bluuebunny/crossref_metadata_2025 huggingface dataset:
National evidence-based oncology navigation metrics: Multisite exploratory study to demonstrate value and sustainability of navigation programs.
Authors: Strusowski Tricia , Johnston Danelle
Published: May 2020

e14040

Background: As evidence guides practice, it is essential for navigation programs to identify core metrics and standardize data collection to demonstrate program outcomes. Through extensive literature review three main areas of measurements have been defined as patient experience (PE), clinical outcomes (CO), and business performance or return-on-investment (ROI) metrics. To advance the field towards the goal of standardized metrics, a team from Academy of Oncology Nurse & Patient Navigators (AONN+), American Cancer Society (ACS), and The Chartis Group, Chartis Oncology Solutions Practice conducted a first of its kind pilot study to assess the extent to which navigation programs could implement 10 of the 35 AONN+ national evidence-based metrics with the goal of validating these metrics and establishing benchmarks. Methods: Using a mixed methods approach, the study team selected eight sites to collect the metrics over a six-month period. Metrics data were uploaded into the ONC iQ NAV metrics cloud-based business intelligence platform to create participant-specific dashboards. Prior to study launch, sites also submitted three years of historical data, as available. Qualitative data on facilitators and barriers to metrics. Results: The following are the results of the study, (total patients navigated 4682). 10,295 total barriers identified, 2.2 barriers/pt. Time to treatment decreased by 11 days (initial 57 days decreased to 43 days). Readmission rates decreased: 30-day readmission 10.8%-9.8%, 60-day readmission 15.5%-14.3%, 90-day readmission 17.9% - 16.3%. Psychosocial distress screens: 1,987 completed (42% of patients screened). Social support referrals: 0.4/patient. Palliative care referrals: 687 pts. 15%. Satisfaction/Patient Experience survey: discuss goals 82%, encouraged participation in decisions 90%, listened to your needs 82%, listen to emotional concerns 82%, assisted with additional needs 74%. Conclusions: Further research will be needed in navigation metrics.
Patient trial matching using pseudo-siamese network.
Authors: Xiao Cao , Gao Junyi , Glass Lucas , Sun Jimeng
Published: May 2020

e14050

Background: Clinical trials suffer from insufficient patient (pt) recruitment. The availability of electronic health records (EHR) and trial eligibility criteria (EC) is promising for data driven pt-trial matching. The objective is to find qualified pt given patients' EHR and trial EC in unstructured text EC. Pseudo-Siamese network is a novel subfield within information retrieval and has shown great success in the cross-modal information retrieval problems such as semantic image-text retrieval (e.g., match images with text descriptions). The objective is to find the match between pts and clinical trials using Pseudo-Siamese network based cross-modal retrieval. Our model addresses the following challenges: (1) How to match unstructured EC text with structured EHR where EC often encode more general disease concepts and EHR represent pt conditions using more specific medical codes. (2) How to capture pts' evolving health conditions. (3) How to explicitly handle the difference for inclusion and exclusion criteria. Methods: Our matching model addresses these challenges as follows: (1) we augment the medical codes in pts’ records with their textual descriptions and hierarchical taxonomies, such that concepts can be embedded in finer and more coarse levels for better concept alignment across pt data and ECs. (2) We include an attentive dynamic memory network that extracts the best matching and more recent pt EHR to match with ECs. (3) We introduce a composite loss term to maximize the similarity between pt records and inclusion criteria while minimizes the similarity between pt records and exclusion criteria. Results: We evaluated our model on a pt-trial match dataset on the ECs collected from 590 clinical trials from ClinicalTrials.gov. We also extract 83,371 pt claims data from IQVIA database collected (2002-2018), where each pt is eligible for at least one trial. We compared our model with leading pt-trial matching models. Our model significantly outperforms the best baseline model by 24.3% relatively higher accuracy score. We also tested these models in 34 oncology trials in 25 cancers. Results will be reported. Conclusions: Pseudo-Siamese network successfully solved the cross-modal information retrieval problems. We therefore propose a new pt-trial matching model based on Pseudo-Siamese network model. Experiments on real-world datasets demonstrated that our model significantly outperforms existing works in pt-trial matching for oncology trials.
Automated abstraction of real-world clinical outcome in lung cancer: A natural language processing and artificial intelligence approach from electronic health records.
Authors: Ma Meng , Redfern Arielle , Zhou Xiang , Li Dan , Ru Ying , Lee Kyeryoung , Gilman Christopher , Liu Zongzhi , Jones Scott , Mai Yun , Deitz Matthew , Gong Yunrou , Mullaney Tommy , Prentice Tony , Chen Rong , Schadt Eric , Wang Xiaoyan
Published: May 2020

