Cancer systems biology

Solute carrier transporter superfamily member SLC16A1 is a potential prognostic biomarker and associated with immune infiltration in skin cutaneous melanoma

Melanoma is a type of cancer with a relatively poor prognosis. The development of immunotherapy for the treatment of patients with melanoma has drawn considerable attention in recent years. It is of great clinical significance to identify novel promising prognostic biomarkers and to explore their roles in the immune microenvironment. The solute carrier (SLC) superfamily is a group of transporters predominantly expressed on the cell membrane and are involved in substance transport. SLC16A1 is a member of the SLC family, participating in the transport of lactate, pyruvate, amino acids, ketone bodies, etc. The role of SLC16A1 in tumor immunity has been recently elucidated, while its role in melanoma remains unclear. In this study, bioinformatics analysis was performed to explore the role of SLC16A1 in melanoma. The results showed that high SLC16A1 expression was correlated with decreased overall survival in patients with melanoma. The genes co-expressed with SLC16A1 were significantly enriched in metabolic regulation, protein ubiquitination, and substance localization. Moreover, SLC16A1 was correlated with the infiltration of immune cells. In conclusion, SLC16A1 is a robust prognostic biomarker for melanoma and may be used as a novel target in immunotherapy.

Current Challenges in Glioblastoma: Intratumour Heterogeneity, Residual Disease, and Models to Predict Disease Recurrence

Glioblastoma (GB) is the most common primary malignant brain tumor, and despite the availability of chemotherapy and radiotherapy to combat the disease, overall survival remains low with a high incidence of tumor recurrence. Technological advances are continually improving our understanding of the disease, and in particular, our knowledge of clonal evolution, intratumor heterogeneity, and possible reservoirs of residual disease. These may inform how we approach clinical treatment and recurrence in GB. Mathematical modeling (including neural networks) and strategies such as multiple sampling during tumor resection and genetic analysis of circulating cancer cells, may be of great future benefit to help predict the nature of residual disease and resistance to standard and molecular therapies in GB.

Personalized medicine approaches for colon cancer driven by genomics and systems biology: OncoTrack

The post-genomic era promises to pave the way to a personalized understanding of disease processes, with technological and analytical advances helping to solve some of the world's health challenges. Despite extraordinary progress in our understanding of cancer pathogenesis, the disease remains one of the world's major medical problems. New therapies and diagnostic procedures to guide their clinical application are urgently required. OncoTrack, a consortium between industry and academia, supported by the Innovative Medicines Initiative, signifies a new era in personalized medicine, which synthesizes current technological advances in omics techniques, systems biology approaches, and mathematical modeling. A truly personalized molecular imprint of the tumor micro-environment and subsequent diagnostic and therapeutic insight is gained, with the ultimate goal of matching the "right" patient to the "right" drug and identifying predictive biomarkers for clinical application. This comprehensive mapping of the colon cancer molecular landscape in tandem with crucial, clinical functional annotation for systems biology analysis provides unprecedented insight and predictive power for colon cancer management. Overall, we show that major biotechnological developments in tandem with changes in clinical thinking have laid the foundations for the OncoTrack approach and the future clinical application of a truly personalized approach to colon cancer theranostics.

Biochemical Systems Theory: A Review

Biochemical systems theory (BST) is the foundation for a set of analytical andmodeling tools that facilitate the analysis of dynamic biological systems. This paper depicts major developments in BST up to the current state of the art in 2012. It discusses its rationale, describes the typical strategies and methods of designing, diagnosing, analyzing, and utilizing BST models, and reviews areas of application. The paper is intended as a guide for investigators entering the fascinating field of biological systems analysis and as a resource for practitioners and experts.

Bioinformatics and variability in drug response: a protein structural perspective

Marketed drugs frequently perform worse in clinical practice than in the clinical trials on which their approval is based. Many therapeutic compounds are ineffective for a large subpopulation of patients to whom they are prescribed; worse, a significant fraction of patients experience adverse effects more severe than anticipated. The unacceptable risk-benefit profile for many drugs mandates a paradigm shift towards personalized medicine. However, prior to adoption of patient-specific approaches, it is useful to understand the molecular details underlying variable drug response among diverse patient populations. Over the past decade, progress in structural genomics led to an explosion of available three-dimensional structures of drug target proteins while efforts in pharmacogenetics offered insights into polymorphisms correlated with differential therapeutic outcomes. Together these advances provide the opportunity to examine how altered protein structures arising from genetic differences affect protein-drug interactions and, ultimately, drug response. In this review, we first summarize structural characteristics of protein targets and common mechanisms of drug interactions. Next, we describe the impact of coding mutations on protein structures and drug response. Finally, we highlight tools for analysing protein structures and protein-drug interactions and discuss their application for understanding altered drug responses associated with protein structural variants.

The use of automated quantitative analysis to evaluate epithelial-to-mesenchymal transition associated proteins in clear cell renal cell carcinoma

Background

Epithelial-to-mesenchymal transition (EMT) has recently been implicated in the initiation and progression of renal cell carcinoma (RCC). Some mRNA gene expression studies have suggested a link between the EMT phenotype and poorer clinical outcome from RCC. This study evaluated expression of EMT-associated proteins in RCC using in situ automated quantitative analysis immunofluorescence (AQUA) and compared expression levels with clinical outcome.

