bluuebunny/crossref_metadata_2025 huggingface dataset:
Time to initial review by oncology clinical pathways committees for new FDA approvals/label expansions.
Authors: Caranfa Christine L. , Addonizio Joanna , Desravines Belinda , Rezaee Daniel , Waid Omar , Hamilton Joanna M. , Selesnick Victoria H. , Scalzo Michael , Jackman David Michael
Published: June 2023

e13523

Background: Dana-Farber Cancer Institute’s (DFCI) clinical pathways provide expert oncology decision-support. We schedule 2-4 reviews/year for each of over 40 medical oncology pathways, plus ad hoc reviews. When FDA issues new approvals or label expansions, the relevant medical director assigns it to the next scheduled review or a more urgent ad hoc meeting. Here we explore the time to initial review (TTIR) for FDA approvals. Methods: This analysis includes FDA approvals/label expansions for adult oncology patients from 01/01/2021 – 12/31/2022. We excluded CAR-T therapies as well as FDA approvals not directed at one of our existing pathways. TTIR was calculated from date of FDA approval to date of DFCI clinical pathways review. Univariate analyses examined the impact of prior in-class competitors in the specific approval setting; prior approvals for the treatment class in general; and scheduled meeting cadence. Results: From January 1, 2021 – December 31, 2022, there were 58 FDA approvals/label expansions in diseases for which a DFCI pathway exists. Eighteen represented items that had been previously addressed (e.g., conversion of accelerated approval to full, or a label expansion adopted into pathways earlier based on published data). For the remaining 40 new approvals or label expansions, the median TTIR was 3.4 months. 43% of FDA approvals were addressed prior to or within 30 days after announcement. There were 9 ad hoc reviews over this period, including review of 3 new FDA approvals. Median TTIR for items that were a first-in-class therapy for that specific indication (n= 20) was 2.1 months, compared to 3.5 months for agents with an existing in-class competitor in that setting (n=20). Median TTIR for items that represented the first approval for a novel first-in-class agent (n=13) was 1.5 months; TTIR for agents with existing in-class approvals in any setting (n=27) was 3.5 months. Median TTIR for FDA actions involving diseases/pathways that have > 3 scheduled review meetings/yr (n=28) was 2.8 months, compared to 5.2 months in pathways with 1-2 scheduled reviews/yr (n=12). Conclusions: Clinical Pathways provide a structured mechanism for reviewing whether, when, and how new treatments are best used in oncology practice, and for disseminating those recommendations to providers. The time to initial review was shorter for initial registrational approvals for novel agents, and for treatments in diseases with more frequent meetings. [Table: see text]
A comparison between a clinical decision support system and clinicians with guidelines in breast cancer.
Authors: Li Jianbin , Yuan Yang , Bian Li , Yang Hua , Ma Li , Xin Ling , Li Feng , Zhang Shaohua , Wang Tao , Liu Yinhua , Jiang Zefei
Published: June 2023

e13551

Background: We are building a clinical decision support system (CSCO AI) for breast cancer patients to improve the efficiency of clinical decision-making. We aimed to assess cancer treatment regimens, in neoadjuvant therapy, adjuvant chemotherapy, adjuvant endocrine therapy, first line therapy and second line therapy, given by CSCO AI and clinicians. Methods: 400 breast cancer patients were screened from the CSCO database. Clinicians with similar levels were randomly assigned one of the volumes (200 cases). After that, clinicians with guidelines were asked to answer the same cases again. CSCO AI was asked to assess all cases. Three reviewers were independently asked to evaluate the regimens from clinicians and CSCO AI. Regimens were masked before evaluation. The primary outcome was the proportion of high-level conformity (HLC), which were defined as the proportions of regimens in accordance with CSCO guidelines. Results: The overall concordance between clinicians and CSCO AI was 67.4% (2350/3500). After referring to the guideline, a total of 22.6% (792/3500) regimens were modified by clinicians, 12.9% (451/3500) had a higher grades and 9.7% (341/3500) had a lower grades. In early stage, the concordance was elevated with statistical significance from 71.3% (1497/2100) to 76.1% (1598/2100, p＜0.001). In the metastatic stage, the concordance was improved form 61.7% (864/1400) to 66.0% (924/1400, p=0.018). HLC in CSCO AI was 95.8% (95%CI:94.0%-97.6%), significantly higher than that in clinicians (90.8%, 95%CI:89.8%-91.8%) and in clinicians with guidelines (92.1%, 95%CI:91.0%-93.4%). In early stage, high-level conformity in CSCO AI was 95.7%, with no statistical significance when compared with clinicians (92.7%, p=0.078) and clinicians with guidelines (92.3%, p=0.050). In metastatic stage, high-level conformity in clinicians was only 88.0%, lower than that in CSCO AI (96.0%, p=0.001). However, after referring guidelines, high-level conformity in clinicians was elevated to 91.9%, with no significant difference when compared with that in CSCO AI (p=0.058). Considering professions, the high level conformity of surgeons was 85.9%, lower than that of CSCO AI (OR=0.25,95%CI: 0.16-0.41). The most significant difference in HLC was in first-line therapy (OR=0.06, 95%CI:0.01-0.41). When clinicians were divided according to their levels, there was no statistical significance between CSCO AI and higher-level clinicians. Conclusions: Clinical decision support for breast cancer was superior for most process outcomes except for second-line therapy. The improvements in process outcomes suggest that CSCO AI can be widely used in clinical practice.
Novel machine learning algorithm to predict response to immunotherapy in patients with small cell and non-small cell lung cancer.
Authors: Sharma Lakshya , Choi Sunyoung , Balaji Vaishnavi , Joshi Adwait , Mporas Iosif , Shi Jiaqi , Zhao Yi , Tailor Vipula , Shah Riyaz N.H. , Waters Justin S. , Adeleke Sola Michael
Published: June 2023

