A functional role for tumor cell heterogeneity in a mouse model of small cell lung cancer

Small Cell Lung Cancer-An Update on Chemotherapy Resistance

OPINION STATEMENT

Compared to other types of lung cancer, small cell lung cancer (SCLC) exhibits aggressive characteristics that promote drug resistance. Despite platinum-etoposide chemotherapy combined with immunotherapy being the current standard treatment, the rapid development of drug resistance has led to unsatisfactory clinical outcomes. This review focuses on the mechanisms contributing to the chemotherapy resistance phenotype in SCLC, such as increased intra-tumoral heterogeneity, alterations in the tumor microenvironment, changes in cellular metabolism, and dysregulation of apoptotic pathways. A comprehensive understanding of these drug resistance mechanisms in SCLC is imperative for ushering in a new era in cancer research, which will promise revolutionary advancements in cancer diagnosis and treatment methodologies.

SMARCA4 controls state plasticity in small cell lung cancer through regulation of neuroendocrine transcription factors and REST splicing

INTRODUCTION

Small Cell Lung Cancer (SCLC) can be classified into transcriptional subtypes with distinct degrees of neuroendocrine (NE) differentiation. Recent evidence supports plasticity among subtypes with a bias toward adoption of low-NE states during disease progression or upon acquired chemotherapy resistance. Here, we identify a role for SMARCA4, the catalytic subunit of the SWI/SNF complex, as a regulator of subtype shift in SCLC.

METHODS

ATACseq and RNAseq experiments were performed in SCLC cells after pharmacological inhibition of SMARCA4. DNA binding of SMARCA4 was characterized by ChIPseq in high-NE SCLC patient derived xenografts (PDXs). Enrichment analyses were applied to transcriptomic data. Combination of FHD-286 and afatinib was tested in vitro and in a set of chemo-resistant SCLC PDXs in vivo.

RESULTS

SMARCA4 expression positively correlates with that of NE genes in both SCLC cell lines and patient tumors. Pharmacological inhibition of SMARCA4 with FHD-286 induces the loss of NE features and downregulates neuroendocrine and neuronal signaling pathways while activating non-NE factors. SMARCA4 binds to gene loci encoding NE-lineage transcription factors ASCL1 and NEUROD1 and alters chromatin accessibility, enhancing NE programs. Enrichment analysis applied to high-confidence SMARCA4 targets confirmed neuron related pathways as the top GO Biological processes regulated by SMARCA4 in SCLC. In parallel, SMARCA4 also controls REST, a known suppressor of the NE phenotype, by regulating SRRM4-dependent REST transcript splicing. Furthermore, SMARCA4 inhibition drives ERBB pathway activation in SCLC, rendering SCLC tumors sensitive to afatinib.

CONCLUSIONS

This study nominates SMARCA4 as a key regulator of the NE state plasticity and defines a novel therapeutic strategy for SCLC.

Microenvironment shapes small-cell lung cancer neuroendocrine states and presents therapeutic opportunities

Small-cell lung cancer (SCLC) is the most fatal form of lung cancer. Intratumoral heterogeneity, marked by neuroendocrine (NE) and non-neuroendocrine (non-NE) cell states, defines SCLC, but the cell-extrinsic drivers of SCLC plasticity are poorly understood. To map the landscape of SCLC tumor microenvironment (TME), we apply spatially resolved transcriptomics and quantitative mass spectrometry-based proteomics to metastatic SCLC tumors obtained via rapid autopsy. The phenotype and overall composition of non-malignant cells in the TME exhibit substantial variability, closely mirroring the tumor phenotype, suggesting TME-driven reprogramming of NE cell states. We identify cancer-associated fibroblasts (CAFs) as a crucial element of SCLC TME heterogeneity, contributing to immune exclusion, and predicting exceptionally poor prognosis. Our work provides a comprehensive map of SCLC tumor and TME ecosystems, emphasizing their pivotal role in SCLC's adaptable nature, opening possibilities for reprogramming the TME-tumor communications that shape SCLC tumor states.

Gastrointestinal stromal tumor with small cell carcinoma infiltration: a case report

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors of the digestive system. They usually occur in the gastrointestinal tract. However, we discovered a rare phenomenon in which small cell carcinoma infiltrated the GIST of a patient. The patient came to the hospital and presented with chest tightness and shortness of breath for 2 months and a dry cough for half a month. As the ancillary tests were refined, it was discovered that he also had a lesion in the pelvic cavity. After pathological examination of the core needle biopsy (CNB) samples from the pelvic cavity lesion, the patient was diagnosed with GIST with small cell carcinoma infiltration. The patient is currently receiving a chemotherapy regimen of etoposide combined with cisplatin.

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.

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.

Transforming lung cancer types

Lung cancer cells can escape targeted therapy by switching oncogenic drivers and cell identity.

