Precision medicine driven by cancer systems biology

Clinical Network Systems Biology: Traversing the Cancer Multiverse

In recent decades, cancer biology and medicine have ushered in a new age of precision medicine through high-throughput approaches that led to the development of novel targeted therapies and immunotherapies for different cancers. The availability of multifaceted high-throughput omics data has revealed that cancer, beyond its genomic heterogeneity, is a complex system of microenvironments, sub-clonal tumor populations, and a variety of other cell types that impinge on the genetic and non-genetic mechanisms underlying the disease. Thus, a systems approach to cancer biology has become instrumental in identifying the key components of tumor initiation, progression, and the eventual emergence of drug resistance. Through the union of clinical medicine and basic sciences, there has been a revolution in the development and approval of cancer therapeutic drug options including tyrosine kinase inhibitors, antibody-drug conjugates, and immunotherapy. This 'Team Medicine' approach within the cancer systems biology framework can be further improved upon through the development of high-throughput clinical trial models that utilize machine learning models, rapid sample processing to grow patient tumor cell cultures, test multiple therapeutic options and assign appropriate therapy to individual patients quickly and efficiently. The integration of systems biology into the clinical network would allow for rapid advances in personalized medicine that are often hindered by a lack of drug development and drug testing.

Translating precision medicine for autism spectrum disorder: A pressing need

Autism spectrum disorder (ASD) is a heterogenous group of neurodevelopmental disorders (NDDs) with a high unmet medical need. Currently, ASD is diagnosed according to behavior-based criteria that overlook clinical and genomic heterogeneity, thus repeatedly resulting in failed clinical trials. Here, we summarize the scientific evidence pointing to the pressing need to create a precision medicine framework for ASD and other NDDs. We discuss the role of omics and systems biology to characterize more homogeneous disease subtypes with different underlying pathophysiological mechanisms and to determine corresponding tailored treatments. Finally, we provide recent initiatives towards tackling the complexity in NDDs for precision medicine and cost-effective drug discovery.

Can Systems Biology Advance Clinical Precision Oncology?

Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems' level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research.

Performance Comparisons of AlexNet and GoogLeNet in Cell Growth Inhibition IC50 Prediction

Drug responses in cancer are diverse due to heterogenous genomic profiles. Drug responsiveness prediction is important in clinical response to specific cancer treatments. Recently, multi-class drug responsiveness models based on deep learning (DL) models using molecular fingerprints and mutation statuses have emerged. However, for multi-class models for drug responsiveness prediction, comparisons between convolution neural network (CNN) models (e.g., AlexNet and GoogLeNet) have not been performed. Therefore, in this study, we compared the two CNN models, GoogLeNet and AlexNet, along with the least absolute shrinkage and selection operator (LASSO) model as a baseline model. We constructed the models by taking drug molecular fingerprints of drugs and cell line mutation statuses, as input, to predict high-, intermediate-, and low-class for half-maximal inhibitory concentration (IC50) values of the drugs in the cancer cell lines. Additionally, we compared the models in breast cancer patients as well as in an independent gastric cancer cell line drug responsiveness data. We measured the model performance based on the area under receiver operating characteristic (ROC) curves (AUROC) value. In this study, we compared CNN models for multi-class drug responsiveness prediction. The AlexNet and GoogLeNet showed better performances in comparison to LASSO. Thus, DL models will be useful tools for precision oncology in terms of drug responsiveness prediction.

Recent advances in activity-based probes (ABPs) and affinity-based probes (AfBPs) for profiling of enzymes

Activity-based protein profiling (ABPP) is a technique that uses highly selective active-site targeted chemical probes to label and monitor the state of proteins. ABPP integrates the strengths of both chemical and biological disciplines. By utilizing chemically synthesized or modified bioactive molecules, ABPP is able to reveal complex physiological and pathological enzyme-substrate interactions at molecular and cellular levels. It is also able to provide critical information of the catalytic activity changes of enzymes, annotate new functions of enzymes, discover new substrates of enzymes, and allow real-time monitoring of the cellular location of enzymes. Based on the mechanism of probe-enzyme interaction, two types of probes that have been used in ABPP are activity-based probes (ABPs) and affinity-based probes (AfBPs). This review highlights the recent advances in the use of ABPs and AfBPs, and summarizes their design strategies (based on inhibitors and substrates) and detection approaches.

