Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers

Emerging advances in defining the molecular and therapeutic landscape of small-cell lung cancer

Small-cell lung cancer (SCLC) has traditionally been considered a recalcitrant cancer with a dismal prognosis, with only modest advances in therapeutic strategies over the past several decades. Comprehensive genomic assessments of SCLC have revealed that most of these tumours harbour deletions of the tumour-suppressor genes TP53 and RB1 but, in contrast to non-small-cell lung cancer, have failed to identify targetable alterations. The expression status of four transcription factors with key roles in SCLC pathogenesis defines distinct molecular subtypes of the disease, potentially enabling specific therapeutic approaches. Overexpression and amplification of MYC paralogues also affect the biology and therapeutic vulnerabilities of SCLC. Several other attractive targets have emerged in the past few years, including inhibitors of DNA-damage-response pathways, epigenetic modifiers, antibody-drug conjugates and chimeric antigen receptor T cells. However, the rapid development of therapeutic resistance and lack of biomarkers for effective selection of patients with SCLC are ongoing challenges. Emerging single-cell RNA sequencing data are providing insights into the plasticity and intratumoural and intertumoural heterogeneity of SCLC that might be associated with therapeutic resistance. In this Review, we provide a comprehensive overview of the latest advances in genomic and transcriptomic characterization of SCLC with a particular focus on opportunities for translation into new therapeutic approaches to improve patient outcomes.

Differential NEUROD1, ASCL1, and POU2F3 Expression Defines Molecular Subsets of Bladder Small Cell/Neuroendocrine Carcinoma with Prognostic Implications

Small cell carcinomas (SMC) of the lung are now molecularly classified based on the expression of transcriptional regulators (NEUROD1, ASCL1, POU2F3, YAP1) and DLL3, which has emerged as an investigational therapeutic target. PLCG2 has been shown to identify a distinct subpopulation of lung SMC with stem cell-like and pro-metastasis features and poor prognosis. We analyzed the expression of these novel neuroendocrine markers and their association with traditional neuroendocrine markers and patient outcomes in a cohort of bladder neuroendocrine carcinoma (NEC) consisting of 103 SMC and 19 large cell neuroendocrine carcinomas (LCNEC) assembled in tissue microarrays. Co-expression patterns were assessed and integrated with detailed clinical annotation including overall (OS) and recurrence free survival (RFS) and response to neoadjuvant/adjuvant chemotherapy. We identified five distinct molecular subtypes in bladder SMC based on expression of ASCL1, NEUROD1 and POU2F3: ASCL1+/NEUROD1- (n=33; 34%), ASCL1-/NEUROD1+ (n=21; 21%), ASCL1+/NEUROD1+ (n=17; 17%), POU2F3+ (n=22, 22%), and ASCL1-/NEUROD1-/POU2F3- (n=5, 5%). POU2F3+ tumors were mutually exclusive with those expressing ASCL1 and NEUROD1 and exhibited lower expression of traditional neuroendocrine markers. PLCG2 expression was noted in 33 tumors (32%) and was highly correlated with POU2F3 expression (p < 0.001). DLL3 expression was high in both SMC (n=72, 82%) and LCNEC (n=11, 85%). YAP1 expression was enriched in non- neuroendocrine components and negatively correlated with all neuroendocrine markers. In patients without metastatic disease who underwent radical cystectomy, PLCG2+ or POU2F3+ tumors had shorter RFS and OS (p<0.05), but their expression was not associated with metastasis status or response to neoadjuvant/adjuvant chemotherapy. In conclusion, NEC of the bladder can be divided into distinct molecular subtypes based on the expression of ASCL1, NEUROD1 and POU2F3. POU2F3 expressing tumors represent an ASCL1/NEUROD1-negative subset of bladder NEC characterized by lower expression of traditional neuroendocrine markers. Marker expression patterns were similar in SMC and LCNEC. Expression of PLCG2 and POU2F3 was associated with shorter recurrence-free and overall survival. DLL3 was expressed at high levels in both SMC and LCNEC of the bladder, nominating it as a potential therapeutic target.

Olfactory neuroblastoma mimics molecular heterogeneity and lineage trajectories of small-cell lung cancer

The olfactory epithelium undergoes neuronal regeneration from basal stem cells and is susceptible to olfactory neuroblastoma (ONB), a rare tumor of unclear origins. Employing alterations in Rb1/Trp53/Myc (RPM), we establish a genetically engineered mouse model of high-grade metastatic ONB exhibiting a NEUROD1+ immature neuronal phenotype. We demonstrate that globose basal cells (GBCs) are a permissive cell of origin for ONB and that ONBs exhibit cell fate heterogeneity that mimics normal GBC developmental trajectories. ASCL1 loss in RPM ONB leads to emergence of non-neuronal histopathologies, including a POU2F3+ microvillar-like state. Similar to small-cell lung cancer (SCLC), mouse and human ONBs exhibit mutually exclusive NEUROD1 and POU2F3-like states, an immune-cold tumor microenvironment, intratumoral cell fate heterogeneity comprising neuronal and non-neuronal lineages, and cell fate plasticity-evidenced by barcode-based lineage tracing and single-cell transcriptomics. Collectively, our findings highlight conserved similarities between ONB and neuroendocrine tumors with significant implications for ONB classification and treatment.

Extracellular vesicles from non-neuroendocrine SCLC cells promote adhesion and survival of neuroendocrine SCLC cells

Small cell lung cancer (SCLC) tumors are made up of distinct cell subpopulations, including neuroendocrine (NE) and non-neuroendocrine (non-NE) cells. While secreted factors from non-NE SCLC cells have been shown to support the growth of the NE cells, the underlying molecular factors are not well understood. Here, we show that exosome-type small extracellular vesicles (SEVs) secreted from non-NE SCLC cells promote adhesion and survival of NE SCLC cells. Proteomic analysis of purified SEVs revealed that extracellular matrix (ECM) proteins and integrins are highly enriched in SEVs of non-NE cells whereas nucleic acid-binding proteins are enriched in SEVs purified from NE cells. Addition of select purified ECM proteins identified in purified extracellular vesicles (EVs), specifically fibronectin, laminin 411, and laminin 511, were able to substitute for the role of non-NE-derived SEVs in promoting adhesion and survival of NE SCLC cells. Those same proteins were differentially expressed by human SCLC subtypes. These data suggest that ECM-carrying SEVs secreted by non-NE cells play a key role in supporting the growth and survival of NE SCLC cells.