Dynamic phenotypic reprogramming and chemoresistance induced by lung fibroblasts in small cell lung cancer

Small cell lung cancer (SCLC) is heterogenous in phenotype and microenvironment. Dynamic phenotypic reprogramming, leading to heterogeneity, is prevalent in SCLC, while the mechanisms remain incompletely understood. Cancer-associated fibroblasts (CAFs) possess comprehensive roles in cancer progression, while their function in phenotypic reprogramming of SCLC remain elusive. Here, we obtained transcriptome data of SCLC tissues from publicly available databases, subsequently estimated abundance of CAFs. We found CAF-abundant SCLC exhibited non-neuroendocrine (Non-NE) characteristics. Supporting this, the positive correlation of expression level of α-SMA, the CAF marker, and expression level of REST, protein typically expressed in Non-NE type SCLC, was identified in SCLC tissue arrays. Moreover, we revealed that fibroblasts inhibited NE markers expression and cell proliferation of SCLC cells in the co-culture system comprising lung fibroblasts and SCLC cells, indicating a phenotypic reprogramming from NE to Non-NE. During this process, fibroblast-derived IL-6 activated the JAK2/STAT3 signaling, upregulated c-MYC expression, and subsequently activated the NOTCH pathway, driving phenotypic reprogramming. Moreover, CAF-enriched SCLC exhibited increased immune cell infiltration, elevated expression of immune activation-related signatures, and checkpoint molecules. Our data also highlighted the chemoresistance induced by fibroblasts in SCLC cells, which was effectively reversed by JAK inhibitor. In conclusion, fibroblasts induced phenotypic reprogramming of SCLC cells from NE to Non-NE, likely contributes to inflamed immune microenvironment and chemoresistance. These findings provide novel insights into the clinical implications of CAFs in SCLC.

Genetically-engineered mouse models of small cell lung cancer: the next generation

Small cell lung cancer (SCLC) remains the most fatal form of lung cancer, with patients in dire need of new and effective therapeutic approaches. Modeling SCLC in an immunocompetent host is essential for understanding SCLC pathogenesis and ultimately discovering and testing new experimental therapeutic strategies. Human SCLC is characterized by near universal genetic loss of the RB1 and TP53 tumor suppressor genes. Twenty years ago, the first genetically-engineered mouse model (GEMM) of SCLC was generated using conditional deletion of both Rb1 and Trp53 in the lungs of adult mice. Since then, several other GEMMs of SCLC have been developed coupling genomic alterations found in human SCLC with Rb1 and Trp53 deletion. Here we summarize how GEMMs of SCLC have contributed significantly to our understanding of the disease in the past two decades. We also review recent advances in modeling SCLC in mice that allow investigators to bypass limitations of the previous generation of GEMMs while studying new genes of interest in SCLC. In particular, CRISPR/Cas9-mediated somatic gene editing can accelerate how new genes of interest are functionally interrogated in SCLC tumorigenesis. Notably, the development of allograft models and precancerous precursor models from SCLC GEMMs provides complementary approaches to GEMMs to study tumor cell-immune microenvironment interactions and test new therapeutic strategies to enhance response to immunotherapy. Ultimately, the new generation of SCLC models can accelerate research and help develop new therapeutic strategies for SCLC.

Dynamic altruistic cooperation within breast tumors

BACKGROUND

Social behaviors such as altruism, where one self-sacrifices for collective benefits, critically influence an organism's survival and responses to the environment. Such behaviors are widely exemplified in nature but have been underexplored in cancer cells which are conventionally seen as selfish competitive players. This multidisciplinary study explores altruism and its mechanism in breast cancer cells and its contribution to chemoresistance.

METHODS

MicroRNA profiling was performed on circulating tumor cells collected from the blood of treated breast cancer patients. Cancer cell lines ectopically expressing candidate miRNA were used in co-culture experiments and treated with docetaxel. Ecological parameters like relative survival and relative fitness were assessed using flow cytometry. Functional studies and characterization performed in vitro and in vivo include proliferation, iTRAQ-mass spectrometry, RNA sequencing, inhibition by small molecules and antibodies, siRNA knockdown, CRISPR/dCas9 inhibition and fluorescence imaging of promoter reporter-expressing cells. Mathematical modeling based on evolutionary game theory was performed to simulate spatial organization of cancer cells.

RESULTS

Opposing cancer processes underlie altruism: an oncogenic process involving secretion of IGFBP2 and CCL28 by the altruists to induce survival benefits in neighboring cells under taxane exposure, and a self-sacrificial tumor suppressive process impeding proliferation of altruists via cell cycle arrest. Both processes are regulated concurrently in the altruists by miR-125b, via differential NF-κB signaling specifically through IKKβ. Altruistic cells persist in the tumor despite their self-sacrifice, as they can regenerate epigenetically from non-altruists via a KLF2/PCAF-mediated mechanism. The altruists maintain a sparse spatial organization by inhibiting surrounding cells from adopting the altruistic fate via a lateral inhibition mechanism involving a GAB1-PI3K-AKT-miR-125b signaling circuit.

CONCLUSIONS

Our data reveal molecular mechanisms underlying manifestation, persistence and spatial spread of cancer cell altruism. A minor population behave altruistically at a cost to itself producing a collective benefit for the tumor, suggesting tumors to be dynamic social systems governed by the same rules of cooperation in social organisms. Understanding cancer cell altruism may lead to more holistic models of tumor evolution and drug response, as well as therapeutic paradigms that account for social interactions. Cancer cells constitute tractable experimental models for fields beyond oncology, like evolutionary ecology and game theory.

