Evolutionary dynamics of carcinogenesis and why targeted therapy does not work

Maintenance therapy for FLT3-ITD-mutated acute myeloid leukemia

FLT3-ITD is a constitutively activated variant of the FLT3 tyrosine kinase receptor. Its expression in acute myeloid leukemia (AML) is associated with a poor prognosis. Due to this, the development of tyrosine kinase inhibitors (TKI) blocking FLT3-ITD became a rational therapeutic concept. This review describes key milestones in the clinical development of different FLT3-specific TKI with a particular focus on FLT3-TKI maintenance therapy in remission after allogeneic hematopoietic stem cell transplantation (HCT). Recent evidence from randomized trials using sorafenib in FLT3-ITD mutated AML provided a proof of concept that targeted post-HCT maintenance therapy could become a new treatment paradigm in AML.

Understanding tumour cell heterogeneity and its implication for immunotherapy in liver cancer using single-cell analysis

Over the last decade, precision medicine and immunotherapeutic approaches have become increasingly popular in oncology. Early clinical trials reported promising results, but response rates in phase III clinical trials have been suboptimal. Knowledge gained from subsequent translational studies indicates the importance of targeting the tumour microenvironment to overcome resistance to immunotherapy. In this era of precision medicine, it is crucial to consider inter- as well as intratumoural heterogeneity. Single-cell analysis is a cutting-edge technology that enables us to better define the tumour cell community and to identify potential targets for immunotherapy or combination treatments. This review focuses on single-cell analysis in the context of immunotherapy in liver cancer, including the rationale behind studying hepatocellular carcinoma biology at a single-cell level. Single-cell technologies have the potential to revolutionise our understanding of resistance mechanisms and to guide drug discovery efforts, leading to further advances in personalised medicine.

Role of synergy and antagonism in designing multidrug adaptive chemotherapy schedules

Chemotherapeutic resistance via the mechanism of competitive release of resistant tumor cell subpopulations is a major problem associated with cancer treatments and one of the main causes of tumor recurrence. Often, chemoresistance is mitigated by using multidrug schedules (two or more combination therapies) that can act synergistically, additively, or antagonistically on the heterogeneous population of cells as they evolve. In this paper, we develop a three-component evolutionary game theory model to design two-drug adaptive schedules that mitigate chemoresistance and delay tumor recurrence in an evolving collection of tumor cells with two resistant subpopulations and one chemosensitive population that has a higher baseline fitness but is not resistant to either drug. Using the nonlinear replicator dynamical system with a payoff matrix of Prisoner's Dilemma (PD) type (enforcing a cost to resistance), we investigate the nonlinear dynamics of this three-component system along with an additional tumor growth model whose growth rate is a function of the fitness landscape of the tumor cell populations. A key parameter determines whether the two drugs interact synergistically, additively, or antagonistically. We show that antagonistic drug interactions generally result in slower rates of adaptation of the resistant cells than synergistic ones, making them more effective in combating the evolution of resistance. We then design evolutionary cycles (closed loops) in the three-component phase space by shaping the fitness landscape of the cell populations (i.e., altering the evolutionary stable states of the game) using appropriately designed time-dependent schedules (adaptive therapy), altering the dosages and timing of the two drugs. We describe two key bifurcations associated with our drug interaction parameter which help explain why antagonistic interactions are more effective at controlling competitive release of the resistant population than synergistic interactions in the context of an evolving tumor.

The impact of phenotypic heterogeneity of tumour cells on treatment and relapse dynamics

Intratumour heterogeneity is increasingly recognized as a frequent problem for cancer treatment as it allows for the evolution of resistance against treatment. While cancer genotyping becomes more and more established and allows to determine the genetic heterogeneity, less is known about the phenotypic heterogeneity among cancer cells. We investigate how phenotypic differences can impact the efficiency of therapy options that select on this diversity, compared to therapy options that are independent of the phenotype. We employ the ecological concept of trait distributions and characterize the cancer cell population as a collection of subpopulations that differ in their growth rate. We show in a deterministic model that growth rate-dependent treatment types alter the trait distribution of the cell population, resulting in a delayed relapse compared to a growth rate-independent treatment. Whether the cancer cell population goes extinct or relapse occurs is determined by stochastic dynamics, which we investigate using a stochastic model. Again, we find that relapse is delayed for the growth rate-dependent treatment type, albeit an increased relapse probability, suggesting that slowly growing subpopulations are shielded from extinction. Sequential application of growth rate-dependent and growth rate-independent treatment types can largely increase treatment efficiency and delay relapse. Interestingly, even longer intervals between decisions to change the treatment type may achieve close-to-optimal efficiencies and relapse times. Monitoring patients at regular check-ups may thus provide the temporally resolved guidance to tailor treatments to the changing cancer cell trait distribution and allow clinicians to cope with this dynamic heterogeneity.

The individual cells within a cancer cell population are not all equal. The heterogeneity among them can strongly affect disease progression and treatment success. Recent diagnostic advances allow measuring how the characteristics of this heterogeneity change over time. To match these advances, we developed deterministic and stochastic trait-based models that capture important characteristics of the intratumour heterogeneity and allow to evaluate different treatment types that either do or do not interact with this heterogeneity. We focus on growth rate as the decisive characteristic of the intratumour heterogeneity. We find that by shifting the trait distribution of the cancer cell population, the growth rate-dependent treatment delays an eventual relapse compared to the growth rate-independent treatment. As a downside, however, we observe a refuge effect where slower-growing subpopulations are less affected by the growth rate-dependent treatment, which may decrease the likelihood of successful therapy. We find that navigating along this trade-off may be achieved by sequentially combining both treatment types, which agrees qualitatively with current clinical practice. Interestingly, even rather large intervals between treatment changes allow for close-to-optimal treatment results, which again hints towards a practical applicability.

Physics approaches to the spatial distribution of immune cells in tumors

The goal of immunotherapy is to mobilize the immune system to kill cancer cells. Immunotherapy is more effective and, in general, the prognosis is better, when more immune cells infiltrate the tumor. We explore the question of whether the spatial distribution rather than just the density of immune cells in the tumor is important in forecasting whether cancer recurs. After reviewing previous work on this issue, we introduce a novel application of maximum entropy to quantify the spatial distribution of discrete point-like objects. We apply our approach to B and T cells in images of tumor tissue taken from triple negative breast cancer patients. We find that the immune cells are more spatially dispersed in good clinical outcome (no recurrence of cancer within at least 5 years of diagnosis) compared to poor clinical outcome (recurrence within 3 years of diagnosis). Our results highlight the importance of spatial distribution of immune cells within tumors with regard to clinical outcome, and raise new questions on their role in cancer recurrence.

