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BackgroundTreatment of multidrug- and rifampin-resistant tuberculosis (MDR/RR-TB) is expensive, labour-intensive, and associated with substantial adverse events and poor outcomes. While most MDR/RR-TB patients do not receive treatment, many who do are treated for 18 months or more. A shorter all-oral regimen is currently recommended for only a sub-set of MDR/RR-TB. Its use is only conditionally recommended because of very low-quality evidence underpinning the recommendation. Novel combinations of newer and repurposed drugs bring hope in the fight against MDR/RR-TB, but their use has not been optimized in all-oral, shorter regimens. This has greatly limited their impact on the burden of disease. There is, therefore, dire need for high-quality evidence on the performance of new, shortened, injectable-sparing regimens for MDR-TB which can be adapted to individual patients and different settings.MethodsendTB is a phase III, pragmatic, multi-country, adaptive, randomized, controlled, parallel, open-label clinical trial evaluating the efficacy and safety of shorter treatment regimens containing new drugs for patients with fluoroquinolone-susceptible, rifampin-resistant tuberculosis. Study participants are randomized to either the control arm, based on the current standard of care for MDR/RR-TB, or to one of five 39-week multi-drug regimens containing newly approved and repurposed drugs. Study participation in all arms lasts at least 73 and up to 104 weeks post-randomization. Randomization is response-adapted using interim Bayesian analysis of efficacy endpoints. The primary objective is to assess whether the efficacy of experimental regimens at 73 weeks is non-inferior to that of the control. A sample size of 750 patients across 6 arms affords at least 80% power to detect the non-inferiority of at least 1 (and up to 3) experimental regimens, with a one-sided alpha of 0.025 and a non-inferiority margin of 12%, against the control in both modified intention-to-treat and per protocol populations.DiscussionThe lack of a safe and effective regimen that can be used in all patients is a major obstacle to delivering appropriate treatment to all patients with active MDR/RR-TB. Identifying multiple shorter, safe, and effective regimens has the potential to greatly reduce the burden of this deadly disease worldwide.Trial registrationClinicalTrials.gov Identifier NCT02754765. Registered on 28 April 2016; the record was last updated for study protocol version 3.3, on 27 August 2019.

The development and evaluation of novel treatment combinations is a key component of modern clinical research. The primary goals of factorial clinical trials of treatment combinations range from the estimation of intervention-specific effects, or the discovery of potential synergies, to the identification of combinations with the highest response probabilities. Most factorial studies use balanced or block randomization, with an equal number of patients assigned to each treatment combination, irrespective of the specific goals of the trial. Here, we introduce a class of Bayesian response-adaptive designs for factorial clinical trials with binary outcomes. The study design was developed using Bayesian decision-theoretic arguments and adapts the randomization probabilities to treatment combinations during the enrollment period based on the available data. Our approach enables the investigator to specify a utility function representative of the aims of the trial, and the Bayesian response-adaptive randomization algorithm aims to maximize this utility function. We considered several utility functions and factorial designs tailored to them. Then, we conducted a comparative simulation study to illustrate relevant differences of key operating characteristics across the resulting designs. We also investigated the asymptotic behavior of the proposed adaptive designs. We also used data summaries from three recent factorial trials in perioperative care, smoking cessation, and infectious disease prevention to define realistic simulation scenarios and illustrate advantages of the introduced trial designs compared to other study designs., (© The Author 2024. Published by Oxford University Press. All rights reserved. For Permissions, email: journals.permissions@oup.com.)

Background: NF2 -related schwannomatosis ( NF2 -SWN, formerly called neurofibromatosis type 2) is a tumor predisposition syndrome that is manifested by multiple vestibular schwannomas, nonvestibular schwannomas, meningiomas, and ependymomas. The condition is relentlessly progressive with no approved therapies. On the basis of preclinical activity of brigatinib (an inhibitor of multiple tyrosine kinases) in NF2 -driven nonvestibular schwannoma and meningioma, data were needed on the use of brigatinib in patients with multiple types of progressive NF2 -SWN tumors., Methods: In this phase 2 platform trial with a basket design, patients who were 12 years of age or older with NF2 -SWN and progressive tumors were treated with oral brigatinib at a dose of 180 mg daily. A central review committee evaluated one target tumor and up to five nontarget tumors in each patient. The primary outcome was radiographic response in target tumors. Key secondary outcomes were safety, response rate in all tumors, hearing response, and patient-reported outcomes., Results: A total of 40 patients (median age, 26 years) with progressive target tumors (10 vestibular schwannomas, 8 nonvestibular schwannomas, 20 meningiomas, and 2 ependymomas) received treatment with brigatinib. After a median follow-up of 10.4 months, the percentage of tumors with a radiographic response was 10% (95% confidence interval [CI], 3 to 24) for target tumors and 23% (95% CI, 16 to 30) for all tumors; meningiomas and nonvestibular schwannomas had the greatest benefit. Annualized growth rates decreased for all tumor types during treatment. Hearing improvement occurred in 35% (95% CI, 20 to 53) of eligible ears. Exploratory analyses suggested a decrease in self-reported pain severity during treatment (-0.013 units per month; 95% CI, -0.002 to -0.029) on a scale from 0 (no pain) to 3 (severe pain). No grade 4 or 5 treatment-related adverse events were reported., Conclusions: Brigatinib treatment resulted in radiographic responses in multiple tumor types and clinical benefit in a heavily pretreated cohort of patients with NF2 -SWN. (Funded by the Children's Tumor Foundation and others; INTUITT-NF2 ClinicalTrials.gov number, NCT04374305.)., (Copyright © 2024 Massachusetts Medical Society.)

