Alterations in the Ca2+ toolkit in oesophageal adenocarcinoma

Aim: To investigate alterations in transcription of genes, encoding Ca2+ toolkit proteins, in oesophageal adenocarcinoma (OAC) and to assess associations between gene expression, tumor grade, nodal-metastatic stage, and patient survival. Methods: The expression of 275 transcripts, encoding components of the Ca2+ toolkit, was analyzed in two OAC datasets: the Cancer Genome Atlas [via the University of Alabama Cancer (UALCAN) portal] and the oesophageal-cancer, clinical, and molecular stratification [Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS)] dataset. Effects of differential expression of these genes on patient survival were determined using Kaplan-Meier log-rank tests. OAC grade- and metastatic-stage status was investigated for a subset of genes. Adjustment for the multiplicity of testing was made throughout. Results: Of the 275 Ca2+-toolkit genes analyzed, 75 displayed consistent changes in expression between OAC and normal tissue in both datasets. The channel-encoding genes, N-methyl-D-aspartate receptor 2D (GRIN2D), transient receptor potential (TRP) ion channel classical or canonical 4 (TRPC4), and TRP ion channel melastatin 2 (TRPM2) demonstrated the greatest increase in expression in OAC in both datasets. Nine genes were consistently upregulated in both datasets and were also associated with improved survival outcomes. The 6 top-ranking genes for the weighted significance of altered expression and survival outcomes were selected for further analysis: voltage-gated Ca2+ channel subunit α 1D (CACNA1D), voltage-gated Ca2+ channel auxiliary subunit α2 δ4 (CACNA2D4), junctophilin 1 (JPH1), acid-sensing ion channel 4 (ACCN4), TRPM5, and secretory pathway Ca2+ ATPase 2 (ATP2C2). CACNA1D, JPH1, and ATP2C2 were also upregulated in advanced OAC tumor grades and nodal-metastatic stages in both datasets. Conclusions: This study has unveiled alterations of the Ca2+ toolkit in OAC, compared to normal tissue. Such Ca2+ signalling findings are consistent with those from studies on other cancers. Genes that were consistently upregulated in both datasets might represent useful markers for patient diagnosis. Genes that were consistently upregulated, and which were associated with improved survival, might be useful markers for patient outcome. These survival-associated genes may also represent targets for the development of novel chemotherapeutic agents.

Epigenetic loss of heterogeneity from low to high grade localized prostate tumours

Identifying precise molecular subtypes attributable to specific stages of localized prostate cancer has proven difficult due to high levels of heterogeneity. Bulk assays represent a population-average, which mask the heterogeneity that exists at the single-cell level. In this work, we sequence the accessible chromatin regions of 14,424 single-cells from 18 flash-frozen prostate tumours. We observe shared chromatin features among low-grade prostate cancer cells are lost in high-grade tumours. Despite this loss, high-grade tumours exhibit an enrichment for FOXA1, HOXB13 and CDX2 transcription factor binding sites, indicating a shared trans-regulatory programme. We identify two unique genes encoding neuronal adhesion molecules that are highly accessible in high-grade prostate tumours. We show NRXN1 and NLGN1 expression in epithelial, endothelial, immune and neuronal cells in prostate cancer using cyclic immunofluorescence. Our results provide a deeper understanding of the active gene regulatory networks in primary prostate tumours, critical for molecular stratification of the disease.

PATH-10. MULTI-OMICS PROFILING AND MACHINE LEARNING PROPOSE A REFINED MOLECULAR CLASSIFICATION SYSTEM FOR GLIOMA

Accurate molecular stratification of glioma patients is key to an optimal design of therapeutic strategy to maximize patient survival. Here we leveraged multi-omics analysis of glioma and detailed clinical follow-up to build a refined classification system for glioma patients using support vector machines. The model input included the number of non-synonymous mutations in cancer driver genes, the number of non-synonymous mutations in cancer related genes, the transcriptomic grouping information, the immune infiltrations predicted by RNA-seq dataset, the site of tumor occurrence, as well as other well-known markers including IDH mutation status and 1p19q co-deletion status. We validated key model predictions using TCGA and CGGA datasets. Our refined classification system outperforms current state-of-the-art framework used in clinic. Taken together, we propose a refined molecular classification for glioma combining multi-omics profiling and machine learning approaches.

