Patient-Derived Xenografts for Prognostication and Personalized Treatment for Head and Neck Squamous Cell Carcinoma

Resource

Patient-Derived Xenografts for Prognostication and Personalized Treatment for Head and Neck Squamous Cell Carcinoma Graphical Abstract

Authors Christina Karamboulas, Jeffrey P. Bruce, Andrew J. Hope, ..., Scott V. Bratman, Wei Xu, Laurie Ailles

Correspondence [email protected]

In Brief Karamboulas et al. show that the ability of head and neck squamous cell carcinoma samples to form patient-derived xenografts in mice is correlated with patient clinical outcomes and provide proof of principle for the potential use of genomically characterized xenograft models to identify effective targeted therapies with accompanying predictive biomarkers.

Highlights d

A large collection of HNSCC patient-derived xenograft models was established

d

‘‘Rapid’’ engraftment of patient samples is highly prognostic

d

Genomic deregulation of the G1/S checkpoint pathway correlates with engraftment

d

CCND1 and CDKN2A genomic alterations are predictive of response to abemaciclib

Karamboulas et al., 2018, Cell Reports 25, 1318–1331 October 30, 2018 ª 2018 The Author(s). https://doi.org/10.1016/j.celrep.2018.10.004

Cell Reports

Resource Patient-Derived Xenografts for Prognostication and Personalized Treatment for Head and Neck Squamous Cell Carcinoma Christina Karamboulas,1 Jeffrey P. Bruce,1 Andrew J. Hope,2,3 Jalna Meens,1 Shao Hui Huang,2,3 Natalie Erdmann,1 Elzbieta Hyatt,4 Keira Pereira,1 David P. Goldstein,5,6 Ilan Weinreb,7 Jie Su,8 Brian O’Sullivan,2,3 Rodger Tiedemann,1 Fei-Fei Liu,2,3,9 Trevor J. Pugh,1,9 Scott V. Bratman,2,3,9 Wei Xu,1,8 and Laurie Ailles1,9,10,* 1Princess

Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada Medicine Program, Princess Margaret Cancer Centre, Toronto, ON M5G 2C1, Canada 3Department of Radiation Oncology, University of Toronto, Toronto, ON M5G 2M9, Canada 4Hospital for Sick Children, Program in Genetics and Genome Biology, Toronto, ON M5G 0A4, Canada 5Department of Otolaryngology–Head and Neck Surgery, Princess Margaret Cancer Centre, Toronto, ON M5G 2C1, Canada 6University of Toronto, Toronto, ON M5G 2M9, Canada 7Department of Pathology, University Health Network, Toronto, ON M5G 2C4, Canada 8Division of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada 9Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada 10Lead Contact *Correspondence: [email protected] https://doi.org/10.1016/j.celrep.2018.10.004 2Radiation

SUMMARY

Overall survival remains very poor for patients diagnosed as having head and neck squamous cell carcinoma (HNSCC). Identification of additional biomarkers and novel therapeutic strategies are important for improving patient outcomes. Patient-derived xenografts (PDXs), generated by implanting fresh tumor tissue directly from patients into immunodeficient mice, recapitulate many of the features of their corresponding clinical cancers, including histopathological and molecular profiles. Using a large collection of PDX models of HNSCC, we demonstrate that rapid engraftment into immunocompromised mice is highly prognostic and show that genomic deregulation of the G1/S checkpoint pathway correlates with engraftment. Furthermore, CCND1 and CDKN2A genomic alterations are predictive of response to the CDK4and CDK6 inhibitor abemaciclib. Overall, our study supports the pursuit of CDK4 and CDK6 inhibitors as a therapeutic strategy for a substantial proportion of HNSCC patients and demonstrates the potential of using PDX models to identify targeted therapies that will benefit patients who have the poorest outcomes. INTRODUCTION The overall outcomes for human papillomavirus-negative (HPV
) head and neck squamous cell carcinoma (HNSCC) patients remain poor, with 5-year overall survival (OS) rates of 50% (Cooper et al., 2004; Tiwana et al., 2014). The majority of these patients are treated with surgery followed by observation or with adjuvant therapies such as radiation therapy (RT) or radia-

tion plus concurrent chemotherapy, chemoradiation therapy (CRT). The use of post-operative RT or CRT is based on clinicopathologic features such as nodal involvement, perineural invasion (PNI), lymphovascular invasion (LVI), positive margins, or nodal extracapsular extension (ECE), all of which suggest that patients are at higher risk of local or regional recurrence (Bernier et al., 2004; Cooper et al., 2004). While these features are helpful in identifying patients in need of more aggressive therapy, it is clear from the rate of locoregional or distant failures that more accurate methods of risk stratification could greatly improve outcomes for HPV
HNSCC patients. The implementation of risk stratification, however, requires identifying biomarkers for patients who would benefit from adjuvant RT or CRT and evaluating their effectiveness. The several months that typically transpire between surgery and recovery from adjuvant therapy afford a window of opportunity for the interrogation of candidate targeted treatments and predictive biomarkers before relapse. Despite decades of research, no validated molecular biomarkers have been clinically implemented for the personalized treatment of HNSCC. In addition to biomarkers for better risk stratification, there is also a need for novel therapeutic strategies leading to improved outcomes. Genomic aberrations that represent therapeutic targets and thus predict responses to therapies have been identified in many cancer types (Santos et al., 2017). In HNSCC, cetuximab, an anti-epidermal growth factor receptor (EGFR) antibody therapy, has provided some benefit in the setting of platinum-refractory metastatic and/or recurrent HNSCC, as well as being an addition to radiotherapy for locally or regionally advanced disease (Bonner et al., 2006, 2010). However, the activity of EGFR-targeted therapy is modest, predictive biomarkers have not been established, and mortality rates remain high (Juergens et al., 2017). More recently, immune checkpoint inhibitors have shown some promise (Ferris et al., 2016; Seiwert et al., 2016), but only a subset of patients respond, with objective response rates ranging from 11% to 18% (De Meulenaere et al., 2017).

1318 Cell Reports 25, 1318–1331, October 30, 2018 ª 2018 The Author(s). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Thus, additional, more effective targeted therapies are urgently needed for HNSCC patients. Recently, patient-derived xenografts (PDXs) have been shown to faithfully recapitulate human tumor biology and predict drug responses, supporting their relevance as preclinical models for new drug development (Gao et al., 2015; Townsend et al., 2016). Upon implantation of surgically resected specimens of human cancers under the skin of immunocompromised mice, not every sample successfully forms a graft, and it has been shown for several cancers, including HNSCC, that PDX formation is an indicator of worse clinical outcomes (John et al., 2011; Joshua et al., 2012; McAuliffe et al., 2015). In the present study, analysis of PDX formation and corresponding clinical data from a large cohort of HPV
HNSCC indicates that this feature is indeed highly correlated with outcomes and, furthermore, that rapid PDX formation represents a risk stratification biomarker that can be used in a clinically relevant time frame. Engraftment ability was also significantly correlated with amplification of a region of chromosome 11q that harbors the CCND1 gene, and treatment of PDX models with the CDK4 and CDK6 inhibitor abemaciclib indicated that CCND1 amplification and/or CDKN2A loss via mutation or deletion may be predictive of drug response. Thus, we provide proof of principle for the utility of PDX models for prognostication, preclinical drug evaluation, and identification of predictive biomarkers.

RESULTS PDX Formation Is Predictive of Increased Risk of Disease Recurrence Between 2008 and 2015, 243 surgically resected HPV
HNSCC specimens were implanted subcutaneously into NOD/SCID/ IL2Rg
/
(NSG) mice, and 161 formed xenografts within 6 months (Tables 1, S1, S2, and S3). Patients demonstrating PDX formation had a higher frequency of advanced nodal stage (p < 0.01) and close margins (p = 0.04), and they were more likely to receive adjuvant therapies (p = 0.02). There was no significant difference in primary tumor stage (T) or overall clinical stage between engrafters and non-engrafters. Histological comparison of PDX and corresponding primary tumors revealed a high degree of similarity (Figure 1A). PDX formation correlated with a significantly lower 5-year OS of 53% in engrafters versus 80% in non-engrafters (Figure 1B; p < 0.001). Engrafting patients also had inferior 5-year diseasefree survival (DFS; 47% versus 65%; p = 0.01), as well as a higher risk of distant metastases (5-year distant metastasis rate of 22% versus 6%; p = 0.002). The correlation between PDX formation and locoregional control (LRC) did not reach statistical significance (p = 0.07). The overall survival difference in PDX-forming patients was observed for both patients treated with surgery alone and those receiving adjuvant RT or CRT (Figure 1C). For patients treated with surgery alone, the 5-year OS was 64% versus 88% for engrafting versus non-engrafting patients, respectively (p = 0.011). Similarly, for patients treated with surgery and adjuvant radiation (with or without chemotherapy), the 5-year OS was 52% versus 72% in the respective groups (p = 0.023).

