Evolutionary triage governs fitness in driver and passenger mutations and suggests targeting never mutations

Targeting STAT3, FOXO3a, and Pim-1 kinase by FDA-approved tyrosine kinase inhibitor-Radotinib: An in silico and in vitro approach

Cancer, a multifactorial pathological condition, is primarily caused due to mutations in multiple genes. Hepatocellular carcinoma (HCC) is a form of primary liver cancer that is often diagnosed at the advanced stage. Current treatment strategies for advanced HCC involve systemic therapies which are often hindered due to the emergence of resistance and toxicity. Therefore, a multitarget approach might prove more effective in HCC treatment. The present study focuses on targeting signal transducer and activator of transcription 3 (STAT3), forkhead box class O3a (FOXO3a), and proviral integration site for Moloney murine leukemia virus-1 (Pim-1) kinase, using a Food and Drug Administration (FDA)-approved anticancer drug library. Two compounds, namely, radotinib and capmatinib, were identified as top compounds using molecular docking. Among the two compounds, radotinib exhibited significant binding values towards the targeted proteins and their heterodimers. Furthermore, in vitro experiments involving 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT), live/dead, 4',6-diamidino-2-phenylindole, and clonogenic assays were performed to evaluate the effect of radotinib in human hepatoblastoma cell line/hepatocellular carcinoma cells. The gene expression data indicated reduced expression of FOXO3a and Pim-1, but no basal-level alteration of STAT3. The Western blot analysis assay showed that the phosphorylation level of STAT3 was significantly decreased upon radotinib treatment. Taken together, our findings suggest that radotinib, which is currently used in the treatment of chronic myeloid leukemia (CML), could be considered as a potential candidate for repurposing in the treatment of HCC.

DiPRO1 distinctly reprograms muscle and mesenchymal cancer cells

We have recently identified the uncharacterized ZNF555 protein as a component of a productive complex involved in the morbid function of the 4qA locus in facioscapulohumeral dystrophy. Subsequently named DiPRO1 (Death, Differentiation, and PROliferation related PROtein 1), our study provides substantial evidence of its role in the differentiation and proliferation of human myoblasts. DiPRO1 operates through the regulatory binding regions of SIX1, a master regulator of myogenesis. Its relevance extends to mesenchymal tumors, such as rhabdomyosarcoma (RMS) and Ewing sarcoma, where DiPRO1 acts as a repressor via the epigenetic regulators TIF1B and UHRF1, maintaining methylation of cis-regulatory elements and gene promoters. Loss of DiPRO1 mimics the host defense response to virus, awakening retrotransposable repeats and the ZNF/KZFP gene family. This enables the eradication of cancer cells, reprogramming the cellular decision balance towards inflammation and/or apoptosis by controlling TNF-α via NF-kappaB signaling. Finally, our results highlight the vulnerability of mesenchymal cancer tumors to si/shDiPRO1-based nanomedicines, positioning DiPRO1 as a potential therapeutic target.

An agent-based modelling framework to study growth mechanisms in EGFR-L858R mutant cell alveolar type II cells

Mutations in the epidermal growth factor receptor (EGFR) are common in non-small cell lung cancer (NSCLC), particularly in never-smoker patients. However, these mutations are not always carcinogenic, and have recently been reported in histologically normal lung tissue from patients with and without lung cancer. To investigate the outcome of EGFR mutation in healthy lung stem cells, we grow murine alveolar type II organoids monoclonally in a three-dimensional Matrigel. Our experiments show that the EGFR-L858R mutation induces a change in organoid structure: mutated organoids display more 'budding', in comparison with non-mutant controls, which are nearly spherical. We perform on-lattice computational simulations, which suggest that this can be explained by the concentration of division among a small number of cells on the surface of the mutated organoids. We are currently unable to distinguish the cell-based mechanisms that lead to this spatial heterogeneity in growth, but suggest a number of future experiments which could be used to do so. We suggest that the likelihood of L858R-fuelled tumorigenesis is affected by whether the mutation arises in a spatial environment that allows the development of these surface protrusions. These data may have implications for cancer prevention strategies and for understanding NSCLC progression.

Lung adenocarcinomas without driver genes converge to common adaptive strategies through diverse genetic, epigenetic, and niche construction evolutionary pathways

Somatic evolution selects cancer cell phenotypes that maximize survival and proliferation in dynamic environments. Although cancer cells are molecularly heterogeneous, we hypothesized convergent adaptive strategies to common host selection forces can be inferred from patterns of epigenetic and genetic evolutionary selection in similar tumors. We systematically investigated gene mutations and expression changes in lung adenocarcinomas with no common driver genes (n = 313). Although 13,461 genes were mutated in at least one sample, only 376 non-synonymous mutations evidenced positive evolutionary selection with conservation of 224 genes, while 1736 and 2430 genes exhibited ≥ two-fold increased and ≥ 50% decreased expression, respectively. Mutations under positive selection are more frequent in genes with significantly altered expression suggesting they often "hardwire" pre-existing epigenetically driven adaptations. Conserved genes averaged 16-fold higher expression in normal lung tissue compared to those with selected mutations demonstrating pathways necessary for both normal cell function and optimal cancer cell fitness. The convergent LUAD phenotype exhibits loss of differentiated functions and cell-cell interactions governing tissue organization. Conservation with increased expression is found in genes associated with cell cycle, DNA repair, p53 pathway, epigenetic modifiers, and glucose metabolism. No canonical driver gene pathways exhibit strong positive selection, but extensive down-regulation of membrane ion channels suggests decreased transmembrane potential may generate persistent proliferative signals. NCD LUADs perform niche construction generating a stiff, immunosuppressive microenvironment through selection of specific collagens and proteases. NCD LUADs evolve to a convergent phenotype through a network of interconnected genetic, epigenetic, and ecological pathways.

N-of-1 medicine

ABSTRACT

The fields of precision and personalised medicine have led to promising advances in tailoring treatment to individual patients. Examples include genome/molecular alteration-guided drug selection, single-patient gene therapy design and synergy-based drug combination development, and these approaches can yield substantially diverse recommendations. Therefore, it is important to define each domain and delineate their commonalities and differences in an effort to develop novel clinical trial designs, streamline workflow development, rethink regulatory considerations, create value in healthcare and economics assessments, and other factors. These and other segments are essential to recognise the diversity within these domains to accelerate their respective workflows towards practice-changing healthcare. To emphasise these points, this article elaborates on the concept of digital health and digital medicine-enabled N-of-1 medicine, which individualises combination regimen and dosing using a patient's own data. We will conclude with recommendations for consideration when developing novel workflows based on emerging digital-based platforms.

The Rise of Hypothesis-Driven Artificial Intelligence in Oncology

Cancer is a complex disease involving the deregulation of intricate cellular systems beyond genetic aberrations and, as such, requires sophisticated computational approaches and high-dimensional data for optimal interpretation. While conventional artificial intelligence (AI) models excel in many prediction tasks, they often lack interpretability and are blind to the scientific hypotheses generated by researchers to enable cancer discoveries. Here we propose that hypothesis-driven AI, a new emerging class of AI algorithm, is an innovative approach to uncovering the complex etiology of cancer from big omics data. This review exemplifies how hypothesis-driven AI is different from conventional AI by citing its application in various areas of oncology including tumor classification, patient stratification, cancer gene discovery, drug response prediction, and tumor spatial organization. Our aim is to stress the feasibility of incorporating domain knowledge and scientific hypotheses to craft the design of new AI algorithms. We showcase the power of hypothesis-driven AI in making novel cancer discoveries that can be overlooked by conventional AI methods. Since hypothesis-driven AI is still in its infancy, open questions such as how to better incorporate new knowledge and biological perspectives to ameliorate bias and improve interpretability in the design of AI algorithms still need to be addressed. In conclusion, hypothesis-driven AI holds great promise in the discovery of new mechanistic and functional insights that explain the complexity of cancer etiology and potentially chart a new roadmap to improve treatment regimens for individual patients.