Antibody-drug conjugates: an evolving approach for melanoma treatment

Melanoma continues to be an aggressive and deadly form of skin cancer while therapeutic options are continuously developing in an effort to provide long-term solutions for patients. Immunotherapeutic strategies incorporating antibody-drug conjugates (ADCs) have seen varied levels of success across tumor types and represent a promising approach for melanoma. This review will explore the successes of FDA-approved ADCs to date compared to the ongoing efforts of melanoma-targeting ADCs. The challenges and opportunities for future therapeutic development are also examined to distinguish how ADCs may better impact individuals with malignancies such as melanoma.

Advances in Research of Adult Gliomas

Diffuse gliomas are the most frequent brain tumours, representing 75% of all primary malignant brain tumours in adults. Because of their locally aggressive behaviour and the fact that they cannot be cured by current therapies, they represent one of the most devastating cancers. The present review summarises recent advances in our understanding of glioma development and progression by use of various in vitro and in vivo models, as well as more complex techniques including cultures of 3D organoids and organotypic slices. We discuss the progress that has been made in understanding glioma heterogeneity, alteration in gene expression and DNA methylation, as well as advances in various in silico models. Lastly current treatment options and future clinical trials, which aim to improve early diagnosis and disease monitoring, are also discussed.

Cancer cells employ an evolutionarily conserved polyploidization program to resist therapy

Unusually large cancer cells with abnormal nuclei have been documented in the cancer literature since 1858. For more than 100 years, they have been generally disregarded as irreversibly senescent or dying cells, too morphologically misshapen and chromatin too disorganized to be functional. Cell enlargement, accompanied by whole genome doubling or more, is observed across organisms, often associated with mitigation strategies against environmental change, severe stress, or the lack of nutrients. Our comparison of the mechanisms for polyploidization in other organisms and non-transformed tissues suggest that cancer cells draw from a conserved program for their survival, utilizing whole genome doubling and pausing proliferation to survive stress. These polyaneuploid cancer cells (PACCs) are the source of therapeutic resistance, responsible for cancer recurrence and, ultimately, cancer lethality.

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.

The biogenesis and roles of extrachromosomal oncogene involved in carcinogenesis and evolution

More and more extrachromosomal DNA (ecDNA) was found in human tumor cells in recent years, which has a high copy number in tumors and changes the expression of oncogenes, thus different from normal chromosomal DNA. These circular structures were identified to originate from chromosomes, and play critical roles in rapid carcinogenesis, tumor evolution and multidrug resistance. Therefore, this review mostly focuses on the biogenesis and regulation of extrachromosomal oncogene in ecDNA as well as its function and mechanism in tumors, which are of great significance for our comprehensive understanding of the role of ecDNA in tumor carcinogenic mechanism and are expected to provide ecDNA with the potential to be a new molecular target for the diagnosis and treatment of tumors.

Mathematical model predicts response to chemotherapy in advanced non-resectable non-small cell lung cancer patients treated with platinum-based doublet

We developed a computational platform including machine learning and a mechanistic mathematical model to find the optimal protocol for administration of platinum-doublet chemotherapy in a palliative setting. The platform has been applied to advanced metastatic non-small cell lung cancer (NSCLC). The 42 NSCLC patients treated with palliative intent at Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, were collected from a retrospective cohort of patients diagnosed in 2004–2014. Patients were followed-up, for three years. Clinical data collected include complete information about the clinical course of the patients including treatment schedule, response according to RECIST classification, and survival. The core of the platform is the mathematical model, in the form of a system of ordinary differential equations, describing dynamics of platinum-sensitive and platinum-resistant cancer cells and interactions reflecting competition for space and resources. The model is simulated stochastically by sampling the parameter values from a joint probability distribution function. The machine learning model is applied to calibrate the mathematical model and to fit it to the overall survival curve. The model simulations faithfully reproduce the clinical cohort at three levels long-term response (OS), the initial response (according to RECIST criteria), and the relationship between the number of chemotherapy cycles and time between two consecutive chemotherapy cycles. In addition, we investigated the relationship between initial and long-term response. We showed that those two variables do not correlate which means that we cannot predict patient survival solely based on the initial response. We also tested several chemotherapy schedules to find the best one for patients treated with palliative intent. We found that the optimal treatment schedule depends, among others, on the strength of competition among various subclones in a tumor. The computational platform developed allows optimizing chemotherapy protocols, within admissible limits of toxicity, for palliative treatment of metastatic NSCLC. The simplicity of the method allows its application to chemotherapy optimization in different cancers.

Lung cancer is usually diagnosed at an advanced stage because of non-specific symptoms. The most common subtype of lung cancer is non-small cell lung cancer, which constitutes 80% of lung cancer cases. Here, we developed the methodology for finding the optimal treatment schedule for patients treated with palliative intent. The goal is not to cure the patients who are at an advanced stage but to prolong their survival by the administration of platinum-based chemotherapy. The method is based on the mathematical model describing the growth of tumors and its response to chemotherapy which is calibrated using real clinical data.

Cancer and pH Dynamics: Transcriptional Regulation, Proteostasis, and the Need for New Molecular Tools

An emerging hallmark of cancer cells is dysregulated pH dynamics. Recent work has suggested that dysregulated intracellular pH (pHi) dynamics enable diverse cancer cellular behaviors at the population level, including cell proliferation, cell migration and metastasis, evasion of apoptosis, and metabolic adaptation. However, the molecular mechanisms driving pH-dependent cancer-associated cell behaviors are largely unknown. In this review article, we explore recent literature suggesting pHi dynamics may play a causative role in regulating or reinforcing tumorigenic transcriptional and proteostatic changes at the molecular level, and discuss outcomes on tumorigenesis and tumor heterogeneity. Most of the data we discuss are population-level analyses; lack of single-cell data is driven by a lack of tools to experimentally change pHi with spatiotemporal control. Data is also sparse on how pHi dynamics play out in complex in vivo microenvironments. To address this need, at the end of this review, we cover recent advances for live-cell pHi measurement at single-cell resolution. We also discuss the essential role for tool development in revealing mechanisms by which pHi dynamics drive tumor initiation, progression, and metastasis.

