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Evolutionary Action Score of TP53 Analysis in Pathologically High-Risk HPV-Negative Head and Neck Cancer from a Phase II Clinical Trial: NRG Oncology RTOG 0234
Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Maxillofacial Surgery, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
Corresponding Author: Jeffrey N. Myers, MD Anderson Cancer Center, Department of Head and Neck Surgery, 1515 Holcombe Boulevard, Unit 1445. Houston, TX 77030-4009 US. Phone: (713) 745-2667
An evolutionary action scoring algorithm (EAp53) based on phylogenetic sequence variations stratifies head and neck squamous cell carcinoma (HNSCC) patients bearing TP53 missense mutations as high-risk, associated with poor outcomes, or low-risk, with similar outcomes as TP53 wild-type, and has been validated as a reliable prognostic marker. We performed this study to further validate prior findings demonstrating that EAp53 is a prognostic marker for locally advanced HNSCC patients, and explore its predictive value for treatment outcomes to adjuvant bio-chemoradiotherapy.
METHODS
Eighty-one resection samples from patients treated surgically for stage III or IV human papillomavirus-negative HNSCC with high-risk pathologic features, who received either radiotherapy+cetuximab+cisplatin (cisplatin) or radiotherapy+cetuximab+docetaxel (docetaxel), as adjuvant treatment in a phase II study were subjected to TP53 targeted sequencing and EAp53 scoring to correlate with clinical outcomes. Due to the limited sample size, patients were combined into two EAp53 groups: wild-type/low-risk and high-risk/other.
RESULTS
At a median follow-up of 9.8 years, there was a significant interaction between EAp53 group and treatment for overall survival (OS) (P =.008), disease-free survival (DFS) (P = .05) and distant metastasis (DM) (P = .004). In wild-type/low-risk group, the docetaxel arm showed significantly better OS [HR 0.11 (0.03-0.36)]. DFS [HR 0.24 (0.09-0.61)], and less DM [HR 0.04 (0.01-0.31)] than the cisplatin arm. In high-risk/other group, differences between treatments were not statistically significant.
CONCLUSION
The docetaxel arm was associated with better survival than the cisplatin arm for patients with wild-type/low-risk EAp53. These benefits appear to be largely driven by a reduction in DM.
Head and neck squamous cell carcinoma (HNSCC) is a common cause of cancer-related deaths worldwide.1 Surgery followed by RT and concurrent administration of high-dose cisplatin is a current global standard therapy for those patients who have operable HNSCC with either extranodal extension (ENE) and/or positive margin upon pathologic review.2-6 Subsequent studies have been conducted to determine whether more effective and less toxic post-operative chemoradiotherapeutic regimens can be used in this disease setting. One such study is xxxx, a phase II randomized clinical trial in which patients treated surgically for stage III or IV HNSCC with high-risk pathologic features received either RT+cetuximab+cisplatin (cisplatin) or RT+cetuximab+docetaxel (docetaxel) as adjuvant treatment.7 The primary endpoint of this trial was published and demonstrated that both arms had improved outcomes with better overall survival (OS) and disease-free survival (DFS) rates than in the historical standard, which is the RT+cisplatin arm of —-, but the largest improvement was noted for the docetaxel arm.7
Whereas most treatment decisions for HNSCC patients have been based on clinical staging and pathologic evaluation, molecular biomarkers that could provide unique insight into tumor biology have great potential to complement imaging- and pathology-based staging. One potential candidate biomarker for HNSCC is the TP53 gene, as multiple studies have demonstrated an association between TP53 mutations and decreased survival rates.8-11 Additionally, recent next-generation sequencing technology confirmed that TP53 is the most common somatically mutated gene in HNSCC.12-14 However, TP53 mutational status has yet to be incorporated into clinical practice for treatment selection. To that end, Katsonis et al. established a novel computational scoring system named EAp53,15 which uses an evolutionary action (EA) score based on phylogenetic sequence variations and speciation to stratify HNSCC patients bearing TP53 missense mutations into either a high-risk group associated with poor outcomes or a low-risk group with similar outcomes to patients with wild-type TP53. The EAp53 score has been validated as a reliable prognostic marker in several clinical cohorts.16,17 Additionally, in pre-clinical study, TP53-mutated and -wild-type HNSCC cell lines treated in vitro or in vivo with cisplatin indicated that high-risk EAp53 confer cisplatin resistance not seen in cell with wild-type or low-risk EAp5316,17 while we have not observed TP53 mutations to be associated with taxane resistance using same model (unpublished data).
