To explore several serum and genetic-based biomarkers that may prove useful in following men being managed with active surveillance for localized prostate cancer by predicting those that either have the potential to develop, or already harbor occult high grade disease.There is increasing evidence that serum biomarkers human Kallikrein 2, early prostate cancer antigen, urokinase-type plasminogen activator/urokinase-type plasminogen activator receptor, transforming growth factor-β1 and interleukin-6/interleukin-6 receptor and genetic biomarkers BRCA1 and BRCA2, Phosphatase and tensin homolog, cellular myelocytomatosis oncogene and NKX3.1 may predict for aggressive high grade disease and are identifiable early in prostate carcinogenesis.One of the barriers of widespread adoption of active surveillance for low risk, localized prostate cancer is the concern that some patients may harbor occult high-risk disease at diagnosis, or develop more aggressive/noncurable disease not detected by our current well established prognostic factors. This review examines several serum and genetic-based biomarkers that appear to be of value in localized prostate cancer, unlike the vast majority of more established prostate cancer biomarkers that have been validated in far more advanced disease. Although the biomarkers discussed show exciting promise, their clinical utility is unknown, and their role in the active surveillance scenario needs further study.
Here we present the result of our work on achieving sub-pM limit of quantitation in direct quantitative analysis of multiple miRNAs by capillary electophoresis with laser-induced fluorescence detection. The PDF file contains the description of the results with figures, references, and supporting information. The <i>raw data.ZIP</i> file is the archive containing (i) Excel files of raw data used to build figures presented in the PDF file and (ii) the <i>red-me.PDF </i>file explaining how to read and use the Excel files. The <i>figure data.ZIP</i> file is the archive of all figure files in Origin and Adobe Illustrator formats. <div><b>Abstract:</b> Thousands of putative miRNA-based cancer biomarkers have been reported but none has been validated for approval by the Food and Drug Administration. One of the reasons for this alarming discrepancy is the lack of a method which is sufficiently robust for carrying out validation studies, which may require analysis of samples from hundreds of patients across multiple institutions and pooling the results together. Capillary electrophoresis (CE)-based hybridization assay proved to be more robust than reversed transcription polymerase chain reaction (the current standard) but its limit of quantification (LOQ) exceeds 10 pM while miRNA concentrations in cell lysates are below 1 pM. Thus, CE-based separation must be preceded by on-column sample preconcentration. Here we explain challenges of sample preconcentration for CE-based miRNA analyses and introduce a preconcentration method that can suit CE-based miRNA analysis utilizing peptide nucleic acid (PNA) hybridization probes. The method combines field-amplified sample stacking (FASS) with isotachophoresis (ITP). We proved that FASS-ITP could retain and concentrate both near-neutral PNA with highly-negatively charged PNA–miRNA hybrids. We demonstrated that preconcentration by FASS-ITP could be combined with the CE-based separation of the unreacted PNA probes from the PNA–miRNA hybrids and facilitate improvement in LOQ by a factor of 140, down to 0.1 pM. Finally, we applied FASS-ITP-CE for simultaneous detection of two miRNAs in crude cell lysates and proved that the method was robust when used in complex biological matrices. The 140-fold improvement in LOQ and the robustness to biological matrices will significantly expand the applicability of CE-based miRNA analysis, bringing it closer to becoming a practical tool for validation of miRNA biomarkers. <div><br></div></div>
Aims Basal and luminal molecular subgroups of muscle‐invasive urothelial carcinoma (UC) can be recognised by the use of immunohistochemical markers. Studies have shown that responses to chemotherapy and outcomes differ among these subtypes. High‐grade UC of the bladder is an immunogenic neoplasm that induces a substantial intratumoral and peritumoral immune response; the phenotype of infiltrating immune cells may yield prognostic information and predict response to therapy. In this study, we aimed to correlate the immunohistochemical phenotype of high‐grade UC with immune microenvironment composition. Methods and results Two hundred and thirty‐five cases of high‐grade UC treated with cystectomy were reviewed. Clinicopathological variables for each case were recorded, and disease‐free survival at last follow‐up was calculated. Invasive front inflammation and tumour‐infiltrating lymphocytes were scored for each case. Two hundred and seven cases were used to construct a triplicate‐core tissue microarray (TMA), with sections stained for cytokeratin (CK) 5/6 and GATA3. Of the evaluable cases, 167 were designated as luminal (CK5/6− and GATA3+) and 29 as basal (CK5/6+ and GATA3−). Additional sequential TMA sections were stained for CD3, CD4, CD8, CD20, CD68, CD163, FOXP3, programmed cell death protein 1 (PD‐1), and programmed death‐ligand 1 (PD‐L1) (SP263). Basal‐subtype tumours showed a trend towards worse disease‐specific survival ( P = 0.078). There were statistically significant associations between basal subtype and CD8 expression ( P = 0.008), PD‐1 expression ( P = 0.001), and PD‐L1 expression ( P = 0.014). Lower CD4/CD8 and increased CD8/FOXP3 ratios ( P = 0.047 and P = 0.031, respectively) were also identified in the basal‐subtype group. Conclusions Basal‐subtype high‐grade UC has an abundance of CD8+ T cells with increased expression of inhibitory markers, indicative of a ‘hot’ immunophenotype.
