Exploiting evolutionary steering to induce collateral drug sensitivity in cancer

MYC ecDNA promotes intratumour heterogeneity and plasticity in PDAC

Intratumour heterogeneity and phenotypic plasticity drive tumour progression and therapy resistance1,2. Oncogene dosage variation contributes to cell-state transitions and phenotypic heterogeneity3, thereby providing a substrate for somatic evolution. Nonetheless, the genetic mechanisms underlying phenotypic heterogeneity are still poorly understood. Here we show that extrachromosomal DNA (ecDNA) is a major source of high-level focal amplification in key oncogenes and a major contributor of MYC heterogeneity in pancreatic ductal adenocarcinoma (PDAC). We demonstrate that ecDNAs drive varying levels of MYC dosage, depending on their regulatory landscape, enabling cancer cells to rapidly and reversibly adapt to microenvironmental changes. In the absence of selective pressure, a high ecDNA copy number imposes a substantial fitness cost on PDAC cells. We also show that MYC dosage affects cell morphology and dependence of cancer cells on stromal niche factors. Our work provides a detailed analysis of ecDNAs in PDAC and describes a new genetic mechanism driving MYC heterogeneity in PDAC.

Lineage Tracing Reveals Clone-Specific Responses to Doxorubicin in Triple-Negative Breast Cancer

Triple-negative breast cancer, characterized by aggressive growth and high intratumor heterogeneity, presents a significant clinical challenge. Here, we use a lineage-tracing system, ClonMapper, which couples heritable clonal identifying tags with single-cell RNA-sequencing (scRNA-seq), to better elucidate the response to doxorubicin in a model of TNBC. We demonstrate that, while there is a dose-dependent reduction in overall clonal diversity, there is no pre-existing resistance signature among surviving clones. Separately, we found the existence of two transcriptomically distinct clonal subpopulations that remain through the course of treatment. Among clones persisting across multiple samples we identified divergent phenotypes, suggesting a response to treament independent of clonal identity. Finally, a subset of clones harbor novel changes in expression following treatment. The clone and sample specific responses to treatment identified herein highlight the need for better personalized treatment strategies to overcome tumor heterogeneity.

Natural Killer Cell-Mediated Cytotoxicity Shapes the Clonal Evolution of B-cell Leukemia

The term cancer immunoediting describes the dual role by which the immune system can suppress and promote tumor growth and is divided into three phases: elimination, equilibrium, and escape. The role of NK cells has mainly been attributed to the elimination phase. Here, we show that NK cells play a role in all three phases of cancer immunoediting. Extended co-culturing of DNA-barcoded mouse BCR/ABLp185+ B-cell acute lymphoblastic leukemia (B-ALL) cells with NK cells allowed for a quantitative measure of NK cell-mediated immunoediting. Although most tumor cell clones were efficiently eliminated by NK cells, a certain fraction of tumor cells harbored an intrinsic primary resistance. Furthermore, DNA barcoding revealed tumor cell clones with secondary resistance, which stochastically acquired resistance to NK cells. NK cell-mediated cytotoxicity put a selective pressure on B-ALL cells, which led to an outgrowth of primary and secondary resistant tumor cell clones, which were characterized by an IFNγ signature. Besides well-known regulators of immune evasion, our analysis of NK cell-resistant tumor cells revealed the upregulation of genes, including lymphocyte antigen 6 complex, locus A (Ly6a), which we found to promote leukemic cell resistance to NK cells. Translation of our findings to the human system showed that high expression of LY6E on tumor cells impaired their physical interaction with NK cells and led to worse prognosis in patients with leukemia. Our results demonstrate that tumor cells are actively edited by NK cells during the equilibrium phase and use different avenues to escape NK cell-mediated eradication.

Normal tissue transcriptional signatures for tumor-type-agnostic phenotype prediction

Cancer transcriptional patterns reflect both unique features and shared hallmarks across diverse cancer types, but whether differences in these patterns are sufficient to characterize the full breadth of tumor phenotype heterogeneity remains an open question. We hypothesized that these shared transcriptomic signatures reflect repurposed versions of functional tasks performed by normal tissues. Starting with normal tissue transcriptomic profiles, we use non-negative matrix factorization to derive six distinct transcriptomic phenotypes, called archetypes, which combine to describe both normal tissue patterns and variations across a broad spectrum of malignancies. We show that differential enrichment of these signatures correlates with key tumor characteristics, including overall patient survival and drug sensitivity, independent of clinically actionable DNA alterations. Additionally, we show that in HR+/HER2- breast cancers, metastatic tumors adopt transcriptomic signatures consistent with the invaded tissue. Broadly, our findings suggest that cancer often arrogates normal tissue transcriptomic characteristics as a component of both malignant progression and drug response. This quantitative framework provides a strategy for connecting the diversity of cancer phenotypes and could potentially help manage individual patients.

Star wars against leukemia: attacking the clones

Leukemia, although most likely starts as a monoclonal genetic/epigenetic anomaly, is a polyclonal disease at manifestation. This polyclonal nature results from ongoing evolutionary changes in the genome/epigenome of leukemia cells to promote their survival and proliferation advantages. We discuss here how genetic and/or epigenetic aberrations alter intracellular microenvironment in individual leukemia clones and how extracellular microenvironment selects the best fitted clones. This dynamic polyclonal composition of leukemia makes designing an effective therapy a challenging task especially because individual leukemia clones often display substantial differences in response to treatment. Here, we discuss novel therapeutic approach employing single cell multiomics to identify and eradicate all individual clones in a patient.

Evolutionary graph theory beyond single mutation dynamics: on how network-structured populations cross fitness landscapes

Spatially resolved datasets are revolutionizing knowledge in molecular biology, yet are under-utilized for questions in evolutionary biology. To gain insight from these large-scale datasets of spatial organization, we need mathematical representations and modeling techniques that can both capture their complexity, but also allow for mathematical tractability. Evolutionary graph theory utilizes the mathematical representation of networks as a proxy for heterogeneous population structure and has started to reshape our understanding of how spatial structure can direct evolutionary dynamics. However, previous results are derived for the case of a single new mutation appearing in the population and the role of network structure in shaping fitness landscape crossing is still poorly understood. Here we study how network-structured populations cross fitness landscapes and show that even a simple extension to a two-mutational landscape can exhibit complex evolutionary dynamics that cannot be predicted using previous single-mutation results. We show how our results can be intuitively understood through the lens of how the two main evolutionary properties of a network, the amplification and acceleration factors, change the expected fate of the intermediate mutant in the population and further discuss how to link these models to spatially resolved datasets of cellular organization.

Using nanotechnology to progress the utilization of marine natural products in combating multidrug resistance in cancer: A prospective strategy

Achieving targeted, customized, and combination therapies with clarity of the involved molecular pathways is crucial in the treatment as well as overcoming multidrug resistance (MDR) in cancer. Nanotechnology has emerged as an innovative and promising approach to address the problem of drug resistance. Developing nano-formulation-based therapies using therapeutic agents poses a synergistic effect to overcome MDR in cancer. In this review, we aimed to highlight the important pathways involved in the progression of MDR in cancer mediated through nanotechnology-based approaches that have been employed to circumvent them in recent years. Here, we also discussed the potential use of marine metabolites to treat MDR in cancer, utilizing active drug-targeting nanomedicine-based techniques to enhance selective drug accumulation in cancer cells. The discussion also provides future insights for developing complex targeted, multistage responsive nanomedical drug delivery systems for effective cancer treatments. We propose more combinational studies and their validation for the possible marine-based nanoformulations for future development.