Modeling phenotypic heterogeneity towards evolutionarily inspired osteosarcoma therapy

Osteosarcoma is the most common bone sarcoma in children and young adults. While universally delivered, chemotherapy only benefits roughly half of patients with localized disease. Increasingly, intratumoral heterogeneity is recognized as a source of therapeutic resistance. In this study, we develop and evaluate an in vitro model of osteosarcoma heterogeneity based on phenotype and genotype. Cancer cell populations vary in their environment-specific growth rates and in their sensitivity to chemotherapy. We present the genotypic and phenotypic characterization of an osteosarcoma cell line panel with a focus on co-cultures of the most phenotypically divergent cell lines, 143B and SAOS2. Modest environmental (pH, glutamine) or chemical perturbations dramatically shift the success and composition of cell lines. We demonstrate that in nutrient rich culture conditions 143B outcompetes SAOS2. But, under nutrient deprivation or conventional chemotherapy, SAOS2 growth can be favored in spheroids. Importantly, when the simplest heterogeneity state is evaluated, a two-cell line coculture, perturbations that affect the faster growing cell line have only a modest effect on final spheroid size. Thus the only evaluated therapies to eliminate the spheroids were by switching therapies from a first strike to a second strike. This extensively characterized, widely available system, can be modeled and scaled to allow for improved strategies to anticipate resistance in osteosarcoma due to heterogeneity.

Cell population growth kinetics in the presence of stochastic heterogeneity of cell phenotype

Recent studies at individual cell resolution have revealed phenotypic heterogeneity in nominally clonal tumor cell populations. The heterogeneity affects cell growth behaviors, which can result in departure from the idealized uniform exponential growth of the cell population. Here we measured the stochastic time courses of growth of an ensemble of populations of HL60 leukemia cells in cultures, starting with distinct initial cell numbers to capture a departure from the uniform exponential growth model for the initial growth ("take-off"). Despite being derived from the same cell clone, we observed significant variations in the early growth patterns of individual cultures with statistically significant differences in growth dynamics, which could be explained by the presence of inter-converting subpopulations with different growth rates, and which could last for many generations. Based on the hypothesis of existence of multiple subpopulations, we developed a branching process model that was consistent with the experimental observations.

Cell Population Growth Kinetics in the Presence of Stochastic Heterogeneity of Cell Phenotype

Recent studies at individual cell resolution have revealed phenotypic heterogeneity in nominally clonal tumor cell populations. The heterogeneity affects cell growth behaviors, which can result in departure from the idealized uniform exponential growth of the cell population. Here we measured the stochastic time courses of growth of an ensemble of populations of HL60 leukemia cells in cultures, starting with distinct initial cell numbers to capture a departure from the uniform exponential growth model for the initial growth ("take-off"). Despite being derived from the same cell clone, we observed significant variations in the early growth patterns of individual cultures with statistically significant differences in growth dynamics, which could be explained by the presence of inter-converting subpopulations with different growth rates, and which could last for many generations. Based on the hypothesis of existence of multiple subpopulations, we developed a branching process model that was consistent with the experimental observations.

Predator: Predicting the Impact of Cancer Somatic Mutations on Protein-Protein Interactions

Since many biological processes are governed by protein-protein interactions, understanding which mutations lead to a disruption in these interactions is profoundly important for cancer research. Most of the existing methods focus on the stability of the protein without considering the specific effects of a mutation on its interactions with other proteins. Here, we focus on somatic mutations that appear on the interface regions of the protein and predict the interactions that would be affected by a mutation of interest. We build an ensemble model, Predator, that classifies the interface mutations as disruptive or nondisruptive based on the predicted effects of mutations on specific protein-protein interactions. We show that Predator outperforms existing approaches in literature in terms of prediction accuracy. We then apply Predator on various TCGA cancer cohorts and perform comprehensive analysis at cohort level, patient level, and gene level in determining the genes whose interface mutations tend to yield a disruption in its interactions. The predictions obtained by Predator shed light on interesting patterns on several genes for each cohort regarding their potential as cancer drivers. Our analyses further reveal that the identified genes and their frequently disrupted partners exhibit patterns of mutually exclusivity across cancer cohorts under study.

Cell Population Growth Kinetics in the Presence of Stochastic Heterogeneity of Cell Phenotype

Recent studies at individual cell resolution have revealed phenotypic heterogeneity in nominally clonal tumor cell populations. The heterogeneity affects cell growth behaviors, which can result in departure from the idealized exponential growth. Here we measured the stochastic time courses of growth of an ensemble of populations of HL60 leukemia cells in cultures, starting with distinct initial cell numbers to capture the departure from the exponential growth model in the initial growth phase. Despite being derived from the same cell clone, we observed significant variations in the early growth patterns of individual cultures with statistically significant differences in growth kinetics and the presence of subpopulations with different growth rates that endured for many generations. Based on the hypothesis of existence of multiple inter-converting subpopulations, we developed a branching process model that captures the experimental observations.

Identifying Liver Metastasis-Related Genes Through a Coexpression Network to Construct a 5-Gene Model for Predicting Pancreatic Ductal Adenocarcinoma Patient Prognosis

OBJECTIVES

This study aimed to develop a liver metastasis-related gene prognostic index (LMPI) for pancreatic ductal adenocarcinoma prognosis and therapy.

METHODS

The Cancer Genome Atlas data set was used to identify liver metastasis-related hub genes via weighted gene coexpression network analysis. The core genes were identified to construct an LMPI by using the Cox regression method. An immune cell abundance identifier was applied to determine the immune cell abundance.

RESULTS

A total of 78 hub liver metastasis-related genes in the black module were significantly enriched in complement and coagulation cascades, fat digestion and absorption, and the PPAR signaling pathway. Then, an LMPI was constructed on the basis of the 5 prognostic genes (MOGAT3, ASGR1, TRPM8, SGSM1, and LOC101927851). Patients with higher LMPI scores had poor overall survival, more co-occurring or mutually exclusive pairs of driver gene mutations, and less benefit from immunotherapy than patients with lower LMPI scores. In addition, a high correlation was also found between LMPI scores and immune infiltration, such as CD4 naive, CD8 T, cytotoxic T, T helper 2, follicular helper T, and natural killer cells.

CONCLUSIONS

The core genes of the LMPI developed may be independent factors for predicting prognosis, immune characteristics, and immunotherapy efficacy in pancreatic ductal adenocarcinoma.

Evolutionary Analysis of TCGA Data Using Over- and Under- Mutated Genes Identify Key Molecular Pathways and Cellular Functions in Lung Cancer Subtypes

We identify critical conserved and mutated genes through a theoretical model linking a gene’s fitness contribution to its observed mutational frequency in a clinical cohort. “Passenger” gene mutations do not alter fitness and have mutational frequencies determined by gene size and the mutation rate. Driver mutations, which increase fitness (and proliferation), are observed more frequently than expected. Non-synonymous mutations in essential genes reduce fitness and are eliminated by natural selection resulting in lower prevalence than expected. We apply this “evolutionary triage” principle to TCGA data from EGFR-mutant, KRAS-mutant, and NEK (non-EGFR/KRAS) lung adenocarcinomas. We find frequent overlap of evolutionarily selected non-synonymous gene mutations among the subtypes suggesting enrichment for adaptations to common local tissue selection forces. Overlap of conserved genes in the LUAD subtypes is rare suggesting negative evolutionary selection is strongly dependent on initiating mutational events during carcinogenesis. Highly expressed genes are more likely to be conserved and significant changes in expression (>20% increased/decreased) are common in genes with evolutionarily selected mutations but not in conserved genes. EGFR-mut cancers have fewer average mutations (89) than KRAS-mut (228) and NEK (313). Subtype-specific variation in conserved and mutated genes identify critical molecular components in cell signaling, extracellular matrix remodeling, and membrane transporters. These findings demonstrate subtype-specific patterns of co-adaptations between the defining driver mutation and somatically conserved genes as well as novel insights into epigenetic versus genetic contributions to cancer evolution.