e14062

Background: Real world evidence generated from electronic health records (EHRs) is playing an increasing role in health care decisions. It has been recognized as an essential element to assess cancer outcomes in real-world settings. Automatically abstracting outcomes from notes is becoming a fundamental challenge in medical informatics. In this study, we aim to develop a system to automatically abstract outcomes (Progression, Response, Stable Disease) from notes in lung cancer. Methods: A lung cancer cohort (n = 5,003) was obtained from the Mount Sinai Data Warehouse. The progress, pathology and radiology notes of patients were used. We integrated various techniques of Natural Language Processing (NLP) and Artificial Intelligence (AI) and developed a system to automatically abstract outcomes. The corresponding images, biopsies and lines of treatments (LOTs) were abstracted as attributes of outcomes. This system includes four information models: 1. Customized NLP annotator model: preprocessor, section detector, sentence splitter, named entity recognition, relation detector; CRF and LSTM methods were applied to recognize entities and relations. 2. Clinical Outcome container model: biopsy evidence extractor, lines of treatment detector, image evidence extractor, clinical outcome event recognizer, date detector, and temporal reasoning; Domain-specific rules were crafted to automatically infer outcomes. 3. Document Summarizer; 4. Longitudinal Outcome Summarizer. Results: To evaluate the outcomes abstracted, we curated a subset (n = 792) from patient cohort for which LOTs were available. About 61% of the outcomes identified were supported by radiologic images (time window = ±14 days) or biopsy pathology results (time window = ±100 days). In 91% (720/792) of patients, Progression was abstracted within a time window of 90 days prior to first-line treatment. Also, 72% of the Progression events identified were accompanied by a downstream event (e.g., treatment change or death). We randomly selected 250 outcomes for manual curation, and 197 outcomes were assessed to be correct (precision = 79%). Moreover, our automated abstraction system improved human abstractor efficiency to curate outcomes, reducing curation time per patient by 90%. Conclusions: We have demonstrated the feasibility and effectiveness of NLP and AI approaches to abstract outcomes from lung cancer EHR data. It promises to automatically abstract outcomes and other clinical entities from notes across all cancers.
Clinical trial feasibility assessment and start-up tool (CTFAST).
Authors: Tice Amanda , Dima Richard , Saif Wasif M. , Panzo Melissa , Vaiselbuh Sarah R.
Published: May 2020

e14089

Background: Selecting the right clinical trials for patients remains a challenging job. The better the match between a clinical trial and the target patient population before conducting a study, the more likely the study will successfully reach the target goal of recruitment. We developed a feasibility assessment scoring system based on our vast clinical research experience, managing multiple sites across a large health system, applicable to oncology as well. Methods: Our feasibility team (FT) is responsible for identifying clinical trials and determining if they are an appropriate match for our institution and unique patient population. Once a potential trial is identified, FT performs a feasibility assessment of the research protocol to determine the resources required to conduct the trial. We developed a Clinical Trial Feasibility Assessment & Start-up Tool (CTFAST) to help sites streamline their feasibility assessments and track their trial through the start-up phase. With CTFAST a feasibility score is generated based on the cumulative value assigned to several items such as: sponsor, study type, pharmacokinetic studies, trial phase, etc. A feasibility scale is assigned as follows: > 25 – Accepted; 15-25 – On Hold; < 15- Rejected. Accepted studies are assigned a color coded priority track (fast, intermediate, routine), that allows team members to prioritize their studies accordingly. Once a study is accepted, the study is processed for enrollment. Results: CTFAST has increased productivity and clinical trial revenues by 40%. CTFAST allows for early identification of bottlenecks in workflow, thereby improving outcomes. By appropriately matching of clinical trials to our site, enrollments increased by 50% with an expanded clinical trial portfolio across 9 different departments. Study start-up times have been reduced to a minimum of 21 days and the use of time & effort has been optimized. Conclusions: CTFAST is replicable across all clinical trial sites and provides an expansive and critical feasibility analysis that is not attainable by traditional querying of investigators and questionnaires. It is an excellent work flow improvement tool as it critically analyzes all aspects of a study, prior to enrollment. When conducting an effective feasibility analysis the clinical trial site can optimize clinical trial outcomes.
Assessment of current approaches of starting dose selection and dose escalation for oncology biologics in first-in-patient trials.
Authors: Liao Kai Hsin , Chen Xiaoying , Beaupre Darrin M. , Yin Donghua , Udata Chandrasekhar
Published: May 2020