Evolution of tumor cells during AsiDNA treatment results in energy exhaustion, decrease in responsiveness to signal, and higher sensitivity to the drug

It is increasingly suggested that ecological and evolutionary sciences could inspire novel therapies against cancer but medical evidence of this remains scarce at the moment. The Achilles heel of conventional and targeted anticancer treatments is intrinsic or acquired resistance following Darwinian selection; that is, treatment toxicity places the surviving cells under intense evolutionary selective pressure to develop resistance. Here, we review a set of data that demonstrate that Darwinian principles derived from the "smoke detector" principle can instead drive the evolution of malignant cells toward a different trajectory. Specifically, long-term exposure of cancer cells to a strong alarm signal, generated by the DNA repair inhibitor AsiDNA, induces a stable new state characterized by a down-regulation of the targeted pathways and does not generate resistant clones. This property is due to the original mechanism of action of AsiDNA, which acts by overactivating a "false" signaling of DNA damage through DNA-PK and PARP enzymes, and is not observed with classical DNA repair inhibitors such as the PARP inhibitors. Long-term treatment with AsiDNA induces a new "alarm down" state in the tumor cells with decrease in NAD level and reactiveness to it. These results suggest that agonist drugs such as AsiDNA could promote a state-dependent tumor cell evolution by lowering their ability to respond to high "danger" signal. This analysis provides a compelling argument that evolutionary ecology could help drug design development in overcoming fundamental limitation of novel therapies against cancer due to the modification of the targeted tumor cell population during treatment.

Extracellular vesicle long non-coding RNA-mediated crosstalk in the tumor microenvironment: Tiny molecules, huge roles

Emerging evidence has shown that dynamic crosstalk among cells in the tumor microenvironment modulates the progression and chemotherapeutic responses of cancer. Extracellular vesicles comprise a crucial form of intracellular communication through horizontal transfer of bioactive molecules, including long non-coding RNA (lncRNA), to neighboring cells. Three main types of extracellular vesicles are exosomes, microvesicles and apoptotic bodies, exhibiting a wide range of sizes and different biogenesis. Over the last decade, dysregulation of extracellular vesicle lncRNA has been revealed to remodel the tumor microenvironment and induce aggressive phenotypes of tumor cells, thereby facilitating tumor growth and development. This review will focus on extracellular vesicle lncRNA-mediated crosstalk between tumor cells and recipient cells, including tumor cells as well as stromal cells in the tumor microenvironment, and overview the mechanisms by which lncRNA are selectively sorted into extracellular vesicles, which may pave the way for their clinical application in cancer diagnosis and treatment.

Eco-oncology: Applying ecological principles to understand and manage cancer

Cancer is a disease of single cells that expresses itself at the population level. The striking similarities between initiation and growth of tumors and dynamics of biological populations, and between metastasis and ecological invasion and community dynamics suggest that oncology can benefit from an ecological perspective to improve our understanding of cancer biology. Tumors can be viewed as complex, adaptive, and evolving systems as they are spatially and temporally heterogeneous, continually interacting with each other and with the microenvironment and evolving to increase the fitness of the cancer cells. We argue that an eco-evolutionary perspective is essential to understand cancer biology better. Furthermore, we suggest that ecologically informed therapeutic approaches that combine standard of care treatments with strategies aimed at decreasing the evolutionary potential and fitness of neoplastic cells, such as disrupting cell-to-cell communication and cooperation, and preventing successful colonization of distant organs by migrating cancer cells, may be effective in managing cancer as a chronic condition.

An integrative framework of heterogeneous genomic data for cancer dynamic modules based on matrix decomposition

Cancer progression is dynamic, and tracking dynamic modules is promising for cancer diagnosis and therapy. Accumulated genomic data provide us an opportunity to investigate the underlying mechanisms of cancers. However, as far as we know, no algorithm has been designed for dynamic modules by integrating heterogeneous omics data. To address this issue, we propose an integrative framework for dynamic module detection based on regularized nonnegative matrix factorization method (DrNMF) by integrating the gene expression and protein interaction network. To remove the heterogeneity of genomic data, we divide the samples of expression profiles into groups to construct gene co-expression networks. To characterize the dynamics of modules, the temporal smoothness framework is adopted, in which the gene co-expression network at the previous stage and protein interaction network are incorporated into the objective function of DrNMF via regularization. The experimental results demonstrate that DrNMF is superior to state-of-the-art methods in terms of accuracy. For breast cancer data, the obtained dynamic modules are more enriched by the known pathways, and can be used to predict the stages of cancers and survival time of patients. The proposed model and algorithm provide an effective integrative analysis of heterogeneous genomic data for cancer progression.

Oxygen-Enhanced Optoacoustic Tomography Reveals the Effectiveness of Targeting Heme and Oxidative Phosphorylation at Normalizing Tumor Vascular Oxygenation

Multispectral optoacoustic tomography (MSOT) is an emerging noninvasive imaging modality that can detect real-time dynamic information about the tumor microenvironment in humans and animals. Oxygen enhanced (OE)-MSOT can monitor tumor vasculature and oxygenation during disease development or therapy. Here, we used MSOT and OE-MSOT to examine in mice the response of human non-small cell lung cancer (NSCLC) xenografts to a new class of antitumor drugs, heme-targeting agents heme-sequestering peptide 2 (HSP2) and cyclopamine tartrate (CycT). HSP2 inhibits heme uptake, while CycT inhibits heme synthesis in NSCLC cells, where heme is essential for ATP generation via oxidative phosphorylation. HSP2 and CycT can inhibit ATP generation and thereby suppress NSCLC cell tumorigenic functions. MSOT showed that treatment of NSCLC tumors with HSP2 or CycT reduced total hemoglobin, increased oxygen saturation, and enhanced the amplitude of response to oxygen gas breathing challenge. HSP2 and CycT normalized tumor vasculature and improved tumor oxygenation, where levels of several hypoxia markers in NSCLC tumors were reduced by treatment with HSP2 or CycT. Furthermore, treatment with HSP2 or CycT reduced levels of angiogenic factor VEGFA, its receptor VEGFR1, and vascular marker CD34. Together, our data show that heme-targeting drugs HSP2 and CycT elicit multiple tumor-suppressing functions, such as inhibiting angiogenic function, normalizing tumor vasculature, alleviating tumor hypoxia, and inhibiting oxygen consumption and ATP generation. SIGNIFICANCE: Heme-targeting agents HSP2 and CycT effectively normalize tumor vasculature and alleviate tumor hypoxia, raising the possibility of their combination with chemo-, radio-, and immunotherapies to improve antitumor efficacy.See related commentary by Tomaszewski, p. 3461.

The issues with tissues: the wide range of cell fate separation enables the evolution of multicellularity and cancer

Our understanding of the rises of animal and cancer multicellularity face the same conceptual hurdles: what makes the clade originate and what makes it diversify. Between the events of origination and diversification lies complex tissue organization that gave rise to novel functionality for organisms and, unfortunately, for malignant transformation in cells. Tissue specialization with distinctly separated cell fates allowed novel functionality at organism level, such as for vertebrate animals, but also involved trade-offs at the cellular level that are potentially disruptive. These trade-offs are under-appreciated and here we discuss how the wide separation of cell phenotypes may contribute to cancer evolution by (a) how factors can reverse differentiated cells into a window of phenotypic plasticity, (b) the reversal to phenotypic plasticity coupled with asexual reproduction occurs in a way that the host cannot adapt, and (c) the power of the transformation factor correlates to the power needed to reverse tissue specialization. The role of reversed cell fate separation for cancer evolution is strengthened by how some tissues and organisms maintain high cell proliferation and plasticity without developing tumours at a corresponding rate. This demonstrates a potential proliferation paradox that requires further explanation. These insights from the cancer field, which observes tissue evolution in real time and closer than any other field, allow inferences to be made on evolutionary events in animal history. If a sweet spot of phenotypic and reproductive versatility is key to transformation, factors stimulating cell fate separation may have promoted also animal diversification on Earth.

