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Published OnlineFirst September 18, 2018; DOI: 10.1158/2159-8290.CD-17-0987

Research Article

Intertumoral Heterogeneity in SCLC Is Influenced by the Cell Type of Origin

Dian Yang1,2,3, Sarah K. Denny2,4, Peyton G. Greenside5, Andrea C. Chaikovsky1,2,3, Jennifer J. Brady2, Youcef Ouadah1,6, Jeffrey M. Granja2,4, Nadine S. Jahchan2,3, Jing Shan Lim1,2,3, Shirley Kwok7, Christina S. Kong7, Anna S. Berghoff8,9,10, Anna Schmitt11, H. Christian Reinhardt11,12,13, Kwon-Sik Park14, Matthias Preusser9,10, Anshul Kundaje2,15, William J. Greenleaf2, Julien Sage1,2,3, and Monte M. Winslow1,2,7

aBstRact The extent to which early events shape tumor evolution is largely uncharacterized, even though a better understanding of these early events may help identify key vul- nerabilities in advanced tumors. Here, using genetically defi ned mouse models of small cell lung cancer (SCLC), we uncovered distinct metastatic programs attributable to the cell type of origin. In one model, tumors gain metastatic ability through amplifi cation of the transcription factor NFIB and a widespread increase in chromatin accessibility, whereas in the other model, tumors become metastatic in the absence of NFIB-driven chromatin alterations. Gene-expression and chromatin accessibility analyses identify distinct mechanisms as well as markers predictive of metastatic progression in both groups. Underlying the difference between the two programs was the cell type of origin of the tumors, with NFIB- independent metastases arising from mature neuroendocrine cells. Our fi ndings underscore the impor- tance of the identity of cell type of origin in infl uencing tumor evolution and metastatic mechanisms.

SIGNIFICANCE : We show that SCLC can arise from different cell types of origin, which profoundly infl u- ences the eventual genetic and epigenetic changes that enable metastatic progression. Understanding intertumoral heterogeneity in SCLC, and across cancer types, may illuminate mechanisms of tumor progression and uncover how the cell type of origin affects tumor evolution. Cancer Discov; 8(10); 1316–31. ©2018 AACR.

See related commentary by Pozo et al., p. 1216.

iNtRODUctiON to acquire metastatic ability early in the course of tumor progression, but emerging evidence suggests that this metastatic ability Most patients with cancer die from metastatic disease; how- is not inherent. Rather, genetic events such as upregulation of ever, many key aspects of metastatic progression remain poorly CXCR4 and NEUROD1 and/or amplifi cation ofNFIB are critical understood. In particular, the nature of the changes that drive for SCLC invasion and metastasis in at least a subset of patients metastasis and the potential impact of early events in tumori- (5–9 ). Recent data from genetically engineered mouse models genesis upon the direction of this evolution are largely unex- and human tumors have uncovered multiple levels of hetero- plored. A better understanding of these mechanisms could geneity in SCLC; however, how this heterogeneity pertains to help diagnose and treat patients more effectively (1, 2). metastatic progression is largely unexplored (10–19 ). Small cell lung cancer (SCLC) is one of the most metastatic SCLC is a neuroendocrine cancer; thus, it made sense when and lethal of all major cancer types (3, 4). SCLC is thought deletion of the key SCLC tumor suppressor genes Rb1 and Trp53 in pulmonary neuroendocrine cells uncovered these cells as one cell type of origin for SCLC in mouse models (refs. 1 Cancer Biology Program, Stanford University, Stanford, California. 2 Depart- ment of Genetics, Stanford University, Stanford, California . 3 Department of 20–22 ; reviewed in ref 4 ). Interestingly, although pulmonary Pediatrics, Stanford University, Stanford, California. 4 Biophysics Program, neuroendocrine cells are quite rare, induction of these same Stanford University, Stanford, California. 5 Program in Biomedical Informat- genetic alterations in the much more prevalent lung epithelial 6 ics, Stanford University, Stanford, California. Department of Biochemis- cell types expressing either Scgb1a1 (coding for CC10, a marker try, Stanford University, Stanford, California. 7 Department of Pathology, Stanford University, Stanford, California. 8 Institute of Neurology, Medical of club cells ) or Sftpc (coding for SPC, a marker of alveolar type II University of Vienna, Vienna, Austria. 9 Department of Medicine I, Medical cells) demonstrated that these cells have very little, if any, ability University of Vienna, Vienna, Austria. 10 Comprehensive Cancer Center CNS to serve as the cell of origin of SCLC ( 20, 21 ). Nonetheless, the Tumors Unit, Medical University of Vienna, Vienna, Austria. 11 Department lung epithelium contains many diverse cell types ( 23–25 ), and of Internal Medicine I, University Hospital of Cologne, Cologne, Germany. whether SCLC can be initiated from other cell types is unknown. 12Cologne Excellence Cluster on Cellular Stress Response in Aging- Associated Diseases, University of Cologne, Cologne, Germany. 13 Center Here, through detailed molecular characterization of pri- for Molecular Medicine Cologne, University of Cologne, Cologne, Germany. mary tumors and metastases, we identify two discrete paths 14 Department of Microbiology, Immunology, and Cancer Biology, University by which SCLC gains metastatic ability. Our data indicate 15 of Virginia School of Medicine, Charlottesville, Virginia. Department of that the same genomic alterations in different cell types give Computer Science, Stanford University, Stanford, California. rise to distinct subtypes of SCLC, and that the founding cell Note: Supplementary data for this article are available at Cancer Discovery type of origin can defi ne the trajectory of tumor progression. Online (http://cancerdiscovery.aacrjournals.org/). Corresponding Authors: Monte M. Winslow, Stanford University School of Medicine, 279 Campus Drive, Beckman Center B256 , Stanford, CA 94305. ResUlts Phone: 650-725-8696; Fax: 650 - 725 - 1534 ; E-mail: mwinslow@stanford. Mouse SCLC Initiated from Adult Neuroendocrine edu; and Julien Sage, Stanford University School of Medicine, Mail code 5149, 265 Campus Drive, SIM1, Stanford, CA 94305. Phone: 650-724- Cells Gains Metastatic Ability without 9246; E-mail: [email protected] Upregulation of NFIB doi: 10.1158/2159-8290.CD-17-0987 To study the mechanisms underlying SCLC metastasis, we ini- © 2018 American Association for Cancer Research. tially used the well-characterized Rb1 fl o x / fl ox ;Trp53fl o x / fl ox ; p130fl o x / fl ox ;

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A B Adeno Adeno CMV-Cre “CMV TKO” CGRP-Cre “CGRP TKO”

Rb1flox/flox;Trp53 flox/flox;p130 flox/flox;R26 mTmG Rb1flox/flox;Trp53 flox/flox;p130 flox/flox;R26 mTmG

C CMV-Cre D CGRP-Cre

Primary tumor Liver metastasis Primary tumor Liver metastasis

H&E H&E

NFIB NFIB

E F Score = 0 Score = 1 G 100 100 NFIB NFIB High score Medium 75 Low/neg 3 2 50 50 1 Score = 2 Score = 3 0 Percentage Percentage 25 human 0 SCLC NFIB 0 metastases Human SCLC metastases CMV-Cre CMV-Cre CGRP-Cre CGRP-Cre Primary Liver tumors met

Figure 1. SCLC initiated from pulmonary neuroendocrine cells metastasizes without upregulating NFIB. A and B, Mouse models of SCLC. Rb1flox/flox; Trp53flox/flox;p130flox/flox;R26mTmG (TKO;mTmG) mice were transduced with either Adeno-CMV-Cre (“CMV TKO”) or Adeno-CGRP-Cre (“CGRP TKO”) to initiate SCLC. C and D, Representative hematoxylin and eosin (H&E) images of SCLC tumors from CMV TKO and CGRP TKO mice. Immunostaining for NFIB on primary tumors and metastases is shown. Scale bars, 100 μm. E, Quantification of NFIB expression in primary tumors and liver metastases (met) from CMV TKO and CGRP TKO mice. F, Representative immunostaining for NFIB on human SCLC brain metastases. Expression score for NFIB is indicated. Scale bars, 100 μm. G, Quantification of NFIB expression in human SCLC metastases (including lymph node and brain metastases,N = 43). CGRP, calcitonin gene-related peptide.

