Keio University Research: Genomic Insights For Personalized Cancer Treatment

(MENAFN- PR Newswire) TOKYO, Oct. 18, 2024 /PRNewswire/ -- In a recent paper published in Cancer Discovery, Keio University's Sara Horie and her colleagues conducted an in-depth analysis of cancer driver alterations using genomic data from Japanese and international cohorts. The study elucidates the genomic landscape of Japanese patients and identifies significant genomic differences between Asian and white populations, providing valuable resources for cancer genomic medicine. These findings have important implications for appropriate patient selection in molecularly targeted therapies.

Figure LINK and caption

Caption: [Fig. 1A,
Fig. 4A,
Fig. 5BC,
from the paper
[1]

Background

The advent of next-generation sequencing (NGS) has transformed our understanding of cancer, enabling detailed analyses of genetic mutations driving cancer development. Large-scale genomic studies
like The Cancer Genome Atlas (TCGA) and the AACR Project Genomics Evidence Neoplasia Information Exchange (GENIE) have been instrumental in identifying mutations that affect cancer progression. These efforts have been fundamental to the rise of precision medicine, which tailors treatment strategies to the unique genetic profile of each patient.

However, the majority of existing data
primarily consist of white populations, with few studies focused on Asian populations. Horie and colleagues addressed this
gap by analyzing cancer gene panel data from Japan's Center for Cancer Genomics and Advanced Therapeutics (C-CAT), which, as of
December 2023, has collected data from 67,630 patients.

Study focus and methodology

This study presents a pan-cancer clinico-genomic analysis
of 48,627
Japanese patients registered in C-CAT between June 2019 and August 2023. It compares the patterns
of driver mutations between the C-CAT cohort
and white patients in the GENIE cohort. Additionally, C-CAT, GENIE, and TCGA datasets consisting of more than 100,000 patients were integrated to uncover numerous co-occurring and mutually exclusive relationships between driver mutations across various cancer types.

Key Findings

• Genomic landscape and clinical
  actionability of Japanese cancer patients

The study provides an overview of the genomic landscape across multiple cancer types in Japanese patients. It highlights the spectrum of driver mutations, fusions, and tumor mutation burden, along with their clinical actionability. Clinically actionable genetic alterations were identified in 15.3% of patients in the C-CAT cohort, with the highest frequencies observed in well-differentiated thyroid cancer.

• Genetic differences between Asian and White populations

A comparative analysis of the mutational landscape between Japanese and white populations revealed a significantly higher frequency of TP53 mutations in the Japanese cohort across multiple cancer types, suggesting potential racial differences in mutation prevalence. When assessing clinical actionability, the overall proportion of actionable genetic alterations was higher in the GENIE white cohort. This disparity is largely due to the C-CAT cohort including more cases of cancers with few targetable alterations, such as pancreatobiliary cancers, highlighting the need to explore alternative therapeutic approaches for these cancer types.

• Co-occurrence and mutual exclusivity of driver mutations

Through the integration of multi-cohort registry data, the research
identified numerous co-occurring and mutually exclusive relationships of driver mutations. A key finding is the significant co-occurrence of epigenetic driver mutations in ten cancer types. Analysis of RNA-sequencing data from TCGA and CRISPR screening data from cell lines revealed that the accumulation of mutations in epigenetic regulators promotes cancer growth.

Significance and Future Prospects

This research provides valuable insights into the genetic differences in cancer patients from different racial populations, particularly between Japanese and white
populations. The findings highlight the need for cancer treatment strategies that are tailored to the Japanese population, ensuring that advances in precision medicine are inclusive of diverse genetic profiles. The study serves as a foundation for improving cancer diagnosis and treatment in Japan, with future large-scale genomic research expected to further refine personalized treatment approaches.

Reflecting on the study's impact, the authors state, "This research highlights the critical need to consider population-specific genetic profiles in cancer treatment. By understanding these unique genomic differences, we can significantly improve personalized therapies and outcomes for Japanese cancer patients, ensuring that precision medicine can benefit a broader range of patients."

This research highlights the importance of continuing to explore genetic diversity in cancer research, as it plays a crucial role in advancing personalized treatments and improving patient outcomes globally.

About the researchers

Sara Horie -
Graduate medical student
School of Medicine

Sara Horie graduated from the Keio University School of Medicine in 2016 and completed a senior residency program in gastroenterology. She subsequently joined the Division of Molecular Oncology at the National Cancer Center Research Institute in Tokyo, Japan, where she is now conducting research focusing on cancer genetics. Her work involves integrating large-scale datasets of clinico-genomic data, aiming to contribute to the understanding of the molecular mechanisms driving human cancers.

Keisuke Kataoka -
Professor
School of Medicine

Keisuke Kataoka is a professor of the Division of Hematology, Department of Medicine, Keio University School of Medicine. He is also a chief of the Division of Molecular Oncology at the National Cancer Center Research Institute in Japan. He received his M.D. and Ph.D. in medicine from the University of Tokyo. As a hematologist, he aims to delineate the entire picture of genetic aberrations and clarify the molecular pathogenesis in various cancers.

Links

Reference