Statistical Signal Detection Algorithm in Safety Data: A Proprietary Method Compared to Industry Standard Methods.

INTRODUCTION: Several quantitative methods have been established, in pharmacovigilance, to detect signals of disproportionate reporting (SDRs) from databases containing reports of adverse drug reactions (ADRs). The signal detection algorithms (SDAs) and the source of the reporting per product vary, but it is unclear whether any algorithm can provide satisfactory performance using data with such large variance factors. OBJECTIVE: Determine the appropriate SDA for Biogen's internal Global Safety Database (GSD) given the characteristics of the database including frequencies of events, data skewness, outliers, and missing information. Compare performance of standard approaches (EBGM, EB05, PRR, and ROR), well accepted by industry, to a Biogen-developed Machine Learning (ML) Regression Decision Tree (RDT) model, across several Biogen products, to determine a champion SDA. METHODS: All data associated with seven marketed Biogen products were chosen and a historical subset of reported ADRs were considered. Six SDAs (five common industry disproportionality methods) and RDT were evaluated. The SDRs were calculated on training and test data composed of quarterly reporting intervals from 2004-2019. The performance measures used were sensitivity, precision, time to detect new events, and frequency of detected cases for each algorithm for each product. Outcomes in the test data are known a priori and easily compared to predicted outcomes. Validation was performed via rates of misclassification. This work solely represents Biogen's internal information, intentionally chosen to serve the performance review of its signal detection systems, and results will not necessarily be generalizable to other external sources. RESULTS: Several algorithms performed differently among products, but no one method dominated any other. Performance was dependent on the thresholds used to define a signal according to different criteria. However, those different statistics subtly influenced the achievable performance. The relative performance of RDT and Medicines and Healthcare products Regulatory Agency (MHRA) algorithms were superior and paired across products. A reduction in precision for all methods spanning the products was present. Hence, companies evaluating signal detection approaches, search for innovative methods to minimize this effect. CONCLUSIONS: In designing signal detection systems, careful consideration should be given to the criteria that are used to define SDRs. The choice of disproportionality statistics does not affect the achievable range of signal detection performance. These choices should consider mainly ease of implementation and interpretation. The implementation of a method is specific to its accuracy. The RDT attempted to take advantage of known methods and compare results on a per-product basis. Many factors influencing ADRs may improve RDT in future efforts. In this experiment, RDT demonstrated superiority in terms of quickest time to detect and capturing of the highest number of ADRs. Next steps include expansion of data for products representing other indications and testing models in external databases to investigate generalizability of estimates when comparing SDAs.

An evaluation of novel oncology approvals with a PMR/C for assessing data in racial and ethnic populations underrepresented in premarket clinical trials.

Clinical trials supporting oncology drug approvals frequently underrepresent diverse racial and ethnic populations. Recent policies have focused on ensuring premarket clinical trials are more inclusive and representative of racial and ethnic diversity in the general U.S. population or intended patient population; however, recent U.S. Food and Drug Administration (FDA) guidance on postmarketing approaches to collecting data in underrepresented populations demonstrates that, in certain circumstances, PMR/Cs may be issued to conduct more representative studies if there are remaining questions about safety or efficacy. This analysis demonstrates that prior to 2020 no drugs had a PMR/C to further characterize use in a more representative population, and in the last three years, more than half of novel oncology approvals have had such a PMR/C (21/40, 53%). In addition, this analysis helps to identify characteristics, such as single-arm pivotal trial design, U.S. enrollment, and results of safety subgroup analyses based on race and ethnicity, that may contribute to decisions to issue a PMR/C to conduct a study that is more representative of the racial and ethnic diversity of the U.S. or intended patient population. These results can inform efforts to improve premarket clinical trials to ensure they are representative and able to characterize use in any patient who may need the drug.

Advancing Evidence Generation for Circulating Tumor DNA: Lessons Learned from A Multi-Assay Study of Baseline Circulating Tumor DNA Levels across Cancer Types and Stages.