Current updates on precision therapy for breast cancer associated brain metastasis: Emphasis on combination therapy

Cancer therapies have undergone a tremendous progress over the past decade. Precision medicine provides a more tailored approach, making the combination of existing therapies more precise. Different types of cancers are characterized by unique biomarkers that are targeted using various genomic approaches by clinicians and companies worldwide to achieve efficient treatment with minimal side effects. Precision medicine has two broad approaches namely stratified and personalized medicine. The driver mutations could vary within a subtype while the same driver mutations could be found across different subtypes. Precision medicine has recently gained a lot of importance for breast cancer therapy. Various kinds of mutations like hotspot mutations, gene alterations, gene amplification mutations are targeted to design a more specific therapy. Apart from these known gene mutations there are various unknown mutations. Thus, tumor heterogeneity can pose a challenge to precision medicine. For breast cancer, one of the most successful models developed in case of precision medicine is the anti-HER2 therapies as HER2 was considered to have the worst prognosis being highly malignant. But now due to the advent of HER2 receptor targeted therapies, it has a good prognosis. Moreover, precision medicine helps in identifying if the drug molecules being used for the treatment of one kind of cancer can be beneficial in the treatment of another kind of cancer as well, considering the signaling pathways and machinery is similar in most of the cancers. This reduces the time for new drug development and is economically more feasible. Precision medicine will prove to be very advantageous in case of brain metastasis.

Integrating 3D structural information into systems biology

Systems biology is a data-heavy field that focuses on systems-wide depictions of biological phenomena necessarily sacrificing a detailed characterization of individual components. As an example, genome-wide protein interaction networks are widely used in systems biology and continuously extended and refined as new sources of evidence become available. Despite the vast amount of information about individual protein structures and protein complexes that has accumulated in the past 50 years in the Protein Data Bank, the data, computational tools, and language of structural biology are not an integral part of systems biology. However, increasing effort has been devoted to this integration, and the related literature is reviewed here. Relationships between proteins that are detected via structural similarity offer a rich source of information not available from sequence similarity, and homology modeling can be used to leverage Protein Data Bank structures to produce 3D models for a significant fraction of many proteomes. A number of structure-informed genomic and cross-species (i.e., virus–host) interactomes will be described, and the unique information they provide will be illustrated with a number of examples. Tissue- and tumor-specific interactomes have also been developed through computational strategies that exploit patient information and through genetic interactions available from increasingly sensitive screens. Strategies to integrate structural information with these alternate data sources will be described. Finally, efforts to link protein structure space with chemical compound space offer novel sources of information in drug design, off-target identification, and the identification of targets for compounds found to be effective in phenotypic screens.

Systems Biology and Experimental Model Systems of Cancer

Over the past decade, we have witnessed an increasing number of large-scale studies that have provided multi-omics data by high-throughput sequencing approaches. This has particularly helped with identifying key (epi)genetic alterations in cancers. Importantly, aberrations that lead to the activation of signaling networks through the disruption of normal cellular homeostasis is seen both in cancer cells and also in the neighboring tumor microenvironment. Cancer systems biology approaches have enabled the efficient integration of experimental data with computational algorithms and the implementation of actionable targeted therapies, as the exceptions, for the treatment of cancer. Comprehensive multi-omics data obtained through the sequencing of tumor samples and experimental model systems will be important in implementing novel cancer systems biology approaches and increasing their efficacy for tailoring novel personalized treatment modalities in cancer. In this review, we discuss emerging cancer systems biology approaches based on multi-omics data derived from bulk and single-cell genomics studies in addition to existing experimental model systems that play a critical role in understanding (epi)genetic heterogeneity and therapy resistance in cancer.

