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Benjamin J. Raphael

Princeton University

ORCID: 0000-0003-1274-048X

Publishes on Cancer Genomics and Diagnostics, Single-cell and spatial transcriptomics, Genomic variations and chromosomal abnormalities. 448 papers and 71.6k citations.

448Publications
71.6kTotal Citations

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Top publicationsby citations

Mutational landscape and significance across 12 major cancer types
Cited by 4.5kOpen Access

The Cancer Genome Atlas (TCGA) has used the latest sequencing and analysis methods to identify somatic variants across thousands of tumours. Here we present data and analytical results for point mutations and small insertions/deletions from 3,281 tumours across 12 tumour types as part of the TCGA Pan-Cancer effort. We illustrate the distributions of mutation frequencies, types and contexts across tumour types, and establish their links to tissues of origin, environmental/carcinogen influences, and DNA repair defects. Using the integrated data sets, we identified 127 significantly mutated genes from well-known (for example, mitogen-activated protein kinase, phosphatidylinositol-3-OH kinase, Wnt/β-catenin and receptor tyrosine kinase signalling pathways, and cell cycle control) and emerging (for example, histone, histone modification, splicing, metabolism and proteolysis) cellular processes in cancer. The average number of mutations in these significantly mutated genes varies across tumour types; most tumours have two to six, indicating that the number of driver mutations required during oncogenesis is relatively small. Mutations in transcriptional factors/regulators show tissue specificity, whereas histone modifiers are often mutated across several cancer types. Clinical association analysis identifies genes having a significant effect on survival, and investigations of mutations with respect to clonal/subclonal architecture delineate their temporal orders during tumorigenesis. Taken together, these results lay the groundwork for developing new diagnostics and individualizing cancer treatment. As part of The Cancer Genome Atlas Pan-Cancer effort, data analysis for point mutations and small indels from 3,281 tumours and 12 tumour types is presented; among the findings are 127 significantly mutated genes from cellular processes with both established and emerging links in cancer, and an indication that the number of driver mutations required for oncogenesis is relatively small. As part of The Cancer Genome Atlas Pan-Cancer project, these authors present data analysis for point mutations and small indels from more than 3,000 tumours representing 12 tumour types. Among the findings are 127 significantly mutated genes from cellular processes with both established and emerging links to cancer, and an indication that the number of driver mutations required for oncogenesis is relatively small. Additional analyses also identify genes with significant impact on survival and a likely temporal order of mutational events during tumorigenesis.

Eleven grand challenges in single-cell data science
David Lähnemann, Johannes Köster, Ewa Szczurek et al.|Genome biology|2020
Cited by 1.4kOpen Access

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.