Antoine M. Snijders

Microarray techniques in pathology: tool or toy?

Microarray technology allows the simultaneous analysis of up to thousands of different genes in histological or cytological specimens. Although microarray technology has so far mainly been applied in the research setting, its clinical application in pathology is expected in the foreseeable future. This paper presents an overview of the technical “ins and outs” of microarray technology, and discusses several putative applications in diagnostic pathology, which include tumour classification, the prediction of responses to certain chemotherapeutical or hormonal agents, the biological staging of tumours, the risk assessment of premalignant lesions, and the detection of microorganisms.

DNA Repair and Cell Cycle Biomarkers of Radiation Exposure and Inflammation Stress in Human Blood

DNA damage and repair are hallmarks of cellular responses to ionizing radiation. We hypothesized that monitoring the expression of DNA repair-associated genes would enhance the detection of individuals exposed to radiation versus other forms of physiological stress. We employed the human blood ex vivo radiation model to investigate the expression responses of DNA repair genes in repeated blood samples from healthy, non-smoking men and women exposed to 2 Gy of X-rays in the context of inflammation stress mimicked by the bacterial endotoxin lipopolysaccharide (LPS). Radiation exposure significantly modulated the transcript expression of 12 genes of 40 tested (2.2E-06<p<0.03), of which 8 showed no overlap between unirradiated and irradiated samples (CDKN1A, FDXR, BBC3, PCNA, GADD45a, XPC, POLH and DDB2). This panel demonstrated excellent dose response discrimination (0.5 to 8 Gy) in an independent human blood ex vivo dataset, and 100% accuracy for discriminating patients who received total body radiation. Three genes of this panel (CDKN1A, FDXR and BBC3) were also highly sensitive to LPS treatment in the absence of radiation exposure, and LPS co-treatment significantly affected their radiation responses. At the protein level, BAX and pCHK2-thr68 were elevated after radiation exposure, but the pCHK2-thr68 response was significantly decreased in the presence of LPS. Our combined panel yields an estimated 4-group accuracy of ∼90% to discriminate between radiation alone, inflammation alone, or combined exposures. Our findings suggest that DNA repair gene expression may be helpful to identify biodosimeters of exposure to radiation, especially within high-complexity exposure scenarios.

Understanding cancer development processes after HZE-particle exposure: roles of ROS, DNA damage repair and inflammation

During space travel astronauts are exposed to a variety of radiations, including galactic cosmic rays composed of high-energy protons and high-energy charged (HZE) nuclei, and solar particle events containing low- to medium-energy protons. Risks from these exposures include carcinogenesis, central nervous system damage and degenerative tissue effects. Currently, career radiation limits are based on estimates of fatal cancer risks calculated using a model that incorporates human epidemiological data from exposed populations, estimates of relative biological effectiveness and dose-response data from relevant mammalian experimental models. A major goal of space radiation risk assessment is to link mechanistic data from biological studies at NASA Space Radiation Laboratory and other particle accelerators with risk models. Early phenotypes of HZE exposure, such as the induction of reactive oxygen species, DNA damage signaling and inflammation, are sensitive to HZE damage complexity. This review summarizes our current understanding of critical areas within the DNA damage and oxidative stress arena and provides insight into their mechanistic interdependence and their usefulness in accurately modeling cancer and other risks in astronauts exposed to space radiation. Our ultimate goals are to examine potential links and crosstalk between early response modules activated by charged particle exposure, to identify critical areas that require further research and to use these data to reduced uncertainties in modeling cancer risk for astronauts. A clearer understanding of the links between early mechanistic aspects of high-LET response and later surrogate cancer end points could reveal key nodes that can be therapeutically targeted to mitigate the health effects from charged particle exposures.

