Research Topic

Computational tools in inferring cancer tissue-of-origin and molecular classification towards personalized cancer therapy, Volume III

About this Research Topic

Our second Research Topic in this series, Computational tools in inferring cancer tissue-of-origin and molecular classification towards personalized cancer therapy, Volume II (https://fro.ntiers.in/14361) has over 8 accepted articles and further manuscripts currently under review. Due to the continued success of these Research Topics and the interest in the subject, we will launch a third volume on the same topic.

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.

Our second Research Topic in this series, Computational tools in inferring cancer tissue-of-origin and molecular classification towards personalized cancer therapy, Volume II (https://fro.ntiers.in/14361) has over 8 accepted articles and further manuscripts currently under review. Due to the continued success of these Research Topics and the interest in the subject, we will launch a third volume on the same topic.

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.

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With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

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Submission Deadlines

14 May 2021 Abstract
14 October 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

14 May 2021 Abstract
14 October 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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