Intratumor heterogeneity alters most effective drugs in designed combinations

Intratumoral heterogeneity and drug resistance in cancer

Intratumoral heterogeneity is the main cause of tumor treatment failure, varying across disease sites (spatial heterogeneity) and polyclonal properties of tumors that evolve over time (temporal heterogeneity). As our understanding of intratumoral heterogeneity, the formation of which is mainly related to the genomic instability, epigenetic modifications, plastic gene expression, and different microenvironments, plays a substantial role in drug-resistant as far as tumor metastasis and recurrence. Understanding the role of intratumoral heterogeneity, it becomes clear that a single therapeutic agent or regimen may only be effective for subsets of cells with certain features, but not for others. This necessitates a shift from our current, unchanging treatment approach to one that is tailored against the killing patterns of cancer cells in different clones. In this review, we discuss recent evidence concerning global perturbations of intratumoral heterogeneity, associations of specific intratumoral heterogeneity in lung cancer, the underlying mechanisms of intratumoral heterogeneity potentially leading to formation, and how it drives drug resistance. Our findings highlight the most up-to-date progress in intratumoral heterogeneity and its role in mediating tumor drug resistance, which could support the development of future treatment strategies.

Prediction of Patient Drug Response via 3D Bioprinted Gastric Cancer Model Utilized Patient-Derived Tissue Laden Tissue-Specific Bioink

Despite significant research progress, tumor heterogeneity remains elusive, and its complexity poses a barrier to anticancer drug discovery and cancer treatment. Response to the same drug varies across patients, and the timing of treatment is an important factor in determining prognosis. Therefore, development of patient-specific preclinical models that can predict a patient's drug response within a short period is imperative. In this study, a printed gastric cancer (pGC) model is developed for preclinical chemotherapy using extrusion-based 3D bioprinting technology and tissue-specific bioinks containing patient-derived tumor chunks. The pGC model retained the original tumor characteristics and enabled rapid drug evaluation within 2 weeks of its isolation from the patient. In fact, it is confirmed that the drug response-related gene profile of pGC tissues co-cultured with human gastric fibroblasts (hGaFibro) is similar to that of patient tissues. This suggested that the application of the pGC model can potentially overcome the challenges associated with accurate drug evaluation in preclinical models (e.g., patient-derived xenografts) owing to the deficiency of stromal cells derived from the patient. Consequently, the pGC model manifested a remarkable similarity with patients in terms of response to chemotherapy and prognostic predictability. Hence, it is considered a promising preclinical tool for personalized and precise treatments.

Allostery in Disease: Anticancer Drugs, Pockets, and the Tumor Heterogeneity Challenge

Charting future innovations is challenging. Yet, allosteric and orthosteric anticancer drugs are undergoing a revolution and taxing unresolved dilemmas await. Among the imaginative innovations, here we discuss cereblon and thalidomide derivatives as a means of recruiting neosubstrates and their degradation, allosteric heterogeneous bifunctional drugs like PROTACs, drugging phosphatases, inducers of targeted posttranslational protein modifications, antibody-drug conjugates, exploiting membrane interactions to increase local concentration, stabilizing the folded state, and more. These couple with harnessing allosteric cryptic pockets whose discovery offers more options to modulate the affinity of orthosteric, active site inhibitors. Added to these are strategies to counter drug resistance through drug combinations co-targeting pathways to bypass signaling blockades. Here, we discuss on the molecular and cellular levels, such inspiring advances, provide examples of their applications, their mechanisms and rational. We start with an overview on difficult to target proteins and their properties-rarely, if ever-conceptualized before, discuss emerging innovative drugs, and proceed to the increasingly popular allosteric cryptic pockets-their advantages-and critically, issues to be aware of. We follow with drug resistance and in-depth discussion of tumor heterogeneity. Heterogeneity is a hallmark of highly aggressive cancers, the core of drug resistance unresolved challenge. We discuss potential ways to target heterogeneity by predicting it. The increase in experimental and clinical data, computed (cell-type specific) interactomes, capturing transient cryptic pockets, learned drug resistance, workings of regulatory mechanisms, heterogeneity, and resistance-based cell signaling drug combinations, assisted by AI-driven reasoning and recognition, couple with creative allosteric drug discovery, charting future innovations.

Designing SERS nanotags for profiling overexpressed surface markers on single cancer cells: A review

This review focuses on the chemical design and the use of Surface-Enhanced Raman Scattering (SERS)-active nanotags for measuring surface markers that can be overexpressed at the surface of single cancer cells. Indeed, providing analytical tools with true single-cell measurements capabilities is capital, especially since cancer research is increasingly leaning toward single-cell analysis, either to guide treatment decisions or to understand complex tumor behaviour including the single-cell heterogeneity and the appearance of treatment resistance. Over the past two decades, SERS nanotags have triggered significant interest in the scientific community owing their advantages over fluorescent tags, mainly because SERS nanotags resist photobleaching and exhibit sharper signal bands, which reduces possible spectral overlap and enables the discrimination between the SERS signals and the autofluorescence background from the sample itself. The extensive efforts invested in harnessing SERS nanotags for biomedical purposes, particularly in cancer research, highlight their potential as the next generation of optical labels for single-cell studies. The review unfolds in two main parts. The first part focuses on the structure of SERS nanotags, detailing their chemical composition and the role of each building block of the tags. The second part explores applications in measuring overexpressed surface markers on single-cells. The latter encompasses studies using single nanotags, multiplexed measurements, quantitative information extraction, monitoring treatment responses, and integrating phenotype measurements with SERS nanotags on single cells isolated from complex biological matrices. This comprehensive review anticipates SERS nanotags to persist as a pivotal technology in advancing single-cell analytical methods, particularly in the context of cancer research and personalized medicine.

Relatively Rare Populations of Invasive Cells Drive Progression of Heterogeneous Tumors

Introduction

Breast tumors often display an astonishing degree of spatial and temporal heterogeneity, which are associated with cancer progression, drug resistance, and relapse. Triple-negative breast cancer (TNBC) is a particularly aggressive and heterogeneous subtype for which targeted therapies are scarce. Consequently, patients with TNBC have a poorer overall prognosis compared to other breast cancer patients. Within heterogeneous tumors, individual clonal subpopulations may exhibit differences in their rates of growth and degrees of invasiveness. We hypothesized that such phenotypic heterogeneity at the single-cell level may accelerate tumor progression by enhancing the overall growth and invasion of the entire tumor.