Landscape and Treatment Options of Shapeshifting Small Cell Lung Cancer

Small cell lung cancer (SCLC) is a deadly neuroendocrine malignancy, notorious for its rapid tumor growth, early metastasis, and relatively "cold" immune environment. Only standard chemotherapies and a few immune checkpoint inhibitors have been approved for SCLC treatment, revealing an urgent need for novel therapeutic approaches. Moreover, SCLC has been recently recognized as a malignancy with high intratumoral and intertumoral heterogeneity, which explains the modest response rate in some patients and the early relapse. Molecular subtypes defined by the expression of lineage-specific transcription factors (ASCL1, NEUROD1, POU2F3, and, in some studies, YAP1) or immune-related genes display different degrees of neuroendocrine differentiation, immune cell infiltration, and response to treatment. Despite the complexity of this malignancy, a few biomarkers and targets have been identified and many promising drugs are currently undergoing clinical trials. In this review, we integrate the current progress on the genomic landscape of this shapeshifting malignancy, the characteristics and treatment vulnerabilities of each subtype, and promising drugs in clinical phases.

Maboss for HPC environments: implementations of the continuous time Boolean model simulator for large CPU clusters and GPU accelerators

BACKGROUND

Computational models in systems biology are becoming more important with the advancement of experimental techniques to query the mechanistic details responsible for leading to phenotypes of interest. In particular, Boolean models are well fit to describe the complexity of signaling networks while being simple enough to scale to a very large number of components. With the advance of Boolean model inference techniques, the field is transforming from an artisanal way of building models of moderate size to a more automatized one, leading to very large models. In this context, adapting the simulation software for such increases in complexity is crucial.

RESULTS

We present two new developments in the continuous time Boolean simulators: MaBoSS.MPI, a parallel implementation of MaBoSS which can exploit the computational power of very large CPU clusters, and MaBoSS.GPU, which can use GPU accelerators to perform these simulations.

CONCLUSION

These implementations enable simulation and exploration of the behavior of very large models, thus becoming a valuable analysis tool for the systems biology community.

Applied models and molecular characteristics of small cell lung cancer

Small cell lung cancer (SCLC) is a highly aggressive type of cancer frequently diagnosed with metastatic spread, rendering it surgically unresectable for the majority of patients. Although initial responses to platinum-based therapies are often observed, SCLC invariably relapses within months, frequently developing drug-resistance ultimately contributing to short overall survival rates. Recently, SCLC research aimed to elucidate the dynamic changes in the genetic and epigenetic landscape. These have revealed distinct subtypes of SCLC, each characterized by unique molecular signatures. The recent understanding of the molecular heterogeneity of SCLC has opened up potential avenues for precision medicine, enabling the development of targeted therapeutic strategies. In this review, we delve into the applied models and computational approaches that have been instrumental in the identification of promising drug candidates. We also explore the emerging molecular diagnostic tools that hold the potential to transform clinical practice and patient care.

Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML

Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a new optimization procedure to identify the top network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression.

Assessing NGS-based computational methods for predicting transcriptional regulators with query gene sets

This article provides an in-depth review of computational methods for predicting transcriptional regulators with query gene sets. Identification of transcriptional regulators is of utmost importance in many biological applications, including but not limited to elucidating biological development mechanisms, identifying key disease genes, and predicting therapeutic targets. Various computational methods based on next-generation sequencing (NGS) data have been developed in the past decade, yet no systematic evaluation of NGS-based methods has been offered. We classified these methods into two categories based on shared characteristics, namely library-based and region-based methods. We further conducted benchmark studies to evaluate the accuracy, sensitivity, coverage, and usability of NGS-based methods with molecular experimental datasets. Results show that BART, ChIP-Atlas, and Lisa have relatively better performance. Besides, we point out the limitations of NGS-based methods and explore potential directions for further improvement.

Key points

An introduction to available computational methods for predicting functional TRs from a query gene set.A detailed walk-through along with practical concerns and limitations.A systematic benchmark of NGS-based methods in terms of accuracy, sensitivity, coverage, and usability, using 570 TR perturbation-derived gene sets.NGS-based methods outperform motif-based methods. Among NGS methods, those utilizing larger databases and adopting region-centric approaches demonstrate favorable performance. BART, ChIP-Atlas, and Lisa are recommended as these methods have overall better performance in evaluated scenarios.

Predicting drug response of small cell lung cancer cell lines based on enrichment analysis of complex gene signatures

Advances in the field of genomics and transcriptomics have enabled researchers to identify gene signatures related to development and treatment of Small Cell Lung Cancer. In most cases, complex gene expression patterns are identified, comprising of genes with differential behavior. Most tools use single-genes as predictors of drug response, with only limited options for multi-gene use. Here we examine the potential of predicting drug response using these complex gene expression signatures by employing clustering and signal enrichment in Small Cell Lung Cancer. Our results demonstrate clustering genes from complex expression patterns helps identify differential activity of gene groups with alternate function which can then be used to predict drug response.

Small cells - big issues: biological implications and preclinical advancements in small cell lung cancer

Current treatment guidelines refer to small cell lung cancer (SCLC), one of the deadliest human malignancies, as a homogeneous disease. Accordingly, SCLC therapy comprises chemoradiation with or without immunotherapy. Meanwhile, recent studies have made significant advances in subclassifying SCLC based on the elevated expression of the transcription factors ASCL1, NEUROD1, and POU2F3, as well as on certain inflammatory characteristics. The role of the transcription regulator YAP1 in defining a unique SCLC subset remains to be established. Although preclinical analyses have described numerous subtype-specific characteristics and vulnerabilities, the so far non-existing clinical subtype distinction may be a contributor to negative clinical trial outcomes. This comprehensive review aims to provide a framework for the development of novel personalized therapeutic approaches by compiling the most recent discoveries achieved by preclinical SCLC research. We highlight the challenges faced due to limited access to patient material as well as the advances accomplished by implementing state-of-the-art models and methodologies.

