Recounting the FANTOM CAGE-Associated Transcriptome.

Long noncoding RNAs (lncRNAs) have emerged as key coordinators of biological and cellular processes. Characterizing lncRNA expression across cells and tissues is key to understanding their role in determining phenotypes, including human diseases. We present here FC-R2, a comprehensive expression atlas across a broadly defined human transcriptome, inclusive of over 109,000 coding and noncoding genes, as described in the FANTOM CAGE-Associated Transcriptome (FANTOM-CAT) study. This atlas greatly extends the gene annotation used in the original recount2 resource. We demonstrate the utility of the FC-R2 atlas by reproducing key findings from published large studies and by generating new results across normal and diseased human samples. In particular, we (a) identify tissue-specific transcription profiles for distinct classes of coding and noncoding genes, (b) perform differential expression analysis across thirteen cancer types, identifying novel noncoding genes potentially involved in tumor pathogenesis and progression, and (c) confirm the prognostic value for several enhancer lncRNAs expression in cancer. Our resource is instrumental for the systematic molecular characterization of lncRNA by the FANTOM6 Consortium. In conclusion, comprised of over 70,000 samples, the FC-R2 atlas will empower other researchers to investigate functions and biological roles of both known coding genes and novel lncRNAs.

Efficient representations of tumor diversity with paired DNA-RNA aberrations.

Cancer cells display massive dysregulation of key regulatory pathways due to now well-catalogued mutations and other DNA-related aberrations. Moreover, enormous heterogeneity has been commonly observed in the identity, frequency and location of these aberrations across individuals with the same cancer type or subtype, and this variation naturally propagates to the transcriptome, resulting in myriad types of dysregulated gene expression programs. Many have argued that a more integrative and quantitative analysis of heterogeneity of DNA and RNA molecular profiles may be necessary for designing more systematic explorations of alternative therapies and improving predictive accuracy. We introduce a representation of multi-omics profiles which is sufficiently rich to account for observed heterogeneity and support the construction of quantitative, integrated, metrics of variation. Starting from the network of interactions existing in Reactome, we build a library of "paired DNA-RNA aberrations" that represent prototypical and recurrent patterns of dysregulation in cancer; each two-gene "Source-Target Pair" (STP) consists of a "source" regulatory gene and a "target" gene whose expression is plausibly "controlled" by the source gene. The STP is then "aberrant" in a joint DNA-RNA profile if the source gene is DNA-aberrant (e.g., mutated, deleted, or duplicated), and the downstream target gene is "RNA-aberrant", meaning its expression level is outside the normal, baseline range. With M STPs, each sample profile has exactly one of the 2M possible configurations. We concentrate on subsets of STPs, and the corresponding reduced configurations, by selecting tissue-dependent minimal coverings, defined as the smallest family of STPs with the property that every sample in the considered population displays at least one aberrant STP within that family. These minimal coverings can be computed with integer programming. Given such a covering, a natural measure of cross-sample diversity is the extent to which the particular aberrant STPs composing a covering vary from sample to sample; this variability is captured by the entropy of the distribution over configurations. We apply this program to data from TCGA for six distinct tumor types (breast, prostate, lung, colon, liver, and kidney cancer). This enables an efficient simplification of the complex landscape observed in cancer populations, resulting in the identification of novel signatures of molecular alterations which are not detected with frequency-based criteria. Estimates of cancer heterogeneity across tumor phenotypes reveals a stable pattern: entropy increases with disease severity. This framework is then well-suited to accommodate the expanding complexity of cancer genomes and epigenomes emerging from large consortia projects.

Transcriptional landscape of PTEN loss in primary prostate cancer.

