Molecular portraits of colorectal cancer morphological regions.

Heterogeneity of colorectal carcinoma (CRC) represents a major hurdle towards personalized medicine. Efforts based on whole tumor profiling demonstrated that the CRC molecular subtypes were associated with specific tumor morphological patterns representing tumor subregions. We hypothesize that whole-tumor molecular descriptors depend on the morphological heterogeneity with significant impact on current molecular predictors. We investigated intra-tumor heterogeneity by morphology-guided transcriptomics to better understand the links between gene expression and tumor morphology represented by six morphological patterns (morphotypes): complex tubular, desmoplastic, mucinous, papillary, serrated, and solid/trabecular. Whole-transcriptome profiling by microarrays of 202 tumor regions (morphotypes, tumor-adjacent normal tissue, supportive stroma, and matched whole tumors) from 111 stage II-IV CRCs identified morphotype-specific gene expression profiles and molecular programs and differences in their cellular buildup. The proportion of cell types (fibroblasts, epithelial and immune cells) and differentiation of epithelial cells were the main drivers of the observed disparities with activation of EMT and TNF-α signaling in contrast to MYC and E2F targets signaling, defining major gradients of changes at molecular level. Several gene expression-based (including single-cell) classifiers, prognostic and predictive signatures were examined to study their behavior across morphotypes. Most exhibited important morphotype-dependent variability within same tumor sections, with regional predictions often contradicting the whole-tumor classification. The results show that morphotype-based tumor sampling allows the detection of molecular features that would otherwise be distilled in whole tumor profile, while maintaining histopathology context for their interpretation. This represents a practical approach at improving the reproducibility of expression profiling and, by consequence, of gene-based classifiers.

Combination of Urinary MiR-501 and MiR-335 With Current Clinical Diagnostic Parameters as Potential Predictive Factors of Prostate Biopsy Outcome.

BACKGROUND: The detection of prostate cancer (PCa) is currently based on prostate-specific antigen (PSA) quantification as an initial screening followed by ultrasound-guided transrectal biopsy. However, the high rate of false-negative biopsies often leads to inappropriate treatment. Therefore, new molecular biomarkers, such as urine microRNAs (miRNAs), are a possible way to redefine PCa diagnostics. PATIENTS AND METHODS: Urine samples of 356 patients undergoing prostate biopsy (256 cases with confirmed prostate cancer, 100 cases with negative prostate biopsy) at the Masaryk Memorial Cancer Institute (Czech Republic) and additional 36 control subjects (healthy controls, benign prostatic hyperplasia - BPH) were divided into the discovery and validation cohorts and analyzed. In the discovery phase, small RNA sequencing was performed using the QIAseq miRNA Library Kit and the NextSeq 500 platform. Identified miRNA candidates were validated by the RT-qPCR method in the independent validation phase. RESULTS: Using the small RNA sequencing method, we identified 12 urine miRNAs significantly dysregulated between PCa patients and controls. Furthermore, independent validation showed the ability of miR-501-3p and the quantitative miR-335:miR-501 ratio to distinguish between PCa patients and patients with negative prostate biopsy. The subsequent combination of the miR-335:miR-501 ratio with PSA and total prostate volume (TPV) using logistic regression exceeded the analytical accuracy of standalone parameters [area under curve (AUC)=0.75, positive predictive value (PPV)=0.85, negative predictive value (NPV)=0.51)] and discriminated patients according to biopsy outcome. CONCLUSION: Combination of miR-335:miR-501 ratio with PSA and total prostate volume was able to identify patients with negative prostate biopsy and could potentially streamline decision making for biopsy indication.

Long non-coding RNAs enable precise diagnosis and prediction of early relapse after nephrectomy in patients with renal cell carcinoma.

PURPOSE: Renal cell carcinoma belongs among the deadliest malignancies despite great progress in therapy and accessibility of primary care. One of the main unmet medical needs remains the possibility of early diagnosis before the tumor dissemination and prediction of early relapse and disease progression after a successful nephrectomy. In our study, we aimed to identify novel diagnostic and prognostic biomarkers using next-generation sequencing on a novel cohort of RCC patients. METHODS: Global expression profiles have been obtained using next-generation sequencing of paired tumor and non-tumor tissue of 48 RCC patients. Twenty candidate lncRNA have been selected for further validation on an independent cohort of paired tumor and non-tumor tissue of 198 RCC patients. RESULTS: Sequencing data analysis showed significant dysregulation of more than 2800 lncRNAs. Out of 20 candidate lncRNAs selected for validation, we confirmed that 14 of them are statistically significantly dysregulated. In order to yield better discriminatory results, we combined several best performing lncRNAs into diagnostic and prognostic models. A diagnostic model consisting of AZGP1P1, CDKN2B-AS1, COL18A1, and RMST achieved AUC 0.9808, sensitivity 95.96%, and specificity 90.4%. The model for prediction of early relapse after nephrectomy consists of COLCA1, RMST, SNHG3, and ZNF667-AS1 and achieved AUC 0.9241 with sensitivity 93.75% and specificity 71.07%. Notably, no combination has outperformed COLCA1 alone. Lastly, a model for stage consists of ZNF667-AS1, PVT1, RMST, LINC00955, and TCL6 and achieves AUC 0.812, sensitivity 85.71%, and specificity 69.41%. CONCLUSION: In our work, we identified several lncRNAs as potential biomarkers and developed models for diagnosis and prognostication in relation to stage and early relapse after nephrectomy.