e14093

Background: Current approaches for starting dose selection for first-in-patient (FIP) trials of oncology biologics vary according to pharmacological properties and modality of the investigational agent. For biologics with immune agonistic properties, the FIP starting dose is typically based on the minimal anticipated biological effect level (MABEL). For non-immune activating biologics, the starting dose is typically based on allometric scaling of a threshold toxic dose determined in the most appropriate animal species. The aim of this work is to assess the performance of current approaches of FIP starting dose selection and dose escalation for oncology biologics. Methods: FIP clinical trials for oncology biologics licensed by the US FDA from 2010 to 2019 were analyzed to extract information related to starting dose and dose escalation. Source materials for the relevant FIP trials included the Drugs@FDA database, PubMed, and conference proceedings. The data were further analyzed to evaluate parameters related to appropriateness of the starting dose and dose escalation efficiency, which include the number of cohorts to reach the maximum tolerated dose [MTD] (or, when MTD was not identified, the highest human dose [HHD]) and the ratio of MTD/HHD to starting dose. Results: The 26 oncology biologics licensed by the US FDA between 2010 and 2019 included 8 ADCs/immunotoxins, 7 checkpoint inhibitors and 11 other biologics (monoclonal antibody, fusion protein or CD3 bispecific construct). The majority (69%) of these biologics were well tolerated without MTD identified in the FIP trials. All 26 FIP trials with biologics appeared to follow ICH S9 Guidance for starting dose selection, and the dose increment ranged from 1.2- to 10-fold. These FIP trials had a median of 6 cohorts (range: 3-10) to reach MTD (or HHD) from the starting dose. About 50% of these trials involved ≥ 6 cohorts to reach MTD (or HHD). The MTD/HHD to starting dose ratio was > 100 for 4 of the biologics. The median label dose to starting dose ratio was 10 (range: 1-3200), suggesting that a large number of patients in these FIP trials received subtherapeutic dose levels. Conclusions: Current approaches of starting dose selection and dose escalation led to a very low starting dose and many dose escalation cohorts for the FIP trials of a large portion of recently licensed oncology biologics. There exist opportunities to improve current approaches to increase FIP trial efficiency and reduce the number of patients receiving subtherapeutic dose levels in these trials.
Comparison of the predictive power of survival of the Royal Marsden Score (RMS), the Gustave Rousy Immunescore (GRIM score), the MDACC score (MDAS) and the LIPI score in phase I trials patients.
Authors: Castanon Alvarez Eduardo , Resano Leyre , Barba Luis , Vidal Covadonga , Sanchez Arraez Alvaro , Gonzalez Martin Antonio , Ponz-Sarvisé Mariano
Published: May 2020

e14094

Background: Selection of patients for enrolment in phase I trials is critical. Initial scores (RMS or MDAS) were designed to select patients with higher probability to obtain benefit in a clinical trial, followed by others such as GRIM score and the LIPI score. In this study, we have performed a comparison of the four scores in order to detect the most optimal one to select patients for immunotherapy phase I trials. Methods: We retrospectively studied 248 patients who participated in immunotherapy phase I trials in our institution from 2012-2019. We calculated high or low risk for each patient according to punctuation in RMS (number of metastasis,LDH, albumin), GRIM score (LDH, Albumin, NLR > 6), MDAS (RMS, ECOG > = 1, gastrointestinal tumors) and LIPI score (LDH, NLR > 3) at the time of screening. Overall survival (OS) was defined as the time between first dose and date of death or lost to follow up. Cox regression model was performed for predicting OS. C-Harrell index and its confidence interval were calculated for each score for measuring the power of prediction. Differences between c-Harrell indexes were calculated. All calculations were performed with STATA v16.0. Results: In terms of predicting overall survival we found that a higher punctuation in RMS (high vs low HR = 2.14 95% CI 1.47-3.10), Grim score (high vs low HR = 2.14; 95% CI1.51-3.02), MDAS (HR = 1.17 95% CI 1.04-1.31) and LIPI score (high vs low HR = 1.83; 95% CI 1.42-2.37) were all statistically significant. In terms of power of prediction, we found that LIPI score presented a c-Harrel index slightly higher (0.63; 95% CI 0.59-0.68) than GRIM score (0.62; 95% CI 0.57-0.66), MDACC score (0.55 95% CI 0.50-0.62) and RMS (0.62; 95% CI 0.59-0.66). Differences between c-index of each score were not statistically significant. In terms of predicting survival at 90 days, differences between ROC areas for LIPI score (0.64 95% CI 0.56-0.72), Grim Score (0.69 95% CI 0.61-0.76), MDAS (0.57 95% CI 0.48-0.66), and RMS (0.68 95% CI 0.62-0.73) were not statistically significant. Conclusions: Although in this study we have seen a slightly higher predictive power for LIPI score in comparison to GRIM score, MDAS or RMS, differences were not statistically significant. Nevertheless, and assuming the most parsimonious model, LIPI score seemed to be an easy and accurate model for predicting survival. These results might suggest that further prospective analysis are needed to avoid excluding patients based on these scores and who may benefit from phase I trials.
HIV status and history of prior malignancy in immunotherapy trials.
Authors: Xie John , Deshotels Leigh , Chaaya Gerard , Komiya Takefumi
Published: May 2020

e14096

Background: Patients with human immunodeficiency virus (HIV) and history of prior malignancy have been historically excluded from clinical trials. In late 2017, ASCO and Friends of Cancer Research proposed to include patients with HIV and history of prior malignancy for which treatment has been completed for at least 2 years. We aimed to determine if these recommendations were followed. Methods: Using clinicaltrials.gov, we performed a systematic review of immunotherapy trials involving pembrolizumab, nivolumab, avelumab, atezolizumab, and durvalumab. We included all solid tumor trials in patients aged 18 years and older performed between January 1, 2016 and December 31, 2019. The following data were recorded for each trial: study period (2016-17 vs. 2018-19), drug type (pembrolizumab vs other), inclusion of HIV positive population (yes or no), and inclusion of prior malignancy per guidelines (yes or no). A Chi-square analysis was performed on the two study periods in relation to HIV status and history of prior malignancy. The same analysis was also performed on HIV status/ history of prior malignancy in relation to the drug type. Significance was set at p-value < 0.05. Results: A total of 305 studies reached final analysis, of which 123 involved pembrolizumab. For all drugs combined, there was no significant change in the two study periods in the inclusion of HIV patients (p = 0.48) or history of prior malignancy (p = 0.68). A subanalysis of the pembrolizumab trials showed a statistically significant inclusion of history of prior malignancy compared to other drugs in the entire study period of 2016-2019 (p = 0.03), but this did not hold true for HIV status (p = 0.69). Conclusions: Our study suggests that recent recommendations by ASCO and Friends of Cancer Research regarding HIV status and history of prior malignancy are not being followed. This could be due to limited awareness surrounding the recommendations or continued fears regarding treatment-related adverse events in this subset of patients. [Table: see text]
The role of an open-label non-intervention design versus a placebo-control arm in oncology randomized trials.
Authors: Kushnir Igal , Clemons Mark J. , Fergusson Dean , Bosse Dominick , Reaume M. Neil
Published: May 2020

