Systems pathology—taking molecular pathology into a new dimension

System biology approaches for drug repurposing

Drug repurposing, or drug repositioning, refers to the identification of alternative therapeutic applications for established medications that go beyond their initial indications. This strategy has becoming increasingly popular since it has the potential to significantly reduce the overall costs of drug development by around $300 million. System biology methodologies have been employed to facilitate medication repurposing, encompassing computational techniques such as signature matching and network-based strategies. These techniques utilize pre-existing drug-related data types and databases to find prospective repurposed medications that have minimal or acceptable harmful effects on patients. The primary benefit of medication repurposing in comparison to drug development lies in the fact that approved pharmaceuticals have already undergone multiple phases of clinical studies, thereby possessing well-established safety and pharmacokinetic properties. Utilizing system biology methodologies in medication repurposing offers the capacity to expedite the discovery of viable candidates for drug repurposing and offer novel perspectives for structure-based drug design.

A dynamical systems approach for multiscale synthesis of Alzheimer's pathogenesis

Alzheimer's disease (AD) is a spatially dynamic pathology that implicates a growing volume of multiscale data spanning genetic, cellular, tissue, and organ levels of the organization. These data and bioinformatics analyses provide clear evidence for the interactions within and between these levels. The resulting heterarchy precludes a linear neuron-centric approach and necessitates that the numerous interactions are measured in a way that predicts their impact on the emergent dynamics of the disease. This level of complexity confounds intuition, and we propose a new methodology that uses non-linear dynamical systems modeling to augment intuition and that links with a community-wide participatory platform to co-create and test system-level hypotheses and interventions. In addition to enabling the integration of multiscale knowledge, key benefits include a more rapid innovation cycle and a rational process for prioritization of data campaigns. We argue that such an approach is essential to support the discovery of multilevel-coordinated polypharmaceutical interventions.

Noncoding RNAs and Deep Learning Neural Network Discriminate Multi-Cancer Types

Detecting cancers at early stages can dramatically reduce mortality rates. Therefore, practical cancer screening at the population level is needed. To develop a comprehensive detection system to classify multiple cancer types, we integrated an artificial intelligence deep learning neural network and noncoding RNA biomarkers selected from massive data. Our system can accurately detect cancer vs. healthy objects with 96.3% of AUC of ROC (Area Under Curve of a Receiver Operating Characteristic curve), and it surprisingly reaches 78.77% of AUC when validated by real-world raw data from a completely independent data set. Even validating with raw exosome data from blood, our system can reach 72% of AUC. Moreover, our system significantly outperforms conventional machine learning models, such as random forest. Intriguingly, with no more than six biomarkers, our approach can easily discriminate any individual cancer type vs. normal with 99% to 100% AUC. Furthermore, a comprehensive marker panel can simultaneously multi-classify common cancers with a stable 82.15% accuracy rate for heterogeneous cancerous tissues and conditions. This detection system provides a promising practical framework for automatic cancer screening at population level. Key points: (1) We developed a practical cancer screening system, which is simple, accurate, affordable, and easy to operate. (2) Our system binarily classify cancers vs. normal with >96% AUC. (3) In total, 26 individual cancer types can be easily detected by our system with 99 to 100% AUC. (4) The system can detect multiple cancer types simultaneously with >82% accuracy.

System and network biology-based computational approaches for drug repositioning

Recent advances in computational biology have not only fastened the drug discovery process but have also proven to be a powerful tool for the search of existing molecules of therapeutic value for drug repurposing. The system biology-based drug repurposing approaches shorten the time and reduced the cost of the whole process when compared to de novo drug discovery. In the present pandemic situation, these computational approaches have emerged as a boon to tackle the COVID-19 associated morbidities and mortalities. In this chapter, we present the overview of system biology-based network system approaches which can be exploited for the drug repurposing of disease. Besides, we have included information on relevant repurposed drugs which are currently used for the treatment of COVID-19.

Elevated Neutrophil-to-Lymphocyte Ratio in Perioperative Periods is Suggestive of Poor Prognosis in Patients with Colorectal Cancer

Background

Recent data suggest that alterations in the neutrophil-to-lymphocyte ratio (NLR) in the perioperative periods can serve as prognostic factors. However, research on the clinical impact has been limited and even discordant in patients with colorectal cancer (CRC).

