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.<p>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.<p>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.<p>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.<p>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.
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.<p>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.<p>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.<p>With the ongoing attacks on the Affordable Care Act, I have the distinct feeling of traveling back in time.<p>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".
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:<p><i>"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)."</i>
<a href="https://www.ncbi.nlm.nih.gov/pubmed/18283116" rel="nofollow">https://www.ncbi.nlm.nih.gov/pubmed/18283116</a><p>Many people claim that aneuploidy is found in nearly all cancer cells:<p><a href="https://www.ncbi.nlm.nih.gov/pubmed/17046232" rel="nofollow">https://www.ncbi.nlm.nih.gov/pubmed/17046232</a><p><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4443636/" rel="nofollow">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4443636/</a><p><a href="https://www.ncbi.nlm.nih.gov/pubmed/10687734" rel="nofollow">https://www.ncbi.nlm.nih.gov/pubmed/10687734</a>
This is my peers thesis. I may be doing this type or along this line.<p>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.<p>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.<p>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.<p>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.<p>I also saw other comment about using genetic data against them. I think this is FUD because we have a law in place, GINA.
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.<p>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).<p>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.<p>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.
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.<p>[1] <a href="https://www.genome.gov/sequencingcosts/" rel="nofollow">https://www.genome.gov/sequencingcosts/</a><p>[2] <a href="http://www.nature.com/nrg/journal/v17/n2/abs/nrg.2015.17.html" rel="nofollow">http://www.nature.com/nrg/journal/v17/n2/abs/nrg.2015.17.htm...</a>