Small Cell Lung Cancer Subtypes Research Articles

Abstract 6392: Activity of HER2-and TROP2- antibody drug conjugates (ADCs) in small cell lung cancer (SCLC) models with SLFN11 as a predictive biomarker

Abstract Background: Small-cell lung cancer (SCLC) is a highly aggressive malignancy with its few treatment options devoid of biomarker selection, and a 5-year survival rate of less than 7%. Our group previously established four transcriptionally defined subtypes of SCLC, each with distinct cell surface targets (Gay et al, Cancer Cell 2021). Further, we identified SLFN11 (a putative DNA/RNA helicase) as a candidate biomarker of sensitivity to DNA-damaging agents, including topoisomerase inhibitors. Human epidermal growth factor receptor 2 (HER2/ERBB2) expression has been previously described in a subset of SCLCs and has been linked to poor prognosis. HER2 antibody-drug conjugates (ADCs) like trastuzumab deruxtecan (T-DXd) (where the payload is a topoisomerase I inhibitor) have received FDA approval for various solid tumors, highlighting their therapeutic potential. Similarly, TROP2 (TACSTD2) ADC sacituzumab govitecan has shown promising clinical efficacy in a SCLC (TROPiCS-03 basket trial). However, it is unclear what the predictive biomarkers are to these ADCs in SCLC. Methods: Expression of ERBB2 and TACSTD2 mRNA was analyzed using publicly available patient datasets. We also characterized surface expression of HER2 and TROP2 by flow cytometry in SCLC cell lines at baseline and after cisplatin/etoposide treatment. We then assessed the cytotoxicity of T-Dxd and sacituzumab govitecan in a panel of SCLC cell lines representative of each SCLC subtype, as well as SLFN11 status. Results: Analyses of HER2/ERBB2 and TROP2/TACSTD2 in SCLC cell lines and patient datasets showed a range of ERBB2 and TACSTD2 expression with enrichment in the non-neuroendocrine subtypes (SCLC-P and SCLC- I subtypes). We further validated HER2 and TROP2 surface levels by flow cytometry in a subset of SCLC cell lines. Intriguingly, we observed that surface expression of both HER2 and TROP2 increase after frontline chemotherapy cisplatin/etoposide treatment. As expected, T-Dxd and sacituzumab govitecan exhibit cytotoxicity in a subset of SCLC cell lines. We found a strong correlation between the in vitro cytotoxicity (IC50) of sacituzumab govitecan with SLFN11 (rho= 0.74, p=0.015). Importantly, SLFN11 is bimodally distributed in SCLC and we found ERBB2 and TACSTD2 to be enriched in the SLFN11-high group of SCLC patients. We also found ERBB2 and TACSTD2 to be correlated with each other (rho = 0.53, p <0.001). Conclusion: In this study, we demonstrate that HER2/ERBB2 and TROP2/TACSTD2 are enriched in a subset of SCLC. FDA-approved ADCs, T-DXd and sacituzumab govitecan each have preclinical activity in SCLC with SLFN11 as a predictive biomarker of payload cytotoxicity. We also found HER2 and TROP2 target levels increase after frontline chemotherapy (cisplatin/etoposide) in SCLC, which suggest their efficacy may be enhanced in relapsed SCLC where treatment options are extremely limited. Citation Format: Bingnan Zhang, Ali Ibrahim, C.Allison Stewart, Lixia Diao, Qi Wang, Li Shen, Yuann xi, Alejandra Serrano, Luisa M. Solis, Wei-Lien Wang, Kavya Ramkumar, Robert J. Cardnell, Runsheng Wang, Alberto Duarte, Jing Wang, Lauren A. Byers, Carl Gay. Activity of HER2-and TROP2- antibody drug conjugates (ADCs) in small cell lung cancer (SCLC) models with SLFN11 as a predictive biomarker [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6392.

Abstract 6602: Targeting the SWI/SNF complex in POU2F-POU2AF transcription factor-driven malignancies

