MYST3/CREBBP Rearranged Acute Myeloid Leukemia after Adjuvant Chemotherapy for Breast Cancer

Identification of Triple-Negative Breast Cancer Genes and a Novel High-Risk Breast Cancer Prediction Model Development Based on PPI Data and Support Vector Machines

Triple-negative breast cancer (TNBC) is a special subtype of breast cancer that is difficult to treat. It is crucial to identify breast cancer-related genes that could provide new biomarkers for breast cancer diagnosis and potential treatment goals. In the development of our new high-risk breast cancer prediction model, seven raw gene expression datasets from the NCBI gene expression omnibus (GEO) database (GSE31519, GSE9574, GSE20194, GSE20271, GSE32646, GSE45255, and GSE15852) were used. Using the maximum relevance minimum redundancy (mRMR) method, we selected significant genes. Then, we mapped transcripts of the genes on the protein-protein interaction (PPI) network from the Search Tool for the Retrieval of Interacting Genes (STRING) database, as well as traced the shortest path between each pair of proteins. Genes with higher betweenness values were selected from the shortest path proteins. In order to ensure validity and precision, a permutation test was performed. We randomly selected 248 proteins from the PPI network for shortest path tracing and repeated the procedure 100 times. We also removed genes that appeared more frequently in randomized results. As a result, 54 genes were selected as potential TNBC-related genes. Using 14 out the 54 genes, which are potential TNBC associated genes, as input features into a support vector machine (SVM), a novel model was trained to predict high-risk breast cancer. The prediction accuracy of normal tissues and TNBC tissues reached 95.394%, and the predictions of Stage II and Stage III TNBC reached 86.598%, indicating that such genes play important roles in distinguishing breast cancers, and that the method could be promising in practical use. According to reports, some of the 54 genes we identified from the PPI network are associated with breast cancer in the literature. Several other genes have not yet been reported but have functional resemblance with known cancer genes. These may be novel breast cancer-related genes and need further experimental validation. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to appraise the 54 genes. It was indicated that cellular response to organic cyclic compounds has an influence in breast cancer, and most genes may be related with viral carcinogenesis.

Paclitaxel Induced MDS and AML: A Case Report and Literature Review

Therapy related acute myelogenous leukemia (AML) and myelodysplastic syndromes (MDS) have been classically linked to alkylating agents and topoisomerase inhibitors. They constitute about 1% of all AMLs. There is less evidence on association of taxanes (paclitaxel and docetaxel) with these myeloid neoplasms. We present a case of paclitaxel therapy related acute myelogenous leukemia after treatment of endometrial cancer with a regimen containing paclitaxel and carboplatin. A 63-year-old female underwent surgery followed by a total of 6 cycles of chemotherapy with carboplatin and paclitaxel. Six months after last cycle of chemotherapy, she was diagnosed with myelodysplastic syndrome with refractory anemia and excess blasts. Six weeks later, she had worsening anemia and thrombocytopenia which prompted a bone marrow biopsy which revealed acute myelomonocytic leukemia. A thorough literature review revealed 12 other case reports where taxanes have been implicated in the development of therapy related myeloid neoplasm. Based on the timeline of events in our patient, paclitaxel is the likely culprit in the pathogenesis of this myeloid neoplasm. This rare but significantly grave adverse effect should be kept in consideration when deciding on treatment options for gynecological malignancies.