Macrophage HIF-1α Is an Independent Prognostic Indicator in Kidney Cancer.

PURPOSE: Clear cell renal cell carcinoma (ccRCC) is frequently associated with inactivation of the von Hippel-Lindau tumor suppressor, resulting in activation of HIF-1α and HIF-2α. The current paradigm, established using mechanistic cell-based studies, supports a tumor promoting role for HIF-2α, and a tumor suppressor role for HIF-1α. However, few studies have comprehensively examined the clinical relevance of this paradigm. Furthermore, the hypoxia-associated factor (HAF), which regulates the HIFs, has not been comprehensively evaluated in ccRCC. EXPERIMENTAL DESIGN: To assess the involvement of HAF/HIFs in ccRCC, we analyzed their relationship to tumor grade/stage/outcome using tissue from 380 patients, and validated these associations using tissue from 72 additional patients and a further 57 patients treated with antiangiogenic therapy for associations with response. Further characterization was performed using single-cell mRNA sequencing (scRNA-seq), RNA-in situ hybridization (RNA-ISH), and IHC. RESULTS: HIF-1α was primarily expressed in tumor-associated macrophages (TAMs), whereas HIF-2α and HAF were expressed primarily in tumor cells. TAM-associated HIF-1α was significantly associated with high tumor grade and increased metastasis and was independently associated with decreased overall survival. Furthermore, elevated TAM HIF-1α was significantly associated with resistance to antiangiogenic therapy. In contrast, high HAF or HIF-2α were associated with low grade, decreased metastasis, and increased overall survival. scRNA-seq, RNA-ISH, and Western blotting confirmed the expression of HIF-1α in M2-polarized CD163-expressing TAMs. CONCLUSIONS: These findings highlight a potential role of TAM HIF-1α in ccRCC progression and support the reevaluation of HIF-1α as a therapeutic target and marker of disease progression.

CIPR: a web-based R/shiny app and R package to annotate cell clusters in single cell RNA sequencing experiments.

BACKGROUND: Single cell RNA sequencing (scRNAseq) has provided invaluable insights into cellular heterogeneity and functional states in health and disease. During the analysis of scRNAseq data, annotating the biological identity of cell clusters is an important step before downstream analyses and it remains technically challenging. The current solutions for annotating single cell clusters generally lack a graphical user interface, can be computationally intensive or have a limited scope. On the other hand, manually annotating single cell clusters by examining the expression of marker genes can be subjective and labor-intensive. To improve the quality and efficiency of annotating cell clusters in scRNAseq data, we present a web-based R/Shiny app and R package, Cluster Identity PRedictor (CIPR), which provides a graphical user interface to quickly score gene expression profiles of unknown cell clusters against mouse or human references, or a custom dataset provided by the user. CIPR can be easily integrated into the current pipelines to facilitate scRNAseq data analysis. RESULTS: CIPR employs multiple approaches for calculating the identity score at the cluster level and can accept inputs generated by popular scRNAseq analysis software. CIPR provides 2 mouse and 5 human reference datasets, and its pipeline allows inter-species comparisons and the ability to upload a custom reference dataset for specialized studies. The option to filter out lowly variable genes and to exclude irrelevant reference cell subsets from the analysis can improve the discriminatory power of CIPR suggesting that it can be tailored to different experimental contexts. Benchmarking CIPR against existing functionally similar software revealed that our algorithm is less computationally demanding, it performs significantly faster and provides accurate predictions for multiple cell clusters in a scRNAseq experiment involving tumor-infiltrating immune cells. CONCLUSIONS: CIPR facilitates scRNAseq data analysis by annotating unknown cell clusters in an objective and efficient manner. Platform independence owing to Shiny framework and the requirement for a minimal programming experience allows this software to be used by researchers from different backgrounds. CIPR can accurately predict the identity of a variety of cell clusters and can be used in various experimental contexts across a broad spectrum of research areas.

The Lineage-Defining Transcription Factors SOX2 and NKX2-1 Determine Lung Cancer Cell Fate and Shape the Tumor Immune Microenvironment.

The major types of non-small-cell lung cancer (NSCLC)-squamous cell carcinoma and adenocarcinoma-have distinct immune microenvironments. We developed a genetic model of squamous NSCLC on the basis of overexpression of the transcription factor Sox2, which specifies lung basal cell fate, and loss of the tumor suppressor Lkb1 (SL mice). SL tumors recapitulated gene-expression and immune-infiltrate features of human squamous NSCLC; such features included enrichment of tumor-associated neutrophils (TANs) and decreased expression of NKX2-1, a transcriptional regulator that specifies alveolar cell fate. In Kras-driven adenocarcinomas, mis-expression of Sox2 or loss of Nkx2-1 led to TAN recruitment. TAN recruitment involved SOX2-mediated production of the chemokine CXCL5. Deletion of Nkx2-1 in SL mice (SNL) revealed that NKX2-1 suppresses SOX2-driven squamous tumorigenesis by repressing adeno-to-squamous transdifferentiation. Depletion of TANs in SNL mice reduced squamous tumors, suggesting that TANs foster squamous cell fate. Thus, lineage-defining transcription factors determine the tumor immune microenvironment, which in turn might impact the nature of the tumor.

Characterizing functional consequences of DNA copy number alterations in breast and ovarian tumors by spaceMap.