Abstract Background: Small cell lung cancer (SCLC) is a rapidly progressing subtype of lung cancer with a high growth rate and early metastasis propensity, often resulting in a more advanced disease stage at diagnosis. A recent transcriptome analysis of human SCLC tumors revealed that SCLC could be characterized by the expression pattern of certain transcription factors (TFs), including ASCL1 (achaete-scute family bHLH transcription factor 1) and POU2F3 (POU domain class 2 transcription factor 3), exemplifying SCLC as a TF-driven malignancy. ASCL1-driven SCLC (SCLC-A) manifests a neuroendocrine phenotype, while POU2F3-driven SCLC (SCLC-P) is characterized as a tuft-cell-like variant. Recent studies revealed that POU domain class 2 transcription factors uniquely rely on coactivators to achieve their lineage-defining functions in the tuft cell and B cell lineages. In tuft-cell-like SCLC cells, the coactivators of POU2F3 (POU2AF2 and POU2AF3) endow POU2F3 with a critical transactivation domain by forming a master regulator complex, which supports enhancer-mediated cancer-promoting gene activation in SCLC-P cells. This indicates a potential therapeutic vulnerability of tuft-cell-like SCLC whereby strategies aimed at blocking POU2F3-POU2AF2/3 function may lead to clinical benefit. Methods: We conducted a domain-targeted CRISPR screen to identify druggable targets for SCLC-P, followed by pharmacological validation in preclinical SCLC models. Multi-omics techniques, including ATAC-seq, ChIP-seq, RNA-seq, and RIME, were employed to elucidate the SWI/SNF complex's regulatory influence on the POU2F-POU2AF axis. Results: Here we identified that the POU2F3 molecular subtype of SCLC (SCLC-P) exhibits an exquisite dependence on the activity of the SWI/SNF epigenetic complex. SCLC-P cell lines were sensitive to low nanomolar levels of a SWI/SNF ATPase degrader when compared to other molecular subtypes of SCLC. Co-factors of POU2F were found to interact with components of the SWI/SNF complex. The POU2F3 transcription factor complex is evicted from chromatin upon SWI/SNF ATPase degradation. A novel, orally bioavailable SWI/SNF ATPase PROTAC degrader demonstrated preferential efficacy in SCLC-P relative to the SCLC-A subtype. The SWI/SNF ATPase PROTAC degrader did not alter normal tuft cell numbers in lung or colon, nor did it exhibit toxicity in mice. B cell malignancies which displayed a dependency on the POU2F co-factor, POU2AF1, were also remarkably sensitive to SWI/SNF ATPase degradation. In a POU2AF1-dependent, disseminated murine model of multiple myeloma, AU-24118 had an enhanced survival benefit as compared to pomalidomide, an approved treatment for multiple myeloma. Conclusions: Taken together, our studies suggest that POU2F-POU2AF driven malignancies have an intrinsic dependence on the SWI/SNF complex representing a striking therapeutic vulnerability. Citation Format: Lanbo Xiao, Tongchen He, Olaf Klingbeil, Young Eleanor, Xiaoli Wu, Yuanyuan Qiao, Abhijit Parolia, Sanjana Eyunni, Rahul Mannan, Somnath Mahapatra, NamHoon Kim, Heng Zheng, Fengyun Su, Xuhong Cao, Susanta Samajdar, Murali Ramachandra, Christopher R. Vakoc, Arul M. Chinnaiyan. Targeting the SWI/SNF complex in POU2F-POU2AF transcription factor-driven malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6602.

Comprehensive analysis of transcription factor-based molecular subtypes and their correlation to clinical outcomes in small-cell lung cancer

Recent studies have reported the predictive and prognostic value of novel transcriptional factor-based molecular subtypes in small-cell lung cancer (SCLC). We conducted an in-depth analysis pairing multi-omics data with immunohistochemistry (IHC) to elucidate the underlying characteristics associated with differences in clinical outcomes between subtypes. IHC (n=252), target exome sequencing (n=422), and whole transcriptome sequencing (WTS, n=189) data generated from 427 patients (86.4% males, 13.6% females) with SCLC were comprehensively analysed. The differences in the mutation profile, gene expression profile, and inflammed signatures were analysed according to the IHC-based molecular subtype. IHC-based molecular subtyping, comprised of 90 limited-disease (35.7%) and 162 extensive-disease (64.3%), revealed a high incidence of ASCL1 subtype (IHC-A, 56.3%) followed by ASCL1/NEUROD1 co-expressed (IHC-AN, 17.9%), NEUROD1 (IHC-N, 12.3%), POU2F3 (IHC-P, 9.1%), triple-negative (IHC-TN, 4.4%) subtypes. IHC-based subtype showing high concordance with WTS-based subtyping and non-negative matrix factorization (NMF) clusterization method. IHC-AN subtype resembled IHC-A (rather than IHC-N) in terms of both gene expression profiles and clinical outcomes. Favourable median overall survival was observed in IHC-A (15.2 months) compared to IHC-N (8.0 months, adjusted HR 2.3, 95% CI 1.4-3.9, p=0.002) and IHC-P (8.3 months, adjusted HR 1.7, 95% CI 0.9-3.2, p=0.076). Inflamed tumours made up 25% of cases (including 53% of IHC-P, 26% of IHC-A, 17% of IHC-AN, but only 11% of IHC-N). Consistent with recent findings, inflamed tumours were more likely to benefit from first-line immunotherapy treatment than non-inflamed phenotype (p=0.002). This study provides fundamental data, including the incidence and basic demographics of molecular subtypes of SCLC using both IHC and WTS from a comparably large, real-world Asian/non-Western patient cohort, showing high concordance with the previous NMF-based SCLC model. In addition, we revealed underlying biological pathway activities, immunogenicity, and treatment outcomes based on molecular subtype, possibly related to the difference in clinical outcomes, including immunotherapy response. This work was supported by AstraZeneca, Future Medicine 2030 Project of the Samsung Medical Center [grant number SMX1240011], the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) [grant number 2020R1C1C1010626] and the 7th AstraZeneca-KHIDI (Korea Health Industry Development Institute) oncology research program.

