Computational analysis of receptor tyrosine kinase inhibitors and cancer metabolism: implications for treatment and discovery of potential therapeutic signatures.

BACKGROUND: Receptor tyrosine kinase (RTK) inhibitors are frequently used to treat cancers and the results have been mixed, some of these small molecule drugs are highly successful while others show a more modest response. A high number of studies have been conducted to investigate the signaling mechanisms and corresponding therapeutic influence of RTK inhibitors in order to explore the therapeutic potential of RTK inhibitors. However, most of these studies neglected the potential metabolic impact of RTK inhibitors, which could be highly associated with drug efficacy and adverse effects during treatment. METHODS: In order to fill these knowledge gaps and improve the therapeutic utilization of RTK inhibitors a large-scale computational simulation/analysis over multiple types of cancers with the treatment responses of RTK inhibitors was performed. The pharmacological data of all eight RTK inhibitor and gene expression profiles of 479 cell lines from The Cancer Cell Line Encyclopedia were used. RESULTS: The potential metabolic impact of RTK inhibitors on different types of cancers were analyzed resulting in cancer-specific (breast, liver, pancreas, central nervous system) metabolic signatures. Many of these are in line with results from different independent studies, thereby providing indirect verification of the obtained results. CONCLUSIONS: Our study demonstrates the potential of using a computational approach on signature-based-analysis over multiple cancer types. The results reveal the strength of multiple-cancer analysis over conventional signature-based analysis on a single cancer type.

A Bayesian scoring rule on clustered event data for familial risk assessment - An example from colorectal cancer screening.

Colorectal cancer screening is well established. The identification of high risk populations is the key to implement effective risk-adjusted screening. Good statistical approaches for risk prediction do not exist. The family's colorectal cancer history is used for identification of high risk families and usually assessed by a questionnaire. This paper introduces a prediction algorithm to designate a family for colorectal cancer risk and discusses its statistical properties. The new algorithm uses Bayesian reasoning and a detailed family history illustrated by a pedigree and a Lexis diagram. The algorithm is able to integrate different hereditary mechanisms that define complex latent class or random factor structures. They are generic and do not reflect specific genetic models. This is comparable to strategies in complex segregation analysis. Furthermore, the algorithm can integrate different statistical penetrance models for right censored event data. Computational challenges related to the handling of the likelihood are discussed. Simulation studies assess the predictive quality of the new algorithm in terms of ROC curves and corresponding AUCs. The algorithm is applied to data of a recent study on familial colorectal cancer risk. Its predictive performance is compared to that of a questionnaire currently used in screening for familial colorectal cancer. The results of the proposed algorithm are robust against different inheritance models. Using the simplest hereditary mechanism, the simulation study provides evidence that the algorithm improves detection of families with high cancer risk in comparison to the currently used questionnaire. The applicability of the algorithm goes beyond the field of colorectal cancer.

A microRNA molecular modeling extension for prediction of colorectal cancer treatment.

BACKGROUND: Several studies show that the regulatory impact of microRNAs (miRNAs) is an essential contribution to the pathogenesis of colorectal cancer (CRC). The expression levels of diverse miRNAs are associated with specific clinical diagnoses and prognoses of CRC. However, this association reveals very little actionable information with regard to how or whether to treat a CRC patient. To address this problem, we use miRNA expression data along with other molecular information to predict individual response of CRC cell lines and CRC patients. METHODS: A strategy has been developed to join four types of information: molecular, kinetic, genetic and treatment data for prediction of individual treatment response of CRC. RESULTS: Information on miRNA regulation, including miRNA target regulation and transcriptional regulation of miRNA, in integrated into an in silico molecular model for colon cancer. This molecular model is applied to study responses of seven CRC cell lines from NCI-60 to ten agents targeting signaling pathways. Predictive results of models without and with implemented miRNA information are compared and advantages are shown for the extended model. Finally, the extended model was applied to the data of 22 CRC patients to predict response to treatments of sirolimus and LY294002. The in silico results can also replicate the oncogenic and tumor suppression roles of miRNA on the therapeutic response as reported in the literature. CONCLUSIONS: In summary, the results reveal that detailed molecular events can be combined with individual genetic data, including gene/miRNA expression data, to enhance in silico prediction of therapeutic response of individual CRC tumors. The study demonstrates that miRNA information can be applied as actionable information regarding individual therapeutic response.

