Activation of cellular transcriptional responses, mediated by hypoxia-inducible factor (HIF), is common in many types of cancer, and generally confers a poor prognosis. Known to induce many hundreds of protein-coding genes, HIF has also recently been shown to be a key regulator of the non-coding transcriptional response. Here we show that NEAT1 lncRNA is a direct transcriptional target of HIF in many breast cancer cell lines and in solid tumors. Unlike previously described lncRNAs, NEAT1 is regulated principally by HIF-2 rather than by HIF-1. NEAT1 is a nuclear lncRNA that is an essential structural component of paraspeckles and the hypoxic induction of NEAT1 induces paraspeckle formation in a manner that is dependent upon both NEAT1 and on HIF-2. Paraspeckles are multifunction nuclear structures that sequester transcriptionally active proteins as well as RNA transcripts that have been subjected to A-to-I editing. We show that the nuclear retention of one such transcript, F11R (also known as junctional adhesion molecule 1 – JAM1), in hypoxia is dependent upon the hypoxic increase in NEAT1, thereby conferring a novel mechanism of HIF-dependent gene regulation. Induction of NEAT1 in hypoxia also leads to accelerated cellular proliferation, improved clonogenic survival and reduced apoptosis, all of which are hallmarks of increased tumorigenesis. Furthermore, in patients with breast cancer, high tumor NEAT1 expression correlates with poor survival. Taken together, these results indicate a new role for HIF transcriptional pathways in the regulation of nuclear structure and that this contributes to the pro-tumorigenic hypoxia-phenotype in breast cancer.

The adipocyte-rich microenvironment forms a niche for ovarian cancer metastasis, but the mechanisms driving this process are incompletely understood. Here we show that salt-inducible kinase 2 (SIK2) is overexpressed in adipocyte-rich metastatic deposits compared with ovarian primary lesions. Overexpression of SIK2 in ovarian cancer cells promotes abdominal metastasis while SIK2 depletion prevents metastasis in vivo. Importantly, adipocytes induce calcium-dependent activation and autophosphorylation of SIK2. Activated SIK2 plays a dual role in augmenting AMPK-induced phosphorylation of acetyl-CoA carboxylase and in activating the PI3K/AKT pathway through p85α-S154 phosphorylation. These findings identify SIK2 at the apex of the adipocyte-induced signaling cascades in cancer cells and make a compelling case for targeting SIK2 for therapy in ovarian cancer.

Cancer is a leading cause of death worldwide and, despite new targeted therapies and immunotherapies, many patients with advanced-stage- or high-risk cancers still die, owing to metastatic disease. Adoptive T-cell therapy, involving the autologous or allogeneic transplant of tumour-infiltrating lymphocytes or genetically modified T cells expressing novel T-cell receptors or chimeric antigen receptors, has shown promise in the treatment of cancer patients, leading to durable responses and, in some cases, cure. Technological advances in genomics, computational biology, immunology and cell manufacturing have brought the aspiration of individualised therapies for cancer patients closer to reality. This new era of cell-based individualised therapeutics challenges the traditional standards of therapeutic interventions and provides opportunities for a paradigm shift in our approach to cancer therapy. Invited speakers at a 2020 symposium discussed three areas—cancer genomics, cancer immunology and cell-therapy manufacturing—that are essential to the effective translation of T-cell therapies in the treatment of solid malignancies. Key advances have been made in understanding genetic intratumour heterogeneity, and strategies to accurately identify neoantigens, overcome T-cell exhaustion and circumvent tumour immunosuppression after cell-therapy infusion are being developed. Advances are being made in cell-manufacturing approaches that have the potential to establish cell-therapies as credible therapeutic options. T-cell therapies face many challenges but hold great promise for improving clinical outcomes for patients with solid tumours.