Cetuximab chemotherapy resistance: Insight into the homeostatic evolution of head and neck cancer (Review)

The complex evolution of genetic alterations in cancer that occurs in vivo is a selective process involving numerous factors and mechanisms. Chemotherapeutic agents that prevent the growth and spread of cancer cells induce selective pressure, leading to rapid artificial selection of resistant subclones. This rapid evolution is possible because antineoplastic drugs promote alterations in tumor‑cell metabolism, thus creating a bottleneck event. The few resistant cells that survive in this new environment obtain differential reproductive success that enables them to pass down the newly selected resistant gene pool. The present review aims to summarize key findings of tumor evolution, epithelial‑mesenchymal transition and resistance to cetuximab therapy in head and neck squamous cell carcinoma.

Optimizing cancer therapy: a review of the multifaceted effects of metronomic chemotherapy

Metronomic chemotherapy (MCT), characterized by the continuous administration of chemotherapeutics at a lower dose without prolonged drug-free periods, has garnered significant attention over the last 2 decades. Extensive evidence from both pre-clinical and clinical settings indicates that MCT induces distinct biological effects than the standard Maximum Tolerated Dose (MTD) chemotherapy. The low toxicity profile, reduced likelihood of inducing acquired therapeutic resistance, and low cost of MCT render it an attractive chemotherapeutic regimen option. One of the most prominent aspects of MCT is its anti-angiogenesis effects. It has been shown to stimulate the expression of anti-angiogenic molecules, thereby inhibiting angiogenesis. In addition, MCT has been shown to decrease the regulatory T-cell population and promote anti-tumor immune response through inducing dendritic cell maturation and increasing the number of cytotoxic T-cells. Combination therapies utilizing MCT along with oncolytic virotherapy, radiotherapy or other chemotherapeutic regimens have been studied extensively. This review provides an overview of the current status of MCT research and the established mechanisms of action of MCT treatment and also offers insights into potential avenues of development for MCT in the future.

Long-term Multimodal Recording Reveals Epigenetic Adaptation Routes in Dormant Breast Cancer Cells

Patients with estrogen receptor-positive breast cancer receive adjuvant endocrine therapies (ET) that delay relapse by targeting clinically undetectable micrometastatic deposits. Yet, up to 50% of patients relapse even decades after surgery through unknown mechanisms likely involving dormancy. To investigate genetic and transcriptional changes underlying tumor awakening, we analyzed late relapse patients and longitudinally profiled a rare cohort treated with long-term neoadjuvant ETs until progression. Next, we developed an in vitro evolutionary study to record the adaptive strategies of individual lineages in unperturbed parallel experiments. Our data demonstrate that ETs induce nongenetic cell state transitions into dormancy in a stochastic subset of cells via epigenetic reprogramming. Single lineages with divergent phenotypes awaken unpredictably in the absence of recurrent genetic alterations. Targeting the dormant epigenome shows promising activity against adapting cancer cells. Overall, this study uncovers the contribution of epigenetic adaptation to the evolution of resistance to ETs.

SIGNIFICANCE

This study advances the understanding of therapy-induced dormancy with potential clinical implications for breast cancer. Estrogen receptor-positive breast cancer cells adapt to endocrine treatment by entering a dormant state characterized by strong heterochromatinization with no recurrent genetic changes. Targeting the epigenetic rewiring impairs the adaptation of cancer cells to ETs. See related commentary by Llinas-Bertran et al., p. 704. This article is featured in Selected Articles from This Issue, p. 695.

Tumour Growth Mechanisms Determine Effectiveness of Adaptive Therapy in Glandular Tumours

In cancer treatment, adaptive therapy holds promise for delaying the onset of recurrence through regulating the competition between drug-sensitive and drug-resistant cells. Adaptive therapy has been studied in well-mixed models assuming free mixing of all cells and spatial models considering the interactions of single cells with their immediate adjacent cells. Both models do not reflect the spatial structure in glandular tumours where intra-gland cellular interaction is high, while inter-gland interaction is limited. Here, we use mathematical modelling to study the effects of adaptive therapy on glandular tumours that expand using either glandular fission or invasive growth. A two-dimensional, lattice-based model of sites containing sensitive and resistant cells within individual glands is developed to study the evolution of glandular tumour cells under continuous and adaptive therapies. We found that although both growth models benefit from adaptive therapy's ability to prevent recurrence, invasive growth benefits more from it than fission growth. This difference is due to the migration of daughter cells into neighboring glands that is absent in fission but present in invasive growth. The migration resulted in greater mixing of cells, enhancing competition induced by adaptive therapy. By varying the initial spatial spread and location of the resistant cells within the tumour, we found that modifying the conditions within the resistant cells containing glands affect both fission and invasive growth. However, modifying the conditions surrounding these glands affect invasive growth only. Our work reveals the interplay between growth mechanism and tumour topology in modulating the effectiveness of cancer therapy.

On the possibility of engineering social evolution in microfluidic environments

Many species of microbes cooperate by producing public goods from which they collectively benefit. However, these populations are under the risk of being taken over by cheating mutants that do not contribute to the pool of public goods. Here we present theoretical findings that address how the social evolution of microbes can be manipulated by external perturbations to inhibit or promote the fixation of cheaters. To control social evolution, we determine the effects of fluid-dynamical properties such as flow rate or domain geometry. We also study the social evolutionary consequences of introducing beneficial or harmful chemicals at steady state and in a time-dependent fashion. We show that by modulating the flow rate and by applying pulsed chemical signals, we can modulate the spatial structure and dynamics of the population in a way that can select for more or less cooperative microbial populations.

Cancer Evolution: A Multifaceted Affair

Cancer cells adapt and survive through the acquisition and selection of molecular modifications. This process defines cancer evolution. Building on a theoretical framework based on heritable genetic changes has provided insights into the mechanisms supporting cancer evolution. However, cancer hallmarks also emerge via heritable nongenetic mechanisms, including epigenetic and chromatin topological changes, and interactions between tumor cells and the tumor microenvironment. Recent findings on tumor evolutionary mechanisms draw a multifaceted picture where heterogeneous forces interact and influence each other while shaping tumor progression. A comprehensive characterization of the cancer evolutionary toolkit is required to improve personalized medicine and biomarker discovery.

SIGNIFICANCE

Tumor evolution is fueled by multiple enabling mechanisms. Importantly, genetic instability, epigenetic reprogramming, and interactions with the tumor microenvironment are neither alternative nor independent evolutionary mechanisms. As demonstrated by findings highlighted in this perspective, experimental and theoretical approaches must account for multiple evolutionary mechanisms and their interactions to ultimately understand, predict, and steer tumor evolution.

Characterization of dabrafenib-induced drug insensitivity via cellular barcoding and collateral sensitivity to second-line therapeutics

Drug insensitivity is arguably one of the biggest challenges in cancer therapeutics. Although effective therapeutic solutions in cancer are limited due to the emergence of drug insensitivity, exploiting evolutionary understanding in this context can provide potential second-line therapeutics sensitizing the drug insensitive populations. Targeted therapeutic agent dabrafenib is used to treat CRC patients with BRAF V600E genotype and insensitivity to dabrafenib is often observed. Understanding underlying clonal architecture of dabrafenib-induced drug insensitivity and identification of potential second-line therapeutics that could sensitize dabrafenib insensitive populations remain to be elucidated. For this purpose, we utilized cellular barcoding technology to decipher dabrafenib-induced clonal evolution in BRAF V600E mutant HT-29 cells. This approach revealed the detection of both pre-existing and de novo barcodes with increased frequencies as a result of dabrafenib insensitivity. Furthermore, our longitudinal monitoring of drug insensitivity based on barcode detection from floating DNA within used medium enabled to identify temporal dynamics of pre-existing and de novo barcodes in relation to dabrafenib insensitivity in HT-29 cells. Moreover, whole-exome sequencing analysis exhibited possible somatic CNVs and SNVs contributing to dabrafenib insensitivity in HT-29 cells. Last, collateral drug sensitivity testing demonstrated oxaliplatin and capecitabine, alone or in combination, as successful second-like therapeutics in inducing collateral sensitivity in dabrafenib-insensitive HT-29 cells. Overall, our findings demonstrate clonal dynamics of dabrafenib-insensitivity in HT-29 cells. In addition, oxaliplatin and capecitabine, alone or in combination, were successful second-line therapeutics in inducing collateral sensitivity in dabrafenib-insensitive HT-29 cells.

