Proteogenomic Characterization of Bladder Cancer Reveals Sensitivity to Apoptosis Induced by Tumor Necrosis Factor-related Apoptosis-inducing Ligand in FGFR3-mutated Tumors

BACKGROUND

Molecular understanding of muscle-invasive (MIBC) and non-muscle-invasive (NMIBC) bladder cancer is currently based primarily on transcriptomic and genomic analyses.

OBJECTIVE

To conduct proteogenomic analyses to gain insights into bladder cancer (BC) heterogeneity and identify underlying processes specific to tumor subgroups and therapeutic outcomes.

DESIGN, SETTING, AND PARTICIPANTS

Proteomic data were obtained for 40 MIBC and 23 NMIBC cases for which transcriptomic and genomic data were already available. Four BC-derived cell lines harboring FGFR3 alterations were tested with interventions.

INTERVENTION

Recombinant tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), second mitochondrial-derived activator of caspases mimetic (birinapant), pan-FGFR inhibitor (erdafitinib), and FGFR3 knockdown.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Proteomic groups from unsupervised analyses (uPGs) were characterized using clinicopathological, proteomic, genomic, transcriptomic, and pathway enrichment analyses. Additional enrichment analyses were performed for FGFR3-mutated tumors. Treatment effects on cell viability for FGFR3-altered cell lines were evaluated. Synergistic treatment effects were evaluated using the zero interaction potency model.

RESULTS AND LIMITATIONS

Five uPGs, covering both NMIBC and MIBC, were identified and bore coarse-grained similarity to transcriptomic subtypes underlying common features of these different entities; uPG-E was associated with the Ta pathway and enriched in FGFR3 mutations. Our analyses also highlighted enrichment of proteins involved in apoptosis in FGFR3-mutated tumors, not captured through transcriptomics. Genetic and pharmacological inhibition demonstrated that FGFR3 activation regulates TRAIL receptor expression and sensitizes cells to TRAIL-mediated apoptosis, further increased by combination with birinapant.

CONCLUSIONS

This proteogenomic study provides a comprehensive resource for investigating NMIBC and MIBC heterogeneity and highlights the potential of TRAIL-induced apoptosis as a treatment option for FGFR3-mutated bladder tumors, warranting a clinical investigation.

PATIENT SUMMARY

We integrated proteomics, genomics, and transcriptomics to refine molecular classification of bladder cancer, which, combined with clinical and pathological classification, should lead to more appropriate management of patients. Moreover, we identified new biological processes altered in FGFR3-mutated tumors and showed that inducing apoptosis represents a new potential therapeutic option.

Transcriptomic Profiling of Upper Tract Urothelial Carcinoma: Bladder Cancer Consensus Classification Relevance, Molecular Heterogeneity, and Differential Immune Signatures

Analyses of large transcriptomics data sets of muscle-invasive bladder cancer (MIBC) have led to a consensus classification. Molecular subtypes of upper tract urothelial carcinomas (UTUCs) are less known. Our objective was to determine the relevance of the consensus classification in UTUCs by characterizing a novel cohort of surgically treated ≥pT1 tumors. Using immunohistochemistry (IHC), subtype markers GATA3-CK5/6-TUBB2B in multiplex, CK20, p16, Ki67, mismatch repair system proteins, and PD-L1 were evaluated. Heterogeneity was assessed morphologically and/or with subtype IHC. FGFR3 mutations were identified by pyrosequencing. We performed 3'RNA sequencing of each tumor, with multisampling in heterogeneous cases. Consensus classes, unsupervised groups, and microenvironment cell abundance were determined using gene expression. Most of the 66 patients were men (77.3%), with pT1 (n = 23, 34.8%) or pT2-4 stage UTUC (n = 43, 65.2%). FGFR3 mutations and mismatch repair-deficient status were identified in 40% and 4.7% of cases, respectively. Consensus subtypes robustly classified UTUCs and reflected intrinsic subgroups. All pT1 tumors were classified as luminal papillary (LumP). Combining our consensus classification results with those of previously published UTUC cohorts, LumP tumors represented 57.2% of ≥pT2 UTUCs, which was significantly higher than MIBCs. Ten patients (15.2%) harbored areas of distinct subtypes. Consensus classes were associated with FGFR3 mutations, stage, morphology, and IHC. The majority of LumP tumors were characterized by low immune infiltration and PD-L1 expression, in particular, if FGFR3 mutated. Our study shows that MIBC consensus classification robustly classified UTUCs and highlighted intratumoral molecular heterogeneity. The proportion of LumP was significantly higher in UTUCs than in MIBCs. Most LumP tumors showed low immune infiltration and PD-L1 expression and high proportion of FGFR3 mutations. These findings suggest differential response to novel therapies between patients with UTUC and those with MIBC.