e13556

Background: Immune checkpoint inhibitors (ICI) are used to manage patients with both small cell (SCLC) and non-small cell (NSCLC) lung cancer. However, ICI response rates are often low, and identifying patients that will benefit from ICIs can be challenging. The value of biomarkers used to predict ICI response, such as PD-L1, Combined Positive Score (CPS) or tumor mutational burden (TMB) have been debated. Furthermore, the resources needed to assess these biomarkers may not be available in many centres. Developing more accurate and accessible tools that predict ICI responses could enable a precision medicine approach that improves patient outcomes. This study aimed to use a novel machine-learning (ML) algorithm to predict response to different ICI therapies in patients with lung cancer based on clinically available data. Methods: 334 eligible records were cleaned and reprocessed from textual to categorical data using one hot encoding. Complete datasets were available for 161 patients. Differences in the data distribution were handled using the Synthetic Minority Oversampling Technique. Six ML algorithms were trained, including Linear regression, Support Vector Classifier, XGBoost Classifier, Random Forest, Decision Tree, and Gaussian Naive Bayes Classifier. These algorithms used 80% of the training data, were tested on 20% of validation data and used the Grid Search Cross-Validation technique for hyperparameter optimization. Results: For the 161 patients included in the final analysis, the mean age was 68 years and 48% were female. 9% of patients had SCLC and 80% had NSCLC. Patients receiving Pembrolizumab, Nivolumab and Atezolizumab comprised 62%, 11% and 25%, respectively. The artificial intelligence (AI) algorithm predicted and stratified ICI response better than PD-L1 levels. Of the ML algorithms, XGBoost Classifier predicted response with the most accuracy, 64% (0.61 F1 score). This model found that good performance status (0-1), female gender and adenocarcinoma sub-type predicted response to ICI. On the other hand, M1, N2 staging, male gender, squamous cell carcinoma sub-type and receiving Atezolizumab were predictive of disease progression. Conclusions: This study developed multiple novel ML models to predict responses to ICIs in lung cancer. XGBoost Classifier used clinically available data to show that the type of ICI a patient receives, their histopathology sub-type and their TMN staging impact ICI response. Future work will aim to improve accuracy and predict ICI toxicity by including data from multiple centres, different cancer types and additional clinical variables. [Table: see text]
Cost and time estimates for integrating a cardiovascular health application for cancer survivors in the electronic health record: Case studies from the NCORP network.
Authors: Foraker Randi E. , Durkin Michael , Lackey Ian , Heard Kevin , Wells Brian J. , Fesperman Susan , Davidson Eleanor , Payne Philip , Lai Albert M. , Gendron Melissa , Weaver Kathryn E.
Published: June 2023

e13557

Background: Clinical decision support (CDS) tools may support the delivery of guideline concordant cancer care yet must be integrated into EHR platforms and optimized for clinical workflow. Implementation outcomes are not well-documented in the literature for deployment of CDS tools in community oncology practices. Here we provide best practices for teams looking to implement CDS tools in diverse cancer care settings. Methods: The Automated Heart-Health Assessment (AH-HA-WF-1804CD) study is a clinic-randomized trial coordinated by the Wake Forest NCI Community Oncology Research Program (NCORP) Research Base to support cardiovascular health assessment among cancer survivors. The AH-HA team developed and guided the implementation of the AH-HA tool, an EHR-based CDS visualization of the American Heart Association's Life's Simple 7 modifiable risk factors supplemented with cancer treatment information. Four NCORP clinics were randomized to the intervention arm and implemented the tool within their EHR leveraging Fast Healthcare Interoperable Resources (FHIR) standards and best practice advisories (BPAs). Information technology (IT) team authorization was required prior to randomization. We provided an implementation checklist to each clinic alongside virtual support from experts in implementing CDS for research and operational purposes. We describe our successful CDS tool implementation strategy and report the timeline for implementation at each clinic. Results: Although all clinics utilized the same EHR platform (Epic), clinics differed in terms of their time to approval, implementation, and tool activation, ranging in a total from 4 to 15 months. Although we provided step-by-step guidance for CDS deployment, each site required at least one technical assistance consultation. The estimated cost at each implementation site to deploy the tool, given a range of 40 to 80 hours at $150/hour, was $6,000 to $12,000 US dollars. Conclusions: Strengths of the implementation process included extensive documentation and clear communication of the steps for implementation. Clinics desired flexibility to adapt BPAs to their workflows. Site-level differences may pose challenges for interpretation of study implementation outcomes, including patient-level reach measures. Studies testing the impact of similar CDS tools should plan for clinic-level adaption and consider variability in timelines and assistance needed within the overall study timeline and budget. Clinical trial information: NCT03935282 .
Morphomic phenotyping of oncologic populations.
Authors: Wang Stewart , Wang Nicholas C.
Published: June 2023