Tumor- and circulating-free DNA methylation identifies clinically relevant small cell lung cancer subtypes

Small cell lung cancer (SCLC) is an aggressive malignancy composed of distinct transcriptional subtypes, but implementing subtyping in the clinic has remained challenging, particularly due to limited tissue availability. Given the known epigenetic regulation of critical SCLC transcriptional programs, we hypothesized that subtype-specific patterns of DNA methylation could be detected in tumor or blood from SCLC patients. Using genomic-wide reduced-representation bisulfite sequencing (RRBS) in two cohorts totaling 179 SCLC patients and using machine learning approaches, we report a highly accurate DNA methylation-based classifier (SCLC-DMC) that can distinguish SCLC subtypes. We further adjust the classifier for circulating-free DNA (cfDNA) to subtype SCLC from plasma. Using the cfDNA classifier (cfDMC), we demonstrate that SCLC phenotypes can evolve during disease progression, highlighting the need for longitudinal tracking of SCLC during clinical treatment. These data establish that tumor and cfDNA methylation can be used to identify SCLC subtypes and might guide precision SCLC therapy.

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.

Molecular and Pathologic Characterization of YAP1-Expressing Small Cell Lung Cancer Cell Lines Leads to Reclassification as SMARCA4-Deficient Malignancies.

The classification of small cell lung cancer (SCLC) into distinct molecular subtypes defined by ASCL1, NEUROD1, POU2F3, or YAP1 (SCLC-A, -N, -P, or -Y) expression, paves the way for a personalized treatment approach. However, the existence of a distinct YAP1-expressing SCLC subtype remains controversial. To better understand YAP1-expressing SCLC, the mutational landscape of human SCLC cell lines was interrogated to identify pathogenic alterations unique to SCLC-Y. Xenograft tumors, generated from cell lines representing the four SCLC molecular subtypes, were evaluated by a panel of pathologists who routinely diagnose thoracic malignancies. Diagnoses were complemented by transcriptomic analysis of primary tumors and human cell line datasets. Protein expression profiles were validated in patient tumor tissue. Unexpectedly, pathogenic mutations in SMARCA4 were identified in six of eight SCLC-Y cell lines and correlated with reduced SMARCA4 mRNA and protein expression. Pathologist evaluations revealed that SMARCA4-deficient SCLC-Y tumors exhibited features consistent with thoracic SMARCA4-deficient undifferentiated tumors (SMARCA4-UT). Similarly, the transcriptional profile SMARCA4-mutant SCLC-Y lines more closely resembled primary SMARCA4-UT, or SMARCA4-deficient non-small cell carcinoma, than SCLC. Furthermore, SMARCA4-UT patient samples were associated with a YAP1 transcriptional signature and exhibited strong YAP1 protein expression. Together, we found little evidence to support a diagnosis of SCLC for any of the YAP1-expressing cell lines originally used to define the SCLC-Y subtype. SMARCA4-mutant SCLC-Y cell lines exhibit characteristics consistent with SMARCA4-deficient malignancies rather than SCLC. Our findings suggest that, unlike ASCL1, NEUROD1, and POU2F3, YAP1 is not a subtype defining transcription factor in SCLC. See related commentary by Rekhtman, p. 1708.

