Notch receptors participate in a conserved signaling pathway in which successive ligand-mediated proteolytic cleavages by ADAM10 and γ-secretase permit intracellular Notch (ICN) to translocate to the nucleus and form a Notch transcription complex (NTC) with the DNA-binding factor RBPJ and co-activators of the Mastermind-like (MAML) family (for review, see Bray, 2016). Outcomes of Notch activation are dose and cell-context-dependent, in part because most Notch response elements lie within lineage-specific enhancers (Castel et al., 2013; Ryan et al., 2017; Skalska et al., 2015; Wang et al., 2014). As a result, Notch-dependent transcriptional programs vary widely across cell types.

The context-dependency of outcomes produced by Notch signaling is reflected in the varied patterns of Notch mutations that are found in different cancers (for review, see Aster et al., 2017). In some cancers oncogenic gain-of-function Notch mutations predominate, but in human cutaneous squamous cell carcinoma (SCC) (South et al., 2014; Wang et al., 2011) loss-of-function mutations are common, early driver events, observations presaged by work showing that loss of Notch function promotes skin cancer development in mouse models (Nicolas et al., 2003; Proweller et al., 2006). The mechanism underlying the tumor suppressive effect of Notch appears to involve its ability to promote squamous differentiation at the expense of self-renewal, a function that is operative in other squamous epithelia (Alcolea et al., 2014), where Notch also has tumor suppressive activities (Agrawal et al., 2011; Agrawal et al., 2012; Loganathan et al., 2020). In line with this idea, conditional ablation of Notch1 in postnatal mice results in epidermal hyperplasia and expansion of proliferating basal-like cells (Nicolas et al., 2003; Rangarajan et al., 2001). Moreover, murine and human β-papilloma viruses express E6 proteins that target MAML1 and inhibit Notch function (Meyers et al., 2017; Tan et al., 2012), thereby causing epidermal hyperplasia and delayed differentiation of infected keratinocytes. Conversely, constitutively active forms of Notch enhance keratinocyte differentiation in vitro and in vivo (Nickoloff et al., 2002; Rangarajan et al., 2001; Uyttendaele et al., 2004).

While these studies delineate a pro-differentiation, tumor suppressive role for Notch in squamous cells, little is known about the Notch target genes that confer this phenotype. Work to date has focused on candidate genes chosen for their known activities in keratinocytes or their roles as Notch target genes in other cell types. These include CDKN1A/p21 (Rangarajan et al., 2001), which has been linked to cell cycle arrest and differentiation (Missero et al., 1996); HES1, which represses basal fate/self-renewal (Blanpain et al., 2006); and IRF6, expression of which positively correlates with Notch activation in keratinocytes (Restivo et al., 2011). However, dose- and time-controlled genome-wide studies to determine the immediate, direct effects of Notch activation in squamous-lineage cells have yet to be performed.

To this end, we developed and validated 2D and 3D culture models of malignant and non-transformed human squamous epithelial cells in which tightly regulated Notch activation produces growth arrest and squamous differentiation. We find that immediate, direct Notch target genes are largely keratinocyte-specific and are associated with lineage-specific NTC-binding enhancers enriched for the motifs of transcription factors linked to regulation of keratinocyte differentiation, particularly AP1. Among these targets are multiple genes previously shown to be upregulated by DNA damage and cell stress, including IER5, a member of the AP1-regulated immediate early response gene family (Williams et al., 1999). Here, we show that IER5 is required for Notch-induced differentiation of human SCC cells and TERT-immortalized human keratinocytes, and that this requirement is abolished by knockout of the B55α regulatory subunit of PP2A, to which IER5 directly binds. Our studies provide the first genome-wide view of the effects of Notch on gene expression in cutaneous squamous carcinoma cells, highlight previously unrecognized crosstalk between Notch and DNA response genes, and point to the existence of a Notch-IER5-PP2A signaling axis that coordinates keratinocyte differentiation.

Establishment of a conditional Notch-on SCC model

Determination of the immediate, direct effects of Notch in a model system requires tightly timed, switch-like Notch activation. This is difficult to achieve with ligands because simple addition of soluble Notch ligands does not induce signaling (Sun and Artavanis-Tsakonas, 1997). Methods of triggering Notch activation include plating of cells on immobilized ligands (Varnum-Finney et al., 2000); treatment with EDTA, which renders Notch susceptible to activating cleavages by chelating Ca²⁺ and thereby destabilizing the Notch negative regulatory region (Rand et al., 2000); and γ-secretase inhibitor (GSI) washout, which reliably delivers a pulse of ICN in 15–30 min to the nuclei of cells expressing mutated or truncated forms of membrane-tethered Notch (Petrovic et al., 2019; Ryan et al., 2017; Wang et al., 2014; Weng et al., 2006). Plating of adherent cells on substrate coated with immobilized ligand is confounded by the need to first produce cell suspensions with trypsin and/or EDTA, which activates Notch in cells expressing Notch receptors. EDTA treatment also suffers from several limitations: (i) Notch activation is confined to a period of several minutes immediately following EDTA addition and is therefore limited in degree and duration, possibly because chelation of Zn²⁺ also rapidly inactivates ADAM metalloproteases and (ii) off-target effects of EDTA, including on surface proteins that mediate cell adhesion. GSI washout is open to criticism because γ-secretase has numerous substrates in addition to Notch receptors, raising questions about specificity. However, major phenotypes induced by treatment of flies (Micchelli et al., 2003), mice (van Es et al., 2005), and humans (Aster and Blacklow, 2012) with GSI are all related to Notch inhibition, strongly suggesting that Notch is the dominant GSI substrate at the organismal level. In line with these observations, in prior work we have noted that cells lacking ongoing Notch signaling show little or no change in phenotype when treated with GSI, and we therefore selected GSI washout to produce timed activation of Notch in cells of squamous lineage.

To create a squamous cell model in which GSI washout activates NOTCH1 (Figure 1A), we first engineered a cDNA encoding a mutated truncated form of NOTCH1, ΔEGF-L1596H, that cannot respond to ligand and that has a point substitution in its negative regulatory region that produces ligand-independent, γ-secretase-dependent Notch activation (Gordon et al., 2009; Malecki et al., 2006). Notably, when expressed from retroviruses NOTCH1 alleles bearing negative regulatory region mutations like L1596H generate Notch signals that are sufficient to produce physiologic effects in hematopoietic stem cells (induction of T cell differentiation) without causing pathophysiologic effects (induction of T cell acute lymphoblastic leukemia) (Chiang et al., 2008). Because Notch transcription complexes appear to largely act through ‘poised’ enhancers primed by lineage-specific ‘pioneer’ transcription factors (Falo-Sanjuan et al., 2019), we reasoned that squamous cell carcinoma lines with loss-of-function Notch mutations and little/no ongoing Notch signaling would be an ideal context in which to identify direct downstream targets of Notch. We therefore transduced ΔEGF-L1596H into two human SCC cell lines, IC8 and SCCT2, that have biallelic inactivating mutations in NOTCH1 and TP53 (Inman et al., 2018), lesions that were confirmed by resequencing on a clinical-grade targeted exome NGS platform (summarized in Tables 1 and 2).

Figure 1 with 3 supplements see all

Download asset Open asset

Table 1

Gene	Variant	Variant allele frequency
IC8 cell
CASP8	c.971T > C(p.M324T)	66% of 411 reads
FBXW7	c.1633T > C(p.Y545H)	33% of 195 reads
KMT2D	c.7412G > A(p.R2471Q)	42% of 255 reads
MGA	c.5599G > A(p.V1867I)	39% of 710 reads
MTOR	c.4828G > A(p.E1610K)	56% of 280 reads
NOTCH1	c.5059C > T (p.Q1687*)	100% of 412 reads
PAXIP1	c.2023C > T(p.H675Y)	17% of 384 reads
PMS1	c.566_567delTCinsAT(p.V189D)	36% of 108 reads
RIF1	c.658G > A(p.E220K)	62% of 251 reads
ROS1	c.1144T > G(p.Y382D)	87% of 169 reads
ROS1	c.1164+2_1164+8delTTAGTCC ()	19% of 191 reads
SDHA	c.1627T > C(p.Y543H)	56% of 668 reads
SF3B1	c.2549T > C(p.I850T)	31% of 246 reads
TERT	CC242-243TT promoter mutation	50% of 26 reads
TP53	c.451C > T(p.P151S)	100% of 366 reads
WHSC1	c.2185C > T(p.R729C)	66% of 410 reads
WWTR1	c.551T > G (p.V184G)	64% of 256 reads
ZNF217	c.2590C > T(p.L864F)	39% of 835 reads
ZNF217	c.1162delC(p.H388Tfs*77)	55% of 822 reads
SCCT2 Cell
ALK	c.2854G > A (p.G952R)	50% of 441 reads
ASXL1	c.3959C > T (p.A1320V)	31% of 930 reads
BRD3	c.533C > T (p.S178F)	49% of 281 reads
BRD4	c.3915_3917dupTGC (p.A1306dup)	45% of 170 reads
CDH4	c.1801C > T (p.L601F)	30% of 447 reads
CDKN2A	c.*151–1G > A ()	100% of 172 reads
CDKN2A	c.212A > T (p.N71I)	100% of 184 reads
CREBBP	c.5842C > T (p.P1948S)	74% of 77 reads
CREBBP	c.2116G > A (p.G706R)	45% of 172 reads
DDB1	c.327+6G > A ()	47% of 451 reads
DICER1	c.775C > T (p.P259S)	42% of 301 reads
DOCK8	c.185T > A (p.V62E)	100% of 597 reads
EGFR	c.1955G > A (p.G652E)	48% of 518 reads
EGFR	c.298C > T (p.P100S)	49% of 595 reads
ERCC2	c.886A > T (p.S296C)	48% of 165 reads
ERCC5	c.264+1G > A ()	50% of 442 reads
ETV4	c.1298C > G (p.P433R)	45% of 302 reads
FANCF	c.494C > T (p.T165I)	50% of 644 reads
FANCL	c.155+1G > A ()	51% of 220 reads
FAT1	c.9076–1G > A ()	49% of 367 reads
FH	c.681G > T (p.Q227H)	4% of 756 reads
FLT4	c.2224G > A (p.D742N)	49% of 346 reads
GALNT12	c.1035+5G > A ()	52% of 523 reads
GLI2	c.1859C > A (p.T620K)	45% of 351 reads
HNF1A	c.1640C > T (p.T547I)	54% of 392 reads
JAZF1	c.477C > T (p.I159I)	47% of 606 reads
JAZF1	c.328C > T (p.P110S)	44% of 211 reads
KMT2D	c.10355+1G > A ()	49% of 622 reads
LIG4	c.1271_1275delAAAGA (p.K424Rfs*20)	40% of 659 reads
MAP2K1	c.568+1G > A ()	53% of 239 reads
MED12	c.2080G > A (p.E694K)	100% of 269 reads
MYB	c.1461+5G > A ()	41% of 430 reads
NF1	c.2608G > A (p.V870I)	50% of 615 reads
NF2	c.813T > G (p.F271L)	48% of 168 reads
NOTCH1	c.1226G > T (p.C409F)	44% of 519 reads
NOTCH1	c.1406A > G (p.D469G)	50% of 912 reads
NOTCH1	c.1245G > T (p.E415D)	42% of 495 reads
NOTCH2	c.5252G > A (p.G1751D)	44% of 459 reads
NOTCH2	c.1298G > A (p.C433Y)	50% of 484 reads
NOTCH2	c.1108+1G > A ()	53% of 305 reads
NSD1	c.7669G > A (p.G2557R)	49% of 743 reads
PDGFRB	c.2586+2T > A ()	43% of 380 reads
PHOX2B	c.181A > T (p.T61S)	52% of 222 reads
POLQ	c.6565G > A (p.A2189T)	27% of 462 reads
POLQ	c.1634G > A (p.S545N)	33% of 667 reads
PPARG	c.819+6T > C ()	100% of 134 reads
PRKDC	c.6436G > A (p.A2146T)	42% of 471 reads
RAD51C	c.996G > A (p.Q332Q)	45% of 302 reads
RHEB	c.443C > T (p.S148F)	46% of 120 reads
ROS1	c.6871C > T (p.P2291S)	45% of 605 reads
ROS1	c.3342A > T (p.Q1114H)	48% of 274 reads
ROS1	c.137A > T (p.D46V)	42% of 215 reads
RPTOR	c.2992G > A (p.V998I)	48% of 352 reads
RUNX1T1	c.1039G > A (p.D347N)	45% of 715 reads
SDHA	c.1151C > T (p.S384L)	53% of 446 reads
SLC34A2	c.1700T > A (p.I567N)	50% of 460 reads
SMARCA4	c.3947T > G (p.F1316C)	55% of 431 reads
SMARCE1	c.395C > T (p.A132V)	51% of 587 reads
STAT3	c.1852G > A (p.G618S)	47% of 527 reads
TDG	c.166+4G > A ()	48% of 329 reads
TP53	c.375+1G > T	47% of 173 reads
TP53	c.832_833delCCinsTT (p.P278F)	46% of 418 reads
UIMC1	c.971T > C (p.V324A)	48% of 745 reads
XPC	c.571C > T (p.R191W)	100% of 219 reads

1. ^*Based on analysis of 16,131,317 unique, high-quality sequencing reads (mean, 406 reads per targeted exon, with 98% of exons having more than 30 reads).