The primary objective of this study was to determine whether EAp53 is prognostic for patients in xxxx. The secondary objective was to determine whether EAp53 is predictive of treatment outcome. We hypothesized that high-risk EAp53 would show worse clinical outcomes in the entire XXXX cohort compared to low-risk EAp53 and that high-risk EAp53 would show worse clinical outcomes in the cisplatin arm compared to the docetaxel arm while there will be no difference in outcomes between treatment arms among low-risk EAp53 group. Lastly, for comparison purposes, we performed the same analyses with the TP53 classification method established by Poeta and colleagues, which is based on protein folding.9,18
METHODS and MATERIALS
All studies here were approved by the Institutional Review Board and conducted in accordance with Declaration of Helsinki. Waiver of written informed consent was provided as part of the approval process prior to sample collection.
EAp53 Scoring
EA scores for individual TP53 mutations were obtained from the EAp53 server at xxxx College of Medicine (http://xxxx). Based on previous analysis,16 scores below 75 were categorized as low-risk EAp53, and those 75 or above were categorized as high-risk EAp53. Mutations that were not missense mutations were designated “other”. Patients who had both missense and other mutations were classified using the missense mutation. Those whose tumors had both low-risk and high-risk EAp53 were classified as having high-risk EAp53.
The Cancer Genome Atlas (TCGA) Database
In order to monitor the EAp53 impact on survival, the clinico-pathological and TP53 sequencing information for latest HNSCC patients in TCGA were extracted from the National Cancer Institute Genomic Data Commons (https://gdc.cancer.gov) or firebrowse.org. Patient criteria, analysis method, and TP53 mutation status were described in the Supplementary Data and Supplementary Table S1.
XXXX Sample Collection
Tumor samples resected from patients enrolled on XXXX were used to extract genomic DNA and determine EAp53 status. All patients had American Joint Committee on Cancer (AJCC) pathologic stage III or IV SCC of the oral cavity, oropharynx, hypopharynx, or larynx and had undergone gross total resection. Also, patients had one or more pathologically high-risk factors: positive margin and/or ENE and/or two or more positive nodes.
Among 203 analyzable patients, all 151 available resected tumor samples were received as 5- or 10-μm-thick sections of formalin-fixed paraffin-embedded tissue samples from the yyyy bank (Figure 1). Samples were quality-controlled, reviewed to confirm the diagnosis, annotated, and anonymized before being provided to our laboratory through an approval by the National Clinical Trials Network Core Correlative Sciences Committee.
Figure 1CONSORT diagram. HPV, human papillomavirus; RT, radiation therapy.
Isolated DNA was sequenced on MiSeq (Illumina, San Diego, CA). Precise information was described in the Supplementary Data.
Statistical Analysis of XXXX Cohort
DFS and OS rates were estimated by the Kaplan-Meier method. Local-regional failure (LRF) and distant metastasis (DM) rates were estimated by the cumulative incidence method. Patients excluded from XXXX primary analysis were also excluded here.7 Missing data analysis was performed by comparing patients with known EAp53 against those with unknown EAp53 for DFS, OS, LRF, DM, and patient characteristics. Patient characteristics between groups were compared by Fisher's exact test or Wilcoxon rank-sum test. Automated immunohistochemistry staining and in situ hybridization were performed for detecting p16 and human papillomavirus (HPV) status, respectively.7 Due to low prevalence of TP53 mutations in HPV-positive tumors, primary analysis was limited to patients with HPV-negative tumors, defined as follows: for oropharynx primary site, both HPV-negative and p16-negative were defined as negative and HPV-positive and/or p16-positive were defined as positive; for other primary sites, only HPV status was considered. To be included in analysis, patients had to have no missing data for the following variables: assigned treatment, age, gender, race, Zubrod performance status, smoking history, primary site, T stage, N stage, ENE, positive margin, and number of positive nodes. Due to small sample sizes, patients were combined into two EAp53 groups: wild-type/low-risk and high-risk/other. For prognostic analysis, EAp53 groups were compared by log-rank test. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated by Cox model with and without assigned treatment and additional covariates. The number of additional covariates was limited to treatment+2 for OS, treatment+3 for DFS, and only treatment for LRF and DM, due to low numbers of events. Model selection was performed using AIC. For predictive analysis, HRs were estimated for models including EAp53, treatment, and the interaction of EAp53 and treatment with and without additional covariates as described above. All statistical tests were two-sided with alpha of 0.05. With the 81 patients available for these analyses, the statistical power to detect a HR of 2.5 on OS, DFS, LRF and DM associated with EAp53 status was 87%, 92%, 67% and 66%, respectively, based on a Cox model. The SAS version 9.4 was used for analysis.
