SLFN11 Blocks Stressed Replication Forks Independently of ATR.

SLFN11 sensitizes cancer cells to a broad range of DNA-targeted therapies. Here we show that, in response to replication stress induced by camptothecin, SLFN11 tightly binds chromatin at stressed replication foci via RPA1 together with the replication helicase subunit MCM3. Unlike ATR, SLFN11 neither interferes with the loading of CDC45 and PCNA nor inhibits the initiation of DNA replication but selectively blocks fork progression while inducing chromatin opening across replication initiation sites. The ATPase domain of SLFN11 is required for chromatin opening, replication block, and cell death but not for the tight binding of SLFN11 to chromatin. Replication stress by the CHK1 inhibitor Prexasertib also recruits SLFN11 to nascent replicating DNA together with CDC45 and PCNA. We conclude that SLFN11 is recruited to stressed replication forks carrying extended RPA filaments where it blocks replication by changing chromatin structure across replication sites.

Proteomic Dynamics of Breast Cancer Cell Lines Identifies Potential Therapeutic Protein Targets.

Treatment and relevant targets for breast cancer (BC) remain limited, especially for triple-negative BC (TNBC). We identified 6091 proteins of 76 human BC cell lines using data-independent acquisition (DIA). Integrating our proteomic findings with prior multi-omics datasets, we found that including proteomics data improved drug sensitivity predictions and provided insights into the mechanisms of action. We subsequently profiled the proteomic changes in nine cell lines (five TNBC and four non-TNBC) treated with EGFR/AKT/mTOR inhibitors. In TNBC, metabolism pathways were dysregulated after EGFR/mTOR inhibitor treatment, while RNA modification and cell cycle pathways were affected by AKT inhibitor. This systematic multi-omics and in-depth analysis of the proteome of BC cells can help prioritize potential therapeutic targets and provide insights into adaptive resistance in TNBC.

CellMiner Cross-Database (CellMinerCDB) version 1.2: Exploration of patient-derived cancer cell line pharmacogenomics.

CellMiner Cross-Database (CellMinerCDB, discover.nci.nih.gov/cellminercdb) allows integration and analysis of molecular and pharmacological data within and across cancer cell line datasets from the National Cancer Institute (NCI), Broad Institute, Sanger/MGH and MD Anderson Cancer Center (MDACC). We present CellMinerCDB 1.2 with updates to datasets from NCI-60, Broad Cancer Cell Line Encyclopedia and Sanger/MGH, and the addition of new datasets, including NCI-ALMANAC drug combination, MDACC Cell Line Project proteomic, NCI-SCLC DNA copy number and methylation data, and Broad methylation, genetic dependency and metabolomic datasets. CellMinerCDB (v1.2) includes several improvements over the previously published version: (i) new and updated datasets; (ii) support for pattern comparisons and multivariate analyses across data sources; (iii) updated annotations with drug mechanism of action information and biologically relevant multigene signatures; (iv) analysis speedups via caching; (v) a new dataset download feature; (vi) improved visualization of subsets of multiple tissue types; (vii) breakdown of univariate associations by tissue type; and (viii) enhanced help information. The curation and common annotations (e.g. tissues of origin and identifiers) provided here across pharmacogenomic datasets increase the utility of the individual datasets to address multiple researcher question types, including data reproducibility, biomarker discovery and multivariate analysis of drug activity.

rcellminer: exploring molecular profiles and drug response of the NCI-60 cell lines in R.

PURPOSE: The rcellminer R package provides a wide range of functionality to help R users access and explore molecular profiling and drug response data for the NCI-60. The package enables flexible programmatic access to CellMiner's unparalleled breadth of NCI-60 data, including gene and protein expression, copy number, whole exome mutations, as well as activity data for ∼21K compounds, with information on their structure, mechanism of action and repeat screens. Functions are available to easily visualize compound structures, activity patterns and molecular feature profiles. Additionally, embedded R Shiny applications allow interactive data exploration. AVAILABILITY AND IMPLEMENTATION: rcellminer is compatible with R 3.2 and above on Windows, Mac OS X and Linux. The package, documentation, tutorials and Shiny-based applications are available through Bioconductor (http://www.bioconductor.org/packages/rcellminer); ongoing updates will occur according to the Bioconductor release schedule with new CellMiner data. The package is free and open-source (LGPL 3). CONTACT: lunaa@cbio.mskcc.org or vinodh.rajapakse@nih.gov.