Molecular classification of small cell lung cancer subtypes: Characteristics, prognostic factors, and clinical translation

ABSTRACT

Small cell lung cancer (SCLC) is a highly malignant tumor with a very poor prognosis; therefore, more effective treatments are urgently needed for patients afflicted with the disease. In recent years, emerging molecular classifications based on key transcription factors of SCLC have provided more information on the tumor pathophysiology, metastasis, immune microenvironment, and acquired therapeutic resistance and reflected the intertumoral heterogeneity of the various SCLC phenotypes. Additionally, advances in genomics and single-cell sequencing analysis have further revealed the high intratumoral heterogeneity and plasticity of the disease. Herein, we review and summarize these recent lines of evidence and discuss the possible pathogenesis of SCLC.

Clinical insights into small cell lung cancer: Tumor heterogeneity, diagnosis, therapy, and future directions

Small cell lung cancer (SCLC) is characterized by rapid growth and high metastatic capacity. It has strong epidemiologic and biologic links to tobacco carcinogens. Although the majority of SCLCs exhibit neuroendocrine features, an important subset of tumors lacks these properties. Genomic profiling of SCLC reveals genetic instability, almost universal inactivation of the tumor suppressor genes TP53 and RB1, and a high mutation burden. Because of early metastasis, only a small fraction of patients are amenable to curative-intent lung resection, and these individuals require adjuvant platinum-etoposide chemotherapy. Therefore, the vast majority of patients are currently being treated with chemoradiation with or without immunotherapy. In patients with disease confined to the chest, standard therapy includes thoracic radiotherapy and concurrent platinum-etoposide chemotherapy. Patients with metastatic (extensive-stage) disease are treated with a combination of platinum-etoposide chemotherapy plus immunotherapy with an anti-programmed death-ligand 1 monoclonal antibody. Although SCLC is initially very responsive to platinum-based chemotherapy, these responses are transient because of the development of drug resistance. In recent years, the authors have witnessed an accelerating pace of biologic insights into the disease, leading to the redefinition of the SCLC classification scheme. This emerging knowledge of SCLC molecular subtypes has the potential to define unique therapeutic vulnerabilities. Synthesizing these new discoveries with the current knowledge of SCLC biology and clinical management may lead to unprecedented advances in SCLC patient care. Here, the authors present an overview of multimodal clinical approaches in SCLC, with a special focus on illuminating how recent advancements in SCLC research could accelerate clinical development.

Data-driven structural analysis of small cell lung cancer transcription factor network suggests potential subtype regulators and transition pathways

Small cell lung cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated. Here, we analyze the structure of an SCLC TF network by using graph theory concepts and identify its structurally important components responsible for complex signal processing, called hubs. We show that the hubs of the network are regulators of different SCLC subtypes by analyzing first the unbiased network structure and then integrating RNA-seq data as weights assigned to each interaction. Data-driven analysis emphasizes MYC as a hub, consistent with recent reports. Furthermore, we hypothesize that the pathways connecting functionally distinct hubs may control subtype transitions and test this hypothesis via network simulations on a candidate pathway and observe subtype transition. Overall, structural analyses of complex networks can identify their functionally important components and pathways driving the network dynamics. Such analyses can be an initial step for generating hypotheses and can guide the discovery of target pathways whose perturbation may change the network dynamics phenotypically.

YAP1 expression is associated with survival and immunosuppression in small cell lung cancer

Immunotherapy is considered a major breakthrough in the treatment of small cell lung cancer (SCLC), although its anti-tumor efficacy is limited. With a high degree of malignancy and high heterogeneity, SCLC is difficult to treat in the clinic. A new combination strategy is urgently needed to further improve the efficacy of immunotherapy in patients with SCLC. By immunofluorescence, 100 SCLC patients in a local cohort were classified into the SCLC-A (high ASCL1 expression; n = 36), SCLC-N (high NEUROD1 expression; n = 32), SCLC-P (high POU2F3 expression; n = 14), and SCLC-Y (high YAP1 expression; n = 18) subtypes. Each SCLC molecular subtype represented different prognoses, tumor microenvironment traits, and immunotherapy sensitivities. Analysis of both the local and public cohorts suggested that the SCLC-Y subtype exhibited the worst clinical outcome (p < 0.05) when compared with other subtypes. SCLC with high YAP1 expression was characterized by high PD-L1 expression, high stromal score, T-cell functional impairment, and a close relationship with immune-related pathways. YAP1 upregulated PD-L1 expression and suppressed T cell activation, thus leading to immune evasion. In in vitro experiments, blockade of YAP1 promoted cancer cell apoptosis, immune cell proliferation, T-cell activation, and cytotoxic T-cell infiltration, thus further potentiating the efficacy of immunotherapy in patients with the SCLC-Y subtype.

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.

Subtype of SCLC Is an Intrinsic and Persistent Feature Through Systemic Treatment

Introduction

SCLC is an aggressive malignancy with poor outcome. Most patients have disease recurrence despite treatments with multiple modalities. Subtyping of SCLC has been proposed recently, and novel agents targeting specific subtypes are actively being investigated. In this study, we evaluated the plasticity of subtypes in paired pre- and post-treatment samples. The aim was to understand possible subtype evolution after chemotherapy resistance that could lead to alternate targeted therapy strategies.

Methods

A total of 68 samples from 32 patients with sufficient paired specimens were identified from 1998 to 2022. ASCL1, NEUROD1, and POU2F3 immunohistochemistry studies were performed on all cases, and subtyping by predominant expression was determined. Subtype comparison in each patient was performed, and expression analysis was performed on the basis of subtypes.

Results

Of 32 cases, 28 (88%) had the same subtype in pre- and first post-treatment specimens. Protein expression level of subtype-specific transcription factor remained stable after chemotherapy. Two of five (40%) NEUROD1-predominant SCLC switched to ASCL1-predominant phenotype after treatment. One case had a pitfall of scoring ASCL1 on specimen with marked crushing artifacts. One case revealed the challenge of proper subtyping for samples with borderline POU2F3 expression.

Conclusions

Subtype of SCLC generally remains the same after acquiring chemotherapy resistance. Plasticity was observed with rare cases switching from NEUROD1-predominant to ASC1-predominant SCLC. Resubtyping is unnecessary for the consideration of novel subtype-specific targeted agents, except cases with NEUROD1-predominant subtype.

Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML

Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a novel optimization procedure to identify the optimal network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression.