Poeta Classification Method
The same analyses for the same patients were performed replacing EAp53 with Poeta rules+Splice method.9,18 Precise information is described in the Supplementary Data.
RESULTS
EAp53 Impact on Outcome in TCGA Cohort
Among 432 HPV-negative HNSCC patients in TCGA, 78 patients (18%) were categorized as low-risk, 140 (32%) were categorized as high-risk, 135 (31%) were categorized as other, with 79 (18%) wild-type for EAp53. Kaplan-Meier survival curve in Figure 2A showed that the 5-year OS rate was 60.2% for wild-type, 51.6% for low-risk [HR low-risk/wild-type 1.15 (0.67-1.99); P = .61], 38.2% for high-risk [HR high-risk/wild-type 1.67 (1.04-2.69); P = .03], and 41.3% for other EAp53 [HR other/wild-type 1.55 (0.96-2.51); P = .07]. Given the similarity between the outcomes for wild-type and low-risk or high-risk and other, we combined them into two groups: wild-type/low-risk and high-risk/other, to improve the statistical power of our analysis. The 5-year OS rate was therefore 55.3% for wild-type/low-risk, 39.8% for high-risk/other group [HR 1.49 (1.08-2.06); P = .014, Figure 2B].
Figure 2Overall survival (OS) estimates in 432 patients with HPV-negative Head and Neck Squamous Cell Carcinoma (HNSCC) from The Cancer Genome Atlas (TCGA), (A) by each EAp53 status and (B) by EAp53 group. WT, wild-type
Figure 2Overall survival (OS) estimates in 432 patients with HPV-negative Head and Neck Squamous Cell Carcinoma (HNSCC) from The Cancer Genome Atlas (TCGA), (A) by each EAp53 status and (B) by EAp53 group. WT, wild-type
One hundred forty-one of 151 patients had successful TP53 mutation calls (Supplementary Figure S1, Tables S2 and S3). We observed 10 sequencing failures: 9 due to low DNA yield and 1 due to low DNA quality. Seventy-nine of 141 sequenced patients (56%) had a total of 101 mutations. Of these mutations, 62 were missense mutations that could be scored by EAp53. Twenty of 79 patients (25%) had multiple mutations. Twenty-one of 141 patients (15%) were classified as low-risk, 33 (23%) were classified as high-risk, 25 (18%) were classified as other, with 62 (44%) wild-type for EAp53.
Patient Characteristics in XXXX HPV-Negative Cohort
The patient and tumor characteristics for 81 patients with HPV-negative tumors and complete data for covariates are summarized in Table 1. There was no imbalance in all covariates between treatment arms for EAp53 subset of patients. Sixty-eight of 81 patients (84%) had TP53 mutations; low-risk were 19 (23%), high-risk were 27 (33%), other mutations were 22 (27%), with 13 (16%) wild-type. Overall, 52% had Zubrod performance status 1, 58% had ENE, and 37% had positive margins. Primary sites were oral cavity (65%), oropharynx (14%), larynx (14%), and hypopharynx (7%). In wild-type/low-risk group, 34% of patients had Zubrod 1, compared with 63% in high-risk/other group. Seventy-five percent and 59% of patients in wild-type/low-risk and high-risk/other groups, respectively, had oral cavity tumors. In wild-type/low-risk group, 47% of patients had ENE, compared with 65% in high-risk/other group. Fifty percent and 29% in wild-type/low-risk and high-risk/other groups, respectively, had positive margins.