Harnessing hypoxia as an evolutionary driver of complex multicellularity

Animal tissue requires low-oxygen conditions for its maintenance. The need for low-oxygen conditions contrasts with the idea of an evolutionary leap in animal diversity as a result of expanding oxic conditions. To accommodate tissue renewal at oxic conditions, however, vertebrate animals and vascular plants demonstrate abilities to access hypoxia. Here, I argue that multicellular organisms sustain oxic conditions first after internalizing hypoxic conditions. The 'harnessing' of hypoxia has allowed multicellular evolution to leave niches that were stable in terms of oxygen concentrations for those where oxygen fluctuates. Since oxygen fluctuates in most settings on Earth's surface, the ancestral niche would have been a deep marine setting. The hypothesis that 'large life' depends on harnessing hypoxia is illustrated in the context of conditions that promote the immature cell phenotype (stemness) in animal physiology and tumour biology and offers one explanation for the general rarity of diverse multicellularity over most of Earth's history.

DECO: decompose heterogeneous population cohorts for patient stratification and discovery of sample biomarkers using omic data profiling

Motivation

Patient and sample diversity is one of the main challenges when dealing with clinical cohorts in biomedical genomics studies. During last decade, several methods have been developed to identify biomarkers assigned to specific individuals or subtypes of samples. However, current methods still fail to discover markers in complex scenarios where heterogeneity or hidden phenotypical factors are present. Here, we propose a method to analyze and understand heterogeneous data avoiding classical normalization approaches of reducing or removing variation.

Results

DEcomposing heterogeneous Cohorts using Omic data profiling (DECO) is a method to find significant association among biological features (biomarkers) and samples (individuals) analyzing large-scale omic data. The method identifies and categorizes biomarkers of specific phenotypic conditions based on a recurrent differential analysis integrated with a non-symmetrical correspondence analysis. DECO integrates both omic data dispersion and predictor–response relationship from non-symmetrical correspondence analysis in a unique statistic (called h-statistic), allowing the identification of closely related sample categories within complex cohorts. The performance is demonstrated using simulated data and five experimental transcriptomic datasets, and comparing to seven other methods. We show DECO greatly enhances the discovery and subtle identification of biomarkers, making it especially suited for deep and accurate patient stratification.

Availability and implementation

DECO is freely available as an R package (including a practical vignette) at Bioconductor repository (http://bioconductor.org/packages/deco/).

Supplementary information

Supplementary data are available at Bioinformatics online.

Beyond the limitation of targeted therapy: Improve the application of targeted drugs combining genomic data with machine learning

Precision oncology involves effectively selecting drugs for cancer patients and planning an effective treatment regimen. However, for Molecular targeted drug, using genomic state of the drug target to select drugs has limitations. Many patients who could benefit from molecularly targeted drugs, but they are being missed due to the insufficient labelling ability of the existing target genes. For non-specific chemotherapy drugs, most of the first-line anticancer drugs do not have biomarkers to guide doctor make treatment regimen. Furthermore, it is important to determine a long-term treatment plan based on the patient's genomic data during tumor evolution. Therefore, it is necessary to establish a tumor drug sensitivity prediction model, which can assist doctors in designing a personalized tumor treatment regimen. This paper proposed a novel model to predict tumor drug sensitivity including targeted drugs and non-specific chemotherapy drugs. This model uses statistical methods based on Bimodal distribution to select multimodal genetic data to solve dimensional challenges and reduce noise and to establish a classification model to predict the effectiveness of the drug in the tumor cell line using machine learning. The experimental test 87 molecular targeted drugs and non-specific chemotherapy drugs. The results show that the method can effectively predict the sensitivity of tumor drugs with an average sensitivity of 0.98 and specificity of 0.97. This model is worth to promotion. If it can be successfully used in clinical trials, it will effectively assist doctors to develop personalized cancer treatment programs and expand the application of molecularly targeted drugs.

Metabolic rewiring in melanoma drug-resistant cells

Several evidences indicate that melanoma, one of the deadliest types of cancer, presents the ability to transiently shift its phenotype under treatment or microenvironmental pressure to an invasive and treatment-resistant phenotype, which is characterized by cells with slow division cycle (also called slow-cycling cells) and high-OXPHOS metabolism. Many cellular marks have been proposed to track this phenotype, such as the expression levels of the master regulator of melanocyte differentiation (MITF) and the epigenetic factor JARID1B. It seems that the slow-cycling phenotype does not necessarily present a single gene expression signature. However, many lines of evidence lead to a common metabolic rewiring process in resistant cells that activates mitochondrial metabolism and changes the mitochondrial network morphology. Here, we propose that mitochondria-targeted drugs could increase not only the efficiency of target therapy, bypassing the dynamics between fast-cycling and slow-cycling, but also the sensitivity to immunotherapy by modulation of the melanoma microenvironment.

Herb-sourced emodin inhibits angiogenesis of breast cancer by targeting VEGFA transcription

Anti-angiogenesis is an important and promising strategy in cancer therapy. However, the current methods using anti-vascular endothelial growth factor A (VEGFA) antibodies or inhibitors targeting VEGFA receptors are not as efficient as expected partly due to their low efficiencies in blocking VEGFA signaling in vivo. Until now, there is still no method to effectively block VEGFA production in cancer cells from the very beginning, i.e., from the transcriptional level. Here, we aimed to find bioactive small molecules to block VEGFA transcription. Methods: We screened our natural compound pool containing 330 small molecules derived from Chinese traditional herbs for small molecules activating the expression of seryl-tRNA synthetase (SerRS), which is a newly identified potent transcriptional repressor of VEGFA, by a cell-based screening system in MDA-MB-231 cell line. The activities of the candidate molecules on regulating SerRS and VEGFA expression were first tested in breast cancer cells. We next investigated the antiangiogenic activity in vivo by testing the effects of candidate drugs on the vascular development in zebrafish and by matrigel plug angiogenesis assay in mice. We further examined the antitumor activities of candidate drugs in two triple-negative breast cancer (TNBC)-bearing mouse models. Furthermore, streptavidin-biotin affinity pull-down assay, coimmunoprecipitation assays, docking analysis and chromatin immunoprecipitation were performed to identify the direct targets of candidate drugs. Results: We identified emodin that could greatly increase SerRS expression in TNBC cells, consequently reducing VEGFA transcription. Emodin potently inhibited vascular development of zebrafish and blocked tumor angiogenesis in TNBC-bearing mice, greatly improving the survival. We also identified nuclear receptor corepressor 2 (NCOR2) to be the direct target of emodin. Once bound by emodin, NCOR2 got released from SerRS promoter, resulting in the activation of SerRS expression and eventually the suppression of VEGFA transcription. Conclusion: We discovered a herb-sourced small molecule emodin with the potential for the therapy of TNBC by targeting transcriptional regulators NCOR2 and SerRS to suppress VEGFA transcription and tumor angiogenesis.