Small Cell Lung Carcinoma: Current Diagnosis, Biomarkers, and Treatment Options with Future Perspectives

Small cell lung cancer (SCLC) is an aggressive malignancy characterized by rapid proliferation, early dissemination, acquired therapy resistance, and poor prognosis. Early diagnosis of SCLC is crucial since most patients present with advanced/metastatic disease, limiting the potential for curative treatment. While SCLC exhibits initial responsiveness to chemotherapy and radiotherapy, treatment resistance commonly emerges, leading to a five-year overall survival rate of up to 10%. New effective biomarkers, early detection, and advancements in therapeutic strategies are crucial for improving survival rates and reducing the impact of this devastating disease. This review aims to comprehensively summarize current knowledge on diagnostic options, well-known and emerging biomarkers, and SCLC treatment strategies and discuss future perspectives on this aggressive malignancy.

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.

Targeted Therapies in Small Cell Lung Cancer: From Old Failures to Novel Therapeutic Strategies

The clinical management of small cell lung cancer (SCLC) treatment remains a major challenge for thoracic oncologists, with very few therapeutic advances significantly impacting patients' survival. The recent introduction of immunotherapy in the clinical setting produced a marginal benefit for a limited subset of metastatic patients, while the therapeutic scenario for relapsing extended-disease small cell lung cancers (ED-SCLCs) remains almost deserted. Recent efforts clarified the molecular features of this disease, leading to the identification of key signalling pathways which may serve as potential targets for clinical use. Despite the large number of molecules tested and the numerous therapeutic failures, some targeted therapies have recently shown interesting preliminary results. In this review, we describe the main molecular pathways involved in SCLC development/progression and provide an updated summary of the targeted therapies currently under investigation in SCLC patients.

Data-driven structural analysis of Small Cell Lung Cancer transcription factor network suggests potential subtype regulators and transition pathways

Small Cell Lung Cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated. Here, we analyze the structure of an SCLC TF network by using graph theory concepts and identify its structurally important components responsible for complex signal processing, called hubs. We show that the hubs of the network are regulators of different SCLC subtypes by analyzing first the unbiased network structure and then integrating RNA-seq data as weights assigned to each interaction. Data-driven analysis emphasizes MYC as a hub, consistent with recent reports. Furthermore, we hypothesize that the pathways connecting functionally distinct hubs may control subtype transitions and test this hypothesis via network simulations on a candidate pathway and observe subtype transition. Overall, structural analyses of complex networks can identify their functionally important components and pathways driving the network dynamics. Such analyses can be an initial step for generating hypotheses and can guide the discovery of target pathways whose perturbation may change the network dynamics phenotypically.

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.

CXCR2 expression during melanoma tumorigenesis controls transcriptional programs that facilitate tumor growth

Background

Though the CXCR2 chemokine receptor is known to play a key role in cancer growth and response to therapy, a direct link between expression of CXCR2 in tumor progenitor cells during induction of tumorigenesis has not been established.

Methods

To characterize the role of CXCR2 during melanoma tumorigenesis, we generated tamoxifen-inducible tyrosinase-promoter driven Braf ^V600E /Pten ^-/- /Cxcr2 ^-/- and NRas ^Q61R /INK4a ^-/- /Cxcr2 ^-/- melanoma models. In addition, the effects of a CXCR1/CXCR2 antagonist, SX-682, on melanoma tumorigenesis were evaluated in Braf ^V600E /Pten ^-/- and NRas ^Q61R /INK4a ^-/- mice and in melanoma cell lines. Potential mechanisms by which Cxcr2 affects melanoma tumorigenesis in these murine models were explored using RNAseq, mMCP-counter, ChIPseq, and qRT-PCR; flow cytometry, and reverse phosphoprotein analysis (RPPA).

Results

Genetic loss of Cxcr2 or pharmacological inhibition of CXCR1/CXCR2 during melanoma tumor induction resulted in key changes in gene expression that reduced tumor incidence/growth and increased anti-tumor immunity. Interestingly, after Cxcr2 ablation, Tfcp2l1 , a key tumor suppressive transcription factor, was the only gene significantly induced with a log ₂ fold-change greater than 2 in these three different melanoma models.

Conclusions

Here, we provide novel mechanistic insight revealing how loss of Cxcr2 expression/activity in melanoma tumor progenitor cells results in reduced tumor burden and creation of an anti-tumor immune microenvironment. This mechanism entails an increase in expression of the tumor suppressive transcription factor, Tfcp2l1, along with alteration in the expression of genes involved in growth regulation, tumor suppression, stemness, differentiation, and immune modulation. These gene expression changes are coincident with reduction in the activation of key growth regulatory pathways, including AKT and mTOR.

Protein Expression of immune checkpoints STING and MHCII in small cell lung cancer

BACKGROUND

SCLC is an aggressive malignancy where immunotherapies show limited efficacy. We aimed to characterize the SCLC microenvironment according to the expression patterns of SCLC subtype markers and novel immune checkpoints to identify therapeutic vulnerabilities.

METHODS

We included SCLC tissue samples from 219 surgically resected, limited-stage patients in this cross-sectional study. We performed immunohistochemistry for STING and MHCII, as well as for the novel subtype markers (ASCL1, NEUROD1, POU2F3, YAP1). Moreover, we assessed CD45 + , CD8 + and CD68 + immune cell infiltration.

RESULTS

36% of SCLC tumors showed significant stromal or intraepithelial CD45 + immune cell infiltration. These patients exhibited significantly increased overall survival (OS) (vs. patients with immune-deserted tumors). High CD8 expression was associated with increased median OS. We found STING expression on cancer-associated fibroblasts in the stroma and on T-cells and macrophages in both tumorous and stromal compartments. STING expression positively correlated with immune cell infiltration. Increased STING-positivity in tumor nests was an independent favorable prognosticator for OS. ASCL1 was the most frequently expressed subtype-specific protein. Concomitant expression of three or four subtype-defining markers was seen in 13.8% of the included samples, whereas 24.1% of the cases were classified as quadruple negative tumors. YAP1 expression was associated with increased immune infiltrates. Tumor cell MHCII expression positively correlated with immune cell infiltration and with STING- and YAP1 expressions.

CONCLUSIONS

STING and MHCII are expressed in SCLC. The majority of immune-infiltrated SCLCs exhibit increased STING expression. Immune infiltration and STING expression are prognostic in limited-stage SCLC, making STING a potential therapeutic target.