Biomarkers and experimental models for cancer immunology investigation

The rapid advancement of tumor immunotherapies poses challenges for the tools used in cancer immunology research, highlighting the need for highly effective biomarkers and reproducible experimental models. Current immunotherapy biomarkers encompass surface protein markers such as PD-L1, genetic features such as microsatellite instability, tumor-infiltrating lymphocytes, and biomarkers in liquid biopsy such as circulating tumor DNAs. Experimental models, ranging from 3D in vitro cultures (spheroids, submerged models, air-liquid interface models, organ-on-a-chips) to advanced 3D bioprinting techniques, have emerged as valuable platforms for cancer immunology investigations and immunotherapy biomarker research. By preserving native immune components or coculturing with exogenous immune cells, these models replicate the tumor microenvironment in vitro. Animal models like syngeneic models, genetically engineered models, and patient-derived xenografts provide opportunities to study in vivo tumor-immune interactions. Humanized animal models further enable the simulation of the human-specific tumor microenvironment. Here, we provide a comprehensive overview of the advantages, limitations, and prospects of different biomarkers and experimental models, specifically focusing on the role of biomarkers in predicting immunotherapy outcomes and the ability of experimental models to replicate the tumor microenvironment. By integrating cutting-edge biomarkers and experimental models, this review serves as a valuable resource for accessing the forefront of cancer immunology investigation.

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.

Novel Mouse Cell Lines and In Vivo Models for Human High-Grade Neuroendocrine Lung Carcinoma, Small Cell Lung Carcinoma (SCLC), and Large Cell Neuroendocrine Carcinoma (LCNEC)

There is a clear need to expand the toolkit of adequate mouse models and cell lines available for preclinical studies of high-grade neuroendocrine lung carcinoma (small cell lung carcinoma (SCLC) and large cell neuroendocrine carcinoma (LCNEC)). SCLC and LCNEC are two highly aggressive tumor types with dismal prognoses and few therapeutic options. Currently, there is an extreme paucity of material, particularly in the case of LCNEC. Given the lack of murine cell lines and transplant models of LCNEC, the need is imperative. In this study, we generated and examined new models of LCNEC and SCLC transplantable cell lines derived from our previously developed primary mouse LCNEC and SCLC tumors. RNA-seq analysis demonstrated that our cell lines and syngeneic tumors maintained the transcriptome program from the original transgenic primary tumor and displayed strong similarities to human SCLC or LCNEC. Importantly, the SCLC transplanted cell lines showed the ability to metastasize and mimic this characteristic of the human condition. In summary, we generated mouse cell line tools that allow further basic and translational research as well as preclinical testing of new treatment strategies for SCLC and LCNEC. These tools retain important features of their human counterparts and address the lack of LCNEC disease models.

Crosstalk between small-cell lung cancer cells and astrocytes mimics brain development to promote brain metastasis

Brain metastases represent an important clinical problem for patients with small-cell lung cancer (SCLC). However, the mechanisms underlying SCLC growth in the brain remain poorly understood. Here, using intracranial injections in mice and assembloids between SCLC aggregates and human cortical organoids in culture, we found that SCLC cells recruit reactive astrocytes to the tumour microenvironment. This crosstalk between SCLC cells and astrocytes drives the induction of gene expression programmes that are similar to those found during early brain development in neurons and astrocytes. Mechanistically, the brain development factor Reelin, secreted by SCLC cells, recruits astrocytes to brain metastases. These astrocytes in turn promote SCLC growth by secreting neuronal pro-survival factors such as SERPINE1. Thus, SCLC brain metastases grow by co-opting mechanisms involved in reciprocal neuron-astrocyte interactions during brain development. Targeting such developmental programmes activated in this cancer ecosystem may help prevent and treat brain metastases.

Lineage Plasticity in SCLC Generates Non-Neuroendocrine Cells Primed for Vasculogenic Mimicry

INTRODUCTION

Vasculogenic mimicry (VM), the process of tumor cell transdifferentiation to endow endothelial-like characteristics supporting de novo vessel formation, is associated with poor prognosis in several tumor types, including SCLC. In genetically engineered mouse models (GEMMs) of SCLC, NOTCH, and MYC co-operate to drive a neuroendocrine (NE) to non-NE phenotypic switch, and co-operation between NE and non-NE cells is required for metastasis. Here, we define the phenotype of VM-competent cells and molecular mechanisms underpinning SCLC VM using circulating tumor cell-derived explant (CDX) models and GEMMs.

METHODS

We analyzed perfusion within VM vessels and their association with NE and non-NE phenotypes using multiplex immunohistochemistry in CDX, GEMMs, and patient biopsies. We evaluated their three-dimensional structure and defined collagen-integrin interactions.

RESULTS

We found that VM vessels are present in 23/25 CDX models, 2 GEMMs, and in 20 patient biopsies of SCLC. Perfused VM vessels support tumor growth and only NOTCH-active non-NE cells are VM-competent in vivo and ex vivo, expressing pseudohypoxia, blood vessel development, and extracellular matrix organization signatures. On Matrigel, VM-primed non-NE cells remodel extracellular matrix into hollow tubules in an integrin β1-dependent process.

CONCLUSIONS

We identified VM as an exemplar of functional heterogeneity and plasticity in SCLC and these findings take considerable steps toward understanding the molecular events that enable VM. These results support therapeutic co-targeting of both NE and non-NE cells to curtail SCLC progression and to improve the outcomes of patients with SCLC in the future.