Evolutionary selection identifies critical immune-relevant genes in lung cancer subtypes

In an evolving population, proliferation is dependent on fitness so that a numerically dominant population typically possesses the most well adapted phenotype. In contrast, the evolutionary "losers" typically disappear from the population so that their genetic record is lost. Historically, cancer research has focused on observed genetic mutations in the dominant tumor cell populations which presumably increase fitness. Negative selection, i.e., removal of deleterious mutations from a population, is not observable but can provide critical information regarding genes involved in essential cellular processes. Similar to immunoediting, "evolutionary triage" eliminates mutations in tumor cells that increase susceptibility to the host immune response while mutations that shield them from immune attack increase proliferation and are readily observable (e.g., B2M mutations). These dynamics permit an "inverse problem" analysis linking the fitness consequences of a mutation to its prevalence in a tumor cohort. This is evident in "driver mutations" but, equally important, can identify essential genes in which mutations are seen significantly less than expected by chance. Here we utilized this new approach to investigate evolutionary triage in immune-related genes from TCGA lung adenocarcinoma cohorts. Negative selection differs between the two cohorts and is observed in endoplasmic reticulum aminopeptidase genes, ERAP1 and ERAP2 genes, and DNAM-1/TIGIT ligands. Targeting genes or molecular pathways under positive or negative evolutionary selection may permit new treatment options and increase the efficacy of current immunotherapy.

The role of evolutionary game theory in spatial and non-spatial models of the survival of cooperation in cancer: a review

Evolutionary game theory (EGT) is a branch of mathematics which considers populations of individuals interacting with each other to receive pay-offs. An individual’s pay-off is dependent on the strategy of its opponent(s) as well as on its own, and the higher its pay-off, the higher its reproductive fitness. Its offspring generally inherit its interaction strategy, subject to random mutation. Over time, the composition of the population shifts as different strategies spread or are driven extinct. In the last 25 years there has been a flood of interest in applying EGT to cancer modelling, with the aim of explaining how cancerous mutations spread through healthy tissue and how intercellular cooperation persists in tumour-cell populations. This review traces this body of work from theoretical analyses of well-mixed infinite populations through to more realistic spatial models of the development of cooperation between epithelial cells. We also consider work in which EGT has been used to make experimental predictions about the evolution of cancer, and discuss work that remains to be done before EGT can make large-scale contributions to clinical treatment and patient outcomes.

Stochastic models of Mendelian and reverse transcriptional inheritance in state-structured cancer populations

Recent evidence suggests that a polyaneuploid cancer cell (PACC) state may play a key role in the adaptation of cancer cells to stressful environments and in promoting therapeutic resistance. The PACC state allows cancer cells to pause cell division and to avoid DNA damage and programmed cell death. Transition to the PACC state may also lead to an increase in the cancer cell’s ability to generate heritable variation (evolvability). One way this can occur is through evolutionary triage. Under this framework, cells gradually gain resistance by scaling hills on a fitness landscape through a process of mutation and selection. Another way this can happen is through self-genetic modification whereby cells in the PACC state find a viable solution to the stressor and then undergo depolyploidization, passing it on to their heritably resistant progeny. Here, we develop a stochastic model to simulate both of these evolutionary frameworks. We examine the impact of treatment dosage and extent of self-genetic modification on eco-evolutionary dynamics of cancer cells with aneuploid and PACC states. We find that under low doses of therapy, evolutionary triage performs better whereas under high doses of therapy, self-genetic modification is favored. This study generates predictions for teasing apart these biological hypotheses, examines the implications of each in the context of cancer, and provides a modeling framework to compare Mendelian and non-traditional forms of inheritance.

Novel biomarkers for cholangiocarcinoma: how can it enhance diagnosis, prognostication, and investigational drugs? Part-1

INTRODUCTION

The development of novel biomarkers for cancer has exploded over the last decade with advances in novel technologies. Cholangiocarcinoma (CCA), a cancer of the bile ducts, has a dearth of strong disease and pathophysiology biomarkers, making early detection and prognostication a difficult task.

AREAS COVERED

In this comprehensive review, we discuss the spectrum of biomarkers for CCA diagnosis and prognostication. We elaborate on novel biomarker discovery through a comprehensive multi-omics approach. We also cover, how certain biomarkers may also serve as unique and potent targets for therapeutic development.

EXPERT OPINION

Despite the relatively poor diagnostic and prognostic performance of existing biomarkers for CCA, there is a vast range of novel biomarkers with exquisite diagnostic and prognostic performance for CCA in the pipeline. Moreover, these biomarkers may serve as potential targets for precision medicine. Existing strategies to target unique biomolecular classes are discussed, within the context of an overall 'omics' focused profiling strategy. Omics profiling will simultaneously allow for enhanced biomarker development and identification of unique subtypes of cholangiocarcinoma and how they are influenced by an individual's unique context. In this manner, patient management strategy and clinical trial design can be optimized to the individual.

The Contribution of Evolutionary Game Theory to Understanding and Treating Cancer

Evolutionary game theory mathematically conceptualizes and analyzes biological interactions where one's fitness not only depends on one's own traits, but also on the traits of others. Typically, the individuals are not overtly rational and do not select, but rather inherit their traits. Cancer can be framed as such an evolutionary game, as it is composed of cells of heterogeneous types undergoing frequency-dependent selection. In this article, we first summarize existing works where evolutionary game theory has been employed in modeling cancer and improving its treatment. Some of these game-theoretic models suggest how one could anticipate and steer cancer's eco-evolutionary dynamics into states more desirable for the patient via evolutionary therapies. Such therapies offer great promise for increasing patient survival and decreasing drug toxicity, as demonstrated by some recent studies and clinical trials. We discuss clinical relevance of the existing game-theoretic models of cancer and its treatment, and opportunities for future applications. Moreover, we discuss the developments in cancer biology that are needed to better utilize the full potential of game-theoretic models. Ultimately, we demonstrate that viewing tumors with evolutionary game theory has medically useful implications that can inform and create a lockstep between empirical findings and mathematical modeling. We suggest that cancer progression is an evolutionary competition between different cell types and therefore needs to be viewed as an evolutionary game.

Transcriptomic heterogeneity of driver gene mutations reveals novel mutual exclusivity and improves exploration of functional associations

Background

Lung adenocarcinoma (LUAD), as the most common subtype of lung cancer, is the leading cause of cancer deaths in the world. The accumulation of driver gene mutations enables cancer cells to gradually acquire growth advantage. Therefore, it is important to understand the functions and interactions of driver gene mutations in cancer progression.

Methods

We obtained gene mutation data and gene expression profile of 506 LUAD tumors from The Cancer Genome Atlas (TCGA). The subtypes of tumors with driver gene mutations were identified by consensus cluster analysis.

