Functional impact of protein-RNA variation in clinical cancer analyses

Enhanced interpretation of immune cell phenotype and function through a rhesus macaque single-cell atlas

Single-cell RNA sequencing (scRNA-seq) allows cell classification using genome-wide transcriptional state; however, high-dimensional transcriptomic profiles, and the unsupervised analyses employed to interpret them, provide a systematically different view of biology than well-established functional/lineage definitions of immunocytes. Understanding these differences and limits is essential for accurate interpretation of these rich data. We present the Rhesus Immune Reference Atlas (RIRA), the first immune-focused macaque single-cell multi-tissue atlas. We contrasted transcriptional profiles against immune lineages, using surface protein and marker genes as ground truth. While the pattern of clustering can align with cell type, this is not always true. Especially within T and natural killer (NK) cells, many functionally distinct subsets lack defining markers, and strong shared expression programs, such as cytotoxicity, result in systematic intermingling by unsupervised clustering. We identified gene programs with high discriminatory/diagnostic value, including multi-gene signatures that model T/NK cell maturation. Directly measuring these diagnostic programs complements unsupervised analyses.

Clinical and Prognostic Value of VHL in Korean Patients with Rectal Cancer

Background and Objectives: Von Hippel-Lindau (VHL) disease is caused by mutations in the VHL gene and can develop various cancers. Hypoxia-inducible factors 1 and 2 alphas, regulated by the VHL gene, can increase the levels of vascular endothelial growth factor, thereby activating cancer progression. Here, we demonstrated clinical and prognostic values of VHL expression in rectal cancer (RC). Materials and Methods: Von Hippel-Lindau mRNA expression was examined in 60 patients with RC. Furthermore, we evaluated survival to determine the prognostic significance of VHL mRNA expression levels in RC using the Cancer Genome Atlas (TCGA) data. Results: Lower VHL expression was correlated with the recurrence (p = 0.058) and lymphatic invasion (p = 0.078), although it was not statistically significant. In TCGA data, VHL expression level was correlated with the M stage (p = 0.044); however, it had a possible association with lymphatic invasion (p = 0.068) and N stage (p = 0.104). Survival analysis showed that lower VHL gene expression predicted poorer survival in both patients with RC and TCGA data. Conclusions: This study identified a significant correlation between VHL gene expression and RC for the first time using patient tissues and TCGA data, suggesting that the VHL gene expression level could be a potential biomarker or candidate for the treatment of RC. Further studies are required to identify the molecular pathogenesis and clinical characteristics of VHL disease in RC.

Mutation impact on mRNA versus protein expression across human cancers

BACKGROUND

Cancer mutations are often assumed to alter proteins, thus promoting tumorigenesis. However, how mutations affect protein expression-in addition to gene expression-has rarely been systematically investigated. This is significant as mRNA and protein levels frequently show only moderate correlation, driven by factors such as translation efficiency and protein degradation. Proteogenomic datasets from large tumor cohorts provide an opportunity to systematically analyze the effects of somatic mutations on mRNA and protein abundance and identify mutations with distinct impacts on these molecular levels.

RESULTS

We conduct a comprehensive analysis of mutation impacts on mRNA- and protein-level expressions of 953 cancer cases with paired genomics and global proteomic profiling across 6 cancer types. Protein-level impacts are validated for 47.2% of the somatic expression quantitative trait loci (seQTLs), including CDH1 and MSH3 truncations, as well as other mutations from likely "long-tail" driver genes. Devising a statistical pipeline for identifying somatic protein-specific QTLs (spsQTLs), we reveal several gene mutations, including NF1 and MAP2K4 truncations and TP53 missenses showing disproportional influence on protein abundance not readily explained by transcriptomics. Cross-validating with data from massively parallel assays of variant effects (MAVE), TP53 missenses associated with high tumor TP53 proteins are more likely to be experimentally confirmed as functional.

CONCLUSION

This study reveals that somatic mutations can exhibit distinct impacts on mRNA and protein levels, underscoring the necessity of integrating proteogenomic data to comprehensively identify functionally significant cancer mutations. These insights provide a framework for prioritizing mutations for further functional validation and therapeutic targeting.

Cilengitide sensitivity is predicted by overall integrin expression in breast cancer

BACKGROUND

Treatment options for triple-negative breast cancer (TNBC) are limited and patients face a poor prognosis. Here, we sought to identify drugs that target TNBC vulnerabilities and understand the biology underlying these responses. We analyzed the Broad Institute DepMap to identify recurrent TNBC vulnerabilities and performed a 45-compound screen on vulnerability-related pathways on a set of up to 8 TNBC cell lines. We identified a subset of cell lines with an ITGAV vulnerability and a differential sensitivity to cilengitide, an integrin inhibitor targeting ITGAV:ITGB3 and ITGAV:ITGB5. Next, we sought to understand cilengitide resistance and response biomarkers. Clinical trials targeting integrins continue enrolling patients, necessitating an understanding of how these drugs affect tumors.

