Patel, a Et Al. 1 Integrative Genomic and Epigenomic Analyses Identify A

bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 1

Integrative genomic and epigenomic analyses identify a distinct role of c-Myc and L-Myc

for lineage determination in small cell lung cancer

Ayushi S. Patel1,2, Seungyeul Yoo3,4, Ranran Kong1,2,5, Takashi Sato1,2, Maya Fridrikh1,2,

Abhilasha Sinha1,2, German Nudelman6, Charles A. Powell1,2, Mary Beth Beasley7, Jun Zhu2,3,4,

Hideo Watanabe1,2,3,8*

1Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn

School of Medicine at Mount Sinai, New York, NY 10029, USA

2Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA

3Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New

York, NY 10029, USA

4Sema4, a Mount Sinai venture, Stamford, CT 06902, USA.

5Department of Thoracic Surgery, The Second Affiliated Hospital of Medical School, Xi'an

Jiaotong University, Xi'an, Shaanxi, 710004, China

6 Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029,

USA

7Department of Pathology and Laboratory Medicine, Icahn School of Medicine at Mount Sinai,

New York, NY 10029, USA

8Lead Contact

*Correspondence: [email protected]

Patel, A et al. 2

Abstract

Small cell lung cancer (SCLC) is the most aggressive subtype of lung cancer with a dismal

prognosis. The standard-of-care remains to be uniform treatment with chemotherapy and

radiotherapy, even though emerging evidence suggests disease heterogeneity. In primary SCLC,

the gene loci for Myc family members are amplified mutually exclusively, their expression is

correlated with unique neuroendocrine markers and distinct histopathology of xenografts from

SCLC cell lines and murine SCLC. In this study, we explore a novel role of c-Myc and L-Myc as

lineage specific factors to bridge the gap between SCLC molecular subtypes and histological

classification. Integrated analyses of a Bayesian network generated from primary tumor mRNA

expression and chromatin state profiling of cell lines showed that Myc family members impart

distinct transcriptional programs associated with lineage state; wherein L-Myc was enriched for

neuronal pathways and c-Myc for Notch signaling and epithelial-to-mesenchymal transition.

Genetically engineering the exchange of c-Myc with L-Myc in c-Myc amplified SCLC revealed

the insufficiency of L-Myc to induce lineage switch but the requirement for c-Myc to maintain

NeuroD1-lineage state. In contrast, exogenous expression of c-Myc in L-Myc amplified ASCL1-

SCLC revealed incompatibility with c-Myc expression in those SCLC, that eventually trans-

differentiated to NeuroD1-SCLC accompanied by variant histopathological features.

Transcriptomic profiling of trans-differentiated cells revealed Notch signaling target, Re-1

silencing transcription factor (REST) was induced with c-Myc expression mediating the

alteration of ASCL1 expression. Collectively, our findings reveal a previously undescribed role

for historically defined general oncogenes, c-Myc and L-Myc, in regulating lineage plasticity

across molecular subtypes as well as histological classes. bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 3

Introduction

Small cell lung cancer (SCLC) represents about 15% of all lung cancers with a median

survival time of approximately only 10 months and 5-year overall survival at 6% (Siegel et al.,

2019). SCLC is characterized by neuroendocrine differentiation, rapid growth, early metastatic

spread and poor prognosis (Gazdar et al., 2017). The standard of care has remained to be

cytotoxic chemotherapies for decades, mainly etoposide and a platinum agent (Alvarado-Luna et

al., 2016), that is only temporarily effective for the vast majority of patients (Pietanza et al.,

2015). Even with recent developments in immune checkpoint inhibitors, activity against SCLCs

when combined with chemotherapy has been marginal with a modest improvement on the

median survival (10.3 months vs. 12.3 months) for extensive stage SCLCs (Horn et al., 2018).

These data reflect the urgent need for effective therapeutics for patients with SCLC. The lack of

effective therapeutics for SCLC stands in stark contrast to the breadth of targeted therapies for

non-small cell lung cancer (NSCLC), particularly lung adenocarcinoma (Rudin et al., 2019). This

progress in drug development for NSCLCs is largely attributable to a more comprehensive

understanding of molecular subtypes and identifying targetable driver oncogenes (Zappa et al.,

2016). Therefore, better characterization of the molecular subtypes of SCLC should aid future

drug development and patient stratification for targeted therapies.

Morphological classification of SCLC was noted over three decades ago when human

SCLC cell lines were implanted as xenografts and distinguished as two primary subtypes:

classical SCLC and variant SCLC (Carney et al., 1985; Gazdar et al. 1985). The classical

subtype features relatively small cells with high nuclear:cytoplasm ratio while the variant

subtype exhibits relatively larger cells and moderate amounts of cytoplasm. The variant subtype

is correlated with reduced expression of neuroendocrine markers and frequent MYC bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 4

amplification (Gazdar et al. 1985). The variant features were later identified in primary

specimens (Beredensen et al., 1988). However, the World Health Organization (WHO)

classification, updated in 2015, recognizes SCLC as a homogenous disease with neuroendocrine

features defined by histological features including small cells, scant cytoplasm, nuclear

morphology with fine granular chromatin and lacking prominent nucleoli, reminiscent of the

features of classical SCLC with no further sub-classifications (Travis et al., 2015). The variant

histopathology observed in xenografts of SCLC cell lines, according to the WHO classification,

would likely be recognized as “combined SCLC” that includes a component of either large-cell

neuroendocrine carcinomas (LCNEC) or NSCLC, potentially reflective of the discrepancies

between clinical practice and experimental findings (Travis et al., 2015, Rekhtman, 2010).

More recent efforts to distinguish SCLC molecular subtypes include profiling gene

expression and genome-wide methylation in primary human tumors and patient derived

xenografts (PDX), that revealed three clusters, with a dichotomy between achaete-scute

homologue 1 (ASCL1) and neurogenic differentiation factor 1 (NeuroD1) expression, in addition

to a cluster with low expression of both (Poirier et al., 2015). The expression of ASCL1 and

NeuroD1 has been implicated to confer SCLC heterogeneity by imparting distinct transcriptional

profiles (Borromeo et al., 2016). The third neuroendocrine-low cluster led to the further

classification into two subtypes characterized by transcriptional driver YAP1 or POU class 2

homeobox 3 (POU2F3) (McColl et. Al., 2017, Huang et al., 2018).

SCLC cell line xenograft histology has been correlated with these contrasting factors

where variant SCLC was positively correlated with a higher NeuroD1:ASCL1 ratio and classical

SCLC was positively correlated with the inverse in SCLC cell lines (Poirier et al., 2013). A bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 5

recent study showed that additional c-Myc overexpression, in a classical SCLC genetically

engineered mouse model (GEMM) with conditional loss of Rb1 and Trp53 mouse, can drive the

progression of murine SCLC to variant histopathology with reduced neuroendocrine gene

expression including ASCL1 but displayed higher NeuroD1 (Mollaoglu et al., 2017). On the

contrary, the classical SCLC GEMM harbored stochastic MYCL amplifications or

overexpression associated with classical SCLC histopathology (Meuwiseen et al., 2003).

Recapitulating Myc family members in SCLC GEMMs has provided insight in the role for

tumorigenesis and contribution to histopathological characteristics (Brägelmann et al., 2017,

Kim et al., 2016).

c-Myc (MYC) and L-Myc (MYCL) belong to the MYC family of basic helix-loop-helix

(bHLH) transcription factors. The paralogs contain functionally relevant highly conserved amino

acid sequences and are structurally homologous (DePinho et al., 1987b, Conacci-Sorell et al.,

2014). c-Myc is a well-characterized oncogene; L-Myc although understudied is implicated to

have a similar oncogenic role. Amplification of Myc family members are mutually exclusive and

overall account for ~20% of SCLC and overexpression for ~50% of SCLC in primary human

tumors (George et al., 2015). In contrast to the fact that MYC is commonly amplified across all

three major lung cancer subtypes: lung adenocarcinomas, squamous cell lung carcinomas and

SCLC (Weir et al., 2007, Hammerman et al., 2012, George et al., 2015), MYCL and MYCN are

uniquely amplified in SCLC, in a manner suggestive of their role as lineage-amplified genes.

In this study, we explore a novel role for c-Myc and L-Myc as lineage specific factors to

associate SCLC molecular subtypes with histological classes. We investigated the potential of L-

Myc and c-Myc to regulate lineage state and identified transcriptional programs unique to each bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 6

Myc family member, wherein L-Myc regulates neuronal developmental pathways and c-Myc

regulates epithelial-to-mesenchymal transition and Notch signaling, biological pathways that are

associated with distinct molecular subsets. In addition, we showed the requirement of c-Myc to

maintain lineage state marker NeuroD1, in NeuroD1-positive SCLC and the incompatibility of c-

Myc with ASCL1-positive SCLC that ultimately leads to trans-differentiation to NeuroD1-driven

characterized by variant histopathology mediated by a transcriptional repressor, Rest.

Results

SCLC network reveals unique and distinct sub-networks for c-Myc and L-Myc

To investigate the biological processes unique to c-Myc and L-Myc in primary SCLC, we

applied a network built using the Bayesian conditional probability from two independent

transcriptomic SCLC primary specimen datasets (George et al., 2015, Jiang et al., 2016). The

Bayesian estimation uses variations in genes expression across samples to estimate the maximum

likelihood score of association to elucidate the network (Zhu et al., 2004). The Bayesian network

from these datasets comprised of 8,451 unique genes capturing correlation and directionality

amongst these genes with 9,301 edges. Identifying immediate neighbors for genes of interest can

provide insights into biological functions they are associated with (Zhu et al., 2007, Zhu et al.,

2008, Zhu et al., 2012). This powered us to discern transcriptional sub-networks associated with

c-Myc and L-Myc that may reflect their unique biological role. To this end, we first aimed to

generate a signature associated with each factor and then projected the signature to the Bayesian

network to identify specific subnetworks (Figure 1A). To generate this signature, we examined

MYC and MYCL mRNA expression in SCLC cell lines from Cancer Cell Line Encyclopedia bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 7

(CCLE) and selected cell lines with either high expression of MYC or MYCL, then defined gene

signatures for MYC expressing and MYCL expressing subsets (Figure S1A). We identified 457

differentially expressed genes; 147 genes positively correlated with MYC expression and 310

positively correlated with MYCL in SCLC cell lines (Figure S1B).

To explore the sub-networks associated with L-Myc, we projected the genes upregulated in

MYCL expressing subset. We identified one large sub-network (L1, Figure 1B) comprising of

959 gene nodes that included 120 of 310 genes from the signature. Gene ontology analysis of

this L-Myc subnetwork revealed enrichments of two biological processes: cell cycle progression

and neuronal development (Figure 1C). These two pathways have been previously implicated as

core descriptors of classical SCLC (Gazdar et al., 2017). On the other hand, when we projected

the c-Myc signature to the network, the c-Myc network was organized into three unique sub-

networks c1, c2 and c3, comprised of 95, 29 and 25 gene nodes, respectively (Figure 1D). Gene

ontology analysis for each individual c-Myc sub-networks revealed enrichments of three distinct

biological processes. Sub-network c1 was enriched for previously described canonical c-Myc

functions in transcriptional, translational and metabolic regulation (Figure 1E) (Conacci-Sorell et

al., 2014). c-Myc sub-network c2 was enriched for Notch signaling (Figure 1E), which has been

implicated to mediate a transition from neuroendocrine to non-neuroendocrine fate of tumor cells

in a murine SCLC model (Lim et al., 2017). Finally, sub-network c3 was enriched for pathways

in epithelial-to-mesenchymal transition (Figure 1E); that has been shown to be relevant in the

neuroendocrine-low subtype (Zhang et al., 2018). These functional pathways enriched in

subnetworks c2 and c3 imply previously uncharacterized role of c-Myc in lineage state

determination in SCLC. bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 8

Together, these findings suggest that c-Myc and L-Myc are associated with differential

molecular mechanisms that have been implicated in SCLC biology. Specifically, it implicates c-

Myc is associated with neuroendocrine-low differentiation state in addition to its canonical

oncogenic functions in SCLC, and by contrast, L-Myc is associated with classic neuroendocrine

state of SCLC.

c-Myc and L-Myc driven SCLCs exhibit distinct chromatin states

Next, we sought to determine whether the distinct c-Myc and L-Myc networks are a

consequence of transcriptional program each of the factors regulates. To select an appropriate

model system to examine the role of each Myc family member, we examined copy number

alteration and expression for each Myc family member in 49 SCLC cell lines from CCLE and

classified them into four groups representing each Myc family member and low Myc (Figure

S1C). Based on this classification, we selected representative cell lines for c-Myc (NCI-H82,

NCI-H524, NCI-H2171 and NCI-H2081) and L-Myc (CORL-88, NCI-H1963 and NCI-H209)

and confirmed protein expression for these transcription factors (Figure 2A). In order to define

open regulatory elements potentially regulated by c-Myc and L-Myc, we performed the assay for

transposase-accessible chromatin-sequencing (ATAC-seq) on those representative cell lines and

filtered identified accessible chromatin regions with motif matrices for Myc (E-box) referring to

as Myc accessible regions hereafter (Figure 2B). Of note, inferred c-Myc accessible regions

reasonably recapitulated c-Myc binding sites previously determined by c-Myc chromatin-

immunoprecipitation in NCI-H2171 cells (Lin et al., 2012) (Figure S3A and S3B). bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 9

Principal component analysis of peaks containing the E-box motif supported cell line

classification according to Myc status (Figure 2C). Additionally, a correlation matrix of pairwise

Pearson correlation coefficient of Myc accessibility signal captured distinct clusters for the c-

Myc and L-Myc classified cell lines (Figure S3C). We observed a fraction of peaks (cluster 1;

Figure 2D) that overlap between c-Myc classified and L-Myc classified cells, suggestive of

common functional binding that the Myc family members share. We also found distinct groups

of peaks with higher signal intensity unique to L-Myc classified cells (cluster 2; Figure 2D) and

in c-Myc classified cell lines (cluster 3; Figure 2D). Of note, the chromatin landscape of NCI-

H82 appeared to be intermediate to the L-Myc and c-Myc profiles. Together, these findings

suggest that c-Myc and L-Myc impart differential transcriptional programs.

Myc family members exert differential transcriptional programs

To gain insights into the unique biological processes c-Myc and L-Myc may impart, we

analyzed differentially enriched Myc accessible sites comparing between c-Myc cell lines and L-

Myc cell lines. We identified 2,808 differentially accessible regions; 1,235 peaks enriched in L-

Myc classified cell lines and 1,573 peaks enriched in c-Myc classified cell lines (Figure 3A).

Next, we performed GREAT analysis (McLean et al., 2010) on the differentially

accessible peaks. Unique L-Myc accessible sites are enriched for neuronal pathways such as glial

cell proliferation, development of the neural plate, neural fold and future midbrain (Figure 3B).

