Publications

The microbiome plays a fundamental role in how the immune system develops and how inflammatory responses are shaped and regulated. The "gut-lung axis" is a relatively new term that highlights a crucial biological crosstalk between the intestinal microbiome and lung. A growing body of literature suggests that dysbiosis, perturbation of the gut microbiome, is a driving force behind the development, and severity of allergic asthma. Animal models have given researchers new insights into how gut microbe-derived components and metabolites, such as short-chain fatty acids (SCFAs), influence the development of asthma. While the full understanding of how SCFAs influence allergic airway disease remains obscure, a recurring theme of epigenetic regulation of gene expression in several immune cell compartments is emerging. This review will address our current understanding of how SCFAs, and specifically butyrate, orchestrates cell behavior, and epigenetic changes and will provide a detailed overview of the effects of these modifications on immune cells in the context of allergic airway disease.

There has been a rapid expansion in treatment options for the management of metastatic prostate cancer, but individual patient outcomes can be variable due to inter-patient tumor heterogeneity. Fortunately, the disease can be stratified on the basis of common somatic features, providing potential for the development of clinically useful prognostic and predictive biomarkers. Tissue biopsy programs and studies leveraging circulating tumor DNA (ctDNA) have revealed specific genomic alterations that are associated with aggressive disease biology. In this review, we discuss the potential for genomic subtyping to improve prognostication and to help guide treatment selection. We summarize data on associations between AR pathway alterations and patient response to AR signaling inhibitors and other standards of care. We describe the links between detection of different types of DNA damage repair defects and clinical outcomes with targeted therapies such as poly(adenosine diphosphate-ribose) polymerase (PARP) inhibitors or immune checkpoint inhibitors. PI3K signaling pathway inhibitors are also in advanced clinical development and we report upon the potential for these and other novel targeted therapies to have impact in specific molecular subsets of metastatic prostate cancer. Finally, we discuss the growing use of blood-based analytes for prognostic and predictive biomarker development, and summarize ongoing prospective biomarker-driven clinical trials.

Pathological links between neurodegenerative disease and cancer are emerging. LRRK2 overactivity contributes to Parkinson's disease, whereas our previous analyses of public cancer patient data revealed that decreased LRRK2 expression is associated with lung adenocarcinoma (LUAD). The clinical and functional relevance of LRRK2 repression in LUAD is unknown. Here, we investigated associations between LRRK2 expression and clinicopathological variables in LUAD patient data and asked whether LRRK2 knockout promotes murine lung tumorigenesis. In patients, reduced LRRK2 was significantly associated with ongoing smoking and worse survival, as well as signatures of less differentiated LUAD, altered surfactant metabolism and immunosuppression. We identified shared transcriptional signals between LRRK2-low LUAD and postnatal alveolarization in mice, suggesting aberrant activation of a developmental program of alveolar growth and differentiation in these tumors. In a carcinogen-induced murine lung cancer model, multiplex IHC confirmed that LRRK2 was expressed in alveolar type II (AT2) cells, a main LUAD cell-of-origin, while its loss perturbed AT2 cell morphology. LRRK2 knockout in this model significantly increased tumor initiation and size, demonstrating that loss of LRRK2, a key Parkinson's gene, promotes lung tumorigenesis.

Clinical reporting of solid tumor sequencing requires accurate assessment of the accuracy and reproducibility of each assay. Somatic mutation variant allele fractions may be below 10% in many samples due to sample heterogeneity, tumor clonality, and/or sample degradation in fixatives such as formalin. The toolkits available to the clinical sequencing community for correlating assay design parameters with assay sensitivity remain limited, and large-scale empirical assessments are often relied upon due to the lack of clear theoretical grounding. To address this uncertainty, we developed a theoretical model for predicting the expected variant calling sensitivity for a given library complexity and sequencing depth. We found that binomial models were appropriate when assay sensitivity was only limited by library complexity or sequencing depth, but that functional scaling for library complexity was necessary when both library complexity and sequencing depth were co-limiting. We empirically validated this model with sequencing experiments using a series of DNA input amounts and sequencing depths. Based on these findings, we propose a workflow for determining the limiting factors to sensitivity in different assay designs, and present the formulas for these scenarios. The approach described here provides designers of clinical assays with the methods to theoretically predict assay design outcomes a priori, potentially reducing burden in clinical tumor assay design and validation efforts.