Expression-based subtypes define pathologic response to neoadjuvant immune-checkpoint inhibitors in muscle-invasive bladder cancer

Checkpoint immunotherapy (CPI) has increased survival for some patients with advanced-stage bladder cancer (BCa). However, most patients do not respond. Here, we characterized the tumor and immune microenvironment in pre- and post-treatment tumors from the PURE01 neoadjuvant pembrolizumab immunotherapy trial, using a consolidative approach that combined transcriptional and genetic profiling with digital spatial profiling. We identify five distinctive genetic and transcriptomic programs and validate these in an independent neoadjuvant CPI trial to identify the features of response or resistance to CPI. By modeling the regulatory network, we identify the histone demethylase KDM5B as a repressor of tumor immune signaling pathways in one resistant subtype (S1, Luminal-excluded) and demonstrate that inhibition of KDM5B enhances immunogenicity in FGFR3-mutated BCa cells. Our study identifies signatures associated with response to CPI that can be used to molecularly stratify patients and suggests therapeutic alternatives for subtypes with poor response to neoadjuvant immunotherapy.

Epigenomic mapping identifies an enhancer repertoire that regulates cell identity in bladder cancer through distinct transcription factor networks

Muscle-invasive bladder cancer (BLCA) is an aggressive disease. Consensus BLCA transcriptomic subtypes have been proposed, with two major Luminal and Basal subgroups, presenting distinct molecular and clinical characteristics. However, how these distinct subtypes are regulated remains unclear. We hypothesized that epigenetic activation of distinct super-enhancers could drive the transcriptional programs of BLCA subtypes. Through integrated RNA-sequencing and epigenomic profiling of histone marks in primary tumours, cancer cell lines, and normal human urothelia, we established the first integrated epigenetic map of BLCA and demonstrated the link between subtype and epigenetic control. We identified the repertoire of activated super-enhancers and highlighted Basal, Luminal and Normal-associated SEs. We revealed super-enhancer-regulated networks of candidate master transcription factors for Luminal and Basal subgroups including FOXA1 and ZBED2, respectively. FOXA1 CRISPR-Cas9 mutation triggered a shift from Luminal to Basal phenotype, confirming its role in Luminal identity regulation and induced ZBED2 overexpression. In parallel, we showed that both FOXA1 and ZBED2 play concordant roles in preventing inflammatory response in cancer cells through STAT2 inhibition. Our study furthers the understanding of epigenetic regulation of muscle-invasive BLCA and identifies a co-regulated network of super-enhancers and associated transcription factors providing potential targets for the treatment of this aggressive disease.

Integrated molecular and pharmacological characterization of patient-derived xenografts from bladder and ureteral cancers identifies new potential therapies

Background

Muscle-invasive bladder cancer (MIBC) and upper urinary tract urothelial carcinoma (UTUC) are molecularly heterogeneous. Despite chemotherapies, immunotherapies, or anti-fibroblast growth factor receptor (FGFR) treatments, these tumors are still of a poor outcome. Our objective was to develop a bank of patient-derived xenografts (PDXs) recapitulating the molecular heterogeneity of MIBC and UTUC, to facilitate the preclinical identification of therapies.

Methods

Fresh tumors were obtained from patients and subcutaneously engrafted into immune-compromised mice. Patient tumors and matched PDXs were compared regarding histopathology, transcriptomic (microarrays), and genomic profiles [targeted Next-Generation Sequencing (NGS)]. Several PDXs were treated with chemotherapy (cisplatin/gemcitabine) or targeted therapies [FGFR and epidermal growth factor (EGFR) inhibitors].

Results

A total of 31 PDXs were established from 1 non-MIBC, 25 MIBC, and 5 upper urinary tract tumors, including 28 urothelial (UC) and 3 squamous cell carcinomas (SCCs). Integrated genomic and transcriptomic profiling identified the PDXs of three different consensus molecular subtypes [basal/squamous (Ba/Sq), luminal papillary, and luminal unstable] and included FGFR3-mutated PDXs. High histological and genomic concordance was found between matched patient tumor/PDX. Discordance in molecular subtypes, such as a Ba/Sq patient tumor giving rise to a luminal papillary PDX, was observed (n=5) at molecular and histological levels. Ten models were treated with cisplatin-based chemotherapy, and we did not observe any association between subtypes and the response. Of the three Ba/Sq models treated with anti-EGFR therapy, two models were sensitive, and one model, of the sarcomatoid variant, was resistant. The treatment of three FGFR3-mutant PDXs with combined FGFR/EGFR inhibitors was more efficient than anti-FGFR3 treatment alone.