Results

We found 21 significantly mutually exclusive pairs consisting of 20 genes among 506 LUAD patients. Because of the increased transcriptomic heterogeneity of mutations, we identified subtypes among tumors with non‐silent mutations in driver genes. There were 494 mutually exclusive pairs found among driver gene mutations within different subtypes. Furthermore, we identified functions of mutually exclusive pairs based on the hypothesis of functional redundancy of mutual exclusivity. These mutually exclusive pairs were significantly enriched in nuclear division and humoral immune response, which played crucial roles in cancer initiation and progression. We also found 79 mutually exclusive triples among subtypes of tumors with driver gene mutations, which were key roles in cell motility and cellular chemical homeostasis. In addition, two mutually exclusive triples and one mutually exclusive triple were associated with the overall survival and disease‐specific survival of LUAD patients, respectively.

Conclusions

We revealed novel mutual exclusivity and generated a comprehensive functional landscape of driver gene mutations, which could offer a new perspective to understand the mechanisms of cancer development and identify potential biomarkers for LUAD therapy.

Co-occurrence and mutual exclusivity: what cross-cancer mutation patterns can tell us

Cancer is the dysregulated proliferation of cells caused by acquired mutations in key driver genes. The most frequently mutated driver genes promote tumorigenesis in various organisms, cell types, and genetic backgrounds. However, recent cancer genomics studies also point to the existence of context-dependent driver gene functions, where specific mutations occur predominately or even exclusively in certain tumor types or genetic backgrounds. Here, we review examples of co-occurring and mutually exclusive driver gene mutation patterns across cancer genomes and discuss their underlying biology. While co-occurring driver genes typically activate collaborating oncogenic pathways, we identify two distinct biological categories of incompatibilities among the mutually exclusive driver genes depending on whether the mutated drivers trigger the same or divergent tumorigenic pathways. Finally, we discuss possible therapeutic avenues emerging from the study of incompatible driver gene mutations.

Identification of novel prognostic biomarkers by integrating multi-omics data in gastric cancer

BACKGROUND

Gastric cancer is a fatal gastrointestinal cancer with high morbidity and poor prognosis. The dismal 5-year survival rate warrants reliable biomarkers to assess and improve the prognosis of gastric cancer. Distinguishing driver mutations that are required for the cancer phenotype from passenger mutations poses a formidable challenge for cancer genomics.

METHODS

We integrated the multi-omics data of 293 primary gastric cancer patients from The Cancer Genome Atlas (TCGA) to identify key driver genes by establishing a prognostic model of the patients. Analyzing both copy number alteration and somatic mutation data helped us to comprehensively reveal molecular markers of genomic variation. Integrating the transcription level of genes provided a unique perspective for us to discover dysregulated factors in transcriptional regulation.

RESULTS

We comprehensively identified 31 molecular markers of genomic variation. For instance, the copy number alteration of WASHC5 (also known as KIAA0196) frequently occurred in gastric cancer patients, which cannot be discovered using traditional methods based on significant mutations. Furthermore, we revealed that several dysregulation factors played a hub regulatory role in the process of biological metabolism based on dysregulation networks. Cancer hallmark and functional enrichment analysis showed that these key driver (KD) genes played a vital role in regulating programmed cell death. The drug response patterns and transcriptional signatures of KD genes reflected their clinical application value.

CONCLUSIONS

These findings indicated that KD genes could serve as novel prognostic biomarkers for further research on the pathogenesis of gastric cancers. Our study elucidated a multidimensional and comprehensive genomic landscape and highlighted the molecular complexity of GC.

Molecularly Targeted Clinical Trials

Glioblastoma remains incurable despite advances in surgery, radiation, and chemotherapy, underscoring the need for new therapies. The genetic heterogenicity, presence of redundant molecular pathways, and the blood-brain barrier have limited the applicability of molecularly targeted agents. The therapeutic benefit seen with a small subset of patients suggests, however, that patient selection is critical. Recent investigations show that molecularly targeted synthetic lethality is a promising complementary approach. The article provides an overview of the challenges of molecularly targeted therapy in adults with glioblastoma, including current trials and future therapeutic directions.

Mutation-selection balance and compensatory mechanisms in tumour evolution

Intratumour heterogeneity and phenotypic plasticity, sustained by a range of somatic aberrations, as well as epigenetic and metabolic adaptations, are the principal mechanisms that enable cancers to resist treatment and survive under environmental stress. A comprehensive picture of the interplay between different somatic aberrations, from point mutations to whole-genome duplications, in tumour initiation and progression is lacking. We posit that different genomic aberrations generally exhibit a temporal order, shaped by a balance between the levels of mutations and selective pressures. Repeat instability emerges first, followed by larger aberrations, with compensatory effects leading to robust tumour fitness maintained throughout the tumour progression. A better understanding of the interplay between genetic aberrations, the microenvironment, and epigenetic and metabolic cellular states is essential for early detection and prevention of cancer as well as development of efficient therapeutic strategies.

Integrating evolutionary dynamics into cancer therapy

Many effective drugs for metastatic and/or advanced-stage cancers have been developed over the past decade, although the evolution of resistance remains the major barrier to disease control or cure. In large, diverse populations such as the cells that compose metastatic cancers, the emergence of cells that are resistant or that can quickly develop resistance is virtually inevitable and most likely cannot be prevented. However, clinically significant resistance occurs only when the pre-existing resistant phenotypes are able to proliferate extensively, a process governed by eco-evolutionary dynamics. Attempts to disrupt the molecular mechanisms of resistance have generally been unsuccessful in clinical practice. In this Review, we focus on the Darwinian processes driving the eco-evolutionary dynamics of treatment-resistant cancer populations. We describe a variety of evolutionarily informed strategies designed to increase the probability of disease control or cure by anticipating and steering the evolutionary dynamics of acquired resistance.

Population mutation properties of tumor evolution

Tumor growth patterns differ depending on the individual tumor, leading to various patterns of genetic heterogeneity across tumors. Despite their importance, the population mutation properties of tumor evolution have not been well studied, especially in terms of overall genetic heterogeneity. The current study aims to examine factors in tumor evolution influencing overall genetic heterogeneity. Extensive simulations of the representative evolutionary patterns of various tumors were conducted in the current study to determine the overall genetic characteristics of tumor evolution. The variations in cell birth/death rates and angiogenesis duration were examined based on the simplest growth pattern of tumors with avascular growth, angiogenesis, and vascular growth. To examine the impact of evolutionary tree structure, three-step linear evolution and branching evolution were investigated based on various subpopulation initiation times. The population size during the initial growth phase and the duration of angiogenesis are important factors affecting overall genetic heterogeneity. Furthermore, the shape of the evolutionary tree is crucial for defining the genetic heterogeneity of the extant tumor cell population, indicating the importance of the initiation timing of subpopulations. Branching evolution results in slightly greater genetic heterogeneity in terms of allelic distribution than in terms of the number of variants. The results of the current study reveal that the pattern of tumor evolution is critical for defining tumor genetic heterogeneity. These population genetic approaches are important for understanding the properties of tumor cell populations.

Integrating genetic and nongenetic drivers of somatic evolution during carcinogenesis: The biplane model

The multistep transition from a normal to a malignant cellular phenotype is often termed "somatic evolution" caused by accumulating random mutations. Here, we propose an alternative model in which the initial genetic state of a cancer cell is the result of mutations that occurred throughout the lifetime of the host. However, these mutations are not carcinogenic because normal cells in multicellular organism cannot ordinarily evolve. That is, proliferation and death of normal cells are controlled by local tissue constraints typically governed by nongenomic information dynamics in the cell membrane. As a result, the cells of a multicellular organism have a fitness that is identical to the host, which is then the unit of natural selection. Somatic evolution of a cell can occur only when its fate becomes independent of host constraints. Now, survival, proliferation, and death of individual cells are dependent on Darwinian dynamics. This cellular transition from host-defined fitness to self-defined fitness may, consistent with the conventional view of carcinogenesis, result from mutations that render the cell insensitive to host controls. However, an identical state will result when surrounding tissue cannot exert control because of injury, inflammation, aging, or infection. Here, all surviving cells within the site of tissue damage default to self-defined fitness functions allowing them to evolve so that the mutations accumulated over the lifetime of the host now serve as the genetic heritage of an evolutionary unit of selection. Furthermore, tissue injury generates a new ecology cytokines and growth factors that might promote proliferation in cells with prior receptor mutations. This model integrates genetic and nongenetic dynamics into cancer development and is consistent with both clinical observations and prior experiments that divided carcinogenesis to initiation, promotion, and progression steps.

