The impact of DNA methylation on the cancer proteome.

Aberrant DNA methylation disrupts normal gene expression in cancer and broadly contributes to oncogenesis. We previously developed MethylMix, a model-based algorithmic approach to identify epigenetically regulated driver genes. MethylMix identifies genes where methylation likely executes a functional role by using transcriptomic data to select only methylation events that can be linked to changes in gene expression. However, given that proteins more closely link genotype to phenotype recent high-throughput proteomic data provides an opportunity to more accurately identify functionally relevant abnormal methylation events. Here we present a MethylMix analysis that refines nominations for epigenetic driver genes by leveraging quantitative high-throughput proteomic data to select only genes where DNA methylation is predictive of protein abundance. Applying our algorithm across three cancer cohorts we find that using protein abundance data narrows candidate nominations, where the effect of DNA methylation is often buffered at the protein level. Next, we find that MethylMix genes predictive of protein abundance are enriched for biological processes involved in cancer including functions involved in epithelial and mesenchymal transition. Moreover, our results are also enriched for tumor markers which are predictive of clinical features like tumor stage and we find clustering using MethylMix genes predictive of protein abundance captures cancer subtypes.

GeM-Pro: a tool for genome functional mining and microbial profiling.

Gem-Pro is a new tool for gene mining and functional profiling of bacteria. It initially identifies homologous genes using BLAST and then applies three filtering steps to select orthologous gene pairs. The first one uses BLAST score values to identify trivial paralogs. The second filter uses the shared identity percentages of found trivial paralogs as internal witnesses of non-orthology to set orthology cutoff values. The third filtering step uses conditional probabilities of orthology and non-orthology to define new cutoffs and generate supportive information of orthology assignations. Additionally, a subsidiary tool, called q-GeM, was also developed to mine traits of interest using logistic regression (LR) or linear discriminant analysis (LDA) classifiers. q-GeM is more efficient in the use of computing resources than Gem-Pro but needs an initial classified set of homologous genes in order to train LR and LDA classifiers. Hence, q-GeM could be used to analyze new set of strains with available genome sequences, without the need to rerun a complete Gem-Pro analysis. Finally, Gem-Pro and q-GeM perform a synteny analysis to evaluate the integrity and genomic arrangement of specific pathways of interest to infer their presence. The tools were applied to more than 2 million homologous pairs encoded by Bacillus strains generating statistical supported predictions of trait contents. The different patterns of encoded traits of interest were successfully used to perform a descriptive bacterial profiling.

Randomized Controlled Trial Comparing White Light with Near-Infrared Autofluorescence for Parathyroid Gland Identification During Total Thyroidectomy.

BACKGROUND: Parathyroid glands are difficult to identify during total thyroidectomies, and accidental resection can lead to problematic postoperative hypocalcemia. Our main goals were to evaluate the effectiveness of using near-infrared light (NIRL) autofluorescence intraoperatively for parathyroid gland identification and to measure its impact on postoperative hypocalcemia incidence. STUDY DESIGN: Total thyroidectomies were performed on 170 patients with different thyroid pathologies, block-randomized (1:1) into 2 equal groups. Among controls, traditional overhead white light (WL) was used throughout. In the experimental group, NIRL was used to enhance parathyroid gland recognition before thyroid dissection. The number of parathyroid glands identified was compared after thyroid dissection in controls using WL vs pre-dissection in the experimental using NIRL and with WL vs NIRL before thyroid dissection in the experimental group. Postoperative serum calcium levels and hypocalcemia rates were compared. RESULTS: The mean number of parathyroid glands identified pre-dissection with NIRL was the same identified post-dissection with WL (3.5 vs 3.6). In the experimental group, converting from WL to NIRL increased the number of glands detected from 2.6 to 3.5 (p < 0.001), and revealed at least 1 previously missed gland in 67.1% of patients. Calcium levels ≤7.5 mg/dL were one-tenth as common in the NIRL group (p = 0.005). The adjusted odds of hypocalcemia developing increased by 15% for every 5-g increase in thyroid gland weight (odds ratio 1.15; 95% CI 1.06 to 1.25). All hypocalcemia resolved within 6 months. CONCLUSIONS: Using NIRL during thyroidectomy increases intraoperative identification of parathyroid glands, enhances their detection before thyroid dissection, and decreases the incidence of postoperative hypocalcemia.

