Relevant bibliographies by topics / Cancer Clusters

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Cancer Clusters'

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Published: 6 September 2023

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Journal articles on the topic "Cancer Clusters"

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Garfinkel, L. "Cancer Clusters." CA: A Cancer Journal for Clinicians 37, no. 1 (January 1, 1987): 20–25. http://dx.doi.org/10.3322/canjclin.37.1.20.

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Novak, Kristine. "Cancer clusters." Nature Reviews Cancer 3, no. 12 (December 2003): 888. http://dx.doi.org/10.1038/nrc1246.

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Man, Kingson, Alice Lim, Zahra Eftekhari, and Chi Wah Wong. "Clustering whole-exome sequences of breast cancer and associations with staging and molecular subtype." Journal of Clinical Oncology 41, no. 16_suppl (June 1, 2023): e12563-e12563. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e12563.

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e12563 Background: Large-scale genetic sequencing of breast cancer has enabled modern approaches to precision medicine, with the discovery of a handful of variants now known to be associated with breast cancer. However, it is critical to identify additional gene variants in breast cancer that are associated with clinically relevant features of cancers, such as staging and molecular subtype. Methods: We took an unsupervised machine learning approach that clustered the somatic whole exome sequences (WES) of 1533 breast cancers. We performed k-modes clustering on the binarized mutational state of the top 250 most frequently mutated genes. Following two rounds of clustering, 11 distinct “barcodes” for each genetic cluster’s mutation profile became apparent. We systematically tested each genetically defined cluster for associations with molecular subtypes of breast cancer. We performed non-parametric significance testing by randomly permuting cluster assignments to generate an empirical null distribution of the effect of clustering on proportions of the clinical factor of interest. Results: As an example of our set of results, two clusters showed roughly three-fold enrichment of triple-negative breast cancer (TNBC) patients, compared to the whole-group proportion. We calculated SHAP values to provide model explainability and identify the genes that placed a cancer into a particular cluster; TP53 and TTN were the strongest drivers in relation to TNBC. Genetic clusters were also found to associate with T-, N-, and M-stages. Conclusions: Our approach, which uses unsupervised machine learning on WES to create genetic groups of cancers, considers the joint mutational state – present or absent – of multiple genes for their clinical relevance. This reveals many additional variants that may have been previously overlooked or of uncertain significance.
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King, Jennifer, Kimberly Lind, Kristin Morrill, and Cynthia Thomson. "ASSOCIATION OF PSYCHOSOCIAL FACTORS WITH MORTALITY IN OLDER FEMALE SURVIVORS OF CANCER." Innovation in Aging 6, Supplement_1 (November 1, 2022): 601. http://dx.doi.org/10.1093/geroni/igac059.2245.

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Abstract Understanding factors associated with survival after a cancer diagnosis among older adults is critical as the population ages and cancer survivorship increases. The purpose of this study was to: (1) identify clusters of postmenopausal cancer survivor characteristics by demographic and lifestyle factors; 2) describe the characteristics of each cluster; and 3) evaluate the association of cluster assignment with survival. Participants from the Women’s Health Initiative (WHI) who reported either a prevalent cancer diagnosis at baseline (n=14294) or were diagnosed with a first primary incident cancer within the first 10 years of WHI (n=12934) were included. Latent class analysis was used to identify survivor clusters using psychosocial variables. Clusters were characterized using descriptive statistics. We tested for differences in cluster characteristics using ANOVA and Chi-square tests as appropriate. Cox proportional hazards regression was used to evaluate the association between cluster and mortality. Prevalent (n=7) and incident (n=9) cancer survivors clusters were identified. Among both (prevalent and incident) sets of clusters, age at WHI baseline, age at menopause, race, ethnicity, income, education, body mass index, diet quality, smoking, alcohol consumption and exercise all differed by cluster (p<.0001 for all). The most racially and ethnically diverse cluster had higher mortality rates compared to the largest most homogenous cluster; hazard ratio (95%CI) 1.30 (1.15, 1.48) and 1.33 (1.16, 1.53), respectively. Understanding how clusters of risk factors influence cancer survival in postmenopausal women will inform future interventions to improve outcomes and reduce health disparities for cancer survivors.
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Thun, M. J., and T. Sinks. "Understanding Cancer Clusters." CA: A Cancer Journal for Clinicians 54, no. 5 (September 1, 2004): 273–80. http://dx.doi.org/10.3322/canjclin.54.5.273.

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ENTERLINE, PHILIP E. "Evaluating Cancer Clusters." American Industrial Hygiene Association Journal 46, no. 3 (March 1985): B—10—B—13. http://dx.doi.org/10.1080/15298668591394608.

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Aktas, Aynur. "Cancer symptom clusters." Current Opinion in Supportive and Palliative Care 7, no. 1 (March 2013): 38–44. http://dx.doi.org/10.1097/spc.0b013e32835def5b.

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He, Yongshan, Yuanyuan Chen, Xuan Dai, and Shiyong Huang. "Dysregulation of Circadian Clock Genes Associated with Tumor Immunity and Prognosis in Patients with Colon Cancer." Computational and Mathematical Methods in Medicine 2022 (July 16, 2022): 1–19. http://dx.doi.org/10.1155/2022/4957996.