Abstract LB_A22: Advancing personalized therapies for small cell lung cancer: Exploring HER2-targeting antibody-drug conjugates (ADCs) and SLFN11 biomarker insights

Abstract Background: Small-cell lung cancer (SCLC) is a highly aggressive malignancy constituting 15% of all lung cancers, with a 5-year survival rate of less than 7%. Our group has established four transcriptionally defined subtypes of SCLC, each with distinct cell surface targets (Gay et al, Cancer Cell 2021). Further, we identified SLFN11 (a putative DNA/RNA helicase) as a candidate biomarker of sensitivity to DNA-damaging agents, including topoisomerase I inhibitors. Despite these advances, SCLC is still treated clinically with a “one-size-fits-all” approach. Human epidermal growth factor receptor 2 (HER2/ERBB2) has been previously described in a subset of SCLCs and has been linked to poor prognosis. Notably, HER2-targeting antibody-drug conjugates (ADCs) like Trastuzumab Deruxtecan (T-DXd) (where the payload is a topoisomerase I inhibitor) have received FDA approval for various solid tumors, highlighting their therapeutic potential. Methods: We analyzed ERBB2 mRNA expression in publicly available patient datasets and profiled HER2 protein expression in cell lines and patient-derived xenograft models by flow cytometry and IHC. We then assessed the cytotoxicity of Trastuzumab Deruxtecan in a panel of cell lines representative of the heterogeneity of SCLC to test whether levels of HER2 and/or SLFN11 predicted response. Results: Analyses of HER2/ERBB2 in SCLC cell lines and patient-derived xenograft (PDX) models revealed an enrichment in the SCLC-POU2F3 (SCLC-P) subtype. HER2 expression was validated by IHC and flow cytometry in a subset of SCLC cell lines and PDX models. ERBB2 mRNA expression data was analyzed using publicly available SCLC patient datasets, where we uncovered high expression of ERBB2 in a subset of patient tumors. Consistent with this, single-cell RNA-Seq data revealed ERBB2 expression in POU2F3+ cells in tumors from patients with relapsed SCLC. As predicted, we found a strong correlation between the in vitro cytotoxicity of Trastuzumab Deruxtecan and both the level of surface HER2 expression and SLFN11 level. Importantly, the SCLC cell lines with the highest HER2 surface expression exhibited the lowest IC50 of Trastuzumab Deruxtecan, and the response was predicted by the presence of SLFN11. Conclusion: We demonstrate HER2/ERBB2 mRNA and protein expression in a subset of SCLC, with enrichment in the SCLC-P subtype. Further, the FDA-approved HER2-targeting ADC Trastuzumab Deruxtecan had significant cytotoxicity, correlating with expression of the surface target (HER2) as well as with SLFN11 (a candidate predictive biomarker for the topoisomerase inhibitor payload). These findings suggest the potential of tailoring HER2-targeting strategies based on HER2 and SLFN11 expression in SCLC as an effective and personalized approach for tackling SCLC. Citation Format: Ali H. Ibrahim, Bingnan Zhang, C. Allison Stewart, Alejandra Serrano, Luisa M. Solis, Wei-Lien Wang, Kavya Ramkumar, Robert J. Cardnell, Runsheng Wang, Alberto Duarte Jr, Lixia Diao, Yuanxin Xi, Jing Wang, Lauren A. Byers, Carl M. Gay. Advancing personalized therapies for small cell lung cancer: Exploring HER2-targeting antibody-drug conjugates (ADCs) and SLFN11 biomarker insights [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr LB_A22.

Does subtyping of high-grade pulmonary neuroendocrine carcinomas have an impact on therapy selection?

Small cell lung cancer (SCLC) and large cell neuroendocrine carcinomas (LCNEC) are characterized by a rapid progressive course. Therapy for SCLC has not much changed for decades, and in LCNEC controversies exist, favoring either SCLC-like or non-small cell lung cancer (NSCLC)-like therapy. Three subtypes of SCLC identified in cell cultures, namely ASCL1, NeuroD1, and POU2F3 have been confirmed by immunohistochemistry. The fourth type based on the expression of YAP1 was questioned, and another type, inflamed SCLC, was proposed. SCLC and LCNEC samples were investigated by immunohistochemistry for different subtypes. Additionally, immunohistochemical markers as potential tools to identify patients who might respond to targeted treatment were investigated. For validation a biopsy set was added. ASCL1, NeuroD1, and POU2F3 were expressed in different percentages in SCLC and LCNEC. Similar percentages of expression were found in biopsies. ATOH was expressed in combination with one of the subtypes. YAP1 and TAZ were expressed in some SCLC and LCNEC cases. HES1 expression was seen in few cases. Predominantly stroma cells expressed programmed cell death ligand 1 (PD-L1). The dominant MYC protein was N-MYC. Aurora kinase A (AURKA) was expressed in the majority of both carcinomas, whereas fibroblast growth factor receptor 2 (FGFR2) in few. SCLC and LCNEC can be subtyped into ASCL1-, NeuroD1-, and POU2F3-positive types. AURKA expression and positivity for N-MYC protein was not associated with subtypes. AURKA and FGFR2 are both possible targets for inhibition in SCLC and LCNEC, but patients' selection should be based on expression of the enzyme. Combined chemo- and immunotherapy might be decided by PD-L1 staining of stroma cells.

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.

