Julia Chifman

Quartet inference from SNP data under the coalescent model.

MOTIVATION Increasing attention has been devoted to estimation of species-level phylogenetic relationships under the coalescent model. However, existing methods either use summary statistics (gene trees) to carry out estimation, ignoring an important source of variability in the estimates, or involve computationally intensive Bayesian Markov chain Monte Carlo algorithms that do not scale well to whole-genome datasets. RESULTS We develop a method to infer relationships among quartets of taxa under the coalescent model using techniques from algebraic statistics. Uncertainty in the estimated relationships is quantified using the nonparametric bootstrap. The performance of our method is assessed with simulated data. We then describe how our method could be used for species tree inference in larger taxon samples, and demonstrate its utility using datasets for Sistrurus rattlesnakes and for soybeans. AVAILABILITY AND IMPLEMENTATION The method to infer the phylogenetic relationship among quartets is implemented in the software SVDquartets, available at www.stat.osu.edu/∼lkubatko/software/SVDquartets.

Identifiability of the unrooted species tree topology under the coalescent model with time-reversible substitution processes, site-specific rate variation, and invariable sites.

The inference of the evolutionary history of a collection of organisms is a problem of fundamental importance in evolutionary biology. The abundance of DNA sequence data arising from genome sequencing projects has led to significant challenges in the inference of these phylogenetic relationships. Among these challenges is the inference of the evolutionary history of a collection of species based on sequence information from several distinct genes sampled throughout the genome. It is widely accepted that each individual gene has its own phylogeny, which may not agree with the species tree. Many possible causes of this gene tree incongruence are known. The best studied is the incomplete lineage sorting, which is commonly modeled by the coalescent process. Numerous methods based on the coalescent process have been proposed for the estimation of the phylogenetic species tree given DNA sequence data. However, use of these methods assumes that the phylogenetic species tree can be identified from DNA sequence data at the leaves of the tree, although this has not been formally established. We prove that the unrooted topology of the n-leaf phylogenetic species tree is generically identifiable given observed data at the leaves of the tree that are assumed to have arisen from the coalescent process under a time-reversible substitution process with the possibility of site-specific rate variation modeled by the discrete gamma distribution and a proportion of invariable sites.

Immunogenic subtypes of breast cancer delineated by gene classifiers of immune responsiveness

Assessment of expression profiles and clinical data from many breast tumors enabled classifications having prognostic value. Tumors comprising molecularly distinct subtypes differed in potential for metastasis-protective immune responsiveness, perhaps reflecting a differential activation of immunomodulatory pathways The abundance and functional orientation of tumor-infiltrating lymphocytes in breast cancer is associated with distant metastasis-free survival, yet how this association is influenced by tumor phenotypic heterogeneity is poorly understood. Here, a bioinformatics approach defined tumor biologic attributes that influence this association and delineated tumor subtypes that may differ in their ability to sustain durable antitumor immune responses. A large database of breast tumor expression profiles and associated clinical data was compiled, from which the ability of phenotypic markers to significantly influence the prognostic performance of a classification model that incorporates immune cell–specific gene signatures was ascertained. Markers of cell proliferation and intrinsic molecular subtype reproducibly distinguished two breast cancer subtypes that we refer to as immune benefit-enabled (IBE) and immune benefit-disabled (IBD). The IBE tumors, comprised mostly of highly proliferative tumors of the basal-like, HER2-enriched, and luminal B subtypes, could be stratified by the immune classifier into significantly different prognostic groups, while IBD tumors could not, indicating the potential for productive engagement of metastasis-protective immunity in IBE tumors, but not in IBD tumors. The prognostic stratification in IBE was independent of conventional variables. Gene network analysis predicted the activation of TNFα/IFNγ signaling pathways in IBE tumors and the activation of the transforming growth factor-β pathway in IBD tumors. This prediction supports a model in which breast tumors can be distinguished on the basis of their potential for metastasis-protective immune responsiveness. Whether IBE and IBD represent clinically relevant contexts for evaluating sensitivity to immunotherapeutic agents warrants further investigation. Cancer Immunol Res; 4(7); 600–10. ©2016 AACR.