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Early research shows that disrupting the circadian rhythm increases the risk of various cancers. However, the roles of circadian clock genes in colorectal cancer, which is becoming more common and lethal in China, remained to be unclear. In conclusion, the present study has demonstrated that multiple CCGs were dysregulated and frequently mutated in CRC samples by analyzing the TCGA database. The higher expression levels of REV1, ADCYAP1, CSNK1D, NR1D1, CSNK1E, and CRY2 had a strong link with shorter DFS time in CRC patients, demonstrating that CCGs had an important regulatory role in CRC development. Moreover, 513 CRC tumor samples were divided into 3 categories, namely, cluster1 ( n = 428 ), cluster2 ( n = 83 ), and cluster 3 ( n = 109 ), based on the expression levels of the CCGs. Clinical significance analysis showed that the overall survival and disease-free survival of cluster 2 and cluster 3 were significantly shorter than those of cluster 1. The stemness scores in cluster 1 and cluster 2 were significantly higher than those of cluster 3 CRC samples. Clinically, we found that the C3 subtype had significantly higher percentage of T3/T4, N1/N2, and grades III and IV than groups C1 or C2. In addition, we reported that different CRC clusters had significantly different tumor-infiltrating immune cell signatures. Finally, pancancer analysis showed that higher expression of CSNK1D was correlated with shorter DFS time in multiple cancer types, such as COAD and LIHC, and was dysregulated in various cancers. In conclusion, we effectively developed a CCG-related predictive model and opened up new avenues for research into immune regulatory mechanisms and the development of immunotherapy for CRC.
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Rizzi, Maria, and Matteo D'Aloia. "COMPUTER AIDED SYSTEM FOR BREAST CANCER DIAGNOSIS." Biomedical Engineering: Applications, Basis and Communications 26, no. 03 (March 17, 2014): 1450033. http://dx.doi.org/10.4015/s1016237214500331.

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Computer aided detection and Diagnosis systems are becoming very useful and helpful in supporting physicians for early detection and control of some diseases such as neoplastic pathologies. In this paper, a computer aided system for breast cancer diagnosis in mammographic images is presented. In particular, the method looks for microcalcification cluster occurrence and makes the diagnosis of the detected abnormality. The procedure first detects microcalcifications having a cluster pattern and then classifies the abnormalities as benign or malignant clusters. The method formulates the differentiation between malignant and benign microcalcification clusters as a supervised learning problem implementing an artificial neural network classifier. As input to the classifier, the procedure uses image features automatically extracted from the detected clusters. The seven features used are related both to the distribution of microcalcifications within cluster and to the uniformity of their shape. The performance of the implemented system is evaluated taking into account the accuracy of classifying clusters. The obtained results make this method able to operate as a "second opinion" helping radiologists during the routine clinical practice. Moreover, the implemented method has a general validity and can be used to detect and to classify microcalcification clusters independently from the acquisition equipment adopted during the mammographic screening.
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Al Qadire, Mohammad, Omar Shamieh, Sameer Abdullah, and Faisal Albadainah. "Symptom Clusters’ Content, Stability and Correlation with the Quality of Life in a Heterogeneous Group of Cancer Patients: A Large-Scale Longitudinal Study." Clinical Nursing Research 29, no. 8 (June 11, 2020): 561–70. http://dx.doi.org/10.1177/1054773820933449.

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Cancer-related symptoms can negatively affect the quality of life, hinder or delay treatment, and increase suffering. This study aimed to explore symptom clusters among Jordanian cancer patients. A longitudinal survey design was used. The sample consisted of 1280 cancer patients treated in three selected hospitals. Two-thirds of the participants were female (63.5%) with a mean age of 52.7 SD 13.8 years and 40.3% had breast cancer. Five clusters were identified, the first was the psychological cluster of eight symptoms; the second was the treatment side-effects cluster consisting of ten symptoms; the third was the nausea and vomiting cluster comprising four symptoms; the fourth was the pain cluster comprising four symptoms; and last was the fatigue cluster, with three symptoms. Cancer patients through the journey of cancer treatment have several symptoms that tend to occur in five clusters which are negatively correlated with their quality of life.
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Dissertations / Theses on the topic "Cancer Clusters"

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Rattican, Debra. "Symptom Clusters in Lung Cancer Patients." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/352.

