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The PLOS Medicine editors issue a call for papers for translational and clinical cancer research papers with strong potential to advance patient care, public policy, or clinical research agendas. [ABSTRACT FROM AUTHOR]

Although adaptive cancer therapy shows promise in integrating evolutionary dynamics into treatment scheduling, the stochastic nature of cancer evolution has seldom been taken into account. Various sources of random perturbations can impact the evolution of heterogeneous tumors, making performance metrics of any treatment policy random as well. In this paper, we propose an efficient method for selecting optimal adaptive treatment policies under randomly evolving tumor dynamics. The goal is to improve the cumulative "cost" of treatment, a combination of the total amount of drugs used and the total treatment time. As this cost also becomes random in any stochastic setting, we maximize the probability of reaching the treatment goals (tumor stabilization or eradication) without exceeding a pre-specified cost threshold (or a "budget"). We use a novel Stochastic Optimal Control formulation and Dynamic Programming to find such "threshold-aware" optimal treatment policies. Our approach enables an efficient algorithm to compute these policies for a range of threshold values simultaneously. Compared to treatment plans shown to be optimal in a deterministic setting, the new "threshold-aware" policies significantly improve the chances of the therapy succeeding under the budget, which is correlated with a lower general drug usage. We illustrate this method using two specific examples, but our approach is far more general and provides a new tool for optimizing adaptive therapies based on a broad range of stochastic cancer models. Author summary: Tumor heterogeneities provide an opportunity to improve therapies by leveraging complex (often competitive) interactions of different types of cancer cells. These interactions are usually stochastic due to both individual cell differences and random events affecting the patient as a whole. The new generation of cancer models strive to account for this inherent stochasticity, and adaptive treatment plans need to reflect it as well. In optimizing such treatment, the most common approach is to maximize the probability of eventually stabilizing or eradicating the tumor. In this paper, we consider a more nuanced version of success, maximizing the probability of reaching these therapy goals before the cumulative burden from the disease and treatment exceed a chosen threshold. Importantly, our method allows computing such optimal treatment plans efficiently and for a range of thresholds at once. If used on a high-fidelity personalized model, our general approach could potentially be used by clinicians to choose the most suitable threshold after a detailed discussion of a specific patient's goals (e.g., to include the trade-offs between toxicity and quality of life). [ABSTRACT FROM AUTHOR]

Timely access to guideline-recommended cancer treatment is known to be an indicator of the quality and accessibility of a cancer care system. Yet people who are socially disadvantaged experience inequities in access to cancer treatment that have significant impacts on cancer outcomes and quality of life. Among people experiencing the intersecting impacts of poor access to the social determinants of health and personal identities typically marginalized from society ('social disadvantage'), there are significant barriers to accessing cancer, many of which compound one another, making cancer treatment extremely difficult to access. Although some research has focused on barriers to accessing cancer treatment among people who are socially disadvantaged, it is not entirely clear what, if anything, is being done to mitigate these barriers and improve access to care. Increasingly, there is a need to design cancer treatment services and models of care that are flexible, tailored to meet the needs of patients, and innovative in reaching out to socially disadvantaged groups. In this paper, we report the protocol for a planned scoping review which aims to answer the following question: What services, models of care, or interventions have been developed to improve access to or receipt of cancer treatment for adults who are socially disadvantaged? Based on the methodological framework of Arksey and O'Malley, this scoping review is planned in six iterative stages. A comprehensive search strategy will be developed by an academic librarian. OVID Medline, EMBASE, CINAHL (using EBSCOhost) and Scopus will be searched for peer-reviewed published literature; advanced searches in Google will be done to identify relevant online grey literature reports. Descriptive and thematic analysis methods will be used to analyze extracted data. Findings will provide a better understanding of the range and nature of strategies developed to mitigate barriers to accessing cancer treatment. [ABSTRACT FROM AUTHOR]

There is still unmet medical need in cancer treatment mainly due to drug resistance and adverse drug events. Therefore, the search for better drugs is essential. Computer-aided drug design (CADD) and discovery tools are useful to streamline the lengthy and costly drug development process. Anthraquinones are a group of naturally occurring compounds with unique scaffold that exert various biological properties including anticancer activities. This protocol describes a systematic review that provide insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment. It was prepared in accordance with the "Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 guidelines, and published in the "International prospective register of systematic reviews" database (PROSPERO: CRD42023432904). Search strategies will be developed based on the combination of relevant keywords and executed in PubMed, Scopus, Web of Science and MedRxiv. Only original studies that employed CADD as primary tool in virtual screening for the purpose of designing or discovering anti-cancer drugs involving anthraquinone scaffold published in English language will be included. Two independent reviewers will be involved to screen and select the papers, extract the data and assess the risk of bias. Apart from exploring the trends and types of CADD methods used, the target proteins of these compounds in cancer treatment will also be revealed in this review. It is believed that the outcome of this study could be utilized to support the ongoing research in similar area with better quality and greater probability of success, consequently optimizing the resources in subsequent in vitro, in vivo, non-clinical and clinical development. It will also serve as an evidence based scientific guide for new research to design novel anthraquinone-derived drug with improved efficacy and safety profile for cancer treatment. [ABSTRACT FROM AUTHOR]

The unique expression patterns of circRNAs linked to the advancement and prognosis of cancer underscore their considerable potential as valuable biomarkers. Repurposing existing drugs for new indications can significantly reduce the cost of cancer treatment. Computational prediction of circRNA-cancer and drug-cancer relationships is crucial for precise cancer therapy. However, prior computational methods fail to analyze the interaction between circRNAs, drugs, and cancer at the systematic level. It is essential to propose a method that uncover more valuable information for achieving cancer-centered multi-association prediction. In this paper, we present a novel computational method, AutoEdge-CCP, to unveil cancer-associated circRNAs and drugs. We abstract the complex relationships between circRNAs, drugs, and cancer into a multi-source heterogeneous network. In this network, each molecule is represented by two types information, one is the intrinsic attribute information of molecular features, and the other is the link information explicitly modeled by autoGNN, which searches information from both intra-layer and inter-layer of message passing neural network. The significant performance on multi-scenario applications and case studies establishes AutoEdge-CCP as a potent and promising association prediction tool. Author summary: CircRNAs serve as crucial biomarkers and drug targets in cancer therapy. Predicting cancer-associated circRNAs and drugs contributes to uncover intricate molecular mechanisms driving tumorigenesis, thus offering novel insights into cancer diagnosis, treatment, and research. However, prevailing predictive methods often neglect the comprehensive interactions within circRNAs, drugs, and cancer, leading to an incomplete understanding of their complex interplay. In response, we introduce AutoEdge-CCP, a framework that models circRNA-cancer-drug interactions within a multi-source heterogeneous network. Each molecule combines intrinsic attribute information describing molecular features with interaction information derived through autoGNN, revealing pivotal circRNAs and drugs associated with cancer. Experimental results across multi-scenario attest to AutoEdge-CCP's superior performance compared to competing methods, particularly in predicting novel circRNAs and drugs associated with cancer. Additionally, visualization of edge embeddings and case studies provide interpretable insights into the prediction outcomes. [ABSTRACT FROM AUTHOR]

Background: Over 1.3 billion people, or 16% of the world's population, live with some form of disability. Recent studies have reported that people with disabilities (PwD) might not be receiving state-of-the-art treatment for cancer as their non-disabled peers; our objective was to systematically review this topic. Methods: A systematic review was undertaken to compare cancer outcomes and quality of cancer care between adults with and without disabilities (NIHR Prospero register ID number: CRD42022281506). A search of the literature was performed in July 2022 across five databases: EMBASE, Medline, Cochrane Library, Web of Science and CINAHL databases. Peer-reviewed quantitative research articles, published in English from 2000 to 2022, with interventional or observational study designs, comparing cancer outcomes between a sample of adult patients with disabilities and a sample without disabilities were included. Studies focused on cancer screening and not treatment were excluded, as well as editorials, commentaries, opinion papers, reviews, case reports, case series under 10 patients and conference abstracts. Studies were evaluated by one reviewer for risk of bias based on a set of criteria according to the SIGN 50 guidelines. A narrative synthesis was conducted according to the Cochrane SWiM guidelines, with tables summarizing study characteristics and outcomes. This research received no external funding. Results: Thirty-one studies were included in the systematic review. Compared to people without disabilities, PwD had worse cancer outcomes, in terms of poorer survival and higher overall and cancer-specific mortality. There was also evidence that PwD received poorer quality cancer care, including lower access to state-of-the-art care or curative-intent therapies, treatment delays, undertreatment or excessively invasive treatment, worse access to in-hospital services, less specialist healthcare utilization, less access to pain medications and inadequate end-of-life quality of care. Discussion: Limitations of this work include the exclusion of qualitative research, no assessment of publication bias, selection performed by only one reviewer, results from high-income countries only, no meta-analysis and a high risk of bias in 15% of included studies. In spite of these limitations, our results show that PwD often experience severe disparities in cancer care with less guideline-consistent care and higher mortality than people without disabilities. These findings raise urgent questions about how to ensure equitable care for PwD; in order to prevent avoidable morbidity and mortality, cancer care programs need to be evaluated and urgently improved, with specific training of clinical staff, more disability inclusive research, better communication and shared decision-making with patients and elimination of physical, social and cultural barriers. [ABSTRACT FROM AUTHOR]