OS models were generated using parameters that were significant on univariable analysis. When evaluated in a multivariable model including PDX formation, nodal status (N0 versus N+), and treatment (surgery alone versus surgery followed by postoperative RT), only PDX formation (hazard ratio [HR] = 2.25, p < 0.01) and nodal status (HR = 2.78, p < 0.01) remained significant (Table 2). Rapid Engrafters Have Extremely Poor Survival Outcomes We noticed that some patients’ tumors grew extremely rapidly in mice, with xenografts becoming palpable within only a few weeks of implantation, whereas other tumors grew slowly, taking up to 6 months to generate a palpable graft. This suggested the possibility that rapidly growing tumors may represent a particularly aggressive cohort, and that for some patients, knowledge of the engraftment ability of their tumors could be obtained before the initiation of adjuvant therapies, which typically occurs approximately 8 weeks after surgery. For a subset of patient samples obtained between 2013 and 2015, the time between tumor implantation and first detection of a PDX was carefully monitored. We then performed a comparison between samples that demonstrated PDX formation within 8 weeks of implantation versus samples that took >8 weeks during this period and patients who did not engraft at all (during the entire 2008–2015 period; Figure 1D). Slow and non-engrafters were pooled together, because these two cohorts would be indistinguishable at the 8-week time point (see Figure S1A for an analysis of rapid versus slow versus non-engrafters). Patients with ‘‘rapid engraftment’’ demonstrated significantly inferior rates of 1-year OS (59% versus 92%; HR = 3.0, p = 0.011) as well as DFS (60% versus 84%; HR = 2.3, p = 0.018) relative to slow and non-engrafters combined. Rapid engrafters also experienced significantly higher rates of locoregional recurrence (70% versus 89%, HR = 2.44, p = 0.024), an association that was not observed when all engrafters were examined (p = 0.07; Figure 1B). There were, however, no differences in the rate of distant metastases between these two groups. The analysis above used all of the samples collected between 2008 and 2015 for the non-engrafting cohort, and samples collected only from 2013 to 2015 for the rapid and slow engrafters, because this was the period during which we were actively monitoring time to engraftment. To address possible concerns relating to the different time frames during which samples were collected, we conducted a sensitivity analysis using only the samples that were collected between 2013 and 2015, including non-engrafters (Figure S1B). From this analysis, the HRs and 95% confidence intervals (CIs) for OS, DFS, and LRC are 2.39 (0.89–6.44), 1.84 (0.83–4.07), and 1.94 (0.82–4.63), respectively. Although not statistically significant, these association results are consistent with the original analyses (Figure 1D), indicating that the use of non-engrafters from 2008 to 2015 did not confound the results. More important, 8 weeks is a time frame in which clinical decisions relating to adjuvant therapy could be made (Rosenthal et al., 2002), with rapid engrafters being treated as ‘‘high risk’’ (Figure S2). Of the 22 evaluable rapid engrafters in our cohort, 7 were treated with surgery alone, 11 with surgery followed by

Cell Reports 25, 1318–1331, October 30, 2018 1319

Table 1. Patient Demographics and Clinical Characteristics All (n = 243)

Non-engrafters (n = 82)

Engrafters (n = 161)

p

Mean age at diagnosis, y

63.5

64.5

63.1

0.42

Female, n (%)

99 (41)

31 (38)

68 (42)

0.58

Smoking >20 pky, n (%)

109 (45)

40 (49)

69 (43)

0.41

Current smoker, n (%)

91 (39)

34 (43)

57 (37)

0.43

T1

44 (18)

17 (21)

27 (17)

T2

109 (45)

36 (44)

73 (45)

T3

43 (18)

14 (17)

29 (18)

T4a

45 (19)

15 (18)

30 (19)

T4b

2 (1)

–

2 (1)

N0

110 (45)

48 (59)

62 (39)

N1

39 (16)

8 (10)

31 (19)

N2a

3 (1)

1 (1)

2 (1)

N2b

65 (27)

21 (26)

44 (27)

N2c

25 (10)

3 (4)

22 (14)

N3

1 (0.4)

1 (1)

–

I/II

81(33)

32 (39)

49 (30)

III/IV

162 (67)

50 (61)

112 (70)

103 (43)

42 (51)

61 (38)

T-stage, n (%)

0.92

N-stage, n (%)

<0.01

Stage Group, n (%)

0.2

Treatment, n (%) Sx

0.02

Sx + PORT

90 (37)

29 (35)

61 (38)

Sx + PORT (AF)

2 (1)

1 (1)

1 (1)

Sx + PORT + CHT

34 (14)

6 (7)

28 (17)

Other

14

4

10

Widely clear, >5 mm

70 (29)

31 (38)

39 (24)

–

Close, <5mm

140 (58)

38 (46)

102 (63)

–

Positive

33 (14)

13 (16)

20 (12)

–

Lymphovascular invasion, n (%)

14 (6)

2 (2)

12 (7)

0.15

Perineural invasion, n (%)

142 (58)

43 (52)

99 (61)

0.22

No

78 (57)

25 (69)

53 (52)

Yes

60 (43)

11 (31)

49 (48)

Missing

105

46

59

Primary Site Margin status, n (%)

0.04

Extracapsular extension, n (%)

0.08

All patients are HPV
. AF, altered fractionation; CHT, systemic chemotherapy with cetuximab or cisplatin; Other, palliative care, post-op treatment elsewhere or missing; pky, pack-years; PORT, post-operative radiation therapy; Sx, surgery.

RT, and only 4 with surgery followed by CRT (Figure 1E). One patient treated with surgery alone had stage I disease with no pathological indicators of high risk, yet it recurred within 4 months and the patient died within 8 months of surgery. Of the patients receiving RT, >50% recurred, suggesting that these patients could have benefited from an escalation of therapy to CRT. The analysis above was completed at a point at which there was a minimum of 1 year of clinical follow-up data for the cohort of 243 patients obtained between 2008 and 2015. In the following 1.5 years, engraftment data were collected for additional patients, and clinical data for the entire cohort were up-

1320 Cell Reports 25, 1318–1331, October 30, 2018

dated. Survival analysis of the expanded cohort and updated clinical data are shown in Figure S1C, wherein we find that, with this larger number of patients, the differences between rapid, slow, and non-engrafters are maintained and significance is increased. Molecular Profiling of Engrafting versus Non-engrafting Patient Samples While PDX formation itself demonstrated an independent association with patient outcomes, we sought to determine whether successful engraftment was also associated with any underlying

34494

61773

68614

Overall Survival

Disease-Free Survival

35852

p=0.0097

Time in Years

Locoregional Control

Probability

Probability

Overall Survival

Disease-Free Survival

p=0.011

HR=2.35 (1.14, 4.84) p=0.018

Time in Years

E

Time in Years

Recurrences

Deaths due to cancer

Surgery (n=7)

1/7**

1/7**

Surgery+RT (n=11)

7/11

5/11

Surgery+CRT (n=4)

2/4

2/4

Treatment

Surgery + RT or CRT p=0.023 Time in Years

Locoregional Control

Probability

Time in Years

Survival Probability

Survival Probability

p=0.065

Time in Years

HR=3.00 (1.23, 7.34)

p=0.011

Overall Survival Surgery + RT or CRT

p=0.002

D

Surgery alone

Time in Years

Time in Years

Distant Metastasis

Survival Probability

p<0.001

Overall Survival Surgery Alone

C

Survival Probability

Survival Probability

B

Survival Probability

PDX

Patient Tumor

A

HR=2.44 (1.10, 5.52) p=0.024 Time in Years

(legend on next page)

Cell Reports 25, 1318–1331, October 30, 2018 1321

Table 2. Multivariable Regression Model on Overall Survival Covariate

HR (95% CI)

Engraftment

p 0.0099

No

reference

Yes

2.25 (1.21–4.15)

Nodal Status

<0.001

N0

reference

N+

2.78 (1.54–5.02)

RT Intent

0.98

Radiation

reference

Sx only

1.01 (0.59–1.73)

CI, confidence interval; HR, hazard ratio; RT, radiation therapy; Sx, surgery.

DNA mutations or copy-number alterations (CNAs). A total of 112 patient samples (64 engrafters and 48 non-engrafters; Table S4) were analyzed using a custom hybrid capture targeted sequencing panel of 112 genes (Table S5) and 10,000 SNPs. This panel identified both small mutations (Table S6) and CNAs (Table S7) in a cost-effective manner. Copy-number analysis yielded overall copy number profiles in our samples that were qualitatively similar to those reported for the Cancer Genome Atlas (TCGA) HNSCC patient cohort using SNP6 arrays, with many of the same regions identified to be significantly altered in both studies (The Cancer Genome Atlas Network, 2015) (Figure S3). The overall frequency of genetic alterations in our patient cohort was also consistent with previous TCGA findings (The Cancer Genome Atlas Network, 2015) (Figure 2A). No statistically significant associations were observed between any single gene mutation and engraftment. Others have reported survival correlations with loss of chromosome 3p and gains of regions of chromosome 11q (Gross et al., 2014; van Kempen et al., 2015; Vincent-Chong et al., 2017). Interrogation of our data for arm-level CNAs in these two chromosomes showed that loss of chromosome 3p was significantly associated with engraftment (p = 0.038, Wilcoxon rank sum test; Figure 2B). In addition, while whole-arm-level gains of chromosome 11q did not correlate with engraftment, copy number gains at the locus containing CCND1, FADD, and FGF3 were significantly correlated with engraftment (p = 0.036, Fisher’s exact test; Figure 2C). As described above, rapid engraftment was particularly associated with poor outcomes in HNSCC patients. Only 23 patients with time-to-engraftment data were included in the cohort of the 112 samples sequenced, 12 of whom were rapid engrafters. Of