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SYMPTOM CLUSTERS IN LUNG CANCER PATIENTS By Debra Rattican, PhD, RN A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth University, 2012 Major Director: Debra E. Lyon, PhD. Professor and Chair Family and Community Health Nursing The purpose of the study was to examine selected relationships among symptoms common to individuals with lung cancer. The specific aims were: 1) To examine the relationship between the symptoms of dyspnea and anxiety in patients with lung cancer. 2) To examine the relationships among the symptoms of dyspnea, anxiety, and symptom cluster components (depressive symptoms, fatigue, pain) in patients with lung cancer. 3) To examine the correlation between functional ability and quality of life in patients with lung cancer. 4) To explore the relationships among the symptoms of dyspnea, anxiety, and symptom cluster components (depressive symptoms, fatigue, pain) in patients with lung cancer and patients’ functional ability. 5) To explore the relationships among the symptoms of dyspnea, anxiety, and symptom cluster components (depressive symptoms, fatigue, pain) in patients with lung cancer and patients’ quality of life. Data were gathered through online survey and analyzed using descriptive, correlation, principal component analysis, exploratory factor analysis, and forward stepwise regression techniques. A strong positive correlation was found between dyspnea and anxiety (both anxiety in general and anxiety at the time the survey was completed. While results of this study cannot provide conclusive evidence of the existence of a symptom cluster composed of depressive symptoms, fatigue, and pain, the results are consistent with other studies in this area. Significant positive correlations among these three symptoms indicate that this is a possible symptom cluster experienced by lung cancer patients in general. This study provides preliminary data on how these symptoms are related and how they affect functional ability, or the ability to perform routine activities of daily living (ADLS) and instrumental activities of daily living (IADLS), and quality of life in patients with lung cancer. Further study is needed on to better understand the symptom experience of these individuals in order to develop robust interventions targeting effective symptom management.
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Chau, Hau-yan, and 周厚仁. "Symptom clusters among Chinese women with breast cancer." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B48422563.

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Background: breast cancer patients were found to experience multiple concurrent symptoms. These concurrent symptoms (termed symptom cluster) could have synergistic effects on patient functional status and quality of life. Understanding symptom cluster provided us new insight in symptom assessment and symptom management. Previous studies on symptom cluster among breast cancer patients focused on Caucasian. The current study attempt to identify symptom cluster and the factors associated with it among Chinese breast cancer patients. Methods: this study was a secondary analysis on a sample of 348 breast cancer patients. Data on symptom distress (assessed by Memorial symptom Assessment scale Short Form) was retrieved. Symptom clusters were identified through factor analysis using principal components method and Varimax rotation. Demographics and medical characteristics were proposed to be associated with symptom cluster. Uni-variate analysis and linear regression performed on these variables. Results: four symptom clusters (namely gastrointestinal cluster, general malaise cluster, self image cluster, and cutaneous cluster) were identified. Recurrence of breast cancer and chemotherapy were associated with either gastrointestinal cluster or general malaise cluster. Age, cancer status and occupation were associated with self image cluster while no variable was associated with cutaneous cluster. Conclusion: The current study provided empirical evidence that Chinese breast cancer patients experienced similar symptom clusters as Caucasian. Future study could be done to verify these four symptom clusters and identify underlying mechanism. Recommendations: health care providers could pay more attention to those suffer from breast cancer recurrence or currently receiving chemotherapy. These patients tend to experience gastrointestinal cluster and general malaise cluster. Clinical setting and evaluation tools could be adjusted to fit these high risk groups.
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Maguire, Roma. "Where is the person in symptom cluster research? : the experience of symptom clusters in patients with advanced lung cancer." Thesis, University of Stirling, 2011. http://hdl.handle.net/1893/3423.

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Where is the Person in Symptom Cluster Research? The Experience of Symptom Clusters in Patients with Advanced Lung Cancer This thesis describes a three-year qualitative study which aimed to explore the experience of symptom clusters in patients with advanced lung cancer. The study employed a patient-focused approach utilising Interpretative Phenomenological Analysis (IPA) (Smith et al. 2009a). This methodology (IPA), informed by a contextual constructionist stance, was selected to explore the experience of symptom clusters, for its focus on the lived experience, the context and meanings which surround such experiences and its idiographic approach. Ten patients (a sample size which is the upper limit of the number of participants advocated for studies employing IPA (Smith et al. 2009b;Reid et al. 2005;Smith and Osborn 2004)) with advanced lung cancer took part in the study and data were collected using unstructured, in-depth interviews at two time points: on recruitment and three to five weeks later. Data were analysed using Interpretative Phenomenological Analysis, within the framework advocated by Smith and Osborn (2003). The study generated interesting and significant findings. The experience of symptom clusters in patients with advanced lung cancer was characterised by two super-ordinate themes: ‘The lived experience of symptom clusters and the role of context and meaning’ and ‘Symptom clusters and loss of sense of self’. The super-ordinate theme of ‘The lived experience of symptom clusters and the role of context and meaning’ in the first instance, illustrates that the participants in this study were experiencing symptom clusters and providing detail on the components, nature and patterning of the symptom clusters reported, particularly the way that one or two salient symptoms were commonly highlighted from all the other symptoms experienced. This super-ordinate theme also demonstrates the core role that context and meaning play in the lived experience of symptom clusters, with many of the participants in this study framing their experiences of symptom clusters within a fear of death, stigma and loss of sense of self. The second super-ordinate theme informing this thesis is ‘Symptom clusters and loss of sense of self’. This super-ordinate theme illustrates the impact of symptom clusters on the participants’ lives, and how this, in turn, impacted on their sense of self in a number of different ways. For some, their sense of self was compromised by the concurrent symptoms that they were experiencing, as they prevented them from undertaking roles and activities that they were accustomed to in the past. This super-ordinate theme also highlights the role of the body relative to the self, and describes how the participants’ sense of self was transiently lost during periods when they experienced symptom clusters of high severity. The findings presented also demonstrate the knock-on effect of loss of sense of self experienced, with the participants feeling like they were a burden due to their incapacitation, and at times hiding the multiple symptoms that they were experiencing, in a bid to protect their loved ones from their illness. In light of the loss of sense of self experienced, this super-ordinate theme also demonstrates how the participants employed various strategies in a bid to try and maintain a coherent and valued sense of self. The findings presented illustrate how the use of IPA facilitated the collection of data that provided an in-depth understanding of the complexity of the experience of symptom clusters in patients with advanced lung cancer, adding a unique contribution to this body of knowledge. The results of this study highlight the limitations of definitions that currently underpin the study of symptom clusters in patients with cancer and the current empirical base to date, particularly the way that they do not acknowledge the core role that context and meaning play in the lived experience of this phenomenon. This lack of recognition of these core elements of the patient experience of symptom clusters poses the risk of this body of research producing data that have limited relevance to the patient and therefore clinical practice. It is therefore proposed that the study of symptom clusters in patients with cancer needs to move away from the reductionist approach which currently dominates and to broaden its scope, to one that acknowledges the complexity of the experience of symptom clusters, the core role that context and meaning play in such experiences, and contributions that patient experience can make in advancing this important and emerging body of research.
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Kim, Jung-Eun Esther. "Multiple symptoms and symptom clusters in patients with cancer." Diss., Search in ProQuest Dissertations & Theses. UC Only, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3324589.