Purpose: The CONSORT extension for patient reported outcomes (PROs) aims to improve reporting, but guidance on the optimal integration with clinical data is lacking. This study examines in detail the reporting of PROs and clinical data from randomized controlled trials (RCTs) in gastro-intestinal cancer to inform design and reporting of combined PRO and clinical data from trials to improve the ‘take home’ message for clinicians to use in practice. Materials and Methods: The case study was undertaken in gastro-intestinal cancer trials. Well-conducted RCTs reporting PROs with validated instruments were identified and categorized into those combining PRO and clinical data in a single paper, or those separating data into linked primary and supplemental papers. Qualitative methods were developed to examine reporting of the critical interpretation of the trial results (trial exegesis) in the papers in relation of the PRO and clinical outcomes and applied to each publication category. Results were used to inform recommendations for practice. Results: From 1917 screened abstracts, 49 high quality RCTs were identified reported in 36 combined and 15 linked primary and supplemental papers. In-depth analysis of manuscript text identified three categories for understanding trial exegesis: where authors reported a “detailed”, “general”, or absent PRO rationale and integrated interpretation of clinical and PRO results. A total of 11 (30%) and 6 (16%) combined papers reported “detailed” PRO rationale and integrated interpretation of results although only 2 (14%) and 1 (7%) primary papers achieved the same standard respectively. Supplemental papers provide better information with 11 (73%) and 3 (20%) achieving “detailed” rationale and integrated interpretation of results. Supplemental papers, however, were published a median of 20 months after the primary RCT data in lower impact factor journals (median 16.8 versus 5.2). Conclusion: It is recommended that single papers, with detailed PRO rationale and integrated PRO and clinical data are published to optimize trial exegesis. Further work to examine whether this improves the use of PRO data to inform practice is needed. [ABSTRACT FROM AUTHOR]

Publication of accurate and detailed descriptions of methods in research articles involving animals is essential for health scientists to accurately interpret published data, evaluate results and replicate findings. Inadequate reporting of key aspects of experimental design may reduce the impact of studies and could act as a barrier to translation of research findings. Reporting of animal use must be as comprehensive as possible in order to take advantage of every study and every animal used. Animal models are essential to understanding and assessing new chemotherapy candidates for Chagas disease pathology, a widespread parasitic disease with few treatment options currently available. A systematic review was carried out to compare ARRIVE guidelines recommendations with information provided in publications of preclinical studies for new anti-Trypanosoma cruzi compounds. A total of 83 publications were reviewed. Before ARRIVE guidelines, 69% of publications failed to report any macroenvironment information, compared to 57% after ARRIVE publication. Similar proportions were observed when evaluating reporting of microenvironmental information (56% vs. 61%). Also, before ARRIVE guidelines publication, only 13% of papers described animal gender, only 18% specified microbiological status and 13% reported randomized treatment assignment, among other essential information missing or incomplete. Unfortunately, publication of ARRIVE guidelines did not seem to enhance reporting quality, compared to papers appeared before ARRIVE publication. Our results suggest that there is a strong need for the scientific community to improve animal use description, animal models employed, transparent reporting and experiment design to facilitate its transfer and application to the affected human population. Full compliance with ARRIVE guidelines, or similar animal research reporting guidelines, would be an excellent start in this direction. [ABSTRACT FROM AUTHOR]

Cancer therapy selects for cancer cells resistant to treatment, a process that is fundamentally evolutionary. To what extent, however, is the evolutionary perspective employed in research on therapeutic resistance and relapse? We analyzed 6,228 papers on therapeutic resistance and/or relapse in cancers and found that the use of evolution terms in abstracts has remained at about 1% since the 1980s. However, detailed coding of 22 recent papers revealed a higher proportion of papers using evolutionary methods or evolutionary theory, although this number is still less than 10%. Despite the fact that relapse and therapeutic resistance is essentially an evolutionary process, it appears that this framework has not permeated research. This represents an unrealized opportunity for advances in research on therapeutic resistance. [ABSTRACT FROM AUTHOR]

Background: Patients diagnosed with cancer face many challenges and need a good understanding of their diagnosis and proposed treatments to make informed decisions about their care. Health literacy plays an important role in this and low health literacy has been associated with poorer outcomes. The aims of this review are to identify which outcomes relate to health literacy in patients with cancer, and to combine this through a mixed studies approach with the patient experience as described through qualitative studies. Methods: Four electronic databases were searched in January 2021 to identify records relating to health literacy and patients with cancer. Records were independently screened then assessed for inclusion by two reviewers according to the following criteria: patients aged ≥18 years with cancer, English language publication AND health literacy measured with validated tool and measured outcome associated with health literacy OR qualitative study exploring the role of health literacy as patients make decisions about health. Quality was independently assessed by two reviewers. A narrative synthesis was performed, and findings integrated through concept mapping. This systematic review was registered with PROSPERO, entry CRD42020166454. Results: 4441 records were retrieved. Following de-duplication, 2496 titles and abstracts were screened and full texts of 405 papers were reviewed for eligibility. 66 papers relating to 60 studies met the eligibility criteria. Lower health literacy was associated with greater difficulties understanding and processing cancer related information, poorer quality of life and poorer experience of care. Personal and situational influences contributed to how participants processed information and reached decisions about their care. Conclusion: This review highlights the important role of health literacy for patients with cancer. Outcomes are poorer for those who experience difficulties with health literacy. Further efforts should be made to facilitate understanding, develop health literacy and support patients to become more involved in their care. [ABSTRACT FROM AUTHOR]

Introduction: Cancer causes a substantial burden to our society, both from a health and an economic perspective. To improve cancer patient outcomes and lower society expenses, early diagnosis and timely treatment are essential. The recent COVID-19 crisis has disrupted the care trajectory of cancer patients, which may affect their prognosis in a potentially negative way. The purpose of this paper is to present a flexible decision-analytic Markov model methodology allowing the evaluation of the impact of delayed cancer care caused by the COVID-19 pandemic in Belgium which can be used by researchers to respond to diverse research questions in a variety of disruptive events, contexts and settings. Methods: A decision-analytic Markov model was developed for 4 selected cancer types (i.e. breast, colorectal, lung, and head and neck), comparing the estimated costs and quality-adjusted life year losses between the pre-COVID-19 situation and the COVID-19 pandemic in Belgium. Input parameters were derived from published studies (transition probabilities, utilities and indirect costs) and administrative databases (epidemiological data and direct medical costs). One-way and probabilistic sensitivity analyses are proposed to consider uncertainty in the input parameters and to assess the robustness of the model's results. Scenario analyses are suggested to evaluate methodological and structural assumptions. Discussion: The results that such decision-analytic Markov model can provide are of interest to decision makers because they help them to effectively allocate resources to improve the health outcomes of cancer patients and to reduce the costs of care for both patients and healthcare systems. Our study provides insights into methodological aspects of conducting a health economic evaluation of cancer care and COVID-19 including insights on cancer type selection, the elaboration of a Markov model, data inputs and analysis. [ABSTRACT FROM AUTHOR]

In this month's editorial, the PLOS Medicine Editors announce an upcoming Special Issue, with Guest Editors Elaine Mardis and Marc Ladanyi, on actionable advances in research on the cancer genome. [ABSTRACT FROM AUTHOR]

The design of efficient combination therapies is a difficult key challenge in the treatment of complex diseases such as cancers. The large heterogeneity of cancers and the large number of available drugs renders exhaustive in vivo or even in vitro investigation of possible treatments impractical. In recent years, sophisticated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at a molecular level have been developed. However, surprisingly little effort has been put into leveraging these models to find novel therapies. In this paper we use for the first time, to our knowledge, a large-scale state-of-the-art pan-cancer signaling pathway model to identify candidates for novel combination therapies to treat individual cancer cell lines from various tissues (e.g., minimizing proliferation while keeping dosage low to avoid adverse side effects) and populations of heterogeneous cancer cell lines (e.g., minimizing the maximum or average proliferation across the cell lines while keeping dosage low). We also show how our method can be used to optimize the drug combinations used in sequential treatment plans—that is, optimized sequences of potentially different drug combinations—providing additional benefits. In order to solve the treatment optimization problems, we combine the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm with a significantly more scalable sampling scheme for truncated Gaussian distributions, based on a Hamiltonian Monte-Carlo method. These optimization techniques are independent of the signaling pathway model, and can thus be adapted to find treatment candidates for other complex diseases than cancers as well, as long as a suitable predictive model is available. Author summary: Combination therapies are a promising approach to counter complex diseases such as cancers. Two key difficulties in the design of effective cancer combination therapies are the large number of available drugs and the heterogeneity of cancers which render exhaustive laboratory studies impractical. In recent years, sophisticated signaling pathway models that can predict responses to combination treatments at a molecular level have been developed. This motivates the question of how one can leverage mechanistic models to identify candidates for novel combination treatments. In this paper we propose a combination treatment optimization framework which employs a large-scale pan-cancer pathway model. We formulate treatment optimization problems for single cell lines and heterogeneous populations of cancer cells. We further investigate sequential treatment plans and combine an existing evolutionary algorithm with an efficient Hamiltonian Monte-Carlo based sampling scheme. During extensive simulation studies our approach identified combination therapies which are predicted to be more effective than conventional treatments. We hope that one day in silico experiments will be used to identify a small set of promising treatment candidates which can then form a starting point for laboratory studies, allowing for an efficient use of limited resources and accelerated discovery of effective therapies. [ABSTRACT FROM AUTHOR]

