Coordinated single-cell tumor microenvironment dynamics reinforce pancreatic cancer subtype.

Bulk analyses of pancreatic ductal adenocarcinoma (PDAC) samples are complicated by the tumor microenvironment (TME), i.e. signals from fibroblasts, endocrine, exocrine, and immune cells. Despite this, we and others have established tumor and stroma subtypes with prognostic significance. However, understanding of underlying signals driving distinct immune and stromal landscapes is still incomplete. Here we integrate 92 single cell RNA-seq samples from seven independent studies to build a reproducible PDAC atlas with a focus on tumor-TME interdependence. Patients with activated stroma are synonymous with higher myofibroblastic and immunogenic fibroblasts, and furthermore show increased M2-like macrophages and regulatory T-cells. Contrastingly, patients with 'normal' stroma show M1-like recruitment, elevated effector and exhausted T-cells. To aid interoperability of future studies, we provide a pretrained cell type classifier and an atlas of subtype-based signaling factors that we also validate in mouse data. Ultimately, this work leverages the heterogeneity among single-cell studies to create a comprehensive view of the orchestra of signaling interactions governing PDAC.

FaStaNMF: a Fast and Stable Non-negative Matrix Factorization for Gene Expression.

Gene expression analysis of samples with mixed cell types only provides limited insight to the characteristics of specific tissues. In silico deconvolution can be applied to extract cell type specific expression, thus avoiding prohibitively expensive techniques such as cell sorting or single-cell sequencing. Non-negative matrix factorization (NMF) is a deconvolution method shown to be useful for gene expression data, in part due to its constraint of non-negativity. Unlike other methods, NMF provides the capability to deconvolve without prior knowledge of the components of the model. However, NMF is not guaranteed to provide a globally unique solution. In this work, we present FaStaNMF, a method that balances achieving global stability of the NMF results, which is essential for inter-experiment and inter-lab reproducibility, with accuracy and speed. Results: FaStaNMF was applied to four datasets with known ground truth, created based on publicly available data or by using our simulation infrastructure, RNAGinesis. We assessed FaStaNMF on three criteria - speed, accuracy, and stability, and it favorably compared to the standard approach of achieving reproduceable results with NMF. We expect that FaStaNMF can be applied successfully to a wide array of biological data, such as different tumor/immune and other disease microenvironments.

Open-source curation of a pancreatic ductal adenocarcinoma gene expression analysis platform (pdacR) supports a two-subtype model.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease for which potent therapies have limited efficacy. Several studies have described the transcriptomic landscape of PDAC tumors to provide insight into potentially actionable gene expression signatures to improve patient outcomes. Despite centralization efforts from multiple organizations and increased transparency requirements from funding agencies and publishers, analysis of public PDAC data remains difficult. Bioinformatic pitfalls litter public transcriptomic data, such as subtle inclusion of low-purity and non-adenocarcinoma cases. These pitfalls can introduce non-specificity to gene signatures without appropriate data curation, which can negatively impact findings. To reduce barriers to analysis, we have created pdacR ( http://pdacR.bmi.stonybrook.edu , github.com/rmoffitt/pdacR), an open-source software package and web-tool with annotated datasets from landmark studies and an interface for user-friendly analysis in clustering, differential expression, survival, and dimensionality reduction. Using this tool, we present a multi-dataset analysis of PDAC transcriptomics that confirms the basal-like/classical model over alternatives.

Spatial Characterization of Tumor-Infiltrating Lymphocytes and Breast Cancer Progression.

Tumor-infiltrating lymphocytes (TILs) have been established as a robust prognostic biomarker in breast cancer, with emerging utility in predicting treatment response in the adjuvant and neoadjuvant settings. In this study, the role of TILs in predicting overall survival and progression-free interval was evaluated in two independent cohorts of breast cancer from the Cancer Genome Atlas (TCGA BRCA) and the Carolina Breast Cancer Study (UNC CBCS). We utilized machine learning and computer vision algorithms to characterize TIL infiltrates in digital whole-slide images (WSIs) of breast cancer stained with hematoxylin and eosin (H&E). Multiple parameters were used to characterize the global abundance and spatial features of TIL infiltrates. Univariate and multivariate analyses show that large aggregates of peritumoral and intratumoral TILs (forests) were associated with longer survival, whereas the absence of intratumoral TILs (deserts) is associated with increased risk of recurrence. Patients with two or more high-risk spatial features were associated with significantly shorter progression-free interval (PFI). This study demonstrates the practical utility of Pathomics in evaluating the clinical significance of the abundance and spatial patterns of distribution of TIL infiltrates as important biomarkers in breast cancer.

Development of a Cx46 Targeting Strategy for Cancer Stem Cells.