Antibiotics can be used to contain drug-resistant bacteria by maintaining sufficiently large sensitive populations

Standard infectious disease practice calls for aggressive drug treatment that rapidly eliminates the pathogen population before resistance can emerge. When resistance is absent, this elimination strategy can lead to complete cure. However, when resistance is already present, removing drug-sensitive cells as quickly as possible removes competitive barriers that may slow the growth of resistant cells. In contrast to the elimination strategy, a containment strategy aims to maintain the maximum tolerable number of pathogens, exploiting competitive suppression to achieve chronic control. Here, we combine in vitro experiments in computer-controlled bioreactors with mathematical modeling to investigate whether containment strategies can delay failure of antibiotic treatment regimens. To do so, we measured the "escape time" required for drug-resistant Escherichia coli populations to eclipse a threshold density maintained by adaptive antibiotic dosing. Populations containing only resistant cells rapidly escape the threshold density, but we found that matched resistant populations that also contain the maximum possible number of sensitive cells could be contained for significantly longer. The increase in escape time occurs only when the threshold density-the acceptable bacterial burden-is sufficiently high, an effect that mathematical models attribute to increased competition. The findings provide decisive experimental confirmation that maintaining the maximum number of sensitive cells can be used to contain resistance when the size of the population is sufficiently large.

Predicting Glioblastoma Response to Bevacizumab Through MRI Biomarkers of the Tumor Microenvironment

PURPOSE

Glioblastoma (GB) is one of the most vascularized of all solid tumors and, therefore, represents an attractive target for antiangiogenic therapies. Many lesions, however, quickly develop escape mechanisms associated with changes in the tumor microenvironment (TME) resulting in rapid treatment failure. To prevent patients from adverse effects of ineffective therapy, there is a strong need to better predict and monitor antiangiogenic treatment response.

PROCEDURES

We utilized a novel physiological magnetic resonance imaging (MRI) method combining the visualization of oxygen metabolism and neovascularization for classification of five different TME compartments: necrosis, hypoxia with/without neovascularization, oxidative phosphorylation, and aerobic glycolysis. This approach, termed TME mapping, was used to monitor changes in tumor biology and pathophysiology within the TME in response to bevacizumab treatment in 18 patients with recurrent GB.

RESULTS

We detected dramatic changes in the TME by rearrangement of its compartments after the onset of bevacizumab treatment. All patients showed a decrease in active tumor volume and neovascularization as well as an increase in hypoxia and necrosis in the first follow-up after 3 months. We found that recurrent GB with a high percentage of neovascularization and active tumor before bevacizumab onset showed a poor or no treatment response.

CONCLUSIONS

TME mapping might be useful to develop strategies for patient stratification and response prediction before bevacizumab onset.

Targeting Acidic Diseased Tissues by pH-Triggered Membrane-Associated Peptide Folding

The advantages of targeted therapy have motivated many efforts to find distinguishing features between the molecular cell surface landscapes of diseased and normal cells. Typically, the features have been proteins, lipids or carbohydrates, but other approaches are emerging. In this discussion, we examine the use of cell surface acidity as a feature that can be exploited by using pH-sensitive peptide folding to target agents to diseased cell surfaces or cytoplasms.

Exploiting evolutionary steering to induce collateral drug sensitivity in cancer

Drug resistance mediated by clonal evolution is arguably the biggest problem in cancer therapy today. However, evolving resistance to one drug may come at a cost of decreased fecundity or increased sensitivity to another drug. These evolutionary trade-offs can be exploited using 'evolutionary steering' to control the tumour population and delay resistance. However, recapitulating cancer evolutionary dynamics experimentally remains challenging. Here, we present an approach for evolutionary steering based on a combination of single-cell barcoding, large populations of 108-109 cells grown without re-plating, longitudinal non-destructive monitoring of cancer clones, and mathematical modelling of tumour evolution. We demonstrate evolutionary steering in a lung cancer model, showing that it shifts the clonal composition of the tumour in our favour, leading to collateral sensitivity and proliferative costs. Genomic profiling revealed some of the mechanisms that drive evolved sensitivity. This approach allows modelling evolutionary steering strategies that can potentially control treatment resistance.

Chemotherapy, host response and molecular dynamics in periampullary cancer: the CHAMP study

BACKGROUND

Pancreatic cancer is a devastating disease with a dismal prognosis. Despite profound medical advances in systemic therapies for other types of aggressive tumours during recent years, a diagnosis of pancreatic cancer is still often synonymous with a fatal outcome. The term periampullary cancer includes pancreatic cancer and applies to the group of tumours found in proximity to the ampulla of Vater. Molecular events and immune response in the host during chemotherapy remain largely unexplored in this group of tumours. Therefore, the "Chemotherapy, Host Response and Molecular Dynamics in Periampullary Cancer (CHAMP)" study aims to monitor these processes to gain new insight into this perplexing disease.

METHODS

The CHAMP study is a prospective, single-arm observational study. All patients diagnosed with pancreatic or other periampullary adenocarcinoma undergoing adjuvant or palliative chemotherapy treatment in the Department of Oncology, Skåne University Hospital, are invited to participate. Clinical and pathological data will be compiled at study entry. A single tissue microarray (TMA) block is constructed for each patient with a resected tumour and blood samples are drawn before, during and after chemotherapy in order to sample peripheral blood mononuclear cells (PBMC), cytokines and circulating tumour DNA (ctDNA). Next generation sequencing will be performed on tumour tissue and ctDNA to detect changes in the clonal landscape over space and time.

DISCUSSION

Despite the recent emergence of some promising biomarkers for periampullary cancer, there has been a lack of success in clinical implementation. Cancer cells continuously adapt and become resistant to treatment during chemotherapy. To be able to keep pace with and hopefully overtake this rapid evolution we must, with the help of new diagnostic tools, be ready to adapt and alter treatment accordingly. It seems to us that the only way forward is to gain a better understanding of the dynamics of the disease during treatment. With insights gained from the CHAMP study we hope to find answers to key questions in this largely unexplored territory.

