The tidyomics ecosystem: Enhancing omic data analyses.

The growth of omic data presents evolving challenges in data manipulation, analysis, and integration. Addressing these challenges, Bioconductor1 provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming2 offers a revolutionary standard for data organisation and manipulation. Here, we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning, and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analysing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas3, spanning six data frameworks and ten analysis tools.

The tidyomics ecosystem: enhancing omic data analyses.

The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analyzing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas, spanning six data frameworks and ten analysis tools.

cellsig plug-in enhances CIBERSORTx signature selection for multidataset transcriptomes with sparse multilevel modelling.

MOTIVATION: The precise characterization of cell-type transcriptomes is pivotal to understanding cellular lineages, deconvolution of bulk transcriptomes, and clinical applications. Single-cell RNA sequencing resources like the Human Cell Atlas have revolutionised cell-type profiling. However, challenges persist due to data heterogeneity and discrepancies across different studies. One limitation of prevailing tools such as CIBERSORTx is their inability to address hierarchical data structures and handle nonoverlapping gene sets across samples, relying on filtering or imputation. RESULTS: Here, we present cellsig, a Bayesian sparse multilevel model designed to improve signature estimation by adjusting data for multilevel effects and modelling for gene-set sparsity. Our model is tailored to large-scale, heterogeneous pseudobulk and bulk RNA sequencing data collections with nonoverlapping gene sets. We tested the performances of cellsig on a novel curated Human Bulk Cell-type Catalogue, which harmonizes 1435 samples across 58 datasets. We show that cellsig significantly enhances cell-type marker gene ranking performance. This approach is valuable for cell-type signature selection, with implications for marker gene validation, single-cell annotation, and deconvolution benchmarks. AVAILABILITY AND IMPLEMENTATION: Codes and the interactive app are available at https://github.com/stemangiola/cellsig; and the database is available at https://doi.org/10.5281/zenodo.7582421.

Single-cell RNA sequencing captures patient-level heterogeneity and associated molecular phenotypes in breast cancer pleural effusions.

BACKGROUND: Malignant pleural effusions (MPEs) are a common complication of advanced cancers, particularly those adjacent to the pleura, such as lung and breast cancer. The pathophysiology of MPE formation remains poorly understood, and although MPEs are routinely used for the diagnosis of breast cancer patients, their composition and biology are poorly understood. It is difficult to distinguish invading malignant cells from resident mesothelial cells and to identify the directionality of interactions between these populations in the pleura. There is a need to characterize the phenotypic diversity of breast cancer cell populations in the pleural microenvironment, and investigate how this varies across patients. METHODS: Here, we used single-cell RNA-sequencing to study the heterogeneity of 10 MPEs from seven metastatic breast cancer patients, including three Miltenyi-enriched samples using a negative selection approach. This dataset of almost 65 000 cells was analysed using integrative approaches to compare heterogeneous cell populations and phenotypes. RESULTS: We identified substantial inter-patient heterogeneity in the composition of cell types (including malignant, mesothelial and immune cell populations), in expression of subtype-specific gene signatures and in copy number aberration patterns, that captured variability across breast cancer cell populations. Within individual MPEs, we distinguished mesothelial cell populations from malignant cells using key markers, the presence of breast cancer subtype expression patterns and copy number aberration patterns. We also identified pleural mesothelial cells expressing a cancer-associated fibroblast-like transcriptomic program that may support cancer growth. CONCLUSIONS: Our dataset presents the first unbiased assessment of breast cancer-associated MPEs at a single cell resolution, providing the community with a valuable resource for the study of MPEs. Our work highlights the molecular and cellular diversity captured in MPEs and motivates the potential use of these clinically relevant biopsies in the development of targeted therapeutics for patients with advanced breast cancer.

sccomp: Robust differential composition and variability analysis for single-cell data.

