High intratumor genetic heterogeneity is related to worse outcome in patients with head and neck squamous cell carcinoma

Abstract

Background: Although the presence of genetic heterogeneity within the tumors of individual patients is established, it is unclear whether greater heterogeneity predicts a worse outcome. A quantitative measure of genetic heterogeneity based on next-generation sequencing (NGS) data, mutant-allele tumor heterogeneity (MATH), was previously developed and applied to a data set on head and neck squamous cell carcinoma (HNSCC). Whether this measure correlates with clinical outcome was not previously assessed.

Methods: The authors examined the association between MATH and clinical, pathologic, and overall survival data for 74 patients with HNSCC for whom exome sequencing was completed.

Results: High MATH (a MATH value above the median) was found to be significantly associated with shorter overall survival (hazards ratio, 2.5; 95% confidence interval, 1.3-4.8). MATH was similarly found to be associated with adverse outcomes in clinically high-risk patients with an advanced stage of disease, and in those with tumors classified as high risk on the basis of validated biomarkers including those that were negative for human papillomavirus or having disruptive tumor protein p53 mutations. In patients who received chemotherapy, the hazards ratio for high MATH was 4.1 (95% confidence interval, 1.6-10.2).

Conclusions: This novel measure of tumor genetic heterogeneity is significantly associated with tumor progression and adverse treatment outcomes, thereby supporting the hypothesis that higher genetic heterogeneity portends a worse clinical outcome in patients with HNSCC. The prognostic value of some known biomarkers may be the result of their association with high genetic heterogeneity. MATH provides a useful measure of that heterogeneity to be prospectively validated as NGS data from homogeneously treated patient cohorts become available.

Keywords: head and neck cancer; intratumor genetic heterogeneity; next-generation DNA sequencing; overall survival; somatic mutations; tumor biomarkers.

Figure 1. Relation of mutant-allele tumor heterogeneity (MATH) to outcome in clinically defined subsets of HNSCC

Each panel shows overall survival curves for patients whose tumors had MATH above (“High MATH”) versus below (“Low MATH”) the overall median value of 32 units. Survival is calculated from the time of the surgical procedure that obtained the tumor sample subjected to NGS by Stransky et al. Crosses represent the last follow-up times for surviving patients. p-values are for log-rank tests. Panels represent different subsets of cases. The numbers of cases and numbers of deaths in each subset analysis are shown in Table 2. (a) Comparison of High-MATH and Low-MATH groups for all 74 cases. (b) Subset with HPV-negative tumors. (c) Subset in which tumors had disruptive mutations in the TP53 gene; all these tumors were also HPV-negative. (d) Subset with documented perineural invasion. (e) Subset with Stage IV disease. (f) Subset with N classification of 2 or 3; all these cases were Stage IV.

Figure 2. Relation of mutant-allele tumor heterogeneity (MATH) to outcome in HNSCC treated with chemotherapy

(a) Survival curves as in Fig. 1, for MATH values above versus below the median in the 41 cases in which therapy involved chemotherapy (40 also involving radiation; all 41 were primary tumors). (b) Receiver operating characteristic (ROC) curve for MATH estimated from these 41 cases, showing how different choices of MATH cutoff values affect the specificity and sensitivity of outcome classification at survival times 24 months or greater. MATH value cutoffs increase from the top right to the bottom left of the solid curve, with the MATH value shown for every fourth tumor. For example, about 95% of patients whose tumors have MATH values greater than 40 die within 24 months (selectivity), but using 40 as a MATH cutoff only identifies 50% of the patients dying within 24 months (sensitivity). Using the median MATH value as the cutoff, as in panel (a), provides greater sensitivity (70%) but lower selectivity (80%). The dashed line would have been the sensitivity-specificity relation if there were no relation of MATH to outcome. The area under the curve (AUC) of 0.82 means that in 82% of random pairs of patients with one dying and one surviving, the pre-treatment MATH value for the surviving patient would be lower.