e13572

Background: We have previously identified body and organ composition measures in pre-treatment CT scans that predict post-treatment mortality or disease recurrence in melanoma as well as esophageal, pancreatic, hepatocellular and lung cancers within single institution cohorts in the US. Similar trends can be identified in Asian cohorts although the absolute range of these body composition measures differ substantially from those in the US. We have also identified body and organ composition measures that predict drug tissue distribution and optimal dosing. The qualitative and relative nature of body and organ composition measures to date have limited their utility for precision treatment of oncologic populations. We have therefore refined the precision, granularity and reliability of these measures (morphomics) in oncologic and reference populations. Methods: Analytic Morphomics, our group’s imaging analysis platform, extracts granular, anatomically-indexed body and organ composition measures from medical imaging. This platform has been updated to use artificial intelligence and nested, supervised machine learning algorithms. Our digital morphomics “assays” are 100% reproducible due to our extensive use of deterministic software algorithms and scalable due to our use of efficient ML algorithms and software code. Our ML models have been trained using state-of-the art techniques in 2D and 3D convolutional neural networks (CNNs) for image segmentation, utilizing repeated randomization cross-validation, hold-out sets, and ensemble modeling methods, with large, broadly representable training image sets, and verified by extensive user testing on clinical scans from nearly 200,000 live subjects. Results: We analyzed curated cohorts reflecting the general United States and Taiwanese population (Ns=5,286; 1,459). In addition, we analyzed oncological cohorts reflecting patients with colon (N=95), ovarian (216), pancreatic (156), and esophageal cancers (Ns=333, 344). Validated morphomic measures include bone mineral density (BMD), visceral fat area (VAT, subcutaneous fat area (SAT), skeletal muscle area (SMA), skeletal muscle density (MD) at multiple vertebral levels. When referenced against measures taken from general non-oncologic (trauma) populations to adjust for non-specific changes due to age and gender, these referenced morphomic measures gain predictive power for mortality and cancer recurrence. Conclusions: The unprecedented level of granularity and precision of these raw and referenced morphomic measures shows clinical utility for improved patient selection, drug dosing as well as evaluation of treatment response and toxicity.
Generating synthetic samples of chondrosarcoma histopathology with a denoising diffusion probabilistic model.
Authors: Nye Logan , Ghaednia Hamid , Schwab Joseph H.
Published: June 2023

e13592

Background: The emergence of digital pathology — an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis — is changing the pathology ecosystem. The development of machine-learning approaches for the extraction of information from image data, allows for tissue interrogation in a way that was not previously possible. However, creating digital pathology algorithms requires large volumes of training data, often on the order of thousands of histopathology slides. This becomes problematic for rare diseases, where imaging datasets of such size do not exist. This makes it impossible to train digital pathology models for these rare conditions. However, recent advances in generative deep learning models may provide a method for overcoming this lack of histology data for rare diseases. Pre-trained diffusion-based probabilistic models can be used to create photorealistic variations of existing images. In this study, we explored the potential of using a deep generative model created by OpenAI for the purpose of producing synthetic histopathology images, using chondrosarcoma as our rare tumor of interest. Methods: Our team compiled a dataset of 55 chondrosarcoma histolopathology images from the annotated records of Dr. Henry Jaffe, a pioneering authority in musculoskeletal pathology. We built a deep learning image-generation application in a Jupyter notebook environment, iterating upon OpenAI’s DALL-E application processing interface (API) with python programming language. Using the chondrosarcoma histology dataset and NVIDIA GPUs, we trained the deep learning application to generate multiple synthetic variations of each real chondrosarcoma image. Results: After several hours, the deep learning model successfully generated 1,000 images of chondrosarcoma from 55 original images. The synthetic histology images retained photorealistic quality and displayed characteristic cellular features of chondrosarcoma tumor tissue. Conclusions: Deep generative models may be useful in addressing issues of data scarcity in rare diseases, such as chondrosarcoma. For example, in situations where existing imaging data is insufficient for training diagnostic computer vision models, diffusion-based generative models could be applied to create training datasets. However, further exploration of ethical considerations and qualitative analyses of these generated data are needed.
Standardizing the strategy in glioblastoma trials to promote success in cell and gene therapy clinical development.
Authors: Elorza Izaskun , Nirenberg Damian , Maczkiewicz Martin , Hoppener Floris
Published: June 2023