Using drug response data to identify molecular effectors, and molecular "omic" data to identify candidate drugs in cancer.

The current convergence of molecular and pharmacological data provides unprecedented opportunities to gain insights into the relationships between the two types of data. Multiple forms of large-scale molecular data, including but not limited to gene and microRNA transcript expression, DNA somatic and germline variations from next-generation DNA and RNA sequencing, and DNA copy number from array comparative genomic hybridization are all potentially informative when one attempts to recognize the panoply of potentially influential events both for cancer progression and therapeutic outcome. Concurrently, there has also been a substantial expansion of the pharmacological data being accrued in a systematic fashion. For cancer cell lines, the National Cancer Institute cell line panel (NCI-60), the Cancer Cell Line Encyclopedia (CCLE), and the collaborative Genomics of Drug Sensitivity in Cancer (GDSC) databases all provide subsets of these forms of data. For the patient-derived data, The Cancer Genome Atlas (TCGA) provides analogous forms of genomic information along with treatment histories. Integration of these data in turn relies on the fields of statistics and statistical learning. Multiple algorithmic approaches may be chosen, depending on the data being considered, and the nature of the question being asked. Combining these algorithms with prior biological knowledge, the results of molecular biological studies, and the consideration of genes as pathways or functional groups provides both the challenge and the potential of the field. The ultimate goal is to provide a paradigm shift in the way that drugs are selected to provide a more targeted and efficacious outcome for the patient.

Lack of Association Between Depression and Subclinical Hypothyroidism in Adolescents Presenting for Routine Physical Examinations.

Background: There are conflicting results in the existing studies regarding the association between depression and subclinical hypothyroidism in adolescents. Subclinical hypothyroidism is defined as elevated thyroid stimulating hormone (TSH) levels above the reference range without signs or symptoms of hypothyroidism. Objectives: The focus of this study is to determine whether there is any association between depression and subclinical hypothyroidism, (as defined by the serum TSH levels) in a population of healthy adolescents. Design: Quantitative-based cross-sectional study of a representative subset of the adolescent population. Methods: We carried out a cross-sectional study to determine the association between major depressive disorder (MDD) and subclinical hypothyroidism, in adolescents presenting for annual physical examinations during the peak period of the COVID-19 pandemic in the USA, a period deemed high for adolescent depression. All the adolescents were screened for depression by the PHQ-9 screening tool and had their TSH measured. Results: Of the 304 subjects analyzed, 179 (58.88%) were minimally or not depressed according to the Patient Health Questionnaire (PHQ-9) screening tool (mean PHQ 1.80 ± 1.49). 70 (23.03%) had mild depression (mean PHQ 6.59 ± 1.46), 50 (16.45%) had moderate depression (mean PHQ 13.70 ± 2.75), and 5 (1.64%) had severe depression (mean PHQ 21.40 ± 1.67). Mean TSH values were 1.93 ± 0.99, 1.77 ± 1.05, 2.10 ± 0.98, and 1.57 ± 0.32 mIU/L, respectively in the four groups. All values were within the recommended range of 0.50 to 4.30 mIU/L, without statistically significant inter-group differences. Conclusion: We conclude that there is no statistically significant association between depression and subclinical hypothyroidism, in a population of adolescents presenting for physical examinations, and if the screening for depression by the PHQ-9 tool indicates depression, a screening TSH test for subclinical hypothyroidism is not justified.

Genomic alterations and transcriptional phenotypes in circulating tumor DNA and matched metastatic tumor.

