Characterization of cancer genomic heterogeneity by next-generation sequencing advances precision medicine in cancer treatment

Abstract

Cancer is a heterogeneous disease with unique genomic and phenotypic features that differ between individual patients and even among individual tumor regions. In recent years, large-scale genomic studies and new next-generation sequencing technologies have uncovered more scientific details about tumor heterogeneity, with significant implications for the choice of specific molecular biomarkers and clinical decision making. Genomic heterogeneity significantly contributes to the generation of a diverse cell population during tumor development and progression, representing a determining factor for variation in tumor treatment response. It has been considered a prominent contributor to therapeutic failure, and increases the likelihood of resistance to future therapies in most common cancers. The understanding of molecular heterogeneity in cancer is a fundamental component of precision oncology, enabling the identification of genomic alteration of key genes and pathways that can be targeted therapeutically. Here, we review the emerging knowledge of tumor genomics and heterogeneity, as well as potential implications for precision medicine in cancer treatment and new therapeutic discoveries. An analysis and interpretation of the TCGA database was included.

Keywords: Genomics; cancer treatment; heterogeneity; next-generation sequencing; precision medicine.

Figure 1.

Interplay of key contributing factors to tumor heterogeneity. Both cell-intrinsic and cell-extrinsic factors contribute to tumor heterogeneity. Key cell-intrinsic factors include mutation, DNA-repair genes, epigenetic mechanisms, chromosome segregation and stability, as well as intracellular signaling. Non-genetic or phenotypic variations as a result of contributing cell-intrinsic factors are depicted by different cytoplasmic colors. Cell-extrinsic mechanisms affect and contribute to the unequal microenvironment, indirectly contributing to tumor heterogeneity. Multiple cell types and different inter- and intra-cell interactions within a tumor may exist (only representatives are shown here), hence selectively contributing to tumor heterogeneity.

Figure 2.

Contribution of tumor heterogeneity in cancer progression and metastasis. (a) Graphical representation of inter- and intra-tumor heterogeneity origins at macroscopic and microscopic levels. (b) Graphical summary of the two recognized heterogeneity models: clonal (stochastic) evolution and cancer stem cell (CSC), involving either monoclonal evolution or single progenitor, and polyclonal evolution or multiple progenitors, linking tumor cellular paths to different tumor heterogeneity. (c) Contributing role of tumor heterogeneity with respect to cancer progression and metastasis.

Figure 3.

Role of tumor heterogeneity in biomarker prediction and tumor resistance to clinical therapy. Initial cancer diagnosis and first treatment depends on initial cell and molecular characterization, derived from a small tumor fraction (biopsy, here figure shows a complete representation, but in some cases, it may be biased). In most cases, the current first-line treatments can successfully eliminate dominating cancer clones, with the cost of selecting resistant tumor clones through either differential sensitivity (1) or therapy-induced mutagenesis (2). These resistant clones are capable of driving disease progression and eventually metastasis. Hence, the clonal composition of metastatic lesions may significantly differ from clones in the primary tumor. As a result, initial treatment choice may not be effective in progressive metastatic disease. This necessitates a new diagnosis and additional comparative steps after relapse, prior to second and usually combined treatment options (i.e., immunotherapy, selective pathway component targeting and/or gene therapy) (Adapted from Tellez-Gabriel et al., 2016; doi:10.3390/ijms17122142).

Figure 4.

Recurrent somatic alterations across common tumor types. Heatmap of significant genes that were genetically altered across the 18 most common cancers, as evaluated by the TCGA project. Percentage of alteration frequency (white = low to blue = high) for the genes is shown.