Methods

To test this hypothesis, we isolated and characterized clonal subpopulations with distinct morphologies and biomarker expression from the inherently heterogeneous 4T1 mouse mammary carcinoma cell line. We then leveraged a 3D microfluidic tumor model to reverse-engineer intratumoral heterogeneity and thus investigate how interactions between phenotypically distinct subpopulations affect tumor growth and invasion.

Results

We found that the growth and invasion of multiclonal tumors were largely dictated by the presence of cells with epithelial and mesenchymal traits, respectively. The latter accelerated overall tumor invasion, even when these cells comprised less than 1% of the initial population. Consistently, tumor progression was delayed by selectively targeting the mesenchymal subpopulation.

Discussion

This work reveals that highly invasive cells can dominate tumor phenotype and that specifically targeting these cells can slow the progression of heterogeneous tumors, which may help inform therapeutic approaches.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12195-023-00792-w.

Modeling early treatment response in AML from cell-free tumor DNA

Monitoring disease response after intensive chemotherapy for acute myeloid leukemia (AML) currently requires invasive bone marrow biopsies, imposing a significant burden on patients. In contrast, cell-free tumor DNA (ctDNA) in peripheral blood, carrying tumor-specific mutations, offers a less-invasive assessment of residual disease. However, the relationship between ctDNA levels and bone marrow blast kinetics remains unclear. We explored this in 10 AML patients with NPM1 and IDH2 mutations undergoing initial chemotherapy. Comparison of mathematical mixed-effect models showed that (1) inclusion of blast cell death in the bone marrow, (2) transition of ctDNA to peripheral blood, and (3) ctDNA decay in peripheral blood describes kinetics of blast cells and ctDNA best. The fitted model allows prediction of residual bone marrow blast content from ctDNA, and its scaling factor, representing clonal heterogeneity, correlates with relapse risk. Our study provides precise insights into blast and ctDNA kinetics, offering novel avenues for AML disease monitoring.

In Vivo Biorthogonal Antibody Click for Dual Targeting and Augmented Efficacy in Cancer Treatment

Antibody-drug conjugates (ADCs) have emerged as promising therapeutics for cancer treatment; however, their effectiveness has been limited by single antigen targeting, potentially leading to resistance mechanisms triggered by tumor compensatory pathways or reduced expression of the target protein. Here, we present antibody-ADC click, an approach that harnesses bioorthogonal click chemistry for in vivo dual receptor targeting, irrespective of the levels of the tumor's expression of the ADC-targeting antigen. Antibody-ADC click enables targeting heterogeneity and enhances antibody internalization and drug delivery inside cancer cells, resulting in potent toxicity. We conjugated antibodies and ADCs to the bioorthogonal click moieties tetrazine (Tz) and trans-cyclooctene (TCO). Through sequential antibody administration in living biological systems, we achieved dual receptor targeting by in vivo clicking of antibody-TCO with antibody-Tz. We show that the clicked antibody therapy outperformed conventional ADC monotherapy or antibody combinations in preclinical models mimicking ADC-eligible, ADC-resistant, and ADC-ineligible tumors. Antibody-ADC click enables in vivo dual-antigen targeting without extensive antibody bioengineering, sustains tumor treatment, and enhances antibody-mediated cytotoxicity.

Biphasic JNK-Erk signaling separates the induction and maintenance of cell senescence after DNA damage induced by topoisomerase II inhibition

Genotoxic stress in mammalian cells, including those caused by anti-cancer chemotherapy, can induce temporary cell-cycle arrest, DNA damage-induced senescence (DDIS), or apoptotic cell death. Despite obvious clinical importance, it is unclear how the signals emerging from DNA damage are integrated together with other cellular signaling pathways monitoring the cell's environment and/or internal state to control different cell fates. Using single-cell-based signaling measurements combined with tensor partial least square regression (t-PLSR)/principal component analysis (PCA) analysis, we show that JNK and Erk MAPK signaling regulates the initiation of cell senescence through the transcription factor AP-1 at early times after doxorubicin-induced DNA damage and the senescence-associated secretory phenotype (SASP) at late times after damage. These results identify temporally distinct roles for signaling pathways beyond the classic DNA damage response (DDR) that control the cell senescence decision and modulate the tumor microenvironment and reveal fundamental similarities between signaling pathways responsible for oncogene-induced senescence (OIS) and senescence caused by topoisomerase II inhibition. A record of this paper's transparent peer review process is included in the supplemental information.

Challenges and Opportunities Modeling the Dynamic Tumor Matrisome

We need novel strategies to target the complexity of cancer and, particularly, of metastatic disease. As an example of this complexity, certain tissues are particularly hospitable environments for metastases, whereas others do not contain fertile microenvironments to support cancer cell growth. Continuing evidence that the extracellular matrix (ECM) of tissues is one of a host of factors necessary to support cancer cell growth at both primary and secondary tissue sites is emerging. Research on cancer metastasis has largely been focused on the molecular adaptations of tumor cells in various cytokine and growth factor environments on 2-dimensional tissue culture polystyrene plates. Intravital imaging, conversely, has transformed our ability to watch, in real time, tumor cell invasion, intravasation, extravasation, and growth. Because the interstitial ECM that supports all cells in the tumor microenvironment changes over time scales outside the possible window of typical intravital imaging, bioengineers are continuously developing both simple and sophisticated in vitro controlled environments to study tumor (and other) cell interactions with this matrix. In this perspective, we focus on the cellular unit responsible for upholding the pathologic homeostasis of tumor-bearing organs, cancer-associated fibroblasts (CAFs), and their self-generated ECM. The latter, together with tumoral and other cell secreted factors, constitute the "tumor matrisome". We share the challenges and opportunities for modeling this dynamic CAF/ECM unit, the tools and techniques available, and how the tumor matrisome is remodeled (e.g., via ECM proteases). We posit that increasing information on tumor matrisome dynamics may lead the field to alternative strategies for personalized medicine outside genomics.

Lactate-dependent chaperone-mediated autophagy induces oscillatory HIF-1α activity promoting proliferation of hypoxic cells

Response to hypoxia is a highly regulated process, but little is known about single-cell responses to hypoxic conditions. Using fluorescent reporters of hypoxia response factor-1α (HIF-1α) activity in various cancer cell lines and patient-derived cancer cells, we show that hypoxic responses in individual cancer cells can be highly dynamic and variable. These responses fall into three classes, including oscillatory activity. We identify a molecular mechanism that can account for all three response classes, implicating reactive-oxygen-species-dependent chaperone-mediated autophagy of HIF-1α in a subset of cells. Furthermore, we show that oscillatory response is modulated by the abundance of extracellular lactate in a quorum-sensing-like mechanism. We show that oscillatory HIF-1α activity rescues hypoxia-mediated inhibition of cell division and causes broad suppression of genes downregulated in cancers and activation of genes upregulated in many cancers, suggesting a mechanism for aggressive growth in a subset of hypoxic tumor cells.

