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

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.

A Multiplexed Approach to Assess Small Cell Lung Cancer Subtype Heterogeneity in Primary and Patient-Derived Tumor Samples

Unlocking the heterogeneity of cancers is crucial for developing therapeutic approaches that effectively eradicate disease. As our understanding of markers specific to cancer subclones or subtypes expands, there is a growing demand for advanced technologies that enable the simultaneous investigation of multiple targets within an individual tumor sample. Indeed, multiplex approaches offer distinct benefits, particularly when tumor specimens are small and scarce. Here we describe the utility of two fluorescence-based multiplex approaches; fluorescent Western blots, and multiplex immunohistochemistry (Opal™) staining to interrogate heterogeneity, using small cell lung cancer as an example. Critically, the coupling of Opal™ staining with advanced image quantitation, permits the dissection of cancer cell phenotypes at a single cell level. These approaches can be applied to patient biopsies and/or patient-derived xenograft (PDX) models and serve as powerful methodologies for assessing tumor cell heterogeneity in response to therapy or between metastatic lesions across diverse tissue sites.

Epigenetic Regulators Open the Door to SCLC Plasticity

Small-cell lung cancer (SCLC) is a neuroendocrine tumor type with limited treatment options and poor prognosis. SCLC comprises multiple molecular subtypes that are defined by the expression of the lineage-related transcription factors ASCL1, NEUROD1, POU2F3, and more controversially, YAP1. SCLC exhibits remarkable plasticity with the capacity to transition between molecular states; because these states are associated with unique therapeutic susceptibilities, SCLC has been likened to a moving therapeutic target. While MYC's role in driving the ASCL1-to-NEUROD1 (A-to-N) transition is established, additional mechanisms governing SCLC plasticity remain largely obscure. A recent study by Duplaquet and colleagues, published in Nature Cell Biology, employs an innovative genetically engineered mouse model of SCLC harboring loss of KDM6A-a histone lysine demethylase mutated in approximately 2% of SCLC cases. KDM6A loss in SCLC alters chromatin accessibility and increases the potential for A-to-N plasticity in vivo. Through characterization of the epigenetic landscape, Duplaquet and colleagues identified histone methylation as a key regulator of SCLC plasticity. These findings provide not only a new model system for studying SCLC plasticity, but also identify new epigenetic mechanisms involved, which will ultimately be critical for designing more effective therapies.

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.

Tumor intrinsic and extrinsic functions of CD73 and the adenosine pathway in lung cancer

The adenosine pathway is an exciting new target in the field of cancer immunotherapy. CD73 is the main producer of extracellular adenosine. Non-small cell lung cancer (NSCLC) has one of the highest CD73 expression signatures among all cancer types and the presence of common oncogenic drivers of NSCLC, such as mutant epidermal growth factor receptor (EGFR) and KRAS, correlate with increased CD73 expression. Current immune checkpoint blockade (ICB) therapies only benefit a subset of patients, and it has proved challenging to understand which patients might respond even with the current understanding of predictive biomarkers. The adenosine pathway is well known to disrupt cytotoxic function of T cells, which is currently the main target of most clinical agents. Data thus far suggests that combining ICB therapies already in the clinic with adenosine pathway inhibitors provides promise for the treatment of lung cancer. However, antigen loss or lack of good antigens limits efficacy of ICB; simultaneous activation of other cytotoxic immune cells such as natural killer (NK) cells can be explored in these tumors. Clinical trials harnessing both T and NK cell activating treatments are still in their early stages with results expected in the coming years. In this review we provide an overview of new literature on the adenosine pathway and specifically CD73. CD73 is thought of mainly for its role as an immune modulator, however recent studies have demonstrated the tumor cell intrinsic properties of CD73 are potentially as important as its role in immune suppression. We also highlight the current understanding of this pathway in lung cancer, outline ongoing studies examining therapies in combination with adenosine pathway targeting, and discuss future prospects.