Development and validation of a clinical decision support system based on PSA, microRNAs, and MRI for the detection of prostate cancer.

OBJECTIVES: The aims of this study are to develop and validate a clinical decision support system based on demographics, prostate-specific antigen (PSA), microRNA (miRNA), and MRI for the detection of prostate cancer (PCa) and clinical significant (cs) PCa, and to assess if this system performs better compared to MRI alone. METHODS: This retrospective, multicenter, observational study included 222 patients (mean age 66, range 46-75 years) who underwent prostate MRI, miRNA (let-7a-5p and miR-103a-3p) assessment, and biopsy. Monoparametric and multiparametric models including age, PSA, miRNA, and MRI outcome were trained on 65% of the data and then validated on the remaining 35% to predict both PCa (any Gleason grade [GG]) and csPCa (GG ≥ 2 vs GG = 1/negative). Accuracy, sensitivity, specificity, positive and negative predictive value (NPV), and area under the receiver operating characteristic curve were calculated. RESULTS: MRI outcome was the best predictor in the monoparametric model for both detection of PCa, with sensitivity of 90% (95%CI 73-98%) and NPV of 93% (95%CI 82-98%), and for csPCa identification, with sensitivity of 91% (95%CI 72-99%) and NPV of 95% (95%CI 84-99%). Sensitivity and NPV of PSA + miRNA for the detection of csPCa were not statistically different from the other models including MRI alone. CONCLUSION: MRI stand-alone yielded the best prediction models for both PCa and csPCa detection in biopsy-naïve patients. The use of miRNAs let-7a-5p and miR-103a-3p did not improve classification performances compared to MRI stand-alone results. CLINICAL RELEVANCE STATEMENT: The use of miRNA (let-7a-5p and miR-103a-3p), PSA, and MRI in a clinical decision support system (CDSS) does not improve MRI stand-alone performance in the detection of PCa and csPCa. KEY POINTS: • Clinical decision support systems including MRI improve the detection of both prostate cancer and clinically significant prostate cancer with respect to PSA test and/or microRNA. • The use of miRNAs let-7a-5p and miR-103a-3p did not significantly improve MRI stand-alone performance. • Results of this study were in line with previous works on MRI and microRNA.

Identification of a minimum number of genes to predict triple-negative breast cancer subgroups from gene expression profiles.

BACKGROUND: Triple-negative breast cancer (TNBC) is a very heterogeneous disease. Several gene expression and mutation profiling approaches were used to classify it, and all converged to the identification of distinct molecular subtypes, with some overlapping across different approaches. However, a standardised tool to routinely classify TNBC in the clinics and guide personalised treatment is lacking. We aimed at defining a specific gene signature for each of the six TNBC subtypes proposed by Lehman et al. in 2011 (basal-like 1 (BL1); basal-like 2 (BL2); mesenchymal (M); immunomodulatory (IM); mesenchymal stem-like (MSL); and luminal androgen receptor (LAR)), to be able to accurately predict them. METHODS: Lehman's TNBCtype subtyping tool was applied to RNA-sequencing data from 482 TNBC (GSE164458), and a minimal subtype-specific gene signature was defined by combining two class comparison techniques with seven attribute selection methods. Several machine learning algorithms for subtype prediction were used, and the best classifier was applied on microarray data from 72 Italian TNBC and on the TNBC subset of the BRCA-TCGA data set. RESULTS: We identified two signatures with the 120 and 81 top up- and downregulated genes that define the six TNBC subtypes, with prediction accuracy ranging from 88.6 to 89.4%, and even improving after removal of the least important genes. Network analysis was used to identify highly interconnected genes within each subgroup. Two druggable matrix metalloproteinases were found in the BL1 and BL2 subsets, and several druggable targets were complementary to androgen receptor or aromatase in the LAR subset. Several secondary drug-target interactions were found among the upregulated genes in the M, IM and MSL subsets. CONCLUSIONS: Our study took full advantage of available TNBC data sets to stratify samples and genes into distinct subtypes, according to gene expression profiles. The development of a data mining approach to acquire a large amount of information from several data sets has allowed us to identify a well-determined minimal number of genes that may help in the recognition of TNBC subtypes. These genes, most of which have been previously found to be associated with breast cancer, have the potential to become novel diagnostic markers and/or therapeutic targets for specific TNBC subsets.

SOMAscan Proteomics Identifies Novel Plasma Proteins in Amyotrophic Lateral Sclerosis Patients.

