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In a trial comparing preoperative chemoradiotherapy and FOLFOX in patients with rectal cancer undergoing sphincter-sparing surgery, 5-year disease-free survival was 80.8% with FOLFOX and 78.6% with chemoradiotherapy.

Patient-reported outcomes (PROs), such as symptoms, function, and other health-related quality-of-life aspects, are increasingly evaluated in cancer randomised controlled trials (RCTs) to provide information about treatment risks, benefits, and tolerability. However, expert opinion and critical review of the literature showed no consensus on optimal methods of PRO analysis in cancer RCTs, hindering interpretation of results. The Setting International Standards in Analyzing Patient-Reported Outcomes and Quality of Life Endpoints Data Consortium was formed to establish PRO analysis recommendations. Four issues were prioritised: developing a taxonomy of research objectives that can be matched with appropriate statistical methods, identifying appropriate statistical methods for PRO analysis, standardising statistical terminology related to missing data, and determining appropriate ways to manage missing data. This Policy Review presents recommendations for PRO analysis developed through critical literature reviews and a structured collaborative process with diverse international stakeholders, which provides a foundation for endorsement; ongoing developments of these recommendations are also discussed.

There is increasing interest to use patient-reported outcome (PRO) measures to evaluate symptomatic adverse events (AEs) in cancer treatment trials. However, there are currently no standard recommended approaches for integrating patient-reported AE measures into trials. Approaches are identified from previous trials for selecting AEs for solicited patient reporting, administering patient-reported AE measures, and analyzing and reporting results. Approaches for integrating patient-reported AE measures into cancer trials generally combine current standard methods for clinician-reported AEs and established best practices for using PRO measures. Specific AEs can be selected for a PRO questionnaire based on common and expected reactions in a given trial context, derived from literature review and qualitative/mixed-methods evaluations and should be the same set administered across all arms of a trial. A mechanism for collecting unsolicited patient-reported AEs will also ideally be included. Patients will preferably report at baseline and at the end of active treatment as well as on a frequent standardized schedule during active treatment, such as weekly from home, with a recall period corresponding to the frequency of reporting (eg, past 7 days). Less frequent reporting may be considered after an initial intensive monitoring period for trials of prolonged treatments and during long-term follow-up. Electronic PRO data collection is preferred. Backup data collection for missed PRO reports is advisable to boost response rates. Analysis can use a combination of approaches to AE and PRO data. If a high proportion of patients is experiencing baseline symptoms, systematic subtraction of these from on-study AEs should be considered to improve reporting of symptoms related to treatment. More granular longitudinal analyses of individual symptoms can also be useful. Methods are evolving for integrating patient-reported symptomatic AEs into cancer trials. These methods are expected to further evolve as more data from trials become available.

Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM. We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set. We collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients). Multi-parametric MRI and texture analysis can help characterize and visualize GBM's spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.

Purpose Patients with cancer experience acute and chronic symptoms caused by their underlying disease or by the treatment. While numerous studies have examined the impact of various treatments on symptoms experienced by cancer patients, there are inconsistencies regarding the symptoms measured and reported in treatment trials. This article presents a systematic review of the research literature of the prevalence and severity of symptoms in patients undergoing cancer treatment. Methods A systematic search for studies of persons receiving active cancer treatment was performed with the search terms of “multiple symptoms” and “cancer” for studies involving patients over the age of 18 years and published in English during the years 2001 to 2011. Search outputs were reviewed independently by seven authors, resulting in the synthesis of 21 studies meeting criteria for generation of an Evidence Table reporting symptom prevalence and severity ratings. Results Data were extracted from 21 multi-national studies to develop a pooled sample of 4,067 cancer patients in whom the prevalence and severity of individual symptoms was reported. In total, the pooled sample across the 21 studies was comprised of 62 % female, with a mean age of 58 years (range 18 to 97 years). A majority (62 %) of these studies assessed symptoms in homogeneous samples with respect to tumor site (predominantly breast and lung cancer), while 38 % of the included studies utilized samples with mixed diagnoses and treatment regimens. Eighteen instruments and structured interviews were including those measuring single symptoms, multi-symptom inventories, and single symptom items drawn from HRQOL or health status measures. The MD Anderson Symptom Inventory was the most commonly used instrument in the studies analyzed ( n  = 9 studies; 43 %), while the Functional Assessment of Cancer Therapy, Hospital Anxiety and Depression Subscale, Medical Outcomes Survey Short Form-36, and Symptom Distress Scale were each employed in two studies. Forty-seven symptoms were identified across the 21 studies which were then categorized into 17 logical groupings. Symptom prevalence and severity were calculated across the entire cohort and also based upon sample sizes in which the symptoms were measured providing the ability to rank symptoms. Conclusions Symptoms are prevalent and severe among patients with cancer. Therefore, any clinical study seeking to evaluate the impact of treatment on patients should consider including measurement of symptoms. This study demonstrates that a discrete set of symptoms is common across cancer types. This set may serve as the basis for defining a “core” set of symptoms to be recommended for elicitation across cancer clinical trials, particularly among patients with advanced disease.

