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Good Start Genetics Publishes Validation Data for Its MIP Sequencing Strategy; Earns NY Approval


This article was originally published June 24.

Good Start Genetics has published clinical validation data for its carrier screening test and detailed its targeted sequencing strategy, which makes use of molecular inversion probes, automation, and a novel assembly algorithm to achieve 99.98 percent analytical sensitivity and 99.9999 percent analytical specificity.

In addition, the firm said this week that it has received a clinical laboratory permit from the New York State Department of Health to provide its GoodStart Select carrier screening test to in vitro fertilization and other reproductive health practitioners in the state.

The privately held Cambridge, Mass.-based company last year launched a carrier screening test that originally tested for 14 disorders, but has since been expanded to 23, including all those that are recommended in guidelines set by the American Congress of Obstetricians & Gynecologists, the American College of Medical Genetics and Genomics, as well as societies supporting the Ashkenazi Jewish population (CSN 10/24/2012).

This month, it detailed its sequencing strategy and clinical validation in Genetics in Medicine.

In the study, researchers at Good Start performed targeted sequencing of 15 genes from 194 samples that were derived from cell lines.

The firm used molecular inversion probes to capture the coding regions of the 15 genes, a strategy that Gregory Porreca, Good Start's vice president of research and technology, told Clinical Sequencing News was designed to be not only extremely accurate, but also amenable to automation.

"When we started designing the workflow, the major considerations were around the ability to automate, simplicity, and the ability to capture every single base pair in the region," explained Porreca.

The probes were designed to capture the coding regions and well-characterized non-coding regions of 15 genes. Each probe consisted of 5' and 3' targeting arms, totaling 40 bases, and were designed to tile across the 130-base target in such a way that each genomic position was captured by multiple probes with orthogonal targeting arm sequences.

The samples were then barcoded, pooled, and sequenced on the Illumina HiSeq 2000 to an average per-base coverage of 2,399x. The researchers performed single-end sequencing on the HiSeq, sequencing each pooled library in seven lanes with the eighth lane used as a control.

Next, the researchers made use of a novel, proprietary algorithm — Genotyping by Assembly-Templated Alignment, or GATA — to do local assembly before calling variants. This algorithm helped increase the accuracy of variant calling.

Prior to GATA, the firm's researchers did "extensive simulation" using standard tools such as BWA and GATK, said Porreca. "There were specific mutations that we knew we wanted to pick up, and we simulated those," he said. But, "we found that there were certain mutations that that analysis approach would not detect."

Examining why those analysis tools were missing mutations, Porreca said they realized that one problem was due to the fact that those tools rely on first aligning the sequences to a reference genome. The researchers thought that it would be "better if you started with an assembly, rather than aligning directly to the reference, [in order] to get the benefit from the raw reads, and then align those [raw reads] to the assembly," Porecca said.

GATA first performs a local assembly from reads partitioned into subsets by targeting arm sequence and then does base quality- and coverage-informed genotyping, aligning the raw reads back to the assembly.

While the performance of GATA compared to the traditional analysis methods were comparable when looking at SNVs, it excelled in calling indels.

From 147 samples, GATA detected 23 unique insertions and deletions, nine (39 percent) of which were not detected by traditional analysis, including the "most common disease-causing mutation for Bloom syndrome in people of Ashkenazi Jewish descent," and several alleles in SMPD1, the gene associated with Niemann-Pick disease, according to the study's authors.

To assess the overall accuracy of Good Start's method, they compared it to Sanger sequencing.

Across 194 samples, both methods called over 6.9 megabases of sequence, with 1,220 discordant SNV calls. A manual review of the discordant calls found that nine were true discrepancies, corresponding to eight false positives and one false negative.

The method detected 4,000 true positive SNVs and over 6.9 million true negative SNVs.

Positive predictive value for common SNV calls was 100 percent, and 97 percent for novel SNV calls.

The one false negative occurred in a sample that exhibited "skewed allele ratios along the chromosome," the researchers wrote, "which should not commonly occur when testing for germline mutations in clinical specimens derived from whole blood."

The test demonstrated a false positive rate of around 1.1 per one million bases, which should be a "low burden for clinical testing and surpasses values previously reported," according to the study.

Porreca added that it was difficult to characterize the accuracy of the test on indel detection because the sample size in the study was too small, although he said that GATA appears to improve such detection.

Since doing the validation study, the firm has expanded its test from the 14 disorders examined in the Genetics in Medicine study to 23. Porreca said that the method while fundamentally the same, is still continually being improved upon. "The purpose for [the publication] was to show that it is possible to do next-gen sequencing in a very accurate way," he said.

Thus far, Porreca said that test adoption has been going well. Additionally, he said that the company is being reimbursed from insurance companies, and that while the reimbursement rate is still "variable" depending on the company, "most of our revenue comes from private payors."

Good Start brought in around $6 million in net revenue in 2012 and said in February that it is aiming to generate $25 million in revenue in 2013 (CSN 2/13/2013).

Moving forward, Porreca said that while the company's "first priority is to do a very good job servicing the in vitro fertilization market with its current test," it is also considering additional tests in that market, as well as looking beyond the IVF market into the larger fertility market.

Earlier this year, CEO Don Hardison told CSN that the company is looking to eventually market its test to Ob/Gyn practices and move into other areas of reproductive health.