3 ms·

Here's their white-paper on their testing methodology: https://s3.amazonaws.com/color-static-prod/pdfs/validationWhitePaper.pdf https://s3.amazonaws.com/color-s

Here's their white-paper on their testing methodology: https://s3.amazonaws.com/color-static-prod/pdfs/validationWhitePaper.pdf https://s3.amazonaws.com/color-static-prod/pdfs/validationWh... "Color has developed a next-generation sequencing based test for hereditary cancer. This test analyzes 30 genes associated with increased risk to develop breast, ovarian, colorectal, melanoma, pancreatic, prostate, stomach, and uterine cancers... The assay has a high degree of analytical validity for the detection of single nucleotide variants, small insertions and deletions (indels), and larger deletions and duplications (copy number variants, or CNVs)." So not micro-arrays like 23andMe that test for SNPs ($99?), but not full genome sequencing either (~$1,000?); but specific sequencing for the sites of these genomic regions of those 30 genes. Wet Lab sequencing method: "Specifically, it includes target enrichment by Agilent’s SureSelect method (v1.7) and sequencing by Illumina’s NextSeq 500 (paired-end 150bp, High Output kit)"; Unanswered question, are they doing the sequencing in-house or using a facility somewhere else? Computational method: "The bioinformatics pipeline was built using well-established algorithms such as BWA-MEM, SAMtools, Picard and GATK. CNVs are detected using dedicated internally developed algorithms for read depth analysis and split-read alignment detection." So basically perform the standard genome assembly, alignment with human reference genome of your partial assembly, and then identify what variant of these 30 genes the patient sample has; plus a special sauce for counting the number of specific bp repeats, due to in-del events, this is not something I am not too familiar, but presumably the number of a specific k-mer repeats you have in these genes of interest might correlate to a specific type of cancer? (would love to hear someone who is an expert in this field their opinion). "These [30] genes are APC, ATM, BAP1, BARD1, BMPR1A, BRCA1,BRCA2, BRIP1, CDH1, CDK4, CDKN2A (p14ARF and p16INK4a), CHEK2, EPCAM, GREM1, MITF, MLH1, MSH2, MSH6, MUTYH, NBN, PALB2, PMS2, POLD1, POLE, PTEN, RAD51C, RAD51D, SMAD4, STK11, and TP53". (You can follow up by searching them here, e.g., http://www.genecards.org/cgi-bin/carddisp.pl?gene=BRCA2&keywords=BRCA2 http://www.genecards.org/cgi-bin/carddisp.pl?gene=BRCA2&keyw...). Also interesting to note, since it's clinical, each of their test has to be verified by a certified "genetics counselor" and also meet lots of clinical standards.

daemonk 10y ago
This seems very standard to me. The only thing that can't be reproduced immediately is the CNV algorithm?
lunula 10y ago
To make clinical tests it is necessary to focus on single genes or loci. Whole genome is not controlled enough to meet clinical standards.
jrm5100 10y ago
plus a special sauce for counting the number of specific bp repeats, due to in-del events, this is not something I am not too familiar, but presumably the number of a specific k-mer repeats you have in these genes of interest might correlate to a specific type of cancer? (would love to hear someone who is an expert in this field their opinion). "Copy number variant" refers to larger deletions and duplications that can occur in the genome. There isn't some specific cutoff for size, but some examples in these kinds of genes would be an entire exon or gene. There are countless studies that find correlations between specific variants or CNVs and risk of cancers. Standard variant detection is pretty straightforward. CNVs are harder because they are longer (several hundred to several thousand base pairs) than the raw data (150 to 250 bp for Illumina)- you don't get single reads that span the entire variant. You have to normalize then look for differences in coverage, or look for split reads (where the read is aligned on the border of one of these CNVs). This kind of funding baffles me because they don't seem to be proposing anything new at all (maybe slightly better CNV detection?) and there are already lots of labs/companies doing this kind of testing. Maybe they are working on being very efficient to offer a better price.
- noname123 10y ago
  Thanks for your detailed explanation. Just out of curiosity and to follow-up, presumably this is a example of the list of detected CNVs in a TCGA Breast Cancer data-set you're referring to: http://cancer.sanger.ac.uk/cosmic/gene/analysis?ln=BRCA1#cnv_t http://cancer.sanger.ac.uk/cosmic/gene/analysis?ln=BRCA1#cnv... According to Sanger (or maybe TCGA?), a gain is when a genomic region (for a diploid) has more than five absolute copies of this region and a loss is when the genomic region has no reads ((http://cancer.sanger.ac.uk/cosmic/help/cnv/overview http://cancer.sanger.ac.uk/cosmic/help/cnv/overview), where the copy number is perhaps determined by that normalized distribution of read coverage across the reference genome? (http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-S11-S1#Fig1 http://bmcbioinformatics.biomedcentral.com/articles/10.1186/...). This is for CNVs that are longer than the 150-200bp Illumina fragments (Fig1c. Read Depth method, e.g., exome#3 looks like it has two absolute copies vs exome #1 and #2) Then for small CNVs that perhaps span that 150-200bp fragment, we use the split read method to filter for incompletely mapped reads that are only aligned on the edges to the reference. This implies that there was a duplication event that expanded that sequence? (Fig 1b. Split Read method). Presumably, the pipeline would determine the CNV sites in a specific patient sample, then cross-reference with the TCGA CNV data-set and come up with correlation score of how much those CNVs sites match with consensus CNVs in the cancer data-set? Thanks again for your detailed breakdown.

