Archival single-cell genomics reveals persistent subclones during DCIS progression.

Ductal carcinoma in situ (DCIS) is a common precursor of invasive breast cancer. Our understanding of its genomic progression to recurrent disease remains poor, partly due to challenges associated with the genomic profiling of formalin-fixed paraffin-embedded (FFPE) materials. Here, we developed Arc-well, a high-throughput single-cell DNA-sequencing method that is compatible with FFPE materials. We validated our method by profiling 40,330 single cells from cell lines, a frozen tissue, and 27 FFPE samples from breast, lung, and prostate tumors stored for 3-31 years. Analysis of 10 patients with matched DCIS and cancers that recurred 2-16 years later show that many primary DCIS had already undergone whole-genome doubling and clonal diversification and that they shared genomic lineages with persistent subclones in the recurrences. Evolutionary analysis suggests that most DCIS cases in our cohort underwent an evolutionary bottleneck, and further identified chromosome aberrations in the persistent subclones that were associated with recurrence.

Spatial intra-tumor heterogeneity is associated with survival of lung adenocarcinoma patients.

Intra-tumor heterogeneity (ITH) of human tumors is important for tumor progression, treatment response, and drug resistance. However, the spatial distribution of ITH remains incompletely understood. Here, we present spatial analysis of ITH in lung adenocarcinomas from 147 patients using multi-region mass spectrometry of >5,000 regions, single-cell copy number sequencing of ~2,000 single cells, and cyclic immunofluorescence of >10 million cells. We identified two distinct spatial patterns among tumors, termed clustered and random geographic diversification (GD). These patterns were observed in the same samples using both proteomic and genomic data. The random proteomic GD pattern, which is characterized by decreased cell adhesion and lower levels of tumor-interacting endothelial cells, was significantly associated with increased risk of recurrence or death in two independent patient cohorts. Our study presents comprehensive spatial mapping of ITH in lung adenocarcinoma and provides insights into the mechanisms and clinical consequences of GD.

MEDALT: single-cell copy number lineage tracing enabling gene discovery.

We present a Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm that infers the evolution history of a cell population based on single-cell copy number (SCCN) profiles, and a statistical routine named lineage speciation analysis (LSA), whichty facilitates discovery of fitness-associated alterations and genes from SCCN lineage trees. MEDALT appears more accurate than phylogenetics approaches in reconstructing copy number lineage. From data from 20 triple-negative breast cancer patients, our approaches effectively prioritize genes that are essential for breast cancer cell fitness and predict patient survival, including those implicating convergent evolution.The source code of our study is available at https://github.com/KChen-lab/MEDALT .

esiCancer: Evolutionary In Silico Cancer Simulator.

The evolution of cancer is inferred mainly from samples taken at discrete points that represent glimpses of the complete process. In this study, we present esiCancer as a cancer-evolution simulator. It uses a branching process, randomly applying events to a diploid oncogenome, altering probabilities of proliferation and death of the affected cells. Multiple events that occur over hundreds of generations may lead to a gradual change in cell fitness and the establishment of a fast-growing population. esiCancer provides a platform to study the impact of several factors on tumor evolution, including dominance, fitness, event rate, and interactions among genes as well as factors affecting the tumor microenvironment. The output of esiCancer can be used to reconstruct clonal composition and Kaplan-Meier-like survival curves of multiple evolutionary stories. esiCancer is an open-source, standalone software to model evolutionary aspects of cancer biology. SIGNIFICANCE: This study provides a customizable and hands-on simulation tool to model the effect of diverse types of genomic alterations on the fate of tumor cells.

Systematic Functional Annotation of Somatic Mutations in Cancer.

The functional impact of the vast majority of cancer somatic mutations remains unknown, representing a critical knowledge gap for implementing precision oncology. Here, we report the development of a moderate-throughput functional genomic platform consisting of efficient mutant generation, sensitive viability assays using two growth factor-dependent cell models, and functional proteomic profiling of signaling effects for select aberrations. We apply the platform to annotate >1,000 genomic aberrations, including gene amplifications, point mutations, indels, and gene fusions, potentially doubling the number of driver mutations characterized in clinically actionable genes. Further, the platform is sufficiently sensitive to identify weak drivers. Our data are accessible through a user-friendly, public data portal. Our study will facilitate biomarker discovery, prediction algorithm improvement, and drug development.

Analysis of NTPDase2 in the cell membrane using fluorescence recovery after photobleaching (FRAP).

NTPDase2, a member of the CD39/NTPDase family, is an ecto-nucleotidase anchored to the plasma membrane by two transmembrane domains, with a catalytic site facing the extracellular space and preferentially hydrolyzing nucleoside triphosphates. While NTPDase2 is expressed in many cell types, its unique functionality, mobility and dynamics at the cell membrane remain unexplored. We therefore constructed a recombinant NTPDase2 linked to the yellow fluorescent protein (EYFP) to investigate its dynamics by confocal microscopy. The present study shows that the expression of EYFP-NTPDase2 in different cell lines does not affect its proliferation, migration and adhesion to extracellular matrices (ECM). Moreover, in human embryonic kidney cells 293 (HEK293) grown on collagen type I and fibronectin, EYFP-NTPDase2 fluorescence is greater in free plasma membrane regions than in cell-cell contacts, in comparison with cells grown on other substrates. Differences in the time required for fluorescence recovery after photobleaching (FRAP) in free membrane regions and cell-cell contacts indicate that the mobility of EYFP-NTPDase2 depends on the matrix to which the cells are attached. © 2018 International Society for Advancement of Cytometry.

A20 expressing tumors and anticancer drug resistance.

Resistance to anticancer drugs is a major impediment to treating patients with cancer. The molecular mechanisms deciding whether a tumor cell commits to cell death or survives under chemotherapy are complex. Mounting evidence indicates a critical role of cell death and survival pathways in determining the response of human cancers to chemotherapy. Nuclear factor-kappaB (NF-kappaB) is a eukaryotic transcription factor on the crossroad of a cell's decision to live or die. Under physiological conditions, NF-kappaB is regulated by a complex network of endogenous pathway modulators. Tumor necrosis factor alpha induced protein 3 (tnfaip3), a gene encoding the A20 protein, is one of the cell's own inhibitory molecule, which regulates canonical NF-kappaB activation by interacting with upstream signaling pathway components. Interestingly, A20 is also itself a NF-kappaB dependent gene, that has been shown to also exert cell-type specific anti- or pro-apoptotic functions. Recent reports suggest that A20 expression is increased in a number of solid human tumors. This likely contributes to both carcinogenesis and response to chemotherapy. These data uncover the complexities of the mechanisms involved in A20's impact on tumor development and response to treatment, highlighting tumor and drug-type specific outcomes. While A20-targeted therapies may certainly add to the chemotherapeutic armamentarium, better understanding of A20 regulation, molecular targets and function(s) in every single tumor and in response to any given drug is required prior to any clinical implementation. Current renewed appreciation of the unique molecular signature of each tumor holds promise for personalized chemotherapeutic regimen hopefully comprising specific A20-targeting agents i.e., both inhibitors and enhancers.