the 12 rapid engrafters, 10 harbored a CDKN2A mutation or deletion, a CCND1 amplification, or both, whereas only 2 of 11 slow engrafters bore these alterations (p = 0.0033; Figure 2D), suggesting that deregulation of the G1/S cell-cycle checkpoint pathway promotes rapid engraftment. CCND1 Amplifications and CDKN2A Mutations Identified in Patient Samples Are Retained in PDX Models CCND1 amplifications and CDKN2A mutations were identified in patient tumor specimens. To verify that PDX models derived from these patient samples recapitulated the copy number alterations identified by sequencing, fluorescence in situ hybridization (FISH) for CCND1 was performed on tumor cells isolated from 8 HPV
PDX models: 5 with CCND1 amplification and 3 without. Four of the 5 models with CCND1 amplifications in the primary samples also had CCND1 amplifications that were detected by FISH in the matched PDXs. For the fifth model (73412) that was annotated to carry 5 copies of CCND1 in the primary tumor, CCND1 amplification was found in a subset of cells by FISH, suggesting the possibility that the CCND1 amplification was subclonal in this PDX (Figures 3 and S4A). Among another 3 patient samples annotated as lacking CCND1 amplifications, 1 of the matched PDX models (68614) appeared normal by FISH (2 copies of CCND1; Figure S4B). The remaining 2 models were aneuploid for chromosome 11. Patient 61773 was annotated as having 4 copies of chromosome 11 by both sequencing and FISH (Figure 3B). Visualization across the whole genome indicated widespread CNAs in this sample, with an average ploidy of 3.6 (Figure S4C), demonstrating concordance between copy-number analysis and FISH. The CCND1 locus was not found to be amplified by sequencing in patient 35852, and focal amplifications were also not seen by FISH. However, FISH analysis indicated that the majority of cells contained 5 copies of chromosome 11 (Figures 3B and S4A). Cells from this model were then analyzed by FISH using centromere probes for 3 additional chromosomes: 8, 17, and 19. Results indicated that PDX model 35852 was very heterogeneous, with aneuploidy occurring in all 4 chromosomes (Figure S4D). The majority of cells were tetra- or pentaploid for chromosomes 11 and 8 and were mostly triploid for chromosomes 17 and 19. Finally, to verify CDKN2A mutations, PCR amplification and Sanger sequencing of the CDKN2A locus was performed on 5 models derived from patients with CDKN2A mutations. All of the mutations identified in the primary patient samples were corroborated in the PDX models (Figure 4). In all of the cases,

Figure 1. PDX Formation and Rapid Engraftment Are Associated with Poor Clinical Outcomes (A) H&E staining of tissue sections from patient tumors and their matched PDX models indicates conservation of patient tumor histology in xenografts. Scale bars, 200 mm. (B) Kaplan-Meier estimates of the probability of OS, DFS, distant metastasis, and LRC for 243 patients segregated based on PDX formation (red, n = 161) versus no PDX formation (black, n = 82). p values determined by log-rank test. (C) Kaplan-Meier estimates of the probability of OS for 103 patients treated with surgery alone (61 engrafters versus 42 non-engrafters; top) or 138 patients treated with surgery plus post-operative RT or CRT (98 engrafters versus 40 non-engrafters; bottom). p values determined by log-rank test. (D) Kaplan-Meier estimates of the probability of OS, DFS, and LRC for patients with rapidly engrafting tumors (red, n = 24) versus slowly engrafting or nonengrafting tumors (black, n = 115). p values determined by log-rank test. HR, hazard ratio (confidence interval). (E) Table of treatments received and outcomes for rapidly engrafting patients. Two of 24 rapid engrafters are not included because they had no information regarding treatment. RT, radiation therapy; CRT, chemoradiation therapy. See also Tables S1, S2, and S3 and Figures S1 and S2.

1322 Cell Reports 25, 1318–1331, October 30, 2018

A

Figure 2. Molecular Profiling of Engrafting versus Non-engrafting Patient Samples

B

(A) The frequency of alterations, including gains, losses, and mutations, are shown in engrafters versus nonengrafters for the top 100 most frequently altered genes. (B) Losses of chromosome 3p are more frequent in engrafters versus non-engrafters (p = 0.038, Wilcoxon rank sum test). This is confirmed by a significant enrichment for samples with EPHA3 copy number loss in engrafting samples (p = 0.012, Fisher’s exact test). (C) Arm-level gains in chromosome 11q are not significantly different in engrafting versus non-engrafting samples (p = 0.415, Wilcoxon rank sum test). However, more focal amplifications that include the CCND1, FADD, and FGF3 genes are significantly enriched in the engrafting cohort (p = 0.034, Fisher’s exact test). (D) Among the 23 patient tumors that were sequenced for which time-to-engraftment data were available, alterations leading to G1/S checkpoint activation (CCND1 amplification and/ or CDKN2A deletion or mutation) were significantly enriched in rapid engrafters versus slow engrafters (p = 0.0033, Fisher’s exact test). See also Tables S4, S5, S6, and S7 and Figure S3.

C

D

the alteration appears homozygous in the PDX due to the loss of heterozygosity at the CDKN2A locus that occurred in the patient (Figure S5). Preclinical Assessment of CDK4 and CDK6 Inhibition Using Abemaciclib in PDX Models CCND1 amplification and CDKN2A mutation or deletion are common in HPV
HNSCC and lead to the activation of the G1/S checkpoint pathway, suggesting targeted therapy via the inhibition of cyclin-dependent kinases (CDKs). CDKs are serine/threonine kinases that, upon phosphorylation, bind to partner cyclins (e.g., CCND1) and regulate the G1/S phase transition. P16Ink4a (encoded by the CDKN2A gene) is a negative regulator of this axis. Tumors with CCND1 amplification and/or loss of CDKN2A should therefore show an increased dependence on CDK4 and/or CDK6 (Goel and Zhao, 2016). We therefore sought to use our PDX resource to determine whether alterations in these genes would confer sensitivity to the CDK4 and CDK6 inhibitor abemaciclib. Ten PDX models (Table S8), including those that were verified for the fidelity of the CCND1 and CDKN2A alterations described above, were used to establish cohorts of 10 mice per patient. Thus, 6 models with

CCND1 amplifications and/or CDKN2A mutations and 4 models without alterations in these genes (or CDKN2B or RB) were treated. In addition, 2 HPV+ PDX models that we had established in the lab were included; these are predicted to be insensitive to abemaciclib treatment due to the binding of the HPV E7 protein to RB, thereby promoting cell-cycle progression downstream of CDK4 and CDK6 and rendering cells insensitive to CDK4 and CDK6 inhibition, and thus acting as a control for off-target activity of the inhibitor. The HPV status of these 2 models was verified by HPV-E6 PCR (Figure S6). All of the patient samples and mice were short tandem repeat (STR) profiled to verify identity. Five mice per model were treated with abemaciclib and 5 with vehicle control. Five of 6 PDX models derived from patient samples containing CCND1 and/ or CDKN2A alterations demonstrated a significant growth delay in response to abemaciclib treatment (p < 0.0001, using mixed model regression; Figure 5). The single non-responding PDX 73412 was found by FISH analysis to have subclonal CCND1 amplification. Among the 4 HPV
samples that did not harbor CCND1 amplification or CDKN2A mutations, only 1 responded (p < 0.0001). A western blot for phospho-RB (Ser807/811) confirmed a distinct reduction in RB phosphorylation upon abemaciclib treatment in responding PDX models (Figure 5C). DISCUSSION Previous studies have reported the molecular characterization of PDX models from HNSCC patients and have shown that the

Cell Reports 25, 1318–1331, October 30, 2018 1323

A

Chromosome 11 Copy Number Profile in Patient Sample 34994

B

PDX FISH Results 2R-2G+ (40%)

2R-4G+ (16%)

73191

5R-5G+ (43%)

64390

4R-3G+ (22%)

4R-4G+ (18%)

64842

4R-3G+ (22%)

4R-4G+ (18%)

73412

2R-2G (13%)

2R-3G (34%)

61773

4R-4G (76%)

35852

5R-5G (54%)

4R-7G+ (4.4%)

Figure 3. CCND1 Amplifications Identified in Patient Samples Are Retained in PDX Models (A) FISH was carried out on 5 PDX models derived from patients with CCND1 amplifications using a CCND1 probe and a chromosome 11 probe. Chromosome 11 copy-number profiles culled from the sequencing data are shown on the left (red arrows: CCND1 loci) and representative FISH results from matched PDXs are shown on the right (red, chromosome 11 centromere probe; green, CCND1 probe). CCND1 amplifications were observed in all 5 PDX models; however, for PDX 73412, the CCND1 amplification appeared to be subclonal. Scale bars, 5 mm. (B) FISH was carried out as above on 2 PDX models derived from patients lacking CCND1 amplifications according to sequencing. These were found to be tetraploid or pentaploid for chromosome 11. Scale bars, 5 mm. See also Figure S4.

1324 Cell Reports 25, 1318–1331, October 30, 2018

Patient

Mutation found in patient

PDX model

Wild-type sample

Deletion CCGCG

g.chr9:2197115321971157delCCGCG 34994

C>T

73191

g.chr9:21971108C>T

C>G

g.chr9:21971208C>G 64390

G>A

61773

g.chr9:21971120G>A

G>A

73412

g.chr9:21971029C>T (Used forward primer; Opposite strand G>A)

Figure 4. CDKN2A Mutations Identified in Patient Samples Are Retained in PDX Models For patients who were annotated as harboring CDKN2A mutations, DNA was isolated from the matched PDX models and used for PCR amplification of the CDKN2A region containing the mutations. PCR products were then subjected to Sanger sequencing. For each model, the mutation identified in the patient through targeted sequencing was found in the matching PDX model. A wild-type sample is shown next to each for comparison. See also Figure S5.