e14099

Background: Randomized trials are considered the gold standard when assessing the efficacy of new therapeutic agents. In settings where there is no known effective agent available, a randomised study could compare the investigational agent with either a placebo arm or an open-label non-intervention arm (i.e. best supportive care). The use of placebo arm can result in additional inconvenience and time commitments for patients, as well as increased administrative and regulatory costs. We conducted a survey among Canadian medical oncologists to assess whether an open-label non-intervention arm would be an acceptable alternative control to placebo in randomized trials. Methods: Members of the Canadian Association of Medical Oncologists were invited to participate in an anonymous online survey. Standardized case scenarios were used to determine participants’ attitudes regarding the role of open-label randomized-controlled trials instead of a placebo. Results: Eighty-six medical oncologists and trainees responded (response rate was 27%). Eighty-one (94%) of respondents worked at university-affiliated centres and 41 (48%) had been in practice for at least 10 years. Fifty-one (59%) respondents believed that it is acceptable to use an open-label design instead of a placebo arm when assessing a therapeutic agent in the adjuvant setting. Thirty-eight (49%) felt it was acceptable to compare the investigational agent to an open-label arm instead of a placebo to assess overall survival in the metastatic setting. Twenty-eight (38%) of respondents felt using an open-label design when assessing quality of life endpoints was acceptable. Most physicians (75%) were unsure whether or not the US Food and Drug Administration requires a placebo-controlled arm in cancer clinical trials. Conclusions: We report disagreement and uncertainty among Canadian medical oncologists regarding the acceptability of an open-label design in randomized-controlled trials where no standard therapy is approved. Clearer guidance from regulatory bodies on the adequacy of different trial designs could help reduce this equipoise.
Impact of organ function based standard exclusion criteria in diffuse large b-cell lymphoma (DLBCL) patients: Who gets left behind?
Authors: Khurana Arushi , Mwangi Raphael , Nowakowski Grzegorz S. , Habermann Thomas Matthew , Ansell Stephen M. , LaPlant Betsy , Link Brian K. , Cerhan James Robert , Maurer Matthew J. , Witzig Thomas E.
Published: May 2020

e14100

Background: Only 3-5% of adult cancer patients in the US enroll in clinical trials. Patients with organ dysfunction are often excluded from clinical trials, regardless of specific drug metabolism or relative function of the organ. The ASCO and the US FDA recommend modernizing criteria related to baseline organ function and comorbidities. In hematological malignancies, often the disease itself is the reason for organ function derangement. In order to better inform clinical trial eligibility and improve participation in the future, we evaluated the impact of baseline organ function on the potential eligibility for clinical trial enrollment for real world patients with newly diagnosed DLBCL. Methods: Consecutive, newly diagnosed lymphoma patients were offered enrollment from 2002-2015 into the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence. This analysis is based on 1270 DLBCL patients receiving immunochemotherapy. Baseline organ function parameters were identified from the exclusion criteria for hemoglobin, absolute neutrophil count (ANC), platelet count, creatinine, and bilirubin reported in recent frontline trials in DLBCL (Table). Abstracted clinical labs from the MER were used to determine the percent of patients that would be excluded based on the criteria. Results: We determined that 11-23% of MER DLBCL patients receiving standard of care frontline therapy would have been excluded in the various trials utilizing baseline organ function alone (Table). Hemoglobin and renal function had the greatest impact on exclusion. Conclusions: Current national and international (phase II and III) trials are excluding up to 23% of patients from clinical trial participation based on organ function alone in DLBCL. These data will be useful in future clinical trial development to meet ASCO recommendations to increase trial accrual, while balancing the drug toxicities and patient safety. An online tool was developed based on these results to aid future trial development. [Table: see text]
Interventional multicenter study to assess the feasibility of an e-health system (ZEMY) designed to manage symptoms and treatment-related toxicities in patients with breast cancer.
Authors: Teixeira Luis , Levy Christelle , Coeffic David , Ghorbal Rim , Dupin Julien , Esposito Nazario , Derbel Olfa
Published: May 2020