Patients and Methods

The optimal cut-off value of preoperative NLR (NLR-pre), postoperative NLR (NLR-post), and its change (NLR-delta) were determined to maximize differences in overall survival (OS) between groups. Patients were categorized into four groups (NLR-trend) as follows: G1, low NLR-pre and NLR-post; G2, low NLR-pre and high NLR-post; G3, high NLR-pre and low NLR-post; and G4, high NLR-pre and NLR-post. Discriminatory performance was compared using integrated AUC (iAUC) between all indicators.

Results

A total of 576 patients diagnosed with stage I–IV CRC were included. The cut-off points were determined as 2.33 for NLR-pre, 2.06 for NLR-post, and −1.08 for NLR-delta. Subgroup dichotomization using NLR-pre, NLR-post, NLR-delta and NLR-trend were all identified as significant prognostic factors by univariate analysis. However, NLR-trend was only remained as an independent prognostic factor in the multivariate analysis. The iAUC of the NLR-trend was superior to that of NLR-pre (bootstrap iAUC mean difference=0.036; 95% CI 0.013–0.073), NLR-post (bootstrap iAUC mean difference=0.045; 95% CI 0.019–0.081) and NLR-delta (bootstrap iAUC mean difference=0.061; 95% CI 0.025–0.104).

Conclusion

Risk stratification and combining of preoperative and postoperative NLR (NLR-trend) can improve prognostic discrimination compared with single measurements or simple changes in NLR in patients with CRC.

Exaggerated Autophagy in Stanford Type A Aortic Dissection: A Transcriptome Pilot Analysis of Human Ascending Aortic Tissues

Stanford type A aortic dissection (TAAD) is one of the most dangerous diseases of acute aortic syndrome. Molecular pathological studies on TAAD can aid in understanding the disease comprehensively and can provide insights into new diagnostic markers and potential therapeutic targets. In this study, we defined the molecular pathology of TAAD by performing transcriptome sequencing of human ascending aortic tissues. Pathway analysis revealed that activated inflammation, cell death and smooth muscle cell degeneration are the main pathological changes in aortic dissection. However, autophagy is considered to be one of the most important biological processes, regulating inflammatory reactions and degenerative changes. Therefore, we focused on the pathological role of autophagy in aortic dissection and identified 10 autophagy-regulated hub genes, which are all upregulated in TAAD. These results indicate that exaggerated autophagy participates in the pathological process of aortic dissection and may provide new insight for further basic research on TAAD.

Enhancing value of clinical pharmacodynamics in oncology drug development: An alliance between quantitative pharmacology and translational science

Clinical pharmacodynamic evaluation is a key component of the "pharmacologic audit trail" in oncology drug development. We posit that its value can and should be greatly enhanced via application of a robust quantitative pharmacology framework informed by biologically mechanistic considerations. Herein, we illustrate examples of intersectional blindspots across the disciplines of quantitative pharmacology and translational science and offer a roadmap aimed at enhancing the caliber of clinical pharmacodynamic research in the development of oncology therapeutics.

Novel Monte Carlo approach quantifies data assemblage utility and reveals power of integrating molecular and clinical information for cancer prognosis

Current clinical practice in cancer stratifies patients based on tumour histology to determine prognosis. Molecular profiling has been hailed as the path towards personalised care, but molecular data are still typically analysed independently of known clinical information. Conventional clinical and histopathological data, if used, are added only to improve a molecular prediction, placing a high burden upon molecular data to be informative in isolation. Here, we develop a novel Monte Carlo analysis to evaluate the usefulness of data assemblages. We applied our analysis to varying assemblages of clinical data and molecular data in an ovarian cancer dataset, evaluating their ability to discriminate one-year progression-free survival (PFS) and three-year overall survival (OS). We found that Cox proportional hazard regression models based on both data types together provided greater discriminative ability than either alone. In particular, we show that proteomics data assemblages that alone were uninformative (p = 0.245 for PFS, p = 0.526 for OS) became informative when combined with clinical information (p = 0.022 for PFS, p = 0.048 for OS). Thus, concurrent analysis of clinical and molecular data enables exploitation of prognosis-relevant information that may not be accessible from independent analysis of these data types.

