Purpose Understanding the etiology of cancer-related fatigue (CRF) is critical to identify targets to develop therapies to reduce CRF burden. The goal of this systematic review was to expand on the initial work by the National Cancer Institute CRF Working Group to understand the state of the science of the biology of CRF. Specifically, to evaluate studies that examined the relationships between biomarkers and CRF, and to develop an etiologic model of CRF to guide researchers on pathways to explore or therapeutic targets to investigate. Methods This review was completed by the Multinational Association of Supportive Care in Cancer Fatigue Study Group – Biomarker Working Group. The initial search used three terms (biomarkers, fatigue, cancer), which yielded 11,129 articles. After removing duplicates, 7,175 articles remained. Titles were assessed for the keywords, “cancer” and “fatigue” resulting in 3,811 articles. Articles published before 2010 and those with samples <50 were excluded, leaving 75 articles for full-text review. Of the 75 articles, 25 were further excluded for not investigating the associations of biomarkers and CRF. Results Of the 47 articles reviewed, 25 were cross-sectional and 22 were longitudinal studies. Less than half (44%) were published recently (2010-2013). Almost half (46%) enrolled breast cancer participants. A majority of studies assessed fatigue using self-report questionnaires, and only two studies used clinical parameters to measure fatigue. Conclusions The findings from this review suggest that CRF is linked to immune/inflammatory, metabolic, neuroendocrine, and genetic biomarkers. We also identified gaps in knowledge and made recommendations for future research.

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Depression is a common mood disorder that causes severe medical problems and interferes negatively with daily life. Identifying human behavior patterns that are predictive or indicative of depressive disorder is important. Clinical diagnosis of depression relies on costly clinician assessment using survey instruments which may not objectively reflect the fluctuation of daily behavior. Self-administered surveys, such as the Quick Inventory of Depressive Symptomatology (QIDS) commonly used to monitor depression, may show disparities from clinical decision. Smartphones provide easy access to many behavioral parameters, and Fitbit wrist bands are becoming another important tool to assess variables such as heart rates and sleep efficiency that are complementary to smartphone sensors. However, data used to identify depression indicators have been limited to a single platform either iPhone, or Android, or Fitbit alone due to the variation in their methods of data collection. The present work represents a large-scale effort to collect and integrate data from mobile phones, wearable devices, and self reports in depression analysis by designing a new machine learning approach. This approach constructs sparse mappings from sensing variables collected by various tools to two separate targets: self-reported QIDS scores and clinical assessment of depression severity. We propose a so-called heterogeneous multi-task feature learning method that jointly builds inference models for related tasks but of different types including classification and regression tasks. The proposed method was evaluated using data collected from 103 college students and could predict the QIDS score with an R 2 reaching 0.44 and depression severity with an F1-score as high as 0.77. By imposing appropriate regularizers, our approach identified strong depression indicators such as time staying at home and total time asleep.

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Chemotherapy-induced peripheral neuropathy (CIPN) is a common and debilitating condition associated with a number of chemotherapeutic agents. Drugs commonly implicated in the development of CIPN include platinum agents, taxanes, vinca alkaloids, bortezomib, and thalidomide analogues. As a drug response can vary between individuals, it is hypothesized that an individual's specific genetic variants could impact the regulation of genes involved in drug pharmacokinetics, ion channel functioning, neurotoxicity, and DNA repair, which in turn affect CIPN development and severity. Variations of other molecular markers may also affect the incidence and severity of CIPN. Hence, the objective of this review was to summarize the known biological (molecular and genomic) predictors of CIPN and discuss the means to facilitate progress in this field.