Table 1Patient and Tumor Characteristics by EAp53 and Assigned Treatment
Characteristic
Wild-type/Low-risk
High-risk/Other
Cisplatin
Docetaxel
Total
Cisplatin
Docetaxel
Total
(n=12)
(n=20)
(n=32)
(n=25)
(n=24)
(n=49)
Age (years)
P = .39a
P = .94a
Mean
58.2
55.1
56.2
55
53.6
54.3
Standard deviation
9.73
11.57
10.86
11.43
13.71
12.49
Median
57.5
58.5
58
57
56.5
57
Min - max
38 - 69
25 - 77
25-77
27 - 74
21 - 79
21-79
First - third quartiles
53.5 - 66.5
50.5 - 62.5
52-63
49 - 62
47 - 62
49-62
Gender
P = .21b
P = .77b
Male
11 (92%)
14 (70%)
25 (78%)
17 (68%)
15 (63%)
32 (65%)
Female
1 (8%)
6 (30%)
7 (22%)
8 (32%)
9 (38%)
17 (35%)
Race
P = .62b
P = .61b
White
10 (83%)
18 (90%)
28 (88%)
22 (88%)
23 (96%)
45 (92%)
Non-white
2 (17%)
2 (10%)
4 (13%)
3 (12%)
1 (4%)
4 (8%)
Zubrod performance status
P = 1.00b
P = 1.00b
0
8 (67%)
13 (65%)
21 (66%)
9 (36%)
9 (38%)
18 (37%)
1
4 (33%)
7 (35%)
11 (34%)
16 (64%)
15 (63%)
31 (63%)
Smoking history
P = .18a
P = .79a
Never
2 (17%)
7 (35%)
9 (28%)
4 (16%)
5 (21%)
9 (18%)
Former
7 (58%)
11 (55%)
18 (56%)
18 (72%)
16 (67%)
34 (69%)
Current
3 (25%)
2 (10%)
5 (16%)
3 (12%)
3 (13%)
6 (12%)
Primary site
P = .68b
P = 1.00b
Oral Cavity
10 (83%)
14 (70%)
24 (75%)
15 (60%)
14 (58%)
29 (59%)
Oropharynx
1 (8%)
2 (10%)
3 (9%)
4 (16%)
4 (17%)
8 (16%)
Hypopharynx
0
1 (5%)
1 (3%)
2 (8%)
3 (13%)
5 (10%)
Larynx
1 (8%)
3 (15%)
4 (13%)
4 (16%)
3 (13%)
7 (14%)
Surgical-pathologic T stage
P = .54a
P = .44a
T1
2 (17%)
5 (25%)
7 (22%)
1 (4%)
5 (21%)
6 (12%)
T2
5 (42%)
6 (30%)
11 (34%)
7 (28%)
4 (17%)
11 (22%)
T3
1 (8%)
7 (35%)
8 (25%)
5 (20%)
5 (21%)
10 (20%)
T4
4 (33%)
2 (10%)
6 (19%)
12 (48%)
10 (42%)
22 (45%)
Surgical-pathologic N stage
P = .55a
P = .15a
N0
0
2 (10%)
2 (6%)
2 (8%)
0
2 (4%)
N1
2 (17%)
2 (10%)
4 (13%)
4 (16%)
2 (8%)
6 (12%)
N2a
1 (8%)
1 (5%)
2 (6%)
0
0
0
N2b
7 (58%)
14 (70%)
21 (66%)
13 (52%)
14 (58%)
27 (55%)
N2c
2 (17%)
1 (5%)
3 (9%)
6 (24%)
6 (25%)
12 (24%)
N3
0
0
0
0
2 (8%)
2 (4%)
Surgical-pathologic AJCC stage
P = 1.00b
P = .67b
III
1 (8%)
3 (15%)
4 (13%)
4 (16%)
2 (8%)
6 (12%)
IV
11 (92%)
17 (85%)
28 (88%)
21 (84%)
22 (92%)
43 (88%)
Extranodal extension
P = 1.00b
P = .77b
No
6 (50%)
11 (55%)
17 (53%)
8 (32%)
9 (38%)
17 (35%)
Yes
6 (50%)
9 (45%)
15 (47%)
17 (68%)
15 (63%)
32 (65%)
Positive margin
P = 1.00b
P = .75b
No
6 (50%)
10 (50%)
16 (50%)
17 (68%)
18 (75%)
35 (71%)
Yes
6 (50%)
10 (50%)
16 (50%)
8 (32%)
6 (25%)
14 (29%)
Two or more positive nodes
P = .37b
P = .70b
No
1 (8%)
5 (25%)
6 (19%)
5 (20%)
3 (13%)
8 (16%)
Yes
11 (92%)
15 (75%)
26 (81%)
20 (80%)
21 (88%)
41 (84%)
Abbreviation: AJCC, American Joint Committee on Cancer.
a Wilcoxon rank-sum test
b Fisher's exact test; primary site was tested as oral cavity vs. others
EAp53 as a Prognostic Biomarker in XXXX HPV-Negative Cohort
OS, DFS, LRF, and DM estimates by EAp53 group are shown in Figure 3. Before adjustment for treatment or covariates, high-risk/other group had significantly worse OS and DFS than wild-type/low-risk group [HR 1.87 (1.00-3.48); P = .05 and HR 1.74 (1.00-3.01); P = .05, respectively]. However, after adjustment, the difference was no longer significant [HR 1.56 (0.80-3.04); P = .19 for OS and HR 1.55 (0.85-2.81); P = .15 for DFS]. In LRF, we observed no significant difference between the EAp53 groups in analysis both before and after adjustment for treatment; the HRs (high-risk/other vs. wild-type/low-risk) were 1.25 (0.59-2.64) [P = .57] and 1.17 (0.55-2.52) [P = .68], respectively. In DM, we also observed no significant difference between the EAp53 groups in analyses both before and after adjustment for treatment; the HRs (high-risk/other vs. wild-type/low-risk) were 1.62 (0.73-3.59) [P = .24] and 1.31 (0.58-2.97) [P = .52], respectively. Lung was the first distant metastatic site with or without other sites in 22 of 28 patients (79%).
