Elucidation of molecular and functional heterogeneity through differential expression network analyses of discrete tumor subsets

A Multistep Tumor Growth Model of High-Grade Serous Ovarian Carcinoma Identifies Hypoxia-Associated Signatures

High-grade serous ovarian carcinoma (HGSC) is associated with late-stage disease presentation and poor prognosis, with a limited understanding of early transformation events. Our study analyzes HGSC tumor progression and organ-specific metastatic dissemination to identify hypoxia-associated molecular, cellular, and histological alterations. Clinical characteristics of the HGSC were replicated in orthotopic xenografts, which involve metastatic dissemination and the prevalence of group B tumors (volume: >0.0625 ≤ 0.5 cm3). Enhanced hyaluronic acid (HA) deposition, expanded tumor vasculature, and increased necrosis contributed to the remodeling of tumor tissue architecture. The proliferative potential of tumor cells and the ability to form glands were also altered during tumor growth. Flow cytometry and label chase-based molecular profiling across the tumor regenerative hierarchy identified the hypoxia-vasculogenic niche and the hybrid epithelial-mesenchymal tumor-cell state as determinants of self-renewal capabilities of progenitors and cancer stem cells. A regulatory network and mathematical model based on tumor histology and molecular signatures predicted hypoxia-inducible factor 1-alpha (HIF1A) as a central node connecting HA synthesis, epithelial-mesenchymal transition, metabolic, vasculogenic, inflammatory, and necrotic pathways in HGSC tumors. Thus, our findings provide a temporal resolution of hypoxia-associated events that sculpt HGSC tumor growth; an in-depth understanding of it may aid in the early detection and treatment of HGSC.

A molecular signature associated with prolonged survival in glioblastoma patients treated with regorafenib

BACKGROUND

Patients with glioblastoma (GBM) have a dramatically poor prognosis. The recent REGOMA trial suggested an overall survival (OS) benefit of regorafenib in recurrent GBM patients. Considering the extreme genetic heterogeneity of GBMs, we aimed to identify molecular biomarkers predictive of differential response to the drug.

METHODS

Total RNA was extracted from tumor samples of patients enrolled in the REGOMA trial. Genome-wide transcriptome and micro (mi)RNA profiles were associated with patients' OS and progression-free survival.

RESULTS

In the first step, a set of 11 gene transcripts (HIF1A, CTSK, SLC2A1, KLHL12, CDKN1A, CA12, WDR1, CD53, CBR4, NIFK-AS1, RAB30-DT) and 10 miRNAs (miR-93-5p, miR-203a-3p, miR-17-5p, let-7c-3p, miR-101-3p, miR-3607-3p, miR-6516-3p, miR-301a-3p, miR-23b-3p, miR-222-3p) was filtered by comparing survival between regorafenib and lomustine arms. In the second step, a mini-signature of 2 gene transcripts (HIF1A, CDKN1A) and 3 miRNAs (miR-3607-3p, miR-301a-3p, miR-93-5p) identified a subgroup of patients showing prolonged survival after regorafenib administration (median OS range, 10.6-20.8 mo).

CONCLUSIONS

The study provides evidence that a signature based on the expression of 5 biomarkers could help identify a subgroup of GBM patients exhibiting a striking survival advantage when treated with regorafenib. Although the presented results must be confirmed in larger replication cohorts, the study highlights potential biomarker options to help guide the clinical decision among regorafenib and other treatments in patients with relapsing GBM.

Identification of TYROBP and FCER1G as Key Genes with Prognostic Value in Clear Cell Renal Cell Carcinoma by Bioinformatics Analysis

The involvement of aberrantly expressed genes in the pathogenesis and progression of various human malignancies has been widely reported, including clear cell renal cell carcinoma (ccRCC). This study aimed to identify potential crucial genes in ccRCC and further investigate the role of these genes in ccRCC prognosis. Three gene expression profiles (GSE3, GSE6344 and GSE53000) were downloaded from GEO database. GEO2R was performed to identify the differentially expressed genes (DEGs) between ccRCC and normal samples. GO analysis and KEGG pathway enrichment analysis were applied for the function analysis. The DEGs were mapped into the PPI network, then the hub genes were identified and verified using the ONCOMINE database. Kaplan-Meier plotter was used to evaluate of the prognostic value of the identified hub genes. A total of 113 DEGs were identified from the three gene expression profiles, including 64 up-regulated genes and 69 down-regulated genes. DEGs were observed to be enriched in biological processes related to the progress and pathogenesis of human cancers. According to PPI network, 5 hub genes were collected, including TYROBP, C1QB, ITGB2, CD53 and FCER1G. Among them, CD53 was newly identified, and Kaplan-Meier survival curves suggested that high expression of CD53 was significantly associated with poor survival in ccRCC patients (log-rank P < 0.01). The present results may provide new insight into the understanding of molecular mechanisms and the clinical prognosis of ccRCC.