Tumor-Microenvironment-on-Chip Platform for Assessing Drug Response in 3D Dynamic Culture

Microphysiological systems involving microfluidic 3D culture of cancer cells have emerged as a versatile toolkit to study tumor biological problems and evaluate potential treatment strategies. Incorporation of microfluidic technologies in 3D tissue culture offers opportunities for realistic simulation of tumor microenvironment in vitro by facilitating a dynamic culture environment mimicking features of human physiology such as reconstituted ECM, interstitial flow, and gradients of drugs and biomacromolecules. This protocol describes development of 3D microfluidic cell culture based on Tumor-Microenvironment-on-Chip (T-MOC) platform modeling tumor blood and lymphatic capillary vessels and the interstitial space in between. Based on earlier applications of T-MOC for transport characteristics, drug response, and tumor-stroma interactions in mammary carcinoma and pancreatic adenocarcinoma, this protocol provides detailed description of device fabrication, on-chip 3D culture, and drug treatment assays. This protocol can easily be adapted for applications involving other cancer types.

Solving polymicrobial puzzles: evolutionary dynamics and future directions

Polymicrobial infections include various microorganisms, often necessitating different treatment methods than a monomicrobial infection. Scientists have been puzzled by the complex interactions within these communities for generations. The presence of specific microorganisms warrants a chronic infection and impacts crucial factors such as virulence and antibiotic susceptibility. Game theory is valuable for scenarios involving multiple decision-makers, but its relevance to polymicrobial infections is limited. Eco-evolutionary dynamics introduce causation for multiple proteomic interactions like metabolic syntropy and niche segregation. The review culminates both these giants to form evolutionary dynamics (ED). There is a significant amount of literature on inter-bacterial interactions that remain unsynchronised. Such raw data can only be moulded by analysing the ED involved. The review culminates the inter-bacterial interactions in multiple clinically relevant polymicrobial infections like chronic wounds, CAUTI, otitis media and dental carries. The data is further moulded with ED to analyse the niche colonisation of two notoriously competitive bacteria: S.aureus and P.aeruginosa. The review attempts to develop a future trajectory for polymicrobial research by following recent innovative strategies incorporating ED to curb polymicrobial infections.

Evolutionary determinants of curability in cancer

The emergence of drug-resistant cells, most of which have a mutated TP53 gene, prevents curative treatment in most advanced and common metastatic cancers of adults. Yet, a few, rarer malignancies, all of which are TP53 wild type, have high cure rates. In this Perspective, we discuss how common features of curable cancers offer insights into the evolutionary and developmental determinants of drug resistance. Acquired loss of TP53 protein function is the most common genetic change in cancer. This probably reflects positive selection in the context of strong ecosystem pressures including microenvironmental hypoxia. Loss of TP53's functions results in multiple fitness benefits and enhanced evolvability of cancer cells. TP53-null cells survive apoptosis, and tolerate potent oncogenic signalling, DNA damage and genetic instability. In addition, critically, they provide an expanded pool of self-renewing, or stem, cells, the primary units of evolutionary selection in cancer, making subsequent adaptation to therapeutic challenge by drug resistance highly probable. The exceptional malignancies that are curable, including the common genetic subtype of childhood acute lymphoblastic leukaemia and testicular seminoma, differ from the common adult cancers in originating prenatally from embryonic or fetal cells that are developmentally primed for TP53-dependent apoptosis. Plus, they have other genetic and phenotypic features that enable dissemination without exposure to selective pressures for TP53 loss, retaining their intrinsic drug hypersensitivity.

A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing

Single-cell RNA and ATAC sequencing technologies enable the examination of gene expression and chromatin accessibility in individual cells, providing insights into cellular phenotypes. In cancer research, it is important to consistently analyze these states within an evolutionary context on genetic clones. Here we present CONGAS+, a Bayesian model to map single-cell RNA and ATAC profiles onto the latent space of copy number clones. CONGAS+ clusters cells into tumour subclones with similar ploidy, rendering straightforward to compare their expression and chromatin profiles. The framework, implemented on GPU and tested on real and simulated data, scales to analyse seamlessly thousands of cells, demonstrating better performance than single-molecule models, and supporting new multi-omics assays. In prostate cancer, lymphoma and basal cell carcinoma, CONGAS+ successfully identifies complex subclonal architectures while providing a coherent mapping between ATAC and RNA, facilitating the study of genotype-phenotype maps and their connection to genomic instability.

Using picoliter droplet deposition to track clonal competition in adherent and organoid cancer cell cultures

Clonal growth and competition underlie processes of key relevance in etiology, progression and therapy response across all cancers. Here, we demonstrate a novel experimental approach, based on multi-color, fluorescent tagging of cell nuclei, in combination with picoliter droplet deposition, to study the clonal dynamics in two- and three-dimensional cell cultures. The method allows for the simultaneous visualization and analysis of multiple clones in individual multi-clonal colonies, providing a powerful tool for studying clonal dynamics and identifying clonal populations with distinct characteristics. Results of our experiments validate the utility of the method in studying clonal dynamics in vitro, and reveal differences in key aspects of clonal behavior of different cancer cell lines in monoculture conditions, as well as in co-cultures with stromal fibroblasts.

Treatment of evolving cancers will require dynamic decision support

Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency of existing drugs. Based on the modus operandi established in the early days of chemotherapies, most drugs are administered according to predetermined schedules that seek to deliver the maximum tolerated dose and are only adjusted for toxicity. However, we believe that the complex, evolving nature of cancer requires a more dynamic and personalized approach. Chronicling the milestones of the field, we show that the impact of schedule choice crucially depends on processes driving treatment response and failure. As such, cancer heterogeneity and evolution dictate that a one-size-fits-all solution is unlikely-instead, each patient should be mapped to the strategy that best matches their current disease characteristics and treatment objectives (i.e. their 'tumorscape'). To achieve this level of personalization, we need mathematical modeling. In this perspective, we propose a five-step 'Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT)' paradigm to integrate data and understanding across scales and derive dynamic and personalized schedules. We conclude with promising examples of model-guided schedule personalization and a call to action to address key outstanding challenges surrounding data collection, model development, and integration.

Investigation of evolutionary dynamics for drug resistance in 3D spheroid model system using cellular barcoding technology

Complex evolutionary dynamics governing the drug resistance is one of the major challenges in cancer treatment. Understanding these mechanisms requires a sequencing technology with higher resolution to delineate whether pre-existing or de novo drug mechanisms are behind the drug resistance. Combining this technology with clinically very relevant model system, namely 3D spheroids, better mimicking tumorigenesis and drug resistance have so far been lacking. Thus, we sought to establish dabrafenib and irinotecan resistant derivatives of barcoded 3D spheroids with the ultimate aim to quantify the selection-induced clonal dynamics and identify the genomic determinants in this model system. We found that dabrafenib and irinotecan induced drug resistance in 3D-HT-29 and 3D-HCT-116 spheroids are mediated by pre-existing and de novo resistant barcodes, indicating the presence of polyclonal drug resistance in this system. Moreover, whole-exome sequencing analysis found chromosomal gains and mutations associated with dabrafenib and irinotecan resistance in 3D-HT-29 and 3D-HCT-116 spheroids. Last, we show that dabrafenib and irinotecan resistance are also mediated by multiple drug resistance by detection of upregulation of the drug efflux pumps, ABCB1 and ABCG2, in our spheroid model system. Overall, we present the quantification of drug resistance and evolutionary dynamics in spheroids for the first time using cellular barcoding technology and the underlying genomic determinants of the drug resistance in our model system.