Involvement of Epithelial-Mesenchymal Transition Genes in Small Cell Lung Cancer Phenotypic Plasticity

Small cell lung cancer (SCLC) is an aggressive cancer recalcitrant to treatment, arising predominantly from epithelial pulmonary neuroendocrine (NE) cells. Intratumor heterogeneity plays critical roles in SCLC disease progression, metastasis, and treatment resistance. At least five transcriptional SCLC NE and non-NE cell subtypes were recently defined by gene expression signatures. Transition from NE to non-NE cell states and cooperation between subtypes within a tumor likely contribute to SCLC progression by mechanisms of adaptation to perturbations. Therefore, gene regulatory programs distinguishing SCLC subtypes or promoting transitions are of great interest. Here, we systematically analyze the relationship between SCLC NE/non-NE transition and epithelial to mesenchymal transition (EMT)-a well-studied cellular process contributing to cancer invasiveness and resistance-using multiple transcriptome datasets from SCLC mouse tumor models, human cancer cell lines, and tumor samples. The NE SCLC-A2 subtype maps to the epithelial state. In contrast, SCLC-A and SCLC-N (NE) map to a partial mesenchymal state (M1) that is distinct from the non-NE, partial mesenchymal state (M2). The correspondence between SCLC subtypes and the EMT program paves the way for further work to understand gene regulatory mechanisms of SCLC tumor plasticity with applicability to other cancer types.

An integrative epigenomic approach identifies ELF3 as an oncogenic regulator in ASCL1-positive neuroendocrine carcinoma

Neuroendocrine carcinoma (NEC) is a highly aggressive subtype of the neuroendocrine tumor with an extremely poor prognosis. We have previously conducted a comprehensive genomic analysis of over 100 cases of NEC of the gastrointestinal system (GIS-NEC) and unraveled its unique and organ-specific genomic drivers. However, the epigenomic features of GIS-NEC remain unexplored. In this study, we have described the epigenomic landscape of GIS-NEC and small cell lung carcinoma (SCLC) by integrating motif enrichment analysis from the assay of transposase-accessible chromatin sequencing (ATAC-seq) and enhancer profiling from a novel cleavage under targets and tagmentation (CUT&Tag) assay for H3K27ac and identified ELF3 as one of the super-enhancer-related transcriptional factors in NEC. By combining CUT&Tag and knockdown RNA sequencing for ELF3, we uncovered the transcriptional network regulated by ELF3 and defined its distinctive gene signature, including AURKA, CDC25B, CLDN4, ITGB6, and YWAHB. Furthermore, a loss-of-function assay revealed that ELF3 depletion led to poor cell viability. Finally, using gene expression of clinical samples, we successfully divided GIS-NEC patients into two subgroups according to the ELF3 signature and demonstrated that tumor-promoting pathways were activated in the ELF3 signature-high group. Our findings highlight the transcriptional regulation of ELF3 as an oncogenic transcription factor and its tumor-promoting properties in NEC.

Rare thoracic cancers: a comprehensive overview of diagnosis and management of small cell lung cancer, malignant pleural mesothelioma and thymic epithelial tumours

Despite the progress in outcomes seen with immunotherapy in various malignancies, including nonsmall cell lung cancer, the benefits are less in small cell lung cancer, malignant pleural mesothelioma and thymic epithelial tumours. New effective treatment options are needed, guided via more in-depth insights into the pathophysiology of these rare malignancies. This review comprehensively presents an overview of the clinical presentation, diagnostic tools, staging systems, pathophysiology and treatment options for these rare thoracic cancers. In addition, opportunities for further improvement of therapies are discussed.

NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity

A major question in systems biology is how to identify the core gene regulatory circuit that governs the decision-making of a biological process. Here, we develop a computational platform, named NetAct, for constructing core transcription factor regulatory networks using both transcriptomics data and literature-based transcription factor-target databases. NetAct robustly infers regulators' activity using target expression, constructs networks based on transcriptional activity, and integrates mathematical modeling for validation. Our in silico benchmark test shows that NetAct outperforms existing algorithms in inferring transcriptional activity and gene networks. We illustrate the application of NetAct to model networks driving TGF-β-induced epithelial-mesenchymal transition and macrophage polarization.

Transcriptional Circuitry of NKX2-1 and SOX1 Defines an Unrecognized Lineage Subtype of Small-Cell Lung Cancer

Rationale: The current molecular classification of small-cell lung cancer (SCLC) on the basis of the expression of four lineage transcription factors still leaves its major subtype SCLC-A as a heterogeneous group, necessitating more precise characterization of lineage subclasses. Objectives: To refine the current SCLC classification with epigenomic profiles and to identify features of the redefined SCLC subtypes. Methods: We performed unsupervised clustering of epigenomic profiles on 25 SCLC cell lines. Functional significance of NKX2-1 (NK2 homeobox 1) was evaluated by cell growth, apoptosis, and xenograft using clustered regularly interspaced short palindromic repeats-Cas9 (CRISPR-associated protein 9)-mediated deletion. NKX2-1-specific cistromic profiles were determined using chromatin immunoprecipitation followed by sequencing, and its functional transcriptional partners were determined using coimmunoprecipitation followed by mass spectrometry. Rb1flox/flox; Trp53flox/flox and Rb1flox/flox; Trp53flox/flox; Nkx2-1flox/flox mouse models were engineered to explore the function of Nkx2-1 in SCLC tumorigenesis. Epigenomic landscapes of six human SCLC specimens and 20 tumors from two mouse models were characterized. Measurements and Main Results: We identified two epigenomic subclusters of the major SCLC-A subtype: SCLC-Aα and SCLC-Aσ. SCLC-Aα was characterized by the presence of a super-enhancer at the NKX2-1 locus, which was observed in human SCLC specimens and a murine SCLC model. We found that NKX2-1, a dual lung and neural lineage factor, is uniquely relevant in SCLC-Aα. In addition, we found that maintenance of this neural identity in SCLC-Aα is mediated by collaborative transcriptional activity with another neuronal transcriptional factor, SOX1 (SRY-box transcription factor 1). Conclusions: We comprehensively describe additional epigenomic heterogeneity of the major SCLC-A subtype and define the SCLC-Aα subtype by the core regulatory circuitry of NKX2-1 and SOX1 super-enhancers and their functional collaborations to maintain neuronal linage state.

