Lotta Eriksson and Eszter Lakatos.
Photo Credits: Ruben Seyer and Marco Nikic.
Scientific Frontline: Extended "At a Glance" Summary: BayesCNA Blood Analysis Method
The Core Concept: A highly sensitive analytical blood-testing method that uses classical statistics to isolate and analyze samples containing as little as 5% cancer DNA.
Key Distinction/Mechanism: While current clinical methods require 15–20% tumor DNA to function, BayesCNA applies a classical statistical algorithm to amplify extremely weak signals from low-pass whole-genome sequencing. This allows researchers to filter out the "noise" of healthy DNA and bypass the need for machine learning models, which proved less effective for this specific data structure.
Major Frameworks/Components:
- Low-Pass Whole-Genome Sequencing: A rapid, highly cost-effective sequencing technique utilized to generate a broad structural overview of DNA, despite yielding inherently low-quality data.
- Classical Statistical Modeling: The algorithmic foundation that isolates weak pathological signals from overwhelming biological noise to reveal hidden tumor composition.
- Liquid Biopsy Pathology: The clinical framework of utilizing frequent, non-invasive blood draws to map tumor characteristics in lieu of invasive solid tissue sampling.
Branch of Science: Oncology, Statistical Genetics, Mathematical Biology, and Molecular Pathology.
Future Application: The algorithm is advancing toward clinical trials to enable oncologists to continuously monitor micro-fluctuations in tumor composition between standard treatment intervals, paving the way for highly individualized, dynamically adjusted cancer therapies.
Why It Matters: When cancer treatments successfully attack a tumor, the proportion of circulating tumor DNA drops significantly, paradoxically rendering the remaining cancer harder to monitor. By drastically lowering the detection threshold, BayesCNA provides a high-resolution, continuous timeline of treatment efficacy without requiring repeated, invasive tissue biopsies.
Blood tests have proven to be a promising tool for detecting and monitoring cancer. Researchers at Chalmers University of Technology and the University of Gothenburg in Sweden have developed a new method that makes it possible to analyze samples containing as little as 5% cancer DNA in the blood, compared with the 15–20% currently required. This method could lead to better cancer care and improved monitoring of tumor progression.
Analyzing changes in tumor DNA using blood tests is a technique currently being explored in several clinical trials worldwide. Existing analytical methods work well when the amount of cancer DNA is relatively high, at around 15–20% of the total DNA in the blood. However, the level of cancer DNA is often considerably lower, meaning the sample quality may be inadequate for detailed analysis.
"We wanted to develop a method that works particularly well in difficult cases where there is very little cancer DNA in the blood and a lot of what we consider noise—that is, mainly healthy DNA. Our results show that the new method performs better with samples involving low levels of cancer DNA, where the proportion is around 5%. So, it works exactly as we had hoped," says Lotta Eriksson, a doctoral student in the Department of Mathematical Sciences at Chalmers and the University of Gothenburg.
Better Monitoring and Individually Tailored Treatment
Blood-based methods currently tested in clinical trials are often used simply to determine whether cancer is detectable at all. It is difficult to obtain a more detailed picture, partly due to high costs and poor sample quality.
The new method, BayesCNA, can extract information that was previously hidden in low-quality samples and provide more detail about the tumor's composition. This can help provide a better understanding of how a patient's cancer changes over time.
"When the treatment is effective, the amount of cancer DNA in the blood drops significantly. This makes it more difficult both to detect the cancer and to monitor how it changes. It is important to be able to analyze samples containing low levels of cancer DNA to gain a clearer picture of how a patient responds to treatment," says Eszter Lakatos, an assistant professor in the Department of Mathematical Sciences at Chalmers and the University of Gothenburg.
Currently, a tissue sample from the tumor itself is required to obtain detailed information about its composition. The ability to monitor tumor progression using blood tests could lead to significantly better care for cancer patients.
"A patient may undergo surgery once or twice, whereas blood tests may be taken at intervals of just a few weeks during treatment. If we can obtain information about tumor changes from the samples, we can monitor developments much more closely and see what happens between treatment sessions. This can help doctors make more informed decisions, such as tailoring treatment to the tumor's composition," says Eszter Lakatos.
A Statistical Method That Amplifies Weak Signals
The method was developed to analyze data from low-pass whole-genome sequencing, a technique that provides a general overview of the DNA structure. Although the technique offers major financial benefits, it provides limited information because the data quality is low.
"You could compare it to skimming through a book rather than reading it closely. We get an overview of the DNA structure, but not a detailed picture," says Eszter Lakatos.
The new analysis method uses a statistical algorithm to amplify the very weak signals present in this type of sample.
"Nowadays, machine learning is used to solve many problems, and we tried those methods first. But, to our surprise, it turned out that classical statistics worked better in this case, which was particularly pleasing to us mathematicians and statisticians," says Lotta Eriksson.
Aiming for Clinical Trials
The next step is to analyze the information the method provides on tumor composition. The researchers are keen to develop an additional method for identifying the hidden characteristics of the cancer that influence how patients respond to treatment.
"If we can demonstrate that this information is useful, we hope it will lead to more collaborations and wider adoption of our method within the research community. In the long term, I hope that the methods we develop can be used in clinical trials and, with any luck, make a difference in the care of cancer patients," says Eszter Lakatos.
Funding: The study was funded by the Swedish Research Council and Chalmers's Health Engineering Area of Advance
Published in journal: Briefings in Bioinformatics
Title: Sensitive detection of copy number alterations in low-pass liquid biopsy sequencing data
Authors: Lotta Eriksson, and Eszter Lakatos
Source/Credit: Chalmers University of Technology | Julia Romell
Edited by: Scientific Frontline
Reference Number: ongy060926_01
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