Convergent TP53 loss and evolvability in cancer

Cancer cell populations evolve by a stepwise process involving natural selection of the fittest variants within a tissue ecosystem context and as modified by therapy. Genomic scrutiny of patient samples reveals an extraordinary diversity of mutational profiles both between patients with similar cancers and within the cancer cell population of individual patients. Does this signify highly divergent evolutionary trajectories or are there repetitive and predictable patterns?Major evolutionary innovations or adaptations in different species are frequently repeated, or convergent, reflecting both common selective pressures and constraints on optimal solutions. We argue this is true of evolving cancer cells, especially with respect to the TP53 gene. Functional loss variants in TP53 are the most common genetic change in cancer. We discuss the likely microenvironmental selective pressures involved and the profound impact this has on cell fitness, evolvability and probability of subsequent drug resistance.

Wargaming cancer: a strategy for future precision oncology?

Development of drug resistance is a mounting problem in precision oncology, demanding a rethink of treatment strategy. Here, we apply concepts from military theory and espionage to the battle-like dynamics between cancer and its host, thereby identifying system vulnerabilities in cancer and ways of tricking cancer evolution into dead ends.

Identification of collateral sensitivity and evolutionary landscape of chemotherapy-induced drug resistance using cellular barcoding technology

Background: One of the most significant challenges impeding cancer treatment effectiveness is drug resistance. Combining evolutionary understanding with drug resistance can pave the way for the identification of second-line drug options that can overcome drug resistance. Although capecitabine and irinotecan are commonly used therapeutic agents in the treatment of CRC patients, resistance to these agents is common. The underlying clonal dynamics of resistance to these agents using high-resolution barcode technology and identification of effective second-line drugs in this context remain unclear. Methods and materials: Caco-2 and HT-29 cell lines were barcoded, and then capecitabine and irinotecan resistant derivatives of these cell lines were established. The frequencies of barcodes from resistant cell lines and harvested medium, longitudinally, were determined. Collateral drug sensitivity testing was carried out on resistant Caco-2 and HT-29 cell lines using single agents or drug combinations. The SyngeryFinder tool was used to analyse drug combination testing. Results: In Caco-2 and HT-29 cell lines, barcode frequency measurements revealed clonal dynamics of capecitabine and irinotecan formed by both pre-existing and de novo barcodes, indicating the presence of polyclonal drug resistance. The temporal dynamics of clonal evolution in Caco-2 and HT-29 cell lines were demonstrated by longitudinal analysis of pre-existing and de novo barcodes from harvested medium. In Caco-2 and HT-29 cell lines, collateral drug sensitivity revealed a number of drugs that were effective alone and in combination. Conclusion: The use of barcoding technology reveals the clonal dynamics of chemotherapy-induced drug resistance not only from harvested cell populations, but also from longitudinal sampling throughout the course of clonal evolution. Second-line drugs that sensitize drug-resistant CRC cell lines are identified through collateral drug testing.

Clinical trial designs for evaluating and exploiting cancer evolution

The evolution of drug-resistant cell subpopulations causes cancer treatment failure. Current preclinical evidence shows that it is possible to model herding of clonal evolution and collateral sensitivity where an initial treatment could favourably influence the response to a subsequent one. Novel therapy strategies exploiting this understanding are being considered, and clinical trial designs for steering cancer evolution are needed. Furthermore, preclinical evidence suggests that different subsets of drug-sensitive and resistant clones could compete between themselves for nutrients/blood supply, and clones that populate a tumour do so at the expense of other clones. Treatment paradigms based on this clinical application of exploiting cell-cell competition include intermittent dosing regimens or cycling different treatments before progression. This will require clinical trial designs different from the conventional practice of evaluating responses to individual therapy regimens. Next-generation sequencing to assess clonal dynamics longitudinally will improve current radiological assessment of clinical response/resistance and be incorporated into trials exploiting evolution. Furthermore, if understood, clonal evolution can be used to therapeutic advantage, improving patient outcomes based on a new generation of clinical trials.

Barcoding Populations of Pseudomonas fluorescens SBW25

In recent years, evolutionary biologists have developed an increasing interest in the use of barcoding strategies to study eco-evolutionary dynamics of lineages within evolving populations and communities. Although barcoded populations can deliver unprecedented insight into evolutionary change, barcoding microbes presents specific technical challenges. Here, strategies are described for barcoding populations of the model bacterium Pseudomonas fluorescens SBW25, including the design and cloning of barcoded regions, preparation of libraries for amplicon sequencing, and quantification of resulting barcoded lineages. In so doing, we hope to aid the design and implementation of barcoding methodologies in a broad range of model and non-model organisms.

A synthetic lethal screen for Snail-induced enzalutamide resistance identifies JAK/STAT signaling as a therapeutic vulnerability in prostate cancer

Despite substantial improvements in the treatment landscape of prostate cancer, the evolution of hormone therapy-resistant and metastatic prostate cancer remains a major cause of cancer-related death globally. The mainstay of treatment for advanced prostate cancer is targeting of androgen receptor signaling, including androgen deprivation therapy plus second-generation androgen receptor blockade (e.g., enzalutamide, apalutamide, darolutamide), and/or androgen synthesis inhibition (abiraterone). While these agents have significantly prolonged the lives of patients with advanced prostate cancer, is nearly universal. This therapy resistance is mediated by diverse mechanisms, including both androgen receptor-dependent mechanisms, such as androgen receptor mutations, amplifications, alternative splicing, and amplification, as well as non-androgen receptor-mediated mechanisms, such as lineage plasticity toward neuroendocrine-like or epithelial-mesenchymal transition (EMT)-like lineages. Our prior work identified the EMT transcriptional regulator Snail as critical to hormonal therapy resistance and is commonly detected in human metastatic prostate cancer. In the current study, we sought to interrogate the actionable landscape of EMT-mediated hormone therapy resistant prostate cancer to identify synthetic lethality and collateral sensitivity approaches to treating this aggressive, therapy-resistant disease state. Using a combination of high-throughput drug screens and multi-parameter phenotyping by confluence imaging, ATP production, and phenotypic plasticity reporters of EMT, we identified candidate synthetic lethalities to Snail-mediated EMT in prostate cancer. These analyses identified multiple actionable targets, such as XPO1, PI3K/mTOR, aurora kinases, c-MET, polo-like kinases, and JAK/STAT as synthetic lethalities in Snail+ prostate cancer. We validated these targets in a subsequent validation screen in an LNCaP-derived model of resistance to sequential androgen deprivation and enzalutamide. This follow-up screen provided validation of inhibitors of JAK/STAT and PI3K/mTOR as therapeutic vulnerabilities for both Snail+ and enzalutamide-resistant prostate cancer.

