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

Drug-induced tolerant persisters in tumor: mechanism, vulnerability and perspective implication for clinical treatment

Cancer remains a significant global health burden due to its high morbidity and mortality. Oncogene-targeted therapy and immunotherapy have markedly improved the 5-year survival rate in the patients with advanced or metastatic tumors compared to outcomes in the era of chemotherapy/radiation. Nevertheless, the majority of patients remain incurable. Initial therapies eliminate the bulk of tumor cells, yet residual populations termed drug-tolerant persister cells (DTPs) survive, regenerate tumor and even drive distant metastases. Notably, DTPs frequently render tumor cross-resistance, a detrimental phenomenon observed in the patients with suboptimal responses to subsequent therapies. Analogous to species evolution, DTPs emerge as adaptative products at the cellular level, instigated by integrated intracellular stress responses to therapeutic pressures. These cells exhibit profound heterogeneity and adaptability shaped by the intricate feedforward loops among tumor cells, surrounding microenvironments and host ecology, which vary across tumor types and therapeutic regimens. In this review, we revisit the concept of DTPs, with a focus on their generation process upon targeted therapy or immunotherapy. We dissect the critical phenotypes and molecule mechanisms underlying DTPs to therapy from multiple aspects, including intracellular events, intercellular crosstalk and the distant ecologic pre-metastatic niches. We further spotlight therapeutic strategies to target DTP vulnerabilities, including synthetic lethality approaches, adaptive dosing regimens informed by mathematical modeling, and immune-mediated eradication. Additionally, we highlight synergistic interventions such as lifestyle modifications (e.g., exercise, stress reduction) to suppress pro-tumorigenic inflammation. By integrating mechanistic insights with translational perspectives, this work bridges the gap between DTP biology and clinical strategies, aiming for optimal efficacy and preventing relapse.

A Model of Intratumor and Interpatient Heterogeneity Explains Clinical Trials of Curative Combination Therapy for Lymphoma

SIGNIFICANCE

A new model of intratumor and interpatient heterogeneity in response to drug combinations explains and predicts the results of clinical trials of curative-intent treatments for DLBCL. This model can be used to understand and inform optimal design of curative drug combinations and clinical trials. See related commentary by Goldstein et al., p. 153.

Discovery of drug transporter inhibitors tied to long noncoding RNA in resistant cancer cells; a computational model -in silico- study

Chemotherapeutic resistance is a major obstacle to chemotherapeutic failure. Cancer cell resistance involves several mechanisms, including epithelial-to-mesenchymal transition (EMT), signaling pathway bypass, drug efflux activation, and impairment of drug entry. P-glycoproteins (P-gp) are an efflux transporter that pumps chemotherapeutic drugs out of cancer cells, resulting in chemotherapeutic resistance. Several types of long noncoding RNA (lncRNAs) have been identified in resistant cancer cells, including ODRUL, MALAT1, and ANRIL. The high expression level of ODRUL is related to the induction of ATP-binding cassette (ABC) gene expression, resulting in the emergence of doxorubicin resistance in osteosarcoma. lncRNAs are observed to be regulators of drug transporters in cancer cells such as MALAT1 and ANRIL. Targeting P-gp expression using natural products is a new strategy to overcome cancer cell resistance and improve the sensitivity of resistant cells toward chemotherapies. This review validates the inhibitory effects of natural products on P-gp expression and activity using in silico molecular docking. In silico analysis showed that Delphinidin and Asparagoside-f are the most significant natural product inhibitors of p-glycoprotein-1. These inhibitors can reverse multi-drug resistance and induce the sensitivity of resistant cancer cells toward chemotherapy based on in silico molecular docking. It is important to validate that pre-elementary docking can be confirmed using in vitro and in vivo experimental data.