We observed augmented accessibility at neuronal development associated genes such as the

ASCL1 and FOXA2 loci in L-Myc cell lines when compared to c-Myc cell lines (Figure 3D). By

contrast, unique c-Myc accessible sites are enriched for pathways involved in Notch signaling

(Figure 3C). Accordingly, we observed preferential accessibility in c-Myc classified cell lines at bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 10

Notch targets such as REST and at NEUROD1 loci (Figure 3E). While specific Notch signaling

activity in MYC amplified SCLC has not been previously noted, there have been previous reports

showing Rest as a target of the Notch signaling pathway to suppress the expression of neuronal

genes (George et al., 2015, Lim et al., 2017). These findings are consistent with the observations

from the Bayesian network analysis providing additional evidence to suggest distinct

transcriptional programs in c-Myc and L-Myc driven SCLCs.

c-Myc plays a dominant role in lineage specification

Previous literature suggests that deletion of Mycl (L-Myc) in the classical murine SCLC

model led to the formation of mixed and NSCLC morphology instead of classical SCLC features

(Kim et al., 2016) in contrast to the c-Myc expressing SCLC exhibiting ‘variant’ morphology

(Gazdar et al.,1985; Mollaglu et al., 2017). Therefore, given our findings from Bayesian

Network analysis and Myc accessibility profiling, we hypothesized that the distinct programs

regulated by Myc family members establish these unique lineage states in SCLC. To test the

potential of L-Myc to establish the neuroendocrine lineage, we modified a c-Myc expressing

NeuroD1 classified cell line NCI-H82 by exogenous overexpression of L-Myc and CRISPR-

Cas9 mediated deletion of c-Myc.

We found c-Myc expressing cell line (NCI-H82) was able to tolerate the over-expression

of L-Myc (Figure 4A). Cells engineered to express both L-Myc and c-Myc exhibited a modest

increase in proliferation rates compared to parental cells expressing only c-Myc (Figure 4B). We

further demonstrated that replacement of c-Myc with the expression of L-Myc allowed the cells

to retain proliferative potential in the absence of c-Myc in NCI-H82 (Figure 4B). This indicates

redundant roles among the two Myc family members in regards to maintaining cell viability. bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 11

A recent review synthesized SCLC profiling studies to identify four molecular subtypes

that are driven by four transcription factors ASCL1, NeuroD1, YAP1 and POU2F3 (Rudin et al.,

2019). To inquire their contribution to lineage determination, we profiled the expression of

known lineage state markers. Addition of L-Myc neither induced expression of neuroendocrine-

marker ASCL1 nor altered the expression of variant state marker NeuroD1 (Figure 4A),

suggesting that L-Myc is not sufficient to induce lineage switch to classical neuroendocrine state.

This may be consistent with the reported infrequency of a transition from non-neuroendocrine to

neuroendocrine state (Lim et al., 2017). By contrast, the replacement of c-Myc with L-Myc led

to the downregulation of NeuroD1 while ASCL1 expression was not induced even upon the loss

of c-Myc. (Figure 4A). To further investigate if the downregulation of NeuroD1 expression was

a combinatorial effect of L-Myc expression and c-Myc deletion, we compared with simple

ablation of c-Myc in NCI-H82 (Figure 4C). Without L-Myc replacement, consistent with

previous findings (Jain et al., 2002), c-Myc was essential for survival in NCI-H82 (Figure 4B),

precluding us from evaluating converted lineage state. However, during the short window of

survival, c-Myc ablation led to significant downregulation of lineage state marker NeuroD1

suggesting a role for c-Myc on maintaining expression of lineage state marker. The expression of

neuroendocrine-low lineage state marker, YAP1, were not induced, suggesting a lineage negative

state for these cells (Figure 4A).

To investigate the state of these lineage-marker negative cells, we performed

transcriptomic profiling. First, we investigated the effect of expressing of L-Myc in the presence

of c-Myc in NCI-H82, which revealed 235 genes were significantly upregulated and 290 genes

were downregulated (Figure S3A). Gene ontology analysis of upregulated genes revealed

enrichment for neurogenesis and neuronal associated pathways (Figure S3B), suggesting that L- bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 12

Myc imparts neuronal pathways while not fully inducing ASCL1-lineage state. Next, we

investigated the profile of the lineage negative-NCI-H82 cells that were overexpressed with L-

Myc and ablated for c-Myc expression compared to NCI-H82 cells that were overexpressed with

L-Myc, and found genes involved in neuronal structure were enriched in the 170 upregulated

genes, suggesting the cells are further committed to a neuronal state (Figure S3C and S3D). A

direct comparison of the transcriptome control NCI-H82 cells with lineage negative-NCI-H82

cells, revealed significantly 458 upregulated genes were enriched for neuronal associated

pathways and the downregulated genes were enriched for canonical Myc function (Figure 4C

and 4D). Collectively, the data suggest that replacement of c-Myc with L-Myc induces a profile

that represents more neural state but not fully transdifferentiates to ASCL1 positive state and

remains to be negative for lineage markers used in the current consensus molecular classification

(Rudin et al., 2019).

Aurora Kinase A Inhibition sensitivity is altered with change in Myc status

Previous findings suggest that MYC (c-Myc) driven SCLC is more responsive to Aurora

kinase A inhibition (Mollaoglu et al., 2017). We confirmed previous findings that MYC

amplified cell lines exhibit increased sensitivity, but we found that MYCL-amplified cell lines are

resistant to alisertib (Figure S4A). Alisertib treatment of the NCI-H82 cells expressing both c-

Myc and L-Myc revealed no changes in sensitivity to the drug nor did changes in expression of

the variant state marker. However, the cells that replaced c-Myc with L-Myc became resistance

to aurora kinase inhibition (Figure S4B). On the other hand, overexpression of c-Myc increased

sensitivity at lower concentrations of the drug (Figure S4C). This suggests either alisertib bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 13

sensitivity is attributable to c-Myc driven lineage state of the cells, or difference in molecular

interaction of Aurora kinase A with each Myc protein.

c-Myc causes loss of classical neuroendocrine SCLC features

The dependency of lineage state marker NeuroD1 on c-Myc led us to hypothesize that c-

Myc exerts a role in addition to its oncogenic role to regulate and establish a variant

differentiation state. To this end, we genetically engineered a neuroendocrine L-Myc expressing

cell lines NCI-H1963 with inducible exogenous overexpression of c-Myc (Figure 5A). Of note,

exogenous expression of c-Myc led to downregulation of L-Myc in NCI-H1963.

When c-Myc expression is introduced in neuroendocrine-high MYCL-amplified NCI-

H1963, we observed an initial phase of growth suppression (Figure 5B). This observation stands

in contrast to the established role of c-Myc to promote cell cycle and cell proliferation (Dang,

2012). The growth suppression is unlikely to be due to oncogene induced senescence as the

genomic profile of these cells lack Rb1 and p53 and did not stain positive for beta galactosidase

(Figure S5A). Next, we investigated the cell cycle dynamics of NCI-H1963 c-Myc expressing

cells in their growth suppressive phase, and found that initial c-Myc expression (day 2) modestly

increased the proportion of cells in S and G2/M phase but did not significantly alter the

distribution of cells or induce cell cycle arrest to explain slower cell growth (Figure S5B).

Alternatively, we sought to investigate whether NCI-H1963 did not tolerate the expression of c-

Myc and were consequently undergoing apoptosis. We investigated the proportion of Annexin-V

positive cells on day 2 and found with c-Myc overexpression there was an increase in the

proportion of necrotic and apoptotic cells as compared to control cells (Figure 5C). bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 14

We hypothesized that the cell death was a consequence of incompatibility of the

neuroendocrine differentiation state with c-Myc expression and that the persisting cells

eventually grown out were cells that tolerated the expression of c-Myc, potentially by switching

into a differentiation state compatible with c-Myc expression (Figure 5B). Therefore, to inquire

if the tolerant cells transitioned to an altered lineage state, we investigated the expression of

lineage state markers and found that the overexpression of c-Myc downregulated ASCL1 and

upregulated NeuroD1 in H1963 cells (Figure 5A), indicating the trans-differentiation to

NeuroD1-expressing subtype of SCLC. The switch suggests that c-Myc exerts a role in dictating

a distinct neuroendocrine differentiation state defined by NeuroD1 expression. To investigate if

the trans-differentiation between molecular subtypes was accompanied by histological class

switch in vivo, we injected NCI-H1963 cells genetically engineered with stable c-MYC

expression. We observed that NCI-H1963 overexpressed with LacZ (control cells) had oval and

elongated nuclei with a high nuclear:cytoplasm ratio, smooth granular chromatin, no nucleoli

representative of classical SCLC histology and NCI-H1963 overexpressed with c-Myc showed

presence of nucleoli and polygonal shaped larger cells indicative of the variant subtype (Figure

5D, Figure S5C). Together, these results suggest that c-Myc expression drive the transition from

ASCL1-positive state characterized by classical SCLC histology to NeuroD1-positive state

SCLC characterized by variant SCLC histology.

c-Myc mediates trans-differentiation through REST

We sought to investigate the mechanism through which c-Myc mediates trans-

differentiation to the ASCL1-negative/NeuroD1-positive state. To this end, we profiled the

accessible chromatin profile of trans-differentiated NCI-H1963. Filtering the regions to reflect bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 15

regions with Myc (E-box) motif matrices reflected a common pattern (cluster 1 to 3). c-Myc

overexpression resulted in the gain of additional regulatory regions (cluster 4) and a decrease in

accessibility at sites located in cluster 5 (Figure 6A). Thus, revealing an alteration of the MYC

(E-box) cistrome and further suggesting non-redundancy of this family of transcription factors.

To identify the transcriptional programs associated with the changes in regulatory regions of the

MYC cistrome, we performed RNA-seq on trans-differentiated NCI-H1963. We identified 135

differentially expressed genes (70 genes downregulated and 45 genes upregulated) (Figure 6B).

Gene ontology analysis for upregulated genes revealed an enrichment for cell death and

apoptosis (Figure 6C) while gene ontology analysis for downregulated of genes revealed an

enrichment for neuronal associated genes (Figure 6C). These findings are consistent with the

initial growth suppression and the loss in neuroendocrine lineage state marker and (Figure 5A

and Figure 5B), but did not reveal specific mechanistic insights.

Previous works have shown the negative regulation of ASCL1 by Notch signaling during

lung development (Ito et al., 2000) and activation of Notch signaling in SCLC suppresses

ASCL1 expression (George et al., 2015, Augert et al., 2019, Oser et al., 2019). Additionally, our

SCLC transcriptional network analysis and Myc accessibility data revealed Notch pathway

activation preferentially in c-Myc expressing tumors and cell lines (Figures 1E and 3C).

Although we did not observe an enrichment for an active Notch signaling signature amongst the

upregulated genes, when we investigated the expression of known Notch signaling pathway

targets in trans-differentiated NCI-H1963, we identified Notch-target RE-1 Silencing

transcription factor (REST) that is a transcriptional repressor of neuronal genes and ASCL1 (Lim

et al, 2017) as one of the genes that were induced upon c-Myc expression (Figure S6A) and we

confirmed this with protein expression (Figure S6B). Therefore, we sought to investigate the c- bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 16

Myc–Notch–Rest axis in mediating trans-differentiation, we pharmacologically inhibited

Notch1-4 using a γ-secretase inhibitor (GSI), DBZ (Ran et al., 2017) in combination with c-Myc

expression in NCI-H1963 cells. Inhibited Notch signaling, did not alter expression of Rest and

ASCL1 (Figure 6E). These data suggest a novel Notch-independent, c-Myc mediated activation

of Rest that would in turn suppress ASCL1 expression.

Discussion

In this study, we showed a novel distinction in functional enrichment for transcriptional

programs associated with c-Myc and L-Myc, in contrast to the prevailing notion of the similarity

of molecular function of Myc family members in SCLC (Kim et al., 2016). We identified c-Myc

sub-networks and c-Myc accessibility profile are associated with canonical roles including

transcriptional and translational regulation, as well as lineage associated pathways Notch

signaling and epithelial-mesenchymal transitions. Our findings are indeed supported by a

previous report that described active Notch signaling negatively regulating neuroendocrine

lineage by suppressing the expression of ASCL1 (Lim et al., 2017) as well as a study that showed

mesenchymal-like features in neuroendocrine-low sub-type of SCLC (Zhang et al., 2018). By

contrast, we found L-Myc subnetwork and accessibility profile were associated with neuronal

pathways implicating their relevancy in determining the classical neuroendocrine state of SCLC.

These findings are consistent with previous work showing a correlation of expression between L-

Myc and neuronal proteins (Kim et al., 2006). Taken together, contrary to the notion that Myc

family members, c-Myc in particular, exclusively act as oncogenes and general amplifiers of

expression, our data suggest that they additionally exert lineage defining transcriptional

programs (Lin et al., 2012). We propose that the Myc family members regulate a defined set of bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 17

transcriptional programs that are essential in SCLC lineage subtype determination. Of note,

although these biological pathways associated with these factors have been shown to be relevant

for lineage maintenance and determination in distinct subtypes of SCLC but their association

with Myc family members has not been described before.

We found L-Myc failed to induce the expression of neuroendocrine lineage state marker,

ASCL1 suggesting its inability to push trans-differentiation from neuroendocrine-low/variant

state to neuroendocrine high/classical state. However, based on previous work suggesting

requirement of L-Myc to drive tumors of neuroendocrine origin in SCLC GEMM (Kim et al.,

2016) and our findings that suggest L-Myc regulates neuronal development pathways, L-Myc

likely plays roles in lineage state maintenance and/or is more compatible to the neuroendocrine

lineage.

We also report a previously undescribed function for c-Myc in lineage maintenance in

NEUROD1-positive SCLC. c-Myc expression was incompatible with ASCL1-positive SCLC

and induces trans-differentiation to NeuroD1-positive revealing the role for c-Myc in regulating

lineage plasticity. We show that this trans-differentiation between molecular subtypes of SCLC

is accompanied by alterations in the transition of histopathology in SCLC from classical SCLC

features to variant histopathology. These data reflect that the variant histopathology, that would

typically be classified as “mixed” or “combined” SCLC per the current guidelines of WHO, can

indeed be derived from SCLC. This phenotype is perhaps reflective of biology distinct from

classical SCLC and may warrant distinct therapeutic strategies.

We identified that the trans-differentiation is mediated independent of Notch signaling but

through the direct activation of a Notch signaling target, Rest, a transcriptional repressor. These bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 18

findings may be clinically important when evaluating the rationale for targeting SCLC by

activating Notch signaling with drugs including LSD1 and KDM5A (Augert et al., 2019, Oser et

al., 2019). Our data suggests that downstream actors of Notch signaling may induce the trans-

differentiation to NEUROD1-positive or variant subtype. This may not be a favorable outcome

since the variant subtype has been reported to be more frequent in tumors after initial response to

chemotherapy (Gazdar et al., 1985, Mollaglu et al., 2017). We found that the altered

differentiation state with the gain of L-Myc and loss of c-Myc cells conferred resistance to

Aurora Kinase A inhibition and in contrast the trans-differentiation as a result of c-Myc gain and

loss of L-Myc cells conferred sensitivity to Aurora Kinase A inhibition. The differential

sensitivity associated with distinct lineage state with Myc family member status as a biomarker

could translate to significant clinical implications in stratification of patients for targeted therapy.

