identification of deleterious notch mutation as novel ... · 1 1 identification of deleterious...

1

Identification of deleterious NOTCH mutation as novel predictor to efficacious 1

immunotherapy in NSCLC 2

Running title: NOTCH mutation and efficacious immunotherapy in NSCLC 3

4

Kai Zhang1*, Xiaohua Hong

1*, Zhengbo Song

2*, Yu Xu

2*, Chengcheng Li

3, Guoqiang Wang

3, Yuzi 5

Zhang3, Xiaochen Zhao

3, Zhengyi Zhao

3, Jing Zhao

3, Mengli Huang

3, Depei Huang

3, Chuang Qi

3, 6

Chan Gao3, Shangli Cai

3, Feifei Gu

1, Yue Hu

1, Chunwei Xu

4, Wenxian Wang

5, Zhenkun Lou

6#, Yong 7

Zhang7# and Li Liu

1# 8

1Cancer Center, Wuhan Union Hospital, Tongji Medical College, Huazhong University of Science 9

and Technology, Hubei, P.R. China; 2Zhejiang Cancer Hospital, Zhejiang, P.R. China;

3The Medical 10

Department, 3D Medicines Inc., Shanghai, P.R. China; 4Department of Pathology, Fujian Cancer 11

Hospital, Fujian Medical University Cancer Hospital, Fujian, P.R. China; 5Department of 12

Chemotherapy, Chinese Academy of Sciences University Cancer Hospital (Zhejiang Cancer 13

Hospital), Zhejiang, P.R. China; 6Department of Oncology, Mayo Clinic, Rochester, Minnesota, 14

USA; 7Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, 15

Huazhong University of Science and Technology, Hubei, P.R. China. 16

17

*These authors contributed equally to this work. 18

#Corresponding author: 19

Zhenkun Lou, Ph.D., Department of Oncology, Mayo Clinic, Rochester, Minnesota, USA; Email: 20

[email protected]. 21

Yong Zhang, M.D., Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical 22

Research. on July 11, 2020. © 2020 American Association for Cancerclincancerres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on April 2, 2020; DOI: 10.1158/1078-0432.CCR-19-3976

http://clincancerres.aacrjournals.org/

2

College, Huazhong University of Science and Technology, Hubei, P.R. China; Email: 23

[email protected] 24

Li Liu, M.D., Cancer Center, Wuhan Union Hospital, Tongji Medical College, Huazhong University 25

of Science and Technology, Wuhan, P.R. China; Email: [email protected]. 26

27

Conflict of interest 28

The authors declare no potential conflicts of interest. 29



mailto:[email protected]


3

Translational relevance 30

This study involving 5 cohorts (n=1557) identifies NOTCH mutation, especially deleterious 31

NOTCH mutation (del-NOTCHmut

), as novel, frequent determinant of sensitivity to immune 32

checkpoint inhibitor (ICI) in EGFR/ALKWT

NSCLC. ICI, compared to chemotherapy, conferred 33

limited benefit in the NOTCH-wild-type patients, but remarkably prolonged PFS and OS in the 34

patients harboring del-NOTCHmut

. These results indicate the potential that del-NOTCHmut

might 35

impact on the treatment choice (ICI vs. chemotherapy) in advanced EGFR/ALKWT

NSCLC. 36

More importantly, del-NOTCHmut

downregulates NOTCH signaling and is correlated with better 37

ICI efficacy, which unravels a possibility that the monoclonal antibodies or small chemicals aiming 38

NOTCH members or their ligands might enhance the response to ICI. This inference might lead 39

future research to explore the efficacy of adding NOTCH inhibitor to ICI regimen in NSCLC, for the 40

optimization of ICI treatment in clinical practice. 41

42




4

Abstract 43

Purpose: NOTCH signaling is associated with tumorigenesis, mutagenesis, and immune tolerance in 44

NSCLC, indicating its association with the clinical benefit of immune checkpoint inhibitors (ICIs). 45

We hypothesized that NOTCH mutation in NSCLC might be a robust predictor of 46

immunotherapeutic efficacy. 47

Experimental Design: Multiple-dimensional data including genomic, transcriptomic, and clinical 48

data from cohorts of NSCLC internal and public cohorts involving immunotherapeutic patients were 49

analyzed. PolyPhen-2 system was performed to determine deleterious NOTCH mutation (del-50

NOTCHmut

). Further investigation on molecular mechanism was performed in TCGA data via 51

CIBERSORT and GSEA. 52

Results: Our 3DMed cohort (n=58) and other four cohorts (Rizvi, POPLAR/OAK, Van Allen, and 53

MSKCC [n=1499]) uncovered marked correlation between NOTCH1/2/3 mutation and better ICI 54

outcomes in EGFR/ALKWT

population, including ORR (2.20-fold, P=0.001), PFS (HR=0.61, 95%CI 55

0.46-0.81, P=0.001) and OS (HR=0.56, 95%CI 0.32-0.96, P=0.035). Del-NOTCHmut

exhibited better 56

predictive function than non-deleterious NOTCH mutation (non-del-NOTCHmut

), potentially via 57

greater transcription of genes related to DDR and immune activation. Del-NOTCHmut

was not linked 58

with prognosis in TCGA cohorts and chemotherapeutic response, but was independently associated 59

with immunotherapeutic benefit, delineating the predictive, but not prognostic utility of del-60

NOTCHmut

. 61

Conclusions: This work distinguishes del-NOTCHmut

as a potential predictor to favorable ICI 62

response in NSCLC, highlighting the importance of genomic profiling in immunotherapy. More 63

importantly, our results unravel a possibility of personalized combination immunotherapy as adding 64

NOTCH inhibitor to ICI regimen in NSCLC, for the optimization of ICI treatment in clinical 65

practice. 66




5

67

Keyword: Non-small cell lung cancer; NOTCH; immunotherapy; predictive biomarker 68




6

Introduction 69

Immune checkpoint inhibitors (ICIs) have renovated the standard treatment for patients with 70

non-small cell lung cancer (NSCLC), by virtue of the unprecedented prolongation of life (1-3). 71

Despite this, durable response of ICIs merely occurs in a tiny minority, which necessitates further 72

investigation into the biomarkers predicting the clinical benefit. 73

To date, two critical biomarkers, programmed death ligand 1 (PD-L1) expression and tumour 74

mutational burden (TMB) have been validated prospectively in random controlled trials (RCTs) 75

concerning NSCLC (4-6). Meanwhile, retrospective analyses of genomic profiles identified potential 76

biomarkers that are associated with better outcome such as DNA damage response (DDR) pathways 77

co-mutations and TP53/KRAS co-mutations (7,8). The genomic landscape that is associated with 78

better clinical outcome of ICIs has not been fully explored. 79

NOTCH pathway, a highly conserved signaling system, is regulated by short-range cell–cell 80

interaction between NOTCH receptor (NOTCH1–4) and “canonical” ligand (Jagged1, Jagged2, delta 81

like canonical NOTCH ligand 1 [DLL1], DLL3, or DLL4) (9), or non-canonically through activation 82

of other pathways such as nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), 83

WNT, transforming growth factor-β (TGF-β), and signal transducers and activators of transcription 3 84

(STAT3) (10). 85

Of great importance is the irreplaceable action of NOTCH in the development of multiple 86

organs at early stage (11), including the longitudinal regulation of lung growth from trachea/bronchi 87

differentiation during embryogenesis (proximal) to mature alveoli formation in postnatal period 88

(distal) (12). In developed lung, NOTCH modulates the homeostasis between secretory cells (Clara 89

and goblet cells) and ciliated cells, extracellular matrix production and subepithelial fibrosis, 90

myofibroblast differentiation, epithelial-mesenchymal transition (EMT), and pulmonary vascular 91

remodeling, contributing to the initiation and progression of multiple pulmonary diseases, such as 92




7

asthma and chronic obstructive pulmonary disease (COPD) (11,12). Of the NOTCH-related 93

pathogenesis mentioned above, EMT, in which epithelial cells lose polarity together with cell-cell 94

adhesion and acquire properties of mesenchymal cells (13), frequently associates with stemness 95

property (as cancer stem cell) (14,15), and resistance to chemotherapy (16,17), radiotherapy (17-19), 96

and even targeted therapy in NSCLC (17,20,21). 97

As for immunotherapy, plenty of molecular research concerning tumor initiation, 98

immunogenicity, and immune microenvironment strongly support the possible association between 99

NOTCH and immunotherapeutic efficacy. Firstly, tumor initiation via suppressing TP53 requires the 100

regulation of NOTCH1 on MDM2 (22), a robust biomarker related to hyper-progression in 101

immunotherapy (23). Secondly, tumor immunogenicity regulated by DDR pathways might be 102

enhanced by NOTCH1 inhibition (22), possibly via the direct binding between NOTCH1 and ataxia-103

telengiectasia mutated (ATM) (24,25). Thirdly, in immune microenvironment, the NOTCH ligands 104

on myeloid-derived suppressor cells (MDSCs) interact with the NOTCH receptor on tumor cells and 105

thereby improve the CSC capacity (26), which in turn increases the expression of NOTCH ligands on 106