Cell Reports 25, 1318–1331, October 30, 2018 1325

A

CCND1 and/or CDKN2A altered 34994

H PV+ Mo del s

73191

1000

37760 (HPV+)

64482 (HPV+)

800

ns

600 400 200

10

7

3

0

27

24

17

21

14

7

10

3

0

0

CCND1 and/or CDKN2A unaltered 2000

4000

64390

1500

64842

1500

68614

35852

3000 1000

1000

2000

500

1000

0

0

73412

Days

60976

20

14

17

7

10

3

0

20

17

14

10

3

7

0

14

7

11

0

18

Tumor volume (mm3)

61773

0 4

21

23

14

17

7

10

3

0

Tumor volume (mm3)

500

57255

Days

C Patient ID

CCND1

CDKN2A

34494*

AMP

TRUNC (p.AE68fs)

73191*

AMP

MISSENSE (p.D84N)

64390

AMP

MISSENSE (splice site)

64842

AMP

WT

61773

WT

TRUNC (p.R80*)

73412*

AMP

TRUNC (p.W110*)

68614

WT

WT

35852**

WT

WT

60976

WT

WT

57255

WT

WT

*Rapid engrafters; **Slow engrafter

*

D

Non-responders 1.0

HPV+ CCND1/CDKN2A wild-type CCND1/CDKN2A altered Responders

0.5

0.0 64 48 2 37 76 0 73 41 2 60 97 6 68 61 4 57 25 5 64 84 2 64 39 0 34 99 4 35 85 2 73 19 1 61 77 3

B

Figure 5. Preclinical Assessment of CDK4 and CDK6 Inhibition Using Abemaciclib in PDX Models (A) Ten HPV
HNSCC PDX models (6 bearing CCND1 and/or CDKN2A alterations and 4 lacking these alterations) were treated with 100 mg/kg abemaciclib. Two HPV+ models were included as controls, because these are not expected to respond to abemaciclib. Of 6 models with CCND1/CDKN2A alterations, 5 demonstrated significant tumor growth delays with abemaciclib treatment. One of 4 unaltered models responded to abemaciclib. The mean tumor volume ± SE (n=5 mice per group) is shown. ns, nonsignificant; ****p < 0.0001; mixed model regression on repeated measures. (B) Summary of the alterations in CCND1 and CDKN2A genes in each HPV
PDX model treated. (C) Western blots for phospho-RB (Ser807/811) and pan-RB in a subset of treated versus untreated PDX tumors. The decrease in phospho-RB in treated PDX models relative to control untreated models was calculated by normalizing the phospho-RB and pan-RB signals to the glyceraldehyde 3-phosphate dehydrogenase (GAPDH) signals on the same blot, then by normalizing phospho-RB to pan-RB. Normalized phospho-RB was then compared between control versus treated for each PDX model. All of the samples were analyzed on the same western blot. Samples are shown as separated images due to the need to use different

(legend continued on next page)

1326 Cell Reports 25, 1318–1331, October 30, 2018

transcriptome and proteome are generally well conserved (Keysar et al., 2013; Li et al., 2016). Keysar et al. (2013) also performed survival analysis of engrafting (n = 25) versus nonengrafting (n = 21) patients and, in contrast to our findings, found no correlations. This discrepancy may be due to the heterogeneity of their patient cohort, half of which were newly diagnosed and half were relapsed samples, 8 were skin SCCs, and 11 samples were HPV+ oropharynx cancers. By contrast, our cohort of 243 included only newly diagnosed HPV
cancers. In particular, HPV+ HNSCC is now recognized to be a distinct clinical entity, with affected patients having significantly better survival outcomes compared to HPV
patients (Ang et al., 2010; Fakhry et al., 2008). An association between PDX formation and poor patient outcomes has been previously reported in other cancers, including breast cancer (McAuliffe et al., 2015) and early-stage lung cancer (John et al., 2011). This, together with our results for HPV
HNSCC, suggests that engraftment ability is a property that relates to cancer aggressiveness and patient survival across a range of tumor types. The optimal management of HPV
HNSCC is currently dictated by the pathologic features of tumors following surgery. Features such as tumor size, bony invasion, primary site margins, PNI, LVI, ENE, or lymph node metastases are reflections of the invasive character and underlying biology of tumors and have been associated with poor outcomes. Chemoradiotherapy has been shown to be superior to post-operative radiotherapy alone in patients with high-risk HNSCC (Bernier et al., 2004; Cooper et al., 2004). However, some early-stage tumors that lack pathologic high-risk features still recur rapidly and spread to distant sites, underscoring the need for more accurate biomarkers of high-risk tumor biology such as the PDX models described here. When tumor specimens are implanted into mice, the rate of growth is highly variable from patient to patient. Mice are observed for 6 months before the final scoring of a sample as an engrafter or a non-engrafter. This 6-month time frame renders engraftment ability impractical as a clinical biomarker for patient risk stratification. However, some patients’ tumors grow extremely rapidly, with xenografts becoming palpable within only a few weeks of implantation. Upon tracking of timeto-engraftment in a subset of patients, we made the observation that rapid engraftment (within a period of %8 weeks) occurred in a substantial number of models and was strongly associated with poor clinical outcomes. We selected 8 weeks as the cutoff because additional information regarding tumor biology could be clinically useful in this time frame to inform and/or alter therapy (Figure S2). For instance, a patient with no clear indications for radiation (N0, no PNI or LVI, >5 mm margins) that demonstrated PDX formation within 8 weeks may be considered for post-operative therapy to decrease the risk of locoregional failure. One such patient was included in the 7 rapid engrafters in our cohort who were treated with surgery alone, who by all

indications was low risk, but who rapidly relapsed and died within 8 months of diagnosis. Similarly, an intermediate-risk patient who was originally scheduled to receive radiation alone may consider the addition of chemotherapy to maximize locoregional control and/or address the risk of distant metastasis seen in PDX-forming patients (22% versus 6% at 5 years; Figure 1B). Overall, of the 22 rapid engrafters shown in Figure 1E, only 4 were treated with surgery + CRT. Of the 18 remaining patients, 8 recurred rapidly, suggesting that these patients may have benefited from therapy escalation. Our results also suggest that for rapid engrafters who have the worst prognosis and are in the most dire need of novel approaches, the PDX represents a patient ‘‘avatar’’ in which drug responses could be assessed within a relatively short time frame (Figure S2). Given that surgery and post-operative therapy can last several months, interrogation of candidate targeted treatments and predictive biomarkers could feasibly be determined before relapse. Due to the number of patients in this study, it was not possible to explore the interactions between all clinicopathologic risk factors and outcomes in the context of PDX formation. This information will become available as we continue to implant and study more patients over time. Furthermore, future studies will require validation of our results in a prospective setting. However, while determining engraftment ability with an 8-week cutoff is relatively inexpensive (in the same range as next-generation sequencing assays), implementing PDX generation at diverse treatment sites would be logistically challenging due to a lack of specialized expertise and facilities at many of these. Thus, the identification genetic or other stratifiers that could be more easily implemented in a clinical setting is a top priority. We sought to identify whether engrafting versus non-engrafting samples harbored differences in genomic alterations (mutations and CNAs) using a small targeted gene panel in combination with copy-number analysis. We did not identify any mutations that specifically correlated with engraftment; however, chromosome 11q gains that included the CCND1, FADD, and FGF3 genes and the chromosome 3p loss that included the EPHA3 gene were both significantly correlated with engraftment. This agrees with other studies demonstrating that 11q amplification is prognostic in HNSCC (van Kempen et al., 2015; Vincent-Chong et al., 2017). Others have also observed a worse prognosis in patients with 3p loss, in particular when combined with TP53 mutations (Gross et al., 2014; Raju et al., 2015). Of the genes in our panel located at this locus (EPHA3, FHIT, and TGFBR2), deletion of EPHA3 significantly correlated with engraftment (Figure 2C); however, this does not rule out the potential role of other genes located in chromosome 3p, given the large region that is deleted in these tumors. Deeper molecular and biological studies must be conducted to determine the mechanism(s) driving intertumoral differences in HNSCC and identify biomarkers that are more clinically amenable than the rapid engraftment assay. Our cohort of PDX models represent a resource that, with more detailed

exposure times for different samples and to allow responders and non-responders to be grouped together. Asterisk indicates GAPDH from the phospho-RB western blot. (D) Summary of abemaciclib responses showing the tumor volume at endpoint in abemaciclib-treated mice relative to control mice. Patient 73412 is a mixture of blue and red due to the identification of the CCND1 amplification being subclonal in this PDX model. See also Table S8 and Figure S6.