SCLC is a recalcitrant disease typically characterized by neuroendocrine differentiation,

nonetheless approximately 10-20% of SCLCs may lack expression of diagnostic neuroendocrine

markers (Rekhtman, 2010). Here we report, the plasticity between these histological subtypes

and molecular subtypes regulated by c-Myc and L-Myc. The role in lineage determination and

maintenance for this family of transcription factors is striking since the Myc family has

historically been grouped as general oncogenes. Our data suggests that the role of Myc family in

SCLC tumorigenesis could be re-defined. This will enable us to categorize these subtypes to

develop effective therapies to combat this aggressive disease.

Patel, A et al. 19

Experimental Procedures

CCLE RNA-seq analysis

RNA-seq gene expression data and SNP-array copy number data for MYC, MYCL and

MYCN were downloaded from (CCLE) web site (http://www.broadinstitute.org/ccle/home).

Log2(RPKM (reads per kilobase per million) +1) values for mRNA expression and log2(inferred

copy number/2) values for copy numbers were used to depict a heatmap for a 49 SCLC cell lines.

Bayesian Network Analysis

The sub-networks were identified when the gene signature was overlaid on the network

to include genes in the signature and its two-layered neighbors. Key driver analysis was

performed to identify master regulators associated with L-Myc and c-Myc signatures. For each

node in the network, we compared neighboring nodes within two layers for each signature to test

whether their overlap is statistical significant based on Fisher’s exact test (FET) p-value

considering multiple testing (FET p<0.05/(# of nodes)). Enriched functions were identified using

GO terms in Molecular Signatures Database (MSigDB). The significance of the enrichment was

determined based on FET p-value considering multiple testing (FET p<0.05/(# of dataset tested))

in each category.

Cell Lines

All cells were cultured at 37°C in a humidified incubator at 5% CO2. SCLC cell lines

NCI-H2171, NCI-H82, NCI-H524, NCI-H209, NCI-H1963, CORL88, HCC33, NCI-H69, and

NCI-H526 were maintained in Roswell Park Memorial Institute medium (RPMI1640, Gibco),

and supplemented with 10% Fetal Bovine Serum (FBS, Sigma) and 1mM Penicillin bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 20

Streptomycin (P/S). HEK293T cells were maintained in Dulbecco’s Modified Eagle’s Medium

(DMEM), and supplemented with 10% Fetal Bovine Serum (FBS, Sigma) and 1mM Penicillin

Streptomycin (P/S). Cultured cells were regularly tested for mycoplasma using the mycoAlert

Detection Kit (Lonza).

ATAC-seq

ATAC-seq was performed as previously described (ref). Five thousand cells were

harvested and pre-treated with DNase I (Thermo Fisher Scientific) at 37°C for 20 minutes to

eliminate DNA contamination from dead cells. The cells were washed in cold PBS to eliminate

traces of DNase. Nuclei were isolated with nuclear lysis buffer (10 mM Tris, 10mM NaCl, 3mM

MgCl2 0.1% IGEPAL-630) and centrifuged at low speeds. The nuclei were resuspended in

transposase reaction mixture (10mM tris PH 8.0, 5 mM MgCl2, 10% DMF, 0.2 mg/ml Tn5

transposase complex). Transposition was carried out at 37°C for 30 minutes followed by DNA

purification with DNA Clean and Concentrator-25 (Zymo Research) according to manufacturer’s

recommendation. Following purification, library fragments were PCR amplified with Nextera

adapter primers. Sequencing was performed at Tisch Cancer Institute sequencing core on the

NextSeq500 (Illumina) for 38 nucleotides each from paired ends according to manufacturer’s

instructions.

ATAC-seq data analysis

Illumina sequencing adapter was removed using Cutadapt from raw sequence files in

fastq format. The reads were aligned to the hg19 reference genome using Bowtie2 with -k 1

parameters. The aligned reads were used to eliminate PCR duplicates using samtools and bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 21

filtered off an ATAC blacklist for mitochondrial DNA homologs generated by Buenostro et al.,

2015. Fragment ends were shifted +4 nucleotides for positive strand and -5 for negative strand to

account for distance from Tn5 binding and helicase activity to identify cut sites. Extended Tn5

cut sites were used for peak calling with MACS2 with parameters --nomodel --extsize 100 --shift

50 --nolambda --keep-dup all. Fraction of reads in peaks (FRiP) score was calculated for each

sample and was used as a normalization factor for pileup rescaled to a total number of uniquely

alignable sequences by WigMath function of Java-Genomic Toolkit. Peaks were filtered by

MYC motif using findMotifsGenome function in homer using four motif matrices MA0058.2

(MAX), MA0059.1 (MAX::MYC), MA0104.4 (MYCN), and MA0147.3 (MYC). Peaks were

quantized using k-means clustering (k=8) and heatmaps for the peaks filtered with MYC motif

were generated using cistrome (http://cistrome.org/) or using k-means clustering (k=6) and

plotHeatmap function of deepTools (Liu et al., 2011) using wiggle files for genetically

engineered NCI-H1963. Normalized ATAC-seq signals for each sample was visualized on

integrative genome viewer (IGV) genome browser (Robinson et al., 2011).

Differentially bound sites comparing three c-Myc classified cell lines vs three L-Myc

classified cell lines were identified using R package DiffBind with cutoffs set at fold change ³ 5

and FDR £ 0.05. Functional analysis of the differentially bound regions was performed using

Genomic Regions Enrichment of Annotations Tool (GREAT).

Western Blot and Antibodies

Protein lysates were prepared by washing cells with 1 ml of cold PBS and resuspended in

lysis buffer (150 mM NaCl, 50 mM Tris-HCl at pH 8.0, 1% NP-40, 0.5% Na deoxycholate, 0.1%

SDS, protease inhibitors) for 30 min at 4°C. Lysates were centrifuged at 5°C for 15 minutes at bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 22

13,000 rpm to remove insoluble debris. Protein concentrations were quantified using Pierceä

BCA (Thermo Fisher Scientific). Proteins were separated by electrophoresis on a SDS-PAGE gel

(BioRad), transferred to a PVDF membrane (Thermo Fisher Scientific) and blocked with 5%

milk in TBS-T. The membranes were immunoblotted with anti-c-Myc (Santa Cruz

Biotechnology), anti-L-Myc (Proteintech), anti-vinculin (Sigma), anti-ASCL1 (BD Biosciences)

and anti-NEUROD1(Proteintech), anti-β-Actin antibody (Sigma), or anti-vinculin (Sigma).

Lentiviral introduction of genes

c-Myc, L-Myc or LacZ open reading frame (ORF) was cloned into pLEX_307 (a gift

from David Root, Addgene #41392) using the Gateway® cloning methods according to

manufacturer’s recommendations. (Thermo Fisher Scientific). HEK293T cells were seeded in a

10cm tissue culture dish and incubated at 37°C and 5% CO2. At 80% confluency the cells were

co-transfected with 10 µg of plasmid constructs, 7.5 µg of psPAX2 (a gift from Didier Trono,

Addgene #12260) and 2.5 µg of pMD2.G (a gift from Didier Trono, Addgene #12259) vectors

using TransIT-Lenti (Mirus) following manufacturer’s recommendations. At 48h post

transfection, virus-containing supernatants were collected, filtered (0.45 µm) and stored at

−80°C. Cells were infected with lentiviral supernatant supplemented with polybrene at a final

concentration of 8 µg/mL or lentiBlast (OZ BioSciences) at a ratio of 1:1000. Cells were selected

with puromycin (1-2 µg/mL for 4-6 days).

CRISPR-Cas9 genome editing Cells with stable human codon-optimized S. pyogenes Cas9 expression were generated

by infection with the lentiCas9-Blast plasmid (a gift from Feng Zhang, Addgene # 52962).

sgRNAs targeting MYC and MYCL were selected from the Brunello library (Doench, Nat bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 23

Biotechnol. 2016). Non-target sgRNA from the Gecko library v2 (Sanjana et al Nat Methods.

2014) were used as non-target sgRNAs. sgGRNA target sequences are listed in Table S3.

sgRNAs were cloned using BbsI site downstream of the human U6 promoter in a lentiviral

vector containing EGFP downstream of the human PGK promoter (a kind gift of Brown

laboratory, ISMMS). Lentivirus was produced as described above. Cas9 expressing cells were

then infected with pLenti-GFP-sgRNA.

Cell proliferation assay

Cells were plated at a density of 5,000 cells/well with five replicates in a 96-well plate;

four identical plates were prepared. Cell viability was assayed at 0, 2, 4 and 6 days after plating

with alamarBlue Cell Viability Reagent (Thermo Fisher) and fluorescence at 585 nm was

measured on a Spectra Max3 plate reader (Molecular Device) according to the manufacturer’s

protocol at excitation of 555 nm. Cell viability at 2, 4 and 6 days were corrected for the ratio to

control cells from the day 0 reading to account for plating unevenness.

RNA-seq

Total RNAs from engineered NCI-H82 cell lines were extracted using the Qiagen

RNeasy kit. Poly-adenylated RNA was enriched from 1ug of RNA for each sample with the

NEBNext PolyA mRNA Magnetic Isolation Module (NEB, E7490), incubated at 94°C for 15

min and double-strand cDNA was synthesized using SuperScript III reverse transcriptase

(ThermoFisher) and NEBNext® Ultra™ II Directional RNA Second Strand Synthesis Module

(NEB). Up to 10 ng of cDNA was used for the Illumina sequencing library construction using

NEBNext Ultra DNA Library Prep Kit (NEB, E7645). Paired ends sequencing was performed on bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 24

NextSeq 500 (Illumina) for 38 nucleotides from each end according to the manufacturer’s

instructions.

RNA-seq analyses RNA-seq analyses for engineered NCI-H82 cell lines, sequencing reads were

pseudoaligned to the human reference transcriptome GRCh38.95 from Ensembl and transcript

abundance was estimated using kallisto (v0.45.0) (Bray et al., 2016) . Transcript abundance was

aggregated to gene level abundance using biomaRt annotation. Then DEseq2 (Love et al., 2014)

was used to identify the differentially expressed genes between control NCI-H82 cells (LacZ-

overexpressed NCI-H82 cells, LacZ-overexpressed in combination with nontarget sgRNA1 NCI-

H82 and LacZ-overexpressed in combination with nontarget sgRNA2 NCI-H82) and L-Myc-

overexpressed NCI-H82 cells (L-Myc-overexpressed NCI-H82 cells, L-Myc-overexpressed with

nontarget sgRNA1 NCI-H82 and L-Myc-overexpressed with nontarget sgRNA2 NCI-H82). The

same strategy was used for L-Myc-overexpressed NCI-H82 cells (L-Myc-overexpressed NCI-

H82 cells, L-Myc-overexpressed in combination with nontarget sgRNA1 NCI-H82 and L-Myc-

overexpressed in combination with nontarget sgRNA2 NCI-H82) and c-Myc replaced with L-

Myc NCI-H82 cells (L-Myc-overexpressed NCI-H82 with sgRNA-MYC1 cells, L-Myc-

overexpressed with sgRNA-MYC2 NCI-H82 and L-Myc-overexpressed with sgRNA-MYC3

NCI-H82), as well as for LacZ-overexpressed NCI-H1963 cells and c-Myc overexpressed NCI-

H1963 cells.

Total differentially expressed genes were determined based on cutoffs of fold change> 2

and FDR<0.05. To identify potentially enriched functions of selected gene sets of interest, we

compared these gene sets with the genes annotated by the same Gene Ontology (GO) terms

curated in the Molecular Signature Database (MSigDB) (Subramanian et al., 2005). Each of bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 25

5917 GO terms included in “C5” collection (version 7.0) was compared with query gene sets

using Fisher’s exact test. The significance of the overlap was determined based on p-value

adjusted for multiple comparisons (FDR<0.05). Any GO terms consisting of more than 2,000

genes were considered non-specific and removed from the analysis.

Beta-galactosidase staining Cells were stained with Senescence beta-galactosidase Staining Kit (Cell signaling

#9860) according to manufacturer’s recommendations.

Cell cycle analysis Cells were harvested at day 2 after selection and fixed with 70 % ethanol at least

overnight at 4°C. Cell were washed with PBS, then, incubated in PBS containing 100 µg/ml

RNase and 50 µg/ml propidium iodide at room-temperature for 1 hour. DNA content was

analyzed by FACS Canto II (BD Bioscience), and quantitative analyses for the proportions of

cells in cell cycle were performed using FlowJo software.

Annexin-V staining

Cells were harvested at day 2 after selection and washed with PBS. Cells were stained

with Annexin-V and propidium iodide using Alexa Fluor® 488 annexin V/Dead Cell Apoptosis

Kit (Thermofisher Scientific) according to manufacturers’ recommendations. Fluorescence was

measured by FACS Canto II (BD Bioscience). Quantitative analyses for the cell viability

proportions were performed using FlowJo software.

Patel, A et al. 26

Xenograft model

All animal procedures and studies were approved by the Mount Sinai Institutional

Animal Care Use Committee (IACUC) (protocol number, IACUC-2018-0021). H1963 cells

which overexpressed LacZ or cMYC (5 x 106 cells) were injected with a 1:1 mixture of 50 µl cell

suspension and 50 µl Matrigel (Corning) subcutaneously into both flank regions of 4- to 6-week-

old male NOD-scid gamma mice (Jackson Laboratory). Tumor volume (length x width2 /2) was

measured twice a week. When tumor size reached at 1000 mm3, mice were sacrificed and tumors

were immersed in formalin for immunohistological analysis.

Aurora Kinase Inhibition Assay

Cells were seeded in a 96-well plate at a density of 5,000 cells/well with five replicates. Cells

were treated with Alisertib at concentrations ranging from 1nM to 10 µM for 96h. Cell viability

was measured using alamarBlue Cell Viability Reagent (Thermo Fisher Scientific) and

fluorescence at 585nm was measured on a Spectra Max3 plate reader (Molecular Devices, CA)

according to the manufacturer’s protocol at excitation of 555 nm.