MDSCs (27), constituting a positive feedback eventually resulting in immune tolerance (28). Based 107

on these observations, we hypothesized that NOTCH mutation in NSCLC might predict the clinical 108

benefit from immunotherapy. 109




8

Methods 110

Patients 111

Patients with NSCLC treated with PD-1/PD-L1 inhibitors in the Wuhan Union Hospital who 112

had genomic profiling of circulating tumor DNA (150-gene panel, Supplementary Table S1) before 113

treatment were included in our 3DMed cohort. The patient characteristic of our cohort was shown in 114

Supplementary Table S2. Another 4 independent public cohorts were also used in the present study, 115

including Rizvi, POPLAR/OAK, Van Allen, and MSKCC cohorts. 116

The data for the 4 independent cohorts were retrieved from published articles (detailed features 117

were displayed in Supplementary Table S3). (1) The Rizvi cohort contains 240 advanced NSCLC 118

patients treated with anti-PD-1/PD-L1 monotherapy or combination therapy with anti-CTLA-4, and 119

their tumor tissues were profiled with MSK-IMPACT panel (341-gene panel, 0.98 Mb, 56 pts; 410-120

gene panel, 1.06 Mb, 164 pts; 468-gene panel, 1.22 Mb, 20 pts) (29). (2) The POPLAR/OAK cohort 121

of 853 advanced NSCLC patients who had received 1 or 2 cytotoxic chemotherapy from a phase II 122

trial, POPLAR (NCT01903993) (30), and a phase III trial, OAK (NCT02008227) (31), includes two 123

different regimens, atezolizumab and docetaxel. All patients in the POPLAR/OAK trial implemented 124

a genomic profiling of ctDNA with Foundation One panel (315-gene panel, 1.1Mb) (32). (3) The Van 125

Allen cohort was defined as the NSCLC subpopulation of the pan-cancer research on micro-satellite 126

stable (MSS) patients with clinically annotated outcomes to immune checkpoint therapy (33). All 56 127

MSS NSCLC samples and matched normal tissues were profiled by whole-exome sequencing (WES) 128

(33). (4) The MSKCC cohort was identified as the NSCLC subpopulation of the pan-cancer research 129

on the relation between TMB and immunotherapeutic OS. Tumor tissues from 350 NSCLC patients 130

were profiled with MSK-IMPACT panel (341-gene panel, 0.98 Mb, 56 pts; 410-gene panel, 1.06 Mb, 131

239 pts; 468-gene panel, 1.22 Mb, 55 pts) (34). Of note, overlapped patients identified between the 132

Rizvi and MSKCC cohorts do not affect the independence of their clinical outcomes, for the reason 133




9

that the Rizvi study displayed ORR and PFS data and the MSKCC study published OS data (29,34). 134

All human sample collection and usage were in accordance with the principles of the 135

Declaration of Helsinki and approved by the Institution Review Board of Huazhong University of 136

Science and Technology. The written consents were received from all the participated patients. 137

Study assessment 138

In the 3DMed cohort, the objective response rate (ORR) was defined as the percentage of 139

patients with confirmed complete response (CR) or partial response (PR) by Response Evaluation 140

Criteria in Solid Tumors, version 1.1 (RECIST v1.1). PFS was defined as the time from the start of 141

anti-PD-1/PD-L1 treatment until disease progression (assessed by an investigator using RECIST 142

version 1.1) or death from any cause. In other 4 independent cohorts, tumor response was assessed 143

by thoracic radiologists (Rizvi, Van Allen) or investigators (POPLAR/OAK) using RECIST v1.1. 144

PD-L1 expression on tumor cells was assessed by VENTANA PD-L1 (SP263) assay in the 145

3DMed cohort, and the PD-L1 status were characterized as negative or positive. In the OAK trial, 146

PD-L1 expression on tumor cells or immune cells were evaluated simultaneously by VENTANA PD-147

L1 (SP142) assay. Detailed definition of PD-L1 status in tumor cells (TC1/2/3) and immune cells 148

(IC1/2/3) was described in the original article (31). 149

The testing of circulating tumor DNA (ctDNA) in the 3DMed cohort followed the method that 150

had been previously published (35). 151

PolyPhen-2 system evaluating the mutational impact on protein structure 152

PolyPhen-2 (Polymorphism Phenotyping v2) is a software tool from Harvard University which 153

predicts possible impact of amino acid substitutions on the structure and function of human proteins 154

using straightforward physical and evolutionary comparative considerations (36). PolyPhen-2 155

predictions were calculated for all resulting amino acid residue substitutions in human UniProtKB 156




10

proteins with the maximum CDS sequence overlap and identity. PolyPhen-2 uses eight sequence-157

based and three structure-based predictive features which were selected automatically by an iterative 158

greedy algorithm. 159

PolyPhen-2 calculates the naive Bayes posterior probability that a given mutation is damaging 160

and reports estimates of false positive (the chance that the mutation is classified as damaging when it 161

is in fact nondamaging) and true positive (the chance that the mutation is classified as damaging 162

when it is indeed damaging) rates. Based on the final score assessing the harm of missense mutation, 163

we set the cut-off value as 0.800 to categorized NOTCH missense mutation as higher impact group 164

or lower impact group, and thereafter explore to better predictive efficacy of deleterious NOTCH 165

mutation, compared to non-deleterious NOTCH mutation. 166

Gene set enrichment analysis (GSEA) 167

For GSEA (37), the javaGSEA Desktop Application (GSEA 4.0.1) was downloaded from 168

http://software.broadinstitute.org/gsea/index.jsp. GSEA was used to associate the gene signature with 169

deleterious and non-deleterious NOTCH mutation. The signatures tested in the present study was 170

shown in Supplementary Table S4. The genes identified to be on the leading edge of the enrichment 171

profile were subject to pathway analysis. Fold-change values were exported for all genes and 172

analyzed with version 4.0.1 of GSEA, using the GSEA pre-ranked module. The normalized 173

enrichment score (NES) is the primary statistic for examining gene set enrichment results. The 174

nominal P value estimates the statistical significance of the enrichment score. A gene set with 175

nominal P<0.05 was determined to be significantly enriched in genes. 176

The leading edge analysis allows for the GSEA to determine which subsets (referred to as the 177

leading edge subset) of genes contributed the most to the enrichment signal of a given gene set's 178

leading edge or core enrichment. In the present study, leading edge analyses were performed in 179

GSEA 4.0.1 to discern whether a small number of DDR members contribute to multiple significance 180



http://software.broadinstitute.org/gsea/index.jsp


11

of enrichment of DDR signature in the del-NOTCHmut

group. 181

Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) 182

CIBERSORT, an online method (https://cibersort.stanford.edu/index.php) for characterizing cell 183

composition of complex tissues from their gene expression profiles (38), was applied to enumeration 184

of hematopoietic subsets in mRNA mixtures from TCGA database. CIBERSORT outperformed other 185

methods with respect to noise, unknown mixture content and closely related cell types (38). 186

mRNA expression profiling analysis 187

The association between deleterious NOTCH mutation and relevant immune-related genes were 188

analyzed in TCGA database, where both DNA-seq and RNA-seq data are available. The list of DNA 189

damage response-related genes was determined by the leading edge analysis of GSEA in DDR 190

pathways (Supplemental Table S5). The immune gene list was mainly based on a published article 191

that summarized the genes related to activated T cells, immune cytolytic activity, and IFNγ release 192

(8). Other immune genes were added according to two relevant clinical trials (39,40). A list of 47 193

immune genes is provided in Supplementary Table S5. Statistical significance was determined by 194

the DESeq2 method in RStudio 3.6.1 and the normal p value was given in the figure. 195

Statistical analysis 196

The significance with categorical variables (e.g., ORR, PD-L1 status) were evaluated by Fisher 197

exact test. Kaplan-Meier method was performed to delineate the curve of PFS and OS, and Log-rank 198

method was used to assess their significance. Cox regression was implemented to calculate the 199

hazard ratio (HR) on PFS and OS, in both univariable and multivariable analyses and interaction 200

tests exploring the interaction effect between NOTCH mutation and treatment choice (atezolizumab 201

vs. docetaxel). The variables with p value below 0.10 in the univariable analyses were included in the 202

following multivariable analyses. The method of random-effect inverse-variance-weighted were used 203



https://cibersort.stanford.edu/index.php


12

to pool outcomes, which is calculated by HR and its 95% confidence interval (CI) to estimate the 204

size of influence on the clinical benefits including ORR, PFS and OS. Heterogeneity assessment 205

between different studies was applied using the I2 statistics. A result of P > 0.1 and I