Applications of personalised signalling network models in precision oncology

As our ability to provide in-depth, patient-specific characterisation of the molecular alterations within tumours rapidly improves, it is becoming apparent that new approaches will be required to leverage the power of this data and derive the full benefit for each individual patient. Systems biology approaches are beginning to emerge within this field as a potential method of incorporating large volumes of network level data and distilling a coherent, clinically-relevant prediction of drug response. However, the initial promise of this developing field is yet to be realised. Here we argue that in order to develop these precise models of individual drug response and tailor treatment accordingly, we will need to develop mathematical models capable of capturing both the dynamic nature of drug-response signalling networks and key patient-specific information such as mutation status or expression profiles. We also review the modelling approaches commonly utilised within this field, and outline recent examples of their use in furthering the application of systems biology for a precision medicine approach to cancer treatment.

Panel 3: Genomics, precision medicine and targeted therapies

OBJECTIVE

To review the most recent advances in human and bacterial genomics as applied to pathogenesis and clinical management of otitis media.

DATA SOURCES

PubMed articles published since the last meeting in June 2015 up to June 2019.

REVIEW METHODS

A panel of experts in human and bacterial genomics of otitis media was formed. Each panel member reviewed the literature in their respective fields and wrote draft reviews. The reviews were shared with all panel members, and a merged draft was created. The panel met at the 20th International Symposium on Recent Advances in Otitis Media in June 2019, discussed the review and refined the content. A final draft was made, circulated, and approved by the panel members.

CONCLUSION

Trans-disciplinary approaches applying pan-omic technologies to identify human susceptibility to otitis media and to understand microbial population dynamics, patho-adaptation and virulence mechanisms are crucial to the development of novel, personalized therapeutics and prevention strategies for otitis media.

IMPLICATIONS FOR PRACTICE

In the future otitis media prevention strategies may be augmented by mucosal immunization, combination vaccines targeting multiple pathogens, and modulation of the middle ear microbiome. Both treatment and vaccination may be tailored to an individual's otitis media phenotype as defined by molecular profiles obtained by using rapidly developing techniques in microbial and host genomics.

Dissecting Mechanisms of Melanoma Resistance to BRAF and MEK Inhibitors Revealed Genetic and Non-Genetic Patient- and Drug-Specific Alterations and Remarkable Phenotypic Plasticity

The clinical benefit of MAPK pathway inhibition in BRAF-mutant melanoma patients is limited by the development of acquired resistance. Using drug-naïve cell lines derived from tumor specimens, we established a preclinical model of melanoma resistance to vemurafenib or trametinib to provide insight into resistance mechanisms. Dissecting the mechanisms accompanying the development of resistance, we have shown that (i) most of genetic and non-genetic alterations are triggered in a cell line- and/or drug-specific manner; (ii) several changes previously assigned to the development of resistance are induced as the immediate response to the extent measurable at the bulk levels; (iii) reprogramming observed in cross-resistance experiments and growth factor-dependence restricted by the drug presence indicate that phenotypic plasticity of melanoma cells largely contributes to the sustained resistance. Whole-exome sequencing revealed novel genetic alterations, including a frameshift variant of RBMX found exclusively in phospho-AKT^high resistant cell lines. There was no similar pattern of phenotypic alterations among eleven resistant cell lines, including expression/activity of crucial regulators, such as MITF, AXL, SOX, and NGFR, which suggests that patient-to-patient variability is richer and more nuanced than previously described. This diversity should be considered during the development of new strategies to circumvent the acquired resistance to targeted therapies.

Applications of Activity-Based Protein Profiling (ABPP) and Bioimaging in Drug Discovery

Activity-based protein profiling (ABPP) and bioimaging have been developed in recent years as powerful technologies in drug discovery. Specifically, both approaches can be applied in critical steps of drug development, such as therapy target discovery, high-throughput drug screening and target identification of bioactive molecules. We have been focused on the development of various strategies that enable simultaneous activity-based protein profiling and bioimaging studies, thus facilitating an understanding of drug actions and potential toxicities. In this Minireview, we summarize these novel strategies and applications, with the aim of promoting these technologies in drug discovery.

Opportunities for Artificial Intelligence in Advancing Precision Medicine

PURPOSE OF REVIEW

We critically evaluate the future potential of machine learning (ML), deep learning (DL), and artificial intelligence (AI) in precision medicine. The goal of this work is to show progress in ML in digital health, to exemplify future needs and trends, and to identify any essential prerequisites of AI and ML for precision health.