MethylMix 2.0: an R package for identifying DNA methylation genes.

Summary: DNA methylation is an important mechanism regulating gene transcription, and its role in carcinogenesis has been extensively studied. Hyper and hypomethylation of genes is a major mechanism of gene expression deregulation in a wide range of diseases. At the same time, high-throughput DNA methylation assays have been developed generating vast amounts of genome wide DNA methylation measurements. We developed MethylMix, an algorithm implemented in R to identify disease specific hyper and hypomethylated genes. Here we present a new version of MethylMix that automates the construction of DNA-methylation and gene expression datasets from The Cancer Genome Atlas (TCGA). More precisely, MethylMix 2.0 incorporates two major updates: the automated downloading of DNA methylation and gene expression datasets from TCGA and the automated preprocessing of such datasets: value imputation, batch correction and CpG sites clustering within each gene. The resulting datasets can subsequently be analyzed with MethylMix to identify transcriptionally predictive methylation states. We show that the Differential Methylation Values created by MethylMix can be used for cancer subtyping. Availability and implementation: MethylMix 2.0 was implemented as an R package and is available in bioconductor. https://www.bioconductor.org/packages/release/bioc/html/MethylMix.html.

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas.

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smoking and/or human papillomavirus (HPV). SCCs harbor 3q, 5p, and other recurrent chromosomal copy-number alterations (CNAs), DNA mutations, and/or aberrant methylation of genes and microRNAs, which are correlated with the expression of multi-gene programs linked to squamous cell stemness, epithelial-to-mesenchymal differentiation, growth, genomic integrity, oxidative damage, death, and inflammation. Low-CNA SCCs tended to be HPV(+) and display hypermethylation with repression of TET1 demethylase and FANCF, previously linked to predisposition to SCC, or harbor mutations affecting CASP8, RAS-MAPK pathways, chromatin modifiers, and immunoregulatory molecules. We uncovered hypomethylation of the alternative promoter that drives expression of the ΔNp63 oncogene and embedded miR944. Co-expression of immune checkpoint, T-regulatory, and Myeloid suppressor cells signatures may explain reduced efficacy of immune therapy. These findings support possibilities for molecular classification and therapeutic approaches.

NSD1 inactivation defines an immune cold, DNA hypomethylated subtype in squamous cell carcinoma.

Chromatin modifying enzymes are frequently mutated in cancer, resulting in widespread epigenetic deregulation. Recent reports indicate that inactivating mutations in the histone methyltransferase NSD1 define an intrinsic subtype of head and neck squamous cell carcinoma (HNSC) that features pronounced DNA hypomethylation. Here, we describe a similar hypomethylated subtype of lung squamous cell carcinoma (LUSC) that is enriched for both inactivating mutations and deletions in NSD1. The 'NSD1 subtypes' of HNSC and LUSC are highly correlated at the DNA methylation and gene expression levels, featuring ectopic expression of developmental transcription factors and genes that are also hypomethylated in Sotos syndrome, a congenital disorder caused by germline NSD1 mutations. Further, the NSD1 subtype of HNSC displays an 'immune cold' phenotype characterized by low infiltration of tumor-associated leukocytes, particularly macrophages and CD8+ T cells, as well as low expression of genes encoding the immunotherapy target PD-1 immune checkpoint receptor and its ligands. Using an in vivo model, we demonstrate that NSD1 inactivation results in reduced T cell infiltration into the tumor microenvironment, implicating NSD1 as a tumor cell-intrinsic driver of an immune cold phenotype. NSD1 inactivation therefore causes epigenetic deregulation across cancer sites, and has implications for immunotherapy.

Increased identification of parathyroid glands using near infrared light during thyroid and parathyroid surgery.