e14107

Background: There is increasing interest in using e-health systems for symptom monitoring and alert triggering. ZEMY, a new software medical device, was developed to improve symptom management and enhance patient and health care team interactions. Methods: This was a three-month open-label, interventional, single arm study conducted at five French sites. Adult women with breast cancer (BC) initiating oral and/or parenteral cancer treatment received a smartphone with ZEMY installed and verbal training. Patients started cancer treatment on Day 1 and were followed for three months. Patients entered data for ten prespecified symptoms. The ZEMY software made recommendations to patients on the self-management of symptoms and transmitted automatic messages to the health care team. Primary outcome was patient feasibility response at 3 months (completion of ≥3 symptom reports and a report completion rate of ≥60% per patient). Secondary objectives included ZEMY symptom management, usability and satisfaction, and device deficiencies (DDs). Results: Overall, 54 patients were enrolled (Jun 2018 to Jan 2019) and 52 (96.3%) completed the study. Thirty-one (57.4%) patients were responders; this was not significantly higher than the predefined cut-off and ZEMY feasibility was not demonstrated. Forty-seven (87.0%) patients had ≥3 completed symptom reports and 33 (66.0%) patients had a report completion rate of ≥60% (missing: n = 4). In subgroup analyses, 22/30 (73.3%) patients with locally advanced BC and 24/36 (66.7%) patients aged 40–60 years were responders. ZEMY patient recommendations and health care team automatic messages were considered relevant at least once in > 74% of cases for five of the six symptoms that were reported at least once by more than 1/3 of patients (diarrhea, nausea, fatigue, cutaneous and mucosal toxicities, and anxiety/depression). Using a 0–10 visual analogue scale, mean ZEMY satisfaction score was higher for patients than investigators (6.3 ±2.9 vs 4.4 ±1.5). Overall, 95 DDs were reported in 37 (68.5%) patients. Main reasons for DDs were inappropriate recommendations (n = 51) and device malfunction (n = 36). Conclusions: The primary endpoint of the study was not reached; however, higher response rates were observed in subgroups versus the overall population, indicating that ZEMY may be useful in symptom management in these patients. Generally, patients had a favorable opinion of ZEMY. The DD rate observed indicates the need for better user support in the future. Clinical trial information: NCT03558490.
Provider preferences for attributes of artificial intelligence in breast cancer screening: A discrete choice experiment.
Authors: Hendrix Nathaniel , Hauber A. Brett , Lee Christoph I. , Bansal Aasthaa , Veenstra David Leroy
Published: May 2020

e14118

Background: One of the emerging medical applications of artificial intelligence (AI) is the interpretation of mammograms for breast cancer screening. It is uncertain what attributes would result in acceptance of AI for breast cancer screening (AI BCS) among ordering clinicians. Methods: We performed qualitative interviews to identify the most important attributes of AI BCS for ordering clinicians. We then invited US-based primary care providers (PCPs) to participate in a discrete choice experiment (DCE). The experiment featured 15 choices between radiologist alone and two AI BCS alternatives where respondents traded better metrics on some attributes for worse metrics on others. Responses were analyzed using a mixed logit model adjusting for preference heterogeneity to determine the probability of recommending AI BCS. Results: In qualitative interviews, the six most important attributes to PCPs were AI sensitivity, specificity, radiologist involvement, understandability of AI decision-making, supporting evidence, and diversity of training data. Forty PCPs completed the DCE. Sensitivity was the most important attribute: a 4 percentage point improvement in sensitivity over the average radiologist increased the probability of recommending AI by 0.41 (95% confidence interval (CI), 0.38-0.42). Specificity was approximately half as important. Respondents were indifferent to whether radiologists confirmed all or only screens likely to be abnormal. However, no radiologist involvement reduced the probability of recommendation by 0.31 (95% CI, 0.29-0.31). An AI developed using data from diverse populations increased the probability of recommendation by 0.38 (95% CI, 0.36-0.39). Lastly, an AI that is transparent in the rationale for its decisions increased the probability of recommendation by 0.41 (95% CI, 0.39-0.41). Conclusions: PCPs prefer AI BCS that improves sensitivity versus specificity, and involves radiologists in the confirmation of abnormal screens. Improving sensitivity alone, however, will likely not be sufficient to support widespread PCP acceptance – algorithms will need to be developed with diverse data and more transparent explanations of their decisions.
Role of real-world evidence for oncology product registration in the United States: A review of approvals by the U.S. Food and Drug Administration from 2015 to 2019.
Authors: Arondekar Bhakti , Bhak Rachel , DerSarkissian Maral , Huynh Lynn , Wang Kelsey , Davis Eric , Wornson Bryon , Duh Mei Sheng
Published: May 2020

e14130

Background: There are few concrete examples of real world evidence (RWE) used to support clinical development in regulatory filings despite growing interest in this field. This study systematically reviewed FDA oncology approvals to identify use cases of RWE. Methods: FDA’s new drug application (NDA) and biologics license application (BLA) approvals for oncology products from 2015-2019 were systematically reviewed. Among cases with RWE, data characterizing the submission and RWE details (data source, study design, FDA comments) were synthesized. Results: 93 approved NDAs and BLAs were identified; 6 included RWE in support of efficacy (see Table), approved on or after 2017, and were largely retrospective studies that contextualized results to pivotal trial, with primary endpoints overall survival (OS), overall response rate (ORR), and time to treatment discontinuation (TTD). Flatiron data were used in 3 of these as database analyses, 1 was an expanded access program (EAP), 1 was a meta-analysis, and 1 was a retrospective chart review. Conclusions: In the past 5 years, few FDA decisions incorporated RWE in oncology drug approvals. When used, RWE has been a complement rather than a supplement for clinical trial data. Early engagement, a priori protocol development, and robust research design (adjusting for bias, comparability to clinical trial population) remain key determinants for successful use of RWE in FDA decision making. [Table: see text]
Optimization of screening tools in patients eligible to receive immune-checkpoint inhibitors.
Authors: Ostios-Garcia Lorena , Higuera Oliver , Martinez-Fdez Sara , Pinto Alvaro , Espinosa Enrique , Castelo Beatriz , Cruz Patricia , Yubero Paloma , Feliu Batlle Jaime
Published: May 2020