RECENT FINDINGS

High-throughput technologies are delivering growing volumes of biomedical data, such as large-scale genome-wide sequencing assays; libraries of medical images; or drug perturbation screens of healthy, developing, and diseased tissue. Multi-omics data in biomedicine is deep and complex, offering an opportunity for data-driven insights and automated disease classification. Learning from these data will open our understanding and definition of healthy baselines and disease signatures. State-of-the-art applications of deep neural networks include digital image recognition, single-cell clustering, and virtual drug screens, demonstrating breadths and power of ML in biomedicine.

SUMMARY

Significantly, AI and systems biology have embraced big data challenges and may enable novel biotechnology-derived therapies to facilitate the implementation of precision medicine approaches.

Cancer Cell Lines Are Useful Model Systems for Medical Research

Cell lines are in vitro model systems that are widely used in different fields of medical research, especially basic cancer research and drug discovery. Their usefulness is primarily linked to their ability to provide an indefinite source of biological material for experimental purposes. Under the right conditions and with appropriate controls, authenticated cancer cell lines retain most of the genetic properties of the cancer of origin. During the last few years, comparing genomic data of most cancer cell lines has corroborated this statement and those that were observed studying the tumoral tissue equivalents included in the The Cancer Genome Atlas (TCGA) database. We are at the disposal of comprehensive open access cell line datasets describing their molecular and cellular alterations at an unprecedented level of accuracy. This aspect, in association with the possibility of setting up accurate culture conditions that mimic the in vivo microenvironment (e.g., three-dimensional (3D) coculture), has strengthened the importance of cancer cell lines for continuing to sustain medical research fields. However, it is important to consider that the appropriate use of cell lines needs to follow established guidelines for guaranteed data reproducibility and quality, and to prevent the occurrence of detrimental events (i.e., those that are linked to cross-contamination and mycoplasma contamination).

Visualization of drug target interactions in the contexts of pathways and networks with ReactomeFIViz

The precision medicine paradigm is centered on therapies targeted to particular molecular entities that will elicit an anticipated and controlled therapeutic response. However, genetic alterations in the drug targets themselves or in genes whose products interact with the targets can affect how well a drug actually works for an individual patient. To better understand the effects of targeted therapies in patients, we need software tools capable of simultaneously visualizing patient-specific variations and drug targets in their biological context. This context can be provided using pathways, which are process-oriented representations of biological reactions, or biological networks, which represent pathway-spanning interactions among genes, proteins, and other biological entities. To address this need, we have recently enhanced the Reactome Cytoscape app, ReactomeFIViz, to assist researchers in visualizing and modeling drug and target interactions. ReactomeFIViz integrates drug-target interaction information with high quality manually curated pathways and a genome-wide human functional interaction network. Both the pathways and the functional interaction network are provided by Reactome, the most comprehensive open source biological pathway knowledgebase. We describe several examples demonstrating the application of these new features to the visualization of drugs in the contexts of pathways and networks. Complementing previous features in ReactomeFIViz, these new features enable researchers to ask focused questions about targeted therapies, such as drug sensitivity for patients with different mutation profiles, using a pathway or network perspective.

CD271 is a molecular switch with divergent roles in melanoma and melanocyte development

Dysregulation of signaling networks controlling self-renewal and migration of developmental cell lineages is closely linked to the proliferative and invasive properties of tumors. Identification of such signaling pathways and their critical regulators is vital for successful design of effective targeted therapies against neoplastic tissue growth. The neurotrophin receptor (CD271/NGFR/p75NTR) is a key regulator of the melanocytic cell lineage through its ability to mediate cell growth, survival, and differentiation. Using clinical melanoma samples, normal melanocytes and global gene expression profiling we have investigated the role of CD271 in rewiring signal transduction networks of melanoma cells during neoplastic transformation. Our analysis demonstrates that depending on the cell fate of tumor initiation vs normal development, elevated levels of CD271 can serve as a switch between proliferation/survival and differentiation/cell death. Two divergent arms of neurotrophin signaling hold the balance between positive regulators of tumor growth controlled by E2F, MYC, SREBP1 and AKT3 pathways on the one hand, and differentiation, senescence, and apoptosis controlled by TRAF6/IRAK-dependent activation of AP1 and TP53 mediated processes on the other hand. A molecular network map revealed in this study uncovers CD271 as a context-specific molecular switch between normal development and malignant transformation.