Background: Treatment nonadherence in cancer patients remains high with most interventions having had limited success. Most studies omit the multi-factorial aspects of treatment adherence and refer to medication adherence. The behaviour is rarely defined as intentional or unintentional. Aim: The aim of this Scoping Review is to increase understanding of modifiable factors in treatment nonadherence through the relationships that physicians have with their patients. This knowledge can help define when treatment nonadherence is intentional or unintentional and can assist in predicting cancer patients at risk of nonadherence and in intervention design. The scoping review provides the basis for method triangulation in two subsequent qualitative studies: 1. Sentiment analysis of online cancer support groups in relation to treatment nonadherence; 2. A qualitative validation survey to refute / or validate claims from this scoping review. Thereafter, framework development for a future (cancer patient) online peer support intervention. Methods: A Scoping Review was performed to identify peer reviewed studies that concern treatment / medication nonadherence in cancer patients—published between 2000 to 2021 (and partial 2022). The review was registered in the Prospero database CRD42020210340 and follows the PRISMA-S: an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Searches. The principles of meta-ethnography are used in a synthesis of qualitative findings that preserve the context of primary data. An aim of meta-ethnography is to identify common and refuted themes across studies. This is not a mixed methods study, but due to a limited qualitativevidence base and to broaden findings, the qualitative elements (author interpretations) found within relevant quantitative studies have been included. Results: Of 7510 articles identified, 240 full texts were reviewed with 35 included. These comprise 15 qualitative and 20 quantitative studies. One major theme, that embraces 6 sub themes has emerged: 'Physician factors can influence patient factors in treatment nonadherence'. The six (6) subthemes are: 1. Suboptimal Communication; 2. The concept of Information differs between Patient and Physician; 3.Inadequate time. 4. The need for Treatment Concordance is vague or missing from concepts; 5. The importance of Trust in the physician / patient relationship is understated in papers; 6. Treatment concordance as a concept is rarely defined and largely missing from studies. Line of argument was drawn: Treatment (or medication) nonadherence that is intentional or unintentional is often attributed to patient factors—with far less attention to the potential influence of physician communication factors. The differentation between intentional or unintentional nonadherence is missing from most qualitative and quantitative studies. The holistic inter-dimensional / multi-factorial concept of 'treatment adherence' receives scant attention. The main focus is on medication adherence / nonadherence in the singular context. Nonadherence that is unintentional is not necessarily passive behaviour and may overlap with intentional nonadherence. The absence of treatment concordance is a barrier to treatment adherence and is rarely articulated or defined in studies. Conclusion: This review demonstrates how cancer patient treatment nonadherence is often a shared outcome. An equal focus on physican and patient factors can increase understanding of the two main types of nonadherence (intentional or unintentional). This differentation should help improve the fundamentals of intervention design. [ABSTRACT FROM AUTHOR]

Background: Decision impact studies have become increasingly prevalent in genomic medicine, particularly in cancer research. Such studies are designed to provide evidence of clinical utility for genomic tests by evaluating their impact on clinical decision-making. This paper offers insights into understanding of the origins and intentions of these studies through an analysis of the actors and institutions responsible for the production of this new type of evidence. Methods: We conducted bibliometric and funding analyses of decision impact studies in genomic medicine research. We searched databases from inception to June 2022. The datasets used were primarily from Web of Science. Biblioshiny, additional R-based applications, and Microsoft Excel were used for publication, co-authorship and co-word analyses. Results: 163 publications were included for the bibliometric analysis; a subset of 125 studies were included for the funding analysis. Included publications started in 2010 and increased steadily over time. Decision impact studies were primarily produced for proprietary genomic assays for use in cancer care. The author and affiliate analyses reveal that these studies were produced by 'invisible colleges' of researchers and industry actors with collaborations focused on producing evidence for proprietary assays. Most authors had an industry affiliation, and the majority of studies were funded by industry. While studies were conducted in 22 countries, the majority had at least one author from the USA. Discussion: This study is a critical step in understanding the role of industry in the production of new types of research. Based on the data collected, we conclude that decision impact studies are industry-conceived and -produced evidence. The findings of this study demonstrate the depth of industry involvement and highlight a need for further research into the use of these studies in decision-making for coverage and reimbursement. [ABSTRACT FROM AUTHOR]

Background: Distant metastasis or local recurrence after primary tumour resection remain a major clinical problem. The anaesthetic technique used during oncologic surgery is suggested to influence the metastatic process. While awaiting the results of ongoing randomised controlled trials (RCTs), we have analyzed the evidence regarding the influence of anaesthetic drugs on experimental tumour metastasis in animal studies. Methods: PubMed and Embase were searched until April 21st, 2015. Studies were included in the systematic review when they 1) assessed the effect of an anaesthetic drug used in clinical practice on the number or incidence of metastasis in animal models with experimental cancer, 2) included an appropriate control group, and 3) presented unique data. Results: 20 studies met the inclusion criteria (published between 1958–2010). Data on number of metastases could be retrieved from 17 studies. These studies described 41 independent comparisons, 33 of which could be included in the meta-analysis (MA). The incidence of metastases was studied in 3 unique papers. From these 3 papers, data on 7 independent comparisons could be extracted and included in the MA. Locally administered local anaesthetics appear to decrease the number of metastases (SMD -6.15 [-8.42; -3.88]), whereas general anaesthetics (RD: 0.136 [0.045, 0.226]), and more specifically volatile anaesthetics (SMD 0.54 [0.24; 0.84]), appear to increase the number and risk of metastases in animal models for cancer. Conclusions: Anaesthetics influence the number and incidence of metastases in experimental cancer models. Although more high quality experimental research is necessary, based on the currently available evidence from animal studies, there is no indication to suggest that locally administered local anaesthetics are harmful during surgery in cancer patients. Volatile anaesthetics, however, might increase metastasis in animal models and clinical trials investigating this possibly harmful effect should receive priority. The results of our systematic review in animal studies are broadly consistent with clinical reports that anaesthetic technique does seem to affect the tumour metastasis process. [ABSTRACT FROM AUTHOR]

Background: Cancer is a leading cause of disease burden worldwide and the first cause of mortality in Canada with 30.2% of deaths attributable to cancer. Given aging of the population and the improvement of prevention and treatment protocols, the number of cancer survivors is steadily increasing. These individuals have unique physical and mental health needs some of which can be addressed by integrating physical activity promotion into ongoing and long-term care. Despite the benefits of being active, delivery of PA programs for cancer patients in both clinical and community settings remains challenging. This knowledge-to-action protocol–called Kiné-Onco–aims to develop a practice guideline for the delivery, implementation, and scaling-up of cancer-specific physical activity promotion programs and services in clinical and community settings located in Québec, Canada. Method: The Kiné-Onco project involves knowledge synthesis of scientific and grey literature to establish the benefits and added value of physical activity for cancer patients and survivors, describes current practices in delivering physical activity programs, analyses quantitative data from electronic health records (EHR) of patients participating in a novel hospital-based physical activity program, collects and analyses qualitative data from patients and healthcare providers interviews about lived experience, facilitators, and barriers to physical activity promotion, outlines deliberative workshops among multidisciplinary team members to develop implementation guidelines for physical activity promotion, and summarizes a variety of knowledge transfer and exchange activities to disseminate the practice guidelines. Discussion: This paper describes the protocol for a knowledge-to-action project aimed at producing and sharing actionable evidence. Our aim is that physical activity promotion programs and services be scaled up in such a way as to successfully integrate physical activity promotion throughout cancer treatment and survivorship in order to improve the physical and mental health of the growing population of individuals having received a cancer diagnosis. [ABSTRACT FROM AUTHOR]

Academic networks are expected to enhance scientific collaboration and thereby increase research outputs. However, little is known about whether and how the initial steps of getting to know other researchers translates into effective collaborations. In this paper, we investigate the evolution and co-evolution of an academic social network and a collaborative research network (using co-authorship as a proxy measure of the latter), and simultaneously examine the effect of individual researcher characteristics (e.g. gender, seniority or workplace) on their evolving relationships. We used longitudinal data from an international network in primary care cancer research: the CanTest Collaborative (CanTest). Surveys were distributed amongst CanTest researchers to map who knows who (the 'academic social network'). Co-authorship relations were derived from Scopus (the 'collaborative network'). Stochastic actor-oriented models were employed to investigate the evolution and co-evolution of both networks. Visualizing the development of the CanTest network revealed that researchers within CanTest get to know each other quickly and also start collaborating over time (evolution of the academic social network and collaborative network respectively). Results point to a stable and solid academic social network that is particularly encouraging towards more junior researchers; yet differing for male and female researchers (the effect of individual researcher characteristics). Moreover, although the academic social network and the research collaborations do not grow at the same pace, the benefit of creating academic social relationships to stimulate effective research collaboration is clearly demonstrated (co-evolution of both networks). [ABSTRACT FROM AUTHOR]

Although chemotherapy is a standard treatment for cancer, it comes with significant side effects. In particular, certain agents can induce severe muscle loss, known as cachexia, worsening patient quality of life and treatment outcomes. 5-fluorouracil, an anti-cancer agent used to treat several cancers, has been shown to cause muscle loss. Experimental data indicates a non-linear dose-dependence for muscle loss in mice treated with daily or week-day schedules. We present a mathematical model of chemotherapy-induced muscle wasting that captures this non-linear dose-dependence. Area-under-the-curve metrics are proposed to quantify the treatment's effects on lean mass and tumour control. Model simulations are used to explore alternate dosing schedules, aging effects, and morphine use in chemotherapy treatment with the aim of better protecting lean mass while actively targeting the tumour, ultimately leading to improved personalization of treatment planning and improved patient quality of life. Author summary: In this paper we present a novel mathematical model for muscle loss due to cancer chemotherapy treatment. Loss of muscle mass relates to increased drug toxicity and side-effects, and to decreased patient quality of life and survival rates. With our model, we examine the therapeutic efficacy of various dosing schedules with the aim of controlling a growing tumour while also preserving lean mass. Preservation of body composition, in addition to consideration of inflammation and immune interactions, the gut microbiome, and other systemic health measures, may lead to improved patient-specific treatment plans that improve patient quality of life. [ABSTRACT FROM AUTHOR]