Patel, A et al. 27

Acknowledgements

We thank Dawei Yang, Bhavana Shewale, Nicole Stokes, David Dominique-Sola, Christie

Nguyen, Yifei Sun for helpful discussions. Aleksandra Wroblewska and Brian D. Brown for

providing a lentiviral vector for cloning sgRNAs; NextSeq Sequencing Facility of the

Department of Oncological Sciences at Icahn School of Medicine at Mount Sinai (ISMMS),

Saboor Hekmaty, Gayatri Panda and Ravi Sachidanandam for sequencing assistance. The

authors also thank the Flow Cytometry Core facility and the Biorepository and Pathology Core

Facility at ISMMS. This work was supported in part through Tisch Cancer Institute at ISMMS

and the computational resources and staff expertise provided by Scientific Computing at

ISMMS. H. Watanabe is supported by 2017 ATS Foundation Unrestricted Grant: Pulmonary, the

American Lung Association of the Northeast and NIH (R01CA240342). T. Sato is supported by

the Japanese Respiratory Society the 6th Lilly Oncology Fellowship Program and the Uehara

Memorial Foundation. R. Kong is supported by Shaanxi Provincial Natural Science Foundation,

China (No. 2017JM8046). C.A. Powell and H. Watanabe are supported by NIH

(R01CA163772).

Author Contribution

Conceptualization & Methodology, A.S.P. and H.W.; Data Collection: A.S.P., S.Y., R.K., T.S., M.F., A.S., and H.W.; Resources, M.B.B., C.A.P. and H.W.; Data Analysis, A.S.P., S.Y., G.N., M.B.B. and H.W.; Writing – Original Draft, A.P. and H.W.; Writing –Review & Editing, A.S.P., S.Y., G.N., C.A.P., M.B.B, J.Z. and H.W.; Supervision, C.A.P., J.Z. and H.W.; Project Administration & Funding Acquisition, H.W.

Patel, A et al. 28

Declaration of Interests

Jun Zhu and Seungyeul Yoo are employees of Sema4, a for-profit organization that promotes

genomic sequencing for patient-centered healthcare. bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 29

Figure legends

Figure 1: Bayesian Network analysis reveals unique L-Myc and c-Myc networks associated with

distinct biological processes

A. Schematic of workflow to use SCLC Bayesian Network to identify c-Myc and L-Myc

networks.

B. Sub-network showing directionality and association of genes when L-Myc gene signature

(Figure S1B) is projected to SCLC Bayesian network. Circles colored in pink represent

nodes from L-Myc gene signature. Size of circle is directly proportional to the number of

outgoing nodes. Nodes indicated in larger text are Key drivers of the sub-network (Table

S1).

C. Gene Ontology analysis for L-Myc neighbor subnetwork. Enriched functions for these

genes are identified based on Fisher’s exact test against GO terms.

D. Three sub-networks showing directionality and association of genes when c-Myc

associated gene signature (Figure S1B) is projected to SCLC Bayesian network. Circles

colored in blue represent nodes from c-Myc gene signature. Size of circle is directly

proportional to the number of outgoing nodes. Nodes indicated in larger text are Key

drivers of the sub-network (Table S2).

E. Gene Ontology analysis for corresponding c-Myc neighbor subnetwork. Enriched

functions for these genes are identified based on Fisher’s exact test against GO terms.

Figure 2: c-Myc and L-Myc driven SCLCs exhibit distinct chromatin states

A. Protein expression of c-Myc and L-Myc as well as vinculin as a loading control in a panel

of representative SCLC cell lines bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 30

B. Schematic showing the analytic approach to define Myc accessible regions.

C. Principal component analysis of open chromatin regions with E-Box motif in six

SCLC cell lines. Each point represents a SCLC cell line that is colored based on Myc

status (pink: LMYC, blue: cMYC).

D. Each heatmap depicting global Myc (E-box) accessibility in individual SCLC cell lines

at 72,833 combined peaks. ATAC-seq signal intensity is shown by color shading.

Figure 3: Myc accessibility reveals distinct transcriptional programs regulated by c-Myc and L-

Myc

A. Heatmap showing 2,808 differentially accessible regions (fold change ³ 5 and FDR £

0.05) between 3 L-Myc cell lines shown in pink (H209, H1963, CORL88) and 3 c-Myc

cell lines shown in blue (H524, H82, H2171)

B. Enriched ontology by GREAT analyses for regions differentially accessible in L-Myc

classified cells.

C. Enriched ontology by GREAT analyses for regions differentially accessible in c-Myc

classified cells.

D. Genome browser view tracks of ATAC-seq signals in L-Myc cell lines peaks shown in

pink (NCI-H209, NCI-H1963, CORL88) and c-Myc cell lines shown in blue (NCI-H524,

NCI-H82, NCI-H2171) at the FOXA2 (top panel) and ASCL1 (bottom panel) loci.

E. Genome browser view tracks of ATAC-seq signals in L-Myc cell lines peaks shown in

pink (NCI-H209, NCI-H1963, CORL88) and c-Myc cell lines shown in blue NCI-H524,

NCI-H82, NCI-H2171) at the REST (top panel) and NEUROD1 (bottom panel) loci.

Patel, A et al. 31

Figure 4: c-Myc is required to maintain NeuroD1-positive lineage state

A. Protein expression of L-Myc, c-Myc, ASCL1, NeuroD1 and YAP1 as well as vinculin as

a loading control in genetically engineered NCI-H82 cells to replace c-Myc with L-Myc.

B. Cell growth curve for NCI-H82 cells with over-expression of L-Myc and genetic deletion

of c-Myc.

C. Protein expression of c-Myc and NeuroD1 with vinculin as loading control in genetically

engineered NCI-H82 cells with c-Myc ablation

D. Volcano plot showing 525 differentially expressed genes (458 upregulated in pink and

294 downregulated in blue) between control NCI-H82 cells (LacZ-overexpressed cells

and LacZ-overexpressed cells with nontarget gRNA) and NCI-H82 cells (LMYC-

overexpressed cells and LMYC-overexpressed cells with three gRNA targeting MYC).

E. Gene Ontology analysis for differentially upregulated (top panel) and downregulated

genes (bottom panel) for comparison in Figure S5C. Enriched functions for these genes

are identified based on Fisher’s exact test against GO terms.

Figure 5: c-Myc expression in ASCL1-positive SCLC cells induces trans-differentiation to

histologically distinct NeuroD1-positive state

A. Protein expression of c-Myc, L-Myc, ASCL1, and NeuroD1, as well as vinculin as

loading control in genetically engineered NCI-H1963 cells.

B. Cell growth curve for NCI-H1963 cells with over-expression of c-Myc and control Lac-Z

cells. bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 32

C. Dot plot showing Annexin-V staining for NCI-H1963 cells with control Lac-Z cells (left

panel) and over-expression of c-Myc (right panel) with proportion of viable, necrotic and

apoptotic cells.

D. Hematoxylin-eosin staining in LacZ-overexpressed (left) or c-Myc-overexpressed (right)

NCI-H1963 xenograft. Original Images, x20.

Figure 6: c-Myc mediates lineage switch through REST

A. Volcano plot showing 145 differentially expressed genes (45 upregulated in purple and

70 downregulated in pink) between control NCI-H1963 cells LacZ-overexpressed cells

and NCI-H1963 cells c-Myc- overexpressed.

B. Gene Ontology analysis for differentially upregulated (top panel) and downregulated

(bottom panel) genes. Enriched functions for these genes are identified based on Fisher’s

exact test against GO terms.

C. Each heatmap depicting k-means clustering of global Myc (E-box) accessibility in

NCI-H1963 overexpressing LacZ (left) and NCI-H1963 overexpressing cMYC (right) at

44,722 combined peaks. ATAC-seq signal intensity is shown by color shading.

D. Protein expression of c-Myc, L-Myc, ASCL1, and Rest, as well as vinculin as loading

control in genetically engineered NCI-H1963 cells treated with vehicle (DMSO) or 0.5

µM of DBZ for 48 hours.

Patel, A et al. 33

Supplementary Figure Legends

Figure S1: Myc family status and signature in SCLC CCLE cell lines

A. Scatter-plot showing c-Myc expression and L-Myc expression for 49 SCLC cell lines.

Cell lines selected to generate signature are highlighted L-Myc in pink and c-Myc in

blue.

B. Heatmap showing 457 differentially expressed (t-test p-value < 0.01 and fold change >

1.5) between four c-Myc expressing CCLE SCLC cell lines (H1341, H2081, H211,

H524, SCLC21H) and eight L-Myc expressing CCLE SCLC cell lines (HCC33, H1092,

H1184, H1836, H1963, H2029, H209). 147 genes upregulated in c-Myc (condition

shown in blue) and 310 genes upregulated in L-Myc (condition shown in pink).

C. Classification of 49 SCLC cell lines using copy number and expression of MYC, MYCL

and MYCN from CCLE

Figure S2: ATAC-seq inferred c-Myc accessibility is comparable to c-Myc ChIP-seq bound

regions and classifies by Myc status

A. Heatmap comparing Myc (E-box) accessibility inferred from ATAC-seq with c-Myc

bound sites with E-box motif inferred from ChIP-seq (Lin et al., 2012) in NCI-H2171.

ATAC-seq/ChIP-seq signal intensity is shown by color shading.

B. Genome browser view tracks of ATAC-seq signals and c-Myc ChIP-seq at HES1 locus.

C. Heatmap showing unsupervised clustering of degree of correlation for pairwise

comparisons of identified open chromatin regions with MYC motif in 3 L-Myc classified

cell lines H209, H1963, CORL88 (condition shown in pink) and 3 c-Myc classified cell

lines H524, H82, H2171 (condition shown in blue) bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 34

Figure S3: L-Myc induces neuronal state not defined by ASCL-1

A. Volcano plot showing 525 differentially expressed genes (235 upregulated in purple and

290 downregulated in blue) between control NCI-H82 cells (LacZ-overexpressed cells

and LacZ-overexpressed cells with nontarget gRNA) and NCI-H82 cells (LMYC-

overexpressed cells and LMYC-overexpressed cells with nontarget gRNA).

B. Gene Ontology analysis for differentially upregulated (top panel) and downregulated

(bottom panel) genes. Enriched functions for these genes are identified based on Fisher’s

exact test against GO terms.

C. Volcano plot showing 214 differentially expressed genes (170 upregulated in pink and 44

downregulated in purple) between NCI-H82 cells (L-Myc-overexpressed cells and L-

Myc-overexpressed cells with nontarget gRNA) and NCI-H82 cells (L-Myc-

overexpressed with three gRNA targeting MYC).

D. Gene Ontology analysis for differentially upregulated (top panel) and downregulated

(bottom panel) genes. Enriched functions for these genes are identified based on Fisher’s

exact test against GO terms.

Figure S4: Aurora Kinase A inhibition shows differential sensitivity between c-Myc and L-Myc

expressing cells

A. Heatmap showing dose-dependent anti-proliferative activity of Alisertib as screened

across a panel of 3 c-Myc (shown in blue) and 3 L-Myc cell lines (shown in pink) at 96

hours. Cell viability (Alamar Blue) was calculated relative to the control. Data are means

from at least n = 3 biological replicates. bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 35

B. Dose-response curves of H82–replacement cells treated with Alisertib for 96 hours.

Viability was assessed with the Alamar Blue assay and calculated relative to the control.

Data are means from at least n = 3 biological replicates.

C. Dose-response curves of H1963–replacement cells treated with Alisertib for 96 hours.

Viability was assessed with the AlamarBlue assay and calculated relative to the control.

Data are means from at least n = 3 biological replicates.

Figure S5: c-Myc induced growth-suppression is not a consequence of altered cell cycle

dynamics

A. Beta-galactosidase staining for NCI-H1963 LacZ-overexpressed (top panel) or c-Myc-

overexpressed (bottom panel) at day 2.

B. Propidium iodide staining followed by flow cytometry showing cell cycle distribution in

NCI-H1963 LacZ-overexpressed control cells and c-Myc overexpressed cells at day 2.

C. Hematoxylin-eosin staining in LacZ-overexpressed (left) or c-Myc-overexpressed (right)

NCI-H1963 xenograft. Original Images, ×4 (top) and x10 (bottom).

Figure S6: c-Myc induces expression of neuronal repressor REST

A. Expression plot for REST in NCI-H1963 cells LacZ-overexpressed cells and NCI-H1963

cells c-Myc- overexpressed.

B. Protein expression of c-Myc, and Rest, as well as vinculin as loading control in

genetically engineered NCI-H1963 cells.

Patel, A et al. 36

References Alvarado-Luna, G., D. Morales-Espinosa, 2016. Treatment for small cell lung cancer, where are we now?—a review. Transl. Lung Cancer Res 5(1): 26-38.

Amati, B., Brooks, M.W., Levy, N., Littlewood, T.D., Evan, G.I., Land, H., 1993. Oncogenic activity of the c-Myc protein requires dimerization with Max. Cell 72, 233–245. https://doi.org/10.1016/0092-8674(93)90663-B

Borromeo, M.D., Savage, T.K., Kollipara, R.K., He, M., Augustyn, A., Osborne, J.K., Girard, L., Minna, J.D., Gazdar, A.F., Cobb, M.H., Johnson, J.E., 2016. ASCL1 and NEUROD1 Reveal Heterogeneity in Pulmonary Neuroendocrine Tumors and Regulate Distinct Genetic Programs. Cell Reports 16, 1259–1272. https://doi.org/10.1016/j.celrep.2016.06.081

Brägelmann, J., Böhm, S., Guthrie, M.R., Mollaoglu, G., Oliver, T.G., Sos, M.L., 2017. Family matters: How MYC family oncogenes impact small cell lung cancer. Cell Cycle 16, 1489– 1498. https://doi.org/10.1080/15384101.2017.1339849

Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 34, 525–527 (2016).

Carney, D.N., Gazdar, A.F., Bepler, G., Guccion, J.G., Marangos, P.J., Moody, T.W., Zweig, M.H., Minna, J.D., 1985. Establishment and Identification of Small Cell Lung Cancer Cell Lines Having Classic and Variant Features. Cancer Res 45, 2913.

