| dc.identifier.uri | http://hdl.handle.net/11401/78372 | |
| 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.format.mimetype | Application/PDF | en_US |
| dc.language.iso | en_US | |
| dc.type | Dissertation | |
| dcterms.abstract | It is well recognized that cancer results from multi-stage mutation acquisitions. To this end, both intrinsic and extrinsic factors contribute to mutagenesis in cancer and subsequently the risk of cancer. To better understand the process of cancer initiation and the contributions of various risk factors, we build stochastic models for carcinogenesis based on modern cancer stem cell theory with clonal expansion. In our extended risk model, we have incorporated all three types of cell lineages including stem cells, progenitor cells and terminal cells. We have also included major ingredients for cancer development, including general cell behaviors, tissue homeostasis, multi-stage mutation acquisition, as well as how driver mutations may alter cell behaviors through cell fitness or clonal expansion. Our model provides a general framework for estimating cancer risk and cancer mutation distributions at any age in a lifetime. With these models, we can simulate and analyze the effect of different factors on the speed, magnitude and risk of cancer onset. In particular, for each cancer, based on observed cancer risk data, we can quantify (1) the amount of lifetime risk due to the intrinsic mutations alone, that is, the intrinsic risk, or as the media calls, the ‘bad luck’, and (2) the percent of mutations due to intrinsic risk alone. Applying our modeling in conjunction with the US and the World cancer registry data, we found that non-intrinsic risk accounts for not only the major percentage of lifetime cancer risk, but also the major proportion of lifetime cancer mutations. | |
| dcterms.available | 2018-07-09T14:34:54Z | |
| dcterms.contributor | Wu, Song | en_US |
| dcterms.contributor | Zhu, Wei. | en_US |
| dcterms.contributor | Zhu, Wei | en_US |
| dcterms.contributor | Kuan, Pei Fen | en_US |
| dcterms.contributor | Hannun, Yusuf. | en_US |
| dcterms.creator | Tian, Mu | |
| dcterms.dateAccepted | 2018-07-09T14:34:54Z | |
| dcterms.dateSubmitted | 2018-07-09T14:34:54Z | |
| dcterms.description | Department of Applied Mathematics and Statistics. | en_US |
| dcterms.extent | 160 pg. | en_US |
| dcterms.format | Monograph | |
| dcterms.identifier | http://hdl.handle.net/11401/78372 | |
| dcterms.identifier | Tian_grad.sunysb_0771E_13450.pdf | en_US |
| dcterms.issued | 2017-08-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Submitted by Jason Torre (fjason.torre@stonybrook.edu) on 2018-07-09T14:34:54Z
No. of bitstreams: 1
Tian_grad.sunysb_0771E_13450.pdf: 9383816 bytes, checksum: 9d4232dd2f5c0637f1243e1afd6b2e63 (MD5) | en |
| dcterms.provenance | Made available in DSpace on 2018-07-09T14:34:54Z (GMT). No. of bitstreams: 1
Tian_grad.sunysb_0771E_13450.pdf: 9383816 bytes, checksum: 9d4232dd2f5c0637f1243e1afd6b2e63 (MD5)
Previous issue date: 2017-08-01 | en |
| dcterms.subject | Statistics | |
| dcterms.subject | Biometry | |
| dcterms.title | Stochastic Modeling of Cell Dynamics, Mutation Acquisition and Cancer Risk | |
| dcterms.type | Dissertation | |
