Relevant Genetic Pathways Research Articles

Sotos Syndrome: Deep Neuroimaging Phenotyping Reveals a High Prevalence of Malformations of Cortical Development.

Sotos syndrome is a rare autosomal dominant condition caused by pathogenic mutations in the NSD1 gene that presents with craniofacial dysmorphism, overgrowth, seizures, and neurodevelopmental delay. Macrocephaly, ventriculomegaly, and corpus callosal dysmorphism are typical neuroimaging features that have been described in the medical literature. The purpose of this study was to expand on the neuroimaging phenotype by detailed analysis of a large cohort of patients with genetically proved Sotos syndrome. This multicenter, multinational, retrospective observational cohort study systematically analyzed the clinical characteristics and neuroimaging features of 77 individuals with genetically diagnosed Sotos syndrome, via central consensus review with 3 pediatric neuroradiologists. In addition to previously described features, malformations of cortical development were identified in most patients (95.0%), typically dysgyria (92.2%) and polymicrogyria (22.1%), varying in location and distribution. Incomplete rotation of the hippocampus was observed in 50.6% of patients and was associated with other imaging findings, in particular with dysgyria (100% versus 84.2%, P = .012). Our findings show a link between the genetic-biochemical basis and the neuroimaging features and aid in better understanding the underlying clinical manifestations and possible treatment options. These findings have yet to be described to this extent and correspond with recent studies that show that NSD1 participates in brain development and has interactions with other known relevant genetic pathways.

Multigraph Cluster Analysis of AD Candidate Genes via Human Brain Mapping

AbstractBackgroundBrain imaging genetics examines associations between imaging quantitative traits (QTs) and genetic factors such as single nucleotide polymorphisms (SNPs) to provide important insights into the pathogenesis of Alzheimer’s diseases (AD). Given the high dimensionality, the individual level SNP‐QT signals typically have small effect sizes and are hard to be detected and replicated. To overcome this limitation, this work proposes a new approach to identify high‐level imaging genetic associations through applying multi‐graph clustering to SNP‐QT association maps.MethodsParticipants include 255 cognitive normal (CN) and 218 late mild cognitive impairment (LMCI) subjects from the ADNI data. Linear regression is performed to regress regional AV‐45 measures on each of the 54 AD SNPs to obtain a brain association map across 116 AAL‐ROIs. To quantify the similarity between each SNP pair, 5 scoring functions are used to calculate 5 different similarity measures between the respective brain maps of the two SNPs. Each scoring function yields a similarity graph of all 54 SNPs. Multigraph min‐max clustering is applied to 5 different graphs simultaneously to group similar SNPs together. Functional annotation is performed on each SNP cluster and the corresponding average brain association map to provide high level interpretations.ResultsFigure 1 shows the genetic effect on regional AV‐45 measures for each pair of SNP and ROI. The multi‐graph clustering result is shown in Figure 2, where 54 AD‐related SNPs are clustered into two groups. Figure 3a shows the results of Enrichr Elsevier pathway analysis. SNP group 1 is associated with pathways such as amyloid beta clearance, while SNP group 2 is associated with pathways including amyloid beta formation and APP processing in AD. Figure 3b shows the NeuroSynth annotation of the average brain effect maps associated with the two SNP clusters, respectively.ConclusionWe proposed an approach that transforms individual level imaging genetic associations (SNP vs ROI) to high‐level associations (i.e., SNP cluster vs brain association map), and applied it to an LMCI study. These high‐level findings have the potential to provide valuable insights into relevant genetic pathways and brain circuits, which can help form new hypotheses for more detailed imaging and genetics studies in independent cohorts.

Mining High-Level Imaging Genetic Associations via Clustering AD Candidate Variants with Similar Brain Association Patterns.

Brain imaging genetics examines associations between imaging quantitative traits (QTs) and genetic factors such as single nucleotide polymorphisms (SNPs) to provide important insights into the pathogenesis of Alzheimer’s disease (AD). The individual level SNP-QT signals are high dimensional and typically have small effect sizes, making them hard to be detected and replicated. To overcome this limitation, this work proposes a new approach that identifies high-level imaging genetic associations through applying multigraph clustering to the SNP-QT association maps. Given an SNP set and a brain QT set, the association between each SNP and each QT is evaluated using a linear regression model. Based on the resulting SNP-QT association map, five SNP–SNP similarity networks (or graphs) are created using five different scoring functions, respectively. Multigraph clustering is applied to these networks to identify SNP clusters with similar association patterns with all the brain QTs. After that, functional annotation is performed for each identified SNP cluster and its corresponding brain association pattern. We applied this pipeline to an AD imaging genetic study, which yielded promising results. For example, in an association study between 54 AD SNPs and 116 amyloid QTs, we identified two SNP clusters with one responsible for amyloid beta clearances and the other regulating amyloid beta formation. These high-level findings have the potential to provide valuable insights into relevant genetic pathways and brain circuits, which can help form new hypotheses for more detailed imaging and genetics studies in independent cohorts.

