should become a diagnostic assay of first resort. That said, further improvements are required as it appears the majority of cancer causing sequence variants are found in non-coding regions of the genome [2], suggesting that greater sequencing coverage is tremendously valuable. [1] https://www.genome.gov/sequencingcosts/ [2] http://www.nature.com/…
> the majority of cancer causing sequence variants are found in non-coding regions of the genome
That's not entirely accurate. The majority of variants occurs in the non-coding genome, but that's also because the vast majority of the genome is non-coding. There certainly are non-coding variants that are oncogenic (e.g. hTERT promoter), but for the most part the functional significance of a given non-coding variant is unknown.
I'm not suggesting greater sequencing isn't important, but there are a lot of considerations that goes into what part of a cancer genome get sequenced (WGS, exome, gene panels, specific variants). We simply don't know what a lot of the variants do, but for those that suggest a particular therapy (BRAF V600E), it can be quite effective.
Re: How Big Data Can Help Fight Cancer
#5As whole-exome sequencing has now dipped below $1,000 [1], this really should become a diagnostic assay of first resort. That said, further improvements are required as it appears the majority of cancer causing sequence variants are found in non-coding regions of the genome [2], suggesting that greater sequencing coverage is tremendously valuable. [1] https://www.genome.gov/sequencingcosts/ [2] http://www.nature.com/…
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 little more cost) because of the extra types of information it adds about structural variants and copy number changes.
2) We're reasonably sure that most cancer causing variants are in the coding space, but there are undoubtedly some in non-coding regions (and classes of large structural events like duplications or deletions that affect both).
It's the best time in the history of the world to have cancer, and it is only getting better. Survival curves are slowly bending, and new classes of treatments like immunotherapies are helping to bend them even more.
The bottom line is, If you get cancer, fight like hell to get your tumor sequenced. Most insurers cover at least some kind of genomic test for cancer these days.
Re: How Big Data Can Help Fight Cancer
#6Well, 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 through mitotic recovery from either monastrol or nocodazole treatment ( Fig. 3 C ). These basal and induced rates of chromosome missegregation are similar to those previously measured in primary human fibroblasts ( Cimini et al., 1999 ). Assuming all chromosomes behave equivalently, RPE-1 and HCT116 cells missegregate a chromosome every 100 cell divisions unless merotely is experimentally elevated, whereupon they missegregate a chromosome every third cell division. Chromosome missegregation rates in three aneuploid tumor cell lines with CIN range from 0.3 to 1.0% per chromosome (Fig. 3 C ). Depending on the modal chromosome number in each cell line, these cells missegregate a chromosome every cell division (Caco2), every other cell division (MCF-7), or every fifth cell division (HT29)." https://www.ncbi.nlm.nih.gov/pubmed/18283116
Many people claim that aneuploidy is found in nearly all cancer cells:
https://www.ncbi.nlm.nih.gov/pubmed/17046232
Re: How Big Data Can Help Fight Cancer
#7I would embrace big-data epidemiological studies (in the U.S.) if society would legally and practically-irreversibly guarantee me that the results would not be used to discriminate, against me nor against others. In health care insurance and health care delivery. In employment. Etc.
As it is, I fear any and every bit of data I provide the system may well be used against me at a future point.
Right now, I'm going through some extensive testing, and I've decided to provide further historical data in my possession for the sake of a better analysis and diagnosis. However, that same data -- or rather, one datum of the data it is comprised of -- a few decades ago, was used as the basis to deny my application to purchase individual health care insurance.
With the ongoing attacks on the Affordable Care Act, I have the distinct feeling of traveling back in time.
If we are going to have cooperative buy-in on big data, we are going to need to ensure that the resulting benefits are shared across the population and are not used to discriminate against subjects having data "on the left side of the bell curve".
Re: How Big Data Can Help Fight Cancer
#8As whole-exome sequencing has now dipped below $1,000 [1], this really should become a diagnostic assay of first resort. That said, further improvements are required as it appears the majority of cancer causing sequence variants are found in non-coding regions of the genome [2], suggesting that greater sequencing coverage is tremendously valuable. [1] https://www.genome.gov/sequencingcosts/ [2] http://www.nature.com/…
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…
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 genetic tools like immunotherapies are today's superweapon - tomorrow's is just to fix the mutation. All the more reason to pour money into the DNA side of things (sequencing, synthesis, analysis) over the small molecule side of things ('drug development'.
Re: How Big Data Can Help Fight Cancer
#9Earlier 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…
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 is crispr/cas9 going to fix that?
Re: How Big Data Can Help Fight Cancer
#10As whole-exome sequencing has now dipped below $1,000 [1], this really should become a diagnostic assay of first resort. That said, further improvements are required as it appears the majority of cancer causing sequence variants are found in non-coding regions of the genome [2], suggesting that greater sequencing coverage is tremendously valuable. [1] https://www.genome.gov/sequencingcosts/ [2] http://www.nature.com/…
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 more.
There are patients that could be treated from the analysis of their genome that aren't even considered if their prognosis is shorter than the amount of time it will take to get clinically actionable results from the lab, which is the really sad part. This problem is actually what led us to create our company, Genomenon, to help make the analysis much faster and alleviate the bioinformatics bottleneck at the sequencing labs.