Clonal cooperation through soluble metabolite exchange facilitates metastatic outgrowth by modulating Allee effect

Cancers feature substantial intratumoral heterogeneity of genetic and phenotypically distinct lineages. Although interactions between coexisting lineages are emerging as a potential contributor to tumor evolution, the extent and nature of these interactions remain largely unknown. We postulated that tumors develop ecological interactions that sustain diversity and facilitate metastasis. Using a combination of fluorescent barcoding, mathematical modeling, metabolic analysis, and in vivo models, we show that the Allee effect, i.e., growth dependency on population size, is a feature of tumor lineages and that cooperative ecological interactions between lineages alleviate the Allee barriers to growth in a model of triple-negative breast cancer. Soluble metabolite exchange formed the basis for these cooperative interactions and catalyzed the establishment of a polyclonal community that displayed enhanced metastatic dissemination and outgrowth in xenograft models. Our results highlight interclonal metabolite exchange as a key modulator of tumor ecology and a contributing factor to overcoming Allee effect-associated growth barriers to metastasis.

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.

A Comparative Study of Neuroendocrine Heterogeneity in Small Cell Lung Cancer and Neuroblastoma

Lineage plasticity has long been documented in both small cell lung cancer (SCLC) and neuroblastoma, two clinically distinct neuroendocrine (NE) cancers. In this study, we quantified the NE features of cancer as NE scores and performed a systematic comparison of SCLC and neuroblastoma. We found neuroblastoma and SCLC cell lines have highly similar molecular profiles and shared therapeutic sensitivity. In addition, NE heterogeneity was observed at both the inter- and intra-cell line levels. Surprisingly, we did not find a significant association between NE scores and overall survival in SCLC or neuroblastoma. We described many shared and unique NE score-associated features between SCLC and neuroblastoma, including dysregulation of Myc oncogenes, alterations in protein expression, metabolism, drug resistance, and selective gene dependencies.

IMPLICATIONS

Our work establishes a reference for molecular changes and vulnerabilities associated with NE to non-NE transdifferentiation through mutual validation of SCLC and neuroblastoma samples.

Transcription factor networks link B-lymphocyte development and malignant transformation in leukemia

Rapid advances in genomics have opened unprecedented possibilities to explore the mutational landscapes in malignant diseases, such as B-cell acute lymphoblastic leukemia (B-ALL). This disease is manifested as a severe defect in the production of normal blood cells due to the uncontrolled expansion of transformed B-lymphocyte progenitors in the bone marrow. Even though classical genetics identified translocations of transcription factor-coding genes in B-ALL, the extent of the targeting of regulatory networks in malignant transformation was not evident until the emergence of large-scale genomic analyses. There is now evidence that many B-ALL cases present with mutations in genes that encode transcription factors with critical roles in normal B-lymphocyte development. These include PAX5, IKZF1, EBF1, and TCF3, all of which are targeted by translocations or, more commonly, partial inactivation in cases of B-ALL. Even though there is support for the notion that germline polymorphisms in the PAX5 and IKZF1 genes predispose for B-ALL, the majority of leukemias present with somatic mutations in transcription factor-encoding genes. These genetic aberrations are often found in combination with mutations in genes that encode components of the pre-B-cell receptor or the IL-7/TSLP signaling pathways, all of which are important for early B-cell development. This review provides an overview of our current understanding of the molecular interplay that occurs between transcription factors and signaling events during normal and malignant B-lymphocyte development.

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.

The paradox of cooperation among selfish cancer cells

It is traditionally assumed that during cancer development, tumor cells abort their initially cooperative behavior (i.e., cheat) in favor of evolutionary strategies designed solely to enhance their own fitness (i.e., a "selfish" life style) at the expense of that of the multicellular organism. However, the growth and progress of solid tumors can also involve cooperation among these presumed selfish cells (which, by definition, should be noncooperative) and with stromal cells. The ultimate and proximate reasons behind this paradox are not fully understood. Here, in the light of current theories on the evolution of cooperation, we discuss the possible evolutionary mechanisms that could explain the apparent cooperative behaviors among selfish malignant cells. In addition to the most classical explanations for cooperation in cancer and in general (by-product mutualism, kin selection, direct reciprocity, indirect reciprocity, network reciprocity, group selection), we propose the idea that "greenbeard" effects are relevant to explaining some cooperative behaviors in cancer. Also, we discuss the possibility that malignant cooperative cells express or co-opt cooperative traits normally expressed by healthy cells. We provide examples where considerations of these processes could help understand tumorigenesis and metastasis and argue that this framework provides novel insights into cancer biology and potential strategies for cancer prevention and treatment.

Clonal interactions in cancer: integrating quantitative models with experimental and clinical data

Tumors consist of different genotypically distinct subpopulations-or subclones-of cells. These subclones can influence neighboring clones in a process called "clonal interaction." Conventionally, research on driver mutations in cancer has focused on their cell-autonomous effects that lead to an increase in fitness of the cells containing the driver. Recently, with the advent of improved experimental and computational technologies for investigating tumor heterogeneity and clonal dynamics, new studies have shown the importance of clonal interactions in cancer initiation, progression, and metastasis. In this review we provide an overview of clonal interactions in cancer, discussing key discoveries from a diverse range of approaches to cancer biology research. We discuss common types of clonal interactions, such as cooperation and competition, its mechanisms, and the overall effect on tumorigenesis, with important implications for tumor heterogeneity, resistance to treatment, and tumor suppression. Quantitative models-in coordination with cell culture and animal model experiments-have played a vital role in investigating the nature of clonal interactions and the complex clonal dynamics they generate. We present mathematical and computational models that can be used to represent clonal interactions and provide examples of the roles they have played in identifying and quantifying the strength of clonal interactions in experimental systems. Clonal interactions have proved difficult to observe in clinical data; however, several very recent quantitative approaches enable their detection. We conclude by discussing ways in which researchers can further integrate quantitative methods with experimental and clinical data to elucidate the critical-and often surprising-roles of clonal interactions in human cancers.