Evaluating the agreement between tumour volumetry and the estimated volumes of tumour lesions using an algorithm.

OBJECTIVES: To evaluate the agreement between tumour volume derived from semiautomated volumetry (SaV) and tumor volume defined by spherical volume using longest lesion diameter (LD) according to Response Evaluation Criteria In Solid Tumors (RECIST) or ellipsoid volume using LD and longest orthogonal diameter (LOD) according to World Health Organization (WHO) criteria. MATERIALS AND METHODS: Twenty patients with metastatic colorectal cancer from the CIOX trial were included. A total of 151 target lesions were defined by baseline computed tomography and followed until disease progression. All assessments were performed by a single reader. A variance component model was used to compare the three volume versions. RESULTS: There was a significant difference between the SaV and RECIST-based tumour volumes. The same model showed no significant difference between the SaV and WHO-based volumes. Scatter plots showed that the RECIST-based volumes overestimate lesion volume. The agreement between the SaV and WHO-based relative changes in tumour volume, evaluated by intraclass correlation, showed nearly perfect agreement. CONCLUSIONS: Estimating the volume of metastatic lesions using both the LD and LOD (WHO) is more accurate than those based on LD only (RECIST), which overestimates lesion volume. The good agreement between the SaV and WHO-based relative changes in tumour volume enables a reasonable approximation of three-dimensional tumour burden. KEY POINTS: • Tumour response in patients undergoing chemotherapy is assessed using CT images • Measurements are based on RECIST (unidimensional)-based or WHO (bidimensional)-based criteria • We calculated tumour volume from bidimensional target lesion measurements • This formula provides good tumour volume approximation, based on semiautomated volumetry.

Early tumor shrinkage in metastatic colorectal cancer: retrospective analysis from an irinotecan-based randomized first-line trial.

Early tumor shrinkage (ETS) has been highlighted as a favorable prognostic factor related to progression-free survival (PFS) and overall survival (OS) in cytotoxic treatment of metastatic colorectal cancer. Data from a randomized phase III study comparing infusional 5-fluorouracil plus irinotecan (FUFIRI) versus irinotecan plus oxaliplatin (mIROX) were evaluated. Patient groups were analyzed according to the relative change in maximum tumor diameter between baseline and after 7 weeks of treatment. The ETS cohort was defined as a decrease of ≥20%. Additionally, the non-ETS cohort was subdivided into "minor shrinkage" (0-19%), "tumor progression" (any increase) and development of "new metastatic lesions". Progression-free survival and OS were estimated in all patient subgroups. Assessment of ETS was possible in 201 patients. Early tumor shrinkage was observed in 47% (94/201) and non-ETS in 53% (107/201) of patients. Patients with ETS had a more favorable outcome with regard to PFS (9.9 months vs 6.1 months, P = 0.029) and OS (27.5 months vs 17.8 months, P = 0.002). In the non-ETS subgroups, patients with "minor shrinkage" (PFS 8.4 months, OS 21.6 months) showed a markedly better outcome than patients with "early tumor progression" (PFS 4.0 months, OS 15.3 months) or with "new metastatic lesions (PFS 2.2 months, OS 7.6 months). In conclusion, ETS assessment offers accelerated response evaluation when compared to RECIST. In patients treated with chemotherapy alone, ETS ≥20% is associated with excellent outcome. Non-ETS is a heterogeneous subgroup where patients with minor shrinkage clearly benefit from treatment, and patients with early progression or development of new lesions have an unfavorable prognosis.

AutoAnalyze in Systems Biology.

AutoAnalyze is a highly customizable framework for the visualization and analysis of large-scale model graphs. Originally developed for use in the automotive domain, it also supports efficient computation within molecular networks represented by reaction equations. A static analysis approach is used for efficient treatment-condition-specific simulation. The chosen method relies on the computation of a global network data-flow resulting from the evaluation of individual genetic data. The approach facilitates complex analyses of biological components from a molecular network under specific therapeutic perturbations, as demonstrated in a case study. In addition to simulating the complex networks in a stable and reproducible way, kinetic constants can also be fine-tuned using a genetic algorithm and built-in statistical tools.