<p>The genomic landscape of CF- and HF-resistant prostate cancer cells. <b>A,</b> Schematic of experimental design and workflow. <b>B,</b> Somatic SNV count for CF- vs. HF-resistant cells. CF-resistant cells gained twice more SNVs than HF (<i>P</i> = 0.1; Mann–Whitney <i>U</i> test). <b>C,</b> SNVs in cancer driver genes. Presented are all driver genes that are predicted to be strongly influenced by SNVs. Considered are SNVs that were identified in all three replicates for each cell type. Top, single-base substitution types. Bottom, the predicted annotation. <b>D,</b> Gained SNVs converged on partly similar cancer mutational signatures. Most signatures of known etiology, irrespective of the treatment schedule, are associated with defective DNA mismatch repair. Signature etiologies: SBS5, unknown; SBS26 and SBS15, defective DNA mismatch repair; SBS1, spontaneous deamination of 5-methylcytosine; SBS14, concurrent polymerase epsilon mutation and defective DNA mismatch repair; SBS20, concurrent <i>POLD1</i> mutations and defective DNA mismatch repair; SBS44, defective DNA mismatch repair. <b>E,</b> Somatic SV counts for CF- and HF-resistant cells. The number of somatic SVs is similar between CF- and HF-resistant cells (<i>P</i> = 1; Mann–Whitney <i>U</i> test). <b>F,</b> Distinct SVs in CF-resistant cells compared to HF across the genome. Considered are SVs that were identified in all three replicates for each cell type. Chromosome numbers are presented on the <i>x</i>-axis. The colored lines represent types of SVs: DEL, deletion; DUP, duplication; INV, inversion; TRA, translocation. <b>G,</b> Fusion transcripts that were identified in either CF- and/or HF-resistant cells. Purple, the fusion transcript was identified; white, the fusion transcript was not identified. The results are presented for three replicates for each cell line.</p>
<p>Fractionation-dependent protein profiles in radioresistant cells. <b>A,</b> The difference in relationships of consensus-module eigengenes and cellular fractions between CF- and HF-resistant cells. The colors represent the difference in correlations between consensus module eigengenes and a specific subcellular fraction of HF-resistant cells compared to CF: Blue, a higher correlation in CF-resistant cells; Red, a higher correlation in HF-resistant cells. At the top, each color represents a module, which is a detected group of positively correlated genes that are highly interconnected. <b>B,</b> Gene ontology enrichment of module genes for biological processes. The top two enrichments for each module are presented. <b>C,</b> Differences in protein abundances of driver, across different subcellular fractions and in whole cell lysates. Main, protein Cohen’s d effect sizes of significant proteins across cell fractions. Only significant changes at the level of FDR ≤ 0.025 were plotted. The dot color is the directionality: magenta and green represent upregulation and downregulation, respectively, toward CF- or HF-resistant cells. Right, the consensus modules that each gene was assigned to. For all figures, three replicates were used for CF- and HF- resistant cells. For the parental cells, at least two replicates were used.</p>
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