«Authors: Curtis R Pickering1, Jiexin Zhang2, David M Neskey1, Mei Zhao1, Samar A Jasser1, Jiping Wang1, Alexandra Ward1, C Jillian Tsai3, Marcus V ...»
Title: Squamous cell carcinoma of the oral tongue in young non-smokers is genomically
similar to tumors in older smokers.
Curtis R Pickering1, Jiexin Zhang2, David M Neskey1, Mei Zhao1, Samar A Jasser1, Jiping
Wang1, Alexandra Ward1, C Jillian Tsai3, Marcus V Ortega Alves1, Jane H Zhou4, Jennifer
Drummond6, Adel K El-Naggar4, Richard Gibbs6,7, John N Weinstein2,5, David A Wheeler6, Jing
Wang2, Mitchell J Frederick1, Jeffrey N Myers1
1 Department of Head and Neck Surgery, 2Department of Bioinformatics and Computational Biology, 3Department of Radiation Oncology, 4Department of Pathology, 5Department of Systems Biology, The University of Texas MD Anderson Cancer Center, 6Human Genome Sequencing Center, 7Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas;
Running Title: Genomic analysis of young tongue cancer.
Keywords: Young tongue cancer, SCCOT, Head and neck/oral cancers, Tobacco Abbreviations: squamous cell carcinoma of the oral tongue (SCCOT), head and neck squamous cell carcinoma (HNSCC), young tongue (YT), old tongue (OT), MD Anderson Cancer Center (MDA), TCGA HNSCC project (HNSC), copy number alterations (CNA), lung adenocarcinoma (LUAD), bladder urothelial carcinoma (BLCA) Notes: word count 3498, 2 figures and 2 tables Financial support: This work was supported by the Cancer Prevention Research Institute of Texas grant RP100233; National Institutes of Health (NIH)/National Institute of Dental and Craniofacial Research grant RC2DE020958; NIH Specialized Program of Research Excellence grants P50CA097007; Cancer Center Support Grant P30CA16672 and the Pantheon Program.
Corresponding author: Jeffrey N Myers, Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 1445, Houston TX 77030. Phone: 713-745-2667; Fax: 713-794-4662; Email: email@example.com Conflicts: The authors disclose no potential conflicts of interest.
Statement of Translational Relevance: Genomic alterations do not explain the increasing incidence of oral tongue cancer in young patients, and smoking does not dramatically alter the genome of tongue cancer at any age. Therefore, the causes of tongue cancer are still largely unknown, and their identification could provide novel avenues for therapeutic intervention.
Purpose: Epidemiological studies have identified an increasing incidence of squamous cell carcinoma of the oral tongue (SCCOT) in younger patients.
Experimental Design: DNA isolated from tongue tumors of young (45 yrs, non-smokers) and old (45 yrs) patients at was subjected to whole-exome sequencing and copy number analysis.
These data were compared to data from similar patients in the The Cancer Genome Atlas (TCGA) project.
Results: In this study, we found that gene-specific mutation and copy number alteration frequencies were similar between young and old SCCOT patients in two independent cohorts.
Likewise, the types of base changes observed in the young cohort were similar to those in the old cohort even though they differed in smoking history. TCGA data also demonstrate that the genomic effects of smoking are tumor-site specific, and we find that smoking has only a minor impact on the types of mutations observed in SCCOT.
Conclusions: Overall, tumors from young SCCOT patients appear genomically similar to those of older SCCOT patients, and the cause for the increasing incidence of young SCCOT remains unknown. These data indicate that the functional impact of smoking on carcinogenesis in SCCOT is still poorly understood.
Introduction: Epidemiological studies have recently identified increasing incidence of squamous cell carcinoma of the oral tongue (SCCOT) in younger patients(1-3). These tumors are not HPV-related and are often found in women who are non-smokers(2, 4). Those observations contrast with most SCCOT cases which are seen in older men with a history of cigarette smoking(5). The causes for this increasing incidence in the young are unknown. We hypothesized that this epidemiologically distinct disease would also prove to be genomically distinct, especially with respect to alterations caused by smoking, and that a better understanding of the differences would identify novel opportunities for treatment and/or prevention. Hence, we undertook the sequencing and integrated genomic profiling of a cohort of younger SCCOT patients, as reported here.
