Identification of biomarkers and their functions in dasatinib-resistant pancreatic cancer using bioinformatics analysis

Laminin 332 expression levels predict clinical outcomes and chemotherapy response in patients with pancreatic adenocarcinoma

Poor outcomes and chemotherapy resistance for patients with pancreatic adenocarcinoma (PAAD) are a challenge worldwide, and new or improved prognostic biomarkers are urgently required. Individual laminin family members have been established as cancer-associated markers, predicting patient outcomes in many cancer types, including PAAD. Here, we used multiple modalities including RNAseq and gene chip, and genomic and proteomic data to examine the relationships of all laminin genes in PAAD with clinical outcomes. These analyses identified that LAMA3, LAMB3, and LAMC2 expression levels are increased at the mRNA and protein levels in PAAD tumours with evidence of co-regulation. Increased expression of all three genes was associated with decreased promoter methylation status, TP53 mutations, and altered receptor tyrosine kinase (RTK) pathways. Clinically, high LAMA3, LAMB3, and LAMC2 transcript abundance was each related to an advanced histological grade. Moreover, high expression of these genes individually predicted poor patient survival, while a signature of combined high expression of LAMA3, LAMB3, and LAMC2 was a stronger predictor of patient outcomes than each gene alone. Interestingly, cell lines with high expression of LM332 chains were not sensitive to the commonly used PAAD chemotherapy drugs paclitaxel and gemcitabine; however, increased sensitivity was evident for erlotinib, afatinib, gefitinib, and cetuximab epidermal growth factor (EGFR) RTK inhibitors. To explore possible mechanisms, we investigated co-expressed genes, identifying eight hub genes, namely, GJB3, ITGB6, SERPINB5, GPRC5A, PLEK2, TMPRSS4, P2RY2, and TRIM29, which are co-expressed with all three of LAMA3, LAMB3, and LAMC2. Of these, only SERPINB5 provided a stronger predictive value than the laminin-encoding genes. Together, these multiple integrated analyses suggest that the combined expression of LM332 is a useful prognostic biomarker for PAAD and could help patient stratification and therapeutic selection.

Network controllability solutions for computational drug repurposing using genetic algorithms

Control theory has seen recently impactful applications in network science, especially in connections with applications in network medicine. A key topic of research is that of finding minimal external interventions that offer control over the dynamics of a given network, a problem known as network controllability. We propose in this article a new solution for this problem based on genetic algorithms. We tailor our solution for applications in computational drug repurposing, seeking to maximize its use of FDA-approved drug targets in a given disease-specific protein-protein interaction network. We demonstrate our algorithm on several cancer networks and on several random networks with their edges distributed according to the Erdős-Rényi, the Scale-Free, and the Small World properties. Overall, we show that our new algorithm is more efficient in identifying relevant drug targets in a disease network, advancing the computational solutions needed for new therapeutic and drug repurposing approaches.

N-Glycoproteomics Study of Putative N-Glycoprotein Biomarkers of Drug Resistance in MCF-7/ADR Cells

Currently, drug resistance of anti-cancer therapy has become the main cause of low survival rate and poor prognosis. Full understanding of drug resistance mechanisms is an urgent request for further development of anti-cancer therapy and improvement of prognosis. Here we present our N-glycoproteomics study of putative N-glycoprotein biomarkers of drug resistance in doxorubicin resistance breast cancer cell line michigan cancer foundation-7 (MCF-7/ADR) relative to parental michigan cancer foundation-7 (MCF-7) cells. Intact N-glycopeptides (IDs) from MCF-7/ADR and MCF-7 cells were enriched with zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC), labeled with stable isotopic diethylation (SIDE), and analyzed with C18-RPLC-MS/MS (HCD with stepped normalized collision energies); these IDs were identified with database search engine GPSeeker, and the differentially expressed intact N-glycopeptides (DEGPs) were quantified with GPSeekerQuan. With target-decoy searches and control of spectrum-level FDR ≤ 1%, 322 intact N-glycopeptides were identified; these intact N-glycopeptides come from the combination of 249 unique peptide backbones (corresponding to 234 intact N-glycoproteins) and 90 monosaccharide compositions (corresponding to 248 putative N-glycosites). The sequence structures of 165 IDs were confirmed with structure-diagnostic fragment ions. With the criteria of observation at least twice among the three technical replicates, ≥ 1.5-fold change and p value < 0.05, 20 DEGPs were quantified, where five of them were up-regulated and 15 of them were down-regulated; the corresponding intact N-glycoproteins as putative markers of drug resistance were discussed.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-021-00029-8.

The State-of-the-Art of Phase II/III Clinical Trials for Targeted Pancreatic Cancer Therapies

Pancreatic cancer is a devastating disease with very poor prognosis. Currently, surgery followed by adjuvant chemotherapy represents the only curative option which, unfortunately, is only available for a small group of patients. The majority of pancreatic cancer cases are diagnosed at advanced or metastatic stage when surgical resection is not possible and treatment options are limited. Thus, novel and more effective therapeutic strategies are urgently needed. Molecular profiling together with targeted therapies against key hallmarks of pancreatic cancer appear as a promising approach that could overcome the limitations of conventional chemo- and radio-therapy. In this review, we focus on the latest personalised and multimodal targeted therapies currently undergoing phase II or III clinical trials. We discuss the most promising findings of agents targeting surface receptors, angiogenesis, DNA damage and cell cycle arrest, key signalling pathways, immunotherapies, and the tumour microenvironment.