Christensen, C.L., Kwiatkowski, N., Abraham, B.J., Carretero, J., Al-Shahrour, F., Zhang, T., Chipumuro, E., Herter-Sprie, G.S., Akbay, E.A., Altabef, A., Zhang, J., Shimamura, T., Capelletti, M., Reibel, J.B., Cavanaugh, J.D., Gao, P., Liu, Y., Michaelsen, S.R., Poulsen, H.S., Aref, A.R., Barbie, D.A., Bradner, J.E., George, R.E., Gray, N.S., Young, R.A., Wong, K.-K., 2014. Targeting Transcriptional Addictions in Small Cell Lung Cancer with a Covalent CDK7 Inhibitor. Cancer Cell 26, 909–922. https://doi.org/10.1016/j.ccell.2014.10.019

Conacci-Sorrell, M., McFerrin, L., Eisenman, R.N., 2014. An Overview of MYC and Its Interactome. Cold Spring Harbor Perspectives in Medicine 4, a014357–a014357. https://doi.org/10.1101/cshperspect.a014357

Dalla-Favera, R., Bregni, M., Erikson, J., Patterson, D., Gallo, R.C., Croce, C.M., 1982. Human c-myc onc gene is located on the region of chromosome 8 that is translocated in Burkitt lymphoma cells. Proceedings of the National Academy of Sciences 79, 7824–7827. https://doi.org/10.1073/pnas.79.24.7824

Dauch, D., Rudalska, R., Cossa, G., Nault, J.-C., Kang, T.-W., Wuestefeld, T., Hohmeyer, A., Imbeaud, S., Yevsa, T., Hoenicke, L., Pantsar, T., Bozko, P., Malek, N.P., Longerich, T., Laufer, S., Poso, A., Zucman-Rossi, J., Eilers, M., Zender, L., 2016. A MYC–aurora kinase A protein complex represents an actionable drug target in p53-altered liver cancer. Nat Med 22, 744–753. https://doi.org/10.1038/nm.4107 bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 37

Delmore, J.E., Issa, G.C., Lemieux, M.E., Rahl, P.B., Shi, J., Jacobs, H.M., Kastritis, E., Gilpatrick, T., Paranal, R.M., Qi, J., Chesi, M., Schinzel, A.C., McKeown, M.R., Heffernan, T.P., Vakoc, C.R., Bergsagel, P.L., Ghobrial, I.M., Richardson, P.G., Young, R.A., Hahn, W.C., Anderson, K.C., Kung, A.L., Bradner, J.E., Mitsiades, C.S., 2011. BET Bromodomain Inhibition as a Therapeutic Strategy to Target c-Myc. Cell 146, 904–917. https://doi.org/10.1016/j.cell.2011.08.017

DePinho, R., Mitsock, L., Hatton, K., Ferrier, P., Zimmerman, K., Legouy, E., Tesfaye, A., Collum, R., Yancopoulos, G., Nisen, P., Kriz, R., Alt, F., 1987a. Myc family of cellular oncogenes. J. Cell. Biochem. 33, 257–266. https://doi.org/10.1002/jcb.240330404

DePinho, R.A., Hatton, K.S., Tesfaye, A., Yancopoulos, G.D., Alt, F.W., 1987b. The human myc gene family: structure and activity of L-myc and an L-myc pseudogene. Genes & Development 1, 1311–1326. https://doi.org/10.1101/gad.1.10.1311

Fujino, K., Motooka, Y., Hassan, W.A., Ali Abdalla, M.O., Sato, Y., Kudoh, S., Hasegawa, K., Niimori-Kita, K., Kobayashi, H., Kubota, I., Wakimoto, J.,(Alvarado-Luna and Morales- Espinosa 2016) Suzuki, M., Ito, T., 2015. Insulinoma-Associated Protein 1 Is a Crucial Regulator of Neuroendocrine Differentiation in Lung Cancer. The American Journal of Pathology 185, 3164–3177. https://doi.org/10.1016/j.ajpath.2015.08.018

Gazdar, A.F., Carney, D.N., Nau, M.M., Minna, J.D., 1985. Characterization of Variant Subclasses of Cell Lines Derived from Small Cell Lung Cancer Having Distinctive Biochemical, Morphological, and Growth Properties. Cancer Res 45, 2924.

Gazdar, A.F., Bunn, P.A., Minna, J.D., 2017. Small-cell lung cancer: what we know, what we need to know and the path forward. Nat Rev Cancer 17, 725–737. https://doi.org/10.1038/nrc.2017.87

George, J., Lim, J.S., Jang, S.J., Cun, Y., Ozretić, L., Kong, G., Leenders, F., Lu, X., Fernández- Cuesta, L., Bosco, G., Müller, C., Dahmen, I., Jahchan, N.S., Park, K.-S., Yang, D., Karnezis, A.N., Vaka, D., Torres, A., Wang, M.S., Korbel, J.O., Menon, R., Chun, S.-M., Kim, D., Wilkerson, M., Hayes, N., Engelmann, D., Pützer, B., Bos, M., Michels, S., Vlasic, I., Seidel, D., Pinther, B., Schaub, P., Becker, C., Altmüller, J., Yokota, J., Kohno, T., Iwakawa, R., Tsuta, K., Noguchi, M., Muley, T., Hoffmann, H., Schnabel, P.A., Petersen, I., Chen, Y., Soltermann, A., Tischler, V., Choi, C., Kim, Y.-H., Massion, P.P., Zou, Y., Jovanovic, D., Kontic, M., Wright, G.M., Russell, P.A., Solomon, B., Koch, I., Lindner, M., Muscarella, L.A., la Torre, A., Field, J.K., Jakopovic, M., Knezevic, J., Castaños-Vélez, E., Roz, L., Pastorino, U., Brustugun, O.-T., Lund-Iversen, M., Thunnissen, E., Köhler, J., Schuler, M., Botling, J., Sandelin, M., Sanchez-Cespedes, M., Salvesen, H.B., Achter, V., Lang, U., Bogus, M., Schneider, P.M., Zander, T., Ansén, S., Hallek, M., Wolf, J., Vingron, M., Yatabe, Y., Travis, W.D., Nürnberg, P., Reinhardt, C., Perner, S., Heukamp, L., Büttner, R., Haas, S.A., Brambilla, E., Peifer, M., Sage, J., Thomas, R.K., 2015. Comprehensive genomic profiles of small cell lung cancer. Nature 524, 47–53. https://doi.org/10.1038/nature14664 bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 38

Goga, A., Yang, D., Tward, A.D., Morgan, D.O., Bishop, J.M., 2007. Inhibition of CDK1 as a potential therapy for tumors over-expressing MYC. Nat Med 13, 820–827. https://doi.org/10.1038/nm1606

He, T., 1998. Identification of c-MYC as a Target of the APC Pathway. Science 281, 1509–1512. https://doi.org/10.1126/science.281.5382.1509

Hirvonen, H., T.P. Makela, M. Sandberg, H. Kalimo, E. Vuorio, K. Alitalo. 1990. Expression of the myc proto-oncogenes in developing human fetal brain. Oncogene 5: 1787–1797

Ho, Y.-T., Shimbo, T., Wijaya, E., Ouchi, Y., Takaki, E., Yamamoto, R., Kikuchi, Y., Kaneda, Y., Tamai, K., 2018. Chromatin accessibility identifies diversity in mesenchymal stem cells from different tissue origins. Sci Rep 8, 17765. https://doi.org/10.1038/s41598-018-36057-0

Horn, L., Mansfield, A.S., Szczęsna, A., Havel, L., Krzakowski, M., Hochmair, M.J., Huemer, F., Losonczy, G., Johnson, M.L., Nishio, M., Reck, M., Mok, T., Lam, S., Shames, D.S., Liu, J., Ding, B., Lopez-Chavez, A., Kabbinavar, F., Lin, W., Sandler, A., Liu, S.V., 2018. First-Line Atezolizumab plus Chemotherapy in Extensive-Stage Small-Cell Lung Cancer. N Engl J Med 379, 2220–2229. https://doi.org/10.1056/NEJMoa1809064

Huang, Y.-H., Klingbeil, O., He, X.-Y., Wu, X.S., Arun, G., Lu, B., Somerville, T.D.D., Milazzo, J.P., Wilkinson, J.E., Demerdash, O.E., Spector, D.L., Egeblad, M., Shi, J., Vakoc, C.R., 2018. POU2F3 is a master regulator of a tuft cell-like variant of small cell lung cancer. Genes Dev. 32, 915–928. https://doi.org/10.1101/gad.314815.118

Ito, T., Kudoh, S., Ichimura, T., Fujino, K., Hassan, W.A.M.A., Udaka, N., 2017. Small cell lung cancer, an epithelial to mesenchymal transition (EMT)-like cancer: significance of inactive Notch signaling and expression of achaete-scute complex homologue 1. Human Cell 30, 1– 10. https://doi.org/10.1007/s13577-016-0149-3

Jain, M., 2002. Sustained Loss of a Neoplastic Phenotype by Brief Inactivation of MYC. Science 297, 102–104. https://doi.org/10.1126/science.1071489

Key Statistics for Lung Cancer. (n.d.). Retrieved from https://www.cancer.org/cancer/non-small- cell-lung-cancer/about/key-statistics.html

Kim, Y.H., Girard, L., Giacomini, C.P., Wang, P., Hernandez-Boussard, T., Tibshirani, R., Minna, J.D., Pollack, J.R., 2006. Combined microarray analysis of small cell lung cancer reveals altered apoptotic balance and distinct expression signatures of MYC family gene amplification. Oncogene 25, 130–138. https://doi.org/10.1038/sj.onc.1208997

Kim, D.-W., Wu, N., Kim, Y.-C., Cheng, P.F., Basom, R., Kim, D., Dunn, C.T., Lee, A.Y., Kim, K., Lee, C.S., Singh, A., Gazdar, A.F., Harris, C.R., Eisenman, R.N., Park, K.-S., MacPherson, D., 2016. Genetic requirement for Mycl and efficacy of RNA Pol I inhibition in mouse models of small cell lung cancer. Genes Dev. 30, 1289–1299. https://doi.org/10.1101/gad.279307.116 bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 39

Kretzner, L., Blackwood, E.M., Eisenman, R.N., 1992. Myc and Max proteins possess distinct transcriptional activities. Nature 359, 426–429. https://doi.org/10.1038/359426a0

Lim, J.S., Ibaseta, A., Fischer, M.M., Cancilla, B., O’Young, G., Cristea, S., Luca, V.C., Yang, D., Jahchan, N.S., Hamard, C., Antoine, M., Wislez, M., Kong, C., Cain, J., Liu, Y.-W., Kapoun, A.M., Garcia, K.C., Hoey, T., Murriel, C.L., Sage, J., 2017. Intratumoural heterogeneity generated by Notch signalling promotes small-cell lung cancer. Nature 545, 360–364. https://doi.org/10.1038/nature22323

Lin, C.Y., Lovén, J., Rahl, P.B., Paranal, R.M., Burge, C.B., Bradner, J.E., Lee, T.I., Young, R.A., 2012. Transcriptional Amplification in Tumor Cells with Elevated c-Myc. Cell 151, 56–67. https://doi.org/10.1016/j.cell.2012.08.026

McColl, K., Wildey, G., Sakre, N., Lipka, M.B., Behtaj, M., Kresak, A., Chen, Y., Yang, M., Velcheti, V., Fu, P., Dowlati, A., 2017. Reciprocal expression of INSM1 and YAP1 defines subgroups in small cell lung cancer. Oncotarget 8. https://doi.org/10.18632/oncotarget.20572

Meuwissen, R., Linn, S.C., Linnoila, R.I., Zevenhoven, J., Mooi, W.J., Berns, A., 2003. Induction of small cell lung cancer by somatic inactivation of both Trp53 and Rb1 in a conditional mouse model. Cancer Cell 4, 181–189. https://doi.org/10.1016/S1535- 6108(03)00220-4

Peifer, M., Fernández-Cuesta, L., Sos, M.L., George, J., Seidel, D., Kasper, L.H., Plenker, D., Leenders, F., Sun, R., Zander, T., Menon, R., Koker, M., Dahmen, I., Müller, C., Di Cerbo, V., Schildhaus, H.-U., Altmüller, J., Baessmann, I., Becker, C., de Wilde, B., Vandesompele, J., Böhm, D., Ansén, S., Gabler, F., Wilkening, I., Heynck, S., Heuckmann, J.M., Lu, X., Carter, S.L., Cibulskis, K., Banerji, S., Getz, G., Park, K.-S., Rauh, D., Grütter, C., Fischer, M., Pasqualucci, L., Wright, G., Wainer, Z., Russell, P., Petersen, I., Chen, Y., Stoelben, E., Ludwig, C., Schnabel, P., Hoffmann, H., Muley, T., Brockmann, M., Engel-Riedel, W., Muscarella, L.A., Fazio, V.M., Groen, H., Timens, W., Sietsma, H., Thunnissen, E., Smit, E., Heideman, D.A.M., Snijders, P.J.F., Cappuzzo, F., Ligorio, C., Damiani, S., Field, J., Solberg, S., Brustugun, O.T., Lund-Iversen, M., Sänger, J., Clement, J.H., Soltermann, A., Moch, H., Weder, W., Solomon, B., Soria, J.-C., Validire, P., Besse, B., Brambilla, E., Brambilla, C., Lantuejoul, S., Lorimier, P., Schneider, P.M., Hallek, M., Pao, W., Meyerson, M., Sage, J., Shendure, J., Schneider, R., Büttner, R., Wolf, J., Nürnberg, P., Perner, S., Heukamp, L.C., Brindle, P.K., Haas, S., Thomas, R.K., 2012. Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer. Nat Genet 44, 1104–1110. https://doi.org/10.1038/ng.2396

Pietanza, M.C., Byers, L.A., Minna, J.D., Rudin, C.M., 2015. Small Cell Lung Cancer: Will Recent Progress Lead to Improved Outcomes? Clinical Cancer Research 21, 2244–2255. https://doi.org/10.1158/1078-0432.CCR-14-2958

Poirier, J.T., Dobromilskaya, I., Moriarty, W.F., Peacock, C.D., Hann, C.L., Rudin, C.M., 2013. Selective Tropism of Seneca Valley Virus for Variant Subtype Small Cell Lung Cancer. bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 40

JNCI: Journal of the National Cancer Institute 105, 1059–1065. https://doi.org/10.1093/jnci/djt130

Poirier, J.T., Gardner, E.E., Connis, N., Moreira, A.L., de Stanchina, E., Hann, C.L., Rudin, C.M., 2015. DNA methylation in small cell lung cancer defines distinct disease subtypes and correlates with high expression of EZH2. Oncogene 34, 5869–5878. https://doi.org/10.1038/onc.2015.38

Roussel, M., Saule, S., Lagrou, C., Rommens, C., Beug, H., Graf, T., Stehelin, D., 1979. Three new types of viral oncogene of cellular origin specific for haematopoietic cell transformation. Nature 281, 452–455. https://doi.org/10.1038/281452a0

Rudin, C.M., Poirier, J.T., Byers, L.A., Dive, C., Dowlati, A., George, J., Heymach, J.V., Johnson, J.E., Lehman, J.M., MacPherson, D., Massion, P.P., Minna, J.D., Oliver, T.G., Quaranta, V., Sage, J., Thomas, R.K., Vakoc, C.R., Gazdar, A.F., 2019. Molecular subtypes of small cell lung cancer: a synthesis of human and mouse model data. Nat Rev Cancer 19, 289–297. https://doi.org/10.1038/s41568-019-0133-9

Sabari, J.K., Lok, B.H., Laird, J.H., Poirier, J.T., Rudin, C.M., 2017. Unravelling the biology of SCLC: implications for therapy. Nat Rev Clin Oncol 14, 549–561. https://doi.org/10.1038/nrclinonc.2017.71

Schwab, M., Alitalo, K., Klempnauer, K.-H., Varmus, H.E., Bishop, J.M., Gilbert, F., Brodeur, G., Goldstein, M., Trent, J., 1983. Amplified DNA with limited homology to myc cellular oncogene is shared by human neuroblastoma cell lines and a neuroblastoma tumour. Nature 305, 245–248. https://doi.org/10.1038/305245a0

Semenova, E.A., Kwon, M., Monkhorst, K., Song, J.-Y., Bhaskaran, R., Krijgsman, O., Kuilman, T., Peters, D., Buikhuisen, W.A., Smit, E.F., Pritchard, C., Cozijnsen, M., van der Vliet, J., Zevenhoven, J., Lambooij, J.-P., Proost, N., van Montfort, E., Velds, A., Huijbers, I.J., Berns, A., 2016. Transcription Factor NFIB Is a Driver of Small Cell Lung Cancer Progression in Mice and Marks Metastatic Disease in Patients. Cell Reports 16, 631–643. https://doi.org/10.1016/j.celrep.2016.06.020

Sheiness, D., Bishop, J.M., 1979. DNA and RNA from uninfected vertebrate cells contain nucleotide sequences related to the putative transforming gene of avian myelocytomatosis virus. J Virol 31, 514–521.