Overcoming Cancer Multi-drug Resistance (MDR): Reasons, mechanisms, nanotherapeutic solutions, and challenges

Multi-drug resistance (MDR) in cancer cells, either intrinsic or acquired through various mechanisms, significantly hinders the therapeutic efficacy of drugs. Typically, the reduced therapeutic performance of various drugs is predominantly due to the inherent over expression of ATP-binding cassette (ABC) transporter proteins on the cell membrane, resulting in the deprived uptake of drugs, augmenting drug detoxification, and DNA repair. In addition to various physiological abnormalities and extensive blood flow, MDR cancer phenotypes exhibit improved apoptotic threshold and drug efflux efficiency. These severe consequences have substantially directed researchers in the fabrication of various advanced therapeutic strategies, such as co-delivery of drugs along with various generations of MDR inhibitors, augmented dosage regimens and frequency of administration, as well as combinatorial treatment options, among others. In this review, we emphasize different reasons and mechanisms responsible for MDR in cancer, including but not limited to the known drug efflux mechanisms mediated by permeability glycoprotein (P-gp) and other pumps, reduced drug uptake, altered DNA repair, and drug targets, among others. Further, an emphasis on specific cancers that share pathogenesis in executing MDR and effluxed drugs in common is provided. Then, the aspects related to various nanomaterials-based supramolecular programmable designs (organic- and inorganic-based materials), as well as physical approaches (light- and ultrasound-based therapies), are discussed, highlighting the unsolved issues and future advancements. Finally, we summarize the review with interesting perspectives and future trends, exploring further opportunities to overcome MDR.

A survey of open questions in adaptive therapy: Bridging mathematics and clinical translation

Adaptive therapy is a dynamic cancer treatment protocol that updates (or 'adapts') treatment decisions in anticipation of evolving tumor dynamics. This broad term encompasses many possible dynamic treatment protocols of patient-specific dose modulation or dose timing. Adaptive therapy maintains high levels of tumor burden to benefit from the competitive suppression of treatment-sensitive subpopulations on treatment-resistant subpopulations. This evolution-based approach to cancer treatment has been integrated into several ongoing or planned clinical trials, including treatment of metastatic castrate resistant prostate cancer, ovarian cancer, and BRAF-mutant melanoma. In the previous few decades, experimental and clinical investigation of adaptive therapy has progressed synergistically with mathematical and computational modeling. In this work, we discuss 11 open questions in cancer adaptive therapy mathematical modeling. The questions are split into three sections: (1) integrating the appropriate components into mathematical models (2) design and validation of dosing protocols, and (3) challenges and opportunities in clinical translation.

Integration of quantitative methods and mathematical approaches for the modeling of cancer cell proliferation dynamics

Physiological processes rely on the control of cell proliferation, and the dysregulation of these processes underlies various pathological conditions, including cancer. Mathematical modeling can provide new insights into the complex regulation of cell proliferation dynamics. In this review, we first examine quantitative experimental approaches for measuring cell proliferation dynamics in vitro and compare the various types of data that can be obtained in these settings. We then explore the toolbox of common mathematical modeling frameworks that can describe cell behavior, dynamics, and interactions of proliferation. We discuss how these wet-laboratory studies may be integrated with different mathematical modeling approaches to aid the interpretation of the results and to enable the prediction of cell behaviors, specifically in the context of cancer.

MCB-613 exploits a collateral sensitivity in drug resistant EGFR-mutant non-small cell lung cancer through covalent inhibition of KEAP1

Targeted therapies have revolutionized cancer chemotherapy. Unfortunately, most patients develop multifocal resistance to these drugs within a matter of months. Here, we used a high-throughput phenotypic small molecule screen to identify MCB-613 as a compound that selectively targets EGFR-mutant, EGFR inhibitor-resistant non-small cell lung cancer (NSCLC) cells harboring diverse resistance mechanisms. Subsequent proteomic and functional genomic screens involving MCB-613 identified its target in this context to be KEAP1, revealing that this gene is selectively essential in the setting of EGFR inhibitor resistance. In-depth molecular characterization demonstrated that (1) MCB-613 binds KEAP1 covalently; (2) a single molecule of MCB-613 is capable of bridging two KEAP1 monomers together; and, (3) this modification interferes with the degradation of canonical KEAP1 substrates such as NRF2. Surprisingly, NRF2 knockout sensitizes cells to MCB-613, suggesting that the drug functions through modulation of an alternative KEAP1 substrate. Together, these findings advance MCB-613 as a new tool for exploiting the selective essentiality of KEAP1 in drug-resistant, EGFR-mutant NSCLC cells.

Evolutionary analysis of replicator dynamics about anti-cancer combination therapy

The emergence and growth of drug-resistant cancer cell subpopulations during anti-cancer treatment is a major challenge for cancer therapies. Combination therapies are usually applied for overcoming drug resistance. In the present paper, we explored the evolution outcome of tumor cell populations under different combination schedules of chemotherapy and p53 vaccine, by construction of replicator dynamical model for sensitive cells, chemotherapy-resistant cells and p53 vaccine-resistant cells. The local asymptotic stability analysis of the evolutionary stable points revealed that cancer population could evolve to the population with single subpopulation, or coexistence of sensitive cells and p53 vaccine-resistant cells, or coexistence of chemotherapy-resistant cells and p53 vaccine-resistant cells under different monotherapy or combination schedules. The design of adaptive therapy schedules that maintain the subpopulations under control is also demonstrated by sequential and periodic application of combination treatment strategies based on the evolutionary velocity and evolutionary absorbing regions. Applying a new replicator dynamical model, we further explored the supportive effects of sensitive cancer cells on targeted therapy-resistant cells revealed in mice experiments. It was shown that the supportive effects of sensitive cells could drive the evolution of cell population from sensitive cells to coexistence of sensitive cells and one type of targeted therapy-resistant cells.

Drug resistance mechanisms create targetable proteostatic vulnerabilities in Her2+ breast cancers

Oncogenic kinase inhibitors show short-lived responses in the clinic due to high rate of acquired resistance. We previously showed that pharmacologically exploiting oncogene-induced proteotoxic stress can be a viable alternative to oncogene-targeted therapy. Here, we performed extensive analyses of the transcriptomic, metabolomic and proteostatic perturbations during the course of treatment of Her2+ breast cancer cells with a Her2 inhibitor covering the drug response, resistance, relapse and drug withdrawal phases. We found that acute Her2 inhibition, in addition to blocking mitogenic signaling, leads to significant decline in the glucose uptake, and shutdown of glycolysis and of global protein synthesis. During prolonged therapy, compensatory overexpression of Her3 allows for the reactivation of mitogenic signaling pathways, but fails to re-engage the glucose uptake and glycolysis, resulting in proteotoxic ER stress, which maintains the protein synthesis block and growth inhibition. Her3-mediated cell proliferation under ER stress during prolonged Her2 inhibition is enabled due to the overexpression of the eIF2 phosphatase GADD34, which uncouples protein synthesis block from the ER stress response to allow for active cell growth. We show that this imbalance in the mitogenic and proteostatic signaling created during the acquired resistance to anti-Her2 therapy imposes a specific vulnerability to the inhibition of the endoplasmic reticulum quality control machinery. The latter is more pronounced in the drug withdrawal phase, where the de-inhibition of Her2 creates an acute surge in the downstream signaling pathways and exacerbates the proteostatic imbalance. Therefore, the acquired resistance mechanisms to oncogenic kinase inhibitors may create secondary vulnerabilities that could be exploited in the clinic.

Epistasis and evolutionary dependencies in human cancers

Cancer evolution is driven by the concerted action of multiple molecular alterations, which emerge and are selected during tumor progression. An alteration is selected when it provides an advantage to the tumor cell. However, the advantage provided by a specific alteration depends on the tumor lineage, cell epigenetic state, and presence of additional alterations. In this case, we say that an evolutionary dependency exists between an alteration and what influences its selection. Epistatic interactions between altered genes lead to evolutionary dependencies (EDs), by favoring or vetoing specific combinations of events. Large-scale cancer genomics studies have discovered examples of such dependencies, and showed that they influence tumor progression, disease phenotypes, and therapeutic response. In the past decade, several algorithmic approaches have been proposed to infer EDs from large-scale genomics datasets. These methods adopt diverse strategies to address common challenges and shed new light on cancer evolutionary trajectories. Here, we review these efforts starting from a simple conceptualization of the problem, presenting the tackled and still unmet needs in the field, and discussing the implications of EDs in cancer biology and precision oncology.