The use of simulation-based sensitivity analyses is fundamental for evaluating and comparing candidate designs of future clinical trials. In this context, sensitivity analyses are especially useful to assess the dependence of important design operating characteristics with respect to various unknown parameters. Typical examples of operating characteristics include the likelihood of detecting treatment effects and the average study duration, which depend on parameters that are unknown until after the onset of the clinical study, such as the distributions of the primary outcomes and patient profiles. Two crucial components of sensitivity analyses are (i) the choice of a set of plausible simulation scenarios and (ii) the list of operating characteristics of interest. We propose a new approach for choosing the set of scenarios to be included in a sensitivity analysis. We maximize a utility criterion that formalizes whether a specific set of sensitivity scenarios is adequate to summarize how the operating characteristics of the trial design vary across plausible values of the unknown parameters. Then, we use optimization techniques to select the best set of simulation scenarios (according to the criteria specified by the investigator) to exemplify the operating characteristics of the trial design. We illustrate our proposal in three trial designs., Competing Interests: Conflicts of interest The authors report there are no competing interests to declare.

Purpose: The Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT) is a phase II platform trial that uses response adaptive randomization and genomic profiling to efficiently identify novel therapies for phase III testing. Three initial experimental arms (abemaciclib [a cyclin-dependent kinase [CDK]4/6 inhibitor], neratinib [an epidermal growth factor receptor [EGFR]/human epidermal growth factor receptor 2 inhibitor], and CC-115 [a deoxyribonucleic acid-dependent protein kinase/mammalian target of rapamycin inhibitor]) were simultaneously evaluated against a common control arm. We report the results for each arm and examine the feasibility and conduct of the adaptive platform design., Patients and Methods: Patients with newly diagnosed O 6 -methylguanine-DNA methyltransferase-unmethylated glioblastoma were eligible if they had tumor genotyping to identify prespecified biomarker subpopulations of dominant glioblastoma signaling pathways (EGFR, phosphatidylinositol 3-kinase, and CDK). Initial random assignment was 1:1:1:1 between control (radiation therapy and temozolomide) and the experimental arms. Subsequent Bayesian adaptive randomization was incorporated on the basis of biomarker-specific progression-free survival (PFS) data. The primary end point was overall survival (OS), and one-sided P values are reported. The trial is registered with ClinicalTrials.gov (identifier: NCT02977780)., Results: Two hundred thirty-seven patients were treated (71 control; 73 abemaciclib; 81 neratinib; 12 CC-115) in years 2017-2021. Abemaciclib and neratinib were well tolerated, but CC-115 was associated with ≥ grade 3 treatment-related toxicity in 58% of patients. PFS was significantly longer with abemaciclib (hazard ratio [HR], 0.72; 95% CI, 0.49 to 1.06; one-sided P = .046) and neratinib (HR, 0.72; 95% CI, 0.50 to 1.02; one-sided P = .033) relative to the control arm but there was no PFS benefit with CC-115 (one-sided P = .523). None of the experimental therapies demonstrated a significant OS benefit ( P > .05)., Conclusion: The INSIGhT design enabled efficient simultaneous testing of three experimental agents using a shared control arm and adaptive randomization. Two investigational arms had superior PFS compared with the control arm, but none demonstrated an OS benefit. The INSIGhT design may promote improved and more efficient therapeutic discovery in glioblastoma. New arms have been added to the trial.

Mutations in the BRCA1 and BRCA2 genes are known to be highly associated with breast cancer. Identifying both shared and unique transcript expression patterns in blood samples from these groups can shed insight into if and how the disease mechanisms differ among individuals by mutation status, but this is challenging in the high-dimensional setting. A recent method, Bayesian Multi-Study Factor Analysis (BMSFA), identifies latent factors common to all studies (or equivalently, groups) and latent factors specific to individual studies. However, BMSFA does not allow for factors shared by more than one but less than all studies. This is critical in our context, as we may expect some but not all signals to be shared by BRCA1-and BRCA2-mutation carriers but not necessarily other high-risk groups. We extend BMSFA by introducing a new method, Tetris, for Bayesian combinatorial multi-study factor analysis, which identifies latent factors that any combination of studies or groups can share. We model the subsets of studies that share latent factors with an Indian Buffet Process, and offer a way to summarize uncertainty in the sharing patterns using credible balls. We test our method with an extensive range of simulations, and showcase its utility not only in dimension reduction but also in covariance estimation. When applied to transcript expression data from high-risk families grouped by mutation status, Tetris reveals the features and pathways characterizing each group and the sharing patterns among them. Finally, we further extend Tetris to discover groupings of samples when group labels are not provided, which can elucidate additional structure in these data.

