Precision Medicine Approach to Improving Reconstructive Surgery Outcomes for Breast Cancer Survivors
| dc.contributor.author | Degen, Katherine Emily | en |
| dc.contributor.committeechair | Gourdie, Robert G. | en |
| dc.contributor.committeemember | Moyer, Kurtis E. | en |
| dc.contributor.committeemember | Whittington, Abby R. | en |
| dc.contributor.committeemember | Van Dyke, Mark | en |
| dc.contributor.committeemember | Poelzing, Steven | en |
| dc.contributor.department | Biomedical Engineering and Mechanics | en |
| dc.date.accessioned | 2018-07-26T08:00:35Z | en |
| dc.date.available | 2018-07-26T08:00:35Z | en |
| dc.date.issued | 2018-07-25 | en |
| dc.description.abstract | As the survival rate increases, the importance of quality of life post-cancer is increasing. This, in conjunction with genetic screening, has increase the number of breast reconstructions 36%. The most common complication causing revision of reconstructive surgery is the formation of a dense scar capsule around the silicone implant called capsular contracture. Nearly all patients will experience this complication, though with different degrees of response, ranging from moderate scarring to major disfigurement and pain at the implant site. Presently, there is no way to predict the degree of contraction capsule formation that individual patients will suffer prospectively, nor is there clinical approach to preventing this complication. Patient information and tissue was collected in a uniform manner to address these lingering problems. Clinical data was used to construct a predictive model which can accurately predict capsular contracture severity in breast reconstruction patients. Histological analysis demonstrated differences in structure and cell composition between different capsule severities. Of particular note, a new region was described which could serve as the communication interface between innate immune cells and fibroblasts. RNA-seq analysis identified 1029 significantly dysregulated genes in severe capsules. Pathway enrichment was then performed which highlights IL4/13 signaling, extracellular matrix organization, antigen presentation, and interferon signaling as importantly dysregulated pathways. These RNA results were also compared to various clinical and histological measurements to evaluate novel correlations. PVT-1, a long non-coding RNA associated with cancer, was strongly correlated to capsules formed after cancer removal. This suggests cancerous transformations of cell types that remain after the tumor is removed. Furthermore, transgelin and caspase 7 correlated to myofibroblasts density, suggesting an abnormal fibroblasts that are resistant to cell death and may have enhanced contractile abilities. Capsule formation is a complex process however, with well controlled clinical models quantitative differences can be found. These results serve as stepping stone for the field to move beyond retrospective clinical trials and pursue treatments and preventative measures. | en |
| dc.description.abstractgeneral | As the survival rate increases, the importance of quality of life post-cancer is increasing. This, in conjunction with genetic screening, has increase the number of breast reconstructions 36%. The most common complication causing revision of reconstructive surgery is the formation of a dense scar capsule around the silicone implant called capsular contracture. Nearly all patients will experience this complication, though with different degrees of response, ranging from moderate scarring to major disfigurement and pain at the implant site. Presently, there is no way to predict the degree of contraction capsule formation that individual patients will suffer prospectively, nor is there clinical approach to preventing this complication. Patient information and tissue was collected in a uniform manner to address these lingering problems. Clinical data was used to construct a predictive model which can accurately predict capsular contracture severity in breast reconstruction patients. Histological analysis demonstrated differences in structure and cell composition between different capsule severities. Of particular note, a new region was described which could serve as the communication interface between innate immune cells and fibroblasts. RNA-seq analysis identified 1029 significantly dysregulated genes in severe capsules. Pathway enrichment was then performed which highlights IL4/13 signaling, extracellular matrix organization, antigen presentation, and interferon signaling as importantly dysregulated pathways. These RNA results were also compared to various clinical and histological measurements to evaluate novel correlations. PVT-1, a long non-coding RNA associated with cancer, was strongly correlated to capsules formed after cancer removal. This suggests cancerous transformations of cell types that remain after the tumor is removed. Furthermore, transgelin and caspase 7 correlated to myofibroblasts density, suggesting an abnormal fibroblasts that are resistant to cell death and may have enhanced contractile abilities. Capsule formation is a complex process however, with well controlled clinical models quantitative differences can be found. These results serve as stepping stone for the field to move beyond retrospective clinical trials and pursue treatments and preventative measures. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:15540 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/84398 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | capsular contracture | en |
| dc.subject | foreign body response | en |
| dc.subject | breast reconstruction | en |
| dc.subject | RNA-seq | en |
| dc.subject | Artificial Neural Network | en |
| dc.title | Precision Medicine Approach to Improving Reconstructive Surgery Outcomes for Breast Cancer Survivors | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Biomedical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
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