TRIAL REGISTRATION

This study has been registered 30th October 2018 at clinicaltrials.gov as NCT03724994.

Intratumor Heterogeneity: The Rosetta Stone of Therapy Resistance

Advances in our understanding of molecular mechanisms of tumorigenesis have translated into knowledge-based therapies directed against specific oncogenic signaling targets. These therapies often induce dramatic responses in susceptible tumors. Unfortunately, most advanced cancers, including those with robust initial responses, eventually acquire resistance to targeted therapies and relapse. Even though immune-based therapies are more likely to achieve complete cures, acquired resistance remains an obstacle to their success as well. Acquired resistance is the direct consequence of pre-existing intratumor heterogeneity and ongoing diversification during therapy, which enables some tumor cells to survive treatment and facilitates the development of new therapy-resistant phenotypes. In this review, we discuss the sources of intratumor heterogeneity and approaches to capture and account for it during clinical decision making. Finally, we outline potential strategies to improve therapeutic outcomes by directly targeting intratumor heterogeneity.

Tumour heterogeneity and the evolutionary trade-offs of cancer

Tumours vary in gene expression programmes and genetic alterations. Understanding this diversity and its biological meaning requires a theoretical framework, which could in turn guide the development of more accurate prognosis and therapy. Here, we review the theory of multi-task evolution of cancer, which is based upon the premise that tumours evolve in the host and face selection trade-offs between multiple biological functions. This theory can help identify the major biological tasks that cancer cells perform and the trade-offs between these tasks. It introduces the concept of specialist tumours, which focus on one task, and generalist tumours, which perform several tasks. Specialist tumours are suggested to be sensitive to therapy targeting their main task. Driver mutations tune gene expression towards specific tasks in a tissue-dependent manner and thus help to determine whether a tumour is specialist or generalist. We discuss potential applications of the theory of multi-task evolution to interpret the spatial organization of tumours and intratumour heterogeneity.

Specific "Unlocking" of a Nanozyme-Based Butterfly Effect To Break the Evolutionary Fitness of Chaotic Tumors

Chaos and the natural evolution of tumor systems can lead to the failure of tumor therapies. Herein, we demonstrate that iridium oxide nanoparticles (IrO_x ) possess acid-activated oxidase and peroxidase-like functions and wide pH-dependent catalase-like properties. The integration of glucose oxidase (GOD) unlocked the oxidase and peroxidase activities of IrO_x by the production of gluconic acid from glucose by GOD catalysis in cancer cells, and the produced H₂ O₂ was converted into O₂ to compensate its consumption in GOD catalysis owing to the catalase-like function of the nanozyme, thus resulting in the continual consumption of glucose and the self-supply of substrates to generate superoxide anion and hydroxyl radical. Moreover, IrO_x can constantly consume glutathione (GSH) by self-cyclic valence alternation of Ir^IV and Ir^III . These cascade reactions lead to a "butterfly effect" of initial starvation therapy and the subsequent pressure of multiple reactive oxygen species (ROS) to completely break the self-adaption of cancer cells.

Somatic mutations and copy number variations in breast cancers with heterogeneous HER2 amplification

Intratumour heterogeneity fuels carcinogenesis and allows circumventing specific targeted therapies. HER2 gene amplification is associated with poor outcome in invasive breast cancer. Heterogeneous HER2 amplification has been described in 5-41% of breast cancers. Here, we investigated the genetic differences between HER2-positive and HER2-negative admixed breast cancer components. We performed an in-depth analysis to explore the potential heterogeneity in the somatic mutational landscape of each individual tumour component. Formalin-fixed, paraffin-embedded breast cancer tissue of ten patients with at least one HER2-negative and at least one HER2-positive component was microdissected. Targeted next-generation sequencing was performed using a customized 53-gene panel. Somatic mutations and copy number variations were analysed. Overall, the tumours showed a heterogeneous distribution of 12 deletions, 9 insertions, 32 missense variants and 7 nonsense variants in 26 different genes, which are (likely) pathogenic. Three splice site alterations were identified. One patient had an EGFR copy number gain restricted to a HER2-negative in situ component, resulting in EGFR protein overexpression. Two patients had FGFR1 copy number gains in at least one tumour component. Two patients had an 8q24 gain in at least one tumour component, resulting in a copy number increase in MYC and PVT1. One patient had a CCND1 copy number gain restricted to a HER2-negative tumour component. No common alternative drivers were identified in the HER2-negative tumour components. This series of 10 breast cancers with heterogeneous HER2 gene amplification illustrates that HER2 positivity is not an unconditional prerequisite for the maintenance of tumour growth. Many other molecular aberrations are likely to act as alternative or collaborative drivers. This study demonstrates that breast carcinogenesis is a dynamically evolving process characterized by a versatile somatic mutational profile, of which some genetic aberrations will be crucial for cancer progression, and others will be mere 'passenger' molecular anomalies.

Are Synapse-Like Structures a Possible Way for Crosstalk of Cancer with Its Microenvironment?

The failure of therapies directed at targets within cancer cells highlight the necessity for a paradigm change in cancer therapy. The attention of researchers has shifted towards the disruption of cancer cell interactions with the tumor microenvironment. A typical example of such a disruption is the immune checkpoint cancer therapy that disrupts interactions between the immune and the cancer cells. The interaction of cancer antigens with T cells occurs in the immunological synapses. This is characterized by several special features, i.e., the proximity of the immune cells and their target cells, strong intercellular adhesion, and secretion of signaling cytokines into the intercellular cleft. Earlier, we hypothesized that the cancer-associated fibroblasts interacting with cancer cells through a synapse-like adhesion might play an important role in cancer tumors. Studies of the interactions between cancer cells and cancer-associated fibroblasts showed that their clusterization on the membrane surface determined their strength and specificity. The hundreds of interacting pairs are involved in the binding that may indicate the formation of synapse-like structures. These interactions may be responsible for successful metastasis of cancer cells, and their identification and disruption may open new therapeutic possibilities.

How hypoxia regulate exosomes in ischemic diseases and cancer microenvironment?

Exosomes, as natural occurring vesicles, play highly important roles in the behavior and fate of ischemic diseases and different tumors. Secretion, composition, and function of exosomes are remarkably influenced by hypoxia in ischemic diseases and tumor microenvironment. Exosomes secreted from hypoxic cells affect development, growth, angiogenesis, and progression in ischemic diseases and tumors through a variety of signaling pathways. In this review article, we discuss how hypoxia affects the quantity and quality of exosomes, and review the mechanisms by which hypoxic cell-derived exosomes regulate ischemic cell behaviors in both cancerous and noncancerous cells.

