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Roth, A.

Publications and source records attributed to Roth, A..

2 recordsLinked to original sources

The interface of malignant and immunologic clonal dynamics in high-grade serous ovarian cancer

High-grade serous ovarian cancer exhibits extensive intratumoral heterogeneity coupled with widespread intraperitoneal disease. Despite this, metastatic spread of tumor clones is non-random, implying the existence of local microenvironmental factors that shape tumor progression. We interrogated the molecular interface between tumor-infiltrating lymphocytes (TIL) and cancer cells in 143 samples from 21 patients using whole-genome sequencing, immunohistochemistry, histologic image analysis, gene expression profiling, and T- and B-cell receptor sequencing. We identify 3 immunologic response categories, which frequently co-exist within individual patients. Furthermore, epithelial CD8+ TIL were inversely associated with malignant cell diversity, evidenced by subclonal neoepitope elimination and spatial tracking between tumor and T-cell clones. Intersecting mutational signatures and immune analysis showed that foldback inversion genomic aberrations lead to worse outcomes even in the presence of cytotoxic TIL (n=433). Thus, regional variation in immune contexture mirrors the pattern of intraperitoneal malignant spread, provoking new perspectives for treatment of this challenging disease.

cancer biology

E-scape: Interactive visualization of single cell phylogenetics and spatio-temporal evolution in cancer

Inference of clonal dynamics and tumour evolution has fundamental importance in understanding the major clinical endpoints in cancer: development of treatment resistance, relapse and metastasis. DNA sequencing technology has made measuring clonal dynamics through mutation analysis accessible at scale, facilitating computational inference of informative patterns of interest. However, currently no tools allow for biomedical experts to meaningfully interact with the often complex and voluminous dataset to inject domain knowledge into the inference process. We developed an interactive, web-based visual analytics software suite called E-scape which supports dynamically linked, multi-faceted views of cancer evolution data. Developed using R and javascript d3.js libraries, the suite includes three tools: TimeScape and MapScape for visualizing population dynamics over time and space, respectively, and CellScape for visualizing evolution at single cell resolution. The tool suite integrates phylogenetic, clonal prevalence, mutation and imaging data to generate intuitive, dynamically linked views of data which update in real time as a function of user actions. The system supports visualization of both point mutation and copy number alterations, rendering how mutations distribute in clones in both bulk and single cell experiment data in multiple representations including phylogenies, heatmaps, growth trajectories, spatial distributions and mutation tables. E-scape is open source and is freely available to the community at large.

cancer biology