Background: Clinical trial design must consider the specific resource constraints and overall goals of the drug development process (DDP); for example, in designing a phase I trial to evaluate the safety of a drug and recommend a dose for a subsequent phase II trial. Here, we focus on design considerations that involve the sequence of clinical trials, from early phase I to late phase III, that constitute the DDP., Methods: We discuss how stylized simulation models of clinical trials in an oncology DDP can quantify important relationships between early-phase trial designs and their consequences for the remaining phases of development. Simulations for three illustrative settings are presented, using stylized models of the DDP that mimic trial designs and decisions, such as the potential discontinuation of the DDP., Results: We describe: (1) the relationship between a phase II single-arm trial sample size and the likelihood of a positive result in a subsequent phase III confirmatory trial; (2) the impact of a phase I dose-finding design on the likelihood that the DDP will produce evidence of a safe and effective therapy; and (3) the impact of a phase II enrichment trial design on the operating characteristics of a subsequent phase III confirmatory trial., Conclusions: Stylized models of the DDP can support key decisions, such as the sample size, in the design of early-phase trials. Simulation models can be used to estimate performance metrics of the DDP under realistic scenarios; for example, the duration and the total number of patients enrolled. These estimates complement the evaluation of the operating characteristics of early-phase trial design, such as power or accuracy in selecting safe and effective dose levels., (© 2023. The Author(s).)

Drug development can be associated with slow timelines, particularly for rare or difficult-to-treat solid tumors such as glioblastoma. The use of external data in the design and analysis of trials has attracted significant interest because it has the potential to improve the efficiency and precision of drug development. A recurring challenge in the use of external data for clinical trials, however, is the difficulty in accessing high-quality patient-level data. Academic research groups generally do not have access to suitable datasets to effectively leverage external data for planning and analyses of new clinical trials. Given the need for resources to enable investigators to benefit from existing data assets, we have developed the Glioblastoma External (GBM-X) Data Platform which will allow investigators in neuro-oncology to leverage our data collection and obtain analyses. GBM-X strives to provide an uncomplicated process to use external data, contextualize single-arm trials, and improve inference on treatment effects early in drug development. The platform is designed to welcome interested collaborators and integrate new data into the platform, with the expectation that the data collection can continue to grow and remain updated. With such features, GBM-X is designed to help to accelerate evaluation of therapies, to grow with collaborations, and to serve as a model to improve drug discovery for rare and difficult-to-treat tumors in oncology., (©2023 American Association for Cancer Research.)

Sharing data from control groups across concurrent randomised clinical trials with identical enrolment criteria and the same control therapy can translate into efficiencies for the drug development process. We discuss potential benefits and risks of prospective data-sharing plans for concurrent randomised trials., Competing Interests: Conflict of interest statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: Patients with precursors to multiple myeloma are dichotomised as having monoclonal gammopathy of undetermined significance or smouldering multiple myeloma on the basis of monoclonal protein concentrations or bone marrow plasma cell percentage. Current risk stratifications use laboratory measurements at diagnosis and do not incorporate time-varying biomarkers. Our goal was to develop a monoclonal gammopathy of undetermined significance and smouldering multiple myeloma stratification algorithm that utilised accessible, time-varying biomarkers to model risk of progression to multiple myeloma., Methods: In this retrospective, multicohort study, we included patients who were 18 years or older with monoclonal gammopathy of undetermined significance or smouldering multiple myeloma. We evaluated several modelling approaches for predicting disease progression to multiple myeloma using a training cohort (with patients at Dana-Farber Cancer Institute, Boston, MA, USA; annotated from Nov, 13, 2019, to April, 13, 2022). We created the PANGEA models, which used data on biomarkers (monoclonal protein concentration, free light chain ratio, age, creatinine concentration, and bone marrow plasma cell percentage) and haemoglobin trajectories from medical records to predict progression from precursor disease to multiple myeloma. The models were validated in two independent validation cohorts from National and Kapodistrian University of Athens (Athens, Greece; from Jan 26, 2020, to Feb 7, 2022; validation cohort 1), University College London (London, UK; from June 9, 2020, to April 10, 2022; validation cohort 1), and Registry of Monoclonal Gammopathies (Czech Republic, Czech Republic; Jan 5, 2004, to March 10, 2022; validation cohort 2). We compared the PANGEA models (with bone marrow [BM] data and without bone marrow [no BM] data) to current criteria (International Myeloma Working Group [IMWG] monoclonal gammopathy of undetermined significance and 20/2/20 smouldering multiple myeloma risk criteria)., Findings: We included 6441 patients, 4931 (77%) with monoclonal gammopathy of undetermined significance and 1510 (23%) with smouldering multiple myeloma. 3430 (53%) of 6441 participants were female. The PANGEA model (BM) improved prediction of progression from smouldering multiple myeloma to multiple myeloma compared with the 20/2/20 model, with a C-statistic increase from 0·533 (0·480-0·709) to 0·756 (0·629-0·785) at patient visit 1 to the clinic, 0·613 (0·504-0·704) to 0·720 (0·592-0·775) at visit 2, and 0·637 (0·386-0·841) to 0·756 (0·547-0·830) at visit three in validation cohort 1. The PANGEA model (no BM) improved prediction of smouldering multiple myeloma progression to multiple myeloma compared with the 20/2/20 model with a C-statistic increase from 0·534 (0·501-0·672) to 0·692 (0·614-0·736) at visit 1, 0·573 (0·518-0·647) to 0·693 (0·605-0·734) at visit 2, and 0·560 (0·497-0·645) to 0·692 (0·570-0·708) at visit 3 in validation cohort 1. The PANGEA models improved prediction of monoclonal gammopathy of undetermined significance progression to multiple myeloma compared with the IMWG rolling model at visit 1 in validation cohort 2, with C-statistics increases from 0·640 (0·518-0·718) to 0·729 (0·643-0·941) for the PANGEA model (BM) and 0·670 (0·523-0·729) to 0·879 (0·586-0·938) for the PANGEA model (no BM)., Interpretation: Use of the PANGEA models in clinical practice will allow patients with precursor disease to receive more accurate measures of their risk of progression to multiple myeloma, thus prompting for more appropriate treatment strategies., Funding: SU2C Dream Team and Cancer Research UK., Competing Interests: Declaration of interests This study was previously presented on April 12, 2022, at the 2022 American Association for Cancer Research Annual Meeting and on Aug 25, 2022, at the International Myeloma Society Annual Meeting. AC declares grants from the International Myeloma Society for travel and conference expenses. FF is employed by Biostatistics and Research Decision Sciences, Merck & Co. SSF declares that their salary is partly supported by research funding from International Business Machines (IBM) and has patent applications (EP14807512·0A and US16/084 890) and a provisional patent application (62/866 261). LA declares grants from the International Myeloma Society for travel and conference expenses. JR declares honoraria from Sanofi, Janssen, Amgen, GSK, and Bristol Myers Squibb; travel grants from BMS, Janssen, and Amgen; and funding from a consulting or advisory role from Sanofi, Janssen, Amgen, GSK, and BMS. EK reports honoraria from Amgen, Janssen, Takeda, Genesis Pharma, Pfizer, and GSK; travel grants from Janssen; and is an advisory board member at Janssen and Prothena. MAD declares honoraria from Amgen, BMS, Takeda, and Janssen and is an advisory board member at Amgen, BMS, Takeda, and Janssen. CRM reports research funding from GRAIL. GG declares honoraria for lectures from Society for Neuro-oncology, Society of Tumor Oncology, and MD Anderson; honoraria as a Paul C Zamecnik Chair in Oncology; research funding from IBM and Pharmacyclics; patents, royalties, other intellectual property as Inventor on patent applications related to MSMuTect, MSMutSig, MSIDetect, POLYSOLVER, and SignatureAnalyzer-GPU; and stock and other ownership interests from Founder as a consultant and has privately-held equity in Scorpion Therapeutics. IMG declares honoraria from Celgene, Bristol-Myers Squibb, Takeda, Amgen, Janssen, and Vor Biopharma; consulting or advisory roles at Bristol-Myers Squibb, Novartis, Amgen, Takeda, Celgene, Cellectar, Sanofi, Janssen, Pfizer, Menarini Silicon Biosystems, Oncopeptides, The Binding Site, GSK, AbbVie, Adaptive, and 10xGenomics; and a spouse who is the Chief Medical Officer at Disc Medicine and holds equity in the company. AC, FF, SSF, GG, LT, and IMG have applied for a patent for the application of the PANGEA models described in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