Circulating tumor DNA (ctDNA) holds promise as a biomarker for predicting clinical responses to therapy in solid tumors, and multiple ctDNA assays are in development. However, the heterogeneity in ctDNA levels prior to treatment (baseline) across different cancer types and stages and across ctDNA assays has not been widely studied. Friends of Cancer Research formed a collaboration across multiple commercial ctDNA assay developers to assess baseline ctDNA levels across five cancer types in early- and late-stage disease. This retrospective study included eight commercial ctDNA assay developers providing summary-level de-identified data for patients with non-small cell lung cancer (NSCLC), bladder, breast, prostate, and head and neck squamous cell carcinoma following a common analysis protocol. Baseline ctDNA levels across late-stage cancer types were similarly detected, highlighting the potential use of ctDNA as a biomarker in these cancer types. Variability was observed in ctDNA levels across assays in early-stage NSCLC, indicative of the contribution of assay analytical performance and methodology on variability. We identified key data elements, including assay characteristics and clinicopathological metadata, that need to be standardized for future meta-analyses across multiple assays. This work facilitates evidence generation opportunities to support the use of ctDNA as a biomarker for clinical response.

Accelerating the development of genetically engineered cellular therapies: a framework for extrapolating data across related products.

BACKGROUND: Significant advancements have been made in the field of cellular therapy as anti-cancer treatments, with the approval of chimeric antigen receptor (CAR)-T cell therapies and the development of other genetically engineered cellular therapies. CAR-T cell therapies have demonstrated remarkable clinical outcomes in various hematological malignancies, establishing their potential to change the current cancer treatment paradigm. Due to the increasing importance of genetically engineered cellular therapies in the oncology treatment landscape, implementing strategies to expedite development and evidence generation for the next generation of cellular therapy products can have a positive impact on patients. METHODS: We outline a risk-based methodology and assessment aid for the data extrapolation approach across related genetically engineered cellular therapy products. This systematic data extrapolation approach has applicability beyond CAR-T cells and can influence clinical development strategies for a variety of immune therapies such as T cell receptor (TCR) or genetically engineered and other cell-based therapies (e.g., tumor infiltrating lymphocytes, natural killer cells and macrophages). RESULTS: By analyzing commonalities in manufacturing processes, clinical trial designs, and regulatory considerations, key learnings were identified. These insights support optimization of the development and regulatory approval of novel cellular therapies. CONCLUSIONS: The field of cellular therapy holds immense promise in safely and effectively treating cancer. The ability to extrapolate data across related products presents opportunities to streamline the development process and accelerate the delivery of novel therapies to patients.

A new commercial boundary dataset for metropolitan areas in the USA and Canada, built from open data.

The purpose of this study is to define the geographic boundaries of commercial areas by creating a consistent definition, combining various commercial area types, including downtowns, retail centres, financial districts, and other employment subcentres. Our research involved the collection of office, retail and job density data from 69 metropolitan regions across USA and Canada. Using this data, we conducted an unsupervised image segmentation model and clustering methods to identify distinctive commercial geographic boundaries. As a result, we identified 23,751 commercial areas, providing a detailed perspective on the commercial landscape of metropolitan areas in the USA and Canada. In addition, the generated boundaries were successfully validated through comparison with previously established commerce-related boundaries. The output of this study has implications for urban and regional planning and economic development, delivering valuable insights into the overall commercial geography in the region. The commercial boundary and used codes are freely available on the School of Cities Github, and users can reuse, reproduce and modify the boundaries.

Level of Agreement Between Child and Parent Reporting on the Child Sport Concussion Assessment Tool (SCAT5).

