{"type": "root", "children": [{"type": "p", "children": [{"type": "t", "text": "\n CT47A10 is a member of the cancer/testis (CT) antigen family—genes normally expressed only in germ cells but aberrantly activated in various cancers—that has been implicated in eliciting immune responses and in serving as a potential biomarker for cancer diagnosis and therapy. In the context of breast cancer, CT antigens (including CT47A10) have been observed to be expressed in high‐risk, estrogen receptor–negative lesions such as ductal carcinoma in situ, where their re‐expression is associated with other oncogenic markers (for example, HER2 positivity) and a marked stromal immune response."}, {"type": "fg", "children": [{"type": "fg_f", "ref": "1"}]}, {"type": "t", "text": " Similarly, in prostate cancer the evaluation of autoantibody profiles against a panel of tumor-associated antigens highlights the promise for CT antigens to contribute to high throughput diagnostic and immunotherapeutic strategies."}, {"type": "fg", "children": [{"type": "fg_f", "ref": "2"}]}, {"type": "t", "text": " Large-scale transcript cataloging projects have expanded our understanding of the human transcriptome—and by extension the repertoire of CT antigens such as CT47A10—underscoring the complexity and potential clinical relevance of these normally “silent” genes."}, {"type": "fg", "children": [{"type": "fg_f", "ref": "3"}]}, {"type": "t", "text": " Finally, integrated genomic profiling approaches in breast cancer survivors indicate that the expression of such biomarkers can contribute to personalized treatment strategies and survivability prediction, reinforcing their potential utility in clinical decision making."}, {"type": "fg", "children": [{"type": "fg_f", "ref": "4"}]}, {"type": "t", "text": "\n "}]}, {"type": "rg", "children": [{"type": "r", "ref": 1, "children": [{"type": "t", "text": "Otavia L Caballero, Sami Shousha, Qi Zhao, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Expression of Cancer/Testis genes in ductal carcinoma in situ and benign lesions of the breast."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Oncoscience (2013)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.18632/oncoscience.4"}], "href": "https://doi.org/10.18632/oncoscience.4"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "25593980"}], "href": "https://pubmed.ncbi.nlm.nih.gov/25593980"}]}, {"type": "r", "ref": 2, "children": [{"type": "t", "text": "Henry A Adeola, Muneerah Smith, Lisa Kaestner, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Novel potential serological prostate cancer biomarkers using CT100+ cancer antigen microarray platform in a multi-cultural South African cohort."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Oncotarget (2016)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.18632/oncotarget.7359"}], "href": "https://doi.org/10.18632/oncotarget.7359"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "26885621"}], "href": "https://pubmed.ncbi.nlm.nih.gov/26885621"}]}, {"type": "r", "ref": 3, "children": [{"type": "t", "text": "Mihaela Pertea, Alaina Shumate, Geo Pertea, et al. "}, {"type": "b", "children": [{"type": "t", "text": "CHESS: a new human gene catalog curated from thousands of large-scale RNA sequencing experiments reveals extensive transcriptional noise."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Genome Biol (2018)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1186/s13059-018-1590-2"}], "href": "https://doi.org/10.1186/s13059-018-1590-2"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "30486838"}], "href": "https://pubmed.ncbi.nlm.nih.gov/30486838"}]}, {"type": "r", "ref": 4, "children": [{"type": "t", "text": "Ashraf Abou Tabl, Abedalrhman Alkhateeb, Waguih ElMaraghy, et al. "}, {"type": "b", "children": [{"type": "t", "text": "A Machine Learning Approach for Identifying Gene Biomarkers Guiding the Treatment of Breast Cancer."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Front Genet (2019)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.3389/fgene.2019.00256"}], "href": "https://doi.org/10.3389/fgene.2019.00256"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "30972106"}], "href": "https://pubmed.ncbi.nlm.nih.gov/30972106"}]}]}]}