Figure 3(A) Overall survival, (B) disease-free survival, (C) local-regional failure, and (D) distant metastasis estimates by EAp53 group with treatment arms combined in XXXX HPV-negative cohort. WT, wild-type.
EAp53 as a Predictive Biomarker in XXXX HPV-Negative Cohort
OS, DFS, LRF, and DM estimates by EAp53 group and treatment assignment are shown in Figure 4. Final multivariate models are shown in Table 2. For OS, before adjustment for covariates, we found a significant interaction between EAp53 group and assigned treatment (P = .01); the HR comparing the docetaxel to the cisplatin arm was 0.12 (0.04-0.39) in wild-type/low-risk and 0.67 (0.34-1.33) in high-risk/other group. After adjustment for covariates, we again found a significant interaction between EAp53 group and assigned treatment (P = .008); the HR comparing the docetaxel to the cisplatin arm was 0.11 (0.03-0.36) in wild-type/low-risk and 0.71 (0.36-1.40) in high-risk/other group. For DFS, before adjustment for covariates, we did not observe a significant interaction between EAp53 group and assigned treatment (P = .14); the HR comparing the docetaxel to the cisplatin arm was 0.29 (0.12-0.71) in wild-type/low-risk and 0.67 (0.35-1.26) in high-risk/other group. However, after adjustment for covariates, we found a significant interaction between EAp53 group and assigned treatment (P = .05); the HR comparing the docetaxel to the cisplatin arm was 0.24 (0.09-0.61) in wild-type/low-risk and 0.74 (0.39-1.39) in high-risk/other group. For LRF, we did not see a significant interaction between EAp53 group and assigned treatment without adjustment for covariates (P = .42); the HR comparing the docetaxel to the cisplatin arm was 0.49 (0.15-1.62) in wild-type/low-risk and 0.92 (0.36-2.31) in high-risk/other group. For DM, we found a significant interaction between EAp53 group and assigned treatment without adjustment for covariates (P = .004); the HR comparing the docetaxel to the cisplatin arm was 0.04 (0.01-0.31) in wild-type/low-risk and 1.05 (0.42-2.59) in high-risk/other group. There were too few events to adjust for covariates for both LRF and DM.
Figure 4(A) Overall survival, (B) disease-free survival, (C) local-regional failure, and (D) distant metastasis estimates by EAp53 group and treatment arm in XXXX HPV-negative cohort. WT, wild-type; LR, low-risk; HR, high-risk; cis, cisplatin; doc, docetaxel.
In both TCGA and XXXX HPV-negative cohort, disruptive mutation showed no discriminatory power of prognosis comparing to nondisruptive mutation (Supplementary Figures S2 and S3). As a predictive biomarker to treatment outcome in XXXX HPV-negative cohort, we found a significant interaction between Poeta rules+Splice group and assigned treatment after adjusting for covariates for OS (P = .04). However, regarding other outcomes, we did not see any significance (Supplementary Figure S4 and Table S4).
DISCUSSION
We confirmed that EAp53 status is a prognostic marker in HNSCC in HPV-negative TCGA cohort, however, EAp53 was not prognostic in XXXX HPV-negative cohort (n=81) when adjusting for additional covariates. This difference may be due to the poor prognostic features used as inclusion criteria in XXXX, the small sample size, or potential baseline covariate imbalance between biomarker subgroups, as suggested by the adjusted analysis (Supplementary Table S5). Interestingly, EAp53 was also not prognostic in HPV-negative TCGA data when the HNSCC cohort was filtered with the same criteria as XXXX (Supplementary Figure S5). Overall, these results might suggest that the importance of EAp53 as a prognostic biomarker could vary depending on the disease setting. For example, it may not be prognostic in locally-advanced tumors that are treated with cisplatin-based therapy, but could be prognostic in lower stage tumors. These hypotheses should be confirmed with well-designed studies involving a larger number of patients for each disease setting.
