Matching Items (5)
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- All Subjects: Cancer
- Creators: Anderson, Karen
- Creators: Maley, Carlo
- Member of: ASU Electronic Theses and Dissertations
Description
Adaptive therapy utilizes competitive interactions between resistant and sensitive cells by keeping some sensitive cells to control tumor burden with the aim of increasing overall survival and time to progression. The use of adaptive therapy to treat breast cancer, ovarian cancer, and pancreatic cancer in preclinical models has shown significant results in controlling tumor growth. The adaptive therapy model comes from the integrated pest management agricultural strategy, predator prey model, and the unique intra- and inter-tumor heterogeneity of tumors. The purpose of this thesis is to analyze and compare gemcitabine dose response on hormone refractory breast cancer cells retrieved from mice using an adaptive therapy strategy with standard therapy treatment. In this study, we compared intermittent (drug holiday) adaptive therapy with maximum tolerated dose therapy. The MCF7 resistant cell lines to both fulvestrant and palbociclib were injected into the mammary fat pads of 8 weeks old NOD/SCID gamma (NSG) mice which were then treated with gemcitabine. Tumor burden graphs were made to track tumor growth/decline during different treatments while Drug Dose Response (DDR) curves were made to test the sensitivity of the cell lines to the drug gemcitabine. The tumor burden graphs showed success in controlling the tumor burden with intermittent treatment. The DDR curves showed a positive result in using the adaptive therapy treatment method to treat mice with gemcitabine. Due to some fluctuating DDR results, the sensitivity of the cell lines to gemcitabine needs to be further studied by repeating the DDR experiment on the other mice cell lines for stronger results.
ContributorsConti, Aviona Christina (Author) / Maley, Carlo (Thesis advisor) / Blattman, Joseph (Committee member) / Anderson, Karen (Committee member) / Arizona State University (Publisher)
Created2022
Description
A big part of understanding cancer is understanding the cellular environment itthrives in by analyzing it from a microecological perspective. Humans and other species
are affected by different cancer types, and this highlights the notion that there may be a
correlation between specific tissues and neoplasia prevalence. Research shows that
humans are the most susceptible to adenocarcinomas and carcinomas which include the
following tissues: lungs, breast, prostate, and pancreas. Furthermore, research shows that
adenocarcinoma accounts for 38.5% of all lung cancer cases, 20% of small cell
carcinomas, and 2.9% of large cell carcinoma. The incidence of the most common cancer
types in humans is consistently increasing annually. This study analyzes trends of tissue-specific cancers across species to examine possible contributors to vulnerability to
cancer. I predicted that adenocarcinomas would be the most prevalent cancer type across
the tree of life. To test this hypothesis, I reviewed over 130 species that reported equal to
or greater than 50 individual necropsy pathology records across 4 classes (Mammalia,
amphibia, Reptilia, Aves) and ranked them by neoplasia prevalence. This information was
then organized in tables in descending order. The study’s resulting tables and data
concluded that the hypothesis was correct. I found that across all species
adenocarcinomas were the most common cancer type and account for 30.4% of
malignancies reported among species. Future research should investigate how organ size
contributes to neoplasia prevalence.
ContributorsPERAZA, ASHLEY (Author) / Maley, Carlo (Thesis advisor) / Boddy, Amy (Thesis advisor) / Baciu, Cristina (Committee member) / Arizona State University (Publisher)
Created2022
Description
Adoptive transfer of T cells engineered to express synthetic antigen-specific T cell receptors (TCRs) has provocative therapeutic applications for treating cancer. However, expressing these synthetic TCRs in a CD4+ T cell line is a challenge. The CD4+ Jurkat T cell line expresses endogenous TCRs that compete for space, accessory proteins, and proliferative signaling, and there is the potential for mixed dimer formation between the α and β chains of the endogenous receptor and that of the synthetic cancer-specific TCRs. To prevent hybridization between the receptors and to ensure the binding affinity measured with flow cytometry analysis is between the tetramer and the TCR construct, a CRISPR-Cas9 gene editing pipeline was developed. The guide RNAs (gRNAs) within the complex were designed to target the constant region of the α and β chains, as they are conserved between TCR clonotypes. To minimize further interference and confer cytotoxic capabilities, gRNAs were designed to target the CD4 coreceptor, and the CD8 coreceptor was delivered in a mammalian expression vector. Further, Golden Gate cloning methods were validated in integrating the gRNAs into a CRISPR-compatible mammalian expression vector. These constructs were transfected via electroporation into CD4+ Jurkat T cells to create a CD8+ knockout TCR Jurkat cell line for broadly applicable uses in T cell immunotherapies.