Primary sclerosing cholangitis: detailed histologic assessment and integration using bioinformatics highlights arterial fibrointimal hyperplasia as a novel feature

OBJECTIVES

Liver biopsy diagnosis of primary sclerosing cholangitis (PSC) is difficult. We performed a detailed histologic analysis of PSC cases using novel bioinformatics analysis to identify histologic features that may be useful in its diagnosis.

METHODS

PSC liver explants were examined and compared with primary biliary cirrhosis and hepatitis C explants to act as controls. Demographic, macroscopic, and histologic variables were analyzed using both conventional statistics and an integrative bioinformatics approach, significance analysis of microarrays (SAM), and hierarchical clustering analysis (HCA).

RESULTS

The PSC group was younger and had distinctive PSC features, including bile duct scars, onion-skin fibrosis, and arterial fibrointimal hyperplasia. SAM allowed the integration of variables by comparing PSC and control groups, whereas HCA was able to correctly categorize each group.

CONCLUSIONS

This study demonstrates characteristic PSC histology as well as arterial hyperplasia to be distinctive features that may aid in PSC diagnosis and be confirmed by bioinformatics.

Dynamic contrast-enhanced MRI evaluates the early response of human head and neck tumor xenografts following anti-EMMPRIN therapy with cisplatin or irradiation

PURPOSE

To assess the early therapeutic effects of anti-EMMPRIN (extracellular matrix metalloprotease inducer) antibody with/without cisplatin or X-ray radiation in head and neck cancer mouse models using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

MATERIALS AND METHODS

Mice bearing SCC1 (or OSC19) tumor xenografts were treated with anti-EMMPRIN antibody, radiation, cisplatin, or anti-EMMPRIN antibody plus cisplatin (or radiation) for a week (n = 4-5 per group). DCE-MRI was carried out on a 9.4T small animal MR scanner on days 0, 3, and 7, and K(trans) values were averaged in a 0.5-mm-thick peripheral tumor region. Ki67 and CD31 staining were implemented for all tumors after imaging.

RESULTS

The K(trans) changes of SCC1 and OSC19 tumors treated with anti-EMMPRIN antibody for 3 days were -18 ± 8% and 4 ± 7%, respectively, which were significantly lower than those of control groups (39 ± 5% and 45 ± 7%; P = 0.0025 and 0.0220, respectively). When cisplatin was added, those were -42 ± 9% and -44 ± 9%, respectively, and with radiation, -45 ± 9% and -27 ± 10%, respectively, which were also significantly lower than those of control groups (P < 0.0001 for all four comparisons). In the eight groups untreated (served as control) or treated with anti-EMMPRIN antibody with/without cisplatin or radiation, the mean K(trans) change for 3 days was significantly correlated with the mean tumor volume change for 7 days (r = 0.74, P = 0.0346), Ki67-expressing cell density (r = 0.96, P = 0.0001), and CD31 density (r = 0.84, P = 0.0084).

CONCLUSION

DCE-MRI might be utilized to assess the early therapeutic effects of anti-EMMPRIN antibody with/without chemotherapy or radiotherapy in head and neck cancer.

A hierarchical Bayesian framework to infer the progression level to diabetes based on deficient clinical data

The increase in lifestyle-related diseases such as heart disease, diabetes, and high blood pressure is a challenging problem that should be resolved. The physiological mechanisms of the human body have long been studied using mathematical models. In particular, to study glucose metabolism, several models that infer insulin sensitivity and β-cell function have been developed. The use of mathematical models to assess progression to diabetes based on clinical data could be effective for preventing the onset of diabetes. However, to assess the progression level, we need clinical data including data from oral glucose tolerance tests, which are not typically performed on patients whose glucose tolerance may be impaired. To address this shortcoming, we developed a hierarchical Bayesian framework to infer the progression of glucose intolerance based on deficient data. We demonstrated how the framework infers the level of progression to diabetes and showed that glucose disposal capacity and insulin-secretory function depend on the fasting glucose and glycated hemoglobin (HbA1c) levels.