Conclusions

We developed preclinical PDX models that recapitulate the molecular heterogeneity of MIBCs and UTUC, including actionable mutations, which will represent an essential tool in therapy development. The pharmacological characterization of the PDXs suggested that the upper urinary tract and MIBCs, not only UC but also SCC, with similar molecular characteristics could benefit from the same treatments including anti-FGFR for FGFR3-mutated tumors and anti-EGFR for basal ones and showed a benefit for combined FGFR/EGFR inhibition in FGFR3-mutant PDXs, compared to FGFR inhibition alone.

TreeAndLeaf: an R/Bioconductor package for graphs and trees with focus on the leaves

MOTIVATION

Dendrogram is a classical diagram for visualizing binary trees. Although efficient to represent hierarchical relations, it provides limited space for displaying information on the leaf elements, especially for large trees.

RESULTS

Here, we present TreeAndLeaf, an R/Bioconductor package that implements a hybrid layout strategy to represent tree diagrams with focus on the leaves. The TreeAndLeaf package combines force-directed graph and tree layout algorithms using a single visualization system, allowing projection of multiple layers of information onto a graph-tree diagram. The Supplementary Information provides two case studies that use breast cancer data from epidemiological and experimental studies.

AVAILABILITY AND IMPLEMENTATION

TreeAndLeaf is written in the R language, and is available from the Bioconductor project at http://bioconductor.org/packages/TreeAndLeaf/ (version≥1.4.2).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Modeling Progression of Single Cell Populations Through the Cell Cycle as a Sequence of Switches

Cell cycle is a biological process underlying the existence and propagation of life in time and space. It has been an object for mathematical modeling for long, with several alternative mechanistic modeling principles suggested, describing in more or less details the known molecular mechanisms. Recently, cell cycle has been investigated at single cell level in snapshots of unsynchronized cell populations, exploiting the new methods for transcriptomic and proteomic molecular profiling. This raises a need for simplified semi-phenomenological cell cycle models, in order to formalize the processes underlying the cell cycle, at a higher abstracted level. Here we suggest a modeling framework, recapitulating the most important properties of the cell cycle as a limit trajectory of a dynamical process characterized by several internal states with switches between them. In the simplest form, this leads to a limit cycle trajectory, composed by linear segments in logarithmic coordinates describing some extensive (depending on system size) cell properties. We prove a theorem connecting the effective embedding dimensionality of the cell cycle trajectory with the number of its linear segments. We also develop a simplified kinetic model with piecewise-constant kinetic rates describing the dynamics of lumps of genes involved in S-phase and G2/M phases. We show how the developed cell cycle models can be applied to analyze the available single cell datasets and simulate certain properties of the observed cell cycle trajectories. Based on our model, we can predict with good accuracy the cell line doubling time from the length of cell cycle trajectory.

An integrated multi-omics analysis identifies prognostic molecular subtypes of non-muscle-invasive bladder cancer

The molecular landscape in non-muscle-invasive bladder cancer (NMIBC) is characterized by large biological heterogeneity with variable clinical outcomes. Here, we perform an integrative multi-omics analysis of patients diagnosed with NMIBC (n = 834). Transcriptomic analysis identifies four classes (1, 2a, 2b and 3) reflecting tumor biology and disease aggressiveness. Both transcriptome-based subtyping and the level of chromosomal instability provide independent prognostic value beyond established prognostic clinicopathological parameters. High chromosomal instability, p53-pathway disruption and APOBEC-related mutations are significantly associated with transcriptomic class 2a and poor outcome. RNA-derived immune cell infiltration is associated with chromosomally unstable tumors and enriched in class 2b. Spatial proteomics analysis confirms the higher infiltration of class 2b tumors and demonstrates an association between higher immune cell infiltration and lower recurrence rates. Finally, the independent prognostic value of the transcriptomic classes is documented in 1228 validation samples using a single sample classification tool. The classifier provides a framework for biomarker discovery and for optimizing treatment and surveillance in next-generation clinical trials.