Background: Profiling circulating cell-free DNA (cfDNA) has become a fundamental practice in cancer medicine, but the effectiveness of cfDNA at elucidating tumor-derived molecular features has not been systematically compared to standard single-lesion tumor biopsies in prospective cohorts of patients. The use of plasma instead of tissue to guide therapy is particularly attractive for patients with small cell lung cancer (SCLC), a cancer whose aggressive clinical course making it exceedingly challenging to obtain tumor biopsies. Methods: Here, a prospective cohort of 49 plasma samples obtained before, during, and after treatment from 20 patients with recurrent SCLC, we study cfDNA low pass whole genome (0.1X coverage) and exome (130X) sequencing in comparison with time-point matched tumor, characterized using exome and transcriptome sequencing. Results: Direct comparison of cfDNA versus tumor biopsy reveals that cfDNA not only mirrors the mutation and copy number landscape of the corresponding tumor but also identifies clinically relevant resistance mechanisms and cancer driver alterations not found in matched tumor biopsies. Longitudinal cfDNA analysis reliably tracks tumor response, progression, and clonal evolution. Genomic sequencing coverage of plasma DNA fragments around transcription start sites shows distinct treatment-related changes and captures the expression of key transcription factors such as NEUROD1 and REST in the corresponding SCLC tumors, allowing prediction of SCLC neuroendocrine phenotypes and treatment responses. Conclusions: These findings have important implications for non-invasive stratification and subtype-specific therapies for patients with SCLC, now treated as a single disease.

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.

Extrachromosomal DNA Amplification Contributes to Small Cell Lung Cancer Heterogeneity and Is Associated with Worse Outcomes.

Small-cell lung cancer (SCLC) is an aggressive neuroendocrine lung cancer. Oncogenic MYC amplifications drive SCLC heterogeneity, but the genetic mechanisms of MYC amplification and phenotypic plasticity, characterized by neuroendocrine and nonneuroendocrine cell states, are not known. Here, we integrate whole-genome sequencing, long-range optical mapping, single-cell DNA sequencing, and fluorescence in situ hybridization to find extrachromosomal DNA (ecDNA) as a primary source of SCLC oncogene amplifications and driver fusions. ecDNAs bring to proximity enhancer elements and oncogenes, creating SCLC transcription-amplifying units, driving exceptionally high MYC gene dosage. We demonstrate that cell-free nucleosome profiling can noninvasively detect ecDNA amplifications in plasma, facilitating its genome-wide interrogation in SCLC and other cancers. Altogether, our work provides the first comprehensive map of SCLC ecDNA and describes a new mechanism that governs MYC-driven SCLC heterogeneity. ecDNA-enabled transcriptional flexibility may explain the significantly worse survival outcomes of SCLC harboring complex ecDNA amplifications. SIGNIFICANCE: MYC drives SCLC progression, but the genetic basis of MYC-driven SCLC evolution is unknown. Using SCLC as a paradigm, we report how ecDNA amplifications function as MYC-amplifying units, fostering tumor plasticity and a high degree of tumor heterogeneity. This article is highlighted in the In This Issue feature, p. 799.

Preinvasive and invasive ductal pancreatic cancer and its early detection in the mouse.

To evaluate the role of oncogenic RAS mutations in pancreatic tumorigenesis, we directed endogenous expression of KRAS(G12D) to progenitor cells of the mouse pancreas. We find that physiological levels of Kras(G12D) induce ductal lesions that recapitulate the full spectrum of human pancreatic intraepithelial neoplasias (PanINs), putative precursors to invasive pancreatic cancer. The PanINs are highly proliferative, show evidence of histological progression, and activate signaling pathways normally quiescent in ductal epithelium, suggesting potential therapeutic and chemopreventive targets for the cognate human condition. At low frequency, these lesions also progress spontaneously to invasive and metastatic adenocarcinomas, establishing PanINs as definitive precursors to the invasive disease. Finally, mice with PanINs have an identifiable serum proteomic signature, suggesting a means of detecting the preinvasive state in patients.

Replication stress defines distinct molecular subtypes across cancers.