e13604

Background: Glioblastoma is the most common and aggressive brain tumor, with a high recurrence rate and low survival. We are increasing our understanding of its biology, and more targeted therapeutical agents are available. However, the GBM prognosis has not improved over the last decades. Multiple clinical trials on Cell and Gene Therapies are ongoing but, there is a huge need to define standardized and innovative strategies to promote better patient selection, implement adaptative designs and specific laboratory assays, and tailor the clinical endpoint assessments. Methods: Medical experts panel agreed on the following insights and gave advice to improve the design and conduct of GBM trials. Results: The criteria for selecting patients are not uniform among studies. The 2021 WHO Classification of central nervous system tumors has yet to be universally adopted, and different tumor entities are being treated as GBM according to the prior 2016 classification. Furthermore, the required genetic testing panel is only available in some countries as part of routine testing. A wide range of CGT therapies are being studied with different mechanisms of action and are well suited to be administered as combinatory regimens and multiple-dosing schedules to boost the antitumor effect. The implementation of master protocols offers the possibility to test different schedules to identify the most promising for further investigation during early phase development. There is a lack of biomarkers for the patient- or cancer-specific sensitivity and the efficacy of CGT. This is important not only to target clinical treatments to the appropriate patients and identify responding/non-responding patients early during treatment but also to improve the preclinical models and identify targets for further therapeutic development. Investigators should use standard endpoint criteria when reporting clinical outcomes. It has been common to refrain from introducing any preliminary-effect objective in the early stages. This tendency changes after guidance from experts and regulatory agencies about maximizing early-phase data. Another vital endpoint in the early phases is assessing patient immunogenicity against CG therapy based on humoral and cell virus-specific and tumor-specific immune responses. This is especially important as there are significant concerns in translating preclinical safety results to the clinic due to the species-specificity of different viruses and critical differences between immune systems. Conclusions: Today, the purpose of treatment for GBM is to extend the survival period and to manage the disease while preserving the patient’s quality of life. The goal of CGT is a durable complete response, but to achieve this target, we will need a deep analysis of how we perform clinical trials and move to an expert and tailored research environment.
Imbalanced censoring as a bias in evaluation of randomized controlled trials: A simulation-based study.
Authors: Rumyantsev Alexey , Tyulyandina Alexandra , Tjulandin Sergei
Published: June 2023

e13606

Background: Censoring imbalances between experimental and control arms in clinical trials efficacy can compromise efficacy analysis especially if surrogate endpoints are used. We conducted a simulation-based study to assess the impact of imbalanced censoring in clinical trials on the probability of false-positive outcomes. Methods: With BayesCTDesign R package we created a data set of 500,000 simulated trials. The trials had initially negative results, varying sample sizes (100-2000 patients) and median survival times (1.5-9.5 mo.) with no baseline censoring. Trials with no significant baseline differences (p > 0.2) between experimental and control arms were selected. We randomly censored samples of patients from control (0-20% of sample) and experimental (0-40%) arms for analysis. A logistic regression model was created to assess probability of positive results due to censoring (HR<1 and p value after censoring <0.05). Sample sizes and difference in % of censored subjects were predictors as well as interaction term between these variables. Results: A data set consisting of 399,946 trials was included in the analysis. After random censoring, a statistically significant positive transformation was observed in 61797 trials with corresponding HR<1 (0.59-0.91; p < 0.05) in favor of experimental arm. A logistic regression model with above mentioned variables returned a following equation (p <0.05 for all the variables): y = -6.45005797 + 0.27661437*B1 + -0.000070584*B2 + 0.000304167*B3 where B0 is the intercept, B1 – difference in percentage of censored subjects between arms (per %), B2 – sample size (per subject) and B3 – an interaction term (B1*B2). Pseudo R2 coefficient was 0.715. Based on the model we estimated a predicted probability of false-positive results for the trials. Conclusions: Clinicians and researchers should pay special attention to censoring imbalances in the clinical trials as these may have a significant impact on results consistency, validity and lead to false-positive findings. [Table: see text]
Symptomatic adverse events in lung cancer clinical trials included in US drug labels from 2015 to 2021 to inform patient-reported outcomes.
Authors: Horodniceanu Erica , Datla Tejaswi , Murugappan Meena N. , Larkins Erin A. , Singh Harpreet , Bhatnagar Vishal , Kluetz Paul
Published: June 2023

e13642

Background: FDA recommends assessing expected symptomatic adverse events (symAEs) as a core patient-reported outcome (PRO) in cancer clinical trials (CTs). Several core PRO symptom lists exist that measure the same symptoms across trial contexts; however, many were developed in an era when cytotoxic chemotherapy was the main mechanism of action (MOA). To further research relevant PROs in modern cancer CTs, we identified common symAEs from recently approved lung cancer drugs across MOAs. Methods: We identified lung cancer drug approvals from 2015 through 2021 using an FDA database. The drug US Prescribing Information (PI) was used to extract indication, MOA, CT information, and all-grade symAEs reported in ≥20% of patients in the investigational arm. To improve symAE attribution to the drug, we limited analysis to CTs with a monotherapy investigational arm. We determined the most common symAEs based on the number of CTs reporting a specific symAE in ≥20% of patients. Results: Of 54 approvals identified, 31 lung cancer CTs (30 for NSCLC, 1 for SCLC) with monotherapy investigational arms were included, with AE data from 9,829 patients. Drug MOAs were classified broadly as growth pathway inhibitors (GPI) for 21 CTs (68%), immunotherapy (IO) for 9 CTs (29%), and cytotoxic chemotherapy for 1 CT (3%). Most common symAEs are presented in Table 1 (truncated to symAEs in ≥9 CTs, based on All CTs). In comparing MOAs, symAEs reported at ≥20% incidence in ≥5 GPI CTs but in no IO CTs included: diarrhea (76% of GPI CTs), edema (38%), nail toxicity/paronychia (29%), vomiting (29%), stomatitis (29%), and dry skin (24%). Fifteen other symAEs were reported at ≥20% incidence in ≥3 GPI CTs, but in no IO CTs [data not shown]. Conclusions: While there were several common symAEs across lung cancer CTs, our analysis using AE data in PIs of recently approved drugs showed important variability between CTs and broad GPI and IO groups, demonstrating that consideration of MOA and expected toxicity are important for ensuring relevant high frequency symAEs are included as PROs. Final selection of expected symAEs from a PRO item library is context dependent, requiring consideration of all available clinical data and patient input.[Table: see text]
The impact of SARS-CoV2 Omicron variants epidemic on patients with cancer in China.
Authors: Xiao Ping , Zhong Diansheng , Zhang Linlin
Published: June 2023

