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Abstract Biomarkers predictive of drug-specific outcomes are important tools for personalized medicine. In this study, we present an integrative analysis to identify miRNAs that are predictive of drug-specific survival outcome in cancer. Using the clinical data from TCGA, we defined subsets of cancer patients who suffered from the same cancer and received the same drug treatment, which we call cancer-drug groups. We then used the miRNA expression data in TCGA to evaluate each miRNA’s ability to predict the survival outcome of patients in each cancer-drug group. As a result, the identified miRNAs are predictive of survival outcomes in a cancer-specific and drug-specific manner. Notably, most of the drug-specific miRNA survival markers and their target genes showed consistency in terms of correlations in their expression and their correlations with survival. Some of the identified miRNAs were supported by published literature in contexts of various cancers. We explored several additional breast cancer datasets that provided miRNA expression and survival data, and showed that our drug-specific miRNA survival markers for breast cancer were able to effectively stratify the prognosis of patients in those additional datasets. Together, this analysis revealed drug-specific miRNA markers for cancer survival, which can be promising tools toward personalized medicine. 

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. 

Cancer‐associated fibroblasts (CAFs) are present in many types of tumors and play a pivotal role in tumor progression and immunosuppression. Fibroblast‐activation protein (FAP), which is overexpressed on CAFs, has been indicated as a universal tumor target. However, FAP expression is not restricted to tumors, and systemic treatment against FAP often causes severe side effects. To solve this problem, a photodynamic therapy (PDT) approach is developed based on ZnF₁₆Pc‐loaded and FAP‐specific single chain variable fragment (scFv)‐conjugated apoferritin nanoparticles, or αFAP‐Z@FRT. αFAP‐Z@FRT PDT efficiently eradicates CAFs in tumors without inducing systemic toxicity. When tested in murine 4T1 models, the treatment elicits anti‐cancer immunity, causing suppression of both primary and distant tumors, that is, the abscopal effect. Treatment efficacy is enhanced when αFAP‐Z@FRT PDT is used in combination with anti‐PD1 antibodies. Interestingly, it is found that the PDT treatment not only elicits a cellular immunity against cancer cells, but also stimulates an anti‐CAFs immunity. This is supported by an adoptive cell transfer study, where T cells taken from 4T1‐tumor‐bearing animals treated with αFAP PDT retard the growth of A549 tumors established on nude mice. Overall, this approach is unique for permitting site‐specific eradication of CAFs and inducing a broad spectrum anti‐cancer immunity.