Concurrent Targeting of Glutaminolysis and Metabotropic Glutamate Receptor 1 (GRM1) Reduces Glutamate Bioavailability in GRM1+ Melanoma

Aberrant glutamatergic signaling has been implicated in altered metabolic activity in many cancer types, including malignant melanoma. Previously, we have illustrated the role of metabotropic glutamate receptor 1 (GRM1) in neoplastic transformation of melanocytes in vitro and spontaneous metastatic melanoma in vivo. In this study, we showed that autocrine stimulation constitutively activates the GRM1 receptor and its downstream mitogenic signaling. GRM1-activated (GRM1+) melanomas exhibited significantly increased expression of glutaminase (GLS), which catalyzes the first step in the conversion of glutamine to glutamate. In cultured GRM1+ melanoma cell lines, CB-839, a potent, selective, and orally bioavailable inhibitor of GLS, suppressed cell proliferation, while riluzole, an inhibitor of glutamate release, promoted apoptotic cell death in vitro and in vivo. Combined treatment with CB-839 and riluzole treatment proved to be superior to single-agent treatment, restricting glutamate bioavailability and leading to effective suppression of tumor cell proliferation in vitro and tumor progression in vivo. Hyperactivation of GRM1 in malignant melanoma is an oncogenic driver, which acts independently of canonical melanoma proto-oncogenes, BRAF or NRAS. Overall, these results indicate that expression of GRM1 promotes a metabolic phenotype that supports increased glutamate production and autocrine glutamatergic signaling, which can be pharmacologically targeted by decreasing glutamate bioavailability and the GLS-dependent glutamine to glutamate conversion. SIGNIFICANCE: These findings demonstrate that targeting glutaminolytic glutamate bioavailability is an effective therapeutic strategy for GRM1-activated tumors.

Precision medicine review: rare driver mutations and their biophysical classification

How can biophysical principles help precision medicine identify rare driver mutations? A major tenet of pragmatic approaches to precision oncology and pharmacology is that driver mutations are very frequent. However, frequency is a statistical attribute, not a mechanistic one. Rare mutations can also act through the same mechanism, and as we discuss below, “latent driver” mutations may also follow the same route, with “helper” mutations. Here, we review how biophysics provides mechanistic guidelines that extend precision medicine. We outline principles and strategies, especially focusing on mutations that drive cancer. Biophysics has contributed profoundly to deciphering biological processes. However, driven by data science, precision medicine has skirted some of its major tenets. Data science embodies genomics, tissue- and cell-specific expression levels, making it capable of defining genome- and systems-wide molecular disease signatures. It classifies cancer driver genes/mutations and affected pathways, and its associated protein structural data guide drug discovery. Biophysics complements data science. It considers structures and their heterogeneous ensembles, explains how mutational variants can signal through distinct pathways, and how allo-network drugs can be harnessed. Biophysics clarifies how one mutation—frequent or rare—can affect multiple phenotypic traits by populating conformations that favor interactions with other network modules. It also suggests how to identify such mutations and their signaling consequences. Biophysics offers principles and strategies that can help precision medicine push the boundaries to transform our insight into biological processes and the practice of personalized medicine. By contrast, “phenotypic drug discovery,” which capitalizes on physiological cellular conditions and first-in-class drug discovery, may not capture the proper molecular variant. This is because variants of the same protein can express more than one phenotype, and a phenotype can be encoded by several variants.