Multivalent state transitions shape the intratumoral composition of small cell lung carcinoma

Studies to date have not resolved how diverse transcriptional programs contribute to the intratumoral heterogeneity of small cell lung carcinoma (SCLC), an aggressive tumor associated with a dismal prognosis. Here, we identify distinct and commutable transcriptional states that confer discrete functional attributes in individual SCLC tumors. We combine an integrative approach comprising the transcriptomes of 52,975 single cells, high-resolution measurement of cell state dynamics at the single-cell level, and functional and correlative studies using treatment naïve xenografts with associated clinical outcomes. We show that individual SCLC tumors contain distinctive proportions of stable cellular states that are governed by bidirectional cell state transitions. Using drugs that target the epigenome, we reconfigure tumor state composition in part by altering individual state transition rates. Our results reveal new insights into how single-cell transition behaviors promote cell state equilibrium in SCLC and suggest that facile plasticity underlies its resistance to therapy and lethality.

Identification of the Transcriptional Regulatory Role of RUNX2 by Network Analysis in Lung Cancer Cells

The use of a new bioinformatics pipeline allowed the identification of deregulated transcription factors (TFs) coexpressed in lung cancer that could become biomarkers of tumor establishment and progression. A gene regulatory network (GRN) of lung cancer was created with the normalized gene expression levels of differentially expressed genes (DEGs) from the microarray dataset GSE19804. Moreover, coregulatory and transcriptional regulatory network (TRN) analyses were performed for the main regulators identified in the GRN analysis. The gene targets and binding motifs of all potentially implicated regulators were identified in the TRN and with multiple alignments of the TFs' target gene sequences. Six transcription factors (E2F3, FHL2, ETS1, KAT6B, TWIST1, and RUNX2) were identified in the GRN as essential regulators of gene expression in non-small-cell lung cancer (NSCLC) and related to the lung tumoral process. Our findings indicate that RUNX2 could be an important regulator of the lung cancer GRN through the formation of coregulatory complexes with other TFs related to the establishment and progression of lung cancer. Therefore, RUNX2 could become an essential biomarker for developing diagnostic tools and specific treatments against tumoral diseases in the lung after the experimental validation of its regulatory function.

Molecular Characterization and Therapeutic Approaches to Small Cell Lung Cancer: Imaging Implications

Small cell lung cancer (SCLC) is a highly aggressive malignancy with exceptionally poor prognosis, comprising approximately 15% of lung cancers. Emerging knowledge of the molecular and genomic landscape of SCLC and recent successful clinical applications of new systemic agents have allowed for precision oncology treatment approaches. Imaging is essential for the diagnosis, staging, and treatment monitoring of patients with SCLC. The role of imaging is increasing with the approval of new treatment agents, including immune checkpoint inhibitors, which lead to novel imaging manifestations of response and toxicities. The purpose of this state-of-the-art review is to provide the reader with the latest information about SCLC, focusing on the subtyping of this malignancy (molecular characterization) and the emerging systemic therapeutic approaches and their implications for imaging. The review will also discuss the future directions of SCLC imaging, radiomics and machine learning.

Single-cell transcriptomic profiling reveals the tumor heterogeneity of small-cell lung cancer

Small-cell lung cancer (SCLC) is the most aggressive and lethal subtype of lung cancer, for which, better understandings of its biology are urgently needed. Single-cell sequencing technologies provide an opportunity to profile individual cells within the tumor microenvironment (TME) and investigate their roles in tumorigenic processes. Here, we performed high-precision single-cell transcriptomic analysis of ~5000 individual cells from primary tumors (PTs) and matched normal adjacent tissues (NATs) from 11 SCLC patients, including one patient with both PT and relapsed tumor (RT). The comparison revealed an immunosuppressive landscape of human SCLC. Malignant cells in SCLC tumors exhibited diverse states mainly related to the cell cycle, immune, and hypoxic properties. Our data also revealed the intratumor heterogeneity (ITH) of key transcription factors (TFs) in SCLC and related gene expression patterns and functions. The non-neuroendocrine (non-NE) tumors were correlated with increased inflammatory gene signatures and immune cell infiltrates in SCLC, which contributed to better responses to immune checkpoint inhibitors. These findings indicate a significant heterogeneity of human SCLC, and intensive crosstalk between cancer cells and the TME at single-cell resolution, and thus, set the stage for a better understanding of the biology of SCLC as well as for developing new therapeutics for SCLC.

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.

Organoids as a Systems Platform for SCLC Brain Metastasis

Small Cell Lung Cancer (SCLC) is a highly aggressive, neuroendocrine tumor. Traditional reductionist approaches have proven ineffective to ameliorate the uniformly dismal outcomes for SCLC - survival at 5 years remains less than 5%. A major obstacle to improving treatment is that SCLC tumor cells disseminate early, with a strong propensity for metastasizing to the brain. Accumulating evidence indicates that, contrary to previous textbook knowledge, virtually every SCLC tumor is comprised of multiple subtypes. Important questions persist regarding the role that this intra-tumor subtype heterogeneity may play in supporting the invasive properties of SCLC. A recurrent hypothesis in the field is that subtype interactions and/or transition dynamics are major determinants of SCLC metastatic seeding and progression. Here, we review the advantages of cerebral organoids as an experimentally accessible platform for SCLC brain metastasis, amenable to genetic manipulations, drug perturbations, and assessment of subtype interactions when coupled, e.g., to temporal longitudinal monitoring by high-content imaging or high-throughput omics data generation. We then consider systems approaches that can produce mathematical and computational models useful to generalize lessons learned from ex vivo organoid cultures, and integrate them with in vivo observations. In summary, systems approaches combined with ex vivo SCLC cultures in brain organoids may effectively capture both tumor-tumor and host-tumor interactions that underlie general principles of brain metastasis.

