Heterogeneity in the gene regulatory landscape of leiomyosarcoma

Patient-specific gene co-expression networks reveal novel subtypes and predictive biomarkers in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a highly heterogenous and aggressive form of non-small cell lung cancer (NSCLC). The use of genome-wide gene co-expression networks (GCNs) has been paramount to describe changes in the transcriptional regulatory programs found between diseased and healthy states of LUAD. Recently, studies have shown that multiple cancerous phenotypes share a distinct GCN architecture, suggesting that network topology holds promise for understanding disease pathology. However, conventional GCN inference methods struggle to capture the inherent context-specificity within a patient population, thus flattening its heterogeneity. To address this issue, the use of single-sample network (SSN) modelling has emerged as a promising solution into studying heterogeneous traits of cancer through network-based approaches. Here, we reconstructed patient-specific GCNs (n=334) using the LIONESS equation and mutual information as the network inference method. Unsupervised analysis revealed six novel LUAD subtypes based on inter-patient network similarity, each with distinct network motifs reflecting unique biological programs. Supervised analysis, employing regularized Cox regression, identified 12 genes (CHRDL2, SPP2, VAC14, IRF5, GUCY1B1, NCS1, RRM2B, EIF5A2, CCDC62, CTCFL, XG, and TP53INP2) whose weighted degree in SSNs is predictive of patient survival in LUAD. These findings suggest that topological features of SSNs offer valuable insights into the context-specific nature of LUAD malignancy, highlighting the potential of SSN-based approaches for further research.

Gene co-expression network analysis reveals relationship between leukocyte fraction and genomic instability in dedifferentiated liposarcoma

Dedifferentiated Liposarcoma (DDLPS) is one of the common subtypes of liposarcoma that is considered a highly malignant category. This study aims to investigate DDLPS through a system biology approach. The gene expression profiles and clinical traits of the DDLPS were acquired from The Cancer Genome Atlas (TCGA). The identification of co-expressed modules was conducted using the weighted gene co-expression network analysis. The immune cell-related gene function was studied by a web-based tool, TIMER, and, the survival analysis was performed at both the module and single-gene levels through Cox Regression analysis. Gene enrichment analysis was also conducted using the DAVID tool. One of the nine co-expressed DDLPS modules was significantly correlated with leukocyte fraction, hyper/hypo methylation, tumor purity, and chromosome instability (CIN). Based on the biological processes used to classify genes, the hub genes in a particular module play important roles in DNA repair, microtubule organizing clusters, mitotic checkpoint dysregulation, and cell proliferation signaling pathways. After screening the genes based on intra-module connectivity, module membership, and gene significance RAD54L was selected as one of the important hub genes. RAD54L showed poor prognosis to the overall survival (OS) analysis (HR=1.6, 95% CI=1.1-2.4, p=0.02). No co-expressed modules had relationship with OS. Through DDLPS traits, CIN and hyper/hypo methylation had significant negative relationship with OS. Our achievement confirmed the inverse association between tumor purity for DDLPS gene profiles and leukocyte fraction and negative leukocyte fraction (LF) gene significance in some genes was justified according to the sub-population analyses of immune cells in TIMER.

Reproducible processing of TCGA regulatory networks

Background

Technological advances in sequencing and computation have allowed deep exploration of the molecular basis of diseases. Biological networks have proven to be a useful framework for interrogating omics data and modeling regulatory gene and protein interactions. Large collaborative projects, such as The Cancer Genome Atlas (TCGA), have provided a rich resource for building and validating new computational methods resulting in a plethora of open-source software for downloading, pre-processing, and analyzing those data. However, for an end-to-end analysis of regulatory networks a coherent and reusable workflow is essential to integrate all relevant packages into a robust pipeline.

Findings

We developed tcga-data-nf, a Nextflow workflow that allows users to reproducibly infer regulatory networks from the thousands of samples in TCGA using a single command. The workflow can be divided into three main steps: multi-omics data, such as RNA-seq and methylation, are downloaded, preprocessed, and lastly used to infer regulatory network models with the netZoo software tools. The workflow is powered by the NetworkDataCompanion R package, a standalone collection of functions for managing, mapping, and filtering TCGA data. Here we show how the pipeline can be used to study the differences between colon cancer subtypes that could be explained by epigenetic mechanisms. Lastly, we provide pre-generated networks for the 10 most common cancer types that can be readily accessed.

Conclusions

tcga-data-nf is a complete yet flexible and extensible framework that enables the reproducible inference and analysis of cancer regulatory networks, bridging a gap in the current universe of software tools.

Conjunctival leiomyosarcoma: A clinico-pathological study with in deep molecular characterization

BACKGROUND

Primary and metastatic leiomyosarcomas (LMS) involving the orbital region are well known to occur however, the conjunctiva represents an extremely rare site of occurrence.

METHODS

A 97-year-old male was referred to the Ocular Oncology Unit due to a rapidly growing painful mass (16×12×20 mm) in the nasal conjunctiva of his left eye. Wide excision followed by radiotherapy was performed.

RESULTS

Based on the microscopic features (hypercellular neoplasm composed of spindle cells with cigar shaped and blunt ended nuclei with brightly eosinophilic fibrillary cytoplasm) and immunohistochemical findings (positive staining for Vimentin, Desmin, Caldesmon, and SMA and negative staining for AE1/AE3, EMA, CD117, S100, MelanA, SOX10, HMB45, TLE1, CD99, EMA and AE1 / AE3) the final diagnosis of grade 2 leyomiosarcoma was rendered. Moreover, 'in deep' DNA sequencing (>500 genes analysis) revealed a neoplasm with high TMB: 64 muts/Mb and numerous VUS and several pathogenic/oncogenic molecular alterations, including CNV loss or gain in > 10 genes. At the last follow-up visit, residual disease was observed in the superior fornix, at the nasal limbus and the cornea. At the time of writing, after a follow-up of 2 month the patients is still alive without evidence of metastatic disease.

CONCLUSION

An uncommon molecular finding observed in our case was the presence of TSC1 gene mutation usually associated with soft tissue and gynecological PEComas. Our finding may harbor important therapeutic implications since the inactivation of the tumor suppressor genes TSC1 and TSC2 lead to upregulation of mTOR signaling, providing the rationale for target therapy with mTOR inhibitors. Additional studies on larger series are needed to validate our findings.