Genetic risk score for ovarian cancer based on chromosomal-scale length variation.

Abstract

IntroductionTwin studies indicate that a substantial fraction of ovarian cancers should be predictable from genetic testing. Genetic risk scores can stratify women into different classes of risk. Higher risk women can be treated or screened for ovarian cancer, which should reduce overall death rates due to ovarian cancer. However, current ovarian cancer genetic risk scores, based on SNPs, do not work that well. We developed a genetic risk score based on structural variation, quantified by variations in the length of chromosomes. MethodsWe evaluated this genetic risk score using data collected by The Cancer Genome Atlas. From this dataset, we synthesized a dataset of 414 women who had ovarian serous carcinoma and 4225 women who had no form of ovarian cancer. We characterized each woman by 22 numbers, representing the length of each chromosome in their germ line DNA. We used a gradient boosting machine, a machine learning algorithm, to build a classifier that can predict whether a woman had been diagnosed with ovarian cancer in this dataset. ResultsThe genetic risk score based on chromosomal-scale length variation could stratify women such that the highest 20% had a 160x risk (95% confidence interval 50x-450x) compared to the lowest 20%. The genetic risk score we developed had an area under the curve of the receiver operating characteristic curve of 0.88 (estimated 95% confidence interval 0.86-0.91). ConclusionA genetic risk score based on chromosomal-scale length variation of germ line DNA provides an effective means of predicting whether or not a woman will develop ovarian cancer.