Classification of Widely and Rarely Expressed Genes with Recurrent Neural Network

A tissue-specific gene expression shapes the formation of tissues, while gene expression changes reflect the immune response of the human body to environmental stimulations or pressure, particularly in disease conditions, such as cancers. A few genes are commonly expressed across tissues or various cancers, while others are not. To investigate the functional differences between widely and rarely expressed genes, we defined the genes that were expressed in 32 normal tissues/cancers (i.e., called widely expressed genes; FPKM >1 in all samples) and those that were not detected (i.e., called rarely expressed genes; FPKM <1 in all samples) based on the large gene expression data set provided by Uhlen et al. Each gene was encoded using the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment scores. Minimum redundancy maximum relevance (mRMR) was used to measure and rank these features on the mRMR feature list. Thereafter, we applied the incremental feature selection method with a supervised classifier recurrent neural network (RNN) to select the discriminate features for classifying widely expressed genes from rarely expressed genes and construct an optimum RNN classifier. The Youden's indexes generated by the optimum RNN classifier and evaluated using a 10-fold cross validation were 0.739 for normal tissues and 0.639 for cancers. Furthermore, the underlying mechanisms of the key discriminate GO and KEGG features were analyzed. Results can facilitate the identification of the expression landscape of genes and elucidation of how gene expression shapes tissues and the microenvironment of cancers.

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Some genes are widely expressed across tissues or various cancers.

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A number of genes are rarely expressed across tissues or various cancers.

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The functional differences between widely and rarely expressed genes were studied.

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Several GO terms and KEGG pathways were extracted and analyzed.

Systems Biology, Systems Medicine, Systems Pharmacology: The What and The Why

Systems biology is today such a widespread discipline that it becomes difficult to propose a clear definition of what it really is. For some, it remains restricted to the genomic field. For many, it designates the integrated approach or the corpus of computational methods employed to handle the vast amount of biological or medical data and investigate the complexity of the living. Although defining systems biology might be difficult, on the other hand its purpose is clear: systems biology, with its emerging subfields systems medicine and systems pharmacology, clearly aims at making sense of complex observations/experimental and clinical datasets to improve our understanding of diseases and their treatments without putting aside the context in which they appear and develop. In this short review, we aim to specifically focus on these new subfields with the new theoretical tools and approaches that were developed in the context of cancer. Systems pharmacology and medicine now give hope for major improvements in cancer therapy, making personalized medicine closer to reality. As we will see, the current challenge is to be able to improve the clinical practice according to the paradigm shift of systems sciences.

The triennial International Pigment Cell Conference (IPCC)

The International Federation of Pigment Cell Societies (IFPCS) held its XXIII triennial International Pigment Cell Conference (IPCC) in Denver, Colorado in August 2017. The goal of the summit was to provide a venue promoting a vibrant interchange among leading basic and clinical researchers working on leading-edge aspects of melanocyte biology and disease. The philosophy of the meeting, entitled Breakthroughs in Pigment Cell and Melanoma Research, was to deliver a comprehensive program in an inclusive environment fostering scientific exchange and building new academic bridges. This document provides an outlook on the history, accomplishments, and sustainability of the pigment cell and melanoma research community. Shared progress in the understanding of cellular homeostasis of pigment cells but also clinical successes and hurdles in the treatment of melanoma and dermatological disorders continue to drive future research activities. A sustainable direction of the societies creates an international forum identifying key areas of imminent needs in laboratory research and clinical care and ensures the future of this vibrant, diverse and unique research community at the same time. Important advances showcase wealth and breadth of the field in melanocyte and melanoma research and include emerging frontiers in melanoma immunotherapy, medical and surgical oncology, dermatology, vitiligo, albinism, genomics and systems biology, precision bench-to-bedside approaches, epidemiology, pigment biophysics and chemistry, and evolution. This report recapitulates highlights of the federate meeting agenda designed to advance clinical and basic research frontiers from melanoma and dermatological sciences followed by a historical perspective of the associated societies and conferences.

Frontiers in pigment cell and melanoma research

In this perspective, we identify emerging frontiers in clinical and basic research of melanocyte biology and its associated biomedical disciplines. We describe challenges and opportunities in clinical and basic research of normal and diseased melanocytes that impact current approaches to research in melanoma and the dermatological sciences. We focus on four themes: (1) clinical melanoma research, (2) basic melanoma research, (3) clinical dermatology, and (4) basic pigment cell research, with the goal of outlining current highlights, challenges, and frontiers associated with pigmentation and melanocyte biology. Significantly, this document encapsulates important advances in melanocyte and melanoma research including emerging frontiers in melanoma immunotherapy, medical and surgical oncology, dermatology, vitiligo, albinism, genomics and systems biology, epidemiology, pigment biophysics and chemistry, and evolution.

