April 4, 2022
Prognosis vs Treatment
How linked are the research strategies for cancer prognosis and cancer treatment? While these two areas go hand in hand in the clinic, it turns out that they may be dictated by different cellular markers (also known as biomarkers).
Cancer treatment research tries to identify aspects of cell biology unique to cancerous tumors and ways to target those without affecting healthy, normal cells of the body. This has led to the discoveries of many oncogenes, or genes that are commonly mutated in cancers, and drive cancer initiation and progression, as well as targeted therapies.
Are Therapeutic Targets also Good Prognostic Biomarkers?
Targeted therapies, like anti-PD-1 or HER2 antibodies, have been extremely successful tools in improving the specificity of cancer treatments, and thus improving outcomes. But have there been similar improvements in the ability to predict cancer’s severity and progression?
It turns out that the classical cancer markers, including oncogenes and cancer drug targets, are rarely effective as prognostic tools. A recent study in Cell Reports by Smith and Sheltzer from Yale Medical School leverages modern genomic analysis tools to uncover better prognostic biomarkers for cancer.
Complex Genomic Analysis Uncovers New Prognostic Markers
From an atlas of nearly 11,000 patients encompassing 33 cancer types, the researchers performed a comprehensive genomic analysis. RNA-seq for transcriptomics, exome sequencing, and more paired with clinical data yielded interesting results: the genes implicated most heavily in predicting cancer prognosis are typical “housekeeping” genes, responsible for normal maintenance of the cell cycle and homeostasis of the cell.
Housekeeping genes make extremely poor cancer drug targets because they are found in almost every cell in the body, but this conversely makes them good biomarkers for cancer prognosis due to their abundance.
The analysis from this paper highlights the massive utility of genomic analysis techniques to establish comprehensive databases for clinicians and researchers. This helps them to better integrate genomic data with clinical data and get to the holy grail, to predict cancer patient outcomes.
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Processing and analyzing scRNA-seq data is a complex computational task. Outsourcing your bioinformatic work can save time, eliminate common challenges, and improve reproducibility. Book a free discovery call with us at Bridge Informatics to discuss 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].