BACKGROUND: PTEN is the most frequently lost tumor suppressor in primary prostate cancer (PCa) and its loss is associated with aggressive disease. However, the transcriptional changes associated with PTEN loss in PCa have not been described in detail. In this study, we highlight the transcriptional changes associated with PTEN loss in PCa. METHODS: Using a meta-analysis approach, we leveraged two large PCa cohorts with experimentally validated PTEN and ERG status by Immunohistochemistry (IHC), to derive a transcriptomic signature of PTEN loss, while also accounting for potential confounders due to ERG rearrangements. This signature was expanded to lncRNAs using the TCGA quantifications from the FC-R2 expression atlas. RESULTS: The signatures indicate a strong activation of both innate and adaptive immune systems upon PTEN loss, as well as an expected activation of cell-cycle genes. Moreover, we made use of our recently developed FC-R2 expression atlas to expand this signature to include many non-coding RNAs recently annotated by the FANTOM consortium. Highlighting potential novel lncRNAs associated with PTEN loss and PCa progression. CONCLUSION: We created a PCa specific signature of the transcriptional landscape of PTEN loss that comprises both the coding and an extensive non-coding counterpart, highlighting potential new players in PCa progression. We also show that contrary to what is observed in other cancers, PTEN loss in PCa leads to increased activation of the immune system. These findings can help the development of new biomarkers and help guide therapy choices.

Digitizing omics profiles by divergence from a baseline.

Data collected from omics technologies have revealed pervasive heterogeneity and stochasticity of molecular states within and between phenotypes. A prominent example of such heterogeneity occurs between genome-wide mRNA, microRNA, and methylation profiles from one individual tumor to another, even within a cancer subtype. However, current methods in bioinformatics, such as detecting differentially expressed genes or CpG sites, are population-based and therefore do not effectively model intersample diversity. Here we introduce a unified theory to quantify sample-level heterogeneity that is applicable to a single omics profile. Specifically, we simplify an omics profile to a digital representation based on the omics profiles from a set of samples from a reference or baseline population (e.g., normal tissues). The state of any subprofile (e.g., expression vector for a subset of genes) is said to be "divergent" if it lies outside the estimated support of the baseline distribution and is consequently interpreted as "dysregulated" relative to that baseline. We focus on two cases: single features (e.g., individual genes) and distinguished subsets (e.g., regulatory pathways). Notably, since the divergence analysis is at the individual sample level, dysregulation can be analyzed probabilistically; for example, one can estimate the probability that a gene or pathway is divergent in some population. Finally, the reduction in complexity facilitates a more "personalized" and biologically interpretable analysis of variation, as illustrated by experiments involving tissue characterization, disease detection and progression, and disease-pathway associations.

Comparison of three machine learning algorithms for classification of B-cell neoplasms using clinical flow cytometry data.

Multiparameter flow cytometry data is visually inspected by expert personnel as part of standard clinical disease diagnosis practice. This is a demanding and costly process, and recent research has demonstrated that it is possible to utilize artificial intelligence (AI) algorithms to assist in the interpretive process. Here we report our examination of three previously published machine learning methods for classification of flow cytometry data and apply these to a B-cell neoplasm dataset to obtain predicted disease subtypes. Each of the examined methods classifies samples according to specific disease categories using ungated flow cytometry data. We compare and contrast the three algorithms with respect to their architectures, and we report the multiclass classification accuracies and relative required computation times. Despite different architectures, two of the methods, flowCat and EnsembleCNN, had similarly good accuracies with relatively fast computational times. We note a speed advantage for EnsembleCNN, particularly in the case of addition of training data and retraining of the classifier.

Distinct mesenchymal cell states mediate prostate cancer progression.

In the complex tumor microenvironment (TME), mesenchymal cells are key players, yet their specific roles in prostate cancer (PCa) progression remain to be fully deciphered. This study employs single-cell RNA sequencing to delineate molecular changes in tumor stroma that influence PCa progression and metastasis. Analyzing mesenchymal cells from four genetically engineered mouse models (GEMMs) and correlating these findings with human tumors, we identify eight stromal cell populations with distinct transcriptional identities consistent across both species. Notably, stromal signatures in advanced mouse disease reflect those in human bone metastases, highlighting periostin's role in invasion and differentiation. From these insights, we derive a gene signature that predicts metastatic progression in localized disease beyond traditional Gleason scores. Our results illuminate the critical influence of stromal dynamics on PCa progression, suggesting new prognostic tools and therapeutic targets.

Using biological constraints to improve prediction in precision oncology.