Molecular Pathways in Melanomagenesis: What We Learned from Next-Generation Sequencing Approaches

PURPOSE OF REVIEW

Conventional clinico-pathological features in melanoma patients should be integrated with new molecular diagnostic, predictive, and prognostic factors coming from the expanding genomic profiles. Cutaneous melanoma (CM), even differing in biological behavior according to sun-exposure levels on the skin areas where it arises, is molecularly heterogeneous. The next-generation sequencing (NGS) approaches are providing data on mutation landscapes in driver genes that may account for distinct pathogenetic mechanisms and pathways. The purpose was to group and classify all somatic driver mutations observed in the main NGS-based studies.

RECENT FINDINGS

Whole exome and whole genome sequencing approaches have provided data on spectrum and distribution of genetic and genomic alterations as well as allowed to discover new cancer genes underlying CM pathogenesis. After evaluating the mutational status in a cohort of 686 CM cases from the most representative NGS studies, three molecular CM subtypes were proposed: BRAFmut, RASmut, and non-BRAFmut/non-RASmut.

Crosstalk between epigenetics and metabolism-Yin and Yang of histone demethylases and methyltransferases in cancer

Histone methylation is an epigenetic modification of chromatin undergoing dynamic changes and balancing tissue-specific demands of proliferation and differentiation. In cancer, aberrant histone methylation can facilitate oncogenic and tumor suppression programs by modulating gene expression. Histone remodelers such as lysine methyltransferases and lysine demethylases are seemingly opposite or contrary forces but may be part of an interconnected network complementing each other. We identify several layers of molecular communication where epigenetic master regulators engage in crosstalk between tumor metabolism and histone remodeling. Epigenetic master regulators have the ability to cooperate with members of the transcriptional machinery, DNA methyltransferases, as well as other histone modifiers. High-throughput sequencing and omics data in combination with cancer systems biology analysis have the power to prioritize regulatory events epigenome-wide.

A network of epigenomic and transcriptional cooperation encompassing an epigenomic master regulator in cancer

Coordinated experiments focused on transcriptional responses and chromatin states are well-equipped to capture different epigenomic and transcriptomic levels governing the circuitry of a regulatory network. We propose a workflow for the genome-wide identification of epigenomic and transcriptional cooperation to elucidate transcriptional networks in cancer. Gene promoter annotation in combination with network analysis and sequence-resolution of enriched transcriptional motifs in epigenomic data reveals transcription factor families that act synergistically with epigenomic master regulators. By investigating complementary omics levels, a close teamwork of the transcriptional and epigenomic machinery was discovered. The discovered network is tightly connected and surrounds the histone lysine demethylase KDM3A, basic helix-loop-helix factors MYC, HIF1A, and SREBF1, as well as differentiation factors AP1, MYOD1, SP1, MEIS1, ZEB1, and ELK1. In such a cooperative network, one component opens the chromatin, another one recognizes gene-specific DNA motifs, others scaffold between histones, cofactors, and the transcriptional complex. In cancer, due to the ability to team up with transcription factors, epigenetic factors concert mitogenic and metabolic gene networks, claiming the role of a cancer master regulators or epioncogenes. Significantly, specific histone modification patterns are commonly associated with open or closed chromatin states, and are linked to distinct biological outcomes by transcriptional activation or repression. Disruption of patterns of histone modifications is associated with the loss of proliferative control and cancer. There is tremendous therapeutic potential in understanding and targeting histone modification pathways. Thus, investigating cooperation of chromatin remodelers and the transcriptional machinery is not only important for elucidating fundamental mechanisms of chromatin regulation, but also necessary for the design of targeted therapeutics.

