The heterogeneity of breast cancer plays a major role in drug response and resistance and has been extensively characterized at the genomic level. Here, a single-cell breast cancer mass cytometry (BCMC) panel is optimized to identify cell phenotypes and their oncogenic signalling states in a biobank of patient-derived tumour xenograft (PDTX) models representing the diversity of human breast cancer. The BCMC panel identifies 13 cellular phenotypes (11 human and 2 murine), associated with both breast cancer subtypes and specific genomic features. Pre-treatment cellular phenotypic composition is a determinant of response to anticancer therapies. Single-cell profiling also reveals drug-induced cellular phenotypic dynamics, unravelling previously unnoticed intra-tumour response diversity. The comprehensive view of the landscapes of cellular phenotypic heterogeneity in PDTXs uncovered by the BCMC panel, which is mirrored in primary human tumours, has profound implications for understanding and predicting therapy response and resistance.

Mitochondrial dysfunction has pleiotropic effects and is frequently caused by mitochondrial DNA mutations. However, factors such as significant variability in clinical manifestations make interpreting the pathogenicity of variants in the mitochondrial genome challenging. Here, we present APOGEE 2, a mitochondrially-centered ensemble method designed to improve the accuracy of pathogenicity predictions for interpreting missense mitochondrial variants. Built on the joint consensus recommendations by the American College of Medical Genetics and Genomics/Association for Molecular Pathology, APOGEE 2 features an improved machine learning method and a curated training set for enhanced performance metrics. It offers region-wise assessments of genome fragility and mechanistic analyses of specific amino acids that cause perceptible long-range effects on protein structure. With clinical and research use in mind, APOGEE 2 scores and pathogenicity probabilities are precompiled and available in MitImpact. APOGEE 2’s ability to address challenges in interpreting mitochondrial missense variants makes it an essential tool in the field of mitochondrial genetics.

Background: Endometriosis is a widespread multifactorial disease in which environmental, genetic, and epigenetic factors contribute to the phenotype. Single Nucleotide Polymorphisms (SNPs) in genes implicated in pivotal molecular mechanisms have been investigated as susceptible risk factors in distinct populations. Among these, Toll-like receptor 4 (TLR4) represents a good candidate due to its role in the immune/inflammatory response and endometriosis pathogenesis. Methods: The TRL4 gene T399I SNP (C/T transition, rs4986791) was investigated in 236 Italian endometriosis patients and 150 controls by using the PCR-RFLP method. One-tailed Fisher’s exact test was used to compare differences between categorical variables. T399I genotype distribution was evaluated for Hardy–Weinberg equilibrium in both groups using the Chi-squared test for given probabilities. Results: Fisher’s exact test comparing C and T allele frequencies showed a difference in the frequency of T alleles between patients and controls (OR = 1.96, 95% confidence interval 0.91–4.23; p-value = 0.0552). Genotype frequencies did not show any significant difference between patients and controls. The homozygous TT genotype was observed in 2% of endometriosis women and not in controls. Conclusions: Our results show that the TLR4 rs4986791 T variant may be considered a genetic risk factor for endometriosis in Italian women. More extensive studies in other populations are needed to confirm this result.

Tooth development, or odontogenesis, is a complex process in which several molecular pathways play a key role. Recently, microRNAs, a class of approximately 20-nucleotide small RNA molecules that regulate gene expression, have been implicated in the odontogenesis process. This study aimed to assess the role of miRNAs in odontogenesis anomalies, specifically agenesis and impaction. We analyzed a manually curated list of 82 miRNAs associated with human odontogenesis, sourced from literature data. Employing two different approaches to validate findings, we conducted functional enrichment analysis to evaluate the cell pathways, diseases, and phenotypes enriched for those miRNAs. Our findings indicate that the analyzed miRNAs regulate pathways linked to tooth anomalies, including the TGFꞵ and Wnt signaling pathways, and those governing the pluripotency of stem cells, known to mediate various cellular processes, and interconnected with odontogenesis-related pathways. Furthermore, the analysis disclosed several pathways associated with tumors, including small cell lung and gastric cancer. These results were confirmed also by diseases and phenotypes enrichment evaluation. Moreover, cell network analysis disclosed that miRNAs are embedded and interconnected in networks associated with dental diseases and cancer development, thus confirming the functional enrichment analyses. In summary, our results offer a quantitative measure of the potential involvement of miRNAs in regulating pathways crucial for developmental processes, notably odontogenesis, and provide results suggesting potential association with oncogenesis processes as well.

The intertumor and intratumor heterogeneity of triple-negative breast cancers, which is reflected in diverse drug responses, interplays with tumor evolution. In this study, we developed a preclinical experimental and analytical framework using patient-derived tumor xenografts (PDTX) from patients with treatment-naïve triple-negative breast cancers to test their predictive value in personalized cancer treatment approaches. Patients and their matched PDTXs exhibited concordant drug responses to neoadjuvant therapy using two trial designs and dosing schedules. This platform enabled analysis of nongenetic mechanisms involved in relapse dynamics. Treatment resulted in permanent phenotypic changes, with functional and therapeutic consequences. High-throughput drug screening methods in ex vivo PDTX cells revealed patient-specific drug response changes dependent on first-line therapy. This was validated in vivo, as exemplified by a change in olaparib sensitivity in tumors previously treated with clinically relevant cycles of standard-of-care chemotherapy. In summary, PDTXs provide a robust tool to test patient drug responses and therapeutic regimens and to model evolutionary trajectories. However, high intermodel variability and permanent nongenomic transcriptional changes constrain their use for personalized cancer therapy. This work highlights important considerations associated with preclinical drug response modeling and potential uses of the platform to identify efficacious and preferential sequential therapeutic regimens. Significance: Patient-derived tumor xenografts from treatment-naïve breast cancer samples can predict patient drug responses and model treatment-induced phenotypic and functional evolution, making them valuable preclinical tools.

The recent identification of noncoding variants with pathogenic effects suggests that these variations could underlie a significant number of undiagnosed cases. Several computational methods have been developed to predict the functional impact of noncoding variants, but they exhibit only partial concordance and are not integrated with functional annotation resources, making the interpretation of these variants still challenging. MicroRNAs (miRNAs) are small noncoding RNA molecules that act as fine regulators of gene expression and play crucial functions in several biological processes, such as cell proliferation and differentiation. An increasing number of studies demonstrate a significant impact of miRNA single nucleotide variants (SNVs) both in Mendelian diseases and complex traits. To predict the functional effect of miRNA SNVs, we implemented a new meta-predictor, MiRLog, and we integrated it into a comprehensive database, dbmiR, which includes a precompiled list of all possible miRNA allelic SNVs, providing their biological annotations at nucleotide and miRNA levels. MiRLog and dbmiR were used to explore the genetic variability of miRNAs in 15,708 human genomes included in the gnomAD project, finding several ultra-rare SNVs with a potentially deleterious effect on miRNA biogenesis and function representing putative contributors to human phenotypes.