Evolving therapeutic landscape of advanced hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common solid malignancies worldwide. A large proportion of patients with HCC are diagnosed at advanced stages and are only amenable to systemic therapies. We have witnessed the evolution of systemic therapies from single-agent targeted therapy (sorafenib and lenvatinib) to the combination of a checkpoint inhibitor plus targeted therapy (atezolizumab plus bevacizumab therapy). Despite remarkable advances, only a small subset of patients can obtain durable clinical benefit, and therefore substantial therapeutic challenges remain. In the past few years, emerging systemic therapies, including new molecular-targeted monotherapies (for example, donafenib), new immuno-oncology monotherapies (for example, durvalumab) and new combination therapies (for example, durvalumab plus tremelimumab), have shown encouraging results in clinical trials. In addition, many novel therapeutic approaches with the potential to offer improved treatment effects in patients with advanced HCC, such as sequential combination targeted therapy and next-generation adoptive cell therapy, have also been proposed and developed. In this Review, we summarize the latest clinical advances in the treatment of advanced HCC and discuss future perspectives that might inform the development of more effective therapeutics for advanced HCC.

Drug resistance dependent on allostery: A P-loop rigor Eg5 mutant exhibits resistance to allosteric inhibition by STLC

The mitotic kinesin Eg5 has emerged as a potential anti-mitotic target for the purposes of cancer chemotherapy. Whether clinical resistance to these inhibitors can arise is unclear. We exploited HCT116 cancer cell line to select resistant clones to S-trityl-L-cysteine (STLC), an extensively studied Eg5 loop-L5 binding inhibitor. The STLC resistant clones differed in their resistance to other loop-L5 binding inhibitors but remained sensitive to the ATP class of competitive Eg5 specific inhibitors. Eg5 is still necessary for bipolar spindle formation in the resistant clones since the cells were sensitive to RNAi mediated depletion of Eg5. One clone expressing Eg5(T107N), a dominant point mutation in the P-loop of the ATP binding domain of the motor, appeared to be not only resistant but also dependent on the presence of STLC. Eg5(T107N) expression was associated also with resistance to the clinical relevant loop-L5 Eg5 inhibitors, Arry-520 and ispinesib. Ectopic expression of the Eg5(T107N) mutant in the absence of STLC was associated with strong non-exchangeable binding to microtubules causing them to bundle. Biochemical assays showed that in contrast to the wild type Eg5-STLC complex, the ATP binding site of the Eg5(T107N) is accessible for nucleotide exchange only when the inhibitor is present. We predict that resistance can be overcome by inhibitors that bind to other than the Eg5 loop-L5 binding site having different chemical scaffolds, and that allostery-dependent resistance to Eg5 inhibitors may also occur in cells and may have positive implications in chemotherapy since once diagnosed may be beneficial following cessation of the chemotherapeutic regimen.

Accurate determination of CRISPR-mediated gene fitness in transplantable tumours

Assessing tumour gene fitness in physiologically-relevant model systems is challenging due to biological features of in vivo tumour regeneration, including extreme variations in single cell lineage progeny. Here we develop a reproducible, quantitative approach to pooled genetic perturbation in patient-derived xenografts (PDXs), by encoding single cell output from transplanted CRISPR-transduced cells in combination with a Bayesian hierarchical model. We apply this to 181 PDX transplants from 21 breast cancer patients. We show that uncertainty in fitness estimates depends critically on the number of transplant cell clones and the variability in clone sizes. We use a pathway-directed allelic series to characterize Notch signaling, and quantify TP53 / MDM2 drug-gene conditional fitness in outlier patients. We show that fitness outlier identification can be mirrored by pharmacological perturbation. Overall, we demonstrate that the gene fitness landscape in breast PDXs is dominated by inter-patient differences.

Scales of Cancer Evolution: Selfish Genome or Cooperating Cells?

Simple Summary

Cancer continuously evolves its ability to survive in time-varying microenvironment, which results, regarding the therapeutic context, in its therapeutic resistance. As it is accepted that the development of resistance is the direct consequence of intratumour heterogeneity, its evolutionary etiology is intensively studied. Models of carinogenesis are often assessed accordingly to how well they fit into the evolutionary scenario. In the paper, the relevant observations and concepts in cancer research, such as intratumour heterogeneity, cell plasticity, and Markov cell state dynamics, are reviewed and integrated into an evolutionary model. The possibility that the interaction between cancer cells can be interpreted as cooperation is proposed.

Abstract

The exploitation of the evolutionary modus operandi of cancer to steer its progression towards drug sensitive cancer cells is a challenging research topic. Integrating evolutionary principles into cancer therapy requires properly identified selection level, the relevant timescale, and the respective fitness of the principal selection unit on that timescale. Interpretation of some features of cancer progression, such as increased heterogeneity of isogenic cancer cells, is difficult from the most straightforward evolutionary view with the cancer cell as the principal selection unit. In the paper, the relation between the two levels of intratumour heterogeneity, genetic, due to genetic instability, and non-genetic, due to phenotypic plasticity, is reviewed and the evolutionary role of the latter is outlined. In analogy to the evolutionary optimization in a changing environment, the cell state dynamics in cancer clones are interpreted as the risk diversifying strategy bet hedging, optimizing the balance between the exploitation and exploration of the cell state space.

A Bayesian method to cluster single-cell RNA sequencing data using Copy Number Alterations

MOTIVATION

Cancers are composed by several heterogeneous subpopulations, each one harbouring different genetic and epigenetic somatic alterations that contribute to disease onset and therapy response. In recent years, copy number alterations leading to tumour aneuploidy have been identified as potential key drivers of such populations, but the definition of the precise makeup of cancer subclones from sequencing assays remains challenging. In the end, little is known about the mapping between complex copy number alterations and their effect on cancer phenotypes.

RESULTS

We introduce CONGAS, a Bayesian probabilistic method to phase bulk DNA and single-cell RNA measurements from independent assays. CONGAS jointly identifies clusters of single cells with subclonal copy number alterations, and differences in RNA expression. The model builds statistical priors leveraging bulk DNA sequencing data, does not require a normal reference and scales fast thanks to a GPU backend and variational inference. We test CONGAS on both simulated and real data, and find that it can determine the tumour subclonal composition at the single-cell level together with clone-specific RNA phenotypes in tumour data generated from both 10x and Smart-Seq assays.

AVAILABILITY

CONGAS is available as 2 packages: CONGAS (https://github.com/caravagnalab/congas), which implements the model in Python, and RCONGAS (https://caravagnalab.github.io/rcongas/), which provides R functions to process inputs, outputs, and run CONGAS fits. The analysis of real data and scripts to generate figures of this paper are available via RCONGAS; code associated to simulations is available at https://github.com/caravagnalab/rcongas_test.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DIPG Harbors Alterations Targetable by MEK Inhibitors, with Acquired Resistance Mechanisms Overcome by Combinatorial Inhibition

The survival of children with diffuse intrinsic pontine glioma (DIPG) remains dismal, with new treatments desperately needed. In a prospective biopsy-stratified clinical trial, we combined detailed molecular profiling and drug screening in newly established patient-derived models in vitro and in vivo. We identified in vitro sensitivity to MEK inhibitors in DIPGs harboring MAPK pathway alterations, but treatment of patient-derived xenograft models and a patient at relapse failed to elicit a significant response. We generated trametinib-resistant clones in a BRAFG469V model through continuous drug exposure and identified acquired mutations in MEK1/2 with sustained pathway upregulation. These cells showed hallmarks of mesenchymal transition and expression signatures overlapping with inherently trametinib-insensitive patient-derived cells, predicting sensitivity to dasatinib. Combined trametinib and dasatinib showed highly synergistic effects in vitro and on ex vivo brain slices. We highlight the MAPK pathway as a therapeutic target in DIPG and show the importance of parallel resistance modeling and combinatorial treatments for meaningful clinical translation.