2 < 50% indicates 206

that no significant between-study heterogeneity was present. The significance with continuous 207

variables (e.g., tumour mutational burden/count and fraction of copy number alteration) were 208

assessed by one-way ANOVA or two-way ANOVA, with Tukey post-test, with the requirement of 209

homoscedasticity between different groups. If the data failed to meet the criteria for parametric test, 210

non-parametric analyses would be implemented, i.e., chi-square test, and rank sum tests. All 211

statistical analyses mentioned above were performed using IBM SPSS Statistics 22 or Stata/SE 15.1, 212

and the graphs in the present study were drawn by GraphPad Prism 8. We set the nominal level of 213

significance as 10% for heterogeneity test and 5% for the rest statistical analysis, and all 95% CIs 214

were 2-sided. 215




13

Results 216

Association between NOTCH mutation and better clinical benefit to ICIs in the 3DMed cohort. 217

In the present study, 58 NSCLC patients with ctDNA sequencing before anti-PD-1/PD-L1 218

treatment were included to investigate the association between ICI efficacy and genomic alterations. 219

The baseline characteristics are described in Supplementary Table S2. In brief, this cohort was 220

representative of the general population of advanced NSCLC patients with median age of 59 (range, 221

36-72), major proportion of male (74.1%), and high percentage of ever-smokers (56.9%). In this 222

cohort, 86.2% of the patients received anti-PD-1 and the rest underwent anti-PD-L1 treatment. The 223

lines of anti-PD-1/PD-L1 immunotherapy varied from first to seventh (first-line, 20.7%; second-line, 224

39.7%; third-line or later, 39.7%), and the overall response rate reached 20.7% in total. The median 225

PFS and OS were comparable to previously published trials, as 3.1 months and 16.0 months 226

respectively. 227

The genomic mutational landscape of 58 NSCLC patients categorized according to response in 228

the 3DMed cohort is displayed in Fig. 1A. Consistent with previous studies, higher mutational rate of 229

TP53 and KRAS were shown in responders, relative to non-responders (8). Besides these, NOTCH 230

mutation discovered to be enriched in responders as well (Fig. 1B). 231

Due to the limited efficacy of ICIs in NSCLC patients with EGFR or ALK driver mutation, we 232

excluded these individuals in the following exploration. The mutation of NOTCH1/2/3 was 233

significantly associated with higher ORR (60.0% vs. 11.9%, P=0.003), longer PFS (HR 0.30, 94%CI 234

0.12-0.78, log-rank P=0.009, Fig. 1C) and OS (HR 0.21, 95%CI 0.05-0.91, log-rank P=0.020, Fig. 235

1D) in EGFR/ALKWT

NSCLC patients. These results suggest that NOTCH mutation might be 236

associated with the clinical benefit of immunotherapy. 237




14

Association between NOTCH1/2/3, but not NOTCH4 mutation and better benefit to ICIs in 238

NSCLC patients from independent cohorts 239

To further evaluate the predictive value of NOTCH family, another 4 public cohorts of ICIs 240

with adequate information of the genomic alterations in tumor tissue or ctDNA and survival were 241

analyzed, including the cohorts from Rizvi, POPLAR/OAK, Van Allen, and MSKCC (characteristics 242

displayed in Supplementary Table S3). 243

NOTCH family consists of 4 members, with potentially distinct mechanisms underlying the 244

development of NSCLC. Among the samples from the OAK/POPLAR and Rizvi/MSKCC cohorts, 245

the mutational rates of NOTCH were 6.11% (NOTCH1), 5.19% (NOTCH2), 4.12% (NOTCH3) and 246

6.72% (NOTCH4), which exhibited similar distribution in lung squamous cell carcinoma and lung 247

adenocarcinoma (Supplementary Fig. S1). Univariable and multivariable analyses of the impact 248

from non-synonymous mutation in each NOTCH gene on PFS and OS benefit from ICI were 249

performed in the POPLAR/OAK, Rizvi and MSKCC cohorts. In each cohort, the multivariable HR 250

value of NOTCH4 mutation exceeded 1, and on the contrary, the HR values of NOTCH1, NOTCH2, 251

and NOTCH3 mutations were inferior to 1 (Supplementary Fig. S2), which indicates the 252

contradictory predictive value of NOTCH4 mutation to NOTCH1/2/3 mutation. Thus, we further 253

focused on the potential of predictive function of NOTCH1/2/3 mutation in these cohorts. 254

Among the EGFR/ALKWT

population, the beneficial trend of NOTCH1/2/3 mutation in 255

immunotherapeutic ORR (Fig. 2A-C), PFS (Fig. 2A-C), and OS (Fig. 2E-G) were observed in all 256

cohorts. Pooled estimates demonstrated that compared to the NOTCHWT

group, the NOTCH1/2/3mut

257

group exhibited better ORR (RR 2.2, 95% CI 1.39-3.51, P=0.001, Fig. 2D), longer PFS (HR 0.61, 258

95% CI 0.46-0.81, P=0.001, Fig. 2D) and OS (HR 0.56, 95% CI 0.32-0.96, P=0.035, Fig. 2H). 259

Statistical analyses for heterogeneity were insignificant in all pooled estimates (P>0.10), indicating 260

the consistency of the association between NOTCH1/2/3 mutation and favorable benefit to ICIs 261




15

across these cohorts. 262

Enrichment of deleterious NOTCH mutation in EGFR/ALKWT

NSCLC with higher TMB, 263

irrespective of PD-L1 status 264

As illustrated in Fig. 3A, missense mutations were predominant among all kinds of NOTCH 265

mutations in the POPLAR/OAK and MSKCC cohorts. Unlike truncating mutations that strikingly 266

impact upon tumour cells by the loss of gene expression, missense mutations might be deleterious or 267

benign, on account of their effects on protein structure. To rule out the benign mutations that 268

generally do not affect the protein function, PolyPhen-2 analysis was implemented to distinguish 269

between the missense mutations of NOTCH with higher or lower probability of impact on protein 270

structure (abbreviated as NOTCHmis-high

and NOTCHmis-low

, respectively). Since both truncating and 271

mis-high mutations explicitly affect the biological function of the involved gene, we hereby 272

combined these two kinds of alterations and defined them as deleterious NOTCH mutations (del-273

NOTCHmut

). Relatively, mis-low mutations of NOTCH1/2/3 and all NOTCH4 mutations were 274

identified as non-deleterious NOTCH mutations (non-del-NOTCHmut

) and the patients without any 275

NOTCH mutation were categorized as the control group (NOTCHWT

). The work flow is illustrated in 276

Fig. 3B. 277

To investigate the possible mechanism underlying the predictive role of NOTCH mutation, we 278

firstly aimed to ascertain whether co-occurrence takes place between del-NOTCHmut

with robust 279

predictors in EGFR/ALKWT

NSCLC, including PD-L1 expression and higher TMB. We identified a 280

cohort from the OAK trial where the data of both ctDNA and PD-L1 testing is available (n=637). In 281

this cohort, participants were divided into 9 subgroups by the two variables, bTMB (bTMB-L: 282

bTMB<8; bTMB-I: 8≤bTMB<16; bTMB-H: bTMB≥16) and PD-L1 expression (negative: TC0 and 283

IC0; intermediate: TC1/2 and/or IC1/2; strong expression: TC3 or IC3). As illustrated in Fig. 3C, the 284

digits in these nine squares representing nine subgroups are the incidence rates of del-NOTCHmut

, 285




16

which was enriched in bTMB-H subgroups, irrespective of PD-L1 expression. Furthermore, the 286

distribution of PD-L1 status (SP142 antibody) was parallel among three groups (Fig. 3D), but the 287

level of bTMB was significantly higher in the del-NOTCHmut

and non-del-NOTCHmut

groups, 288

compared to NOTCHWT

individuals (Fig. 3E). In addition, a similar trend of TMB was observed in 289

the EGFR/ALKWT

NSCLC patients of TCGA database. 290

Impact of deleterious NOTCH mutation on DNA damage response 291

Despite the similar levels of mutational burden were discovered in the del-NOTCHmut

and non-292

del-NOTCHmut

groups, the underlying mechanisms to repair the miscoding DNA, i.e., DNA damage 293

response (DDR) pathways, might be dissimilar between these two groups. GSEA revealed prominent 294

enrichment of signatures related to DNA repair in the del-NOTCHmut

group, compared to the non-del-295

NOTCHmut

(P<0.001, Fig. 3G) and NOTCHWT

(P<0.001, Fig. 3G) groups. In detail, DDR could be 296

defined as 8 different pathways according to their diverse functions, including homologous 297

recombination repair (HRR), mismatch repair (MMR), base excision repair (BER), nucleotide 298

excision repair (NER), Fanconi anemia pathway (FA), translesion DNA synthesis (TLS), non-299

homologous end-joining (NHEJ), and checkpoint factors (CPF). Here we further explored that the 300

signatures of all pathways except NHEJ displayed enrichment in the del-NOTCHmut

group, relative to 301

the NOTCHWT

(Fig. 3H) and non-del-NOTCHmut

(Fig. 3I) groups, while no difference was found 302

between the non-del-NOTCHmut

group and the NOTCHWT

group (Fig. 3J). There are members 303

shared among different DDR pathways, and to exclude the possibility that identical genes contribute 304

to multiple significances in several DDR pathways, leading edge analyses were performed and 305

merely limited DDR genes were found to impact various enrichments (Supplementary Fig. S3). In 306

addition, the expression of all genes in the leading edge analysis (113 genes in total) was further 307

analyzed in comparison between the del-NOTCHmut

group and the NOTCHWT

group. The heatmap 308

and box plots of the most significant 30 DDR genes were displayed in Supplementary Fig. S4. 309