A common assumption of data analysis in clinical trials is that the patient population, as well as treatment effects, do not vary during the course of the study. However, when trials enroll patients over several years, this hypothesis may be violated. Ignoring variations of the outcome distributions over time, under the control and experimental treatments, can lead to biased treatment effect estimates and poor control of false positive results. We propose and compare two procedures that account for possible variations of the outcome distributions over time, to correct treatment effect estimates, and to control type-I error rates. The first procedure models trends of patient outcomes with splines. The second leverages conditional inference principles, which have been introduced to analyze randomized trials when patient prognostic profiles are unbalanced across arms. These two procedures are applicable in response-adaptive clinical trials. We illustrate the consequences of trends in the outcome distributions in response-adaptive designs and in platform trials, and investigate the proposed methods in the analysis of a glioblastoma study., (© 2021 The International Biometric Society.)

Accurate risk stratification is key to reducing cancer morbidity through targeted screening and preventative interventions. Multiple breast cancer risk prediction models are used in clinical practice, and often provide a range of different predictions for the same patient. Integrating information from different models may improve the accuracy of predictions, which would be valuable for both clinicians and patients. BRCAPRO is a widely used model that predicts breast cancer risk based on detailed family history information. A major limitation of this model is that it does not consider non-genetic risk factors. To address this limitation, we expand BRCAPRO by combining it with another popular existing model, BCRAT (i.e., Gail), which uses a largely complementary set of risk factors, most of them non-genetic. We consider two approaches for combining BRCAPRO and BCRAT: (1) modifying the penetrance (age-specific probability of developing cancer given genotype) functions in BRCAPRO using relative hazard estimates from BCRAT, and (2) training an ensemble model that takes BRCAPRO and BCRAT predictions as input. Using both simulated data and data from Newton-Wellesley Hospital and the Cancer Genetics Network, we show that the combination models are able to achieve performance gains over both BRCAPRO and BCRAT. In the Cancer Genetics Network cohort, we show that the proposed BRCAPRO + BCRAT penetrance modification model performs comparably to IBIS, an existing model that combines detailed family history with non-genetic risk factors.

Purpose: Adaptive clinical trials use algorithms to predict, during the study, patient outcomes and final study results. These predictions trigger interim decisions, such as early discontinuation of the trial, and can change the course of the study. Poor selection of the Prediction Analyses and Interim Decisions (PAID) plan in an adaptive clinical trial can have negative consequences, including the risk of exposing patients to ineffective or toxic treatments., Methods: We present an approach that leverages data sets from completed trials to evaluate and compare candidate PAIDs using interpretable validation metrics. The goal is to determine whether and how to incorporate predictions into major interim decisions in a clinical trial. Candidate PAIDs can differ in several aspects, such as the prediction models used, timing of interim analyses, and potential use of external data sets. To illustrate our approach, we considered a randomized clinical trial in glioblastoma. The study design includes interim futility analyses on the basis of the predictive probability that the final analysis, at the completion of the study, will provide significant evidence of treatment effects. We examined various PAIDs with different levels of complexity to investigate if the use of biomarkers, external data, or novel algorithms improved interim decisions in the glioblastoma clinical trial., Results: Validation analyses on the basis of completed trials and electronic health records support the selection of algorithms, predictive models, and other aspects of PAIDs for use in adaptive clinical trials. By contrast, PAID evaluations on the basis of arbitrarily defined ad hoc simulation scenarios, which are not tailored to previous clinical data and experience, tend to overvalue complex prediction procedures and produce poor estimates of trial operating characteristics such as power and the number of enrolled patients., Conclusion: Validation analyses on the basis of completed trials and real world data support the selection of predictive models, interim analysis rules, and other aspects of PAIDs in future clinical trials.