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.

Targeting the Notch signaling pathway and the Notch ligand, DLL3, in small cell lung cancer

Small cell lung cancer (SCLC) is a highly aggressive and poorly differentiated cancer with high-grade neuroendocrine (NE) features, accounting for approximately 15 % of all lung cancers. For decades, chemotherapy and radiotherapy have predominated the treatment strategy for SCLC, but relapses ensue quickly and result in poor survival of patients. Immunotherapy has brought novel insights, yet the efficacy is still restricted to a limited population with SCLC. Notch signaling is identified to play a key role in the initiation and development of SCLC, and the Notch ligand, Delta-like ligand 3 (DLL3) is found broadly and specifically expressed in SCLC cells. Thus, Notch signaling is under active exploration as a potential therapeutic target in SCLC. Herein, we summarized and updated the functional relevance of Notch signaling in SCLC, discussed Notch signaling-targeted therapy for SCLC and the correspondent preclinical and clinical trials, and investigated the promising synergy effects of Notch signaling targeted therapy and immune checkpoint inhibitors (ICIs) treatment.

Molecular features and evolutionary trajectory of ASCL1+ and NEUROD1+ SCLC cells

BACKGROUND

Small cell lung cancer (SCLC) is the most aggressive subtype of lung cancer without recognised morphologic or genetic heterogeneity. Based on the expression of four transcription factors, ASCL1, NEUROD1, POU2F3, and YAP1, SCLCs are classified into four subtypes. However, biological functions of these different subtypes are largely uncharacterised.

METHODS

We studied intratumoural heterogeneity of resected human primary SCLC tissues using single-cell RNA-Seq. In addition, we undertook a series of in vitro and in vivo functional studies to reveal the distinct features of SCLC subtypes.

RESULTS

We identify the coexistence of ASCL1⁺ and NEUROD1⁺ SCLC cells within the same human primary SCLC tissue. Compared with ASCL1⁺ SCLC cells, NEUROD1⁺ SCLC cells show reduced epithelial features and lack EPCAM expression. Thus, EPCAM can be considered as a cell surface marker to distinguish ASCL1⁺ SCLC cells from NEUROD1⁺ SCLC cells. We further demonstrate that NEUROD1⁺ SCLC cells exhibit higher metastatic capability than ASCL1⁺ SCLC cells and can be derived from ASCL1⁺ SCLC cells.

CONCLUSIONS

Our studies unveil the biology and evolutionary trajectory of ASCL1⁺ and NEUROD1⁺ SCLC cells, shedding light on SCLC tumourigenesis and progression.

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.

A multiplexed in vivo approach to identify driver genes in small cell lung cancer

Small cell lung cancer (SCLC) is a lethal form of lung cancer. Here, we develop a quantitative multiplexed approach on the basis of lentiviral barcoding with somatic CRISPR-Cas9-mediated genome editing to functionally investigate candidate regulators of tumor initiation and growth in genetically engineered mouse models of SCLC. We found that naphthalene pre-treatment enhances lentiviral vector-mediated SCLC initiation, enabling high multiplicity of tumor clones for analysis through high-throughput sequencing methods. Candidate drivers of SCLC identified from a meta-analysis across multiple human SCLC genomic datasets were tested using this approach, which defines both positive and detrimental impacts of inactivating 40 genes across candidate pathways on SCLC development. This analysis and subsequent validation in human SCLC cells establish TSC1 in the PI3K-AKT-mTOR pathway as a robust tumor suppressor in SCLC. This approach should illuminate drivers of SCLC, facilitate the development of precision therapies for defined SCLC genotypes, and identify therapeutic targets.

Orchestration of Collective Migration and Metastasis by Tumor Cell Clusters

Metastatic dissemination has lethal consequences for cancer patients. Accruing evidence supports the hypothesis that tumor cells can migrate and metastasize as clusters of cells while maintaining contacts with one another. Collective metastasis enables tumor cells to colonize secondary sites more efficiently, resist cell death, and evade the immune system. On the other hand, tumor cell clusters face unique challenges for dissemination particularly during systemic dissemination. Here, we review recent progress toward understanding how tumor cell clusters overcome these disadvantages as well as mechanisms they utilize to gain advantages throughout the metastatic process. We consider useful models for studying collective metastasis and reflect on how the study of collective metastasis suggests new opportunities for eradicating and preventing metastatic disease.

Neuroendocrine neoplasms of the lung and gastrointestinal system: convergent biology and a path to better therapies

Neuroendocrine neoplasms (NENs) can develop in almost any organ and span a spectrum from well-differentiated and indolent neuroendocrine tumours (NETs) to poorly differentiated and highly aggressive neuroendocrine carcinomas (NECs), including small-cell lung cancer (SCLC). These neoplasms are thought to primarily derive from neuroendocrine precursor cells located throughout the body and can also arise through neuroendocrine transdifferentiation of organ-specific epithelial cell types. Hence, NENs constitute a group of tumour types that share key genomic and phenotypic characteristics irrespective of their site of origin, albeit with some organ-specific differences. The establishment of representative preclinical models for several of these disease entities together with analyses of human tumour specimens has provided important insights into crucial aspects of their biology with therapeutic implications. In this Review, we provide a comprehensive overview of the current understanding of NENs of the gastrointestinal system and lung from clinical and biological perspectives. Research on NENs has typically been siloed by the tumour site of origin, and a cross-cutting view might enable advances in one area to accelerate research in others. Therefore, we aim to emphasize that a better understanding of the commonalities and differences of NENs arising in different organs might more effectively inform clinical research to define therapeutic targets and ultimately optimize patient care.