17

Taken together, these results demonstrate the potentially intensified activation of DDR pathways 310

in the EGFR/ALKWT

NSCLC with deleterious NOTCH mutation, in comparison with the one with 311

non-deleterious NOTCH mutation, despite the comparable level of TMB in these two clusters. Tumor 312

mutational burden is the consequence of the confront between mutagenesis and DNA repair. The 313

comparable TMB but higher activation of DDR system in the del-NOTCHmut

NSCLC might suggest 314

the possibility of more drastic mutagenesis in the NSCLC harboring deleterious NOTCH mutation. 315

Impact of deleterious NOTCH mutation on immune infiltration and response 316

Using CIBERSORT, we estimated the degree of infiltrated immune cells, discovering an 317

increase of M1 macrophage in the EGFR/ALKWT

NSCLC with deleterious NOTCH mutation, 318

relative to those without NOTCH mutation (Supplemental Fig. S5). Despite no distinctions that 319

were uncovered in other subsets of immune cells, the immune reaction to tumoral neoantigens might 320

be higher in patients with deleterious NOTCH mutation, which was explored by GSEA in the 321

following section. 322

Displayed in Fig. 4A, multiple significant enrichments of immune activation were revealed in 323

the del-NOTCHmut

group, compared to the NOTCHWT

group, including antigen processing and 324

presentation, BCR/TCR downstream signaling, activation of CD4/CD8 T cell and NKT cell, 325

inhibition of regulatory T cell (Treg), programmed cell death, and metabolism of steroid hormones. 326

In contrast, the non-del-NOTCHmut

group has much less enrichment compared to the NOTCHWT

327

group. Detailed curves of GSEA were shown in Supplemental Fig. S6-9. 328

In addition, the interleukin pathways were also taken into consideration. GSEA of interleukin 329

signatures unmasked the activations of IL-1 and IL-12 pathways in the del-NOTCHmut

group, and the 330

activations of IL-6 family, IL-10, and IL-12 family in the non-del-NOTCHmut

group, relative to the 331

NOTCHWT

group. Of great importance, the IL-10 signaling enriched in the non-del-NOTCHmut

332

NSCLC associated with an anti-inflammatory and immunosuppressive microenvironment, in 333




18

accordance with the previous result of a Treg activation in the non-del-NOTCHmut

NSCLC. 334

To sum up, deleterious NOTCH mutation was positively associated with infiltration of M1 335

macrophage, antigen processing via degradation in proteasome, cross presentation of antigen, 336

BCR/TCR downstream signaling, activation of CD4 T cell/CD8 T cell/NKT cell, deactivation of 337

Treg, programmed cell death of tumor cell, and metabolism of steroid hormones. These results 338

delineate the hyper-active immune microenvironment and the robust immune reaction in the 339

EGFR/ALKWT

NSCLC with deleterious NOTCH mutation, relative to the ones with non-deleterious 340

NOTCH mutation or without any NOTCH mutation, which might be linked with the larger benefit 341

from immunotherapy in the del-NOTCHmut

patients with EGFR/ALKWT

NSCLC. 342

Deleterious NOTCH1/2/3 mutation is predictive, not prognostic biomarker of ICI benefit 343

As shown above, del-NOTCHmut

, compared to non-del-NOTCHmut

, exhibited greater association 344

with potentially higher transcription of DDR genes and activated immune microenvironment, which 345

is plausibly linked with better efficacy of immunotherapy. We next sought to firstly validate this 346

hypothesis in the POPLAR/OAK cohort, where the PFS/OS data of both ICI (atezolizumab) and 347

chemotherapeutic agent (docetaxel) are available and of high credibility due to the RCT setting. 348

Previously in Supplemental Figure S2, we ruled out the probability of better ICI outcome 349

associated with NOTCH4 mutation. Here we further distinguished NOTCH4 mutation by Polyphen-2 350

to explore whether deleterious NOTCH4 mutation could be beneficial. The patients harboring 351

deleterious NOTCH1, NOTCH2 or NOTCH3 mutations exhibited a trend of better ORR and PFS 352

than the patients with wildtype NOTCH genes (Supplemental Figure S10A-C). However, as 353

illustrated in Supplemental Figure S10D, the lines representing deleterious NOTCH4 mutation and 354

wildtype NOTCH are mingled, and none of the patients with NOTCH4 mutation acquired objective 355

response, regardless of the deleterious status of the mutation. Taken together, this further analysis 356

supports the results in Supplemental Fig. S2 that unlike the mutations in NOTCH1/2/3, NOTCH4 357




19

mutation might not be associated with immunotherapeutic outcomes in EGFR/ALKWT

NSCLC. 358

To directly evaluate the utility of deleterious NOTCH mutation in clinical decision on 359

immunotherapy or chemotherapy in advanced EGFR/ALKWT

NSCLC, the PFS and OS benefits from 360

atezolizumab relative to docetaxel in the del-NOTCHmut

, non-del-NOTCHmut

, and NOTCHWT

groups 361

were separately calculated and compared. As shown in Fig. 5A, in ITT population, limited ORR and 362

PFS benefits were observed (ORR: 15.6% vs. 12.3%, P=0.204; PFS: HR 0.84, 95%CI 0.72-0.98, 363

P=0.025), and this weak improvement became even milder and insignificant in the NOTCHWT

364

subpopulation (ORR: 14.5% vs. 13.3%, P=0.726; PFS: HR 0.91, 95%CI 0.77-1.08, P=0.279). In 365

contrast, moderate benefit was observed in the non-del-NOTCHmut

group (ORR: 10.7% vs. 10.3%, 366

P>0.999; PFS: HR 0.65, 95% CI 0.37-1.13, P=0.126), and remarkably, considerable benefit was 367

achieved in the del-NOTCHmut

group (ORR: 29.4% vs. 9.3%, P=0.008; HR 0.45, 95% CI 0.27-0.77, 368

P=0.004). As a result, the interaction effect on PFS between NOTCH mutation (del-NOTCHmut

vs. 369

non-del-NOTCHmut

vs. WT) and treatment choice (atezolizumab vs. docetaxel) was significant (HR 370

0.73, 95% CI 0.57-0.93, Pinteraction=0.012), recognizing del-NOTCHmut

mutation as a predictive 371

biomarker of PFS benefit from immunotherapy over chemotherapy. 372

Identical analyses were performed on OS benefit (Fig. 5B). Compared to docetaxel, 373

atezolizumab monotherapy decreased the hazard of death by 33% in the NOTCHWT

group (HR 0.67, 374

95% CI 0.55-0.80, P<0.001), 49% in the non-del-NOTCHmut

group (HR 0.51, 95% CI 0.27-0.96, 375

P=0.038), and by higher proportion as 52% in the del-NOTCHmut

group (HR 0.48, 95% CI 0.28-0.85, 376

P=0.011). 377

Furthermore, multivariable analyses were performed to explore whether deleterious NOTCH 378

mutation is independently associated with immunotherapeutic efficacy. In the POPLAR/OAK cohort 379

where mutations were detected in ctDNA, del-NOTCHmut

independently associated with better 380

immunotherapeutic PFS (HR 0.57, 95% CI 0.38-0.86, P=0.006, Table 1) and OS (HR=0.63, 95% CI 381




20

0.40-0.99, P=0.045, Table 1), but not chemotherapeutic benefit (PFS: HR 1.04, 95% CI 0.73-1.49, 382

P=0.840; OS: HR=0.86, 95% CI 0.58-1.29, P=0.462, Supplemental Table S6). These results 383

demonstrate that deleterious NOTCH mutation is a predictive, not prognostic biomarker in the 384

