Single Cell Tracking Explains Why Certain Cancer Cells Survive Treatments

A comprehensive multi-cancer study from researchers at The University of Texas MD Anderson Cancer Center has revealed that cancer cells within tumors are genetically diverse, yet all carry the same core genetic changes that can be traced back to a common ancestral cell, providing a single-cell view of how tumors adapt, survive and diversify. Understanding this helps explain why some cancer cells manage to survive treatments, paving the way for more tailored diagnostic and therapeutic strategies.

The study, published in Cancer Discovery, was led by Nicholas Navin, Ph.D., chair of Systems Biology. The research shows that cancer cells do not evolve slowly over time but, rather, grow through sudden bursts of rapid genetic changes that include copy number alterations (CNAs) – gains or losses of entire sections of DNA. This creates a family tree of distinct new subpopulations that can influence tumor aggressiveness, metastasis and treatment response.

Our findings provide the clearest views to date of how cancers originate and evolve at the single-cell level. By revealing both the shared early genetic events and the bursts that drive ongoing diversity, we now have a roadmap for developing smarter clinical diagnostic and treatment strategies to improve patient outcomes."

Nicholas Navin, Chair of Systems Biology, University of Texas MD Anderson Cancer Center

How Does Understanding Tumor Evolution Help Patients?

Aneuploidy – when a cell contains an abnormal number of chromosomes – is a hallmark of tumors, but few studies have closely examined these genetic differences and how they evolve within tumors at a single-cell level.

Historically, cancer genomes have been analyzed using bulk sequencing methods, which blend cells together and mask minor cell populations. However, tumors are not uniform, and a single biopsy may miss important cell subpopulations, leading to potentially ineffective treatments.

Understanding this complexity helps guide better predictive diagnostic and treatment strategies that are more likely to work for patients with certain genomic features, such as high genetic diversity in genes that are used as biomarkers. Single-cell sequencing allows researchers to answer the pressing question of how many cells in a normal organ give rise to cancer.

These findings could help lead to better prognostics by understanding which patients are more likely to have aggressive disease, metastasis, or therapeutic resistance based on the diversity of the cancer cells in their tumors.

How Did the Researchers Investigate Tumor Diversity and What Does it Mean to Have More Genetic Diversity?

The researchers analyzed 94 tumors across seven cancer types (bladder, breast, colon, glioblastoma, kidney, lung, and ovarian) and used single-cell sequencing on over 62,000 aneuploid cells, along with whole-exome and RNA sequencing of patient samples.

They found that tumor cells share early-stage CNAs, meaning they have a common single ancestral origin starting from just one cell in the tissue. Key features, such as TP53 mutations, genome doubling and elevated CNA burden, were frequent in many cancers and were linked to subclonal diversity, chromosome loss and more aggressive disease.

The researchers also found that tumors with higher genetic diversity were more likely to form distinct spatial regions within the tumor, allowing researchers to create a Punctuated Evolution Index (PEI) to quantify the evolutionary dynamics of CNAs. PEI measures the degree to which these gains happen as a sudden punctuated burst at a given point in time or evolve gradually over time. Tumors with high PEI tend to acquire key genetic drivers rapidly and were associated with poorer clinical outcomes and advanced stages of disease.

The researchers say these findings provide a foundational framework for future larger-scale investigations that account for intratumoral diversity, which can include more patients and other cancer types to better understand tumor evolution. This, in turn, could lead to more accurate diagnosis and personalized treatment to improve clinical care and outcomes.

This work was supported by the National Institutes of Health, the National Cancer Institute, the Cancer Prevention and Research Institute of Texas (CPRIT), the Dana-Farber Cancer Institute Center for Cancer Evolution, the American Association for the Advancement of Science, the Damon Runyon-Rachleff Innovation Award, the Andrew Sabin Family Fellowship, the Jack and Beverly Randall Prize for Excellence in Cancer Research, the Vivian L. Smith Foundation, the Andrew Sowell-Wade Huggins Scholarship in Cancer Research, and the Cancer Research UK Brain Tumour Centres of Excellence. A full list of collaborating authors and their disclosures can be found with the full paper in Cancer Discovery.