Comparison of oral cavity protein abundance among caries-free and caries-affected individuals-a systematic review and meta-analysis.

Objective: Some salivary proteins seem to be differently abundant among caries-free (CF) and caries-affected (CA) individuals, but previous results are contradictory precluding that definitive conclusion be drawn. A pooled analysis of the available evidence may provide more robust data on identifying oral cavity protein patterns among CF and CA individuals. This systematic review and meta-analysis (PROSPERO CRD42021269079) aimed to compare the oral cavity protein abundance among caries-free and caries-affected individuals. Methods: This study was conducted following PRISMA guidelines. PubMed, Embase, and Web of Science databases were systematically assessed (up to February 2023) to retrieve clinical studies written in English, German, or in Latin-based languages that compared the oral cavity protein abundance among CF and CA individuals. Data extraction and methodological quality assessment (NIH guidelines) were independently performed by two investigators. Qualitative synthesis was performed from all included studies and meta-analysis was performed using a random-effects model with inverse variance for studies that reported the concentration of proteins or enzymatic activity. Standardized mean difference (SMD) with respective 95% confidence interval (CI) were calculated for each outcome. Results: A total of 90 studies (two cohort and 88 cross-sectional designs) of more than 6,000 participants were selected for data extraction, being the quality of evidence graded as "fair" for most of them. The oral cavity of CF individuals presented lower total protein concentration [SMD = 0.37 (95% CI: 0.07-0.68; 18 studies)], lower total antioxidant capacity [SMD = 1.29 (95% CI: 0.74-1.85); 17 studies], and lower carbonic anhydrase activity [SMD = 0.83 (95% CI: 0.58-1.09); three studies], whereas CA individuals presented lower carbonic anhydrase concentration [SMD = -0.66 (95% CI: -1.00 to -0.32); three studies], urease [SMD = -0.95 (IC 95%: -1.72 to -0.17); four studies], and arginine deiminase system [SMD = -2.07 (95% CI: -3.53 to -0.62); three studies] activities. Antimicrobial peptides, secretory immunoglobulin-A concentrations and alpha-amylase activity were similar among individuals. Conclusion: Differences on oral cavity protein abundance were observed among CF and CA individuals. These data indicate some protein patterns for the oral health and dental caries conditions. Even when statistically significant, some of the results were not very consistent. Cohort studies need to be conducted to validate these results.

Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysis.

Introduction: Identification of tumor specific neoantigen (TSN) immunogenicity is crucial to develop peptide/mRNA based anti-tumoral vaccines and/or adoptive T-cell immunotherapies; thus, accurate in-silico classification/prioritization proves critical for cost-effective clinical applications. Several methods were proposed as TSNs immunogenicity predictors; however, comprehensive performance comparison is still lacking due to the absence of well documented and adequate TSN databases. Methods: Here, by developing a new curated database having 199 TSNs with experimentally-validated MHC-I presentation and positive/negative immune response (ITSNdb), sixteen metrics were evaluated as immunogenicity predictors. In addition, by using a dataset emulating patient derived TSNs and immunotherapy cohorts containing predicted TSNs for tumor neoantigen burden (TNB) with outcome association, the metrics were evaluated as TSNs prioritizers and as immunotherapy response biomarkers. Results: Our results show high performance variability among methods, highlighting the need for substantial improvement. Deep learning predictors were top ranked on ITSNdb but show discrepancy on validation databases. In overall, current predicted TNB did not outperform existing biomarkers. Conclusion: Recommendations for their clinical application and the ITSNdb are presented to promote development and comparison of computational TSNs immunogenicity predictors.

Novel evaluation approach for molecular signature-based deconvolution methods.

The tumoral immune microenvironment (TIME) plays a key role in prognosis, therapeutic approach and pathophysiological understanding over oncological processes. Several computational immune cell-type deconvolution methods (DM), supported by diverse molecular signatures (MS), have been developed to uncover such TIME interplay from RNA-seq tumor biopsies. MS-DM pairs were benchmarked against each other by means of different metrics, such as Pearson's correlation, R2 and RMSE, but these only evaluate the linear association of the estimated proportion related to the expected one, missing the analysis of prediction-dependent bias trends and cell identification accuracy. We present a novel protocol composed of four tests allowing appropriate evaluation of the cell type identification performance and proportion prediction accuracy of molecular signature-deconvolution method pair by means of certainty and confidence cell-type identification scores (F1-score, distance to the optimal point and error rates) as well the Bland-Altman method for error-trend analysis. Our protocol was used to benchmark six state-of-the-art DMs (CIBERSORTx, DCQ, DeconRNASeq, EPIC, MIXTURE and quanTIseq) paired to five murine tissue-specific MSs, revealing a systematic overestimation of the number of different cell types across almost all methods.

Pan-Cancer Molecular Patterns and Biological Implications Associated with a Tumor-Specific Molecular Signature.

Studying tissue-independent components of cancer and defining pan-cancer subtypes could be addressed using tissue-specific molecular signatures if classification errors are controlled. Since PAM50 is a well-known, United States Food and Drug Administration (FDA)-approved and commercially available breast cancer signature, we applied it with uncertainty assessment to classify tumor samples from over 33 cancer types, discarded unassigned samples, and studied the emerging tumor-agnostic molecular patterns. The percentage of unassigned samples ranged between 55.5% and 86.9% in non-breast tissues, and gene set analysis suggested that the remaining samples could be grouped into two classes (named C1 and C2) regardless of the tissue. The C2 class was more dedifferentiated, more proliferative, with higher centrosome amplification, and potentially more TP53 and RB1 mutations. We identified 28 gene sets and 95 genes mainly associated with cell-cycle progression, cell-cycle checkpoints, and DNA damage that were consistently exacerbated in the C2 class. In some cancer types, the C1/C2 classification was associated with survival and drug sensitivity, and modulated the prognostic meaning of the immune infiltrate. Our results suggest that PAM50 could be repurposed for a pan-cancer context when paired with uncertainty assessment, resulting in two classes with molecular, biological, and clinical implications.