Profiling of hepatocellular carcinoma neoantigens reveals immune microenvironment and clonal evolution related patterns.

OBJECTIVE: Neoantigens derived from tumor-specific genomic alterations have demonstrated great potential for immunotherapeutic interventions in cancers. However, the comprehensive profile of hepatocellular carcinoma (HCC) neoantigens and their complex interplay with immune microenvironment and tumor evolution have not been fully addressed. METHODS: Here we integrated whole exome sequencing data, transcriptome sequencing data and clinical information of 72 primary HCC patients to characterize the HCC neoantigen profile, and systematically explored its interactions with tumor clonal evolution, driver mutations and immune microenvironments. RESULTS: We observed that higher somatic mutation/neoantigen load was associated with better clinical outcomes and HCC patients could be further divided into two subgroups with distinct prognosis based on their neoantigen expression patterns. HCC subgroup with neoantigen expression probability high (NEP-H) showed more aggressive pathologic features including increased incidence of tumor thrombus (P=0.038), higher recurrence rate (P=0.029), more inclined to lack tumor capsule (P=0.026) and with more microsatellite instability sites (P=0.006). In addition, NEP-H subgroup was also characterized by higher chance to be involved in tumor clonal evolution [odds ratio (OR)=46.7, P<0.001]. Gene set enrichment analysis revealed that upregulation of MYC and its targets could suppress immune responses, leading to elevated neoantigen expression proportion in tumor cells. Furthermore, we discovered an immune escape mechanism that tumors could become more inconspicuous by evolving subclones with less immunogenicity. We observed that smaller clonal mutation clusters with higher immunogenicity in tumor were more likely to involve in clonal evolution. Based on identified neoantigen profiles, we also discovered series of neoantigenic hotspot genes, which could serve as potential actionable targets in future. CONCLUSIONS: Our results revealed the landscape of HCC neoantigens and discovered two clinically relevant subgroups with distinct neoantigen expression patterns, suggesting the neoantigen expression should be fully considered in future immunotherapeutic interventions.

Accumulation of STR-Loci Aberrations in Subclones of Jurkat Cell Line as a Model of Tumor Clonal Evolution.

Many genetic markers are known to distinguish tumor cells from normal. Genetic lesions found at disease onset often belong to a predominant tumor clone, and further observation makes it possible to assess the fate of this clone during therapy. However, minor clones escape monitoring and become unidentified, leading to relapses. Here we report the results of in vitro study of clonal evolution in cultured tumor cell line (Jurkat) compared to the cell line of non-tumor origin (WIL2-S). Cell lines were cultured and cloned by limiting dilutions. Subclones were tested by short tandem repeats (STR) profiling. Spontaneous STR aberrations in cells of non-tumor origin occur in less than 1 of 100 cultured cells. While in the cells of tumor origin, new aberrations appear in 1 or even more of 3 cultured cells. At the same time, a significant relationship was found between the accumulation of aberrations in the pool of subclones and the rate of cell growth. One can speculate that this approach could be applied for the analysis of primary patient tumor cell culture to obtain information concerning the evolutionary potential of the tumor cells that may be useful for the selection of a therapy approach.

Modeling Tumor Clonal Evolution for Drug Combinations Design.

Cancer is a clonal evolutionary process. This presents challenges for effective therapeutic intervention, given the constant selective pressure towards drug resistance. Mathematical modeling from population genetics, evolutionary dynamics, and engineering perspectives are being increasingly employed to study tumor progression, intratumoral heterogeneity, drug resistance, and rational drug scheduling and combinations design. In this review, we discuss promising opportunities these inter-disciplinary approaches hold for advances in cancer biology and treatment. We propose that quantitative modeling perspectives can complement emerging experimental technologies to facilitate enhanced understanding of disease progression and improved capabilities for therapeutic drug regimen designs.