Phylogenetic Tree Inference: A Top-Down Approach to Track Tumor Evolution

Oncogene-Driven Non-Small Cell Lung Cancers in Patients with a History of Smoking Lack Smoking-Induced Mutations

Non-small cell lung cancers (NSCLC) in nonsmokers are mostly driven by mutations in the oncogenes EGFR, ERBB2, and MET and fusions involving ALK and RET. In addition to occurring in nonsmokers, alterations in these "nonsmoking-related oncogenes" (NSRO) also occur in smokers. To better understand the clonal architecture and genomic landscape of NSRO-driven tumors in smokers compared with typical-smoking NSCLCs, we investigated genomic and transcriptomic alterations in 173 tumor sectors from 48 NSCLC patients. NSRO-driven NSCLCs in smokers and nonsmokers had similar genomic landscapes. Surprisingly, even in patients with prominent smoking histories, the mutational signature caused by tobacco smoking was essentially absent in NSRO-driven NSCLCs, which was confirmed in two large NSCLC data sets from other geographic regions. However, NSRO-driven NSCLCs in smokers had higher transcriptomic activities related to the regulation of the cell cycle. These findings suggest that, whereas the genomic landscape is similar between NSRO-driven NSCLC in smokers and nonsmokers, smoking still affects the tumor phenotype independently of genomic alterations.

SIGNIFICANCE

Non-small cell lung cancers driven by nonsmoking-related oncogenes do not harbor genomic scars caused by smoking regardless of smoking history, indicating that the impact of smoking on these tumors is mainly nongenomic.

Integrated multi-omics profiling to dissect the spatiotemporal evolution of metastatic hepatocellular carcinoma

Comprehensive molecular analyses of metastatic hepatocellular carcinoma (HCC) are lacking. Here, we generate multi-omic profiling of 257 primary and 176 metastatic regions from 182 HCC patients. Primary tumors rich in hypoxia signatures facilitated polyclonal dissemination. Genomic divergence between primary and metastatic HCC is high, and early dissemination is prevalent. The remarkable neoantigen intratumor heterogeneity observed in metastases is associated with decreased T cell reactivity, resulting from disruptions to neoantigen presentation. We identify somatic copy number alterations as highly selected events driving metastasis. Subclones without Wnt mutations show a stronger selective advantage for metastasis than those with Wnt mutations and are characterized by a microenvironment rich in activated fibroblasts favoring a pro-metastatic phenotype. Finally, metastases without Wnt mutations exhibit higher enrichment of immunosuppressive B cells that mediate terminal exhaustion of CD8+ T cells via HLA-E:CD94-NKG2A checkpoint axis. Collectively, our results provide a multi-dimensional dissection of the complex evolutionary process of metastasis.

Mutational landscape of primary breast angiosarcoma with repeated resection and recurrence over a 15-year period: A case report

Angiosarcoma is a rare malignant tumor derived from vascular endothelial cells and has a poor prognosis. We have experienced a case of multiple breast angiosarcoma for which multiple resections had been performed during the course of its progression over a period of more than 15 years, allowing comprehensive genetic mutation analysis. Somatic mutations in several cancer-related genes were detected, but no previously reported driver gene mutations of angiosarcoma were evident. Several germline mutations associated with malignancy, such as single nucleotide polymorphisms in Fibroblast Growth Factor Receptor 4 (FGFR4) (p.Gly388Arg, rs351855), Kinase Insert Domain Receptor (KDR) (Gln472His, rs1870377) and tumor protein p53 (TP53) (p.Pro72Arg, rs1042522) were detected. Common signatures and genetic mutations were scarce in the tumor samples subjected to genetic mutational analysis. These findings suggested that this case was very probably a multiprimary angiosarcoma.

Multiregional Sequencing of IDH-WT Glioblastoma Reveals High Genetic Heterogeneity and a Dynamic Evolutionary History

Simple Summary

Glioblastoma is the most common and aggressive primary brain malignancy in adults. In addition to extensive inter-patient heterogeneity, glioblastoma shows intra-tumor extensive cellular and molecular heterogeneity, both spatially and temporally. This heterogeneity is one of the main reasons for the poor prognosis and overall survival. Moreover, it raises the important question of whether the molecular characterization of a single biopsy sample, as performed in standard diagnostics, actually represents the entire lesion. In this study, we sequenced the whole exome of nine spatially different cancer regions of three primary glioblastomas. We characterized their mutational profiles and copy number alterations, with implications for our understanding of tumor biology in relation to clonal architecture and evolutionary dynamics, as well as therapeutically relevant alterations.

Abstract

Glioblastoma is one of the most common and lethal primary neoplasms of the brain. Patient survival has not improved significantly over the past three decades and the patient median survival is just over one year. Tumor heterogeneity is thought to be a major determinant of therapeutic failure and a major reason for poor overall survival. This work aims to comprehensively define intra- and inter-tumor heterogeneity by mapping the genomic and mutational landscape of multiple areas of three primary IDH wild-type (IDH-WT) glioblastomas. Using whole exome sequencing, we explored how copy number variation, chromosomal and single loci amplifications/deletions, and mutational burden are spatially distributed across nine different tumor regions. The results show that all tumors exhibit a different signature despite the same diagnosis. Above all, a high inter-tumor heterogeneity emerges. The evolutionary dynamics of all identified mutations within each region underline the questionable value of a single biopsy and thus the therapeutic approach for the patient. Multiregional collection and subsequent sequencing are essential to try to address the clinical challenge of precision medicine. Especially in glioblastoma, this approach could provide powerful support to pathologists and oncologists in evaluating the diagnosis and defining the best treatment option.

Hierarchical Modelling of Haplotype Effects on a Phylogeny

We introduce a hierarchical model to estimate haplotype effects based on phylogenetic relationships between haplotypes and their association with observed phenotypes. In a population there are many, but not all possible, distinct haplotypes and few observations per haplotype. Further, haplotype frequencies tend to vary substantially. Such data structure challenge estimation of haplotype effects. However, haplotypes often differ only due to few mutations, and leveraging similarities can improve the estimation of effects. We build on extensive literature and develop an autoregressive model of order one that models haplotype effects by leveraging phylogenetic relationships described with a directed acyclic graph. The phylogenetic relationships can be either in a form of a tree or a network, and we refer to the model as the haplotype network model. The model can be included as a component in a phenotype model to estimate associations between haplotypes and phenotypes. Our key contribution is that we obtain a sparse model, and by using hierarchical autoregression, the flow of information between similar haplotypes is estimated from the data. A simulation study shows that the hierarchical model can improve estimates of haplotype effects compared to an independent haplotype model, especially with few observations for a specific haplotype. We also compared it to a mutation model and observed comparable performance, though the haplotype model has the potential to capture background specific effects. We demonstrate the model with a study of mitochondrial haplotype effects on milk yield in cattle. We provide R code to fit the model with the INLA package.