Single-cell RNA-seq reveals diverse molecular signatures associated with Methotrexate resistance in primary central nervous system lymphoma cells

PURPOSE

Methotrexate is one of the most essential single agents in patients with primary central nervous system lymphoma (PCNSL). However, 25-50% result in relapse with a poor prognosis. Therefore, studies on methotrexate resistance are warranted to explore salvage chemotherapy for recurrent PCNSL. Single-cell sequence analysis enables the characterization of novel cell types and provides a precise understanding of cancer biology.

METHODS

Single-cell sequence analysis of parental and methotrexate-resistant PCNSL cells was performed. We used a Weighted Gene Co-expression Network Analysis to identify groups of significantly connected genes.

RESULTS

We identified consensus modules in both the HKBML and TK datasets. HLA-DRβ1, HLA-DQβ1,and SNRPG were hub genes those detected in both datasets revealed by network analysis. Cyclosporine A was selected as the candidate drug for treating methotrexate-resistant cells.

CONCLUSION

The results of the present study characterized the methotrexate resistance-related signaling pathways in cultured PCNSL cells. Overall, these results may account for variations in treatment responses and lead potential novel therapeutic strategies for patients with PCNSL.

BESTDR: Bayesian quantification of mechanism-specific drug response in cell culture

Understanding drug responses at the cellular level is essential for elucidating mechanisms of action and advancing preclinical drug development. Traditional dose-response models rely on simplified metrics, limiting their ability to quantify parameters like cell division, death, and transition rates between cell states. To address these limitations, we developed Bayesian Estimation of STochastic processes for Dose-Response (BESTDR), a novel framework modeling cell growth and treatment response dynamics to estimate concentration-response relationships using longitudinal cell count data. BESTDR quantifies rates in multi-state systems across multiple cell lines using hierarchical modeling to support high-throughput screening. We validated BESTDR with synthetic and experimental datasets, demonstrating its robustness and accuracy in estimating drug response. By integrating mechanistic modeling of cytotoxic, cytostatic and other effects, BESTDR enhances dose-response studies, facilitating robust drug comparisons and mechanism-specific analyses. BESTDR offers a versatile tool for early-stage preclinical research, paving the way for drug discovery and informed experimental design.

Lung cancer organoid-based drug evaluation models and new drug development application trends

Lung cancer is a malignant tumor with high incidence and mortality rates in both men and women worldwide. Although anticancer drugs are prescribed to treat lung cancer patients, individual responses to these drugs vary, making it crucial to identify the most suitable treatment for each patient. Therefore, it is necessary to develop an anticancer drug efficacy prediction model that can analyze drug efficacy before patient treatment and establish personalized treatment strategies. Unlike two-dimensional (2D) cultured lung cancer cells, lung cancer organoid (LCO) models have a three-dimensional (3D) structure that effectively mimics the characteristics and heterogeneity of lung cancer cells. Lung cancer patient-derived organoids (PDOs) also have the advantage of recapitulating histological and genetic characteristics similar to those of patient tissues under in vitro conditions. Due to these advantages, LCO models are utilized in various fields, including cancer research, and precision medicine, and are especially employed in various new drug development processes, such as targeted therapies and immunotherapy. LCO models demonstrate potential applications in precision medicine and new drug development research. This review discusses the various methods for implementing LCO models, LCO-based anticancer drug efficacy analysis models, and new trends in lung cancer-targeted drug development.

Comprehensive review of drug resistance in mammalian cancer stem cells: implications for cancer therapy