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Stults, Dawn Michelle. "Human ribosomal RNA gene clusters are recombinational hotspots in cancer." Lexington, Ky. : [University of Kentucky Libraries], 2009. http://hdl.handle.net/10225/1122.

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Thesis (M.S.)--University of Kentucky, 2009.
Title from document title page (viewed on May 6, 2009). Document formatted into pages; contains: v, 27 p. : ill. Includes abstract and vita. Includes bibliographical references (p. 25-26).
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Morrison, Eleshia JP. "Examining the Presence of Symptom Clusters in Patients with Advanced Cancer." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1259769198.

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Dong, Skye Tian. "Symptom Clusters in Patients with Advanced Cancer: A mixed methods investigation." Thesis, The University of Sydney, 2016. http://hdl.handle.net/2123/14987.

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Patients with advanced cancer typically experience symptom clusters or multiple concurrent symptoms, which have a detrimental impact on patient outcomes. Research in symptom management has predominantly focused on investigating single symptoms in isolation. Although the literature on symptom clusters is growing, the composition of symptom clusters differs depending on the various clinical variables and the method of their assessment and statistical analysis. Effective management of multiple concurrent symptoms in advanced cancer patients remains a clinical challenge across healthcare settings. Minimal attention has been given to gaining a broad and in-depth understanding about the experiences, perspectives, and attitudes of patients with multiple symptoms and clinicians treating symptom clusters. This thesis describes a systematic review of symptom clusters in advanced cancer (Study 1) and three empirical studies that aimed to explore: the composition of symptom clusters in advanced cancer and their influence on patient outcomes (Study 2); patients’ experiences of living with multiple symptoms (Study 3); and multidisciplinary clinicians’ attitudes and strategies towards managing symptom clusters in advanced cancer (Study 4). The overarching purpose of this thesis was to qualitatively investigate the experience of multiple concurrent symptoms from patients’ and clinicians’ perspectives, whilst simutaneously examine how this converges with and diverges from a quantitative investigation of symptom clusters in advanced cancer patients. This mixed methods study design allows for a complete understanding of the impact of symptom clusters on both qualitative and quantitative accounts of quality of life and functioning. Chapter 3 – Systematic review Method: In Study 1, to understand what is known about the composition, stability, consistency, and impact of symptom clusters in advanced cancer patients, the relevant literature was systematically reviewed. Medline, CINAHL, Embase, Web of Science, and PsychINFO were searched to identify studies investigating variants of symptom clusters, cancer, and palliative care. After screening 977 potentially eligible articles, 33 articles were systematically reviewed and assessed for quality. Results: Four common groupings of symptom clusters were found: anxiety-depression, nausea-vomiting, nausea-appetite loss, and fatigue-dyspnea-drowsiness-pain. Symptom clusters in most cases were not stable longitudinally and the various statistical methods used tended to reveal different symptom clusters. The predictors and outcomes of symptom clusters and measurement tools used were also inconsistent across studies. Future studies need to explore the influence of statistical methods on symptom cluster composition, which symptom clusters predict patient outcomes, and patients’ subjective experience of symptom clusters. Chapter 5 – Quantitative study Method: Study 2 examined the extent to which different statistical methodologies differ in the symptom cluster composition solutions they produce across five primary cancer sites, and determine which symptom clusters predict patient outcomes. One hundred and fifty variations of principal component analysis, exploratory factor analysis, and hierarchical cluster analysis were used on an existing data set (N = 1562) of advanced cancer patients who completed the European Organisation for the Research and Treatment of Cancer Quality of Life Questionnaire–Core 30. Results: Four clusters consistently formed for many of the methods and cancer sites: tense-worry-irritable-depressed (emotional cluster), fatigue-pain, nausea-vomiting, and concentration memory (cognitive cluster). The emotional cluster was a stronger predictor of overall quality of life than the other clusters. Fatigue-pain was a stronger predictor of overall health than the other clusters. The cognitive cluster and fatigue-pain predicted physical functioning, role functioning, and social functioning. Chapter 6 – Qualitative study on patients with advanced cancer Method: Study 3 examined advanced cancer patients’ experiences of multiple concurrent symptoms using thematic analysis and an adapted grounded theory approach to data collection and analysis. Semi-structured interviews (N = 58) were conducted with 23 inpatients and 35 outpatients recruited purposively from two palliative care centres and two hospital-based oncology departments in Sydney, Australia. Participants were sampled to include a range of cancers and phases in advanced cancer. Results: Six major themes were identified: imminence of death and deterioration; overwhelming loss of control; impinging on autonomy and identity; psychological adaptation; burden of self-management responsibility; and valuing security and empowerment. Multiple symptoms have a profound impact on patients’ autonomy, function, and psychological state and patients transitioning from oncology to palliative care settings were more vulnerable to self-management burden. Chapter 7 – Qualitative study on clinicians managing symptom clusters in advanced cancer Method: Study 4 examined clinicians’ experiences, strategies, and attitudes towards the management of multiple concurrent symptoms in advanced cancer patients using thematic analysis and adapted grounded theory. In-depth semi-structured interviews (N = 48) were conducted with 10 palliative care physicians, 6 oncologists, 6 general practitioners, 12 nurses, and 14 allied health providers, purposively recruited from two acute hospitals, two palliative care centres, and four community general practices in Sydney, Australia. Results: Six themes were identified: uncertainty in decision making; attunement to patient and family; deciphering cause to guide intervention; balancing complexities in medical management; fostering hope and empowerment; and depending on multidisciplinary expertise. Managing symptom clusters, is both an art and a science currently fraught with uncertainty in decision making. Increased scientific evidence for treating symptom clusters and effective collaboration across settings is vital. Conclusions Integration of symptom cluster research into clinical practice is in its infancy. A psychosocial pathway in the management of symptom clusters was proposed as possibly improving quality of life, and biological mechanisms underpinning symptom clusters require further investigation. Multiple symptom management and integrated care is needed to empower advanced cancer patients and reduce their struggles with self-management burden, particularly as they transition between oncologic and palliative care. Strengthening multidisciplinary collaboration, continuity of care, more pragmatic planning of clinical trials to address the multiple symptom experience, and training in symptom cluster assessment and management is crucial. Future research needs to target translating the body of symptom cluster literature into clinically relevant guidelines and interventions across settings, to support a move towards advancing healthcare and research for symptom management in advanced cancer patients.
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Skerman, Helen Mary. "Alternative analytical methods for the identification of cancer-related symptom clusters." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/34503/1/Helen_Skerman_Thesis.pdf.

