Identifying Fraud, Waste, and Abuse in Genetic Testing

Dr. Tina Hernandez-Boussard and her team of researchers from the Stanford Center for Biomedical Informatics Research (BMIR) at Stanford identify and examine in a recently published white paper, the potential for fraud, waste, and abuse in genetic testing.

Genetic testing has rapidly grown over the past decade. Collectively, the healthcare industry faces billions in annual costs for these tests. This emerging area of medicine is susceptible to wide-scale fraud, waste, and abuse that increases healthcare spending and negatively affects the integrity and quality of the current healthcare system.

This white paper was developed in partnership with the Healthcare Fraud Prevention Partnership (HFPP), a public-private partnership of healthcare payers and allied organizations that seeks to identify waste, fraud, and abuse across the healthcare sector, and the Stanford University School of Medicine.

Researcher
Tina Hernandez-Boussard,  Ph.D., is Associate Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science within Stanford’s Center for Biomedical Informatics Research division.
 

Dr. Olivier Gevaert led a study on “Whole-slide Images Reflect DNA Methylation Patterns of Human Tumors” published by npj Genomic Medicine showing an intriguing connection between epigenomic signatures and whole slide images of tumor tissues discovered using machine learning. Epigenomic patterns, in particular DNA methylation signatures, are changes on top of the DNA. DNA methylation changes do not change the DNA sequence, but instead, they affect how cells activate genes. In this study we show that these epigenomic patterns are reflected in whole slide images of tissues that are routinely captured by pathologists examining tumors. The authors concluded that their results provide new insights into the link between histopathological whole slide images and epigenomic signatures in human cancers. This work underlines the potential of associations between tumor tissue as visualized on whole slide images with the underlying DNA methylation states, providing new insights and understanding of how tumors develop at multiple scales. This may provide benefits in the future for pathologists to more routinely use available whole slide images as a proxy for determining the epigenomic make-up of human tumors.

Abstract
DNA methylation is an important epigenetic mechanism regulating gene expression and its role in carcino-genesis has been extensively studied. High-throughput DNA methylation assays have been used broadly in cancer research. Histopathology images are commonly obtained in cancer treatment, given that tissue sampling remains the clinical gold-standard for diagnosis.
In this work, we investigate the interaction between cancer histopathology images and DNA methylation profiles to provide a better understanding of tumor pathobiology at the epigenetic level. We demonstrate that classical machine-learning algorithms can associate the DNA methylation profiles of cancer samples with morphometric features extracted from whole-slide images.
Furthermore, grouping the genes into methylation clusters greatly improves the performance of the models. The well-predicted genes are enriched in key pathways in carcinogenesis including hypoxia in glioma and angiogenesis in renal cell carcinoma. Our results provide new insights into the link between histopathological and molecular data.
In addition to Dr. Gevaert, the team further involved the first author, Dr. Hong Zheng, a postdoc in the Gevaert Lab; Mr. Alexandre Momeni and Pierre-Louis Cedoz, two students in the lab; and Dr. Hannes Vogel, a professor in the Department of Pathology at Stanford.
 

Dr. Olivier Gevaert is the lead author of the article “Benchmark of Long non-coding RNA Quantification for RNA Sequencing of Cancer Samples,” which was published in December 2019. The authors (Hong Zheng, Kevin Brennan, Mikel Hernaez and Olivier Gevaert) concluded that “considering the consistency with ground truth and computational resources, pseudoalignment methods Kallisto or Salmon in combination with full transcriptome annotation is our recommended strategy for RNA-Seq analysis for lncRNAs.

Researcher
Olivier Gevaert, Ph.D., is Assistant Professor of Medicine (Biomedical informatics) and of Biomedical Data Science within Stanford’s Center for Biomedical Informatics Research division.