Biomarkers are important determinants of appropriate and effective therapeutic approaches for various diseases including cancer. There is ample evidence pointing toward the significance of immune check point inhibitors (ICI) against cancer, and they showed promising clinical benefits to a specific group of patients with colorectal cancer (CRC).
Several reports demonstrated the efficacy of biomarkers such as programmed death-1 protein ligand (PD-L1), density of tumor-infiltrating lymphocytes (TILs), and tumor mutational burden (TMB), to determine the patient responsiveness for the efficient use of ICIs as therapeutics against cancer.
A high level of TMB (TMB-H), which reflects elevated total number of non-synonymous somatic mutations per coding area of a tumor genome and normally derived from gene panel testing, is recognized as a promising biomarker for the ICI therapies of various solid cancers. However, in clinical practice, it is not feasible to perform gene panel testing for all cancer patients.
Dr. Yoshifumi Shimada and coworkers from the Division of Digestive and General Surgery, Graduate School of Medical and Dental Sciences, Niigata University, regarded TMB-H from a specific CRC patient subgroup, as a more robust marker for predicting the efficacy of ICIs, and developed a convolutional neural network (CNN) – based algorithm to predict TMB-H CRC directly from the histopathological characteristics, in particular, the TIL, obtained from the hematoxylin and eosin (H&E) stained slides.
A representative microscopic image of the H&E stained tumor mutational burden-high colorectal cancer tumor is shown in the accompanying figure, demonstrating the presence of tumor-infiltrating lymphocytes in significantly elevated level compared to normal surrounding tissue. Digital information from such this neoplastic and also non-neoplastic images obtained from JP-CRC-cohort is transformed and normalized for building a predictive Convoluted Neural Network model employing Inception V3 learning model, by Dr. Shimada group.
The CNN-based model developed by Dr. Shimada and coworkers has the potential to not only reduce the burden of proper diagnosis on pathologists but also provide the necessary information on the patients’ responsiveness to the ICI based therapeutics, without the use of expensive, time consuming and not easily available gene panel testing. This study by Dr. Shimada and coworkers is published in a recent issue of Journal of Gastroenterology (2021; vol. 56: pp. 547–559; https://doi.org/10.1007/s00535-021-01789-w).
In addition, the studies of Dr. Shimada group also provided means to predict TMB-H CRC only by using the TIL information from the H&E slides from the patients’ tumor tissues. However, considering that the patients in the studied cohort were not treated with any ICIs, no conclusions could be drawn regarding their ICI responsiveness following the TMB-H diagnosis and it was suggested that future clinical trials need to be conducted to address whether TIL alone can be useful as a predictive biomarker for the efficacy of ICIs. Dr. Shimada says about the present study: “We have developed artificial intelligence to predict genetic alterations in colorectal cancer by deep learning using hematoxylin and eosin slides. This artificial intelligence is important in solving the cost problems associated with genetic analysis and facilitating personalized medicine in colorectal cancer.”
Overall, the studies by Dr. Shimada and associates provide a cost and time effective and reliable method to inform the clinicians if the CRC patient they are managing can benefit from Immune Checkpoint Inhibitor (including inhibitors of the PD-1 protein and its ligand, PD-L1) therapy, without implicating the use of gene panel.
Journal: Journal of Gastroenterology
Title: Histopathological characteristics and artificial intelligence for predicting tumor mutational burden-high colorectal cancer
Authors: Yoshifumi Shimada, Shujiro Okuda, Yu Watanabe, Yosuke Tajima, Masayuki Nagahashi, Hiroshi Ichikawa, Masato Nakano, Jun Sakata, Yasumasa Takii, Takashi Kawasaki, Kei-Ichi Homma, Tomohiro Kamori, Eiji Oki, Yiwei Ling, Shiho Takeuchi, Toshifumi Wakai
Source: Niigata University