Cancer remains a significant global challenge, and despite the numerous strategies developed to advance cancer therapy, an effective cure for metastatic cancer remains elusive. A major hurdle in treatment success is the ability of cancer cells, particularly cancer stem cells (CSCs), to resist therapy. These CSCs possess unique abilities, including self-renewal, differentiation, and repair, which drive tumor progression and chemotherapy resistance. The resilience of CSCs is linked to certain signaling pathways. Tumors with pathway-dependent CSCs often develop genetic resistance, whereas those with pathway-independent CSCs undergo epigenetic changes that affect gene regulation. CSCs can evade cytotoxic drugs, radiation, and apoptosis by increasing drug efflux transporter activity and activating survival mechanisms. Future research should prioritize the identification of new biomarkers and signaling molecules to better understand drug resistance. The use of cutting-edge approaches, such as bioinformatics, genomics, proteomics, and nanotechnology, offers potential solutions to this challenge. Key strategies include developing targeted therapies, employing nanocarriers for precise drug delivery, and focusing on CSC-targeted pathways such as the Wnt, Notch, and Hedgehog pathways. Additionally, investigating multitarget inhibitors, immunotherapy, and nanodrug delivery systems is critical for overcoming drug resistance in cancer cells.

Machine learning in oncological pharmacogenomics: advancing personalized chemotherapy

This review analyzes the application of machine learning (ML) in oncological pharmacogenomics, focusing on customizing chemotherapy treatments. It explores how ML can analyze extensive genomic, proteomic, and other omics datasets to identify genetic patterns associated with drug responses. This, in turn, facilitates personalized therapies that are more effective and have fewer side effects. Recent studies have emphasized ML's revolutionary role of ML in personalized oncology treatment by identifying genetic variability and understanding cancer pharmacodynamics. Integrating ML with electronic health records and clinical data shows promise in refining chemotherapy recommendations by considering the complex influencing factors. Although standard chemotherapy depends on population-based doses and treatment regimens, customized techniques use genetic information to tailor treatments for specific patients, potentially enhancing efficacy and reducing adverse effects.However, challenges, such as model interpretability, data quality, transparency, ethical issues related to data privacy, and health disparities, remain. Machine learning has been used to transform oncological pharmacogenomics by enabling personalized chemotherapy treatments. This review highlights ML's potential of ML to enhance treatment effectiveness and minimize side effects through detailed genetic analysis. It also addresses ongoing challenges including improved model interpretability, data quality, and ethical considerations. The review concludes by emphasizing the importance of rigorous clinical trials and interdisciplinary collaboration in the ethical implementation of ML-driven personalized medicine, paving the way for improved outcomes in cancer patients and marking a new frontier in cancer treatment.

A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations

Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms.

ORC6 acts as an effective prognostic predictor for non‑small cell lung cancer and is closely associated with tumor progression

Origin recognition complexes (ORCs) are vital in the control of DNA replication and the progression of the cell cycle, however the precise function and mechanism of ORC6 in non-small cell lung cancer (NSCLC) is still not well understood. The present study used bioinformatics methods to assess the predictive significance of ORC6 expression in NSCLC. Moreover, the expression of ORC6 was further evaluated using reverse transcription-quantitative PCR and western blotting, and its functional significance in lung cancer was assessed via knockdown experiments using small interfering RNA. A significant association was demonstrated between the expression of ORC6 and the clinical features of NSCLC. In particular, elevated levels of ORC6 were significantly strongly correlated with an unfavorable prognosis. Multivariate analysis demonstrated that increased ORC6 expression independently contributed to the risk of overall survival (HR 1.304; P=0.015) in individuals diagnosed with NSCLC. Analysis of Kaplan-Meier plots demonstrated that ORC6 expression served as a valuable indicator for diagnosing and predicting the prognosis of NSCLC. Moreover, in vitro studies demonstrated that modified ORC6 expression had a significant impact on the proliferation, migration and metastasis of NSCLC cells. NSCLC cell lines (H1299 and mH1650) exhibited markedly higher ORC6 expression than normal lung cell lines. The results of the present study indicated a strong association between the expression of ORC6 and the clinicopathological characteristics of NSCLC, which suggested its potential as a reliable biomarker for predicting NSCLC. Furthermore, ORC6 may have important therapeutic implications in the management of NSCLC.

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.