Patients and Methods: Fresh-frozen surgically resected previously untreated tumor tissue and matched nonmalignant adjacent tissue were obtained from consented patients treated for head and neck squamous cell carcinoma (HNSCC) at The University of Texas MD Anderson Cancer Center (Houston, TX), under an Institutional Review Board-approved protocol. Young tongue patients were chosen based on oral tongue primary tumor site, age less than 46 and less than 1 pack-year smoking history. Old tongue patients were chosen based on oral tongue primary tumor site and age greater than 45. Patient characteristics are shown in Table S1. Exome DNA was captured with Nimblegen reagents (Nimblegen) and sequenced on a SOLiD or Illumina platform as described previously(6). SNP analysis was conducted on SNP6.0 or CytoscanHD arrays (Affymetrix), and the data were analyzed with Partek (v6.6, Partek Inc.), ASCAT (v2.1), GISTIC (v2.0.12), and R software, as described previously(6). We have previously reported sequencing and copy number data from some of these patients(6, 7). Mutation data can be found in Supplemental Table S3.
A similar cohort of patients was identified from within the TCGA HNSCC project. YT patients were chosen based on oral tongue site, age less than 46 and lifelong non-smoker or reformed smoker with unknown pack-year history. OT were chosen based on oral tongue site and age greater than 45. Patient characteristics are shown in Table S1. Mutation and copy number data were obtained from the TCGA pan-cancer project(8) and the Synapse website(9) (www.synapse.org, doi:10.7303/syn300013).
The statistical significance of mutation and copy number frequencies was determined by Fisher’s exact test. Mutation type frequencies were determined for each patient, excluding indels and multiple-base mutations. Group frequencies were determined by averaging the individual patient frequencies: this prevented skewing of the data by patients with large numbers of mutations.
Mutation type frequencies were compared by using the t-test on arcsine transformed data.
Results: DNA from 16 young tongue (YT) and 28 old tongue (OT) patients treated at MD Anderson Cancer Center (MDA) was subjected to whole-exome sequencing (Table S1). A median of 29 and 83 mutations were identified in the YT and OT tumors, respectively. The elevated number of mutations in the OT is not surprising, since mutation number is known to be related to both age and smoking (10). This finding was validated in The Cancer Genome Atlas (TCGA) cohort, in which the median number of mutations increased from 63 in YT to 112 in OT. Figure S1 shows the relationships between mutation number and age. We next asked whether any specific genes were mutated at a different frequency in the YT cohort. Because of the low sample size we limited our initial search to the most significantly mutated genes identified by MutSig in the entire TCGA HNSCC project (HNSC). TP53 (unadjusted p=0.015) showed a slight increase in mutation frequency in the MDA YT cohort, but it was not statistically significant when adjusted for multiple testing (Table 1, Fig 1). Comparable analysis was then performed on the TCGA cohort. The TP53 mutation frequency was also elevated in the TCGA YT patients (Table 1, Fig 1). In order to increase statistical power the two cohorts were combined. Three genes showed trends toward statistical significance; FAT1, TP53, and PIK3CA (Table 1, Fig 1). However, none of those genes showed a statistically significant difference between the combined YT and OT patient cohorts. The trend of increased TP53 mutations in YT is provocative since the YT lack exposure to cigarette smoke, which has been associated with TP53 mutations. FAT1 and PIK3CA showed a lower mutation frequency in the YT cohort.
Mutation frequencies for HPV-positive tumors and the entire TCGA cohort are shown for comparison (Table 1). An analysis of mutation frequencies in all genes in the combined cohorts was also performed, but no genes were found to be significantly different. Additional subset analysis for really young tongues (30yo), OT smokers, and OT non-smokers are shown in Table S2.
Whole genome copy number analysis was also performed. We compared the number and size of copy number alterations (CNA) between the YT and OT cohorts, identifing an average of 129 CNAs in YT samples and 72 in the OT samples from the MDA cohort (Table 2), and 62 and 64 CNAs in the YT and OT samples from the TCGA cohort, respectively (Table 2). These differences were not statistically significant, although the YT had a smaller mean segment length for copy number gains (p=0.00817) (Table 2) in the MDA cohort. There was no difference in segment length of losses in between YT and OT in either cohort (Table 2). The mean copy number of gains or losses was also not different for gains or losses in either cohort (Table 2).
Finally, no specific genomic regions were found to be significantly different between the YT and OT patients in either the MDA cohort or the TCGA cohort. Analysis of some genes frequently gained or lost in HNSCC revealed modest differences in frequency between YT and OT (Fig 1).
