Serially measured pre-diagnostic levels of serum cytokines and risk of brain cancer in active component military personnel

Alkylating agents are possible inducers of glioblastoma and other brain tumors

Epidemiological evidence of an association between exposure to chemical carcinogens and an increased risk for development of glioblastoma (GBM) is limited to weak statistical associations in cohorts of firefighters, farmers, residents exposed to air pollution, and soldiers exposed to toxic chemicals (e.g., military burn pits, oil-well fire smoke). A history of ionizing radiation therapy to the head or neck is associated with an increased risk of GBM. Ionizing radiation induces point mutations, frameshift mutations, double-strand breaks, and chromosomal insertions or deletions. Mutational profiles associated with chemical exposures overlap with the broad mutational patterns seen with ionizing radiation. Data on 16 agents (15 chemicals and radio frequency radiation) that induced tumors in the rodent brain were extracted from 602 Technical Reports on 2-years cancer bioassays found in the National Toxicology Program database. Ten of the 15 chemical agents that induce brain tumors are alkylating agents. Three of the 15 chemical agents have idiosyncratic structures and might be alkylating agents. Only two of the 15 chemical agents are definitively not alkylating agents. The rat model is thought to be of possible relevance to humans suggesting that exposure to alkylating chemicals should be considered in epidemiology studies on GBM and other brain tumors.

Pre-diagnostic blood biomarkers for adult glioma

Glioma is one of the most common malignant primary brain tumours in adults, of which, glioblastoma is the most prevalent and malignant entity. Glioma is often diagnosed at a later stage of disease progression, which means it is associated with significant mortality and morbidity. Therefore, there is a need for earlier diagnosis of these tumours, which would require sensitive and specific biomarkers. These biomarkers could better predict glioma onset to improve diagnosis and therapeutic options for patients. While liquid biopsies could provide a cheap and non-invasive test to improve the earlier detection of glioma, there is little known on pre-diagnostic biomarkers which predate disease detection. In this review, we examine the evidence in the literature for pre-diagnostic biomarkers in glioma, including metabolomics and proteomics. We also consider the limitations of these approaches and future research directions of pre-diagnostic biomarkers for glioma.

Structured time-dependent inverse regression (STIR)

We propose and study structured time-dependent inverse regression (STIR), a novel sufficient dimension reduction model, to analyze longitudinally measured, correlated biomarkers in relation to an outcome. The time structure is accommodated in an inverse regression model for the markers that can be applied both to equally and unequally spaced time points for each sample. The inverse regression structure also naturally accommodates retrospectively sampled markers, that is, markers measured in case-control studies. We estimate the corresponding linear combinations of the markers, the reduction, using least squares. We show that under additional distributional assumptions the reduction contains sufficient information about the outcome. In extensive simulations the STIR linear combinations perform well in predictive models based on samples of realistic size. A Wald-type test for association of a particular marker with outcome at any time point based on the STIR reduction has better power overall than assessing associations based on logistic or linear regression models that include all longitudinally measured markers as independent predictors. As illustrations we estimate the STIR reductions for a cohort study of diabetes and hyperlipidemia and a case-control study of brain cancer with multiple longitudinally measured biomarkers. We assess the STIR reductions' predictive performance and identify outcome-associated biomarkers.

Prediagnostic biomarkers for early detection of glioma—using case–control studies from cohorts as study approach

Background

Understanding the trajectory and development of disease is important and the knowledge can be used to find novel targets for therapy and new diagnostic tools for early diagnosis.

Methods

Large cohorts from different parts of the world are unique assets for research as they have systematically collected plasma and DNA over long-time periods in healthy individuals, sometimes even with repeated samples. Over time, the population in the cohort are diagnosed with many different diseases, including brain tumors.

Results

Recent studies have detected genetic variants that are associated with increased risk of glioblastoma and lower grade gliomas specifically. The impact for genetic markers to predict disease in a healthy population has been deemed low, and a relevant question is if the genetic variants for glioma are associated with risk of disease or partly consist of genes associated to survival. Both metabolite and protein spectra are currently being explored for early detection of cancer.

Conclusions

We here present a focused review of studies of genetic variants, metabolomics, and proteomics studied in prediagnostic glioma samples and discuss their potential in early diagnostics.

Pre‐diagnostic levels of sVEGFR2, sTNFR2, sIL‐2Rα and sIL‐6R are associated with glioma risk: A nested case–control study of repeated samples

No strong aetiological factors have been established for glioma aside from genetic mutations and variants, ionising radiation and an inverse relationship with asthmas and allergies. Our aim was to investigate the association between pre‐diagnostic immune protein levels and glioma risk. We conducted a case–control study nested in the Northern Sweden Health and Disease Study cohort. We analysed 133 glioma cases and 133 control subjects matched by age, sex and date of blood donation. ELISA or Luminex bead‐based multiplex assays were used to measure plasma levels of 19 proteins. Conditional logistic regression models were used to estimate the odds ratios and 95% CIs. To further model the protein trajectories over time, the linear mixed‐effects models were conducted. We found that the levels of sVEGFR2, sTNFR2, sIL‐2Rα and sIL‐6R were associated with glioma risk. After adjusting for the time between blood sample collection and glioma diagnosis, the odds ratios were 1.72 (95% CI = 1.01–2.93), 1.48 (95% CI = 1.01–2.16) and 1.90 (95% CI = 1.14–3.17) for sTNFR2, sIL‐2Rα and sIL‐6R, respectively. The trajectory of sVEGFR2 concentrations over time was different between cases and controls (p‐value = 0.031), increasing for cases (0.8% per year) and constant for controls. Our findings suggest these proteins play important roles in gliomagenesis.

Least squares and maximum likelihood estimation of sufficient reductions in regressions with matrix-valued predictors

We propose methods to estimate sufficient reductions in matrix-valued predictors for regression or classification. We assume that the first moment of the predictor matrix given the response can be decomposed into a row and column component via a Kronecker product structure. We obtain least squares and maximum likelihood estimates of the sufficient reductions in the matrix predictors, derive statistical properties of the resulting estimates and present fast computational algorithms with assured convergence. The performance of the proposed approaches in regression and classification is compared in simulations.We illustrate the methods on two examples, using longitudinally measured serum biomarker and neuroimaging data.