Endogenous replication stress is a major driver of genomic instability. Current assessments of replication stress are low throughput precluding its comprehensive assessment across tumors. Here we develop and validate a transcriptional profile of replication stress by leveraging established cellular characteristics that portend replication stress. The repstress gene signature defines a subset of tumors across lineages characterized by activated oncogenes, aneuploidy, extrachromosomal DNA amplification, immune evasion, high genomic instability, and poor survival, and importantly predicts response to agents targeting replication stress more robustly than previously reported transcriptomic measures of replication stress. Repstress score profiles the dual roles of replication stress during tumorigenesis and in established cancers and defines distinct molecular subtypes within cancers that may be more vulnerable to drugs targeting this dependency. Altogether, our study provides a molecular profile of replication stress, providing novel biological insights of the replication stress phenotype, with clinical implications.

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

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

RedundancyMiner: De-replication of redundant GO categories in microarray and proteomics analysis.

BACKGROUND: The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process, molecular function and subcellular localization. Tools such as GoMiner can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Two or more of the categories are often redundant, in the sense that identical or nearly-identical sets of genes map to the categories. The redundancy might typically inflate the report of significant categories by a factor of three-fold, create an illusion of an overly long list of significant categories, and obscure the relevant biological interpretation. RESULTS: We now introduce a new resource, RedundancyMiner, that de-replicates the redundant and nearly-redundant GO categories that had been determined by first running GoMiner. The main algorithm of RedundancyMiner, MultiClust, performs a novel form of cluster analysis in which a GO category might belong to several category clusters. Each category cluster follows a "complete linkage" paradigm. The metric is a similarity measure that captures the overlap in gene mapping between pairs of categories. CONCLUSIONS: RedundancyMiner effectively eliminated redundancies from a set of GO categories. For illustration, we have applied it to the clarification of the results arising from two current studies: (1) assessment of the gene expression profiles obtained by laser capture microdissection (LCM) of serial cryosections of the retina at the site of final optic fissure closure in the mouse embryos at specific embryonic stages, and (2) analysis of a conceptual data set obtained by examining a list of genes deemed to be "kinetochore" genes.

Immunohistochemical analysis of SLFN11 expression uncovers potential non-responders to DNA-damaging agents overlooked by tissue RNA-seq.

DNA-damaging agents include first-line drugs such as platinum (cisplatin, carboplatin), topoisomerase inhibitors (etoposide, doxorubicin), and replication inhibitors (cytarabine, gemcitabine). Despite their wide and long usage, there is no clinically available biomarker to predict responses to these drugs. Schlafen 11 (SLFN11), a putative DNA/RNA helicase, recently emerged as a dominant determinant of sensitivity to these drugs by enforcing the replication block in response to DNA damage. Since the clinical importance of SLFN11 is implicated, a comprehensive analysis of SLFN11 expression across human organs will provide a practical resource to develop the utility of SLFN11 in the clinic. In this study, we established a scoring system of SLFN11 expression by immunohistochemistry (IHC) and assessed SLFN11 expression in ~ 700 malignant as well as the adjacent non-tumor tissues across 16 major human adult organs. We found that the SLFN11 expression is tissue specific and varies during tumorigenesis. Although The Cancer Genome Atlas (TCGA) is a prevailing tool to assess gene expression in various malignant and normal tissues, our IHC data exhibited obvious discrepancy from the TCGA data in several organs. Importantly, SLFN11-negative tumors, potentially non-responders to DNA-damaging agents, were largely overrated in TCGA because TCGA samples are a mixture of infiltrating immune cells, including T cells, B cells, and macrophages, which have strong SLFN11 expression. Thus, our study reveals the significance of immunohistochemical procedures for evaluating expression of SLFN11 in patient samples and provides a robust resource of SLFN11 expression across adult human organs.

Novel and Highly Potent ATR Inhibitor M4344 Kills Cancer Cells With Replication Stress, and Enhances the Chemotherapeutic Activity of Widely Used DNA Damaging Agents.

