MAIN BODY: In this review, we summarized the evidence and unique properties of TME in pancreatic cancer that may contribute to its resistance towards immunotherapies as well as strategies to overcome those barriers. We reviewed the current strategies and future perspectives of combination therapies that (1) promote T cell priming through tumor associated antigen presentation; (2) inhibit tumor immunosuppressive environment; and (3) break-down the desmoplastic barrier which improves tumor infiltrating lymphocytes entry into the TME.
CONCLUSIONS: It is imperative for clinicians and scientists to understand tumor immunology, identify novel biomarkers, and optimize the position of immunotherapy in therapeutic sequence, in order to improve pancreatic cancer clinical trial outcomes. Our collaborative efforts in targeting pancreatic TME will be the mainstay of achieving better clinical prognosis among pancreatic cancer patients. Ultimately, pancreatic cancer will be a treatable medical condition instead of a death sentence for a patient.
RESULTS: We present an automated gene prediction pipeline, Seqping that uses self-training HMM models and transcriptomic data. The pipeline processes the genome and transcriptome sequences of the target species using GlimmerHMM, SNAP, and AUGUSTUS pipelines, followed by MAKER2 program to combine predictions from the three tools in association with the transcriptomic evidence. Seqping generates species-specific HMMs that are able to offer unbiased gene predictions. The pipeline was evaluated using the Oryza sativa and Arabidopsis thaliana genomes. Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis showed that the pipeline was able to identify at least 95% of BUSCO's plantae dataset. Our evaluation shows that Seqping was able to generate better gene predictions compared to three HMM-based programs (MAKER2, GlimmerHMM and AUGUSTUS) using their respective available HMMs. Seqping had the highest accuracy in rice (0.5648 for CDS, 0.4468 for exon, and 0.6695 nucleotide structure) and A. thaliana (0.5808 for CDS, 0.5955 for exon, and 0.8839 nucleotide structure).
CONCLUSIONS: Seqping provides researchers a seamless pipeline to train species-specific HMMs and predict genes in newly sequenced or less-studied genomes. We conclude that the Seqping pipeline predictions are more accurate than gene predictions using the other three approaches with the default or available HMMs.
METHODS: The correlation of these variants to the plasma BDNF level among Malaysian MDD patients was assessed. A total of 300 cases and 300 matched controls recruited from four public hospitals within the Klang Valley of Selangor State, Malaysia and matched for age, sex and ethnicity were screened for BDNF rs6265, rs1048218 and rs1048220 using high resolution melting (HRM).
FINDINGS: BDNF rs1048218 and BDNF rs1048220 were monomorphic and were excluded from further analysis. The distribution of the alleles and genotypes for BDNF rs6265 was in Hardy-Weinberg equilibrium for the controls (p = 0.13) but was in Hardy Weinberg disequilibrium for the cases (p = 0.011). Findings from this study indicated that having BDNF rs6265 in the Malaysian population increase the odds of developing MDD by 2.05 folds (95% CI = 1.48-3.65). Plasma from 206 cases and 206 controls were randomly selected to measure the BDNF level using enzyme-linked immunosorbent assay (ELISA). A significant decrease in the plasma BDNF level of the cases as compared to controls (p<0.0001) was observed. However, there was no evidence of the effect of the rs6265 genotypes on the BDNF level indicating a possible role of other factors in modulating the BDNF level that warrants further investigation.
CONCLUSION: The study indicated that having the BDNF rs6265 allele (A) increase the risk of developing MDD in the Malaysian population suggesting a possible role of BDNF in the etiology of the disorder.