Conservation of immune gene signatures in solid tumors and prognostic implications

BackgroundTumor-infiltrating leukocytes can either limit cancer growth or facilitate its spread. Diagnostic strategies that comprehensively assess the functional complexity of tumor immune infiltrates could have wide-reaching clinical value. In previous work we identified distinct immune gene signatures in breast tumors that reflect the relative abundance of infiltrating immune cells and exhibited significant associations with patient outcomes. Here we hypothesized that immune gene signatures agnostic to tumor type can be identified by de novo discovery of gene clusters enriched for immunological functions and possessing internal correlation structure conserved across solid tumors from different anatomic sites.MethodsWe assembled microarray expression datasets encompassing 5,295 tumors of the breast, colon, lung, ovarian and prostate. Unsupervised clustering methods were used to determine number and composition of gene clusters within each dataset. Immune-enriched gene clusters (signatures) identified by gene ontology enrichment were analyzed for internal correlation structure and conservation across tumors then compared against expression profiles of: 1) flow-sorted leukocytes from peripheral blood and 2) >300 cancer cell lines from solid and hematologic cancers. Cox regression analysis was used to identify signatures with significant associations with clinical outcome.ResultsWe identified nine distinct immune-enriched gene signatures conserved across all five tumor types. The signatures differentiated specific leukocyte lineages with moderate discernment overall, and naturally organized into six discrete groups indicative of admixed lineages. Moreover, seven of the signatures exhibit minimal and uncorrelated expression in cancer cell lines, suggesting that these signatures derive predominantly from infiltrating immune cells. All nine immune signatures achieved statistically significant associations with patient prognosis (p<0.05) in one or more tumor types with greatest significance observed in breast and skin cancers. Several signatures indicative of myeloid lineages exhibited poor outcome associations that were most apparent in brain and colon cancers.ConclusionsThese findings suggest that tumor infiltrating immune cells can be differentiated by immune-specific gene expression patterns that quantify the relative abundance of multiple immune infiltrates across a range of solid tumor types. That these markers of immune involvement are significantly associated with patient prognosis in diverse cancers suggests their clinical utility as pan-cancer markers of tumor behavior and immune responsiveness.

A Systems Biology Approach to Iron Metabolism

Iron is critical to the survival of almost all living organisms. However, inappropriately low or high levels of iron are detrimental and contribute to a wide range of diseases. Recent advances in the study of iron metabolism have revealed multiple intricate pathways that are essential to the maintenance of iron homeostasis. Further, iron regulation involves processes at several scales, ranging from the subcellular to the organismal. This complexity makes a systems biology approach crucial, with its enabling technology of computational models based on a mathematical description of regulatory systems. Systems biology may represent a new strategy for understanding imbalances in iron metabolism and their underlying causes.

Activated Oncogenic Pathway Modifies Iron Network in Breast Epithelial Cells: A Dynamic Modeling Perspective.

Dysregulation of iron metabolism in cancer is well documented and it has been suggested that there is interdependence between excess iron and increased cancer incidence and progression. In an effort to better understand the linkages between iron metabolism and breast cancer, a predictive mathematical model of an expanded iron homeostasis pathway was constructed that includes species involved in iron utilization, oxidative stress response and oncogenic pathways. The model leads to three predictions. The first is that overexpression of iron regulatory protein 2 (IRP2) recapitulates many aspects of the alterations in free iron and iron-related proteins in cancer cells without affecting the oxidative stress response or the oncogenic pathways included in the model. This prediction was validated by experimentation. The second prediction is that iron-related proteins are dramatically affected by mitochondrial ferritin overexpression. This prediction was validated by results in the pertinent literature not used for model construction. The third prediction is that oncogenic Ras pathways contribute to altered iron homeostasis in cancer cells. This prediction was validated by a combination of simulation experiments of Ras overexpression and catalase knockout in conjunction with the literature. The model successfully captures key aspects of iron metabolism in breast cancer cells and provides a framework upon which more detailed models can be built.

