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

Small Cell Lung Cancer Neuronal Features and Their Implications for Tumor Progression, Metastasis, and Therapy

Small cell lung cancer (SCLC) is an epithelial neuroendocrine form of lung cancer for which survival rates remain dismal and new therapeutic approaches are greatly needed. Key biological features of SCLC tumors include fast growth and widespread metastasis, as well as rapid resistance to treatment. Similar to pulmonary neuroendocrine cells, SCLC cells have traits of both hormone-producing cells and neurons. In this study, we specifically discuss the neuronal features of SCLC. We consider how neuronal G protein-coupled receptors and other neuronal molecules on the surface of SCLC cells can contribute to the growth of SCLC tumors and serve as therapeutic targets in SCLC. We also review recent evidence for the role of neuronal programs expressed by SCLC cells in the fast proliferation, migration, and metastasis of these cells. We further highlight how these neuronal programs may be particularly relevant for the development of brain metastases and how they can assist SCLC cells to functionally interact with neurons and astrocytes. A greater understanding of the molecular and cellular neuronal features of SCLC is likely to uncover new vulnerabilities in SCLC cells, which may help develop novel therapeutic approaches. More generally, the epithelial-to-neuronal transition observed during tumor progression in SCLC and other cancer types can contribute significantly to tumor development and response to therapy.

Deciphering Pre-existing and Induced 3D Genome Architecture Changes involved in Constricted Melanoma Migration

Metastatic cancer cells traverse constricted spaces that exert forces on their nucleus and the genomic contents within. Cancerous tumors are highly heterogeneous and not all cells within them can achieve such a feat. Here, we investigated what initial genome architecture characteristics favor the constricted migratory ability of cancer cells and which arise only after passage through multiple constrictions. We identified a cell surface protein (ITGB4) whose expression correlates with increased initial constricted migration ability in human melanoma A375 cells. Sorting out this subpopulation allowed us to identify cellular and nuclear features that pre-exist and favor migration, as well as alterations that only appear after cells have passed through constrictions. We identified specific genomic regions that experienced altered genome spatial compartment profiles only after constricted migration. Our study reveals 3D genome structure contributions to both selection and induction mechanisms of cell fate change during cancer metastasis.

Diffusion on PCA-UMAP Manifold: The Impact of Data Structure Preservation to Denoise High-Dimensional Single-Cell RNA Sequencing Data

Single-cell transcriptomics (scRNA-seq) is revolutionizing biological research, yet it faces challenges such as inefficient transcript capture and noise. To address these challenges, methods like neighbor averaging or graph diffusion are used. These methods often rely on k-nearest neighbor graphs from low-dimensional manifolds. However, scRNA-seq data suffer from the 'curse of dimensionality', leading to the over-smoothing of data when using imputation methods. To overcome this, sc-PHENIX employs a PCA-UMAP diffusion method, which enhances the preservation of data structures and allows for a refined use of PCA dimensions and diffusion parameters (e.g., k-nearest neighbors, exponentiation of the Markov matrix) to minimize noise introduction. This approach enables a more accurate construction of the exponentiated Markov matrix (cell neighborhood graph), surpassing methods like MAGIC. sc-PHENIX significantly mitigates over-smoothing, as validated through various scRNA-seq datasets, demonstrating improved cell phenotype representation. Applied to a multicellular tumor spheroid dataset, sc-PHENIX identified known extreme phenotype states, showcasing its effectiveness. sc-PHENIX is open-source and available for use and modification.

Deep learning identifies heterogeneous subpopulations in breast cancer cell lines

Motivation

Cells exhibit a wide array of morphological features, enabling computer vision methods to identify and track relevant parameters. Morphological analysis has long been implemented to identify specific cell types and cell responses. Here we asked whether morphological features might also be used to classify transcriptomic subpopulations within in vitro cancer cell lines. Identifying cell subpopulations furthers our understanding of morphology as a reflection of underlying cell phenotype and could enable a better understanding of how subsets of cells compete and cooperate in disease progression and treatment.

Results

We demonstrate that cell morphology can reflect underlying transcriptomic differences in vitro using convolutional neural networks. First, we find that changes induced by chemotherapy treatment are highly identifiable in a breast cancer cell line. We then show that the intra cell line subpopulations that comprise breast cancer cell lines under standard growth conditions are also identifiable using cell morphology. We find that cell morphology is influenced by neighborhood effects beyond the cell boundary, and that including image information surrounding the cell can improve model discrimination ability.

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.