R26mTmG (TKO;mTmG) mouse model, in which we initiated Fig. 1A and B). These “CGRP TKO” mice developed many tumors via intratracheal delivery of an adenoviral vector fewer SCLCs than CMV TKO mice even when transduced with expressing Cre recombinase under the control of the broadly a 10- to 20-fold higher titer of Ad-CGRP-Cre (Supplementary expressed CMV promoter (Ad-CMV-Cre; refs. 7, 26). Thus, Fig. S1A). Nonetheless, both CMV TKO and CGRP TKO in this model, Trp53, Rb1, and p130 are inactivated in many mice developed SCLC and widespread metastatic disease with different cell types in the lung. We initially uncovered that in metastasis to multiple organs, including the lymph nodes this “CMV TKO” model, amplification of theNfib genomic and liver, 6 to 9 months after tumor initiation (Fig. 1A and D; locus and high expression of the NFIB transcription factor Supplementary Fig. S1B–S1H). Previous studies showed that in primary tumors is an important step during metastatic Ad-CMV-Cre and Ad-CGRP-Cre each can also initiate meta- progression (7). To further investigate SCLC metastatic pro- static SCLC in Rb1flox/flox;Trp53flox/flox mice, but again, 15-fold gression in a model in which the tumors arise from a defined more Ad-CGRP-Cre was used to initiate tumors (21, 28). cell type, we subsequently initiated tumors in TKO;mTmG mice As part of our characterization of primary tumors and with Ad-CGRP-Cre, which specifically directs Cre expression to metastases in CMV TKO and CGRP TKO mice, we performed mature, CGRP-expressing neuroendocrine cells (refs. 21, 22, 27; immunostaining for the prometastatic transcription factor

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SCLC Cell of Origin Influences Metastatic Progression RESEARCH ARTICLE

NFIB. Surprisingly, in contrast to CMV TKO metastases, NFIB gin, but DTCs in the pleural cavity were also almost always was undetectable in the vast majority of CGRP TKO metastases of one color (Fig. 2B–D; Supplementary Fig. S3B). Thus, our (Fig. 1C–E; Supplementary Fig. S1C–S1H). In general, primary data are consistent with not all tumors in CGRP TKO mice tumors in CMV TKO mice were characterized by a “rosette” containing cells with the ability to readily disseminate and growth pattern with more differentiated glandular structures metastasize. intermixed with less well-differentiated areas, whereas primary Although NFIB levels are low in the CGRP TKO model, we tumors in CGRP TKO mice were predominantly character- considered whether NFIB might be transiently upregulated ized by a “solid-nested” growth pattern. Notably, metastases during metastatic dissemination, then downregulated to gen- in both models uniformly exhibited solid-nested growth and erate NFIBnegative/low metastases. To investigate this possibility, no rosette formation. Primary tumors with rosette histology we examined NFIB expression by immunofluorescence stain- were uniformly NFIBnegative/low in both models (Supplementary ing of FACS-isolated SCLC cells. DTCs and cancer cells from Table S1). Most of the solid-nested areas in CMV TKO mice metastases from CGRP TKO mice had low NFIB expression, were NFIBhigh, whereas the solid-nested areas in CGRP TKO whereas DTCs and metastatic cancer cells in the CMV TKO were mostly NFIBnegative/low (Supplementary Fig. S1G–S1H). The model nearly always had high NFIB expression (Fig. 2E and majority of the macrometastases in the lymph nodes and liver, F). This argues against transient expression of NFIB during as well as most of the cancer cells growing within the pulmonary metastasis in the CGRP TKO model. Collectively, these data lymphatics, were NFIBhigh in CMV TKO mice. In contrast, most indicate that metastatic ability is an acquired property of metastases and lymphatic invasive cells in CGRP TKO mice were cancer cells in both models of SCLC, and that SCLC can take NFIBnegative/low (Fig. 1C–E; Supplementary Fig. S1E–S1F). Similarly, different paths during metastatic progression (Supplemen- across multiple other genotypes (including Rb1flox/flox;Trp53flox/flox tary Fig. S3C). Rb1flox/flox;Trp53flox/flox;Ptenflox/+, and Rb1flox/flox;Trp53flox/flox;Ptenflox/flox mice), SCLC tumors initiated by Ad-CMV-Cre generally gave Absence of Widespread Chromatin rise to NFIBhigh metastases, whereas SCLC tumors initiated Changes during Metastatic Progression by Ad-CGRP-Cre generally gave rise to NFIBnegative/low metas- of SCLC in CGRP TKO Mice tases (refs. 5, 29; Supplementary Fig. S2A). Importantly, the During metastatic progression of CMV TKO tumors, expression of NFIB is also heterogeneous in human SCLC upregulation of NFIB drives a dramatic global increase in lymph node and brain metastases, suggesting that diverse chromatin accessibility (7). Metastasis in CGRP-Cre mice mechanisms of tumor progression also exist in human SCLC could be driven either by widespread chromatin accessibil- (Fig. 1F and G; Supplementary Fig. S2D–S2E; refs. 6, 7, 30). ity changes that are independent of NFIB upregulation or Collectively, these data indicate that SCLC initiated from by mechanisms that are independent of changes in chro- CGRPpositive cells in the mouse lung can metastasize in the matin accessibility. To characterize the changes in chroma- absence of NFIB upregulation, and this may recapitulate tin accessibility during metastasis, we performed Assay for tumor progression in a subset of patients with SCLC. Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) on primary tumors and metastases Murine SCLC Requires Progression Prior from CGRP TKO mice (Supplementary Figs. S4–S5; Sup- to Dissemination plementary Table S2 and Methods; ref. 32). In contrast to In the CMV TKO model, genomic amplification ofNfib our previous analysis of CMV TKO tumors and metastases precedes dissemination, consistent with tumor evolu- (7), unsupervised hierarchical clustering based on chroma- tion as a prerequisite for metastasis (5–7). The absence tin accessibility did not clearly separate the primary tumors of NFIB upregulation in most tumors in CGRP TKO and liver metastases from CGRP TKO mice (Fig. 3A). Com- mice prompted us to investigate whether SCLC in this parison of the accessible chromatin regions in CGRP-Cre model also progresses to gain metastatic ability or if these primary tumors and liver metastases uncovered only 1,020 tumors were inherently metastatic. Even at a late time point genomic regions that were less accessible in metastases and (7–11 months after tumor initiation), when they harbored 130 genomic regions that were more accessible in metas- multiple large primary tumors, not every CMV TKO or tases (Fig. 3B; Supplementary Fig. S6A–S6C). Hierarchical CGRP TKO mouse had detectable macrometastases, micro- clustering of chromatin accessibility of samples from both metastases, or even disseminated tumor cells (DTC; Fig. the CMV TKO and CGRP TKO models showed that CGRP 2A). These initial data suggested that not all CMV-Cre or TKO primary tumors, CGRP TKO liver metastases, and CGRP-Cre TKO tumors have the ability to disseminate and NFIBlow CMV TKO primary tumors clustered together but metastasize. were separate from the NFIBhigh CMV TKO samples (Fig. 3C To further investigate whether only some tumors possess and D). Direct comparison between CGRP TKO and NFIBlow metastatic ability, we incorporated a multicolor reporter CMV TKO primary tumors uncovered very few regions with allele (R26LSL-Motley; ref. 31) into the Rb1flox/flox;Trp53flox/flox; differential chromatin accessibility (260 regions more open p130flox/flox TKO model. In these TKO;Motley mice, clonally in CMV TKO samples and 160 regions more open in CGRP derived tumors are labeled with different combinations TKO samples; Fig. 3E). Collectively, these data indicate that of fluorescent markers (Supplementary Fig. S3A). Impor- the chromatin state alterations during metastatic progres- tantly, in individual TKO;Motley mice with Ad-CMV-Cre– or sion of SCLC in CGRP TKO mice are categorically different Ad-CGRP-Cre–initiated SCLC, the metastases were almost from those in CMV TKO mice, not just by virtue of NFIB always of one color, suggesting that they originated from a expression, but also at the level of global differences in chro- single primary tumor. Not only were metastases clonal in ori- matin accessibility.