We introduce a statistical procedure that integrates datasets from multiple biomedical studies to predict patients' survival, based on individual clinical and genomic profiles. The proposed procedure accounts for potential differences in the relation between predictors and outcomes across studies, due to distinct patient populations, treatments and technologies to measure outcomes and biomarkers. These differences are modeled explicitly with study-specific parameters. We use hierarchical regularization to shrink the study-specific parameters towards each other and to borrow information across studies. The estimation of the study-specific parameters utilizes a similarity matrix, which summarizes differences and similarities of the relations between covariates and outcomes across studies. We illustrate the method in a simulation study and using a collection of gene expression datasets in ovarian cancer. We show that the proposed model increases the accuracy of survival predictions compared to alternative meta-analytic methods., (© 2021 The International Biometric Society.)

Bayesian response adaptive clinical trials are currently evaluating experimental therapies for several diseases. Adaptive decisions, such as pre-planned variations of the randomization probabilities, attempt to accelerate the development of new treatments. The design of response adaptive trials, in most cases, requires time consuming simulation studies to describe operating characteristics, such as type I/II error rates, across plausible scenarios. We investigate large sample approximations of pivotal operating characteristics in Bayesian Uncertainty directed trial Designs (BUDs). A BUD trial utilizes an explicit metric u to quantify the information accrued during the study on parameters of interest, for example the treatment effects. The randomization probabilities vary during time to minimize the uncertainty summary u at completion of the study. We provide an asymptotic analysis (i) of the allocation of patients to treatment arms and (ii) of the randomization probabilities. For BUDs with outcome distributions belonging to the natural exponential family with quadratic variance function, we illustrate the asymptotic normality of the number of patients assigned to each arm and of the randomization probabilities. We use these results to approximate relevant operating characteristics such as the power of the BUD. We evaluate the accuracy of the approximations through simulations under several scenarios for binary, time-to-event and continuous outcome models.

Patients with smoldering multiple myeloma (SMM) are observed until progression, but early treatment may improve outcomes. We conducted a phase II trial of elotuzumab, lenalidomide, and dexamethasone (EloLenDex) in patients with high-risk SMM and performed single-cell RNA and T cell receptor (TCR) sequencing on 149 bone marrow (BM) and peripheral blood (PB) samples from patients and healthy donors (HDs). We find that early treatment with EloLenDex is safe and effective and provide a comprehensive characterization of alterations in immune cell composition and TCR repertoire diversity in patients. We show that the similarity of a patient's immune cell composition to that of HDs may have prognostic relevance at diagnosis and after treatment and that the abundance of granzyme K (GZMK) + CD8 + effector memory T (TEM) cells may be associated with treatment response. Last, we uncover similarities between immune alterations observed in the BM and PB, suggesting that PB-based immune profiling may have diagnostic and prognostic utility., Competing Interests: Declaration of interests N.J.H. is a consultant for Constellation Pharmaceuticals. F.A. is an employee of Illumina Inc. O.Z. is an employee of Ikena Oncology and a stockholder in Ikena Oncology and Morphosys AG. G.G. receives research funds from IBM and Pharmacyclics and is an inventor on patent applications filed by the Broad Institute related to MSMuTect, MSMutSig, POLYSOLVER, SignatureAnalyzer-GPU, and MSIDetect. He is also a founder and consultant of and holds privately held equity in Scorpion Therapeutics. I.M.G. has a consulting or advisory role with AbbVie, Adaptive, Amgen, Aptitude Health, Bristol Myers Squibb, GlaxoSmithKline, Huron Consulting, Janssen, Menarini Silicon Biosystems, Oncopeptides, Pfizer, Sanofi, Sognef, Takeda, The Binding Site, and Window Therapeutics and has received speaker fees from Vor Biopharma and Veeva Systems, Inc., and her spouse is the CMO and equity holder of Disc Medicine. S.M. has a consulting role with Abbvie, Adaptive Biotechnology, Amgen, Celgene/BMS, GlaxoSmithKline, Janssen, Novartis, Oncopeptides, Regeneron, Roche, and Takeda and has received research funding from Abbvie, Adaptive Biotechnology, Amgen, Celgene/BMS, GlaxoSmithKline, Janssen, Novartis, Oncopeptides, Regeneron, Roche, and Takeda. A.J.Y. has a consulting role with Adaptive Biotechnologies, Amgen, BMS, Celgene, GSK, Janssen, Karyopharm, Oncopeptides, Sanofi, and Takeda and has received research funding from Amgen, Janssen, and Takeda. M.B. is a consultant for Sanofi, Genzyme, and Janssen and has received research funding from MedImmune, Janssen, Legend Biotech, Amgen, Celularity, Bristol Myers Squibb, Celgene, Bluebird bio, Millennium, Takeda, Cerecor (currently Avalo Therapeutics), and C4 Therapeutics. M.B has an advisory role and received honoraria from Bristol Myers Squibb, Takeda, Janssen, and Menarini. T.H.M. received advisory board fees from Legend Biotech. R.S.-P., G.G., and I.M.G. are co-inventors on a patent application related to this work (PCT/US22/74839)., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Patient-level data from completed clinical studies or electronic health records can be used in the design and analysis of clinical trials. However, these external data can bias the evaluation of the experimental treatment when the statistical design does not appropriately account for potential confounders. In this work, we introduce a hybrid clinical trial design that combines the use of external control datasets and randomization to experimental and control arms, with the aim of producing efficient inference on the experimental treatment effects. Our analysis of the hybrid trial design includes scenarios where the distributions of measured and unmeasured prognostic patient characteristics differ across studies. Using simulations and datasets from clinical studies in extensive-stage small cell lung cancer and glioblastoma, we illustrate the potential advantages of hybrid trial designs compared to externally controlled trials and randomized trial designs., (© 2022. The Author(s).)