Cell Reports 25, 1318–1331, October 30, 2018 1327

genomic and proteomic interrogation, will be invaluable for identifying such biomarkers in the future. Recent studies have shown that drug responses in PDXs mimicked human clinical trials and that PDXs could be used to characterize drug efficacy, improve preclinical evaluation of treatment modalities, and enhance the ability to predict clinical trial responses (Gao et al., 2015; Townsend et al., 2016). We sought to demonstrate the utility of our HNSCC PDX models as tools for the evaluation of drug responses and their correlations with molecular features of individual tumors. CCND1 amplifications and CDKN2A mutations or deletions were observed in 25% and 53%, respectively, among the HNSCC patients in the TCGA study (The Cancer Genome Atlas Network, 2015), and in 27% and 44%, respectively, in our patient cohort, suggesting the potential utility of CDK inhibitors in a substantial number of patients. Abemaciclib was selected over other CDK4 and CDK6 inhibitors (palbociclib and ribociclib) due to its higher potency (half-maximal inhibitory concentration [IC50] for CDK4 and CDK6, 2 nM and 5 nM, respectively) in comparison to the other two (IC50 for CDK4 and CDK6, 10 nM and 15 nM for palbociclib; 10 nM and 39 nM for ribociclib) (Kwapisz, 2017). Our results suggest that abemaciclib has anti-tumor activity in a substantial fraction of HNSCC patients and that CCND1 amplifications and/or CDKN2A mutations or deletions may be predictive of such response. Specifically, 5 of 6 PDX models bearing CCND1 amplifications/CDKN2A deletions responded to the drug; among these 6 patients, 5 had both CCND1 amplifications and a CDKN2A mutation, and 1 had only a CDKN2A mutation. The latter patient (61773), however, was tetraploid for chromosome 11 by FISH, and the copy-number analysis indicated 4 copies of chromosome 11 (Figure 3). The genome-wide copynumber profile indicated an average ploidy of 3.6 for this sample (Figure S4); thus, this sample may in fact have had a low-level amplification of the CCND1 gene relative to the rest of the genome through aneuploidy. The single CCND1 amplified model that did not respond to abemaciclib (73412, which had both CCND1 amplification and CDKN2A mutation in the patient sample) showed, upon analysis of CCND1 copy number by FISH, that CCND1 amplification was subclonal in the PDX, whereas the CDKN2A sequencing result for this sample indicated that all of the cells in the PDX were CDKN2A mutant. This suggests that CCND1 amplification may be more predictive of abemaciclib response than CDKN2A mutation. Our FISH analysis of this PDX was performed on an untreated sample; to truly determine whether there is differential sensitivity to the drug in CCND1 amplified cells, it would be of interest to perform FISH on samples that were treated to determine whether any cells with CCND1 amplification remained in those unresponsive tumors. Furthermore, more models with CCND1 amplifications and CDKN2A mutations alone must be treated. Two HPV+ PDX models were treated with abemaciclib and showed no response. This was expected due to the expression of HPV E7 and its sequestration of RB and serves to demonstrate the specificity of abemaciclib (in essence, acting as a control for off-target effects). Of 4 HPV
models that did not harbor CCND1 and/or CDKN2A alterations, 3 were unresponsive and 1 (35852) showed a significant growth delay. The FISH results for this

1328 Cell Reports 25, 1318–1331, October 30, 2018

model showed that the majority of the cells in this sample were pentaploid for chromosome 11 (and thus CCND1), suggesting a mechanism whereby extra copies of CCND1 may in fact be present. FISH for 3 additional chromosomes indicated extreme aneuploidy in this sample, with 4–6 copies of chromosome 8 in the majority of cells, but lower copy numbers of chromosomes 17 and 19, supporting this possibility. It is also possible that CDKN2A is silenced by hypermethylation (Maruya et al., 2004) or that there are alterations in genes other than CCND1 and CDKN2A that could result in sensitivity to abemaciclib. The tumor of patient 35852 also has an EGFR amplification, which could lead to increased cyclin-dependent CDK4 and CDK6 activity (Wee and Wang, 2017) and render cells sensitive to CDK4 and CDK6 inhibition. The addition of the CDK4 and CDK6 inhibitor palbociclib to letrozole in women with advanced ER+/Her2
breast cancer was shown to improve progression-free survival (Cristofanilli et al., 2016; Finn et al., 2015; Iwata et al., 2017) and is currently approved for this indication. A recent phase I clinical trial in other solid tumors showed single-agent activity in several malignancies (Patnaik et al., 2016). A phase I trial investigating the use of palbociclib in combination with cetuximab in recurrent and/or metastatic HNSCC patients reported that this combination therapy is safe and that tumor responses were observed even in cetuximab or platin-resistant disease (Michel et al., 2016). Several clinical trials to evaluate the use of these drugs in HNSCC are now under way, but only one of them (NCT03088059) includes an analysis of CCND1 amplification status as part of the trial (https://clinicaltrials.gov). Overall, our results indicate that alterations in the G1/S checkpoint pathway may predict for response to CDK4 and CDK6 inhibition in HNSCC, supporting the assessment of biomarkers in clinical trials moving forward. We also provide proof of principle for the use of PDX models for preclinical drug evaluation and identification of predictive biomarkers. As with other preclinical models, PDX models also have limitations, including the lack of an intact immune system and differences between mouse and human stroma. In addition, we have not performed a comparison between surgical samples and matched PDXs at the molecular level. While it has been shown by others that the transcriptome and proteome are generally well conserved in HNSCC PDX models (Keysar et al., 2013; Li et al., 2016), we will prioritize carrying out such analyses on our cohort in the future to verify this. Furthermore, a recent in silico analysis of published data reported that PDX models underwent mouse-specific tumor evolution, with rapid accumulation of CNA during PDX passaging that differed from those acquired during tumor evolution in patients (Ben-David et al., 2017). This study included only 5 HNSCC models and was done largely using inference of copy number from published gene expression (microarray or RNA-sequencing) data. Our resource will allow us to carry out deep next-generation sequencing analyses of matched patient and PDX models to directly assess clonal dynamics during PDX generation and passaging and to determine whether changes in clonal dynamics within PDX models have functional consequences. In spite of these limitations, it has been observed in several studies that PDX models do predict clinical trial drug responses

(Bertotti et al., 2011; Gao et al., 2015; Townsend et al., 2016). It should also be noted that while substantial growth delays were observed in our responding models, the tumors did not shrink or disappear. Furthermore, as with many targeted therapies, resistance is likely to develop with extended drug exposure. Thus, future studies must also focus on the identification of mechanisms of resistance, along with the use of abemaciclib in combination with therapies such as ionizing radiation. PDX models such as those described here provide invaluable model systems for achieving these goals and have significant potential to change the paradigm in the future management of HNSCC patients. STAR+METHODS Detailed methods are provided in the online version of this paper and include the following: d d d

d

d d

KEY RESOURCES TABLE CONTACT FOR REAGENT AND RESOURCE SHARING EXPERIMENTAL MODEL AND SUBJECT DETAILS B Patients and Tissue Samples B PDX models METHOD DETAILS B DNA extraction and sequencing B Analysis of targeted sequencing data B Fluorescence in situ hybridization B CDKN2A PCR and Sanger sequencing B Abemaciclib Treatment of PDX models B Validation of HPV status of HPV+ PDX models B Western Blots QUANTIFICATION AND STATISTICAL ANALYSIS DATA AND SOFTWARE AVAILABILITY

SUPPLEMENTAL INFORMATION Supplemental Information includes six figures and eight tables and can be found with this article online at https://doi.org/10.1016/j.celrep.2018.10.004. ACKNOWLEDGMENTS We thank the University Health Network (UHN) Program for Biospecimen Sciences for fresh tissue acquisition and the UHN Animal Resources Centre staff for animal care. We also thank Marco Di Grappa for help and reagents for HPV validation. This work was supported by grants from the Canadian Institutes of Health Research (MOP126203), the Ontario Institute for Cancer Research (Investigator Award), the Princess Margaret Cancer Foundation, and the Joe and Cara Finley Centre for Head and Neck Cancer Translational Research, with additional philanthropic funds contributed by the Wharton Family and Gordon Tozer. AUTHOR CONTRIBUTIONS C.K., J.M., E.H., and K.P. implanted patient samples into mice, and J.M. tracked tumor growth. C.K. coordinated samples and extracted DNA for sequencing, performed drug treatments, and carried out PCR for the validation of the CDKN2A mutations. J.P.B. and T.J.P. performed the analysis of the sequencing data. N.E. and R.T. performed the FISH. S.H.H. and B.O. collected and compiled the clinical data. W.X. and J.S. performed the statistical analysis of the engraftment and clinical data. I.W. assessed the histology of the primary patients and PDXs. A.J.H., D.P.G., F.-F.L., S.V.B., and L.A. contributed to the

