Overcoming cancer heterogeneity in drug selection through the application of AI and Radiogenomics.

2022 Fiscal Year Final Research Report

• Project/Area Number: 20K09115
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 55020:Digestive surgery-related
• Research Institution: Chiba Cancer Center (Research Institute)
• Principal Investigator: Hoshino Isamu 千葉県がんセンター(研究所), 消化器外科, 主任医長 (10400904)
• Co-Investigator(Kenkyū-buntansha): 横田 元 千葉大学, 大学院医学研究院, 講師 (20649280) ; 森 康久仁 千葉大学, 大学院工学研究院, 助教 (40361414)
• Project Period (FY): 2020-04-01 – 2023-03-31
• Keywords: Radiogenomics / 人工知能 / AI / 癌不均一性 / TMB / 大腸癌

Outline of Final Research Achievements

The study population included 24 CRC patients with liver metastases. DNA was extracted from primary and liver metastatic lesions obtained from the patients and TMB values were evaluated by next-generation sequencing. The TMB value was considered high when it equaled to or exceeded 10/100 Mb. Radiogenomic analysis of TMB was performed by machine learning using CT images and the construction of prediction models. In 7 out of 24 patients (29.2%), the TMB status differed between the primary and liver metastatic lesions. Radiogenomic analysis was performed to predict whether TMB status was high or low. The maximum values for the area under the receiver operating characteristic curve were 0.732 and 0.812 for primary CRC and CRC with liver metastasis, respectively. The sensitivity, specificity, and accuracy of the constructed models for TMB status discordance were 0.857, 0.600, and 0.682, respectively.

Free Research Field

腫瘍学

Academic Significance and Societal Importance of the Research Achievements

Radiogenomicsの手法を用いれば、生検困難な病巣の遺伝学的情報を類推し、更にはmultisamplingによる弊害（implantationや施行による副次的障害の発生）を回避することが可能となる。また、遺伝学的な検索は時間的・経済的に高コストであるが（遺伝子パネル検査：約60万円/1回、3から4週）であるが、この方法論が確立すれば日常の画像検査で分子生物学的手法を用いた遺伝学的検索の代替となり得る。今後はこのようなアプローチが癌の不均一性の克服、ひいては個別化医療の一助となる可能性がある。