   ^†Based on analysis of 20,972,158 unique, high-quality sequencing reads (mean, 413 reads per targeted exon, with 99% of exons having more than 30 reads).

Table 2

Chromosome	Type	Genes affected
IC8 cell line
1q	Gain	MCL1, GBA, RIT1, NTRK1, DDR2, PVRL4, SDHC, CDC73, MDM4, PIK3C2B, UBE2T, PTPN14, H3F3A, EGLN1, AKT3, EXO1, FH
2	Loss	XPO1, FANCL, REL, MSH6, EPCAM, MSH2, SOS1, ALK, BRE, DNMT3A, GEN1, MYCN, TMEM127, GLI2, ERCC3, CXCR4, RIF1, ACVR1, ABCB11, NFE2L2, PMS1, CASP8, SF3B1, CTLA4, ERBB4, IDH1, BARD1, XRCC5, DIS3L2
3p	Loss	MITF, BAP1, PBRM1, COL7A1, RHOA, SETD2, CTNNB1, MLH1, MYD88, XPC, PPARG, RAF1, FANCD2, OGG1, VHL
3q	Gain	NFKBIZ, CBLB, POLQ, GATA2, MBD4, TOPBP1, FOXL2, ATR, MECOM, PRKCI, TERC, PIK3CA, SOX2, ETV5, BCL6
4	Loss	PHOX2B, RHOH, SLC34A2, FGFR3, WHSC1, KDR, KIT, PDGFRA, FAM175A, HELQ, TET2, FBXW7, NEIL3, FAT1
5	Gain	RICTOR, IL7R, SDHA, TERT, MAP3K1, PIK3R1, XRCC4, RASA1, APC, RAD50, CTNNA1, PDGFRB, ITK, NPM1, TLX3, FGFR4, NSD1, UIMC1, FLT4
6	Gain	CCND3, NFKBIE, POLH, VEGFA, CDKN1A, PIM1, RNF8, FANCE, DAXX, HFE, HIST1H3B, HIST1H3C, ID4, PRDM1, ROS1, RSPO3, MYB, TNFAIP3, ESR1, ARID1B, PARK2, QKI
7	Gain	EGFR, IKZF1, JAZF1, ETV1, PMS2, RAC1, CARD11, SBDS, CDK6, SLC25A13, CUX1, RINT1, MET, POT1, SMO, BRAF, PRSS1, EZH2, RHEB, XRCC2, PAXIP1
8p	Loss	KAT6A, POLB, FGFR1, WHSC1L1, NRG1, WRN, NKX3-1, PTK2B, GATA4, NEIL2
8q11.21-q21.11	Loss	PRKDC, MYBL1, TCEB1
8q21.3-q24.3	Gain	NBN, RUNX1T1, RAD54B, RSPO2, EXT1, RAD21, MYC, RECQL4
9p13.2-p21.3	Loss	PAX5, FANCG, RMRP, CDKN2A, CDKN2B, MTAP
9p24.1-p24.3	Gain	CD274, JAK2, PDCD1LG2, DOCK8
11p11.2-p13	Gain	EXT2, LMO2
13q33.1	Loss	ERCC5
15q	Gain	FAN1, GREM1, BUB1B, MGA, RAD51, TP53BP1, B2M, USP8, MAP2K1, PML, NEIL1, FAH, NTRK3, BLM, FANCI, IDH2, IGF1R
16p13.3	Loss	CREBBP, SLX4
19	Loss	BABAM1, CRTC1, JAK3, KLF2, MEF2B, BRD4, NOTCH3, CALR, KEAP1, SMARCA4, ELANE, GNA11, MAP2K2, STK11, TCF3, CCNE1, C19orf40, CEBPA, AKT2, AXL, CIC, XRCC1, ARHGAP35, ERCC1, ERCC2, BCL2L12, PNKP, POLD1, PPP2R1A
20	Gain	MCM8, ASXL1, BCL2L1, MAFB, AURKA, ZNF217, GNAS, CDH4
SCCT2 Cell Line
1q32.1	Loss	UBE2T
1q42.12-q42.2	Gain	H3F3A, EGLN1
1q43	Loss	AKT3, EXO1
1q43	Gain	FH
3 p Arm level	Loss	MITF, BAP1, PBRM1, COL7A1, RHOA, SETD2, CTNNB1, MLH1, MYD88, XPC, PPARG, RAF1, FANCD2, OGG1, VHL
3q Arm level	Gain	NFKBIZ, CBLB, POLQ, GATA2, MBD4, TOPBP1, FOXL2, ATR, MECOM, PRKCI, TERC, PIK3CA, SOX2, ETV5, BCL6
8q Arm level	Gain	PRKDC, MYBL1, TCEB1, NBN, RUNX1T1, RAD54B, RSPO2, EXT1, RAD21, MYC, RECQL4
9q Arm level	Gain	GNAQ, NTRK2, FANCC, PTCH1, GALNT12, XPA, KLF4, TAL2, ENG, ABL1, TSC1, BRD3, NOTCH1
18q11.2	Gain	GATA6, RBBP8
18q11.2-q21.33	Gain	SS18, SETBP1, SMAD2, SMAD4, BCL2
20	Gain	MCM8, ASXL1, BCL2L1, MAFB, AURKA, ZNF217, GNAS, CDH4

In pilot studies, we observed that the growth of IC8 and SCCT2 cells transduced with empty virus was unaffected by the presence or absence of GSI, whereas the growth of lines transduced with ΔEGF-L1596H was reduced by GSI washout (Figure 1—figure supplement 1A, B). GSI washout was accompanied by rapid activation of NOTCH1 (ICN1) followed by upregulation of markers of differentiation, such as involucrin (Figure 1—figure supplement 1C, D). Of interest, ICN1 levels reproducibly peaked at around 4 hr and then declined, suggesting that sustained NOTCH1 activation and accompanying changes in cell state led to induction of feedback loops that negatively regulate ICN1. We also observed that IC8-ΔEGF-L1596H cells formed ‘skin-like’ epithelia when seeded onto organotypic 3D cultures, whereas SCCT2-ΔEGF-L1596H cells did not (data not shown); therefore, additional studies focused on IC8 cells and derivatives thereof.

To further characterize and validate our system, we performed single-cell cloning of IC8−ΔEGF-L1596H cells and observed that differentiation following GSI washout correlated with ICN1 accumulation (Figure 1—figure supplement 2A and B). The subclone SC2, which showed moderate accumulation of ICN1 and sharply reduced growth following GSI washout, was selected for further study. The ability of SC2 cells to form a multilayered epithelium in the Notch-on, growth suppressive state appears to stem from an unexpected property that emerged in 3D cultures, namely the self-organization of these cells into a proliferating, ICN1-low basal layer in contact with matrix and a non-proliferating, ICN1-high suprabasal layer (Figure 1—figure supplement 2C). The self-organization of Notch-on SC2 cells grown on collagen rafts into ICN1-low basal proliferating cells and ICN1-high suprabasal non-proliferating cells suggested that contact of SC2 cells with collagen reduces ICN1 levels. To test this idea, we plated SC2 cells on plastic or collagen, washed out GSI, and compared ICN1 levels by western blotting. As shown in Figure 1—figure supplement 2D, culture of SC2 cells on collagen sharply reduced ICN1 levels, whereas the level of total NOTCH1 polypeptides was unchanged or slightly increased in cells grown on collagen. These observations point to the existence of one or more matrix-dependent effects that decrease ICN1 levels and which may serve to reinforce the ‘Notch-low’ status of basal keratinocytes.

We also noted that the growth arrest induced by GSI washout in SC2 cells was blocked by dominant-negative MAML1 (DN-MAML), a specific inhibitor of Notch-dependent transcription (Figure 1B; Nam et al., 2007; Weng et al., 2003), confirming that the growth inhibitory effects of GSI washout are mediated through Notch activation. Growth arrest occurred several days after Notch activation (compare Figure 1B and C) and was accompanied by upregulation of multiple markers of squamous differentiation, such as involucrin, keratin1, and plakophilin1, in 2D (Figure 1C–E) and in 3D cultures (Figure 1F). In addition, we also noted that staining for keratin14, a prototypic marker of proliferating basal keratinocytes (Fuchs, 1995), became more sharply localized to basal cells in Notch-on SC2 cells (Figure 1F).

Although Notch activation in IC8 and SC2 cells clearly induced expression of squamous differentiation markers, the distribution of these markers in 3D cultures failed to precisely mimic that of normal epidermis, as staining for involucrin and plakophilin-1 was seen in proliferating keratin14-positive basal cells. The observed expression of spinous markers in ICN1-low proliferating basal cells may be explained by differing Notch dose requirements for growth arrest (relatively high) versus induction of spinous differentiation (relatively low). To further compare and contrast the effects of Notch activation in SC2 cells with normal keratinocyte differentiation, we performed staining for a series of additional markers (Figure 1—figure supplement 3). Similar to normal keratinocyte differentiation, Notch activation was associated with increased staining for BCL6, particularly in suprabasal SC2 cells. By contrast, p63 staining, which is normally confined to basal keratinocytes, was seen in suprabasal as well as basal SC2 cells, and keratin5 staining was observed throughout SC2 cell rafts, in contradistinction to keratin14 staining (Figure 1F). Notch activation also failed to induce staining for markers of terminal differentiation such as filaggrin (Figure 1—figure supplement 3) and loricrin (not shown). Thus, Notch activation in SC2 cells induces genes associated with spinous differentiation but is insufficient to down-regulate certain genes associated with basal cell fate, such as p63 and keratin5, or to push SC2 cells to terminally differentiate.