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Advances in symptom management strategies through a better understanding of cancer symptom clusters depend on the identification of symptom clusters that are valid and reliable. The purpose of this exploratory research was to investigate alternative analytical approaches to identify symptom clusters for patients with cancer, using readily accessible statistical methods, and to justify which methods of identification may be appropriate for this context. Three studies were undertaken: (1) a systematic review of the literature, to identify analytical methods commonly used for symptom cluster identification for cancer patients; (2) a secondary data analysis to identify symptom clusters and compare alternative methods, as a guide to best practice approaches in cross-sectional studies; and (3) a secondary data analysis to investigate the stability of symptom clusters over time. The systematic literature review identified, in 10 years prior to March 2007, 13 cross-sectional studies implementing multivariate methods to identify cancer related symptom clusters. The methods commonly used to group symptoms were exploratory factor analysis, hierarchical cluster analysis and principal components analysis. Common factor analysis methods were recommended as the best practice cross-sectional methods for cancer symptom cluster identification. A comparison of alternative common factor analysis methods was conducted, in a secondary analysis of a sample of 219 ambulatory cancer patients with mixed diagnoses, assessed within one month of commencing chemotherapy treatment. Principal axis factoring, unweighted least squares and image factor analysis identified five consistent symptom clusters, based on patient self-reported distress ratings of 42 physical symptoms. Extraction of an additional cluster was necessary when using alpha factor analysis to determine clinically relevant symptom clusters. The recommended approaches for symptom cluster identification using nonmultivariate normal data were: principal axis factoring or unweighted least squares for factor extraction, followed by oblique rotation; and use of the scree plot and Minimum Average Partial procedure to determine the number of factors. In contrast to other studies which typically interpret pattern coefficients alone, in these studies symptom clusters were determined on the basis of structure coefficients. This approach was adopted for the stability of the results as structure coefficients are correlations between factors and symptoms unaffected by the correlations between factors. Symptoms could be associated with multiple clusters as a foundation for investigating potential interventions. The stability of these five symptom clusters was investigated in separate common factor analyses, 6 and 12 months after chemotherapy commenced. Five qualitatively consistent symptom clusters were identified over time (Musculoskeletal-discomforts/lethargy, Oral-discomforts, Gastrointestinaldiscomforts, Vasomotor-symptoms, Gastrointestinal-toxicities), but at 12 months two additional clusters were determined (Lethargy and Gastrointestinal/digestive symptoms). Future studies should include physical, psychological, and cognitive symptoms. Further investigation of the identified symptom clusters is required for validation, to examine causality, and potentially to suggest interventions for symptom management. Future studies should use longitudinal analyses to investigate change in symptom clusters, the influence of patient related factors, and the impact on outcomes (e.g., daily functioning) over time.
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Crane, Tracy E., and Tracy E. Crane. "Symptom Clusters and Trajectories of Depression and Anxiety in Latina Breast Cancer Survivors." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/621858.