We next explored the use of EAp53 as a predictive biomarker. We found EAp53 status to be predictive of outcome to cisplatin- and docetaxel-based combination bio-chemoradiotherapy in patients with pathologically high-risk HPV-negative HNSCC. In wild-type/low-risk group, the docetaxel arm was associated with better OS, DFS, and lower DM rates than the cisplatin arm. We found a significant treatment effect favoring docetaxel over cisplatin in wild-type/low-risk but there was no evidence of difference in clinical outcomes in high-risk/other group by treatment arm. We therefore concluded that these observed differences in outcome were driven by differential response to the treatments in the wild-type/low-risk group.
These results are not consistent with our original hypothesis, especially for wild-type/low-risk group. Preclinical findings in HPV-negative HNSCC cell lines demonstrated relative cisplatin resistance in cell lines with high-risk EAp53 compared to those with wild-type or low-risk EAp53.16,17 Besides, multiple clinical cohorts have shown that tumors bearing TP53 mutations are relatively resistant to cisplatin-based treatment compared to those with wild-type TP53.19,20 Conversely, both in vitro and in vivo, docetaxel has shown no difference in response based on TP53 mutation status, or slightly better impact in TP53 mutant cell lines than wild-type (unpublished data).21,22 Because we observed a difference in DM but not LRF, we hypothesized that tumors with wild-type/low-risk EAp53 might not only be resistant to the cisplatin-containing treatment but more aggressive after the treatment. Our most current experiments partially support this hypothesis as TP53 wild-type HPV-negative HNSCC cell line selected for cisplatin resistance retains TP53 wild-type status, and when being injected into the tongues of mice, it develops distance metastases whereas the parental cell line does not (unpublished data).
How much the inclusion of cetuximab may have affected the observed differences in outcomes between the two arms is not clear. The phase III clinical trial —- determined that the addition of cetuximab did not show significantly different outcomes compared to RT+cisplatin alone for 891 stage III or IV HNSCC patients although these patients didn't undergo surgery.23 The lack of difference could have been caused by the substantial rates of incomplete treatment in the cetuximab arm due to severe toxicity. In XXXX, both arms showed feasible and tolerated with predictive toxicity resulting in better survival rates relative to historical control, RT+cisplatin. However, the docetaxel arm showed most favorable outcomes compared with the cisplatin arm with regard to OS and DFS, therefore only this arm has commenced formal testing in the following randomized phase II/III trial zzzz, which is currently accruing patients. Validation of our findings in a cohort of patients treated without cetuximab will be necessary to determine whether cetuximab plays a role in the phenotype that can result in different treatment outcomes. Fortunately, zzzz includes the appropriate arms to test this. Another ongoing phase II randomized EA3132 trial for HNSCC patients with pathological stage III or IVA (AJCC 8): T3-T4a, N0-3, M0 or T1-T2, N1-3, M0 after total resection of the primary tumor may also be complementary used as a validation.24,25 This study stratifies patient by TP53 mutational status in adjuvant RT alone versus RT+cisplatin. The fundamental concepts underlying this study are similar to our xxxx trial in that they are both designed with the long-term goal of potentially selecting post-operative adjuvant therapy for surgically treated HNSCC patients based on TP53 mutational status. However, the studies differ in that EA3132 trial determines the TP53 mutational status prospectively and uses Poeta rules+Splice method. In addition, it is targeting patients with lower risk surgical pathology (no positive margins, ENE, and/or gross residual disease) than xxxx or zzzz.
Our findings may have only been apparent because we analyzed pathologically advanced HNSCC, the biology of which is different from early stage HNSCC. Sandulache et al. previously reported a strong association between high-risk/other EAp53 and ENE in oral SCC (OSCC) patients in TCGA.26 In the present study, 58% of the patients had ENE, and they were similarly enriched in high-risk/other EAp53. Of note, in the ENE-positive OSCC cohort of TCGA, we also observed a worse OS rate in wild-type/low-risk than in high-risk/other group (Supplementary Figure S6). Adjuvant treatments in the TCGA cohort were not controlled in any way, but many of the ENE-positive patients likely received adjuvant therapy including RT+cisplatin. Therefore, the worse outcomes in wild-type/low-risk patients may have been related to both the treatment regimen and the pathologically high-risk tumor biology, particularly ENE positivity. What this means mechanistically is unclear.
A limitation of this study is that the sample size is small and validation in other cohorts is necessary. Additionally, the sequenced tissue sites included not only primary tumor but also metastatic lymph nodes. Mutations can be heterogeneous within a primary tumor and across metastatic sites, so it is possible that some detected mutations may not be representative of the whole tumor.24 This variability is inherent to most sequencing studies but could be more controlled in future studies.