Purpose: Models used to predict the probability of an individual having a pathogenic homozygous or heterozygous variant in a mismatch repair gene, such as MMRpro, are widely used. Recently, MMRpro was updated with new colorectal cancer penetrance estimates. The purpose of this study was to evaluate the predictive performance of MMRpro and other models for individuals with a family history of colorectal cancer., Methods: We performed a validation study of 4 models, Leiden, MMRpredict, PREMM 5 , and MMRpro, using 784 members of clinic-based families from the United States. Predicted probabilities were compared with germline testing results and evaluated for discrimination, calibration, and predictive accuracy. We analyzed several strategies to combine models and improve predictive performance., Results: MMRpro with additional tumor information (MMRpro+) and PREMM 5 outperformed the other models in discrimination and predictive accuracy. MMRpro+ was the best calibrated with an observed to expected ratio of 0.98 (95% CI = 0.89-1.08). The combination models showed improvement over PREMM 5 and performed similar to MMRpro+., Conclusion: MMRpro+ and PREMM 5 performed well in predicting the probability of having a pathogenic homozygous or heterozygous variant in a mismatch repair gene. They serve as useful clinical decision tools for identifying individuals who would benefit greatly from screening and prevention strategies., Competing Interests: Conflict of Interest G.P. is a cofounder and equity holder in Phaeno Inc., a member of the Scientific Advisory Board of Konica Minolta Precision Medicine, Inc (which includes Ambry Genetics and Invicro), and a consultant for Delfi Diagnostics and Foundation Medicine, Inc. D.B. and G.P. colead the BayesMendel lab, which develops and maintains the BayesMendel software package. This includes a variety of risk assessment tools including BRCAPRO, PancPRO, MelaPRO, MMRpro, and PanelPRO and is licensed for commercial use. All licensing revenues are used for software maintenance and upgrades. Neither BayesMendel lab leaders nor members derive personal income from BayesMendel licenses. D.B. and G.P. are coinventor of the Ask2me tool, which is commercially licensed. D.B.’s conflicts of interest are managed by Harvard T.H. Chan School of Public Health. S.S. has been a consultant for Myriad Genetics, Inc and has rights to the inventor portion of licensing revenues for the PREMM model. Z.K.S.’s immediate family member serves as a consultant in Ophthalmology for Alcon, Adverum Biotechnologies, Gyroscope Therapeutics Limited, Neurogene Inc, and REGENXBIO Inc, outside the submitted work. All other authors declare no conflicts of interest., (Copyright © 2022 American College of Medical Genetics and Genomics. Published by Elsevier Inc. All rights reserved.)

Scientific and technologic advances have led to a boon of candidate therapeutics for patients with malignancies of the central nervous system. The path from drug development to clinical use has generally followed a regimented order of sequential clinical trial phases. The recent increase in novel therapies, however, has strained the regulatory process and unearthed limitations of the current system, including significant cost, prolonged development time, and difficulties in testing therapies for rarer tumors. Novel clinical trial designs have emerged to increase efficiencies in clinical trial conduct to better evaluate and bring impactful drugs to patients in a timely manner. In order to better capture meaningful benefits for brain tumor patients, new endpoints to complement or replace traditional endpoints are also an increasingly important consideration. This review will explore the current challenges in the current clinical trial landscape and discuss novel clinical trial concepts, including consideration of limitations and risks of novel trial designs, within the context of neuro-oncology., (© 2022. The American Society for Experimental Neurotherapeutics, Inc.)

The predictive probabilities of the hierarchical Pitman-Yor process are approximated through Monte Carlo algorithms that exploits the Chinese Restaurant Franchise (CRF) representation. However, in order to simulate the posterior distribution of the hierarchical Pitman-Yor process, a set of auxiliary variables representing the arrangement of customers in tables of the CRF must be sampled through Markov chain Monte Carlo. This paper develops a perfect sampler for these latent variables employing ideas from the Propp-Wilson algorithm and evaluates its average running time by extensive simulations. The simulations reveal a significant dependence of running time on the parameters of the model, which exhibits sharp transitions. The algorithm is compared to simpler Gibbs sampling procedures, as well as a procedure for unbiased Monte Carlo estimation proposed by Glynn and Rhee. We illustrate its use with an example in microbial genomics studies.