Tertiary lymphoid structures marker CXCL13 is associated with better survival for patients with advanced-stage bladder cancer treated with immunotherapy

INTRODUCTION

Immune checkpoint inhibitors (ICIs) have proved to be an effective treatment for up to 40% of muscle-invasive bladder cancer (MIBC), but there is still a need for better performing biomarkers allowing to improve prediction of response to ICI. Response to immunotherapy in soft-tissue sarcoma, melanoma and renal cell carcinoma have been recently linked to the presence of tertiary lymphoid structures (TLS) in the tumour. TLS are organised aggregates of T, B and dendritic cells, participating in adaptive antitumor immune response. The chemokine CXCL13 is involved in the formation of TLS, and is reported as a reliable transcriptomic marker of TLS.

OBJECTIVES

In this study, we sought to assess whether CXCL13 transcript expression can be a prognostic biomarker for ICI-treated MIBC patients and also investigated whether it can serve a biomarker of TLS in MIBC.

METHODS

We analysed transcriptomic data from three publicly available MIBC cohorts and evaluated pathological slides from the TCGA-BLCA cohort for TLS presence and stage of maturation.

RESULTS

We showed that CXCL13 was independently associated with both prolonged survival (HR = 0.8, 95% CI [0.68-0.94]) and objective response (p < 0.0001) in patients treated with ICI, at the difference of others immunological signatures. However, it was not a predictor for non-ICI-treated MIBC, suggesting a predictive effect of ICI efficacy. Finally, we validated that CXCL13 expression was correlated with tumour TLS in TCGA data set (p < 0.001), and can serve as a marker of TLS in bladder cancer.

CONCLUSION

These results support that CXCL13 expression, as a surrogate for tumour TLS, is a relevant candidate predictive biomarker of response to ICI for patients with advanced-stage bladder cancer.

Novel lncRNAs Co-Expression Networks Identifies LINC00504 with Oncogenic Role in Luminal A Breast Cancer Cells

Long non-coding RNAs (lncRNAs) are functional transcripts with more than 200 nucleotides. These molecules exhibit great regulatory capacity and may act at different levels of gene expression regulation. Despite this regulatory versatility, the biology of these molecules is still poorly understood. Computational approaches are being increasingly used to elucidate biological mechanisms in which these lncRNAs may be involved. Co-expression networks can serve as great allies in elucidating the possible regulatory contexts in which these molecules are involved. Herein, we propose the use of the pipeline deposited in the RTN package to build lncRNAs co-expression networks using TCGA breast cancer (BC) cohort data. Worldwide, BC is the most common cancer in women and has great molecular heterogeneity. We identified an enriched co-expression network for the validation of relevant cell processes in the context of BC, including LINC00504. This lncRNA has increased expression in luminal subtype A samples, and is associated with prognosis in basal-like subtype. Silencing this lncRNA in luminal A cell lines resulted in decreased cell viability and colony formation. These results highlight the relevance of the proposed method for the identification of lncRNAs in specific biological contexts.

RTNduals: an R/Bioconductor package for analysis of co-regulation and inference of dual regulons

MOTIVATION

Transcription factors (TFs) are key regulators of gene expression, and can activate or repress multiple target genes, forming regulatory units, or regulons. Understanding downstream effects of these regulators includes evaluating how TFs cooperate or compete within regulatory networks. Here we present RTNduals, an R/Bioconductor package that implements a general method for analyzing pairs of regulons.

RESULTS

RTNduals identifies a dual regulon when the number of targets shared between a pair of regulators is statistically significant. The package extends the RTN (Reconstruction of Transcriptional Networks) package, and uses RTN transcriptional networks to identify significant co-regulatory associations between regulons. The Supplementary Information reports two case studies for TFs using the METABRIC and TCGA breast cancer cohorts.

AVAILABILITY AND IMPLEMENTATION

RTNduals is written in the R language, and is available from the Bioconductor project at http://bioconductor.org/packages/RTNduals/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

RTNsurvival: an R/Bioconductor package for regulatory network survival analysis

MOTIVATION

Transcriptional networks are models that allow the biological state of cells or tumours to be described. Such networks consist of connected regulatory units known as regulons, each comprised of a regulator and its targets. Inferring a transcriptional network can be a helpful initial step in characterizing the different phenotypes within a cohort. While the network itself provides no information on molecular differences between samples, the per-sample state of each regulon, i.e. the regulon activity, can be used for describing subtypes in a cohort. Integrating regulon activities with clinical data and outcomes would extend this characterization of differences between subtypes.