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A B C 100 DTCs No met CGRP TKO;Motley With met Lung LN met

YY 50 CFP YC YC RY YFP Percent of mice Percent Pleural met Liver met

0 YC CMV CGRP YC TKO TKO (N = 18) (N = 17) RFP YFP

D CMV TKO;Motley CGRP TKO;Motley Tumor color RR or R Mouse1 Mouse2 Mouse3 Mouse4 Mouse1 Mouse2 Mouse3 Mouse4 YY or Y Primary CC or C tumors RY DTCs YC LN met RC Liver µMet Liver met Other met ND ND

E F CMV TKO CGRP TKO CMV TKO DTCs DTCs DTCs * CGRP TKO

Liver CMV TKO * met CGRP TKO * GFP NFIB GFP NFIB 050 100 NFIBpos cells (%)

Figure 2. Tumor progression is required prior to metastasis of SCLC in both CMV TKO and CGRP TKO mice. A, Occurrence of metastasis (met) in CMV TKO and CGRP TKO mice. Not every mouse developed metastatic disease at the time of analysis even though each mouse in this analysis had several large primary tumors (7–11 months after initiation). B, Representative images of tumors and metastases from TKO;Motley mice with CGRP-Cre-initiated SCLC. Primary tumors existed in multiple colors, although all metastases from the same mouse had the same color, suggesting that these metastases are clonally related to one primary tumor. Scale bar, 2 mm. Color code: R, RFP; Y, YFP; C, CFP. LN, lymph node. C, Representative FACS analysis of DTCs from a CGRP TKO;Motley mouse. All the DTCs in this mouse have the same color (CFPpositive and YFPpositive). D, Summary of results from 4 CMV TKO;Motley mice and 4 CGRP TKO;Motley mice. The colors of the largest primary tumors, DTCs, and metastases are shown. µMet, micrometastases; ND, not detected. All potential tumor color options are indicated. E and F, Immunofluorescence staining for NFIB on FACS-isolated GFPpositive DTCs from the pleural cavity and cancer cells from liver metastases from CMV TKO and CGRP TKO mice. E, Representative images of DTCs from CMV TKO and CGRP TKO mice. Membrane GFP staining is encoded by the recombined R26mTmG allele. Scale bars, 10 μm. F, Quantification of NFIB expression in DTCs and cancer cells from liver metastases from CMV TKO and CGRP TKO mice. CMV TKO data are from Denny et al. (7). Each dot represents one mouse. *, P = 0.0097; ***, P = 0.001; Mann–Whitney test.

SCLC Subtypes Have Different To further understand the basis for the different metastatic Gene-Expression Programs paths of SCLC in CMV TKO versus CGRP TKO mice, we next performed RNA sequencing (RNA-seq) on FACS-isolated can- The overall similarity of the chromatin landscape in CMV cer cells from both models. Unsupervised clustering and prin- TKO and CGRP TKO primary tumors suggests that inherent cipal component analysis (PCA) showed a clear separation differences in chromatin accessibility likely do not explain the of CMV TKO primary tumors from CMV TKO liver metas- different trajectories of tumor progression in these models. tases. CGRP TKO primary tumors and metastases clustered

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ABSpearman FDR correlation G3T2 1 130 G5T1 0.75 G5T1 0.5 More open in met G3L2 1 G3L3 0.25 G1T2 G2L1 0 G2L2 Samples G1T1 G6T1 CGRP tumor −1 G5T2 CGRP met Less open in met G5T2 CGRP met / tumor G6T2 2 −2 G2T1 log Differential accessibility Differential 1020 G2T2 −3 G6L1 G6L2 46810 G6L3 Mean reads/peak (log2)

C D 100 Spearman correlation 0.9 80

0.6 Nfib

0.3 Location on chromosome 4 (Mb) Samples Nfib copy# Samples V1L1 V3L1 V4L1 V5L1 V5L2 V2L1 V4T1 V5T1 V5T3 V5T2 V3T1 V2T1 V1T1 V5T4 G6L1 G6L3 G6L2 G3L3 G2L1 G2L2 G3L2 V5N1 G3T2 G6T2 G6T1 G1T2 G1T1 G2T2 G5T2 G2T1 G5T1 CGRP tumor Samples Nfib copy# Log2 reads observed/ CGRP met CGRP tumor CMV tumor None reads expected CMV tumor CGRP met CMV met Low CMV met High −4 04

E 160 (15) CMV regions NFIBlow CGRP Primary (Mostly primary 260 (16) tumors N = 4) tumors regions (N = 9) 11,600 40,200 1020 130 (6,500) (26,500) (83) (1) regions regions regions regions V1L1 V1L1 V5L1 V5L1 V3L1 V3L1 V5L2 V4L1 V4L1 V2L1 V2L1 V2L1 V2L1 V5T1 V5T1 V4T1 V4T1 V3T1 V3T1 V3T1 V3T1 V5T3 V5T2 V5T2 V5T4 V5T4 V1T1 V2T1 V2T1

G6L3 G6L2 G2L2 G2L1 G6L1 G3L3 G3L2 CMV CGRP V5N1 G1T1 G1T2 G6T1 G5T2 G5T2 G6T2 G2T2 G2T1 G5T1 G5T1 G3T2 NFIBhigh mets NFIB high NFIB low (mostly mets N = 5) (N = 6) CMV tumors CMV tumors and mets CMV mets CGRP tumors and mets

Figure 3. Limited chromatin accessibility changes and lack of Nfib amplification during metastatic progression of CGRP TKO tumors. A, Correlation of chromatin accessibility of 9 primary tumors and 7 liver metastases from CGRP TKO mice. Replicate samples from the same tumors have the same name. Metastases (met) from the same mice (indicated with brackets) correlate most closely with one another. B, Differential accessibility between CGRP primary tumors and metastases. Log2 fold change in reads per peak between groups plotted against the mean number of reads per peak is shown. Num- ber of regions that have significant accessibility changes (FDR< 0.1, |log2 fold change| > 0) is indicated. Color of each point represents the FDR that the absolute value of the log2 fold change was greater than 0. C, Correlation of chromatin accessibility between primary tumors and metastases from CMV TKO and CGRP TKO mice. D, DNA copy-number analysis at the Nfib locus on chromosome 4 in CMV TKO and CGRP TKO primary tumors and metastases. Heat map shows copy-number variation (CNV) at the Nfib locus. None, CNV < 1; low, CNV ≥1 and CNV <1.5; high, CNV ≥1.5. E, Summary of the chromatin accessibility changes during metastatic progression of CMV TKO and CGRP TKO tumors. Numbers of peaks with changed accessibility are listed (|lfc| > 0 at FDR < 0.1, no peak changed <2-fold); numbers in parentheses indicate the number of peaks that change in accessibility (|lfc| > 0.5 at FDR < 0.1; no peak changed <2-fold). together and more closely with CMV TKO primary tumors progression of CMV TKO tumors, we thus performed differ- (Fig. 4A; Supplementary Fig. S7A). ential gene-expression analysis between NFIBlow CMV TKO Some metastatic primary tumors in the CMV TKO model primary tumors and CMV TKO metastases. This analysis express high levels of Nfib (Supplementary Fig. S7B and ref. revealed widespread changes in gene-expression programs 7), and Nfib expression correlated with the clustering of CMV during metastatic progression of this subtype of SCLC (Fig. TKO primary tumors and metastases (Supplementary Fig. 4B; Supplementary Table S3). Genes that were upregulated S7C). To uncover gene-expression changes during metastatic in CMV TKO metastases were enriched for gene ontology