METHODS

We combined in vitro assays with computational approaches to systematically explore the differential sensitivity to cilengitide and resistance mechanisms. We tested an additional pan-ITGAV inhibitor (GLPG0187) to determine how generalizable our findings on cilengitide sensitivity might be to integrin inhibition. ITGB4, ITGA3, and ITGA6 knockdown experiments assessed the importance of integrin monomers in cell attachment during cilengitide treatment. Additionally, we explored the role of extracellular matrix (ECM) proteins in cilengitide response by performing cell replating experiments and by culturing on collagen, fibronectin, or laminin coated plates.

RESULTS

We discovered that cell-derived ECM modulates cilengitide sensitivity and exogenous fibronectin addition conferred resistance to all sensitive TNBC cell lines, though fibronectin expression did not correlate with sensitivity. Instead, elevated overall integrin protein levels, not specific integrins, in TNBC cells positively correlated with resistance. This suggested that high pan-integrin expression promotes cilengitide resistance. Thus, we tested cilengitide in six luminal breast cancer cell lines (which have low integrin levels); all were sensitive. Also, pan-ITGAV inhibitor, GLPG0187, showed the same sensitivity profile across our TNBC cell lines, suggesting our findings apply to other integrin inhibitors.

CONCLUSIONS

Integrin inhibitors are appealing candidates to pursue as anti-cancer drugs because they are generally well-tolerated, but their efficacy is mixed, possibly due to the absence of predictive markers. Cilengitide induces death in breast cancer cells with low integrin abundance, where complementary ECM promotes survival. Thus, integrin inhibition in breast cancer warrants further study.

Activation of platelet-derived growth factor receptors regulate connective tissue growth factor protein levels via the AKT pathway in malignant mesothelioma cells

The incidence of malignant mesothelioma (MM), a disease linked to refractory asbestos exposure, continues to increase globally and remains largely resistant to various treatments. Our previous studies have identified a strong correlation between connective tissue growth factor (CTGF) protein expression and MM malignancy, underscoring the importance of understanding CTGF regulation in MM cells. In this study, we demonstrate for the first time that stimulation with platelet-derived growth factor receptor (PDGFR) ligand, PDGF-BB, increases CTGF protein expression levels without affecting CTGF mRNA levels. Inhibition of PDGFR resulted in a reduction of CTGF protein expression, indicating that PDGFR activation is essential in regulating CTGF protein expression in MM cells. PDGF-BB also activated the protein kinase B (AKT) pathway, and inhibition of AKT phosphorylation abolished the PDGFR-induced CTGF protein expression, suggesting that PDGFR acts upstream of CTGF via the AKT pathway. This reinforces the role of CTGF protein as a key regulator of MM malignancy. Additionally, PDGFR activation led to the phosphorylation of mTOR and 4E-BP1, critical regulators of protein synthesis downstream of AKT, suggesting that PDGFR controls CTGF protein expression through the regulation of CTGF mRNA translation.

mosaicMPI: a framework for modular data integration across cohorts and -omics modalities

Advances in molecular profiling have facilitated generation of large multi-modal datasets that can potentially reveal critical axes of biological variation underlying complex diseases. Distilling biological meaning, however, requires computational strategies that can perform mosaic integration across diverse cohorts and datatypes. Here, we present mosaicMPI, a framework for discovery of low to high-resolution molecular programs representing both cell types and states, and integration within and across datasets into a network representing biological themes. Using existing datasets in glioblastoma, we demonstrate that this approach robustly integrates single cell and bulk programs across multiple platforms. Clinical and molecular annotations from cohorts are statistically propagated onto this network of programs, yielding a richly characterized landscape of biological themes. This enables deep understanding of individual tumor samples, systematic exploration of relationships between modalities, and generation of a reference map onto which new datasets can rapidly be mapped. mosaicMPI is available at https://github.com/MorrissyLab/mosaicMPI.

Mutation Impact on mRNA Versus Protein Expression across Human Cancers

Cancer mutations are often assumed to alter proteins, thus promoting tumorigenesis. However, how mutations affect protein expression has rarely been systematically investigated. We conduct a comprehensive analysis of mutation impacts on mRNA- and protein-level expressions of 953 cancer cases with paired genomics and global proteomic profiling across six cancer types. Protein-level impacts are validated for 47.2% of the somatic expression quantitative trait loci (seQTLs), including mutations from likely "long-tail" driver genes. Devising a statistical pipeline for identifying somatic protein-specific QTLs (spsQTLs), we reveal several gene mutations, including NF1 and MAP2K4 truncations and TP53 missenses showing disproportional influence on protein abundance not readily explained by transcriptomics. Cross-validating with data from massively parallel assays of variant effects (MAVE), TP53 missenses associated with high tumor TP53 proteins were experimentally confirmed as functional. Our study demonstrates the importance of considering protein-level expression to validate mutation impacts and identify functional genes and mutations.