experimental design and the interpretation of the clinical significance of the findings. C.K., A.J.H., and L.A. wrote the manuscript. DECLARATION OF INTERESTS The authors declare no competing interests. Received: November 13, 2017 Revised: August 2, 2018 Accepted: September 28, 2018 Published: October 30, 2018 REFERENCES ^n, Ang, K.K., Harris, J., Wheeler, R., Weber, R., Rosenthal, D.I., Nguyen-Ta P.F., Westra, W.H., Chung, C.H., Jordan, R.C., Lu, C., et al. (2010). Human papillomavirus and survival of patients with oropharyngeal cancer. N. Engl. J. Med. 363, 24–35. Ben-David, U., Ha, G., Tseng, Y.Y., Greenwald, N.F., Oh, C., Shih, J., McFarland, J.M., Wong, B., Boehm, J.S., Beroukhim, R., and Golub, T.R. (2017). Patient-derived xenografts undergo mouse-specific tumor evolution. Nat. Genet. 49, 1567–1575. Bernier, J., Domenge, C., Ozsahin, M., Matuszewska, K., Lefe`bvre, J.L., Greiner, R.H., Giralt, J., Maingon, P., Rolland, F., Bolla, M., et al.; European Organization for Research and Treatment of Cancer Trial 22931 (2004). Postoperative irradiation with or without concomitant chemotherapy for locally advanced head and neck cancer. N. Engl. J. Med. 350, 1945–1952. Bertotti, A., Migliardi, G., Galimi, F., Sassi, F., Torti, D., Isella, C., Cora`, D., Di Nicolantonio, F., Buscarino, M., Petti, C., et al. (2011). A molecularly annotated platform of patient-derived xenografts (‘‘xenopatients’’) identifies HER2 as an effective therapeutic target in cetuximab-resistant colorectal cancer. Cancer Discov. 1, 508–523. Bonner, J.A., Harari, P.M., Giralt, J., Azarnia, N., Shin, D.M., Cohen, R.B., Jones, C.U., Sur, R., Raben, D., Jassem, J., et al. (2006). Radiotherapy plus cetuximab for squamous-cell carcinoma of the head and neck. N. Engl. J. Med. 354, 567–578. Bonner, J.A., Harari, P.M., Giralt, J., Cohen, R.B., Jones, C.U., Sur, R.K., Raben, D., Baselga, J., Spencer, S.A., Zhu, J., et al. (2010). Radiotherapy plus cetuximab for locoregionally advanced head and neck cancer: 5-year survival data from a phase 3 randomised trial, and relation between cetuximabinduced rash and survival. Lancet Oncol. 11, 21–28. Cibulskis, K., Lawrence, M.S., Carter, S.L., Sivachenko, A., Jaffe, D., Sougnez, C., Gabriel, S., Meyerson, M., Lander, E.S., and Getz, G. (2013). Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219. Cooper, J.S., Pajak, T.F., Forastiere, A.A., Jacobs, J., Campbell, B.H., Saxman, S.B., Kish, J.A., Kim, H.E., Cmelak, A.J., Rotman, M., et al.; Radiation Therapy Oncology Group 9501/Intergroup (2004). Postoperative concurrent radiotherapy and chemotherapy for high-risk squamous-cell carcinoma of the head and neck. N. Engl. J. Med. 350, 1937–1944. Cristofanilli, M., Turner, N.C., Bondarenko, I., Ro, J., Im, S.A., Masuda, N., Colleoni, M., DeMichele, A., Loi, S., Verma, S., et al. (2016). Fulvestrant plus palbociclib versus fulvestrant plus placebo for treatment of hormone-receptor-positive, HER2-negative metastatic breast cancer that progressed on previous endocrine therapy (PALOMA-3): final analysis of the multicentre, doubleblind, phase 3 randomised controlled trial. Lancet Oncol. 17, 425–439. De Meulenaere, A., Vermassen, T., Aspeslagh, S., Huvenne, W., Van Dorpe, J., Ferdinande, L., and Rottey, S. (2017). Turning the tide: clinical utility of PD-L1 expression in squamous cell carcinoma of the head and neck. Oral Oncol. 70, 34–42. Fakhry, C., Westra, W.H., Li, S., Cmelak, A., Ridge, J.A., Pinto, H., Forastiere, A., and Gillison, M.L. (2008). Improved survival of patients with human papillomavirus-positive head and neck squamous cell carcinoma in a prospective clinical trial. J. Natl. Cancer Inst. 100, 261–269.

Cell Reports 25, 1318–1331, October 30, 2018 1329

Favero, F., Joshi, T., Marquard, A.M., Birkbak, N.J., Krzystanek, M., Li, Q., Szallasi, Z., and Eklund, A.C. (2015). Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data. Ann. Oncol. 26, 64–70. Ferris, R.L., Blumenschein, G., Jr., Fayette, J., Guigay, J., Colevas, A.D., Licitra, L., Harrington, K., Kasper, S., Vokes, E.E., Even, C., et al. (2016). Nivolumab for recurrent squamous-cell carcinoma of the head and neck. N. Engl. J. Med. 375, 1856–1867. Finn, R.S., Crown, J.P., Lang, I., Boer, K., Bondarenko, I.M., Kulyk, S.O., Ettl, J., Patel, R., Pinter, T., Schmidt, M., et al. (2015). The cyclin-dependent kinase 4/6 inhibitor palbociclib in combination with letrozole versus letrozole alone as first-line treatment of oestrogen receptor-positive, HER2-negative, advanced breast cancer (PALOMA-1/TRIO-18): a randomised phase 2 study. Lancet Oncol. 16, 25–35. Gao, H., Korn, J.M., Ferretti, S., Monahan, J.E., Wang, Y., Singh, M., Zhang, C., Schnell, C., Yang, G., Zhang, Y., et al. (2015). High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med. 21, 1318–1325. Goel, S., and Zhao, J.J. (2016). CDK4/6 inhibition: the late harvest cycle begins. Oncotarget 7, 48854–48856. Gross, A.M., Orosco, R.K., Shen, J.P., Egloff, A.M., Carter, H., Hofree, M., Choueiri, M., Coffey, C.S., Lippman, S.M., Hayes, D.N., et al. (2014). Multitiered genomic analysis of head and neck cancer ties TP53 mutation to 3p loss. Nat. Genet. 46, 939–943. Iwata, H., Im, S.A., Masuda, N., Im, Y.H., Inoue, K., Rai, Y., Nakamura, R., Kim, J.H., Hoffman, J.T., Zhang, K., et al. (2017). PALOMA-3: Phase III Trial of Fulvestrant With or Without Palbociclib in Premenopausal and Postmenopausal Women With Hormone Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Negative Metastatic Breast Cancer That Progressed on Prior Endocrine Therapy-Safety and Efficacy in Asian Patients. J. Glob. Oncol. 3, 289–303. John, T., Kohler, D., Pintilie, M., Yanagawa, N., Pham, N.A., Li, M., Panchal, D., Hui, F., Meng, F., Shepherd, F.A., et al. (2011). The ability to form primary tumor xenografts is predictive of increased risk of disease recurrence in early-stage non-small cell lung cancer. Clin. Cancer Res. 17, 134–141. Joshua, B., Kaplan, M.J., Doweck, I., Pai, R., Weissman, I.L., Prince, M.E., and Ailles, L.E. (2012). Frequency of cells expressing CD44, a head and neck cancer stem cell marker: correlation with tumor aggressiveness. Head Neck 34, 42–49. Juergens, R.A., Bratman, S.V., Tsao, M.S., Laurie, S.A., Sara Kuruvilla, M., Razak, A.R., and Hansen, A.R. (2017). Biology and patterns of response to EGFR-inhibition in squamous cell cancers of the lung and head & neck. Cancer Treat. Rev. 54, 43–57. Keysar, S.B., Astling, D.P., Anderson, R.T., Vogler, B.W., Bowles, D.W., Morton, J.J., Paylor, J.J., Glogowska, M.J., Le, P.N., Eagles-Soukup, J.R., et al. (2013). A patient tumor transplant model of squamous cell cancer identifies PI3K inhibitors as candidate therapeutics in defined molecular bins. Mol. Oncol. 7, 776–790. Koboldt, D.C., Zhang, Q., Larson, D.E., Shen, D., McLellan, M.D., Lin, L., Miller, C.A., Mardis, E.R., Ding, L., and Wilson, R.K. (2012). VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576. Kwapisz, D. (2017). Cyclin-dependent kinase 4/6 inhibitors in breast cancer: palbociclib, ribociclib, and abemaciclib. Breast Cancer Res. Treat. 166, 41–54.

genes in head and neck squamous carcinoma: incidence and potential implications. Clin. Cancer Res. 10, 3825–3830. McAuliffe, P.F., Evans, K.W., Akcakanat, A., Chen, K., Zheng, X., Zhao, H., Eterovic, A.K., Sangai, T., Holder, A.M., Sharma, C., et al. (2015). Ability to Generate Patient-Derived Breast Cancer Xenografts Is Enhanced in Chemoresistant Disease and Predicts Poor Patient Outcomes. PLoS One 10, e0136851. McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., and DePristo, M.A. (2010). The Genome Analysis Toolkit: a MapReduce framework for analyzing nextgeneration DNA sequencing data. Genome Res. 20, 1297–1303. Mermel, C.H., Schumacher, S.E., Hill, B., Meyerson, M.L., Beroukhim, R., and Getz, G. (2011). GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41. Michel, L., Ley, J., Wildes, T.M., Schaffer, A., Robinson, A., Chun, S.E., Lee, W., Lewis, J., Jr., Trinkaus, K., and Adkins, D. (2016). Phase I trial of palbociclib, a selective cyclin dependent kinase 4/6 inhibitor, in combination with cetuximab in patients with recurrent/metastatic head and neck squamous cell carcinoma. Oral Oncol. 58, 41–48. Patnaik, A., Rosen, L.S., Tolaney, S.M., Tolcher, A.W., Goldman, J.W., Gandhi, L., Papadopoulos, K.P., Beeram, M., Rasco, D.W., Hilton, J.F., et al. (2016). Efficacy and Safety of Abemaciclib, an Inhibitor of CDK4 and CDK6, for Patients with Breast Cancer, Non-Small Cell Lung Cancer, and Other Solid Tumors. Cancer Discov. 6, 740–753. Raju, S.C., Hauff, S.J., Lemieux, A.J., Orosco, R.K., Gross, A.M., Nguyen, L.T., Savariar, E., Moss, W., Whitney, M., Cohen, E.E., et al. (2015). Combined TP53 mutation/3p loss correlates with decreased radiosensitivity and increased matrix-metalloproteinase activity in head and neck carcinoma. Oral Oncol. 51, 470–475. Rosenthal, D.I., Liu, L., Lee, J.H., Vapiwala, N., Chalian, A.A., Weinstein, G.S., Chilian, I., Weber, R.S., and Machtay, M. (2002). Importance of the treatment package time in surgery and postoperative radiation therapy for squamous carcinoma of the head and neck. Head Neck 24, 115–126. Sakano, S., Berggren, P., Kumar, R., Steineck, G., Adolfsson, J., Onelo¨v, E., Hemminki, K., and Larsson, P. (2003). Clinical course of bladder neoplasms and single nucleotide polymorphisms in the CDKN2A gene. Int. J. Cancer 104, 98–103. Santos, R., Ursu, O., Gaulton, A., Bento, A.P., Donadi, R.S., Bologa, C.G., Karlsson, A., Al-Lazikani, B., Hersey, A., Oprea, T.I., and Overington, J.P. (2017). A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov. 16, 19–34. Saunders, C.T., Wong, W.S., Swamy, S., Becq, J., Murray, L.J., and Cheetham, R.K. (2012). Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics 28, 1811– 1817. Seiwert, T.Y., Burtness, B., Mehra, R., Weiss, J., Berger, R., Eder, J.P., Heath, K., McClanahan, T., Lunceford, J., Gause, C., et al. (2016). Safety and clinical activity of pembrolizumab for treatment of recurrent or metastatic squamous cell carcinoma of the head and neck (KEYNOTE-012): an open-label, multicentre, phase 1b trial. Lancet Oncol. 17, 956–965. The Cancer Genome Atlas Network (2015). Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature 517, 576–582.