Shou, Y., Martelli, M.L., Gabrea, A., Qi, Y., Brents, L.A., Roschke, A., Dewald, G., Kirsch, I.R., Bergsagel, P.L., Kuehl, W.M., 2000. Diverse karyotypic abnormalities of the c-myc locus associated with c-myc dysregulation and tumor progression in multiple myeloma. Proceedings of the National Academy of Sciences 97, 228–233. https://doi.org/10.1073/pnas.97.1.228 bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 41

Soucek, L., Whitfield, J., Martins, C.P., Finch, A.J., Murphy, D.J., Sodir, N.M., Karnezis, A.N., Swigart, L.B., Nasi, S., Evan, G.I., 2008. Modelling Myc inhibition as a cancer therapy. Nature 455, 679–683. https://doi.org/10.1038/nature07260

The Cancer Genome Atlas Research Network, 2012. Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519–525. https://doi.org/10.1038/nature11404

Travis, W.D., Brambilla, E., Nicholson, A.G., Yatabe, Y., Austin, J.H.M., Beasley, M.B., Chirieac, Lucian.R., Dacic, S., Duhig, E., Flieder, D.B., Geisinger, K., Hirsch, F.R., Ishikawa, Y., Kerr, K.M., Noguchi, M., Pelosi, G., Powell, C.A., Tsao, M.S., Wistuba, I., 2015. The 2015 World Health Organization Classification of Lung Tumors. Journal of Thoracic Oncology 10, 1243–1260. https://doi.org/10.1097/JTO.0000000000000630

Varghese, A.M., Zakowski, M.F., Yu, H.A., Won, H.H., Riely, G.J., Krug, L.M., Kris, M.G., Rekhtman, N., Ladanyi, M., Wang, L., Berger, M.F., Pietanza, M.C., 2014. Small-Cell Lung Cancers in Patients Who Never Smoked Cigarettes. Journal of Thoracic Oncology 9, 892–896. https://doi.org/10.1097/JTO.0000000000000142

Weir, B.A., Woo, M.S., Getz, G., Perner, S., Ding, L., Beroukhim, R., Lin, W.M., Province, M.A., Kraja, A., Johnson, L.A., Shah, K., Sato, M., Thomas, R.K., Barletta, J.A., Borecki, I.B., Broderick, S., Chang, A.C., Chiang, D.Y., Chirieac, L.R., Cho, J., Fujii, Y., Gazdar, A.F., Giordano, T., Greulich, H., Hanna, M., Johnson, B.E., Kris, M.G., Lash, A., Lin, L., Lindeman, N., Mardis, E.R., McPherson, J.D., Minna, J.D., Morgan, M.B., Nadel, M., Orringer, M.B., Osborne, J.R., Ozenberger, B., Ramos, A.H., Robinson, J., Roth, J.A., Rusch, V., Sasaki, H., Shepherd, F., Sougnez, C., Spitz, M.R., Tsao, M.-S., Twomey, D., Verhaak, R.G.W., Weinstock, G.M., Wheeler, D.A., Winckler, W., Yoshizawa, A., Yu, S., Zakowski, M.F., Zhang, Q., Beer, D.G., Wistuba, I.I., Watson, M.A., Garraway, L.A., Ladanyi, M., Travis, W.D., Pao, W., Rubin, M.A., Gabriel, S.B., Gibbs, R.A., Varmus, H.E., Wilson, R.K., Lander, E.S., Meyerson, M., 2007. Characterizing the cancer genome in lung adenocarcinoma. Nature 450, 893–898. https://doi.org/10.1038/nature06358

Weng, A.P., 2006. c-Myc is an important direct target of Notch1 in T-cell acute lymphoblastic leukemia/lymphoma. Genes & Development 20, 2096–2109. https://doi.org/10.1101/gad.1450406

Zappa, C., Mousa, S.A., 2016. Non-small cell lung cancer: current treatment and future advances. Transl. Lung Cancer Res. 5, 288–300. https://doi.org/10.21037/tlcr.2016.06.07

Zhang, W., Girard, L., Zhang, Y.-A., Haruki, T., Papari-Zareei, M., Stastny, V., Ghayee, H.K., Pacak, K., Oliver, T.G., Minna, J.D., Gazdar, A.F., 2018. Small cell lung cancer tumors and preclinical models display heterogeneity of neuroendocrine phenotypes. Transl. Lung Cancer Res. 7, 32–49. https://doi.org/10.21037/tlcr.2018.02.02

Zhu, J., Lum, P.Y., Lamb, J., GuhaThakurta, D., Edwards, S.W., Thieringer, R., Berger, J.P., Wu, M.S., Thompson, J., Sachs, A.B., Schadt, E.E., 2004. An integrative genomics bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Patel, A et al. 42

approach to the reconstruction of gene networks in segregating populations. Cytogenet Genome Res 105, 363–374. https://doi.org/10.1159/000078209

Zhu J, Wiener MC, Zhang C, Fridman A, Minch E, Lum PY, Sachs JR, & Schadt EE Increasing the power to detect causal associations by combining genotypic and expression data in segregating populations. PLoS Comput Biol 3, e69. (2007) Zhu, J., Zhang, B., Smith, E.N., Drees, B., Brem, R.B., Kruglyak, L., Bumgarner, R.E. and Schadt, E.E.Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks, Nat Genet, 40, 854-861 (2008)

Zhu J, Sova P, Xu Q, Dombek KM, Xu EY, Vu H, Tu Z, Brem RB, Bumgarner RE, Schadt EE Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation. . PLoS Biol. (2012)

bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was Figure 1 not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. A LMYC

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CCDC80 LUM COL10A1

IGFBP3 COL5A2 COL3A1

LOC100133315 TET2 ANTXR1 PAPSS1 LRFN3 CDK2AP2

ARHGAP5

NetworkANKRD50 ABHD8 PLPP2 COX6B1 FAM234A SYCE2 CBR4 RAD9A AKR7A3 CAPNS1 CANT1

WDR83 ELMOD2 LSR COPE VEGFB ATP6AP1 CXorf40B CLDN3 RCE1 ZNF275 FKBP2 HCFC1 RPL10 SLC1A5 METTL14

PPDPF EMD DDX49 GEMIN4 FANCGSMARCA5 ZNF787 RAI1 SGSM2 RAP1GAP2 PGAM5 SRD5A3 DNAAF5 ERI3 FXR2 COX10−AS1 FAM127C RNF126 FAM127A DVL2 MPND GPS2 SHPK ABHD12 KCNAB3 FIBP FAM127B NEURL4 UBXN6 CPTP DRG2 GSS CNTROB TMED10 RPA1 POLR2E NR1H2 KEAP1 TOP2B BSG EPN1 SH3GL1 CDH24 ZNF579 TIMM13 EBLN3 ACD PIGL AKT1S1 UBE2R2 KIAA0753 SF3A2 TOP3A MFSD12 FIZ1 AURKAIP1 U2AF2 PRPF8 PAFAH1B1 DCAF12 ARHGAP33 PPP6R1 ITPR2 ZNF865 USP22 RNF38 ZNF594 FNBP1L GLTSCR1 NCBP3 SCAF1 GPAT4 SNRNP200 RANBP2 METTL16 ZFP3

ZZEF1 VEZF1 TRIM33 ZNF444 DAGLA ASXL2 TUBGCP6 GLUD1P3 NBAS SMYD4 NPIPB3 ALDH16A1 UQCC3 USP34 ATF5 TSR1 ATG16L2 ZNHIT2 ZNF580 RHOT2 PHC2 PNKP VPS13D MAD2L2 VPS53 NUP153 ITSN1 R3HDM1 UBFD1 UBN1 PTOV1−AS2 TSSK3 FLCN BIRC6 LOC102724814 Classifying c-Myc & Generating L-Myc & PMF1 TMEM39B MRPL2 TMEM184B KLHL36 TMEM86B TMEM223 SAMD10 PDPK1 EIF4G3 NCOR1 SZT2 SGK494 SMG6 FAM168B SLC30A1 CBX6 SNRPC RGL4 TM7SF2 HNRNPA0 TMEM222 PPL EXOC4 TTC31 AGO3 ZNF587 JRK SRRM2 TMEM141 UBR4 MEA1 CLUHP3 SETD1A TNFRSF13C NUTM2A−AS1 LDOC1L EXOC6B MYH10 BRAF SOX11 C2orf68 TAOK2 HMGXB3 ANKRD10 TULP3 PHACTR1 MAT2A ZNF785 SMARCD1 SP1 TUBA1B GDI1 TRIM41 WDR24 BACH1 TUBA1A CDC26 ZNF768 SLC1A4 CPXM1 ZNF646 SCARB2 MED13 FAM57B SRCAP ROGDI NHLRC2 AASS FAM53C HAGH SLC25A42 KIAA1143 ALMS1 PSMG4 MALAT1 NUDT16L1 ADD2 NCS1 SNN CDAN1 GMDS−AS1 MIR600HG STARD9 MAML1 KIF7 HGSNAT YIPF3 CDK2 ZNF629 RNF40 DNAJC5 PALM CELSR3 NEMP1 DOK4 DBN1 GNB2L1 LOC101928324 DECR2 DCHS1 DPYSL5 ADRBK2 NR1D2 RFX1 NNAT EIF1B BMP8B FBN3 PRRC2B CD2BP2

ZNF213−AS1 RAB11FIP3 DDX17 CACNA2D1 C19orf54 MTCL1 KIAA0141 NCLN SLC38A1 ENOPH1 LOC171391 NFX1 FBRSL1 ABHD16B CELF1 NDST1 RPUSD1 H2AFZ MYCL LOC101926943 BMP8A LARP1B KIF2A DCAF16 HNRNPUL2 TFCP2L1 ULK1 PCDH17 C5orf66 FGD5−AS1 CCNA2 TCERG1L CAMK4 MGAT4B FASN ACAD8 MAP1B TBC1D9B ATP9A DLL3 ELF2 SCNN1A RFC2 PPP3CA BRD8 TUBGCP4 CBX5 KIF19 BARX1 KCNK3 STMN2 PNP NUCKS1 STARD10 MCMBP DBH CAMSAP1 DDX5 LOC202181 DUS1L EMP3 MICU1 FAM175B ORMDL3 FAM84A GATAD2B CBX2 ZNF532 FAAP100 BRK1 GAL3ST4 ASCL1 FOXJ1 TCERG1 MBD3 HNRNPD SMPD3 IP6K1 TTC3 TIMM23 HS3ST1 DNAJA1 POLI TPGS2 HNRNPH1 SMU1 KCNA1 NR0B2 VDAC2 LOC101928307 BTBD2 RBM23 EPHB4 CAMTA1 ACAA2 CASKIN2 GHITM GATSL2 RFNG INO80C PPP1R16A SEC22C CCDC154 SCAMP4 RHPN1 ADO HN1 ZNF397 ERMP1 TTLL7 KLHL14 ELAVL3 TMTC2 FN3KRP ZNF271P TOPORS MAP3K14 NPTX1 IQCG EXOSC3 PIAS2 TXNL1 RRAGA KIAA2026 CEP19 CEP192 RRM2 SMAD4 AP3M1 C14orf132 C18orf25 ZNF236 TCTEX1D2 GATS PLPP6 GRP ADIPOR2 GLI4 C19orf52 COL22A1 SLC15A4 BIRC5 RNF138 DCAF7 DCAF10 NKX2−2 ACTB ME2 KLHL9 ZNF713 ATP5A1 FAM64A B4GALT6 DENND4C DNAJC17 MEX3C DDX46 TMED2 LANCL2 WDR53 STARD7 SNX8 SBNO1 MIB1 SMAD2 SIK2 CHMP5 ZSCAN30 LINC00403 DDX21 INSR MLEC ZNF24 HDHD2 ARID1B TUBA1C LINC00909 CBL SOX1 CFL1 LPIN2 ATXN7L3B MLLT4 UHRF2 NRBP1 PPP4R1 SLC35E1 RBBP8 UBE4A ADARB2−AS1 ZNF664 KIAA1468 EZR PICALM UBP1 TYMS EPG5 PPA1 MIAT FTH1 BRAP SLC39A6 ZDHHC21 ELAC1 ANAPC5 CACNA1ASEC11C GANAB QRSL1 TEAD1 KIAA1211L RFC4 ATP2A2 FBXO21 DNAJC14 NARS PPP1R12A SRRM4 ERP44 RNF10 SBF2 DDX6 PRDM4 SPTLC1 LEMD3 KIDINS220 DOPEY2 TMEM255B PTOV1−AS1CBFA2T2 HACE1 TEX10 SAE1 FAM120B WDR7 PREP KIF5C SPPL3 SS18 CAMK1DTANGO6 TMX3 LMAN1 SERINC1 EFR3B PDCL ABCD1 DYM NUP160 TOR1A VMA21 YWHAQ ARHGAP32 ARVCF TRA2B NDC80 CBY1 ATP9B CAPRIN1 SH3BP1 ZNF292 PPP6R3 ZFP91 MCF2L GALNT1 EIF4G2 ATP6V1G1 ARFGEF3 YPEL1 EPM2AIP1 CBX1 CREB1 ANKRD54 MAP3K11 ELOVL2 NCAM1 NCAPH2 SRSF1 PLEKHM3 STIP1 FBXO45 TRAPPC10 SYNGR2 PIK3C3 PHF6 CCAR1 RIBC2 RNASEH2C TRAPPC8 PPFIA1 FBXW5 RIMS2 MAP3K4 KHDRBS1 PHF13 PSMD5 DIP2A FAM126B PFKL AGPAT3 PPM1D ECT2 CDCA4 UBE2G2 C21orf58 NELFB PHF19 RBMX NIPSNAP3A GOLGA5 MXD3 SMARCA1 NIN OSBP MCM3AP MAP2 EPC2 ARHGEF7 NGFRAP1 NOL11 EPHB3 RBM4B HMGN2 MORC3 SMURF2 RNPEPL1 LMNB2 ESCO1 FAM120AOS CCDC9 FADS1 MBD5 SNHG5 PDSS1 NUDT5 PAIP2B TXNDC16 SLC36A4 RMND5B SAPCD2 HNRNPA1 MPP5 TOP2A C12orf75 KPNB1 COMMD3 WDR34 STMN1 XPO4 SMARCE1 WAC IRS2 BCL7A TIA1 UBR7 PLEKHA3 HSPA14 PPP2R5D ZBTB1 CECR2 CASD1 GTF3C4 TLK2 LARP4B DDHD2 CNKSR3 DPH7 TRAPPC6B QSOX2 GDAP1 PHF10 FBXW2 ARID4A PCNT FAM102A PAPOLA GMFB RABL6 ZBTB34 L-Myc Cell Lines KIF4A NUDT21 c-Myc Signature TUBB4B PATL1 MLLT10 FRAS1 PCDHB14 DHX8 MTPAP ZNF654 TFDP1 SCG5 NPDC1 ADNP SKA2 PPM1A PRKD1 SGCE CPE NMNAT3 ZDHHC12 SOS2 RAB3BTUBGCP3 ID4 DLX6−AS1 COQ4 TIGD2 SH3GLB2 FAM168A MPPED1 SPIN1 TUBB SSNA1 DGKQ ARRDC1−AS1 CCNG2 CENPF CCNI YME1L1 STK32A DLX6 ZNF329 DNALI1 ZMYND19 PCSK1 SCG3 NMT1 SPAG5 DTL RAD51C JAM3 USP20 MTSS1 DDC ZMYM4 ADH5 HNRNPF CASP8AP2 GUSBP4 CRKL PLXNA2 MDK SYT4 PTPRN2 STX17 RALGAPB KIFC1 CERS6 DDX39A PTK7 ATG2B PRPF39 PATZ1 LOC100506985 UFL1 GPATCH8 C11orf80 TTC6 FOXA1 SESTD1 SCN3A LINC00261 TMEM150C SYT13 MMP25−AS1 ICA1 ZNF782 RIMKLA ERCC6L2 KPNA6 TRAPPC6A ZWINT ITGB1 SMOC2 PPM1E ETS2 STXBP5 ASPM NEMP2 CNTNAP2 NEK9 CDC6 CHAMP1 TRIM37 SECISBP2 PEX26 CDK1 CA8 ASRGL1 ST8SIA6−AS1EXTL3 MXD4 CXXC4 TNRC6A RIF1 ZER1 HNRNPL PSMD5−AS1 GOLGA1 AGTPBP1 FANCC FAM155A FAAH2 CENPA DNAJC12 RABGAP1 TBC1D13 SCGN DDX50 LOC728613 SRSF6 LOC100288152 NKTR DNAJC9 RTN3 REV1 FAM149B1 SAR1A EIF4EBP2 UBXN4 ABL1 NCBP1 SLF2 FOXJ3 PCMTD2 FAM155A−IT1 RPRD1B CNTRL SLIT1 KIAA0368 DAPL1 KIF11 REEP3 SETX AGO4 SFXN3 DMTF1 TET1 ZKSCAN1 PRRC2A CEBPG VPS13A ZNF451 SMC5 WDR11 MGEA5 TMTC4 KLF12 KIAA1279 SLC38A2 PRPF38A DEK DLEU2 LINC01578 PGAP1 PTAR1 HTATSF1 KIAA0430 TNKS2 SEC16A MARCH5 PPM1F CEP55 SGPL1 BAZ2B KAT6B MARCKS ABI2 TTC21B PSIP1 TDP2 BCOR RMI1 GLTSCR1L CDC5L CHML FAM210B GNG4 PTMA ZNF37BP TUG1 CNTLN RANBP9 STK24 LRBA RNF187 SMC3 LINC00641 MTAP SF1 HAUS6 HMGB2 CENPC IGFBPL1 TAS2R31 FARP1 CDC16 NUP50 WSB2 PRR14L ADAM1A IPO5 PAXIP1−AS1 MASP2 UBAC2 EP400 NFYB HOMEZ SAP30 CDH2 SHROOM3 SMS EIF4ENIF1 SON SNAPC5 GPR180 MNS1 REV3L CENPE CPSF6 YWHAH RBM45 DIS3 ABHD13RNF219 AHI1 RBFOX2 PARP2 SYVN1 BRWD1 USP1 BCL3 NR2C2 EIF2B1 RAP2AMZT1 CPSF7 SYNE2 GOLGA3 PHIP SPAG9 NOLC1 VMAC ATXN2 TM9SF2 KIAA1109 PLBD2 TMEM214 SCAF4 LTN1 NAA40 CERK PITPNB AK9 VCP CAPS MAPK1 DYRK1A BAZ2A

