Jacob Turner

Erratum: Personalized immunomonitoring uncovers molecular networks that stratify lupus patients ((Cell (2016) 165 (551-565))

Romain Banchereau, Seunghee Hong, Brandi Cantarel, Nicole Baldwin, Jeanine Baisch, Michelle Edens, Alma-Martina Cepika, Peter Acs, Jacob Turner, Esperanza Anguiano, Parvathi Vinod, Shaheen Khan, Gerlinde Obermoser, Derek Blankenship, Edward Wakeland, Lorien Nassi, Alisa Gotte, Marilynn Punaro, Yong-Jun Liu, Jacques Banchereau, Jose Rossello-Urgell, Tracey Wright, and Virginia Pascual* *Correspondence: virginia.pascual@bswhealth.org http://dx.doi.org/10.1016/j.cell.2016.05.057

A 380-gene meta-signature of active tuberculosis compared with healthy controls

Mycobacterium tuberculosis is estimated to have infected one third of the worlds population and continues to be a significant cause of mortality and morbidity [1]. There is a need for new and improved diagnostics or treatment-monitoring tools and blood-based mRNA diagnostics are a potential solution [2]. Gene expression microarray analysis of human blood has been widely used to profile the host transcriptional response in active tuberculosis (TB) to identify potential biomarkers and better understand the host immune response [2]. So far, there has been a relative lack of concordance in the actual genes being identified from the published studies [2, 3], although there has been agreement in some of the pathways identified. Interferon (IFN) signalling has been identified as a dominant signature in many of the individual studies [2, 4]; however, when significant gene lists were combined from eight publicly available TB datasets, TREM1 (triggering receptor expressed on myeloid cells 1) signalling became the most significant pathway [5]. Modular and meta-profiling identify a common transcriptional response of patients with TB versus healthy controls http://ow.ly/YvEP7

The Transcriptional Signature of Active Tuberculosis Reflects Symptom Status in Extra-Pulmonary and Pulmonary Tuberculosis.

Background Mycobacterium tuberculosis infection is a leading cause of infectious death worldwide. Gene-expression microarray studies profiling the blood transcriptional response of tuberculosis (TB) patients have been undertaken in order to better understand the host immune response as well as to identify potential biomarkers of disease. To date most of these studies have focused on pulmonary TB patients with gene-expression profiles of extra-pulmonary TB patients yet to be compared to those of patients with pulmonary TB or sarcoidosis. Methods A novel cohort of patients with extra-pulmonary TB and sarcoidosis was recruited and the transcriptional response of these patients compared to those with pulmonary TB using a variety of transcriptomic approaches including testing a previously defined 380 gene meta-signature of active TB. Results The 380 meta-signature broadly differentiated active TB from healthy controls in this new dataset consisting of pulmonary and extra-pulmonary TB. The top 15 genes from this meta-signature had a lower sensitivity for differentiating extra-pulmonary TB from healthy controls as compared to pulmonary TB. We found the blood transcriptional responses in pulmonary and extra-pulmonary TB to be heterogeneous and to reflect the extent of symptoms of disease. Conclusions The transcriptional signature in extra-pulmonary TB demonstrated heterogeneity of gene expression reflective of symptom status, while the signature of pulmonary TB was distinct, based on a higher proportion of symptomatic individuals. These findings are of importance for the rational design and implementation of mRNA based TB diagnostics.

The E-Id Protein Axis Specifies Adaptive Lymphoid Cell Identity and Suppresses Thymic Innate Lymphoid Cell Development