SIGNIFICANCE

We report alterations in the MAPK pathway in DIPGs to confer initial sensitivity to targeted MEK inhibition. We further identify for the first time the mechanism of resistance to single-agent targeted therapy in these tumors and suggest a novel combinatorial treatment strategy to overcome it in the clinic. This article is highlighted in the In This Issue feature, p. 587.

Quantification of long-term doxorubicin response dynamics in breast cancer cell lines to direct treatment schedules

While acquired chemoresistance is recognized as a key challenge to treating many types of cancer, the dynamics with which drug sensitivity changes after exposure are poorly characterized. Most chemotherapeutic regimens call for repeated dosing at regular intervals, and if drug sensitivity changes on a similar time scale then the treatment interval could be optimized to improve treatment performance. Theoretical work suggests that such optimal schedules exist, but experimental confirmation has been obstructed by the difficulty of deconvolving the simultaneous processes of death, adaptation, and regrowth taking place in cancer cell populations. Here we present a method of optimizing drug schedules in vitro through iterative application of experimentally calibrated models, and demonstrate its ability to characterize dynamic changes in sensitivity to the chemotherapeutic doxorubicin in three breast cancer cell lines subjected to treatment schedules varying in concentration, interval between pulse treatments, and number of sequential pulse treatments. Cell populations are monitored longitudinally through automated imaging for 600–800 hours, and this data is used to calibrate a family of cancer growth models, each consisting of a system of ordinary differential equations, derived from the bi-exponential model which characterizes resistant and sensitive subpopulations. We identify a model incorporating both a period of growth arrest in surviving cells and a delay in the death of chemosensitive cells which outperforms the original bi-exponential growth model in Akaike Information Criterion based model selection, and use the calibrated model to quantify the performance of each drug schedule. We find that the inter-treatment interval is a key variable in determining the performance of sequential dosing schedules and identify an optimal retreatment time for each cell line which extends regrowth time by 40%-239%, demonstrating that the time scale of changes in chemosensitivity following doxorubicin exposure allows optimization of drug scheduling by varying this inter-treatment interval.

Acquired chemoresistance is a common cause of treatment failure in cancer. The scheduling of a multi-dose course of chemotherapeutic treatment may influence the dynamics of acquired chemoresistance, and drug schedule optimization may increase the duration of effectiveness of a particular chemotherapeutic agent for a particular patient. Here we present a method for experimentally optimizing an in vitro drug schedule through iterative rounds of experimentation and computational analysis, and demonstrate the method’s ability to improve the performance of doxorubicin treatment in three breast carcinoma cell lines. Specifically, we find that the interval between drug exposures can be optimized while holding drug concentration and number of treatments constant, suggesting that this may be a key variable to explore in future drug schedule optimization efforts. We further use this method’s model calibration and selection process to extract information about the underlying biology of the doxorubicin response, and find that the incorporation of delays on both cell death and regrowth are necessary for accurate parameterization of cell growth data.

Evidence for the early emergence of piperaquine-resistant Plasmodium falciparum malaria and modeling strategies to mitigate resistance

Multidrug-resistant Plasmodium falciparum parasites have emerged in Cambodia and neighboring countries in Southeast Asia, compromising the efficacy of first-line antimalarial combinations. Dihydroartemisinin + piperaquine (PPQ) treatment failure rates have risen to as high as 50% in some areas in this region. For PPQ, resistance is driven primarily by a series of mutant alleles of the P. falciparum chloroquine resistance transporter (PfCRT). PPQ resistance was reported in China three decades earlier, but the molecular driver remained unknown. Herein, we identify a PPQ-resistant pfcrt allele (China C) from Yunnan Province, China, whose genotypic lineage is distinct from the PPQ-resistant pfcrt alleles currently observed in Cambodia. Combining gene editing and competitive growth assays, we report that PfCRT China C confers moderate PPQ resistance while re-sensitizing parasites to chloroquine (CQ) and incurring a fitness cost that manifests as a reduced rate of parasite growth. PPQ transport assays using purified PfCRT isoforms, combined with molecular dynamics simulations, highlight differences in drug transport kinetics and in this transporter’s central cavity conformation between China C and the current Southeast Asian PPQ-resistant isoforms. We also report a novel computational model that incorporates empirically determined fitness landscapes at varying drug concentrations, combined with antimalarial susceptibility profiles, mutation rates, and drug pharmacokinetics. Our simulations with PPQ-resistant or -sensitive parasite lines predict that a three-day regimen of PPQ combined with CQ can effectively clear infections and prevent the evolution of PfCRT variants. This work suggests that including CQ in combination therapies could be effective in suppressing the evolution of PfCRT-mediated multidrug resistance in regions where PPQ has lost efficacy.

The recent emergence of Plasmodium falciparum parasite resistance to the antimalarial drug piperaquine (PPQ) has contributed to frequent treatment failures across Southeast Asia, originating in Cambodia. Here, we show that earlier reports of PPQ resistance in Yunnan Province, China could be explained by the unique China C variant of the P. falciparum chloroquine resistance transporter PfCRT. Gene-edited parasites show a loss of fitness and parasite resensitization to the chemically related former first-line antimalarial chloroquine, while acquiring PPQ resistance via drug efflux. Molecular features of drug resistance were examined using biochemical assays to measure mutant PfCRT-mediated drug transport and molecular dynamics simulations with the recently solved PfCRT structure to assess changes in the central drug-binding cavity. We also describe a new computational model that incorporates parasite mutation rates, fitness costs, antimalarial susceptibilities, and drug pharmacological profiles to predict how infections with parasite strains expressing distinct PfCRT variants can evolve and be selected in response to different drug pressures and regimens. Simulations predict that a three-day regimen of PPQ plus chloroquine would be fully effective at preventing recrudescence of drug-resistant infections.

Single-cell profiling of tumour evolution in multiple myeloma - opportunities for precision medicine

Multiple myeloma (MM) is a haematological malignancy of plasma cells characterized by substantial intraclonal genetic heterogeneity. Although therapeutic advances made in the past few years have led to improved outcomes and longer survival, MM remains largely incurable. Over the past decade, genomic analyses of patient samples have demonstrated that MM is not a single disease but rather a spectrum of haematological entities that all share similar clinical symptoms. Moreover, analyses of samples from monoclonal gammopathy of undetermined significance and smouldering MM have also shown the existence of genetic heterogeneity in precursor stages, in some cases remarkably similar to that of MM. This heterogeneity highlights the need for a greater dissection of underlying disease biology, especially the clonal diversity and molecular events underpinning MM at each stage to enable the stratification of individuals with a high risk of progression. Emerging single-cell sequencing technologies present a superlative solution to delineate the complexity of monoclonal gammopathy of undetermined significance, smouldering MM and MM. In this Review, we discuss how genomics has revealed novel insights into clonal evolution patterns of MM and provide examples from single-cell studies that are beginning to unravel the mutational and phenotypic characteristics of individual cells within the bone marrow tumour, immune microenvironment and peripheral blood. We also address future perspectives on clinical application, proposing that multi-omics single-cell profiling can guide early patient diagnosis, risk stratification and treatment strategies.