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A CMV primary tumor B CMV metastasis CGRP primary tumor CGRP metastasis Higher in CMV Higher in CMV 160 NFIBlow tumors metastases 15 140 1251 492

120 )

10 10 100

80 ( − log 5 Height G3L3 G2T2

60 2L1 value Adjusted P value 53Lm G6T1 G3L2 9Lm G6L1 G1T2 G2L1 G1T1 9T2 DY DY G6L2 G6L3 9T1 9N1 DY G3T2 G6T2 G2L2 11N1

40 G2T1 0 DY 5T1 DY DY 2N1 DY 2T2 11T2 5T2 5T4 DY5N1 2T3 DY 53T2 53T1 DY53N1 4T2 4T4 4T1 −550 20 DY DY 11T3 DY DY DY DY 4T1** 53L1** DY

DY low DY DY DY53T3 11T3** DY DY53L1 DY5L1 DY5L2 CMV mets/CMV NFIB tumors DY DY2T1 2L2 2L2* DY DY2T1* DY DY

DY (fold change, log ) DY 2

C D E

GO terms higher in CMV GO Higher in Higher in CGRP Higher in Higher in CMV metastases vs. CMV tumors terms # CGRP tumors metastases CGRP tumors NFIBlow tumors 15 15 Neuronal differentiation 15 40 978 1138 & migration ) )

value value 10 value value 10 10 P

DNA replication 14 10 P

& cell cycle log log − − ( Translation & RNA 9 ( 5 5 processing Adjusted Translation 6 Adjusted 0 0 Chromatin modification 2 −550 −550 Metabolism 2 low CGRP mets/CGRP tumors CMV NFIB tumors/CGRP tumors Other 2 (fold change, log2) (fold change, log2)

F G H GO terms higher in CGRP GO High in CMV CMV 978 Higher CGRP tumors vs. CMV tumors terms # metastases NFIBlow primary primary tumors tumors Specification and 22 (N = 7) differentiation 171 (N = 9) 1138 Higher 12 Synaptic signaling 492 Down 0 Down 2+ Ca and G protein 7 7 1251 Up 4 Up signaling Other 9 CMV mets CGRP 403 (NFIBhigh) mets (N = 12) (N = 7) High in CGRP tumors

Figure 4. Molecularly distinct SCLC subtypes. A, Unsupervised hierarchical clustering of all CMV TKO and CGRP TKO tumors and metastases. *, technical replicates; **, batch replicates. B, Differential gene expression in CMV TKO NFIBlow primary tumors and CMV TKO metastases. (|Fold change| > 2, adj. P < 0.05). C, A summary of top 50 gene ontology (GO) terms (FDR q < 0.05) enriched in CMV TKO metastases in comparison with CMV NFIBlow primary tumors. D, Differential gene expression in CGRP TKO primary tumors and CGRP TKO metastases. (|Fold change| > 2, adj. P < 0.05). E, Differential gene expression in CMV TKO NFIBlow tumors and CGRP TKO tumors (|fold change| > 2, adj. P < 0.05). F, A summary of top 50 GO terms (FDR q < 0.05) enriched low in CGRP TKO tumors in comparison with CMV NFIB primary tumors. G, Genes that are significantly upregulated (|log2 (fold change)| > 1.5) in CMV TKO metastases and CGRP TKO tumors compared with CMV TKO NFIBlow tumors are mostly mutually exclusive. H, Summary of RNA expression differences between the 4 different tumor states. Numbers of genes with changed expression are listed (|fold change| > 2, adj. P < 0.05).

annotations associated with neuronal differentiation and cell in CGRP TKO metastases. In contrast, more than 2,000 cycle, consistent with what we and others have observed upon genes were differentially expressed between CMV TKO and ectopic expression of NFIB in SCLC cells (refs. 6, 7; Fig. 4C; CGRP TKO primary tumors (Fig. 4E; Supplementary Table Supplementary Fig. S8 and Supplementary Table S4). S3). Genes that were more highly expressed in CGRP TKO Surprisingly, very few genes were significantly differentially tumors than in NFIBlow CMV TKO tumors were enriched for expressed by more than 2-fold between CGRP TKO primary gene ontology annotations associated with neuronal differen- tumors and metastases (Fig. 4D). As expected from our tiation, including synaptic signaling (Fig. 4F; Supplementary immunostaining analysis, Nfib expression was not increased Fig. S9 and Supplementary Table S4). Notably, although

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SCLC Cell of Origin Influences Metastatic Progression RESEARCH ARTICLE neuronal gene sets were enriched in both CMV TKO metas- Together, these analyses further underscore the difference tases and CGRP TKO primary tumors, the genes driving between CMV TKO and CGRP TKO primary tumors and these annotations had little overlap and the neuronal gene uncover the presence of subsets of cancer cells expressing programs enriched in CGRP TKO primary tumors relative to markers of multiple lung epithelial lineages in CMV TKO NFIBlow CMV TKO primary tumors were specifically related primary tumors. to a more mature neuronal state (Fig. 4G; Supplementary Fig. S9). Together, these data (Fig. 4H) underscore the dis- The Two Distinct Subtypes of Mouse SCLC Arise tinct molecular paths taken by SCLC tumors in the CMV and from Different Cell Types of Origin CGRP TKO models. The simplest explanation for the differences observed dur- To relate the gene-expression states of CMV TKO and ing tumor progression of the CMV TKO and CGRP TKO CGRP TKO tumors to human SCLC, we merged human models is that the tumors are initiated from different cell SCLC data sets (13) and our mouse SCLC data sets and per- types. Ad-CMV-Cre and Ad-CGRP-Cre both lead to expres- formed PCA. Many principal components seem to be driven sion of Cre in neuroendocrine cells but Ad-CMV-Cre also primarily by the CMV versus CGRP tumor types, and human leads to the expression of Cre in many other cell types in the tumors were distributed broadly with some being more simi- lung (20, 21). The development of fewer tumors in CGRP lar to CGRP TKO tumors and others being more similar to TKO mice than in CMV TKO mice, even with a higher titer CMV TKO tumors (Supplementary Fig. S10). of Ad-CGRP-Cre (Supplementary Fig. S1A), supports the idea that SCLC is initiated from rare neuroendocrine cells in the Nonmetastatic CMV TKO Primary Tumors Contain CGRP TKO model but from a larger pool of cells in the CMV Cells with Diverse Lung Epithelial Lineage Markers TKO model. Although tumors and metastases in both the CMV and Neuroendocrine cells are often present at bifurcation the CGRP TKO models express neuroendocrine genes, points in bronchioles as well as in larger airways but are not CGRP TKO primary tumors and metastases had a slight normally found in alveoli (27, 35, 36). Accordingly, CGRP overall higher expression of neuroendocrine programs com- TKO tumors grow predominantly within the proximal air- pared with CMV TKO primary tumors or metastases (Sup- ways, in both the large and small bronchioles. In contrast, plementary Fig. S11). In contrast, compared with CGRP CMV TKO tumors grew in both the proximal and distal lung TKO primary tumors, CMV TKO primary tumors specifi- with early-stage CMV TKO lesions located at large and small cally had higher expression of lineage markers of several bronchioles, terminal bronchioles, and at bronchial–alveolar diverse lung epithelial cell types, including club cells (e.g., duct junctions (BADJ; Fig. 6A; Supplementary Fig. S14). Scgb1a1/CC10 and Scgb3a2), alveolar type II cells (e.g., Sftpb In addition to the spatial analysis, we also quantitatively and Lamp3), and alveolar type I cells (e.g., Aqp5 and Ager; Fig. reevaluated the potential of the major lung cell types to give 5A; Supplementary Table S3). Neither SCLC subtype highly rise to SCLC. Cancer initiation with Ad-CMV-Cre is more than expressed canonical markers of basal cells or lineage-negative 50-fold more efficient than adenoviral vectors with tissue- epithelial progenitors (LNEP; Krt5 and Trp63) or ciliated specific promoters targeting Cre expression to CGRPpositive cells (Foxj1; Supplementary Fig. S12A). Thus, although CMV NE cells, SPCpositive cells (mostly alveolar type II cells), and TKO tumors still express neuroendocrine markers, they also CC10positive cells (mostly club cells). The high number of express a wider range of lung epithelial markers. tumors initiated by Ad-CMV-Cre relative to other vectors was Many of these lineage genes have diverse expression in further confirmed by analysis at late stage (10 months after human SCLC, consistent with the intertumoral heterogeneity tumor initiation). None of the cell types that we targeted reca- uncovered in the mouse SCLC models (Supplementary Fig. pitulate the high tumorigenic efficiency of Ad-CMV-Cre (Fig. S12B). At the protein level, primary tumors and metastases 6B and C; Supplementary Fig. S15A–S15B). from both mouse models expressed neuroendocrine mark- Collectively, although our data support the idea that dif- ers, including ASCL1 and UCHL1 (Supplementary Fig. S1B; ferent cell types of origin provide the most likely explanation Fig. 5B). However, as anticipated from our RNA-seq analysis, for the difference in tumor number and metastatic programs other markers including CC10 and SELENBP1 (a marker between the CMV TKO and CGRP TKO mouse models, we of bronchiolar and alveolar non-neuroendocrine cells) were carefully investigated other variables. First, the time of tumor specifically expressed in CMV TKO nonmetastatic primary development was not a major determinant of the acquisition tumors (Fig. 5B–D; Supplementary Fig. S12A and S13A– of NFIB-driven metastatic ability, as CMV TKO and CGRP S13C). Cells expressing these non-neuroendocrine markers TKO mice analyzed at the same time point after tumor initia- were uniquely present in the NFIBnegative areas within primary tion still had NFIBpositive and NFIBnegative metastases, respec- CMV TKO SCLC (Supplementary Fig. S13D). tively (Supplementary Fig. S16A–S16B). Second, incomplete Interestingly, CC10 and SELENBP1 were highly expressed recombination of the conditional alleles due to lower Cre in only a subset of cells in CMV TKO primary tumors (Fig. 5B; expression from one of the viral vectors was largely ruled Fig. S13C). Because CC10 and SELENBP1 are also expressed out by PCR genotyping of the floxed alleles in CMV TKO in normal lung epithelial cells (33, 34), we determined whether and CGRP TKO tumors (Supplementary Fig. S4B). Third, these cells were cancer cells or normal cells within the tumors. although 10- to 20-fold higher titers of Ad-CGRP-Cre were In CMV-Cre–initiated tumors in TKO;Motley mice, CC10positive used to initiate tumors, a nonspecific effect of overall adeno- and SELENBP1positive existed within the clonal cancer cell viral titer did not explain our findings: Mice with tumors population, indicating that these cells are cancer cells (Fig. initiated with Ad-CMV-Cre combined with high titers of con- 5E; Supplementary Fig. S13E). trol Ad-CMV-GFP or Ad-CC10-Cre still developed CC10positive