Although several ATR inhibitors are in development, there are unresolved questions regarding their differential potency, molecular signatures of patients with cancer for predicting activity, and most effective therapeutic combinations. Here, we elucidate how to improve ATR-based chemotherapy with the newly developed ATR inhibitor, M4344 using in vitro and in vivo models. The potency of M4344 was compared with the clinically developed ATR inhibitors BAY1895344, berzosertib, and ceralasertib. The anticancer activity of M4344 was investigated as monotherapy and combination with clinical DNA damaging agents in multiple cancer cell lines, patient-derived tumor organoids, and mouse xenograft models. We also elucidated the anticancer mechanisms and potential biomarkers for M4344. We demonstrate that M4344 is highly potent among the clinically developed ATR inhibitors. Replication stress (RepStress) and neuroendocrine (NE) gene expression signatures are significantly associated with a response to M4344 treatment. M4344 kills cancer cells by inducing cellular catastrophe and DNA damage. M4344 is highly synergistic with a broad range of DNA-targeting anticancer agents. It significantly synergizes with topotecan and irinotecan in patient-derived tumor organoids and xenograft models. Taken together, M4344 is a promising and highly potent ATR inhibitor. It enhances the activity of clinical DNA damaging agents commonly used in cancer treatment including topoisomerase inhibitors, gemcitabine, cisplatin, and talazoparib. RepStress and NE gene expression signatures can be exploited as predictive markers for M4344.

Whole-exome sequencing reveals germline-mutated small cell lung cancer subtype with favorable response to DNA repair-targeted therapies.

Because tobacco is a potent carcinogen, secondary causes of lung cancer are often diminished in perceived importance. To assess the extent of inherited susceptibility to small cell lung cancer (SCLC), the most lethal type of lung cancer, we sequenced germline exomes of 87 patients (77 SCLC and 10 extrapulmonary small cell) and considered 607 genes, discovering 42 deleterious variants in 35 cancer-predisposition genes among 43.7% of patients. These findings were validated in an independent cohort of 79 patients with SCLC. Loss of heterozygosity was observed in 3 of 14 (21.4%) tumors. Identification of variants influenced medical management and family member testing in nine (10.3%) patients. Unselected patients with SCLC were more likely to carry germline RAD51 paralog D (RAD51D), checkpoint kinase 1 (CHEK1), breast cancer 2 (BRCA2), and mutY DNA glycosylase (MUTYH) pathogenic variants than healthy controls. Germline genotype was significantly associated with the likelihood of a first-degree relative with cancer or lung cancer (odds ratio: 1.82, P = 0.008; and 2.60, P = 0.028), and longer recurrence-free survival after platinum-based chemotherapy (P = 0.002), independent of known prognostic factors. Treatment of a patient with relapsed SCLC and germline pathogenic mutation of BRCA1 interacting protein C-terminal helicase 1 (BRIP1), a homologous recombination-related gene, using agents synthetically lethal with homologous recombination deficiency, resulted in a notable disease response. This work demonstrates that SCLC, currently thought to result almost exclusively from tobacco exposure, may have an inherited predisposition and lays the groundwork for targeted therapies based on the genes involved.

Therapeutic targeting of ATR yields durable regressions in small cell lung cancers with high replication stress.

Small cell neuroendocrine cancers (SCNCs) are recalcitrant cancers arising from diverse primary sites that lack effective treatments. Using chemical genetic screens, we identified inhibition of ataxia telangiectasia and rad3 related (ATR), the primary activator of the replication stress response, and topoisomerase I (TOP1), nuclear enzyme that suppresses genomic instability, as synergistically cytotoxic in small cell lung cancer (SCLC). In a proof-of-concept study, we combined M6620 (berzosertib), first-in-class ATR inhibitor, and TOP1 inhibitor topotecan in patients with relapsed SCNCs. Objective response rate among patients with SCLC was 36% (9/25), achieving the primary efficacy endpoint. Durable tumor regressions were observed in patients with platinum-resistant SCNCs, typically fatal within weeks of recurrence. SCNCs with high neuroendocrine differentiation, characterized by enhanced replication stress, were more likely to respond. These findings highlight replication stress as a potentially transformative vulnerability of SCNCs, paving the way for rational patient selection in these cancers, now treated as a single disease.

Phenotype stability under dynamic brain-tumor environment stimuli maps glioblastoma progression in patients.