CONTEXT: The Child Sport Concussion Assessment Tool, fifth edition (SCAT5), remains the consensus instrument for concussion evaluation in youth athletes. Both child and parent are recommended to complete the athlete background and symptom reporting. OBJECTIVE: To determine the level of agreement between child and parent medical history and symptom reporting and quantify their performance on the Child SCAT5 in male football athletes. DESIGN: Cross-sectional study. SETTING: National Collegiate Athletic Association Division I college football facility. PATIENTS OR OTHER PARTICIPANTS: A total of 157 youth male football athletes (age = 10.7 ± 1.3 years) participating in a university-sanctioned youth football camp and their parent or legal guardian. MAIN OUTCOME MEASURE(S): Youth athletes and their parent completed the athlete background (demographics, diagnosed medical history) and symptom evaluation (symptom items, total number of symptoms, and symptom severity score) of the Child SCAT5 and were instructed not to discuss reporting with each other during testing. Cronbach α tests were conducted to determine the internal consistency, and descriptive statistics determined the level of agreement between medical history, symptom reporting, and baseline performance. RESULTS: The internal consistency of the symptom items was high for both child (Cronbach α = 0.91) and parent (α = 0.92). Agreement on medical history ranged from 67% (learning disability or dyslexia) to 85% (attention-deficit/hyperactivity disorder), with 82% agreement on sustaining a previous concussion. Fourteen youth athletes reported having been hospitalized for a head injury, with zero matched parent confirmations. Individual symptom agreement ranged from 70.7% (gets distracted easily) to 94.9% (going to faint). Agreement was 35% on total number of symptoms and severity. Abnormal scoring ranged from 2% (going to faint) to 25% (headache) for child and 2% (double vision) to 28% (gets distracted easily) for parent reporting. CONCLUSIONS: Fair agreement was shown between children and their parent on medical history and self-reported symptoms on the Child SCAT5 at baseline. When available, child and parent reporting should be used for concussion assessment and clinical decision-making.

Lenalidomide in the treatment of anti-myelin-associated glycoprotein neuropathy: A phase 1 study to identify the maximum tolerated dose.

BACKGROUND: Anti-myelin-associated glycoprotein (MAG) neuropathy is a debilitating demyelinating polyneuropathy with no approved therapies. Our primary objective was to ascertain lenalidomide safety and maximum tolerated dose (MTD) in anti-MAG neuropathy. METHODS: This phase 1b, open-label, single-arm, dose-finding trial was conducted from 2019 through 2022. The original design included a dose-escalation/extension phase followed by a dose-expansion phase. Three doses of lenalidomide were evaluated: 10, 15, and 25 mg. The main outcome was the MTD. RESULTS: Eleven patients enrolled (10 men), with a mean age of 67.6 years (SD = 6.18, range 58-77 years) and mean disease duration of 8.5 years (SD = 10.9, range 1-40 years). The study terminated early due to higher-than-expected non-dose-limiting toxicity venous thromboembolism (VTE) events. The calculated MTD was 25 mg (posterior mean of toxicity probability was 0.01 with a 95% credible interval of 0.00, 0.06), but a recommended phase 2 dose of 15 mg was advised. For secondary exploratory outcomes, only EQ-5D (-0.95, 95% CI -1.81 to -0.09) and total IgM (-162 mg/dL, 95% CI -298 to -26) showed signs of improvement by month 12. CONCLUSIONS: Lenalidomide was associated with higher-than-expected VTE events in anti-MAG neuropathy patients, despite a calculated MTD of 25 mg. A recommended phase 2 dose of 15 mg was advised. Lenalidomide did not improve disability or impairment at 12 months, although this study was not powered for efficacy. The risks of long term lenalidomide may outweigh benefit for patients with anti-MAG neuropathy. Any future efficacy study should address VTE risk, as current myeloma guidelines appear inadequate. TRIAL REGISTRATION: Lenalidomide in Anti-MAG Neuropathy: Phase 1b Study, ClinicalTrials.gov Identifier: NCT03701711, https://clinicaltrials.gov/ct2/show/NCT03701711. First submitted October 10, 2018. First patient enrolled in January 2019.

An Analysis of Dosing-Related Postmarketing Requirements for Novel Oncology Drugs Approved by the U.S. Food and Drug Administration, 2012-2022.

The FDA's Oncology Center of Excellence's (OCE) launch of Project Optimus signals increased focus on dose optimization approaches in oncology drug development, particularly toward optimization in the premarket setting. Although sponsors continue to adapt premarket study designs and approaches to align with FDA's expectations for dose optimization, including consideration of the optimal dosage(s), there are still instances where questions remain at the time of approval about whether the approved doses or schedules are optimal. In these cases, FDA can exercise regulatory flexibility by issuing postmarketing requirements (PMR) and avoid delaying patient access to promising therapies. This landscape analysis demonstrates that over the past decade (2012-2022), FDA frequently used PMRs to answer additional questions about dosing for novel oncology approvals. We found more than half of drugs (78/132, 59.1%) had a dosing PMR and observed a recent increase in PMRs intended to evaluate whether a lower dose could be more optimal. These results suggest there are opportunities to adapt premarket dose optimization strategies and leverage innovative development tools to ensure timely identification of the optimal dose.

