Polo like kinase 2 tumour suppressor and cancer biomarker: new perspectives on drug sensitivity/resistance in ovarian cancer

The polo-like kinase PLK2 has recently been identified as a potential theranostic marker in the management of chemotherapy sensitive cancers. The methylation status of the PLK2 CpG island varies with sensitivity to paclitaxel and platinum in ovarian cancer cell lines. Importantly, extrapolation of these in vitro data to the clinical setting confirms that the methylation status of the PLK2 CpG island predicts outcomes in patients treated with carboplatin and paclitaxel chemotherapy. A second cell cycle regulator, p57Kip2, is also subject to epigenetic silencing in carboplatin resistance in vitro and in vivo, emphasising that cell cycle regulators are important determinants of sensitivity to chemotherapeutic agents and providing insights into the phenomenon of collateral drug sensitivity in oncology. Understanding the mechanistic basis and identification of robust biomarkers to predict collateral sensitivity may inform optimal use of chemotherapy in patients receiving multiple lines of treatment

Rational treatment of metastatic colorectal cancer: A reverse tale of men, mice, and culture dishes

Stratification of colorectal cancer into subgroups with different response to therapy was initially guided by descriptive associations between specific biomarkers and treatment outcome. Recently, preclinical models based on propagatable patient-derived tumor samples have yielded an improved understanding of disease biology, which has facilitated the functional validation of correlative information and the discovery of novel response determinants, therapeutic targets, and mechanisms of tumor adaptation and drug resistance. We review the contribution of patient-derived models to advancing colorectal cancer characterization, discuss their influence on clinical decision-making, and highlight emerging challenges in the interpretation and clinical transferability of results obtainable with such approaches. SIGNIFICANCE: Association studies in patients with colorectal cancer have led to the identification of response biomarkers, some of which have been implemented as companion diagnostics for therapeutic decisions. By enabling biological investigation in a clinically relevant experimental context, patient-derived colorectal cancer models have proved useful to examine the causal role of such biomarkers in dictating drug sensitivity and are providing fresh knowledge on new actionable targets, dynamics of tumor evolution and adaptation, and mechanisms of drug resistance

Clinical proteomics for precision medicine: the bladder cancer case

Precision medicine can improve patient management by guiding therapeutic decision based on molecular characteristics. The concept has been extensively addressed through the application of –omics based approaches. Proteomics attract high interest, as proteins reflect a “real-time” dynamic molecular phenotype. Focusing on proteomics applications for personalized medicine, a literature search was conducted to cover: a) disease prevention, b) monitoring/ prediction of treatment response, c) stratification to guide intervention and d) identification of drug targets. The review indicates the potential of proteomics for personalized medicine by also highlighting multiple challenges to be addressed prior to actual implementation. In oncology, particularly bladder cancer, application of precision medicine appears especially promising. The high heterogeneity and recurrence rates together with the limited treatment options, suggests that earlier and more efficient intervention, continuous monitoring and the development of alternative therapies could be accomplished by applying proteomics-guided personalized approaches. This notion is backed by studies presenting biomarkers that are of value in patient stratification and prognosis, and by recent studies demonstrating the identification of promising therapeutic targets. Herein, we aim to present an approach whereby combining the knowledge on biomarkers and therapeutic targets in bladder cancer could serve as basis towards proteomics- guided personalized patient management