e13652

Background: In December 2022, SARS-CoV2 Omicron variants were widely spread in China, and its impact on cancer patients is unclear. Methods: We retrospectively analyzed 170 cancer patients admitted to Department of Medical Oncology, Tianjin Medical University General Hospital from December 1,2022 to January 31,2023. Results: The proportion of the vaccinated population was 62% (106/170). The prevalence of Omicron variants infection was 82.35% (140/170), 86% (55/64) in unvaccinated patients and 80% (85/106) in vaccinated patients (P＞0.05). Symptoms were 77% fever (108/140), 64% cough (90/140), 35% pharyngalgia (49/140), 25% loss of taste or smell (35/140), 23% fatigue (32/140), 20% aches and pains (28/140), 19% dyspnea (27/140), 9% stuffy or runny nose (13/140) and 8% nausea or diarrhea (11/140). Among them, 48% (67/140) had 1-2 clinical symptoms; 47% (66/140) had 3-5 clinical symptoms and only 4% had more than 5 clinical symptoms. The proportion of hospitalization due to Omicron variants infection was 4%. Duration of symptoms of positive patients was 7 days (1-30)，and it was not related to vaccination status, cancer type and pulmonary disease(chronic obstructive pulmonary disease、emphysema、tuberculosis and bronchiectasis) (p＞0.05). Proportion of patients admitted to hospitalization for anti-tumor therapy at normal time (delayed 0-7 days) after Omicron variants infection was 39%（57/145）, and the hospital stay delayed 8-14 days was 20%（29/145）and 24% (35/145) more than 14 days. And it was not related to vaccination status, cancer type and pulmonary disease(chronic obstructive pulmonary disease、emphysema、tuberculosis and bronchiectasis)(p＞0.05). Conclusions: Patients with cancer had mild symptoms during Omicron variants epidemic, and the impact of Omicron on the rate of hospitalization and anti-cancer delayed treatment were relatively mild.
Recent analysis of frequency of surrogate end points used in oncology clinical trials 2006-2022.
Authors: Chitkara Akshit , Rai Manoj P. , Thawani Rajat , Chen Emerson Yu-sheng
Published: June 2023

e13658

Background: Surrogate endpoints expedite the clinical trial results at the cost of more uncertainty with clinical efficacy, as they may have a weak correlation with definite endpoints like overall survival (OS). Increased use of surrogate measures such as progression-free survival (PFS) and response rate (RR) are increasingly used by the FDA for drug registration trials in oncology. We conducted this analysis to update our previous analysis (2006-2017) and to study the recent trends in using these surrogate endpoints for approval by FDA. We also study how these surrogate markers are used across various therapy lines. Methods: Our retrospective analysis of US FDA oncology approvals from 2006 to 2022 examined FDA documents from the FDA website, package inserts, and related published trials. Only drug approvals for non-hematologic solid tumors were included. We collected data about the approval type, conversion to regular approval, drug withdrawals (until February 11, 2022), lines of therapy (first-line, second-line, and third or other lines), and endpoints used for FDA approval. All surrogate endpoints were categorized into response rate (RR), generally a proportion, or progression-free survival (PFS), a time-to-event endpoint. Definite endpoints, including patient-reported outcomes (PRO), were all included within the OS category. Results: We identified that OS/PRO endpoints accounted for only 29% (98 of 342) of drug indication approvals. In contrast, 71% of drug approvals (244 of 342) were based on surrogate endpoints; 37% (127 of 342) of drug indications used RR endpoints, followed by 34% (117 of 342) drug indications that used PFS endpoints. These surrogate endpoints (RR, PFS, CR, DFS, EFS, pCR, iDFS, MCR, MFS, ORR, RFS, rPFS combined) showed a trend toward being used in the third- or later-line line setting followed by second-line (91 of 145 drug indications [63%] for first-line vs. 102 of 138 drug indications [74%] for second-line vs. 38 of 43 drug indications [88%] for third- or later-line). Still, they were used in all lines of therapy. Conclusions: FDA commonly accepts surrogate endpoints as criteria for drug approval in oncology since 2006. There has been a trend toward using surrogate endpoints in the third or later lines of drug approvals by the FDA. There is a tradeoff between providing access to cancer therapies and potentially approving therapies that still have unverified clinical benefits.
Trends in FDA approval of solid tumor therapies with analysis of single-agent vs. combination therapies.
Authors: Rai Manoj P. , Chitkara Akshit , Thawani Rajat , Chen Emerson Yu-sheng
Published: June 2023

