| dc.identifier.uri | http://hdl.handle.net/11401/76484 | |
| dc.description.sponsorship | This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. | en_US |
| dc.format | Monograph | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Dissertation | |
| dcterms.abstract | Intra-tumoral genetic heterogeneity has long been recognized, yet remains poorly understood. This has primarily been due to the lack of sensitive technologies to measure it. Genome wide analysis at the level of single cells has recently emerged as a powerful tool to dissect cancer genome heterogeneity. However, to be truly transformative, single cell approaches must accommodate the analysis of large numbers of single cells. Here, using integrative informatics and molecular biology approaches this study presents a robust, low-cost, and high-throughput method to retrieve the genome-wide copy number landscape of hundreds of single cancer cells. Application of the method to human cancer cell lines and clinical cancer tissue illustrates the underlying genetic heterogeneity present in both and further reveals mosaicism of chromosomal amplifications in clinical cancer samples. The capacity of the method to facilitate the rapid profiling of hundreds and thousands of single cell genomes is bound to illuminate the biology of intra-tumoral heterogeneity. | |
| dcterms.available | 2017-09-20T16:50:23Z | |
| dcterms.contributor | Shroyer, Kenneth | en_US |
| dcterms.contributor | Hicks, James | en_US |
| dcterms.contributor | Ju, Jingfang | en_US |
| dcterms.contributor | Offit, Kenneth | en_US |
| dcterms.contributor | Krasnitz, Alexander. | en_US |
| dcterms.creator | Baslan, Taimour | |
| dcterms.dateAccepted | 2017-09-20T16:50:23Z | |
| dcterms.dateSubmitted | 2017-09-20T16:50:23Z | |
| dcterms.description | Department of Molecular and Cellular Biology. | en_US |
| dcterms.extent | 99 pg. | en_US |
| dcterms.format | Application/PDF | en_US |
| dcterms.format | Monograph | |
| dcterms.identifier | http://hdl.handle.net/11401/76484 | |
| dcterms.issued | 2014-12-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2017-09-20T16:50:23Z (GMT). No. of bitstreams: 1
Baslan_grad.sunysb_0771E_12105.pdf: 29921219 bytes, checksum: e677504493d2616f6f5dc7193ae08c57 (MD5)
Previous issue date: 1 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | Copy Number Variation, Genome Evolution, Intra-Tumoral Heterogeneity, Multiplexing, Sequencing, Single Cell | |
| dcterms.subject | Genetics | |
| dcterms.title | High-Throughput Single-Cell Copy Number Profiling for Cancer Heterogeneity Analysis | |
| dcterms.type | Dissertation | |