Heterogeneity of neuroendocrine transcriptional states in metastatic small cell lung cancers and patient-derived models

Molecular subtypes of small cell lung cancer (SCLC) defined by the expression of key transcription regulators have recently been proposed in cell lines and limited number of primary tumors. The clinical and biological implications of neuroendocrine (NE) subtypes in metastatic SCLC, and the extent to which they vary within and between patient tumors and in patient-derived models is not known. We integrate histology, transcriptome, exome, and treatment outcomes of SCLC from a range of metastatic sites, revealing complex intra- and intertumoral heterogeneity of NE differentiation. Transcriptomic analysis confirms previously described subtypes based on ASCL1, NEUROD1, POU2F3, YAP1, and ATOH1 expression, and reveal a clinical subtype with hybrid NE and non-NE phenotypes, marked by chemotherapy-resistance and exceedingly poor outcomes. NE tumors are more likely to have RB1, NOTCH, and chromatin modifier gene mutations, upregulation of DNA damage response genes, and are more likely to respond to replication stress targeted therapies. In contrast, patients preferentially benefited from immunotherapy if their tumors were non-NE. Transcriptional phenotypes strongly skew towards the NE state in patient-derived model systems, an observation that was confirmed in paired patient-matched tumors and xenografts. We provide a framework that unifies transcriptomic and genomic dimensions of metastatic SCLC. The marked differences in transcriptional diversity between patient tumors and model systems are likely to have implications in development of novel therapeutic agents.

Killing SCLC: insights into how to target a shapeshifting tumor

Small cell lung cancer (SCLC) is a rapidly growing, highly metastatic, and relatively immune-cold lung cancer subtype. Historically viewed in the laboratory and clinic as a single disease, new discoveries suggest that SCLC comprises multiple molecular subsets. Expression of MYC family members and lineage-related transcription factors ASCL1, NEUROD1, and POU2F3 (and, in some studies, YAP1) define unique molecular states that have been associated with distinct responses to a variety of therapies. However, SCLC tumors exhibit a high degree of intratumoral heterogeneity, with recent studies suggesting the existence of tumor cell plasticity and phenotypic switching between subtype states. While SCLC plasticity is correlated with, and likely drives, therapeutic resistance, the mechanisms underlying this plasticity are still largely unknown. Subtype states are also associated with immune-related gene expression, which likely impacts response to immune checkpoint blockade and may reveal novel targets for alternative immunotherapeutic approaches. In this review, we synthesize recent discoveries on the mechanisms of SCLC plasticity and how these processes may impinge on antitumor immunity.

Small cell lung cancer; recent advances of its biology and therapeutic perspective

Lung cancer is historically divided into two major categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). While the therapeutic efficacy of NSCLC has improved due to the development of molecular targeted therapy and immune checkpoint inhibitors (ICIs) treatment, there has been very slow progress in the therapeutic advances of SCLC. Since SCLC is a deadly disease with rapid progression and early metastasis and comprises approximately 10% of lung cancer cases, more attention should be given to the therapeutic strategy for SCLC. Most SCLC cases respond to cytotoxic drugs, cisplatin, and etoposide. The objective response rate to the standard regimen is reported to be approximately 70% that is sufficient as standard therapy. However, almost all tumors recur and become refractory to chemotherapy which is the most important problem of this deadly disease. Recently, for the first time in several decades, ICIs have changed the standard therapy for SCLC. It must be emphasized that although ICIs paved the new way for SCLC therapy, more precise and effective therapy for SCLC is desired. Unfortunately, precise molecular mechanisms of SCLC are yet to be understood. Recent elaborate studies on the cell biology of SCLC uncovered several important aspects of molecular mechanisms. Gene profiling of cancer cells can be done using modern technology like next-generation sequencing (NGS). In this minireview, we describe the advances of modern technology in SCLC research and consider future therapeutic strategies based on the molecular mechanisms of SCLC.

TIAM1-RAC1 promote small-cell lung cancer cell survival through antagonizing Nur77-induced BCL2 conformational change

Small-cell lung cancer (SCLC), an aggressive neuroendocrine malignancy, has limited treatment options beyond platinum-based chemotherapy, whereafter acquired resistance is rapid and common. By analyzing expression data from SCLC tumors, patient-derived models, and established cell lines, we show that the expression of TIAM1, an activator of the small GTPase RAC1, is associated with a neuroendocrine gene program. TIAM1 depletion or RAC1 inhibition reduces viability and tumorigenicity of SCLC cells by increasing apoptosis associated with conversion of BCL2 from its pro-survival to pro-apoptotic function via BH3 domain exposure. This conversion is dependent upon cytoplasmic translocation of Nur77, an orphan nuclear receptor. TIAM1 interacts with and sequesters Nur77 in SCLC cell nuclei and TIAM1 depletion or RAC1 inhibition promotes Nur77 translocation to the cytoplasm. Mutant TIAM1 with reduced Nur77 binding fails to suppress apoptosis triggered by TIAM1 depletion. In conclusion, TIAM1-RAC1 signaling promotes SCLC cell survival via Nur77 nuclear sequestration.

Signatures of plasticity, metastasis, and immunosuppression in an atlas of human small cell lung cancer

Small cell lung cancer (SCLC) is an aggressive malignancy that includes subtypes defined by differential expression of ASCL1, NEUROD1, and POU2F3 (SCLC-A, -N, and -P, respectively). To define the heterogeneity of tumors and their associated microenvironments across subtypes, we sequenced 155,098 transcriptomes from 21 human biospecimens, including 54,523 SCLC transcriptomes. We observe greater tumor diversity in SCLC than lung adenocarcinoma, driven by canonical, intermediate, and admixed subtypes. We discover a PLCG2-high SCLC phenotype with stem-like, pro-metastatic features that recurs across subtypes and predicts worse overall survival. SCLC exhibits greater immune sequestration and less immune infiltration than lung adenocarcinoma, and SCLC-N shows less immune infiltrate and greater T cell dysfunction than SCLC-A. We identify a profibrotic, immunosuppressive monocyte/macrophage population in SCLC tumors that is particularly associated with the recurrent, PLCG2-high subpopulation.

Systems-level network modeling deciphers the master regulators of phenotypic plasticity and heterogeneity in melanoma

Phenotypic (i.e. non-genetic) heterogeneity in melanoma drives dedifferentiation, recalcitrance to targeted therapy and immunotherapy, and consequent tumor relapse and metastasis. Various markers or regulators associated with distinct phenotypes in melanoma have been identified, but, how does a network of interactions among these regulators give rise to multiple "attractor" states and phenotypic switching remains elusive. Here, we inferred a network of transcription factors (TFs) that act as master regulators for gene signatures of diverse cell-states in melanoma. Dynamical simulations of this network predicted how this network can settle into different "attractors" (TF expression patterns), suggesting that TF network dynamics drives the emergence of phenotypic heterogeneity. These simulations can recapitulate major phenotypes observed in melanoma and explain de-differentiation trajectory observed upon BRAF inhibition. Our systems-level modeling framework offers a platform to understand trajectories of phenotypic transitions in the landscape of a regulatory TF network and identify novel therapeutic strategies targeting melanoma plasticity.

