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1. Identification of Protein Markers Predictive of Drug-Specific Survival Outcome in Cancers

   https://doi.org/10.1007/978-3-030-91415-8_6  

   Lin, Shuting; Zhou, Jie; Xiao, Yiqiong; Neary, Bridget; Teng, Yong; Qiu, Peng (November 2021, In: Wei, Y., Li, M., Skums, P., Cai, Z. (eds) Bioinformatics Research and Applications. ISBRA 2021. Lecture Notes in Computer Science)

   Wei, Yanjie; Li, Min; Skums, Pavel; Cai, Zhipeng (Ed.)

   Novel discoveries of biomarkers predictive of drug-specific responses not only play a pivotal role in revealing the drug mechanisms in cancers, but are also critical to personalized medicine. In this study, we identified drug-specific biomarkers by integrating protein expression data, drug treatment data and survival outcome of 7076 patients from The Cancer Genome Atlas (TCGA). We first defined cancer-drug groups, where each cancer-drug group contains patients with the same cancer and treated with the same drug. For each protein, we stratified the patients in each cancer-drug group by high or low expression of the protein, and applied log-rank test to examine whether the stratified patients show significant survival difference. We examined 336 proteins in 98 cancer-drug groups and identified 65 protein-cancer-drug combinations involving 55 unique proteins, where the protein expression levels are predictive of drug-specific survival outcomes. Some of the identified proteins were supported by published literature. Using the gene expression data from TCGA, we found the mRNA expression of ∼11% of the drug-specific proteins also showed significant correlation with drug-specific survival, and most of these drug-specific proteins and their corresponding genes are strongly correlated. 

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2. Circulating Exosomal microRNAs as Predictive Biomarkers of Neoadjuvant Chemotherapy Response in Breast Cancer

   https://doi.org/10.3390/curroncol29020055  

   Todorova, Valentina K.; Byrum, Stephanie D.; Gies, Allen J.; Haynie, Cade; Smith, Hunter; Reyna, Nathan S.; Makhoul, Issam (February 2022, Current Oncology)

   Background: Neoadjuvant chemotherapy (NACT) is an increasingly used approach for treatment of breast cancer. The pathological complete response (pCR) is considered a good predictor of disease-specific survival. This study investigated whether circulating exosomal microRNAs could predict pCR in breast cancer patients treated with NACT. Method: Plasma samples of 20 breast cancer patients treated with NACT were collected prior to and after the first cycle. RNA sequencing was used to determine microRNA profiling. The Cancer Genome Atlas (TCGA) was used to explore the expression patterns and survivability of the candidate miRNAs, and their potential targets based on the expression levels and copy number variation (CNV) data. Results: Three miRNAs before that NACT (miR-30b, miR-328 and miR-423) predicted pCR in all of the analyzed samples. Upregulation of miR-127 correlated with pCR in triple-negative breast cancer (TNBC). After the first NACT dose, pCR was predicted by exo-miR-141, while miR-34a, exo-miR182, and exo-miR-183 predicted non-pCR. A significant correlation between the candidate miRNAs and the overall survival, subtype, and metastasis in breast cancer, suggesting their potential role as predictive biomarkers of pCR. Conclusions: If the miRNAs identified in this study are validated in a large cohort of patients, they might serve as predictive non-invasive liquid biopsy biomarkers for monitoring pCR to NACT in breast cancer. 

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3. MicroRNA-Based Discovery of Biomarkers, Therapeutic Targets, and Repositioning Drugs for Breast Cancer

   https://doi.org/10.3390/cells12141917  

   Ye, Qing; Raese, Rebecca A.; Luo, Dajie; Feng, Juan; Xin, Wenjun; Dong, Chunlin; Qian, Yong; Guo, Nancy Lan (July 2023, Cells)