Social and Emotional Skills Predict Postsecondary Enrollment and Retention.

INTRODUCTION: Social and emotional (SE) skills are known to be linked to important life outcomes, many of which fall into the academic domain. For example, meta-analytic data show that the skill of Sustaining Effort is nearly or just as important for academic performance as intelligence. In a recent study with long-term tracking of high school students, those who came from schools with a strong emphasis on SE skill development were more likely to enroll in college within two years of high school graduation. Longitudinal studies like this one are rare, however. METHOD: The focus of the present study is on the SE skills of 6662 students assessed during high school and their relationship with high school academic performance, standardized college admissions test performance, and ultimately postsecondary enrollment and retention. RESULTS: We examined mean-level differences in household income, high school GPA, ACT Composite scores, and SE skills by college enrollment and retention status and found several significant differences, often favoring the enrolled or retained group. Moreover, we found support for the incremental validity of SE skills as they predicted enrollment and retention above household income, high school GPA, and ACT scores. DISCUSSION: Understanding SE skills' effects on later academic outcomes is important to help inform early SE skill intervention and development efforts in secondary and postsecondary settings. Additional implications and future directions are discussed.

The smallest functional antibody fragment: Ultralong CDR H3 antibody knob regions potently neutralize SARS-CoV-2.

Cows produce antibodies with a disulfide-bonded antigen-binding domain embedded within ultralong heavy chain third complementarity determining regions. This "knob" domain is analogous to natural cysteine-rich peptides such as knottins in that it is small and stable but can accommodate diverse loops and disulfide bonding patterns. We immunized cattle with SARS-CoV-2 spike and found ultralong CDR H3 antibodies that could neutralize several viral variants at picomolar IC50 potencies in vitro and could protect from disease in vivo. The independent CDR H3 peptide knobs were expressed and maintained the properties of the parent antibodies. The knob interaction with SARS-CoV-2 spike was revealed by electron microscopy, X-ray crystallography, NMR spectroscopy, and mass spectrometry and established ultralong CDR H3-derived knobs as the smallest known recombinant independent antigen-binding fragment. Unlike other vertebrate antibody fragments, these knobs are not reliant on the immunoglobulin domain and have potential as a new class of therapeutics.

Real-world performance of SARS-Cov-2 serology tests in the United States, 2020.

BACKGROUND: Real-world performance of COVID-19 diagnostic tests under Emergency Use Authorization (EUA) must be assessed. We describe overall trends in the performance of serology tests in the context of real-world implementation. METHODS: Six health systems estimated the odds of seropositivity and positive percent agreement (PPA) of serology test among people with confirmed SARS-CoV-2 infection by molecular test. In each dataset, we present the odds ratio and PPA, overall and by key clinical, demographic, and practice parameters. RESULTS: A total of 15,615 people were observed to have at least one serology test 14-90 days after a positive molecular test for SARS-CoV-2. We observed higher PPA in Hispanic (PPA range: 79-96%) compared to non-Hispanic (60-89%) patients; in those presenting with at least one COVID-19 related symptom (69-93%) as compared to no such symptoms (63-91%); and in inpatient (70-97%) and emergency department (93-99%) compared to outpatient (63-92%) settings across datasets. PPA was highest in those with diabetes (75-94%) and kidney disease (83-95%); and lowest in those with auto-immune conditions or who are immunocompromised (56-93%). The odds ratios (OR) for seropositivity were higher in Hispanics compared to non-Hispanics (OR range: 2.59-3.86), patients with diabetes (1.49-1.56), and obesity (1.63-2.23); and lower in those with immunocompromised or autoimmune conditions (0.25-0.70), as compared to those without those comorbidities. In a subset of three datasets with robust information on serology test name, seven tests were used, two of which were used in multiple settings and met the EUA requirement of PPA ≥87%. Tests performed similarly across datasets. CONCLUSION: Although the EUA requirement was not consistently met, more investigation is needed to understand how serology and molecular tests are used, including indication and protocol fidelity. Improved data interoperability of test and clinical/demographic data are needed to enable rapid assessment of the real-world performance of in vitro diagnostic tests.

