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

Newton, Yulia; Novak, Adam M.; Swatloski, Teresa; McColl, Duncan C.; Chopra, Sahil; Graim, Kiley; Weinstein, Alana S.; Baertsch, Robert; Salama, Sofie R.; Ellrott, Kyle; Chopra, M. R.; Goldstein, Theodore C.; Haussler, David; Morozova, Olena; Stuart, Joshua M.

doi:10.1158/0008-5472.can-17-0580

Cited by 64 publications

(72 citation statements)

References 12 publications

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“…iC was optimized using k-means for 28 major clusters, and visualized by TM. The latent variables were used to generate a TM layout of the samples (Newton et al, 2017). The TM layout was computed using the Euclidean similarity between each pair of samples in the iC latent space.…”

Section: Star*methodsmentioning

confidence: 99%

“…We used TumorMap (TM) (Newton et al, 2017), an interactive visualization and analysis portal, coupled with integrated Cluster (iCluster [iC]) (Shen et al, 2009), and we found high overlap with original histopathologic classifications of SCC. Further, these tools uncovered broader and subtype-related genetic and epigenetic alterations that distinguish SCCs from other cancers and from one another.…”

Section: Introductionmentioning

confidence: 99%

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Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

Campbell¹,

Yau²,

Bowlby³

et al. 2018

Cell Reports

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265

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SUMMARY 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.

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Section: Star*methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

Campbell¹,

Yau²,

Bowlby³

et al. 2018

Cell Reports

Self Cite

265

303

View full text Add to dashboard Cite

show abstract

“…We used the UCSC TumorMap (Newton et al 2017) to visualize clusters of expression profiles across PDX histologies (Figure 5A). We observed clear separation among unrelated histologies and overlapping clustering among related histologies.…”

Section: Resultsmentioning

confidence: 99%

Genomic profiling of childhood tumor patient-derived xenograft models to enable rational clinical trial design

Rokita

Rathi

Cardenas

et al. 2019

Preprint

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Summary Accelerating cures for children with cancer remains an immediate challenge due to extensive oncogenic heterogeneity between and within histologies, distinct molecular mechanisms evolving between diagnosis and relapsed disease, and limited therapeutic options. To systematically prioritize and rationally test novel agents in preclinical murine models, researchers within the Pediatric Preclinical Testing Consortium are continuously developing patient-derived xenografts (PDXs) from high-risk childhood cancers, many refractory to current standard-of-care treatments. Here, we genomically characterize 261 PDX models from 29 unique pediatric cancer malignancies and demonstrate faithful recapitulation of histologies, subtypes, and refine our understanding of relapsed disease. Expression and mutational signatures are used to classify tumors for TP53 and NF1 inactivation, as well as impaired DNA repair. We anticipate that these data will serve as a resource for pediatric oncology drug development and guide rational clinical trial design for children with cancer. Highlights Multiplatform genomic analysis defines landscape of 261 pediatric cancer patient derived xenograft (PDX) models Pediatric patient derived xenografts faithfully recapitulate relapsed disease Inferred TP53 pathway inactivation correlates with pediatric cancer copy number burden Somatic mutational signatures predict impaired DNA repair across multiple histologies

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“…We then compared the clustering patterns across MYCN-NA neuroblastoma, osteosarcoma, Ewing sarcoma, embryonal rhabdomyosarcoma, alveolar rhabdomyosarcoma, and synovial sarcoma. We applied the TumorMap dimensionality reduction method [5] to visualize clustering of the full small blue round cell tumor gene expression matrix. We then applied the hydra framework to explore expression variation within each disease.…”

Section: Small Blue Round Cell Tumor Analysismentioning

confidence: 99%

“…Large cancer sequencing projects, including The Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET), have facilitated the development of cancer gene expression compendia [1][2][3][4][5][6], but these compendia often lack expression data from corresponding normal tissue. Without the normal comparator, Hoadley et al (2018) found that cell-of-origin signals drive integrative clustering of TCGA data.…”

Section: Introductionmentioning

confidence: 99%

Hydra: A mixture modeling framework for subtyping pediatric cancer cohorts using multimodal gene expression signatures

et al. 2020

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Precision oncology has primarily relied on coding mutations as biomarkers of response to therapies. While transcriptome analysis can provide valuable information, incorporation into workflows has been difficult. For example, the relative rather than absolute gene expression level needs to be considered, requiring differential expression analysis across samples. However, expression programs related to the cell-of-origin and tumor microenvironment effects confound the search for cancer-specific expression changes. To address these challenges, we developed an unsupervised clustering approach for discovering differential pathway expression within cancer cohorts using gene expression measurements. The hydra approach uses a Dirichlet process mixture model to automatically detect multimodally distributed genes and expression signatures without the need for matched normal tissue. We demonstrate that the hydra approach is more sensitive than widely-used gene set enrichment approaches for detecting multimodal expression signatures. Application of the hydra analysis framework to small blue round cell tumors (including rhabdomyosarcoma, synovial sarcoma, neuroblastoma, Ewing sarcoma, and osteosarcoma) identified expression signatures associated with changes in the tumor microenvironment. The hydra approach also identified an association between ATRX deletions and elevated immune marker expression in high-risk neuroblastoma. Notably, hydra analysis of all small blue round cell tumors revealed similar subtypes, characterized by changes to infiltrating immune and stromal expression signatures.

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

Cited by 64 publications

References 12 publications

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

Genomic profiling of childhood tumor patient-derived xenograft models to enable rational clinical trial design

Hydra: A mixture modeling framework for subtyping pediatric cancer cohorts using multimodal gene expression signatures

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