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- All Subjects: Cancer
Description
Native American communities face an ongoing challenge of effectively addressing cancer health disparities, as well as environmental racism issues that may compound these inequities. This dissertation identified the shared cultural knowledge and beliefs about cancer in a southwest American Indian community utilizing a cultural consensus method, an approach that combines qualitative and quantitative data. A community-based participatory research (CBPR) approach was applied at all stages of the study. The three phases of research that were undertaken included: 1) ethnographic interviews - to identifying the themes or the content of the participants' cultural model, 2A) ranking of themes - to provide an understanding of the relative importance of the content of the cultural model, 2B) pile sorts - identify the organization of items within specific domains, and 3) a community survey - access whether the model is shared in the greater community. The cultural consensus method has not been utilized to date in identifying the collective cultural beliefs about cancer prevention, treatment or survivorship in a Native American community. Its use represents a methodological step forward in two areas: 1) the traditional ethnographic inferences used in identifying and defining cultural meaning as it relates to health can be tested more rigorously than in the past, and 2) it addresses the challenge of providing reliable results based on a small number of community informants. This is especially significant when working with smaller tribal/cultural groups where the small sample size has led to questions concerning the reliability and validity of health-related research. Results showed that the key consultants shared strong agreement or consensus on a cultural model regarding the importance of environmental and lifestyle causes of cancer. However, there was no consensus found among the key consultants on the prevention and treatment of cancer. The results of the community survey indicated agreement or consensus in the sub-domains of descriptions of cancer, risk/cause, prevention, treatment, remission/cure and living with cancer. Identifying cultural beliefs and models regarding cancer could contribute to the effective development of culturally responsive cancer prevention education and treatment programs.
ContributorsClaus, Cynthia (Author) / Koss, Joan (Thesis advisor) / Brandt, Elizabeth, (Thesis advisor) / Joe, Jennie (Committee member) / Maupin, Jonathan (Committee member) / Arizona State University (Publisher)
Created2011
Description
Through a standpoint feminist perspective (Harding 2009) I conducted a situational analysis (Clarke, 2015) that examined academic literature and cancer support discussion boards (DBs) to identify how Western biomedicine, specifically oncology, can integrate complementary and alternative medicine (CAM) to improve cancer treatment in children. The aims of this project were: 1) to identify the CAM treatments that are being used to alleviate the side effects from oncological treatments and/or treat pediatric cancers; 2) to compare the subjective experience of CAM to Western biomedicine of cancer patients who leave comments on Group Loop, Cancer Compass and Cancer Forums, which are online support groups (N=20). I used grounded theory and situational mapping to analyze discussion threads. The participants identified using the following CAM treatments: herbs, imagery, prayer, stinging nettle, meditation, mind-body therapies and supplements. The participants turned to CAM treatments when their cancer was late-stage or terminal, often as an integrative and not exclusively to treat their cancer. CAM was more "effective" than biomedical oncology treatment at improving their overall quality of life and functionality. We found that youth on discussion boards did not discuss CAM treatments like the adult participants, but all participants visited these sites for support and verification of their cancer treatments. My main integration recommendation is to combine mind-body CAM therapies with biomedical treatment. This project fills the gap in literature that ignores the ideas of vulnerable populations by providing the experiences of adult and pediatric cancer patients, and that of their families. It is applicable to areas of the social studies of medicine, patient care, and families suffering from cancer. KEYWORDS: Cancer; Complementary and Alternative Medicine; Situational Analysis; Standpoint Feminism
ContributorsEsposito, Sydney Maria (Author) / Martinez, AirÃÂn (Thesis director) / Hruschka, Daniel (Committee member) / School of Human Evolution and Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