A Study Design to Harmonize Patient-Reported Outcomes Across Data Sets.

PURPOSE: Using patient-reported outcomes (PROs) provides important insights from the patient's perspective and can be valuable to monitor and manage treatment-related adverse events during cancer treatment. Additionally, the digital administration of PROs (electronic PROs [ePROs]) provides real-time updates to clinical care teams on treatment-related symptoms in-between clinic visits. However, given the variability in the methodology and timing of the data collection, using and harmonizing these data across different systems remains challenging. Identifying data elements to capture and operating procedures for harmonization across ePRO tools will expedite efforts to generate relevant and robust data on use of ePRO data in clinical care. METHODS: Friends of Cancer Research assembled a consortium of project partners from key health care sectors to align on a framework for ePRO data capture across ePRO tools and assessment of the impact of ePRO data capture on patient outcomes. RESULTS: We identified challenges and opportunities to align ePRO data capture across ePRO tools and aligned on key data elements for assessing the impact of ePRO data capture on patient care and outcomes. Ultimately, we proposed a study protocol to leverage ePRO data for symptom and adverse event management to measure real-world effectiveness of ePRO tool implementation on patient care and outcomes. CONCLUSION: This work provides considerations for harmonizing ePRO data sets and a common framework to align across multiple ePRO tools to assess the value of ePROs for improving patient outcomes. Future efforts to interpret evidence and evaluate the impact of ePRO tools on patient outcomes will be aided by improved alignment across studies.

Real-world utilization of SARS-CoV-2 serological testing in RNA positive patients across the United States.

BACKGROUND: As diagnostic tests for COVID-19 were broadly deployed under Emergency Use Authorization, there emerged a need to understand the real-world utilization and performance of serological testing across the United States. METHODS: Six health systems contributed electronic health records and/or claims data, jointly developed a master protocol, and used it to execute the analysis in parallel. We used descriptive statistics to examine demographic, clinical, and geographic characteristics of serology testing among patients with RNA positive for SARS-CoV-2. RESULTS: Across datasets, we observed 930,669 individuals with positive RNA for SARS-CoV-2. Of these, 35,806 (4%) were serotested within 90 days; 15% of which occurred <14 days from the RNA positive test. The proportion of people with a history of cardiovascular disease, obesity, chronic lung, or kidney disease; or presenting with shortness of breath or pneumonia appeared higher among those serotested compared to those who were not. Even in a population of people with active infection, race/ethnicity data were largely missing (>30%) in some datasets-limiting our ability to examine differences in serological testing by race. In datasets where race/ethnicity information was available, we observed a greater distribution of White individuals among those serotested; however, the time between RNA and serology tests appeared shorter in Black compared to White individuals. Test manufacturer data was available in half of the datasets contributing to the analysis. CONCLUSION: Our results inform the underlying context of serotesting during the first year of the COVID-19 pandemic and differences observed between claims and EHR data sources-a critical first step to understanding the real-world accuracy of serological tests. Incomplete reporting of race/ethnicity data and a limited ability to link test manufacturer data, lab results, and clinical data challenge the ability to assess the real-world performance of SARS-CoV-2 tests in different contexts and the overall U.S. response to current and future disease pandemics.

Breakthrough Therapy Designation Criteria Identify Drugs that Improve Clinical Outcomes for Patients: A Case for More Streamlined Coverage of Promising Therapies.

The breakthrough therapy designation (BTD) process was created to expedite clinical development timelines for drugs intended to treat serious conditions and preliminary clinical evidence indicates the drug may demonstrate substantial improvement over existing therapies. This analysis demonstrates that BTD is a valuable tool for expediting approval of promising therapies in oncology. By comparing drugs indicated to treat non-small cell lung cancer (NSCLC) approved with BTD or without BTD between January 2013 and October 2021, BTD drugs reduced the risk of death by a median of 31% and progression by a median of 48%, while drugs never receiving BTD reduced the risk of death and progression by a median of 15% and 41.9%, respectively. These findings show that BTD criteria accurately identify drugs that improve long-term outcomes for patients with cancer and warrant coordinated efforts to ensure timely coverage decisions and access for patients.

