A gold standard to improve cancer genome analysis

Oncologists are increasingly using information obtained from investigations of the tumor genome in order to find individualized therapies for patients. They specifically search the hereditary information of cancer cells for mutations that drive malignant growth. By now, targeted drugs against many of these cancer-typical cellular alterations have become available.

However, how precisely and reliably do the numerous laboratories that specialize in this search around the globe identify individual cancer mutations? And how does the quality and type of sequencing influence results? A team of experts collaborating within the International Cancer Genome Consortium (ICGC) launched an interlaboratory test to find this out. They distributed the DNA of a tumor to five ICGC laboratories and compared the quality of the resulting sequencing data records. The data record that had the highest quality was subsequently sent out to another 17 ICGC institutes for bioinformatic evaluation.

The investigators found significant variations both in sequencing and evaluation results in some of the cases. Only 40 percent out of one thousand small mutations, which each affected the exchange of only a single DNA base, were identified uniformly by all participating teams. The outcome for small DNA losses and insertions was even less favorable: Only a single one out of 337 of these genomic changes was identified by all of the centers.

The team of experts led by Ivo Gut from the Spanish National Center for Genome Analysis* and Roland Eils from the German Cancer Research Center therefore devised measures to improve this situation.

The DNA sequence from the circular experiment, which the participating ICGC labs have by now sequenced up to 300 times and analyzed with almost unprecedented precision, has now been made available for download. It serves as a kind of gold standard. Laboratories that start out in the field of genome analysis can use this data record as a basis to check whether the bioinformatic methods that they are using are capable of detecting all mutations concealed therein. In addition, the team developed evaluation guidelines that stipulate, among others, threshold values for detecting a particular mutation.

“Since tumor genome analysis is becoming increasingly common in cancer medicine, rigorous quality control is necessary – like in any other diagnostic method,” says David Jones. “After all, whether or not a patient survives may depend on the detection of a particular mutation that can be treated efficiently with a drug that is already available.” Ivo Buchhalter from the DKFZ, who is one of the first authors of the present study, is very pleased: “Several groups have already been able to substantially improve their results thanks to our measures.”

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