Doxorubicin is an important anticancer drug in the clinic. Unfortunately, it causes cumulative and dose-dependent cardiotoxic side effects. As the population of cancer survivors who have been exposed to treatment continues to grow, there is increased interest in assessing the long-term cardiac effects of doxorubicin and understanding the underlying mechanisms at play. In this study, we investigated doxorubicin-induced transcriptomic changes using RNA-sequencing (RNAseq) and a cellular model comprised of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). Analyses of predicted upstream regulators identified the p53 protein as a key regulator of transcriptomic changes induced by doxorubicin. Clustering and pathway analyses showed that increased death receptor (DR) expression and enrichment of the extrinsic apoptotic pathway are significantly associated with doxorubicin-induced cardiotoxicity. Increased expression of p53 and DRs were confirmed via immunoblotting. Our data pinpoints increased DR expression as an early transcriptomic indicator of cardiotoxicity, suggesting that DR expression might function as a predictive biomarker for cardiac damage.
Anthracyclines are a class of chemotherapy drugs that are highly effective for the treatment of human cancers, but their clinical use is limited by associated dose-dependent cardiotoxicity. The precise mechanisms by which individual anthracycline induces cardiotoxicity are not fully understood. Human-induced pluripotent stem cell–derived cardiomyocytes (hiPSC-CMs) are emerging as a physiologically relevant model to assess drugs cardiotoxicity. Here, we describe an assay platform by coupling hiPSC-CMs and impedance measurement, which allows real-time monitoring of cardiomyocyte cellular index, beating amplitude, and beating rate. Using this approach, we have performed comparative studies on a panel of four anthracycline drugs (doxorubicin, epirubicin, idarubicin, and daunorubicin) which share a high degree of structural similarity but are associated with distinct cardiotoxicity profiles and maximum cumulative dose limits. Notably, results from our hiPSC-CMs impedance model (dose-dependent responses and EC50 values) agree well with the recommended clinical dose limits for these drugs. Using time-lapse imaging and RNAseq, we found that the differences in anthracycline cardiotoxicity are closely linked to extent of cardiomyocyte uptake and magnitude of activation/inhibition of several cellular pathways such as death receptor signaling, ROS production, and dysregulation of calcium signaling. The results provide molecular insights into anthracycline cardiac interactions and offer a novel assay system to more robustly assess potential cardiotoxicity during drug development.
To evaluate the expression of immune checkpoint genes, their concordance with expression of IFNγ, and to identify potential novel ICP related genes (ICPRG) in colorectal cancer (CRC), the biological connectivity of six well documented ("classical") ICPs (CTLA4, PD1, PDL1, Tim3, IDO1, and LAG3) with IFNγ and its co-expressed genes was examined by NGS in 79 CRC/healthy colon tissue pairs. Identification of novel IFNγ-induced molecules with potential ICP activity was also sought. In our study, the six classical ICPs were statistically upregulated and correlated with IFNγ, CD8A, CD8B, CD4, and 180 additional immunologically related genes in IFNγ positive (FPKM > 1) tumors. By ICP co-expression analysis, we also identified three IFNγ-induced genes [(IFNγ-inducible lysosomal thiol reductase (IFI30), guanylate binding protein1 (GBP1), and guanylate binding protein 4 (GBP4)] as potential novel ICPRGs. These three genes were upregulated in tumor compared to normal tissues in IFNγ positive tumors, co-expressed with CD8A and had relatively high abundance (average FPKM = 362, 51, and 25, respectively), compared to the abundance of the 5 well-defined ICPs (Tim3, LAG3, PDL1, CTLA4, PD1; average FPKM = 10, 9, 6, 6, and 2, respectively), although IDO1 is expressed at comparably high levels (FPKM = 39). We extended our evaluation by querying the TCGA database which revealed the commonality of IFNγ dependent expression of the three potential ICPRGs in 638 CRCs, 103 skin cutaneous melanomas (SKCM), 1105 breast cancers (BC), 184 esophageal cancers (ESC), 416 stomach cancers (STC), and 501 lung squamous carcinomas (LUSC). In terms of prognosis, based on Pathology Atlas data, correlation of GBP1 and GBP4, but not IFI30, with 5-year survival rate was favorable in CRC, BC, SKCM, and STC. Thus, further studies defining the role of IFI30, GBP1, and GBP4 in CRC are warranted.
Autophagy drives drug resistance and drug-induced cancer cell cytotoxicity. Targeting the autophagy process could greatly improve chemotherapy outcomes. The discovery of specific inhibitors or activators has been hindered by challenges with reliably measuring autophagy levels in a clinical setting. We investigated drug-induced autophagy in breast cancer cell lines with differing ER/PR/Her2 receptor status by exposing them to known but divergent autophagy inducers each with a unique molecular target, tamoxifen, trastuzumab, bortezomib or rapamycin. Differential gene expression analysis from total RNA extracted during the earliest sign of autophagy flux showed both cell- and drug-specific changes. We analyzed the list of differentially expressed genes to find a common, cell- and drug-agnostic autophagy signature. Twelve mRNAs were significantly modulated by all the drugs and 11 were orthogonally verified with Q-RT-PCR (Klhl24, Hbp1, Crebrf, Ypel2, Fbxo32, Gdf15, Cdc25a, Ddit4, Psat1, Cd22, Ypel3). The drug agnostic mRNA signature was similarly induced by a mitochondrially targeted agent, MitoQ. In-silico analysis on the KM-plotter cancer database showed that the levels of these mRNAs are detectable in human samples and associated with breast cancer prognosis outcomes of Relapse-Free Survival in all patients (RSF), Overall Survival in all patients (OS), and Relapse-Free Survival in ER+ Patients (RSF ER+). High levels of Klhl24, Hbp1, Crebrf, Ypel2, CD22 and Ypel3 were correlated with better outcomes, whereas lower levels of Gdf15, Cdc25a, Ddit4 and Psat1 were associated with better prognosis in breast cancer patients. This gene signature uncovers candidate autophagy biomarkers that could be tested during preclinical and clinical studies to monitor the autophagy process.
AI for infectious disease modelling and therapeutics is an emerging area that leverages new computational approaches and data in this area. Genomics, proteomics, biomedical literature, social media, and other resources are proving to be critical tools to help understand and solve complicated issues ranging from understanding the process of infection, diagnosis and discovery of the precise molecular details, to developing possible interventions and safety profiling of possible treatments.
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