Assessing incentives to increase digital payment acceptance and usage: A machine learning approach.

An important step to achieve greater financial inclusion is to increase the acceptance and usage of digital payments. Although consumer adoption of digital payments has improved dramatically globally, the acceptance and usage of digital payments for micro, small, and medium-sized retailers (MSMRs) remain challenging. Using random forest estimation, we identify 14 key predictors out of 190 variables with the largest predictive power for MSMR adoption and usage of digital payments. Using conditional inference trees, we study the importance of sequencing and interactions of various factors such as public policy initiatives, technological advancements, and private sector incentives. We find that in countries with low POS terminal adoption, killer applications such as mobile phone payment apps increase the likelihood of P2B digital transactions. We also find the likelihood of digital P2B payments at MSMRs increases when MSMRs pay their employees and suppliers digitally. The level of ownership of basic financial accounts by consumers and the size of the shadow economy are also important predictors of greater adoption and usage of digital payments. Using causal forest estimation, we find a positive and economically significant marginal effect for merchant and consumer fiscal incentives on POS terminal adoption on average. When countries implement financial inclusion initiatives, POS terminal adoption increases significantly and MSMRs' share of P2B digital payments also increases. Merchant and consumer fiscal incentives also increase MSMRs' share of P2B electronic payments.

Facing the future of transit ridership: shifting attitudes towards public transit and auto ownership among transit riders during COVID-19.

Public transit agencies face a transformed landscape of rider demand and political support as the COVID-19 pandemic recedes. We explore people's motivations for returning to or avoiding public transit a year into the pandemic. We draw on a March 2021 follow-up survey of over 1,900 people who rode transit regularly prior to the COVID-19 pandemic in Toronto and Vancouver, Canada, and who took part in a prior survey on the topic in May 2020. We investigate how transit demand changes associated with the pandemic relate to changes in automobile ownership and its desirability. We find that pre-COVID frequent transit users between the ages of 18-29, a part of the so-called "Gen Z," and recent immigrants are more attracted to driving due to the pandemic, with the latter group more likely to have actually purchased a vehicle. Getting COVID-19 or living with someone who did is also a strong and positive predictor of buying a car and anticipating less transit use after the pandemic. Our results suggest that COVID-19  may have increased the attractiveness of auto ownership among transit riders likely to eventually purchase cars anyway (immigrants, twentysomethings), at least in the North American context. We also conclude that getting COVID-19 or living with someone who did is a positive predictor of having bought a car. Future research should consider how having COVID-19 transformed some travelers' views, values, and behaviour. Supplementary Information: The online version contains supplementary material available at 10.1007/s11116-022-10344-2.

Changes in Circulating Tumor DNA Reflect Clinical Benefit Across Multiple Studies of Patients With Non-Small-Cell Lung Cancer Treated With Immune Checkpoint Inhibitors.

PURPOSE: As immune checkpoint inhibitors (ICI) become increasingly used in frontline settings, identifying early indicators of response is needed. Recent studies suggest a role for circulating tumor DNA (ctDNA) in monitoring response to ICI, but uncertainty exists in the generalizability of these studies. Here, the role of ctDNA for monitoring response to ICI is assessed through a standardized approach by assessing clinical trial data from five independent studies. PATIENTS AND METHODS: Patient-level clinical and ctDNA data were pooled and harmonized from 200 patients across five independent clinical trials investigating the treatment of patients with non-small-cell lung cancer with programmed cell death-1 (PD-1)/programmed death ligand-1 (PD-L1)-directed monotherapy or in combination with chemotherapy. CtDNA levels were measured using different ctDNA assays across the studies. Maximum variant allele frequencies were calculated using all somatic tumor-derived variants in each unique patient sample to correlate ctDNA changes with overall survival (OS) and progression-free survival (PFS). RESULTS: We observed strong associations between reductions in ctDNA levels from on-treatment liquid biopsies with improved OS (OS; hazard ratio, 2.28; 95% CI, 1.62 to 3.20; P < .001) and PFS (PFS; hazard ratio 1.76; 95% CI, 1.31 to 2.36; P < .001). Changes in the maximum variant allele frequencies ctDNA values showed strong association across different outcomes. CONCLUSION: In this pooled analysis of five independent clinical trials, consistent and robust associations between reductions in ctDNA and outcomes were found across multiple end points assessed in patients with non-small-cell lung cancer treated with an ICI. Additional tumor types, stages, and drug classes should be included in future analyses to further validate this. CtDNA may serve as an important tool in clinical development and an early indicator of treatment benefit.