Towards the introduction of the 'Immunoscore' in the classification of malignant tumours

The American Joint Committee on Cancer/Union Internationale Contre le Cancer (AJCC/UICC) TNM staging system provides the most reliable guidelines for the routine prognostication and treatment of colorectal carcinoma. This traditional tumour staging summarizes data on tumour burden (T), the presence of cancer cells in draining and regional lymph nodes (N) and evidence for distant metastases (M). However, it is now recognized that the clinical outcome can vary significantly among patients within the same stage. The current classification provides limited prognostic information and does not predict response to therapy. Multiple ways to classify cancer and to distinguish different subtypes of colorectal cancer have been proposed, including morphology, cell origin, molecular pathways, mutation status and gene expression-based stratification. These parameters rely on tumour-cell characteristics. Extensive literature has investigated the host immune response against cancer and demonstrated the prognostic impact of the in situ immune cell infiltrate in tumours. A methodology named ‘Immunoscore’ has been defined to quantify the in situ immune infiltrate. In colorectal cancer, the Immunoscore may add to the significance of the current AJCC/UICC TNM classification, since it has been demonstrated to be a prognostic factor superior to the AJCC/UICC TNM classification. An international consortium has been initiated to validate and promote the Immunoscore in routine clinical settings. The results of this international consortium may result in the implementation of the Immunoscore as a new component for the classification of cancer, designated TNM-I (TNM-Immune). © 2013 The Authors. Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Reconsidering the bio-detection of tolerance in renal transplantation

Operational tolerance in kidney transplantation tolerance is rare phenomenon. It concerns recipients who keep a good function of their graft without immunosuppressors for more than one year. A critical need in the field of transplantation tolerance is the identification of biomarkers able to detect precociously tolerance phenotype in stable recipient in order to adapt treatment and progressively stop immunosuppressive therapy. But many limitations in these studies slow the application in clinics of such tolerance signature. In this addendum article we talk about these limitations and potential new directions to improve our approach in the quest of tolerance biomarkers.

Digitization of medicine: how radiology can take advantage of the digital revolution

In the era of medical cost containment, radiologists must continually maintain their actual and perceived value to patients, payers, and referring providers. Exploitation of current and future digital technologies may be the key to defining and promoting radiology's "brand" and assure our continued relevance in providing predictive, preventive, personalized, and participatory medicine. The Association of University of Radiologists Radiology Research Alliance Digitization of Medicine Task Force was formed to explore the opportunities and challenges of the digitization of medicine that are relevant to radiologists, which include the reporting paradigm, computational biology, and imaging informatics. In addition to discussing these opportunities and challenges, we consider how change occurs in medicine, and how change may be effected in medical imaging community. This review article is a summary of the research of the task force and hopefully can be used as a stimulus for further discussions and development of action plans by radiology leaders.

Systems biology and the integration of mechanistic explanation and mathematical explanation

The paper discusses how systems biology is working toward complex accounts that integrate explanation in terms of mechanisms and explanation by mathematical models-which some philosophers have viewed as rival models of explanation. Systems biology is an integrative approach, and it strongly relies on mathematical modeling. Philosophical accounts of mechanisms capture integrative in the sense of multilevel and multifield explanations, yet accounts of mechanistic explanation (as the analysis of a whole in terms of its structural parts and their qualitative interactions) have failed to address how a mathematical model could contribute to such explanations. I discuss how mathematical equations can be explanatorily relevant. Several cases from systems biology are discussed to illustrate the interplay between mechanistic research and mathematical modeling, and I point to questions about qualitative phenomena (rather than the explanation of quantitative details), where quantitative models are still indispensable to the explanation. Systems biology shows that a broader philosophical conception of mechanisms is needed, which takes into account functional-dynamical aspects, interaction in complex networks with feedback loops, system-wide functional properties such as distributed functionality and robustness, and a mechanism's ability to respond to perturbations (beyond its actual operation). I offer general conclusions for philosophical accounts of explanation.

Human tissue in systems medicine

Histopathology, the examination of an architecturally artefactual, two-dimensional and static image remains a potent tool allowing diagnosis and empirical expectation of prognosis. Considerable optimism exists that the advent of molecular genetic testing and other biomarker strategies will improve or even replace this ancient technology. A number of biomarkers already add considerable value for prediction of whether a treatment will work. In this short review we argue that a systems medicine approach to pathology will not seek to replace traditional pathology, but rather augment it. Systems approaches need to incorporate quantitative morphological, protein, mRNA and DNA data. A significant challenge for clinical implementation of systems pathology is how to optimize information available from tissue, which is frequently sub-optimal in quality and amount, and yet generate useful predictive models that work. The transition of histopathology to systems pathophysiology and the use of multiscale data sets usher in a new era in diagnosis, prognosis and prediction based on the analysis of human tissue.