e13659

Background: Single agents replacing previous therapies are ideal, but combination therapies with two or more active agents have been proposed to decrease drug resistance, promote mechanistic synergy, and/or have additive cytotoxic effects in cancer treatment. However, approval of combination therapies may increase exposure to new and former drugs, leading to more costs and time commitments to patients. Methods: This retrospective analysis examines all FDA-approved drugs for malignant solid tumors from January 2011 to December 2022. We used the data presented in the package inserts on the US FDA website. At least two reviewers selected all new and clinically meaningful indication changes for data extraction. Drug approvals for topical medications, precancerous conditions, hematologic conditions, primarily pediatric cancers, primary genetic syndrome neoplasms, biosimilars, and minor indication changes were all excluded. The primary endpoint was the proportion of new approvals that replaced former therapies rather than adding to existing therapy to become multi-agent combinations. We also compared single and combination regimes with the differences regarding cancer types, line of therapy, primary endpoints leading to approval, year of approval, type of approval, and type of expedited programs at the FDA. Results: Our analysis identified 247 FDA solid tumor drug indication approvals among 279 drugs from 2011 to 2022, with non-small cell lung cancer (52, 21%), followed by breast cancer (28, 11.3%) and melanoma (22, 8.9%), being the most common. The most common class of drug was small molecular targeted therapies (99, 40%), followed by immunotherapy checkpoint inhibitors (84, 34%). Most approvals were for 1st line (111, 44.9%), followed by 2nd line (106, 42.9%), and adjuvant setting (17, 6.9%). Among 247 approvals, 167 (67.6%) replaced a former therapy to become a new single standard of cancer therapy for that clinical setting, while 80 (32%) added to an existing regimen as a combination. 32 (12.9%) approvals have 3 or more drugs in the new therapy, 49 (19.8%) with 2 drugs in the new therapy. When comparing single-agent vs. combination regimen approvals, there was difference in cancer types, type of regimen, line of therapy, highest endpoints leading to approval, novel therapy, and type of drug approval (p-value < 0.05; test statistic > critical value). However, the year of approval, fast-track, and priority-review status were not statistically significant. Conclusions: Approximately two-thirds (68%) of drug approvals over the past 12 years for non-hematologic solid cancers are replacing or adding a new standard. Still, a significant minority (32%) are adding to existing regimens increasing the complexity of cancer therapies, especially in highly prevalent solid tumors, increasing potential time commitment for patients, costs, and exposure to therapies with notable side effects.
Better together: A team-based approach to improve prior authorization efficiencies and wellness in physicians and advanced care providers.
Authors: Pintova Sofya , Royer Jessica , Sheng Tianxiang , Liu Mark , Noble-Kirk Alana , Smith Cardinale B. , Bhardwaj Aarti Sonia
Published: June 2023

e13664

Background: The burden of prior authorizations (PAs) required for costly oncologic treatments is high. Upon PA denials, peer-to-peer requests are performed. These requests consume time and effort of physicians and advanced care providers (APPs) contributing to burnout and may have a negative impact on patient care. We aimed to assess the degree to which the PA process contributed to burnout among oncology clinicians prior to implementation of a new streamlined workflow aimed at improving clinician wellness. Methods: A baseline wellness survey was sent to all hematology-oncology faculty and APPs at the largest clinical site of our academic cancer center. Burn out, our primary outcome, was measured using the work-related Copenhagen Burnout Inventory. We included selected questions from the American Medical Association (AMA) surveys regarding physician perceived impact of PAs on clinical workload and care delivery. We asked physicians and APPs to rate the impact of PAs on burnout on a scale of 0-100. Analysis was conducted using descriptive statistics. This study was sponsored by our Office of Well-Being and Resilience. Results: The wellness survey was completed by 35 physicians and 21 APPs with a response rate of 53% in both. The median Copenhagen burnout score for physicians was 46.43 (35.71-60.71) with higher burn out scores among those identifying as females, in practice less than 15 years and with more than 60% time in direct patient care. The median burn out score for APPs was 64.28 (50-83.03). Similar to physicians, APPs with less clinical experience had higher burn out scores. When asked about the impact of PAs on burnout on a scale of 0-100, physicians at the main site reported a median of 13.5, while APPs reported a median of 53.5. Conclusions: Physicians and APPs report noteworthy baseline rates of burnout and dedicate a significant portion of the week to completing PAs. Importantly, APPs perceived the PAs to have a high impact on their burn out compared to physicians. We will use these data to determine the efficacy of a new process that utilizes a single EMR platform for finance and cancer registry teams to abstract and document all of the required elements to streamline the PA process for intravenous/injection cancer treatments with the goal to reduce the number of PAs and improve wellness. [Table: see text]
Combination of regorafenib with BRAF-/MEK-inhibitors for the treatment of patients with refractory melanoma brain metastases.
Authors: Dirven Iris , Vander Mijnsbrugge An-Sofie , Vounckx Manon , Tijtgat Jens , Wilgenhof Sofie , Neyns Bart
Published: June 2023

