About this Research Topic
Our first Research Topic Volume I: Computational Methods in Inferring Cancer Tissue-of-Origin and Cancer Molecular Classification (https://fro.ntiers.in/jBPY) has over 11 accepted articles and another 15 currently under review. Based on this success, we are launching a second volume for further submissions.
Inferring cancer tissue-of-origin and molecular classification are two critical problems in personalized cancer therapy. It is known that there are about 5% cancers of unknown primary (CUP) site. These kinds of patients are under empirical chemotherapy, which leads to a very low survival rate. Thus, it is important to infer cancer tissue-of-origin. However, experimental methods usually fail to identify the exact tissue-of-origin even after the death of a patient, which provides a need for computational methods especially in the era of big biomedical data. Based on the finding that gene expressions of metastasis cancer cells are more similar to those of original tissue than metastasis tissue, there have been a few computational methods developed in this area. However, the accuracy of the methods is yet to be improved to assure a clinical usage. In addition to CUP, inferring cancer tissue-of-origin is also important in avoiding misdiagnosis even if the cancer origin is known.
On the other hand, cancer molecular classification has been proven to be useful in optimizing treatment methods. With the accumulation of genetic and prognosis data, it is possible to mining a better 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. We aim to comprehensively discuss themes covering cancer tissue-of-origin and molecular classification towards personalized cancer therapy. This topic can involve not only many kinds of methods/algorithms or code/tools to infer the origins and molecular classification, but also translational studies for cancer treatment in hospitals.
As in the previous topic, we welcome investigators to contribute Original Research as well as Review articles on methods/algorithms/code/tools and clinical applications. 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, acetylation, somatic mutation and so on;
- Inferring tissue-of-origin for pan cancers based on liquid biopsy including cfDNA,
cfRNA, circulating tumor cells and so on;
-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, including biomarkers for cancer subtype identifications;
- Clinical applications of cancer molecular classification on different treatment strategies;
- The association between cancer molecular classification and immunotherapies;
- The association between cancer molecular classification and other important cancer features like MSI, TMB, PD1-PDL1, and so on;
- Single cell analyses for tissue-of-origin and cancer molecular classification;
- Medical image-based methods in cancer tissue-of-origin and molecular classification.
Inferring cancer tissue-of-origin and molecular classification are two critical problems in personalized cancer therapy. It is known that there are about 5% cancers of unknown primary (CUP) site. These kinds of patients are under empirical chemotherapy, which leads to a very low survival rate. Thus, it is important to infer cancer tissue-of-origin. However, experimental methods usually fail to identify the exact tissue-of-origin even after the death of a patient, which provides a need for computational methods especially in the era of big biomedical data. Based on the finding that gene expressions of metastasis cancer cells are more similar to those of original tissue than metastasis tissue, there have been a few computational methods developed in this area. However, the accuracy of the methods is yet to be improved to assure a clinical usage. In addition to CUP, inferring cancer tissue-of-origin is also important in avoiding misdiagnosis even if the cancer origin is known.
On the other hand, cancer molecular classification has been proven to be useful in optimizing treatment methods. With the accumulation of genetic and prognosis data, it is possible to mining a better 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. We aim to comprehensively discuss themes covering cancer tissue-of-origin and molecular classification towards personalized cancer therapy. This topic can involve not only many kinds of methods/algorithms or code/tools to infer the origins and molecular classification, but also translational studies for cancer treatment in hospitals.
As in the previous topic, we welcome investigators to contribute Original Research as well as Review articles on methods/algorithms/code/tools and clinical applications. 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, acetylation, somatic mutation and so on;
- Inferring tissue-of-origin for pan cancers based on liquid biopsy including cfDNA,
cfRNA, circulating tumor cells and so on;
-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, including biomarkers for cancer subtype identifications;
- Clinical applications of cancer molecular classification on different treatment strategies;
- The association between cancer molecular classification and immunotherapies;
- The association between cancer molecular classification and other important cancer features like MSI, TMB, PD1-PDL1, and so on;
- Single cell analyses for tissue-of-origin and cancer molecular classification;
- Medical image-based methods in cancer tissue-of-origin and molecular classification.
Keywords: Cancer tissue-of-origin, cancer molecular classification, liquid biopsy, machine learning, single-cell
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.