Cellular omics such as single-cell genomics, proteomics, and microbiomics allow the characterization of tissue and microbial community composition, which can be compared between conditions to identify biological drivers. This strategy has been critical to revealing markers of disease progression, such as cancer and pathogen infection. A dedicated statistical method for differential variability analysis is lacking for cellular omics data, and existing methods for differential composition analysis do not model some compositional data properties, suggesting there is room to improve model performance. Here, we introduce sccomp, a method for differential composition and variability analyses that jointly models data count distribution, compositionality, group-specific variability, and proportion mean-variability association, being aware of outliers. sccomp provides a comprehensive analysis framework that offers realistic data simulation and cross-study knowledge transfer. Here, we demonstrate that mean-variability association is ubiquitous across technologies, highlighting the inadequacy of the very popular Dirichlet-multinomial distribution. We show that sccomp accurately fits experimental data, significantly improving performance over state-of-the-art algorithms. Using sccomp, we identified differential constraints and composition in the microenvironment of primary breast cancer.

Taurine deficiency as a driver of aging.

Aging is associated with changes in circulating levels of various molecules, some of which remain undefined. We find that concentrations of circulating taurine decline with aging in mice, monkeys, and humans. A reversal of this decline through taurine supplementation increased the health span (the period of healthy living) and life span in mice and health span in monkeys. Mechanistically, taurine reduced cellular senescence, protected against telomerase deficiency, suppressed mitochondrial dysfunction, decreased DNA damage, and attenuated inflammaging. In humans, lower taurine concentrations correlated with several age-related diseases and taurine concentrations increased after acute endurance exercise. Thus, taurine deficiency may be a driver of aging because its reversal increases health span in worms, rodents, and primates and life span in worms and rodents. Clinical trials in humans seem warranted to test whether taurine deficiency might drive aging in humans.

IL17RB and IL17REL Expression Are Associated with Improved Prognosis in HPV-Infected Head and Neck Squamous Cell Carcinomas.

Changes in the cellular secretome are implicated in virus infection, malignancy, and anti-tumor immunity. We analyzed the association between transcriptional signatures (TS) from 24 different immune and stromal cell types on the prognosis of HPV-infected and HPV-free head and neck squamous carcinoma (HNSCC) patients from The Cancer Genome Atlas (TCGA) cohort. We found that HPV-positive HNSCC patients have tumors with elevated immune cell TS and improved prognosis, which was specifically associated with an increased tumor abundance of memory B and activated natural killer (NK) cell TS, compared to HPV-free HNSCC patients. HPV-infected patients upregulated many transcripts encoding secreted factors, such as growth factors, hormones, chemokines and cytokines, and their cognate receptors. Analysis of secretome transcripts and cognate receptors revealed that tumor expression of IL17RB and IL17REL are associated with a higher viral load and memory B and activated NK cell TS, as well as improved prognosis in HPV-infected HNSCC patients. The transcriptional parameters that we describe may be optimized to improve prognosis and risk stratification in the clinic and provide insights into gene and cellular targets that may potentially enhance anti-tumor immunity mediated by NK cells and memory B cells in HPV-infected HNSCC patients.

Somatic mutation landscape in a cohort of meningiomas that have undergone grade progression.

BACKGROUND: A subset of meningiomas progress in histopathological grade but drivers of progression are poorly understood. We aimed to identify somatic mutations and copy number alterations (CNAs) associated with grade progression in a unique matched tumour dataset. METHODS: Utilising a prospective database, we identified 10 patients with meningiomas that had undergone grade progression and for whom matched pre- and post-progression tissue (n = 50 samples) was available for targeted next-generation sequencing. RESULTS: Mutations in NF2 were identified in 4/10 patients, of these 94% were non-skull base tumours. In one patient, three different NF2 mutations were identified in four tumours. NF2 mutated tumours showed large-scale CNAs, with highly recurrent losses in 1p, 10, 22q, and frequent CNAs on chromosomes 2, 3 and 4. There was a correlation between grade and CNAs in two patients. Two patients with tumours without detected NF2 mutations showed a combination of loss and high gain on chromosome 17q. Mutations in SETD2, TP53, TERT promoter and NF2 were not uniform across recurrent tumours, however did not correspond with the onset of grade progression. CONCLUSION: Meningiomas that progress in grade generally have a mutational profile already detectable in the pre-progressed tumour, suggesting an aggressive phenotype. CNA profiling shows frequent alterations in NF2 mutated tumours compared to non NF2 mutated tumours. The pattern of CNAs may be associated with grade progression in a subset of cases.