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A Club markers AT II markers AT I markers

5,000 250 80 15 25 15

4,000 200 20 60 10 10 3,000 150 15 40 2,000 100 10 5 5 expression (TPM) 20 expression (TPM) 1,000 50 5 Age r Aqp 5 Sftpb expression (TPM) CC10 expression (TPM) Lamp3 expression (TPM)

0 Scgb3a2 expression (TPM) 0 0 0 0 0 low low low low low low high high high high high high CMV met CMV met CMV met CMV met CMV met CMV met CGRP met CGRP CGRP met CGRP met CGRP met CGRP met CGRP met CGRP CGRP tumor CGRP CGRP tumor CGRP tumor CGRP tumor CGRP tumor CGRP tumor CGRP CMV NFIB CMV NFIB CMV NFIB CMV NFIB CMV NFIB CMV NFIB CMV NFIB CMV NFIB CMV NFIB CMV NFIB CMV NFIB CMV NFIB

B C CMV TKO CGRP TKO CMV TKO lung tumor areas Tumor Metastasis Tumor Metastasis CC10 Neg. Pos.

16 158

H&E Neg. (6%) (66%) NFIB

s. 64 2

Po (27%) (1%)

UCHL1 D CGRP TKO lung tumor areas CC10 Neg. Pos. CC10 57 3

Neg. (82%) (4%) NFIB

s. 10 0

Po (14%) (0%) NFIB

E CMV TKO;Motley Clonal lineage markers Cell state markers

H&E UCHL1 RFP YFP/CFP CC10 SELENBP1

Figure 5. Multilineage differentiation in CMV TKO NFIBlow primary tumors. A, Gene expression of multilineage markers in tumors and metastases from CMV TKO and CGRP TKO mice. CMV TKO primary tumors express markers of club cells, alveolar type II cells (AT II), and alveolar type I (AT I) cells. B, IHC staining for UCHL1, NFIB, and CC10. Representative images show the presence of CC10pos cells specifically in NFIBlow primary tumors in CMV TKO mice. Scale bars, 100 μm. C and D, Quantification of CC10 and NFIB expression. CC10 and NFIB are mostly mutually exclusive in CMV TKO lung tumors (C), although CC10 and NFIB are only rarely expressed in CGRP TKO lung tumors (D). E, IHC staining for tumor clonal lineage markers (RFP, YFP/CFP) as well as CC10 and SELENBP1 on tumors from CMV TKO;Motley mice. The uniform staining of tumor lineage markers in individual tumors is consistent with their clonal origin. CC10positive and SELENBP1positive cells are cancer cells that are clonally related to the CC10negative and SELENBP1negative cells. Scale bars: top, 100 μm; bottom, 20 μm. TPM, transcript per million.

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A Small/terminal B Bronchi bronchiole BADJ CMV-Cre CC10-Cre 39(24%) 77(48%) 45(28%) T A T T TB T

CMV-Cre B TB

4(31%) TB 9(69%) 0% CGRP-Cre SPC-Cre

T TB T TB B

CGRP-Cre A GFP Tom SA

C D Tumor # Ad-CMV-Cre Ad-CGRP-Cre Virus Time mean +/− SD months (# mice) CGRPpos NE cells CMV-Cre 3.5 54.3 +/− 12.4 (3) CC10pos cells CGRP-Cre 3.5 0.6 +/− 0.8 (5) SPCpos cells 10 5.8 +/− 2.2 (4) CC10-Cre 3.5 0 +/− 0 (5) 10 0 +/− 0 (5) 3.5 0 +/− 0 (4) SPC-Cre SCLC SCLC Mature neuronal 10 1 +/− 1 (4) Multilineage potential CGRP-Cre 3.5 0.4 +/− 0.5 (5) differentiation +CC10-Cre 10 3 +/− 2 (2) NFIBhigh More CGRP-Cre 3.5 0.3 / 0.5 (3) Neuroendocrine + − Metastasis Metastasis +CC10-Cre 10 2.5 +/− 1.1 (4) +SPC-Cre Neuronal differentiation Axonal guidance Cell cycle

Figure 6. SCLC in CMV TKO mice arises from an alternative cell of origin. A, Representative immunofluorescent images of whole-mount staining and confocal imaging of tumors in CMV TKO and CGRP TKO mice. Tumors are circled. Number and percentage of lesions at each location are indicated. T, tumor; B, bronchi; TB, terminal bronchiole; A, alveolar space; SA, streptavidin (which nonspecifically labels lung epithelial cells). Scale bars, 200μ m. B, Representative H&E images of the lungs of mice 3.5 months after transduction by the same titer of adenoviral vectors with distinct promoters driving Cre expression. Scale bars, 1 mm. C, Quantification of SCLC tumor number 3.5 months and 10 months after transduction with the same total titer of different types and combinations of adenoviral vectors with distinct promoters driving Cre expression. Tumor number is quantified from H&E lung sections. The number of mice in each cohort is indicated in parentheses. D, Model of SCLC development and progression from two distinct cell types of origin.