Summary Innate and adaptive lymphoid development is orchestrated by the activities of E proteins and their antagonist Id proteins, but how these factors regulate early T cell progenitor (ETP) and innate lymphoid cell (ILC) development remains unclear. Using multiple genetic strategies, we demonstrated that E proteins E2A and HEB acted in synergy in the thymus to establish T cell identity and to suppress the aberrant development of ILCs, including ILC2s and lymphoid‐tissue‐inducer‐like cells. E2A and HEB orchestrated T cell fate and suppressed the ILC transcription signature by activating the expression of genes associated with Notch receptors, T cell receptor (TCR) assembly, and TCR‐mediated signaling. E2A and HEB acted in ETPs to establish and maintain a T‐cell‐lineage‐specific enhancer repertoire, including regulatory elements associated with the Notch1, Rag1, and Rag2 loci. On the basis of these and previous observations, we propose that the E‐Id protein axis specifies innate and adaptive lymphoid cell fate. Graphical Abstract Figure. No Caption available. HighlightsE2A and HEB act in concert to specify T cell fateE protein activity in lymphoid progenitors suppresses aberrant ILC developmentE2A and HEB establish a T‐lineage‐specific program of gene expressionThe E‐Id protein axis specifies the adaptive and innate lymphoid cell fate &NA; Previous studies established that E proteins act at multiple stages to promote T‐cell‐lineage development. Miyazaki et al. demonstrate that E proteins establish T cell identity and suppress the development of thymic ILCs by modulating enhancer repertoires of genes associated with Notch signaling and TCR&bgr; locus assembly.

Quantitative gene set analysis generalized for repeated measures, confounder adjustment, and continuous covariates

BackgroundGene set analysis (GSA) of gene expression data can be highly powerful when the biological signal is weak compared to other sources of variability in the data. However, many gene set analysis approaches utilize permutation tests which are not appropriate for complex study designs. For example, the correlation of subjects is broken when comparing time points within a longitudinal study. Linear mixed models provide a method to analyze longitudinal studies as well as adjust for potential confounding factors and account for sources of variability that are not of primary interest. Currently, there are no known gene set analysis approaches that fully account for these study design and analysis aspects. In order to do so, we generalize the QuSAGE gene set analysis algorithm, denoted Q-Gen, and provide the necessary estimation adjustments to incorporate linear mixed model analyses.ResultsWe assessed the performance of our generalized method in comparison to the original QuSAGE method in settings such as longitudinal repeated measures analysis and accounting for potential confounders. We demonstrate that the original QuSAGE method can not control for type-I error when these complexities exist. In addition to statistical appropriateness, analysis of a longitudinal influenza study suggests Q-Gen can allow for greater sensitivity when exploring a large number of gene sets.ConclusionsQ-Gen is an extension to the gene set analysis method of QuSAGE, and allows for linear mixed models to be applied appropriately within a gene set analysis framework. It provides GSA an added layer of flexibility that was not currently available. This flexibility allows for more appropriate statistical modeling of complex data structures that are inherent to many microarray study designs and can provide more sensitivity.

IL1 Receptor Antagonist Controls Transcriptional Signature of Inflammation in Patients with Metastatic Breast Cancer

Inflammation affects tumor immune surveillance and resistance to therapy. Here, we show that production of IL1β in primary breast cancer tumors is linked with advanced disease and originates from tumor-infiltrating CD11c+ myeloid cells. IL1β production is triggered by cancer cell membrane-derived TGFβ. Neutralizing TGFβ or IL1 receptor prevents breast cancer progression in humanized mouse model. Patients with metastatic HER2- breast cancer display a transcriptional signature of inflammation in the blood leukocytes, which is attenuated after IL1 blockade. When present in primary breast cancer tumors, this signature discriminates patients with poor clinical outcomes in two independent public datasets (TCGA and METABRIC).Significance: IL1β orchestrates tumor-promoting inflammation in breast cancer and can be targeted in patients using an IL1 receptor antagonist. Cancer Res; 78(18); 5243-58. ©2018 AACRSee related commentary by Dinarello, p. 5200.