Amyotrophic lateral sclerosis (ALS) is a complex disease characterized by the interplay of genetic and environmental factors for which, despite decades of intense research, diagnosis remains rather delayed, and most therapeutic options fail. Therefore, unravelling other potential pathogenetic mechanisms and searching for reliable markers are high priorities. In the present study, we employ the SOMAscan assay, an aptamer-based proteomic technology, to determine the circulating proteomic profile of ALS patients. The expression levels of ~1300 proteins were assessed in plasma, and 42 proteins with statistically significant differential expression between ALS patients and healthy controls were identified. Among these, four were upregulated proteins, Thymus- and activation-regulated chemokine, metalloproteinase inhibitor 3 and nidogen 1 and 2 were selected and validated by enzyme-linked immunosorbent assays in an overlapping cohort of patients. Following statistical analyses, different expression patterns of these proteins were observed in the familial and sporadic ALS patients. The proteins identified in this study might provide insight into ALS pathogenesis and represent potential candidates to develop novel targeted therapies.

Meta-analysis of diagnostic cell-free circulating microRNAs for breast cancer detection.

BACKGROUND: Breast cancer (BC) is the most frequently diagnosed cancer among women. Numerous studies explored cell-free circulating microRNAs as diagnostic biomarkers of BC. As inconsistent and rarely intersecting microRNA panels have been reported thus far, we aim to evaluate the overall diagnostic performance as well as the sources of heterogeneity between studies. METHODS: Based on the search of three online search engines performed up to March 21st 2022, 56 eligible publications that investigated diagnostic circulating microRNAs by utilizing Real-Time Quantitative Reverse Transcription PCR (qRT-PCR) were obtained. Primary studies' potential for bias was evaluated with the revised tool for the quality assessment of diagnostic accuracy studies (QUADAS-2). A bivariate generalized linear mixed-effects model was applied to obtain pooled sensitivity and specificity. A novel methodology was utilized in which the sample and study models' characteristics were analysed to determine the potential preference of studies for sensitivity or specificity. RESULTS: Pooled sensitivity and specificity of 0.85 [0.81-0.88] and 0.83 [0.79-0.87] were obtained, respectively. Subgroup analysis showed a significantly better performance of multiple (sensitivity: 0.90 [0.86-0.93]; specificity: 0.86 [0.80-0.90]) vs single (sensitivity: 0.82 [0.77-0.86], specificity: 0.83 [0.78-0.87]) microRNA panels and a comparable pooled diagnostic performance between studies using serum (sensitivity: 0.87 [0.81-0.91]; specificity: 0.83 [0.78-0.87]) and plasma (sensitivity: 0.83 [0.77-0.87]; specificity: 0.85 [0.78-0.91]) as specimen type. In addition, based on bivariate and univariate analyses, miRNA(s) based on endogenous normalizers tend to have a higher diagnostic performance than miRNA(s) based on exogenous ones. Moreover, a slight tendency of studies to prefer specificity over sensitivity was observed. CONCLUSIONS: In this study the diagnostic ability of circulating microRNAs to diagnose BC was reaffirmed. Nonetheless, some subgroup analyses showed between-study heterogeneity. Finally, lack of standardization and of result reproducibility remain the biggest issues regarding the diagnostic application of circulating cell-free microRNAs.

TRF2 positively regulates SULF2 expression increasing VEGF-A release and activity in tumor microenvironment.

The telomeric protein TRF2 is overexpressed in several human malignancies and contributes to tumorigenesis even though the molecular mechanism is not completely understood. By using a high-throughput approach based on the multiplexed Luminex X-MAP technology, we demonstrated that TRF2 dramatically affects VEGF-A level in the secretome of cancer cells, promoting endothelial cell-differentiation and angiogenesis. The pro-angiogenic effect of TRF2 is independent from its role in telomere capping. Instead, TRF2 binding to a distal regulatory element promotes the expression of SULF2, an endoglucosamine-6-sulfatase that impairs the VEGF-A association to the plasma membrane by inducing post-synthetic modification of heparan sulfate proteoglycans (HSPGs). Finally, we addressed the clinical relevance of our findings showing that TRF2/SULF2 expression is a worse prognostic biomarker in colorectal cancer (CRC) patients.

Gene Expression Signature Predictive of Neuroendocrine Transformation in Prostate Adenocarcinoma.

Neuroendocrine prostate cancer (NEPC) can arise de novo, but much more commonly occurs as a consequence of a selective pressure from androgen deprivation therapy or androgen receptor antagonists used for prostate cancer (PCa) treatment. The process is known as neuroendocrine transdifferentiation. There is little molecular characterization of NEPCs and consequently there is no standard treatment for this kind of tumors, characterized by highly metastases rates and poor survival. For this purpose, we profiled 54 PCa samples with more than 10-years follow-up for gene and miRNA expression. We divided samples into two groups (NE-like vs. AdenoPCa), according to their clinical and molecular features. NE-like tumors were characterized by a neuroendocrine fingerprint made of known neuroendocrine markers and novel molecules, including long non-coding RNAs and components of the estrogen receptor signaling. A gene expression signature able to predict NEPC was built and tested on independently published datasets. This study identified molecular features (protein-coding, long non-coding, and microRNAs), at the time of surgery, that may anticipate the NE transformation process of prostate adenocarcinoma. Our results may contribute to improving the diagnosis and treatment of this subgroup of tumors for which traditional therapy regimens do not show beneficial effects.