primary tumors and NFIBhigh metastases (Supplementary leads to tumor suppressor inactivation in one or more cell Fig. S16C–S16E). Finally, in the CMV TKO model, Cre is types that do not express Cre after transduction with Ad- expressed broadly in the lung, leading to the generation of CGRP-Cre. These cells ultimately give rise to SCLC tumors many “normal” epithelial cells that lack the floxed tumor sup- that are sufficiently molecularly distinct to result in pressor genes. The coexistence of many other cells with these strikingly different evolutionary paths toward metastasis genetic alterations did not contribute to the high number of (Fig. 6D). tumors initiated by CMV-Cre, as TKO;mTmG mice transduced with Ad-CGRP-Cre with or without the addition of Ad-CC10- Cre or Ad-CC10-Cre and Ad-SPC-Cre still developed compa- DISCUSSION rable and low numbers of tumors (Fig. 6C; Supplementary SCLC is an aggressive cancer type and patients often pre- Fig. S15C). sent or relapse with widespread metastatic disease. Our data Thus, the most likely explanation for the difference in from defined genetically engineered mouse models indicate tumor number and progression remains that Ad-CMV-Cre that SCLC is not inherently metastatic and that key genetic

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RESEARCH ARTICLE Yang et al. and epigenetic changes occur during cancer progression be required to determine whether different SCLC subtypes (this study and refs. 5–7, 37). Furthermore, we provide evi- define therapeutic responses to immunotherapy, epigenetic dence that SCLC can arise from different cell types, which therapies, or other treatments. has profound influences on the course of tumor evolution. In addition to recent observations that heterogeneity within NFIB is highly expressed in more than 50% of human individual tumors can play a role in the SCLC progression SCLC metastases (refs. 6, 7, 13; Fig. 1G; Supplementary Fig. (14, 16, 18, 19), accumulating evidence supports the notion S2), suggesting that upregulation of this transcription fac- that SCLC is not one disease, but rather many diseases that tor could influence the metastatic ability of a large fraction are distinguishable only at the molecular level (12, 13, 17). of human SCLC. Deletion of Rb/Trp53 or Rb/Trp53/p130 in Across many cancer types, the genetic and epigenetic changes mice after delivery of Ad-CMV-Cre to the lungs may provide that occur during tumor evolution are a major focus of efforts an accurate model for this subset of human tumors. SCLC to understand this intertumoral heterogeneity. However, the initiated specifically from mature neuroendocrine cells impact of the cell type of origin on cancer initiation and using Ad-CGRP-Cre may model a different subset of human progression has garnered less interest (43–47). Distinct cell SCLC that does not amplify or upregulate Nfib as often and types may give rise to different tumor subtypes with distinct thus may metastasize via different mechanisms. We can- histologic features; however, in the case of SCLC, although not exclude, however, that specific combinations of genetic the two subtypes metastasize using different mechanisms, alterations in mature neuroendocrine cells may affect the the resulting metastases were histologically indistinguishable. frequency of Nfib upregulation during SCLC progression. Our results indicate that fundamental differences in Notably, although SCLC in humans often develops within tumor development between the two mouse models may the lobar or main bronchi and is identified by imaging be endowed upon the tumor by the cell type of origin. within the central aspect of the chest, 5% to 15% of cases of The identity of the cell type(s) of origin in the CMV TKO SCLC are more peripheral or even subpleural (38–41). These model remains unknown. Previous work using Rb1flox/flox; peripheral tumors are histologically similar to central SCLC Trp53flox/flox DKO mice and our analysis of Rb1flox/flox; and are also highly metastatic (42). The more peripheral Trp53flox/flox;p130flox/flox TKO mice suggests that lung cell location of tumors in the CMV TKO model suggests that types expressing CC10 or SPC (including club cells and CMV TKO mice may model this poorly characterized subset alveolar type II cells) are unlikely to be cell types of origin of human tumors. for SCLC (4, 20, 21). Based on the expression of classic Why CGRP TKO tumors and a fraction of human SCLC markers of several lung epithelial lineages specifically in tumors metastasize without upregulation of NFIB is CMV TKO primary tumors, including markers of club cells unknown, especially when this factor can be such a strong and alveolar type II and I cells, we speculate that SCLC in oncogene and prometastatic driver. We did not observe large- this model arises from stem/progenitor cells or facultative scale differences in genome-wide chromatin accessibility stem/progenitor cells that gain multilineage potential upon between CMV TKO and CGRP TKO primary tumors, suggest- inactivation of Rb1 and Trp53. Indeed, RB1 loss promotes ing that cancer cells in both models could be responsive to cellular plasticity (48) and can lead to neuroendocrine dif- NFIB. However, small changes in accessibility at specific loci ferentiation/transdifferentiation in lung adenocarcinoma and/or other chromatin differences may render cancer cells and prostate cancer (49–51). The formal identification of in CGRP TKO tumors less responsive to NFIB upregulation. this cell type or types will require new tools to express Cre SCLC derived from mature neuroendocrine cells may be in a specifically in defined subpopulations of lung epithelial more differentiated state that is less amenable to epigenetic cells (23, 24, 52). transformation by NFIB. The Nfib locus itself could be less fre- Patients with SCLC continue to have one of the worst sur- quently amplified or the expression ofNfib could be regulated vival rates of all patients with cancer. Our work indicates that in a different manner in SCLC derived from mature neuroen- multiple cell types in the lung can serve as the cell of origin of docrine cells. Finally, it is possible that cofactors required for SCLC and suggests that biomarkers of these different subsets NFIB action are not present in SCLC derived from CGRPpositive of tumors may help predict their evolutionary trajectories mature neuroendocrine cells. Interestingly, some primary toward malignancy. Understanding this diversity may ulti- tumors in the CGRP TKO model do express high levels of mately have value in enabling better patient stratification and NFIB (Fig. 1E). The expansion of NFIBhigh cancer cells in these more precise treatment plans. tumors could suggest that NFIB still has oncogenic properties in this cellular context, but without promoting metastasis. Whether the molecular paths taken by distinct SCLC METHODS tumors affect their response to specific treatments remains Ethics Statement an important question. In culture, we did not identify sig- Mice were maintained according to practices prescribed by the nificant differences in the responses of cell lines derived from NIH at Stanford’s Research Animal Facility accredited by the Ameri- high low NFIB CMV-Cre and NFIB CGRP-Cre tumors to three can Association for Accreditation of Laboratory Animal Care. All different drugs: cisplatin, etoposide, and a CHK1 inhibitor. In animal studies were conducted following approval from the Stanford general, these cell lines had diverse sensitivities to these drugs. Animal Care and Use Committee (protocol 13565). Even given this diversity, CGRP TKO cell lines had a trend toward higher IC50 values for each drug (Supplementary Fig. Mice, Adenoviral Infections, and Cancer Cell Isolation S17). Future studies, including in vivo studies on the geneti- Rb1flox, Trp53flox, p130flox, R26mTmG, and R26Motley alleles have been cally engineered mouse models and/or in clinical trials, will described (7, 26, 31, 53, 54). The SCLC mouse model bearing deletions

