Genomic Test Predicts Survival Rates after Cardiac Surgery

The team collected blood samples one day before surgery and took clinical data both before surgery and eight days afterward. The patients were classified into two groups depending on their level of organ function. They calculated two validated and commonly used composite organ dysfunction (OD) scores, SOFA and MELD-XI. The SOFA score is a validated and widely accepted measure that rates degree of organ failure across six major organ systems (cardiovascular, respiratory, neurological, renal, hepatic, and coagulation).

Peripheral blood mononuclear cells (PBMC) were collected and processed using next-generation RNA sequencing transcriptome analysis. The quality of the total RNA was assessed using NanoDrop ND-1000 spectrophotometer and Agilent 2100 Bioanalyzer. Library construction consists of random fragmentation of the polyA mRNA, followed by cDNA production using random polymers and the cDNA libraries were quantitated. After RNA extraction, quantification and quality assessment, total mRNA was amplified and sequenced on the whole-genome Illumina HiSeq 2500.

The patients were classified into two groups depending on their level of organ function and 17 patients showed improvement and 12 did not. One year later, 88% of the people in the "improved" group were still alive, compared with 27% in the other group. The scientists identified a set of 28 genes from the pre-surgery blood samples that predicted how well the patients' organ function would recover shortly after surgery and of those 28 genes, 12 helped predict whether organ function would improve after surgery and in forecasting whether the patients would live at least a year after the surgery. The technology used in this study is called MyLeukoMap and builds on the methods used in developing FDA-approved AlloMap, which is used to diagnose organ rejection in heart transplant recipients.

The authors concluded that that preoperative peripheral blood mononuclear cell (PBMC) gene expression profiles (GEP) can predict early postoperative improvement or non-improvement in patients undergoing MCS implantation. They believe this information may be useful in developing prognostic biomarkers. The study was published on December 13, 2017, in the journal Pubic Library of Science ONE.

Related Links:
David Geffen School of Medicine