Drug response prediction is a critical step for personalized treatment of cancer patients and ultimately leads to precision medicine. A lot of machine-learning based methods have been proposed to predict drug response from different types of genomic data. However, currently available methods could only give a “point” prediction of drug response value but fail to provide the reliability and distribution of the prediction, which are of equal interest in clinical practice. In this paper, we proposed a method based on quantile regression forest and applied it to the CCLE dataset. Through the out-of-bag validation, our method achieved much higher prediction accuracy of drug response than other available tools. The assessment of prediction reliability by prediction intervals and its significance in personalized medicine were illustrated by several examples. Functional analysis of selected drug response associated genes showed that the proposed method achieves more biologically plausible results. [ABSTRACT FROM AUTHOR]

Background: Recent advances in targeted therapies have raised expectations that the clinical application of biomarkers would improve patient’s health outcomes and potentially save costs. However, the cost-effectiveness of biomarkers remains unclear irrespective of the cost-effectiveness of corresponding therapies. It is thus important to determine whether biomarkers for targeted therapies provide good value for money. This study systematically reviews economic evaluations of biomarkers for targeted therapies in metastatic colorectal cancer (mCRC) and assesses the cost-effectiveness of predictive biomarkers in mCRC. Methods: A literature search was performed using Medline, Embase, EconLit, NHSEED. Papers published from 2000 until June 2018 were searched. All economic evaluations assessing biomarker-guided therapies with companion diagnostics in mCRC were searched. To make studies more comparable, cost-effectiveness results were synthesized as per biomarker tests and corresponding therapies. Methodological quality was assessed using the Quality of Health Economic Studies (QHES) instrument. Results: Forty-six studies were included in this review. Of these, 17 studies evaluated the intrinsic value of cancer biomarkers, whereas the remaining studies focused on assessing the cost-effectiveness of corresponding drugs. Most studies indicated favourable cost-effectiveness of biomarkers for targeted therapies in mCRC. Some studies reported that biomarkers were cost-effective, while their corresponding therapies were not cost-effective. A considerable number of economic evaluations were conducted in pre-defined genetic populations and thus, often failed to fully capture the biomarker’s clinical and economic values. The average QHES score was 73.6. Conclusion: Cancer biomarkers for targeted therapies in mCRC were mostly found to be cost-effective; otherwise, they at least improved the cost-effectiveness of targeted therapies by saving some costs. However, this did not necessarily make their corresponding therapies cost-effective. While companion biomarkers reduced therapy costs, the savings were not sufficient to make the corresponding agents cost-effective. Evaluation of biomarkers was often restricted to the cost of tests and did not consider their clinical values or biomarker prevalence. [ABSTRACT FROM AUTHOR]

Introduction: The classical lymphoscintigraphic investigations of lower-limb lymphatic edema [LLLE] sometimes reveal either no or few lymph nodes [LNs] at the root of the limb[s] and/or in the abdomen. The aim of the present paper is to report the results of performing one additional injection at the root of the edematous limb[s] to force the visualization of the LNs and/or to demonstrate the collateral lymphatic pathways in such patients. Methods and findings: We retrospectively reviewed our database and found 99 patients [44 primary LLLE with 47 limbs injected and 55 with LLLE secondary to treatments for cancer with 64 limbs injected] where such an additional injection had been performed. In the 43 LLLE patients where no LNs were seen at the end of the classical exam [15 primary LLLE and 28 secondary LLLE], the extra injection showed lymphatic drainage toward LN[s] in all except 3 and when at least one LN was seen, the injection showed lymphatic drainage in every case toward the same ipsilateral [inguinal and/or iliac] LNs [as shown by the classical injection] and/or toward additional LNs. In 40.7% of patients, we observed one or more additional lymphatic pathways: prepubic superficial lymphatic vessels [LV] crossing the midline anteriorly toward contralateral inguinal LNs in 21 [18.9%], "posterior" LV [toward contralateral inguinal LNs and/or ipsi- or contralateral lumbo-aortic and/or para-renal LNs] in 14 [12.6%], but deep LV toward the ipsilateral common iliac LNs passing between the gluteal muscles in 32 [28.8%]. Conclusion: Our work pinpoints one limitation of classical bipedal radionuclide lymphangiography. In patients with primary and secondary LLLE where inguinal and/or iliac LNs cannot be seen on bipedal radionuclide lymphangiography, this additional injection reveals the true lympho-nodal status and shows unexpected collateral lymphatic pathways in 40% of cases. Such information is of the utmost importance in LLLE management and its acquisition is consequently recommended in these patients. [ABSTRACT FROM AUTHOR]

Many different physiological processes affect the growth of malignant lesions and their response to therapy. Each of these processes is spatially and genetically heterogeneous; dynamically evolving in time; controlled by many other physiological processes, and intrinsically random and unpredictable. The objective of this paper is to show that all of these properties of cancer physiology can be treated in a unified, mathematically rigorous way via the theory of random processes. We treat each physiological process as a random function of position and time within a tumor, defining the joint statistics of such functions via the infinite-dimensional characteristic functional. The theory is illustrated by analyzing several models of drug delivery and response of a tumor to therapy. To apply the methodology to precision cancer therapy, we use maximum-likelihood estimation with Emission Computed Tomography (ECT) data to estimate unknown patient-specific physiological parameters, ultimately demonstrating how to predict the probability of tumor control for an individual patient undergoing a proposed therapeutic regimen. [ABSTRACT FROM AUTHOR]

A closer association has been found between the microRNA-146a rs2910164 polymorphism and the risk of head and neck carcinoma in some molecular epidemiological studies. Recently two meta-analyses were performed to explore the relationship between miRNA-146a polymorphisms and the susceptibility of squamous cell carcinoma of the head and neck (SCCHN); however, they yielded conflicting results in susceptibility regarding ethnic variations. Hence, the present study was performed to explain the relationship between the miRNA-146a rs2910164 polymorphism and the risk of SCCHN development of Chinese patients. We retrieved databases and screened eligible papers up to March 10, 2017 and then we extracted the essential data. The subgroup analyses were also performed based on the tumor site, region, and genotyping means. Crude odds ratios (OR) at 95% confidence intervals (CI) were chosen to describe the strength of the association. As a result, 6 publications were included in our study which involved 8 independent case-control studies. A significant association was found between miR-146a rs2910164 polymorphisms and the risk of SCCHN in Chinese patients according to the overall data [CC+CG vs. GG: OR = 1.13; 95%CI = 1.00–1.29; CC vs. GG: OR = 1.19; 95%CI = 1.03–1.38]. According to the subgroup analysis based on tumor site, the risk of cancer was significantly increased among laryngeal cancer (dominant model: OR = 1.76, 95%CI = 1.26~2.46, P = 0.001; homozygote model: OR = 1.83, 95%CI = 1.25~2.67, P = 0.002) and nasopharyngeal carcinoma (homozygote model: OR = 1.41, 95%CI = 1.05~1.90, P = 0.022). In summary, variant alleles of miR-146a rs2910164 alleles may have an association with the increased risk of SCCHN in Chinese patients, and these associations differed based on tumor site. Further studies including a larger sample size will be necessary to clarify these results. [ABSTRACT FROM AUTHOR]

Target agents are peculiar oncological drugs which differ from the traditional therapies in their ability of recognizing specific molecules expressed by tumor cells and microenvironment. Thus, their toxicity is generally lower than that associated to chemotherapy, and they represent nowadays a new standard of care in a number of tumors. This paper deals with the relationship between economic costs and toxicity of target agents. At this aim, a cluster analysis-based exploration of the main features of a large collection of them is carried out, with a specific focus on the variables leading to the identification of their toxicity and related costs. The analysis of the toxicity is based on the Severe Adverse Events (SAE) and Discontinuation (D) rates of each target agent considering data published on PubMed from 1965 to 2016 in the phase II and III studies that have led to the approval of these drugs for cancer patients by US Food and Drug Administration. The construction of the dataset represents a key step of the research, and is grounded on the critical analysis of a wide set of clinical studies. In order to capture different evaluation strategies of the toxicity, clustering is performed according to three different criteria (including Voronoi tessellation). Our procedure allows us to identify 5 different groups of target agents pooled by similar SAE and D rates and, at the same time, 3 groups based on target agents’ costs for 1 month and for the median whole duration of therapy. Results highlight several specific regularities for toxicity and costs. This study present several limitations, being realized starting from clinical trials and not from individual patients’ data. However, a macroscopic perspective suggests that costs are rather heterogeneous, and they do not clearly follow the clustering based on SAE and D rates. [ABSTRACT FROM AUTHOR]