Puzzles, challenges, and information reservoir of SARS-CoV-2 quasispecies

Upon the emergence of SARS-CoV-2 in the human population, it was conjectured that for this coronavirus the dynamic intra-host heterogeneity typical of RNA viruses would be toned down. Nothing of this sort is observed. Here we review the main observations on the complexity and diverse composition of SARS-CoV-2 mutant spectra sampled from infected patients, within the framework of quasispecies dynamics. The analyses suggest that the information provided by myriads of genomic sequences within infected individuals may have a predictive value of the genomic sequences that acquire epidemiological relevance. Possibilities to reconcile the presence of broad mutant spectra in the large RNA coronavirus genome with its encoding a 3' to 5' exonuclease proofreading-repair activity are considered. Indeterminations in the behavior of individual viral genomes provide a benefit for the survival of the ensemble. We propose that this concept falls in the domain of "stochastic thinking," a notion that applies also to cellular processes, as a means for biological systems to face unexpected needs.

Immunofluorescence-Based Assay for High-Throughput Analysis of Multidrug Resistance Markers in Non-Small Cell Lung Carcinoma Patient-Derived Cells

Lung cancer remains the leading cause of cancer death globally, with non-small cell lung cancer (NSCLC) accounting for the majority of cases. Multidrug resistance (MDR), often caused by ATP-binding cassette (ABC) transporters, represents a significant obstacle in the treatment of NSCLC. While genetic profiling has an important role in personalized therapy, functional assays that measure cellular responses to drugs are gaining in importance. We developed an automated microplate-based immunofluorescence assay for the evaluation of MDR markers ABCB1, ABCC1, and ABCG2 in cells obtained from NSCLC patients through high-content imaging and image analysis, as part of a functional diagnostic approach. This assay effectively discriminated cancer from non-cancer cells within mixed cultures, which is vital for accurate assessment of changes in MDR marker expression in different cell populations in response to anticancer drugs. Validation was performed using established drug-sensitive (NCI-H460) and drug-resistant (NCI-H460/R) NSCLC cell lines, demonstrating the assay's capacity to distinguish and evaluate different MDR profiles. The obtained results revealed wide-ranging effects of various chemotherapeutic agents on MDR marker expression in different patient-derived NSCLC cultures, emphasizing the need for MDR diagnostics in NSCLC. In addition to being a valuable tool for assessing drug effects on MDR markers in different cell populations, the assay can complement genetic profiling to optimize treatment. Further assay adaptations may extend its application to other cancer types, improving treatment efficacy while minimizing the development of resistance.

Clonal heterogeneity in ER+ breast cancer reveals the proteasome and PKC as potential therapeutic targets

Intratumoral heterogeneity impacts the success or failure of anti-cancer therapies. Here, we investigated the evolution and mechanistic heterogeneity in clonal populations of cell models for estrogen receptor positive breast cancer. To this end, we established barcoded models of luminal breast cancer and rendered them resistant to commonly applied first line endocrine therapies. By isolating single clones from the resistant cell pools and characterizing replicates of individual clones we observed inter- (between cell lines) and intra-tumor (between different clones from the same cell line) heterogeneity. Molecular characterization at RNA and phospho-proteomic levels revealed private clonal activation of the unfolded protein response and respective sensitivity to inhibition of the proteasome, and potentially shared sensitivities for repression of protein kinase C. Our in vitro findings are consistent with tumor-heterogeneity that is observed in breast cancer patients thus highlighting the need to uncover heterogeneity at an individual patient level and to adjust therapies accordingly.

