VAV2: a novel prognostic marker and a druggable target for adrenocortical carcinoma

Personalized Driver Gene Prediction Using Graph Convolutional Networks with Conditional Random Fields

Cancer is a complex and evolutionary disease mainly driven by the accumulation of genetic variations in genes. Identifying cancer driver genes is important. However, most related studies have focused on the population level. Cancer is a disease with high heterogeneity. Thus, the discovery of driver genes at the individual level is becoming more valuable but is a great challenge. Although there have been some computational methods proposed to tackle this challenge, few can cover all patient samples well, and there is still room for performance improvement. In this study, to identify individual-level driver genes more efficiently, we propose the PDGCN method. PDGCN integrates multiple types of data features, including mutation, expression, methylation, copy number data, and system-level gene features, along with network structural features extracted using Node2vec in order to construct a sample-gene interaction network. Prediction is performed using a graphical convolutional neural network model with a conditional random field layer, which is able to better combine the network structural features with biological attribute features. Experiments on the ACC (Adrenocortical Cancer) and KICH (Kidney Chromophobe) datasets from TCGA (The Cancer Genome Atlas) demonstrated that the method performs better compared to other similar methods. It can identify not only frequently mutated driver genes, but also rare candidate driver genes and novel biomarker genes. The results of the survival and enrichment analyses of these detected genes demonstrate that the method can identify important driver genes at the individual level.

Histopathologic Features of Adrenal Cortical Carcinoma

Adrenal cortical carcinoma (ACC) is a rare and aggressive malignancy that poses challenging issues regarding the diagnostic workup. Indeed, no presurgical technique or clinical parameters can reliably distinguish between adrenal cortical adenomas, which are more frequent and have a favorable outcome, and ACC, and the final diagnosis largely relies on histopathologic analysis of the surgical specimen. However, even the pathologic assessment of malignancy in an adrenal cortical lesion is not straightforward and requires a combined evaluation of multiple histopathologic features. Starting from the Weiss score, which was developed in 1984, several histopathologic scoring systems have been designed to tackle the difficulties of ACC diagnosis. Dealing with specific histopathologic variants (eg, Liss-Weiss-Bisceglia scoring system for oncocytic ACC) or patient characteristics (eg, Wieneke index in the pediatric setting), these scores remarkably improved the diagnostic workup of ACC and its subtypes. Nevertheless, cases with misleading features or discordant correlations between pathologic findings and clinical behavior still occur. Owing to multicentric collaborative studies integrating morphologic features with ancillary immunohistochemical markers and molecular analysis, ACC has eventually emerged as a multifaceted, heterogenous malignancy, and, while innovative and promising approaches are currently being tested, the future clinical management of patients with ACC will mainly rely on personalized medicine and target-therapy protocols. At the dawn of the new Fifth World Health Organization classification of endocrine tumors, this review will tackle ACC from the pathologist's perspective, thus focusing on the main available diagnostic, prognostic, and predictive tissue-tethered features and biomarkers and providing relevant clinical and molecular correlates.

Vav2 is a novel APP-interacting protein that regulates APP protein level

Amyloid precursor protein (APP) is a transmembrane protein that plays critical role in the pathogenesis of Alzheimer's disease (AD). It is also involved in many types of cancers. Increasing evidence has shown that the tyrosine phosphorylation site Y682 in the intracellular tail of APP is crucial for APP function. Here, we report that Vav2, a guanine nucleotide exchange factor (GEF) for Rho family GTPase, is a novel interaction partner of APP. We found that Vav2-SH2 domain was able to bind directly to the Y682-phosphorylated intracellular tail of APP through isothermal titration calorimetry and NMR titrating experiments. The crystal structure of Vav2-SH2 in complex with an APP-derived phosphopeptide was determined to understand the structural basis of this recognition specificity. The interaction of APP and Vav2 in a full-length manner was further confirmed in cells by GST pull-down, co-immunoprecipitation and immunofluorescence staining experiments. In addition, we found overexpression of Vav2 could inhibit APP degradation and markedly increase the protein levels of APP and its cleavage productions in 20E2 cells, and this function of Vav2 required a functional SH2 domain.