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SCLC Cell of Origin Influences Metastatic Progression RESEARCH ARTICLE of Trp53, Rb, and p130 was previously described (26). Ad-CMV-Cre, utes. Cells were fixed with 4% paraformaldehyde for 15 minutes Ad-CGRP-Cre, Ad-SPC-Cre, Ad-CC10-Cre, and Ad-CMV-GFP were and stained for NFIB (Abcam; ab186738; 1:2,000) and with a goat purchased from the University of Iowa viral vector core. Intratracheal anti-rabbit secondary antibody (Invitrogen). For imaging, membrane administration was performed as previously described (7, 26). Unless GFP staining was confirmed to indicate a DTC, and NFIB expression specified in the text, for long-term experiments, we used Ad-CMV-Cre was checked through the far-red channel using a fluorescence scope at 4 × 107 Pfu. Ad-CGRP-Cre or Ad-CC10-Cre were used at 4 × 108 or (Leica). NFIB staining was quantified by counting directly under the 8 × 108 Pfu per mouse, respectively. Primary tumors and metastases scope. On average, we quantified 30 to 100 cells per sample based on were harvested when mice became moribund. Mice were maintained how many cells were harvested. at the Stanford Research Animal Facility accredited by the Association for Assessment and Accreditation of Laboratory Animal Care. Cancer Whole-Mount Immunofluorescence Staining cells were isolated and purified by FACS as previously described (7). Lungs were dissected from mice after perfusion with ∼5 mL PBS into the right cardiac ventricle and intratracheal inflation with∼ 2 Cell Culture mL 2% low melting point agarose (Thermo Fisher; cat. #16520) SCLC cell lines were passaged as previously described (7). For drug in PBS. The lungs were fixed in 4% paraformaldehyde (EMS; cat. response assays, cells were plated in a 96-well plate at 10,000 cells per #15714) for 6 hours at 4°C and then sectioned with a vibrating well, in triplicate, in 100 μL of media. After 24 hours, drugs or vehicle blade microtome (Leica Biosystems; cat. #VT1000S) at 500-μm control were added to the cells at a 2× concentration in 100 μL, for a thickness. Lung slices were stained with rabbit anti-CGRP (Enzo total volume of 200 μL per well. For each drug, seven different con- Life Sciences; cat. #BML-CA1137; 1:1,000 dilution) for 5 to 7 days centrations were tested at 10-fold dilutions. Forty-eight hours after at 4°C, then with Alexa Fluor 647–conjugated anti-rabbit IgG the drugs were added, 20 μL of Alamar Blue was added to each well, (Thermo Fisher; cat. #A-21244; 1:500 dilution) and streptavidin- and fluorescence values were measured 4 hours later. IC50 values were conjugated Alexa Fluor 405 (Thermo Fisher; cat. #S32351, 4 μg/ calculated using GraphPad Prism7. Cisplatin and etoposide were mL final concentration) as a pan-lung epithelial identifier (55) for obtained from the Lucile Packard Children’s Hospital at Stanford. 3 to 4 days at 4°C. Finally, stained sections were optically cleared The CHK1 inhibitor LY2606368 was a gift from the lab of Dr. Lauren using the CUBIC method (56), comprised of a 3-hour incubation Byers (MD Anderson Cancer Center) and reconstituted in DMSO. at room temperature in CUBIC 1 reagent and long-term storage in CUBIC 2 at 4°C. Sections were imaged using a Zeiss LSM 780 laser Immunoassays scanning confocal microscope with an inverted 5× air objective Protein levels of NFIB and ASCL1 for the tumor-derived cell lines (Carl Zeiss AG, NA = 0.45), and optical sections were collected at 10 μm resolution. used in IC50 experiments were determined using the Simple Western quantitative immunoassay and the Compass software, according to the manufacturer’s protocol. Cells were lysed in TNESV lysis buffer ATAC-seq Library Preparation, Sequencing, and Analysis (50 mmol/L Tris–HCl at pH 7.6, 1% IGEPAL, 20 mmol/L EDTA at ATAC-seq library preparation was performed as described (7). pH 8.0, 100 mmol/L NaCl), supplemented with proteasome and phosphatase inhibitors, and lysates were cleared by centrifugation Calling Peaks at maximum speed for 10 minutes. Total protein was quantified Accessible regions were called by performing peak calling to obtain using the Pierce BCA Protein Assay Kit (Thermo Fisher, cat. #23277). peak summits, merging peak calls obtained in different sequenc- Whole-cell lysates were diluted to a final concentration of 0.2 μL/mL. ing batches, and subsequently filtering to remove any overlapping The antibodies and dilutions used were as follows: NFIB (Abcam, regions. Macs2 (v 2.1.0.20140616) was used to call peak summits ab186738) 1:20,000; ASCL1 (BD Pharmingen, 556604) 1:1,000; and on the merged bam file of each sequencing batch of samples (three HSP90 (Cell Signaling Technology, 4877S) 1:2,000. total) using the command “macs2 callpeak –nomodel –call-summits –keep-dup all.” Peak summits were expanded by 250 bps on each Histology and IHC side to form 500-bp windows. Each set of accessible regions was Tumor samples were fixed in 4% formalin and paraffin embedded. filtered to remove blacklist regions (mm9; https://sites.google.com/ Hematoxylin and eosin staining was performed using standard meth- site/anshulkundaje/projects/blacklists) and copy-number amplifica- ods. Images were quantified using ImageJ. For IHC, we used anti- tion (any peak that overlapped a region that differed from expected bodies to NFIB (1:1,000; Abcam ab186738), UCHL1 (1:500; sigma for any sample by more than 2-fold (up or down) was removed; copy- HPA005993), CC10 (CCSP; 1:1,000; Millipore 07-623), SELENBP1 number amplification was determined as described below). Finally, (1:200; Abcam), GFP (1:500; Abcam ab6673), ASCL1 (1:200; BD Bio- the three peak sets were concatenated, while retaining overlapping sciences, 556604), and RFP (1:500; Rockland). peak windows. Initially, all accessible regions were filtered to obtain only regions SCLC Patient Brain Metastasis Sections with sufficient read depth to compare across samples. The number and IHC Staining Score of overlapping reads per peak was found for all samples using the bedtools multicov module (v2.25.0), using only reads with high Human SCLC brain metastasis tissue sections were collected by mapping quality (−q 30). The read counts per peak for each sample Anna S. Berghoff and Matthias Preusser from the Medical University was normalized by the mean read counts per peak for that sample, of Vienna, Austria. IHC staining for NFIB (1:1,000; Abcam ab186738) such that all samples have the same mean reads per peak. Peaks was performed on 4-μm formalin-fixed paraffin-embedded sec- were kept if they fulfilled either of two criteria: (i) the peak had at tions. NFIB expression was scored by a board-certified pathologist, least 1 read for all samples or (ii) the peak had at least 1 sample C.S. Kong, as follows: 0, negative or weak staining of less than 10% of with a large number of normalized reads (>1 standard deviation cells; 1, weak staining of more than 10% of cells; 2, moderate intensity above the mean). This operation was performed to filter out peaks staining; 3, strong intensity staining. with low counts while retaining peaks that may be differentially accessible. Immunofluorescence Finally, any overlapping windows were filtered to obtain a set of FACS-sorted cancer cells from primary tumors, DTCs, and liver nonoverlapping windows. Overlapping windows were identified metastases were cytospun onto glass slides at 500 rpm for 5 min- using the bedtools cluster module. For each set of overlapping

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RESEARCH ARTICLE Yang et al. windows, only the one with the greatest number of normalized used in DESeq was then “∼model_met_amp.” For the CGRP distal reads was kept. In the end, ∼114,000 accessible regions (nonover- metastases versus primary tumor comparison, results were extracted lapping, uniform width) were obtained and used in subsequent using the command “res_G_LvsT ← results(dds, contrast=c(’model_ analyses. met_amp’, ’G_L_0′, ’G_T_0′))” (Supplementary Table S5).