Computational modeling of methionine cycle-based metabolism and DNA methylation and the implications for anti-cancer drug response prediction.

The relationship between metabolism and methylation is considered to be an important aspect of cancer development and drug efficacy. However, it remains poorly defined how to apply this aspect to improve preclinical disease characterization and clinical treatment outcome. Using available molecular information from Kyoto Encyclopedia of Genes and Genomes (KEGG) and literature, we constructed a large-scale knowledge-based metabolic in silico model. For the purpose of model validation, we applied data from the Cancer Cell Line Encyclopedia (CCLE) to investigate computationally the impact of metabolism on chemotherapy efficacy. In our model, different metabolic components such as MAT2A, ATP6V0E1, NNMT involved in methionine cycle correlate with biologically measured chemotherapy outcome (IC50) that are in agreement with findings of independent studies. These proteins are potentially also involved in cellular methylation processes. In addition, several components such as 3,4-dihydoxymandelate, PAPSS2, UPP1 from metabolic pathways involved in the production of purine and pyrimidine correlate with IC50. This study clearly demonstrates that complex computational approaches can reflect findings of biological experiments. This demonstrates their high potential to grasp complex issues within systems medicine such as response prediction, biomarker identification using available data resources.

Comparing Patient-Derived Xenograft and Computational Response Prediction for Targeted Therapy in Patients of Early-Stage Large Cell Lung Cancer.

PURPOSE: Targeted therapy (TT) provides highly effective cancer treatment for appropriately selected individuals. A major challenge of TT is to select patients who would benefit most. EXPERIMENTAL DESIGN: The study uses cancer material from 25 patients primarily diagnosed with non-small cell lung cancer (NSCLC). Patient-derived xenografts (PDXs) are treated with cetuximab and erlotinib. Treatment response is measured by tumor shrinkage comparing tumor volume at day 25 (V25) with tumor volume at baseline (V0). Shrinkage below 40% is considered as treatment response: V25/V0 < 0.4 (<40%). Furthermore, RNA-seq data from each tumor sample are used to predict tumor response to either treatment using an in silico molecular signaling map (MSM) approach. RESULTS: PDX response was 40% (10/25; 95% CI [21.13%, 61.34%]) under cetuximab and 20% (5/25; 95% CI [6.83%, 40.70%]) under erlotinib. MSM predicted response was 48% (12/25; 95% CI [27.8%, 68.7%]) under cetuximab and 40% (10/25; 95% CI [21.13%, 61.34%]) under erlotinib. Agreement between PDX and MSM response prediction is substantial under cetuximab and erlotinib: 84% (21/25, P = 0.001) and 80% (20/25, P = 0.003). A total of 5 from the 25 patients have been treated with cetuximab showing a clinical response identical to both predictions. CONCLUSIONS: For NSCLC patients, this proof-of-concept study shows a considerable agreement in response prediction from MSM and PDX approaches, but MSM saves time and laboratory resources. Our result indicates the potential of MSM-based approach for clinical decision making when selecting cancer TTs. Clin Cancer Res; 22(9); 2167-76. ©2015 AACR.

An NGS Workflow Blueprint for DNA Sequencing Data and Its Application in Individualized Molecular Oncology.

Next-generation sequencing (NGS) technologies that have advanced rapidly in the past few years possess the potential to classify diseases, decipher the molecular code of related cell processes, identify targets for decision-making on targeted therapy or prevention strategies, and predict clinical treatment response. Thus, NGS is on its way to revolutionize oncology. With the help of NGS, we can draw a finer map for the genetic basis of diseases and can improve our understanding of diagnostic and prognostic applications and therapeutic methods. Despite these advantages and its potential, NGS is facing several critical challenges, including reduction of sequencing cost, enhancement of sequencing quality, improvement of technical simplicity and reliability, and development of semiautomated and integrated analysis workflow. In order to address these challenges, we conducted a literature research and summarized a four-stage NGS workflow for providing a systematic review on NGS-based analysis, explaining the strength and weakness of diverse NGS-based software tools, and elucidating its potential connection to individualized medicine. By presenting this four-stage NGS workflow, we try to provide a minimal structural layout required for NGS data storage and reproducibility.

Early tumour shrinkage (ETS) and depth of response (DpR) in the treatment of patients with metastatic colorectal cancer (mCRC).