An evolutionary framework for treating pediatric sarcomas

Lessons from extinction can be used in trials designed to pursue a cure for cancer. When cancer cannot be cured, similar strategies may be unwise, and strategies that leverage the adaptations of cancer to therapy should be considered.

Biomechanical modeling of invasive breast carcinoma under a dynamic change in cell phenotype: collective migration of large groups of cells

According to recent studies, cancer is an evolving complex ecosystem. It means that tumor cells are well differentiated and involved in heterotypic interactions with their microenvironment competing for available resources to proliferate and survive. In this paper, we propose a chemo-mechanical model for the growth of specific subtypes of an invasive breast carcinoma. The model suggests that a carcinoma is a heterogeneous entity comprising cells of different phenotypes, which perform different functions in a tumor. Every cell is represented by an elastic polygon changing its form and size under pressure from the tissue. The mechanical model is based on the elastic potential energy of the tissue including the effects of contractile forces within the cell perimeter and the elastic resistance to stretching or compressing the cell with respect to the reference area. A tissue can evolve via mechanisms of cell division and intercalation. The phenotype of each cell is determined by its environment and can dynamically change via an epithelial-mesenchymal transition and vice versa. The phenotype defines the cell adhesion to the adjacent tissue and the ability to divide. In this part, we focus on the forms of collective migration of large groups of cells. Numerical simulations show the different architectural subtypes of invasive carcinoma. For each communication, we examine the dynamics of the cell population and evaluate the complexity of the pattern in terms of the synergistic paradigm. The patterns are compared with the morphological structures previously identified in clinical studies.

Genotyping of circulating tumor DNA in cholangiocarcinoma reveals diagnostic and prognostic information

Diagnosis of Cholangiocarcinoma (CCA) is difficult, thus a noninvasive approach towards (i) assessing and (ii) monitoring the tumor-specific mutational profile is desirable to improve diagnosis and tailor treatment. Tumor tissue and corresponding ctDNA samples were collected from patients with CCA prior to and during chemotherapy and were subjected to deep sequencing of 15 genes frequently mutated in CCA. A set of ctDNA samples was also submitted for 710 gene oncopanel sequencing to identify progression signatures. The blood/tissue concordance was 74% overall and 92% for intrahepatic tumors only. Variant allele frequency (VAF) in ctDNA correlated with tumor load and in the group of intrahepatic CCA with PFS. 63% of therapy naive patients had their mutational profile changed during chemotherapy. A set of 76 potential progression driver genes was identified among 710 candidates. The molecular landscape of CCA is accessible via ctDNA. This could be helpful to facilitate diagnosis and personalize and adapt therapeutic strategies.

Passenger mutations can accelerate tumour suppressor gene inactivation in cancer evolution

Carcinogenesis is an evolutionary process whereby cells accumulate multiple mutations. Besides the 'driver mutations' that cause the disease, cells also accumulate a number of other mutations with seemingly no direct role in this evolutionary process. They are called passenger mutations. While it has been argued that passenger mutations render tumours more fragile due to reduced fitness, the role of passenger mutations remains understudied. Using evolutionary computational models, we demonstrate that in the context of tumour suppressor gene inactivation (and hence fitness valley crossing), the presence of passenger mutations can accelerate the rate of evolution by reducing overall population fitness and increasing the relative fitness of intermediate mutants in the fitness valley crossing pathway. Hence, the baseline rate of tumour suppressor gene inactivation might be faster than previously thought. Conceptually, parallels are found in the field of turbulence and pattern formation, where instabilities can be driven by perturbations that are damped (disadvantageous), but provide a richer set of pathways such that a system can achieve some desired goal more readily. This highlights, through a number of novel parallels, the relevance of physical sciences in oncology.

Fine-grained simulations of the microenvironment of vascularized tumours

One of many important features of the tumour microenvironment is that it is a place of active Darwinian selection where different tumour clones become adapted to the variety of ecological niches that make up the microenvironment. These evolutionary processes turn the microenvironment into a powerful source of tumour heterogeneity and contribute to the development of drug resistance in cancer. Here, we describe a computational tool to study the ecology of the microenvironment and report results about the ecology of the tumour microenvironment and its evolutionary dynamics.

Multidrug Cancer Therapy in Metastatic Castrate-Resistant Prostate Cancer: An Evolution-Based Strategy

PURPOSE

Integration of evolutionary dynamics into systemic therapy for metastatic cancers can prolong tumor control compared with standard maximum tolerated dose (MTD) strategies. Prior investigations have focused on monotherapy, but many clinical cancer treatments combine two or more drugs. Optimizing the evolutionary dynamics in multidrug therapy is challenging because of the complex cellular interactions and the large parameter space of potential variations in drugs, doses, and treatment schedules. However, multidrug therapy also represents an opportunity to further improve outcomes using evolution-based strategies.

EXPERIMENTAL DESIGN

We examine evolution-based strategies for two-drug therapy and identify an approach that divides the treatment drugs into primary and secondary roles. The primary drug has the greatest efficacy and/or lowest toxicity. The secondary drug is applied solely to reduce the resistant population to the primary drug.

RESULTS

Simulations from the mathematical model demonstrate that the primary-secondary approach increases time to progression (TTP) compared with conventional strategies in which drugs are administered without regard to evolutionary dynamics. We apply our model to an ongoing adaptive therapy clinical trial of evolution-based administration of abiraterone to treat metastatic castrate-resistant prostate cancer. Model simulations, parameterized with data from individual patients who progressed, demonstrate that strategic application of docetaxel during abiraterone therapy would have significantly increased their TTP.

CONCLUSIONS

Mathematical models can integrate evolutionary dynamics into multidrug cancer clinical trials. This has the potential to improve outcomes and to develop clinical trials in which these mathematical models are also used to estimate the mechanism(s) of treatment failure and explore alternative strategies to improve outcomes in future trials.

The role of clonal communication and heterogeneity in breast cancer

Background

Cancer is a rapidly evolving, multifactorial disease that accumulates numerous genetic and epigenetic alterations. This results in molecular and phenotypic heterogeneity within the tumor, the complexity of which is further amplified through specific interactions between cancer cells. We aimed to dissect the molecular mechanisms underlying the cooperation between different clones.

Methods

We produced clonal cell lines derived from the MDA-MB-231 breast cancer cell line, using the UbC-StarTrack system, which allowed tracking of multiple clones by color: GFP C3, mKO E10 and Sapphire D7. Characterization of these clones was performed by growth rate, cell metabolic activity, wound healing, invasion assays and genetic and epigenetic arrays. Tumorigenicity was tested by orthotopic and intravenous injections. Clonal cooperation was evaluated by medium complementation, co-culture and co-injection assays.