Cell signaling activation and extracellular matrix remodeling underpin glioma tumor microenvironment heterogeneity and organization.

PURPOSE: Tumor cells thrive by adapting to the signals in their microenvironment. To adapt, cancer cells activate signaling and transcriptional programs and migrate to establish micro-niches, in response to signals from neighboring cells and non-cellular stromal factors. Understanding how the tumor microenvironment evolves during disease progression is crucial to deciphering the mechanisms underlying the functional behavior of cancer cells. METHODS: Multiplex immunohistochemistry, spatial analysis and histological dyes were used to identify and measure immune cell infiltration, cell signal activation and extracellular matrix deposition in low-grade, high-grade astrocytoma and glioblastoma. RESULTS: We show that lower grade astrocytoma tissue is largely devoid of infiltrating immune cells and extracellular matrix proteins, while high-grade astrocytoma exhibits abundant immune cell infiltration, activation, and extensive tissue remodeling. Spatial analysis shows that most T-cells are restricted to perivascular regions, but bone marrow-derived macrophages penetrate deep into neoplastic cell-rich regions. The tumor microenvironment is characterized by heterogeneous PI3K, MAPK and CREB signaling, with specific signaling profiles correlating with distinct pathological hallmarks, including angiogenesis, tumor cell density and regions where neoplastic cells border the extracellular matrix. Our results also show that tissue remodeling is important in regulating the architecture of the tumor microenvironment during tumor progression. CONCLUSION: The tumor microenvironment in malignant astrocytoma, exhibits changes in cell composition, cell signaling activation and extracellular matrix deposition during disease development and that targeting the extracellular matrix, as well as cell signaling activation will be critical to designing personalized therapy.

A Transcriptional Signature of IL-2 Expanded Natural Killer Cells Predicts More Favorable Prognosis in Bladder Cancer.

Activation of natural killer (NK) cell function is regulated by cytokines, such as IL-2, and secreted factors upregulated in the tumor microenvironment, such as platelet-derived growth factor D (PDGF-DD). In order to elucidate a clinical role for these important regulators of NK cell function in antitumor immunity, we generated transcriptional signatures representing resting, IL-2-expanded, and PDGF-DD-activated, NK cell phenotypes and established their abundance in The Cancer Genome Atlas bladder cancer (BLCA) dataset using CIBERSORT. The IL-2-expanded NK cell phenotype was the most abundant in low and high grades of BLCA tumors and was associated with improved prognosis. In contrast, PDGFD expression was associated with numerous cancer hallmark pathways in BLCA tumors compared with normal bladder tissue, and a high tumor abundance of PDGFD transcripts and the PDGF-DD-activated NK cell phenotype were associated with a poor BLCA prognosis. Finally, high tumor expression of transcripts encoding the activating NK cell receptors, KLRK1 and the CD160-TNFRSF14 receptor-ligand pair, was strongly correlated with the IL-2-expanded NK cell phenotype and improved BLCA prognosis. The transcriptional parameters we describe may be optimized to improve BLCA patient prognosis and risk stratification in the clinic and potentially provide gene targets of therapeutic significance for enhancing NK cell antitumor immunity in BLCA.

A Transcriptional Signature of PDGF-DD Activated Natural Killer Cells Predicts More Favorable Prognosis in Low-Grade Glioma.