The processes of a human somatic cell are very complex with various genetic mechanisms governing its fate. Such cells undergo various genetic mutations, which translate to the genetic aberrations that we see in cancer. There are more than 100 types of cancer, each having many more subtypes with aberrations being unique to each. In the past two decades, the widespread application of high-throughput genomic technologies, such as micro-arrays and next-generation sequencing, has led to the revelation of many such aberrations. Known types and subtypes can be readily identified using gene-expression profiling and more importantly, high-throughput genomic datasets have helped identify novel sub-types with distinct signatures. Recent studies showing usage of gene-expression profiling in clinical decision making in breast cancer patients underscore the utility of high-throughput datasets. Beyond prognosis, understanding the underlying cellular processes is essential for effective cancer treatment. Various high-throughput techniques are now available to look at a particular aspect of a genetic mechanism in cancer tissue. To look at these mechanisms individually is akin to looking at a broken watch; taking apart each of its parts, looking at them individually and finally making a list of all the faulty ones. Integrative approaches are needed to transform one-dimensional cancer signatures into multi-dimensional interaction and regulatory networks, consequently bettering our understanding of cellular processes in cancer. Here, I attempt to (i) address ways to effectively identify high quality variants when multiple assays on the same sample samples are available through two novel tools, snpSniffer and NGSPE; (ii) glean new biological insight into multiple myeloma through two novel integrative analysis approaches making use of disparate high-throughput datasets. While these methods focus on multiple myeloma datasets, the informatics approaches are applicable to all cancer datasets and will thus help advance cancer genomics.
ContributorsYellapantula, Venkata (Author) / Dinu, Valentin (Thesis advisor) / Scotch, Matthew (Committee member) / Wallstrom, Garrick (Committee member) / Keats, Jonathan (Committee member) / Arizona State University (Publisher)
Created2014
Description
Genomic structural variation (SV) is defined as gross alterations in the genome broadly classified as insertions/duplications, deletions inversions and translocations. DNA sequencing ushered structural variant discovery beyond laboratory detection techniques to high resolution informatics approaches. Bioinformatics tools for computational discovery of SVs however are still missing variants in the complex cancer genome. This study aimed to define genomic context leading to tool failure and design novel algorithm addressing this context. Methods: The study tested the widely held but unproven hypothesis that tools fail to detect variants which lie in repeat regions. Publicly available 1000-Genomes dataset with experimentally validated variants was tested with SVDetect-tool for presence of true positives (TP) SVs versus false negative (FN) SVs, expecting that FNs would be overrepresented in repeat regions. Further, the novel algorithm designed to informatically capture the biological etiology of translocations (non-allelic homologous recombination and 3&ndashD; placement of chromosomes in cells –context) was tested using simulated dataset. Translocations were created in known translocation hotspots and the novel&ndashalgorithm; tool compared with SVDetect and BreakDancer. Results: 53% of false negative (FN) deletions were within repeat structure compared to 81% true positive (TP) deletions. Similarly, 33% FN insertions versus 42% TP, 26% FN duplication versus 57% TP and 54% FN novel sequences versus 62% TP were within repeats. Repeat structure was not driving the tool's inability to detect variants and could not be used as context. The novel algorithm with a redefined context, when tested against SVDetect and BreakDancer was able to detect 10/10 simulated translocations with 30X coverage dataset and 100% allele frequency, while SVDetect captured 4/10 and BreakDancer detected 6/10. For 15X coverage dataset with 100% allele frequency, novel algorithm was able to detect all ten translocations albeit with fewer reads supporting the same. BreakDancer detected 4/10 and SVDetect detected 2/10 Conclusion: This study showed that presence of repetitive elements in general within a structural variant did not influence the tool's ability to capture it. This context-based algorithm proved better than current tools even with half the genome coverage than accepted protocol and provides an important first step for novel translocation discovery in cancer genome.
ContributorsShetty, Sheetal (Author) / Dinu, Valentin (Thesis advisor) / Bussey, Kimberly (Committee member) / Scotch, Matthew (Committee member) / Wallstrom, Garrick (Committee member) / Arizona State University (Publisher)
Created2014
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