Taken together, our results demonstrate that RT+cetuximab+docetaxel may be a good post-operative treatment option for locally advanced HPV-negative HNSCC patients with wild-type/low-risk EAp53. This benefit appears to be largely driven by reduction in DM in wild-type/low-risk patients, and who had better survival rates in docetaxel-based treatment. These findings need validation before changing clinical practice. The ongoing zzzz addressed which adjuvant treatment has more promising DFS by comparing RT+cisplatin, RT+docetaxel, and RT+cetuximab+docetaxel in phase II, and is addressing which combination has better OS by comparing RT+cisplatin, RT+cetuximab+docetaxel, and RT+cisplatin+atezolizumab (new arm) in phase III for pathologically high-risk HNSCC in the post-operative setting. The planned TP53 mutation analysis in that trial will answer at least two questions related to our findings: 1) Can we validate the good outcome for wild-type/low-risk patients treated with RT+cetuximab+docetaxel? 2) Does cetuximab play a role in different outcomes? If the results of the zzzz trial validate our findings and justify clinical use, then incorporating TP53 sequencing and EAp53 scoring into standard clinical practice would be easy, given the availability of Clinical Laboratory Improvement Amendments (CLIA)-certified TP53 sequencing assays.
CONCLUSIONS
EAp53 status is a statistically significant predictive biomarker to adjuvant treatment outcome and superior to the stratification by Poeta rules+Splice method in this cohort. However, the Poeta method should still be analyzed in future studies, together with EAp53, because of its demonstrated utility in other previous studies.9,18
REFERENCES
1
Ferlay J, Soerjomataram I, Dikshit R, et al: Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 136:E359-86, 2015
2
Cooper JS, Pajak TF, Forastiere AA, et al: Postoperative concurrent radiotherapy and chemotherapy for high-risk squamous-cell carcinoma of the head and neck. N Engl J Med 350:1937-44, 2004
3
Bernier J, Domenge C, Ozsahin M, et al: Postoperative irradiation with or without concomitant chemotherapy for locally advanced head and neck cancer. N Engl J Med 350:1945-52, 2004
Erber R, Conradt C, Homann N, et al: TP53 DNA contact mutations are selectively associated with allelic loss and have a strong clinical impact in head and neck cancer. Oncogene 16:1671-9, 1998
9
Poeta ML, Manola J, Goldwasser MA, et al: TP53 mutations and survival in squamous-cell carcinoma of the head and neck. N Engl J Med 357:2552-61, 2007
10
Lindenbergh-van der Plas M, Brakenhoff RH, Kuik DJ, et al: Prognostic significance of truncating TP53 mutations in head and neck squamous cell carcinoma. Clin Cancer Res 17:3733-41, 2011
11
Sano D, Xie TX, Ow TJ, et al: Disruptive TP53 mutation is associated with aggressive disease characteristics in an orthotopic murine model of oral tongue cancer. Clin Cancer Res 17:6658-70, 2011
12
****
13
Stransky N, Egloff AM, Tward AD, et al: The mutational landscape of head and neck squamous cell carcinoma. Science 333:1157-60, 2011
14
Cancer Genome Atlas N: Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature 517:576-82, 2015
15
****
16
Neskey DM, Osman AA, Ow TJ, et al: Evolutionary Action Score of TP53 Identifies High-Risk Mutations Associated with Decreased Survival and Increased Distant Metastases in Head and Neck Cancer. Cancer Res 75:1527-36, 2015
17
Osman AA, Neskey DM, Katsonis P, et al: Evolutionary Action Score of TP53 Coding Variants Is Predictive of Platinum Response in Head and Neck Cancer Patients. Cancer Res 75:1205-15, 2015
18
Masica DL, Li S, Douville C, et al: Predicting survival in head and neck squamous cell carcinoma from TP53 mutation. Hum Genet 134:497-507, 2015
19
Temam S, Flahault A, Perie S, et al: p53 gene status as a predictor of tumor response to induction chemotherapy of patients with locoregionally advanced squamous cell carcinomas of the head and neck. J Clin Oncol 18:385-94, 2000
20
Perrone F, Bossi P, Cortelazzi B, et al: TP53 mutations and pathologic complete response to neoadjuvant cisplatin and fluorouracil chemotherapy in resected oral cavity squamous cell carcinoma. J Clin Oncol 28:761-6, 2010
21