Normalizing Read Counts within Accessible Regions Assessing Copy-Number Amplification from ATAC-seq Data The number of reads per peak was obtained to assess the differ- Copy-number amplification was estimated from ATAC-seq data by ential chromatin landscape across samples. To estimate the effect determining read counts in large (10 Mb) intervals across the genome of sequencing depth, a set of “housekeeping” regions was obtained (2 Mb step size) that have different read counts than expected, where that was expected to have similar mean accessibility across samples. the expectation is based on the average read count across 100 GC- These “housekeeping” regions were determined by finding the set of matched intervals. Specifically, the GC content of each interval was genes that were uniformly expressed across CMV tumors and metas- found using bedtools nuc module. For each interval, the 100 other tases (as in Denny and colleagues; ref. 7); peaks corresponding to intervals with the closest GC content to that region were found and the promoters of these genes were determined to be “housekeeping” used as the GC-matched intervals. peaks (Supplementary Table S2). The inverse of the mean read count across all “housekeeping” peaks was used as the sample-specific size RNA-seq Library Preparation and RNA-seq Analysis factor for normalization in DESeq2 (57). These factors were divided by the geometric mean of all the size factors, as recommended in the RNA was extracted from FACS-sorted cancer cells using Qiagen DESeq2 vignette (https://bioconductor.org/packages/release/bioc/ Allprep DNA/RNA kit. All the samples were checked by BioAnalyzer vignettes/DESeq2/inst/doc/DESeq2.html#sample-gene-dependent- (Agilent) for RNA integrity (RIN > 8). cDNA was prepared from 10 normalization-factors). Size factors were converted to peak-specific ng of RNA using the Nugen Ovation V2 kit, according to the manu- normalization factors by using the sample-specific size factor for facturer’s instructions. RNA-seq libraries were generated using the all peaks. Illumina Truseq kit. We used Kallisto to quantify expression (58). We kept only tran- scripts with max transcript per million (TPM) value greater than 1 in Number of Reads Expected from at least one sample and with standard deviation greater than 1. We Background Fragmentation performed the asinh() transformation to variance stabilize the data. The number of reads expected to come from background frag- Because the Illumina library preparation method had been slightly mentation during transposition was obtained by counting reads modified between the two library preparations, we observed a strong overlapping a set of background intervals (entire genome, exclud- batch effect when comparing the two data sets. To correct for the ing blacklist regions, and excluding any 10-Mb interval with evi- batch effect, we included three RNA samples of our old CMV TKO dence of copy-number amplification in any sample), to form a total samples while generating the CGRP TKO libraries. number of reads M for any sample. The number of reads within We used SVA to remove batch effects from the data (59). To iden- accessible regions for each sample (m) was subtracted from M and tify batch effects between the two categories of samples, we strategi- divided by the number of base pairs in the background intervals cally incorporated three overlapping samples between the CGRP (L), less the number of base pairs in accessible regions (l), such that TKO and CMV TKO samples. These otherwise identical samples the number of background reads per base pair per sample was b = give an idea of what differences can arise purely from experimental (M–m)/(L–l). For accessible region k of size lk, the number of reads procedure and other confounding effects. We used SVA to estimate expected simply from background fragmentation was nk = lkb. In confounding factors by protecting the sample name so that the algo- practice, because all of the accessible regions were of uniform size, rithm only removed batch effects confounding the identical samples all accessible regions had the same lk. and otherwise knew nothing about the identity of the samples. We To obtain background-subtracted read counts within accessible then removed the estimated confounding factors from the data. After regions, nk was subtracted from the number of reads overlapping batch correction, the batch difference of the three repeated samples each accessible region for each sample. These subtracted values were was comparable to the difference between technical replicates gener- rounded to the nearest integer, and any intervals with negative read ated in a single RNA-seq experiment. counts were rounded up to zero. For all samples with enrichment score 11, the number of background reads per each peak was less > Differential Expression and Gene Ontology Analysis than ∼5 (normalized for sequence depth). To discover the genes driving transitions between different stages Assessing Differential Accessibility of metastasis, we used the following procedure on data with the experimental batch corrected (see previous section): (i) fit a linear Through unsupervised clustering and PCA, we determined that model (lmFit in R) modeling metastatic effect, (ii) rank genes in samples with low enrichment (calculated as the maximum number order of evidence for differential expression using an empirical of reads around transcription start sites over the minimum number Bayes method (60). We compared only genes with nonzero variance. of reads, as in ref. 7) were associated with differences in chromatin We compared CGRP primary tumors and CGRP metastases, CGRP accessibility. This effect was associated with batch and not with Nfib primary tumors and CMV primary tumors, CMV primary tumors amplification (Supplementary Fig. S5). To minimize this techni- and CMV metastases, and CMV metastases versus CGRP metasta- cal artifact, samples with low enrichment (<11) were filtered from ses. Significantly differential genes were those that had an adjusted the analysis. P value below 0.05. Gene ontology analysis and neuroendocrine Differential accessibility was determined using the package DESeq2 signature enrichment analysis were performed using gene set enrich- (57). Counts per peak were background subtracted to account for ment analysis (61). expected counts per peak due to background fragmentation (see the above section). Each sample was annotated by the model (CGRP vs. CMV), the metastatic state (either T for primary tumor or L for distal Accession Numbers metastases (liver or lymph node), and the Nfib amplification state (1 The accession number for the RNA-seq data reported in this if amplified, 0 if unamplified). This annotation can be found in the paper is GEO: GSE116977, and for the ATAC-seq data is GEO: column “model_met_amp” in Supplementary Table S5. The model GSE117177.

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SCLC Cell of Origin Influences Metastatic Progression RESEARCH ARTICLE

Comparison with Human Samples tion and Research (BMBF e:Med 01ZX1303A to H.C. Reinhardt). We downloaded human SCLC RNA-seq studies EGAD00001001431 D. Yang was supported by a Stanford Graduate Fellowship and by and EGAD00001001244 from the European Genome–Phenome a TRDRP Dissertation Award (24DT-0001). S.K. Denny was sup- Archive. We quantified gene expression for all samples n( = 74) ported by the Stanford Biophysics training grant (T32 GM008294). from the two studies again using Kallisto to generate TPM for each A.C. Chaikovsky was supported by the Stanford Cancer Biology sample. training grant (T32 CA009302). A. Kundaje was supported by NIH In order to compare expression values for human samples and grant DP2OD022870. P.G. Greenside was supported by the Bio-X mouse samples, we subset all data sets to use only genes that Stanford Interdisciplinary Graduate Fellowship (SIGF). Y. Ouadah shared the same gene names between the two species (n = 14,813 was supported by a Stanford Graduate Fellowship. S.K. Denny, A.C. genes). After merging human and mouse data sets on shared Chaikovsky, and Y. Ouadah were supported by the NSF GRFP. J.S. genes, we performed the asinh transformation for variance sta- Lim was supported by an A*STAR scholarship (Singapore). J. Sage bilization. is the Harriet and Mary Zelencik Scientist in Children’s Cancer and We then performed PCA on this merged data set. PCA allows us Blood Diseases. to inspect different axes of variation, such as those driven by species, cell type of origin, and metastatic state without further transforming Received September 5, 2017; revised May 14, 2018; accepted August human and mouse data to make them comparable. We plotted PC1 9, 2018; published first September 18, 2018. versus PC2, PC1 versus PC3, and PC2 versus PC3. We found that PC1, as expected, is driven primarily by differences between mouse and human samples. We found that PC3 seems to be driven primarily by tumor type with a clear continuum among mouse samples from REFERENCES CGRP to CMV Met samples. We used this PC3 in order to understand . 1 Lambert AW, Pattabiraman DR, Weinberg RA. Emerging biological how the distribution of human samples falls relative to the mouse principles of metastasis. Cell 2017;168:670–91. distribution of samples. 2. Nguyen DX, Massague J. Genetic determinants of cancer metastasis. Nat Rev Genet 2007;8:341–52. Disclosure of Potential Conflicts of Interest 3. Bunn PA Jr., Minna JD, Augustyn A, Gazdar AF, Ouadah Y, Krasnow MA, et al. Small cell lung cancer: can recent advances in biology and H.C. Reinhardt reports receiving consulting and lecture fees molecular biology be translated into improved outcomes? 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Intertumoral Heterogeneity in SCLC Is Influenced by the Cell Type of Origin

Dian Yang, Sarah K. Denny, Peyton G. Greenside, et al.

Cancer Discov 2018;8:1316-1331. Published OnlineFirst September 18, 2018.

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