Circulating microRNAs combined with PSA for accurate and non-invasive prostate cancer detection.

The dosage of prostate-specific antigen (PSA), an easily evaluable and non-invasive biomarker, has made early detection of prostate cancer (PCa) possible. However, it leads to high percentages of unnecessary biopsies and may miss aggressive tumors in men with PSA levels below 4 ng/ml. Therefore, we propose to combine circulating microRNAs (miRs) with PSA, to improve the diagnostic route for PCa. Plasma miR profiling identified candidate diagnostic miRs in a discovery cohort of 60 tumors and 60 controls (men with benign prostatic hyperplasia or healthy donors). Linear models with an empirical Bayesian approach and multivariate penalized logistic regression were applied to select tumor-associated miRs and/or clinical variables. A classifier was developed and tested on a validation cohort of 68 tumors and 174 controls consecutively collected, where miRs were evaluated by quantitative real-time polymerase chain reaction. A classifier based on miR-103a-3p, let-7a-5p and PSA could detect both overall and clinically significant tumors better than PSA alone, even in 50-69 years aged men with PSA ≤ 4 ng/ml. Even in the validation cohort, the classifier performed better than PSA alone in terms of specificity and positive predictive value, allowing to correctly identify eight out of nine tumors undetected by PSA, including three high-risk and three tumors in 50-69 years old men. Of carriers of non-malignant lesions with PSA in the 4-16 ng/ml interval, who may avoid unnecessary biopsies, 34% were correctly identified. Coupling two circulating miRs with PSA could be a useful strategy to diagnose clinically significant PCa and avoid an important fraction of unnecessary biopsies.

Systematic evaluation of the microRNAome through miR-CATCHv2.0 identifies positive and negative regulators of BRAF-X1 mRNA.

Here we present miR-CATCHv2.0, an implemented experimental method that allows the identification of the microRNA species directly bound to an RNA of interest. After cross-linking of microRNA::RNA::Ago2 complexes using formaldehyde, the RNA is fragmented using sonication and then subjected to affinity purification using two sets of biotinylated tiling probes (ODD and EVEN). Finally, enriched microRNA species are retrieved by means of small RNA sequencing coupled with an ad hoc analytical workflow. In BRAFV600E mutant A375 melanoma cells, miR-CATCHv2.0 allowed us to identify 20 microRNAs that target X1, the most abundant isoform of BRAF mRNA. These microRNAs fall into different functional classes, according to the effect that they exert (decrease/increase in BRAFV600E mRNA and protein levels) and to the mechanism they use to achieve it (destabilization/stabilization of X1 mRNA or decrease/increase in its translation). microRNA-induced variations in BRAFV600E protein levels are most of the times coupled to consistent variations in pMEK levels, in melanoma cell proliferation in vitro and in sensitivity to the BRAF inhibitor vemurafenib in a xenograft model in zebrafish. However, microRNAs exist that uncouple the degree of activation of the ERK pathway from the levels of BRAFV600E protein. Our study proposes miR-CATCHv2.0 as an effective tool for the identification of direct microRNA-target interactions and, by using such a tool, unveils the complexity of the post-transcriptional regulation to which BRAFV600E and the ERK pathway are subjected in melanoma cells.

Truncating mutations of TP53AIP1 gene predispose to cutaneous melanoma.

Genetic predisposition to cutaneous malignant melanoma (CMM) involves highly penetrant predisposing genes and low and intermediate penetrant predisposing alleles. However, the missing heritability in (CMM) is still high. For such and in order to identify new genetic factors for CMM, we conducted an exome sequencing study in high-risk CMM patients. Two rounds of exome sequencing were successively performed in 33 and 27 high-risk patients. We focused on genes carrying rare nonsense, frameshift, and splice variants (allelic frequency <1%) that were present in both series of exomes. An extension study was then conducted in a large cohort (1 079 CMM patients and 1 230 Caucasian ethnically matched healthy controls), and the inactivating variants frequency was compared between groups using two-sided Fisher exact test. Two TP53AIP1 truncating mutations were identified in four patients: a frameshift c.63_64insG, p.Q22Afs*81 in two patients from the same family and in the proband of a second family; and a nonsense mutation c.95 C > A, p.Ser32Stop in a patient with multiple CMMs. In all patients, TP53AIP1 truncating variants were strongly associated with CMM risk (two-sided Fisher exact test = 0.004, OR = 3.3[1.3-8.5]). Additionally, we showed that TP53AIP1 mRNA was strongly down-regulated throughout different phases of melanoma progression. TP53AIP1 gene is a TP53 target which plays a key role by inducting apoptosis in response to UV-induced DNA damage. Constitutional mutations of TP53AIP1 had previously been involved in susceptibility to prostate cancer. Our results show that constitutional truncating TP53AIP1 mutations predispose to CMM in the French population. Replication studies in other populations should be performed.