MoSBi: Automated signature mining for molecular stratification and subtyping

The improving access to increasing amounts of biomedical data provides completely new chances for advanced patient stratification and disease subtyping strategies. This requires computational tools that produce uniformly robust results across highly heterogeneous molecular data. Unsupervised machine learning methodologies are able to discover de-novo patterns in such data. Biclustering is especially suited by simultaneously identifying sample groups and corresponding feature sets across heterogeneous omics data. The performance of available biclustering algorithms heavily depends on individual parameterization and varies with their application. Here, we developed MoSBi (Molecular Signature identification using Biclustering), an automated multi-algorithm ensemble approach that integrates results utilizing an error model-supported similarity network. We evaluated the performance of MoSBi on transcriptomics, proteomics, and metabolomics data, as well as synthetic datasets covering various data properties. Profiting from multi-algorithm integration, MoSBi identified robust group and disease-specific signatures across all scenarios overcoming single algorithm specificities. Furthermore, we developed a scalable network-based visualization of bicluster communities that support biological hypothesis generation. MoSBi is available as an R package and web service to make automated biclustering analysis accessible for application in molecular sample stratification.

Molecular Stratification of Childhood Ependymomas as a Basis for Personalized Diagnostics and Treatment

Ependymomas are among the most enigmatic tumors of the central nervous system, posing enormous challenges for pathologists and clinicians. Despite the efforts made, the treatment options are still limited to surgical resection and radiation therapy, while none of conventional chemotherapies is beneficial. While being histologically similar, ependymomas show considerable clinical and molecular diversity. Their histopathological evaluation alone is not sufficient for reliable diagnostics, prognosis, and choice of treatment strategy. The importance of integrated diagnosis for ependymomas is underscored in the recommendations of Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy. These updated recommendations were adopted and implemented by WHO experts. This minireview highlights recent advances in comprehensive molecular-genetic characterization of ependymomas. Strong emphasis is made on the use of molecular approaches for verification and specification of histological diagnoses, as well as identification of prognostic markers for ependymomas in children.

Molecular Stratification of Chronic Kidney Disease

Current classification of chronic kidney disease (CKD) into stages based on the indirect measures of kidney functional state, estimated glomerular filtration rate and albuminuria, is agnostic to the heterogeneity of underlying etiologies, histopathology, and molecular processes. We used genome-wide transcriptomics from patients kidney biopsies, directly reflecting kidney biological processes, to stratify patients from three independent CKD cohorts. Unsupervised Self-Organizing Maps (SOM), an artificial neural network algorithm, assembled CKD patients into four novel subgroups, molecular categories, based on the similarity of their kidney transcriptomics profiles. The unbiased, molecular categories were present across CKD stages and histopathological diagnoses, highlighting heterogeneity of conventional clinical subgroups at the molecular level. CKD molecular categories were distinct in terms of biological pathways, transcriptional regulation and associated kidney cell types, indicating that the molecular categorization is founded on biologically meaningful mechanisms. Importantly, our results revealed that not all biological pathways are equally activated in all patients; instead, different pathways could be more dominant in different subgroups and thereby differentially influencing disease progression and outcomes. This first kidney-centric unbiased categorization of CKD paves the way to an integrated clinical, morphological and molecular diagnosis. This is a key step towards enabling precision medicine for this heterogeneous condition with the potential to advance biological understanding, clinical management, and drug development, as well as establish a roadmap for molecular reclassification of CKD and other complex diseases.

Copy Number Signatures and Clinical Outcomes in Upper Tract Urothelial Carcinoma

Tumor staging of upper tract urothelial carcinomas (UTUCs) is relatively difficult to assert accurately before surgery. Here, we used copy number (CN) signatures as a tool to explore their clinical significance of molecular stratification in UTUC. CN signatures were extracted by non-negative matrix factorization from the whole-genome sequencing (WGS) data of 90 Chinese UTUC primary tumor samples. A validation UTUC cohort (n = 56) and a cohort from urinary cell-free DNA (cfDNA) of urothelial cancer patients (n = 94) and matched primary tumors were also examined. Survival analyses were measured using the Kaplan–Meier, and Cox regression was used for multivariate analysis. Here, we identified six CN signatures (Sig1–6). Patients with a high contribution of Sig6 (Sig6high) were associated with higher microsatellite instability level and papillary architecture and had a favorable outcome. Patients with a low weighted genome integrity index were associated with positive lymph node and showed the worst outcome. Sig6high was identified to be an independently prognostic factor. The predictive significance of CN signature was identified by a validation UTUC cohort. CN signatures retained great concordance between primary tumor and urinary cfDNA. In conclusion, our results reveal that CN signature assessment for risk stratification is feasible and provides a basis for clinical studies that evaluate therapeutic interventions and prognosis.