Furuse S, Adachi M, Ijichi K, et al: Pre-radiation enhances the cytotoxicity of docetaxel in head and neck squamous cell carcinoma cells. Oncol Rep 23:1339-43, 2010
22
Tanaka N, Osman AA, Takahashi Y, et al: Head and neck cancer organoids established by modification of the CTOS method can be used to predict in vivo drug sensitivity. Oral Oncol 87:49-57, 2018
Chieko Michikawa, for The Cancer Genome Atlas (TCGA) Cohort, MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030 US. Phone: (713) 563-4764
Pedro A. Torres-Saavedra, for RTOG cohort, NRG Oncology, 50 South 16th Street, Suite 2800, Philadelphia, PA 19102 US. Phone: (215) 717-0851
Conflict of Interest:
C. Michikawa, N. Silver, P.M. Harari, M.S. Kies, D.I. Rosenthal, Q. Le, D.Y. Duose, S. Mallampati, S. Trivedi, R. Luthra, A.A. Osman, O. Lichtarge, U. Parvathaneni, D.N. Hayes, and J.N. Myers have nothing to disclose. R.L. Foote reports Grants or contracts from any entity-Unrestricted research funding from endowed named professorship paid to Mayo Clinic from Hitachi, Ltd, Royalties from textbook sales and writing content, royalties from licensing of patent, paid to me from Elsevier, UpToDate, Bionix. Honoraria was paid to me from Opportunity and Progress of Proton Therapy Clinical Opportunities to advance the field of particle therapy Mayo Clinic Guangzhou, China. Patent licensed to Bionix. Royalties paid to Mayo Clinic and to my department and to myself from Patent issued for TruGuard intra-oral radiotherapy stent. R.C. Jordan reports Grants or contracts from National Cancer Institute U24CA196067, National Institute of Allergy and Infectious Diseases P30AI027763, National Institute of Dental & Craniofacial Diseases R01DE026502. Royalties or licenses from Elsevier – Oral Pathology Clinical Pathologic Correlations Ed 7. Payment for expert testimony for 2021- 2 hours Evans Dixon Law Firm. Stock or stock options-Exact Sciences 4 shares. C.R. Pickering reports All support for the present manuscript-NIH grants. P.A.T-Saavedra reports All support for the present manuscript- NRG Oncology SDMC Grant from NCI. I.I. Wistuba reports Grants or contracts from any entity- Genentech, Oncoplex, HTG Molecular, DepArray, Merck, Bristol-Myers Squibb, Medimmune, Adaptive, Adaptimmune, EMD Serono, Pfizer, Takeda, Amgen, Karus, Johnson & Johnson, Bayer, Iovance, 4D, Novartis, and Akoya. Consulting fees from Genentech/Roche, Bayer, Bristol-Myers Squibb, Astra Zeneca/Medimmune, Pfizer, HTG Molecular, Asuragen, Daiichi Sankyo, Merck, GlaxoSmithKline, Guardant Health, Flame, Novartis, Sanofi, Oncocyte, and MSD. Payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Medscape, MSD, Genentech/Roche, Platform Health, Pfizer, AstraZeneca, Merck.
Funding Statement:
This project was supported by grants UG1CA189867 (NRG Oncology NCORP), U10CA180868 (NRG Oncology Operations), U10CA180822 (NRG Oncology SDMC), U24CA196067 (NRG Oncology Biospecimen Bank), from the National Cancer Institute (NCI), 5R01DE024601-05 from the National Institute of Dental & Craniofacial Research (NIDCR), Eli Lilly and Aventis Pharmaceuticals. This project was funded, in part, under a grant with the Pennsylvania Department of Health; the Department specifically disclaims responsibility for any analyses, interpretations, or conclusions. P.M. Harari acknowledges support from the NIH (P50 DE026787 - UW Head and Neck SPORE Grant). C. Michikawa was supported by JSPS KAKENHI (grant number 16K11718), and supported in part by the Fellowship of Astellas Foundation for Research on Metabolic Disorders.
Data Availability Statement
All original data of The Cancer Genome Atlas (TCGA) cohort are at the National Cancer Institute Genomic Data Commons (https://gdc.cancer.gov) or firebrowse.org. All published data of RTOG 0234 cohort from this paper will be available upon request in accordance with NRG Oncology's data sharing policy which can be found at https://www.nrgoncology.org/Resources/Ancillary-Projects-Data-Sharing-Application. All analyzed data during this study are in this published article or the Supplementary Data.
ACKNOWLEDGMENT
The authors thank Justin P. Windham for technical support of DNA extraction, Barbara Burtness and Christine H. Chung for advice about the Poeta rules+Splice method, and Scientific Publication Services (Donald R. Norwood).
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