PRDX4 overexpression is associated with poor prognosis in gastric cancer

Peroxiredoxin IV (PRDX4) is a multifunctional protein that is involved in cell protection against oxidative injury, regulation of cell proliferation, modulation of intracellular signaling, and the pathogenesis of tumors. We previously conducted a proteomic analysis to investigate tumor-specific protein expression in gastric cancer. The aim of the present study was to investigate whether PRDX4 could be a marker of poor prognosis in patients with gastric cancer. Immunohistochemistry was used to validate PRDX4 as a prognostic marker for gastric cancer. Short hairpin RNA (shRNA)-mediated knockdown of PRDX4 expression in AGS cells and MKN28 cells was used for functional studies, and PRDX4 overexpression in PRDX4-depleted cells was used for knock-in studies. Based on immunohistochemistry data, TNM stage and PRDX4 were independent prognostic factors in the Cox proportional hazard model (P<0.05). In the survival analysis, the PRDX4-overexpressing group demonstrated significantly worse survival than the PRDX4-underexpression group (P<0.01). In vitro, knockdown of PRDX4 expression by shRNA caused a significant decrease in cancer invasion. Conversely, overexpression of PRDX4 in PRDX4-depleted cancer cells promoted migration and invasion. By measuring the expression of EMT-related genes, we found that E-cadherin was increased in shPRDX4 cells compared with control shMKN28 cells, and snail and slug were decreased in shPRDX4-1 cells compared with sh-control cells. Furthermore, the expression levels of these genes could be recovered in rescue experiments. In conclusion, the results of the present study suggested that PRDX4 is a marker of poor prognosis in gastric cancer and that PRDX4 is associated with cancer cell migration and invasion via EMT.

A Bayesian hidden Potts mixture model for analyzing lung cancer pathology images

Digital pathology imaging of tumor tissues, which captures histological details in high resolution, is fast becoming a routine clinical procedure. Recent developments in deep-learning methods have enabled the identification, characterization, and classification of individual cells from pathology images analysis at a large scale. This creates new opportunities to study the spatial patterns of and interactions among different types of cells. Reliable statistical approaches to modeling such spatial patterns and interactions can provide insight into tumor progression and shed light on the biological mechanisms of cancer. In this article, we consider the problem of modeling a pathology image with irregular locations of three different types of cells: lymphocyte, stromal, and tumor cells. We propose a novel Bayesian hierarchical model, which incorporates a hidden Potts model to project the irregularly distributed cells to a square lattice and a Markov random field prior model to identify regions in a heterogeneous pathology image. The model allows us to quantify the interactions between different types of cells, some of which are clinically meaningful. We use Markov chain Monte Carlo sampling techniques, combined with a double Metropolis-Hastings algorithm, in order to simulate samples approximately from a distribution with an intractable normalizing constant. The proposed model was applied to the pathology images of $205$ lung cancer patients from the National Lung Screening trial, and the results show that the interaction strength between tumor and stromal cells predicts patient prognosis (P = $0.005$). This statistical methodology provides a new perspective for understanding the role of cell-cell interactions in cancer progression.

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.

Proton Pump Inhibitors Reduce Pancreatic Adenocarcinoma Progression by Selectively Targeting H+, K+-ATPases in Pancreatic Cancer and Stellate Cells

Pancreatic duct cells are equipped with acid/base transporters important for exocrine secretion. Pancreatic ductal adenocarcinoma (PDAC) cells may utilize such transporters to acidify extracellular tumor microenvironment, creating a niche favoring cell proliferation, fibrosis and resistance to chemotherapy-all contributing to the notoriously bad prognosis of this disease. Here, we report that gastric and non-gastric H⁺, K⁺-ATPases (coded by ATP4A and ATP12A) are overexpressed in human and murine pancreatic cancer and that we can target them specifically with proton pump inhibitors (PPIs) and potassium-competitive acid blockers (P-CABs) in in vitro models of PDAC. Focusing on pantoprazole, we show that it significantly reduced human cancer cell proliferation by inhibiting cellular H⁺ extrusion, increasing K⁺ conductance and promoting cyclin D1-dependent cell cycle arrest and preventing STAT3 activation. Pantoprazole also decreased collagen secretion from pancreatic stellate cells. Importantly, in vivo studies show that pantoprazole treatment of tumor-bearing mice reduced tumor size, fibrosis and expression of angiogenic markers. This work provides the first evidence that H⁺, K⁺-ATPases contribute to PDAC progression and that these can be targeted by inhibitors of these pumps, thus proving a promising therapeutic strategy.

Unmet Clinical Needs in the Treatment of Patients with Thyroid Cancer

The increased incidence of thyroid cancer is a worldwide phenomenon; however, the issue of overdiagnosis has been most prominent in South Korea. The age-standardized mortality rate of thyroid cancer in Korea steeply increased from 1985 to 2004 (from 0.17 per 100,000 to 0.85 per 100,000), and then decreased until 2015 to 0.42 per 100,000, suggesting that early detection reduced mortality. However, early detection of thyroid cancer may be cost-ineffective, considering its very high prevalence and indolent course. Therefore, risk stratification and tailored management are vitally important, but many prognostic markers can only be evaluated postoperatively. Discovery of preoperative marker(s), especially for small cancers, is the most important unmet clinical need for thyroid cancer. Herein, we discuss some such factors that we recently discovered. Another unmet clinical need is better treatment of radioiodine-refractory (RAIR) differentiated thyroid cancer (DTC) and undifferentiated cancers. Although sorafenib and lenvatinib are available, better drugs are needed. We found that phosphoglycerate dehydrogenase, a critical enzyme for serine biosynthesis, could be a novel therapeutic target, and that the lymphocyte-to-monocyte ratio is a prognostic marker of survival in patients with anaplastic thyroid carcinoma or RAIR DTC. Deeper insights are needed into tumor-host interactions in thyroid cancer to improve treatment.

Physiologic MR imaging of the tumor microenvironment revealed switching of metabolic phenotype upon recurrence of glioblastoma in humans

Treating recurrent glioblastoma (GB) is one of the challenges in modern neurooncology. Hypoxia, neovascularization, and energy metabolism are of crucial importance for therapy failure and recurrence. Twenty-one patients with initially untreated GB who developed recurrence were examined with a novel MRI approach for noninvasive visualization of the tumor microenvironment (TME). Imaging biomarker information about oxygen metabolism (mitochondrial oxygen tension) and neovascularization (microvascular density and type) were fused for classification of five different TME compartments: necrosis, hypoxia with/without neovascularization, oxidative phosphorylation, and glycolysis. Volume percentages of these TME compartments were compared between untreated and recurrent GB. At initial diagnosis, all 21 GB showed either the features of a glycolytic dominant phenotype with a high percentage of functional neovasculature (N = 12) or those of a necrotic/hypoxic dominant phenotype with a high percentage of defective tumor neovasculature (N = 9). At recurrence, all 21 GB revealed switching of the initial metabolic phenotype: either from the glycolytic to the necrotic/hypoxic dominant phenotype or vice-versa. A necrotic/hypoxic phenotype at recurrence was associated with a higher rate of multifocality of the recurrent lesions. Our MRI approach may be helpful for a better understanding of treatment-induced metabolic phenotype switching and for future studies developing targeted therapeutic strategies for recurrent GB.