Methods/Principal Findings

Unsupervised hierarchical cluster analysis of pre-existing RCC gene expression array data (GSE16449) from 36 patients revealed the presence of an EMT transcriptional signature in RCC [E-cadherin high/SLUG low/SNAIL low]. As automated immunofluorescence technology is dependent on accurate definition of the tumour cells in which measurements take place is critical, extensive optimisation was carried out resulting in a novel pan-cadherin based tumour mask that distinguishes renal cancer cells from stromal components. 61 patients with ccRCC and clinical follow-up were subsequently assessed for expression of EMT-associated proteins (WT1, SNAIL, SLUG, E-cadherin and phospho-β-catenin) on tissue microarrays. Using Kaplan-Meier analysis both SLUG (p = 0.029) and SNAIL (p = 0.024) (log rank Mantel-Cox) were significantly associated with prolonged progression free survival (PFS). Using Cox regression univariate and multivariate analysis none of the biomarkers were significantly correlated with outcome. 14 of the 61 patients expressed the gene expression analysis predicted EMT-protein signature [E-cadherin high/SLUG low/SNAIL low], which was not found to be associated to PFS when measured at the protein level. A combination of high expression of SNAIL and low stage was able to stratify patients with greater significance (p = 0.001) then either variable alone (high SNAIL p = 0.024, low stage p = 0.029).

Conclusions

AQUA has been shown to have the potential to identify EMT related protein targets in RCC allowing for stratification of patients into high and low risk groups, as well the ability to assess the association of reputed EMT signatures to progression of the disease.

Systems biology approaches to decoding the genome of liver cancer

Molecular classification of cancers has been significantly improved patient outcomes through the implementation of treatment protocols tailored to the abnormalities present in each patient's cancer cells. Breast cancer represents the poster child with marked improvements in outcome occurring due to the implementation of targeted therapies for estrogen receptor or human epidermal growth factor receptor-2 positive breast cancers. Important subtypes with characteristic molecular features as potential therapeutic targets are likely to exist for all tumor lineages including hepatocellular carcinoma (HCC) but have yet to be discovered and validated as targets. Because each tumor accumulates hundreds or thousands of genomic and epigenetic alterations of critical genes, it is challenging to identify and validate candidate tumor aberrations as therapeutic targets or biomarkers that predict prognosis or response to therapy. Therefore, there is an urgent need to devise new experimental and analytical strategies to overcome this problem. Systems biology approaches integrating multiple data sets and technologies analyzing patient tissues holds great promise for the identification of novel therapeutic targets and linked predictive biomarkers allowing implementation of personalized medicine for HCC patients.

Features of the reversible sensitivity-resistance transition in PI3K/PTEN/AKT signalling network after HER2 inhibition

Systems biology approaches that combine experimental data and theoretical modelling to understand cellular signalling network dynamics offer a useful platform to investigate the mechanisms of resistance to drug interventions and to identify combination drug treatments. Extending our work on modelling the PI3K/PTEN/AKT signalling network (SN), we analyse the sensitivity of the SN output signal, phospho-AKT, to inhibition of HER2 receptor. We model typical aberrations in this SN identified in cancer development and drug resistance: loss of PTEN activity, PI3K and AKT mutations, HER2 overexpression, and overproduction of GSK3β and CK2 kinases controlling PTEN phosphorylation. We show that HER2 inhibition by the monoclonal antibody pertuzumab increases SN sensitivity, both to external signals and to changes in kinetic parameters of the proteins and their expression levels induced by mutations in the SN. This increase in sensitivity arises from the transition of SN functioning from saturation to non-saturation mode in response to HER2 inhibition. PTEN loss or PIK3CA mutation causes resistance to anti-HER2 inhibitor and leads to the restoration of saturation mode in SN functioning with a consequent decrease in SN sensitivity. We suggest that a drug-induced increase in SN sensitivity to internal perturbations, and specifically mutations, causes SN fragility. In particular, the SN is vulnerable to mutations that compensate for drug action and this may result in a sensitivity-to-resistance transition. The combination of HER2 and PI3K inhibition does not sensitise the SN to internal perturbations (mutations) in the PI3K/PTEN/AKT pathway: this combination treatment provides both synergetic inhibition and may prevent the SN from acquired mutations causing drug resistance. Through combination inhibition treatments, we studied the impact of upstream and downstream interventions to suppress resistance to the HER2 inhibitor in the SN with PTEN loss. Comparison of experimental results of PI3K inhibition in the PTEN upstream pathway with PDK1 inhibition in the PTEN downstream pathway shows that upstream inhibition abrogates resistance to pertuzumab more effectively than downstream inhibition. This difference in inhibition effect arises from the compensatory mechanism of an activation loop induced in the downstream pathway by PTEN loss. We highlight that drug target identification for combination anti-cancer therapy needs to account for the mutation effects on the upstream and downstream pathways.

What can molecular pathology contribute to the management of renal cell carcinoma?

The incidence of renal cell carcinoma (RCC) is increasing and outcomes remain poor. One-third of patients with localized disease will relapse, and 5-year survival for patients with metastatic disease is less than 10%. No molecular test is currently available to identify which patients who have undergone 'curative' surgery will relapse, and which patients will respond to targeted therapy. Some well characterized biochemical pathways, such as those associated with von Hippel-Lindau disease, are aberrantly regulated in RCC and are associated with histological subtype, but the understanding of these pathways contributes little to the clinical management of patients with RCC. Gene expression and sequencing studies have increased our understanding of the genetic basis of the disease but have failed to establish any unified classification to improve molecular stratification or to predict which patients are likely to relapse or respond to targeted therapy. Instead, they have served to highlight that RCC is heterogeneous at histological, morphological, and molecular levels, and that novel approaches are required to resolve the complexity of RCC prognostication and prediction of treatment response.