Deep Learning Based on Enhanced MRI T1 Imaging to Differentiate Small-cell and Non-small-cell Primary Lung Cancers in Patients with Brain Metastases.

To differentiate the primary small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) for patients with brain metastases (BMs) based on a deep learning (DL) model using contrast-enhanced magnetic resonance imaging (MRI) T1 weighted (T1CE) images. Out of 711 patients with BMs of lung cancer origin (SCLC 232, NSCLC 479), the MRI datasets of 192 patients (lesions' widths and heights > 30 pixels) with BMs from lung cancer (73 SCLC and 119 NSCLC) confirmed pathologically were enrolled, retrospectively. A typical convolutional neural network ResNet18 was applied for the automatic classification of BMs lesions from lung cancer based on T1CE images, with training and testing groups randomized per patient to eliminate learning bias. A 5-fold cross-validation was performed to evaluate the classification of the model. The receiver operating characteristic (ROC) curve, accuracy, precision, recall and f1 score were calculated. For a 5-fold cross-validation test, the DL model achieved AUCs of 0.8019 and 0.8024 for SCLC and NSCLC patients with BMs, respectively, and a mean overall accuracy of 0.7515±0.04. The DL model performed well in differentiating the primary SCLC and NSCLC with BMs. The proposed DL model is feasible and effective in differentiating the pathological subtypes of SCLC and NSCLC causing BMs, which may be used as a new tool for oncologists to diagnose noninvasively BMs and guide therapy based on the imaging structure of tumors.

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.

The Effect of PARP Inhibitor Radiosensitization on the mRNA Translational Regulation of T Cell Chemokines

Immunotherapy has modestly improved survival for small cell lung cancer (SCLC) patients. Low response rate and rapid disease progression remain an intractable challenge. One of the factors that contribute to immunotherapy resistance is the lack of cytotoxic T cell infiltration. The expression of chemoattractant cytokines, like CCL5 and CXCL10, are essential for T cell infiltration. The control of chemokine expression is not fully understood, but both transcriptional and translational control pathways could play a major role. Previous studies have shown a correlation between DNA damage and chemokine expression and that PARP inhibitors (PARPi) are radiosensitizers for SCLC that increases DNA damage. The objectives of this study were to define this potential PARPi immunogenic radiosensitizing relationship. We identified doses of olaparib+ radiation treatment (RT) that conferred radiosensitization in SCLC cell-lines by cell viability and/or clonogenic assays. Olaparib+RT induced CCL5 and CXCL10 mRNA expression was measured by qPCR across SCLC cell-lines. Protein level of chemokines was assessed by immunoblotting. SBC5 cells were treated with olaparib+RT and submitted for RNA sequencing analysis. Genes with adjusted p value<0.05 were considered significant. Protein level changes and target gene knock-out (KO) were confirmed by immunoblotting. Chemokine CXCL10 mRNA and protein level in wildtype (WT) and KO cells were measured by qPCR and western blot, respectively. A mRNA decay assay and dual-luciferase reporter assay was used to identify the region of CXCL10 mRNA that confers mRNA stability control. In vivo anti-tumor efficacy and tumor T cell infiltration studies were done in B6129F mice bearing KP1 tumors. And the T cell infiltration was measured by immune profiling. In vitro, olaparib+RT significantly increased CXCL10 mRNA in all four SCLC subtype cell-lines in comparison to vehicle control. Consistently, the increase of CXCL10 protein levels (3-fold) was observed in SBC5 cells. By RNA-Seq, a top-ranking translational repressor was EIF4E2 (4EHP) mRNA. The downregulation of EIF4E2 protein by olaparib+RT was validated in four SCLC subtypes by western blot. EIF4E2 KO in HEK293 and SBC5 cells increased CXCL10 mRNA and protein level. By mRNA decay assay and western blot, the absence of EIF4E2 stabilized CXCL10 mRNA and increased CXCL10 protein levels. The dual-luciferase assay demonstrated EIF4E2 destabilizes CXCL10 mRNA via the 3'UTR of CXCL10. In vivo, immune profiling showed olaparib+RT significantly increased the total T cell and CD8+ T cell infiltration. Finally, anti-PD-L1 inhibition potentiated olaparib + IR to improve tumor control in KP1 allograft. Our study demonstrated olaparib + RT increases CXCL10 protein levels through downregulating EIF4E2 to subsequently increase T cell infiltration. Olaparib + RT enhanced anti-PD-L1 immunotherapy efficacy and has therapeutic potential as an immunogenic radiosensitizer.

