About this Research Topic
Cancer molecular classification has also been proven to be useful in optimizing treatment methods. With the accumulation of genetic and prognosis data, it is possible to improve molecular classification for most cancer types based on historical data, especially when more and more single-cell data are available. A better cancer molecular classification and better set of markers are critical for developing more efficient cancer treatment and new drugs.
This Research Topic will comprehensively discuss cancer tissue-of-origin and molecular classification, including not only methods/algorithms to infer the origins and molecular classification, but also translational studies for cancer treatment in hospitals. We welcome investigators to contribute Original Research as well as Review articles on methods and clinical applications of these two topics. Potential topics include but are not limited to the following:
-Inferring tissue-of-origin for pan-cancers based on tissue data including gene expression, methylation, somatic mutation, etc.
-Inferring tissue-of-origin for pan-cancers based on liquid biopsy including cfDNA, cfRNA, methylation, circulating tumor cells, etc.
-Tissue and liquid biopsy-based methods in inferring tissue-of-origin for cancer subtypes
-Inferring cancer site-of-origin for multiple nodules at a specific tissue
-Machine learning methods in molecular classification of a specific cancer
-Clinical applications of cancer tissue-of-origin identification
-Clinical applications of cancer molecular classification
-The association between cancer molecular classification and immunotherapies
-The association between cancer molecular classification and other important cancer features like MSI, TMB, PD1-PDL1, etc.
-Single-cell analyses for tissue-of-origin and cancer molecular classification
-Medical image-based methods in cancer tissue-of-origin and molecular classification
Topic Editor Dr. Jialiang Yang is employed by Geneis Co. Ltd. The rest of the Topic Editors declare no conflicts of interest with regards to this Research Topic.
Keywords: cancer, NGS, tissue-of-origin, molecular classification, cancer treatment, medical image, machine learning, liquid biopsy
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.