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Background: Latinas experience a greater number of symptoms and are at an elevated risk for depression and anxiety following a diagnosis of breast cancer compared to Non-Hispanic Whites and African Americans. Cancer-related symptom clusters are frequently reported in women with breast cancer and research suggests these women follow distinct trajectories for depression and anxiety. However, little is known about the trajectories of anxiety and depression or cancer-related symptom clusters in Latinas with breast cancer. Methods: 296 Latinas previously recruited and diagnosed with breast cancer comprised this sample. Questionnaires for depression (the Center for Epidemiological Studies-Depression) and Anxiety (Speilberger State-Trait Inventory and PROMIS Anxiety) were administered at baseline, 2 and 4 months post enrollment. To identify classes of Latina breast cancer survivors based on patterns of symptom occurrence, symptoms latent class analysis was used to describe symptom clusters. Group-based growth mixture modeling was utilized to identify classes of women who followed distinct trajectories of depression and anxiety. Results: On average women reported 4.2±3 symptoms with an overall symptom distress score of 6.4±2.5 (out of a scale of 10). Three symptom classes were identified: Weary and Sleepy (class 1), Weary (class 2) and Weary, Sleepy and Hurting (class 3). Women were most likely to cluster in class 1, followed by class 2 and 3 with fatigue (labeled weary) being the most prevalent symptom for all three classes. Three trajectories emerged for both depression and anxiety. For depression, the majority of women (79.6%) fell in the high then reducing trajectory for depression followed by the low and remaining low (17%) and the high and increasing (worsening) trajectories of depression (3%). For anxiety the majority (78% of women) followed the moderate to increasing (worsening) trajectory of anxiety followed by 14% in the moderate to declining (improving) and 8% in the low to slightly increasing (worsening) trajectories for anxiety. Conclusion: This study suggests Latina breast cancer survivors experience burdensome cancer-related symptom clusters and distinct trajectories for depression and anxiety. Further research is needed in minority women with breast cancer to adequately understand and treat cancer-related symptom clusters as well as depression and anxiety.
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Stults, Dawn Michelle. "STRUCTURAL INSTABILITY OF HUMAN RIBOSOMAL RNA GENE CLUSTERS." UKnowledge, 2010. http://uknowledge.uky.edu/gradschool_diss/68.

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The human ribosomal RNA genes are critically important for cell metabolism and viability. They code for the catalytic RNAs which, encased in a housing of more than 80 ribosomal proteins, link together amino acids by peptide bonds to generate all cellular proteins. Because the RNAs are not repeatedly translated, as is the case with messenger RNAs, multiple copies are required. The genes which code for the human ribosomal RNAs (rRNAs) are arranged as clusters of tandemly repeated sequences. Three of four catalytic RNAs are spliced from a single transcript. The genes are located on the short arms of the five acrocentric chromosomes (13, 14, 15, 21, and 22). The genes for the fourth rRNA are on chromosome 1q42, also arranged as a cluster of tandem repeats. The repeats are extremely similar in sequence, which makes them ideal for misalignment, non‐allelic homologous recombination (NAHR), and genomic destabilization during meiosis , replication, and damage repair. In this dissertation, I have used pulse‐field gel electrophoresis and in‐blot Southern hybridization to explore the physical structure of the human rRNA genes and determine their stability and heritability in normal, healthy individuals. I have also compared their structure in solid tumors compared to normal, healthy tissue from the same patient to determine whether dysregulated homologous recombination is an important means of genomic destabilization in cancer progression. Finally, I used the NCI‐60 panel of human cancer cell lines to compare the results from the pulsed‐field analysis, now called the gene cluster instability (GCI) assay, to two other indicators of homologous‐recombination-mediated genomic instability: sister chromatid exchange, and 5‐hydroxymethyl‐2’deoxyuridine sensitivity.
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Books on the topic "Cancer Clusters"

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United States. Congress. Senate. Committee on Environment and Public Works. Cancer clusters in Long Island, NY: Field hearing before the Committee on Environment and Public Works, United States Senate, One Hundred Seventh Congress, first session on assessing the potential links between environmental contamination and chronic diseases, June 11, 2001, Garden City, NY. Washington: U.S. G.P.O., 2003.

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Oversight on disease clusters and environmental health: Hearing before the Committee on Environment and Public Works, United States Senate, One Hundred Twelfth Congress, first session, March 29, 2011. Washington: U.S. Government Printing Office, 2014.

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Strengthening Protections for Children and Communities from Disease Clusters Act: Report together with minority views (to accompany S. 76) (including cost estimate of the Congressional Budget Office). Washington, D.C: U.S. G.P.O., 2012.

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Advances in cancer research: Clusterin. London: Academic, 2009.

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Aldrich, Tim E. Evaluation of cancer cluster reports in North Carolina. [Raleigh, N.C.]: N.C. Dept. of Environment, Health, and Natural Resources, Division of Statistics and Information Services, Center for Health and Environmental Statistics, 1991.