POPLAR/OAK cohort. 385

In other two immunotherapeutic cohorts (Rizvi and MSKCC) where mutations were detected in 386

tissue sample, TMB was significantly associated with immunotherapeutic outcomes in the 387

univariable analyses and therefore was involved in the further multivariable analyses. The 388

association between del-NOTCHmut

and immunotherapeutic benefits remained significant after the 389

adjustment for TMB (Rizvi-PFS: HR 0.56, 95%CI 0.32-0.99, P=0.047, Supplemental Table S7; 390

MSKCC-OS: HR 0.54, 95%CI 0.29-1.00, P=0.049, Supplemental Table S8), suggesting that the 391

predictive utility of del-NOTCHmut

is independent of TMB in EGFR/ALKWT

NSCLC. Taking TCGA 392

cohort as a comparison, which includes the NSCLC patients with predominantly early-stage and 393

surgically-resected tumors without receiving immunotherapy, we discovered no significant 394

association between deleterious NOTCH mutation and both the DFS and OS in EGFR/ALKWT

395

NSCLC (Supplemental Fig. S11). 396

Taken together, deleterious NOTCH mutation detected by either ctDNA or tumour tissue was 397

independently associated with significant improvement of immunotherapy and was not linked with 398

prognosis, delineating the predictive, but not prognostic value of del-NOTCHmut

in 399

immunotherapeutic treatment against NSCLC. 400




21

Discussion 401

In this study, NOTCH mutation, especially del-NOTCHmut

, was identified as tumor cell-intrinsic 402

determinant of better response to ICI treatment in five cohorts, involving 1557 advanced NSCLC 403

patients in total. Of great attention are their noticeable correlations with better clinical outcome of 404

ICI, instead of chemotherapeutic agents, elucidating the predictive, but not prognostic role in 405

immunotherapy. Of all kinds of NOTCH mutations, deleterious, relative to non-deleterious alteration, 406

might possess more predictive power. Furthermore, we distinguished greater transcription of genes 407

related to DDR and immune activation as potential mechanisms underlying the predictive value of 408

del-NOTCHmut

in NSCLC population (diagram shown in Supplementary Fig. S12). 409

This study represents the one of the first reports to examine the association between ICI 410

response and NOTCH family. As reported by a previous study mainly involving melanoma and 411

NSCLC, NOTCH1 mutation seldom occurred in patients who hyper-progressed while 412

immunotherapy (23). Based on the similarity among different NOTCH members, we took all 4 413

members of NOTCH family into consideration and discovered that the mutation in NOTCH1/2/3, 414

instead of NOTCH4, was associated with higher ORR and longer PFS/OS with ICI treatment in 415

EGFR/ALKWT

NSCLC. 416

Of the NOTCH mutations in these cohorts, frameshift and nonsense mutations are overtly loss-417

of-function, while missense mutations might be damageable or merely benign as nonpathogenic 418

passenger events. Earlier NSCLC studies in NOTCH1 implemented PolyPhen-2 to distinguish 419

detrimental mutations, and confirmed their down-regulating function on transcriptional activity of 420

NOTCH1 by both reporter gene assay and electrophoretic mobility shift assay (41), suggesting the 421

missense mutations of NOTCH might mainly be inhibitory in NSCLC, similar to truncating 422

mutations. In addition, in light of the data in COSMIC database, the pattern of mutational loci in 423

NSCLC is sporadic, unlike the enriched distribution in hematopoietic and lymphoid where NOTCH 424




22

mutations are recognized to be oncogenic and gain-of-function, which further suggests that the 425

NOTCH mutation in NSCLC may be dominated by inhibitory function. Inhibition of NOTCH1 was 426

discovered to stabilize TP53 via downregulating the phosphorylation and protein level of MDM2 427

(42), a prominent biomarker linked with ICI-induced hyper-progression across multiple tumor types 428

(23). These results indicate the possibility that deactivation of MDM2 might account for the 429

predictive function of inhibitory NOTCH mutation in immunotherapy. 430

Further analyses in the POPLAR/OAK cohort comparing atezolizumab and docetaxel revealed 431

that relative to the limited benefit in the NOTCHWT

group, moderate ICI benefit was shown in the 432

non-del-NOTCHmut

group, and remarkable immunotherapeutic outcome was observed in the del-433

NOTCHmut

individuals (detailed comparisons shown in Supplementary Fig. S12). Many missense 434

mutations may have little or no effect on protein function; this is likely the basis for the weaker 435

correlation between clinical outcomes and identified non-del-NOTCHmut

. 436

Consistent with clinical benefit, similar PD-L1 staining but higher TMB was uncovered in both 437

del-NOTCHmut

and non-del-NOTCHmut

groups. The PD-L1 antibody used in the OAK trial is SP142, 438

which was indicated to be poorly consistent to other antibodies including FDA-approved 439

standardized assays, 22C3 and SP263, especially in the high-staining samples (43,44), which might 440

reduce the credibility of the PD-L1 analysis in the present study. As for tumor mutational burden, it 441

is the consequence of the confront between mutagenesis and DNA repair. In the present study, 442

similar level of TMB was revealed in NSCLC with deleterious or non-deleterious NOTCH mutation, 443

while the DNA damage response is much stronger in the NSCLC with del-NOTCHmut

, relative to 444

non-del-NOTCHmut

, suggesting a possibility of fiercer mutagenesis in NSCLC with deleterious 445

NOTCH mutation. Multivariable analyses in the Rizvi and the MSKCC cohorts, where tissue TMB 446

was significantly associated with immunotherapeutic outcome, indicating the predictive utility of 447

del-NOTCHmut

is independent to the level of TMB. 448




23

After the translation of mutated protein, immune procedures including processing and 449

presentation of neoantigen, BCR and TCR downstream signaling, activation of CD4 T cell, CD8 T 450

cell, and NKT cell, inhibition of Treg, contributing to the superior degree of programmed cell death 451

and the success of immunity-induced tumor rejection. Here in the present study, we observed the 452

association between del-NOTCHmut

and these procedures, which might be part of the mechanism of 453

del-NOTCHmut

in predicting better immunotherapeutic outcome. 454

As for limitations, the retrospective setting and pooled-estimate methodology of this study 455

might introduce multiple biases. The limitation from retrospective setting could be greatly minimized 456

by the large sample size (5 cohorts involving 1557 patients), by which the experimental features 457

might be balanced, such as race, ICI regimen, treatment line, and the platform/panel/used sample of 458

NGS testing, etc. However, the pooled-estimate methodology does probably weaken the credibility 459

of the conclusion to some extent, where the included studies with larger sample size tend to possess 460

more weight in the final pooled estimate. In the present study, the predictive utility of NOTCH 461

mutation was mainly driven by small datasets, which introduce minor-to-moderate heterogeneity to 462

the pooled estimates. 463

In our 3DMed cohort, merely NOTCH1-3, instead of NOTCH4, were involved in the gene 464

panel, and the comparison among of 4 NOTCH genes were comprehensively analyzed in the public 465

cohorts. Fortunately, the further analyses indicate that NOTCH4 mutation was not associated with 466

better immunotherapeutic outcome, making it logical to put all the cohorts together for a meta-cohort 467

analysis. In addition, our attempts to recruit functional NOTCH mutations was handicapped by the 468

limited information available regarding the functions of different mutations and the lack of hotspots 469

in NOTCH gene mutations, as illustrated in TCGA and COSMIC databases. To alleviate this 470

limitation, we used PolyPhen-2 system which provides powerful evaluation of the mutational 471

functions on NOTCH protein, assisting us to distinguish the del-NOTCHmut

out of all NOTCH 472




24

missense mutations. However, this system fails to decipher the precise function of missense mutation 473

as activation or deactivation. Despite the similar trend of increased ICI benefit in patients with 474

missense or nonsense mutations, we cannot simply assume that missense mutations at different loci 475

are all inactivating, due to the lack of direct functional data in the present study. Activating and 476

inactivating mutations might lead to distinct influences on immunotherapeutic efficacy, which might 477

be addressed by further molecular studies in cell line and xenograft model. 478

Despite the identification of deleterious NOTCH mutation, the predictive efficacy in cohort 479

(POPLAR/OAK) using the similar detection methods with our cohort is not ideal, which might 480

decrease the robustness of this biomarker. This poorer result might be explained from two angles. 481

Firstly, the confounding variables significantly associated with the PFS and OS in the atezolizumab 482

arm (including sex, race, histology, ECOG, number of metastatic sites, mutations of STK11, KEAP1, 483

and TP53) might influence the predictive efficacy of NOTCH mutation in univariable analysis. After 484

adjusting these factors, the association between del-NOTCHmut

and OS became significant. Secondly, 485

unlike routine administration of ICI in our cohort, the POPLAR/OAK trial allowed the continuation 486

of atezolizumab despite of radiological progression, if the investigator deemed the patient to be 487

receiving clinical benefit which might cover part of the predictive utility of NOTCH mutation in the 488