Advanced systems biology methods in drug discovery and translational biomedicine

Systems biology is in an exponential development stage in recent years and has been widely utilized in biomedicine to better understand the molecular basis of human disease and the mechanism of drug action. Here, we discuss the fundamental concept of systems biology and its two computational methods that have been commonly used, that is, network analysis and dynamical modeling. The applications of systems biology in elucidating human disease are highlighted, consisting of human disease networks, treatment response prediction, investigation of disease mechanisms, and disease-associated gene prediction. In addition, important advances in drug discovery, to which systems biology makes significant contributions, are discussed, including drug-target networks, prediction of drug-target interactions, investigation of drug adverse effects, drug repositioning, and drug combination prediction. The systems biology methods and applications covered in this review provide a framework for addressing disease mechanism and approaching drug discovery, which will facilitate the translation of research findings into clinical benefits such as novel biomarkers and promising therapies.

Re-overcoming barriers in translating biomarkers to clinical practice

IMPORTANCE OF THE FIELD

Recently, there has been growing evidence for the concept of personalized medicine as the implementation of genomic and molecular information in the delivery of healthcare. In parallel, the identification of biomarkers has become of enormous significance as a prerequisite for individualized intervention regimens.

AREAS COVERED IN THIS REVIEW

Biomarkers are developed to improve prevention, diagnosis or therapeutic outcome of a given disease. Although each application reveals distinct developmental strategies, evidence-based approval of new biomarkers is important for the success of new drugs, diagnostic tests or recommendations in preventive medicine. Current hurdles to bringing biomarkers into clinical practice are reviewed, thereby focusing on adequate approaches to overcome these limitations in the future.

WHAT THE READER WILL GAIN

The reader will get an introduction to strategies resolving actual barriers in clinical biomarker development.

TAKE HOME MESSAGE

The identification of evidence-based biomarkers is crucial for the success of individualized therapeutic approaches. Developmental strategies have to be adapted to clinical need, thereby focusing on biomarker validation in clinical settings as well as on the establishment of standardized biomarker test systems for routine application. Consortia have been established bringing together representatives of government, academia and industry to improve future biomarker development.

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.

Nanoproteomics enabling personalized nanomedicine

Nucleic Acid Programmable Protein Arrays utilize a complex mammalian cell free expression system to produce proteins in situ. In alternative to fluorescent-labeled approaches a new label free method, emerging from the combined utilization of three independent and complementary nanotechnological approaches, appears capable to analyze protein function and protein-protein interaction in studies promising for personalized medicine. Quartz Micro Circuit nanogravimetry, based on frequency and dissipation factor, mass spectrometry and anodic porous alumina overcomes indeed the limits of correlated fluorescence detection plagued by the background still present after extensive washes. This could be further optimized by a homogeneous and well defined bacterial cell free expression system capable to realize the ambitious objective to quantify the regulatory protein networks in humans. Implications for personalized medicine of the above label free protein array using different test genes proteins are reported.

Open biomedical pluralism: formalising knowledge about breast cancer phenotypes

We demonstrate a heterogeneity of representation types for breast cancer phenotypes and stress that the characterisation of a tumour phenotype often includes parameters that go beyond the representation of a corresponding empirically observed tumour, thus reflecting significant functional features of the phenotypes as well as epistemic interests that drive the modes of representation. Accordingly, the represented features of cancer phenotypes function as epistemic vehicles aiding various classifications, explanations, and predictions. In order to clarify how the plurality of epistemic motivations can be integrated on a formal level, we give a distinction between six categories of human agents as individuals and groups focused around particular epistemic interests. We analyse the corresponding impact of these groups and individuals on representation types, mapping and reasoning scenarios. Respecting the plurality of representations, related formalisms, expressivities and aims, as they are found across diverse scientific communities, we argue for a pluralistic ontology integration. Moreover, we discuss and illustrate to what extent such a pluralistic integration is supported by the distributed ontology language DOL, a meta-language for heterogeneous ontology representation that is currently under standardisation as ISO WD 17347 within the OntoIOp (Ontology Integration and Interoperability) activity of ISO/TC 37/SC 3. We particularly illustrate how DOL supports representations of parthood on various levels of logical expressivity, mapping of terms, merging of ontologies, as well as non-monotonic extensions based on circumscription allowing a transparent formal modelling of the normal/abnormal distinction in phenotypes.