Smoldering multiple myeloma (SMM) is a precursor condition of multiple myeloma (MM) with significant heterogeneity in disease progression. Existing clinical models of progression risk do not fully capture this heterogeneity. Here we integrate 42 genetic alterations from 214 SMM patients using unsupervised binary matrix factorization (BMF) clustering and identify six distinct genetic subtypes. These subtypes are differentially associated with established MM-related RNA signatures, oncogenic and immune transcriptional profiles, and evolving clinical biomarkers. Three genetic subtypes are associated with increased risk of progression to active MM in both the primary and validation cohorts, indicating they can be used to better predict high and low-risk patients within the currently used clinical risk stratification models., (© 2022. The Author(s).)

Background: Prevalence estimates for monoclonal gammopathy of undetermined significance (MGUS) are based on predominantly White study populations screened by serum protein electrophoresis supplemented with immunofixation electrophoresis. A prevalence of 3% is reported for MGUS in the general population of European ancestry aged 50 years or older. MGUS prevalence is two times higher in individuals of African descent or with a family history of conditions related to multiple myeloma. We aimed to evaluate the prevalence and clinical implications of monoclonal gammopathies in a high-risk US population screened by quantitative mass spectrometry., Methods: We used quantitative matrix-assisted laser desorption ionisation-time of flight (MALDI-TOF) mass spectrometry and EXENT-iQ software to screen for and quantify monoclonal gammopathies in serum from 7622 individuals who consented to the PROMISE screening study between Feb 26, 2019, and Nov 4, 2021, and the Mass General Brigham Biobank (MGBB) between July 28, 2010, and July 1, 2021. M-protein concentrations at the monoclonal gammopathy of indeterminate potential (MGIP) level were confirmed by liquid chromatography mass spectrometry testing. 6305 (83%; 2211 from PROMISE, 4094 from MGBB) of 7622 participants in the cohorts were at high risk for developing a monoclonal gammopathy on the basis of Black race or a family history of haematological malignancies and fell within the eligible high-risk age range (30 years or older for PROMISE cohort and 18 years or older for MGBB cohort); those over 18 years were also eligible if they had two or more family members with a blood cancer (PROMISE cohort). Participants with a plasma cell malignancy diagnosed before screening were excluded. Longitudinal clinical data were available for MGBB participants with a median follow-up time from serum sample screening of 4·5 years (IQR 2·4-6·7). The PROMISE study is registered with ClinicalTrials.gov, NCT03689595., Findings: The median age at time of screening was 56·0 years (IQR 46·8-64·1). 5013 (66%) of 7622 participants were female, 2570 (34%) male, and 39 (<1%) unknown. 2439 (32%) self-identified as Black, 4986 (65%) as White, 119 (2%) as other, and 78 (1%) unknown. Using serum protein electrophoresis with immunofixation electrophoresis, the MGUS prevalence was 6% (101 of 1714) in high-risk individuals aged 50 years or older. Using mass spectrometry, we observed a total prevalence of monoclonal gammopathies of 43% (1788 of 4207) in this group. We termed monoclonal gammopathies below the clinical immunofixation electrophoresis detection level (<0·2 g/L) MGIPs, to differentiate them from those with higher concentrations, termed mass-spectrometry MGUS, which had a 13% (592 of 4207) prevalence by mass spectrometry in high-risk individuals aged 50 years or older. MGIP was predominantly of immunoglobulin M isotype, and its prevalence increased with age (19% [488 of 2564] for individuals aged <50 years, 29% [1464 of 5058] for those aged ≥50 years, and 37% [347 of 946] for those aged ≥70 years). Mass-spectrometry MGUS prevalence increased with age (5% [127 of 2564] for individuals aged <50 years, 13% [678 of 5058] for those aged ≥50 years, and 18% [173 of 946] for those aged ≥70 years) and was higher in men (314 [12%] of 2570) compared with women (485 [10%] 5013; p=0·0002), whereas MGIP prevalence did not differ significantly by gender. In those aged 50 years or older, the prevalence of mass spectrometry was significantly higher in Black participants (224 [17%] of 1356) compared with the controls (p=0·0012) but not in those with family history (368 [13%] of 2851) compared with the controls (p=0·1008). Screen-detected monoclonal gammopathies correlated with increased all-cause mortality in MGBB participants (hazard ratio 1·55, 95% CI 1·16-2·08; p=0·0035). All monoclonal gammopathies were associated with an increased likelihood of comorbidities, including myocardial infarction (odds ratio 1·60, 95% CI 1·26-2·02; p=0·00016 for MGIP-high and 1·39, 1·07-1·80; p=0·015 for mass-spectrometry MGUS)., Interpretation: We detected a high prevalence of monoclonal gammopathies, including age-associated MGIP, and made more precise estimates of mass-spectrometry MGUS compared with conventional gel-based methods. The use of mass spectrometry also highlighted the potential hidden clinical significance of MGIP. Our study suggests the association of monoclonal gammopathies with a variety of clinical phenotypes and decreased overall survival., Funding: Stand Up To Cancer Dream Team, the Multiple Myeloma Research Foundation, and National Institutes of Health., Competing Interests: Declaration of interests DS, DB, MCP are current employees of The Binding Site. MB is a consultant for Takeda and has received honoraria from Takeda, Janssen, and Bristol Myers Squibb (BMS). SH is a current employee, member of the Board of Directors, and holds patents related to The Binding Site. CCM is a consultant for Eli Lilly and Epizyme, is an advisory board member for BMS, has served as a consultant and advisory board member for GlaxoSmithKline (GSK), has received honoraria from Janssen, Karyopharm, and Sanofi; and served as an advisory board member for Karyopharm and Sanofi. PK is a principal investigator of studies for which Mayo Clinic has received research funding from AbbVie, Sanofi, Amgen, GSK, Ichnos, Takeda, Regeneron, and Karyopharm; and has received honoraria from X4 pharmaceuticals, Beigene, Pharmacyclics, Imidex, Clinical Care Options, GSK, Oncopeptides, Cellectar, and Karyopharm. JM is a consultant for Amgen, BMS, GSK, Janssen, Karyopharm, Sanofi, and Takeda. RF is a consultant for AbbVie, Amgen, Bayer, BMS/Celgene, GSK, H3 Therapeutics, Janssen, Juno, Karyopharm, Kite, Merck, Novartis, Oncopeptides, OncoTracker, Pfizer, Pharmacyclics, Regeneron, Sanofi, and Takeda; and is on scientific advisory board of Adaptive Biotechnologies, Caris Life Sciences, OncoMyx, and OncoTracker. GG receives research funds from International Business Machines Corporation and Pharmacyclics and is an inventor on patent applications related to MSMuTect, MSMutSig, MSIDetect, POLYSOLVER, SignatureAnalyzer-GPU and TensorQTL. GG is a founder, consultant and holds privately held equity in Scorpion Therapeutics. CRM has serves as a consultant for JBF Legal and received research funding from GRAIL. IMG has served as a consultant for AbbVie, Adaptive, Aptitude Health, BMS, Cellectar, CurioScience, Genetch, Janssen, Janssen Central American and Caribbean, Karyopharm, Medscape, Oncopeptides, Sanofi, Takeda, The Binding Site, Gene Network Sciences Healthcare, and GSK. IMG's spouse, William Savage is CMO and equity holder at Disc Medicine. All other authors declare no competing interest., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Clonal hematopoiesis (CH) is associated with adverse outcomes in patients with non-Hodgkin lymphoma (NHL) and multiple myeloma undergoing autologous stem cell transplantation. Still, its implications for patients with indolent NHL have not been well studied. We report the prevalence of CH in patients with Waldenström macroglobulinemia (WM) and its association with clinical outcomes. To unambiguously differentiate CH mutations from those in the WM clone, CH was defined by the presence of somatic mutations in DNMT3A, TET2, or ASXL1 (DTA) and was detected in 14% of 587 patients with IgM monoclonal gammopathy of undetermined significance (MGUS), smoldering WM (SWM) or WM. The presence and size of DTA clones were associated with older age. Patients with CH had an increased risk of progression from MGUS or SWM to WM, but not worse overall survival in this cohort. These findings further illuminate the clinical effects of CH in patients with indolent NHL such as WM., (© 2022 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.)