Abstract P3-05-01: Immune and transcriptional signatures of dendritic dell (DC) vaccination combined with chemotherapy in locally advanced, triple-negative breast cancer (TNBC) patients

BACKGROUND: Women with TNBC who do not achieve a pathologic complete response (pCR) with preoperative (preop) chemotherapy have a high risk of recurrence and death from BC. Immunotherapy is an attractive strategy as human BCs can be immunogenic, and enhancing the immune effector function may augment the cytotoxic effects of standard therapies. CLINICAL TRIAL: Following IRB-approved informed consent, 10 pts with locally advanced TNBC received preop dose-dense doxorubicin/cyclophosphamide (AC) followed by paclitaxel and carboplatin (TCb) chemotherapy, combined with antigen-loaded (TNBC antigens: Cyclin B1, WT1, and control viral antigens: CEF) autologous monocyte-derived DC vaccinations administered intratumorally and subcutaneously. DCs were generated with GM-CSF and type I interferon, loaded with antigen in the form of long peptides and activated with innate ligands (LPS and Clo75) and CD40 ligand. Vaccines were given at 4 time points prior to definitive surgery, and 3 times post-surgery, pre- and post-radiation therapy (RT). Safety was the primary study endpoint, and pCR rate in breast and axilla was a secondary endpoint. Correlative studies included assessment of immune response via ELISpot and transcriptional profiling of blood samples collected over time. RESULTS: All pts received the 4 vaccines during preop chemotherapy, and 7/10 received all 7 vaccines. At the time of definitive surgery, 4 pts achieved a pCR, 3 pts had macroscopic residual disease in the breast and axillary lymph nodes, and 3 pts had residual cancer burden scores of 1. As of June 1, 2017, all pts have been in follow-up for at least 1 year s/p completion of all vaccines, and 7/10 patients have no evidence of disease. To assess immune signatures with IFN-γ-ELISpot, PBMCs from baseline (BL) and several time points during vaccine treatment were cultured with control peptides or with peptide libraries covering vaccine antigens. Using a linear mixed model to account for repeated and missing observations we found statistically significant (α = 0.05) increases in Cyclin B1, WT1, and CEF ELISpots in at least 1 time point post-DC vaccination and in follow-up. Compared to BL, Cyclin B1 and WT1 increased at 3 day pre-RT in 8/10 and 7/10 pts, respectively. To assess transcriptional signatures, a linear mixed model was utilized to determine statistically significant differences in fold-change over time compared to the BL and healthy controls. Modular analysis of differentially expressed transcripts at BL revealed downregulation of transcripts related to the monocyte lineage in 7/10 pts. Longitudinal analysis revealed profound transcriptional changes during AC with downregulation of lymphocyte modules and upregulation of innate and inflammation modules. While the latter ones have normalized during TCb and follow-up, T cell module remained substantially downregulated throughout treatment and follow-up. CONCLUSIONS: Combination of preop chemotherapy and intratumoral and subcutaneous autologous DC vaccination is safe in locally advanced TNBC pts and is linked with profound changes in immune transcription signatures and with expansion of antigen-specific immune responses that can be detected in IFN-γ ELISpot. Citation Format: Palucka AK, Roberts LK, Zurawski SM, Tarnowski J, Turner J, Wang X, Blankenship D, Smith JL, Levin MK, Finholt JP, Burkeholder SB, Timis R, Muniz LS, Dao T, Grant M, Banchereau J, Zurawski G, Pascual V, O9Shaughnessy JA. Immune and transcriptional signatures of dendritic dell (DC) vaccination combined with chemotherapy in locally advanced, triple-negative breast cancer (TNBC) patients [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P3-05-01.

Phantom: investigating heterogeneous gene sets in time-course data

Motivation: Gene set analysis is a powerful tool to study the coordinative change of time‐course data. However, most existing methods only model the overall change of a gene set, yet completely overlooked heterogeneous time‐dependent changes within sub‐sets of genes. Results: We have developed a novel statistical method, Phantom, to investigate gene set heterogeneity. Phantom employs the principle of multi‐objective optimization to assess the heterogeneity inside a gene set, which also accounts for the temporal dependency in time‐course data. Phantom improves the performance of gene set based methods to detect biological changes across time. Availability and implementation: Phantom webpage can be accessed at: http://www.baylorhealth.edu/Phantom. R package of Phantom is available at https://cran.r‐project.org/web/packages/phantom/index.html. Contact: jinghua.gu@bswhealth.org Supplementary information: Supplementary data are available at Bioinformatics online.