Overview

Our research aims at understanding the molecular heterogeneity, evolutionary dynamics and progression of cancer. Our focus is towards characterizing how the indivdual’s lifestyle, exposure, immune response, local micro-environment and treatments impinge on the evolution of malignant cells and impact the clinical presentation of the disease. Using this information, we will develop better methods to estimate the likely course of a specific disease, and hopefully suggest new ways to prevent, diagnose and treat cancer by exploiting properties extrinsic to the tumor.

Novel approaches to place the molecular properties of cancer within the context of the patient

The majority of cancer research to date has largely focused on understanding molecular properties of the tumor and its surrounding microenvironment in order to therapeutically target key molecular components that might drive disease progression. However, it is now well understood that cancer cells do not exist in isolation. For example, we presented quantitative evidence that the tumor and microenvironment for many breast cancer patients (~20%) do not provide sufficient information to predict how the disease will progress. There does not appear to be any signals at the tumor site that alone are good markers of response to standard of care treatment and ultimately patient prognosis. In turn, this suggests that other factors external to the tumor including, for example, the patient’s immune system, lifestyle and exposures may play a significant role in disease progression.

Our lab is motivated by studying cancer within the context of the patient in order to create a more holistic understanding of the disease. In our most recent study, we established the existence of several molecular interactions between the primary tumor and the patient systemic response. That is, we identified molecular processes and pathways in the primary breast tumor that are tightly co-expressed with molecular processes and pathways in the patient blood cells (our surrogate to measure the patient systemic response). These interactions and additional information regarding patient exposure and life-style information are the first steps towards a new generation of integrative holistic predictors for a broad range of clinical end-points: diagnostics, response to therapies, prognosis and disease monitoring.

Relevant recent papers:
Interactions between the tumor and the blood systemic response of breast cancer patients
The prognostic ease and difficulty of invasive breast carcinoma
Relevant recent software packages:
MIxT: system designed for exploring and comparing transcriptional profiles from two or more matched tissues across individuals.
Relevant websites:
Tumor-blood interactions in breast cancer patients
Tumor epithelium-stroma interactions in breast cancer

Development of RNA profiles of blood cells as a tool to study individual’s exposure and disease

We have had a long-standing interest in the development of RNA-based biomarker signatures in blood, which can be used to inform on exposure and health status. As a major defense and transport system, blood cells can adjust expression of their genes in response to various clinical, biochemical, and pathological conditions. To develop such surrogate signatures, we first defined robust laboratory methodologies for RNA profiling of blood cells (Dumeaux et al, 2008 Biomarkers in Medicine), and investigated “normal” inter-individual variation in healthy individuals (Dumeaux et al, 2010 PloS Genetics). These studies served as stepping-stones to several projects that investigated how the signatures can be used to detect systemic molecular processes deregulated in response to defined exposure and health status (>5 publications, 200+ citations). These manuscripts highlight specific behavioral programs, such as metabolism or signaling, deregulated in the individual’s blood cells that are biological and/or pathological responses to a given condition in the general population (eg exposure to organic pollutants) or in a diseased population (eg radiation-induced fibrosis in breast cancer survivors). These blood-based signatures can also be used to develop new ways to diagnose the disease. For example, our blood-based diagnostic test for breast cancer may reduce false positive interpretations of suspicious mammographic results (3 publications including Dumeaux et al, 2015 IJC; one patent and one ongoing trial).

Relevant recent work:
Peripheral blood cells inform on the presence of breast cancer: a population-based case-control study
Deciphering normal blood gene expression variation--The NOWAC postgenome study
Using blood gene signatures for assessing effects of exposure to perfluoroalkyl acids (PFAAs) in humans: the NOWAC postgenome study
Sex hormones and gene expression signatures in peripheral blood from postmenopausal women - the NOWAC postgenome study

Single cell genomics of immune responses to cancer

Genomic profiling of RNA from a patient’s blood can be used to assess the state of the immune system in an individual and offer many opportunities to participate in precision medicine efforts. Our efforts to date have harvested RNA from whole blood composed of diverse cell types (eg, T and B lymphocytes, neutrophils, platelets). Thus, molecular profiles of these samples are “averaging” over the transcriptional programs of the different types of cells in blood. Modern approaches based on single-cell RNA sequencing are becoming increasingly routine where RNA from a single cell is isolated and sequenced. This allows transcriptional programs of each type (eg immune) of cell within a blood sample to be measured. Such approaches will have great utility when investigating, how an individual is responding to therapy, to the presence/management of specific adverse effects, and ultimately better inform on long term progression of the disease, including the early diagnosis of recurrence.

Development of novel bioinformatics approaches and softwares

We develop approaches that balance and inter-connect quantitative and biological knowledge. Many acknowledge that no field has generated higher expectations, deeper frustrations, and more “translation anxiety” than advances in human genomics. Early on, we among others have highlighted the role of rigorous epidemiological and statistical approaches in improving the prospect of genomics and big data in personalized medicine. Our approach, termed “Systems Epidemiology” (Lund & Dumeaux, 2008 CEBP), proposes to integrate human -omics data with measurements from observational epidemiologic studies to better characterize the diverse range of factors influencing complex diseases, and help infer causation and support evidence-based research (Lund & Dumeaux, 2010 Int J Epi). In line with these concepts, we supported the development of a large biobank within the Norwegian Women and Cancer Study (Dumeaux et al, 2008 BCR).

Also, critical to these efforts is the development of computational methodologies that support the integration and interpretation of these complex “real-life” data. Specifically, we have developed novel methodologies for the sensitive detection of low amplitude changes in blood profiles across healthy individuals (developed in PloS Genetics 2010), for identifying genes, pathways, and processes that co-vary and interact across tissues and environments, for predicting activation of molecular pathways in a single-patient manner satisfying clinical practice constraints associated with personalized medicine, as well as other methodologies within collaborative manuscripts (Huttenhower et al, 2009 Genome Research; Barutcuoglu Z et al, 2009 Bioinformatics)

Relevant recent papers:
Interactions between the tumor and the blood systemic response of breast cancer patients
Detecting gene signature activation in breast cancer in an absolute, single-patient manner.
Building applications for interactive data exploration in systems biology.
Relevant recent software packages:
MIxT: system designed for exploring and comparing transcriptional profiles from two or more matched tissues across individuals.
Relevant websites:
Tumor-blood interactions in breast cancer patients
Tumor epithelium-stroma interactions in breast cancer

Key collaborators

Funding sources