Identification of a squamous cell-specific Notch-induced program of gene expression

To determine early and delayed effects of Notch on gene expression, we performed RNA-seq on SC2 cells in 2D cultures in the Notch-off state and following Notch activation. Because γ-secretase has numerous substrates, as a control we performed RNA-seq on parental, non-transduced IC8 cells in the presence of GSI and following GSI washout, which revealed no significant GSI-dependent changes in gene expression in the absence of a Notch transgene (Figure 2—figure supplement 1). By contrast, Notch activation in SC2 cells produced significant changes in gene expression by 4 hr that became more pronounced by 24 hr and 72 hr (Figure 2A, see Supplementary files 1–3 for differentially expressed genes). Gene ontology (GO) analysis revealed enrichment among upregulated genes for those that are associated with keratinocyte differentiation and biology (Supplementary files 4 and 5, summarized in Figure 2B). Among the rapidly upregulated genes (genes that increase in expression by 4 hr) were several reported targets of Notch in keratinocytes (e.g, RHOV [Pomrey and Radtke, 2010], HES1 [Blanpain et al., 2006], and IRF6 [Restivo et al., 2011]), but most genes in this class were novel for keratinocytes and included: (1) direct targets of Notch in other lineages (e.g. NRARP, HES4, and HES5); (2) genes linked to keratinocyte differentiation (e.g. RIPK4 [Kwa et al., 2014], recently reported to be upregulated following treatment of keratinocytes with EDTA [Loganathan et al., 2020], and SMAD3 [Meyers et al., 2017]); (3) genes associated with DNA damage responses in keratinocytes and other cell types (e.g. GADD45A, CXCL8, IL1B, ID3, CYR61, BTG2, IER3, and IER5 [Kis et al., 2006; Kumar et al., 1998; Maeda et al., 2002; Rouault et al., 1996; Sesto et al., 2002; Simbulan-Rosenthal et al., 2006]); and (4) genes associated with growth arrest of keratinocytes and other cell types (e.g. HES1 [Blanpain et al., 2006], GADD45A [Maeda et al., 2002], and BTG2 [Rouault et al., 1996]).

Figure 2 with 2 supplements see all

Download asset Open asset

To confirm that these changes in gene expression are general features of Notch activation in IC8-ΔEGF-L1596H cells and to determine the kinetics of response, we performed RT-PCR analyses on a number of known and novel targets in pooled IC8-ΔEGF-L1596H transductants. This confirmed the Notch responsiveness of all genes tested, and also revealed variation in the kinetics of response, even among ‘canonical’ Notch target genes. For example, HES1 showed fast induction followed by rapid down-regulation, consistent with autoinhibition (Hirata et al., 2002), whereas HES4, HES5, and NRARP (a feedback inhibitor of NTC function [Jarrett et al., 2019]) showed more sustained increases in expression (Figure 2C). Genes encoding non-structural proteins known to be linked to squamous differentiation also were ‘early’ responders (Figure 2D), as were genes linked to DNA damage/cell stress response (Figure 2E). In the case of the latter novel targets, we confirmed that protein levels also rose in a Notch-dependent fashion (Figure 2F). By contrast, increased expression of genes encoding structural proteins associated with keratinocyte differentiation (e.g. IVL, KRT1, KRT13) was delayed, only emerging at 24–72 hr (Supplementary files 1–3). These findings suggest that Notch activation induces the expression of a core group of early direct target genes, setting in motion downstream events that lead to differentiation.

Notch activation also down-regulated a smaller set of genes (Figure 2A, summarized in Supplementary files 1–3), possibly via induction of transcriptional repressors of the Hes family. These include multiple genes expressed by basal epidermal stem cells, including genes encoding the Notch ligand DLL1 (Lowell et al., 2000); β1-integrin (Jones and Watt, 1993); LRIG1 (Jensen and Watt, 2006), a negative regulator of epidermal growth factor receptor signaling; and multiple WNT ligands (WNT7A, 7B, 9A, 10A, and 11), of interest because WNT signaling contributes to maintenance of epidermal stem cells (Lim et al., 2013).

To determine the overlap of Notch target genes in squamous cells with other cell lineages, we compared the list of Notch-responsive genes in SC2 cells with three other cell types in which GSI washout has been used to identify genes that are rapidly upregulated by Notch: triple-negative breast cancer cells (Petrovic et al., 2019); mantle cell lymphoma cells (Ryan et al., 2017); and T-cell acute lymphoblastic leukemia (T-ALL) cells. Using fairly stringent cutoffs for Notch-responsiveness (FDR < 0.05, log2 change >1; summarized in Supplementary file 6), we failed to identify any genes that were co-regulated by Notch in all of these cell types (Figure 2—figure supplement 2). Even in two epithelial cell types, SC2 squamous cells and MB157 triple negative breast cancer cells, only 10.8% of Notch-responsive genes in SC2 cells were also Notch-responsive in MB157 cells. As would be expected, the overlap between Notch-responsive genes in SC2 cells and B lineage REC1 cells (9/390, 2.3%) and T lineage DND41 cells (1/390, 0.3%) was even lower. These observations serve to again emphasize the remarkable context-specificity of Notch effects on gene expression.

Notch target genes are associated with lineage-specific NTC-binding enhancer elements

To identify sites of NTC-binding to Notch-responsive regulatory elements in IC8-ΔEGF-L1596H cells, we performed ChIP-seq for Notch transcription complex components (RBPJ and MAML1) 4 hr after Notch activation, as well as for RBPJ prior to Notch activation. The rationale for identifying RBPJ and MAML1-binding sites, rather than NOTCH1-binding sites, was several fold: (i) we wanted to identify binding of endogenous Notch transcription complex components with reliable commercially available monoclonal antibodies; (ii) based on our RNA-seq data sets, MAML1 is the most highly expressed member of the MAML family in IC8 cells (MAML1 log₂ read counts per million = 6.66; MAML2 log₂ read counts per million = 3.59; MAML3 log₂ read counts per million = 0.70), and therefore was the logical member of the family to study; and (iii) we were intrigued by prior studies suggesting that MAML1 might associate with non-Notch transcription factor complexes (Jin et al., 2010; Quaranta et al., 2017; Shen et al., 2006; Zhao et al., 2007). In the Notch-on state, we found that most MAML1 binding sites also bound RBPJ (8533/9,187 sites, 93%; Figure 3A), in line with studies showing that MAML1 association with DNA requires both RBPJ and NICD (Nam et al., 2006). Approximately 92% of RBPJ/MAML1 co-binding sites (hereafter designated NTC binding sites) are in intergenic or intronic regions consistent with enhancers (Figure 3B). As predicted by past studies (Castel et al., 2013; Krejcí and Bray, 2007; Ryan et al., 2017; Wang et al., 2014), NTC binding was associated with increases in RBPJ ChIP-Seq signals and H3K27ac signals at promoter and enhancer sites (Figure 3C), features previously noted to characterize ‘dynamic’ functional Notch response elements. Motif analysis revealed that the most common motif lying within 300 bp of NTC ChIP-Seq signals is that of RBPJ (Figure 3D), and that the motif for AP1, a factor not associated with NTC binding sites in other cell types (Chatr-Aryamontri et al., 2017; Drier et al., 2016; Petrovic et al., 2019; Ryan et al., 2017; Wang et al., 2014), is also highly enriched in this 600 bp window. Based on the method of Severson et al., 2017, approximately 13% of NTC-binding sites in IC8 cells are predicted to be sequence paired sites (Figure 3E), a specialized type of response element that binds NTC dimers (Arnett et al., 2010). Finally, particularly at early time points, NTC-binding sites were spatially associated with genes that are upregulated by Notch, whereas genes that decreased in expression were no more likely to be associated with NTC binding sites than genes that did not change in expression (Figure 3F). Taken together, these studies show that NTCs mainly bind lineage-specific enhancers in SCC cells and that their loading leads to rapid ‘activation’ of Notch-responsive elements and upregulation of adjacent genes.

Figure 3

Download asset Open asset

We also performed motif analysis on sites producing significant signals for only RBPJ or only MAML1. RBPJ ‘only’ sites also were enriched for RBPJ (E value 1.3^e-124) and AP1 (E value 2.8^e-71) motifs but had lower average ChIP-Seq signals, suggesting these may be weak RBPJ-binding sites. MAML1 ‘only’ sites also were enriched for AP1 motifs (E value 2.9^e-181) but were not associated with RBPJ motifs. These sites were relatively few in number (N = 654) and the associated AP1 motifs were distributed broadly around MAML1 signal peaks, arguing against direct physical interaction between MAML1 and AP1 family members on chromatin. Thus, the significance of these ‘MAML1-only’ peaks is uncertain, and it is possible that the observed ChIP-seq signals are non-specific, stemming from over-representation of ‘open’ chromatin in ChIPs.

IER5 is a direct Notch target gene

We were intrigued by the convergence of Notch target genes, which presumably serve to promote and coordinate keratinocyte differentiation, and genes linked to DNA damage/cell stress responses. We selected one gene of this class, IER5, a member of the immediate early response gene family, for detailed analysis based on prior work implicating IER5 in cellular responses to DNA damaging agents and heat shock (Ding et al., 2009; Ishikawa and Sakurai, 2015; Kis et al., 2006), as well as functional studies suggesting that IER5 is a modulator of the serine/threonine kinase PP2A (Asano et al., 2016; Ishikawa et al., 2015; Kawabata et al., 2015) and might therefore serve as point of crosstalk between Notch and signaling pathways that depend on serine/threonine phosphorylation for signal transduction.

To confirm that IER5 is also upregulated by activation of endogenous Notch signaling in non-transformed keratinocytes, we studied TERT-immortalized NOK1 keratinocytes, which undergo squamous differentiation when moved to high Ca²⁺ medium (Piboonniyom et al., 2003). We observed that differentiation of NOK1 cells significantly increased the expression of IER5 as well as the canonical Notch target gene NRARP, effects that were blocked by GSI and by DN-MAML1, confirming that the observed changes in gene expression are Notch-dependent (Figure 4—figure supplement 1A and B). In line with the Notch-dependent increases in IER5 transcripts upon induction of differentiation, we also observed that IER5 protein levels increased in differentiation medium in a GSI- and DN-MAML1-sensitive fashion (Figure 4—figure supplement 1C and D). Differentiation was accompanied by activation of NOTCH2 and NOTCH3 (inferred from the accumulation of smaller polypeptides consistent with ADAM cleavage products under differentiation conditions in the presence of GSI; Figure 4—figure supplement 1C and D), as well as increased expression of NOTCH3, a known target of activated Notch. Suppression of IER5 transcript levels by GSI in some experiments was less pronounced than the abrogation of the accumulation of IER5 protein by Notch inhibitors, suggesting that additional pathways influence IER5 expression (consistent with the complex enhancer landscape around this gene) and that Notch signaling may regulate IER5 both transcriptionally and post-transcriptionally. Unexpectedly, we did not observe any activation of NOTCH1 in NOK1 cells in differentiation medium (data not shown), suggesting that differentiation in this model is directed by NOTCH2 instead of NOTCH1. Notably, given that germline swaps of the coding sequences of the intracellular domains of NOTCH1 and NOTCH2 yield apparently normal mice (Liu et al., 2015), it is likely that NOTCH1 and NOTCH2 activate the same sets of target genes, including IER5 in keratinocytes.

We next sought to confirm that IER5 is a direct Notch target gene. Inspection of chromatin landscapes around IER5 in IC8 cells revealed a series of flanking enhancers, two of which (D and E) showed the largest RBPJ/MAML1 signals and the greatest increase in H3K27ac following Notch activation (Figure 4A), a dynamic change that is strongly correlated with increased transcription of flanking genes (Wang et al., 2014). Notably, a similar enhancer landscape exists in non-transformed human keratinocytes (Figure 4—figure supplement 1E), and expression of IER5 transcripts is readily detectable in normal human skin (Figure 4—figure supplement 1F-H), consistent with the idea that the observed enhancers are involved in physiologic regulation of IER5 in keratinocytes. Reporter gene assays with enhancers D and E in IC8-ΔEGF-L1596H cells (Figure 4B and C, respectively) confirmed that the Notch responsiveness of these elements depend on RBPJ-binding sites and also showed, in the case of enhancer D, that a flanking AP1 consensus site is also required. To determine the contributions of enhancers D and E within the genomic IER5 locus, we used CRISPR/Cas9 targeting to delete the regions containing RBPJ-binding sites in these two enhancers in SC2 cells (Figure 4D). These deletions partially abrogated the Notch-dependent increase in IER5 transcription (Figure 4E) and suppressed the accumulation of IER5 protein following Notch activation (Figure 4F), confirming that IER5 is directly regulated by Notch through these elements.