OS benefit. Taken together, the poorer result in the POPLAR/OAK cohort might be partially 489

attributed to its special procedure and the confounding factors. The significant multivariable p values 490

among multiple cohorts demonstrate the robustness of del-NOTCHmut

in predicting favorable ICI 491

benefit. 492

In summary, our data identified NOTCH mutation, especially del-NOTCHmut

, as a novel, 493

frequent determinant of sensitivity to immune checkpoint blockade in EGFR/ALKWT

NSCLC. More 494

importantly, our results unravel a possibility of personalized combination immunotherapy as adding 495

NOTCH inhibitor to ICI regimen in NSCLC, for the optimization of ICI treatment in clinical 496




25

practice. 497




26

Author contributions 498

Conception and design: Zhengbo Song, Yu Xu, Guoqiang Wang, Zhenkun Lou, Yong Zhang and Li 499

Liu 500

Development of methodology: Yu Xu and Guoqiang Wang 501

Acquisition of data: Kai Zhang, Xiaohua Hong, Yu Xu, Yuzi Zhang, Xiaochen Zhao, Zhengyi Zhao, 502

Jing Zhao, Mengli Huang, Depei Huang, Chuang Qi, Chan Gao, Shangli Cai, Feifei Gu, Yue Hu and 503

Chunwei Xu 504

Analysis and interpretation of data: Kai Zhang, Yu Xu, Chengcheng Li, Guoqiang Wang and 505

Wenxian Wang 506

Writing, review, and/or revision of the manuscript: Kai Zhang, Yu Xu, Chengcheng Li, Guoqiang 507

Wang, Yuzi Zhang, Xiaochen Zhao, Zhengyi Zhao, Jing Zhao, Mengli Huang, Depei Huang, Chuang 508

Qi, Chan Gao, Shangli Cai, Yue Hu, Chunwei Xu and Li Liu 509

Administrative, technical, or material support: Zhengbo Song, Shangli Cai and Li Liu 510

Study supervision: Wenxian Wang and Li Liu 511

Final approval of manuscript: All authors 512

513

Acknowledgments 514

This work was supported by the National Key Research and Development Program of China 515

(2016YFC13038) and National Natural Science Foundation of China (81773056). 516

517

Competing interests 518

The authors declare no competing interests. 519




27

References 520

1. Blumenthal GM, Zhang L, Zhang H, Kazandjian D, Khozin S, Tang S, et al. Milestone 521

Analyses of Immune Checkpoint Inhibitors, Targeted Therapy, and Conventional Therapy in 522

Metastatic Non-Small Cell Lung Cancer Trials: A Meta-analysis. JAMA Oncol 2017;3(8):e171029 523

doi 10.1001/jamaoncol.2017.1029. 524

2. Gettinger S, Horn L, Jackman D, Spigel D, Antonia S, Hellmann M, et al. Five-Year Follow-525

Up of Nivolumab in Previously Treated Advanced Non-Small-Cell Lung Cancer: Results From the 526

CA209-003 Study. J Clin Oncol 2018;36(17):1675-84 doi 10.1200/JCO.2017.77.0412. 527

3. Garon EB, Hellmann MD, Rizvi NA, Carcereny E, Leighl NB, Ahn MJ, et al. Five-Year 528

Overall Survival for Patients With Advanced Non Small-Cell Lung Cancer Treated With 529

Pembrolizumab: Results From the Phase I KEYNOTE-001 Study. J Clin Oncol 2019:JCO1900934 530

doi 10.1200/JCO.19.00934. 531

4. Reck M, Rodriguez-Abreu D, Robinson AG, Hui R, Csoszi T, Fulop A, et al. Pembrolizumab 532

versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer. N Engl J Med 533

2016;375(19):1823-33 doi 10.1056/NEJMoa1606774. 534

5. Hellmann MD, Ciuleanu TE, Pluzanski A, Lee JS, Otterson GA, Audigier-Valette C, et al. 535

Nivolumab plus Ipilimumab in Lung Cancer with a High Tumor Mutational Burden. N Engl J Med 536

2018;378(22):2093-104 doi 10.1056/NEJMoa1801946. 537

6. Mok TSK, Wu Y-L, Kudaba I, Kowalski DM, Cho BC, Turna HZ, et al. Pembrolizumab 538

versus chemotherapy for previously untreated, PD-L1-expressing, locally advanced or metastatic 539

non-small-cell lung cancer (KEYNOTE-042): a randomised, open-label, controlled, phase 3 trial. 540

The Lancet 2019 doi 10.1016/s0140-6736(18)32409-7. 541

7. Wang Z, Zhao J, Wang G, Zhang F, Zhang Z, Zhang F, et al. Comutations in DNA Damage 542

Response Pathways Serve as Potential Biomarkers for Immune Checkpoint Blockade. Cancer Res 543




28

2018;78(22):6486-96 doi 10.1158/0008-5472.CAN-18-1814. 544

8. Dong ZY, Zhong WZ, Zhang XC, Su J, Xie Z, Liu SY, et al. Potential Predictive Value of 545

TP53 and KRAS Mutation Status for Response to PD-1 Blockade Immunotherapy in Lung 546

Adenocarcinoma. Clin Cancer Res 2017;23(12):3012-24 doi 10.1158/1078-0432.CCR-16-2554. 547

9. Radtke F, MacDonald HR, Tacchini-Cottier F. Regulation of innate and adaptive immunity by 548

Notch. Nat Rev Immunol 2013;13(6):427-37 doi 10.1038/nri3445. 549

10. Ayaz F, Osborne BA. Non-canonical notch signaling in cancer and immunity. Front Oncol 550

2014;4:345 doi 10.3389/fonc.2014.00345. 551

11. Siebel C, Lendahl U. Notch Signaling in Development, Tissue Homeostasis, and Disease. 552

Physiol Rev 2017;97(4):1235-94 doi 10.1152/physrev.00005.2017. 553

12. Hussain M, Xu C, Ahmad M, Yang Y, Lu M, Wu X, et al. Notch Signaling: Linking 554

Embryonic Lung Development and Asthmatic Airway Remodeling. Mol Pharmacol 2017;92(6):676-555

93 doi 10.1124/mol.117.110254. 556

13. Yuan X, Wu H, Han N, Xu H, Chu Q, Yu S, et al. Notch signaling and EMT in non-small cell 557

lung cancer: biological significance and therapeutic application. J Hematol Oncol 2014;7:87 doi 558

10.1186/s13045-014-0087-z. 559

14. Kostina AS, Uspensky Vcapital Ie C, Irtyuga OB, Ignatieva EV, Freylikhman O, Gavriliuk 560

ND, et al. Notch-dependent EMT is attenuated in patients with aortic aneurysm and bicuspid aortic 561

valve. Biochim Biophys Acta 2016;1862(4):733-40 doi 10.1016/j.bbadis.2016.02.006. 562

15. Zheng Y, de la Cruz CC, Sayles LC, Alleyne-Chin C, Vaka D, Knaak TD, et al. A rare 563

population of CD24(+)ITGB4(+)Notch(hi) cells drives tumor propagation in NSCLC and requires 564

Notch3 for self-renewal. Cancer Cell 2013;24(1):59-74 doi 10.1016/j.ccr.2013.05.021. 565

16. Liu YP, Yang CJ, Huang MS, Yeh CT, Wu AT, Lee YC, et al. Cisplatin selects for multidrug-566

resistant CD133+ cells in lung adenocarcinoma by activating Notch signaling. Cancer Res 567




29

2013;73(1):406-16 doi 10.1158/0008-5472.CAN-12-1733. 568

17. Sosa Iglesias V, Theys J, Groot AJ, Barbeau LMO, Lemmens A, Yaromina A, et al. 569

Synergistic Effects of NOTCH/gamma-Secretase Inhibition and Standard of Care Treatment 570

Modalities in Non-small Cell Lung Cancer Cells. Front Oncol 2018;8:460 doi 571

10.3389/fonc.2018.00460. 572

18. Theys J, Yahyanejad S, Habets R, Span P, Dubois L, Paesmans K, et al. High NOTCH 573

activity induces radiation resistance in non small cell lung cancer. Radiother Oncol 2013;108(3):440-574

5 doi 10.1016/j.radonc.2013.06.020. 575

19. Ikezawa Y, Sakakibara-Konishi J, Mizugaki H, Oizumi S, Nishimura M. Inhibition of Notch 576

and HIF enhances the antitumor effect of radiation in Notch expressing lung cancer. Int J Clin Oncol 577

2017;22(1):59-69 doi 10.1007/s10147-016-1031-8. 578

20. Xie M, Zhang L, He C-S, Xu F, Liu J-L, Hu Z-H, et al. Activation of Notch-1 enhances 579

epithelial-mesenchymal transition in gefitinib-acquired resistant lung cancer cells. Journal of Cellular 580