COLGALT1 U2AF1

STON2 FIG4 FBXO42 ZMYM3 RPS18P9 CDKN1B MECP2 PCNX3

TUT1 AGAP1 DEPDC1 SAMD4B CIC TMEM97 ASB1 SOX13 ADRBK1

KDM4B FBXO46 HOOK1 ANKRD13C NCAPG2 GPSM2

H2AFV POLD3

TMEM108

LSM8 PAK1 RHEB

RFC5

FAM188A RNF34

PCBP2 TCTN2 Project to SCLC Network

LMYC cMYC Network LOC101929188

LAD1

JAG1 PDLIM1 MEIS1 YBX3

TRIB1 LHFPL4

NOTCH2NL NOTCH2 MYC A2M ANXA1 RUNX1 LPP GRASP HPS1 SOCS1 TAGLN2 JPX MFGE8 EHD4 HEG1 HMSD NAB2 TMEM41A AHNAK TOR3A AHR MGAT1 CTSA DHRS3 PXDC1 CORO1B SLC39A1 FURIN COQ10A CDC42EP1 CDC42SE1 PODXL SQSTM1 LASP1 ACBD7 FKBP10 TCEAL5 MMP14 OLMALINC LDLRAP1 SHC1 TMC6 HLA−E CKAP4 MEF2D

EFNA1 EMILIN1 CTDSP1 cMYC LMYC CNDP2 PCOLCE EPT1 XPO1 EMP1 CD74 CHM

CTSH ALDH2

HDAC7 CYBB ACCS TMEM140 ANXA4

PPP1R9B PLEK ZBTB4 cMYC PUM2 HLA−DRA BCL9L NOTCH1 SLC4A2 TGFBR2

HSPG2 KIAA1522 C16orf87 CYFIP1 TNS1 OAF TRIM14 B4GALT2 NANS FAM120A PLEKHO2 ADPGK LRPPRC GPNMB

CHST11 LGALS3

L2HGDH DDX1 C1QA C10orf54 TSPAN9

RANBP9 ZNF845 SH3BGRL3

TMEM214 CREG1 ASUN ZYX EIF5B MYL12B PIK3C2A TMEM109 SPSB1 CEBPZ PLBD2 FCGR2A FEZ2 CFDP1 MYL12A STX4 SMC6 SFRP2 MMP2 SUCLG2 ARF3 PRKD3 FTL ELF1 MTIF2 KCNK1 SMIM3 NPTX2 PNPT1 SRBD1 GABARAPL1 CDK5R2 CASP4 MPHOSPH10 ZSWIM5 F2R SERPINF1 N WDR43 FAM114A1 STR SESN2 CDR2

RBM28 SLC4A1AP

DNAJC2 HSP90AB1

B LMYC Network C

COL1A2 COL6A3 FOXN4

CCDC80 LUM

COL10A1

IGFBP3 COL5A2 COL3A1

LOC100133315 TET2 Sub-Network L1

ANTXR1 PAPSS1 LRFN3 CDK2AP2

ARHGAP5

ANKRD50 ABHD8 PLPP2 COX6B1 FAM234A SYCE2 CBR4 RAD9A AKR7A3 CAPNS1 CANT1

WDR83 COPE ELMOD2 LSR VEGFB ATP6AP1 CXorf40B CLDN3 RCE1

ZNF275 FKBP2 HCFC1 RPL10 SLC1A5 METTL14

PPDPF EMD DDX49 GEMIN4 FANCGSMARCA5 ZNF787 RAI1 SGSM2 RAP1GAP2 SRD5A3 PGAM5 DNAAF5 ERI3 FXR2 COX10−AS1 FAM127C RNF126 FAM127A DVL2 MPND GPS2 SHPK ABHD12 KCNAB3 FIBP FAM127B NEURL4 UBXN6 CPTP DRG2 GSS CNTROB Mitotic Spindle Organization TMED10 RPA1 POLR2E KEAP1 NR1H2 BSG TOP2B EPN1 CDH24 SH3GL1 ZNF579 TIMM13

EBLN3 ACD PIGL AKT1S1 UBE2R2 KIAA0753 SF3A2 TOP3A MFSD12 FIZ1 AURKAIP1 PAFAH1B1 U2AF2 PRPF8 DCAF12 ARHGAP33 PPP6R1 ITPR2 ZNF865 USP22 ZNF594 RNF38 FNBP1L GLTSCR1 NCBP3 SCAF1 GPAT4 SNRNP200 RANBP2 METTL16 ZFP3

ZZEF1 VEZF1 TRIM33 ZNF444 DAGLA ASXL2 GLUD1P3 TUBGCP6 NBAS SMYD4 NPIPB3 ALDH16A1 UQCC3 USP34 ATF5 TSR1 ATG16L2 ZNHIT2 Mitotic Nuclear Division ZNF580 PNKP RHOT2 PHC2 VPS13D MAD2L2 VPS53 NUP153 ITSN1 R3HDM1 UBFD1 UBN1 PTOV1−AS2 TSSK3 FLCN BIRC6 LOC102724814 PMF1 TMEM39B MRPL2 TMEM184B KLHL36 TMEM86B SAMD10 TMEM223 PDPK1 NCOR1 EIF4G3 SZT2 SGK494 SMG6 FAM168B SLC30A1 CBX6 SNRPC RGL4 TM7SF2 HNRNPA0 TMEM222 PPL EXOC4 TTC31

AGO3 ZNF587 JRK SRRM2 TMEM141 UBR4 MEA1 CLUHP3 SETD1A NUTM2A−AS1 TNFRSF13C EXOC6B LDOC1L MYH10 BRAF SOX11 C2orf68 TAOK2 HMGXB3 ANKRD10 TULP3 PHACTR1 MAT2A ZNF785 SMARCD1 SP1 TUBA1B GDI1 TRIM41 WDR24 BACH1 TUBA1A ZNF768 SLC1A4 CDC26 SCARB2 MED13 CPXM1 FAM57B ZNF646 ROGDI Cell Cycle SRCAP NHLRC2 AASS FAM53C HAGH SLC25A42 KIAA1143 ALMS1 PSMG4 MALAT1 NUDT16L1 ADD2 NCS1 SNN CDAN1 GMDS−AS1 MIR600HG STARD9 MAML1 CDK2 KIF7 HGSNAT YIPF3 ZNF629 RNF40 DNAJC5 NEMP1 PALM CELSR3 DOK4 DBN1 GNB2L1 LOC101928324 DECR2 DCHS1 DPYSL5 ADRBK2 NR1D2 RFX1 NNAT EIF1B BMP8B FBN3 PRRC2B CD2BP2

ZNF213−AS1 RAB11FIP3 DDX17 CACNA2D1 C19orf54 MTCL1 KIAA0141 NCLN SLC38A1 ENOPH1 LOC171391 NFX1 FBRSL1 ABHD16B CELF1 NDST1 RPUSD1 H2AFZ MYCL LOC101926943 BMP8A HNRNPUL2 LARP1B KIF2A DCAF16 TFCP2L1 ULK1 PCDH17 C5orf66 FGD5−AS1 CCNA2 TCERG1L CAMK4 MGAT4B FASN ACAD8 MAP1B TBC1D9B ATP9A ELF2 DLL3 SCNN1A RFC2 PPP3CA BRD8 TUBGCP4 CBX5 KIF19 BARX1 KCNK3 STMN2 PNP NUCKS1 STARD10 DUS1L MCMBP DBH CAMSAP1 DDX5 LOC202181 EMP3 MICU1 FAM175B ORMDL3 FAM84A GATAD2B CBX2 ZNF532 FAAP100 BRK1 Spinal Cord Patterning GAL3ST4 ASCL1 FOXJ1 TCERG1 MBD3 HNRNPD SMPD3 IP6K1 TTC3 TIMM23 HS3ST1 DNAJA1 POLI TPGS2 HNRNPH1 SMU1 KCNA1 NR0B2 VDAC2 LOC101928307 BTBD2 RBM23 EPHB4 CAMTA1 ACAA2 CASKIN2 GHITM GATSL2 RFNG INO80C PPP1R16A SEC22C CCDC154 SCAMP4 RHPN1 HN1 ADO KLHL14 ELAVL3 ZNF397 ERMP1 TTLL7 TMTC2 FN3KRP ZNF271P TOPORS MAP3K14 NPTX1 IQCG EXOSC3 PIAS2 TXNL1 RRAGA KIAA2026 CEP19 CEP192 RRM2 SMAD4 AP3M1 C14orf132 C18orf25 ZNF236 TCTEX1D2 GATS PLPP6 ADIPOR2 GLI4 C19orf52 GRP BIRC5 RNF138 DCAF10 COL22A1 SLC15A4 ME2 DCAF7 NKX2−2 ACTB KLHL9 ZNF713 ATP5A1 FAM64A B4GALT6 DENND4C DNAJC17 MEX3C DDX46 TMED2 LANCL2 WDR53 SNX8 STARD7 MIB1 SMAD2 SIK2 CHMP5 SBNO1 ZSCAN30 LINC00403 DDX21 INSR MLEC ZNF24 HDHD2 ARID1B TUBA1C LINC00909 CBL CFL1 SOX1 LPIN2 ATXN7L3B MLLT4 UHRF2 NRBP1 Regulation of Nervous System Development PPP4R1 SLC35E1 RBBP8 ADARB2−AS1 KIAA1468 EZR UBE4A PICALM UBP1 TYMS ZNF664 EPG5 PPA1 MIAT FTH1 BRAP SLC39A6 ZDHHC21 ELAC1 CACNA1A ANAPC5 SEC11C GANAB QRSL1 TEAD1 KIAA1211L RFC4 FBXO21 PPP1R12A ATP2A2 DNAJC14 NARS SRRM4 SBF2 ERP44 RNF10 DDX6 PRDM4 SPTLC1 LEMD3 TMEM255B KIDINS220 DOPEY2 PTOV1−AS1CBFA2T2 HACE1 FAM120B TEX10 SAE1 PREP KIF5C SS18 WDR7 SPPL3 TMX3 CAMK1DTANGO6 LMAN1 SERINC1 EFR3B PDCL ABCD1 DYM NUP160 TOR1A VMA21 YWHAQ ARHGAP32 ARVCF TRA2B NDC80 CBY1 ATP9B CAPRIN1 SH3BP1 ZNF292 PPP6R3 ZFP91 MCF2L GALNT1 EIF4G2 ATP6V1G1 ARFGEF3 YPEL1 EPM2AIP1 CBX1 CREB1 ANKRD54 ELOVL2 MAP3K11 PLEKHM3 STIP1 FBXO45 NCAM1 NCAPH2 SRSF1 TRAPPC10 PIK3C3 SYNGR2 CCAR1 RNASEH2C TRAPPC8 PHF6 RIBC2 FBXW5 PPFIA1 RIMS2 KHDRBS1 MAP3K4 PHF13 FAM126B PSMD5 DIP2A PFKL AGPAT3 PPM1D ECT2 CDCA4 UBE2G2 C21orf58 NELFB MXD3 PHF19 RBMX NIPSNAP3A GOLGA5 MCM3AP SMARCA1 NIN OSBP MAP2 EPC2 ARHGEF7 NGFRAP1 NOL11 EPHB3 RBM4B HMGN2 SMURF2 RNPEPL1 LMNB2 ESCO1 MORC3 CCDC9 FAM120AOS SNHG5 FADS1 MBD5 PDSS1 NUDT5 PAIP2B TXNDC16 SLC36A4 RMND5B SAPCD2 HNRNPA1 0 1 2 3 4 5 TOP2A MPP5 C12orf75 KPNB1 COMMD3 BCL7A WDR34 STMN1 XPO4 SMARCE1 WAC IRS2 TIA1 UBR7 PLEKHA3 HSPA14 PPP2R5D ZBTB1 CECR2 CASD1 GTF3C4 TLK2 LARP4B DDHD2 CNKSR3 TRAPPC6B