Figure 4 with 1 supplement see all

Download asset Open asset

IER5 is required for ‘late’ Notch-dependent differentiation events in squamous cells

To systematically determine the contribution of IER5 to Notch-dependent changes in gene expression, we compared the transcriptional response to Notch activation in SC2 cells, SC2 cells in which IER5 was knocked out (I5 cells), and I5 cells to which IER5 expression was added back (I5AB cells, Figure 5A). Different doses of IER5 had no effect on gene expression in the absence of Notch signaling, or on the expression of genes that are induced by Notch within 4 hr (Figure 5B, denoted with a blue box); however, by 24 hr and 72 hr of Notch activation, I5 cells failed to upregulate a large group of Notch-responsive genes that were rescued by add-back of IER5 (Figure 5B, denoted with a red box). GO analysis revealed that IER5-dependent genes were associated with various aspects of keratinocyte differentiation and biology (Figure 5C; summarized in Supplementary files 7 and 8). An example of a differentiation-associated gene impacted by loss of IER5 is KRT1, a marker of spinous differentiation, expression of which is markedly impaired by IER5 knockout and restored by IER5 add-back (Figure 5D). Similarly, IER5 was required for Notch-dependent expression of the late marker involucrin in 3D cultures (Figure 5G). Thus, IER5 is necessary but not sufficient for expression of a group of genes that respond to Notch with delayed kinetics.

Figure 5

Download asset Open asset

To extend these observations to non-transformed keratinocytes that rely on endogenous Notch signaling for differentiation, we targeted IER5 with CRISPR/Cas9 and also enforced expression of IER5 through retroviral transduction in NOK1 cells. Although CRISPR/Cas9 targeting of bulk NOK1 cells was only partially effective, it was sufficient to diminish the expression of multiple differentiation-associated genes (Figure 5F), whereas overexpression of IER5 increased expression of each of these markers (Figure 5G). Thus, IER5 is required for Notch-dependent differentiation of malignant and non-transformed keratinocytes.

IER5 binds B55α/PP2A complexes

IER5 encodes a 327 amino acid protein with a ~ 50 amino acid N-terminal IER domain and a C-terminal domain predicted to be unstructured, suggesting that it functions through protein-protein interactions. To identify interacting proteins in an unbiased way, we expressed a tagged form of IER5 in IER5 null I5 cells and performed affinity purification followed by mass spectrometry (Adelmant et al., 2019), which identified the B55α regulatory subunit of PP2A (encoded by the PPP2RA2 gene) and PP2A scaffolding and catalytic subunits as potential interactors (Figure 6A; summarized in Supplementary file 9). We confirmed these associations by expressing tagged IER5 in I5 cells and tagged B55α in SC2 cells (Figure 6B). Full-length IER5 and the N-terminal IER domain of IER5 co-precipitated endogenous B55α in Notch-independent fashion, whereas the C-terminal portion of IER5 did not (Figure 6C). Similarly, tagged B55α co-precipitated endogenous IER5 in a fashion that was augmented by Notch activation (Figure 6D), consistent with increased recovery of IER5 due to induction of IER5 expression by Notch. To confirm that IER5 binds B55α directly, we studied the interaction of purified recombinant proteins. IER5 exhibited saturable binding to B55α-coated beads (Figure 6E), and additional microscale thermophoresis studies showed that IER5 binds B55α with a Kd of approximately 100 nM (Figure 6F).

Figure 6

Download asset Open asset

IER5 is epistatic to PPP2R2A in SCC cells

To gain insight into the role of IER5-B55α interaction in the regulation of Notch- and IER5-sensitive genes, we prepared SCC cells that were knocked out for IER5, PPP2R2A, or both genes (Figure 7A). Knockout of PPP2R2A did not affect the levels of ICN1 following GSI washout (Figure 7A), but markedly increased the expression of the late differentiation gene KRT1 in 2D culture (Figure 7B) and the accumulation of involucrin in 3D cultures (Figure 7C), effects that were suppressed by add-back of B55α, suggesting a model in which IER5 suppresses a B55α-dependent activity. This was supported by assays performed with IER5/PPP2R2A double knockout cells (Figure 7A), in which expression of the late genes such as KRT1 was also restored (Figure 7D). These results suggest that Notch modulation of B55α-PP2A activity via IER5 is important in regulating the complex series of events downstream of Notch that lead to squamous cell differentiation.

Figure 7

Download asset Open asset

Our work provides a genome-wide view of the direct effects of Notch in SCC cells, in which Notch activation induces growth arrest and differentiation. The phenotypic changes induced by Notch are mediated by a largely squamous-cell-specific transcriptional program that includes genes linked to keratinocyte differentiation and DNA damage responses, including IER5, which modulates the activity of B55α-containing PP2A complexes. Upregulation of these Notch-responsive genes are associated with binding of NTCs to RBPJ sites within lineage-specific enhancers, including a minority of sequence-paired sites, a specialized dimeric NTC binding element recently implicated in anti-parasite immune responses in mice (Kobia et al., 2020). The Notch target genes and elements identified here in malignant squamous cells are likely to be relevant for understanding Notch function in non-transformed squamous cells, as many of the NTC-binding enhancers found near Notch target genes in SCC cells are also active in normal human keratinocytes (based on review of ENCODE data for non-transformed human keratinocytes). These observations have a number of implications for understanding how Notch regulates the growth and differentiation of squamous cells and highlight the potential for Notch to influence the activity of diverse signaling pathways in keratinocytes through modulation of PP2A.

Prior work has suggested that p53-mediated upregulation of Notch expression and activity is a component of the DNA damage response in keratinocytes (Mandinova et al., 2008). Conversely, our work shows that Notch activation, even in a TP53 mutant background, induces the expression of genes that are components of the keratinocyte DNA damage/cell stress response, suggesting that these two pathways converge on a set of genes that induce the differentiation (and thus, the elimination) of damaged cells. Consistent with this idea, recent work has shown that low-dose radiation induces the differentiation of esophageal squamous cells at the expense of self-renewal in vivo (Fernandez-Antoran et al., 2019). The frequent co-mutation of TP53 and Notch genes in squamous carcinoma may also reflect, at least in part, the convergence of these pathways on a core set of genes with anti-oncogenic activities. Further work delineating the crosstalk between p53 and Notch signaling in well controlled model systems will be needed to test this idea.

Among the genes linked to Notch and p53 is IER5, an immediate early response gene that is a component of the DNA-damage response in a number of cell types (Ding et al., 2009; Kis et al., 2006; Kumar et al., 1998; Yang et al., 2016). Several lines of investigation suggest that IER5 modulates the function of PP2A complexes containing B55 (Asano et al., 2016; Ishikawa et al., 2015; Kawabata et al., 2015) regulatory subunits, and here we demonstrate that IER5 directly binds B55α protein in a purified system. However, the exact effect of IER5 on PP2A function is uncertain. One model suggests that IER5 augments the ability of B55/PP2A complexes to recognize and dephosphorylate specific substrates such as S6 kinase and HSF1 (Ishikawa et al., 2015; Kawabata et al., 2015), the latter leading to HSF1 activation as part of the heat shock response. However, our work with double knockout cells suggests IER5 inhibits at least some activities that are attributable to B55α-containing PP2A complexes. Work in purified systems, which is now feasible, may help to clarify how IER5 influences B55/PP2A function.

We also note that IER3, a putative regulator of B56/PP2A complexes, behaves as a direct Notch target in our SCC model system. Given the pleotropic role of PP2A isoforms and their myriad substrates, it appears likely that Notch-dependent modulation of PP2A regulators such as IER5 and IER3 will alter the activity of many factors that are regulated by serine/threonine phosphorylation. Because IER5 is required for expression of a large number of Notch-sensitive genes with delayed response kinetics, B55α/PP2A complexes are likely to regulate one or more transcription factors that coordinately induce squamous differentiation with Notch, another idea that is readily testable in our model system.

Finally, we note that our small screen of SCC cell lines suggests that squamous cell carcinomas retain the capacity to respond to Notch signals by undergoing growth arrest and differentiation. Although originally identified as an oncogene, sequencing of cancer genomes has revealed that Notch most commonly acts as a tumor suppressor, particularly in squamous cell carcinoma, which is difficult to treat when advanced in stage. While restoring the expression of defective Notch receptors is problematic, detailed analysis of crosstalk between Notch and other pathways may reveal druggable targets leading to reactivation of tumor suppressive signaling nodes downstream of Notch, which would constitute a new rational therapeutic approach for squamous cancers.

Sequencing data have been deposited in GEO under accession codes GSE156488 and GSE156624.

1. 1. Pan L

   (2020) NCBI Gene Expression Omnibus

   ID GSE156488. Notch target genes in keratinocytes.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE156488
2. 1. Rogers JM

   (2020) NCBI Gene Expression Omnibus

   ID GSE156624. Gamma-secretase inhibition does not affect gene expression in squamous cell carcinoma cells that do not express Notch1.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE156624

1. 1. Abo RP
   2. Ducar M
   3. Garcia EP
   4. Thorner AR
   5. Rojas-Rudilla V
   6. Lin L
   7. Sholl LM
   8. Hahn WC
   9. Meyerson M
   10. Lindeman NI
   11. Van Hummelen P
   12. MacConaill LE

   (2015) BreaKmer: detection of structural variation in targeted massively parallel sequencing data using kmers

   Nucleic Acids Research 43:e19.

   https://doi.org/10.1093/nar/gku1211

   • PubMed
   • Google Scholar
2. 1. Adelmant G
   2. Garg BK
   3. Tavares M
   4. Card JD
   5. Marto JA

   (2019) Tandem affinity purification and mass spectrometry (TAP-MS) for the analysis of protein complexes

   Current Protocols in Protein Science 96:e84.

   https://doi.org/10.1002/cpps.84

   • PubMed
   • Google Scholar
3. 1. Agrawal N
   2. Frederick MJ
   3. Pickering CR
   4. Bettegowda C
   5. Chang K
   6. Li RJ
   7. Fakhry C
   8. Xie T-X
   9. Zhang J
   10. Wang J
   11. Zhang N
   12. El-Naggar AK
   13. Jasser SA
   14. Weinstein JN
   15. Trevino L
   16. Drummond JA
   17. Muzny DM
   18. Wu Y
   19. Wood LD
   20. Hruban RH
   21. Westra WH
   22. Koch WM
   23. Califano JA
   24. Gibbs RA
   25. Sidransky D
   26. Vogelstein B
   27. Velculescu VE
   28. Papadopoulos N
   29. Wheeler DA
   30. Kinzler KW
   31. Myers JN

   (2011) Exome sequencing of head and neck squamous cell carcinoma reveals inactivating mutations in NOTCH1

   Science 333:1154–1157.

   https://doi.org/10.1126/science.1206923

   • Google Scholar
4. 1. Agrawal N
   2. Jiao Y
   3. Bettegowda C
   4. Hutfless SM
   5. Wang Y
   6. David S
   7. Cheng Y
   8. Twaddell WS
   9. Latt NL
   10. Shin EJ
   11. Wang LD
   12. Wang L
   13. Yang W
   14. Velculescu VE
   15. Vogelstein B
   16. Papadopoulos N
   17. Kinzler KW
   18. Meltzer SJ