Background: Cervical cancer is a major public health problem. Even though readily preventable, it is the fourth leading cause of death in women globally. Women living with HIV are at increased risk of invasive cervical cancer, highlighting the need for access to screening and treatment for this population. Integration of services has been proposed as an effective way of improving access to cervical cancer screening especially in areas of high HIV prevalence as well as lower resourced settings. This paper presents the results of a systematic review of programs integrating cervical cancer and HIV services globally, including feasibility, acceptability, clinical outcomes and facilitators for service delivery. Methods: This is part of a larger systematic review on integration of services for HIV and non-communicable diseases. To be considered for inclusion studies had to report on programs to integrate cervical cancer and HIV services at the level of service delivery. We searched multiple databases including Global Health, Medline and Embase from inception until December 2015. Articles were screened independently by two reviewers for inclusion and data were extracted and assessed for risk of bias. Main results: 11,057 records were identified initially. 7,616 articles were screened by title and abstract for inclusion. A total of 21 papers reporting interventions integrating cervical cancer care and HIV services met the criteria for inclusion. All but one study described integration of cervical cancer screening services into existing HIV services. Most programs also offered treatment of minor lesions, a ‘screen-and-treat’ approach, with some also offering treatment of larger lesions within the same visit. Three distinct models of integration were identified. One model described integration within the same clinic through training of existing staff. Another model described integration through co-location of services, with the third model describing programs of integration through complex coordination across the care pathway. The studies suggested that integration of cervical cancer services with HIV services using all models was feasible and acceptable to patients. However, several barriers were reported, including high loss to follow up for further treatment, limited human-resources, and logistical and chain management support. Using visual screening methods can facilitate screening and treatment of minor to larger lesions in a single ‘screen-and-treat’ visit. Complex integration in a single-visit was shown to reduce loss to follow up. The use of existing health infrastructure and funding together with comprehensive staff training and supervision, community engagement and digital technology were some of the many other facilitators for integration reported across models. Conclusions: This review shows that integration of cervical cancer screening and treatment with HIV services using different models of service delivery is feasible as well as acceptable to women living with HIV. However, the descriptive nature of most papers and lack of data on the effect on long-term outcomes for HIV or cervical cancer limits the inference on the effectiveness of the integrated programs. There is a need for strengthening of health systems across the care continuum and for high quality studies evaluating the effect of integration on HIV as well as on cervical cancer outcomes. [ABSTRACT FROM AUTHOR]

Grade II gliomas are slowly growing primary brain tumors that affect mostly young patients. Cytotoxic therapies (radiotherapy and/or chemotherapy) are used initially only for patients having a bad prognosis. These therapies are planned following the “maximum dose in minimum time” principle, i. e. the same schedule used for high-grade brain tumors in spite of their very different behavior. These tumors transform after a variable time into high-grade gliomas, which significantly decreases the patient’s life expectancy. In this paper we study mathematical models describing the growth of grade II gliomas in response to radiotherapy. We find that protracted metronomic fractionations, i.e. therapeutical schedules enlarging the time interval between low-dose radiotherapy fractions, may lead to a better tumor control without an increase in toxicity. Other non-standard fractionations such as protracted or hypoprotracted schemes may also be beneficial. The potential survival improvement depends on the tumor’s proliferation rate and can be even of the order of years. A conservative metronomic scheme, still being a suboptimal treatment, delays the time to malignant progression by at least one year when compared to the standard scheme. [ABSTRACT FROM AUTHOR]

Background: Cancer and Diabetes Mellitus (DM) are leading causes of death worldwide and the prevalence of both is escalating. People with co-morbid cancer and DM have increased morbidity and premature mortality compared with cancer patients with no DM. The reasons for this are likely to be multifaceted but will include the impact of hypo/hyperglycaemia and diabetes therapies on cancer treatment and disease progression. A useful step toward addressing this disparity in treatment outcomes is to establish the impact of cancer treatment on diabetes control. Aim: The aim of this review is to identify and analyse current evidence reporting glycaemic control (HbA1c) during and after cancer treatment. Methods: Systematic searches of published quantitative research relating to comorbid cancer and type 2 diabetes mellitus were conducted using databases, including Medline, Embase, PsychINFO, CINAHL and Web of Science (February 2017). Full text publications were eligible for inclusion if they: were quantitative, published in English language, investigated the effects of cancer treatment on glycaemic control, reported HbA1c (%/mmols/mol) and included adult populations with diabetes. Means, standard deviations and sample sizes were extracted from each paper; missing standard deviations were imputed. The completed datasets were analysed using a random effects model. A mixed-effects analysis was undertaken to calculate mean HbA1c (%/mmols/mol) change over three time periods compared to baseline. Results: The available literature exploring glycaemic control post-diagnosis was mixed. There was increased risk of poor glycaemic control during this time if studies of surgical treatment for gastric cancer are excluded, with significant differences between baseline and 12 months (p < 0.001) and baseline and 24 months (p = 0.002). Conclusion: We found some evidence to support the contention that glycaemic control during and/or after non-surgical cancer treatment is worsened, and the reasons are not well defined in individual studies. Future studies should consider the reasons why this is the case. [ABSTRACT FROM AUTHOR]

Background: The recently adopted Sustainable Development Goals call for the end of poverty and the equitable provision of healthcare. These goals are often at odds, however: health seeking can lead to catastrophic spending, an outcome for which cancer patients and the poor in resource-limited settings are at particularly high risk. How various health policies affect the additional aims of financial wellbeing and equity is poorly understood. This paper evaluates the health, financial, and equity impacts of governmental and charitable policies for surgical oncology in a resource-limited setting. Methods: Three charitable platforms for surgical oncology delivery in Uganda were compared to six governmental policies aimed at improving healthcare access. An extended cost-effectiveness analysis using an agent-based simulation model examined the numbers of lives saved, catastrophic expenditure averted, impoverishment averted, costs, and the distribution of benefits across the wealth spectrum. Findings: Of the nine policies and platforms evaluated, two were able to provide simultaneous health and financial benefits efficiently and equitably: mobile surgical units and governmental policies that simultaneously address surgical scaleup, the cost of surgery, and the cost of transportation. Policies that only remove user fees are dominated, as is the commonly employed short-term “surgical mission trip”. These results are robust to scenario and sensitivity analyses. Interpretation: The most common platforms for increasing access to surgical care appear unable to provide health and financial risk protection equitably. On the other hand, mobile surgical units, to date an underutilized delivery platform, are able to deliver surgical oncology in a manner that meets sustainable development goals by improving health, financial solvency, and equity. These platforms compare favorably with policies that holistically address surgical delivery and should be considered as countries strengthen health systems. [ABSTRACT FROM AUTHOR]

Meta-analysis of longitudinal studies combines effect sizes measured at pre-determined time points. The most common approach involves performing separate univariate metaanalyses at individual time points. This simplistic approach ignores dependence between longitudinal effect sizes, which might result in less precise parameter estimates. In this paper, we show how to conduct a meta-analysis of longitudinal effect sizes where we contrast different covariance structures for dependence between effect sizes, both within and between studies. We propose new combinations of covariance structures for the dependence between effect size and utilize a practical example involving meta-analysis of 17 trials comparing postoperative treatments for a type of cancer, where survival is measured at 6, 12, 18 and 24 months post randomization. Although the results from this particular data set show the benefit of accounting for within-study serial correlation between effect sizes, simulations are required to confirm these results. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer is one of the leading causes of cancer deaths in both sexes in the world. Improvement of existing therapy modalities and implementing new ones in order to improve survival of patients with colorectal cancer represents a great challenge for medicine. The aim of this paper was to assess the impact that adding bevacizumab to chemotherapy has on survival in patients with metastatic colorectal cancer, compared to the use of chemotherapy alone. Methods: Hazard ratios (HRs) with their 95% confidence intervals (CI) were determined from the studies and pooled. Two-sided p values were reported and considered to indicate statistical significance if less than 0.05. Results: A total of 12 studies that meet the inclusion criteria were identified in the literature search, 3 phase II studies and 9 phase III studies. Based on the random effects meta-analysis, a statistically significant improvement was identified for both overall survival (HR = 0.84; 95% CI: 0.74–0.94; p = 0.003) and progression free survival (HR = 0.64; 95% CI: 0.55–0.73; p<0.00001) in patients with metastatic colorectal cancer when bevacizumab was added to chemotherapy, compared to chemotherapy treatment alone. Conclusion: The findings of this meta analysis confirm the benefit of adding bevacizumab to chemotherapy in terms of survival and progression free survival, but the magnitude of this effect is not consistent throughout the included studies. This suggests the need for further research of interaction of bevacizumab with chemotherapeutic agents as well as recognition of patients’ characteristics important for the treatment selection criteria. [ABSTRACT FROM AUTHOR]

Colorectal Cancer (CRC) is becoming a major threat to people’s life in China. Screening methods adopted by many other countries as effective counter-cancer methods have not been explicitly explored for people there. Thus, we present a Markov model with detailed precancerous adenoma states and then evaluate various screening strategies in this paper. Different from current researches, our model considers the population’s heterogeneous risk of developing adenomas and observation-based screening strategies. Furthermore, we also give a new cost-effectiveness metric. After calibrating, the model is simulated using the Monte Carlo method. Numerical results show that there are threshold values of compliance rates below which strategy with every ten-year colonoscopy becomes the most cost-effective method; otherwise, an observation-based screening strategy is the most cost-effective. We also find that strategy with single colonoscopy for adenoma-free individuals and every three-year colonoscopy for those with adenoma is recommended when the observation-based strategy is not considered. Our findings give an explicit and complete instruction in CRC screening protocol in average-risk Chinese. [ABSTRACT FROM AUTHOR]