RESULTS

We describe RTNsurvival, an R/Bioconductor package that calculates regulon activity profiles using transcriptional networks reconstructed by the RTN package, gene expression data, and a two-tailed Gene Set Enrichment Analysis. Given regulon activity profiles across a cohort, RTNsurvival can perform Kaplan-Meier analyses and Cox Proportional Hazards regressions, while also considering confounding variables. The Supplementary Information provides two case studies that use data from breast and liver cancer cohorts and features uni- and multivariate regulon survival analysis.

AVAILABILITY AND IMPLEMENTATION

RTNsurvival is written in the R language, and is available from the Bioconductor project at http://bioconductor.org/packages/RTNsurvival/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Long non-coding RNAs identify a subset of luminal muscle-invasive bladder cancer patients with favorable prognosis

BACKGROUND

Muscle-invasive bladder cancer (MIBC) is a heterogeneous disease, and gene expression profiling has identified several molecular subtypes with distinct biological and clinicopathological characteristics. While MIBC subtyping has primarily been based on messenger RNA (mRNA), long non-coding RNAs (lncRNAs) may provide additional resolution.

METHODS

LncRNA expression was quantified from microarray data of a MIBC cohort treated with neoadjuvant chemotherapy (NAC) and radical cystectomy (RC) (n = 223). Unsupervised consensus clustering of highly variant lncRNAs identified a four-cluster solution, which was characterized using a panel of MIBC biomarkers, regulon activity profiles, gene signatures, and survival analysis. The four-cluster solution was confirmed in The Cancer Genome Atlas (TCGA) cohort (n = 405). A single-sample genomic classifier (GC) was trained using ridge-penalized logistic regression and validated in two independent cohorts (n = 255 and n = 94).

RESULTS

NAC and TCGA cohorts both contained an lncRNA cluster (LC3) with favorable prognosis that was enriched with tumors of the luminal-papillary (LP) subtype. In both cohorts, patients with LP tumors in LC3 (LPL-C3) were younger and had organ-confined, node-negative disease. The LPL-C3 tumors had enhanced FGFR3, SHH, and wild-type p53 pathway activity. In the TCGA cohort, LPL-C3 tumors were enriched for FGFR3 mutations and depleted for TP53 and RB1 mutations. A GC trained to identify these LPL-C3 patients showed robust performance in two validation cohorts.

CONCLUSIONS

Using lncRNA expression profiles, we identified a biologically distinct subgroup of luminal-papillary MIBC with a favorable prognosis. These data suggest that lncRNAs provide additional information for higher-resolution subtyping, potentially improving precision patient management.

A Consensus Molecular Classification of Muscle-invasive Bladder Cancer

Background:

Muscle-invasive bladder cancer (MIBC) is a molecularly diverse disease with heterogeneous clinical outcomes. Several molecular classifications have been proposed, but the diversity of their subtype sets impedes their clinical application.

Objective:

To achieve an international consensus on MIBC molecular subtypes that reconciles the published classification schemes.

Design, setting, and participants:

We used 1750 MIBC transcriptomic profiles from 16 published datasets and two additional cohorts.

Outcome measurements and statistical analysis:

We performed a network-based analysis of six independent MIBC classification systems to identify a consensus set of molecular classes. Association with survival was assessed using multivariable Cox models.

Results and limitations:

We report the results of an international effort to reach a consensus on MIBC molecular subtypes. We identified a consensus set of six molecular classes: luminal papillary (24%), luminal nonspecified (8%), luminal unstable (15%), stroma-rich (15%), basal/squamous (35%), and neuroendocrine-like (3%). These consensus classes differ regarding underlying oncogenic mechanisms, infiltration by immune and stromal cells, and histological and clinical characteristics, including outcomes. We provide a single-sample classifier that assigns a consensus class label to a tumor sample’s transcriptome. Limitations of the work are retrospective clinical data collection and a lack of complete information regarding patient treatment.

Conclusions:

This consensus system offers a robust framework that will enable testing and validation of predictive biomarkers in future prospective clinical trials.

Patient summary:

Bladder cancers are heterogeneous at the molecular level, and scientists have proposed several classifications into sets of molecular classes. While these classifications may be useful to stratify patients for prognosis or response to treatment, a consensus classification would facilitate the clinical use of molecular classes. Conducted by multidisciplinary expert teams in the field, this study proposes such a consensus and provides a tool for applying the consensus classification in the clinical setting.