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.

The engineering challenges and opportunities when designing potent ionizable materials for the delivery of ribonucleic acids

INTRODUCTION

Ionizable lipids are critical components in lipid nanoparticles. These molecules sequester nucleic acids for delivery to cells. However, to build more efficacious delivery molecules, the field must continue to broaden structure-function studies for greater insight. While nucleic acid-binding efficiency, degradability and nanoparticle stability are vitally important, this review offers perspective on additional factors that must be addressed to improve delivery efficiency.

AREAS COVERED

We discuss how administration route, cellular heterogeneity, uptake pathway, endosomal escape timing, age, sex, and threshold effects can change depending on the type of LNP ionizable lipid.

EXPERT OPINION

Ionizable lipid structure-function studies often focus on the efficiency of RNA utilization and biodistribution. While these focus areas are critical, they remain high-level observations. As our tools for observation and system interrogation improve, we believe that the field should begin collecting additional data. At the cellular level, this data should include age (dividing or senescent cells), sex and phenotype, cell entry pathway, and endosome type. Additionally, administration route and dose are essential to track. This additional data will allow us to identify and understand heterogeneity in LNP efficacy across patient populations, which will help us provide better ionizable lipid options for different groups.

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.

The role of evolutionary game theory in spatial and non-spatial models of the survival of cooperation in cancer: a review

Evolutionary game theory (EGT) is a branch of mathematics which considers populations of individuals interacting with each other to receive pay-offs. An individual’s pay-off is dependent on the strategy of its opponent(s) as well as on its own, and the higher its pay-off, the higher its reproductive fitness. Its offspring generally inherit its interaction strategy, subject to random mutation. Over time, the composition of the population shifts as different strategies spread or are driven extinct. In the last 25 years there has been a flood of interest in applying EGT to cancer modelling, with the aim of explaining how cancerous mutations spread through healthy tissue and how intercellular cooperation persists in tumour-cell populations. This review traces this body of work from theoretical analyses of well-mixed infinite populations through to more realistic spatial models of the development of cooperation between epithelial cells. We also consider work in which EGT has been used to make experimental predictions about the evolution of cancer, and discuss work that remains to be done before EGT can make large-scale contributions to clinical treatment and patient outcomes.

Clonal evolution and expansion associated with therapy resistance and relapse of colorectal cancer

Colorectal cancer (CRC) arises by a continuous process of genetic diversification and clonal evolution. Multiple genes and pathways have a role in tumor initiation and progression. The gradual accumulation of genetic and epigenetic processes leads to the establishment of adenoma and cancer. The important 'driver' mutations in tumor suppressor genes (such as TP53, APC, and SMAD4) and oncogenes (such as KRAS, NRAS, MET, and PIK3CA) confer selective growth advantages and cause CRC advancement. Clonal evolution induced by therapeutic pressure, as well as intra-tumoral heterogeneity, has been a great challenge in the treatment of metastatic CRC. Tumors often develop resistance to treatments as a result of intra-tumor heterogeneity, clonal evolution, and selection. Hence, the development of a multidrug personalized approach should be prioritized to pave the way for therapeutics repurposing and combination therapy to arrest tumor progression. This review summarizes how selective drug pressure can impact tumor evolution, resulting in the formation of polyclonal resistance mechanisms, ultimately promoting cancer progression. Current strategies for targeting clonal evolution are described. By understanding sources and consequences of tumor heterogeneity, customized and effective treatment plans to combat drug resistance may be devised.

Identification of TAZ as the essential molecular switch in orchestrating SCLC phenotypic transition and metastasis

Small-cell lung cancer (SCLC) is a recalcitrant cancer characterized by high metastasis. However, the exact cell type contributing to metastasis remains elusive. Using a Rb1 L/L /Trp53 L/L mouse model, we identify the NCAMhiCD44lo/- subpopulation as the SCLC metastasizing cell (SMC), which is progressively transitioned from the non-metastasizing NCAMloCD44hi cell (non-SMC). Integrative chromatin accessibility and gene expression profiling studies reveal the important role of the SWI/SNF complex, and knockout of its central component, Brg1, significantly inhibits such phenotypic transition and metastasis. Mechanistically, TAZ is silenced by the SWI/SNF complex during SCLC malignant progression, and its knockdown promotes SMC transition and metastasis. Importantly, ectopic TAZ expression reversely drives SMC-to-non-SMC transition and alleviates metastasis. Single-cell RNA-sequencing analyses identify SMC as the dominant subpopulation in human SCLC metastasis, and immunostaining data show a positive correlation between TAZ and patient prognosis. These data uncover high SCLC plasticity and identify TAZ as the key molecular switch in orchestrating SCLC phenotypic transition and metastasis.