The binding of platelet-derived growth factor D (PDGF-DD) to the NKp44 receptor activates a distinct transcriptional program in primary IL-2 expanded human natural killer (NK) cells. We were interested in knowing if the PDGF-DD-NKp44 pathway of NK cell activation might play a clinically relevant role in anti-tumor immunity. In order to address this question, we determined transcriptional signatures unique to resting, IL-2 expanded, and PDGF-DD activated, NK cells, in addition to different T cell subsets, and established the abundance of these immune cell phenotypes in The Cancer Genome Atlas (TCGA) low-grade glioma (LGG) dataset using CIBERSORT. Our results show that LGG patient tumors enriched for either the PDGF-DD activated NK cell or memory CD8+ T cell phenotypes are associated with a more favorable prognosis. Combined cell phenotype analyses revealed that patients with LGG tumors enriched for the PDGF-DD activated NK cell phenotype and the CD4+ T helper cell phenotype had a more favorable prognosis. High expression of transcripts encoding members of the killer cell lectin-like receptor (KLR) family, such as KLRK1 and KLRC2, KLRC3 and KLRC4 in LGG tumors were associated with more favorable prognosis, suggesting that these NK cell family receptors may play a prominent role in LGG anti-tumor immunity. Finally, many of the TCGA findings were reciprocated in LGG patients from the Chinese Glioma Genome Atlas (CGGA) dataset. Our results provide transcriptomic evidence that PDGF-DD activated NK cells and KLR family receptors may play an important clinical role in immune surveillance of LGG.

Transcriptome sequencing and multi-plex imaging of prostate cancer microenvironment reveals a dominant role for monocytic cells in progression.

BACKGROUND: Prostate cancer is caused by genomic aberrations in normal epithelial cells, however clinical translation of findings from analyses of cancer cells alone has been very limited. A deeper understanding of the tumour microenvironment is needed to identify the key drivers of disease progression and reveal novel therapeutic opportunities. RESULTS: In this study, the experimental enrichment of selected cell-types, the development of a Bayesian inference model for continuous differential transcript abundance, and multiplex immunohistochemistry permitted us to define the transcriptional landscape of the prostate cancer microenvironment along the disease progression axis. An important role of monocytes and macrophages in prostate cancer progression and disease recurrence was uncovered, supported by both transcriptional landscape findings and by differential tissue composition analyses. These findings were corroborated and validated by spatial analyses at the single-cell level using multiplex immunohistochemistry. CONCLUSIONS: This study advances our knowledge concerning the role of monocyte-derived recruitment in primary prostate cancer, and supports their key role in disease progression, patient survival and prostate microenvironment immune modulation.

The site of breast cancer metastases dictates their clonal composition and reversible transcriptomic profile.

Intratumoral heterogeneity is a driver of breast cancer progression, but the nature of the clonal interactive network involved in this process remains unclear. Here, we optimized the use of optical barcoding to visualize and characterize 31 cancer subclones in vivo. By mapping the clonal composition of thousands of metastases in two clinically relevant sites, the lungs and liver, we found that metastases were highly polyclonal in lungs but not in the liver. Furthermore, the transcriptome of the subclones varied according to their metastatic niche. We also identified a reversible niche-driven signature that was conserved in lung and liver metastases collected during patient autopsies. Among this signature, we found that the tumor necrosis factor-α pathway was up-regulated in lung compared to liver metastases, and inhibition of this pathway affected metastasis diversity. These results highlight that the cellular and molecular heterogeneity observed in metastases is largely dictated by the tumor microenvironment.

Loss of SNAI2 in Prostate Cancer Correlates With Clinical Response to Androgen Deprivation Therapy.