Li, H., and Durbin, R. (2010). Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595.

Tiwana, M.S., Wu, J., Hay, J., Wong, F., Cheung, W., and Olson, R.A. (2014). 25 year survival outcomes for squamous cell carcinomas of the head and neck: population-based outcomes from a Canadian province. Oral Oncol. 50, 651–656.

Li, H., Wheeler, S., Park, Y., Ju, Z., Thomas, S.M., Fichera, M., Egloff, A.M., Lui, V.W., Duvvuri, U., Bauman, J.E., et al. (2016). Proteomic Characterization of Head and Neck Cancer Patient-Derived Xenografts. Mol. Cancer Res. 14, 278–286.

Townsend, E.C., Murakami, M.A., Christodoulou, A., Christie, A.L., Ko¨ster, J., DeSouza, T.A., Morgan, E.A., Kallgren, S.P., Liu, H., Wu, S.C., et al. (2016). The Public Repository of Xenografts Enables Discovery and Randomized Phase IIlike Trials in Mice. Cancer Cell 30, 183.

Maruya, S., Issa, J.P., Weber, R.S., Rosenthal, D.I., Haviland, J.C., Lotan, R., and El-Naggar, A.K. (2004). Differential methylation status of tumor-associated

van Kempen, P.M., Noorlag, R., Braunius, W.W., Moelans, C.B., Rifi, W., Savola, S., Koole, R., Grolman, W., van Es, R.J., and Willems, S.M. (2015). Clinical

1330 Cell Reports 25, 1318–1331, October 30, 2018

relevance of copy number profiling in oral and oropharyngeal squamous cell carcinoma. Cancer Med. 4, 1525–1535.

Wee, P., and Wang, Z. (2017). Epidermal growth factor receptor cell proliferation signaling pathways. Cancers (Basel) 9, pii: E52.

Vincent-Chong, V.K., Salahshourifar, I., Woo, K.M., Anwar, A., Razali, R., Gudimella, R., Rahman, Z.A., Ismail, S.M., Kallarakkal, T.G., Ramanathan, A., et al. (2017). Genome wide profiling in oral squamous cell carcinoma identifies a four genetic marker signature of prognostic significance. PLoS One 12, e0174865.

Wong, K., Huang, S.H., O’Sullivan, B., Lockwood, G., Dale, D., Michaelson, T., Waldron, J., Bayley, A., Cummings, B., Dawson, L.A., et al. (2010). Point-ofcare outcome assessment in the cancer clinic: audit of data quality. Radiother. Oncol. 95, 339–343.

Cell Reports 25, 1318–1331, October 30, 2018 1331

STAR+METHODS KEY RESOURCES TABLE

REAGENT or RESOURCE

SOURCE

IDENTIFIER

Cell Signaling Technology

Cat# 9308; RRID: AB_331472

Antibodies Rabbit polyclonal phospho-Rb (Ser807/811) Mouse monoclonal pan-Rb

BD Biosciences

Cat# 554136; RRID: AB_395259

Mouse anti-GAPDH

Santa Cruz

Cat# sc-47724; RRID: AB_627678

HNSCC tissue samples

University Health Network

N/A

Patient-derived xenografts (PDX)

This paper

N/A

MedChem Express

Cat# HY-16297; CAS: 1231930-82-7

Agilent

N/A

Aligned sequencing reads

This paper

European Genome-phenome Archive: EGAS00001002979

Analyzed data (mutations and CNAs)

This paper

Tables S6 and S7

Original source: Jackson Labs. Bred in-house at University Health Network Animal Resources Centre

Cat# 4142; RRID:BCBC_4142

CDKN2A forward primer; 50 -GGGCTCTACACAAGCTTCCTT-30

This paper

N/A

CDKN2A reverse primer; 50 - AGCTCCTCAGCCAGGTCCAC
30

This paper

N/A

HPV forward primer: 50 - GAGCGACCCAGAAAGTTACCA
30

This paper

N/A

HPV reverse primer: 50 - TGCTTGCAGTACACACATTCT
30

This paper

N/A

Li and Durbin, 2010; PMID: 19451168

https://sourceforge.net/projects/bio-bwa/ files/

Picard (v.1.130)

N/A

https://broadinstitute.github.io/picard/

GATK IndelRealigner(v3.3-0)

McKenna et al., 2010; PMID: 20644199

https://software.broadinstitute.org/gatk/

Mutect

Cibulskis et al., 2013; PMID: 23396013

https://www.broadinstitute.org/cancer/ cga/mutect

Strelka

Saunders et al., 2012; PMID: 22581179

https://github.com/Illumina/strelka

IndelGenotyper

McKenna et al., 2010; PMID: 20644199

https://software.broadinstitute.org/gatk/

Varscan2

Koboldt et al., 2012; PMID: 22300766

http://massgenomics.org/varscan

Sequenza

Favero et al., 2015; PMID: 25319062

http://www.cbs.dtu.dk/biotools/sequenza/

GISTIC (v2.0.23)

Mermel et al., 2011; PMID: 21527027

https://www.broadinstitute.org/cancer/ cga/gistic

FISH probe: SureFISH Chr11 CEP 789 kb

Agilent

Cat# G101083R-8

FISH probe: SureFISH Chr17 CEP 436 kb

Agilent

Cat# G101105R-8

Biological Samples

Chemicals, Peptides, and Recombinant Proteins LY2835219 (abemaciclib) Critical Commercial Assays Agilent SureSelect custom in-solution hybrid capture panel Deposited Data

Experimental Models: Organisms/Strains NOD/SCID/IL2Rg
/
(NSG) mice

Oligonucleotides

Software and Algorithms BWA-MEM (v0.7.12)

Other

(Continued on next page)

e1 Cell Reports 25, 1318–1331.e1–e4, October 30, 2018

Continued REAGENT or RESOURCE

SOURCE

IDENTIFIER

FISH probe: SureFISH Chr19 CEP 527 kb

Agilent

Cat# G101075R-8

FISH probe: SureFISH 11q13.3 CCND1 30 BA 352 kb

Agilent

Cat# G101213G-8

CONTACT FOR REAGENT AND RESOURCE SHARING Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Laurie Ailles ([email protected]). EXPERIMENTAL MODEL AND SUBJECT DETAILS Patients and Tissue Samples Tumor samples were obtained from patients undergoing surgery for head and neck squamous cell carcinoma at the University Health Network. All samples were collected from patients with informed consent, and all related procedures were performed with the approval of the Research Ethics Board (REB# 12-5639) of the University Health Network. Patient data were extracted from the Anthology of Outomes (Wong et al., 2010). For patients not included in the Anthology, patient records were reviewed. PDX models All animal experiments were performed with the approval of the University Health Network Animal Care Committee and adhered to the Canadian Council on Animal Care guidelines (protocol #1542). NOD/SCID/IL2Rg
/
(NSG) mice were bred in-house at the University Health Network Animal Resources Centre. Tumor tissues were dissected into small fragments (1mm3) and ten fragments per patient were implanted under the skin on the flanks of NSG mice (two fragments per mouse). Tumor fragments from established PDXs were viably banked by cryopreservation in 90% fetal bovine serum (FBS)/10% dimethylsulfoxide (DMSO). Each group of five mice was monitored weekly for tumor growth and mice were euthanized when tumors reached an end point of 1.5 cm diameter, or at 6 months post-implantation. If PDX formation did not occur by 6 months, the patient was scored as a ‘‘non-engrafter.’’ Treatment of PDX models with abemaciclib is described in the Method Details section below. METHOD DETAILS DNA extraction and sequencing HNSCC tissues that were frozen in OCT embedding media were sectioned (10 mm) and 25-40 sections were collected in an Eppendorf tube containing phosphate buffered saline (PBS). The tissue was pelleted and DNA was extracted using QiaAMP DNA Minikit (QIAGEN). Patient-matched normal DNA was extracted from peripheral blood lymphocytes (PBL). If PBLs were not available, normal adjacent tissue was used instead. A custom in-solution hybrid capture targeted sequencing panel (Agilent SureSelect) was designed to isolate 2 Mb of genomic DNA containing the exons of the most commonly altered genes from the TCGA analysis of 279 HNSCC samples (The Cancer Genome Atlas Network, 2015). Baits for 10,000 single nucleotide polymorphism (SNP) loci were included to enable CNA analysis, using the genome coordinates of the SNPs from the Affymetrix 10K array, +/
5 base pairs. Analysis of targeted sequencing data Raw paired-end reads were aligned to hg19 using BWA-MEM (v0.7.12) (Li and Durbin, 2010). Duplicates were marked using Picard (v1.130) and indel realignment was performed using GATK IndelRealigner (v3.3-0) (McKenna et al., 2010). For mutation detection, single nucleotide variants (SNVs) were called using Mutect (v1.1.4)(Cibulskis et al., 2013). Non-coding variants and SNVs present at a rate > 1% in 1000 Genomes were removed. Small insertions and deletions (indels) were called using Strelka (v1.0.14) (Saunders et al., 2012), IndelGenotyper (v36.3336) (McKenna et al., 2010) and Varscan2 (v2.3.6) (Koboldt et al., 2012). All Strelka called indels were retained as well as any indel called by 2 or more out of 3 callers. Copy number profiles were generated using Varscan2 (v2.3.6) (Koboldt et al., 2012) and the Sequenza package (v 2.1.0) (Favero et al., 2015) in the R statistical environment (v3.1.1). Significantly altered regions as well as amplified (4+ copies) and homozygousdeleted genes were identified using GISTIC2.0 (v2.0.23) (Mermel et al., 2011). Fluorescence in situ hybridization Bulk single cell suspensions in Hank’s balanced salt solution (HBSS) with 2% FBS from PDX tumors were blocked using mouse and rat IgG (2 mg/ml) and stained with mouse lineage markers (biotin mouse anti-mouse H2Kd, clone SF1-1.1 (1:1000); biotin rat

