Genomic landscape of cancer in racially and ethnically diverse populations

Abstract

Cancer incidence and mortality rates can vary widely among different racial and ethnic groups, attributed to a complex interplay of genetic, environmental and social factors. Recently, substantial progress has been made in investigating hereditary genetic risk factors and in characterizing tumour genomes. However, most research has been conducted in individuals of European ancestries and, increasingly, in individuals of Asian ancestries. The study of germline and somatic genetics in cancer across racial and ethnic groups using omics technologies offers opportunities to identify similarities and differences in both heritable traits and the molecular features of cancer genomes. An improved understanding of population-specific cancer genomics, as well as translation of those findings across populations, will help reduce cancer disparities and ensure that personalized medicine and public health approaches are equitable across racial and ethnic groups.

Fig. 1 |. A multi-omics approach to assess cancer disparities for germline and somatic tumour investigations.

Using different omics technologies across different racial and ethnic groups to identify features related to clinical outcomes can preserve statistical power to find differences between groups that are clinically relevant. Identifying germline variants that predict disease outcomes can help tailor prognostic models to specific populations. Polygenic risk score (PRS), which aggregates the effects of multiple genetic variants, can be more accurate when developed for specific populations, improving cancer risk prediction. Moreover, different populations can have distinct patterns of somatic mutation, which can aid in more precise cancer subtype classification. Understanding population-specific germline variation and somatic mutations can also help identify unique therapeutic targets and predict responses to treatments, leading to more personalized and effective therapies. CMS, consensus molecular subtype.

Fig. 2 |. Linkage disequilibrium differences between ancestries can affect the identification of causal germline variants.

a, Haplotype block differences between two populations (red triangles represent high correlation within haplotype blocks). b, Forest plots depicting tag SNP association differences in two populations of different ancestries (left panel) and true casual variant association for the same two populations (right panel).

Fig. 3 |. Mutational signatures.

A, Various exposures, such as specific microbiota, viral infections, UV radiation, smoking and toxins, can induce somatic mutations that occur more frequently within a specific nucleotide content. In aggregate, these exposures lead to unique mutational signatures that affect cancer development, progression and treatment response. From a mutational dataset, mutational signatures that are recurrent across samples can be inferred and their relative contributory exposures estimated, with mutations attributable to each signature in a sample. Comparisons across populations can identify population-specific risk factors, as exposure to environmental mutagens could underlie the differential incidence of specific cancer types. Ba, The single base substitution 22a (SBS22a) signature is associated with exposure to aristolochic acid, a group of naturally occurring compounds present in many plant species of the Aristolochiaceae family. Aristolochic acid exposure is a risk factor for clear cell renal cell carcinomas, urological and hepatobiliary cancers, as well as severe nephropathy. Bb, A recent study found that somatic mutation profiles differed between countries, with Romania, Serbia and Thailand showing mutational signatures characteristic of aristolochic acid compounds in most cases. Part Bb is reprinted from ref. , CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).