Biomarkers: Symptoms, Survivorship, and Quality of Life

Department of Physiological Nursing, University of California, San Francisco, CA 94143-0610, USA.
Seminars in Oncology Nursing 05/2012; 28(2):129-38. DOI: 10.1016/j.soncn.2012.03.008
Source: PubMed


To review the evidence on a number of biomarkers that show potential clinical utility in the prediction of and treatment responsiveness for the four most common symptoms associated with cancer and its treatment (ie, pain, fatigue, sleep disturbance, depression).
Review and synthesis of review articles and data-based publications.
A growing body of evidence suggests that sensitive and specific biomarkers will be available to assist clinicians with the assessment and management of symptoms.
Nurses will play a critical role in educating patients about their risk for specific symptoms based on an evaluation of specific biomarkers. Nurses will be involved in using biomarker data to titrate medications based on patient's responses to symptom management interventions.

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    • "It has been well established that a variety of biomarkers are used in risk assessment, early detection, diagnosis, treatment, and management of cancer.13,14 Molecular analyses at the protein, DNA, RNA, or miRNA levels can contribute to the identification of novel tumor subclasses, each with a unique prognostic outcome or response to treatment.15 "
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    ABSTRACT: Cancer biomarkers have provided great opportunities for improving the management of cancer patients by enhancing the efficiency of early detection, diagnosis, and efficacy of treatment. Every cell type has a unique molecular signature, referred to as biomarkers, which are identifiable characteristics such as levels or activities of a myriad of genes, proteins, or other molecular features. Biomarkers can facilitate the molecular definition of cancer, provide information about the course of cancer, and predict response to chemotherapy. They offer the hope of early detection as well as tracking disease progression and recurrence. Current progress in the characterization of molecular genetics of HIV-associated cancers may form the basis for improved patient stratification and future targeted or individualized therapies. Biomarker use for cancer staging and personalization of therapy at the time of diagnosis could improve patient care. This review focuses on the relevance of biomarkers in the most common HIV-associated malignancies, namely, Kaposi sarcoma, non-Hodgkin's lymphoma, and invasive cervical cancer.
    07/2014; 6:11-20. DOI:10.4137/BIC.S15056
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    ABSTRACT: Introduction: Men with prostate cancer undergoing radiation treatment frequently report fatigue, insomnia, depression, anxiety and urinary, bowel, sexual, and hormonal symptoms. Plasma concentrations of cytokines may be related to these symptoms, but few studies have examined these relationships. The study purpose was to explore the association between prostate cancer symptoms and cytokine levels at pretreatment and posttreatment. Method: In this longitudinal, correlational study, 29 men with nonmetastatic prostate cancer completed symptom questionnaires at preradiation and postradiation treatment. Blood drawn at these same time points was used to determine levels of tumor necrosis factor-alpha (TNF-α) and interleukins-1β, 6, 10, and 4 (IL-1β, IL-6, IL-10, and IL-4). Results: Men reported symptom severity at pretreatment and posttreatment as low to moderate. There were significant differences from pretreatment to posttreatment in fatigue, insomnia, urinary irritative and incontinence, bowel, sexual, and hormonal problems. There were no significant differences in TNF-α, IL-6, IL-10, or IL-4. At pretreatment, TNF-α was associated with depression, anxiety, urinary irritative, and bowel problems, and IL-4 was related to urinary irritative symptoms. At posttreatment, IL-4 was associated with urinary irritative symptoms. Findings suggest that, in men with prostate cancer, there is no strong association between symptom reporting and cytokine levels. Ongoing research focused on neuroendocrine and genetic markers and their associations with symptoms is promising and may result in the provision of better markers for quantifying the symptom experience in patients with cancer.
    Biological Research for Nursing 05/2013; 16(3). DOI:10.1177/1099800413490228 · 1.43 Impact Factor
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    ABSTRACT: Over the past decades, some scientific progress has been made in understanding and treating cancer-related fatigue (CRF). However, three major problems have limited further progress: lack of agreement about measurement, inadequate understanding of the underlying biology, and problems in the conduct of clinical trials for CRF. This commentary reports the recommendations of a National Cancer Institute Clinical Trials Planning Meeting and an ongoing National Cancer Institute working group to address these problems so that high-priority research and clinical trials can be conducted to advance the science of CRF and its treatment. Recommendations to address measurement issues included revising the current case definition to reflect more rigorous criteria, adopting the Patient Reported Outcomes Measurement Information System fatigue scales as standard measures of CRF, and linking legacy measures to the scales. With regard to the biology of CRF, the group identified the need for longitudinal research to examine biobehavioral mechanisms underlying CRF and testing mechanistic hypotheses within the context of intervention research. To address clinical trial issues, recommendations included using only placebo-controlled trial designs. setting eligibility to minimize sample heterogeneity or enable subgroup analysis, establishing a CRF severity threshold for participation in clinical trials, conducting dissemination trials of efficacious interventions (such as exercise), and combining nonpharmacologic and pharmacologic interventions to exploit the potential synergy between these approaches. Accomplishing these goals has the potential to advance the science of CRF and improve the clinical management of this troubling symptom.
    Journal of the National Cancer Institute 09/2013; 105(19). DOI:10.1093/jnci/djt242 · 12.58 Impact Factor
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