PURPOSE: Androgen receptor (AR) signaling is important in prostate cancer progression, and therapies that target this pathway have been the mainstay of treatment for advanced disease for over 70 years. Tumors eventually progress despite castration through a number of well-characterized mechanisms; however, little is known about what determines the magnitude of response to short-term pathway inhibition. METHODS: We evaluated a novel combination of AR-targeting therapies (degarelix, abiraterone, and bicalutamide) and noted that the objective patient response to therapy was highly variable. To investigate what was driving treatment resistance in poorly responding patients, as a secondary outcome we comprehensively characterized pre- and post-treatment samples using both whole-genome and RNA sequencing. RESULTS: We find that resistance following short-term treatment differs molecularly from typical progressive castration-resistant disease, associated with transcriptional reprogramming, to a transitional epithelial-to-mesenchymal transition (EMT) phenotype rather than an upregulation of AR signaling. Unexpectedly, tolerance to therapy appears to be the default state, with treatment response correlating with the prevalence of tumor cells deficient for SNAI2, a key regulator of EMT reprogramming. CONCLUSION: We show that EMT characterizes acutely resistant prostate tumors and that deletion of SNAI2, a key transcriptional regulator of EMT, correlates with clinical response.

MSH2-deficient prostate tumours have a distinct immune response and clinical outcome compared to MSH2-deficient colorectal or endometrial cancer.

BACKGROUND: Recent publications have shown patients with defects in the DNA mismatch repair (MMR) pathway driven by either MSH2 or MSH6 loss experience a significant increase in the incidence of prostate cancer. Moreover, this increased incidence of prostate cancer is accompanied by rapid disease progression and poor clinical outcomes. METHODS AND RESULTS: We show that androgen-receptor activation, a key driver of prostate carcinogenesis, can disrupt the MSH2 gene in prostate cancer. We screened tumours from two cohorts (recurrent/non-recurrent) of prostate cancer patients to confirm the loss of MSH2 protein expression and identified decreased MSH2 expression in recurrent cases. Stratifying the independent TCGA prostate cancer cohort for MSH2/6 expression revealed that patients with lower levels of MSH2/6 had significant worse outcomes, in contrast, endometrial and colorectal cancer patients with lower MSH2/6 levels. MMRd endometrial and colorectal tumours showed the expected increase in mutational burden, microsatellite instability and enhanced immune cell mobilisation but this was not evident in prostate tumours. CONCLUSIONS: We have shown that loss or reduced levels of MSH2/MSH6 protein in prostate cancer is associated with poor outcome. However, our data indicate that this is not associated with a statistically significant increase in mutational burden, microsatellite instability or immune cell mobilisation in a cohort of primary prostate cancers.

Interfacing Seurat with the R tidy universe.

MOTIVATION: Seurat is one of the most popular software suites for the analysis of single-cell RNA sequencing data. Considering the popularity of the tidyverse ecosystem, which offers a large set of data display, query, manipulation, integration and visualization utilities, a great opportunity exists to interface the Seurat object with the tidyverse. This interface gives the large data science community of tidyverse users the possibility to operate with familiar grammar. RESULTS: To provide Seurat with a tidyverse-oriented interface without compromising efficiency, we developed tidyseurat, a lightweight adapter to the tidyverse. Tidyseurat displays cell information as a tibble abstraction, allowing intuitively interfacing Seurat with dplyr, tidyr, ggplot2 and plotly packages powering efficient data manipulation, integration and visualization. Iterative analyses on data subsets are enabled by interfacing with the popular nest-map framework. AVAILABILITY AND IMPLEMENTATION: The software is freely available at cran.r-project.org/web/packages/tidyseurat and github.com/stemangiola/tidyseurat. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Probabilistic outlier identification for RNA sequencing generalized linear models.

Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq to analyse several publicly available datasets using popular tools, we show that from 3 to 10 percent of differentially abundant transcripts across algorithms and datasets had statistics inflated by the presence of outliers.

tidybulk: an R tidy framework for modular transcriptomic data analysis.

