Biology subjects

Landry, A.

Publications and source records attributed to Landry, A..

4 recordsLinked to original sources

bioRxiv2019

Meta-gene markers predict meningioma recurrence with high accuracy

BackgroundMeningiomas, the most common adult brain tumors, recur in up to half of cases[1-3]. This requires timely intervention and therefore accurate risk assessment of recurrence is essential. Our current practice relies heavily on histological grade and extent of surgical excision to predict meningioma recurrence. However, prediction accuracy can be as poor as 50% for low or intermediate grade tumors, which constitute more than 90% of all cases. Moreover, attempts to find molecular markers, particularly for predicting recurrence of intermediate tumors have been impeded by low[4] or heterogenous[5-8] genetic signal. We therefore sought to apply systems-biology approaches to transcriptomic data to better predict meningioma recurrence.\n\nMethodsWe apply gene co-expression networks to a cohort of 252 adult patients from the publicly-available genetic repository Gene Expression Omnibus. Resultant gene clusters (\"modules\") were represented by the first principle component of their expression levels, and their ability to predict recurrence assessed with a logistic regression model. External validation was done using an independent merged cohort of 108 patients using the same modules. We used the bioinformatics database DAVID[9] to examine the gene ontology associations of each module.\n\nResultsUsing gene co-expression analysis, we were able predict tumor recurrence with high accuracy using a single module which mapped to cell cycle-related functions (AUC of 0.81 {+/-} 0.09 in external validation). Further, the module maintained its accuracy with removal of up to 20 of its most individually predictive genes, suggesting robustness against noise at the single gene level.\n\nConclusionsWith the easy accessibility of gene panels in healthcare diagnostics, our results offer a basis for routine molecular testing in meningioma management.

bioinformatics↗

bioRxiv2019

Data driven analysis reveals shared transcriptome response and immune cell composition across aetiologies of critical illness.

Sepsis and trauma are frequent and challenging health problems in critical care. The diversity of patient response to these conditions complicates both disease management and outcome prediction. Whole blood transcriptomics allows the analysis of response in the critically ill at a molecular level. Prior results in this field demonstrate robust and diverse genomic response in the acute phase and others have shown shared biological mechanisms across wide disease aetiologies. We hypothesize that specific biological mechanisms, particularly those related to immune processes, are shared between sepsis and trauma cohorts. These may serve as universal markers for patients vulnerable to a complicated clinical course and/or mortality. We present a systems level analysis of gene expression for a total of 317 patients with abdominal sepsis (51), pulmonary sepsis (108) or trauma (158) and compare them to healthy controls (68). Our results confirm that immune processes are shared across disease aetiologies in critical illnesses. We identify two consistent and distinct subgroups of critical illness: 1) increased dendritic cell and CD4 T helper fractions but suppressed neutrophils and 2) high neutrophils and otherwise suppressed leukocyte fractions. These subgroups validate in an independent cohort of 181 paediatric patients suffering from septic shock of diverse aetiologies. Furthermore, we found immune and inflammatory processes derived from gene co-expression networks were downregulated in subgroup 1. This paralleled prior results which find similar leukocyte configuration by deconvolving whole blood transcriptomics, and this leukocyte configuration associates with greater susceptibility to multi organ failure. We are the first to identify a patient subgroup with a preserved leukocyte configuration across aetiologies of critical illness, which may serve as a universal predictor of complicated clinical course/poor outcome.

immunology↗

bioRxiv2019

Skull base meningiomas have a distinct immune landscape.

Modulation of tumor microenvironment is an emerging frontier for new therapeutics. However in meningiomas, the most frequent adult brain tumor, the correlation of microenvironment with tumor phenotype is scarcely studied. We applied a variety of systems biology approaches to bulk tumor transcriptomics to explore the immune environments of both skull base and convexity (hemispheric) meningiomas. We hypothesized that the more benign biology of skull base meningiomas parallels the relative composition and activity of immune cells that oppose tumor growth and/or survival. We firstly applied gene co-expression networks to tumor bulk transcriptomics from 107 meningiomas (derived from 3 independent studies) and found immune processes to be the sole biological mechanism correlated with anatomical location while correcting for tumour grade. We then derived tumor immune cell fractions from bulk transcriptomics data and examined the immune cell-cytokine interactions using a network-based approach. We demonstrate that oncolytic M1 macrophages dominate skull base meningiomas while mast cells, known to play a role in oncogenesis, show greater activity in convexity tumors. Our results are the first to suggest the importance of tumor microenvironment in meningioma biology in the context of anatomic location and immune landscape. These findings may help better inform surgical decision making and yield location-specific therapies through modulation of immune microenvironment.

cancer biology↗

bioRxiv2018

Homogenous subgroups of atypical meningiomas defined using oncogenic signatures: basis for a new grading system?

Meningiomas are the most common brain tumor with a prevalence of 3% in the population. Histological grading of meningiomas (1 through 3) has a major role in determining treatment choice and predicting outcome. While largely indolent grade 1 and the highly aggressive grade 3 meningiomas as considered mostly homogenous in clinical behavior, atypical or grade 2 meningiomas have highly diverse biological properties. Our aim was to identify homogenous subgroups of atypical meningiomas with the working hypothesis that these subgroups would share features with grade 1 and grade 3 counterparts. We carried out systems level analysis by gene module discovery using co-expression networks on the transcriptomics of 212 meningiomas. The newly identified subgroups were characterized in terms of recurrence rate and overlapping biological processes in gene ontology. We were able to reclassify 33 of 46 atypical meningiomas (72%) into a benign \"grade 1-like\" (14/46) and malignant \"grade 3-like\" (19/46) subgroup based on oncogenic signatures. Recurrence rates of \"Grade 1-like\" and \"grade 3-like\" tumors was 0% and 72% respectively. These two new subgroups showed similar recurrence rates and concordant biological processes with the respected grades. Our findings help resolve the heterogeneity/uncertainty around atypical meningioma biology and identify subgroups more homogenous than in prior studies. These results may help reshape prediction, follow-up planning, treatment decisions and recruitment protocols for future and ongoing clinical trials. The findings demonstrate the conceptual advantage of systems biology approaches and underpin the utility of molecular signatures as complements to the current histological grading system.

cancer biology↗