There is emerging evidence that the immune biology associated with lung and other solid tumors, as well as patient immune genetic traits, contributes to individual survival. At this time, dramatic advances in immunologic approaches to the study and management of human cancers are taking place, including lung and head and neck squamous cell carcinoma. However, major obstacles for therapies are the profound immune alterations in blood and in the tumor microenvironment that arise in tandem with the cancer. Although there is a significant current effort underway across the cancer research community to probe the tumor environment to uncover the dynamics of the immune response, little similar work is being done to understand the dynamics of immune alterations in peripheral blood, despite evidence showing the prognostic relevance of the neutrophil/lymphocyte ratio for these cancers. A prominent feature of cancer-associated inflammation is the generation of myeloid-derived suppressor cells, which arise centrally in bone marrow myelopoiesis and peripherally in response to tumor factors. Two classes of myeloid-derived suppressor cells are recognized: granulocytic and monocytic. To date, such immune factors have not been integrated into molecular classification or prognostication. Here, we advocate for a more complete characterization of patient immune profiles, using DNA from archival peripheral blood after application of methylation profiling (immunomethylomics). At the heart of this technology are cell libraries of differentially methylated regions that provide the "fingerprints" of immune cell subtypes. Going forward, opportunities exist to explore aberrant immune profiles in the context of cancer-associated inflammation, potentially adding significantly to prognostic and mechanistic information for solid tumors.

Cigarette smoking by children and adolescents continues to be prevalent, and this fact represents a major public health problem and challenge. Epidemiologic work has previously suggested that exposure of the lung to tobacco carcinogens at an early age may be an independent risk factor for lung cancer. Recent studies at the molecular and cellular levels are consistent with this, now suggesting that early exposure enhances DNA damage and is associated with the induction of DNA alterations in specific chromosomal regions. In this paper we hypothesize that adolescence, which is known to be the period of greatest development for the lung, may constitute a "critical period" in which tobacco carcinogens can induce fields of genetic alterations that make the early smoker more susceptible to the damaging effects of continued smoking. The fact that lung development differs by sex might also contribute to apparent gender differences in lung cancer susceptibility. Because this hypothesis has important implications for health policy and tobacco control, additional resources need to be devoted to its further evaluation. Targeted intervention in adolescent smoking may yield even greater reductions in lung cancer occurrence than otherwise anticipated.

The WHO 2007 glioma classification system (based primarily on tumour histology) resulted in considerable interobserver variability and substantial variation in patient survival within grades. Furthermore, few risk factors for glioma were known. Discoveries over the past decade have deepened our understanding of the molecular alterations underlying glioma and have led to the identification of numerous genetic risk factors. The advances in molecular characterization of glioma have reframed our understanding of its biology and led to the development of a new classification system for glioma. The WHO 2016 classification system comprises five glioma subtypes, categorized by both tumour morphology and molecular genetic information, which led to reduced misclassification and improved consistency of outcomes within glioma subtypes. To date, 25 risk loci for glioma have been identified and several rare inherited mutations that might cause glioma in some families have been discovered. This Review focuses on the two dominant trends in glioma science: the characterization of diagnostic and prognostic tumour markers and the identification of genetic and other risk factors. An overview of the many challenges still facing glioma researchers is also included.

Stem cell maturation is a fundamental, yet poorly understood aspect of human development. We devised a DNA methylation signature deeply reminiscent of embryonic stem cells (a fetal cell origin signature, FCO) to interrogate the evolving character of multiple human tissues. The cell fraction displaying this FCO signature was highly dependent upon developmental stage (fetal versus adult), and in leukocytes, it described a dynamic transition during the first 5 yr of life. Significant individual variation in the FCO signature of leukocytes was evident at birth, in childhood, and throughout adult life. The genes characterizing the signature included transcription factors and proteins intimately involved in embryonic development. We defined and applied a DNA methylation signature common among human fetal hematopoietic progenitor cells and have shown that this signature traces the lineage of cells and informs the study of stem cell heterogeneity in humans under homeostatic conditions.

Background

Differentiated cells that arise from stem cells in early development contain DNA methylation features that provide a memory trace of their fetal cell origin (FCO). The FCO signature was developed to estimate the proportion of cells in a mixture of cell types that are of fetal origin and are reminiscent of embryonic stem cell lineage. Here we implemented the FCO signature estimation method to compare the fraction of cells with the FCO signature in tumor tissues and their corresponding nontumor normal tissues.

Methods

We applied our FCO algorithm to discovery data sets obtained from The Cancer Genome Atlas (TCGA) and replication data sets obtained from the Gene Expression Omnibus (GEO) data repository. Wilcoxon rank sum tests, linear regression models with adjustments for potential confounders and non-parametric randomization-based tests were used to test the association of FCO proportion between tumor tissues and nontumor normal tissues. P-values of < 0.05 were considered statistically significant.

Results

Across 20 different tumor types we observed a consistently lower FCO signature in tumor tissues compared with nontumor normal tissues, with 18 observed to have significantly lower FCO fractions in tumor tissue (total n = 6,795 tumor, n = 922 nontumor, P < 0.05). We replicated our findings in 15 tumor types using data from independent subjects in 15 publicly available data sets (total n = 740 tumor, n = 424 nontumor, P < 0.05).

Conclusions

The results suggest that cancer development itself is substantially devoid of recapitulation of normal embryologic processes. Our results emphasize the distinction between DNA methylation in normal tightly regulated stem cell driven differentiation and cancer stem cell reprogramming that involves altered methylation in the service of great cell heterogeneity and plasticity.