HyDe: A Python Package for Genome-Scale Hybridization Detection

Abstract.— The analysis of hybridization and gene flow among closely related taxa is a common goal for researchers studying speciation and phylogeography. Many methods for hybridization detection use simple site pattern frequencies from observed genomic data and compare them to null models that predict an absence of gene flow. The theory underlying the detection of hybridization using these site pattern probabilities exploits the relationship between the coalescent process for gene trees within population trees and the process of mutation along the branches of the gene trees. For certain models, site patterns are predicted to occur in equal frequency (i.e., their difference is 0), producing a set of functions called phylogenetic invariants. In this article, we introduce HyDe, a software package for detecting hybridization using phylogenetic invariants arising under the coalescent model with hybridization. HyDe is written in Python and can be used interactively or through the command line using pre‐packaged scripts. We demonstrate the use of HyDe on simulated data, as well as on two empirical data sets from the literature. We focus in particular on identifying individual hybrids within population samples and on distinguishing between hybrid speciation and gene flow. HyDe is freely available as an open source Python package under the GNU GPL v3 on both GitHub (https://github.com/pblischak/HyDe) and the Python Package Index (PyPI: https://pypi.python.org/pypi/phyde).

Immune gene signatures and tumor intrinsic markers delineate novel immunogenic subtypes of breast cancer

The abundance and functional orientation of tumor-infiltrating effector cells has long been observed to predict for reduced incidence of clinical metastasis and cancer-specific death. Using bioinformatics to mine large breast tumor microarray datasets, we and others have identified prognostic immune gene signatures, or metagenes. Robust evidence indicates that these metagenes are: 1) positively correlated with distant metastasis-free survival (DMFS) of patients, 2) comprised of genes that regulate immune cell-specific biology, and 3) reflective of the relative abundance of discernible populations of tumor infiltrating leukocytes. In recent work we have leveraged the statistical associations between the immune metagenes and the DMFS of breast cancer patients to explore the underlying phenotypes that differ in their ability to potentiate long-term, immune-mediated tumor rejection. Using a tumor classification model that combines the prognostic attributes of three distinct immune metagenes, termed the B/P, T/NK and M/D metagenes, we have identified molecular subtypes of breast cancer that either permit or prohibit prognostication by the immune metagenes. On this basis, we have delineated the phenotypic attributes of breast cancer that distinguish two novel immunogenic tumor subtypes, which we have defined as: immune benefit-enabled (IBE) and immune benefit-disabled (IBD). Phenotypically, IBE tumors comprise of Basal-like tumors and highly-proliferative HER2-Enriched and Luminal-B subtypes, while IBD tumors comprise of Claudin-Low, Luminal-A, and low-proliferative HER2-Enriched and Luminal-B tumors. Prognostically, IBE tumors (n = 666) can be stratified by the immune metagene model into prognostic subgroups with high statistical significance (P<0.0001,log-rank test), while IBD tumors cannot (n = 1005, P = 0.3) consistent with the capacity for an innate anti-tumor immunity against IBE tumors, but not IBD tumors, that guards against distant metastasis. Furthermore, these observations were independent of adjuvant treatment, and may owe to differential activation of immunomodulatory pathways. Network analysis revealed that IBE/IBD differentially-expressed genes (q<0.01) underlie highly-significant pathway activation scores for TGF-beta signaling in IBD (p < 0.0001), and Interferon-gamma signaling in IBE (p < 0.0001). Furthermore, 15 of 19 genes comprising the previously described Immunologic Constant of Rejection (Marincola and colleagues) were significantly overexpressed in IBE tumors (P-value range: 0.05-3.5E-14). Thus, we conclude that breast tumors can be dichotomized into two subtypes fundamentally distinct with respect to their potential for metastasis-protective immune responsiveness. These findings indicate new contexts for studying anti-tumor immunity and oncogenic mechanisms of immunosuppression in breast cancer. Whether IBE and IBD subtypes represent clinically-relevant contexts for assessing patient prognosis or evaluating the efficacy of immunotherapeutic treatments warrants further investigation (See Figure ​Figure11). Figure 1 (A-D) The immune metagenes are prognostic of IBE but no IBD breast cancer. (A) Heatmaps of metagene expression levels (rows) across 1,954 tumors (columns). Key shows color scale of mean centered, log2-transformed gene signal intensities. For each metagene, ...