The chernobyl tissue bank - a repository for biomaterial and data used in integrative and systems biology modeling the human response to radiation

The only unequivocal radiological effect of the Chernobyl accident on human health is the increase in thyroid cancer in those exposed in childhood or early adolescence. In response to the scientific interest in studying the molecular biology of thyroid cancer post Chernobyl, the Chernobyl Tissue Bank (CTB: www.chernobyltissuebank.com) was established in 1998. Thus far it is has collected biological samples from 3,861 individuals, and provided 27 research projects with 11,254 samples. The CTB was designed from its outset as a resource to promote the integration of research and clinical data to facilitate a systems biology approach to radiation related thyroid cancer. The project has therefore developed as a multidisciplinary collaboration between clinicians, dosimetrists, molecular biologists and bioinformaticians and serves as a paradigm for tissue banking in the omics era.

Multi-biomarker pattern for tumor identification and prognosis

In last decades, the basic, clinical, and translational research efforts have been directed to the identification of standard biomarkers associated with the degree of malignancy. There is an increasingly public health concern for earlier detection of cancer development at stages in which successful treatments can be achieved. To meet this urgent clinical demand, early stage cancer biomarkers supported by reliable and robust experimental data that can be readily applicable in the clinical practice, are required. In the current standard protocols, when one or two of the canonical proliferating index biomarkers are analyzed, contradictory results are frequently reached leading to incorrect cancer diagnostic and unsuccessful therapies. Therefore, the identification of other cellular characteristics or signatures present in the tumor cells either alone or in combination with the well-established proliferation markers emerge as an alternative strategy in the improvement of cancer diagnosis and treatment. Because it is well known that several pathways and processes are altered in tumor cells, the concept of "single marker" in cancer results incorrect. Therefore, this review aims to analyze and discuss the proposal that the molecular profile of different genes or proteins in different altered tumor pathways must be established to provide a better global clinical pattern for cancer detection and prognosis.

Systems pathology: a critical review

The technological advances of the last twenty years together with the dramatic increase in computational power have injected new life into systems-level thinking in Medicine. This review emphasizes the close relationship of Systems Pathology to Systems Biology and delineates the differences between Systems Pathology and Clinical Systems Pathology. It also suggests an algorithm to support the application of systems-level thinking to clinical research, proposes applying systems-level thinking to the health care systems and forecasts an acceleration of preventive medicine as a result of the coupling of personal genomics with systems pathology.

Model-based global sensitivity analysis as applied to identification of anti-cancer drug targets and biomarkers of drug resistance in the ErbB2/3 network

High levels of variability in cancer-related cellular signalling networks and a lack of parameter identifiability in large-scale network models hamper translation of the results of modelling studies into the process of anti-cancer drug development. Recently global sensitivity analysis (GSA) has been recognised as a useful technique, capable of addressing the uncertainty of the model parameters and generating valid predictions on parametric sensitivities. Here we propose a novel implementation of model-based GSA specially designed to explore how multi-parametric network perturbations affect signal propagation through cancer-related networks. We use area-under-the-curve for time course of changes in phosphorylation of proteins as a characteristic for sensitivity analysis and rank network parameters with regard to their impact on the level of key cancer-related outputs, separating strong inhibitory from stimulatory effects. This allows interpretation of the results in terms which can incorporate the effects of potential anti-cancer drugs on targets and the associated biological markers of cancer. To illustrate the method we applied it to an ErbB signalling network model and explored the sensitivity profile of its key model readout, phosphorylated Akt, in the absence and presence of the ErbB2 inhibitor pertuzumab. The method successfully identified the parameters associated with elevation or suppression of Akt phosphorylation in the ErbB2/3 network. From analysis and comparison of the sensitivity profiles of pAkt in the absence and presence of targeted drugs we derived predictions of drug targets, cancer-related biomarkers and generated hypotheses for combinatorial therapy. Several key predictions have been confirmed in experiments using human ovarian carcinoma cell lines. We also compared GSA-derived predictions with the results of local sensitivity analysis and discuss the applicability of both methods. We propose that the developed GSA procedure can serve as a refining tool in combinatorial anti-cancer drug discovery.

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.

