Investigators at Rutgers Cancer Institute of New Jersey have developed a computational method that uncovers clinically relevant gene expression patterns in large cohorts of breast cancer patients.

This method, which is applicable to the analysis of all cancers, can robustly describe molecular processes that are associated with tumor subtypes and can identify predictive markers of response to treatment or disease recurrence.

Rutgers Cancer Institute of New Jersey research member Hossein Khiabanian, an assistant professor of pathology and laboratory medicine at Rutgers Robert Wood Johnson Medical School, is the senior author of the research.

Rutgers Cancer Institute associate research member Gyan Bhanot, professor of molecular biology and biochemistry and professor of physics in the School of Arts and Sciences at Rutgers University, and lead author Amartya Singh, a Rutgers Physics Department PhD candidate, have their work published in the June 18 online edition of GigaScience.

According to Khiabanian, this topic is important to study because changes in patterns of gene expression in tumor cells play a key role in cancer development, its progression, and therefore its treatment.

“Gene expression is the process of transcribing genomic information that is stored in the DNA to messenger RNA, which in turn is used for synthesizing a functioning protein,” said Khiabanian.

He added that investigating changes in gene expression helps researchers identify molecular biomarkers that are predictive of disease subtype and stage.

Khiabanian pointed out that high-throughput sequencing technologies, such as RNA sequencing, have enabled precise and unbiased quantification of transcription levels for thousands of genes in large cohorts of patients that include hundreds of samples.

“Clustering approaches that group together both genes and samples simultaneously in an unsupervised manner, known as biclustering, cannot only discover genes that are co-expressed aberrantly but also allow us to uncover associations between tumor samples with similar changes in their gene expression and clinical attributes such as survival and therapeutic response,” said Khiabanian.

Exploring the diversity of aberrant signatures, said Khiabanian, would enable the identification of potential biomarkers of clinical relevance that can further improve treatment outcomes for breast as well as other cancers.