Biochemistry 2012:n/a-n/a doi 10.1002/jcb.24019. 581

21. Xie M, He CS, Wei SH, Zhang L. Notch-1 contributes to epidermal growth factor receptor 582

tyrosine kinase inhibitor acquired resistance in non-small cell lung cancer in vitro and in vivo. Eur J 583

Cancer 2013;49(16):3559-72 doi 10.1016/j.ejca.2013.07.007. 584

22. Licciulli S, Avila JL, Hanlon L, Troutman S, Cesaroni M, Kota S, et al. Notch1 is required for 585

Kras-induced lung adenocarcinoma and controls tumor cell survival via p53. Cancer Res 586

2013;73(19):5974-84 doi 10.1158/0008-5472.CAN-13-1384. 587

23. Kato S, Goodman A, Walavalkar V, Barkauskas DA, Sharabi A, Kurzrock R. 588

Hyperprogressors after Immunotherapy: Analysis of Genomic Alterations Associated with 589

Accelerated Growth Rate. Clin Cancer Res 2017;23(15):4242-50 doi 10.1158/1078-0432.CCR-16-590

3133. 591




30

24. Adamowicz M, Vermezovic J, d'Adda di Fagagna F. NOTCH1 Inhibits Activation of ATM by 592

Impairing the Formation of an ATM-FOXO3a-KAT5/Tip60 Complex. Cell Rep 2016;16(8):2068-76 593

doi 10.1016/j.celrep.2016.07.038. 594

25. Vermezovic J, Adamowicz M, Santarpia L, Rustighi A, Forcato M, Lucano C, et al. Notch is 595

a direct negative regulator of the DNA-damage response. Nat Struct Mol Biol 2015;22(5):417-24 doi 596

10.1038/nsmb.3013. 597

26. Welte T, Kim IS, Tian L, Gao X, Wang H, Li J, et al. Oncogenic mTOR signalling recruits 598

myeloid-derived suppressor cells to promote tumour initiation. Nat Cell Biol 2016;18(6):632-44 doi 599

10.1038/ncb3355. 600

27. Sierra RA, Trillo-Tinoco J, Mohamed E, Yu L, Achyut BR, Arbab A, et al. Anti-Jagged 601

Immunotherapy Inhibits MDSCs and Overcomes Tumor-Induced Tolerance. Cancer Res 602

2017;77(20):5628-38 doi 10.1158/0008-5472.CAN-17-0357. 603

28. Janghorban M, Xin L, Rosen JM, Zhang XH. Notch Signaling as a Regulator of the Tumor 604

Immune Response: To Target or Not To Target? Front Immunol 2018;9:1649 doi 605

10.3389/fimmu.2018.01649. 606

29. Rizvi H, Sanchez-Vega F, La K, Chatila W, Jonsson P, Halpenny D, et al. Molecular 607

Determinants of Response to Anti-Programmed Cell Death (PD)-1 and Anti-Programmed Death-608

Ligand 1 (PD-L1) Blockade in Patients With Non-Small-Cell Lung Cancer Profiled With Targeted 609

Next-Generation Sequencing. J Clin Oncol 2018;36(7):633-41 doi 10.1200/JCO.2017.75.3384. 610

30. Fehrenbacher L, Spira A, Ballinger M, Kowanetz M, Vansteenkiste J, Mazieres J, et al. 611

Atezolizumab versus docetaxel for patients with previously treated non-small-cell lung cancer 612

(POPLAR): a multicentre, open-label, phase 2 randomised controlled trial. Lancet 613

2016;387(10030):1837-46 doi 10.1016/S0140-6736(16)00587-0. 614

31. Rittmeyer A, Barlesi F, Waterkamp D, Park K, Ciardiello F, von Pawel J, et al. Atezolizumab 615




31

versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): a phase 3, 616

open-label, multicentre randomised controlled trial. Lancet 2017;389(10066):255-65 doi 617

10.1016/S0140-6736(16)32517-X. 618

32. Gandara DR, Paul SM, Kowanetz M, Schleifman E, Zou W, Li Y, et al. Blood-based tumor 619

mutational burden as a predictor of clinical benefit in non-small-cell lung cancer patients treated with 620

atezolizumab. Nat Med 2018;24(9):1441-8 doi 10.1038/s41591-018-0134-3. 621

33. Miao D, Margolis CA, Vokes NI, Liu D, Taylor-Weiner A, Wankowicz SM, et al. Genomic 622

correlates of response to immune checkpoint blockade in microsatellite-stable solid tumors. Nat 623

Genet 2018;50(9):1271-81 doi 10.1038/s41588-018-0200-2. 624

34. Samstein RM, Lee CH, Shoushtari AN, Hellmann MD, Shen R, Janjigian YY, et al. Tumor 625

mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet 626

2019;51(2):202-6 doi 10.1038/s41588-018-0312-8. 627

35. Wang Z, Duan J, Cai S, Han M, Dong H, Zhao J, et al. Assessment of Blood Tumor 628

Mutational Burden as a Potential Biomarker for Immunotherapy in Patients With Non-Small Cell 629

Lung Cancer With Use of a Next-Generation Sequencing Cancer Gene Panel. JAMA Oncol 2019 doi 630

10.1001/jamaoncol.2018.7098. 631

36. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method 632

and server for predicting damaging missense mutations. Nat Methods 2010;7(4):248-9 doi 633

10.1038/nmeth0410-248. 634

37. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set 635

enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. 636

Proc Natl Acad Sci U S A 2005;102(43):15545-50 doi 10.1073/pnas.0506580102. 637

38. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of 638

cell subsets from tissue expression profiles. Nat Methods 2015;12(5):453-7 doi 10.1038/nmeth.3337. 639




32

39. Higgs BW, Morehouse CA, Streicher K, Brohawn PZ, Pilataxi F, Gupta A, et al. Interferon 640

Gamma Messenger RNA Signature in Tumor Biopsies Predicts Outcomes in Patients with Non-641

Small Cell Lung Carcinoma or Urothelial Cancer Treated with Durvalumab. Clin Cancer Res 642

2018;24(16):3857-66 doi 10.1158/1078-0432.CCR-17-3451. 643

40. Ayers M, Lunceford J, Nebozhyn M, Murphy E, Loboda A, Kaufman DR, et al. IFN-gamma-644

related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest 2017;127(8):2930-645

40 doi 10.1172/JCI91190. 646

41. Wang NJ, Sanborn Z, Arnett KL, Bayston LJ, Liao W, Proby CM, et al. Loss-of-function 647

mutations in Notch receptors in cutaneous and lung squamous cell carcinoma. Proc Natl Acad Sci U 648

S A 2011;108(43):17761-6 doi 10.1073/pnas.1114669108. 649

42. Li S, Ren B, Shi Y, Gao H, Wang J, Xin Y, et al. Notch1 inhibition enhances DNA damage 650

induced by cisplatin in cervical cancer. Exp Cell Res 2019;376(1):27-38 doi 651

10.1016/j.yexcr.2019.01.014. 652

43. Hirsch FR, McElhinny A, Stanforth D, Ranger-Moore J, Jansson M, Kulangara K, et al. PD-653

L1 Immunohistochemistry Assays for Lung Cancer: Results from Phase 1 of the Blueprint PD-L1 654

IHC Assay Comparison Project. J Thorac Oncol 2017;12(2):208-22 doi 10.1016/j.jtho.2016.11.2228. 655

44. Tsao MS, Kerr KM, Kockx M, Beasley MB, Borczuk AC, Botling J, et al. PD-L1 656

Immunohistochemistry Comparability Study in Real-Life Clinical Samples: Results of Blueprint 657

Phase 2 Project. J Thorac Oncol 2018;13(9):1302-11 doi 10.1016/j.jtho.2018.05.013. 658

659

660




33

Figure legend 661

Fig 1. NOTCH1/2/3 mutations are associated with better response to ICIs in NSCLC. A. 662

Stacked plots show mutational burden (histogram, top), mutations in TP53, LRP1B, KRAS, AR, 663

RBM10, CDKN2A, EGFR, FAT1, IRS2, NOTCH2, NOTCH1, NOTCH3, BRCA1, FAM135B, RB1 664

(tile plot, middle), their mutational rates in patients having achieved objective response or 665

progressive disease (histogram, right), and mutational marks (bottom). B. Scatter diagram displays 666

the mutational rate in patients having achieved objective response or progressive disease. NOTCH 667

members are highlighted in orange. C. Best response of the patients with available radiographic 668

assessment. NOTCH1/2/3 mutations are emphasized in orange, pink and purple, respectively. 669

Individuals with mutations in 2 NOTCH members are marked with the intermingled color. D-E. ORR 670

