26 ms·

How Big Data Can Help Fight Cancer

66 points by DarkContinent 10y ago

joshenglish 10y ago
It’s crazy how many ailments are now being heavily data mined.
- j6m8 10y ago
  I think it's a little crazy how many are not.
aradhakrishnan 10y ago
As 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/ https://www.genome.gov/sequencingcosts/ [2] http://www.nature.com/nrg/journal/v17/n2/abs/nrg.2015.17.html http://www.nature.com/nrg/journal/v17/n2/abs/nrg.2015.17.htm...
- mbreese 10y ago
  > 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.
- chrisamiller 10y ago
  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.
  - jfarlow 10y ago
    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'.
    - nonbel 10y ago
      >"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?
nonbel 10y ago
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 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 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 https://www.ncbi.nlm.nih.gov/pubmed/17046232 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4443636/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4443636/ https://www.ncbi.nlm.nih.gov/pubmed/10687734 https://www.ncbi.nlm.nih.gov/pubmed/10687734
- deleted 10y ago
  [deleted]
pasbesoin 10y ago
I 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".
dkural 10y ago
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.
digitalzombie 10y ago
This is my peers thesis. I may be doing this type or along this line. I think the problem is big data, in a non statistican sense, where you cannot get large amount of observation from patient. Either because of legal loop holes and/or the cost of getting enough patients for experiment and trials. This is for trials. Even with phase III clinical trial it's less than 200 obs. This is not big data in the non statistician world. In our world big data mean tons of predictors. Medical data is usually high dimensional, less obs but tons of predictors, more columns than rows. Also hospitals are wary of giving out data, either because of legal issues or because they know it's valuable so they don't want to share it. These two issue is compound it on the fact that the infrastructure is not there to share the data in one spot, it's fragmented across many other databases with different schema and what not. But my peers and I have thesis involving cancer using genetic data. It's very promising, one of the recent thesis is about base on genetic data if the patient should take the surgery route or the chemo route and the model had a 80% accuracy rate and nice sensitivity rate (forgot what it was). The prediction is survival rate. I also saw other comment about using genetic data against them. I think this is FUD because we have a law in place, GINA.
Gatsky 10y ago
Sequencing DNA in archival specimens like Foundation has significant limitations. The ability to predict drug responses from this data alone seems quite limited. For example, the most common alteration, loss of function variants in TP53, is not at present druggable. Additionally, one of the greatest revolutions in cancer therapy - immunotherapy - does not have a great genomic-based predictive biomarker (Foundation Medicine has created a surrogate test using mutation burden, but this is not really good enough, and not validated prospectively). So DNA sequencing is only part of the story. Some tumours in particular seem driven by epigenetic changes, or structural variants that Foundation cannot detect. Sequencing is in a place where it has reached technical maturity, and has collided with big data hype. But in reality, the benefit to patients will be incremental. There is at present zero good quality evidence that panel based genomic screening like Foundation Medicine provides improves patient outcomes as a general strategy.

66 points by DarkContinent 10y ago

joshenglish 10y ago

It’s crazy how many ailments are now being heavily data mined.

j6m8 10y ago
I think it's a little crazy how many are not.

aradhakrishnan 10y ago

As 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/ https://www.genome.gov/sequencingcosts/ [2] http://www.nature.com/nrg/journal/v17/n2/abs/nrg.2015.17.html http://www.nature.com/nrg/journal/v17/n2/abs/nrg.2015.17.htm...

mbreese 10y ago
> 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.
chrisamiller 10y ago
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.
- jfarlow 10y ago
  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'.
  - nonbel 10y ago
    >"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?

nonbel 10y ago

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 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 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 https://www.ncbi.nlm.nih.gov/pubmed/17046232 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4443636/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4443636/ https://www.ncbi.nlm.nih.gov/pubmed/10687734 https://www.ncbi.nlm.nih.gov/pubmed/10687734

deleted 10y ago
[deleted]

pasbesoin 10y ago

I 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".

dkural 10y ago

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.

digitalzombie 10y ago

This is my peers thesis. I may be doing this type or along this line. I think the problem is big data, in a non statistican sense, where you cannot get large amount of observation from patient. Either because of legal loop holes and/or the cost of getting enough patients for experiment and trials. This is for trials. Even with phase III clinical trial it's less than 200 obs. This is not big data in the non statistician world. In our world big data mean tons of predictors. Medical data is usually high dimensional, less obs but tons of predictors, more columns than rows. Also hospitals are wary of giving out data, either because of legal issues or because they know it's valuable so they don't want to share it. These two issue is compound it on the fact that the infrastructure is not there to share the data in one spot, it's fragmented across many other databases with different schema and what not. But my peers and I have thesis involving cancer using genetic data. It's very promising, one of the recent thesis is about base on genetic data if the patient should take the surgery route or the chemo route and the model had a 80% accuracy rate and nice sensitivity rate (forgot what it was). The prediction is survival rate. I also saw other comment about using genetic data against them. I think this is FUD because we have a law in place, GINA.

Gatsky 10y ago

Sequencing DNA in archival specimens like Foundation has significant limitations. The ability to predict drug responses from this data alone seems quite limited. For example, the most common alteration, loss of function variants in TP53, is not at present druggable. Additionally, one of the greatest revolutions in cancer therapy - immunotherapy - does not have a great genomic-based predictive biomarker (Foundation Medicine has created a surrogate test using mutation burden, but this is not really good enough, and not validated prospectively). So DNA sequencing is only part of the story. Some tumours in particular seem driven by epigenetic changes, or structural variants that Foundation cannot detect. Sequencing is in a place where it has reached technical maturity, and has collided with big data hype. But in reality, the benefit to patients will be incremental. There is at present zero good quality evidence that panel based genomic screening like Foundation Medicine provides improves patient outcomes as a general strategy.