Copy number aberrations drive kinase rewiring, leading to genetic vulnerabilities in cancer

Insights into the Clinical, Biological and Therapeutic Impact of Copy Number Alteration in Cancer

Copy number alterations (CNAs), resulting from the gain or loss of genetic material from as little as 50 base pairs or as big as entire chromosome(s), have been associated with many congenital diseases, de novo syndromes and cancer. It is established that CNAs disturb the dosage of genomic regions including enhancers/promoters, long non-coding RNA and gene(s) among others, ultimately leading to an altered balance of key cellular functions. In cancer, CNAs have been associated with almost all steps of the disease: predisposition, initiation, development, maintenance, response to treatment, resistance, and relapse. Therefore, understanding how specific CNAs contribute to tumourigenesis may provide prognostic insight and ultimately lead to the development of new therapeutic approaches to improve patient outcomes. In this review, we provide a snapshot of what is currently known about CNAs and cancer, incorporating topics regarding their detection, clinical impact, origin, and nature, and discuss the integration of innovative genetic engineering strategies, to highlight the potential for targeting CNAs using novel, dosage-sensitive and less toxic therapies for CNA-driven cancer.

The Reign of Follistatin in Tumors and Their Microenvironment: Implications for Drug Resistance

Follistatin (FST) is a potent neutralizer of the transforming growth factor-β superfamily and is associated with normal cellular programs and various hallmarks of cancer, such as proliferation, migration, angiogenesis, and immune evasion. The aberrant expression of FST by solid tumors is a well-documented observation, yet how FST influences tumor progression and therapy response remains unclear. The recent surge in omics data has revealed new insights into the molecular foundation underpinning tumor heterogeneity and its microenvironment, offering novel precision medicine-based opportunities to combat cancer. In this review, we discuss these recent FST-centric studies, thereby offering an updated perspective on the protean role of FST isoforms in shaping the complex cellular ecosystem of tumors and in mediating drug resistance.

Immune Cell Abundance and T-cell Receptor Landscapes Suggest New Patient Stratification Strategies in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a molecularly and spatially heterogeneous disease frequently characterized by impairment of immunosurveillance mechanisms. Despite recent success with immunotherapy treatment, disease progression still occurs quickly after treatment in the majority of cases, suggesting the need to improve patient selection strategies. In the quest for biomarkers that may help inform response to checkpoint blockade, we characterized the tumor microenvironment (TME) of 162 HNSCC primary tumors of diverse etiologic and spatial origin, through gene expression and IHC profiling of relevant immune proteins, T-cell receptor (TCR) repertoire analysis, and whole-exome sequencing. We identified five HNSCC TME categories based on immune/stromal composition: (i) cytotoxic, (ii) plasma cell rich, (iii) dendritic cell rich, (iv) macrophage rich, and (v) immune-excluded. Remarkably, the cytotoxic and plasma cell rich subgroups exhibited a phenotype similar to tertiary lymphoid structures (TLS), which have been previously linked to immunotherapy response. We also found an increased richness of the TCR repertoire in these two subgroups and in never smokers. Mutational patterns evidencing APOBEC activity were enriched in the plasma cell high subgroup. Furthermore, specific signal propagation patterns within the Ras/ERK and PI3K/AKT pathways associated with distinct immune phenotypes. While traditionally CD8/CD3 T-cell infiltration and immune checkpoint expression (e.g., PD-L1) have been used in the patient selection process for checkpoint blockade treatment, we suggest that additional biomarkers, such as TCR productive clonality, smoking history, and TLS index, may have the ability to pull out potential responders to benefit from immunotherapeutic agents.

SIGNIFICANCE

Here we present our findings on the genomic and immune landscape of primary disease in a cohort of 162 patients with HNSCC, benefitting from detailed molecular and clinical characterization. By employing whole-exome sequencing and gene expression analysis of relevant immune markers, TCR profiling, and staining of relevant proteins involved in immune response, we highlight how distinct etiologies, cell intrinsic, and environmental factors combine to shape the landscape of HNSCC primary disease.