Nanosensor dosimetry of mouse blood proteins after exposure to ionizing radiation

Giant magnetoresistive (GMR) nanosensors provide a novel approach for measuring protein concentrations in blood for medical diagnosis. Using an in vivo mouse radiation model, we developed protocols for measuring Flt3 ligand (Flt3lg) and serum amyloid A1 (Saa1) in small amounts of blood collected during the first week after X-ray exposures of sham, 0.1, 1, 2, 3, or 6 Gy. Flt3lg concentrations showed excellent dose discrimination at ≥ 1 Gy in the time window of 1 to 7 days after exposure except 1 Gy at day 7. Saa1 dose response was limited to the first two days after exposure. A multiplex assay with both proteins showed improved dose classification accuracy. Our magneto-nanosensor assay demonstrates the dose and time responses, low-dose sensitivity, small volume requirements, and rapid speed that have important advantages in radiation triage biodosimetry.

Genetic Differences in Transcript Responses to Low-Dose Ionizing Radiation Identify Tissue Functions Associated with Breast Cancer Susceptibility

High dose ionizing radiation (IR) is a well-known risk factor for breast cancer but the health effects after low-dose (LD, <10 cGy) exposures remain highly uncertain. We explored a systems approach that compared LD-induced chromosome damage and transcriptional responses in strains of mice with genetic differences in their sensitivity to radiation-induced mammary cancer (BALB/c and C57BL/6) for the purpose of identifying mechanisms of mammary cancer susceptibility. Unirradiated mammary and blood tissues of these strains differed significantly in baseline expressions of DNA repair, tumor suppressor, and stress response genes. LD exposures of 7.5 cGy (weekly for 4 weeks) did not induce detectable genomic instability in either strain. However, the mammary glands of the sensitive strain but not the resistant strain showed early transcriptional responses involving: (a) diminished immune response, (b) increased cellular stress, (c) altered TGFβ-signaling, and (d) inappropriate expression of developmental genes. One month after LD exposure, the two strains showed opposing responses in transcriptional signatures linked to proliferation, senescence, and microenvironment functions. We also discovered a pre-exposure expression signature in both blood and mammary tissues that is predictive for poor survival among human cancer patients (p = 0.0001), and a post-LD-exposure signature also predictive for poor patient survival (p<0.0001). There is concordant direction of expression in the LD-exposed sensitive mouse strain, in biomarkers of human DCIS and in biomarkers of human breast tumors. Our findings support the hypothesis that genetic mechanisms that determine susceptibility to LD radiation induced mammary cancer in mice are similar to the tissue mechanisms that determine poor-survival in breast cancer patients. We observed non-linearity of the LD responses providing molecular evidence against the LNT risk model and obtained new evidence that LD responses are strongly influenced by genotype. Our findings suggest that the biological assumptions concerning the mechanisms by which LD radiation is translated into breast cancer risk should be reexamined and suggest a new strategy to identify genetic features that predispose or protect individuals from LD-induced breast cancer.

Influence of early life exposure, host genetics and diet on the mouse gut microbiome and metabolome

Although the gut microbiome plays important roles in host physiology, health and disease1, we lack understanding of the complex interplay between host genetics and early life environment on the microbial and metabolic composition of the gut. We used the genetically diverse Collaborative Cross mouse system2 to discover that early life history impacts the microbiome composition, whereas dietary changes have only a moderate effect. By contrast, the gut metabolome was shaped mostly by diet, with specific non-dietary metabolites explained by microbial metabolism. Quantitative trait analysis identified mouse genetic trait loci (QTL) that impact the abundances of specific microbes. Human orthologues of genes in the mouse QTL are implicated in gastrointestinal cancer. Additionally, genes located in mouse QTL for Lactobacillales abundance are implicated in arthritis, rheumatic disease and diabetes. Furthermore, Lactobacillales abundance was predictive of higher host T-helper cell counts, suggesting an important link between Lactobacillales and host adaptive immunity.

Identification of genetic loci that control mammary tumor susceptibility through the host microenvironment

