Deciphering cancer heterogeneity: the biological space

Abstract

Most lethal solid tumors including hepatocellular carcinoma (HCC) are considered incurable due to extensive heterogeneity in clinical presentation and tumor biology. Tumor heterogeneity may result from different cells of origin, patient ethnicity, etiology, underlying disease, and diversity of genomic and epigenomic changes which drive tumor development. Cancer genomic heterogeneity thereby impedes treatment options and poses a significant challenge to cancer management. Studies of the HCC genome have revealed that although various genomic signatures identified in different HCC subgroups share a common prognosis, each carries unique molecular changes which are linked to different sets of cancer hallmarks whose misregulation has been proposed by Hanahan and Weinberg to be essential for tumorigenesis. We hypothesize that these specific sets of cancer hallmarks collectively occupy different tumor biological space representing the misregulation of different biological processes. In principle, a combination of different cancer hallmarks can result in new convergent molecular networks that are unique to each tumor subgroup and represent ideal druggable targets. Due to the ability of the tumor to adapt to external factors such as treatment or changes in the tumor microenvironment, the tumor biological space is elastic. Our ability to identify distinct groups of cancer patients with similar tumor biology who are most likely to respond to a specific therapy would have a significant impact on improving patient outcome. It is currently a challenge to identify a particular hallmark or a newly emerged convergent molecular network for a particular tumor. Thus, it is anticipated that the integration of multiple levels of data such as genomic mutations, somatic copy number aberration, gene expression, proteomics, and metabolomics, may help us grasp the tumor biological space occupied by each individual, leading to improved therapeutic intervention and outcome.

Keywords: cancer drivers; cancer genomic heterogeneity; gene signatures; hepatocellular carcinoma; integrated genomics; primary liver cancer.

Figure 1

Seven independent gene signatures predict concordant outcome groups. (A) Each of the 241 HCC patients (columns) was assigned into high and low risk groups based on hierarchical clustering of the prediction results of the 7 independent gene signatures, TP53 mutation status (wild type vs. mutated), tumor size (<5 cm vs. >5 cm), TNM staging (I vs. II/III) and BCLC staging (A vs. B/C); each tumor subclassification (rows) based on the clustering results was found to be independently associated with prognosis based on Cox Regression model. High risk, poor survival; low risk, better survival. (B) The numbers represent the overlap of genes among the 7 signatures. Color intensities represent the negative log10 hypergeometric p-values. A color intensity scale bar and the significance level at p < 0.001 are indicated.

Figure 2

A model depicts the relationship between the biological space and the prognostic space. Multiple patient subgroups exist because of tumor heterogeneity. Analogous to planets, various patients' subgroups reside on orbits, each of which carries key unique signaling pathways. Representative altered signaling pathways such as MYC, TP53, etc, are indicated. The patients' subgroups whose orbits intersect with the prognostic space are considered those with similar prognostic outcome.