e14139

Background: The management of immune-related adverse events (irAEs) produced by immune-checkpoint inhibitors can be challenging, considering differences with the toxicity of classical chemotherapy. In this study, we explored the limitations in the preventions and diagnosis of irAEs at our institution and developed a strategic plan to solve them according to guidelines of the European Society for Medical Oncology and the American Society of Clinical Oncology. Methods: This is a two-phase, single center, exploratory study. In the first phase, we analyzed a random sample of 20 patients (pts) treated with nivolumab or pembrolizumab in the years 2017-2018. A Pareto chart was designed to identify issues in the baseline screening, as well as potential ways to improve the management of irAEs. After that, we implemented corrective measures based on educational lectures, early identification of patients eligible for immunotherapy, standardized protocols, and a profile of screening tests in the electronic medical records. We applied the new strategy on 15 pts treated from January-April 2019. To compare the impact of the implemented measures we used a run chart, frequencies/percentages for categorical variables, and medians/ranges for continuous variables. Adverse events were assessed according to CTCAEv5.0. Results: In the first phase, 38% of the required tests were done before the start of immunotherapy. We identified a deficit in the evaluation of cardiac and pulmonary function, chronic infections, and skin disorders. 45% of pts had some irAEs (15% G1, 15% G2 and 15% G3). Due to toxicity, 33% of pts had delays in the administration of treatment, and 11% required early interruption. After applying corrective measures, 81% of tests were requested before treatment initiation. However, screening for tuberculosis infection and echocardiogram continued to be deficient. We identified irAEs in 53% of pts (16% G1, 50% G2 and 25% G3), and 37% of them presented two or more different irAEs during the period of treatment. 37,5% and 12,5% of pts had delays or early interruption of immunotherapy, respectively. All of pts with irAEs had presented any basal alterations in screening tests. The most frequent basal alterations were found in pancreatic (66%) and thyroid activity (50%). Conclusions: Simple improvement strategies such as education programs, standardized protocols, and an electronic profile can improve the basal screening for potential irAEs. The basal alterations found in some pts can be related to the development of side effects or interactions with the immunotherapy.
A template for generic asssessment of tumor response: "myRECIST".
Authors: Raskin Stephen , Kharizman Tehila , Rachutin Zalogin Tatyana , Ackerstein Aliza , Konen Eli , Berger Raanan , Amitai Michal
Published: May 2020

e14149

Background: Oncologists often need an objective, quantitative assessment of a patient's response to therapy. Lacking a standardized format, the oncologist may request a RECIST (Response Evaluation Criteria in Solid Tumors) analysis. RECIST, however, was developed to provide standardized assessment in the context of randomized clinical trials. RECIST is based on the prospective analysis of a limited sample of tumor lesions or lymph nodes, whereas the clinician naturally makes clinical decisions retrospectively. Furthermore, RECIST has definitions, rules, and criteria for classifying responses that may not apply in the clinical setting, and it does not include consistent rules for merging or splitting lymph nodes, mixed tumor responses, lesions that may cavitate, and a wide range of individualistic responses encountered in clinical practice. Methods: To address these issues, we have modified our working Excel-based template for RECIST 1.1 assessment to include a "myRECIST" feature, so that the radiologist can enter data and examine a range of pre-defined or customizable scenarios, including RECIST1.1, iRECIST, Lugano, volumetric and other parametric protocols. With a few interactions, this template can produce exportable tables and graphs of tumor responses that can guide therapy, as shown in the attached image. Definitions are standardized and linked to RADLEX ontology for specificity and subsequent analysis. Results: This template has been developed for clinical use and is available for downloading from our institutional web-site. Conclusions: We have developed, for public use, a free, easy-to-use, down-loadable Excel template for evaluating prospective or retrospective scenarios of tumor response to therapy that avoids the restrictions of the RECIST methodology. This template may prove useful to oncologists both in and out of the context of randomized clinical trials. We call this "myRECIST."
The impact of telemedicine on patient-reported outcomes in urologic oncology.
Authors: Gadzinski Adam John , Abarro Isabelle O. , Stewart Blair , Gore John L.
Published: May 2020