Patient-reported outcomes (PROs) are used in clinical trials to provide valuable evidence on the impact of disease and treatment on patients’ symptoms, function and quality of life. High-quality PRO data from trials can inform shared decision-making, regulatory and economic analyses and health policy. Recent evidence suggests the PRO content of past trial protocols was often incomplete or unclear, leading to research waste. To address this issue, international, consensus-based, PRO-specific guidelines were developed: the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT)-PRO Extension. The SPIRIT-PRO Extension is a 16-item checklist which aims to improve the content and quality of aspects of clinical trial protocols relating to PRO data collection to minimise research waste, and ultimately better inform patient-centred care. This SPIRIT-PRO explanation and elaboration (E&E) paper provides information to promote understanding and facilitate uptake of the recommended checklist items, including a comprehensive protocol template. For each SPIRIT-PRO item, we provide a detailed description, one or more examples from existing trial protocols and supporting empirical evidence of the item’s importance. We recommend this paper and protocol template be used alongside the SPIRIT 2013 and SPIRIT-PRO Extension paper to optimise the transparent development and review of trial protocols with PROs.

Measures of health-related quality of life (HRQOL) and other patient-reported outcomes generate important data in cancer randomised trials to assist in assessing the risks and benefits of cancer therapies and fostering patient-centred cancer care. However, the various ways these measures are analysed and interpreted make it difficult to compare results across trials, and hinders the application of research findings to inform publications, product labelling, clinical guidelines, and health policy. To address these problems, the Setting International Standards in Analyzing Patient-Reported Outcomes and Quality of Life Endpoints Data (SISAQOL) initiative has been established. This consortium, directed by the European Organisation for Research and Treatment of Cancer (EORTC), was convened to provide recommendations on how to standardise the analysis of HRQOL and other patient-reported outcomes data in cancer randomised trials. This Personal View discusses the reasons why this project was initiated, the rationale for the planned work, and the expected benefits to cancer research, patient and provider decision making, care delivery, and policy making.

Although patient-reported outcomes (PROs), such as health-related quality of life, are important endpoints in randomised controlled trials (RCTs), there is little consensus about the analysis, interpretation, and reporting of these data. We did a systematic review to assess the variability, quality, and standards of PRO data analyses in advanced breast cancer RCTs. We searched PubMed for English language articles published in peer-reviewed journals between Jan 1, 2001, and Oct 30, 2017. Eligible articles were those that reported PRO results from RCTs of adult patients with advanced breast cancer receiving anti-cancer treatments with reported sample sizes of at least 50 patients—66 RCTs met the selection criteria. Only eight (12%) RCTs reported a specific PRO research hypothesis. Heterogeneity in the statistical methods used to assess PRO data was observed, with a mixture of longitudinal and cross-sectional techniques. Not all articles addressed the problem of multiple testing. Fewer than half of RCTs (28 [42%]) reported the clinical significance of their findings. 48 (73%) did not report how missing data were handled. Our systematic review shows a need to improve standards in the analysis, interpretation, and reporting of PRO data in cancer RCTs. Lack of standardisation makes it difficult to draw robust conclusions and compare findings across trials. The Setting International Standards in the Analyzing Patient-Reported Outcomes and Quality of Life Data Consortium was set up to address this need and develop recommendations on the analysis of PRO data in RCTs.

Blockade of cIAP1 and cIAP2 induces a tumor cell-autonomous type-I IFN response that activates myeloid cells and potentiates anti-tumor immunity in pre-clinical models and patients with multiple myeloma. The cellular inhibitors of apoptosis (cIAP) 1 and 2 are amplified in about 3% of cancers and have been identified in multiple malignancies as being potential therapeutic targets as a result of their role in the evasion of apoptosis. Consequently, small-molecule IAP antagonists, such as LCL161, have entered clinical trials for their ability to induce tumor necrosis factor (TNF)-mediated apoptosis of cancer cells. However, cIAP1 and cIAP2 are recurrently homozygously deleted in multiple myeloma (MM), resulting in constitutive activation of the noncanonical nuclear factor (NF)-κB pathway. To our surprise, we observed robust in vivo anti-myeloma activity of LCL161 in a transgenic myeloma mouse model and in patients with relapsed-refractory MM, where the addition of cyclophosphamide resulted in a median progression-free-survival of 10 months. This effect was not a result of direct induction of tumor cell death, but rather of upregulation of tumor-cell-autonomous type I interferon (IFN) signaling and a strong inflammatory response that resulted in the activation of macrophages and dendritic cells, leading to phagocytosis of tumor cells. Treatment of a MM mouse model with LCL161 established long-term anti-tumor protection and induced regression in a fraction of the mice. Notably, combination of LCL161 with the immune-checkpoint inhibitor anti-PD1 was curative in all of the treated mice.

The use of item libraries for patient-reported outcome (PRO) measurement in oncology allows for the customisation of PRO assessment to measure key health-related quality of life concepts of relevance to the target population and intervention. However, no high-level recommendations exist to guide users on the design and implementation of these customised PRO measures (item lists) across different PRO measurement systems. To address this issue, a working group was set up, including international stakeholders (academic, independent, industry, health technology assessment, regulatory, and patient advocacy), with the goal of creating recommendations for the use of item libraries in oncology trials. A scoping review was carried out to identify relevant publications and highlight any gaps. Stakeholders commented on the available guidance for each research question, proposed recommendations on how to address gaps in the literature, and came to an agreement using discussion-based methods. Nine primary research questions were identified that formed the scope and structure of the recommendations on how to select items and implement item lists created from item libraries. These recommendations address methods to drive item selection, plan the structure and analysis of item lists, and facilitate their use in conjunction with other measures. The findings resulted in high-level, instrument-agnostic recommendations on the use of item-library-derived item lists in oncology trials.