Gap-junction-mediated cell-cell communication enables tumor cells to synchronize complex processes. We previously found that glioblastoma cancer stem cells (CSCs) express higher levels of the gap junction protein Cx46 compared to non-stem tumor cells (non-CSCs) and that this was necessary and sufficient for CSC maintenance. To understand the mechanism underlying this requirement, we use point mutants to disrupt specific functions of Cx46 and find that Cx46-mediated gap-junction coupling is critical for CSCs. To develop a Cx46 targeting strategy, we screen a clinically relevant small molecule library and identify clofazimine as an inhibitor of Cx46-specific cell-cell communication. Clofazimine attenuates proliferation, self-renewal, and tumor growth and synergizes with temozolomide to induce apoptosis. Although clofazimine does not cross the blood-brain barrier, the combination of clofazimine derivatives optimized for brain penetrance with standard-of-care therapies may target glioblastoma CSCs. Furthermore, these results demonstrate the importance of targeting cell-cell communication as an anti-cancer therapy.

Cx26 drives self-renewal in triple-negative breast cancer via interaction with NANOG and focal adhesion kinase.

Tumors adapt their phenotypes during growth and in response to therapies through dynamic changes in cellular processes. Connexin proteins enable such dynamic changes during development, and their dysregulation leads to disease states. The gap junction communication channels formed by connexins have been reported to exhibit tumor-suppressive functions, including in triple-negative breast cancer (TNBC). However, we find that connexin 26 (Cx26) is elevated in self-renewing cancer stem cells (CSCs) and is necessary and sufficient for their maintenance. Cx26 promotes CSC self-renewal by forming a signaling complex with the pluripotency transcription factor NANOG and focal adhesion kinase (FAK), resulting in NANOG stabilization and FAK activation. This FAK/NANOG-containing complex is not formed in mammary epithelial or luminal breast cancer cells. These findings challenge the paradigm that connexins are tumor suppressors in TNBC and reveal a unique function for Cx26 in regulating the core self-renewal signaling that controls CSC maintenance.

Heterogeneity of Reporting Outcomes in the Spine Surgery Literature.

STUDY DESIGN: Review of spine surgery literature between 2005 and 2014 to assess the reporting of patient outcomes by determining the variability of use of patient outcomes metrics in the following categories: pain and disability, patient satisfaction, readmission, and depression. OBJECTIVE: Expose the heterogeneity of outcomes reporting and discuss current initiatives to create more homogenous outcomes databases. SUMMARY OF BACKGROUND DATA: There has been a recent focus on the reporting of quality metrics associated with spine surgery outcomes. However, little consensus exists on the optimal metrics that should be used to measure spine surgery outcomes. MATERIALS AND METHODS: A PubMed search of all spine surgery manuscripts from January 2005 through December 2014 was performed. Linear regression analyses were performed on individual metrics as well as outcomes categories as a fraction of total papers reviewing surgical outcomes. RESULTS: Outcomes reporting has increased significantly between January 1, 2005 and December 31, 2014 [175/2871 (6.1%) vs. 764/5603 (13.6%), respectively; P<0.001; R=98.1%]. For the category of pain and disability reporting, Visual Analog Score demonstrated a statistically significant decrease in use from 2005 through 2014 [56/76 (73.7%) vs. 300/520 (57.7%), respectively; P<0.001], whereas Oswestry Disability Index increased significantly in use [19/76 (25.0%) vs. 182/520 (35.0%), respectively; P<0.001]. For quality of life, EuroQOL-5 Dimensions increased significantly in use between 2005 and 2014 [4/23 (17.4%) vs. 30/87 (34.5%), respectively; P<0.01]. In contrast, use of 36 Item Short Form Survey significantly decreased [19/23 (82.6%) vs. 57/87 (65.5%), respectively; P<0.01]. For depression, only the Zung Depression Scale underwent a significant increase in usage between 2005 and 2014 [0/0 (0%) vs. 7/13 (53.8%), respectively; P<0.01]. CONCLUSIONS: Although spine surgery outcome reporting has increased significantly over the past 10 years, there remains considerable heterogeneity in regards to individual outcomes metrics utilized. This heterogeneity makes it difficult to compare outcomes across studies and to accurately extrapolate outcomes to clinical practice.

Isolation, Characterization, and Expansion of Cancer Stem Cells.

The ability to isolate, characterize, and expand distinct tumor cell populations from primary tissue or xenografts is vital to identifying molecular mechanisms specific to cancer stem cells. Once cells have been extracted from tissue, there are multiple methods by which they can be sorted and cultured. We will describe the approaches that can be taken from cancer stem cell isolation through expansion, including Magnetic-activated Cell Sorting (MACS), Fluorescence-activated Cell Sorting (FACS), the use of reporter systems, and various cell culture methods.