Single-Cell Analysis of Circulating Tumor Cells: How Far Have We Come in the -Omics Era?

Tumor cells detach from the primary tumor or metastatic sites and enter the peripheral blood, often causing metastasis. These cells, named Circulating Tumor Cells (CTCs), display the same spatial and temporal heterogeneity as the primary tumor. Since CTCs are involved in tumor progression, they represent a privileged window to disclose mechanisms of metastases, while -omic analyses at the single-cell level allow dissection of the complex relationships between the tumor subpopulations and the surrounding normal tissue. However, in addition to reporting the proof of concept that we can query CTCs to reveal tumor evolution throughout the continuum of treatment for early detection of resistance to therapy, the scientific literature has also been highlighting the disadvantages of CTCs, which hampers a routine use of this approach in clinical practice. To date, an increasing number of CTC technologies, as well as -omics methods, have been employed, mostly lacking strong comparative analyses. The rarity of CTCs also represents a major challenge, because there is no consensus regarding the minimal criteria necessary and sufficient to define an event as CTC; moreover, we cannot often compare data from of one study with that of another. Finally, the availability of an individual tumor profile undermines the traditional histology-based treatment. Applying molecular data for patient benefit implies a collective effort by biologists, bioengineers, and clinicians, to create tools to interpret molecular data and manage precision medicine in every single patient. Herein, we focus on the most recent findings in CTC −omics to learn how far we have come.

Characterizing the heterogeneity of tumor tissues from spatially resolved molecular measures

Background

Tumor heterogeneity can manifest itself by sub-populations of cells having distinct phenotypic profiles expressed as diverse molecular, morphological and spatial distributions. This inherent heterogeneity poses challenges in terms of diagnosis, prognosis and efficient treatment. Consequently, tools and techniques are being developed to properly characterize and quantify tumor heterogeneity. Multiplexed immunofluorescence (MxIF) is one such technology that offers molecular insight into both inter-individual and intratumor heterogeneity. It enables the quantification of both the concentration and spatial distribution of 60+ proteins across a tissue section. Upon bioimage processing, protein expression data can be generated for each cell from a tissue field of view.

Results

The Multi-Omics Heterogeneity Analysis (MOHA) tool was developed to compute tissue heterogeneity metrics from MxIF spatially resolved tissue imaging data. This technique computes the molecular state of each cell in a sample based on a pathway or gene set. Spatial states are then computed based on the spatial arrangements of the cells as distinguished by their respective molecular states. MOHA computes tissue heterogeneity metrics from the distributions of these molecular and spatially defined states. A colorectal cancer cohort of approximately 700 subjects with MxIF data is presented to demonstrate the MOHA methodology. Within this dataset, statistically significant correlations were found between the intratumor AKT pathway state diversity and cancer stage and histological tumor grade. Furthermore, intratumor spatial diversity metrics were found to correlate with cancer recurrence.

Conclusions

MOHA provides a simple and robust approach to characterize molecular and spatial heterogeneity of tissues. Research projects that generate spatially resolved tissue imaging data can take full advantage of this useful technique. The MOHA algorithm is implemented as a freely available R script (see supplementary information).

Ovarian cancer stem cells: still an elusive entity?

The cancer stem cell (CSC) model proposes that tumor development and progression are fueled and sustained by undifferentiated cancer cells, endowed with self-renewal and tumor-initiating capacity. Ovarian carcinoma, based on its biological features and clinical evolution, appears as a prototypical example of CSC-driven disease. Indeed, ovarian cancer stem cells (OCSC) would account not only for the primary tumor growth, the peritoneal spread and the relapse, but also for the development of chemoresistance, thus having profound implication for the treatment of this deadly disease. In the last decade, an increasing body of experimental evidence has supported the existence of OCSC and their pathogenic role in the disease. Nevertheless, the identification of OCSC and the definition of their phenotypical and functional traits have proven quite challenging, mainly because of the heterogeneity of the disease and of the difficulties in establishing reliable biological models. A deeper understanding of OCSC pathobiology will shed light on the mechanisms that underlie the clinical behaviour of OC. In addition, it will favour the design of innovative treatment regimens that, on one hand, would counteract the resistance to conventional chemotherapy, and, on the other, would aim at the eradication of OC through the elimination of its CSC component.