C-Myc-induced hypersialylation of small cell lung cancer facilitates pro-tumoral phenotypes of macrophages

Immunosuppressive myeloid cell populations have been documented in small cell lung cancer (SCLC) subtypes, playing a key role in remolding the tumor microenvironment (TME). However, the cancer-associated transcriptional features of monocytes and tumor-associated macrophages (TAMs) in SCLC remain poorly understood. Herein, we analyzed the molecular features and functions of monocyte/macrophage subsets aiming to inhibit monocyte recruitment and pro-tumor behavior of macrophages. We observe that NEUROD1-high SCLC subtype (SCLC-N) exhibits subtype-specific hypersialylation induced by the unique target c-Myc (MYC) of NEUROD1. The hypersialylation can alter macrophage phenotypes and pro-tumor behavior by regulating the expression of the immune-inhibiting lectin receptors on monocyte-derived macrophages (MDMs) in SCLC-N. Inhibiting the aberrant sialic acid metabolic pathways in SCLC can significantly enhance the phagocytosis of macrophages. This study provides a comprehensive overview of the cancer-specific immune signature of monocytes and macrophages and reveals tumor-associated biomarkers as potential therapeutic targets for SCLC.

KDM6A epigenetically regulates subtype plasticity in small cell lung cancer.

Small cell lung cancer (SCLC) exists broadly in four molecular subtypes: ASCL1, NEUROD1, POU2F3 and Inflammatory. Initially, SCLC subtypes were thought to be mutually exclusive, but recent evidence shows intra-tumoural subtype heterogeneity and plasticity between subtypes. Here, using a CRISPR-based autochthonous SCLC genetically engineered mouse model to study the consequences of KDM6A/UTX inactivation, we show that KDM6A inactivation induced plasticity from ASCL1 to NEUROD1 resulting in SCLC tumours that express both ASCL1 and NEUROD1. Mechanistically, KDM6A normally maintains an active chromatin state that favours the ASCL1 subtype with its loss decreasing H3K4me1 and increasing H3K27me3 at enhancers of neuroendocrine genes leading to a cell state that is primed for ASCL1-to-NEUROD1 subtype switching. This work identifies KDM6A as an epigenetic regulator that controls ASCL1 to NEUROD1 subtype plasticity and provides an autochthonous SCLC genetically engineered mouse model to model ASCL1 and NEUROD1 subtype heterogeneity and plasticity, which is found in 35-40% of human SCLCs.

Molecular Subtypes and Tumor Microenvironment Characteristics of Small-Cell Lung Cancer Associated with Platinum-Resistance.

Although molecular subtypes of small-cell lung cancer (SCLC) have been proposed, their clinical relevance and therapeutic implications are not fully understood. Thus, we aimed to refine molecular subtypes and to uncover therapeutic targets. We classified the subtypes based on gene expression (n = 81) and validated them in our samples (n = 87). Non-SCLC samples were compared with SCLC subtypes to identify the early development stage of SCLC. Single-cell transcriptome analysis was applied to dissect the TME of bulk samples. Finally, to overcome platinum resistance, we performed drug screening of patient-derived cells and cell lines. Four subtypes were identified: the ASCL1+ (SCLC-A) subtype identified as TP53/RB-mutated non-SCLC representing the early development stage of SCLC; the immune activation (SCLC-I) subtype, showing high CD8+/PD-L1+ T-cell infiltration and endothelial-to-mesenchymal transition (EndMT); the NEUROD1 (SCLC-N) subtype, which showed neurotransmission process; and the POU2F3+ (SCLC-P) subtype with epithelial-to-mesenchymal transition (EMT). EndMT was associated with the worst prognosis. While SCLC-A/N exhibited platinum sensitivity, the EndMT signal of SCLC-I conferred platinum resistance. A BET inhibitor suppressed the aggressive angiogenesis phenotype of SCLC-I. We revealed that EndMT development contributed to a poor outcome in SCLC-I. Moreover, heterogenous TME development facilitated platinum resistance. BET inhibitors are novel candidates for overcoming platinum resistance.

Identification of Immune Subtypes and Candidate mRNA Vaccine Antigens in Small Cell Lung Cancer.