e14154

Background: Nearly 20% of Americans live in rural communities. These individuals face barriers to accessing cancer care, including prevalent poverty and substantial travel burden to seeing cancer providers. We aimed to assess the impact of a rurally focused telemedicine program on patient outcomes in our urologic oncology outpatient clinic. Methods: We prospectively identified patients from rural Washington State, or who lived outside Washington, with a known or suspected urological malignancy being evaluated at the University of Washington Urology Clinic via an in person clinic or a telemedicine appointment. Patients were invited to complete a post-visit survey that assessed satisfaction, travel time, costs, and work absenteeism. We compared patient-reported outcomes between those seen as in-person versus telemedicine visits. Results: We invited 291 eligible patients from June 2019 – February 2020 to participate, 140 patients (48%) completed the survey. One-hundred and thirty-three patients had in person visits and 7 had telemedicine visits. Median age was 68, male 86%, and 69% Caucasian. Eighty-seven patients (62%) were from rural Washington; the remainder resided out-of-state. Patients were being evaluated for prostate cancer (57%), kidney cancer (18%), urothelial cancer (24%), and testis cancer (1%). Patient-reported outcomes are displayed in Table. Seventeen patients coming for in-person visits (13%) paid ≥ $1000 in total travel costs. No differences were noted in patient satisfaction. Conclusions: Patients traveling to our clinic from out-of-state and rural Washington incur significant travel time, costs, and time away from work to receive outpatient urologic cancer care. Telemedicine provides a medium for cancer care delivery that eliminates the significant travel burden associated with in person clinic appointments. [Table: see text]
DCE-MRI Evaluation of 10 patients with brain metastases treated with RRx-001, a Myc inhibitor and a CD47 and PD-L1 downregulator, in a phase I/II trial called BRAINSTORM.
Authors: Kim Michelle Miran , Parmar Hemant , Aryal Madhava P , Caroen Scott , Devasia Theresa , Schipper Matthew J , Morikawa Aki , Spratt Daniel Eidelberg , Hayman James , Junck Larry , Lawrence Theodore Steven , Lao Christopher D. , Cao Yue
Published: May 2020

e14509

Background: In a Phase 1/2 trial called BRAINSTORM (NCT02215512) for brain metastases from any histology, quantitative changes in perfusion MRI after administration of RRx-001, a mic inhibitor and CD47 and PD-L1 downregulator with vascular normalizing properties, were determined and correlated with response. Methods: Ten patients with 64 total lesions evaluable at baseline, 24 hours, and end of radiotherapy (RT) that participated in BRAINSTORM were subjected to a correlative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) examination four days prior to the start of whole brain radiotherapy (WBRT) that evaluated Ktrans (capillary permeability) and Vp (plasma volume). The treatment comprised RRx-001 on Day -4, pre-WBRT then twice weekly during WBRT. Four dose levels were administered (5 mg/m2, 8.4 mg/m2, 16.5 mg/m2, and 27.5 mg/m2. Results: 10 patients underwent DCE-MRI scans and eight patients with 44 total evaluable lesions had available imaging at 1 month, and 6 patients with 29 total evaluable lesions had imaging at 4 months. On univariate analysis, only a decrease in 24-hour Vp from baseline after a single dose of RRx-001 was marginally associated with absolute tumor volume response 1 month after treatment (p-0.07). In a multivariate model, only Vp prior to therapy and 24-hour change in Vp were retained in the model after stepwise selection. A reduction in Vp 24 hours after RRx-001 (prior to WBRT) was associated with reduced tumor volume at 1 month (Estimate 0.88, 95% CI 0.37-1.40, p = 0.001) and 4 months (Estimate 1.51, 95% CI 0.58-2.43, p = 0.003). Likewise, a lower Vp prior to therapy was associated with reduced tumor volume at 1 month (Estimate 0.73, 95% CI 0.29-1.17, p = 0.002) and 4 months (Estimate 1.8, 95% CI 0.95-2.65, p = 0.0002), suggesting anti-angiogenic activity and early potential vascular normalization after a single dose of RRx-001 predictive of longer-term tumor response. Conclusions: RRx-001 induced a reduction in blood plasma volume, which was associated with tumor response and which suggests a vascular normalizing effect that merits further investigation in future planned studies. Clinical trial information: NCT02215512.
Efficacy of anti-programmed cell death-1/ligand 1 monotherapy for non-small cell lung cancer patients with active brain metastases.
Authors: Tozuka Takehiro , Kitazono Satoru , Sakamoto Hiroaki , Yoshida Hiroshi , Amino Yoshiaki , Uematsu Shinya , Yoshizawa Takahiro , Hasegawa Tsukasa , Ariyasu Ryo , Uchibori Ken , Yanagitani Noriko , Horai Takeshi , Nishio Makoto
Published: May 2020