Cell Reports 25, 1318–1331.e1–e4, October 30, 2018 e2

anti-mouse CD31, clone 390 (1:100); biotin rat anti-mouse CD45, clone 30-F11 (1:100) (BD Biosciences) for 20 minutes. Stained cells were washed, pelleted and resuspended in HBSS with 2%FBS containing anti-biotin microbeads (Miltenyi Biotec) for 20 minutes. Cells were again washed and re-suspended in MACS buffer (PBS/2mM EDTA/0.5% BSA), then passed through LS columns attached to the MACS Separator (Miltenyi Biotec) for depletion of mouse lineage cells. Purified human tumor cells were collected and Ficoll treated to remove dead cells. Cytospin slides were then prepared containing 8,000-10,000 tumor cells per slide and fixed in icecold 3:1 methanol/acetic acid, air-dried, incubated in 2X SSC pH 7.0 (10 minutes) at 37 C, 0.005% pepsin in 0.01N HCl (10 minutes) at 37 C, washed in PBS (3 minutes), fixed in 1% formaldehyde/50mM MgCl2 solution (5 minutes), washed in PBS (3 minutes), dehydrated in a series of ethanol washes, denatured in 70% formamide/1X SSC pH 5.3 (5 minutes) at 37 C, and hybridized with FISH probes overnight at 37 C (Agilent SureFISH Chr11 CEP 798kb orange-red and SureFISH 11q13.3 CCND1 30 BA 352kb green). Hybridized slides were washed in 0.4X SSC pH7.0/0.3% NP-40 for 2 minutes at 73 C, 2X SSC pH7.0/0.1% NP-40 for 1 minute at room temperature and mounted in a 1:7 mix of Vectashield mounting medium with DAPI (Vector Laboratories). Images were acquired and analyzed on a BioView Duet automated scanning system (BioView Ltd, Rehovot, Israel). 250 cells were scored for each sample when possible (except 64390 N = 118 and 34994 N = 86). CDKN2A PCR and Sanger sequencing PCR was performed to amplify the CDKN2A gene from genomic DNA isolated from primary patient and patient-derived xenograft tumors. Primer sequences were from Sakano et al. (2003), as follows: Forward: 50 - gggctctacacaagcttcctt
30 Reverse: 50 - agctcctcagccaggtccac
30 These primers were used to amplify a 285 base pair sequence in exon 2 of CDKN2A that spans the region containing genetic alterations detected by targeted gene sequencing. PCR reactions were prepared using AmpliTaq Gold 360 DNA Polymerase (Applied Biosystems). Each 25 ml PCR reaction mix included 100 ng of genomic DNA, 4 mM MgCl2, 0.25 mM dNTPs, 0.4 mM primers, and 5% DMSO (with annealing temperature of 62 C). PCR products were purified using the NucleoSpin Gel and PCR clean up kit (Macherey-Nagel) and Sanger sequencing was performed at The Centre for Applied Genomics, Hospital for Sick Children (Toronto). Abemaciclib Treatment of PDX models Viably banked first passage PDX tumor pieces were thawed and re-implanted into NSG mice for growth and expansion. To ensure that an equivalent number of cells was injected into each mouse and thereby minimizing variability in tumor size, these tumors were dissociated into single cell suspensions and either injected immediately into NSG mice or frozen as bulk cell suspensions in 90% FBS/10% DMSO for later use. To generate cell suspensions tumor tissue was finely minced using a sterile scalpel and incubated in 10 mL of Media199 (Wisent) containing Collagenase/Hyalurodinase (1X) (Stem Cell Technologies) and DNase (125U/ml) (Worthington) at 37 C for 2 hours with frequent trituration. The cell suspension was then filtered using a 70 mm pore filter, washed and pelleted. Cells were suspended in ACK lysis buffer (ThermoFisher) and placed on ice for 5 minutes to lyse contaminating red blood cells, then washed, pelleted and re-suspended in HBSS with 2% FBS for counting. Doses of 100,000 to 250,000 cells per mouse in 50% Matrigel (BD Biosciences) were injected subcutaneously on the left flank into cohorts of 10 mice. Treatment began once tumors reached a volume between 80 to 120 mm3. Five mice were treated daily by oral gavage with 100 mg/kg of the CDK4 and CDK6 inhibitor abemaciclib (LY2835219; MedChem Express) dissolved in water and five mice were treated with water. Tumor measurements were recorded twice a week. Treatment ended once the tumors in the control group reached the humane endpoint size of 15mm in diameter. Validation of HPV status of HPV+ PDX models Quantitative real-time PCR with SSo Advanced SYBR green Universal Mastermix (Bio-Rad) was performed on genomic DNA samples from PDX 37760, PDX 64482, HMS-001 (HPV16+ HNSCC cell line), and CAL-33 (HPV16- HNSCC cell line). A dilution series of genomic DNA from HMS-001 cells was prepared (in triplicate) with DNA amounts of 2000, 200, 20 and 2pg. qPCR reaction mixes were set up to include: 5ml of 2X Mastermix, 1ml of forward and reverse HPV16 E6 primers (2.5uM) (Forward 50 -gagcgacccagaaagttacca-30 and Reverse 50 -tgcttgcagtacacacattct-30 ) and up to 4ml (containing 1pg) of gDNA template. qPCR reactions were performed in Bio-Rad CFX 384 thermocycler under the following conditions: 98 C
3min, (98 C
10 s, 60 C-30 s) x39 cycles. Western Blots Tumor tissue homogenates were prepared in RIPA buffer and normalized for total protein amount. 50 mg of protein from each sample were resolved on 8% SDS-PAGE gels, and transferred onto Immobilon-P membranes (Millipore) using a semi-dry transfer method (Bio-Rad). Blots were probed overnight at 4 C using a rabbit-anti-human phospho-RB (Ser807/811) antibody (1:1000; Cell Signaling Technologies), a mouse-anti-human pan-RB antibody (2 mg/ml; BD Biosciences) and a mouse-anti-human GAPDH antibody (1:2000; Santa Cruz Biotechnology), followed by incubation with appropriate horseradish peroxidase conjugated secondary antibodies (1:2500 for anti-mouse and 1:5000 for anti-rabbit;ThermoFisher). Proteins were detected using enhanced chemiluminescence reagent (ThermoFisher) and autoradiograph exposure (Sigma-Aldrich). Pan-RB and phospho-RB blots were run in parallel and GAPDH was probed on the lower half of each membrane for signal normalization.

e3 Cell Reports 25, 1318–1331.e1–e4, October 30, 2018

QUANTIFICATION AND STATISTICAL ANALYSIS Descriptive statistics were reported as median and range (or mean ± standard deviation) for continuous variables, and frequencies and proportions for categorical variables. The estimated rates of local, regional and distant control were analyzed by the competing risk method. OS was calculated with the Kaplan-Meier method. Univariate and multivariable analysis using a Cox proportional hazards model was applied to identify predictors for OS using two-sided testing. Competing risk methods were conducted on localregional control and distant metastasis. Mixed model regression was applied on the longitudinal samples with repeated-measures. Results were considered significant if the p value was % 0.05. Graphs of tumor growth curves were generated using GraphPad Prism 6. All the statistical analyses were conducted using SAS 9.4 and R (http://CRAN.R-project.org, R Foundation, Vienna, Austria). DATA AND SOFTWARE AVAILABILITY The accession number for the aligned sequencing reads from the targeted DNA sequencing of 112 patient samples reported in this paper (Figure 2) is EGA: EGAS00001002979.

Cell Reports 25, 1318–1331.e1–e4, October 30, 2018 e4