BACKGROUND: Parathyroid gland (PG) identification during thyroid and parathyroid surgery is challenging. Accidental parathyroidectomy increases the rate of postoperative hypocalcaemia. Recently, autofluorescence with near infrared light (NIRL) has been described for PG visualization. The aim of this study is to analyze the increased rate of visualization of PGs with the use of NIRL compared to white light (WL). MATERIALS AND METHODS: All patients undergoing thyroid and parathyroid surgery were included in this study. PGs were identified with both NIRL and WL by experienced head and neck surgeons. The number of PGs identified with NIRL and WL were compared. The identification of PGs was correlated to age, sex, and histopathological diagnosis. RESULTS: Seventy-four patients were included in the study. The mean age was 48.4 (SD ±13.5) years old. Mean PG fluorescence intensity (47.60) was significantly higher compared to the thyroid gland (22.32) and background (9.27) (p < 0.0001). The mean number of PGs identified with NIRL and WL were 3.7 and 2.5 PG, respectively (p < 0.001). The difference in the number of PGs identified with NIRL and WL and fluorescence intensity was not related to age, sex, or histopathological diagnosis, with the exception of the diagnosis of thyroiditis, in which there was a significant increase in the number of PGs visualized with NIRL (p = 0.026). CONCLUSION: The use of NIRL for PG visualization significantly increased the number of PGs identified during thyroid and parathyroid surgery, and the differences in fluorescent intensity among PGs, thyroid glands, and background were not affected by age, sex, and histopathological diagnosis.

Uso del enfoque bayesiano para la estimación y proyección de tasas / Use of a bayesian approach to rate estimation and projection

OBJETIVO: Aplicar y valorar el enfoque bayesiano para realizar proyecciones de tasas de mortalidad por cáncer a través del ajuste de modelos edad-período-cohorte (EPC). MÉTODOS: El método de estimación bayesiano se aplica a datos de mortalidad por cáncer de vejiga en Argentina. Se adopta un esquema autorregresivo de segundo orden para la especificación a priori de los coeficientes del modelo EPC. Se comparan las estimaciones obtenidas con toda la información disponible y excluyendo los grupos de edad con tasas de mortalidad bajas, a fin de valorar el comportamiento del enfoque ante datos esparcidos. Se proyectan las tasas de mortalidad a dos períodos sucesivos a los observados. RESULTADOS: Se comprueba la robustez del método, lo cual evita excluir los grupos de edad con tasas de mortalidad nulas o bajas. Las tasas observadas caen todas dentro de las bandas de credibilidad y confirman la bondad del ajuste del modelo. Se observa una tendencia general decreciente de las tasas de mortalidad por cáncer de vejiga. Las estimaciones y proyecciones de estas tasas resultan más precisas en los grupos etarios que presentan mayor incidencia de mortalidad. CONCLUSIONES: La formulación bayesiana utilizada permite reducir la variación aleatoria entre estimaciones adyacentes al especificar que los efectos de cada escala dependan de los inmediatos anteriores. Se demuestra la capacidad del enfoque para manejar frecuencias bajas y obtener estimaciones confiables de las tasas de mortalidad, como así también proyecciones precisas sin necesidad de realizar supuestos adicionales como sucede en el ajuste clásico de un modelo EPC.

OBJECTIVE: Apply and assess a Bayesian approach to projecting cancer mortality rates by fitting age-period-cohort (APC) models. METHODS: The Bayesian estimation method was applied to bladder cancer mortality data in Argentina. A second-order autoregressive model was adopted for a priori specification of APC model coefficients. The estimates obtained were compared with all available information and excluding age groups with low mortality, to assess behavior of the approach in light of scattered data. Mortality was projected for two successive periods following the ones observed. RESULTS: Robustness of the method was verified, which avoids excluding age groups with null or low mortality. Observed rates all fall within the credibility bands and confirm the model's goodness of fit. An overall downward trend in bladder cancer mortality was observed. Estimates and projections of these rates are more precise in age groups that have greater incidence of mortality. CONCLUSIONS: The Bayesian formulation used herein makes it possible to reduce random variation between adjacent estimates by specifying that the effects of each scale depend on the immediately preceding ones. It was demonstrated that the approach has the capacity to handle low frequencies and obtain reliable mortality estimates, as well as precise projections, without the need for making additional assumptions, as happens in classical APC model fitting.