Results

Characterization of these clones in vitro revealed clear genetic and epigenetic differences that affected growth rate, cell metabolic activity, morphology and cytokine expression among cell lines. In vivo, all clonal cell lines were able to form tumors; however, injection of an equal mix of the different clones led to tumors with very few mKO E10 cells. Additionally, the mKO E10 clonal cell line showed a significant inability to form lung metastases. These results confirm that even in stable cell lines heterogeneity is present. In vitro, the complementation of growth medium with medium or exosomes from parental or clonal cell lines increased the growth rate of the other clones. Complementation assays, co-growth and co-injection of mKO E10 and GFP C3 clonal cell lines increased the efficiency of invasion and migration.

Conclusions

These findings support a model where interplay between clones confers aggressiveness, and which may allow identification of the factors involved in cellular communication that could play a role in clonal cooperation and thus represent new targets for preventing tumor progression.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5883-y) contains supplementary material, which is available to authorized users.

Identifying Cancer Driver lncRNAs Bridged by Functional Effectors through Integrating Multi-omics Data in Human Cancers

The accumulation of somatic driver mutations in the human genome enables cells to gradually acquire a growth advantage and contributes to tumor development. Great efforts on protein-coding cancer drivers have yielded fruitful discoveries and clinical applications. However, investigations on cancer drivers in non-coding regions, especially long non-coding RNAs (lncRNAs), are extremely scarce due to the limitation of functional understanding. Thus, to identify driver lncRNAs integrating multi-omics data in human cancers, we proposed a computational framework, DriverLncNet, which dissected the functional impact of somatic copy number alteration (CNA) of lncRNAs on regulatory networks and captured key functional effectors in dys-regulatory networks. Applying it to 5 cancer types from The Cancer Genome Atlas (TCGA), we portrayed the landscape of 117 driver lncRNAs and revealed their associated cancer hallmarks through their functional effectors. Moreover, lncRNA RP11-571M6.8 was detected to be highly associated with immunotherapeutic targets (PD-1, PD-L1, and CTLA-4) and regulatory T cell infiltration level and their markers (IL2RA and FCGR2B) in glioblastoma multiforme, highlighting its immunosuppressive function. Meanwhile, a high expression of RP11-1020A11.1 in bladder carcinoma was predictive of poor survival independent of clinical characteristics, and CTD-2256P15.2 in lung adenocarcinoma responded to the sensitivity of methyl ethyl ketone (MEK) inhibitors. In summary, this study provided a framework to decipher the mechanisms of tumorigenesis from driver lncRNA level, established a new landscape of driver lncRNAs in human cancers, and offered potential clinical implications for precision oncology.

Integrating clinical, molecular, proteomic and histopathological data within the tissue context: tissunomics

Malignant tumours show a marked degree of morphological, molecular and proteomic heterogeneity. This variability is closely related to microenvironmental factors and the location of the tumour. The activation of genetic alterations is very tissue‐dependent and only few tumours have distinct genetic alterations. Importantly, the activation state of proteins and signaling factors is heterogeneous in the primary tumour and in metastases and recurrences. The molecular diagnosis based only on genetic alterations can lead to treatments with unpredictable responses, depending on the tumour location, such as the tumour response in melanomas versus colon carcinomas with BRAF mutations. Therefore, we understand that the correct evaluation of tumours requires a system that integrates both morphological, molecular and protein information in a clinical and pathological context, where intratumoral heterogeneity can be assessed. Thus, we propose the term ‘tissunomics’, where the diagnosis will be contextualised in each tumour based on the complementation of the pathological, molecular, protein expression, environmental cells and clinical data.

Including Blood Vasculature into a Game-Theoretic Model of Cancer Dynamics

For cancer, we develop a 2-D agent-based continuous-space game-theoretical model that considers cancer cells’ proximity to a blood vessel. Based on castrate resistant metastatic prostate cancer (mCRPC), the model considers the density and frequency (eco-evolutionary) dynamics of three cancer cell types: those that require exogenous testosterone ( T + ), those producing testosterone ( T P ), and those independent of testosterone ( T - ). We model proximity to a blood vessel by imagining four zones around the vessel. Zone 0 is the blood vessel. As rings, zones 1–3 are successively farther from the blood vessel and have successively lower carrying capacities. Zone 4 represents the space too far from the blood vessel and too poor in nutrients for cancer cell proliferation. Within the other three zones that are closer to the blood vessel, the cells’ proliferation probabilities are determined by zone-specific payoff matrices. We analyzed how zone width, dispersal, interactions across zone boundaries, and blood vessel dynamics influence the eco-evolutionary dynamics of cell types within zones and across the entire cancer cell population. At equilibrium, zone 3’s composition deviates from its evolutionary stable strategy (ESS) towards that of zone 2. Zone 2 sees deviations from its ESS because of dispersal from zones 1 and 3; however, its composition begins to resemble zone 1’s more so than zone 3’s. Frequency-dependent interactions between cells across zone boundaries have little effect on zone 2’s and zone 3’s composition but have decisive effects on zone 1. The composition of zone 1 diverges dramatically from both its own ESS, but also that of zone 2. That is because T + cells (highest frequency in zone 1) benefit from interacting with T P cells (highest frequency in zone 2). Zone 1 T + cells interacting with cells in zone 2 experience a higher likelihood of encountering a T P cell than when restricted to their own zone. As expected, increasing the width of zones decreases these impacts of cross-boundary dispersal and interactions. Increasing zone widths increases the persistence likelihood of the cancer subpopulation in the face of blood vessel dynamics, where the vessel may die or become occluded resulting in the “birth” of another blood vessel elsewhere in the space. With small zone widths, the cancer cell subpopulations cannot persist. With large zone widths, blood vessel dynamics create cancer cell subpopulations that resemble the ESS of zone 3 as the larger area of zone 3 and its contribution to cells within the necrotic zone 4 mean that zones 3 and 4 provide the likeliest colonizers for the new blood vessel. In conclusion, our model provides an alternative modeling approach for considering density-dependent, frequency-dependent, and dispersal dynamics into cancer models with spatial gradients around blood vessels. Additionally, our model can consider the occurrence of circulating tumor cells (cells that disperse into the blood vessel from zone 1) and the presence of live cancer cells within the necrotic regions of a tumor.

The impact of proliferation-migration tradeoffs on phenotypic evolution in cancer

Tumors are not static masses of cells but dynamic ecosystems where cancer cells experience constant turnover and evolve fitness-enhancing phenotypes. Selection for different phenotypes may vary with (1) the tumor niche (edge or core), (2) cell turnover rates, (3) the nature of the tradeoff between traits, and (4) whether deaths occur in response to demographic or environmental stochasticity. Using a spatially-explicit agent-based model, we observe how two traits (proliferation rate and migration speed) evolve under different tradeoff conditions with different turnover rates. Migration rate is favored over proliferation at the tumor's edge and vice-versa for the interior. Increasing cell turnover rates slightly slows tumor growth but accelerates the rate of evolution for both proliferation and migration. The absence of a tradeoff favors ever higher values for proliferation and migration, while a convex tradeoff tends to favor proliferation, often promoting the coexistence of a generalist and specialist phenotype. A concave tradeoff favors migration at low death rates, but switches to proliferation at higher death rates. Mortality via demographic stochasticity favors proliferation, and environmental stochasticity favors migration. While all of these diverse factors contribute to the ecology, heterogeneity, and evolution of a tumor, their effects may be predictable and empirically accessible.

Pathogenic BRCA1 mutations may be necessary but not sufficient for tissue genomic heterogeneity: Deep sequencing data from ovarian cancer patients

BACKGROUND

Tissue genomic heterogeneity (t-HET) in patients with epithelial ovarian cancer (OVCA) is related to tissue plasticity, i.e., flexibility to adapt to adverse molecular environments. Here, we interrogated the presence and clinical relevance of OVCA t-HET.