e14511

Background: The efficacy of anti-programmed cell death-1/ligand 1 (anti-PD-(L)1) for active brain metastases (BMs) is not established, because most clinical trials excluded patients (pts) with active BMs such as untreated, symptomatic, or unstable BMs. The aim of this study was to evaluate the efficacy of anti-PD-(L)1 monotherapy in non-small cell lung cancer (NSCLC) pts with active BMs. Methods: This retrospective study included NSCLC pts who had received anti-PD-(L)1 monotherapy in 2nd or later line between December 2015 and August 2019. Pts who had not evaluated BMs by CT/MRI before anti-PD-(L)1 were excluded. Pts were classified into those with or without active BMs which were defined as untreated or symptomatic BMs or BMs requiring systemic steroids. Progression free survival (PFS) and overall survival (OS) of pts with or without active BMs were compared. Intra-cranial and extra-cranial tumor responses were evaluated in pts with active BMs. Results: In this study, 242 pts who had received anti-PD-(L)1 monotherapy were identified and 197 pts were analyzed. Twenty-four pts were classified to pts with active BMs. Among pts without active BMs, 145 pts had no BMs and 28 pts had treated asymptomatic BMs. PFS of pts with active BMs was significantly shorter than that of pts without active BMs (1.3 versus 2.7 months; p < 0.001). OS of pts with active BMs was significantly shorter than that of pts without active BMs (4.5 vs 16.3 months; p = 0.001). Intracranial response rate (RR) was 13.3% (2/15) and extracranial RR was 26.7% (4/15) in pts with active BMs. In multivariate Cox regression analysis, active BM, poor PS and EGFR/ALK(+) were selected as significant factors associated with poor PFS. Active BM and poor PS were selected as significant factors associated with poor OS. Conclusions: Anti-PD-(L)1 monotherapy is not recommend for pts with active BMs.
Results of treating children with medulloblastoma in Azerbaijan of 2020.
Authors: Mammadova Naila Agayar , Nizami Malikov Ramin , Alik Aleskerova Gunel , Sadikh Ismailzade Reiman , Akif Nasirli Asif , Pamir Khalafova Leyla
Published: May 2020

e14519

Background: Medulloblastoma (a primitive neuroectodermal tumor of the posterior cranial fossa) is the most common CNS tumor in children; it takes 20-30% of all brain tumors. In about 40% of cases of primary detected tumors, there are already metastatic lesions of the brain and spinal cord. Treatment of medulloblastoma is a difficult problem. It requires a multidisciplinary approach and modern technical support of the medical institution. Only after the opening of the children's oncology department of the National Oncology Center in 2012, it became possible for children with a medulloblastoma to use modern treatment by HIT-2000 and HIT 2015 protocols. Methods: For the period 2012-2020, a total amount of 45 children were treated with a diagnosis of medulloblastoma (34 boys, girls-11). The average age of children was 12 years. Complete removal of the tumor was reached in 27, subtotal removal of 18 patients. Taking into account the age, the histological variant and the size of the residual tumor after surgery, the patients were divided into high and medium risk groups. Treatment according to the average risk group was carried out in 27 patients and high risk group - in 18. Craniospinal radiation therapy was carried out in 45 patients in the prescribed manner (SOD 25-30 Gy for the entire CNS and additionally for the posterior cranial fossa 25-30 Gy). In parallel with radiation therapy, vincristine is used, in dose of 1.5 mg / m2 weekly, only 8 times, followed by 8 courses of maintenance chemotherapy (cisplatin 70 mg / m2, lomustine 75 mg / m2 and vincristine 1.5 mg / m2). Results: Children in general satisfactorily tolerated both chemo- and radiotherapy. No serious complications of treatment were noted. During the observation period ( 8 years), relapses were observed in 5 children out of 45 treated, in 3 there was a progression during treatment, 8 children died. Thus, out of 45 children, 37 are alive with no signs of illness. 8-year survival rate was 82% ± 7%. Conclusions: The introduction and subsequent modification of modern foreign protocols into the practice of our center made it possible for the first time to achieve quite good results for this difficult category of patients.
Major histocompatibility complex class 1 (MHC1) loss among patients with glioblastoma (GBM).
Authors: Castro Michael , Sipos Bence , Pieper Natalia , Biskup Saskia
Published: May 2020

e14523

Background: Immune evasion represents a hallmark behavior of cancer and may occur through many mechanisms. Among these, intact tumor antigen presentation at the cell surface is a fundamental prerequisite to achieving successful adaptive immunotherapy. The loss of MHC1 expression due to molecular events, including mutation, deletion, or epigenetic silencing of B2M is commonly acquired during immunotherapy. On the other hand, molecular events affecting antigen presentation machinery may be present prior to immunotherapy administration. Among these, TP53 mutations causing loss of ERAP1 and TAP1 expression compromise transport of MHC1 molecules from the endoplasmic reticulum to the cell surface and mutant peptide integration into the HLA context resulting in absent antigen presentation. Thus far, clinical trials of PD-1/L1 agents have failed to demonstrate a benefit for GBM patients and responses are seen only among a minority. Hence, we set out to assess the integrity of MHC1 expression by using immunohistochemistry. Methods: Immunohistochemical (IHC) stains for HLA, B and C were developed and validated with internal controls. Staining intensity and location (membrane-bound or cytoplasmic) was evaluated semi-quantitatively. The first 10 consecutive patients with GBM who were referred for neoepitope vaccine were evaluated. Results: Absent staining was seen among 6/10, negligible, or faint staining was present in 2, and only 2 tumors demonstrated intact membrane-bound expression. Conclusions: In addition to low tumor mutation burden and an immunosuppressive tumor microenvironment, MHC1 loss is a frequent event among patients with GBM, and may be a dominant cause of immunotherapy failure for as many as 80% of patients. Thus, development of strategies to reverse this loss may be an essential component of successful adaptive immunotherapy for this disease. These data suggest that routine assessment of MHC1 should become a component of eligibility checking for GBM patients being considered for an MHC-restricted approaches. [Table: see text]