   (2012) Comparative genomic analysis of esophageal adenocarcinoma and squamous cell carcinoma

   Cancer Discovery 2:899–905.

   https://doi.org/10.1158/2159-8290.CD-12-0189

   • PubMed
   • Google Scholar
5. 1. Alcolea MP
   2. Greulich P
   3. Wabik A
   4. Frede J
   5. Simons BD
   6. Jones PH

   (2014) Differentiation imbalance in single oesophageal progenitor cells causes clonal immortalization and field change

   Nature Cell Biology 16:612–619.

   https://doi.org/10.1038/ncb2963

   • Google Scholar
6. 1. Arnett KL
   2. Hass M
   3. McArthur DG
   4. Ilagan MX
   5. Aster JC
   6. Kopan R
   7. Blacklow SC

   (2010) Structural and mechanistic insights into cooperative assembly of dimeric notch transcription complexes

   Nature Structural & Molecular Biology 17:1312–1317.

   https://doi.org/10.1038/nsmb.1938

   • PubMed
   • Google Scholar
7. 1. Arnette C
   2. Koetsier JL
   3. Hoover P
   4. Getsios S
   5. Green KJ

   (2016) In vitro model of the epidermis: connecting protein function to 3D structure

   Methods in Enzymology 569:287–308.

   https://doi.org/10.1016/bs.mie.2015.07.015

   • PubMed
   • Google Scholar
8. 1. Asano Y
   2. Kawase T
   3. Okabe A
   4. Tsutsumi S
   5. Ichikawa H
   6. Tatebe S
   7. Kitabayashi I
   8. Tashiro F
   9. Namiki H
   10. Kondo T
   11. Semba K
   12. Aburatani H
   13. Taya Y
   14. Nakagama H
   15. Ohki R

   (2016) IER5 generates a novel hypo-phosphorylated active form of HSF1 and contributes to tumorigenesis

   Scientific Reports 6:19174.

   https://doi.org/10.1038/srep19174

   • PubMed
   • Google Scholar
9. 1. Askenazi M
   2. Parikh JR
   3. Marto JA

   (2009) mzAPI: a new strategy for efficiently sharing mass spectrometry data

   Nature Methods 6:240–241.

   https://doi.org/10.1038/nmeth0409-240

   • PubMed
   • Google Scholar
10. 1. Aster JC
    2. Pear WS
    3. Blacklow SC

    (2017) The varied roles of notch in Cancer

    Annual Review of Pathology: Mechanisms of Disease 12:245–275.

    https://doi.org/10.1146/annurev-pathol-052016-100127

    • PubMed
    • Google Scholar
11. 1. Aster JC
    2. Blacklow SC

    (2012) Targeting the notch pathway: twists and turns on the road to rational therapeutics

    Journal of Clinical Oncology 30:2418–2420.

    https://doi.org/10.1200/JCO.2012.42.0992

    • PubMed
    • Google Scholar
12. 1. Benaglia T
    2. Chauveau D
    3. Hunter DR
    4. Young D

    (2009) Mixtools : an R package for analyzing finite mixture models

    Journal of Statistical Software 32:1–29.

    https://doi.org/10.18637/jss.v032.i06

    • Google Scholar
13. 1. Blanpain C
    2. Lowry WE
    3. Pasolli HA
    4. Fuchs E

    (2006) Canonical notch signaling functions as a commitment switch in the epidermal lineage

    Genes & Development 20:3022–3035.

    https://doi.org/10.1101/gad.1477606

    • PubMed
    • Google Scholar
14. 1. Bray SJ

    (2016) Notch signalling in context

    Nature Reviews Molecular Cell Biology 17:722–735.

    https://doi.org/10.1038/nrm.2016.94

    • PubMed
    • Google Scholar
15. 1. Castel D
    2. Mourikis P
    3. Bartels SJJ
    4. Brinkman AB
    5. Tajbakhsh S
    6. Stunnenberg HG

    (2013) Dynamic binding of RBPJ is determined by notch signaling status

    Genes & Development 27:1059–1071.

    https://doi.org/10.1101/gad.211912.112

    • Google Scholar
16. 1. Chatr-Aryamontri A
    2. Oughtred R
    3. Boucher L
    4. Rust J
    5. Chang C
    6. Kolas NK
    7. O'Donnell L
    8. Oster S
    9. Theesfeld C
    10. Sellam A
    11. Stark C
    12. Breitkreutz BJ
    13. Dolinski K
    14. Tyers M

    (2017) The BioGRID interaction database: 2017 update

    Nucleic Acids Research 45:D369–D379.

    https://doi.org/10.1093/nar/gkw1102

    • PubMed
    • Google Scholar
17. 1. Chiang MY
    2. Xu L
    3. Shestova O
    4. Histen G
    5. L'heureux S
    6. Romany C
    7. Childs ME
    8. Gimotty PA
    9. Aster JC
    10. Pear WS

    (2008) Leukemia-associated NOTCH1 alleles are weak tumor initiators but accelerate K-ras-initiated leukemia

    Journal of Clinical Investigation 118:3181–3194.

    https://doi.org/10.1172/JCI35090

    • PubMed
    • Google Scholar
18. 1. Cibulskis K
    2. Lawrence MS
    3. Carter SL
    4. Sivachenko A
    5. Jaffe D
    6. Sougnez C
    7. Gabriel S
    8. Meyerson M
    9. Lander ES
    10. Getz G

    (2013) Sensitive detection of somatic point mutations in impure and heterogeneous Cancer samples

    Nature Biotechnology 31:213–219.

    https://doi.org/10.1038/nbt.2514

    • PubMed
    • Google Scholar
19. 1. DePristo MA
    2. Banks E
    3. Poplin R
    4. Garimella KV
    5. Maguire JR
    6. Hartl C
    7. Philippakis AA
    8. del Angel G
    9. Rivas MA
    10. Hanna M
    11. McKenna A
    12. Fennell TJ
    13. Kernytsky AM
    14. Sivachenko AY
    15. Cibulskis K
    16. Gabriel SB
    17. Altshuler D
    18. Daly MJ

    (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data

    Nature Genetics 43:491–498.

    https://doi.org/10.1038/ng.806

    • PubMed
    • Google Scholar
20. 1. Ding KK
    2. Shang ZF
    3. Hao C
    4. Xu QZ
    5. Shen JJ
    6. Yang CJ
    7. Xie YH
    8. Qiao C
    9. Wang Y
    10. Xu LL
    11. Zhou PK

    (2009) Induced expression of the IER5 gene by gamma-ray irradiation and its involvement in cell cycle checkpoint control and survival

    Radiation and Environmental Biophysics 48:205–213.

    https://doi.org/10.1007/s00411-009-0213-4

    • PubMed
    • Google Scholar
21. 1. Dobin A
    2. Davis CA
    3. Schlesinger F
    4. Drenkow J
    5. Zaleski C
    6. Jha S
    7. Batut P
    8. Chaisson M
    9. Gingeras TR

    (2013) STAR: ultrafast universal RNA-seq aligner

    Bioinformatics 29:15–21.

    https://doi.org/10.1093/bioinformatics/bts635

    • PubMed
    • Google Scholar
22. 1. Drier Y
    2. Cotton MJ
    3. Williamson KE
    4. Gillespie SM
    5. Ryan RJ
    6. Kluk MJ
    7. Carey CD
    8. Rodig SJ
    9. Sholl LM
    10. Afrogheh AH
    11. Faquin WC
    12. Queimado L
    13. Qi J
    14. Wick MJ
    15. El-Naggar AK
    16. Bradner JE
    17. Moskaluk CA
    18. Aster JC
    19. Knoechel B
    20. Bernstein BE

    (2016) An oncogenic MYB feedback loop drives alternate cell fates in adenoid cystic carcinoma

    Nature Genetics 48:265–272.

    https://doi.org/10.1038/ng.3502

    • PubMed
    • Google Scholar
23. 1. Falo-Sanjuan J
    2. Lammers NC
    3. Garcia HG
    4. Bray SJ

    (2019) Enhancer priming enables fast and sustained transcriptional responses to notch signaling

    Developmental Cell 50:411–425.

    https://doi.org/10.1016/j.devcel.2019.07.002

    • PubMed
    • Google Scholar
24. 1. Fernandez-Antoran D
    2. Piedrafita G
    3. Murai K
    4. Ong SH
    5. Herms A
    6. Frezza C
    7. Jones PH

    (2019) Outcompeting p53-Mutant cells in the normal esophagus by redox manipulation

    Cell Stem Cell 25:329–341.

    https://doi.org/10.1016/j.stem.2019.06.011

    • PubMed
    • Google Scholar
25. 1. Fuchs E

    (1995) Keratins and the skin

    Annual Review of Cell and Developmental Biology 11:123–154.

    https://doi.org/10.1146/annurev.cb.11.110195.001011

    • PubMed
    • Google Scholar
26. 1. Gordon WR
    2. Roy M
    3. Vardar-Ulu D
    4. Garfinkel M
    5. Mansour MR
    6. Aster JC
    7. Blacklow SC

    (2009) Structure of the Notch1-negative regulatory region: implications for normal activation and pathogenic signaling in T-ALL

    Blood 113:4381–4390.

    https://doi.org/10.1182/blood-2008-08-174748

    • PubMed
    • Google Scholar
27. 1. Heinz S
    2. Benner C
    3. Spann N
    4. Bertolino E
    5. Lin YC
    6. Laslo P
    7. Cheng JX
    8. Murre C
    9. Singh H
    10. Glass CK

    (2010) Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities

    Molecular Cell 38:576–589.

    https://doi.org/10.1016/j.molcel.2010.05.004

    • PubMed
    • Google Scholar
28. 1. Hirata H
    2. Yoshiura S
    3. Ohtsuka T
    4. Bessho Y
    5. Harada T
    6. Yoshikawa K
    7. Kageyama R

    (2002) Oscillatory expression of the bHLH factor Hes1 regulated by a negative feedback loop

    Science 298:840–843.

    https://doi.org/10.1126/science.1074560

    • PubMed
    • Google Scholar
29. 1. Huang daW
    2. Sherman BT
    3. Lempicki RA

    (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources

    Nature Protocols 4:44–57.

    https://doi.org/10.1038/nprot.2008.211

    • PubMed
    • Google Scholar
30. 1. Inman GJ
    2. Wang J
    3. Nagano A
    4. Alexandrov LB
    5. Purdie KJ
    6. Taylor RG
    7. Sherwood V
    8. Thomson J
    9. Hogan S
    10. Spender LC
    11. South AP
    12. Stratton M
    13. Chelala C
    14. Harwood CA
    15. Proby CM
    16. Leigh IM

    (2018) The genomic landscape of cutaneous SCC reveals drivers and a novel azathioprine associated mutational signature

    Nature Communications 9:3667.

    https://doi.org/10.1038/s41467-018-06027-1

    • PubMed
    • Google Scholar
31. 1. Ishikawa Y
    2. Kawabata S
    3. Sakurai H

    (2015) HSF1 transcriptional activity is modulated by IER5 and PP2A/B55

    FEBS Letters 589:1150–1155.

    https://doi.org/10.1016/j.febslet.2015.03.019

    • PubMed
    • Google Scholar
32. 1. Ishikawa Y
    2. Sakurai H

    (2015) Heat-induced expression of the immediate-early gene IER5 and its involvement in the proliferation of heat-shocked cells

    FEBS Journal 282:332–340.

    https://doi.org/10.1111/febs.13134

    • PubMed
    • Google Scholar
33. 1. Jarrett SM
    2. Seegar TCM
    3. Andrews M
    4. Adelmant G
    5. Marto JA
    6. Aster JC
    7. Blacklow SC