e14003

Background: there are no active treatment options available for patients (pts) diagnosed with melanoma brain metastases (MBM) that progress despite treatment with immune checkpoint inhibitors (ICI), and BRAF-/MEK-inhibitors (BRAF/MEKi) in pts with BRAF V600mutant melanoma. Regorafenib (REGO), an oral multi-target kinase inhibitor, including potent inhibition of RAF-dimers, has single-agent activity in pretreated melanoma (AS Vander Mijnsbrugge et al. SMR 2022). Here we report our single-institution experience with REGO in combination with BRAF/MEKi in pts with refractory MBM. Methods: pts were identified prospectively, medical records were reviewed retrospectively. Results: 13 pts with stage IV-M1d melanoma were included (6F, med age 54y [range 33-72], WHO PS: 0/1/2/3 resp. n = 1/4/6/2 pts, BRAF V600mut: n = 9, NRAS Q61mut: n = 4). Pts previously progressed on ICI (anti-PD-1 and -CTLA-4; 13 pts); BRAF-/MEK-inhibitor (all BRAF V600mut pts); chemotherapy (3 pts), T-VEC (2 pts), REGO mono (2 pts), and REGO+ICI (2 pts). At baseline, 11 pts had active MBM (8 pts on corticosteroids); 10 pts had extracranial evaluable disease. Pts were treated with REGO (40-80 mg QD) combined with BRAF/MEKi in 9 BRAF V600mut pts (dabrafenib/trametinib: n = 4; encorafenib/binimetinib: n = 5) or, in 4 NRAS Q61mut pts, with trametinib or binimetinib (+ low-dose BRAF-inhibitor if needed to mitigate skin toxicity). Treatment was stopped in 8 pts because of PD and is ongoing in 5 pts (3-49 wks after initiating therapy). There were no grade 4-5 TRAE. Grade 3 TRAE included: arterial hypertension (n = 4), hepatotoxicity (n = 2), diverticular bleeding (n = 1), and duodenal perforation, treated conservatively (n = 1). None of the 2 pts without active MBM at baseline progressed within the CNS. In 11 response evaluable pts (incl. 10 with evaluable extracranial metastases), best objective intracranial response (according to RANO-BM) was PR in 4 pts (36%; 3 BRAF V600mut pts), and SD in 4 pts (36%; 3 BRAF V600mut pts). Intracranial responses corresponded with extracranial responses in all but 3 pts (1 NRAS Q61mut pt with a PR intra-, had SD extracranially; 1 BRAF V600mut pt and 1 NRAS Q61mut pt with SD intra-, had PD extracranially); considering a potential clinical benefit of therapy beyond first PD, both pts continued treatment and remained clinically stable at the latest FU (resp. 15 and 49 wks after initiating therapy). Median time on REGO+BRAF/MEKi in BRAF V600mut pts was 15.9 wks (range 3-46), and on REGO+MEKi in NRAS Q61mut pts: 7.6 wks (range 3-49). Median PFS and OS: 12.9 and 26.4 wks (resp. 15.3 and 26.4 wks in BRAF V600mut pts; 8.6 and 10.1 wks in NRAS Q61mut pts). Conclusions: in heavily pretreated patients with MBM combination of REGO with BRAF-/MEK-inhibitors demonstrated promising anti-tumor activity. Further investigation of safety and clinical activity in a prospective trial is warranted.
Baseline characteristics, hospital outcomes, and palliative care utilization in leptomeningeal disease: A nationwide analysis.
Authors: Turk Ekrem , Obeidat Khaldun , Litvin Rafaella , Vakil Jay , Deenadayalan Vaishali , Olafimihan Ayobami Gbenga , Ibrahim Muhammad Bilal , Boduroglu Eda Sevim , Shaka Hafeez , Batra Kunnal
Published: June 2023

e14004

Background: Leptomeningeal metastasis (LM) is a rare but devastating manifestation of cancer metastasis. Most solid tumors are known to cause LM, but the most common etiology is breast cancer and lung cancer. The incidence of LM has been increasing due to increased survival rates secondary to the improved systemic cancer treatment. Here we aim to explore the baseline characteristics, epidemiologic differences, palliative care utilization. and hospital outcomes of LM. Methods: Nationwide Inpatient Sample (NIS) database 2016-2019 was queried to determine adult hospitalized patients with LM and relevant cancers by using ICD-10 codes. We studied the racial and socioeconomic differences, palliative care utilization, length of stay (LOS), total hospital charges (THC), and all-cause mortality in cancer patients with and without LM. Multivariate logistic regression analysis was conducted to adjust for confounders. Results: There were 15,325 admissions with LM, 20% had lung cancer (LC) and 20.4% had breast cancer (BC). The prevalence of LM was 1.9% and 0.5%, respectively, in LC and BC patients. LM patients were younger (LC 63 vs 69; BC 56 vs 64 p<0.001), more likely to be non-White, and more likely to have private insurance (37% vs. 19%, p<0.001). LM admissions had significantly increased LOS and higher THC compared to other cohorts. BC or LC with LM was associated with significantly increased Palliative care consult rate ( LC 31% vs 16%; BC 29% vs 10% p<0.001) compared to BC and LC without LM. Palliative care use was more common in the African American race. The all-cause mortality rate in hospitalizations with LM was increased compared to cancer without LM (LC 11% vs 8.6% p=0.008 aOR 1.41; BC 12.8% vs 4.5% p<0.001 aOR 3.39). The white race (OR 0.6, p<0.001) and Medicare (OR 0.58 p<0.001) were associated with lower odds of mortality, while the Hispanic race (OR 1.43, p=0.042) and Medicaid (OR 1.39, p=0.041) were associated with higher odds of mortality among LM hospitalizations. Conclusions: While actionable mutations and targeted therapies are developing rapidly, therapeutic options for LM remain limited, with a median survival of 4-5 months. Palliative care remains essential for a better quality of life; hence further studies should be conducted to improve the utilization of palliative care and investigate racial and economic disparities in the utilization of such.
Survival in patients with brain metastasis secondary to breast cancer from Saudi Arabia.
Authors: Abusanad Atlal M. , Iskanderani Omar , Al-hajeili Marwan R. , Ujaimi Reem , Alwassia Rolina
Published: June 2023