The interplay between host genetics, tumor microenvironment and environmental exposure in cancer susceptibility remains poorly understood. Here we assessed the genetic control of stromal mediation of mammary tumor susceptibility to low dose ionizing radiation (LDIR) using backcrossed F1 into BALB/c (F1Bx) between cancer susceptible (BALB/c) and resistant (SPRET/EiJ) mouse strains. Tumor formation was evaluated after transplantation of non-irradiated Trp53-/- BALB/c mammary gland fragments into cleared fat pads of F1Bx hosts. Genome-wide linkage analysis revealed 2 genetic loci that constitute the baseline susceptibility via host microenvironment. However, once challenged with LDIR, we discovered 13 additional loci that were enriched for genes involved in cytokines, including TGFβ1 signaling. Surprisingly, LDIR-treated F1Bx cohort significantly reduced incidence of mammary tumors from Trp53-/- fragments as well as prolonged tumor latency, compared to sham-treated controls. We demonstrated further that plasma levels of specific cytokines were significantly correlated with tumor latency. Using an ex vivo 3-D assay, we confirmed TGFβ1 as a strong candidate for reduced mammary invasion in SPRET/EiJ, which could explain resistance of this strain to mammary cancer risk following LDIR. Our results open possible new avenues to understand mechanisms of genes operating via the stroma that affect cancer risk from external environmental exposures.

Unsupervised Transfer Learning via Multi-Scale Convolutional Sparse Coding for Biomedical Applications

The capabilities of (I) learning transferable knowledge across domains; and (II) fine-tuning the pre-learned base knowledge towards tasks with considerably smaller data scale are extremely important. Many of the existing transfer learning techniques are supervised approaches, among which deep learning has the demonstrated power of learning domain transferrable knowledge with large scale network trained on massive amounts of labeled data. However, in many biomedical tasks, both the data and the corresponding label can be very limited, where the unsupervised transfer learning capability is urgently needed. In this paper, we proposed a novel multi-scale convolutional sparse coding (MSCSC) method, that (I) automatically learns filter banks at different scales in a joint fashion with enforced scale-specificity of learned patterns; and (II) provides an unsupervised solution for learning transferable base knowledge and fine-tuning it towards target tasks. Extensive experimental evaluation of MSCSC demonstrates the effectiveness of the proposed MSCSC in both regular and transfer learning tasks in various biomedical domains.

FAM83 family oncogenes are broadly involved in human cancers: an integrative multi‐omics approach

The development of novel targeted therapies for cancer treatment requires identification of reliable targets. FAM83 (‘family with sequence similarity 83’) family members A, B, and D were shown recently to have oncogenic potential. However, the overall oncogenic abilities of FAM83 family genes remain largely unknown. Here, we used a systematic and integrative genomics approach to investigate oncogenic properties of the entire FAM83 family members. We assessed transcriptional expression patterns of eight FAM83 family genes (FAM83A‐H) across tumor types, the relationship between their expression and changes in DNA copy number, and the association with patient survival. By comparing the gene expression levels of FAM83 family members in cancers from 17 different tumor types with those in their corresponding normal tissues, we identified consistent upregulation of FAM83D and FAM83H across the majority of tumor types, which is largely driven by increased DNA copy number. Importantly, we found also that a higher expression level of a signature of FAM83 family members was associated with poor prognosis in a number of human cancers. In breast cancer, we found that alterations in FAM83 family genes correlated significantly with TP53 mutation, whereas significant, but inverse correlation was observed with PIK3CA and CDH1 (E‐cadherin) mutations. We also identified that expression levels of 55 proteins were significantly associated with alterations in FAM83 family genes including a decrease in GATA3, ESR1, and PGR proteins in tumors with alterations in FAM83. Our results provide strong evidence for a critical role of FAM83 family genes in tumor development, with possible relevance for therapeutic target development.

Identification of genetic factors that modify motor performance and body weight using Collaborative Cross mice

Evidence has emerged that suggests a link between motor deficits, obesity and many neurological disorders. However, the contributing genetic risk factors are poorly understood. Here we used the Collaborative Cross (CC), a large panel of newly inbred mice that captures 90% of the known variation among laboratory mice, to identify the genetic loci controlling rotarod performance and its relationship with body weight in a cohort of 365 mice across 16 CC strains. Body weight and rotarod performance varied widely across CC strains and were significantly negatively correlated. Genetic linkage analysis identified 14 loci that were associated with body weight. However, 45 loci affected rotarod performance, seven of which were also associated with body weight, suggesting a strong link at the genetic level. Lastly, we show that genes identified in this study overlap significantly with those related to neurological disorders and obesity found in human GWA studies. In conclusion, our results provide a genetic framework for studies of the connection between body weight, the central nervous system and behavior.