Targeting the cytoskeleton against metastatic dissemination

Cancer is a pathology characterized by a loss or a perturbation of a number of typical features of normal cell behaviour. Indeed, the acquisition of an inappropriate migratory and invasive phenotype has been reported to be one of the hallmarks of cancer. The cytoskeleton is a complex dynamic network of highly ordered interlinking filaments playing a key role in the control of fundamental cellular processes, like cell shape maintenance, motility, division and intracellular transport. Moreover, deregulation of this complex machinery contributes to cancer progression and malignancy, enabling cells to acquire an invasive and metastatic phenotype. Metastasis accounts for 90% of death from patients affected by solid tumours, while an efficient prevention and suppression of metastatic disease still remains elusive. This results in the lack of effective therapeutic options currently available for patients with advanced disease. In this context, the cytoskeleton with its regulatory and structural proteins emerges as a novel and highly effective target to be exploited for a substantial therapeutic effort toward the development of specific anti-metastatic drugs. Here we provide an overview of the role of cytoskeleton components and interacting proteins in cancer metastasis with a special focus on small molecule compounds interfering with the actin cytoskeleton organization and function. The emerging involvement of microtubules and intermediate filaments in cancer metastasis is also reviewed.

Genomic stratification and liquid biopsy in a rare adrenocortical carcinoma (ACC) case, with dual lung metastases

Adrenocortical carcinoma is a rare malignancy with a poor prognosis and few treatment options. Molecular characterization of this cancer remains limited. We present a case of an adrenocortical carcinoma (ACC) in a 37-yr-old female, with dual lung metastases identified 1 yr following commencement of adjuvant mitotane therapy. As standard therapeutic regimens are often unsuccessful in ACC, we undertook a comprehensive genomic study into this case to identify treatment options and monitor disease progress. We performed targeted and whole-genome sequencing of germline, primary tumor, and both metastatic tumors from this patient and monitored recurrence over 2 years using liquid biopsy for ctDNA and steroid hormone measurements. Sequencing revealed the primary and metastatic tumors were hyperhaploid, with extensive loss of heterozygosity but few structural rearrangements. Loss-of-function mutations were identified in MSH2, TP53, RB1, and PTEN, resulting in tumors with mismatch repair signatures and microsatellite instability. At the cellular level, tumors were populated by mitochondria-rich oncocytes. Longitudinal ctDNA mutation and hormone profiles were unable to detect micrometastatic disease, consistent with clinical indicators of disease remission. The molecular signatures in our ACC case suggested immunotherapy in the event of disease progression; however, the patient remains free of cancer. The extensive molecular analysis presented here could be applied to other rare and/or poorly stratified cancers to identify novel or repurpose existing therapeutic options, thereby broadly improving diagnoses, treatments, and prognoses.

Immunohistochemical Biomarkers of Adrenal Cortical Neoplasms

Careful morphological evaluation forms the basis of the workup of an adrenal cortical neoplasm. However, the adoption of immunohistochemical biomarkers has added tremendous value to enhance diagnostic accuracy. The authors provide a brief review of immunohistochemical biomarkers that have been used in the confirmation of adrenal cortical origin and in the detection of the source of functional adrenal cortical proliferations, as well as diagnostic, predictive, and prognostic biomarkers of adrenal cortical carcinoma. In addition, a brief section on potential novel theranostic biomarkers in the prediction of treatment response to mitotane and other relevant chemotherapeutic agents is also provided. In the era of precision and personalized medical practice, adoption of combined morphology and immunohistochemistry provides a new approach to the diagnostic workup of adrenal cortical neoplasms, reflecting the evolution of clinical responsibility of pathologists.