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Aldrich, Tim E. Evaluation of cancer cluster reports in North Carolina. [Raleigh, N.C.]: N.C. Dept. of Environment, Health, and Natural Resources, Division of Statistics and Information Services, Center for Health and Environmental Statistics, 1991.

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United States. Agency for Toxic Substances and Disease Registry. Pancreatic cancer cluster investigation of Livingston and Park County, Montana. Atlanta, Ga: [The Agency?, 1991.

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United States. Agency for Toxic Substances and Disease Registry. Division of Health Studies. Investigation of a cluster of pancreatic cancer deaths, Livingston and Park County, Montana. Atlanta, Ga: [The Agency, 1992.

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Fagin, Dan. Toms River: A small town, a cancer cluster, and the epic quest to expose pollution's hidden consequences. New York: Bantam Books, 2013.

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Toms River: A small town, a cancer cluster, and the epic quest to expose pollution's hidden consequences. New York: Bantam Books, 2013.

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Book chapters on the topic "Cancer Clusters"

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Vasimalai, Nagamalai. "NIR Light-Sensitive Plasmonic Gold Nanomaterials for Cancer Photothermal and Chemotherapy Applications." In Metal Nanoparticles and Clusters, 385–415. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68053-8_10.

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James, Joel, Daniel Andrew M. Gideon, Debasish Roy, and Amritlal Mandal. "Iron Sulfur Clusters and ROS in Cancer." In Handbook of Oxidative Stress in Cancer: Mechanistic Aspects, 1–16. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-4501-6_24-1.

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James, Joel, Daniel Andrew M. Gideon, Debasish Roy, and Amritlal Mandal. "Iron Sulfur Clusters and ROS in Cancer." In Handbook of Oxidative Stress in Cancer: Mechanistic Aspects, 291–306. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-15-9411-3_24.

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Rousseau, Patrick, and Bernd A. Huber. "Ion Collisions with Biomolecules and Biomolecular Clusters." In Nanoscale Insights into Ion-Beam Cancer Therapy, 121–57. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43030-0_4.

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Piñeiro, Roberto, Inés Martínez-Pena, and Rafael López-López. "Relevance of CTC Clusters in Breast Cancer Metastasis." In Circulating Tumor Cells in Breast Cancer Metastatic Disease, 93–115. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35805-1_7.

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Verkhovtsev, Alexey, Andrei V. Korol, and Andrey V. Solov’yov. "Irradiation-Induced Processes with Atomic Clusters and Nanoparticles." In Nanoscale Insights into Ion-Beam Cancer Therapy, 237–76. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43030-0_7.

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Boya, Mert, and A. Fatih Sarioglu. "Cluster-Wells: A Technology for Routine and Rapid Isolation of Extremely Rare Circulating Tumor Cell Clusters from Unprocessed Whole Blood." In Microfluidic Systems for Cancer Diagnosis, 255–68. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3271-0_18.

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Arora, Sandeep, Ajmer Singh Grewal, Neelam Sharma, and Sukhbir Singh. "Microfluidic Devices in Capturing Circulating Metastatic Cancer Cell Clusters." In Metastatic Diseases, 261–78. Boca Raton: Apple Academic Press, 2021. http://dx.doi.org/10.1201/9781003043249-17.

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Kordysh, E., A. Bolotin, M. Barchana, and R. Chen. "Cancer Epidemiology of Small Communities: Using a Novel Approach to Detecting Clusters." In Medical Data Analysis, 126–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45497-7_19.

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Ozdenerol, Esra, and Nina Lam. "Detecting Spatial Clusters of Cancer Mortality in East Baton Rouge Parish, Louisiana." In WorldMinds: Geographical Perspectives on 100 Problems, 75–79. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-1-4020-2352-1_13.

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Conference papers on the topic "Cancer Clusters"

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Haberal, Ismail, and Hasan Ogul. "Detection of microRNA clusters associateci with prostate cancer." In 2014 22nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2014. http://dx.doi.org/10.1109/siu.2014.6830293.

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Jacquez, Geoffrey M., and Jaymie R. Meliker. "Detecting space-time cancer clusters using residential histories." In Defense and Security Symposium, edited by Raja Suresh. SPIE, 2007. http://dx.doi.org/10.1117/12.725638.

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Harouaka, Ramdane, Gabrielle Hodges, Allison David, Rishika Polasani, Spencer Freeman, Katherine Sanchez, Kaitlin Harvey, and Max S. Wicha. "Abstract 4776: Circulating clusters in breast cancer express cancer stem cell phenotypes." In Proceedings: AACR Annual Meeting 2017; April 1-5, 2017; Washington, DC. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7445.am2017-4776.

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Gandhi, Priyanka, Xiao Luo, Susan Storey, Zuoyi Zhang, Zhi Han, and Kun Huang. "Identifying Symptom Clusters in Breast Cancer and Colorectal Cancer Patients using EHR Data." In BCB '19: 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3307339.3342164.

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Sharma, U., P. Miller, K. Medina Saenz, M. Picon-Ruiz, C. Morata-Tarifa, A. Spartz, B. Troness, et al. "Abstract PD9-10: Circulating CAF/cancer stem cell co-clusters bolster breast cancer metastasis." In Abstracts: 2018 San Antonio Breast Cancer Symposium; December 4-8, 2018; San Antonio, Texas. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.sabcs18-pd9-10.