QS61. Irradiated Fibroblast Sub-populations Alter Gene Expression to Heal Murine Skin

Introduction: Radiation-induced skin fibrosis is a well-documented and burdensome sequela of radiation therapy, however the exact mechanism behind this effect is not well understood. We aim to better delineate the cellular mechanisms of acute and chronic radiation-induced skin fibrosis. Methods: Thirty adult B6 mice, with equal proportion of males and females underwent 5 Gy of radiation every other day for 12 days, equaling 30 Gy total, of the dorsal skin. At weeks 0, 1, 2, 4, and 8 after radiation, the skin was harvested and analyzed using histology and immunofluorescence. Dorsal skin also underwent single-cell RNA sequencing, data was demultiplexed and underwent dimension reduction to identify transcriptionally distinct subpopulations, cross-referenced with Enrichr to identify relevant genetic pathways. Results: Dermal thickness was noted to progressively, significantly increase from week 0 to 8. Dermal collagen deposition and disorganization also significantly increased throughout the experiment. CD31+ expression increased in the acute phase, but decreased below baseline levels by the chronic phase. Four distinct fibroblast subpopulations were identified, and the genetic expression of these individual subpopulations demonstrated dynamic changes throughout the experiment. Sca, Dlk-1, and CD26+ immunofluorescence staining further confirmed this dynamicity as their expression fluctuated throughout the 8-week course of the experiment. Conclusion: Identification of this dynamic process amongst the various fibroblast subpopulations during the healing process of irradiation wounds in wild-type mice is a novel finding and paramount to better understanding and treatment of radiation-induced skin fibrosis.

Weighted dimensionality reduction and robust Gaussian mixture model based cancer patient subtyping from gene expression data

Background:The heterogeneous nature of cancer necessitates subtyping of cancer patients into distinct and well separated subgroups. However, computational issues arise because gene expression data is noisy and contains outliers apart from being high dimensional. As such, an attempt to subtype cancer patients from gene expression data leads to highly overlapping Kaplan–Meier (KM) survival plots and thus clear distinction among the discovered subtypes becomes difficult. Here we attempt to achieve a greater separation among the subtypes through a robust clustering pipeline. Methods:We propose a robust framework to achieve a better separation among the discovered subtypes. Our framework is based on dimensionality reduction of a weighted gene expression matrix using t-distributed Stochastic Neighbor Embedding (t-SNE) and a robust Gaussian mixture model based clustering approach. Every gene is weighted according to the median absolute deviation (MAD) of the gene before dimensionality reduction. The results are quantified by measuring the minimum pairwise separation among the KM plots and minimum hazard ratio among the subtypes. We also introduce a novel method, called cumulative survival separation, to quantify the separation among the discovered subtypes. Results:To validate the proposed methodology we obtained five cancer gene expression datasets from The Cancer Genome Atlas (TCGA) and comparisons with Consensus Clustering (CC), Consensus non-negative matrix factorization (CNMF), fast density-aware spectral clustering (Spectrum) and Neighborhood based Multi-Omics clustering (NEMO) methodologies show that the proposed method is able to achieve a greater separation compared to the aforementioned methods in literature. For instance, the minimum pairwise life expectancy difference (in days) between the discovered subtypes for GBM is 61 days for the proposed methodology with MAD scores, whereas it is approximately 33, 19, 49 and 33 days only for CC, Spectrum, Nemo and CNMF respectively. Comparisons are also shown for the proposed framework with and without using the MAD scores and it is observed that MAD score significantly improves the subtype separation. Hazard ratio analysis also shows that the proposed methodology performs better. Furthermore, pathway over-representation analyses were carried to identify relevant genetic pathways which can be possible targets for treatment. Conclusion:The results suggest that the use of median absolute deviation and a robust clustering methodology are helpful in achieving greater separation among the subtypes with better statistical and clinical significance.