A Review of the Recent Advances in Alzheimer's Disease Research and the Utilization of Network Biology Approaches for Prioritizing Diagnostics and Therapeutics

Alzheimer's disease (AD) is a polygenic multifactorial neurodegenerative disease that, after decades of research and development, is still without a cure. There are some symptomatic treatments to manage the psychological symptoms but none of these drugs can halt disease progression. Additionally, over the last few years, many anti-AD drugs failed in late stages of clinical trials and many hypotheses surfaced to explain these failures, including the lack of clear understanding of disease pathways and processes. Recently, different epigenetic factors have been implicated in AD pathogenesis; thus, they could serve as promising AD diagnostic biomarkers. Additionally, network biology approaches have been suggested as effective tools to study AD on the systems level and discover multi-target-directed ligands as novel treatments for AD. Herein, we provide a comprehensive review on Alzheimer's disease pathophysiology to provide a better understanding of disease pathogenesis hypotheses and decipher the role of genetic and epigenetic factors in disease development and progression. We also provide an overview of disease biomarkers and drug targets and suggest network biology approaches as new tools for identifying novel biomarkers and drugs. We also posit that the application of machine learning and artificial intelligence to mining Alzheimer's disease multi-omics data will facilitate drug and biomarker discovery efforts and lead to effective individualized anti-Alzheimer treatments.

Single-cell biology uncovers apoptotic cell death and its spatial organization as a potential modifier of tumor diversity in HCC

BACKGROUND AND AIMS

HCC is a highly aggressive and heterogeneous cancer type with limited treatment options. Identifying drivers of tumor heterogeneity may lead to better therapeutic options and favorable patient outcomes. We investigated whether apoptotic cell death and its spatial architecture is linked to tumor molecular heterogeneity using single-cell in situ hybridization analysis.

APPROACH AND RESULTS

We analyzed 254 tumor samples from two HCC cohorts using tissue microarrays. We developed a mathematical model to quantify cellular diversity among HCC samples using two tumor markers, cyclin-dependent kinase inhibitor 3 and protein regulator of cytokinesis 1 as surrogates for heterogeneity and caspase 3 (CASP3) as an apoptotic cell death marker. We further explored the impact of potential dying-cell hubs on tumor cell diversity and patient outcome by density contour mapping and spatial proximity analysis. We also developed a selectively controlled in vitro model of cell death using CRISPR/CRISPR-associated 9 to determine therapy response and growth under hypoxic conditions. We found that increasing levels of CASP3⁺ tumor cells are associated with higher tumor diversity. Interestingly, we discovered regions of densely populated CASP3⁺ , which we refer to as CASP3⁺ cell islands, in which the nearby cellular heterogeneity was found to be the greatest compared to cells farther away from these islands and that this phenomenon was associated with survival. Additionally, cell culture experiments revealed that higher levels of cell death, accompanied by increased CASP3 expression, led to greater therapy resistance and growth under hypoxia.

CONCLUSIONS

These results are consistent with the hypothesis that increased apoptotic cell death may lead to greater tumor heterogeneity and thus worse patient outcomes.

Recent advances in the development of active hybrid molecules in the treatment of cardiovascular diseases

Multifactorial nature of the underlying pathophysiology of chronic disorders hinders in the effective treatment and management of many complex diseases. The conventional targeted therapies have limited applications due to highly complicated disease etiology. Cardiovascular diseases (CVDs) are the group of disorders of the heart and blood vessels. Currently, there is limited knowledge on the underlying cellular and molecular mechanisms of many of the CVDs due to their complex pathophysiology and co-morbidities. Their management with conventional medications results in failure due to adverse drug reactions and clinical specificity of solo-targeting drug therapy. Therefore, it is critical to introduce an alternative strategy to treat multi-factorial diseases. In the past few years, discovery and use of multi-targeted drug therapy with hybrid molecules have shown promising results with minimal side effects, and thus considered a most effective approach. In this review article, prominent hybrid molecules combining with different active moieties are reported to synergistically and simultaneously block different pathways involved in CVDs. Here, we provide a critical evaluation and discussion on their pharmacology with mechanistic insights and the structure activity relationship. The timely information provided in this article reveals the recent trends of molecular hybridization to the scientific community interested in CVDs and help them in designing the next generation of multi-targeting drug therapeutics.

Combination of tucatinib and neural stem cells secreting anti-HER2 antibody prolongs survival of mice with metastatic brain cancer

Brain metastases are among the most severe complications of systemic breast cancer, and overexpression of the human epidermal growth factor receptor 2 (HER2) in breast cancer cells increases the incidence of brain metastases in patients. In this study, we engineered the human-derived, tumor cell tropic neural stem cells LM-NSC008 (LM008) to continuously secrete antibodies against HER2. These anti-HER2 antibodies impaired tumor cell proliferation by inhibiting the PI3K-Akt signaling pathway in HER2+ breast cancer cells in vitro. Importantly, our results demonstrate that the therapeutic combinatorial regimen consisting of LM-NSC008 anti-HER2 antibody-secreting cells and the HER2 kinase inhibitor tucatinib provide therapeutic benefit and prolong survival in preclinical models of HER2+ breast cancer brain metastases.

Brain metastases are a leading cause of death in patients with breast cancer. The lack of clinical trials and the presence of the blood–brain barrier limit therapeutic options. Furthermore, overexpression of the human epidermal growth factor receptor 2 (HER2) increases the incidence of breast cancer brain metastases (BCBM). HER2-targeting agents, such as the monoclonal antibodies trastuzumab and pertuzumab, improved outcomes in patients with breast cancer and extracranial metastases. However, continued BCBM progression in breast cancer patients highlighted the need for novel and effective targeted therapies against intracranial metastases. In this study, we engineered the highly migratory and brain tumor tropic human neural stem cells (NSCs) LM008 to continuously secrete high amounts of functional, stable, full-length antibodies against HER2 (anti-HER2Ab) without compromising the stemness of LM008 cells. The secreted anti-HER2Ab impaired tumor cell proliferation in vitro in HER2+ BCBM cells by inhibiting the PI3K-Akt signaling pathway and resulted in a significant benefit when injected in intracranial xenograft models. In addition, dual HER2 blockade using anti-HER2Ab LM008 NSCs and the tyrosine kinase inhibitor tucatinib significantly improved the survival of mice in a clinically relevant model of multiple HER2+ BCBM. These findings provide compelling evidence for the use of HER2Ab-secreting LM008 NSCs in combination with tucatinib as a promising therapeutic regimen for patients with HER2+ BCBM.