ContributorsHirneise, Gabrielle Rachel (Author) / Anderson, Karen (Thesis advisor) / Mason, Hugh (Committee member) / Lake, Douglas (Committee member) / Arizona State University (Publisher)
Created2020
Description
Understanding intratumor heterogeneity and their driver genes is critical to
designing personalized treatments and improving clinical outcomes of cancers. Such
investigations require accurate delineation of the subclonal composition of a tumor, which
to date can only be reliably inferred from deep-sequencing data (>300x depth). The
resulting algorithm from the work presented here, incorporates an adaptive error model
into statistical decomposition of mixed populations, which corrects the mean-variance
dependency of sequencing data at the subclonal level and enables accurate subclonal
discovery in tumors sequenced at standard depths (30-50x). Tested on extensive computer
simulations and real-world data, this new method, named model-based adaptive grouping
of subclones (MAGOS), consistently outperforms existing methods on minimum
sequencing depth, decomposition accuracy and computation efficiency. MAGOS supports
subclone analysis using single nucleotide variants and copy number variants from one or
more samples of an individual tumor. GUST algorithm, on the other hand is a novel method
in detecting the cancer type specific driver genes. Combination of MAGOS and GUST
results can provide insights into cancer progression. Applications of MAGOS and GUST
to whole-exome sequencing data of 33 different cancer types’ samples discovered a
significant association between subclonal diversity and their drivers and patient overall
survival.
designing personalized treatments and improving clinical outcomes of cancers. Such
investigations require accurate delineation of the subclonal composition of a tumor, which
to date can only be reliably inferred from deep-sequencing data (>300x depth). The
resulting algorithm from the work presented here, incorporates an adaptive error model
into statistical decomposition of mixed populations, which corrects the mean-variance
dependency of sequencing data at the subclonal level and enables accurate subclonal
discovery in tumors sequenced at standard depths (30-50x). Tested on extensive computer
simulations and real-world data, this new method, named model-based adaptive grouping
of subclones (MAGOS), consistently outperforms existing methods on minimum
sequencing depth, decomposition accuracy and computation efficiency. MAGOS supports
subclone analysis using single nucleotide variants and copy number variants from one or
more samples of an individual tumor. GUST algorithm, on the other hand is a novel method
in detecting the cancer type specific driver genes. Combination of MAGOS and GUST
results can provide insights into cancer progression. Applications of MAGOS and GUST
to whole-exome sequencing data of 33 different cancer types’ samples discovered a
significant association between subclonal diversity and their drivers and patient overall
survival.
ContributorsAhmadinejad, Navid (Author) / Liu, Li (Thesis advisor) / Maley, Carlo (Committee member) / Dinu, Valentin (Committee member) / Arizona State University (Publisher)
Created2019
Description
Human Papillomavirus (HPV) is the most commonly transmitted STI and isresponsible for an estimated 5% of cancer cases worldwide. HPV infection is implicated
in 70% of cervical cancer incidence and is also responsible for a variety of oropharyngeal
and anogenital cancers. While vaccination has greatly reduced the cervical cancer
burden in developed countries, HPV infection remains high in developing countries due
to high cost and poor access to healthcare. Several studies have highlighted the
presence of anti-HPV antibodies following infection and their potential use as
biomarkers for developing novel screening methods. Progression from initial infection to
cancer is slow, thus presenting an opportunity for effective screening programs.
Biomarker screening is an important area of cancer detection and Lateral Flow Assays
(LFA) are a low cost, easy to use alternative to other screening methods that require
extensive training and laboratory space. Therefore, this project proposes as a hypothesis
that the development of an LFA screening for HPV specific IgG can provide clinically
relevant data for the early detection of cervical dysplasia. This project adapts an LFA in a
multiplexed format for fluorescence-based serologic detection of HPV specific IgG in
patient plasma.
ContributorsJohns, William (Author) / Anderson, Karen (Thesis advisor) / Lake, Douglas (Committee member) / Hogue, Brenda (Committee member) / Arizona State University (Publisher)
Created2023