Evaluation of Synthetic Lethality in Guiding Cancer Therapeutics

Background: Synthetic lethality refers to when mutations in two or more genes in the same cell lead to cell death, while a mutation in just one of these genes does not. This principle lets us find and attack such 'partner' genes in cancer, specifically destroying tumour cells while not harming healthy cells. Therefore, synthetic lethality represents a critical mechanism in the pursuit of targeted cancer therapies. While there is growing interest in synthetic lethal biomarkers that can predict the effectiveness of drug treatment based on the presence of these gene targets, research and clinical adoption is limited by the availability of computational resources to detect these biomarkers and the reliability of published biomarkers. Our project aims to address these challenges by creating ready-to-use tools for the scientific community to leverage and by rigorously evaluating published biomarkers in external datasets to ascertain their clinical relevance.

Methods: We have created a computational framework for identifying synthetic lethal biomarkers from patient data. This framework will be optimized for use on MOHCCN cohorts to detect synthetic lethal interactions and their associations with treatment outcome. We will evaluate existing and newly discovered biomarkers across MOHCCN clinical trials using various statistical approaches to measure the consistency of performance and obtain confidence scores that reflect their potential clinical effectiveness.

Significance: Our research has the potential to revolutionize biomarker discovery and patient selection for treatment. This study will further our understanding of synthetic lethality and how it can be harnessed to enhance cancer management. By ensuring transparency in our methods and sharing our findings on public databases, we hope to support future research of synthetic lethal biomarkers and their eventual translation into clinical applications to improve treatment outcomes of cancer patients.

Quotes

 “I am honoured and thrilled to receive the MOHCCN HI&DS Award, which will support my efforts in discovering and validating synthetic lethal biomarkers and candidate targets for cancer therapy. The MOHCCN patient cohorts offers an unprecedented abundance of clinical data for large-scale synthetic lethality investigations. This study promises to uncover valuable insights into the clinical applications of synthetic lethality and guide future synthetic lethal drug development for personalized medicine.”

• Julia Nguyen, HI&DS Awardee
  

 “Exploring synthetic lethality to uncover biomarkers for precision oncology is a promising direction of research. However, inconsistencies in published synthetic lethality predictive performance and limitations in existing software demands further interrogation and validation of these biomarkers. Julia’s study supported by a MOHCCN HI&DS Award will address these challenges by developing new computational tools and predictive methods to identify robust synthetic lethal biomarkers from MOHCCN cohorts for research and clinical use.”

• Dr. Benjamin Haibe-Kains, mentor