Metabolic dependencies of breast cancer cells

Cellular metabolism is one of the core processes for cell growth and proliferation. This process is altered in cancer cells as most solid tumours exhibit increased glucose uptake and lactate secretion, a feature known as the Warburg effect. These metabolic changes are the consequence of oncogene activation, loss of tumour suppressor function and/or mutations in metabolic enzymes. However, cancer cell metabolism is not limited to the Warburg effect and the exact role the metabolic machinery plays in facilitating proliferation and cell survival in different cancer types is still poorly understood and requires further study. Breast cancer is a complex and heterogeneous disease at the molecular level. In addition, the PI3K/AKT signalling pathway is frequently activated in breast cancers due to loss of the PTEN tumour suppressor, oncogenic activation of PIK3CA or overexpression of certain growth factor receptors. This study aimed to investigate whether the metabolic requirements of breast cancer cell lines are determined by their molecular alterations. By using RNA interference (siRNA), the expression of 231 metabolic enzymes, transporters and metabolic regulators of the cellular glucose and lipid metabolism were ablated in a panel of 14 breast cancer cell lines and 3 non-malignant breast cell lines with distinct molecular characteristics. Solid breast tumours are known to have regions of high/low delivery of nutrients and oxygen that facilitate changes in the metabolic dependencies of cancer cells that reside within these areas. Moreover, these solid tumours that contain regions of poor oxygen delivery are associated with cancers refractive to treatment and that have poorer overall survival. Thus, to examine the metabolic dependencies of cells that reside in these regions, an environment of low oxygen was recapitulated and the effect of silencing of metabolic genes on cell survival was assessed. Crucially, this approach has led to the identification of previously known and novel metabolic genes that are essential for survival of breast cancer cells for each of the defined breast cancer subgroups. In addition, the characterisation of the metabolic requirements and processes revealed that each subgroup displays a distinct metabolic phenotype that might provide potential novel molecular targets that could be exploited therapeutically

Brain microenvironment-driven resistance to immune and targeted therapies in acral melanoma.

BACKGROUND: Combination treatments targeting the MEK-ERK pathway and checkpoint inhibitors have improved overall survival in melanoma. Resistance to treatment especially in the brain remains challenging, and rare disease subtypes such as acral melanoma are not typically included in trials. Here we present analyses from longitudinal sampling of a patient with metastatic acral melanoma that became resistant to successive immune and targeted therapies. METHODS: We performed whole-exome sequencing and RNA sequencing on an acral melanoma that progressed on successive immune (nivolumab) and targeted (dabrafenib) therapy in the brain to identify resistance mechanisms. In addition, we performed growth inhibition assays, reverse phase protein arrays and immunoblotting on patient-derived cell lines using dabrafenib in the presence or absence of cerebrospinal fluid (CSF) in vitro. Patient-derived xenografts were also developed to analyse response to dabrafenib. RESULTS: Immune escape following checkpoint blockade was not due to loss of tumour cell recognition by the immune system or low neoantigen burden, but was associated with distinct changes in the microenvironment. Similarly, resistance to targeted therapy was not associated with acquired mutations but upregulation of the AKT/phospho-inositide 3-kinase pathway in the presence of CSF. CONCLUSION: Heterogeneous tumour interactions within the brain microenvironment enable progression on immune and targeted therapies and should be targeted in salvage treatments

Imaging cervical cytology with scanning near-field optical microscopy (SNOM) coupled with an IR-FEL

Cervical cancer remains a major cause of morbidity and mortality among women, especially in the developing world. Increased synthesis of proteins, lipids and nucleic acids is a pre-condition for the rapid proliferation of cancer cells. We show that scanning near-field optical microscopy, in combination with an infrared free electron laser (SNOM-IR-FEL), is able to distinguish between normal and squamous low-grade and high-grade dyskaryosis, and between normal and mixed squamous/glandular pre-invasive and adenocarcinoma cervical lesions, at designated wavelengths associated with DNA, Amide I/II and lipids. These findings evidence the promise of the SNOM-IR-FEL technique in obtaining chemical information relevant to the detection of cervical cell abnormalities and cancer diagnosis at spatial resolutions below the diffraction limit (?0.2 \ensuremathμm). We compare these results with analyses following attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy; although this latter approach has been demonstrated to detect underlying cervical atypia missed by conventional cytology, it is limited by a spatial resolution of ~3 \ensuremathμm to 30 \ensuremathμm due to the optical diffraction limit

A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration

Toxicolog

Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach

Understanding the biological factors that are characteristic of metastasis in melanoma remains a key approach to improving treatment. In this study, we seek to identify a gene signature of metastatic melanoma. We configured a new network-based computational pipeline, combined with a machine learning method, to mine publicly available transcriptomic data from melanoma patient samples. Our method is unbiased and scans a genome-wide protein-protein interaction network using a novel formulation for network scoring. Using this, we identify the most influential, differentially expressed nodes in metastatic as compared to primary melanoma. We evaluated the shortlisted genes by a machine learning method to rank them by their discriminatory capacities. From this, we identified a panel of 6 genes, ALDH1A1, HSP90AB1, KIT, KRT16, SPRR3 and TMEM45B whose expression values discriminated metastatic from primary melanoma (87% classification accuracy). In an independent transcriptomic data set derived from 703 primary melanomas, we showed that all six genes were significant in predicting melanoma specific survival (MSS) in a univariate analysis, which was also consistent with AJCC staging. Further, 3 of these genes, HSP90AB1, SPRR3 and KRT16 remained significant predictors of MSS in a joint analysis (HR = 2.3, P = 0.03) although, HSP90AB1 (HR = 1.9, P = 2 × 10−4) alone remained predictive after adjusting for clinical predictors