Dynamical hallmarks of cancer: Phenotypic switching in melanoma and epithelial-mesenchymal plasticity

Phenotypic plasticity was recently incorporated as a hallmark of cancer. This plasticity can manifest along many interconnected axes, such as stemness and differentiation, drug-sensitive and drug-resistant states, and between epithelial and mesenchymal cell-states. Despite growing acceptance for phenotypic plasticity as a hallmark of cancer, the dynamics of this process remains poorly understood. In particular, the knowledge necessary for a predictive understanding of how individual cancer cells and populations of cells dynamically switch their phenotypes in response to the intensity and/or duration of their current and past environmental stimuli remains far from complete. Here, we present recent investigations of phenotypic plasticity from a systems-level perspective using two exemplars: epithelial-mesenchymal plasticity in carcinomas and phenotypic switching in melanoma. We highlight how an integrated computational-experimental approach has helped unravel insights into specific dynamical hallmarks of phenotypic plasticity in different cancers to address the following questions: a) how many distinct cell-states or phenotypes exist?; b) how reversible are transitions among these cell-states, and what factors control the extent of reversibility?; and c) how might cell-cell communication be able to alter rates of cell-state switching and enable diverse patterns of phenotypic heterogeneity? Understanding these dynamic features of phenotypic plasticity may be a key component in shifting the paradigm of cancer treatment from reactionary to a more predictive, proactive approach.

Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics

Phenotypic plasticity of cancer cells can lead to complex cell state dynamics during tumor progression and acquired resistance. Highly plastic stem-like states may be inherently drug-resistant. Moreover, cell state dynamics in response to therapy allow a tumor to evade treatment. In both scenarios, quantifying plasticity is essential for identifying high-plasticity states or elucidating transition paths between states. Currently, methods to quantify plasticity tend to focus on 1) quantification of quasi-potential based on the underlying gene regulatory network dynamics of the system; or 2) inference of cell potency based on trajectory inference or lineage tracing in single-cell dynamics. Here, we explore both of these approaches and associated computational tools. We then discuss implications of each approach to plasticity metrics, and relevance to cancer treatment strategies.

Unified tumor growth mechanisms from multimodel inference and dataset integration

Mechanistic models of biological processes can explain observed phenomena and predict responses to a perturbation. A mathematical model is typically constructed using expert knowledge and informal reasoning to generate a mechanistic explanation for a given observation. Although this approach works well for simple systems with abundant data and well-established principles, quantitative biology is often faced with a dearth of both data and knowledge about a process, thus making it challenging to identify and validate all possible mechanistic hypothesis underlying a system behavior. To overcome these limitations, we introduce a Bayesian multimodel inference (Bayes-MMI) methodology, which quantifies how mechanistic hypotheses can explain a given experimental datasets, and concurrently, how each dataset informs a given model hypothesis, thus enabling hypothesis space exploration in the context of available data. We demonstrate this approach to probe standing questions about heterogeneity, lineage plasticity, and cell-cell interactions in tumor growth mechanisms of small cell lung cancer (SCLC). We integrate three datasets that each formulated different explanations for tumor growth mechanisms in SCLC, apply Bayes-MMI and find that the data supports model predictions for tumor evolution promoted by high lineage plasticity, rather than through expanding rare stem-like populations. In addition, the models predict that in the presence of cells associated with the SCLC-N or SCLC-A2 subtypes, the transition from the SCLC-A subtype to the SCLC-Y subtype through an intermediate is decelerated. Together, these predictions provide a testable hypothesis for observed juxtaposed results in SCLC growth and a mechanistic interpretation for tumor treatment resistance.

Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools

Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies.

Archetype tasks link intratumoral heterogeneity to plasticity and cancer hallmarks in small cell lung cancer