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Though used widely in cancer therapy, paclitaxel only elicits a response in a fraction of patients. A strong determinant of paclitaxel tumor response is the state of microtubule dynamic instability. However, whether the manipulation of this physiological process can be controlled to enhance paclitaxel response has not been tested. Here, we show a previously unrecognized role of the microtubule-associated protein CRMP2 in inducing microtubule bundling through its carboxy terminus. This activity is significantly decreased when the FER tyrosine kinase phosphorylates CRMP2 at Y479 and Y499. The crystal structures of wild-type CRMP2 and CRMP2-Y479E reveal how mimicking phosphorylation prevents tetramerization of CRMP2. Depletion of FER or reducing its catalytic activity using sub-therapeutic doses of inhibitors increases paclitaxel-induced microtubule stability and cytotoxicity in ovarian cancer cells and in vivo. This work provides a rationale for inhibiting FER-mediated CRMP2 phosphorylation to enhance paclitaxel on-target activity for cancer therapy.

A systematic review of the incidence of STICs in HGSOCs identifies significant methodological inconsistencies.

The epigenetic silencing of tumor suppressor genes (TSGs) is a common finding in several solid and hematological tumors involving various epigenetic readers and writers leading to enhanced cell proliferation and defective apoptosis. Thymoquinone (TQ), the major biologically active compound of black seed oil, has demonstrated anticancer activities in various tumors by targeting several pathways. However, its effects on the epigenetic code of cancer cells are largely unknown. In the present study, we performed RNA sequencing to investigate the anticancer mechanisms of TQ-treated T-cell acute lymphoblastic leukemia cell line (Jurkat cells) and examined gene expression using different tools. We found that many key epigenetic players, including ubiquitin-like containing plant homeodomain (PHD) and really interesting new gene (RING) finger domains 1 ( UHRF1), DNMT1,3A,3B, G9A, HDAC1,4,9, KDM1B , and KMT2A,B,C,D,E , were downregulated in TQ-treated Jurkat cells. Interestingly, several TSGs, such as DLC1, PPARG, ST7, FOXO6, TET2, CYP1B1, SALL4 , and DDIT3 , known to be epigenetically silenced in various tumors, including acute leukemia, were upregulated, along with the upregulation of several downstream pro-apoptotic genes, such as RASL11B, RASD1, GNG3, BAD , and BIK . Data obtained from RNA sequencing were confirmed using quantitative reverse transcription polymerase chain reaction (RT-qPCR) in Jurkat cells, as well as in a human breast cancer cell line (MDA-MB-468 cells). We found that the decrease in cell proliferation and in the expression of UHRF1, DNMT1, G9a , and HDAC1 genes in both cancer cell (Jurkat cells and MDA-MB-468 cells) lines depends on the TQ dose. Our results indicate that the use of TQ as an epigenetic drug represents a promising strategy for epigenetic therapy for both solid and blood tumors by targeting both DNA methylation and histone post-translational modifications.

Clinical Decision Support Systems (CDSS) provide an efficient way to diagnose the presence of diseases such as breast cancer using ultrasound images (USIs). Globally, breast cancer is one of the major causes of increased mortality rates among women. Computer-Aided Diagnosis (CAD) models are widely employed in the detection and classification of tumors in USIs. The CAD systems are designed in such a way that they provide recommendations to help radiologists in diagnosing breast tumors and, furthermore, in disease prognosis. The accuracy of the classification process is decided by the quality of images and the radiologist’s experience. The design of Deep Learning (DL) models is found to be effective in the classification of breast cancer. In the current study, an Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification (EDLCDS-BCDC) technique was developed using USIs. The proposed EDLCDS-BCDC technique was intended to identify the existence of breast cancer using USIs. In this technique, USIs initially undergo pre-processing through two stages, namely wiener filtering and contrast enhancement. Furthermore, Chaotic Krill Herd Algorithm (CKHA) is applied with Kapur’s entropy (KE) for the image segmentation process. In addition, an ensemble of three deep learning models, VGG-16, VGG-19, and SqueezeNet, is used for feature extraction. Finally, Cat Swarm Optimization (CSO) with the Multilayer Perceptron (MLP) model is utilized to classify the images based on whether breast cancer exists or not. A wide range of simulations were carried out on benchmark databases and the extensive results highlight the better outcomes of the proposed EDLCDS-BCDC technique over recent methods.