BACKGROUND: Response evaluation criteria in solid tumours (RECIST) are used to define degrees of response to anti-tumour agents. In retrospective analyses, early tumour shrinkage (ETS) has been investigated as an alternative early-on-treatment predictor of treatment efficacy with regard to progression-free and overall survival. While cut-off based analysis of ETS facilitates the categorisation of patients into responders and non-responders after a defined period of treatment, depth of response (DpR) serves as a continuous measure, which defines the nadir of tumour response. METHODS: A systematic literature search for 'early tumour shrinkage' or 'tumour size decrease' in 'metastatic colorectal cancer' reported from January 2000 to July 2014 was performed. The present review summarises available data concerning ETS and DpR and evaluates their potential as predictive markers for the clinical management of patients with metastatic colorectal cancer (mCRC). RESULTS: A total of 10 clinical trials investigated the role of ETS as a marker of clinical outcome in patients with mCRC. In addition, DpR was investigated using the efficacy data from three of these trials. Available data show that ETS differentiates patients with high sensitivity to treatment and more favourable prognosis from a heterogeneous group of patients classified as non-ETS patients. ETS is an early indicator of the potentially achievable response. In contrast, DpR estimates the nadir of tumour response as a continuous measure, which may affect the subsequent disease history, thus translating into superior survival. CONCLUSIONS: The concepts of ETS and DpR offer potential as clinical end-points to aid the clinical decision making process and thus further optimise mCRC patient management in the era of tailored therapy approaches.

A molecular signaling map and its application.

Cancer research over the past decades has revealed a number of molecular, biochemical, and cellular events that reflect progressive transformation of normal human cells into their malignant derivatives. These findings help to better understand the complexity of human tumorigenesis. In our study, molecular information is organized to chart a comprehensive map of the signaling network for human cancer. It includes transcriptional and translational regulation and diverse feedback-control loops. It is demonstrated that applying this signaling network map allows predicting the effect of targeted therapy before it can be applied into practice to reduce clinical trial risks. Hence, the proposed map with prognosticating potential effect might become part of drug discovery programs for targeted therapy. Applied in individual patient care it helps to reduce the current reliance of cancer treatment on chemotherapies with low therapeutic indices. This study also demonstrates that continuing elucidation of tumorigenesis will not only need heterotypic organ culture systems in vitro and increasingly refined animal models in vivo, but also computationally calculable virtual cell models in silico.

Modeling of non-steroidal anti-inflammatory drug effect within signaling pathways and miRNA-regulation pathways.

To date, it is widely recognized that Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) can exert considerable anti-tumor effects regarding many types of cancers. The prolonged use of NSAIDs is highly associated with diverse side effects. Therefore, tailoring down the NSAID application onto individual patients has become a necessary and relevant step towards personalized medicine. This study conducts the systemsbiological approach to construct a molecular model (NSAID model) containing a cyclooxygenase (COX)-pathway and its related signaling pathways. Four cancer hallmarks are integrated into the model to reflect different developmental aspects of tumorigenesis. In addition, a Flux-Comparative-Analysis (FCA) based on Petri net is developed to transfer the dynamic properties (including drug responsiveness) of individual cellular system into the model. The gene expression profiles of different tumor-types with available drug-response information are applied to validate the predictive ability of the NSAID model. Moreover, two therapeutic developmental strategies, synthetic lethality and microRNA (miRNA) biomarker discovery, are investigated based on the COX-pathway. In conclusion, the result of this study demonstrates that the NSAID model involving gene expression, gene regulation, signal transduction, protein interaction and other cellular processes, is able to predict the individual cellular responses for different therapeutic interventions (such as NS-398 and COX-2 specific siRNA inhibition). This strongly indicates that this type of model is able to reflect the physiological, developmental and pathological processes of an individual. The approach of miRNA biomarker discovery is demonstrated for identifying miRNAs with oncogenic and tumor suppressive functions for individual cell lines of breast-, colon- and lung-tumor. The achieved results are in line with different independent studies that investigated miRNA biomarker related to diagnostics of cancer treatments, therefore it might shed light on the development of biomarker discovery at individual level. Particular results of this study might contribute to step further towards personalized medicine with the systemsbiological approach.