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Every tumour is different. They arise in patients with different genomes, from cells with different epigenetic modifications, and by random processes affecting the genome and/or epigenome of a somatic cell, allowing it to escape the usual controls on its growth. Tumours and patients therefore often respond very differently to the drugs they receive. Cancer precision medicine aims to characterise the tumour (and often also the patient) to be able to predict, with high accuracy, its response to different treatments, with options ranging from the selective characterisation of a few genomic variants considered particularly important to predict the response of the tumour to specific drugs, to deep genome analysis of both tumour and patient, combined with deep transcriptome analysis of the tumour. Here, we compare the expected results of carrying out such analyses at different levels, from different size panels to a comprehensive analysis incorporating both patient and tumour at the DNA and RNA levels. In doing so, we illustrate the additional power gained by this unusually deep analysis strategy, a potential basis for a future precision medicine first strategy in cancer drug therapy. However, this is only a step along the way of increasingly detailed molecular characterisation, which in our view will, in the future, introduce additional molecular characterisation techniques, including systematic analysis of proteins and protein modification states and different types of metabolites in the tumour, systematic analysis of circulating tumour cells and nucleic acids, the use of spatially resolved analysis techniques to address the problem of tumour heterogeneity as well as the deep analyses of the immune system of the patient to, e.g., predict the response of the patient to different types of immunotherapy. Such analyses will generate data sets of even greater complexity, requiring mechanistic modelling approaches to capture enough of the complex situation in the real patient to be able to accurately predict his/her responses to all available therapies., (© 2017 S. Karger AG, Basel.)

A vital quest in biology is comprehensible visualization and interpretation of correlation relationships on a genome scale. Such relationships may be represented in the form of networks, which usually require disassembly into smaller manageable units, or clusters, to facilitate interpretation. Several graph-clustering algorithms that may be used to visualize biological networks are available. However, only some of these support weighted edges, and none provides good control of cluster sizes, which is crucial for comprehensible visualization of large networks. We constructed an interactive coexpression network for the Arabidopsis (Arabidopsis thaliana) genome using a novel Heuristic Cluster Chiseling Algorithm (HCCA) that supports weighted edges and that may control average cluster sizes. Comparative clustering analyses demonstrated that the HCCA performed as well as, or better than, the commonly used Markov, MCODE, and k-means clustering algorithms. We mapped MapMan ontology terms onto coexpressed node vicinities of the network, which revealed transcriptional organization of previously unrelated cellular processes. We further explored the predictive power of this network through mutant analyses and identified six new genes that are essential to plant growth. We show that the HCCA-partitioned network constitutes an ideal "cartographic" platform for visualization of correlation networks. This approach rapidly provides network partitions with relative uniform cluster sizes on a genome-scale level and may thus be used for correlation network layouts also for other species. [ABSTRACT FROM AUTHOR]

Simple Summary: This experimental preclinical study developed a strategy to identify signatures for the personalized treatment of colon cancer focusing on target-specific drug combinations. Tumor growth inhibition was analyzed in a preclinical phase II study using 25 patient-derived xenograft models (PDX) treated with drug combinations blocking alternatively activated oncogenic pathways. Results reveal an improved response by combinatorial treatment in some defined molecular subgroups and potential alternative treatment options in KRAS- and BRAF-mutated colon cancer. The current standard therapies for advanced, recurrent or metastatic colon cancer are the 5-fluorouracil and oxaliplatin or irinotecan schedules (FOxFI) +/− targeted drugs cetuximab or bevacizumab. Treatment with the FOxFI cytotoxic chemotherapy regimens causes significant toxicity and might induce secondary cancers. The overall low efficacy of the targeted drugs seen in colon cancer patients still is hindering the substitution of the chemotherapy. The ONCOTRACK project developed a strategy to identify predictive biomarkers based on a systems biology approach, using omics technologies to identify signatures for personalized treatment based on single drug response data. Here, we describe a follow-up project focusing on target-specific drug combinations. Background for this experimental preclinical study was that, by analyzing the tumor growth inhibition in the PDX models by cetuximab treatment, a broad heterogenic response from complete regression to tumor growth stimulation was observed. To provide confirmation of the hypothesis that drug combinations blocking alternatively activated oncogenic pathways may improve therapy outcomes, 25 models out of the well-characterized ONCOTRACK PDX panel were subjected to treatment with a drug combination scheme using four approved, targeted cancer drugs. [ABSTRACT FROM AUTHOR]