Family history is a major risk factor for many types of cancer. Mendelian risk prediction models translate family histories into cancer risk predictions, based on knowledge of cancer susceptibility genes. These models are widely used in clinical practice to help identify high-risk individuals. Mendelian models leverage the entire family history, but they rely on many assumptions about cancer susceptibility genes that are either unrealistic or challenging to validate, due to low mutation prevalence. Training more flexible models, such as neural networks, on large databases of pedigrees can potentially lead to accuracy gains. In this paper we develop a framework to apply neural networks to family history data and investigate their ability to learn inherited susceptibility to cancer. While there is an extensive literature on neural networks and their state-of-the-art performance in many tasks, there is little work applying them to family history data. We propose adaptations of fully-connected neural networks and convolutional neural networks to pedigrees. In data simulated under Mendelian inheritance, we demonstrate that our proposed neural network models are able to achieve nearly optimal prediction performance. Moreover, when the observed family history includes misreported cancer diagnoses, neural networks are able to outperform the Mendelian BRCAPRO model embedding the correct inheritance laws. Using a large dataset of over 200,000 family histories, the Risk Service cohort, we train prediction models for future risk of breast cancer. We validate the models using data from the Cancer Genetics Network.

Background: Short-term fine particulate matter (PM2.5) exposure is positively associated with acute cardiovascular and respiratory events. Understanding whether this association varies across specific cardiovascular and respiratory conditions has important biologic, clinical, and public health implications., Methods: We conducted a time-stratified case-crossover study of hospitalizations from 2000 through 2014 among United States Medicare beneficiaries aged 65+. The outcomes were hospitalizations with any of 57 cardiovascular and 32 respiratory discharge diagnoses. We estimated associations with two-day moving average PM2.5 as a piecewise linear term with a knot at PM2.5 = 25 g/m3. We used Multi-Outcome Regression with Tree-structured Shrinkage (MOReTreeS) to identify de novo groups of related diseases such that PM2.5 associations are: (1) similar within outcome groups; but (2) different between outcome groups. We adjusted for temperature, humidity, and individual-level characteristics. We introduce an R package, moretrees., Results: Our dataset included 16,007,293 cardiovascular and 8,690,837 respiratory hospitalizations. Of 57 cardiovascular diseases, 51 were grouped and positively associated with PM2.5. We observed a stronger positive association for heart failure, which formed a separate group. We observed negative associations for groups containing the outcomes other aneurysm and intracranial hemorrhage. Of 32 respiratory outcomes, 31 were grouped and were positively associated with PM2.5. Influenza formed a separate group with a negative association., Conclusions: We used a new statistical approach, MOReTreeS, to uncover variation in the association between short-term PM2.5 exposure and hospitalizations for cardiovascular and respiratory causes controlling for patient characteristics, time trends, and environmental confounders., Competing Interests: The authors report no conflicts of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)