Many gene signatures have been developed by applying machine learning (ML) on omics profiles, however, their clinical utility is often hindered by limited interpretability and unstable performance. Here, we show the importance of embedding prior biological knowledge in the decision rules yielded by ML approaches to build robust classifiers. We tested this by applying different ML algorithms on gene expression data to predict three difficult cancer phenotypes: bladder cancer progression to muscle-invasive disease, response to neoadjuvant chemotherapy in triple-negative breast cancer, and prostate cancer metastatic progression. We developed two sets of classifiers: mechanistic, by restricting the training to features capturing specific biological mechanisms; and agnostic, in which the training did not use any a priori biological information. Mechanistic models had a similar or better testing performance than their agnostic counterparts, with enhanced interpretability. Our findings support the use of biological constraints to develop robust gene signatures with high translational potential.

An R package for divergence analysis of omics data.

Given the ever-increasing amount of high-dimensional and complex omics data becoming available, it is increasingly important to discover simple but effective methods of analysis. Divergence analysis transforms each entry of a high-dimensional omics profile into a digitized (binary or ternary) code based on the deviation of the entry from a given baseline population. This is a novel framework that is significantly different from existing omics data analysis methods: it allows digitization of continuous omics data at the univariate or multivariate level, facilitates sample level analysis, and is applicable on many different omics platforms. The divergence package, available on the R platform through the Bioconductor repository collection, provides easy-to-use functions for carrying out this transformation. Here we demonstrate how to use the package with data from the Cancer Genome Atlas.

covid19census: U.S. and Italy COVID-19 metrics and other epidemiological data.

Since the beginning of the coronavirus disease-2019 (COVID-19) pandemic in 2020, there has been a tremendous accumulation of data capturing different statistics including the number of tests, confirmed cases and deaths. This data wealth offers a great opportunity for researchers to model the effect of certain variables on COVID-19 morbidity and mortality and to get a better understanding of the disease at the epidemiological level. However, in order to draw any reliable and unbiased estimate, models also need to take into account other variables and metrics available from a plurality of official and unofficial heterogenous resources. In this study, we introduce covid19census, an R package that extracts from many different repositories and combines together COVID-19 metrics and other demographic, environment- and health-related variables of the USA and Italy at the county and regional levels, respectively. The package is equipped with a number of user-friendly functions that dynamically extract the data over different timepoints and contains a detailed description of the included variables. To demonstrate the utility of this tool, we used it to extract and combine different county-level data from the USA, which we subsequently used to model the effect of diabetes on COVID-19 mortality at the county level, taking into account other variables that may influence such effects. In conclusion, it was observed that the 'covid19census' package allows to easily extract area-level data from both the USA and Italy using few functions. These comprehensive data can be used to provide reliable estimates of the effect of certain variables on COVID-19 outcomes. Database URL: https://github.com/c1au6i0/covid19census.

Influenza Vaccination and COVID-19 Mortality in the USA: An Ecological Study.

The COVID-19 mortality rate is higher in the elderly and in those with pre-existing chronic medical conditions. The elderly also suffer from increased morbidity and mortality from seasonal influenza infections; thus, an annual influenza vaccination is recommended for them. In this study, we explore a possible county-level association between influenza vaccination coverage in people aged 65 years and older and the number of deaths from COVID-19. To this end, we used COVID-19 data up to 14 December 2020 and US population health data at the county level. We fit quasi-Poisson regression models using influenza vaccination coverage in the elderly population as the independent variable and the COVID-19 mortality rate as the outcome variable. We adjusted for an array of potential confounders using different propensity score regression methods. Results show that, on the county level, influenza vaccination coverage in the elderly population is negatively associated with mortality from COVID-19, using different methodologies for confounding adjustment. These findings point to the need for studying the relationship between influenza vaccination and COVID-19 mortality at the individual level to investigate any underlying biological mechanisms.

Identifying Personalized Metabolic Signatures in Breast Cancer.

Cancer cells are adept at reprogramming energy metabolism, and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism, and reactive oxygen species (ROS) detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach.

Influenza Vaccination and COVID19 Mortality in the USA.