(D) and Kaplan-Meier curves of PFS (D) and OS (E) in the EGFR/ALKWT

patients with or without 671

NOTCH mutations. Ticks represent the censored data. 672

Fig. 2. NOTCH1/2/3 mutations are associated with higher ORR, longer PFS and OS with ICI in 673

EGFR/ALKWT

NSCLC. A-C. ORR and Kaplan-Meier curves of PFS in the EGFR/ALKWT

patients 674

with or without NOTCH mutations of the Van Allen (A), POPLAR/OAK (B), and Rizvi cohorts (C). 675

D. Pooled estimates of ORR (left panel) and PFS (right panel). E-G. Kaplan-Meier curves of OS in 676

the EGFR/ALKWT

patients with or without NOTCH mutations of the Van Allen (E), POPLAR/OAK 677

(F), and MSKCC cohorts (G). H. Pooled estimate of OS. In the Kaplan-Meier curves, ticks represent 678

the censored data. In the images of pooled estimates, the squares in light orange represent study-679

specific HRs, and the squares in orange and dashed vertical lines indicate the pooled HRs. Horizontal 680

lines indicate the 95% CIs. The P values for heterogeneity and the values of I2 are from the meta-681

analyses of study-specific HRs. 682

Fig. 3. Deleterious NOTCH mutation on tumour mutational burden and DNA damage response 683

in EGFR/ALKWT

NSCLC. A. Proportion of NOTCH mutations in the POPLAR/OAK and MSKCC 684




34

cohorts classified by different NOTCH members and mutational forms. B. Flow diagram of the 685

identification of deleterious and non-deleterious NOTCH mutations by mutational forms and 686

PolyPhen-2 system. C. Percentages of patients with deleterious NOTCH mutations in subgroups 687

classified by PD-L1 expression (negative, TC0 and IC0; intermediate, TC1/2 and/or IC1/2; high, 688

TC3 or IC3) and bTMB (bTMB-H, ≥16; bTMB-I, ≥8 and <16; bTMB-L, <8). D. PD-L1 expression 689

in EGFR/ALKWT

patients classified by NOTCH mutations (deleterious NOTCH mutation, non-690

deleterious NOTCH mutation, and no NOTCH mutation). E. bTMB in EGFR/ALKWT

patients from 691

POPLAR and OAK trials classified by NOTCH mutations (deleterious NOTCH mutation, non-692

deleterious NOTCH mutation, and no NOTCH mutation). F. TMB in EGFR/ALKWT

patients from 693

TCGA database classified by NOTCH mutations (deleterious NOTCH mutation, non-deleterious 694

NOTCH mutation, and no NOTCH mutation). G. GSEA of DNA repair-related gene signature, in 695

comparisons between NSCLC samples with deleterious NOTCH mutation, non-deleterious NOTCH 696

mutation, and no NOTCH mutation. H-J. Comparisons of signatures related to multiple pathways of 697

DNA damage response between del-NOTCHmut

and NOTCHWT

(H), del-NOTCHmut

and non-del-698

NOTCHmut

(I), and non-del-NOTCHmut

and NOTCHWT

(J). ***P<0.001. 699

Fig. 4. Deleterious NOTCH mutation on expression of immune genes in EGFR/ALKWT

NSCLC. 700

A. GSEA of gene signatures related to immune activation (upper panel) and interleukin pathways 701

(lower panel) in comparisons between del-NOTCHmut

, non-del-NOTCHmut

, and NOTCHWT

groups of 702

EGFR/ALKWT

NSCLC samples. The yellow-blue scale represents normalized enrichment score. The 703

red scale represents p value. B. Heatmap (left panel) and box plot (right panel) of the expression of 704

immune-related genes in comparison of del-NOTCHmut

and NOTCHWT

groups of EGFR/ALKWT

705

NSCLC samples. For the tags representing groups, blue represents the del-NOTCHmut

group and 706

yellow represents the NOTCHWT

group. For the colour indicating mRNA level, red represents higher 707

expression and blue represents lower expression. 708




35

Fig. 5. Deleterious NOTCH mutation as predictive biomarker of ICI treatment in 709

EGFR/ALKWT

NSCLC. A-B. ORR (A) and Kaplan-Meier curves of PFS (A) and OS (B) in the 710

EGFR/ALKWT

patients classified by NOTCH mutations (deleterious NOTCH mutation, orange; non-711

deleterious NOTCH mutation, light blue; no NOTCH mutation, grey) and treatment choice 712

(atezolizumab, solid line; docetaxel, dashed line). ICI benefit comparing atezolizumab and docetaxel 713

is shown in the upper right corner. Circles reflect the HR value and horizontal lines indicate the 95% 714

CIs. The HR values of intention-to-treat population are highlighted in black. 715

716

717




Table 1. Univariable and multivariable analysis of PFS and OS in EGFR/ALKWT

NSCLC receiving atezolizumab in POPLAR/OAK cohort

Atezolizumab group Progression-free survival Overall survival

*PD-L1 IHC performed in OAK cohort Univariable analysis Multivariable analysis Univariable analysis Multivariable analysis

Parameter HR (95%CI) P value HR (95%CI) P value HR (95%CI) P value HR (95%CI) P value

Age (≥65 vs. <65) 0.97 (0.78-1.21) 0.811 0.99 (0.77-1.27) 0.925

Sex (male vs. female) 0.80 (0.64-1.01) 0.065 0.84 (0.66-1.06) 0.135 0.79 (0.60-1.03) 0.080 0.88 (0.66-1.17) 0.383

Race (white vs. non-white) 0.89 (0.70-1.14) 0.351 1.37 (1.02-1.84) 0.039 1.44 (1.07-1.95) 0.018

Histology (LUSC vs. non-LUSC) 1.13 (0.89-1.42) 0.311 1.41 (1.08-1.83) 0.010 1.49 (1.13-1.96) 0.004

ECOG (1 vs. 0) 1.36 (1.08-1.72) 0.010 1.45 (1.14-1.84) 0.003 1.81 (1.38-2.39) <0.001 1.90 (1.44-2.52) <0.001

Smoke (ever vs. never) 0.94 (0.68-1.29) 0.697 1.38 (0.92-2.06) 0.116

Lines (3 vs. 2) 0.81 (0.64-1.03) 0.089 0.70 (0.55-0.90) 0.005 0.95 (0.72-1.25) 0.717

Metastatic sites (≥3 vs. <3) 1.38 (1.10-1.72) 0.005 1.28 (1.02-1.61) 0.033 1.46 (1.12-1.89) 0.005 1.32 (1.01-1.72) 0.043

bTMB (≥16 vs. <16) 0.83 (0.65-1.07) 0.151 1.00 (0.76-1.33) 0.987

PD-L1 (TC1/2/3 or IC1/2/3 vs. TC0 and IC0) 0.93 (0.72-1.19) 0.551 0.94 (0.70-1.26) 0.676

PD-L1 (TC3 or IC3 vs. TC0/1/2 and IC0/1/2) 0.82 (0.59-1.13) 0.217 0.66 (0.47-0.99) 0.043

STK11 (mut vs. WT) 1.53 (1.06-2.22) 0.024 1.51 (1.03-2.19) 0.033 1.58 (1.06-2.34) 0.024 1.54 (1.02-2.32) 0.038

KEAP1 (mut vs. WT) 1.84 (1.37-2.47) <0.001 1.88 (1.39-2.53) <0.001 1.93 (1.41-2.66) <0.001 1.92 (1.39-2.67) <0.001

TP53 (mut vs. WT) 1.11 (0.90-1.38) 0.337 1.40 (1.09-1.80) 0.008 1.20 (0.92-1.57) 0.171

KRAS (mut vs. WT) 1.17 (0.82-1.68) 0.383 1.24 (0.83-1.87) 0.295

TP53 & KRAS (co-mut vs. mono-mut + WT) 0.75 (0.45-1.26) 0.273 0.78 (0.41-1.46) 0.430

del-NOTCHmut

(del-NOTCHmut

vs. the rest) 0.62 (0.41-0.92) 0.018 0.57 (0.38-0.86) 0.006 0.79 (0.51-1.24) 0.305 0.63 (0.40-0.99) 0.045

Abbreviations: bTMB, blood tumor mutational burden; CI, confidence interval; ECOG, Eastern Cooperative Oncology Group; HR, hazard ratio; IHC,

immunohistochemistry; KEAP1, Kelch-like ECH-associated protein 1; LUSC, lung squamous carcinoma; NSCLC, non-small cell lung cancer; PD-L1,

programmed cell death ligand 1; PFS, progression-free survival; STK11, serine/threonine kinase 11; TP53, tumor protein p53; WT, wildtype.




Published OnlineFirst April 2, 2020.Clin Cancer Res Kai Zhang, Xiaohua Hong, Zhengbo Song, et al. predictor to efficacious immunotherapy in NSCLC

mutation as novelNOTCHIdentification of deleterious

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