Strategies to Reduce Long-Term Drug Resistance by Considering Effects of Differential Selective Treatments

Despite great advances in modeling and cancer therapy using optimal control theory, tumor heterogeneity and drug resistance are major obstacles in cancer treatments. Since recent biological studies demonstrated the evidence of tumor heterogeneity and assessed potential biological and clinical implications, tumor heterogeneity should be taken into account in the optimal control problem to improve treatment strategies. Here, first we study the effects of two different treatment strategies (i.e., symmetric and asymmetric) in a minimal two-population model to examine the long-term effects of these treatment methods on the system. Second, by considering tumor adaptation to treatment as a factor of the cost function, the optimal treatment strategy is derived. Numerical examples show that optimal treatment decreases tumor burden for the long-term by decreasing rate of tumor adaptation over time.

A narrative review of tumor heterogeneity and challenges to tumor drug therapy

Objective

To accurately evaluate tumor heterogeneity, make multidimensional diagnosis according to the causes and phenotypes of tumor heterogeneity, and assist in the individualized treatment of tumors.

Background

Tumor heterogeneity is one of the most essential characteristics of malignant tumors. In tumor recurrence, development, and evolution, tumor heterogeneity can lead to the formation of different cell groups with other molecular characteristics. Tumor heterogeneity can be characterized by the uneven distribution of tumor cell subsets of other genes between and within the disease site (spatial heterogeneity) or the time change of cancer cell molecular composition (temporal heterogeneity). The discovery of tumor targeting drugs has dramatically promoted tumor therapy. However, the existence of heterogeneity seriously affects the effect of tumor treatment and the prognosis of patients.

Methods

The literature discussing tumor heterogeneity and its resistance to tumor therapy was broadly searched to analyze tumor heterogeneity as well as the challenges and solutions for gene detection and tumor drug therapy.

Conclusions

Tumor heterogeneity is affected by many factors consist of internal cell factors and cell microenvironment. Tumor heterogeneity greatly hinders effective and individualized tumor treatment. Understanding the fickle of tumors in multiple dimensions and flexibly using a variety of detection methods to capture the changes of tumors can help to improve the design of diagnosis and treatment plans for cancer and benefit millions of patients.

Bivalent chromatin as a therapeutic target in cancer: An in silico predictive approach for combining epigenetic drugs

Tumour cell heterogeneity is a major barrier for efficient design of targeted anti-cancer therapies. A diverse distribution of phenotypically distinct tumour-cell subpopulations prior to drug treatment predisposes to non-uniform responses, leading to the elimination of sensitive cancer cells whilst leaving resistant subpopulations unharmed. Few strategies have been proposed for quantifying the variability associated to individual cancer-cell heterogeneity and minimizing its undesirable impact on clinical outcomes. Here, we report a computational approach that allows the rational design of combinatorial therapies involving epigenetic drugs against chromatin modifiers. We have formulated a stochastic model of a bivalent transcription factor that allows us to characterise three different qualitative behaviours, namely: bistable, high- and low-gene expression. Comparison between analytical results and experimental data determined that the so-called bistable and high-gene expression behaviours can be identified with undifferentiated and differentiated cell types, respectively. Since undifferentiated cells with an aberrant self-renewing potential might exhibit a cancer/metastasis-initiating phenotype, we analysed the efficiency of combining epigenetic drugs against the background of heterogeneity within the bistable sub-ensemble. Whereas single-targeted approaches mostly failed to circumvent the therapeutic problems represented by tumour heterogeneity, combinatorial strategies fared much better. Specifically, the more successful combinations were predicted to involve modulators of the histone H3K4 and H3K27 demethylases KDM5 and KDM6A/UTX. Those strategies involving the H3K4 and H3K27 methyltransferases MLL2 and EZH2, however, were predicted to be less effective. Our theoretical framework provides a coherent basis for the development of an in silico platform capable of identifying the epigenetic drugs combinations best-suited to therapeutically manage non-uniform responses of heterogenous cancer cell populations.

An in vitro model of tumor heterogeneity resolves genetic, epigenetic, and stochastic sources of cell state variability

Tumor heterogeneity is a primary cause of treatment failure and acquired resistance in cancer patients. Even in cancers driven by a single mutated oncogene, variability in response to targeted therapies is well known. The existence of additional genomic alterations among tumor cells can only partially explain this variability. As such, nongenetic factors are increasingly seen as critical contributors to tumor relapse and acquired resistance in cancer. Here, we show that both genetic and nongenetic factors contribute to targeted drug response variability in an experimental model of tumor heterogeneity. We observe significant variability to epidermal growth factor receptor (EGFR) inhibition among and within multiple versions and clonal sublines of PC9, a commonly used EGFR mutant nonsmall cell lung cancer (NSCLC) cell line. We resolve genetic, epigenetic, and stochastic components of this variability using a theoretical framework in which distinct genetic states give rise to multiple epigenetic "basins of attraction," across which cells can transition driven by stochastic noise. Using mutational impact analysis, single-cell differential gene expression, and correlations among Gene Ontology (GO) terms to connect genomics to transcriptomics, we establish a baseline for genetic differences driving drug response variability among PC9 cell line versions. Applying the same approach to clonal sublines, we conclude that drug response variability in all but one of the sublines is due to epigenetic differences; in the other, it is due to genetic alterations. Finally, using a clonal drug response assay together with stochastic simulations, we attribute subclonal drug response variability within sublines to stochastic cell fate decisions and confirm that one subline likely contains genetic resistance mutations that emerged in the absence of drug treatment.

Extending the lymphoblastoid cell line model for drug combination pharmacogenomics

Combination drug therapies have become an integral part of precision oncology, and while evidence of clinical effectiveness continues to grow, the underlying mechanisms supporting synergy are poorly understood. Immortalized human lymphoblastoid cell lines (LCLs) have been proven as a particularly useful, scalable and low-cost model in pharmacogenetics research, and are suitable for elucidating the molecular mechanisms of synergistic combination therapies. In this review, we cover the advantages of LCLs in synergy pharmacogenomics and consider recent studies providing initial evidence of the utility of LCLs in synergy research. We also discuss several opportunities for LCL-based systems to address gaps in the research through the expansion of testing regimens, assessment of new drug classes and higher-order combinations, and utilization of integrated omics technologies.