Purpose: This paper identifies predictors of recovery trajectories of quality of life (QoL), health status and personal wellbeing in the two years following colorectal cancer surgery. Methods: 872 adults receiving curative intent surgery during November 2010 to March 2012. Questionnaires at baseline, 3, 9, 15, 24 months post-surgery assessed QoL, health status, wellbeing, confidence to manage illness-related problems (self-efficacy), social support, co-morbidities, socio-demographic, clinical and treatment characteristics. Group-based trajectory analyses identified distinct trajectories and predictors for QoL, health status and wellbeing. Results: Four recovery trajectories were identified for each outcome. Groups 1 and 2 fared consistently well (scores above/within normal range); 70.5% of participants for QoL, 33.3% health status, 77.6% wellbeing. Group 3 had some problems (24.2% QoL, 59.3% health, 18.2% wellbeing); Group 4 fared consistently poorly (5.3% QoL, 7.4% health, 4.2% wellbeing). Higher pre-surgery depression and lower self-efficacy were significantly associated with poorer trajectories for all three outcomes after adjusting for other important predictors including disease characteristics, stoma, anxiety and social support. Conclusions: Psychosocial factors including self-efficacy and depression before surgery predict recovery trajectories in QoL, health status and wellbeing following colorectal cancer treatment independent of treatment or disease characteristics. This has significant implications for colorectal cancer management as appropriate support may be improved by early intervention resulting in more positive recovery experiences. [ABSTRACT FROM AUTHOR]

Background: Low-dose aspirin has been shown to reduce the incidence of cancer, but its role in the treatment of cancer is uncertain. Objectives: We conducted a systematic search of the scientific literature on aspirin taken by patients following a diagnosis of cancer, together with appropriate meta-analyses. Methods: Searches were completed in Medline and Embase in December 2015 using a pre-defined search strategy. References and abstracts of all the selected papers were scanned and expert colleagues were contacted for additional studies. Two reviewers applied pre-determined eligibility criteria (cross-sectional, cohort and controlled studies, and aspirin taken after a diagnosis of cancer), assessed study quality and extracted data on cancer cause-specific deaths, overall mortality and incidence of metastases. Random effects meta-analyses and planned sub-group analyses were completed separately for observational and experimental studies. Heterogeneity and publication bias were assessed in sensitivity analyses and appropriate omissions made. Papers were examined for any reference to bleeding and authors of the papers were contacted and questioned. Results: Five reports of randomised trials were identified, together with forty two observational studies: sixteen on colorectal cancer, ten on breast and ten on prostate cancer mortality. Pooling of eleven observational reports of the effect of aspirin on cause-specific mortality from colon cancer, after the omission of one report identified on the basis of sensitivity analyses, gave a hazard ratio (HR) of 0.76 (95% CI 0.66, 0.88) with reduced heterogeneity (P = 0.04). The cause specific mortality in five reports of patients with breast cancer showed significant heterogeneity (P<0.0005) but the omission of one outlying study reduced heterogeneity (P = 0.19) and led to an HR = 0.87 (95% CI 0.69, 1.09). Heterogeneity between nine studies of prostate cancer was significant, but again, the omission of one study led to acceptable homogeneity (P = 0.26) and an overall HR = 0.89 (95% CI 0.79–0.99). Six single studies of other cancers suggested reductions in cause specific mortality by aspirin, and in five the effect is statistically significant. There were no significant differences between the pooled HRs for the three main cancers and after the omission of three reports already identified in sensitivity analyses heterogeneity was removed and revealed an overall HR of 0.83 (95% CI 0.76–0.90). A mutation of PIK3CA was present in about 20% of patients, and appeared to explain most of the reduction in colon cancer mortality by aspirin. Data were not adequate to examine the importance of this or any other marker in the effect of aspirin in the other cancers. On bleeding attributable to aspirin two reports stated that there had been no side effect or bleeding attributable to aspirin. Authors on the other reports were written to and 21 replied stating that no data on bleeding were available. Conclusions and Implications: The study highlights the need for randomised trials of aspirin treatment in a variety of cancers. While these are awaited there is an urgent need for evidence from observational studies of aspirin and the less common cancers, and for more evidence of the relevance of possible bio-markers of the aspirin effect on a wide variety of cancers. In the meantime it is urged that patients in whom a cancer is diagnosed should be given details of this research, together with its limitations, to enable each to make an informed decision as to whether or not to take low-dose aspirin. Systematic Review Protocol Number: CRD42015014145 [ABSTRACT FROM AUTHOR]

Background: Identifying cancer subtypes is an important component of the personalised medicine framework. An increasing number of computational methods have been developed to identify cancer subtypes. However, existing methods rarely use information from gene regulatory networks to facilitate the subtype identification. It is widely accepted that gene regulatory networks play crucial roles in understanding the mechanisms of diseases. Different cancer subtypes are likely caused by different regulatory mechanisms. Therefore, there are great opportunities for developing methods that can utilise network information in identifying cancer subtypes. Results: In this paper, we propose a method, weighted similarity network fusion (WSNF), to utilise the information in the complex miRNA-TF-mRNA regulatory network in identifying cancer subtypes. We firstly build the regulatory network where the nodes represent the features, i.e. the microRNAs (miRNAs), transcription factors (TFs) and messenger RNAs (mRNAs) and the edges indicate the interactions between the features. The interactions are retrieved from various interatomic databases. We then use the network information and the expression data of the miRNAs, TFs and mRNAs to calculate the weight of the features, representing the level of importance of the features. The feature weight is then integrated into a network fusion approach to cluster the samples (patients) and thus to identify cancer subtypes. We applied our method to the TCGA breast invasive carcinoma (BRCA) and glioblastoma multiforme (GBM) datasets. The experimental results show that WSNF performs better than the other commonly used computational methods, and the information from miRNA-TF-mRNA regulatory network contributes to the performance improvement. The WSNF method successfully identified five breast cancer subtypes and three GBM subtypes which show significantly different survival patterns. We observed that the expression patterns of the features in some miRNA-TF-mRNA sub-networks vary across different identified subtypes. In addition, pathway enrichment analyses show that the top pathways involving the most differentially expressed genes in each of the identified subtypes are different. The results would provide valuable information for understanding the mechanisms characterising different cancer subtypes and assist the design of treatment therapies. All datasets and the R scripts to reproduce the results are available online at the website: . [ABSTRACT FROM AUTHOR]

Purpose: Organ-at-risk segmentation is essential in adaptive radiotherapy (ART). Learning-based automatic segmentation can reduce committed labor and accelerate the ART process. In this study, an auto-segmentation model was developed by employing individual patient datasets and a deep-learning-based augmentation method for tailoring radiation therapy according to the changes in the target and organ of interest in patients with prostate cancer. Methods: Two computed tomography (CT) datasets with well-defined labels, including contoured prostate, bladder, and rectum, were obtained from 18 patients. The labels of the CT images captured during radiation therapy (CT2nd) were predicted using CT images scanned before radiation therapy (CT1st). From the deformable vector fields (DVFs) created by using the VoxelMorph method, 10 DVFs were extracted when each of the modified CT and CT2nd images were deformed and registered to the fixed CT1st image. Augmented images were acquired by utilizing 110 extracted DVFs and spatially transforming the CT1st images and labels. An nnU-net autosegmentation network was trained by using the augmented images, and the CT2nd label was predicted. A patient-specific model was created for 18 patients, and the performances of the individual models were evaluated. The results were evaluated by employing the Dice similarity coefficient (DSC), average Hausdorff distance, and mean surface distance. The accuracy of the proposed model was compared with those of models trained with large datasets. Results: Patient-specific models were developed successfully. For the proposed method, the DSC values of the actual and predicted labels for the bladder, prostate, and rectum were 0.94 ± 0.03, 0.84 ± 0.07, and 0.83 ± 0.04, respectively. Conclusion: We demonstrated the feasibility of automatic segmentation by employing individual patient datasets and image augmentation techniques. The proposed method has potential for clinical application in automatic prostate segmentation for ART. [ABSTRACT FROM AUTHOR]

Objectives: To 1) explore the experience of patients with lung cancer with low muscle mass or muscle loss during treatment and the ability to cope with treatment, complete self-care, and 2) their receptiveness and preferences for nutrition and exercise interventions to halt or treat low muscle mass/muscle loss. Methods: This was a qualitative study using individual semi-structured interviews conducted using purposive sampling in adults with a diagnosis of non-small cell lung cancer (NSCLC) or small-cell lung cancer (SCLC), treated with curative intent chemo-radiotherapy or radiotherapy. Patients who presented with computed tomography-assessed low muscle mass at treatment commencement or experienced loss of muscle mass throughout treatment were included. Data were analysed using thematic analysis. Results: Eighteen adults (mean age 73 ± SD years, 61% male) with NSCLC (76%) treated with chemo-radiotherapy (76%) were included. Three themes were identified: 1) the effect of cancer and its treatment; 2) engaging in self-management; and 3) impact and influence of extrinsic factors. Although experiences varied, substantial impact on day-to-day functioning, eating, and ability to be physically active was reported. Patients were aware of the overall importance of nutrition and exercise and engaged in self-initiated or health professional supported self-management strategies. Early provision of nutrition and exercise advice, guidance from health professionals, and support from family and friends were valued, albeit with a need for consideration of individual circumstances. Conclusion: Adults with NSCLC with or experiencing muscle loss described a diverse range of experiences regarding treatment. The types of support required were highly individual, highlighting the crucial role of personalised assessment of needs and subsequent intervention. [ABSTRACT FROM AUTHOR]