Small cell lung cancer (SCLC) tumors comprise heterogeneous mixtures of cell states, categorized into neuroendocrine (NE) and non-neuroendocrine (non-NE) transcriptional subtypes. NE to non-NE state transitions, fueled by plasticity, likely underlie adaptability to treatment and dismal survival rates. Here, we apply an archetypal analysis to model plasticity by recasting SCLC phenotypic heterogeneity through multi-task evolutionary theory. Cell line and tumor transcriptomics data fit well in a five-dimensional convex polytope whose vertices optimize tasks reminiscent of pulmonary NE cells, the SCLC normal counterparts. These tasks, supported by knowledge and experimental data, include proliferation, slithering, metabolism, secretion, and injury repair, reflecting cancer hallmarks. SCLC subtypes, either at the population or single-cell level, can be positioned in archetypal space by bulk or single-cell transcriptomics, respectively, and characterized as task specialists or multi-task generalists by the distance from archetype vertex signatures. In the archetype space, modeling single-cell plasticity as a Markovian process along an underlying state manifold indicates that task trade-offs, in response to microenvironmental perturbations or treatment, may drive cell plasticity. Stifling phenotypic transitions and plasticity may provide new targets for much-needed translational advances in SCLC. A record of this paper's Transparent Peer Review process is included in the supplemental information.

Stochastic population dynamics of cancer stemness and adaptive response to therapies

Intratumoral heterogeneity can exist along multiple axes: Cancer stem cells (CSCs)/non-CSCs, drug-sensitive/drug-tolerant states, and a spectrum of epithelial-hybrid-mesenchymal phenotypes. Further, these diverse cell-states can switch reversibly among one another, thereby posing a major challenge to therapeutic efficacy. Therefore, understanding the origins of phenotypic plasticity and heterogeneity remains an active area of investigation. While genomic components (mutations, chromosomal instability) driving heterogeneity have been well-studied, recent reports highlight the role of non-genetic mechanisms in enabling both phenotypic plasticity and heterogeneity. Here, we discuss various processes underlying phenotypic plasticity such as stochastic gene expression, chromatin reprogramming, asymmetric cell division and the presence of multiple stable gene expression patterns ('attractors'). These processes can facilitate a dynamically evolving cell population such that a subpopulation of (drug-tolerant) cells can survive lethal drug exposure and recapitulate population heterogeneity on drug withdrawal, leading to relapse. These drug-tolerant cells can be both pre-existing and also induced by the drug itself through cell-state reprogramming. The dynamics of cell-state transitions both in absence and presence of the drug can be quantified through mathematical models. Such a dynamical systems approach to elucidating patterns of intratumoral heterogeneity by integrating longitudinal experimental data with mathematical models can help design effective combinatorial and/or sequential therapies for better clinical outcomes.

Preclinical Models for Acquired Resistance to Third-Generation EGFR Inhibitors in NSCLC: Functional Studies and Drug Combinations Used to Overcome Resistance

Epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) are currently recommended as first-line treatment for advanced non-small-cell lung cancer (NSCLC) with EGFR-activating mutations. Third-generation (3rd G) EGFR-TKIs, including osimertinib, offer an effective treatment option for patients with NSCLC resistant 1st and 2nd EGFR-TKIs. However, the efficacy of 3rd G EGFR-TKIs is limited by acquired resistance that has become a growing clinical challenge. Several clinical and preclinical studies are being carried out to better understand the mechanisms of resistance to 3rd G EGFR-TKIs and have revealed various genetic aberrations associated with molecular heterogeneity of cancer cells. Studies focusing on epigenetic events are limited despite several indications of their involvement in the development of resistance. Preclinical models, established in most cases in a similar manner, have shown different prevalence of resistance mechanisms from clinical samples. Clinically identified mechanisms include EGFR mutations that were not identified in preclinical models. Thus, NRAS genetic alterations were not observed in patients but have been described in cell lines resistant to 3rd G EGFR-TKI. Mainly, resistance to 3rd G EGFR-TKI in preclinical models is related to the activation of alternative signaling pathways through tyrosine kinase receptor (TKR) activation or to histological and phenotypic transformations. Yet, preclinical models have provided some insight into the complex network between dominant drivers and associated events that lead to the emergence of resistance and consequently have identified new therapeutic targets. This review provides an overview of preclinical studies developed to investigate the mechanisms of acquired resistance to 3rd G EGFR-TKIs, including osimertinib and rociletinib, across all lines of therapy. In fact, some of the models described were first generated to be resistant to first- and second-generation EGFR-TKIs and often carried the T790M mutation, while others had never been exposed to TKIs. The review further describes the therapeutic opportunities to overcome resistance, based on preclinical studies.