Guanine nucleotide exchange factors for Rho GTPases (RhoGEFs) as oncogenic effectors and strategic therapeutic targets in metastatic cancer

Metastatic cancer cells dynamically adjust their shape to adhere, invade, migrate, and expand to generate secondary tumors. Inherent to these processes is the constant assembly and disassembly of cytoskeletal supramolecular structures. The subcellular places where cytoskeletal polymers are built and reorganized are defined by the activation of Rho GTPases. These molecular switches directly respond to signaling cascades integrated by Rho guanine nucleotide exchange factors (RhoGEFs), which are sophisticated multidomain proteins that control morphological behavior of cancer and stromal cells in response to cell-cell interactions, tumor-secreted factors and actions of oncogenic proteins within the tumor microenvironment. Stromal cells, including fibroblasts, immune and endothelial cells, and even projections of neuronal cells, adjust their shapes and move into growing tumoral masses, building tumor-induced structures that eventually serve as metastatic routes. Here we review the role of RhoGEFs in metastatic cancer. They are highly diverse proteins with common catalytic modules that select among a variety of homologous Rho GTPases enabling them to load GTP, acquiring an active conformation that stimulates effectors controlling actin cytoskeleton remodeling. Therefore, due to their strategic position in oncogenic signaling cascades, and their structural diversity flanking common catalytic modules, RhoGEFs possess unique characteristics that make them conceptual targets of antimetastatic precision therapies. Preclinical proof of concept, demonstrating the antimetastatic effect of inhibiting either expression or activity of βPix (ARHGEF7), P-Rex1, Vav1, ARHGEF17, and Dock1, among others, is emerging.

CRISPR screening reveals gleason score and castration resistance related oncodriver ring finger protein 19 A (RNF19A) in prostate cancer

Prostate cancer (PCa) is one of the most lethal causes of cancer-related death in male. It is characterized by chromosomal instability and disturbed signaling transduction. E3 ubiquitin ligases are well-recognized as mediators leading to genomic alterations and malignant phenotypes. There is a lack of systematic study on novel oncodrivers with genomic and clinical significance in PCa. In this study we used clustered regularly interspaced short palindromic repeats (CRISPR) system to screen 656 E3 ubiquitin ligases as oncodrivers or tumor repressors in PCa cells. We identified 51 significantly changed genes, and conducted genomic and clinical analysis on these genes. It was found that the Ring Finger Protein 19 A (RNF19A) was a novel oncodriver in PCa. RNF19A was frequently amplified and highly expressed in PCa and other cancer types. Clinically, higher RNF19A expression correlated with advanced Gleason Score and predicted castration resistance. Mechanistically, transcriptomics, quantitative and ubiquitination proteomic analysis showed that RNF19A ubiquitylated Thyroid Hormone Receptor Interactor 13 (TRIP13) and was transcriptionally activated by androgen receptor (AR) and Hypoxia Inducible Factor 1 Subunit Alpha (HIF1A). This study uncovers the genomic and clinical significance of a oncodriver RNF19A in PCa. The results of this study indicate that targeting AR/HIF1A-RNF19A-TRIP13 signaling axis could be an alternative option for PCa diagnosis and therapy.

CNAViz: An interactive webtool for user-guided segmentation of tumor DNA sequencing data

Copy-number aberrations (CNAs) are genetic alterations that amplify or delete the number of copies of large genomic segments. Although they are ubiquitous in cancer and, thus, a critical area of current cancer research, CNA identification from DNA sequencing data is challenging because it requires partitioning of the genome into complex segments with the same copy-number states that may not be contiguous. Existing segmentation algorithms address these challenges either by leveraging the local information among neighboring genomic regions, or by globally grouping genomic regions that are affected by similar CNAs across the entire genome. However, both approaches have limitations: overclustering in the case of local segmentation, or the omission of clusters corresponding to focal CNAs in the case of global segmentation. Importantly, inaccurate segmentation will lead to inaccurate identification of CNAs. For this reason, most pan-cancer research studies rely on manual procedures of quality control and anomaly correction. To improve copy-number segmentation, we introduce CNAViz, a web-based tool that enables the user to simultaneously perform local and global segmentation, thus overcoming the limitations of each approach. Using simulated data, we demonstrate that by several metrics, CNAViz allows the user to obtain more accurate segmentation relative to existing local and global segmentation methods. Moreover, we analyze six bulk DNA sequencing samples from three breast cancer patients. By validating with parallel single-cell DNA sequencing data from the same samples, we show that by using CNAViz, our user was able to obtain more accurate segmentation and improved accuracy in downstream copy-number calling.

