Research led by UCLA’s Jonsson Comprehensive Cancer Center is testing a cost-effective approach to early detection of cancer from cells without
Early detection remains key to successfully treating many cancers, and early detection via cell-free DNA (cfDNA) circulating in the bloodstream – the so-called “liquid biopsy” – has become a focus of research. But using this method to detect cancer in its early stages has been difficult due to the low concentrations of tumors in DNA blood fragments and the genetic diversity of cancer.
Today, researchers at UCLA’s Jonsson Comprehensive Cancer Center and collaborating organizations report positive results from an experimental cancer detection system that appears to have overcome these challenges in a novel and cost-effective way.
Their work, published in the journal Nature Communications, highlights an approach that offers more than 12 times the cost savings over conventional methods for sequencing the cDNA methylome, as well as a computer model for extracting information from it. DNA sequencing to facilitate early detection and diagnosis.
Cell-free DNA methylation has proven to be one of the most promising biomarkers for the early detection of cancer. However, the cDNA aberration signatures of various cancer types, subtypes, stages, and etiologies are heterogeneous, complicating the identification of suitable methylation markers for early detection. This is of particular concern given that currently available sample sizes are small relative to the diversity of disease and patient population (age, sex, ethnicity and comorbidity). cfDNA methylome profiling can address this challenge, as it preserves genome-wide epigenetic profiles of cancer abnormalities, allowing classification models to learn and exploit important new features as training cohorts are expanding, as well as extending their reach to more types of cancer. However, the conventional way of profiling the cell-free DNA methylome (whole genome bisulfite sequencing) is cost prohibitive for clinical use.
“Our method, cfMethyl-seq, makes cfDNA methylome sequencing a viable option for clinical use,” says Xianghong “Jasmine” Zhou, professor of pathology and laboratory medicine at UCLA and corresponding author of the study. “Despite the inherent challenges, our study shows tremendous potential for accurate early diagnosis of certain cancers from a single blood test.”
Zhou and his colleagues in his UCLA lab focus on precision medicine — using patients’ genomic information to develop more personalized and targeted treatments — and big biological data analytics to integrate complex data from of various platforms and modalities into practical methods that can be used in the clinical setting.
For this study, Zhou and his collaborators put their new approach to the test to see if it could accurately detect four commonly diagnosed cancers – colon, liver, lung and stomach cancer – and do so at a early stage.
The researchers took blood samples from 408 study participants and applied their methylome-based blood test, which can identify a wide range of markers for different types of cancer and their possible causes. Of these, 217 were cancer patients and 191 were control subjects without cancer. Samples were collected from UCLA hospitals or purchased from commercial labs for cross-validation. The researchers also performed cross validations, age validations and independent validations to avoid bias in the study.
Following collection and validation measurements, the researchers entered the data into their sophisticated computer model to measure its accuracy not only in detecting cancer, but also in the specific location of the tumor, referred to as “tissue of origin “.
Their model was 80.7% accurate at detecting cancers at all stages and around 74.5% accurate at detecting early-stage cancers – those at Stages I or II – with a specificity of just under 98. %. There was only one misclassified normal sample (false positive).
For accuracy of tissue of origin, the model correctly identified tumor location with an average accuracy of 89.1% for all cancer stages and around 85% in early-stage patients.
“The key to early cancer detection is to identify true cancer biomarkers, which requires a large cohort of training samples to cover cancer and population heterogeneity, especially for detection of pan -cancer. Our cfDNA methylome approach allows for the inclusion of new markers and better weighting of existing markers as training cohorts increase. Indeed, our data show that as training sample sizes increase, the detection power of our method continues to increase,” said Zhou, who is a member of the UCLA Gene Regulation Program. Jonsson Comprehensive Cancer Center. “With its cost-effective methylome sequencing, cfMethyl-seq can truly facilitate a big data approach to cancer detection.”
The team is currently seeking funding for large clinical trials to validate the technology in hopes of putting it to use for patients.
Article: Cost-effective methylome sequencing of cell-free DNA to accurately detect and localize cancer DOI: 10.1038/s41467-022-32995-6
Co-first authors are Mary L. Stackpole, Weihua Zeng, and Shuo Li, all from the Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles.
This work was supported by the National Cancer Institute (grant no. U01CA230705 to Xianghong Jasmine Zhou, Samuel French and Steven-Huy Han., grant no. R01CA246329 to Xianghong Jasmine Zhou, Wenyuan Li and Steven Dubinett, grant no. U01CA237711 to Wenyuan Li, No. R43CA246941 to Xiaohui Ni, No. R01CA210360 and U01CA214182 to Denise Aberle, and No. R01CA253651 and R01CA246304 to Vatche Agopian), the National Science Foundation Graduate Research Fellowship (No. DGE-1418060 to Mary Stackpole), and the National Institute of Health (Grant No. UM1HG011593 to Frank Alber and Grant No. R01CA255727 to Yazhen Zhu) This work was supported by a Stand Up To Cancer-LUNGevity-American Lung Association Translational Cancer Research Grant. (grant number: SU2C-AACR-DT23-17 to Steve Dubinett). Department of Veterans Affairs research funding.
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