Gene expression classifiers are gaining increasing popularity for stratifying tumors into subgroups with distinct biological features. A fundamental limitation shared by current classifiers is the requirement for comparable training and testing data sets. Here, we describe a self-training implementation of our probability ratio-based classification prediction score method (PRPS-ST), which facilitates the porting of existing classification models to other gene expression data sets. In comparison to gold standards, we demonstrate favorable performance of PRPS-ST in gene expression-based classification of DLBCL and B-ALL using a diverse variety of gene expression data types and pre-processing methods, including in classifications with a high degree of class imbalance. Tumors classified by our method were significantly enriched for prototypical genetic features of their respective subgroups. Interestingly, this included cases that were unclassifiable by established methods, implying the potential enhanced sensitivity of PRPS-ST.

The nucleophosmin 1 (NPM1) protein is frequently overexpressed in various cancers compared to normal tissues and represents a potential biomarker for maliganancy. However, its role in colorectal cancer (CRC) is still not fully understood. In this report, we show that NPM1 levels in CRC correlate with prognosis and sensitivity to chemotherapy. NPM1 expression was found to be significantly increased in CRC tumors (P < .001) and was associated with poor overall 5-year survival (P < .05). For individuals with Stage IV disease, this represented a reduction in survival by 11 months (P < .01; HR = 0.38, CI [0.21, 0.69]. In vitro, we show that NPM1 gene silencing enhanced the chemosensitivity of CRC cells and that pharmacological inhibition of NPM1 by NSC348884 triggered the onset of programmed cell death. Our immunofluorescence microscopy and immunoblot analyses also revealed that blocking NPM1 function sensitized CRC cells to chemotherapy-induced apoptosis through a mechanism that involves proteins in the AKT pathway. Consistent with the in vitro data, our patient-derived CRC xenograft model showed that inhibition of NPM1 suppressed tumor growth and attenuated AKT signaling in vivo. Moreover, LY294002, an inhibitor of the PI3K/AKT pathway, restored the chemosensitivity of CRC cells expressing high levels of NPM1. The findings that NPM1's expression in CRC tissue correlates with prognosis and supports anti-apoptotic activity mediated by AKT signaling, further our understanding of the role of NPM1 in CRC.

Low-grade serous ovarian carcinoma (LGSOC) is a rare tumor subtype with high case fatality rates in patients with metastatic disease. There is a pressing need to develop effective treatments using newly available preclinical models for therapeutic discovery and drug evaluation. Here we use multiomics integration of whole exome sequencing, RNA sequencing, and mass spectrometry-based proteomics on fourteen LGSOC cell lines to elucidate novel biomarkers and therapeutic vulnerabilities. Comparison of LGSOC cell line data to LGSOC tumor data enabled predictive biomarker identification of MEK inhibitor (MEKi) efficacy, with KRAS mutations found exclusively in MEKi-sensitive cell lines and NRAS mutations found mostly in MEKi-resistant cell lines. Distinct patterns of COSMIC mutational signatures were identified in MEKi-sensitive and MEKi-resistant cell lines. Deletions of CDKN2A/B and MTAP genes were more frequent in cell lines than tumor samples and possibly represent key driver events in the absence of KRAS/NRAS/BRAF mutations. These LGSOC cell lines were representative models of the molecular aberrations found in LGSOC tumors. For prediction of in vitro MEKi efficacy, proteomic data provided better discrimination than gene expression data. Condensin, MCM, and RFC protein complexes were identified as potential treatment targets in MEKi-resistant cell lines. This study suggests that CDKN2A/B or MTAP deficiency may be exploited using synthetically lethal treatment strategies, highlighting the importance of using proteomic data as a tool for molecular drug prediction. Multiomics approaches are crucial to improving our understanding of the molecular underpinnings of LGSOC and applying this information to develop new therapies.

Molecular stratification can improve the management of advanced cancers, but requires relevant tumor samples. Metastatic urothelial carcinoma (mUC) is poised to benefit given a recent expansion of treatment options and its high genomic heterogeneity. We profile minimally-invasive plasma circulating tumor DNA (ctDNA) samples from 104 mUC patients, and compare to same-patient tumor tissue obtained during invasive surgery. Patient ctDNA abundance is independently prognostic for overall survival in patients initiating first-line systemic therapy. Importantly, ctDNA analysis reproduces the somatic driver genome as described from tissue-based cohorts. Furthermore, mutation concordance between ctDNA and matched tumor tissue is 83.4%, enabling benchmarking of proposed clinical biomarkers. While 90% of mutations are identified across serial ctDNA samples, concordance for serial tumor tissue is significantly lower. Overall, our exploratory analysis demonstrates that genomic profiling of ctDNA in mUC is reliable and practical, and mitigates against disease undersampling inherent to studying archival primary tumor foci. We urge the incorporation of cell-free DNA profiling into molecularly-guided clinical trials for mUC.