Copy-number aberrations (CNAs) are large genetic alterations that are pervasive in cancer and, therefore, have been the focus of several cancer research studies. Copy-number segmentation is a key step in the process of CNA identification, which consist in partitioning the genome into genomic segments with the same copy-number state. However, segmentation is challenging and the limitations of current segmentation algorithms lead to inaccuracies in the characterization of CNAs. In this paper, we introduce CNAViz, an interactive web-based tool that enables the user to edit segmentation solutions and overcome current limitations. We demonstrate the ability of a user to use CNAViz to improve segmentation solutions on both simulated and real data, analyzing six published bulk DNA sequencing samples from three breast cancer patients. Finally, we demonstrate that these improvements in segmentation solutions improve accuracy in downstream copy-number calling, enabling more accurate analyses of intra-tumor heterogeneity.

A commercial ARHGEF17/TEM4 antibody cross-reacts with Nuclear Mitotic Apparatus protein 1 (NuMA)

The Rho family Guanine nucleotide exchange factor (GEF) ARHGEF17 (also known as TEM4) is a large protein with only 3 annotated regions: an N-terminal actin-binding domain, a Rho-specific dbl homology (DH)- pleckstrin homology (PH) type GEF domain and a seven bladed β propeller fold at the C-terminus with unknown function. TEM4 has been implicated in numerous activities that rely on regulation of the cytoskeleton including cell migration, cell-cell junction formation and the spindle assembly checkpoint during mitosis. Here we have assessed the specificity of a TEM4 polyclonal antibody that has been commonly used as a Western blotting and immunocytochemistry probe for TEM4 in mammalian cells. We find that this antibody, in addition to its intended target, cross-reacts with the Nuclear Mitotic Apparatus Protein 1 (NuMA) in Western blotting and immunoprecipitation, and detects NuMA preferentially in immunocytochemistry. This cross-reactivity, with an abundant chromatin- and mitotic spindle-associated factor, is likely to affect the interpretation of experiments that make use of this antibody probe, in particular by immunocytochemistry and immunoprecipitation.

Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration

Accurate molecular subtypes prediction of cancer patients is significant for personalized cancer diagnosis and treatments. Large amount of multi-omics data and the advancement of data-driven methods are expected to facilitate molecular subtyping of cancer. Most existing machine learning–based methods usually classify samples according to single omics data, fail to integrate multi-omics data to learn comprehensive representations of the samples, and ignore that information transfer and aggregation among samples can better represent them and ultimately help in classification. We propose a novel framework named multi-omics graph convolutional network (M-GCN) for molecular subtyping based on robust graph convolutional networks integrating multi-omics data. We first apply the Hilbert–Schmidt independence criterion least absolute shrinkage and selection operator (HSIC Lasso) to select the molecular subtype-related transcriptomic features and then construct a sample–sample similarity graph with low noise by using these features. Next, we take the selected gene expression, single nucleotide variants (SNV), and copy number variation (CNV) data as input and learn the multi-view representations of samples. On this basis, a robust variant of graph convolutional network (GCN) model is finally developed to obtain samples’ new representations by aggregating their subgraphs. Experimental results of breast and stomach cancer demonstrate that the classification performance of M-GCN is superior to other existing methods. Moreover, the identified subtype-specific biomarkers are highly consistent with current clinical understanding and promising to assist accurate diagnosis and targeted drug development.