Mini-batch optimization enables training of ODE models on large-scale datasets.

Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. Mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark mini-batch optimization for ordinary differential equation (ODE) models, thereby establishing a direct link between dynamic modelling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modelling of even larger and more complex systems than what is currently possible.

Models of Models: A Translational Route for Cancer Treatment and Drug Development.

Every patient and every disease is different. Each patient therefore requires a personalized treatment approach. For technical reasons, a personalized approach is feasible for treatment strategies such as surgery, but not for drug-based therapy or drug development. The development of individual mechanistic models of the disease process in every patient offers the possibility of attaining truly personalized drug-based therapy and prevention. The concept of virtual clinical trials and the integrated use of in silico, in vitro, and in vivo models in preclinical development could lead to significant gains in efficiency and order of magnitude increases in the cost effectiveness of drug development and approval. We have developed mechanistic computational models of large-scale cellular signal transduction networks for prediction of drug effects and functional responses, based on patient-specific multi-level omics profiles. However, a major barrier to the use of such models in a clinical and developmental context is the reliability of predictions. Here we detail how the approach of using "models of models" has the potential to impact cancer treatment and drug development. We describe the iterative refinement process that leverages the flexibility of experimental systems to generate highly dimensional data, which can be used to train and validate computational model parameters and improve model predictions. In this way, highly optimized computational models with robust predictive capacity can be generated. Such models open up a number of opportunities for cancer drug treatment and development, from enhancing the design of experimental studies, reducing costs, and improving animal welfare, to increasing the translational value of results generated.

Cancer Precision Medicine: Why More Is More and DNA Is Not Enough.

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.

Impaired Planar Germ Cell Division in the Testis, Caused by Dissociation of RHAMM from the Spindle, Results in Hypofertility and Seminoma.

Hypofertility is a risk factor for the development of testicular germ cell tumors (TGCT), but the initiating event linking these pathologies is unknown. We hypothesized that excessive planar division of undifferentiated germ cells promotes their self-renewal and TGCT development. However, our results obtained from mouse models and seminoma patients demonstrated the opposite. Defective planar divisions of undifferentiated germ cells caused their premature exit from the seminiferous tubule niche, resulting in germ cell depletion, hypofertility, intratubular germ cell neoplasias, and seminoma development. Oriented divisions of germ cells, which determine their fate, were regulated by spindle-associated RHAMM-a function we found to be abolished in 96% of human seminomas. Mechanistically, RHAMM expression is regulated by the testis-specific polyadenylation protein CFIm25, which is downregulated in the human seminomas. These results suggested that spindle misorientation is oncogenic, not by promoting self-renewing germ cell divisions within the niche, but by prematurely displacing proliferating cells from their normal epithelial milieu. Furthermore, they suggested RHAMM loss-of-function and spindle misorientation as an initiating event underlying both hypofertility and TGCT initiation. These findings identify spindle-associated RHAMM as an intrinsic regulator of male germ cell fate and as a gatekeeper preventing initiation of TGCTs. Cancer Res; 76(21); 6382-95. ©2016 AACR.

Predictive Modeling of Drug Treatment in the Area of Personalized Medicine.

Despite a growing body of knowledge on the mechanisms underlying the onset and progression of cancer, treatment success rates in oncology are at best modest. Current approaches use statistical methods that fail to embrace the inherent and expansive complexity of the tumor/patient/drug interaction. Computational modeling, in particular mechanistic modeling, has the power to resolve this complexity. Using fundamental knowledge on the interactions occurring between the components of a complex biological system, large-scale in silico models with predictive capabilities can be generated. Here, we describe how mechanistic virtual patient models, based on systematic molecular characterization of patients and their diseases, have the potential to shift the theranostic paradigm for oncology, both in the fields of personalized medicine and targeted drug development. In particular, we highlight the mechanistic modeling platform ModCell™ for individualized prediction of patient responses to treatment, emphasizing modeling techniques and avenues of application.

Network and systems biology: essential steps in virtualising drug discovery and development.

The biological processes that keep us healthy or cause disease, as well as the mechanisms of action of possible drugs are inherently complex. In the face of this complexity, attempts at discovering new drugs to treat diseases have alternated between trial-and-error (typically on experimental systems) and grand simplification, usually based on much too little information. We now have the chance to combine these strategies through establishment of 'virtual patient' models, centred on a detailed molecular characterisation of thousands or even, in the future, millions of patients. In doing so, we lay the foundations for truly personalised therapy, as well as a far-reaching virtualisation of drug discovery and development in oncology and other areas of medicine.