RNA-Sequencing of Primary Retinoblastoma Tumors Provides New Insights and Challenges Into Tumor Development

Retinoblastoma is rare tumor of the retina caused by the homozygous loss of the Retinoblastoma 1 tumor suppressor gene (RB1). Loss of the RB1 protein, pRB, results in de-regulated activity of the E2F transcription factors, chromatin changes and developmental defects leading to tumor development. Extensive microarray profiles of these tumors have enabled the identification of genes sensitive to pRB disruption, however, this technology has a number of limitations in the RNA profiles that they generate. The advent of RNA-sequencing has enabled the global profiling of all of the RNA within the cell including both coding and non-coding features and the detection of aberrant RNA processing events. In this perspective, we focus on discussing how RNA-sequencing of rare Retinoblastoma tumors will build on existing data and open up new area's to improve our understanding of the biology of these tumors. In particular, we discuss how the RB-research field may be to use this data to determine how RB1 loss results in the expression of; non-coding RNAs, causes aberrant RNA processing events and how a deeper analysis of metabolic RNA changes can be utilized to model tumor specific shifts in metabolism. Each section discusses new opportunities and challenges associated with these types of analyses and aims to provide an honest assessment of how understanding these different processes may contribute to the treatment of Retinoblastoma.

Systems biology analysis of mitogen activated protein kinase inhibitor resistance in malignant melanoma

BACKGROUND

Kinase inhibition in the mitogen activated protein kinase (MAPK) pathway is a standard therapy for cancer patients with activating BRAF mutations. However, the anti-tumorigenic effect and clinical benefit are only transient, and tumors are prone to treatment resistance and relapse. To elucidate mechanistic insights into drug resistance, we have established an in vitro cellular model of MAPK inhibitor resistance in malignant melanoma.

METHODS

The cellular model evolved in response to clinical dosage of the BRAF inhibitor, vemurafenib, PLX4032. We conducted transcriptomic expression profiling using RNA-Seq and RT-qPCR arrays. Pathways of melanogenesis, MAPK signaling, cell cycle, and metabolism were significantly enriched among the set of differentially expressed genes of vemurafenib-resistant cells vs control. The underlying mechanism of treatment resistance and pathway rewiring was uncovered to be based on non-genomic adaptation and validated in two distinct melanoma models, SK-MEL-28 and A375. Both cell lines have activating BRAF mutations and display metastatic potential.

RESULTS

Downregulation of dual specific phosphatases, tumor suppressors, and negative MAPK regulators reengages mitogenic signaling. Upregulation of growth factors, cytokines, and cognate receptors triggers signaling pathways circumventing BRAF blockage. Further, changes in amino acid and one-carbon metabolism support cellular proliferation despite MAPK inhibitor treatment. In addition, treatment-resistant cells upregulate pigmentation and melanogenesis, pathways which partially overlap with MAPK signaling. Upstream regulator analysis discovered significant perturbation in oncogenic forkhead box and hypoxia inducible factor family transcription factors.

CONCLUSIONS

The established cellular models offer mechanistic insight into cellular changes and therapeutic targets under inhibitor resistance in malignant melanoma. At a systems biology level, the MAPK pathway undergoes major rewiring while acquiring inhibitor resistance. The outcome of this transcriptional plasticity is selection for a set of transcriptional master regulators, which circumvent upstream targeted kinases and provide alternative routes of mitogenic activation. A fine-woven network of redundant signals maintains similar effector genes allowing for tumor cell survival and malignant progression in therapy-resistant cancer.

Trial watch: Immune checkpoint blockers for cancer therapy

Immune checkpoint blockers (ICBs) are literally revolutionizing the clinical management of an ever more diversified panel of oncological indications. Although considerable attention persists around the inhibition of cytotoxic T lymphocyte-associated protein 4 (CTLA4) and programmed cell death 1 (PDCD1, best known as PD-1) signaling, several other co-inhibitory T-cell receptors are being evaluated as potential targets for the development of novel ICBs. Moreover, substantial efforts are being devoted to the identification of biomarkers that reliably predict the likelihood of each patient to obtain clinical benefits from ICBs in the absence of severe toxicity. Tailoring the delivery of specific ICBs or combinations thereof to selected patient populations in the context of precision medicine programs constitutes indeed a major objective of the future of ICB-based immunotherapy. Here, we discuss recent preclinical and clinical advances on the development of ICBs for oncological indications.