Abstract A011: Gene expression signatures of effector immune cell abundance are significantly associated with recurrence risk in colon cancer

Background: Previous studies have suggested that immune infiltrates in colorectal cancer are of clinical importance. We now know that functional orientation, density and location of immune cells within colorectal tumors profoundly influence the clinical outcome irrespective of stage. The currently known prognostic markers are AJCC (American Joint Committee on Cancer) stage, tumor morphology, tumor molecular pathway, tumor gene signatures and tumor mutation status. The immunoscore is currently being validated in a large prospective study to enable its application in clinical practice. The cell types most frequently investigated as antitumor effector cells are tumor infiltrating lymphocytes (TILs) such as cytotoxic T cells (CTLs), natural killer (NK) cells and B cells. Through expression microarray analysis, we recently discovered three separate immune gene signatures, or metagenes, that appear to reflect the relative abundance of distinct tumor-infiltrating leukocyte populations. These metagenes, referred as the B/P (B-cell/Plasma cell), T/NK (T-cell/Natural Killer cell) and M/D (Monocyte/Dendritic cell) immune metagenes, were found to be significantly associated with the distant metastasis-free survival of breast cancer patients with highly proliferative cancer of the Basal-like, HER2-Enriched and Luminal B subtypes, in particular. Aim: Given the histopathological evidence that TIL abundance is prognostic of outcome in colorectal cancer we sought to evaluate the interaction between established clinical and pathologic factors and immune metagenes. We hypothesized that the immune gene signatures would recapitulate the known information and potentially add additional prognostic information. Methods: In a multi-institutional, meta-cohort analysis of 177 colon cancer tumor patients, gene expression profiles of colon tumor biopsies were investigated to determine the relationships between immune gene signatures and disease specific survival (DSS). In separate Cox proportional hazards regression models, B/P and TNK metagenes were both found to be associated with DSS, however M/D was not. Next, stepwise selection models were considered in which other prognostic factors (AJCC stage, grade, gender and age) and both B/P and TNK were considered for inclusion in the Cox proportional hazards regression model. In this analysis, AJCC stage was the first variable to enter the model, followed by B/P and then grade. Results: Age, gender and TNK were not significant predictors in this multivariate model. In the final multivariate model the hazard ratio for B/P was 0.746 with 95% confidence interval (0.61 to 0.91, p=0.0038) suggesting that as B/P signature increases the DSS risk decreases. These analyses support the finding that both TNK and B/P are predictive of DSS; however the impact of TNK was not an independent predictor of DSS once AJCC stage and grade were taken into account, while B/P remained an independent predictor. Further studies need to be conducted to determine whether the TNK effect would reach statistical significance in a multivariate model with a larger sample size. Conclusions: Our results suggest that the prognostic information provided by immune metagenes (B/P and TNK) will have the greatest clinical utility when used as a complement to known clinical predictors, especially AJCC stage. Citation Format: Angela Tatiana Alistar, Julia Chifman, Ralph D9Agostino, Jr., Lance D. Miller. Gene expression signatures of effector immune cell abundance are significantly associated with recurrence risk in colon cancer. [abstract]. In: Proceedings of the CRI-CIMT-EATI-AACR Inaugural International Cancer Immunotherapy Conference: Translating Science into Survival; September 16-19, 2015; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(1 Suppl):Abstract nr A011.