Yale researchers develop software for individualized cancer treatment

In an effort to practice precision medicine – a medical movement to develop specific treatments for individual patients – scientists have created software to quantify and differentiate the impacts of variants that occur in cancer.

Lucy Zha

11:50 pm, January 24, 2023

Collaborating Reporter



Yale daily news

Software that significantly improves the skills of clinicians and researchers to target cancer mutations for individualized patient care was recently developed by researchers at the Yale School of Public Health.

The new program quantifies the impact of single nucleotide variants on cancer proliferation and survival in humans. The software was developed by Jeffrey Townsend, professor of biostatistics and ecology and evolutionary biology at the Yale School of Public Health, Jeffrey Mandell, first author and doctoral student in Townsend’s lab, and Vincent Cannatro, assistant professor of biology at Emmanuel College.

By arranging the data for somatic variants – a mutation that can occur in any cell except germ cells – facilitating mutational signature analysis and calculating site-specific mutation rates, the researchers confirmed that certain variants exhibit a greater impact on development. of cancer than most other variants. This software serves as a better predictor of the variant’s effect on cancer than other techniques, which indirectly make predictions based on protein structures or amino acid sequences.

“Today, in cancer treatment, one of the great movements is to create specific treatments: precision medicine”, said Townsend. “This software examines the mutations that characterize each tumor and quantifies each one of them for each cancer that an individual has. This prioritizes precision treatment.”

The main question that led Mandell on this research journey was his attempt to understand which mutations among thousands of accumulated variants were most important in the development of cancer, as well as to determine which are the appropriate targeted treatments.

According to Mandell, a patient’s tumor is “easily sequenced” using the advanced technology available today. However, he noted that not all mutations provide the same level of insight to determine what medical treatment is needed.

“The question then is: what tools can we use to prioritize the most relevant genetic mutations?” Mandell said.

The implications of this software are multifaceted. First, it is able to contribute to “basic scientific research, academic translational research, and pharmaceutical trials,” according to Townsend. He noted that the software will help medical professionals “create better targets by analyzing cancer mutations and making targeted drugs.” In addition, he said, it can help scientists decide which genetic targets to use resources on during clinical trials, which can be “exhausting”.

This project has been in development since 2016, when the idea was first proposed, according to Cannatro, who at the time was a postdoctoral fellow in Townsend’s lab. The first version of the software was published in 2018 in Journal of the National Cancer Institute. Subsequently, the authors made changes to improve user accessibility, data annotation, and expansion in the cancer variant selection models. The current paper details the final version of the software, published in 2022 in Molecular Biology and Evolution.

“In the literature, there are examples of prevalent variants as a metric of how important variants are,” said Cannatro. “However, the rate of occurrence of variants happens differently.”

For future research directions, Townsend said, improvements can be made in quantifying the average effects of variants in cancer patients and their precise effects in each individual patient. These effects may depend on existing mutations in the patient’s tumor.

Furthermore, current software can only measure single nucleotide somatic mutations, which make up the vast majority of mutations in the early stages of cancer. However, the software could potentially be programmed to include copy number and other mutations that occur at later stages of cancer development.

The first edition of Molecular Biology and Evolution was published in 1983.

Yale researchers develop software for individualized cancer treatment

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