COVID-19 mortality rate is higher in the elderly and in those with preexisting chronic medical conditions. The elderly also suffer from increased morbidity and mortality from seasonal influenza infection, and thus annual influenza vaccination is recommended for them. In this study, we explore a possible area-level association between influenza vaccination coverage in people aged 65 years and older and the number of deaths from COVID-19. To this end, we used COVID-19 data until June 10, 2020 together with population health data for the United States at the county level. We fit quasi-Poisson regression models using influenza vaccination coverage in the elderly population as the independent variable and the number of deaths from COVID-19 as the outcome variable. We adjusted for a wide array of potential confounding variables using both county-level generalized propensity scores for influenza vaccination rates, as well as direct adjustment. Our results suggest that influenza vaccination coverage in the elderly population is negatively associated with mortality from COVID-19. This finding is robust to using different analysis periods, different thresholds for inclusion of counties, and a variety of methodologies for confounding adjustment. In conclusion, our results suggest a potential protective effect of the influenza vaccine on COVID-19 mortality in the elderly population. The significant public health implications of this possibility point to an urgent need for studying the relationship between influenza vaccination and COVID-19 mortality at the individual level, to investigate both the epidemiology and any underlying biological mechanism.

PDCD4 Is an Androgen-Repressed Tumor Suppressor that Regulates Prostate Cancer Growth and Castration Resistance.

Androgen receptor (AR) transcriptional activity contributes to prostate cancer development and castration resistance. The growth and survival pathways driven by AR remain incompletely defined. Here, we found PDCD4 to be a new target of AR signaling and a potent regulator of prostate cancer cell growth, survival, and castration resistance. The 3' untranslated region of PDCD4 is directly targeted by the androgen-induced miRNA, miR-21. Androgen treatment suppressed PDCD4 expression in a dose responsive and miR-21-dependent manner. Correspondingly, AR inhibition dose-responsively induced PDCD4 expression. Using data from prostate cancer tissue samples in The Cancer Genome Atlas (TCGA), we found a significant and inverse correlation between miR-21 and PDCD4 mRNA and protein levels. Higher Gleason grade tumors exhibited significantly higher levels of miR-21 and significantly lower levels of PDCD4 mRNA and protein. PDCD4 knockdown enhanced androgen-dependent cell proliferation and cell-cycle progression, inhibited apoptosis, and was sufficient to drive androgen-independent growth. On the other hand, PDCD4 overexpression inhibited miR-21-mediated growth and androgen independence. The stable knockdown of PDCD4 in androgen-dependent prostate cancer cells enhanced subcutaneous tumor take rate in vivo, accelerated tumor growth, and was sufficient for castration-resistant tumor growth. IMPLICATIONS: This study provides the first evidence that PDCD4 is an androgen-suppressed protein capable of regulating prostate cancer cell proliferation, apoptosis, and castration resistance. These results uncover miR-21 and PDCD4-regulated pathways as potential new targets for castration-resistant prostate cancer.

Epigenetically regulated PAX6 drives cancer cells toward a stem-like state via GLI-SOX2 signaling axis in lung adenocarcinoma.

It remains unclear whether PAX6 acts as a crucial transcription factor for lung cancer stem cell (CSC) traits. We demonstrate that PAX6 acts as an oncogene responsible for induction of cancer stemness properties in lung adenocarcinoma (LUAD). Mechanistically, PAX6 promotes GLI transcription, resulting in SOX2 upregulation directly by the binding of GLI to the proximal promoter region of the SOX2 gene. The overexpressed SOX2 enhances the expression of key pluripotent factors (OCT4 and NANOG) and suppresses differentiation lineage factors (HOPX and NKX2-1), driving cancer cells toward a stem-like state. In contrast, in the differentiated non-CSCs, PAX6 is transcriptionally silenced by its promoter methylation. In human lung cancer tissues, the positive linear correlations of PAX6 expression with GLI and SOX2 expression and its negative correlations with HOPX and NKX2-1 expression were observed. Therapeutically, the blockade of the PAX6-GLI-SOX2 signaling axis elicits a long-lasting therapeutic efficacy by limiting CSC expansion following chemotherapy. Furthermore, a methylation panel including the PAX6 gene yielded a sensitivity of 79.1% and specificity of 83.3% for cancer detection using serum DNA from stage IA LUAD. Our findings provide a rationale for targeting the PAX6-GLI-SOX2 signaling axis with chemotherapy as an effective therapeutic strategy and support the clinical utility of PAX6 gene promoter methylation as a biomarker for early lung cancer detection.

YAP1 and COX2 Coordinately Regulate Urothelial Cancer Stem-like Cells.