Functional transcriptional signatures for tumor-type-agnostic phenotype prediction

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

Immune heterogeneity in small-cell lung cancer and vulnerability to immune checkpoint blockade

Atezolizumab (anti-PD-L1), combined with carboplatin and etoposide (CE), is now a standard of care for extensive-stage small-cell lung cancer (ES-SCLC). A clearer understanding of therapeutically relevant SCLC subsets could identify rational combination strategies and improve outcomes. We conduct transcriptomic analyses and non-negative matrix factorization on 271 pre-treatment patient tumor samples from IMpower133 and identify four subsets with general concordance to previously reported SCLC subtypes (SCLC-A, -N, -P, and -I). Deeper investigation into the immune heterogeneity uncovers two subsets with differing neuroendocrine (NE) versus non-neuroendocrine (non-NE) phenotypes, demonstrating immune cell infiltration hallmarks. The NE tumors with low tumor-associated macrophage (TAM) but high T-effector signals demonstrate longer overall survival with PD-L1 blockade and CE versus CE alone than non-NE tumors with high TAM and high T-effector signal. Our study offers a clinically relevant approach to discriminate SCLC patients likely benefitting most from immunotherapies and highlights the complex mechanisms underlying immunotherapy responses.

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.

Actionable Driver Events in Small Cell Lung Cancer

Small cell lung cancer (SCLC) stands out as the most aggressive form of lung cancer, characterized by an extremely high proliferation rate and a very poor prognosis, with a 5-year survival rate that falls below 7%. Approximately two-thirds of patients receive their diagnosis when the disease has already reached a metastatic or extensive stage, leaving chemotherapy as the remaining first-line treatment option. Other than the recent advances in immunotherapy, which have shown moderate results, SCLC patients cannot yet benefit from any approved targeted therapy, meaning that this cancer remains treated as a uniform entity, disregarding intra- or inter-tumoral heterogeneity. Continuous efforts and technological improvements have enabled the identification of new potential targets that could be used to implement novel therapeutic strategies. In this review, we provide an overview of the most recent approaches for SCLC treatment, providing an extensive compilation of the targeted therapies that are currently under clinical evaluation and inhibitor molecules with promising results in vitro and in vivo.

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.

Unfolding the secrets of small cell lung cancer progression: Novel approaches and insights through rapid autopsies

The understanding of small cell lung cancer (SCLC) biology has increased dramatically in recent years, but the processes that allow SCLC to progress rapidly remain poorly understood. Here, we advocate the integration of rapid autopsies and preclinical models into SCLC research as a comprehensive strategy with the potential to revolutionize current treatment paradigms.

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.

Orchestrating smart therapeutics to achieve optimal treatment in small cell lung cancer: recent progress and future directions

Small cell lung cancer (SCLC) is a recalcitrant malignancy with elusive mechanism of pathogenesis and dismal prognosis. Over the past decades, platinum-based chemotherapy has been the backbone treatment for SCLC. However, subsequent chemoresistance after initial effectiveness urges researchers to explore novel therapeutic targets of SCLC. Recent years have witnessed significant improvements in targeted therapy in SCLC. New molecular candidates such as Ataxia telangiectasia and RAD3-related protein (ATR), WEE1, checkpoint kinase 1 (CHK1) and poly-ADP-ribose polymerase (PARP) have shown promising therapeutic utility in SCLC. While immune checkpoint inhibitor (ICI) has emerged as an indispensable treatment modality for SCLC, approaches to boost efficacy and reduce toxicity as well as selection of reliable biomarkers for ICI in SCLC have remained elusive and warrants our further investigation. Given the increasing importance of precision medicine in SCLC, optimal subtyping of SCLC using multi-omics have gradually applied into clinical practice, which may identify more drug targets and better tailor treatment strategies to each individual patient. The present review summarizes recent progress and future directions in SCLC. In addition to the emerging new therapeutics, we also focus on the establishment of predictive model for early detection of SCLC. More importantly, we also propose a multi-dimensional model in the prognosis of SCLC to ultimately attain the goal of accurate treatment of SCLC.

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.

Network Biology-Inspired Machine Learning Features Predict Cancer Gene Targets and Reveal Target Coordinating Mechanisms

Anticipating and understanding cancers' need for specific gene activities is key for novel therapeutic development. Here we utilized DepMap, a cancer gene dependency screen, to demonstrate that machine learning combined with network biology can produce robust algorithms that both predict what genes a cancer is dependent on and what network features coordinate such gene dependencies. Using network topology and biological annotations, we constructed four groups of novel engineered machine learning features that produced high accuracies when predicting binary gene dependencies. We found that in all examined cancer types, F1 scores were greater than 0.90, and model accuracy remained robust under multiple hyperparameter tests. We then deconstructed these models to identify tumor type-specific coordinators of gene dependency and identified that in certain cancers, such as thyroid and kidney, tumors' dependencies are highly predicted by gene connectivity. In contrast, other histologies relied on pathway-based features such as lung, where gene dependencies were highly predictive by associations with cell death pathway genes. In sum, we show that biologically informed network features can be a valuable and robust addition to predictive pharmacology models while simultaneously providing mechanistic insights.