Immune checkpoint inhibitors (ICIs) have demonstrated promising outcomes in small cell lung cancer (SCLC), but not all patients benefit from it. Thus, developing precise treatments for SCLC is a particularly urgent need. In our study, we constructed a novel phenotype for SCLC based on immune signatures. We clustered patients with SCLC hierarchically in 3 publicly available datasets according to the immune signatures. ESTIMATE and CIBERSORT algorithm were used to evaluate the components of the tumor microenvironment. Moreover, we identified potential mRNA vaccine antigens for patients with SCLC, and qRT-PCR were performed to detect the gene expression. We identified 2 SCLC subtypes and named Immunity High (Immunity_H) and Immunity Low (Immunity_L). Meanwhile, we obtained generally consistent results by analyzing different datasets, suggesting that this classification was reliable. Immunity_H contained the higher number of immune cells and a better prognosis compared to Immunity_L. Gene-set enrichment analysis revealed that several immune-related pathways such as cytokine-cytokine receptor interaction, programmed cell death-Ligand 1 expression and programmed cell death-1 checkpoint pathway in cancer were hyperactivated in the Immunity_H. However, most of the pathways enriched in the Immunity_L were not associated with immunity. Furthermore, we identified 5 potential mRNA vaccine antigens of SCLC (NEK2, NOL4, RALYL, SH3GL2, and ZIC2), and they were expressed higher in Immunity_L, it indicated that Immunity_L maybe more suitable for tumor vaccine development. SCLC can be divided into Immunity_H and Immunity_L subtypes. Immunity_H may be more suitable for treatment with ICIs. NEK2, NOL4, RALYL, SH3GL2, and ZIC2 may be act as potential antigens for SCLC.

Changes in the Histology of Lung Cancer in Northern Italy: Impact on Incidence and Mortality

This study assessed the incidence, mortality, and survival of lung cancer subtypes of NSCSLC (non-small-cell lung cancer), SCLC (small-cell lung cancer), and other morphologies. It is an observational epidemiological study using 7197 cases from the Reggio Emilia Cancer Registry recorded between 2001 and 2020 in males and females. The incidence of NSCLC in 5104 males indicates a significant 3% annual increase until 2013 and then a decline of −3.2% that is not statistically significant; until 2014, mortality increased significantly (3.2%), but it then decreased non-significantly (−12.1%), especially squamous cell cancer. In 2093 females, the incidence and mortality trends continued to rise significantly through 2012, and then they began to slightly decline (not statistically significant). The two-year relative survival of NSCLC increased from 32% to 38% in males and from 42% to 56% in females. SCLC in males decreased significantly both in incidence and mortality, while in women, it showed a slight increase (significantly for incidence but not for mortality). This study is important because it analyzes the decrease in lung cancer in males and the increase in females in relation to the different histotypes. Our study’s findings confirmed a decline in male incidence and death beginning in 2013. We were unable to determine if the drop in cigarette smoking and the introduction of new drugs such as EGFR in first-line therapy were responsible for the lower incidence.

Dissecting small cell lung cancer subtypes with cell-free DNA fragmentomes.

8592 Background: Although small cell lung cancer (SCLC) is managed as a single cancer type, new evidence supports that subtypes (high vs low neuroendocrine) of SCLC acquire diverse transcriptional and epigenetic states. Furthermore, distinct SCLC subtypes acquire unique therapeutic vulnerabilities, such as the low-neuroendocrine highly inflamed SCLC subtype that has been linked to immunotherapy sensitivity. We have previously shown that DELFI (DNA evaluation of fragments for early interception) can be used to non-invasively distinguish SCLC from non-small cell lung cancer (NSCLC). Here, we report a preliminary analysis of using the DELFI approach for distinguishing among SCLC subtypes. Methods: Circulating cell-free DNA (cfDNA) was isolated from plasma samples of patients diagnosed with relapsed SCLC and treated with durvalumab plus olaparib in a phase II trial (NCT02484404). Over 200 cfDNA libraries were prepared for whole genome sequencing in batches with internal controls. To infer tumor gene expression profiles in cfDNA, we investigated genome-wide signals of tissue-specific transcription factors differentially regulated in SCLC, and applied a novel DELFI-based approach to inform SCLC molecular subtypes. Clinical information, patient-matched tissue transcriptome (RNA-seq), and chromatin accessibility data (ATAC-seq) were examined in orthogonal analyses. Results: The DELFI classifier detected SCLC cases (N = 47) with a median DELFI score of 1.0 (95% CI 0.99-1), significantly higher than previously reported scores for other lung cancer subtypes. Comparison of DELFI fragmentome signals to publicly available tumor transcriptomes shows subtype-level concordance ( r = 0.78, p &lt; 0.001, Pearson), particularly in pre-treatment SCLC cases separating high- vs low- neuroendocrine subtypes. High-neuroendocrine SCLCs exhibited a decrease in aggregate cfDNA fragment coverage at ASCL1 transcription factor binding sites relative to low-neuroendocrine SCLCs (r = 0.90, p &lt; 0.001, Pearson). Additionally, low-neuroendocrine SCLCs revealed a significant decrease in aggregate cfDNA fragment coverage at genomic binding sites regulated by hematopoietic transcription factors, reflecting the inflamed phenotype of this SCLC subtype. Integration of these approaches provided a cfDNA fragmentation-based machine learning model that distinguished SCLC subtypes with high performance. Conclusions: Genome-wide cfDNA fragmentome analyses can differentiate high- and low-neuroendocrine SCLC subtypes. Given the challenges for performing SCLC biopsies in a clinical setting, we believe this approach could be a viable method of subtyping SCLC in a non-invasive manner.