Sub-Network L1 DPH7 QSOX2 GDAP1 PHF10 PCNT FBXW2 ARID4A FAM102A PAPOLA GMFB RABL6 ZBTB34 KIF4A TUBB4B NUDT21 PATL1 MLLT10 FRAS1 PCDHB14 DHX8 MTPAP ZNF654 SCG5 SKA2 PPM1A TFDP1 PRKD1 NPDC1 ADNP SGCE CPE NMNAT3 ID4 ZDHHC12 SOS2 RAB3BTUBGCP3 DLX6−AS1 COQ4 TIGD2 SH3GLB2 FAM168A SPIN1 MPPED1 TUBB SSNA1 DGKQ ARRDC1−AS1 CCNI CCNG2 CENPF YME1L1 STK32A DLX6 ZNF329 DNALI1 ZMYND19 PCSK1 SCG3 NMT1 SPAG5 DTL RAD51C JAM3 USP20 ADH5 MTSS1 DDC ZMYM4 HNRNPF GUSBP4 CRKL CASP8AP2 PTPRN2 STX17 PLXNA2 MDK SYT4 RALGAPB CERS6 KIFC1 DDX39A PTK7 LOC100506985 UFL1 ATG2B PRPF39 C11orf80 TTC6 FOXA1 PATZ1 GPATCH8 SESTD1 SCN3A LINC00261 TMEM150C SYT13 MMP25−AS1 ICA1 ZNF782 RIMKLA KPNA6 ERCC6L2 SMOC2 TRAPPC6A ZWINT ITGB1 PPM1E ETS2 STXBP5 ASPM NEMP2 CNTNAP2 NEK9 CDC6 CHAMP1 TRIM37 SECISBP2 PEX26 CDK1 CA8 -Log (p-Value) ASRGL1 ST8SIA6−AS1 EXTL3 CXXC4 MXD4 RIF1 ZER1 TNRC6A HNRNPL PSMD5−AS1 10 GOLGA1 AGTPBP1 FANCC FAM155A FAAH2 CENPA DNAJC12 RABGAP1 DDX50 TBC1D13 SCGN LOC728613 SRSF6 LOC100288152 NKTR DNAJC9 RTN3 FAM149B1 REV1 SAR1A EIF4EBP2 UBXN4 NCBP1 SLF2 ABL1 FOXJ3 PCMTD2 FAM155A−IT1 RPRD1B CNTRL SLIT1 KIAA0368 DAPL1 KIF11 REEP3 DMTF1 SETX AGO4 SFXN3 TET1 PRRC2A ZKSCAN1 CEBPG VPS13A ZNF451 SMC5 WDR11 MGEA5 TMTC4 KLF12 KIAA1279 SLC38A2 PRPF38A DLEU2 PGAP1 PTAR1 DEK LINC01578 KIAA0430 HTATSF1 TNKS2 SEC16A MARCH5 PPM1F CEP55 SGPL1 BAZ2B KAT6B MARCKS ABI2 TTC21B PSIP1 TDP2 BCOR RMI1 GLTSCR1L CDC5L CHML FAM210B GNG4 PTMA ZNF37BP TUG1 CNTLN RANBP9 STK24 LRBA RNF187 SMC3 LINC00641 MTAP SF1 HAUS6 HMGB2 CENPC IGFBPL1 TAS2R31 FARP1 CDC16 NUP50 WSB2 PRR14L ADAM1A IPO5 PAXIP1−AS1 MASP2 UBAC2 NFYB EP400 HOMEZ SAP30 CDH2 SHROOM3 SMS EIF4ENIF1 SNAPC5 SON MNS1 GPR180 REV3L CENPE CPSF6 YWHAH RBM45 DIS3 ABHD13RNF219 AHI1 RBFOX2 PARP2 SYVN1 BRWD1 USP1 BCL3 NR2C2 RAP2AMZT1 EIF2B1 CPSF7 SYNE2 GOLGA3 PHIP SPAG9 VMAC NOLC1 ATXN2 TM9SF2 KIAA1109 PLBD2 TMEM214 NAA40 SCAF4 LTN1 CERK PITPNB AK9 VCP CAPS MAPK1 DYRK1A BAZ2A

COLGALT1 U2AF1

FBXO42 STON2 FIG4 ZMYM3 RPS18P9 CDKN1B MECP2 PCNX3

TUT1 AGAP1 DEPDC1 SAMD4B CIC TMEM97 ASB1 SOX13 ADRBK1

KDM4B FBXO46 HOOK1 ANKRD13C NCAPG2 GPSM2

H2AFV POLD3

TMEM108

LSM8

PAK1 RHEB

RFC5

FAM188A RNF34

PCBP2 D TCTN2 E Sub-Network c1 cMYC Network RNA Binding

HPS1 SOCS1

MFGE8 EHD4

TOR3A Post Transcriptional Regulation of Gene Expression MGAT1 CTSA PXDC1

CORO1B SLC39A1 FURIN CDC42EP1 CDC42SE1

SQSTM1 LASP1 ACBD7 FKBP10 TCEAL5 MMP14

SHC1 Regulation of Cellular Amide Metabolic Processes TMC6 HLA−E CKAP4 MEF2D

EFNA1 EMILIN1 CTDSP1 CNDP2 PCOLCE

EMP1

CD74 Regulation of Translation Initiation CHM

CTSH ALDH2

HDAC7 CYBB ACCS TMEM140 ANXA4

PPP1R9B PLEK ZBTB4

HLA−DRA BCL9L NOTCH1 SLC4A2 TGFBR2 0 2 4 6 HSPG2 KIAA1522 C16orf87 CYFIP1 TNS1 OAF TRIM14 B4GALT2 NANS FAM120A PLEKHO2 ADPGK -Log 10(p-Value) GPNMB

CHST11 LGALS3

L2HGDH C1QA C10orf54 TSPAN9 Sub-Network c1

SH3BGRL3

TMEM214 CREG1 ZYX MYL12B

TMEM109 SPSB1

PLBD2 FCGR2A FEZ2 CFDP1 MYL12A STX4 Sub-Network c2 SFRP2 MMP2

SUCLG2 ARF3 FTL ELF1 SMIM3 NPTX2 GABARAPL1 CDK5R2 CASP4 ZSWIM5 SERPINF1 F2R FAM114A1 CDR2 Regulation of Cellular Component Movement

LOC101929188 Regulation of Epithelial Cell Migraton LAD1

JAG1 PDLIM1 MEIS1 YBX3 Positive Regulation of Locomotion TRIB1 LHFPL4

NOTCH2NL NOTCH2 MYC A2M ANXA1 RUNX1 LPP GRASP TAGLN2 JPX Regulation of Epithelial to Mesenchymal Transition HEG1 HMSD NAB2 TMEM41A AHNAK DHRS3 AHR COQ10A

PODXL

Sub-Network c2 0 2 4 6 OLMALINC LDLRAP1 EPT1 XPO1 -Log 10(p-Value)

PUM2 Sub-Network c3

LRPPRC

DDX1

RANBP9 ZNF845 Negative Regulation of Immune System Process

ASUN EIF5B PIK3C2A CEBPZ Notch Signaling Pathway SMC6

PRKD3 MTIF2 KCNK1 SRBD1 PNPT1 MPHOSPH10 Organ Morphogenesis N WDR43 STR SESN2

RBM28 SLC4A1AP

Sub-Network c3 Negative Regulation of Cell Differentiation DNAJC2 HSP90AB1

0 2 4 6

-Log 10(p-Value) bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was Figure 2 not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. A B

Genome-wide NCI-H82NCI-H524NCI-H2081NCI-H2171NCI-H1963NCI-H209CORL88 Chromatin Accesibility GGACTAGCACACGTGGCATTCAGTCAACG GGACTAGCACACGTGGCATTCAGTCAACG Filter with MYC c-MYC SNPH SNPH (E-Box) Motif L-MYC

GACTAGCACACGTGGCATTCAGTCAACGTA MYC GACTAGCACACGTGGCATTCAGTCAACGTA Vinculin Accesibility WNT2 WNT2

C D MYC (E-Box) Accessibility

LMYC cMYC

CORL88 NCI-H1963 NCI-H209 NCI-H524 NCI-H2171 NCI-H82 0.4 1 NCI-H209 CORL88 0.2 70000 2

0.0 3 NCI-H1963 60000

0.2 50000 NCI-H82 0.4

NCI-H2171 40000 Principal Component #2 [18%] 0.6 NCI-H524 0.5 0.4 0.3 Principal Component #1 [47%] 30000 20000 10000

-400 0 400 -400 0 400 -400 0 400 -400 0 400 -400 0 400 -400 0 400 Distance from peak center (bp) bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was Figure 3 not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. A B Differential Accessible Regions LMYC Accessible Regions

Color Key Abnormal neuronal migration and Histogram LMYC Regulation of Glial Cell Proliferation Count

0 400 1000 cMYC Development of Neural Plate 0 2 4 6 8 10 Score Development of Future Midbrain Hydroencephaly Development of Future Midbrain Neural Fold

0 2 4 6 8 C -Log10(p-Value) cMYC Accessible Regions Notch Signaling pathway Leukotriene Biosynthetic Process N1ICD Regulates Transcription Signaling by NOTCH1 Regulation of Fat Cell Differentiation

CORL88 NCI-H82 cAMP Biosynthetic Process NCI-H209 NCI-H524 NCI-H2171 NCI-H1963 0 2 4 6 8 -Log (p-Value) D E 10 Chr20 Chr4 57,780kb 57,790kb 57,800kb 22,562,000kb 22,563,000kb 22,564,000kb22,565,000kb 22,566,000kb

[0-73] CORL88 [0-46] CORL88

NCI-H1963 NCI-H1963

NCI-H209 NCI-H209

NCI-H2171 NCI-H2171

NCI-H524 NCI-H524

NCI-H82 NCI-H82

FOXA2 REST Chr12 Chr2 182,535kb 182,540kb 182,545kb 103,351,000kb 103,352,000kb 103,353,000kb 103,354,000kb

[0-78] [0-113] CORL88 CORL88

NCI-H1963 NCI-H1963

NCI-H209 NCI-H209 NCI-H2171 NCI-H2171 NCI-H524 NCI-H524 NCI-H82 NCI-H82

ASCL1 NEUROD1 bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was Figure 4 not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. A B C NCI-H82 NCI-H82 NCI-H82

LacZ + + + ------5 sgNT + - - - LMYC - - - + + + + + + sgMYC - + + + 4 sgNT - + + - + + - - - c-MYC sgMYC ------+ + + NCI-H1963 NCI-H841 3 LMYC 2 NeuroD1 1 cMYC Vinculin

ASCL1 Relative Cell Viability 0 0 2 4 6 NEUROD1 Days YAP1 LacZ sgNT LMYC sgNT LacZ sgMYC2 LMYC sgMYC2 Vinculin LacZ sgMYC3 LMYC sgMYC3

D NCI-H82 E LacZ sgNT vs LMYC sgMYC Upregulated genes Intrinsic component of plasma membrane

70 Ion transport MYCL Cell junction Neuron part Neurogenesis 65 Ion transmembrane transport // // Neuron differentiation Regulation of cell differentiation Synapse Neuron projection

0 5 10 15 20

-Log10(p-Value)

Downregulated genes (padj) 10 Organonitrogen compound biosynthetic process Cellular macromolecule localization

-Log Mitochondrion RNA binding Cellular amide metabolic process Small molecule metabolic process Amide biosynthetic process Ribonucleotide binding Peptide biosynthetic process Adenyl nucleotide binding

0 10 20 30 40 50

-Log10(p-Value) 0 5 10 15 20

10 5 0 5 10 Log2FoldChange bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was Figure 5 not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. A B NCI-H1963 6 Cell Growth LacZ cMYC

c-MYC 4

L-MYC 2 ASCL1 Relative Viability

NeuroD1 0 02468 Days Vinculin H1963 LacZ H1963 cMYC

C D NCI-H1963 LacZ NCI-H1963 LacZ

Apoptotic: 28% Necrotic: 4.4%

Annexin-V Viable: 68.6% H&E

Propidium Iodide

NCI-H1963 cMYC NCI-H1963 cMYC

Apoptotic: 48% Necrotic: 14.8%

Annexin-V Viable: 36.2% H&E

Propidium Iodide bioRxiv preprint doi: https://doi.org/10.1101/852939; this version posted November 29, 2019. The copyright holder for this preprint (which was Figure 6 not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. A B Differential Gene Expression NCI-H1963 LacZ cMYC cluster 1 NCI-H1963 LacZ vs NCI-H1963 cMYC cluster 2 35

30 cluster 3

25 -Log10(padj) cluster 4 20

15 0 1 2 3 4 5 6 7

10 4 2 0 2 4 cluster 5 Log2 Fold Change

5 C Upregulated genes Regulation of cell death 0 Apoptotic process -0.5 0 0.5 -0.5 0 0.5 Sequence specific DNA binding Gene distance (bp) Negative regulation of cell death Response to growth factor D 0 2 4 6 8 0 1

NCI-H1963 -Log10(p-Value) LacZ cMYC Downregulated genes Vehicle Vehicle DBZ DBZ Neuron part Axon cMYC Neuron projection Synapse part Tubulin binding LMYC Regulation of nervous system development Synapse Cytoskeletal protein binding Cell projection part ASCL1 Neurogenesis

0 2 4 6 8 -Log (p-Value) Rest 10

Vinculin