METHODS

We applied high-depth (>2000×) sequencing on 297 paraffin tissue samples (fallopian tubes, ovaries, intra-abdominal metastases) from 71 treatment-naïve patients who subsequently received first-line platinum-based chemotherapy. Based on tissue mutation patterns, we distinguished tissue genotypes into: no mutation (33/297 samples; 11.1%), stable (173; 58.2%) and unstable (91; 30.7%). We profiled genotypes per patient and assessed t-HET in 69 patients. Predicted pathogenic mutations refer to germline and/or tissues.

RESULTS

Among all 71 patients, 46 (64.8%) had pathogenic BRCA1 mutations and 15 (21.7%) had BRCA1/2 disruption (i.e., pathogenic mutations with position-LOH). We classified 29 patients with t-HET (42%), all with pathogenic BRCA1; t-HET was observed in 64% with such mutations (p < 0.001). As opposed to non-t-HET, matched tissues in t-HET shared pathogenic BRCA1 (p < 0.001) but not BRCA2 and TP53. Germline BRCA1 mutations in tissues exhibited position-LOH; heterozygous status; or, partial loss of the inherited allele accompanied by additional clonal mutations. Patients with t-HET had worse outcome (log-rank p = 0.048 [progression-free]; p = 0.037 [overall survival]), including 12/15 patients with disrupted BRCA1/2 and 3 BRCA1 carriers with partial germline loss in tissues.

CONCLUSIONS

Pathogenic BRCA1 mutations appear necessary but may not be sufficient for the establishment of t-HET. t-HET may be associated with worse outcome, including in patients with disrupted BRCA1/2, which is usually considered as a favourable marker. OVCA t-HET may need to be addressed for treatment decisions.

CoGAPS matrix factorization algorithm identifies transcriptional changes in AP-2alpha target genes in feedback from therapeutic inhibition of the EGFR network

Patients with oncogene driven tumors are treated with targeted therapeutics including EGFR inhibitors. Genomic data from The Cancer Genome Atlas (TCGA) demonstrates molecular alterations to EGFR, MAPK, and PI3K pathways in previously untreated tumors. Therefore, this study uses bioinformatics algorithms to delineate interactions resulting from EGFR inhibitor use in cancer cells with these genetic alterations. We modify the HaCaT keratinocyte cell line model to simulate cancer cells with constitutive activation of EGFR, HRAS, and PI3K in a controlled genetic background. We then measure gene expression after treating modified HaCaT cells with gefitinib, afatinib, and cetuximab. The CoGAPS algorithm distinguishes a gene expression signature associated with the anticipated silencing of the EGFR network. It also infers a feedback signature with EGFR gene expression itself increasing in cells that are responsive to EGFR inhibitors. This feedback signature has increased expression of several growth factor receptors regulated by the AP-2 family of transcription factors. The gene expression signatures for AP-2alpha are further correlated with sensitivity to cetuximab treatment in HNSCC cell lines and changes in EGFR expression in HNSCC tumors with low CDKN2A gene expression. In addition, the AP-2alpha gene expression signatures are also associated with inhibition of MEK, PI3K, and mTOR pathways in the Library of Integrated Network-Based Cellular Signatures (LINCS) data. These results suggest that AP-2 transcription factors are activated as feedback from EGFR network inhibition and may mediate EGFR inhibitor resistance.

Looking beyond drivers and passengers in cancer genome sequencing data

Cancer arises as a result of acquired changes in the DNA sequence of the genome of somatic cells. A subset of the genetic changes, dubbed driver mutations, propels tumor growth, and the remaining changes are passengers, apparently inconsequential for neoplastic transformation. Massive genome sequencing of thousands of tumors from all major cancer types has enabled cataloging of the so-called driver and passenger mutations, and facilitated molecular classification of cancer, guiding precision medicine approach for the patients. Nonetheless, innovative analyses of cancer genomics data has led to novel, sometimes serendipitous findings that have aided to our understanding of other aspects of the biology of the disease and opened up new frontiers. For instance, emerging findings show that mutational patterns in cancer genomes can help detect signatures of known and novel DNA damage and repair processes, provide a likely chronological account of genomic changes in cancer genomes, and allow revisiting the models of cancer evolution. These findings have stimulated original approaches to identify disease etiology, stratify patients, target the disease, and monitor patient responses, complementing driver-mutation centric approaches. In this review, we discuss these emerging approaches and unexpected breakthroughs, and their implications for basic cancer research and clinical practices.

New Developments in the Molecular Mechanisms of Pancreatic Tumorigenesis

Pancreatic cancer is an aggressive disease with a dismal prognosis in dire need of novel diagnostic and therapeutic approaches. The past decade has witnessed an explosion of data on the genetic alterations that occur in pancreatic cancer, as comprehensive next-generation sequencing analyses have been performed on samples from large cohorts of patients. These studies have defined the genomic landscape of this disease and identified novel candidates whose mutations contribute to pancreatic tumorigenesis. They have also clarified the genetic alterations that underlie multistep tumorigenesis in precursor lesions and provided insights into clonal evolution in pancreatic neoplasia. In addition to these important insights into pancreatic cancer biology, these large scale genomic studies have also provided a foundation for the development of novel early detection strategies and targeted therapies. In this review, we discuss the results of these comprehensive sequencing studies of pancreatic neoplasms, with a particular focus on how their results will impact the clinical care of patients with pancreatic cancer.

Targeted deep sequencing of circulating tumor DNA in metastatic pancreatic cancer

Purpose

Precision medicine in pancreatic ductal adenocarcinoma (PDAC) could be substantially supported by tools that allow to establish and monitor the molecular setup of the tumor. In particular, noninvasive approaches are desirable, but not validated. Characterization of circulating tumor DNA (ctDNA) may help to achieve this goal.

Experimental Design

Blood samples from patients with metastatic PDAC prior to and during palliative treatment were collected. ctDNA and corresponding tumor tissue were analyzed by targeted next generation sequencing and droplet digital PCR for the 7 most frequently mutated genes in PDAC (TP53, SMAD4, CDKN2A, KRAS, APC, ATM, and FBXW7). Findings were correlated with clinical and imaging data.

Results

A total of 20 patients (therapy naïve n = 11; pretreated n = 9) were included. All therapy naïve patients (n = 11/11) presented with detectable ctDNA at baseline. In pretreated patients, 3/7 (prior to 2nd line treatment) and 2/2 (prior to 3rd line chemotherapy) had detectable ctDNA. The combined mutational allele frequency (CMAF) of KRAS and TP53 was chosen to reflect the amount of ctDNA. The median CMAF level significantly decreased during treatment (P = 0.0027) and increased at progression (P = 0.0104). CA19-9 analyses did not show significant differences. In treatment naïve patients, the CMAF levels during therapy significantly correlated with progression-free survival (Spearman, r = -0.8609, P = 0.0013).

Conclusions

Monitoring of ctDNA and its changes during treatment may enable to adapt therapeutic strategies to the specific molecular changes present at a certain time during treatment of mPDAC.

Comprehensive high-throughput image analysis for therapeutic efficacy of architecturally complex heterotypic organoids

Bioengineered three-dimensional (3D) tumor models that incorporate heterotypic cellular communication are gaining interest as they can recapitulate key features regarding the intrinsic heterogeneity of cancer tissues. However, the architectural complexity and heterogeneous contents associated with these models pose a challenge for toxicological assays to accurately report treatment outcomes. To address this issue, we describe a comprehensive image analysis procedure for structurally complex organotypic cultures (CALYPSO) applied to fluorescence-based assays to extract multiparametric readouts of treatment effects for heterotypic tumor cultures that enables advanced analyses. The capacity of this approach is exemplified on various 3D models including adherent/suspension, mono-/heterocellular cultures and several disease types. The subsequent analysis revealed specific morphological effects of oxaliplatin chemotherapy, radiotherapy, and photodynamic therapy. The procedure can be readily implemented in most laboratories to facilitate high-throughput toxicological screening of pharmaceutical agents and treatment regimens on organotypic cultures of human disease to expedite drug and therapy development.