Background

Cellular compositions of solid tumor microenvironments are heterogeneous, varying across patients and tumor types. High-resolution profiling of the tumor microenvironment cell composition is crucial to understanding its biological and clinical implications. Previously, tumor microenvironment gene expression and DNA methylation-based deconvolution approaches have been shown to deconvolve major cell types. However, existing methods lack accuracy and specificity to tumor type and include limited identification of individual cell types.

Results

We employed a novel tumor-type-specific hierarchical model using DNA methylation data to deconvolve the tumor microenvironment with high resolution, accuracy, and specificity. The deconvolution algorithm is named HiTIMED. Seventeen cell types from three major tumor microenvironment components can be profiled (tumor, immune, angiogenic) by HiTIMED, and it provides tumor-type-specific models for twenty carcinoma types. We demonstrate the prognostic significance of cell types that other tumor microenvironment deconvolution methods do not capture.

Conclusion

We developed HiTIMED, a DNA methylation-based algorithm, to estimate cell proportions in the tumor microenvironment with high resolution and accuracy. HiTIMED deconvolution is amenable to archival biospecimens providing high-resolution profiles enabling to study of clinical and biological implications of variation and composition of the tumor microenvironment.

The mammalian kidney has extensive repair capacity; however, identifying adult renal stem cells has proven elusive. We applied an epigenetic marker of foetal cell origin (FCO) in diverse human tissues as a probe for developmental cell persistence, finding a 5.4-fold greater FCO proportion in kidney. Normal kidney FCO proportions averaged 49% with extensive interindividual variation. FCO proportions were significantly negatively correlated with immune-related gene expression and positively correlated with genes expressed in the renal medulla, including those involved in renal organogenesis (e.g., FGF2, PAX8, and HOXB7). FCO associated genes also mapped to medullary nephron segments in mouse and rat, suggesting evolutionary conservation of this cellular compartment. Renal cancer patients whose tumours contained non-zero FCO scores survived longer. The kidney appears unique in possessing substantial foetal epigenetic features. Further study of FCO-related gene methylation may elucidate regenerative regulatory programmes in tissues without apparent discrete stem cell compartments.

Background: The peripheral blood neutrophil-to-lymphocyte ratio (NLR) is a cytologic marker of both inflammation and poor outcomes in patients with cancer. DNA methylation is a key element of the epigenetic program defining different leukocyte subtypes and may provide an alternative to cytology in assessing leukocyte profiles. Our aim was to create a bioinformatic tool to estimate NLR using DNA methylation, and to assess its diagnostic and prognostic performance in human populations.Methods: We developed a DNA methylation-derived NLR (mdNLR) index based on normal isolated leukocyte methylation libraries and established cell-mixture deconvolution algorithms. The method was applied to cancer case-control studies of the bladder, head and neck, ovary, and breast, as well as publicly available data on cancer-free subjects.Results: Across cancer studies, mdNLR scores were either elevated in cases relative to controls, or associated with increased hazard of death. High mdNLR values (>5) were strong indicators of poor survival. In addition, mdNLR scores were elevated in males, in nonHispanic white versus Hispanic ethnicity, and increased with age. We also observed a significant interaction between cigarette smoking history and mdNLR on cancer survival.Conclusions: These results mean that our current understanding of mature leukocyte methylomes is sufficient to allow researchers and clinicians to apply epigenetically based analyses of NLR in clinical and epidemiologic studies of cancer risk and survival.Impact: As cytologic measurements of NLR are not always possible (i.e., archival blood), mdNLR, which is computed from DNA methylation signatures alone, has the potential to expand the scope of epigenome-wide association studies. Cancer Epidemiol Biomarkers Prev; 26(3); 328-38. ©2016 AACR.

Genome-wide methylation arrays are powerful tools for assessing cell composition of complex mixtures. We compare three approaches to select reference libraries for deconvoluting neutrophil, monocyte, B-lymphocyte, natural killer, and CD4+ and CD8+ T-cell fractions based on blood-derived DNA methylation signatures assayed using the Illumina HumanMethylationEPIC array. The IDOL algorithm identifies a library of 450 CpGs, resulting in an average R² = 99.2 across cell types when applied to EPIC methylation data collected on artificial mixtures constructed from the above cell types. Of the 450 CpGs, 69% are unique to EPIC. This library has the potential to reduce unintended technical differences across array platforms.

Glioblastoma multiforme (GBM) is the most aggressive of all brain tumors, with a median survival of less than 1.5 years. Recently, epigenetic alterations were found to play key roles in both glioma genesis and clinical outcome, demonstrating the need to integrate genetic and epigenetic data in predictive models. To enhance current models through discovery of novel predictive biomarkers, we employed a genome-wide, agnostic strategy to specifically capture both methylation-directed changes in gene expression and alternative associations of DNA methylation with disease survival in glioma. Human GBM-associated DNA methylation, gene expression, IDH1 mutation status, and survival data were obtained from The Cancer Genome Atlas. DNA methylation loci and expression probes were paired by gene, and their subsequent association with survival was determined by applying an accelerated failure time model to previously published alternative and expression-based association equations. Significant associations were seen in 27 unique methylation/expression pairs with expression-based, alternative, and combinatorial associations observed (10, 13, and 4 pairs, respectively). The majority of the predictive DNA methylation loci were located within CpG islands, and all but three of the locus pairs were negatively correlated with survival. This finding suggests that for most loci, methylation/expression pairs are inversely related, consistent with methylation-associated gene regulatory action. Our results indicate that changes in DNA methylation are associated with altered survival outcome through both coordinated changes in gene expression and alternative mechanisms. Furthermore, our approach offers an alternative method of biomarker discovery using a priori gene pairing and precise targeting to identify novel sites for locus-specific therapeutic intervention.