LGALS3BP regulates centriole biogenesis and centrosome hypertrophy in cancer cells.

Centrosome morphology and number are frequently deregulated in cancer cells. Here, to identify factors that are functionally relevant for centrosome abnormalities in cancer cells, we established a protein-interaction network around 23 centrosomal and cell-cycle regulatory proteins, selecting the interacting proteins that are deregulated in cancer for further studies. One of these components, LGALS3BP, is a centriole- and basal body-associated protein with a dual role, triggering centrosome hypertrophy when overexpressed and causing accumulation of centriolar substructures when downregulated. The cancer cell line SK-BR-3 that overexpresses LGALS3BP exhibits hypertrophic centrosomes, whereas in seminoma tissues with low expression of LGALS3BP, supernumerary centriole-like structures are present. Centrosome hypertrophy is reversed by depleting LGALS3BP in cells endogenously overexpressing this protein, supporting a direct role in centrosome aberration. We propose that LGALS3BP suppresses assembly of centriolar substructures, and when depleted, causes accumulation of centriolar complexes comprising CPAP, acetylated tubulin and centrin.

The structural impact of cancer-associated missense mutations in oncogenes and tumor suppressors.

BACKGROUND: Current large-scale cancer sequencing projects have identified large numbers of somatic mutations covering an increasing number of different cancer tissues and patients. However, the characterization of these mutations at the structural and functional level remains a challenge. RESULTS: We present results from an analysis of the structural impact of frequent missense cancer mutations using an automated method. We find that inactivation of tumor suppressors in cancer correlates frequently with destabilizing mutations preferably in the core of the protein, while enhanced activity of oncogenes is often linked to specific mutations at functional sites. Furthermore, our results show that this alteration of oncogenic activity is often associated with mutations at ATP or GTP binding sites. CONCLUSIONS: With our findings we can confirm and statistically validate the hypotheses for the gain-of-function and loss-of-function mechanisms of oncogenes and tumor suppressors, respectively. We show that the distinct mutational patterns can potentially be used to pre-classify newly identified cancer-associated genes with yet unknown function.

Health technology assessment in the era of personalized health care.

OBJECTIVES: This article examines the challenges for health technology assessment (HTA) in the light of new developments of personalized health care, focusing on European HTA perspectives. METHODS: Using the example of the Integrated Genome Research Network - Mutanom (IG Mutanom) project, with focus on personalized cancer diagnostics and treatment, we assess the scope of current HTA and examine it prospectively in the context of the translation of basic and clinical research into public health genomics and personalized health care. RESULTS: The approaches developed within the IG-Mutanom project are based on innovative technology potentially providing targeted therapies for cancer; making translation into clinical practice requires a novel course of action, however. New models of HTA are needed that can account for the unique types of evidence inherent to individualized targeted therapies. Using constructive health technology assessment (CTA) models is an option, but further suitable models should be developed. CONCLUSIONS: Integrative, systems biology-based approaches toward personalized medicine call for novel assessment methods. The translation of their highly innovative technologies into the practice of health care requires the development of new HTA concepts.

A flexible multiwell format for immunofluorescence screening microscopy of small-molecule inhibitors.

Large-scale screens in mammalian cells demand for flexible high-throughput screening platforms that allow to analyze cellular traits on a genome-wide level or to identify small-molecule inhibitors (SMIs) from complex compound libraries. In this study we developed and tested high-density cell arrays made out of polydimethylsiloxane (PDMS) that support cell growth directly on standard glass microscope objective slides. We analyzed the effect of 3 reference inhibitors (MLN-8054, VX-680, and flavopiridol) and 4 exploratory, cell permeable small-molecule kinase inhibitors (two benzothiophene-based and two 4-amino-6-arylpyrimidine-based compounds) on different cell lines, using prototype 5 × 5 and 9 × 9 array carpets. We found that high-density PDMS cell arrays support growth of a broad range of cell types, are well suited for compound screens, and are compatible with high-content screening platforms. This novel array format is of particular advantage for compound screening to identify SMIs, when a combination of flexibility with respect to culture volume, well density, and high-resolution imaging is required. In addition, we demonstrated the suitability of this format for reverse transfection and siRNA experiments.