e14018

Background: Breast cancer (BC) is the most common malignancy in women from Saudi Arabia. Nearly one-third of the patients present with metastatic disease. Data on the incidence, pattern and outcome of brain metastasis (BM) in breast cancer patients is yet to be reported to inform the practice and help in assessing the current standard of care. Methods: A retrospective cohort study between the years 2015 and 2020 that included BC patients with BM was conducted. Demographic data, tumor clinico-pathological features, treatment modalities and outcomes were obtained. Survival, factors influencing survival time and the risk of death were examined. A p-value of less than 0.05 was considered statistically significant. Results: 111 patients with BM due to BC were included in the analysis. All are female with a mean age 50.28 ± 11.61 years. IDC histology (93%), grade III (71%), tumor > T2 (64%) and N-positive (60%) BC accounted for the majority. Human epidermal growth factor receptor2 (HER2)-positive/Hormone receptors (HR)-negative was 21%, HER2-positive/HR-positive was 16%, HR-positive/HER2-negative was 31%, TNBC was 22.5% and unknown subtype in 10%. 60% had metastatic disease on presentation with 41% had both skeletal and visceral metastases. Brain lesions > three were reported in nearly half of the cohort. Whole brain radiotherapy (WBRT), surgical resection and SRS were reported in 84%, 16% and 22%, respectively. The mean survival time was 3.31 years with a maximum of 4.23 years and a minimum of 2.39 years. Patients with HR-positive and HER2-positive BC had a significant longer survival (p=0.039, 0.033 respectively). Neither tumor size nor nodal status in BC patients with BM influenced survival time. The presence of visceral metastasesincreased the likelihood of death in BC patients with BM up to 6 folds (p=0.031). The relationship between risk of death, treatment modalities, and response of BM is shown. Conclusions: Although BM secondary to BC represents a therapeutic challenge with an expected unfavorable impact on survival, mortality was associated with the presence of extra-cranial metastases, particularly visceral metastases in this cohort. Longer survival was seen with specific subtypes (HR-positive and HER2-positive), suggesting advancement in systemic treatments against them. [Table: see text]
Phase II study of border zone stereotactic radiosurgery with bevacizumab in patients with recurrent or progressive glioblastoma multiforme.
Authors: Mantica Megan , Drappatz Jan , Lunsford L. Dade , Lieberman Frank S. , Niranjan Ajay
Published: June 2023

e14020

Background: This phase II trial was performed to assess the efficacy of Border Zone Stereotactic Radiosurgery (BZ-SRS) with bevacizumab by overall survival (OS) in patients with recurrent or progressive glioblastoma (GBM) following conventional upfront management. Methods: Patients with histologically confirmed GBM with recurrent disease, received prior first-line treatment with surgery/biopsy, fractionated radiotherapy and chemotherapy, ≥ 2 months since completion of radiotherapy and eligible for SRS were enrolled. Bevacizumab 10mg/kg was given one day before, day 14 and then every 14 days until disease progression. 1 to 14 days before BZ-SRS procedure, patients underwent standard brain MRI /MRS. MRS with measurement of choline-to-N-acetyl aspartate index (CNI) area ≥ 3 was targeted for SRS using standard MRI targeting. Results: From 2015-2017, sixteen patients were enrolled. The median age was 62 (range, 48-74Y). 3 of 16 (0.188) participants experienced grade 2 unacceptable toxicity. The median OS was 11.73 months. No survival difference is seen compared with the University of Pittsburgh historical controls. PFS-6 and OS-6 were 31.2% (p=0.00294) and 81.2%(p= 0.058), respectively. Of 13 evaluable for best response: 1 complete response (p=0.077), 4 partial responses (p=0.308), 7 stable disease (p=0.538), and 1 progressed disease (p=0.077). 11 of 16 participants had MRS scans with an estimated probability that MRS changes a treatment plan of 0 (0,0.285). Conclusions: BZ-SRS with bevacizumab was feasible and well tolerated. There is no significant survival benefit using BZ-SRS with bevacizumab compared to historical controls. Secondary analysis revealed a trend toward improved PFS-6, but not OS-6. MRS scans did not result in any changes in SRS treatment plans. Clinical trial information: NCT02120287 .