Microscopy-based phenotypic monitoring of MDA-MB-231 spheroids allows the evaluation of phenotype-directed therapy

Breast cancer (BC) is the most commonly diagnosed cancer among women. Prognosis has improved over the years, to a large extent, owing to personalized therapy informed by molecular profiling of hormone receptors. However, there is a need for new therapeutic approaches for a subgroup of BCs lacking molecular markers, the Triple Negative Breast Cancer (TNBC) subgroup. TNBC is the most aggressive type of BC, lacks an effective standard of care, shows high levels of resistance and relapse is often inevitable. High resistance to therapy has been hypothesized to be associated with high intratumoral phenotypic heterogeneity. To characterize and treat this phenotypic heterogeneity, we optimized a whole-mount staining and image analysis protocol for three-dimensions (3D) spheroids. Applying this protocol to TNBC spheroids located in the outer region of the spheroid the cells with selected phenotypes: dividing, migrating, and high mitochondrial mass phenotypes. To evaluate the relevance of phenotype-based targeting these cell populations were targeted with Paclitaxel, Trametinib, and Everolimus, respectively, in a dose-dependent manner. Single agents cannot specifically target all phenotypes at the same time. Therefore, we combined drugs that should target independent phenotype. With this rationale we observed that combining Trametinib and Everolimus achieves the highest cytotoxicity at lower doses from all the tested combinations. These findings suggest a rational approach to design treatments can be evaluated in spheroids prior to pre-clinical models and potentially reduce adverse effects.

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.

Spatial epitope barcoding reveals clonal tumor patch behaviors

Intratumoral heterogeneity is a seminal feature of human tumors contributing to tumor progression and response to treatment. Current technologies are still largely unsuitable to accurately track phenotypes and clonal evolution within tumors, especially in response to genetic manipulations. Here, we developed epitopes for imaging using combinatorial tagging (EpicTags), which we coupled to multiplexed ion beam imaging (EpicMIBI) for in situ tracking of barcodes within tissue microenvironments. Using EpicMIBI, we dissected the spatial component of cell lineages and phenotypes in xenograft models of small cell lung cancer. We observed emergent properties from mixed clones leading to the preferential expansion of clonal patches for both neuroendocrine and non-neuroendocrine cancer cell states in these models. In a tumor model harboring a fraction of PTEN-deficient cancer cells, we observed a non-autonomous increase of clonal patch size in PTEN wild-type cancer cells. EpicMIBI facilitates in situ interrogation of cell-intrinsic and cell-extrinsic processes involved in intratumoral heterogeneity.

Cooperative RNA degradation stabilizes intermediate epithelial-mesenchymal states and supports a phenotypic continuum

Multiple intermediate epithelial-mesenchymal transition (EMT) states reflecting hybrid epithelial and mesenchymal phenotypes were observed in physiological and pathological conditions. Previous theoretical models explaining multiple EMT states rely on regulatory loops involving transcriptional feedback, which produce three or four attractors. This is incompatible with the observed continuum-like EMT spectrum. Here, we used mass-action-based models to describe post-transcriptional regulations, finding that cooperative RNA degradation via multiple microRNA binding sites can generate four-attractor systems without transcriptional feedback. Furthermore, the newly identified intermediates-enabling circuits are common in the EMT regulatory network, and they can synergize with transcriptional feedback to support phenotypic continuum. Finally, our model predicted a role of miR-101 in multistate EMT, and we identified evidence from single-cell RNA-sequencing data that support the prediction. Our work reveals a previously unknown role of cooperative RNA degradation and microRNAs in EMT, providing a framework that can bridge the gap between mechanistic models and single-cell experiments.

Asymmetric inheritance of cytoophidia could contribute to determine cell fate and plasticity: The onset of alternative differentiation patterns in daughter cells may rely on the acquisition of either CTPS or IMPDH cytoophidia: The onset of alternative differentiation patterns in daughter cells may rely on the acquisition of either CTPS or IMPDH cytoophidia

Two enzymes involved in the synthesis of pyrimidine and purine nucleotides, CTP synthase (CTPS) and IMP dehydrogenase (IMPDH), can assemble into a single or very few large filaments called rods and rings (RR) or cytoophidia. Most recently, asymmetric cytoplasmic distribution of organelles during cell division has been described as a decisive event in hematopoietic stem cell fate. We propose that cytoophidia, which could be considered as membrane-less organelles, may also be distributed asymmetrically during mammalian cell division as previously described for Schizosaccharomyces pombe. Furthermore, because each type of nucleotide intervenes in distinct processes (e.g., membrane synthesis, glycosylation, and G protein-signaling), alterations in the rate of synthesis of specific nucleotide types could influence cell differentiation in multiple ways. Therefore, we hypothesize that whether a daughter cell inherits or not CTPS or IMPDH filaments determines its fate and that this asymmetric inheritance, together with the dynamic nature of these structures enables plasticity in a cell population.

Unraveling phenotypic plasticity and evolution in small cell lung cancer

Malignant cell populations in a tumor often exist in distinct phenotypic states. Deciphering tumor heterogeneity requires determining how many such unique states exist and what the biological traits associated with each are. Archetype analysis of SCLC transcriptomes reveals key phenotypic states in SCLC tumors and their patterns of evolution.