MATH, a novel measure of intratumor genetic heterogeneity, is high in poor-outcome classes of head and neck squamous cell carcinoma

Abstract

Objectives: Differences among cancer cells within a tumor are important in tumorigenesis and treatment resistance, yet no measure of intratumor heterogeneity is suitable for routine application. We developed a quantitative measure of intratumor genetic heterogeneity, based on differences among mutated loci in the mutant-allele fractions determined by next-generation sequencing (NGS) of tumor DNA. We then evaluated the application of this measure to head and neck squamous cell carcinoma (HNSCC).

Materials and methods: We analyzed published electronically available NGS results for 74 HNSCC. For each tumor we calculated mutant-allele tumor heterogeneity (MATH) as the ratio of the width to the center of its distribution of mutant-allele fractions among tumor-specific mutated loci.

Results: Intratumor heterogeneity assessed by MATH was higher in three poor-outcome classes of HNSCC: tumors with disruptive mutations in the TP53 gene (versus wild-type TP53 or non-disruptive mutations), tumors negative versus positive for human papillomavirus (even when restricted to tumors having wild-type TP53), and HPV-negative tumors from smokers with more pack-years of cigarette exposure (with TP53 status taken into account).

Conclusion: The relation of this type of intratumor heterogeneity to HNSCC outcome classes supports its further evaluation as a prognostic biomarker. As NGS of tumor DNA becomes widespread in clinical research and practice, MATH should provide a simple, quantitative, and clinically practical biomarker to help evaluate relations of intratumor genetic heterogeneity to outcome in any type of cancer.

Fig. 1. Mutant-allele tumor heterogeneity

A, Heterogeneity among mutant-allele fractions. Top, Three heterozygous loci (mutant alleles: lower-case a, b, c) in an idealized homogeneous tumor (left) or in a tumor having 2 populations (40/60 cell ratio, right), processed for sequencing. Middle, Mutant-allele fractions in DNA are 0.5 for all loci from the homogeneous tumor (left) or loci with shared mutations in the heterogeneous tumor (right), versus one-half of the cell fraction for population-specific mutations. Bottom, sampling in single-molecule sequencing smoothes the distributions, illustrated for 100 sequence reads at each of many such loci. The distribution of mutant-allele fractions from the heterogeneous tumor is wider (larger MAD) and has a lower median than for the homogeneous tumor. The ratio of the width to the center of the distribution (MATH = 100 * MAD/median) is thus larger for the heterogeneous tumor. B, Distributions of mutant-allele fractions among tumor-specific mutated loci for 3 HNSCC having similar numbers of mutated loci (top to bottom: 98, 102, 96 loci). Circles are mutant-allele fractions for individual loci. Curves are kernel density estimates from the R densityplot function (lattice package; default parameter values), with y-axes scaled so that the area under each curve equals 1. Each tumor’s MATH value is shown with a standard deviation (SD) estimated by bootstrap resampling; see text. C. Lack of relation between MATH and the number of tumor-specific mutations, a measure of overall mutation rate. Kendall rank correlation, p = 0.21. D, Relation of bootstrap-estimated SD of MATH determinations to the number of mutated loci among 74 HNSCC. r² = 0.86.

Fig. 2. Relation of MATH to TP53 mutation status in HNSCC

Boxplots (thick horizontal lines, medians; whiskers to farthest points within 1 interquartile range of box) and individual MATH values (circles) for HNSCC grouped by TP53 mutation status. p-values are for Wilcoxon rank-sum tests.

Fig. 3. Relation of MATH to HPV status in HNSCC

Boxplots and individual MATH values as in Fig. 2 for HNSCC grouped by HPV status. All HPV-negative cases on the left; HPV-negative cases having wild-type TP53 on the right.