Single-cell RNA sequencing and bioinformatics as tools to decipher cancer heterogenicity and mechanisms of drug resistance

It is well known that cancer is an aggressive disease, often associated with relapse, in many cases due to drug resistance. Cancer stem cell and clonal evolution are frequently causes of innate or acquired drug resistance. Current RNA sequencing technologies do not distinguish gene expression of different cell lineages because they are based on bulk cell studies. Single-cell RNA sequencing technologies and related bioinformatics clustering and differential expression analysis represent a turning point in cancer research. They are emerging as essential tools for dissecting tumors at single-cell resolution and represent novel tools to understand carcinogenesis and drug response. In this review, we will outline the role of these new technologies in addressing cancer heterogeneity and cell lineage-dependent drug resistance.

Orthotopic Versus Allotopic Implantation: Comparison of Radiological and Pathological Characteristics

Background

In experimental animal models, implantation location might influence the heterogeneity and overall development of the tumor, leading to an interpretation bias.

Purpose

To investigate the effects of implantation location in experimental tumor model using magnetic resonance imaging (MRI) and pathological findings.

Study Type

Prospective.

Subjects

Forty‐five breast cancer‐bearing mice underwent orthotopic (N = 15) and heterotopic (intrahepatic [N = 15] and subcutaneous [N = 15]) implantation.

Field Strength/Sequence

Sequences including: T1‐weighted turbo spin echo sequence, T2‐weighted blade sequence, diffusion‐weighted imaging, pre‐ and post‐contrast T1 mapping, multi‐echo T2 mapping at 3.0 T.

Assessment

MRI was performed at 7, 14, and 21 days after implantation. Native T1, post‐contrast T1, T2, and apparent diffusion coefficient (ADC) of tumors, the tumor volume and necrosis volume within tumor were obtained. Lymphocyte cells from H&E staining, Ki67‐positive, and CD31‐positive cells from immunohistochemistry were determined.

Statistical Tests

One‐way analysis of variance and Spearman's rank correlation were performed. P value <0.05 was considered statistically significant.

Results

The tumor volume (intrahepatic vs. orthotopic vs. subcutaneous: 587.50 ± 77.62 mm³ vs. 814.00 ± 43.85 mm³ vs. 956.13 ± 119.22 mm³), necrosis volume within tumor (89.10 ± 26.60 mm³ vs. 292.41 ± 57.92 mm³ vs. 179.91 ± 31.73 mm³, respectively), ADC at day 21 (543.41 ± 42.28 vs. 542.92 ± 99.67 vs. 369.83 ± 42.90, respectively), and post‐contrast T1 at all timepoints (day 7: 442.00 ± 11.52 vs. 435.00 ± 22.90 vs. 394.33 ± 29.95; day 14: 459.00 ± 26.11 vs. 436.83 ± 26.01 vs. 377.00 ± 27.83; day 21: 463.50 ± 23.49 vs. 458.00 ± 34.28 vs. 375.00 ± 30.55) were significantly different between three groups. Necrosis volumes of subcutaneous and intrahepatic tumors were significantly lower than those of orthotopic tumors. The CD31‐positive rate in the intrahepatic implantation was significantly higher than in orthotopic and subcutaneous groups. Necrosis volume (r = −0.71), ADC (r = −0.85), and post‐contrast T1 (r = −0.75) were strongly correlated with vascular invasion index.

Data Conclusion

Orthotopic and heterotopic tumors have their unique growth kinetics, necrosis volume, and vascular invasion. Non‐invasive MR quantitative parameters, including ADC and post‐contrast T1, may reflect vascular invasion in mice.

Level of Evidence

1

Technical Efficacy

Stage 3

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.