    (2019) Extension of the notch intracellular domain ankyrin repeat stack by NRARP promotes feedback inhibition of notch signaling

    Science Signaling 12:eaay2369.

    https://doi.org/10.1126/scisignal.aay2369

    • Google Scholar
34. 1. Jensen KB
    2. Watt FM

    (2006) Single-cell expression profiling of human epidermal stem and transit-amplifying cells: lrig1 is a regulator of stem cell quiescence

    PNAS 103:11958–11963.

    https://doi.org/10.1073/pnas.0601886103

    • PubMed
    • Google Scholar
35. 1. Jin B
    2. Shen H
    3. Lin S
    4. Li JL
    5. Chen Z
    6. Griffin JD
    7. Wu L

    (2010) The mastermind-like 1 (MAML1) co-activator regulates constitutive NF-kappaB signaling and cell survival

    Journal of Biological Chemistry 285:14356–14365.

    https://doi.org/10.1074/jbc.M109.078865

    • PubMed
    • Google Scholar
36. 1. Jones PH
    2. Watt FM

    (1993) Separation of human epidermal stem cells from transit amplifying cells on the basis of differences in integrin function and expression

    Cell 73:713–724.

    https://doi.org/10.1016/0092-8674(93)90251-K

    • PubMed
    • Google Scholar
37. 1. Kawabata S
    2. Ishita Y
    3. Ishikawa Y
    4. Sakurai H

    (2015) Immediate-early response 5 (IER5) interacts with protein phosphatase 2A and regulates the phosphorylation of ribosomal protein S6 kinase and heat shock factor 1

    FEBS Letters 589:3679–3685.

    https://doi.org/10.1016/j.febslet.2015.10.013

    • PubMed
    • Google Scholar
38. 1. Kis E
    2. Szatmári T
    3. Keszei M
    4. Farkas R
    5. Esik O
    6. Lumniczky K
    7. Falus A
    8. Sáfrány G

    (2006) Microarray analysis of radiation response genes in primary human fibroblasts

    International Journal of Radiation Oncology*Biology*Physics 66:1506–1514.

    https://doi.org/10.1016/j.ijrobp.2006.08.004

    • PubMed
    • Google Scholar
39. Preprint

    1. Kobia FM
    2. Preusse K
    3. Dai Q
    4. Weaver N
    5. Eafergen N
    6. Sprinzak D
    7. Kopan R

    (2020) Exposure to mites sensitizes intestinal stem cell maintenance, splenic marginal zone B cell homeostasis, and heart development to notch dosage and cooperativity

    bioRxiv.

    https://doi.org/10.1101/2020.02.13.948224

    • Google Scholar
40. 1. Krejcí A
    2. Bray S

    (2007) Notch activation stimulates transient and selective binding of su(H)/CSL to target enhancers

    Genes & Development 21:1322–1327.

    https://doi.org/10.1101/gad.424607

    • PubMed
    • Google Scholar
41. 1. Kumar R
    2. Kobayashi T
    3. Warner GM
    4. Wu Y
    5. Salisbury JL
    6. Lingle W
    7. Pittelkow MR

    (1998) A novel immediate early response gene, IEX-1, is induced by ultraviolet radiation in human keratinocytes

    Biochemical and Biophysical Research Communications 253:336–341.

    https://doi.org/10.1006/bbrc.1998.9692

    • PubMed
    • Google Scholar
42. 1. Kwa MQ
    2. Huynh J
    3. Aw J
    4. Zhang L
    5. Nguyen T
    6. Reynolds EC
    7. Sweet MJ
    8. Hamilton JA
    9. Scholz GM

    (2014) Receptor-interacting protein kinase 4 and interferon regulatory factor 6 function as a signaling Axis to regulate keratinocyte differentiation

    Journal of Biological Chemistry 289:31077–31087.

    https://doi.org/10.1074/jbc.M114.589382

    • PubMed
    • Google Scholar
43. 1. Langmead B
    2. Trapnell C
    3. Pop M
    4. Salzberg SL

    (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

    Genome Biology 10:R25.

    https://doi.org/10.1186/gb-2009-10-3-r25

    • PubMed
    • Google Scholar
44. 1. Langmead B
    2. Salzberg SL

    (2012) Fast gapped-read alignment with bowtie 2

    Nature Methods 9:357–359.

    https://doi.org/10.1038/nmeth.1923

    • PubMed
    • Google Scholar
45. 1. Li H
    2. Durbin R

    (2009) Fast and accurate short read alignment with Burrows-Wheeler transform

    Bioinformatics 25:1754–1760.

    https://doi.org/10.1093/bioinformatics/btp324

    • PubMed
    • Google Scholar
46. 1. Liao Y
    2. Smyth GK
    3. Shi W

    (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features

    Bioinformatics 30:923–930.

    https://doi.org/10.1093/bioinformatics/btt656

    • PubMed
    • Google Scholar
47. 1. Liao Y
    2. Smyth GK
    3. Shi W

    (2019) The R package rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads

    Nucleic Acids Research 47:e47.

    https://doi.org/10.1093/nar/gkz114

    • PubMed
    • Google Scholar
48. 1. Lim X
    2. Tan SH
    3. Koh WL
    4. Chau RM
    5. Yan KS
    6. Kuo CJ
    7. van Amerongen R
    8. Klein AM
    9. Nusse R

    (2013) Interfollicular epidermal stem cells self-renew via autocrine wnt signaling

    Science 342:1226–1230.

    https://doi.org/10.1126/science.1239730

    • PubMed
    • Google Scholar
49. 1. Liu Z
    2. Brunskill E
    3. Varnum-Finney B
    4. Zhang C
    5. Zhang A
    6. Jay PY
    7. Bernstein I
    8. Morimoto M
    9. Kopan R

    (2015) The intracellular domains of Notch1 and Notch2 are functionally equivalent during development and carcinogenesis

    Development 142:2452–2463.

    https://doi.org/10.1242/dev.125492

    • PubMed
    • Google Scholar
50. 1. Loganathan SK
    2. Schleicher K
    3. Malik A
    4. Quevedo R
    5. Langille E
    6. Teng K
    7. Oh RH
    8. Rathod B
    9. Tsai R
    10. Samavarchi-Tehrani P
    11. Pugh TJ
    12. Gingras AC
    13. Schramek D

    (2020) Rare driver mutations in head and neck squamous cell carcinomas converge on NOTCH signaling

    Science 367:1264–1269.

    https://doi.org/10.1126/science.aax0902

    • PubMed
    • Google Scholar
51. 1. Love MI
    2. Huber W
    3. Anders S

    (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

    Genome Biology 15:550.

    https://doi.org/10.1186/s13059-014-0550-8

    • PubMed
    • Google Scholar
52. 1. Lowell S
    2. Jones P
    3. Le Roux I
    4. Dunne J
    5. Watt FM

    (2000) Stimulation of human epidermal differentiation by delta-notch signalling at the boundaries of stem-cell clusters

    Current Biology 10:491–500.

    https://doi.org/10.1016/S0960-9822(00)00451-6

    • PubMed
    • Google Scholar
53. 1. Machanick P
    2. Bailey TL

    (2011) MEME-ChIP: motif analysis of large DNA datasets

    Bioinformatics 27:1696–1697.

    https://doi.org/10.1093/bioinformatics/btr189

    • PubMed
    • Google Scholar
54. 1. Maeda T
    2. Hanna AN
    3. Sim AB
    4. Chua PP
    5. Chong MT
    6. Tron VA

    (2002) GADD45 regulates G2/M arrest, DNA repair, and cell death in keratinocytes following ultraviolet exposure

    Journal of Investigative Dermatology 119:22–26.

    https://doi.org/10.1046/j.1523-1747.2002.01781.x

    • PubMed
    • Google Scholar
55. 1. Malecki MJ
    2. Sanchez-Irizarry C
    3. Mitchell JL
    4. Histen G
    5. Xu ML
    6. Aster JC
    7. Blacklow SC

    (2006) Leukemia-associated mutations within the NOTCH1 heterodimerization domain fall into at least two distinct mechanistic classes

    Molecular and Cellular Biology 26:4642–4651.

    https://doi.org/10.1128/MCB.01655-05

    • PubMed
    • Google Scholar
56. 1. Mandinova A
    2. Lefort K
    3. Tommasi di Vignano A
    4. Stonely W
    5. Ostano P
    6. Chiorino G
    7. Iwaki H
    8. Nakanishi J
    9. Dotto GP

    (2008) The FoxO3a gene is a key negative target of canonical notch signalling in the keratinocyte UVB response

    The EMBO Journal 27:1243–1254.

    https://doi.org/10.1038/emboj.2008.45

    • PubMed
    • Google Scholar
57. 1. McKenna A
    2. Hanna M
    3. Banks E
    4. Sivachenko A
    5. Cibulskis K
    6. Kernytsky A
    7. Garimella K
    8. Altshuler D
    9. Gabriel S
    10. Daly M
    11. DePristo MA

    (2010) The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data

    Genome Research 20:1297–1303.

    https://doi.org/10.1101/gr.107524.110

    • PubMed
    • Google Scholar
58. 1. Meyers JM
    2. Uberoi A
    3. Grace M
    4. Lambert PF
    5. Munger K

    (2017) Cutaneous HPV8 and MmuPV1 E6 proteins target the NOTCH and TGF-β tumor suppressors to inhibit differentiation and sustain keratinocyte proliferation

    PLOS Pathogens 13:e1006171.

    https://doi.org/10.1371/journal.ppat.1006171

    • Google Scholar
59. 1. Micchelli CA
    2. Esler WP
    3. Kimberly WT
    4. Jack C
    5. Berezovska O
    6. Kornilova A
    7. Hyman BT
    8. Perrimon N
    9. Wolfe MS

    (2003) Gamma-secretase/presenilin inhibitors for alzheimer's disease phenocopy Notch mutations in Drosophila

    The FASEB Journal 17:79–81.

    https://doi.org/10.1096/fj.02-0394fje

    • PubMed
    • Google Scholar
60. 1. Missero C
    2. Di Cunto F
    3. Kiyokawa H
    4. Koff A
    5. Dotto GP

    (1996) The absence of p21Cip1/WAF1 alters keratinocyte growth and differentiation and promotes ras-tumor progression

    Genes & Development 10:3065–3075.

    https://doi.org/10.1101/gad.10.23.3065

    • PubMed
    • Google Scholar
61. 1. Nam Y
    2. Sliz P
    3. Song L
    4. Aster JC
    5. Blacklow SC

    (2006) Structural basis for cooperativity in recruitment of MAML coactivators to notch transcription complexes

    Cell 124:973–983.

    https://doi.org/10.1016/j.cell.2005.12.037

    • PubMed
    • Google Scholar
62. 1. Nam Y
    2. Sliz P
    3. Pear WS
    4. Aster JC
    5. Blacklow SC

    (2007) Cooperative assembly of higher-order notch complexes functions as a switch to induce transcription

    PNAS 104:2103–2108.

    https://doi.org/10.1073/pnas.0611092104

    • PubMed
    • Google Scholar
63. 1. Nickoloff BJ
    2. Qin JZ
    3. Chaturvedi V
    4. Denning MF
    5. Bonish B
    6. Miele L

    (2002) Jagged-1 mediated activation of notch signaling induces complete maturation of human keratinocytes through NF-kappaB and PPARgamma

    Cell Death & Differentiation 9:842–855.

    https://doi.org/10.1038/sj.cdd.4401036

    • PubMed
    • Google Scholar
64. 1. Nicolas M
    2. Wolfer A
    3. Raj K
    4. Kummer JA
    5. Mill P
    6. van Noort M
    7. Hui CC
    8. Clevers H
    9. Dotto GP
    10. Radtke F

    (2003) Notch1 functions as a tumor suppressor in mouse skin

    Nature Genetics 33:416–421.

    https://doi.org/10.1038/ng1099

    • PubMed
    • Google Scholar
65. 1. Parikh JR
    2. Askenazi M
    3. Ficarro SB
    4. Cashorali T
    5. Webber JT
    6. Blank NC
    7. Zhang Y
    8. Marto JA

    (2009) Multiplierz: an extensible API based desktop environment for proteomics data analysis

    BMC Bioinformatics 10:364.

    https://doi.org/10.1186/1471-2105-10-364

    • PubMed
    • Google Scholar
66. 1. Petrovic J
    2. Zhou Y
    3. Fasolino M
    4. Goldman N
    5. Schwartz GW
    6. Mumbach MR
    7. Nguyen SC
    8. Rome KS
    9. Sela Y
    10. Zapataro Z
    11. Blacklow SC
    12. Kruhlak MJ
    13. Shi J
    14. Aster JC
    15. Joyce EF
    16. Little SC
    17. Vahedi G
    18. Pear WS
    19. Faryabi RB

    (2019) Oncogenic notch promotes Long-Range regulatory interactions within hyperconnected 3D cliques

    Molecular Cell 73:1174–1190.

    https://doi.org/10.1016/j.molcel.2019.01.006

    • PubMed
    • Google Scholar
67. 1. Piboonniyom SO
    2. Duensing S
    3. Swilling NW
    4. Hasskarl J
    5. Hinds PW
    6. Münger K

    (2003)

    Abrogation of the retinoblastoma tumor suppressor checkpoint during keratinocyte immortalization is not sufficient for induction of centrosome-mediated genomic instability

    Cancer Research 63:476–483.