Classifying the evolutionary and ecological features of neoplasms

Neoplasms change over time through a process of cell-level evolution, driven by genetic and epigenetic alterations. However, the ecology of the microenvironment of a neoplastic cell determines which changes provide adaptive benefits. There is widespread recognition of the importance of these evolutionary and ecological processes in cancer, but to date, no system has been proposed for drawing clinically relevant distinctions between how different tumours are evolving. On the basis of a consensus conference of experts in the fields of cancer evolution and cancer ecology, we propose a framework for classifying tumours that is based on four relevant components. These are the diversity of neoplastic cells (intratumoural heterogeneity) and changes over time in that diversity, which make up an evolutionary index (Evo-index), as well as the hazards to neoplastic cell survival and the resources available to neoplastic cells, which make up an ecological index (Eco-index). We review evidence demonstrating the importance of each of these factors and describe multiple methods that can be used to measure them. Development of this classification system holds promise for enabling clinicians to personalize optimal interventions based on the evolvability of the patient's tumour. The Evo- and Eco-indices provide a common lexicon for communicating about how neoplasms change in response to interventions, with potential implications for clinical trials, personalized medicine and basic cancer research.

Cancer as an ecomolecular disease and a neoplastic consortium

Current anticancer paradigms largely target driver mutations considered integral for cancer cell survival and tumor progression. Although initially successful, many of these strategies are unable to overcome the tremendous heterogeneity that characterizes advanced tumors, resulting in the emergence of resistant disease. Cancer is a rapidly evolving, multifactorial disease that accumulates numerous genetic and epigenetic alterations. This results in wide phenotypic and molecular heterogeneity within the tumor, the complexity of which is further amplified through specific interactions between cancer cells and the tumor microenvironment. In this context, cancer may be perceived as an "ecomolecular" disease that involves cooperation between several neoplastic clones and their interactions with immune cells, stromal fibroblasts, and other cell types present in the microenvironment. This collaboration is mediated by a variety of secreted factors. Cancer is therefore analogous to complex ecosystems such as microbial consortia. In the present article, we comment on the current paradigms and perspectives guiding the development of cancer diagnostics and therapeutics and the potential application of systems biology to untangle the complexity of neoplasia. In our opinion, conceptualization of neoplasia as an ecomolecular disease is warranted. Advances in knowledge pertinent to the complexity and dynamics of interactions within the cancer ecosystem are likely to improve understanding of tumor etiology, pathogenesis, and progression. This knowledge is anticipated to facilitate the design of new and more effective therapeutic approaches that target the tumor ecosystem in its entirety.

Spatial vs. non-spatial eco-evolutionary dynamics in a tumor growth model

Metastatic prostate cancer is initially treated with androgen deprivation therapy (ADT). However, resistance typically develops in about 1 year - a clinical condition termed metastatic castrate-resistant prostate cancer (mCRPC). We develop and investigate a spatial game (agent based continuous space) of mCRPC that considers three distinct cancer cell types: (1) those dependent on exogenous testosterone (T⁺), (2) those with increased CYP17A expression that produce testosterone and provide it to the environment as a public good (T^P), and (3) those independent of testosterone (T^-). The interactions within and between cancer cell types can be represented by a 3 × 3 matrix. Based on the known biology of this cancer there are 22 potential matrices that give roughly three major outcomes depending upon the absence (good prognosis), near absence or high frequency (poor prognosis) of T^- cells at the evolutionarily stable strategy (ESS). When just two cell types coexist the spatial game faithfully reproduces the ESS of the corresponding matrix game. With three cell types divergences occur, in some cases just two strategies coexist in the spatial game even as a non-spatial matrix game supports all three. Discrepancies between the spatial game and non-spatial ESS happen because different cell types become more or less clumped in the spatial game - leading to non-random assortative interactions between cell types. Three key spatial scales influence the distribution and abundance of cell types in the spatial game: i. Increasing the radius at which cells interact with each other can lead to higher clumping of each type, ii. Increasing the radius at which cells experience limits to population growth can cause densely packed tumor clusters in space, iii. Increasing the dispersal radius of daughter cells promotes increased mixing of cell types. To our knowledge the effects of these spatial scales on eco-evolutionary dynamics have not been explored in cancer models. The fact that cancer interactions are spatially explicit and that our spatial game of mCRPC provides in general different outcomes than the non-spatial game might suggest that non-spatial models are insufficient for capturing key elements of tumorigenesis.

Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks

Emerging evidence indicates the existence of a new class of cancer genes that act as "signal linkers" coordinating oncogenic signals between mutated and differentially expressed genes. While frequently mutated oncogenes and differentially expressed genes, which we term Class I cancer genes, are readily detected by most analytical tools, the new class of cancer-related genes, i.e., Class II, escape detection because they are neither mutated nor differentially expressed. Given this hypothesis, we developed a Machine Learning-Assisted Network Inference (MALANI) algorithm, which assesses all genes regardless of expression or mutational status in the context of cancer etiology. We used 8807 expression arrays, corresponding to 9 cancer types, to build more than 2 × 108 Support Vector Machine (SVM) models for reconstructing a cancer network. We found that ~3% of ~19,000 not differentially expressed genes are Class II cancer gene candidates. Some Class II genes that we found, such as SLC19A1 and ATAD3B, have been recently reported to associate with cancer outcomes. To our knowledge, this is the first study that utilizes both machine learning and network biology approaches to uncover Class II cancer genes in coordinating functionality in cancer networks and will illuminate our understanding of how genes are modulated in a tissue-specific network contribute to tumorigenesis and therapy development.

Evolutionary dynamics and significance of multiple subclonal mutations in cancer

For the last 40 years the authors have collaborated on trying to understand the complexities of human cancer by formulating testable mathematical models that are based on mutation accumulation in human malignancies. We summarize the concepts encompassed by multiple mutations in human cancers in the context of source, accumulation during carcinogenesis and tumor progression, and therapeutic consequences. We conclude that the efficacious treatment of human cancer by targeted therapy will involve individualized, uniquely directed specific agents singly and in simultaneous combinations, and take into account the importance of targeting resistant subclonal mutations, particularly those subclones with alterations in DNA repair genes, DNA polymerase, and other genes required to maintain genetic stability.

Genetic load makes cancer cells more sensitive to common drugs: evidence from Cancer Cell Line Encyclopedia

Genetic alterations initiate tumors and enable the evolution of drug resistance. The pro-cancer view of mutations is however incomplete, and several studies show that mutational load can reduce tumor fitness. Given its negative effect, genetic load should make tumors more sensitive to anticancer drugs. Here, we test this hypothesis across all major types of cancer from the Cancer Cell Line Encyclopedia, which provides genetic and expression data of 496 cell lines together with their response to 24 common anticancer drugs. We found that the efficacy of 9 out of 24 drugs showed significant association with genetic load in a pan-cancer analysis. The associations for some tissue-drug combinations were remarkably strong, with genetic load explaining up to 83% of the variance in the drug response. Overall, the role of genetic load depended on both the drug and the tissue type with 10 tissues being particularly vulnerable to genetic load. We also identified changes in gene expression associated with increased genetic load, which included cell-cycle checkpoints, DNA damage and apoptosis. Our results show that genetic load is an important component of tumor fitness and can predict drug sensitivity. Beyond being a biomarker, genetic load might be a new, unexplored vulnerability of cancer.