A topological approach for cancer subtyping from gene expression data.

Gene expression data contains key information which can be used for subtyping cancer patients. However, computational methods suffer from 'curse of dimensionality' due to very high dimensionality of omics data and therefore are not able to clearly distinguish between the discovered subtypes in terms of separation of survival plots. To address this we propose a framework based on Topological Mapper algorithm. The novelty of this work is that we suggest a method for defining the filter function on which the mapper algorithm heavily depends. Survival analysis of the discovered cancer subtypes is carried out and evaluated in terms of minimum pairwise separation between the Kaplan-Meier plots. Furthermore, we present a method to measure the separation between the discovered subtypes based on hazard ratios. Five cancer genomics datasets obtained from The Cancer Genome Atlas portal have been used for comparisons with Robust Sparse Correlation-Otrimle (RSC-Otrimle) algorithm and Similarity Network Fusion(SNF). Comparisons show that the minimum pairwise life expectancy difference (in days) between the discovered subtypes for lung, colon, breast, glioblastoma and kidney cancers is 107, 204, 20, 88 and 425days, respectively, for the proposed methodology whereas it is only 69, 43, 6, 61 and 282days for RSC-Otrimle and 9, 95, 18, 60 and 148days for SNF. Hazard ratio analysis also shows that the proposed methodology performs better in four of the five datasets. A visual inspection of Kaplan-Meier plots reveals that the proposed methodology achieves lesser overlap in Kaplan-Meier plots especially for lung, breast and kidney cases. Furthermore, relevant genetic pathways for each subtype have been obtained and pathways which can be possible targets for treatment have been discussed. The significance of this work lies in individualized understanding of cancer from patient to patient which is the backbone of Precision Medicine.

Abstract 5336: Methylation patterns in hepatoblastoma: A report from the Children's Oncology Group

Abstract Introduction. Hepatoblastoma (HB) accounts for over 80% of all liver cancer diagnosed in children under 15 years of age in the United States. Aberrant DNA methylation has been implicated in the pathogenesis of multiple types of cancer, including HB. Global hypomethylation and DNA methylation in the promoter region of selected tumor suppressor genes, including RASSF1A, p16, SOCS1, CASP8, and MT1G, has been observed in previous studies of HB. Methods. A total of 47 flash-frozen samples of HB tumors were available for analysis. These tumors include 26 tumors from cases enrolled in Children's Oncology Group study AEPI04C1 (the HOPE study) and 21 tumors obtained as part of the Children's Oncology Group P9346 HB biology protocol. The Cooperative Human Tissue Network Pediatric Division (CHTN; Columbus, OH) provided 23 normal liver tissue samples from children ages 0—6 to serve as controls in this analysis. Initial multi-dimensional scaling (MDS) plots showed that 8 samples, 2 control and 6 patients, did not cluster with the other samples of the same group and were considered outliers. These 8 samples were removed from the study leaving a total of 62 samples, 21 control and 41 patients, remaining. Differentially methylated regions (DMR) were identified between control and patient samples using the bumphunter algorithm included in the minfi package. We used Ingenuity Pathway Analysis (IPA) to identify pathways that were enriched in the group of CpG with a change in methylation beta value of ≥39% between patients and controls. Results. We found 4833 DMR, which are associated with 3543 genes, when comparing HB patients and controls. Pathway analysis revealed 76 pathways enriched among the DMR. Conclusions. Identification of methylation patterns may elucidate the developmental stage at which HB arose and the at-risk period when environmental exposures could be most harmful. Further, understanding the relevant genetic pathways could lead to the development of new targets for therapy. Citation Format: Erin Marcotte, Logan G. Spector, Heather Nelson, Gail Tomlinson, Mark Krailo, Lauren Mills, Jenny Poynter. Methylation patterns in hepatoblastoma: A report from the Children's Oncology Group [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5336.