The Heterogeneity of Lipid Metabolism in Cancer

The study of cancer cell metabolism has traditionally focused on glycolysis and glutaminolysis. However, lipidomic technologies have matured considerably over the last decade and broadened our understanding of how lipid metabolism is relevant to cancer biology [1-3]. Studies now suggest that the reprogramming of cellular lipid metabolism contributes directly to malignant transformation and progression [4, 5]. For example, de novo lipid synthesis can supply proliferating tumor cells with phospholipid components that comprise the plasma and organelle membranes of new daughter cells [6, 7]. Moreover, the upregulation of mitochondrial β-oxidation can support tumor cell energetics and redox homeostasis [8], while lipid-derived messengers can regulate major signaling pathways or coordinate immunosuppressive mechanisms [9-11]. Lipid metabolism has, therefore, become implicated in a variety of oncogenic processes, including metastatic colonization, drug resistance, and cell differentiation [10, 12-16]. However, whether we can safely and effectively modulate the underlying mechanisms of lipid metabolism for cancer therapy is still an open question.

A genome-scale CRISPR Cas9 dropout screen identifies synthetically lethal targets in SRC-3 inhibited cancer cells

Steroid receptor coactivator 3 (SRC-3/NCoA3/AIB1), is a key regulator of gene transcription and it plays a central role in breast cancer (BC) tumorigenesis, making it a potential therapeutic target. Beyond its function as an important regulator of estrogen receptor transcriptional activity, SRC-3 also functions as a coactivator for a wide range of other transcription factors, suggesting SRC-3 inhibition can be beneficial in hormone-independent cancers as well. The recent discovery of a potent SRC-3 small molecule inhibitor, SI-2, enabled the further development of additional related compounds. SI-12 is an improved version of SI-2 that like SI-2 has anti-proliferative activity in various cancer types, including BC. Here, we sought to identify gene targets, that when inhibited in the presence of SI-12, would lead to enhanced BC cell cytotoxicity. We performed a genome-scale CRISPR-Cas9 screen in MCF-7 BC cells under conditions of pharmacological pressure with SI-12. A parallel screen was performed with an ER inhibitor, fulvestrant, to shed light on both common and distinct activities between SRC-3 and ERα inhibition. Bearing in mind the key role of SRC-3 in tumorigenesis of other types of cancer, we extended our study by validating potential hits identified from the MCF-7 screen in other cancer cell lines.

A methodological approach to correlate tumor heterogeneity with drug distribution profile in mass spectrometry imaging data

BACKGROUND

Drug mass spectrometry imaging (MSI) data contain knowledge about drug and several other molecular ions present in a biological sample. However, a proper approach to fully explore the potential of such type of data is still missing. Therefore, a computational pipeline that combines different spatial and non-spatial methods is proposed to link the observed drug distribution profile with tumor heterogeneity in solid tumor. Our data analysis steps include pre-processing of MSI data, cluster analysis, drug local indicators of spatial association (LISA) map, and ions selection.

RESULTS

The number of clusters identified from different tumor tissues. The spatial homogeneity of the individual cluster was measured using a modified version of our drug homogeneity method. The clustered image and drug LISA map were simultaneously analyzed to link identified clusters with observed drug distribution profile. Finally, ions selection was performed using the spatially aware method.

CONCLUSIONS

In this paper, we have shown an approach to correlate the drug distribution with spatial heterogeneity in untargeted MSI data. Our approach is freely available in an R package 'CorrDrugTumorMSI'.

Identification of functionally distinct and interacting cancer cell subpopulations from glioblastoma with intratumoral genetic heterogeneity

Background

Glioblastomas display a high level of intratumoral heterogeneity with regard to both genetic and histological features. Within single tumors, subclones have been shown to communicate with each other to affect overall tumor growth. The aim of this study was to broaden the understanding of interclonal communication in glioblastoma.

Methods

We have used the U-343 model, consisting of U-343 MG, U-343 MGa, U-343 MGa 31L, and U-343 MGa Cl2:6, a set of distinct glioblastoma cell lines that have been derived from the same tumor. We characterized these with regard to temozolomide sensitivity, protein secretome, gene expression, DNA copy number, and cancer cell phenotypic traits. Furthermore, we performed coculture and conditioned media-based experiments to model cell-to-cell signaling in a setting of intratumoral heterogeneity.

Results

Temozolomide treatment of a coculture composed of all 4 U-343 cell lines presents a tumor relapse model where the least sensitive population, U-343 MGa 31L, outlives the others. Interestingly, the U-343 cell lines were shown to have distinct gene expression signatures and phenotypes although they were derived from a single tumor. The DNA copy number analysis revealed both common and unique alterations, indicating the evolutionary relationship between the cells. Moreover, these cells were found to communicate and affect each other’s proliferation, both via contact-dependent and -independent interactions, where NOTCH1, TGFBI, and ADAMTS1 signaling effects were involved, respectively.

Conclusions

These results provide insight into how complex the signaling events may prove to be in a setting of intratumoral heterogeneity in glioblastoma and provide a map for future studies.

Heritable modifiers of the tumor microenvironment influence nanoparticle uptake, distribution and response to photothermal therapy

We report the impact of notch-DLL4-based hereditary vascular heterogeneities on the enhanced permeation and retention (EPR) effect and plasmonic photothermal therapy response in tumors. Methods: We generated two consomic rat strains with differing DLL4 expression on 3rd chromosome. These strains were based on immunocompromised Salt-sensitive or SSIL2Rγ- (DLL4-high) and SS.BN3IL2Rγ- (DLL4-low) rats with 3rd chromosome substituted from Brown Norway rat. We further constructed three novel SS.BN3IL2Rγ- congenic strains by introgressing varying segments of BN chromosome 3 into the parental SSIL2Rγ- strain to localize the role of SSIL2Rγ- DLL4 on tumor EPR effect with precision. We synthesized multimodal theranostic nanoparticles (TNPs) based on Au-nanorods which provide magnetic resonance imaging (MRI), X-ray, and optical contrasts to assess image guided PTT response and quantify host specific therapy response differences in tumors orthotopically xenografted in DLL4-high and -low strains. We tested recovery of therapy sensitivity of PTT resistant strains by employing anti-DLL4 conjugated TNPs in two triple negative breast cancer tumor xenografts. Results: Host strains with high DLL4 allele demonstrated slightly increased tumor nanoparticle uptake but consistently developed photothermal therapy resistance compared to tumors in host strains with low DLL4 allele. Tumor micro-environment with low DLL4 expression altered the geographic distribution of nanoparticles towards closer proximity with vasculature which improved efficacy of PTT in spite of lower overall TNP uptake. Targeting TNPs to tumor endothelium via anti-DLL4 antibody conjugation improved therapy sensitivity in high DLL4 allele hosts for two triple negative human breast cancer xenografts. Conclusions: Inherited DLL4 expression modulates EPR effects in tumors, and molecular targeting of endothelial DLL4 via nanoparticles is an effective personalized nanomedicine strategy.

