Summary
How do we study the earliest stages of cancer development? In cancer types where premalignant tissue is easily accessed, analyzing tissue samples along the timeline of cancer progression provides a clear genetic picture. However, many cancer types are not identified or accessible until they present as an end-stage tumor. Using a suite of computational analysis tools on whole exome data, a new study identifies a way to infer early genetic events in a cancer type using a late-stage tumor sample.
How Does Cancer Begin?
For some cancer subtypes, the progression of early genetic events that transform a cell from normal to cancerous is well-defined. This comprehensive understanding of early-stage disease allows researchers to create accurate models of these cancers during their preliminary stages. These models are essential for studying new therapeutic interventions targeting the initiation of cancer progression and to search for ways to detect cancer earlier.
Understanding the genetic history of a cancer also allows researchers to infer the trajectory of disease progression from patient-derived samples, which could lead to improved diagnostic tools. Our current understanding of cancer progression stems from sampling premalignant and malignant tissue at various stages of tumorigenesis. However, for cancer types with inaccessible premalignant tissue, like the brain, or premalignant tissue that is undetectable, the genetic events that drive their initiation and progression are still largely speculation.
Inferring Early Genetic Progression from Late Stage Tumor Samples
In a recent paper in Nature Cancer, Leshchiner et al. describe a method to infer the order of preceding genetic events from whole exome sequencing (WES) data of a late stage tumor sample. This is a remarkable improvement on the existing strategy of sampling precancerous tissue, as it provides the full genetic history of a known end-stage tumor.
The authors validated their model on WES data of head and neck squamous cell carcinoma (HNSCC), demonstrating that their suite of tools, called PhylogicNDT, could reconstruct the genetic progression of the tumor comparably to the well-established, experimentally determined progression of HNSCC.
Following validation, the authors applied their method to HPV+ HNSCC, an increasingly prevalent variation of this cancer type, for which little is known about its unique genetic progression. The authors successfully identified different types of chromosome number abnormalities, and could trace the timing of the cancer’s main drivers and HPV integration to many years before diagnosis.
Why is Understanding Early Tumorigenesis So Important?
This method can be applied across cancer types to decipher the mechanisms of cancer initiation in types of cancer with rare or otherwise inaccessible premalignant tissue. Findings from understanding early cancer development can be applied to find new drug targets, improve early disease detection, and guide therapeutic strategy.
It is well-known that cancers are their most vulnerable to treatment in their earliest stages, which is why many current research strategies are pivoting to improving cancer prevention and early detection. This method adds another tool to the toolbox for cancer researchers to develop models to better understand this incredibly complex disease.
Outsourcing Bioinformatics Analysis: How Bridge Informatics Can Help
The generation, storage and analysis of biological data is faster and more accessible than ever before. From pipeline development and software engineering to deploying your existing bioinformatic tools, Bridge Informatics can help you on every step of your research journey.
As experts across data types from leading sequencing platforms, we can help you tackle the challenging computational tasks of storing, analyzing and interpreting genomic and transcriptomic data. Bridge Informatics’ bioinformaticians are trained bench biologists, so they understand the biological questions driving your computational analysis. Click here to schedule a free introductory call with a member of our team.
Jane Cook, Biochemist & Content Writer, Bridge Informatics
Jane Cook, the leading Content Writer for Bridge Informatics, has written over 100 articles on the latest topics and trends for the bioinformatics community. Jane’s broad and deep interdisciplinary molecular biology experience spans developing biochemistry assays to genomics. Prior to joining Bridge, Jane held research assistant roles in biochemistry research labs across a variety of therapeutic areas. While obtaining her B.A. in Biochemistry from Trinity College in Dublin, Ireland, Jane also studied journalism at New York University’s Arthur L. Carter Journalism Institute. As a native Texan, she embraces any challenge that comes her way. Jane hails from Dallas but returns to Ireland any and every chance she gets. If you’re interested in reaching out, please email [email protected] or [email protected].
