June 22, 2022
Traditional Cancer Subtyping
Cancers are traditionally named based on the tissue and cell type in the body they originate from. However, today we understand that a more useful way to stratify cancer types is by their molecular composition and biomarkers to predict treatment response.
This includes mutations, immune cell infiltration, response to targeted therapies as well as other features of the tumor microenvironment. Though gaining traction in bioinformatics and genomics studies, this method of cancer subtype delineation is still in development and requires continued study to be robust enough for mainstream use. Classic examples in this area include HER2-positive breast cancer and PD-1-positive non-small cell lung carcinoma, but there is much room for improvement to fine-tune these predictive biomarkers to be more specific and thus more effective.
Multi-Omic Biomarkers for Breast Cancer
A recent paper in Cancer Cell by Wolf et. al. has leveraged pre-treatment gene expression and protein levels and post-treatment response data from ten different drugs used in breast cancer treatment to stratify breast cancer types by their responses to these drugs. Following the initial stratification, the researchers wanted to then identify potential biomarkers to match a cancer type to the treatment most likely to be effective.
The team tested over 11 combinations of data types to subtype the cancers to identify treatment-subtype groups that maximize the pathologic complete response rate. Put simply, they wanted to find the combination of data that produces the most robust biomarker to predict the highest rate of complete treatment success.
The best-performing data combinations included immune cell data, DNA mismatch repair markers, and HER2 phenotypes. This multi-omic approach combines the success of immune cell-based biomarkers and therapies, DNA mismatch biomarkers, and the HER2 biomarker to raise the response rate of the predicted patient cohorts to 63% effective from 51% using HER2 or HR alone.
Outsourcing Bioinformatics Analysis
Multi-omics is an exciting and fast-moving research area, integrating genomic, transcriptomic, proteomic, and more data types to yield complex biological insights. However, two big challenges emerge from the growth in this space: data storage and data analysis. Our experts at Bridge Informatics can help you build custom cloud-based infrastructure to safely and securely store your data, and build bioinformatic pipelines to reproducibly analyze complex datasets and gain actionable biological insights. Book a free discovery call with us now to see how we can help you with your project needs.
Jane Cook, Journalist & Content Writer, Bridge Informatics
Jane is a Content Writer at Bridge Informatics, a professional services firm that helps biotech customers implement advanced techniques in the management and analysis of genomic data. Bridge Informatics focuses on data mining, machine learning, and various bioinformatic techniques to discover biomarkers and companion diagnostics. If you’re interested in reaching out, please email [email protected] or [email protected].