Although tumor invasiveness is known to drive glioblastoma (GBM) recurrence, current approaches to treatment assume a fairly simple GBM phenotype transition map. We provide new analyses to estimate the likelihood of reaching or remaining in a phenotype under dynamic, physiologically likely perturbations of stimuli ("phenotype stability"). We show that higher stability values of the motile phenotype (Go) are associated with reduced patient survival. Moreover, induced motile states are capable of driving GBM recurrence. We found that the Dormancy and Go phenotypes are equally represented in advanced GBM samples, with natural transitioning between the two. Furthermore, Go and Grow phenotype transitions are mostly driven by tumor-brain stimuli. These are difficult to regulate directly, but could be modulated by reprogramming tumor-associated cell types. Our framework provides a foundation for designing targeted perturbations of the tumor-brain environment, by assessing their impact on GBM phenotypic plasticity, and is corroborated by analyses of patient data.

Dynamics of genomic and immune responses during primary immunotherapy resistance in mismatch repair-deficient tumors.

Mismatch repair-deficient (dMMR) cancers generate a substantial number of immunogenic neoantigens, rendering them sensitive to immunotherapy. Yet, there is considerable variability in responses, and roughly one-half of dMMR cancers are refractory to immunotherapy. Here we study a patient with dMMR lung cancer refractory to immunotherapy. The tumor exhibited typical dMMR molecular features, including exceptionally high frameshift insertions and deletions (indels). Despite the treatment inducing abundant intratumoral T-cell infiltrates, it failed to elicit tumor regression, pointing to the T cells lacking cytotoxic activity. A post-treatment tumor demonstrated compound heterozygous frameshift deletions located upstream of the kinase domain in the gene encoding JAK1 protein, down-regulation of JAK1 and mediators of its signal transduction, and total loss of JAK1 phosphorylation. Importantly, one of the JAK1 mutations, despite not being detected in the pretreatment tumor, was found at low variant allele frequency in the pretreatment circulating tumor DNA, suggesting clonal selection of the mutation. To our knowledge, this report provides the most detailed look yet at defective JAK1 signaling in the context of dMMR and immunotherapy resistance. Together with observations of JAK1 frameshift indels being enriched in dMMR compared with MMR-proficient tumors, our findings demonstrate the critical function of JAK1 in immunological surveillance of dMMR cancer.

Clinical and Genomic Characteristics of Small Cell Lung Cancer in Never Smokers: Results From a Retrospective Multicenter Cohort Study.

BACKGROUND: Small cell lung cancer (SCLC) has the strongest association with smoking among lung cancers. The characteristics of never smokers with SCLC is not known. RESEARCH QUESTION: Are the clinical characteristics, prognostic factors, survival, genomic alterations, and tumor mutational burdens of SCLC in patients who have never smoked different from those who have smoked? STUDY DESIGN AND METHODS: A retrospective multicenter cohort study of patients with clinician-confirmed SCLC was performed with the use of a longitudinal and nationally representative electronic medical records database. Smoking history was assessed through technology-enabled abstraction and confirmed for never smokers via chart review. Genomic characteristics of never smoker patients with SCLC were examined with the use of a next-generation sequencing-based gene panel and whole exome sequencing. RESULTS: One hundred of 5,632 patients (1.8%) with SCLC were never smokers. Relative to smokers, never smokers were more likely to be female (66.0% vs 52.4%; P = .009) and present with extensive stage (70.0% vs 62.2%; P = .028). Never smokers had a higher proportion of patients in age groups 35 to 49 years (7.0% vs 3.0%; P = .006) and ≥80 years (17.0% vs 8.2%; P = .006). Known risk factors for lung cancer were found in <20% of never smokers. There were no overall survival differences between never smokers and smokers. Among patients with available genomic data (n = 9), never smoker SCLC were characterized by lower tumor mutational burden, a lower frequency of TP53 mutations, and an absence of mutational signatures related to tobacco exposure. INTERPRETATION: The sex- and age-specific distribution of SCLC among never smokers, along with differences that were identified by genomic analyses, suggests a distinct biology of SCLC in never smokers compared with smokers.