Analysis of tumor- and circulating-free DNA methylation to identify clinically relevant small cell lung cancer subtypes.

8577 Background: Small-cell lung cancer (SCLC) is an aggressive malignancy composed of distinct transcriptional subtypes, defined by the predominant expression of one of the three transcription factors ASCL1 (SCLC-A), NEUROD1 (SCLC-N) and POU2F3 (SCLC-P) as well as an inflamed subtype (SCLC-I; see Gay et al. Cancer Cell. 2021), each with potential therapeutic vulnerabilities. Implementing subtyping in the clinic has remained challenging due to limited tissue availability, particularly for longitudinal monitoring. Given the known epigenetic regulation of critical SCLC transcriptional programs, we hypothesized that there would be subtype-specific patterns of DNA methylation that could be detected in tumor or blood from SCLC patients. Methods: We included 179 patients with SCLC and performed RNA sequencing and genomic-wide reduced-representation bisulfite sequencing (RRBS). We further analyzed DNA methylation in 68 plasma samples including longitudinal samples to track SCLC subtype evolution over time. Results: Using machine learning approaches, we developed a highly accurate DNA methylation-based classifier (SCLC-DMC) that could distinguish SCLC subtypes using clinical tumor samples with 95.8% accuracy in the testing set compared to mRNA-based profiling. We further adjusted the classifier for circulating-free DNA (cfDNA) to subtype SCLC from plasma. Using the cfDNA classifier (cfDMC), we could demonstrate that SCLC subtypes evolve frequently during disease progression, highlighting the need for longitudinal tracking of SCLC during clinical treatment. Furthermore, methylation-based subtyping predicted response to a wide variety of drugs in preclinical models like CDK and AURK inhibitors, and clinical outcomes were indistinguishable in cohorts of patients subtyped using mRNA or SCLC-DMC (p = 0.95). Conclusions: These data establish that tumor and cfDNA methylation can be used to identify SCLC subtypes and guide precision SCLC therapy.

A next-generation sequencing methylation assay for sub-typing small cell lung cancer in cell-free DNA.

e15021 Background: Liquid biopsy provides an easy access and minimally invasive technique to inform cancer patient management in clinics as well as clinical trials. Detecting genomic alterations in circulating tumor DNA (ctDNA) has demonstrated clinical utility and actionability in therapy selection and longitudinal monitoring. The development of a comprehensive assay that provides multi-omic information is imperative to expand the utility of liquid biopsy. Methods: Agilent Resolution ctDNA diagnostic assays can detect all four major types of actionable genomic alterations for cancer patients: SNVs, Indels, fusions, and CNVs. The Agilent Resolution ctDx FIRST assay has recently been approved by the FDA as a companion diagnostic assay for non-small cell lung cancer (NSCLC). Applying the same core technology, we developed an additional assay that enables methylation profiling in cfDNA. The custom methylation-specific panel targets informative regions to detect tumor signals and potentially profile the disease by subtype. Recent reports suggest that small cell lung cancer (SCLC) may be stratified into subtypes based on gene expression, including that of ASCL-1, NEUROD1, and POU2F3. We used the proposed SCLC subtyping model to test the feasibility of subtyping SCLC by cfDNA methylation profiles. A baseline methylation profile was established using cell-free DNA from healthy donors. Test samples included genomic DNA from SCLC cell lines and plasma samples of SCLC patients with matching tissue, where available. Results: We applied a methylation-specific bioinformatic pipeline that generates error-corrected unique molecules. The methylation workflow showed unique coverage comparable to our ctDx assay and produced consistent results from input DNA as low as 1ng. We found consistent methylation patterns in cfDNA among healthy donors. SCLC cell lines showed distinct profiles that could be classified into sub-types. When spiked into healthy donor cfDNA background, the tumor signals were distinguishable down to ~0.5%. Plasma samples from SCLC patients showed profiles consistent with that of SCLC sub-types defined by cell lines. Matching plasma and tissue samples produced concordant sub-typing results, supporting the feasibility of disease subtyping in both sample types. Samples were also tested by a custom SCLC panel to analyze genomic alterations. Prevalent variants in SCLC were detected, including SNVs in TP53, PIK3CA, RB1, and MYC amplifications. Conclusions: We showed a cfDNA methylation profiling platform that could support disease sub-typing for SCLC. This platform has great potential in disease stratification and patient management.