Review and cross-validation of gene expression signatures and melanoma prognosis

In melanoma, there is an urgent need to identify novel biomarkers with prognostic performance superior to traditional clinical and histological parameters. Gene expression-based prognostic signatures offer promise, but studies have been challenged by sample scarcity, cohort heterogeneity, and doubts about the efficacy of such signatures relative to current clinical practices. Motivated by new studies that have begun to address these challenges, we reviewed prognostic signatures derived from gene expression microarray analysis of human melanoma tissue. We used REMARK-based criteria to select the most relevant studies and directly compared their signature gene lists. Through functional ontology enrichment analysis, we observed that these independent data sets converge in part upon immune response processes and the G-protein signaling NRAS-regulation pathway, both important in melanoma development and progression. The signatures correctly predicted patient outcome in independent gene expression data sets with some notably low misclassification rates, particularly among studies involving more advanced-stage tumors. This successful cross-validation indicates that gene expression analysis-based signatures are becoming translationally relevant to care of melanoma patients, as well as improving understanding of the aspects of melanoma biology that determine patient outcome.

Trastuzumab and pertuzumab produce changes in morphology and estrogen receptor signaling in ovarian cancer xenografts revealing new treatment strategies

PURPOSE

The aim of this study was to investigate the antitumor effects of HER2-directed combination therapy in ovarian cancer xenograft models to evaluate their potential. The combinations of trastuzumab and pertuzumab, and trastuzumab and aromatase inhibitor therapy were investigated.

EXPERIMENTAL DESIGN

The effects of trastuzumab, pertuzumab, and letrozole on growth response, apoptosis, morphology, and gene and protein expression were evaluated in the SKOV3 ovarian cancer cell line xenograft and a panel of five human ovarian xenografts derived directly from clinical specimens.

RESULTS

The combination of HER2-directed antibodies showed enhanced antitumor activity compared with single antibody therapy in the SKOV3 xenograft model. Apoptosis, morphology, and estrogen-regulated gene expression were modulated by these antibodies in both spatial and temporal manners. A panel of ovarian cancer xenografts showed differential growth responses to the combination of trastuzumab and pertuzumab. High HER2 expression and increasing HER3 protein expression on treatment were associated with growth response. In trastuzumab-treated SKOV3 tumors, there was a change in tumor morphology, with a reduction in frequency of estrogen receptor alpha (ERα)-negative clear cell areas. Trastuzumab, but not pertuzumab, increased expression of ERα in SKOV3 xenografts when analyzed by quantitative immunofluorescence. ERα and downstream signaling targets were modulated by trastuzumab alone and in combination. Trastuzumab enhanced the responsiveness of SKOV3 xenografts to letrozole when given in combination.

CONCLUSIONS

These data suggest that trastuzumab in combination with pertuzumab could be an effective approach in high HER2-expressing ovarian cancers and could also enhance sensitivity to endocrine therapy in ERα-positive ovarian cancer.

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.

Proof of concept: network and systems biology approaches aid in the discovery of potent anticancer drug combinations

Cancer therapies that target key molecules have not fulfilled expected promises for most common malignancies. Major challenges include the incomplete understanding and validation of these targets in patients, the multiplicity and complexity of genetic and epigenetic changes in the majority of cancers, and the redundancies and cross-talk found in key signaling pathways. Collectively, the uses of single-pathway targeted approaches are not effective therapies for human malignancies. To overcome these barriers, it is important to understand the molecular cross-talk among key signaling pathways and how they may be altered by targeted agents. Innovative approaches are needed, such as understanding the global physiologic environment of target proteins and the effects of modifying them without losing key molecular details. Such strategies will aid the design of novel therapeutics and their combinations against multifaceted diseases, in which efficacious combination therapies will focus on altering multiple pathways rather than single proteins. Integrated network modeling and systems biology have emerged as powerful tools benefiting our understanding of drug mechanisms of action in real time. This review highlights the significance of the network and systems biology-based strategy and presents a proof of concept recently validated in our laboratory using the example of a combination treatment of oxaliplatin and the MDM2 inhibitor MI-219 in genetically complex and incurable pancreatic adenocarcinoma.