   Breast cancer treatment can be improved with biomarkers for early detection and individualized therapy. A set of 86 microRNAs (miRNAs) were identified to separate breast cancer tumors from normal breast tissues (n = 52) with an overall accuracy of 90.4%. Six miRNAs had concordant expression in both tumors and breast cancer patient blood samples compared with the normal control samples. Twelve miRNAs showed concordant expression in tumors vs. normal breast tissues and patient survival (n = 1093), with seven as potential tumor suppressors and five as potential oncomiRs. From experimentally validated target genes of these 86 miRNAs, pan-sensitive and pan-resistant genes with concordant mRNA and protein expression associated with in-vitro drug response to 19 NCCN-recommended breast cancer drugs were selected. Combined with in-vitro proliferation assays using CRISPR-Cas9/RNAi and patient survival analysis, MEK inhibitors PD19830 and BRD-K12244279, pilocarpine, and tremorine were discovered as potential new drug options for treating breast cancer. Multi-omics biomarkers of response to the discovered drugs were identified using human breast cancer cell lines. This study presented an artificial intelligence pipeline of miRNA-based discovery of biomarkers, therapeutic targets, and repositioning drugs that can be applied to many cancer types. 

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4. Identifying gene expression patterns associated with drug-specific survival in cancer patients

   https://doi.org/10.1038/s41598-021-84211-y  

   Neary, Bridget; Zhou, Jie; Qiu, Peng (December 2021, Scientific Reports)

   null (Ed.)

   Abstract The ability to predict the efficacy of cancer treatments is a longstanding goal of precision medicine that requires improved understanding of molecular interactions with drugs and the discovery of biomarkers of drug response. Identifying genes whose expression influences drug sensitivity can help address both of these needs, elucidating the molecular pathways involved in drug efficacy and providing potential ways to predict new patients’ response to available therapies. In this study, we integrated cancer type, drug treatment, and survival data with RNA-seq gene expression data from The Cancer Genome Atlas to identify genes and gene sets whose expression levels in patient tumor biopsies are associated with drug-specific patient survival using a log-rank test comparing survival of patients with low vs. high expression for each gene. This analysis was successful in identifying thousands of such gene–drug relationships across 20 drugs in 14 cancers, several of which have been previously implicated in the respective drug’s efficacy. We then clustered significant genes based on their expression patterns across patients and defined gene sets that are more robust predictors of patient outcome, many of which were significantly enriched for target genes of one or more transcription factors, indicating several upstream regulatory mechanisms that may be involved in drug efficacy. We identified a large number of genes and gene sets that were potentially useful as transcript-level biomarkers for predicting drug-specific patient survival outcome. Our gene sets were robust predictors of drug-specific survival and our results included both novel and previously reported findings, suggesting that the drug-specific survival marker genes reported herein warrant further investigation for insights into drug mechanisms and for validation as biomarkers to aid cancer therapy decisions. 

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5. Impact of Neoantigen Expression and T-Cell Activation on Breast Cancer Survival

   https://doi.org/10.3390/cancers13122879  

   Li, Wenjing; Amei, Amei; Bui, Francis; Norouzifar, Saba; Lu, Lingeng; Wang, Zuoheng (June 2021, Cancers)

   Neoantigens are derived from tumor-specific somatic mutations. Neoantigen-based synthesized peptides have been under clinical investigation to boost cancer immunotherapy efficacy. The promising results prompt us to further elucidate the effect of neoantigen expression on patient survival in breast cancer. We applied Kaplan–Meier survival and multivariable Cox regression models to evaluate the effect of neoantigen expression and its interaction with T-cell activation on overall survival in a cohort of 729 breast cancer patients. Pearson’s chi-squared tests were used to assess the relationships between neoantigen expression and clinical pathological variables. Spearman correlation analysis was conducted to identify correlations between neoantigen expression, mutation load, and DNA repair gene expression. ERCC1, XPA, and XPC were negatively associated with neoantigen expression, while BLM, BRCA2, MSH2, XRCC2, RAD51, CHEK1, and CHEK2 were positively associated with neoantigen expression. Based on the multivariable Cox proportional hazard model, patients with a high level of neoantigen expression and activated T-cell status showed improved overall survival. Similarly, in the T-cell exhaustion and progesterone receptor (PR) positive subgroups, patients with a high level of neoantigen expression showed prolonged survival. In contrast, there was no significant difference in the T-cell activation and PR negative subgroups. In conclusion, neoantigens may serve as immunogenic agents for immunotherapy in breast cancer. 

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