The challenges of tumor genetic diversity

Abstract

The authors review and discuss the implications of genomic analyses documenting the diversity of tumors, both among patients and within individual tumors. Genetic diversity among solid tumors limits targeted therapies, because few mutations that drive tumors are both targetable and at high prevalence. Many more driver mutations and how they affect cellular signaling pathways must be identified if targeted therapy is to become widely useful. Genetic diversity within a tumor-intratumor genetic heterogeneity-makes the tumor a collection of subclones: related yet distinct cancers. Selection for pre-existing, resistant subclones by conventional or targeted therapies may explain many treatment failures. Immune therapy faces the same fundamental challenges. Nevertheless, the processes that generate and maintain heterogeneity might provide novel therapeutic targets. Addressing both types of diversity requires genomic tumor analyses linked to detailed clinical data. The trend toward sequencing restricted cancer gene panels, however, limits the ability to discover new driver mutations and assess intratumor heterogeneity. Clinical data currently collected with genomic analyses often lack critical information, substantially limiting their use in understanding tumor diversity. Now that diversity among and within tumors can no longer be ignored, research and clinical practice must adapt to take diversity into account. Cancer 2017;123:917-27. © 2016 American Cancer Society.

Keywords: driver mutations; immunotherapy; intratumor heterogeneity; next-generation sequencing; targeted therapy.

Figure 1

Subclonal evolution of a tumor. Left, colored regions represent cancer cells; white background, cells with normal DNA. Starting from the tumor-initiating clone (clone 0), subclones are born and expand (numbered triangles) or die (diamonds) over time. Each subclone contains all mutations present in its progenitors, plus its subclone-specific mutations. Right, mutant-allele fractions (MAF), the fraction of DNA in a sample that shows a tumor-specific mutation, at time of tumor sampling. Examples shown for (sub)clone-specific mutations in 2 tumor samples and for the whole tumor, based on heterozygous mutations at normal copy number in cancer cells and 20% of all cells in the sample having normal DNA (80% tumor “purity”). For a heterozygous mutation and 80% purity, the fraction of cells with the mutation, its cancer cell fraction (CCF), is 2.5 times its MAF. Mutations in the originating clone have higher MAF/CCF values than those in subclones. Mutations in subclones can be missed either due to sampling (e.g., subclones 3 and 6 in Sample 1; subclones 4 and 5 in Sample 2) or due to having MAF values too low to be detected (e.g., if the detection limit is 0.05: subclone 2 in Sample 2; subclones 5 and 6 in the whole tumor). Adapted from Bozic et al, PLOS Comp Biol 12: e1004731. doi:10.1371/journal.pcbi.1004731. Used under the Creative Commons Attribution License 4.0, https://creativecommons.org/licenses/by/4.0/.

Figure 2

Using mutant-alllele fraction (MAF) values to assess intra-tumor heterogeneity. Left, a low-heterogeneity tumor; right, a high-heterogeneity tumor. Circles represent MAF values of individual tumor-specific mutated loci; the curves are density plots (smoothed histograms) of the distributions. Mutant-allele tumor heterogeneity (MATH) for each tumor is the percentage ratio of the width to the center of its MAF-value distribution, with the median taken as the center and the median absolute deviation (MAD) taken as the width. From Mroz et al, PLOS Medicine 12: e1001786. doi:10.1371/journal.pmed.1001786, 2015, under the Creative Commons Attribution License, https://creativecommons.org/licenses/by/4.0/.

Figure 3

Intra-tumor genetic heterogeneity is not the same as mutation frequency. Scatter plot of MATH values versus number of tumor-specific mutations for 74 head and neck squamous cell carcinomas (p = 0.21, Kendall rank correlation). From Mroz and Rocco, Oral Oncology 49:211-215, 2013, with permission.

Figure 4

High intra-tumor heterogeneity is related to higher mortality in HNSCC. Kaplan-Meier curves for 305 HNSCC patients in TCGA grouped by high (>32) versus low MATH values. (Hazard ratio, 2.18; 95% CI, 1.44 to 3.30; p < 0.001). A significant relation of high MATH to high mortality was verified in a Cox multiple regression accounting for 9 other covariates (including smoking history, age, and HPV status). From Mroz et al, PLOS Medicine 12: e1001786. doi:10.1371/journal.pmed.1001786, 2015, under the Creative Commons Attribution License, https://creativecommons.org/licenses/by/4.0/.