Single-cell sequencing: expansion, integration and translation

With the rapid advancement in sequencing technologies, the concept of omics has revolutionized our understanding of cellular behaviors. Conventional omics investigation approaches measure the averaged behaviors of multiple cells, which may easily hide signals represented by a small-cell cohort, urging for the development of techniques with enhanced resolution. Single-cell RNA sequencing, investigating cell transcriptomics at the resolution of a single cell, has been rapidly expanded to investigate other omics such as genomics, proteomics and metabolomics since its invention. The requirement for comprehensive understanding of complex cellular behavior has led to the integration of multi-omics and single-cell sequencing data with other layers of information such as spatial data and the CRISPR screening technique towards gained knowledge or innovative functionalities. The development of single-cell sequencing in both dimensions has rendered it a unique field that offers us a versatile toolbox to delineate complex diseases, including cancers.

Colony‑stimulating factor CSF2 mediates the phenotypic plasticity of small‑cell lung cancer by regulating the p‑STAT3/MYC pathway

Relapse and drug resistance are the main causes of mortality in patients with small-cell lung cancer (SCLC). Intratumoral heterogeneity (ITH) is a key biological mechanism that leads to relapse and drug resistance. Phenotypic plasticity is an important factor that leads to ITH in SCLC, although its mechanisms and key regulatory factors remain to be elucidated. In the present study, cell proliferation and cell switch assay were measured using trypan blue. Alamar Blue was used to test drug sensitivity. Differential genes were screened by RNA sequencing. Reverse transcription-quantitative PCR and western blotting were performed to assess the expressions of CSF2/p-STAT3/MYC pathway related molecules, neuroendocrine (NE)/non-neuroendocrine (non-NE), transcription factors and drug-related targets. The present study found that SCLC cell line NCI-H69 exhibited adherent (H69A) and suspensive (H69S) phenotypes, which could switch back and forth. The two phenotypic cells had significant differences in cellular NE and non-NE characteristics, drug sensitivity and expression of drug-related targets. RNA sequencing showed that granulocyte-macrophage colony-stimulating factor [i.e., colony-stimulating factor 2 (CSF2)] was the main differentially expressed gene between the two phenotypes and that H69A cells highly expressed CSF2. The inhibition of CSF2 promoted the transformation from H69A to H69S, increased drug sensitivity and NE marker expression and decreased the non-NE marker expression in H69A. The STRING, Pathway Commons and Reactome databases showed a potential regulatory relationship between CSF2 and phosphorylated signal transducer and activator of transcription 3 (p-STAT3)/MYC. p-STAT3 and MYC expression was higher in H69A cells than in H69S cells and CSF2 silencing inhibited their expression. Taken together, these results indicated that CSF2 may regulate the phenotypic plasticity of SCLC through the phosphorylated STAT3/MYC pathway, thereby limiting the transformation between cell clones with different phenotypes and changing the sensitivity of specific cell clones to targeted drugs. Targeting CSF2 may be a potential therapeutic strategy to overcome drug resistance in SCLC treatment by influencing ITH.

A conserved YAP/Notch/REST network controls the neuroendocrine cell fate in the lungs

The Notch pathway is a conserved cell-cell communication pathway that controls cell fate decisions. Here we sought to determine how Notch pathway activation inhibits the neuroendocrine cell fate in the lungs, an archetypal process for cell fate decisions orchestrated by Notch signaling that has remained poorly understood at the molecular level. Using intratumoral heterogeneity in small-cell lung cancer as a tractable model system, we uncovered a role for the transcriptional regulators REST and YAP as promoters of the neuroendocrine to non-neuroendocrine transition. We further identified the specific neuroendocrine gene programs repressed by REST downstream of Notch in this process. Importantly, we validated the importance of REST and YAP in neuroendocrine to non-neuroendocrine cell fate switches in both developmental and tissue repair processes in the lungs. Altogether, these experiments identify conserved roles for REST and YAP in Notch-driven inhibition of the neuroendocrine cell fate in embryonic lungs, adult lungs, and lung cancer.

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.

Genomic and transcriptomic analysis of a library of small cell lung cancer patient-derived xenografts

Access to clinically relevant small cell lung cancer (SCLC) tissue is limited because surgical resection is rare in metastatic SCLC. Patient-derived xenografts (PDX) and circulating tumor cell-derived xenografts (CDX) have emerged as valuable tools to characterize SCLC. Here, we present a resource of 46 extensively annotated PDX/CDX models derived from 33 patients with SCLC. We perform multi-omic analyses, using targeted tumor next-generation sequencing, RNA-sequencing, and immunohistochemistry to deconvolute the mutational landscapes, global expression profiles, and molecular subtypes of these SCLC models. SCLC subtypes characterized by transcriptional regulators, ASCL1, NEUROD1 and POU2F3 are confirmed in this cohort. A subset of SCLC clinical specimens, including matched PDX/CDX and clinical specimen pairs, confirm that the primary features and genomic and proteomic landscapes of the tumors of origin are preserved in the derivative PDX models. This resource provides a powerful system to study SCLC biology.

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.

Comprehensive Genomic Profiling of Neuroendocrine Carcinomas of the Gastrointestinal System

The neuroendocrine carcinoma of the gastrointestinal system (GIS-NEC) is a rare but highly malignant neoplasm. We analyzed 115 cases using whole-genome/exome sequencing, transcriptome sequencing, DNA methylation assays, and/or ATAC-seq and found GIS-NECs to be genetically distinct from neuroendocrine tumors (GIS-NET) in the same location. Clear genomic differences were also evident between pancreatic NECs (Panc-NEC) and nonpancreatic GIS-NECs (Nonpanc-NEC). Panc-NECs could be classified into two subgroups (i.e., "ductal-type" and "acinar-type") based on genomic features. Alterations in TP53 and RB1 proved common in GIS-NECs, and most Nonpanc-NECs with intact RB1 demonstrated mutually exclusive amplification of CCNE1 or MYC. Alterations of the Notch gene family were characteristic of Nonpanc-NECs. Transcription factors for neuroendocrine differentiation, especially the SOX2 gene, appeared overexpressed in most GIS-NECs due to hypermethylation of the promoter region. This first comprehensive study of genomic alterations in GIS-NECs uncovered several key biological processes underlying genesis of this very lethal form of cancer.