A Time-Programmed Release of Dual Drugs from an Implantable Trilayer Structured Fiber Device for Synergistic Treatment of Breast Cancer

Combination chemotherapy with time-programmed administration of multiple drugs is a promising method for cancer treatment. However, realizing time-programmed release of combined drugs from a single carrier is still a great challenge in enhanced cancer therapy. Here, an implantable trilayer structured fiber device is developed to achieve time-programmed release of combined drugs for synergistic treatment of breast cancer. The fiber device is prepared by a modified microfluidic-electrospinning technique. The glycerol solution containing chemotherapy agent doxorubicin (Dox) forms the internal periodic cavities of the fiber, and poly(l-lactic acid) and poly(ε-caprolactone) containing the angiogenesis inhibitor apatinib (Apa) form the double walls of the fiber. Rapid release of Dox can be obtained by adjusting the wall thickness of the cavities, meanwhile sustained release of Apa is achieved through the slow degradation of the fiber matrix. After the fiber device is implanted subcutaneously near to the implanted solid tumor of mice, an excellent synergistic therapeutic effect is achieved through time-programmed release of the combined dual drugs. The fiber device provides a platform to sequentially co-deliver dual or multiple drugs for enhanced combined therapeutic efficacy.

NanoRNP Overcomes Tumor Heterogeneity in Cancer Treatment

Tumor heterogeneity has been one of the most important factors leading to the failure of conventional cancer therapies due to the accumulation of genetically distinct tumor-cell subpopulations during the tumor development process. Due to the diversity of genetic mutations during tumor growth, combining the use of multiple drugs has only achieved limited success in combating heterogeneous tumors. Herein, we report a novel antitumor strategy that effectively addresses tumor heterogeneity by using a CRISPR/Cas9-based nanoRNP carrying a combination of sgRNAs. Such nanoRNP is synthesized from Cas9 ribonucleoprotein, any combinations of required sgRNAs, and a rationally designed responsive polymer that endows nanoRNP with high circulating stability, enhanced tumor accumulation, and the efficient gene editing in targeted tumor cells eventually. By carrying a combination of sgRNAs that targets STAT3 and RUNX1, the nanoRNP exhibited efficient gene expression disruptions on a heterogeneous tumor model with two subsets of cells whose proliferations were sensitive to the reduced expression of STAT3 and RUNX1, respectively, leading to the effective growth inhibition of the heterogeneous tumor. Considering the close relationship between tumor heterogeneity and cancer progression, resistance to therapy, and recurrences, nanoRNP provides a feasible strategy to overcome tumor heterogeneity in the development of more advanced cancer therapy against malignant tumors.

The fidelity of cancer cells in PDX models: Characteristics, mechanism and clinical significance

Patient-derived xenograft (PDX) models are widely used as preclinical cancer models and are considered better than cell culture models in recapitulating the histological features, molecular characteristics and intratumoral heterogeneity (ITH) of human tumors. While the PDX model is commonly accepted for use in drug discovery and other translational studies, a growing body of evidence has suggested its limitations. Recently, the fidelity of cancer cells within a PDX has been questioned, which may impede the future application of these models. In this review, we will focus the variable phenotypes of xenograft tumors and the genomic instability and molecular inconsistency of PDX tumors after serial transplantation. Next, we will discuss the underlying mechanism of ITH and its clinical relevance. Stochastic selection bias in the sampling process and/or deterministic clonal dynamics due to murine selective pressure may have detrimental effects on the results of personalized medicine and drug screening studies. In addition, we aim to identify a possible solution for the issue of fidelity in current PDX models and to discuss emerging next-generation preclinical models.

Modeling genetic heterogeneity of drug response and resistance in cancer

Heterogeneity in tumors is recognized as a key contributor to drug resistance and spread of advanced disease, but deep characterization of genetic variation within tumors has only recently been quantifiable with the advancement of next generation sequencing and single cell technologies. These data have been essential in developing molecular models of how tumors develop, evolve, and respond to environmental changes, such as therapeutic intervention. A deeper understanding of tumor evolution has subsequently opened up new research efforts to develop mathematical models that account for evolutionary dynamics with the goal of predicting drug response and resistance in cancer. Here, we describe recent advances and limitations of how models of tumor evolution can impact treatment strategies for cancer patients.

Systems biology approaches to measure and model phenotypic heterogeneity in cancer

The recent wide-spread adoption of single cell profiling technologies has revealed that individual cancers are not homogenous collections of deregulated cells, but instead are comprised of multiple genetically and phenotypically distinct cell subpopulations that exhibit a wide range of responses to extracellular signals and therapeutic insult. Such observations point to the urgent need to understand cancer as a complex, adaptive system. Cancer systems biology studies seek to develop the experimental and theoretical methods required to understand how biological components work together to determine how cancer cells function. Ultimately, such approaches will lead to improvements in how cancer is managed and treated. In this review, we discuss recent advances in cancer systems biology approaches to quantify, model, and elucidate mechanisms of heterogeneity.

Deconvolution of transcriptomes and miRNomes by independent component analysis provides insights into biological processes and clinical outcomes of melanoma patients

BACKGROUND

The amount of publicly available cancer-related "omics" data is constantly growing and can potentially be used to gain insights into the tumour biology of new cancer patients, their diagnosis and suitable treatment options. However, the integration of different datasets is not straightforward and requires specialized approaches to deal with heterogeneity at technical and biological levels.

METHODS

Here we present a method that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large discovery datasets. The approach is based on independent component analysis (ICA) - an unsupervised method of signal deconvolution. We developed parallel consensus ICA that robustly decomposes transcriptomics datasets into expression profiles with minimal mutual dependency.

RESULTS

By applying the method to a small cohort of primary melanoma and control samples combined with a large discovery melanoma dataset, we demonstrate that our method distinguishes cell-type specific signals from technical biases and allows to predict clinically relevant patient characteristics. We showed the potential of the method to predict cancer subtypes and estimate the activity of key tumour-related processes such as immune response, angiogenesis and cell proliferation. ICA-based risk score was proposed and its connection to patient survival was validated with an independent cohort of patients. Additionally, through integration of components identified for mRNA and miRNA data, the proposed method helped deducing biological functions of miRNAs, which would otherwise not be possible.

CONCLUSIONS

We present a method that can be used to map new transcriptomic data from cancer patient samples onto large discovery datasets. The method corrects technical biases, helps characterizing activity of biological processes or cell types in the new samples and provides the prognosis of patient survival.