Global transcriptional profiling using RNA sequencing and DNA methylation patterns in highly enriched mesenchymal cells from young versus elderly women

Age-related bone loss in humans is associated with a decrease in bone formation relative to bone resorption, although the mechanisms for this impairment in bone formation with aging are not well understood. It is known that the precursors for the bone-forming osteoblasts reside in the mesenchymal cell population in bone marrow. Thus, in an effort to identify relevant genetic pathways that are altered with aging, we examined the gene expression and DNA methylation patterns from a highly enriched bone marrow mesenchymal cell population from young (mean age, 28.7years) versus old (mean age, 73.3years) women. Bone marrow mononuclear cells from these women were depleted of hematopoietic lineage (lin) and endothelial cells using a combination of magnetic- and fluorescence-activated cell sorting, yielding a previously characterized mesenchymal cell population (lin−/CD34−/CD31− cells) that is capable of osteoblast differentiation. Whole transcriptome RNA sequencing (RNAseq) of freshly isolated cells (without in vitro culture) identified 279 differentially expressed genes (p<0.05, false discovery rate [q]<0.10) between the young and old subjects. Pathway analysis revealed statistically significant (all p<0.05) alterations in protein synthesis and degradation pathways, as well as mTOR, gap junction, calcium, melatonin and NFAT signaling pathways. Further, Reduced Representational Bisulphite sequencing (RRBS DNA methylation sequencing) revealed significant differences in methylation between the young and old subjects surrounding the promoters of 1528 target genes that also exhibited significant differences in gene expression by RNAseq. In summary, these studies provide novel insights into potential pathways affected by aging in a highly enriched human mesenchymal cell population analyzed without the confounding effects of in vitro culture. Specifically, our finding of alterations in several genes and pathways leading to impaired protein synthesis and turnover with aging in bone marrow mesenchymal cells points to the need for further studies examining how these changes, as well as the other alterations with aging that we identified, may contribute to the age-related impairment in osteoblast formation and/or function.

DNA methylation analysis reveals distinct methylation signatures in pediatric germ cell tumors

BackgroundAberrant DNA methylation is a prominent feature of many cancers, and may be especially relevant in germ cell tumors (GCTs) due to the extensive epigenetic reprogramming that occurs in the germ line during normal development.MethodsWe used the Illumina GoldenGate Cancer Methylation Panel to compare DNA methylation in the three main histologic subtypes of pediatric GCTs (germinoma, teratoma and yolk sac tumor (YST); N = 51) and used recursively partitioned mixture models (RPMM) to test associations between methylation pattern and tumor and demographic characteristics. We identified genes and pathways that were differentially methylated using generalized linear models and Ingenuity Pathway Analysis. We also measured global DNA methylation at LINE1 elements and evaluated methylation at selected imprinted loci using pyrosequencing.ResultsMethylation patterns differed by tumor histology, with 18/19 YSTs forming a distinct methylation class. Four pathways showed significant enrichment for YSTs, including a human embryonic stem cell pluripotency pathway. We identified 190 CpG loci with significant methylation differences in mature and immature teratomas (q < 0.05), including a number of CpGs in stem cell and pluripotency-related pathways. Both YST and germinoma showed significantly lower methylation at LINE1 elements compared with normal adjacent tissue while there was no difference between teratoma (mature and immature) and normal tissue. DNA methylation at imprinted loci differed significantly by tumor histology and location.ConclusionUnderstanding methylation patterns may identify the developmental stage at which the GCT arose and the at-risk period when environmental exposures could be most harmful. Further, identification of relevant genetic pathways could lead to the development of new targets for therapy.

Abstract P2-04-05: Prolonged targeted overexpression of Aurora-A in mammary epithelium promotes mammary adenocarcinoma with genomic instability

Abstract Aurora kinase-A (referred to as Aurora-A) identified as a sereine/threonine kinase induces mitotic progression process including centrosome amplification, bipolar spindle formation and chromosome segregation through modulation of activity and localization of its interacting proteins during cell division. Beside its essential roles in mitosis, cancer profiling studies have revealed that Aurora-A function is implicated in tumor relevant signaling pathways and overexpressed in many human tumors. The findings showed highly frequent overexpression of Aurora-A in human breast ductal carcinoma in situ and primary invasive ductal breast carcinoma. Moreover, the Aurora-A overexpression correlates with poor prognosis in breast carcinoma, deregulated overexpression of Aurora-A associates with centrosome anomalies and chromosome instability in rodent models of mammary caricinogenesis. However, published studies in several transgenic mouse models attempting to address the role of Aurora-A in tumorigenesis and genomic instability have raised conflicting results. In this report we targeted expression of Aurora-A transgene in an inducible mouse model in which expression of Aurora-A was restricted to mammary epithelium during multiple pregnancy and lactation cycles under the activity of β-lactoglobulin promotor. The results demonstrate that overexpression of Aurora-A could induce tumorigenesis in mouse mammary epithelium. The tumor incidence was 70% of Aurora-A transgenic mice at 16 months of age. Of note, overexpression of Aurora-A led to genomic instability. Comparative genomic hybridization (CGH) array analyses revealed loss of Trim12a, Trim12c, Trim30b, Trim30d, Trim30e, Cdkn2d, Pten, Cep55 and gain of Adam6. Notably, Aurora-A overexpression caused nuclear accumulation of cyclin D1, activation of AKT, overexpression of TPX2 and PLK1and loss of ERα expression in tumors. Our findings indicated that prolonged Aurora-A expression in mammary epithelium leads to mammary adenocarcinomas with genomic instability. Aurora-A driven adenocarcinoma reveals deregulation of critical tumor relevant genetic pathways involving both oncogenes and tumor suppressor genes. These transgenic mouse model findings indicate that Aurora-A may be an important therapeutic target for mammary cancer. Keywords: Aurora-A, carcinogenesis. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P2-04-05.