Cancer biomarker development from basic science to clinical practice

The amount of published literature on biomarkers has exponentially increased over the last two decades. Cancer biomarkers are molecules that are either part of tumour cells or secreted by tumour cells. Biomarkers can be used for diagnosing cancer (tumour versus normal and differentiation of subtypes), prognosticating patients (progression free survival and overall survival) and predicting response to therapy. However, very few biomarkers are currently used in clinical practice compared to the unprecedented discovery rate. Some of the examples are: carcino-embryonic antigen (CEA) for colon cancer; prostate specific antigen (PSA) for prostate; and estrogen receptor (ER), progesterone receptor (PR) and HER2 for breast cancer. Cancer biomarkers passes through a series of phases before they are used in clinical practice. First phase in biomarker development is identification of biomarkers which involve discovery, demonstration and qualification. This is followed by validation phase, which includes verification, prioritisation and initial validation. More large-scale and outcome-oriented validation studies expedite the clinical translation of biomarkers by providing a strong ‘evidence base’. The final phase in biomarker development is the routine clinical use of biomarker. In summary, careful identification of biomarkers and then validation in well-designed retrospective and prospective studies is a systematic strategy for developing clinically useful biomarkers

A signaling visualization toolkit to support rational design of combination therapies and biomarker discovery: SiViT

Targeted cancer therapy aims to disrupt aberrant cellular signalling pathways. Biomarkers are surrogates of pathway state, but there is limited success in translating candidate biomarkers to clinical practice due to the intrinsic complexity of pathway networks. Systems biology approaches afford better understanding of complex, dynamical interactions in signalling pathways targeted by anticancer drugs. However, adoption of dynamical modelling by clinicians and biologists is impeded by model inaccessibility. Drawing on computer games technology, we present a novel visualisation toolkit, SiViT, that converts systems biology models of cancer cell signalling into interactive simulations that can be used without specialist computational expertise. SiViT allows clinicians and biologists to directly introduce for example loss of function mutations and specific inhibitors. SiViT animates the effects of these introductions on pathway dynamics, suggesting further experiments and assessing candidate biomarker effectiveness. In a systems biology model of Her2 signalling we experimentally validated predictions using SiViT, revealing the dynamics of biomarkers of drug resistance and highlighting the role of pathway crosstalk. No model is ever complete: the iteration of real data and simulation facilitates continued evolution of more accurate, useful models. SiViT will make accessible libraries of models to support preclinical research, combinatorial strategy design and biomarker discovery

Bioinformatic Analysis for the Validation of Novel Biomarkers for Cancer Diagnosis and Drug Sensitivity

Background: The genetic control of tumour progression presents the opportunity for bioinformatics and gene expression data to be used as a basis for tumour grading. The development of a genetic signature based on microarray data allows for the development of personalised chemotherapeutic regimes. Method: ONCOMINE was utilised to create a genetic signature for ovarian serous adenocarcinoma and to compare the expression of genes between normal ovarian and cancerous cells. Ingenuity Pathways Analysis was also utilised to develop molecular pathways and observe interactions with exogenous molecules. Results: The gene signature demonstrated 98.6% predictive capability for the differentiation between borderline ovarian serous neoplasm and ovarian serous adenocarcinoma. The data demonstrated that many genes were related to angiogenesis. Thymidylate synthase, GLUT-3 and HSP90AA1 were related to tanespimycin sensitivity (p=0.005). Conclusions: Genetic profiling with the gene signature demonstrated potential for clinical use. The use of tanespimycin alongside overexpression of thymidylate synthase, GLUT-3 and HSP90AA1 is a novel consideration for ovarian cancer treatment

Cancer biomarkers, and novel techniques for detection

Technologies for early detection of tumors is critical for better therapy outcome and overall change in cancer survival. These assays must be capable of detecting tumors at early stages in order to prevent metastasis of the tumor and help reduce mortality. Biological molecules can serve as markers that can indicate the presence of cancerous cells. Current biomarkers approved by the FDA include CA 125, which is a tumor associated antigen (TAA). However, the sensitivities of these TAAs is not high enough to detect at early stages of disease. Recent technologies have found that antibodies that recognize these TAAs, also known as autoantibodies, provide more sensitive means to screen for tumors. This review aims to present recent literature data relative to the field of cancer diagnosis and treatment. However, one should note that this article covers only fraction of the broad science behind this subject

Integrative analysis of the colorectal cancer proteome : potential clinical impact

Peer reviewedPostprin