Recently, efforts have been made toward the harmonization of transcriptomic data structures and workflows using the concept of data tidiness, to facilitate modularisation. We present tidybulk, a modular framework for bulk transcriptional analyses that introduces a tidy transcriptomic data structure paradigm and analysis grammar. Tidybulk covers a wide variety of analysis procedures and integrates a large ecosystem of publicly available analysis algorithms under a common framework. Tidybulk decreases coding burden, facilitates reproducibility, increases efficiency for expert users, lowers the learning curve for inexperienced users, and bridges transcriptional data analysis with the tidyverse. Tidybulk is available at R/Bioconductor bioconductor.org/packages/tidybulk .

Toward precision immunotherapy using multiplex immunohistochemistry and in silico methods to define the tumor immune microenvironment.

Recent developments in cancer immunotherapy promise better outcomes for cancer patients, although clinical trials for difficult to treat cancers such as malignant brain cancer present special challenges, showing little response to first generation immunotherapies. Reasons for differences in immunotherapy response in some cancer types are likely due to the nature of tumor microenvironment, which harbors multiple cell types which interact with tumor cells to establish immunosuppression. The cell types which appear to hold the key in regulating tumor immunosuppression are the tumor-infiltrating immune cells. The current standard treatment for difficult to treat cancer, including the most malignant brain cancer, glioblastoma, continues to offer a bleak outlook for patients. Immune-profiling and correlation with pathological and clinical data will lead to a deeper understanding of the tumor immune microenvironment and contribute toward the selection, optimization and development of novel precision immunotherapies. Here, we review the current understanding of the tumor microenvironmental landscape in glioblastoma with a focus on next-generation technologies including multiplex immunofluorescence and computational approaches to map the brain tumor microenvironment to decipher the role of the immune system in this lethal malignancy.

Prognostic Utility of a Whole-blood Androgen Receptor-based Gene Signature in Metastatic Castration-resistant Prostate Cancer.

BACKGROUND: The treatment paradigm for metastatic castration-resistant prostate cancer (mCRPC) has evolved significantly in recent years. Identifying predictive and/or prognostic biomarkers in the context of this rapidly expanding therapeutic armamentarium remains a pressing and unmet clinical need. OBJECTIVE: To develop a prognostic whole-blood gene signature for mCRPC patients. DESIGN, SETTING, AND PARTICIPANTS: As part of an ongoing prospective, multicentre biomarker research study (Australian Prostate Biomarker Alliance), we enrolled 115 mCRPC patients commencing chemotherapy (n = 34) or androgen receptor (AR) pathway inhibitors therapy (n = 81) and obtained pretreatment whole-blood samples in PAXgene RNA tubes. Gene expression was assessed using reverse transcription-polymerase chain reaction. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Gene transcripts correlating with overall survival (OS) at p < 0.10 in univariate Cox regression models were incorporated into a multigene signature. Kaplan-Meier survival estimates and multivariate analyses were used to assess association with clinical outcomes. Prognostic strength of the signature was estimated using a concordance probability estimate (CPE). RESULTS AND LIMITATIONS: Based on univariate analysis for OS, the following genes were incorporated into a multigene signature: AR splice variant 7 (AR-V7), and three androgen-regulated genes: GRHL2, HOXB13, and FOXA1. The number of positive transcripts clearly stratified survival outcomes (median OS: not reached vs 24.8 mo vs 16.2 mo for 0, 1, and ≥2 transcripts, respectively; p = 0.0052). Notably, this multigene signature retained prognostic significance on multivariable analysis (hazard ratio, 2.1; 95% confidence interval, 1.1-4.0; p = 0.019). Moreover, CPE for this model was 0.78, indicating strong discriminative capacity. Limitations include short follow-up time. CONCLUSIONS: Our data demonstrate the prognostic utility of a novel whole-blood AR-based signature in mCRPC patients commencing contemporary systemic therapies. Our pragmatic assay requires minimal processing, can be performed in most hospital laboratories, and could represent a key prognostic tool for risk stratification in mCRPC. PATIENT SUMMARY: We found that expression of certain genes associated with the androgen receptor could help determine how long men with advanced prostate cancer survive after starting modern drug therapies.