A tissue miRNA expression pattern is associated with disease aggressiveness of localized prostate cancer.

BACKGROUND: Prostate cancer (PCa) is a heterogeneous malignancy with high variability in clinical course. Insufficient stratification according to the aggressiveness at the time of diagnosis causes unnecessary or delayed treatment. Current stratification systems are not effective enough because they are based on clinical, surgical or biochemical parameters, but do not take into account molecular factors driving PCa cancerogenesis. MicroRNAs (miRNAs) are important players in molecular pathogenesis of PCa and could serve as valuable biomarkers for the assessment of disease aggressiveness and its prognosis. METHODS: In the study, in total, 280 PCa patients were enrolled. The miRNA expression profiles were analyzed in FFPE PCa tissue using the miRCURY LNA miRNA PCR System. The expression levels of candidate miRNAs were further verified by two-level validation using the RT-qPCR method and evaluated in relation to PCa stratification reflecting the disease aggressiveness. RESULTS: MiRNA profiling revealed 172 miRNAs dysregulated between aggressive (ISUP 3-5) and indolent PCa (ISUP 1) (p < 0.05). In the training and validation cohort, miR-15b-5p and miR-106b-5p were confirmed to be significantly upregulated in tissue of aggressive PCa when their level was associated with disease aggressiveness. Furthermore, we established a prognostic score combining the level of miR-15b-5p and miR-106b-5p with serum PSA level, which discriminated indolent PCa from an aggressive form with even higher analytical parameters (AUC being 0.9338 in the training set and 0.8014 in the validation set, respectively). The score was also associated with 5-year biochemical progression-free survival (bPFS) of PCa patients. CONCLUSIONS: We identified a miRNA expression pattern associated with disease aggressiveness in prostate cancer patients. These miRNAs may be of biological interest as the focus can be also set on their specific role within the molecular pathology and the molecular mechanism that underlies the aggressivity of prostate cancer.

HLA-E and HLA-F Are Overexpressed in Glioblastoma and HLA-E Increased After Exposure to Ionizing Radiation.

BACKGROUND/AIM: Glioblastoma (GBM) is one of the deadliest human cancers responding very poorly to therapy. Although the central nervous system has been traditionally considered an immunologically privileged site with an enhanced immune response, GBM appears to benefit from this immunosuppressive milieu. Immunomodulatory molecules play an important role in immune tumor-host interactions. Non-classical human leukocyte antigens (HLA) class Ib molecules HLA-E, HLA-F, and HLA-G have been previously described to be involved in protecting semi-allogeneic fetal allografts from the maternal immune response and in transplant tolerance as well as tumoral immune escape. Unfortunately, their role in GBM remains poorly understood. Our study, therefore, aimed to characterize the relationship between the expression of these molecules in GBM on the transcriptional level and clinicopathological and molecular features of GBM as well as the effect of ionizing radiation. MATERIALS AND METHODS: We performed the analysis of HLA-E, HLA-F, and HLA-G mRNA expression in 69 GBM tissue samples and 21 non-tumor brain tissue samples (controls) by reverse transcription polymerase chain reaction. Furthermore, two primary GBM cell cultures had been irradiated to identify the effect of ionizing radiation on the expression of non-classical HLA molecules. RESULTS: Analyses revealed that both HLA-E and HLA-F are significantly up-regulated in GBM samples. Subsequent survival analysis showed a significant association between low expression of HLA-E and shorter survival of GBM patients. The dysregulated expression of both molecules was also observed between patients with methylated and unmethylated O-6-methylguanine-DNA methyltransferase (MGMT) promoter. Finally, we showed that ionizing radiation increased HLA-E expression level in GBM cells in vitro. CONCLUSION: HLA-E and HLA-F play an important role in GBM biology and could be used as diagnostic biomarkers, and in the case of HLA-E also as a prognostic biomarker.