Abstract 2649: Maternal and offspring folate metabolic genes and the risk of childhood acute lymphoblastic leukemia (ALL)

Abstract Introduction: Childhood ALL is the most common type of cancer in children. Although the survival rate is relatively high, there are significant clinical sequelae associated with treatment and little is known about the etiology of this condition. Family studies suggest a genetic component to ALL, and the peak incidence between 2 and 4 years of age indicates factors during pregnancy may influence risk. The folate metabolic pathway is a strong candidate for genetic epidemiologic studies due to its role in DNA synthesis and methylation. Objective: The purpose of this study was to conduct a case-parent triad analysis to assess both maternal and offspring genetic effects of the folate metabolic pathway on childhood ALL risk. Methods: We recruited pediatric ALL case-parent triads (n = 120) from Texas Children's Hospital. DNA samples were genotyped in the Human Genetics Center at The University of Texas School of Public Health using the Sequenom iPLEX MassARRAY for 80 tagSNPs in six folate metabolic pathway genes (MTHFR, MTRR, MTR, DHFR, BHMT, and TYMS). Log-linear modeling was used to examine the associations between ALL and both maternal and offspring genotypes. Results: After controlling for the false discovery rate (&amp;lt;0.10), there were 19 significant maternal effects in the following genes: BHMT (n = 3), MTR (n = 12), and TYMS (n = 4). For instance, maternal genotypes for BHMT rs558133 (RR = 0.51, 95% CI: 0.30-0.88, p = 0.008, q = 0.08), MTR rs2282369 (RR = 0.46, 95% CI: 0.26-0.79, p = 0.003, q = 0.08), and TYMS rs699517 (RR = 1.92, 95% CI: 1.17-3.17, p = 0.006, q = 0.08) were all associated with ALL. There were no significant offspring effects after controlling for the false discovery rate. Conclusions: This is one of the few studies conducted to evaluate maternal genetic effects in the context of pediatric ALL risk. Furthermore, we employed a family-based design (i.e., case-parent triad) that is robust to confounding due to population structure and allows for the evaluation of both maternal and offspring genotypes. These results provide additional evidence that the risk of pediatric ALL is influenced by variation within folate pathway genes and indicate that studies to explore these relationships further are warranted. We plan to extend these analyses to the evaluate folate pathway haplotypes, as well as other relevant genetic pathways (e.g., DNA repair). Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2649. doi:1538-7445.AM2012-2649

Hruschka et al. Respond

We thank Dr. Bartle for pointing out that clustering of obesity in families would be expected given shared genetics. Of course, we would also expect that common home environment plays a role.1 Our key finding that shared social norms only weakly account for shared body size in a sample including both family and friends leaves room for developing a number of plausible alternative hypotheses for the social bases of obesity clustering, including some that more directly take into account genetics. But as obesity has demonstrated high heritability and a large norm of reaction, we would expect that the most ultimately satisfying answers must be focused on interactions between relevant genetic pathways and the physical and social environment. We are especially encouraged in this regard by recent findings on the developmental origins of obesity,2 interactions between feeding environment and genetic risk,3 and genetic influences on how people construct their social environments.4 A direct focus on mechanisms—whether by examining how body size ideals influence behavior or by mapping specific genetic pathways—will be the crucial step in understanding how and why the social environment matters to the obesity epidemic.