Overcoming acquired drug resistance remains a core challenge in the clinical management of human cancer, including in urothelial carcinoma of the bladder (UCB). Cancer stem-like cells (CSC) have been implicated in the emergence of drug resistance but mechanisms and intervention points are not completely understood. Here, we report that the proinflammatory COX2/PGE2 pathway and the YAP1 growth-regulatory pathway cooperate to recruit the stem cell factor SOX2 in expanding and sustaining the accumulation of urothelial CSCs. Mechanistically, COX2/PGE2 signaling induced promoter methylation of let-7, resulting in its downregulation and subsequent SOX2 upregulation. YAP1 induced SOX2 expression more directly by binding its enhancer region. In UCB clinical specimens, positive correlations in the expression of SOX2, COX2, and YAP1 were observed, with coexpression of COX2 and YAP1 particularly commonly observed. Additional investigations suggested that activation of the COX2/PGE2 and YAP1 pathways also promoted acquired resistance to EGFR inhibitors in basal-type UCB. In a mouse xenograft model of UCB, dual inhibition of COX2 and YAP1 elicited a long-lasting therapeutic response by limiting CSC expansion after chemotherapy and EGFR inhibition. Our findings provide a preclinical rationale to target these pathways concurrently with systemic chemotherapy as a strategy to improve the clinical management of UCB.Significance: These findings offer a preclinical rationale to target the COX2 and YAP1 pathways concurrently with systemic chemotherapy to improve the clinical management of UCB, based on evidence that these two pathways expand cancer stem-like cell populations that mediate resistance to chemotherapy. Cancer Res; 78(1); 168-81. ©2017 AACR.

MicroRNA expression profiling of Xp11 renal cell carcinoma.

Renal cell carcinomas (RCCs) with Xp11 translocation (Xp11 RCC) constitute a distinctive molecular subtype characterized by chromosomal translocations involving the Xp11.2 locus, resulting in gene fusions between the TFE3 transcription factor with a second gene (usually ASPSCR1, PRCC, NONO, or SFPQ). RCCs with Xp11 translocations comprise up to 1% to 4% of adult cases, frequently displaying papillary architecture with epithelioid clear cells. To better understand the biology of this molecularly distinct tumor subtype, we analyze the microRNA (miRNA) expression profiles of Xp11 RCC compared with normal renal parenchyma using microarray and quantitative reverse-transcription polymerase chain reaction. We further compare Xp11 RCC with other RCC histologic subtypes using publically available data sets, identifying common and distinctive miRNA signatures along with the associated signaling pathways and biological processes. Overall, Xp11 RCC more closely resembles clear cell rather than papillary RCC. Furthermore, among the most differentially expressed miRNAs specific for Xp11 RCC, we identify miR-148a-3p, miR-221-3p, miR-185-5p, miR-196b-5p, and miR-642a-5p to be up-regulated, whereas miR-133b and miR-658 were down-regulated. Finally, Xp11 RCC is most strongly associated with miRNA expression profiles modulating DNA damage responses, cell cycle progression and apoptosis, and the Hedgehog signaling pathway. In summary, we describe here for the first time the miRNA expression profiles of a molecularly distinct type of renal cancer associated with Xp11.2 translocations involving the TFE3 gene. Our results might help understanding the molecular underpinning of Xp11 RCC, assisting in developing targeted treatments for this disease.

Gene Expression Signatures Based on Variability can Robustly Predict Tumor Progression and Prognosis.

Gene expression signatures are commonly used to create cancer prognosis and diagnosis methods, yet only a small number of them are successfully deployed in the clinic since many fail to replicate performance on subsequent validation. A primary reason for this lack of reproducibility is the fact that these signatures attempt to model the highly variable and unstable genomic behavior of cancer. Our group recently introduced gene expression anti-profiles as a robust methodology to derive gene expression signatures based on the observation that while gene expression measurements are highly heterogeneous across tumors of a specific cancer type relative to the normal tissue, their degree of deviation from normal tissue expression in specific genes involved in tissue differentiation is a stable tumor mark that is reproducible across experiments and cancer types. Here we show that constructing gene expression signatures based on variability and the anti-profile approach yields classifiers capable of successfully distinguishing benign growths from cancerous growths based on deviation from normal expression. We then show that this same approach generates stable and reproducible signatures that predict probability of relapse and survival based on tumor gene expression. These results suggest that using the anti-profile framework for the discovery of genomic signatures is an avenue leading to the development of reproducible signatures suitable for adoption in clinical settings.