Uso del enfoque bayesiano para la estimación y proyección de tasas / Use of a Bayesian approach to rate estimation and projection

Objetivo. Aplicar y valorar el enfoque bayesiano para realizar proyecciones de tasas de mortalidad por cáncer a través del ajuste de modelos edad-período-cohorte (EPC). Métodos. El método de estimación bayesiano se aplica a datos de mortalidad por cáncer de vejiga en Argentina. Se adopta un esquema autorregresivo de segundo orden para la especificación a priori de los coeficientes del modelo EPC. Se comparan las estimaciones obtenidas con toda la información disponible y excluyendo los grupos de edad con tasas de mortalidad bajas, a fin de valorar el comportamiento del enfoque ante datos esparcidos. Se proyectan las tasas de mortalidad a dos períodos sucesivos a los observados. Resultados. Se comprueba la robustez del método, lo cual evita excluir los grupos de edad con tasas de mortalidad nulas o bajas. Las tasas observadas caen todas dentro de las bandas de credibilidad y confirman la bondad del ajuste del modelo. Se observa una tendencia general decreciente de las tasas de mortalidad por cáncer de vejiga. Las estimaciones y proyecciones de estas tasas resultan más precisas en los grupos etarios que presentan mayor incidencia de mortalidad. Conclusiones. La formulación bayesiana utilizada permite reducir la variación aleatoria entre estimaciones adyacentes al especificar que los efectos de cada escala dependan de los inmediatos anteriores. Se demuestra la capacidad del enfoque para manejar frecuencias bajas y obtener estimaciones confiables de las tasas de mortalidad, como así también proyecciones precisas sin necesidad de realizar supuestos adicionales como sucede en el ajuste clásico de un modelo EPC.

Objective. Apply and assess a Bayesian approach to projecting cancer mortality rates by fitting age-period-cohort (APC) models. Methods. The Bayesian estimation method was applied to bladder cancer mortality data in Argentina. A second-order autoregressive model was adopted for a priori specification of APC model coefficients. The estimates obtained were compared with all available information and excluding age groups with low mortality, to assess behavior of the approach in light of scattered data. Mortality was projected for two successive periods following the ones observed. Results. Robustness of the method was verified, which avoids excluding age groups with null or low mortality. Observed rates all fall within the credibility bands and confirm the model’s goodness of fit. An overall downward trend in bladder cancer mortality was observed. Estimates and projections of these rates are more precise in age groups that have greater incidence of mortality. Conclusions. The Bayesian formulation used herein makes it possible to reduce random variation between adjacent estimates by specifying that the effects of each scale depend on the immediately preceding ones. It was demonstrated that the approach has the capacity to handle low frequencies and obtain reliable mortality estimates, as well as precise projections, without the need for making additional assumptions, as happens in classical APC model fitting.

[Use of a bayesian approach to rate estimation and projection]. / Uso del enfoque bayesiano para la estimación y proyección de tasas.

OBJECTIVE: Apply and assess a Bayesian approach to projecting cancer mortality rates by fitting age-period-cohort (APC) models. METHODS: The Bayesian estimation method was applied to bladder cancer mortality data in Argentina. A second-order autoregressive model was adopted for a priori specification of APC model coefficients. The estimates obtained were compared with all available information and excluding age groups with low mortality, to assess behavior of the approach in light of scattered data. Mortality was projected for two successive periods following the ones observed. RESULTS: Robustness of the method was verified, which avoids excluding age groups with null or low mortality. Observed rates all fall within the credibility bands and confirm the model's goodness of fit. An overall downward trend in bladder cancer mortality was observed. Estimates and projections of these rates are more precise in age groups that have greater incidence of mortality. CONCLUSIONS: The Bayesian formulation used herein makes it possible to reduce random variation between adjacent estimates by specifying that the effects of each scale depend on the immediately preceding ones. It was demonstrated that the approach has the capacity to handle low frequencies and obtain reliable mortality estimates, as well as precise projections, without the need for making additional assumptions, as happens in classical APC model fitting.