daemonk 10y ago

This seems very standard to me. The only thing that can't be reproduced immediately is the CNV algorithm?

lunula 10y ago

To make clinical tests it is necessary to focus on single genes or loci. Whole genome is not controlled enough to meet clinical standards.

jrm5100 10y ago

plus a special sauce for counting the number of specific bp repeats, due to in-del events, this is not something I am not too familiar, but presumably the number of a specific k-mer repeats you have in these genes of interest might correlate to a specific type of cancer? (would love to hear someone who is an expert in this field their opinion). "Copy number variant" refers to larger deletions and duplications that can occur in the genome. There isn't some specific cutoff for size, but some examples in these kinds of genes would be an entire exon or gene. There are countless studies that find correlations between specific variants or CNVs and risk of cancers. Standard variant detection is pretty straightforward. CNVs are harder because they are longer (several hundred to several thousand base pairs) than the raw data (150 to 250 bp for Illumina)- you don't get single reads that span the entire variant. You have to normalize then look for differences in coverage, or look for split reads (where the read is aligned on the border of one of these CNVs). This kind of funding baffles me because they don't seem to be proposing anything new at all (maybe slightly better CNV detection?) and there are already lots of labs/companies doing this kind of testing. Maybe they are working on being very efficient to offer a better price.

noname123 10y ago
Thanks for your detailed explanation. Just out of curiosity and to follow-up, presumably this is a example of the list of detected CNVs in a TCGA Breast Cancer data-set you're referring to: http://cancer.sanger.ac.uk/cosmic/gene/analysis?ln=BRCA1#cnv_t http://cancer.sanger.ac.uk/cosmic/gene/analysis?ln=BRCA1#cnv... According to Sanger (or maybe TCGA?), a gain is when a genomic region (for a diploid) has more than five absolute copies of this region and a loss is when the genomic region has no reads ((http://cancer.sanger.ac.uk/cosmic/help/cnv/overview http://cancer.sanger.ac.uk/cosmic/help/cnv/overview), where the copy number is perhaps determined by that normalized distribution of read coverage across the reference genome? (http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-S11-S1#Fig1 http://bmcbioinformatics.biomedcentral.com/articles/10.1186/...). This is for CNVs that are longer than the 150-200bp Illumina fragments (Fig1c. Read Depth method, e.g., exome#3 looks like it has two absolute copies vs exome #1 and #2) Then for small CNVs that perhaps span that 150-200bp fragment, we use the split read method to filter for incompletely mapped reads that are only aligned on the edges to the reference. This implies that there was a duplication event that expanded that sequence? (Fig 1b. Split Read method). Presumably, the pipeline would determine the CNV sites in a specific patient sample, then cross-reference with the TCGA CNV data-set and come up with correlation score of how much those CNVs sites match with consensus CNVs in the cancer data-set? Thanks again for your detailed breakdown.