    • PubMed
    • Google Scholar
68. Report

    1. Pomrey AB
    2. Radtke F

    (2010)

    RhoV, a Potential Notch Target Gene in Murine Skin, Does Not Play an Essential Role in Epidermis Development and Homeostasis

    École Polytechnique Federale De Lausanne, Lausanne.

    • Google Scholar
69. 1. Proweller A
    2. Tu L
    3. Lepore JJ
    4. Cheng L
    5. Lu MM
    6. Seykora J
    7. Millar SE
    8. Pear WS
    9. Parmacek MS

    (2006) Impaired notch signaling promotes de novo squamous cell carcinoma formation

    Cancer Research 66:7438–7444.

    https://doi.org/10.1158/0008-5472.CAN-06-0793

    • PubMed
    • Google Scholar
70. 1. Purdie KJ
    2. Pourreyron C
    3. South AP

    (2011) Isolation and culture of squamous cell carcinoma lines

    Methods in Molecular Biology 731:151–159.

    https://doi.org/10.1007/978-1-61779-080-5_14

    • PubMed
    • Google Scholar
71. 1. Quaranta R
    2. Pelullo M
    3. Zema S
    4. Nardozza F
    5. Checquolo S
    6. Lauer DM
    7. Bufalieri F
    8. Palermo R
    9. Felli MP
    10. Vacca A
    11. Talora C
    12. Di Marcotullio L
    13. Screpanti I
    14. Bellavia D

    (2017) Maml1 acts cooperatively with gli proteins to regulate sonic hedgehog signaling pathway

    Cell Death & Disease 8:e2942.

    https://doi.org/10.1038/cddis.2017.326

    • PubMed
    • Google Scholar
72. 1. Quinlan AR
    2. Hall IM

    (2010) BEDTools: a flexible suite of utilities for comparing genomic features

    Bioinformatics 26:841–842.

    https://doi.org/10.1093/bioinformatics/btq033

    • PubMed
    • Google Scholar
73. Software

    1. R Development Core Team

    (2014) R: A language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    http://www.r-project.org
74. 1. Rand MD
    2. Grimm LM
    3. Artavanis-Tsakonas S
    4. Patriub V
    5. Blacklow SC
    6. Sklar J
    7. Aster JC

    (2000) Calcium depletion dissociates and activates heterodimeric notch receptors

    Molecular and Cellular Biology 20:1825–1835.

    https://doi.org/10.1128/MCB.20.5.1825-1835.2000

    • PubMed
    • Google Scholar
75. 1. Rangarajan A
    2. Talora C
    3. Okuyama R
    4. Nicolas M
    5. Mammucari C
    6. Oh H
    7. Aster JC
    8. Krishna S
    9. Metzger D
    10. Chambon P
    11. Miele L
    12. Aguet M
    13. Radtke F
    14. Dotto GP

    (2001) Notch signaling is a direct determinant of keratinocyte growth arrest and entry into differentiation

    The EMBO Journal 20:3427–3436.

    https://doi.org/10.1093/emboj/20.13.3427

    • PubMed
    • Google Scholar
76. 1. Restivo G
    2. Nguyen BC
    3. Dziunycz P
    4. Ristorcelli E
    5. Ryan RJ
    6. Özuysal ÖY
    7. Di Piazza M
    8. Radtke F
    9. Dixon MJ
    10. Hofbauer GF
    11. Lefort K
    12. Dotto GP

    (2011) IRF6 is a mediator of notch pro-differentiation and tumour suppressive function in keratinocytes

    The EMBO Journal 30:4571–4585.

    https://doi.org/10.1038/emboj.2011.325

    • PubMed
    • Google Scholar
77. 1. Risso D
    2. Ngai J
    3. Speed TP
    4. Dudoit S

    (2014) Normalization of RNA-seq data using factor analysis of control genes or samples

    Nature Biotechnology 32:896–902.

    https://doi.org/10.1038/nbt.2931

    • PubMed
    • Google Scholar
78. 1. Rouault JP
    2. Falette N
    3. Guéhenneux F
    4. Guillot C
    5. Rimokh R
    6. Wang Q
    7. Berthet C
    8. Moyret-Lalle C
    9. Savatier P
    10. Pain B
    11. Shaw P
    12. Berger R
    13. Samarut J
    14. Magaud JP
    15. Ozturk M
    16. Samarut C
    17. Puisieux A

    (1996) Identification of BTG2, an antiproliferative p53-dependent component of the DNA damage cellular response pathway

    Nature Genetics 14:482–486.

    https://doi.org/10.1038/ng1296-482

    • PubMed
    • Google Scholar
79. 1. Rozenblatt-Rosen O
    2. Deo RC
    3. Padi M
    4. Adelmant G
    5. Calderwood MA
    6. Rolland T
    7. Grace M
    8. Dricot A
    9. Askenazi M
    10. Tavares M
    11. Pevzner SJ
    12. Abderazzaq F
    13. Byrdsong D
    14. Carvunis AR
    15. Chen AA
    16. Cheng J
    17. Correll M
    18. Duarte M
    19. Fan C
    20. Feltkamp MC
    21. Ficarro SB
    22. Franchi R
    23. Garg BK
    24. Gulbahce N
    25. Hao T
    26. Holthaus AM
    27. James R
    28. Korkhin A
    29. Litovchick L
    30. Mar JC
    31. Pak TR
    32. Rabello S
    33. Rubio R
    34. Shen Y
    35. Singh S
    36. Spangle JM
    37. Tasan M
    38. Wanamaker S
    39. Webber JT
    40. Roecklein-Canfield J
    41. Johannsen E
    42. Barabási AL
    43. Beroukhim R
    44. Kieff E
    45. Cusick ME
    46. Hill DE
    47. Münger K
    48. Marto JA
    49. Quackenbush J
    50. Roth FP
    51. DeCaprio JA
    52. Vidal M

    (2012) Interpreting Cancer genomes using systematic host network perturbations by tumour virus proteins

    Nature 487:491–495.

    https://doi.org/10.1038/nature11288

    • PubMed
    • Google Scholar
80. 1. Ryan RJH
    2. Petrovic J
    3. Rausch DM
    4. Zhou Y
    5. Lareau CA
    6. Kluk MJ
    7. Christie AL
    8. Lee WY
    9. Tarjan DR
    10. Guo B
    11. Donohue LKH
    12. Gillespie SM
    13. Nardi V
    14. Hochberg EP
    15. Blacklow SC
    16. Weinstock DM
    17. Faryabi RB
    18. Bernstein BE
    19. Aster JC
    20. Pear WS

    (2017) A B cell regulome links notch to downstream oncogenic pathways in small B cell lymphomas

    Cell Reports 21:784–797.

    https://doi.org/10.1016/j.celrep.2017.09.066

    • PubMed
    • Google Scholar
81. 1. Sesto A
    2. Navarro M
    3. Burslem F
    4. Jorcano JL

    (2002) Analysis of the ultraviolet B response in primary human keratinocytes using oligonucleotide microarrays

    PNAS 99:2965–2970.

    https://doi.org/10.1073/pnas.052678999

    • PubMed
    • Google Scholar
82. 1. Severson E
    2. Arnett KL
    3. Wang H
    4. Zang C
    5. Taing L
    6. Liu H
    7. Pear WS
    8. Shirley Liu X
    9. Blacklow SC
    10. Aster JC

    (2017) Genome-wide identification and characterization of notch transcription complex-binding sequence-paired sites in leukemia cells

    Science Signaling 10:eaag1598.

    https://doi.org/10.1126/scisignal.aag1598

    • PubMed
    • Google Scholar
83. 1. Shen H
    2. McElhinny AS
    3. Cao Y
    4. Gao P
    5. Liu J
    6. Bronson R
    7. Griffin JD
    8. Wu L

    (2006) The notch coactivator, MAML1, functions as a novel coactivator for MEF2C-mediated transcription and is required for normal myogenesis

    Genes & Development 20:675–688.

    https://doi.org/10.1101/gad.1383706

    • PubMed
    • Google Scholar
84. 1. Shen L
    2. Shao N
    3. Liu X
    4. Nestler E

    (2014) Ngs.plot: quick mining and visualization of next-generation sequencing data by integrating genomic databases

    BMC Genomics 15:284.

    https://doi.org/10.1186/1471-2164-15-284

    • PubMed
    • Google Scholar
85. 1. Simbulan-Rosenthal CM
    2. Daher A
    3. Trabosh V
    4. Chen WC
    5. Gerstel D
    6. Soeda E
    7. Rosenthal DS

    (2006) Id3 induces a caspase-3- and -9-dependent apoptosis and mediates UVB sensitization of HPV16 E6/7 immortalized human keratinocytes

    Oncogene 25:3649–3660.

    https://doi.org/10.1038/sj.onc.1209407

    • PubMed
    • Google Scholar
86. 1. Skalska L
    2. Stojnic R
    3. Li J
    4. Fischer B
    5. Cerda-Moya G
    6. Sakai H
    7. Tajbakhsh S
    8. Russell S
    9. Adryan B
    10. Bray SJ

    (2015) Chromatin signatures at Notch-regulated enhancers reveal large-scale changes in H3K56ac upon activation

    The EMBO Journal 34:1889–1904.

    https://doi.org/10.15252/embj.201489923

    • PubMed
    • Google Scholar
87. 1. South AP
    2. Purdie KJ
    3. Watt SA
    4. Haldenby S
    5. den Breems N
    6. Dimon M
    7. Arron ST
    8. Kluk MJ
    9. Aster JC
    10. McHugh A
    11. Xue DJ
    12. Dayal JH
    13. Robinson KS
    14. Rizvi SH
    15. Proby CM
    16. Harwood CA
    17. Leigh IM

    (2014) NOTCH1 mutations occur early during cutaneous squamous cell carcinogenesis

    Journal of Investigative Dermatology 134:2630–2638.

    https://doi.org/10.1038/jid.2014.154

    • PubMed
    • Google Scholar
88. 1. Sun X
    2. Artavanis-Tsakonas S

    (1997)

    Secreted forms of DELTA and SERRATE define antagonists of notch signaling in Drosophila

    Development 124:3439–3448.