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Alkhateeb, Abed, Iman Rezaeian, Siva Singireddy, and Luis Rueda. "Obtaining biomarkers in cancer progression from outliers of time-series clusters." In 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2015. http://dx.doi.org/10.1109/bibm.2015.7359802.

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Yu Peng, Mira Park, Min Xu, Suhuai Luo, Jesse S. Jin, Yue Cui, and W. S. Felix Wong. "Detection of nuclei clusters from cervical cancer microscopic imagery using C4.5." In 2010 2nd International Conference on Computer Engineering and Technology. IEEE, 2010. http://dx.doi.org/10.1109/iccet.2010.5485792.

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Liu, H. "Abstract TS2-1: CTC Clusters, Cancer Stemness, and New Treatment Strategies." In Abstracts: 2019 San Antonio Breast Cancer Symposium; December 10-14, 2019; San Antonio, Texas. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.sabcs19-ts2-1.

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Ma, Yuanjiao, Ziwu Wang, Jeffrey Zheng, Lian Lu, Gang Wang, Peng Li, Tianxin Ma, and Yinfu Xie. "Extracting Micro-calcification Clusters on Mammograms for Early Breast Cancer Detection." In 2006 IEEE International Conference on Information Acquisition. IEEE, 2006. http://dx.doi.org/10.1109/icia.2006.305784.

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Fatichah, Chastine, Dini Adni Navastara, Nanik Suciati, and Lubna Nuraini. "Determining the number of clusters for nuclei segmentation in breast cancer image." In Eighth International Conference on Graphic and Image Processing, edited by Yulin Wang, Tuan D. Pham, Vit Vozenilek, David Zhang, and Yi Xie. SPIE, 2017. http://dx.doi.org/10.1117/12.2266980.

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Reports on the topic "Cancer Clusters"

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Moskowitz, Michal. Symptom Clusters and Work Limitations in Employed Breast Cancer Survivors. Fort Belvoir, VA: Defense Technical Information Center, October 2011. http://dx.doi.org/10.21236/ad1013360.

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Foster, Stephanie, Amy M. Lavery, Suzanne K. Condon, Alisha Etheredge, Kennedy Brian, Svendsen Erik, and Breysse Patrick. Guidelines for examining unusual patterns of cancer and environmental concerns. National Center for Environmental Health (U.S.), December 2022. http://dx.doi.org/10.15620/cdc:122695.

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Abstract:
The Centers for Disease Control and Prevention (CDC) National Center for Environmental Health (NCEH) and the Agency for Toxic Substances and Disease Registry (ATSDR) provide scientific guidance to state, tribal, local, and territorial (STLT) health departments related to environmental health concerns. The guidelines presented here update the 2013 publication, “Investigating Suspected Cancer Clusters and Responding to Community Concerns: Guidelines from the CDC and the Council of State and Territorial Epidemiologists (CSTE)”.1 In general, STLT health departments play the primary role in examining unusual patterns of cancer in communities, including those associated with local environmental concerns. These guidelines expand the approach for these investigations. Occupation-related clusters are not included in these guidelines. Publication date from document properties. CS336302-A Guidelines-for-Examining-Unusual-Patterns-of-Cancer-and-Environmental-Concerns-h.pdf
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Messerle, Louis. PSMA-Targeted Polygadolinium Clusters: A Novel Agent for Imaging Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada483397.

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Kannanganattu, Prasanth K. Analysis of Altered Protein Components of Interchromatin Granule Clusters from Breast Cancer Cells. Fort Belvoir, VA: Defense Technical Information Center, February 2002. http://dx.doi.org/10.21236/ada400947.

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li, Mengyuan, Jing-Yu (Benjamin) Tan, Tao Wang, Xian-Liang Liu, Stephen Wai Hang Kwok, and Li-Qun Yao. Effects of non-pharmacological interventions on symptom clusters in breast cancer survivors: A systematic review of randomized controlled trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2023. http://dx.doi.org/10.37766/inplasy2023.8.0028.

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Hannon, Gregory. A MicroRNA Cluster as a Potential Breast Cancer Oncogene. Fort Belvoir, VA: Defense Technical Information Center, March 2008. http://dx.doi.org/10.21236/ada524956.

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Hannon, Gregory J. A MicroRNA Cluster as a Potential Breast Cancer Oncogene. Fort Belvoir, VA: Defense Technical Information Center, March 2007. http://dx.doi.org/10.21236/ada468511.

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Leskov, Konstantin S., and David A. Boothman. The Role of Clusterin in Estrogen Deprivation-Mediated Cell Death in Breast Cancer Cells. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada407480.

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Criswell, Tracy L., and David A. Boothman. Investigating the Role of Nuclear Clusterin (nCLU) in Lethality and Genomic Instability in Paclitaxel (Taxol) - Treated Human Breast Cancer Cells. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada406785.

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Criswell, Tracy L., and David A. Boothman. Investigating the Role of Nuclear Clusterin (nCLU) in Lethality and Genomic Instability in Paclitaxel (Taxol) - Treated Human Breast Cancer Cells. Fort Belvoir, VA: Defense Technical Information Center, July 2003. http://dx.doi.org/10.21236/ada418107.

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