Homologous Recombination Deficiency: Concepts, Definitions, and Assays.

BACKGROUND: Homologous recombination deficiency (HRD) is a phenotype that is characterized by the inability of a cell to effectively repair DNA double-strand breaks using the homologous recombination repair (HRR) pathway. Loss-of-function genes involved in this pathway can sensitize tumors to poly(adenosine diphosphate [ADP]-ribose) polymerase (PARP) inhibitors and platinum-based chemotherapy, which target the destruction of cancer cells by working in concert with HRD through synthetic lethality. However, to identify patients with these tumors, it is vital to understand how to best measure homologous repair (HR) status and to characterize the level of alignment in these measurements across different diagnostic platforms. A key current challenge is that there is no standardized method to define, measure, and report HR status using diagnostics in the clinical setting. METHODS: Friends of Cancer Research convened a consortium of project partners from key healthcare sectors to address concerns about the lack of consistency in the way HRD is defined and methods for measuring HR status. RESULTS: This publication provides findings from the group's discussions that identified opportunities to align the definition of HRD and the parameters that contribute to the determination of HR status. The consortium proposed recommendations and best practices to benefit the broader cancer community. CONCLUSION: Overall, this publication provides additional perspectives for scientist, physician, laboratory, and patient communities to contextualize the definition of HRD and various platforms that are used to measure HRD in tumors.

Need for aligning the definition and reporting of cytokine release syndrome (CRS) in immuno-oncology clinical trials.

As cancer immunotherapies continue to expand across all areas of oncology, it is imperative to establish a standardized approach for defining and capturing clinically important toxicities, such as cytokine release syndrome (CRS). In this paper, we provide considerations for categorizing the variety of adverse events that may accompany CRS and for recognizing that presentations of CRS may differ among various immunotherapies (e.g., monoclonal antibodies, CAR T cell therapies and T cell engagers, which can include bispecific antibodies and other constructs). The goals of this paper are to ensure accurate and consistent identification of CRS in patients receiving immunotherapies in clinical studies to aid in reporting; enable more precise evaluation of the therapeutic risk-benefit profile and cross-study analyses; support evidence-based monitoring and management of important toxicities related to cancer immunotherapies; and improve patient care and outcomes. These efforts will become more important as the number and variety of molecular targets for immunotherapies broaden and as therapies with novel mechanisms continue to be developed.

Factor structure of borderline personality disorder and response to Systems Training for Emotional Predictability and Problem Solving.

Differential response to the Systems Training for Emotional Predictability and Problem Solving (STEPPS) program was compared in subgroups identified through latent class analysis (LCA). STEPPS is an evidence-based group treatment program for patients with borderline personality disorder (BPD). A reanalysis of data was conducted using data from a 20-week randomized controlled trial and 1-year follow-up. Subjects (n = 164) with DSM-IV BPD were assessed for comorbid Axis I and II disorders and selected clinical variables. Severity was assessed using the Zanarini Rating Scale for BPD (ZAN-BPD) and the Borderline Evaluation of Severity Over Time (BEST). Three- and four-class models were identified with the four-class model having the better fit. The latter included a high severity (HS) class (26%), an affective instability/substance abuse (AISA) class (16%), an empty/dissociation/identity disturbance (EDID) class (27%), and a low severity (LS) class (30%). High impulsiveness predicted membership in the HS class. Improvement was determined using a linear mixed-effects model. Those most likely to benefit were those in the HS group characterized by high symptom severity, Axis I and II comorbidity, problem relationships, abandonment fears, and intense anger. This work should help further efforts to match patients with treatments based on sociodemographic, diagnostic, and other illness characteristics.