Designing combination therapies with modeling chaperoned machine learning

Chemotherapy resistance is a major challenge to the effective treatment of cancer. Thus, a systematic pipeline for the efficient identification of effective combination treatments could bring huge biomedical benefit. In order to facilitate rational design of combination therapies, we developed a comprehensive computational model that incorporates the available biological knowledge and relevant experimental data on the life-and-death response of individual cancer cells to cisplatin or cisplatin combined with the TNF-related apoptosis-inducing ligand (TRAIL). The model's predictions, that a combination treatment of cisplatin and TRAIL would enhance cancer cell death and exhibit a "two-wave killing" temporal pattern, was validated by measuring the dynamics of p53 accumulation, cell fate, and cell death in single cells. The validated model was then subjected to a systematic analysis with an ensemble of diverse machine learning methods. Though each method is characterized by a different algorithm, they collectively identified several molecular players that can sensitize tumor cells to cisplatin-induced apoptosis (sensitizers). The identified sensitizers are consistent with previous experimental observations. Overall, we have illustrated that machine learning analysis of an experimentally validated mechanistic model can convert our available knowledge into the identity of biologically meaningful sensitizers. This knowledge can then be leveraged to design treatment strategies that could improve the efficacy of chemotherapy.

Systems Pharmacology: Defining the Interactions of Drug Combinations

The majority of diseases are associated with alterations in multiple molecular pathways and complex interactions at the cellular and organ levels. Single-target monotherapies therefore have intrinsic limitations with respect to their maximum therapeutic benefits. The potential of combination drug therapies has received interest for the treatment of many diseases and is well established in some areas, such as oncology. Combination drug treatments may allow us to identify synergistic drug effects, reduce adverse drug reactions, and address variability in disease characteristics between patients. Identification of combination therapies remains challenging. We discuss current state-of-the-art systems pharmacology approaches to enable rational identification of combination therapies. These approaches, which include characterization of mechanisms of disease and drug action at a systems level, can enable understanding of drug interactions at the molecular, cellular, physiological, and organismal levels. Such multiscale understanding can enable precision medicine by promoting the rational development of combination therapy at the level of individual patients for many diseases.

[Advances on Recognizing and Managing Tumor Heterogeneity]

肿瘤异质性是恶性肿瘤的特征之一，可使肿瘤的生长速度、侵袭与转移、药物敏感性、预后等各方面产生差异。肿瘤驱动基因和靶向药物的发现发展开启了战胜肿瘤的希望之门，然而异质性的存在又让肿瘤治疗陷入了难以攻克的困境。在肿瘤复发、进展演化的过程中肿瘤异质性如影随形，纷繁复杂。凭借不断进步的检测技术认识和理解肿瘤异质性，针对肿瘤异质性的原因和表型，定制治疗方案已成为当今精准医疗领域的重点范畴。本综述对肿瘤异质性进行了分析和探讨，从而更好的帮助我们了解肿瘤异质性，有利于我们通过多种手段对抗肿瘤异质性。

Harnessing Tumor Evolution to Circumvent Resistance

High-throughput sequencing can be used to measure changes in tumor composition across space and time. Specifically, comparisons of pre- and post-treatment samples can reveal the underlying clonal dynamics and resistance mechanisms. Here, we discuss evidence for distinct modes of tumor evolution and their implications for therapeutic strategies. In addition, we consider the utility of spatial tissue sampling schemes, single-cell analysis, and circulating tumor DNA to track tumor evolution and the emergence of resistance, as well as approaches that seek to forestall resistance by targeting tumor evolution. Ultimately, characterization of the (epi)genomic, transcriptomic, and phenotypic changes that occur during tumor progression coupled with computational and mathematical modeling of tumor evolutionary dynamics may inform personalized treatment strategies.

Analysis of mutation, selection, and epistasis: an informed approach to cancer clinical trials

Currently, drug development efforts and clinical trials to test them are often prioritized by targeting genes with high frequencies of somatic variants among tumors. However, differences in oncogenic mutation rate-not necessarily the effect the variant has on tumor growth-contribute enormously to somatic variant frequency. We argue that decoupling the contributions of mutation and cancer lineage selection to the frequency of somatic variants among tumors is critical to understanding-and predicting-the therapeutic potential of different interventions. To provide an indicator of that strength of selection and therapeutic potential, the frequency at which we observe a given variant across patients must be modulated by our expectation given the mutation rate and target size to provide an indicator of that strength of selection and therapeutic potential. Additionally, antagonistic and synergistic epistasis among mutations also impacts the potential therapeutic benefit of targeted drug development. Quantitative approaches should be fostered that use the known genetic architectures of cancer types, decouple mutation rate, and provide rigorous guidance regarding investment in targeted drug development. By integrating evolutionary principles and detailed mechanistic knowledge into those approaches, we can maximize our ability to identify those targeted therapies most likely to yield substantial clinical benefit.

Computational models for predicting drug responses in cancer research

The computational prediction of drug responses based on the analysis of multiple types of genome-wide molecular data is vital for accomplishing the promise of precision medicine in oncology. This will benefit cancer patients by matching their tumor characteristics to the most effective therapy available. As larger and more diverse layers of patient-related data become available, further demands for new bioinformatics approaches and expertise will arise. This article reviews key strategies, resources and techniques for the prediction of drug sensitivity in cell lines and patient-derived samples. It discusses major advances and challenges associated with the different model development steps. This review highlights major trends in this area, and will assist researchers in the assessment of recent progress and in the selection of approaches to emerging applications in oncology.

A seven-gene prognostic signature for rapid determination of head and neck squamous cell carcinoma survival

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer and displays divergent clinical outcomes. Prognostic biomarkers might improve risk stratification and survival prediction. We aimed to investigate the prognostic genes associated with overall survival. A two-step gene selection method was used to develop a seven-gene-based prognostic model based on the training set collected from The Cancer Genome Atlas (TCGA). In addition, the prognostic model was validated in an independent testing set from Gene Expression Omnibus (GEO). The score based on the model successfully distinguished HNSCC survival into high-risk and low-risk groups in the training set (HR, 2.79; 95% CI, 1.98–3.92; P=4.05×10⁻⁹) and the testing set (HR, 2.05; 95% CI, 1.35–3.11; P=7.98×10⁻⁴). In addition, the score could significantly predict 5-year survival by ROC curves (AUCs for training set, 0.73; testing set, 0.66). Combining risk scores with clinical characteristics improved the AUCs beyond using clinical characteristics alone (training set, from 0.57 to 0.75; testing set, from 0.63 to 0.72). A subgroup sensitivity analysis with HPV status and tumor sites revealed that the risk score was significant in all subgroups except oral cavity tumors of the testing set. Furthermore, HPV-positive status improves survival in oropharyngeal HNSCC but not non-oropharyngeal HNSCC. In conclusion, the seven-gene prognostic signature is a reliable and practical prognostic tool for HNSCC. This approach can add prognostic value to clinical characteristics and provides a new possibility for individualized treatment.