SIGNIFICANCE

GIS-NECs are genetically distinct from GIS-NETs. GIS-NECs arising in different organs show similar histopathologic features and share some genomic features, but considerable differences exist between Panc-NECs and Nonpanc-NECs. In addition, Panc-NECs could be classified into two subgroups (i.e., "ductal-type" and "acinar-type") based on genomic and epigenomic features. This article is highlighted in the In This Issue feature, p. 587.

Machine learning predicts cancer subtypes and progression from blood immune signatures

Clinical adoption of immune checkpoint inhibitors in cancer management has highlighted the interconnection between carcinogenesis and the immune system. Immune cells are integral to the tumour microenvironment and can influence the outcome of therapies. Better understanding of an individual's immune landscape may play an important role in treatment personalisation. Peripheral blood is a readily accessible source of information to study an individual's immune landscape compared to more complex and invasive tumour bioipsies, and may hold immense diagnostic and prognostic potential. Identifying the critical components of these immune signatures in peripheral blood presents an attractive alternative to tumour biopsy-based immune phenotyping strategies. We used two syngeneic solid tumour models, a 4T1 breast cancer model and a CT26 colorectal cancer model, in a longitudinal study of the peripheral blood immune landscape. Our strategy combined two highly accessible approaches, blood leukocyte immune phenotyping and plasma soluble immune factor characterisation, to identify distinguishing immune signatures of the CT26 and 4T1 tumour models using machine learning. Myeloid cells, specifically neutrophils and PD-L1-expressing myeloid cells, were found to correlate with tumour size in both the models. Elevated levels of G-CSF, IL-6 and CXCL13, and B cell counts were associated with 4T1 growth, whereas CCL17, CXCL10, total myeloid cells, CCL2, IL-10, CXCL1, and Ly6Cintermediate monocytes were associated with CT26 tumour development. Peripheral blood appears to be an accessible means to interrogate tumour-dependent changes to the host immune landscape, and to identify blood immune phenotypes for future treatment stratification.

Invasion front dynamics of interactive populations in environments with barriers

Invading populations normally comprise different subpopulations that interact while trying to overcome existing barriers against their way to occupy new areas. However, the majority of studies so far only consider single or multiple population invasion into areas where there is no resistance against the invasion. Here, we developed a model to study how cooperative/competitive populations invade in the presence of a physical barrier that should be degraded during the invasion. For one dimensional (1D) environment, we found that a Langevin equation as [Formula: see text] describing invasion front position. We then obtained how [Formula: see text] and [Formula: see text] depend on population interactions and environmental barrier intensity. In two dimensional (2D) environment, for the average interface position movements we found a Langevin equation as [Formula: see text]. Similar to the 1D case, we calculate how [Formula: see text] and [Formula: see text] respond to population interaction and environmental barrier intensity. Finally, the study of invasion front morphology through dynamic scaling analysis showed that growth exponent, [Formula: see text], depends on both population interaction and environmental barrier intensity. Saturated interface width, [Formula: see text], versus width of the 2D environment (L) also exhibits scaling behavior. Our findings show revealed that competition among subpopulations leads to more rough invasion fronts. Considering the wide range of shreds of evidence for clonal diversity in cancer cell populations, our findings suggest that interactions between such diverse populations can potentially participate in the irregularities of tumor border.

Plasticity in the Absence of NOTCH Uncovers a RUNX2-Dependent Pathway in Small Cell Lung Cancer

Neuroendocrine to nonneuroendocrine plasticity supports small cell lung cancer (SCLC) tumorigenesis and promotes immunogenicity. Approximately 20% to 25% of SCLCs harbor loss-of-function (LOF) NOTCH mutations. Previous studies demonstrated that NOTCH functions as a SCLC tumor suppressor, but can also drive nonneuroendocrine plasticity to support SCLC growth. Given the dual functionality of NOTCH, it is not understood why SCLCs select for LOF NOTCH mutations and how these mutations affect SCLC tumorigenesis. In a CRISPR-based genetically engineered mouse model of SCLC, genetic loss of Notch1 or Notch2 modestly accelerated SCLC tumorigenesis. Interestingly, Notch-mutant SCLCs still formed nonneuroendocrine subpopulations, and these Notch-independent, nonneuroendocrine subpopulations were driven by Runx2-mediated regulation of Rest. Notch2-mutant nonneuroendocrine cells highly express innate immune signaling genes including stimulator of interferon genes (STING) and were sensitive to STING agonists. This work identifies a Notch-independent mechanism to promote nonneuroendocrine plasticity and suggests that therapeutic approaches to activate STING could be selectively beneficial for SCLCs with NOTCH2 mutations. SIGNIFICANCE: A genetically engineered mouse model of NOTCH-mutant SCLC reveals that nonneuroendocrine plasticity persists in the absence of NOTCH, driven by a RUNX2-REST-dependent pathway and innate immune signaling.