Big data visualization identifies the multidimensional molecular landscape of human gliomas

Abstract

We show that visualizing large molecular and clinical datasets enables discovery of molecularly defined categories of highly similar patients. We generated a series of linked 2D sample similarity plots using genome-wide single nucleotide alterations (SNAs), copy number alterations (CNAs), DNA methylation, and RNA expression data. Applying this approach to the combined glioblastoma (GBM) and lower grade glioma (LGG) The Cancer Genome Atlas datasets, we find that combined CNA/SNA data divide gliomas into three highly distinct molecular groups. The mutations commonly used in clinical evaluation of these tumors are regionally distributed in these plots. One of the three groups is a mixture of GBM and LGG that shows similar methylation and survival characteristics to GBM. Altogether, our approach identifies eight molecularly defined glioma groups with distinct sequence/expression/methylation profiles. Importantly, we show that regionally clustered samples are enriched for specific drug targets.big data | glioma | precision medicine | visualization | biomarkers T he primary brain tumors were originally classified histologically by Bailey and Cushing in 1926 (1), named for the CNS cell types that they resembled, and subsequently graded by the appearance of histological structures such as pseudopalisades and vascular proliferation that correlated with outcome (2). More recently, those diagnoses have been embellished by additional molecular characterization, including ki67 staining for proliferation, MGMT methylation status predicting response to temozolomide (3), single copy loss of regions of ch1p and ch19q found in the oligodendrogliomas [predictive of a better outcome in lower grade glioma (LGG)] (4), IDH1 mutations that are common in LGG are predictive of a better outcome when found in glioblastoma (GBM) and associated with the CpG island methylator phenotype (CIMP) (5), and ATRX and p53 mutations that when found in IDH1 mutant tumors predict a worse outcome (6, 7). In addition, new molecularly based classification systems have emerged in the past decade such as a methylation profile-based classification of the gliomas into CIMP and non-CIMP tumors with significant differences in survival between groups and an expression-based division of the non-CIMP GBMs into three or four subclasses (8, 9).None of the currently available molecular strategies use the entire collection of data available to classify the tumors. Rather, they build on, and modify, existing classifications. However, current technology allows the very detailed measurement of multiple types of data. In fact, the The Cancer Genome Atlas (TCGA) glioma databases provide access to measurements of whole exome sequence, copy number across the genome, whole genome methylation profiles, and RNA expression by RNA-seq in a cohort of more than 1,100 grade 4 (GBM) and LGGs (10, 11). The key obstacle to integrating all of these datasets to classify the gliomas in a meaningful way has been designing a method to relate disparate types of high-dimensional data, and the ...

Multidimensional scaling of diffuse gliomas: application to the 2016 World Health Organization classification system with prognostically relevant molecular subtype discovery

“…Classic multidimensional scaling (MDS) of molecular data was performed as previously described [5]. The minimal TCGA tumor purity has been reported at 60-80%, which has been shown to be sufficient (>50% tumor purity) for robust detection of cancer-related copy number alterations via GISTIC 2.0 scores [35], and therefore undersampling of glioma copy number alterations is not likely to affect MDS in this study.…”

TumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal

“…The simultaneous and interactive visualization of multiple genomic datasets can reveal patterns that inspire hypothesis generation (1–4). This is especially true in cancer genomics investigations, where the number of measured features (e.g., 20,000 genes) can be large.…”

Patient Similarity: Emerging Concepts in Systems and Precision Medicine

“…Large values for L-infinity centrality correspond to data points at large distances from the center of the data set (Li et al, 2015). Other pattern analysis and cluster algorithms (Daemen and De Moor, 2009; Chan et al, 2010; Liu et al, 2013a; Mabotuwana et al, 2013; Sundar et al, 2014), or algorithms incorporating distance metric learning (Wang et al, 2011; Bian and Tao, 2012), locally supervised metric learning (Sun et al, 2012; Ng et al, 2015), local spline regression (Wang et al, 2012), or visual analytics (Tsymbal et al, 2009; Ebadollahi et al, 2010; Gotz et al, 2011; Perer, 2012; Heer and Perer, 2014; Bolouri et al, 2016; Ozery-Flato et al, 2016), can also be used for patient similarity to predict diabetes onset, develop treatment recommendations tailored to each patient, or predict survival after chemotherapy (Chan et al, 2010; Liu et al, 2013a; Ng et al, 2015; Ozery-Flato et al, 2016), among other applications. SNOMED CT and other medical terminology frameworks can be used to facilitate communication across platforms in various studies (Melton et al, 2006).…”

“…In this way, patient similarity represents a paradigm shift that introduces disruptive innovation to optimize personalization of patient care. Some promising examples are regarding mental and behavioral disorders (Roque et al, 2011), infectious diseases (Li et al, 2015), cancers (Wu et al, 2005; Teng et al, 2007; Chan et al, 2010, 2015; Klenk et al, 2010; Cho and Przytycka, 2013; Li et al, 2015; Wang, 2015; Bolouri et al, 2016; Wang et al, 2016), endocrine (Li et al, 2015; Wang, 2015), and metabolic diseases (Zhang et al, 2014; Ng et al, 2015). Others involve diseases of the nervous system (Lieberman et al, 2005; Carreiro et al, 2013; Cho and Przytycka, 2013; Qian et al, 2014; Buske et al, 2015a; Li et al, 2015; Bolouri et al, 2016; Wang et al, 2016), eyes (Buske et al, 2015a; Li et al, 2015), skin (Buske et al, 2015a; Li et al, 2015), heart (Wu et al, 2005; Tsymbal et al, 2007; Syed and Guttag, 2011; Buske et al, 2015a; Li et al, 2015; Panahiazar et al, 2015a,b; Wang, 2015; Björnson et al, 2016), liver (Chan et al, 2015), intestines (Buske et al, 2015a), musculoskeletal system (Buske et al, 2015a), congenital malformations (Buske et al, 2015a), and various other conditions or factors influencing health status (Gotz et al, 2012; Subirats et al, 2012; Ng et al, 2015).…”