The cycling hair follicle as an ideal systems biology research model

In the postgenomic era, systems biology has rapidly emerged as an exciting field predicted to enhance the molecular understanding of complex biological systems by the use of quantitative experimental and mathematical approaches. Systems biology studies how the components of a biological system (e.g. genes, transcripts, proteins, metabolites) interact to bring about defined biological function or dysfunction. Living systems may be divided into five dimensions of complexity: (i) molecular; (ii) structural; (iii) temporal; (iv) abstraction and emergence; and (v) algorithmic. Understanding the details of these dimensions in living systems is the challenge that systems biology aims to address. Here, we argue that the hair follicle (HF), one of the signature features of mammals, is a perfect and clinically relevant model for systems biology research. The HF represents a stem cell-rich, essentially autonomous mini-organ, whose cyclic transformations follow a hypothetical intrafollicular "hair cycle clock" (HCC). This prototypic neuroectodermal-mesodermal interaction system, at the cross-roads of systems and chronobiology, encompasses various levels of complexity as it is subject to both intrafollicular and extrafollicular inputs (e.g. intracutaneous timing mechanisms with neural and systemic stimuli). Exploring how the cycling HF addresses the five dimensions of living systems, we argue that a systems biology approach to the study of hair growth and cycling, in man and mice, has great translational medicine potential. Namely, the easily accessible human HF invites preclinical and clinical testing of novel hypotheses generated with this approach.

Translating the genomic architecture of breast cancer into clinical applications

The genetic alterations in breast cancer have in recent years been studied through a variety of techniques: analysis of alterations in individual oncogenes and tumor suppressor genes; gene expression profiling of both messenger RNA and microRNA; global analysis of DNA copy number changes; and most recently, whole-genome sequence analysis. Analysis of the association between genetic alterations and gene expression profiles with prognosis and response to specific treatments will lead to improved possibilities for patient-tailored treatment. Russnes et al. now add an additional view on the complex genetic makeup of breast carcinomas by developing algorithms that can be used to subclassify tumors based on their patterns of genome-wide DNA copy number gains and losses, which vary from very simple (only a few gains and losses) to complex. The algorithms provide indices that can be used in conjunction with results from other genetic analyses to subclassify breast cancer, with the aim of defining subgroups of patients that differ with respect to prognosis and response to therapy.

Overexpression of p73 as a tissue marker for high-risk gastritis

PURPOSE

Histologic assessment of high-risk gastritis for the development of gastric cancer is not well defined. The identification of tissue markers together with the integration of histologic features will be required for this assessment.

EXPERIMENTAL DESIGN

Matched tumor/nontumor adjacent mucosa (NTAM) of 91 early gastric cancer and 148 chronic gastritis cases were evaluated for histologic characteristics (atrophy, intestinal metaplasia, chronic inflammation, polymorphonuclear infiltration, and Helicobacter pylori) by the Sydney System. Atrophy risk assessment was also evaluated by the Operative Link on Gastritis Assessment (OLGA) staging system. Eight tissue markers (BRCA1, HSP90, STAT1, FHIT, EGFR, p73, p53, p16INK4a) and EBV were also evaluated by tissue microarray/immunohistochemistry/in situ hybridization platform. Data were analyzed by contingency tables (2 x 2) using Fisher's exact two-tailed test (P < 0.001) and integrated by Significance Analysis of Microarrays (SAM) and clustering analysis.

RESULTS

Histologically, NTAM have severe intestinal metaplasia/chronic inflammation and severe atrophy assessed by Sydney and OLGA staging systems. H. pylori infection was similar in both groups, and EBV was found only in 5.5% of the tumor samples. Overexpression of p73 was higher in NTAM (50.5%) than in chronic gastritis (10.8%; P < 0.0001). Integration of histologic features and tissue markers showed that overexpression of p73, severe atrophy, and OLGA stage 4 were the most relevant features in NTAM. Clustering analysis correctly assigned NTAM and control cases (P < 0.0001).

CONCLUSIONS

Overexpression of p73 should be considered for the assessment of high-risk chronic gastritis. SAM allows the integration of histology and tissue markers for this assessment.

Would Virchow be a systems biologist? A discourse on the philosophy of science with implications for pathological research

Research in pathology spans from merely descriptive work to functional studies, "-omics" approaches and, more recently, systems biology. The work presented here aims at placing pathological research into an epistemological context. Aided by Rudolf Virchow, we give an overview on the philosophy of science including the Wiener Kreis, Popper, Kuhn, Fleck and Rheinberger and demonstrate their implications for routine diagnostics and science in pathology. A focus is on the fields of "-omics" and systems pathology.