For example, gains in FADD and PIK3CA were less frequent in YT, but the difference was not significant (Fig 1). Overall, the CNAs were very similar between the YT and OT cohorts, and the regions of copy number change were similar to those reported previously(6).
Since smoking is known to leave its mark on the genome by causing certain types of mutations, we compared mutation types in the YT and OT patients. Taking into account directional redundancy, six types of mutations can be distinguished. The frequencies of these 6 types of mutations have been shown to vary across tumor types(8, 11), but we found no significant difference in that respect for YT and OT patients in either the MDA or TCGA cohort (Fig 2A).
The profiles resembled that of all head and neck tumors in the TCGA project. The profile, however, was distinct from that of HPV+ tumors or laryngeal tumors (Fig 2A). HPV+ tumors show an increase in CT mutations (p0.0001) and decreases in CA, AT (both p0.0001) and AG (p=0.0074) mutations when compared with HPV- HNSC tumors. Laryngeal tumors show a decrease in CT mutations (p0.0001) and increases in CA and AT mutations (both p0.0001) when compared with non-laryngeal tumors (Fig 2A). It was expected that OT tumors would exhibit a mutation signature related to cigarette smoking when compared with the YT tumors from non-smokers. The similarity between YT and OT mutation signatures could indicate either the presence of a smoking signature in the YT tumors or a lack of a smoking signature in the OT tumors. To address those alternative possibilities, we investigated the smoking signatures in other tumor sites from the TCGA project.
The mutational events linked to smoking are traditionally reported as an increase in CA mutations and a decrease in CT mutations. TCGA collected smoking history information for lung adenocarcinoma (LUAD), bladder urothelial carcinoma (BLCA), and HNSC. Each of these tumor types was analyzed for the presence of a smoking mutation signature. In LUAD tumors CA mutations increased 23.7 percentage points in smokers when compared to non-smokers, and CT mutation decreased 18.7 percentage points (Fig 2B, C). AT mutations were also found to increase 2.9 percentage points in smokers (Fig 2B, C).
BLCA showed a very different profile, indicating the effect of tissue of origin. BLCA show a much higher frequency of CT and CG mutations and a lower frequency of CA when compared with LUAD (Fig 2B). In BLCA the impact of smoking is quite different from that in LUAD. The largest change with smoking in BLCA is a decrease in CG of 6.6 percentage points (Fig 2B, C). CA is also increased 4.5 percentage points, but neither change is statistically significant (Fig 2B, C).
We next examined the impact of smoking on the mutation profile of HNSC. The changes in HNSC were generally similar to those in LUAD but of lower magnitude. We found a decrease in CT of 13.2 percentage points and an increase in CA of 7.3 percentage points that were consistent with the changes in LUAD (Fig 2B, C). HNSC also demonstrated increases in AG and AT of 3.8 and 3.1 percentage points, respectively (Fig 2B, C, S2A).
Having defined the smoking signature, we could then ask whether YT and OT tumors exhibit such a signature. For this analysis we combined the YT and OT tumors into one cohort (YT/OT) and compared it with the HNSC smoking and non-smoking cohorts. The YT/OT cohort had a lower CA frequency and a higher CT frequency when compared with the HNSC smoking cohort (Fig 2C), indicating that the YT/OT tumors lack the most definitive characteristics of the smoking signature. However, the YT/OT also demonstrated increased AG when compared with the HNSC non-smoking tumors and an intermediate level of AT when compared to both smoking and non-smoking cohorts (Fig 2C). Similar results were obtained when the YT and OT cohorts were separately compared to the non-smoking and smoking cohorts, although with lower statistical significance (data not shown) or when smoking and non-smoking cohorts within the OT cohort were compared to each other or YT (Fig S2B). Overall, the mutation profile of the YT/OT tumors appears most similar to the profile of non-smokers.
Since both YT and OT tumors lack a smoking signature we next searched for the source of the smoking signature in HNSC. We found that tumors from the laryngeal subsite exhibit a smoking signature when compared with tumors from all other sites (Fig 2A, C, S2A, B) with an increase in CA mutations and a decrease in CT mutations. Additionally, the smoking signature within the oral tongue subsite is less recognizable than the overall HNSC smoking signature (Fig 2C).
Those observations suggest that the smoking signature in HNSC is largely driven by the laryngeal subsite.