What is pH regulation, and why do cancer cells need it?

Metabolism is a continuous source of acids. To keep up with a desired metabolic rate, tumors must establish an adequate means of clearing their acidic end-products. This homeostatic priority is achieved by various buffers, enzymes, and transporters connected through the common denominator of H⁺ ions. Whilst this complexity is proportionate to the importance of adequate pH control, it is problematic for developing an intuition for tracking the route taken by acids, assessing the relative importance of various acid-handling proteins, and predicting the outcomes of pharmacological inhibition or genetic alteration. Here, with the help of a simplified mathematical framework, the genesis of cancer pH regulation is explained in terms of the obstacles to efficient acid venting and how these are overcome by specific molecules, often associated with cancer. Ultimately, the pH regulatory apparatus in tumors must (i) provide adequate lactic acid permeability through membranes, (ii) facilitate CO₂/HCO₃⁻/H⁺ diffusivity across the interstitium, (iii) invest in a form of active transport that strikes a favorable balance between intracellular pH and intracellular lactate retention under the energetic constraints of a cell, and (iv) enable the necessary feedback to complete the homeostatic loop. A more informed and quantitative approach to understanding acid-handling in cancer is mandatory for identifying vulnerabilities, which could be exploited as therapeutic targets.

Intracellular pH dynamics and charge-changing somatic mutations in cancer

An unresolved question critical for understanding cancer is how recurring somatic mutations are retained and how selective pressures drive retention. Increased intracellular pH (pHi) is common to most cancers and is an early event in cancer development. Recent work shows that recurrent somatic mutations can confer an adaptive gain in pH sensing to mutant proteins, enhancing tumorigenic phenotypes specifically at the increased pHi of cancer. Newly identified amino acid mutation signatures in cancer suggest charge-changing mutations define and shape the mutational landscape of cancer. Taken together, these results support a new perspective on the functional significance of somatic mutations in cancer. In this review, we explore existing data and new directions for better understanding how changes in dynamic pH sensing by somatic mutation might be conferring a fitness advantage to the high pH of cancer.

Histo-genomics: digital pathology at the forefront of precision medicine

The toughest challenge OMICs face is that they provide extremely high molecular resolution but poor spatial information. Understanding the cellular/histological context of the overwhelming genetic data is critical for a full understanding of the clinical behavior of a malignant tumor. Digital pathology can add an extra layer of information to help visualize in a spatial and microenvironmental context the molecular information of cancer. Thus, histo-genomics provide a unique chance for data integration. In the era of a precision medicine, a four-dimensional (4D) (temporal/spatial) analysis of cancer aided by digital pathology can be a critical step to understand the evolution/progression of different cancers and consequently tailor individual treatment plans. For instance, the integration of molecular biomarkers expression into a three-dimensional (3D) image of a digitally scanned tumor can offer a better understanding of its subtype, behavior, host immune response and prognosis. Using advanced digital image analysis, a larger spectrum of parameters can be analyzed as potential predictors of clinical behavior. Correlation between morphological features and host immune response can be also performed with therapeutic implications. Radio-histomics, or the interface of radiological images and histology is another emerging exciting field which encompasses the integration of radiological imaging with digital pathological images, genomics, and clinical data to portray a more holistic approach to understating and treating disease. These advances in digital slide scanning are not without technical challenges, which will be addressed carefully in this review with quick peek at its future.

In Silico implementation of evolutionary paradigm in therapy design: Towards anti-cancer therapy as Darwinian process

In here presented in silico study we suggest a way how to implement the evolutionary principles into anti-cancer therapy design. We hypothesize that instead of its ongoing supervised adaptation, the therapy may be constructed as a self-sustaining evolutionary process in a dynamic fitness landscape established implicitly by evolving cancer cells, microenvironment and the therapy itself. For these purposes, we replace a unified therapy with the 'therapy species', which is a population of heterogeneous elementary therapies, and propose a way how to turn the toxicity of the elementary therapy into its fitness in a way conforming to evolutionary causation. As a result, not only the therapies govern the evolution of different cell phenotypes, but the cells' resistances govern the evolution of the therapies as well. We illustrate the approach by the minimalistic ad hoc evolutionary model. Its results indicate that the resistant cells could bias the evolution towards more toxic elementary therapies by inhibiting the less toxic ones. As the evolutionary causation of cancer drug resistance has been intensively studied for a few decades, we refer to cancer as a special case to illustrate purely theoretical analysis.

The Promising Connection Between Data Science and Evolutionary Theory in Oncology

Theoretical and empirical work over the past several decades suggests that oncogenesis and disease progression represents an evolutionary story. Despite this knowledge, current anti-resistance strategies to drugs are often managed through treating cancers as independent biological agents divorced from human activity. Yet once drug resistance to cancer treatment is understood as a product of artificial or anthropogenic rather than unconscious selection, oncologists could improve outcomes for their patients by consulting evolutionary studies of oncology prior to clinical trial and treatment plan design. In the setting of multiple cancer types, for example, a machine learning algorithm can predict the genetic changes known to be related to drug resistance. In this way, a unity between technology and theory might have practical clinical implications—and may pave the way for a new paradigm shift in medicine.

Towards Multidrug Adaptive Therapy

A new ecologically inspired paradigm in cancer treatment known as "adaptive therapy" capitalizes on competitive interactions between drug-sensitive and drug-resistant subclones. The goal of adaptive therapy is to maintain a controllable stable tumor burden by allowing a significant population of treatment-sensitive cells to survive. These, in turn, suppress proliferation of the less-fit resistant populations. However, there remain several open challenges in designing adaptive therapies, particularly in extending these therapeutic concepts to multiple treatments. We present a cancer treatment case study (metastatic castrate-resistant prostate cancer) as a point of departure to illustrate three novel concepts to aid the design of multidrug adaptive therapies. First, frequency-dependent "cycles" of tumor evolution can trap tumor evolution in a periodic, controllable loop. Second, the availability and selection of treatments may limit the evolutionary "absorbing region" reachable by the tumor. Third, the velocity of evolution significantly influences the optimal timing of drug sequences. These three conceptual advances provide a path forward for multidrug adaptive therapy. SIGNIFICANCE: Driving tumor evolution into periodic, repeatable treatment cycles provides a path forward for multidrug adaptive therapy.