Background: The state of Sarawak on the island of Borneo in East Malaysia, in working towards developing and strengthening cancer services through a holistic patient-centred approach, must focus on the comprehensive needs of cancer patients by taking into account the psycho-social, cultural and spiritual aspects of Sarawak's multi-ethnic, multi-cultural population. Methods: A 42-item survey questionnaire was developed and validated with a total of 443 patients. The perceived importance of information provided and level of patient satisfaction were assessed with a 5-point Likert scale in 10 domains (Diagnosis, Surgery, Radiotherapy, Systemic therapy, Clinical trials, Pain management, Treatment monitoring, Psychosocial support, Sexual care and fertility issues, and Financial support). A Spearman's rank correlation test was applied to determine the correlation between response in both item and domain categories for perceived importance and satisfaction. Results: Overall, patients were more satisfied with information related to cancer diagnosis, treatment and surgery but less satisfied with information pertaining to sexual aspects of care and family planning, psycho-social support and financial support. The majority of patients were satisfied with the level of treatment-related information received but preferred the information to be delivered in more easily comprehendible formats. Sexual aspects of care and family planning, psychosocial support and treatment monitoring post-discharge were perceived as important but seldom addressed by health care professionals due to lack of professional counsellors, social workers and clinical nurse specialists. Many patients face financial toxicity following a cancer diagnosis, particularly when diagnosed with advanced cancer requiring complex multi-modality treatment. Conclusion: Cancer patients in Sarawak have various unmet information needs. Written information and educational videos in local indigenous languages may be more suitable for Sarawak's multi-ethnic population. Sexual aspects of care and family planning are challenging but essential topics to discuss, in particular due to the high prevalence of breast and cervical cancer amongst young women of reproductive age in Sarawak. Financial assessment and information on support services offered by government and non-government organisations should be provided to eligible patients. A holistic needs assessment of each patient at time of diagnosis and support through their cancer journey requires a multi-disciplinary team of medical, nursing and allied health professionals including clinical nurse specialists, pharmacists, counsellors, physiotherapists, occupational therapists, speech and language therapists, dieticians and social workers. [ABSTRACT FROM AUTHOR]

The histone deacetylase 2 (HDAC2), an enzyme involved in gene regulation, is a potent drug target for the treatment of colon cancer. Phytocompounds having anticancer properties show the ability to interact with HDAC2 enzyme. Among the compounds, docking scores of caffeic acid (CA) and p-coumaric acid (pCA) with HDAC2 showed good binding efficacy of -5.46 kcal/mol and -5.16 kcal/mol, respectively, with small inhibition constants. The higher binding efficacy of CA compared to pCA can be credited to the presence of an extra oxygen atom in the CA molecule, which forms an additional hydrogen bond with Tyr297. The HDAC2 in complex with these molecules was found to be stable by analyzing RMSD, RMSF, Rg, and SASA values obtained through MD simulations. Furthermore, CA and pCA exhibited low MM/GBSA free energies of -16.32 ± 2.62 kcal/mol and -17.01 ± 2.87 kcal/mol, respectively. The HOMO and LUMO energy gaps, dipole moments, global reactivity descriptor values, and MEP surfaces showed the reactivity of the molecules. The favourable physicochemical and pharmacokinetic properties, along with absence of toxicity of the molecules determined using ADMET analysis, suggested both the acids to be regarded as effective drugs in the treatment of colon cancer. [ABSTRACT FROM AUTHOR]

Multimodal medical image fusion is a perennially prominent research topic that can obtain informative medical images and aid radiologists in diagnosing and treating disease more effectively. However, the recent state-of-the-art methods extract and fuse features by subjectively defining constraints, which easily distort the exclusive information of source images. To overcome these problems and get a better fusion method, this study proposes a 2D data fusion method that uses salient structure extraction (SSE) and a swift algorithm via normalized convolution to fuse different types of medical images. First, salient structure extraction (SSE) is used to attenuate the effect of noise and irrelevant data in the source images by preserving the significant structures. The salient structure extraction is performed to ensure that the pixels with a higher gradient magnitude impact the choices of their neighbors and further provide a way to restore the sharply altered pixels to their neighbors. In addition, a Swift algorithm is used to overcome the excessive pixel values and modify the contrast of the source images. Furthermore, the method proposes an efficient method for performing edge-preserving filtering using normalized convolution. In the end,the fused image are obtained through linear combination of the processed image and the input images based on the properties of the filters. A quantitative function composed of structural loss and region mutual data loss is designed to produce restrictions for preserving data at feature level and the structural level. Extensive experiments on CT-MRI images demonstrate that the proposed algorithm exhibits superior performance when compared to some of the state-of-the-art methods in terms of providing detailed information, edge contour, and overall contrasts. [ABSTRACT FROM AUTHOR]

Introduction: Opioids are one of the most important and effective drug classes in pain medicine with a key role in most medical fields. The increase of opioid prescription over time has led to higher numbers of prescription opioid misuse, abuse and opioid-related deaths in most developed OECD (Organisation for Economic Co-operation and Development) countries around the world. Whilst reliable data on the prevalence of opioid treatment is accessible for many countries, data on Germany specifically is still scarce. Considering Germany being the largest country in the European Union, the lack of evidence-based strategies from long-term studies is crucial. The aim of this work is to review and summarise relevant published literature on the prevalence of opioid prescription in Germany to adequately inform health policy strategies. Methods: A systematic review of the epidemiology of opioid prescription in Germany was conducted, searching PubMed and Web of Science. Eligibility criteria were defined prior to conducting the search. Literature concerning Germany, published in English and German was included and the search was replicated by three independent researchers. Two levels of screening were employed. Disagreement was resolved by face-to-face discussion, leading to a consensus judgement. Results: Our electronic search yielded 735 articles. Reviewing titles and abstracts yielded 19 relevant articles. Three authors examined each article’s full text more closely and determined that twelve papers should be included. Of the twelve identified studies—with publication dates ranging from 1985 to 2016—six were retrospective cross-sectional studies and six were retrospective repeated-measures cross-sectional studies. Sample sizes ranged from 92,842 to ≈ 11,000,000 participants. Data sources of included studies showed vast heterogeneity. The reviewed literature suggested an increase in the number of patients with opioid prescriptions and defined daily doses of opioids per recipient in Germany over time. The majority of opioid prescriptions was used for patients with non-cancer pain. Opioid use was more common in older people, women and in the north of Germany. Fentanyl was shown to be the most prescribed strong opioid in outpatient settings in Germany, despite not being the first-line choice for chronic pain conditions. All data published before 2000—but none of the more recent studies—suggested an insufficient treatment of pain using opioids. There were no signs for a current opioid epidemic in Germany. Conclusions: Despite some limitations of the review and the heterogeneity of studies, it can be stated that the number of opioid prescriptions overall as well as the number of people receiving opioid treatment have increased over time. Most prescriptions were found to be for strong opioids and patients with non-cancer pain. Even though patterns of opioid prescription follow trends observed in other developed countries, there are no signs of an opioid epidemic in Germany. Therefore, this review could currently not find a need for urgent health policy interventions regarding opioid prescription practices. However, critical gaps in the literature remain and more research is needed to make more reliable judgements. [ABSTRACT FROM AUTHOR]

Objective: Lung cancer is the most common type of cancer around the world, and it represents the main cause of death in the USA. Surgical treatment is the optimal therapeutic strategy for resectable non-small cell lung cancer. The principal factor for long-term survival after complete resection is the anatomic extension of the neoplasm. However, other factors also have adverse effects on operative mortality, and influence long-term outcome. In this paper we propose an algorithmic solution for the estimation of 5-years survival rate in lung cancer patients undertaking pulmonary resection. Materials and methods: We address the issue of survival analysis through decision-making techniques based on fuzzy and soft set theories. We develop an expert system based on clinical and functional data of lung cancer resections in patients with cancer that can be used to predict the survival of patients. Results: The evaluation of surgical risk in patients undertaking pulmonary resection is a primary target for thoracic surgeons. Lung cancer survival is influenced by many factors. The computational performance of our algorithm is critically analyzed by an experimental study. The correct survival classification is achieved with an accuracy of 79.0%. Our novel soft-set based criterion is an effective and precise diagnosis application for the determination of the survival rate. [ABSTRACT FROM AUTHOR]

Background: The only curative treatment for pancreatic adenocarcinoma is radical surgical resection. Because of the special anatomic features of pancreatic neck, the selection of optimal surgical procedure for treatment of adenocarcinoma of pancreatic neck has always been a dilemma for surgeons. In this paper, we aim to investigate whether different surgical procedures can affect prognosis in the patient with adenocarcinoma of pancreatic neck. Methods: We used the surveillance, epidemiology, and end results database to review patients with adenocarcinoma of pancreatic neck diagnosed between 1998 and 2015. We calculated overall survival (OS) and cancer-specific survival (CSS) of these patients using Kaplan-Meier analysis and Cox regression model. Results: Overall, 1443 patients were included in the study, with 12.5% treated with surgical resection. Among them, 30 (18.8%) patients underwent distal pancreatectomy (DP), 105 (65.6%) patients underwent pancreatoduodenectomy (PD), and 25 (15.6%) patients underwent total pancreatectomy (TP). Patients underwent DP were older than these underwent TP (70.5±10.7 vs. 62.2±14.1, P = 0.027). Patients underwent TP had higher percentages of nodal metastasis (N1 stage) than these underwent DP (68.0% vs. 34.5%, P = 0.014). The surgical procedures did not significantly affect either OS times (P = 0.924) or CSS times (P = 0.786) in Kaplan-Meier analysis, even if in any subgroup of AJCC stage. The multivariate Cox regression model showed that types of surgery were not associated with OS and CSS. Higher tumor grade and AJCC stage are independent prognostic factors for OS and CSS. No radiotherapy was associated with a worse CSS (HR 1.610, 95% CI 1.016–2.554, P = 0.043). Conclusion: Different surgical procedures did not affect prognosis in the patients with adenocarcinoma of pancreatic neck. TP should be performed in carefully selective patients in high-volume pancreatic centers. [ABSTRACT FROM AUTHOR]