Intrinsic and acquired drug resistance to LSD1 inhibitors in small cell lung cancer occurs through a TEAD4-driven transcriptional state

Small-cell lung cancer (SCLC) is a heterogeneous disease, consisting of intratumoral and intertumoral neuroendocrine (ASCL1 and/or NEUROD1), mesenchymal-like, and YAP-driven transcriptional states. Lysine-specific demethylase 1 (LSD1; also known as KDM1A) inhibitors have recently been progressed to clinical trials in SCLC based on a promising preclinical antitumor activity. A potential clinical limitation of LSD1 inhibitors is the heterogeneous drug responses that have been observed in SCLC cell lines and patient-derived models. Based on these observations, we studied molecular and transcriptional signatures that predict patient response to this class of drug. Employing SCLC patient-derived transcriptional signatures, we define that SCLC cell lines sensitive to LSD1 inhibitors are enriched in neuroendocrine transcriptional markers, whereas cell lines enriched in a mesenchymal-like transcriptional program demonstrate intrinsic resistance to LSD1 inhibitors. We have identified a reversible, adaptive resistance mechanism to LSD1 inhibitors through epigenetic reprogramming to a TEAD4-driven mesenchymal-like state. Our data suggest that only a segment of SCLC patients, with a defined neuroendocrine differentiation state, will likely benefit from LSD1 inhibitors. It provides novel evidence for the selection of a TEAD4-driven mesenchymal-like subpopulation resistant to LSD1 inhibitors in SCLC patients that may require effective drug combinations to sustain effective clinical responses.

Coexistence of “Cream Skimmer” and “Crumb Picker” Phenotypes in Nature and in Cancer

Over 40 years ago, seminal papers by Armstrong and McGehee and by Levins showed that temporal fluctuations in resource availability could permit coexistence of two species on a single resource. Such coexistence results from non-linearities or non-additivities in the way resource supply translates into fitness. These reflect trade-offs where one species benefits more than the other during good periods and suffers more (or does less well) than the other during less good periods, be the periods stochastic, unstable population dynamics, or seasonal. Since, coexistence based on fluctuating conditions has been explored under the guises of “grazers” and “diggers,” variance partitioning, relative non-linearity, “opportunists” and “gleaners,” and as the storage effect. Here we focus on two phenotypes, “cream skimmers” and “crumb pickers,” the former having the advantage in richer times and the latter in less rich times. In nature, richer and poorer times, with regular or stochastic appearances, are the norm and occur on many time scales. Fluctuations among richer and poorer times also appear to be the norm in cancer ecosystems. Within tumors, nutrient availability, oxygen, and pH can fluctuate stochastically or periodically, with swings occurring over seconds to minutes to hours. Despite interest in tumor heterogeneity and how it promotes the coexistence of different cancer cell types, the effects of fluctuating resource availability have not been explored for cancer. Here, in the context of pulsed resources, we (1) develop models of foraging consumers who experience pulsed resources to examine four types of trade-offs that can promote coexistence of phenotypes that do relatively better in richer versus in poorer times, (2) establish that conditions in tumors are conducive for this mechanism, (3) propose and empirically explore biomarkers indicative of the two phenotypes (HIF-1, GLUT-1, CA IX, CA XII), and (4) and compare cream skimmer and crumb picker biology and ecology in nature and cancer to provide cross-disciplinary insights into this interesting, and, we argue, likely very common, mechanism of coexistence.

ASCL1, NKX2-1, and PROX1 co-regulate subtype-specific genes in small-cell lung cancer

Lineage-defining transcription factors (LTFs) play key roles in small-cell lung cancer (SCLC) pathophysiology. Delineating the LTF-regulated genes operative in SCLC could provide a road map to identify SCLC dependencies. We integrated chromatin landscape and transcriptome analyses of patient-derived SCLC preclinical models to identify super-enhancers (SEs) and their associated genes in the ASCL1-, NEUROD1-, and POU2F3-high SCLC subtypes. We find SE signatures predict LTF-based classification of SCLC, and the SE-associated genes are enriched with those defined as common essential genes in DepMap. In addition, in ASCL1-high SCLC, we show ASCL1 complexes with NKX2-1 and PROX1 to co-regulate genes functioning in NOTCH signaling, catecholamine biosynthesis, and cell-cycle processes. Depletion of ASCL1 demonstrates it is a key dependency factor in preclinical SCLC models and directly regulates multiple DepMap-defined essential genes. We provide LTF/SE-based subtype-specific gene sets for SCLC for further therapeutic investigation.

•

Super-enhancers support lineage-defining transcription factor SCLC classification

•

SCLC super-enhancer-associated genes represent essential and lineage-identity genes

•

ASCL1, NKX2-1, and PROX1 proteins interact in a complex in SCLC-A

•

ASCL1, NKX2-1, and PROX1 regulate Notch-signaling, NE-specific, and cell-cycle genes

Advances in novel molecular typing and precise treatment strategies for small cell lung cancer

Small cell lung cancer (SCLC) is a high-grade neuroendocrine (NE) cancer characterized by high circulating tumor-cell burden and early extensive metastasis. Considering the complexity of SCLC genes and the immune microenvironment, their unique molecular heterogeneity profiles have been continuously explored. The understanding of SCLC subtypes has recently changed from traditional "classical" and "variant" types to "NE" and "non-NE" phenotypes and to the subtypes defined by major transcriptional regulators, which indicates the gradual revelation of high intratumoral heterogeneity and plasticity characteristics of SCLCs. Advances in genomics as well as the development of single-cell sequencing analysis and new preclinical models have helped investigators gain many new insights into SCLCs and the development of targeted therapy and immunotherapy strategies. This article provides an overview of changes in molecular typing, tumor heterogeneity, and plasticity and that of advances in the precise treatment of different subtypes of SCLC.