Signatures of positive selection reveal a universal role of chromatin modifiers as cancer driver genes

Tumors are composed of an evolving population of cells subjected to tissue-specific selection, which fuels tumor heterogeneity and ultimately complicates cancer driver gene identification. Here, we integrate cancer cell fraction, population recurrence, and functional impact of somatic mutations as signatures of selection into a Bayesian model for driver prediction. We demonstrate that our model, cDriver, outperforms competing methods when analyzing solid tumors, hematological malignancies, and pan-cancer datasets. Applying cDriver to exome sequencing data of 21 cancer types from 6,870 individuals revealed 98 unreported tumor type-driver gene connections. These novel connections are highly enriched for chromatin-modifying proteins, hinting at a universal role of chromatin regulation in cancer etiology. Although infrequently mutated as single genes, we show that chromatin modifiers are altered in a large fraction of cancer patients. In summary, we demonstrate that integration of evolutionary signatures is key for identifying mutational driver genes, thereby facilitating the discovery of novel therapeutic targets for cancer treatment.

Comprehensive benchmarking of SNV callers for highly admixed tumor data

Precision medicine attempts to individualize cancer therapy by matching tumor-specific genetic changes with effective targeted therapies. A crucial first step in this process is the reliable identification of cancer-relevant variants, which is considerably complicated by the impurity and heterogeneity of clinical tumor samples. We compared the impact of admixture of non-cancerous cells and low somatic allele frequencies on the sensitivity and precision of 19 state-of-the-art SNV callers. We studied both whole exome and targeted gene panel data and up to 13 distinct parameter configurations for each tool. We found vast differences among callers. Based on our comprehensive analyses we recommend joint tumor-normal calling with MuTect, EBCall or Strelka for whole exome somatic variant calling, and HaplotypeCaller or FreeBayes for whole exome germline calling. For targeted gene panel data on a single tumor sample, LoFreqStar performed best. We further found that tumor impurity and admixture had a negative impact on precision, and in particular, sensitivity in whole exome experiments. At admixture levels of 60% to 90% sometimes seen in pathological biopsies, sensitivity dropped significantly, even when variants were originally present in the tumor at 100% allele frequency. Sensitivity to low-frequency SNVs improved with targeted panel data, but whole exome data allowed more efficient identification of germline variants. Effective somatic variant calling requires high-quality pathological samples with minimal admixture, a consciously selected sequencing strategy, and the appropriate variant calling tool with settings optimized for the chosen type of data.

Identification of optimal strategies for state transition of complex biological networks

Complex biological networks typically contain numerous parameters, and determining feasible strategies for state transition by parameter perturbation is not a trivial task. In the present study, based on dynamical and structural analyses of the biological network, we optimized strategies for controlling variables in a two-node gene regulatory network and a T-cell large granular lymphocyte signaling network associated with blood cancer by using an efficient dynamic optimization method. Optimization revealed the critical value for each decision variable to steer the system from an undesired state into a desired attractor. In addition, the minimum time for the state transition was determined by defining and solving a time-optimal control problem. Moreover, time-dependent variable profiles for state transitions were achieved rather than constant values commonly adopted in previous studies. Furthermore, the optimization method allows multiple controls to be simultaneously adjusted to drive the system out of an undesired attractor. Optimization improved the results of the parameter perturbation method, thus providing a valuable guidance for experimental design.

Exploiting Synthetic Lethality and Network Biology to Overcome EGFR Inhibitor Resistance in Lung Cancer

Despite the recent approval of third-generation therapies, overcoming resistance to epidermal growth factor receptor (EGFR) inhibitors remains a major challenge in non-small cell lung cancer. Conceptually, synthetic lethality holds the promise of identifying non-intuitive targets for tackling both acquired and intrinsic resistance in this setting. However, translating these laboratory findings into effective clinical strategies continues to be elusive. Here, we provide an overview of the synthetic lethal approaches that have been employed to study EGFR inhibitor resistance and review the oncogene and non-oncogene signalling mechanisms that have thus far been unveiled by synthetic lethality screens. We highlight the potential challenges associated with progressing these discoveries into the clinic including context dependency, signalling plasticity, and tumour heterogeneity, and we offer a perspective on emerging network biology and computational solutions to exploit these phenomena for cancer therapy and biomarker discovery. We conclude by presenting a number of tangible steps to bolster our understanding of fundamental synthetic lethality mechanisms and advance these findings beyond the confines of the laboratory.

Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution

The success of targeted cancer therapy is limited by drug resistance that can result from tumor genetic heterogeneity. The current approach to address resistance typically involves initiating a new treatment after clinical/radiographic disease progression, ultimately resulting in futility in most patients. Towards a potential alternative solution, we developed a novel computational framework that uses human cancer profiling data to systematically identify dynamic, pre-emptive, and sometimes non-intuitive treatment strategies that can better control tumors in real-time. By studying lung adenocarcinoma clinical specimens and preclinical models, our computational analyses revealed that the best anti-cancer strategies addressed existing resistant subpopulations as they emerged dynamically during treatment. In some cases, the best computed treatment strategy used unconventional therapy switching while the bulk tumor was responding, a prediction we confirmed in vitro. The new framework presented here could guide the principled implementation of dynamic molecular monitoring and treatment strategies to improve cancer control.

A population genetics perspective on the determinants of intra-tumor heterogeneity

Cancer results from the acquisition of somatic alterations in a microevolutionary process that typically occurs over many years, much of which is occult. Understanding the evolutionary dynamics that are operative at different stages of progression in individual tumors might inform the earlier detection, diagnosis, and treatment of cancer. Although these processes cannot be directly observed, the resultant spatiotemporal patterns of genetic variation amongst tumor cells encode their evolutionary histories. Such intra-tumor heterogeneity is pervasive not only at the genomic level, but also at the transcriptomic, phenotypic, and cellular levels. Given the implications for precision medicine, the accurate quantification of heterogeneity within and between tumors has become a major focus of current research. In this review, we provide a population genetics perspective on the determinants of intra-tumor heterogeneity and approaches to quantify genetic diversity. We summarize evidence for different modes of evolution based on recent cancer genome sequencing studies and discuss emerging evolutionary strategies to therapeutically exploit tumor heterogeneity. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.