    • PubMed
    • Google Scholar
89. 1. Tan MJ
    2. White EA
    3. Sowa ME
    4. Harper JW
    5. Aster JC
    6. Howley PM

    (2012) Cutaneous -human papillomavirus E6 proteins bind Mastermind-like coactivators and repress notch signaling

    PNAS 109:E1473–E1480.

    https://doi.org/10.1073/pnas.1205991109

    • PubMed
    • Google Scholar
90. 1. Uyttendaele H
    2. Panteleyev AA
    3. de Berker D
    4. Tobin DT
    5. Christiano AM

    (2004) Activation of Notch1 in the hair follicle leads to cell-fate switch and mohawk alopecia

    Differentiation 72:396–409.

    https://doi.org/10.1111/j.1432-0436.2004.07208006.x

    • PubMed
    • Google Scholar
91. 1. van Es JH
    2. van Gijn ME
    3. Riccio O
    4. van den Born M
    5. Vooijs M
    6. Begthel H
    7. Cozijnsen M
    8. Robine S
    9. Winton DJ
    10. Radtke F
    11. Clevers H

    (2005) Notch/gamma-secretase inhibition turns proliferative cells in intestinal crypts and adenomas into goblet cells

    Nature 435:959–963.

    https://doi.org/10.1038/nature03659

    • PubMed
    • Google Scholar
92. 1. Varnum-Finney B
    2. Wu L
    3. Yu M
    4. Brashem-Stein C
    5. Staats S
    6. Flowers D
    7. Griffin JD
    8. Bernstein ID

    (2000)

    Immobilization of notch ligand, Delta-1, is required for induction of notch signaling

    Journal of Cell Science 113:4313–4318.

    • PubMed
    • Google Scholar
93. 1. Wagle N
    2. Berger MF
    3. Davis MJ
    4. Blumenstiel B
    5. Defelice M
    6. Pochanard P
    7. Ducar M
    8. Van Hummelen P
    9. Macconaill LE
    10. Hahn WC
    11. Meyerson M
    12. Gabriel SB
    13. Garraway LA

    (2012) High-throughput detection of actionable genomic alterations in clinical tumor samples by targeted, massively parallel sequencing

    Cancer Discovery 2:82–93.

    https://doi.org/10.1158/2159-8290.CD-11-0184

    • PubMed
    • Google Scholar
94. 1. Wang NJ
    2. Sanborn Z
    3. Arnett KL
    4. Bayston LJ
    5. Liao W
    6. Proby CM
    7. Leigh IM
    8. Collisson EA
    9. Gordon PB
    10. Jakkula L
    11. Pennypacker S
    12. Zou Y
    13. Sharma M
    14. North JP
    15. Vemula SS
    16. Mauro TM
    17. Neuhaus IM
    18. Leboit PE
    19. Hur JS
    20. Park K
    21. Huh N
    22. Kwok PY
    23. Arron ST
    24. Massion PP
    25. Bale AE
    26. Haussler D
    27. Cleaver JE
    28. Gray JW
    29. Spellman PT
    30. South AP
    31. Aster JC
    32. Blacklow SC
    33. Cho RJ

    (2011) Loss-of-function mutations in notch receptors in cutaneous and lung squamous cell carcinoma

    PNAS 108:17761–17766.

    https://doi.org/10.1073/pnas.1114669108

    • PubMed
    • Google Scholar
95. 1. Wang H
    2. Zang C
    3. Taing L
    4. Arnett KL
    5. Wong YJ
    6. Pear WS
    7. Blacklow SC
    8. Liu XS
    9. Aster JC

    (2014) NOTCH1-RBPJ complexes drive target gene expression through dynamic interactions with superenhancers

    PNAS 111:705–710.

    https://doi.org/10.1073/pnas.1315023111

    • PubMed
    • Google Scholar
96. 1. Weng AP
    2. Nam Y
    3. Wolfe MS
    4. Pear WS
    5. Griffin JD
    6. Blacklow SC
    7. Aster JC

    (2003) Growth suppression of pre-T acute lymphoblastic leukemia cells by inhibition of notch signaling

    Molecular and Cellular Biology 23:655–664.

    https://doi.org/10.1128/MCB.23.2.655-664.2003

    • PubMed
    • Google Scholar
97. 1. Weng AP
    2. Millholland JM
    3. Yashiro-Ohtani Y
    4. Arcangeli ML
    5. Lau A
    6. Wai C
    7. Del Bianco C
    8. Rodriguez CG
    9. Sai H
    10. Tobias J
    11. Li Y
    12. Wolfe MS
    13. Shachaf C
    14. Felsher D
    15. Blacklow SC
    16. Pear WS
    17. Aster JC

    (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

    • PubMed
    • Google Scholar
98. 1. Williams M
    2. Lyu MS
    3. Yang YL
    4. Lin EP
    5. Dunbrack R
    6. Birren B
    7. Cunningham J
    8. Hunter K

    (1999) Ier5, a novel member of the slow-kinetics immediate-early genes

    Genomics 55:327–334.

    https://doi.org/10.1006/geno.1998.5679

    • PubMed
    • Google Scholar
99. 1. Yang C
    2. Yin L
    3. Zhou P
    4. Liu X
    5. Yang M
    6. Yang F
    7. Jiang H
    8. Ding K

    (2016) Transcriptional regulation of IER5 in response to radiation in HepG2

    Cancer Gene Therapy 23:61–65.

    https://doi.org/10.1038/cgt.2016.1

    • PubMed
    • Google Scholar
100. 1. Zhang Y
     2. Liu T
     3. Meyer CA
     4. Eeckhoute J
     5. Johnson DS
     6. Bernstein BE
     7. Nusbaum C
     8. Myers RM
     9. Brown M
     10. Li W
     11. Liu XS

     (2008) Model-based analysis of ChIP-Seq (MACS)

     Genome Biology 9:R137.

     https://doi.org/10.1186/gb-2008-9-9-r137

     • PubMed
     • Google Scholar
101. 1. Zhao Y
     2. Katzman RB
     3. Delmolino LM
     4. Bhat I
     5. Zhang Y
     6. Gurumurthy CB
     7. Germaniuk-Kurowska A
     8. Reddi HV
     9. Solomon A
     10. Zeng MS
     11. Kung A
     12. Ma H
     13. Gao Q
     14. Dimri G
     15. Stanculescu A
     16. Miele L
     17. Wu L
     18. Griffin JD
     19. Wazer DE
     20. Band H
     21. Band V

     (2007) The notch regulator MAML1 interacts with p53 and functions as a coactivator

     Journal of Biological Chemistry 282:11969–11981.

     https://doi.org/10.1074/jbc.M608974200

     • PubMed
     • Google Scholar

Author details

1. Li Pan

   Department of Pathology, Brigham and Women’s Hospital, and Harvard Medical School, Boston, United States

   Contribution

   Investigation, Methodology, Writing - original draft, Writing - review and editing, Responsible for conduct of RNA-seq and ChIP-seq experiments, RT-PCR analyses, Western blot analyses, immunofluorescence microscopy, organotypic cultures

   Competing interests

   No competing interests declared

2. Madeleine E Lemieux

   Bioinfo, Plantagenet, Canada

   Contribution

   Formal analysis, Methodology, Writing - original draft, Writing - review and editing, Responsible for design of RNA-seq experiments, responsible for statistical and bioinformatic analyses of RNA-Seq and ChIP-Seq experiments and generation of graphic displays of data (e.g., heat maps)

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6355-8691

3. Tom Thomas

   Department of Pathology, Brigham and Women’s Hospital, and Harvard Medical School, Boston, United States

   Contribution

   Investigation, Methodology, Writing - original draft, Writing - review and editing, Responsible for purification of recombinant proteins and conduct of tandem affinity purification and immunoprecipitation studies

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1840-6776

4. Julia M Rogers

   Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, United States

   Contribution

   Investigation, Writing - original draft, Writing - review and editing, Responsible for conducting RNA-seq experiments studying the effects of GSI on gene expression in parental IC8 cells

   Competing interests

   No competing interests declared

5. Colin H Lipper

   Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, United States

   Contribution

   Investigation, Methodology, Writing - review and editing, Responsible for microscale thermophoresis experiments

   Competing interests

   No competing interests declared

6. Winston Lee

   Department of Pathology, Brigham and Women’s Hospital, and Harvard Medical School, Boston, United States

   Present address

   Departmentof Pathology, University of Michigan Medical School, Ann Arbor, United States

   Contribution

   Investigation, Methodology, Writing - original draft, Writing - review and editing, Responsible for development of organotypic culture system, optimization of the GSI washout method for adherent cell lines

   Competing interests

   No competing interests declared

7. Carl Johnson

   Department of Pathology, Brigham and Women’s Hospital, and Harvard Medical School, Boston, United States

   Contribution

   Investigation, Writing - original draft, Writing - review and editing, Responsible for western blot analyses, RT-PCR analyses, in situ hybridization studies

   Competing interests

   No competing interests declared

8. Lynette M Sholl

   Department of Pathology, Brigham and Women’s Hospital, and Harvard Medical School, Boston, United States

   Contribution

   Data curation, Writing - original draft, Writing - review and editing, Responsible for curation of targeted exome sequencing data for the IC8 and SCCT2 cell lines

   Competing interests

   No competing interests declared

9. Andrew P South

   Department of Dermatology and Cutaneous Biology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, United States

   Contribution

   Conceptualization, Data curation, Writing - original draft, Writing - review and editing, Assisted with development of organotypic cultures, provided key cell line reagents

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7650-0835

10. Jarrod A Marto

    1. Department of Pathology, Brigham and Women’s Hospital, and Harvard Medical School, Boston, United States
    2. Departmentof Oncologic Pathology and Blais Proteomics Center, Dana FarberCancer Institute, HarvardMedical School, Boston, United States

    Contribution

    Data curation, Investigation, Methodology, Writing - original draft, Writing - review and editing, Responsible for conduct of tandem mass spectroscopy, curation of data sets to remove non-specific IER5-interacting proteins

    Competing interests

    No competing interests declared

11. Guillaume O Adelmant

    1. Department of Pathology, Brigham and Women’s Hospital, and Harvard Medical School, Boston, United States
    2. Departmentof Oncologic Pathology and Blais Proteomics Center, Dana FarberCancer Institute, HarvardMedical School, Boston, United States

    Contribution

    Data curation, Formal analysis, Methodology, Writing - original draft, Writing - review and editing, Responsible for conduct of tandem mass spectroscopy, curation of data sets to remove non-specific IER5-interacting proteins

    Competing interests

    No competing interests declared

12. Stephen C Blacklow

    Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, United States

    Contribution

    Conceptualization, Writing - original draft, Writing - review and editing, Responsible for experimental conceptualization and design

    Competing interests

    SCB is on the SAB for Erasca, Inc, receives sponsored research funding from Novartis and Erasca, Inc, and is a consultant for IFM therapeutics and Ayala Pharmaceuticals.

    "This ORCID iD identifies the author of this article:" 0000-0002-6904-1981

13. Jon C Aster

    Department of Pathology, Brigham and Women’s Hospital, and Harvard Medical School, Boston, United States

    Contribution

    Conceptualization, Supervision, Funding acquisition, Methodology, Writing - original draft, Project administration, Writing - review and editing, Responsible for experimental conceptualization and design, documentation of organotypic culture results by light microscopy

    For correspondence

    jaster@rics.bwh.harvard.edu

    Competing interests

    JCA is a consultant for Ayala Pharmaceuticals and for Cellestia, Inc, There is no conflict of interest with the work described in this manuscript.

    "This ORCID iD identifies the author of this article:" 0000-0002-1957-9070

• 1,991

  views

• 213

  downloads

• 13

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.