Blocking cytokine interleukin 10 (IL-10) at the time of immunisation enhances vaccine induced T cell responses and improves control of tumour cell growth in vivo. However, the effect of an IL-10 blockade on the biological function of macrophages has not been explored. In the current paper, a macrophage precursor cell line, U937 cells, was selected to investigate the differential expression of proteins and relevant cell signalling pathway changes, when stimulated with lipopolysaccharide (LPS) in the presence of antibodies to IL-10 or IL-10 receptor. We used a quantitative proteomic strategy to investigate variations in protein profiles of U937 cells following the treatments with LPS, LPS plus human anti-IL10 antibody and anti-IL10R antibody in 24hrs, respectively. The LPS treatment significantly activated actin-related cell matrix formation and immune response pathways. The addition of anti-IL10 and anti-IL10R antibody further promoted the immune response and potentially effect macrophage survival through PI3K/AKT signalling; however, the latter appeared to also upregulated oncogene XRCC5 and Cajal body associated processes. [ABSTRACT FROM AUTHOR]

The presence of treatment-resistant cells is an important factor that limits the efficacy of cancer therapy, and the prospect of resistance is considered the major cause of the treatment strategy. Several recent studies have employed mathematical models to elucidate the dynamics of generating resistant cancer cells and attempted to predict the probability of emerging resistant cells. The purpose of this paper is to present numerical approach to compute the number of resistant cells and the emerging probability of resistance. Stochastic model was designed and developed a method to approximately but efficiently compute the number of resistant cells and the probability of resistance. To model the progression of cancer, a discrete-state, two-dimensional Markov process whose states are the total number of cells and the number of resistant cells was employed. Then exact analysis and approximate aggregation approaches were proposed to calculate the number of resistant cells and the probability of resistance when the cell population reaches detection size. To confirm the accuracy of computed results of approximation, relative errors between exact analysis and approximation were computed. The numerical values of our approximation method were very close to those of exact analysis calculated in the range of small detection size M = 500, 100, and 1500. Then computer simulation was performed to confirm the accuracy of computed results of approximation when the detection size was M = 10000,30000,50000,100000 and 1000000. All the numerical results of approximation fell between the upper level and the lower level of 95% confidential intervals and our method took less time to compute over a broad range of cell size. The effects of parameter change on emerging probabilities of resistance were also investigated by computed values using approximation method. The results showed that the number of divisions until the cell population reached the detection size is important for emerging the probability of resistance. The next step of numerical approach is to compute the emerging probabilities of resistance under drug administration and with multiple mutation. Another effective approximation would be necessary for the analysis of the latter case. [ABSTRACT FROM AUTHOR]

Robustly predicting outcome for cancer patients from gene expression is an important challenge on the road to better personalized treatment. Network-based outcome predictors (NOPs), which considers the cellular wiring diagram in the classification, hold much promise to improve performance, stability and interpretability of identified marker genes. Problematically, reports on the efficacy of NOPs are conflicting and for instance suggest that utilizing random networks performs on par to networks that describe biologically relevant interactions. In this paper we turn the prediction problem around: instead of using a given biological network in the NOP, we aim to identify the network of genes that truly improves outcome prediction. To this end, we propose SyNet, a gene network constructed ab initio from synergistic gene pairs derived from survival-labelled gene expression data. To obtain SyNet, we evaluate synergy for all 69 million pairwise combinations of genes resulting in a network that is specific to the dataset and phenotype under study and can be used to in a NOP model. We evaluated SyNet and 11 other networks on a compendium dataset of >4000 survival-labelled breast cancer samples. For this purpose, we used cross-study validation which more closely emulates real world application of these outcome predictors. We find that SyNet is the only network that truly improves performance, stability and interpretability in several existing NOPs. We show that SyNet overlaps significantly with existing gene networks, and can be confidently predicted (~85% AUC) from graph-topological descriptions of these networks, in particular the breast tissue-specific network. Due to its data-driven nature, SyNet is not biased to well-studied genes and thus facilitates post-hoc interpretation. We find that SyNet is highly enriched for known breast cancer genes and genes related to e.g. histological grade and tamoxifen resistance, suggestive of a role in determining breast cancer outcome. [ABSTRACT FROM AUTHOR]

Anxiety and depression can be heightened among individuals living with chronic diseases. Identifying these individuals is necessary for ensuring they are provided with adequate support. Traditional tools such as clinical interviews or symptom checklists are not always feasible to implement in practice. Robust single-item questions may be a useful alternative. This study aimed to measure agreement, sensitivity, specificity, positive predictive value and negative predictive value of a single-item question about anxiety and depression compared to the widely used Hospital Anxiety and Depression Scale (HADS). A cross-sectional survey of 2,811 people with cancer attending 19 treatment centres in Australia. Patients were approached in the waiting room prior to an outpatient clinic appointment and invited to complete a pen and paper survey. Participants completed the HADS as well as 2 single-items asking if they have felt anxious or depressed in the last week. The single-items for anxiety and depression each demonstrated moderate levels of sensitivity (0.78 for anxiety; 0.63 for depression) and specificity (0.75 for anxiety; 0.84 for depression) against the relevant HADS subscale. Positive predictive values were moderate (0.53 for anxiety and 0.52 for depression) while negative predictive values were high for both single-item questions (0.90 for anxiety and 0.89 for depression). The single-item measures of anxiety and depression may be useful to rule out individuals who do not require further psychological assessment or intervention for anxiety and depression. Further research is needed to explore whether these findings generalise to other chronic diseases. [ABSTRACT FROM AUTHOR]

Purpose: Socioeconomic inequality in return to work after cancer treatment and rehabilitation have been documented, but less is known about its causes. This paper investigates the role played by breast cancer stage at diagnosis and comorbidity. Methods: We used the comprehensive Danish Cancer Registry to follow 7372 women aged 30-60, who were in the labour force when diagnosed with breast cancer in 2000-06 and survived at least three years. Controls were 213,276 women without breast cancer. Inequalities in employment outlook were estimated as interaction effects in linear regression between educational attainment and disease on employment. Results: There is significant interaction between education and breast cancer, but it is only marginally affected by including stage and comorbidity in the regression models. Education, breast cancer stage, and comorbidity all have strong effects on later employment, and a considerable amount of the educational effect is mediated by comorbidity and pre-cancer labour market participation and income. Conclusion: The result of the study is negative in the sense that the stronger effect of breast cancer on employment among low-educated compared to highly educated individuals is not explained by cancer stage or comorbidity. The fact that comorbidity has little impact on inequality may be due to a different social patterning of most comorbidity compared to breast cancer. [ABSTRACT FROM AUTHOR]

Effective symptom management is a critical component of cancer treatment. Computational tools that predict the course and severity of these symptoms have the potential to assist oncology clinicians to personalize the patient’s treatment regimen more efficiently and provide more aggressive and timely interventions. Three common and inter-related symptoms in cancer patients are depression, anxiety, and sleep disturbance. In this paper, we elaborate on the efficiency of Support Vector Regression (SVR) and Non-linear Canonical Correlation Analysis by Neural Networks (n-CCA) to predict the severity of the aforementioned symptoms between two different time points during a cycle of chemotherapy (CTX). Our results demonstrate that these two methods produced equivalent results for all three symptoms. These types of predictive models can be used to identify high risk patients, educate patients about their symptom experience, and improve the timing of pre-emptive and personalized symptom management interventions. [ABSTRACT FROM AUTHOR]

Background: Preclinical evidence suggests statins may have anti-tumor properties. Large observational studies are also consistent with improved survival and cancer-specific outcomes among cancer patients on statins. We sought to evaluate the randomized controlled trials of statins in addition to usual anti-cancer therapy. Methods: A systematic search of MEDLINE, Embase, CINAHL, Cochrane Library, Web of Science, Papers First and Clinicaltrials.gov was performed from inception through to July 4, 2017 to identify randomized clinical trials that investigated statin therapy in cancer patients. Our primary outcome was overall survival and our secondary outcome was progression-free survival. We calculated summary hazard ratio’s (HR) and 95% confidence intervals (CI) based on random-effects models using aggregate data. PROSPERO (CRD42017065503). Results: Ten studies with 1,881 individuals were included with 1,572 deaths and a median follow-up of 23 months. All trials included patients with advanced (stage 3 or higher) disease. There was minimal between-study statistical heterogeneity (I2 = 1.8%, for OS; I2 = 0%, for PFS). The pooled HR for overall survival in patients randomized to statins plus standard anti-cancer therapy versus standard therapy alone was 0.94 (95% CI, 0.85 to 1.04). In the 9 studies that reported progression-free survival (1,798 participants), the pooled HR for statin plus standard therapy versus standard therapy alone was 0.97 (95% CI, 0.87 to 1.07). Conclusions: In patients with advanced cancer and a prognosis <2 years, the addition of statins to standard anti-cancer therapy does not appear to improve overall survival or progression-free survival. Future research should assess if cancer patients with better prognosis benefit from longer-term statin therapy. [ABSTRACT FROM AUTHOR]