Combination treatment optimization using a pan-cancer pathway model

The design of efficient combination therapies is a difficult key challenge in the treatment of complex diseases such as cancers. The large heterogeneity of cancers and the large number of available drugs renders exhaustive in vivo or even in vitro investigation of possible treatments impractical. In recent years, sophisticated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at a molecular level have been developed. However, surprisingly little effort has been put into leveraging these models to find novel therapies. In this paper we use for the first time, to our knowledge, a large-scale state-of-the-art pan-cancer signaling pathway model to identify candidates for novel combination therapies to treat individual cancer cell lines from various tissues (e.g., minimizing proliferation while keeping dosage low to avoid adverse side effects) and populations of heterogeneous cancer cell lines (e.g., minimizing the maximum or average proliferation across the cell lines while keeping dosage low). We also show how our method can be used to optimize the drug combinations used in sequential treatment plans-that is, optimized sequences of potentially different drug combinations-providing additional benefits. In order to solve the treatment optimization problems, we combine the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm with a significantly more scalable sampling scheme for truncated Gaussian distributions, based on a Hamiltonian Monte-Carlo method. These optimization techniques are independent of the signaling pathway model, and can thus be adapted to find treatment candidates for other complex diseases than cancers as well, as long as a suitable predictive model is available.

Targeted therapy for LIMD1-deficient non-small cell lung cancer subtypes

An early event in lung oncogenesis is loss of the tumour suppressor gene LIMD1 (LIM domains containing 1); this encodes a scaffold protein, which suppresses tumorigenesis via a number of different mechanisms. Approximately 45% of non-small cell lung cancers (NSCLC) are deficient in LIMD1, yet this subtype of NSCLC has been overlooked in preclinical and clinical investigations. Defining therapeutic targets in these LIMD1 loss-of-function patients is difficult due to a lack of 'druggable' targets, thus alternative approaches are required. To this end, we performed the first drug repurposing screen to identify compounds that confer synthetic lethality with LIMD1 loss in NSCLC cells. PF-477736 was shown to selectively target LIMD1-deficient cells in vitro through inhibition of multiple kinases, inducing cell death via apoptosis. Furthermore, PF-477736 was effective in treating LIMD1-/- tumours in subcutaneous xenograft models, with no significant effect in LIMD1+/+ cells. We have identified a novel drug tool with significant preclinical characterisation that serves as an excellent candidate to explore and define LIMD1-deficient cancers as a new therapeutic subgroup of critical unmet need.

DNMT3A Harboring Leukemia-Associated Mutations Directs Sensitivity to DNA Damage at Replication Forks

PURPOSE

In acute myeloid leukemia (AML), recurrent DNA methyltransferase 3A (DNMT3A) mutations are associated with chemoresistance and poor prognosis, especially in advanced-age patients. Gene-expression studies in DNMT3A-mutated cells identified signatures implicated in deregulated DNA damage response and replication fork integrity, suggesting sensitivity to replication stress. Here, we tested whether pharmacologically induced replication fork stalling, such as with cytarabine, creates a therapeutic vulnerability in cells with DNMT3A(R882) mutations.

EXPERIMENTAL DESIGN

Leukemia cell lines, genetic mouse models, and isogenic cells with and without DNMT3A(mut) were used to evaluate sensitivity to nucleoside analogues such as cytarabine in vitro and in vivo, followed by analysis of DNA damage and signaling, replication restart, and cell-cycle progression on treatment and after drug removal. Transcriptome profiling identified pathways deregulated by DNMT3A(mut) expression.

RESULTS

We found increased sensitivity to pharmacologically induced replication stress in cells expressing DNMT3A(R882)-mutant, with persistent intra-S-phase checkpoint activation, impaired PARP1 recruitment, and elevated DNA damage, which was incompletely resolved after drug removal and carried through mitosis. Pulse-chase double-labeling experiments with EdU and BrdU after cytarabine washout demonstrated a higher rate of fork collapse in DNMT3A(mut)-expressing cells. RNA-seq studies supported deregulated cell-cycle progression and p53 activation, along with splicing, ribosome biogenesis, and metabolism.

CONCLUSIONS

Together, our studies show that DNMT3A mutations underlie a defect in recovery from replication fork arrest with subsequent accumulation of unresolved DNA damage, which may have therapeutic tractability. These results demonstrate that, in addition to its role in epigenetic control, DNMT3A contributes to preserving genome integrity during replication stress.

Harnessing the predictive power of preclinical models for oncology drug development

Recent progress in understanding the molecular basis of cellular processes, identification of promising therapeutic targets and evolution of the regulatory landscape makes this an exciting and unprecedented time to be in the field of oncology drug development. However, high costs, long development timelines and steep rates of attrition continue to afflict the drug development process. Lack of predictive preclinical models is considered one of the key reasons for the high rate of attrition in oncology. Generating meaningful and predictive results preclinically requires a firm grasp of the relevant biological questions and alignment of the model systems that mirror the patient context. In doing so, the ability to conduct both forward translation, the process of implementing basic research discoveries into practice, as well as reverse translation, the process of elucidating the mechanistic basis of clinical observations, greatly enhances our ability to develop effective anticancer treatments. In this Review, we outline issues in preclinical-to-clinical translatability of molecularly targeted cancer therapies, present concepts and examples of successful reverse translation, and highlight the need to better align tumour biology in patients with preclinical model systems including tracking of strengths and weaknesses of preclinical models throughout programme development.

Clonal Architecture and Evolutionary Dynamics in Acute Myeloid Leukemias

Acute myeloid leukemias (AML) results from the accumulation of genetic and epigenetic alterations, often in the context of an aging hematopoietic environment. The development of high-throughput sequencing-and more recently, of single-cell technologies-has shed light on the intratumoral diversity of leukemic cells. Taking AML as a model disease, we review the multiple sources of genetic, epigenetic, and functional heterogeneity of leukemic cells and discuss the definition of a leukemic clone extending its definition beyond genetics. After introducing the two dimensions contributing to clonal diversity, namely, richness (number of leukemic clones) and evenness (distribution of clone sizes), we discuss the mechanisms at the origin of clonal emergence (mutation rate, number of generations, and effective size of the leukemic population) and the causes of clonal dynamics. We discuss the possible role of neutral drift, but also of cell-intrinsic and -extrinsic influences on clonal fitness. After reviewing available data on the prognostic role of genetic and epigenetic diversity of leukemic cells on patients' outcome, we discuss how a better understanding of AML as an evolutionary process could lead to the design of novel therapeutic strategies in this disease.

Darwinian Approaches for Cancer Treatment: Benefits of Mathematical Modeling

One of the major problems of traditional anti-cancer treatments is that they lead to the emergence of treatment-resistant cells, which results in treatment failure. To avoid or delay this phenomenon, it is relevant to take into account the eco-evolutionary dynamics of tumors. Designing evolution-based treatment strategies may help overcoming the problem of drug resistance. In particular, a promising candidate is adaptive therapy, a containment strategy which adjusts treatment cycles to the evolution of the tumors in order to keep the population of treatment-resistant cells under control. Mathematical modeling is a crucial tool to understand the dynamics of cancer in response to treatments, and to make predictions about the outcomes of these treatments. In this review, we highlight the benefits of in silico modeling to design adaptive therapy strategies, and to assess whether they could effectively improve treatment outcomes. Specifically, we review how two main types of models (i.e., mathematical models based on Lotka-Volterra equations and agent-based models) have been used to model tumor dynamics in response to adaptive therapy. We give examples of the advances they permitted in the field of adaptive therapy and discuss about how these models can be integrated in experimental approaches and clinical trial design.