We propose a novel conditional graphical model - spaceMap - to construct gene regulatory networks from multiple types of high dimensional omic profiles. A motivating application is to characterize the perturbation of DNA copy number alterations (CNAs) on downstream protein levels in tumors. Through a penalized multivariate regression framework, spaceMap jointly models high dimensional protein levels as responses and high dimensional CNAs as predictors. In this setup, spaceMap infers an undirected network among proteins together with a directed network encoding how CNAs perturb the protein network. spaceMap can be applied to learn other types of regulatory relationships from high dimensional molecular profiles, especially those exhibiting hub structures. Simulation studies show spaceMap has greater power in detecting regulatory relationships over competing methods. Additionally, spaceMap includes a network analysis toolkit for biological interpretation of inferred networks. We applies spaceMap to the CNAs, gene expression and proteomics data sets from CPTAC-TCGA breast (n=77) and ovarian (n=174) cancer studies. Each cancer exhibits disruption of 'ion transmembrane transport' and 'regulation from RNA polymerase II promoter' by CNA events unique to each cancer. Moreover, using protein levels as a response yields a more functionally-enriched network than using RNA expressions in both cancer types. The network results also help to pinpoint crucial cancer genes and provide insights on the functional consequences of important CNA in breast and ovarian cancers. The R package spaceMap - including vignettes and documentation - is hosted on https://topherconley.github.io/spacemap.

Similar expression profiles in CD34+ cells from chronic phase chronic myeloid leukemia patients with and without deep molecular responses to nilotinib.

The life expectancy of patients with chronic phase chronic myeloid leukemia on tyrosine kinase inhibitor therapy now approaches that of the general population. Approximately 60% of patients treated with second generation tyrosine kinase inhibitors achieve a deep molecular response, the prerequisite for a trial of treatment-free remission. Those patients unlikely to achieve deep molecular response may benefit from more intensive therapy up front. To identify biomarkers predicting deep molecular response we performed transcriptional profiling on CD34+ progenitor cells from newly diagnosed chronic phase chronic myeloid leukemia patients treated with nilotinib on a prospective clinical trial. Using unsupervised and targeted analytical strategies, we show that gene expression profiles are similar in patients with and without subsequent deep molecular response. This result is in contrast to the distinct expression signature of CD34+ chronic phase chronic myeloid leukemia patients failing to achieve a cytogenetic response on imatinib and suggests that deep molecular response to second-generation tyrosine kinase inhibitors is governed by the biology of more primitive chronic myeloid leukemia cells or extrinsic factors.

Regulation of Zn and Fe transporters by the GPC1 gene during early wheat monocarpic senescence.

BACKGROUND: During wheat senescence, leaf components are degraded in a coordinated manner, releasing amino acids and micronutrients which are subsequently transported to the developing grain. We have previously shown that the simultaneous downregulation of Grain Protein Content (GPC) transcription factors, GPC1 and GPC2, greatly delays senescence and disrupts nutrient remobilization, and therefore provide a valuable entry point to identify genes involved in micronutrient transport to the wheat grain. RESULTS: We generated loss-of-function mutations for GPC1 and GPC2 in tetraploid wheat and showed in field trials that gpc1 mutants exhibit significant delays in senescence and reductions in grain Zn and Fe content, but that mutations in GPC2 had no significant effect on these traits. An RNA-seq study of these mutants at different time points showed a larger proportion of senescence-regulated genes among the GPC1 (64%) than among the GPC2 (37%) regulated genes. Combined, the two GPC genes regulate a subset (21.2%) of the senescence-regulated genes, 76.1% of which are upregulated at 12 days after anthesis, before the appearance of any visible signs of senescence. Taken together, these results demonstrate that GPC1 is a key regulator of nutrient remobilization which acts predominantly during the early stages of senescence. Genes upregulated at this stage include transporters from the ZIP and YSL gene families, which facilitate Zn and Fe export from the cytoplasm to the phloem, and genes involved in the biosynthesis of chelators that facilitate the phloem-based transport of these nutrients to the grains. CONCLUSIONS: This study provides an overview of the transport mechanisms activated in the wheat flag leaf during monocarpic senescence. It also identifies promising targets to improve nutrient remobilization to the wheat grain, which can help mitigate Zn and Fe deficiencies that afflict many regions of the developing world.

Massifquant: open-source Kalman filter-based XC-MS isotope trace feature detection.

MOTIVATION: Isotope trace (IT) detection is a fundamental step for liquid or gas chromatography mass spectrometry (XC-MS) data analysis that faces a multitude of technical challenges on complex samples. The Kalman filter (KF) application to IT detection addresses some of these challenges; it discriminates closely eluting ITs in the m/z dimension, flexibly handles heteroscedastic m/z variances and does not bin the m/z axis. Yet, the behavior of this KF application has not been fully characterized, as no cost-free open-source implementation exists and incomplete evaluation standards for IT detection persist. RESULTS: Massifquant is an open-source solution for KF IT detection that has been subjected to novel and rigorous methods of performance evaluation. The presented evaluation with accompanying annotations and optimization guide sets a new standard for comparative IT detection. Compared with centWave, matchedFilter and MZMine2-alternative IT detection engines-Massifquant detected more true ITs in a real LC-MS complex sample, especially low-intensity ITs. It also offers competitive specificity and equally effective quantitation accuracy. AVAILABILITY AND IMPLEMENTATION: Massifquant is integrated into XCMS with GPL license ≥ 2.0 and hosted by Bioconductor: http://bioconductor.org. Annotation data are archived at http://hdl.lib.byu.edu/1877/3232. Parameter optimization code and documentation is hosted at https://github.com/topherconley/optimize-it.