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PR1, an HLA-A2-restricted peptide derived from both proteinase 3 and neutrophil elastase, is recognized on myeloid leukemia cells by cytotoxic T lymphocytes (CTLs) that preferentially kill leukemia and contribute to cytogenetic remission. To evaluate safety, immunogenicity and clinical activity of PR1 vaccination, a phase I/II trial was conducted. Sixty-six HLA-A2+ patients with acute myeloid leukemia (AML: 42), chronic myeloid leukemia (CML: 13) or myelodysplastic syndrome (MDS: 11) received three to six PR1 peptide vaccinations, administered subcutaneously every 3 weeks at dose levels of 0.25, 0.5 or 1.0 mg. Patients were randomized to the three dose levels after establishing the safety of the highest dose level. Primary end points were safety and immune response, assessed by doubling of PR1/HLA-A2 tetramer-specific CTL, and the secondary end point was clinical response. Immune responses were noted in 35 of 66 (53%) patients. Of the 53 evaluable patients with active disease, 12 (24%) had objective clinical responses (complete: 8; partial: 1 and hematological improvement: 3). PR1-specific immune response was seen in 9 of 25 clinical responders versus 3 of 28 clinical non-responders (P=0.03). In conclusion, PR1 peptide vaccine induces specific immunity that correlates with clinical responses, including molecular remission, in AML, CML and MDS patients.

Background: Recurrence is a major cause of treatment failure after allogeneic transplantation for acute myelogenous leukemia (AML) and myelodysplastic syndrome (MDS), and treatment options are very limited. Azacitidine is a DNA methyltransferase inhibitor with activity in myeloid disease. The authors hypothesized that low-dose azacitidine administered after transplant would reduce recurrence rates, and conducted a study to determine a safe dose/schedule combination.Methods: Forty-five high-risk patients were treated. Median age was 60 years; median number of comorbidities was 3; 67% were not in remission. By using a Bayesian adaptive method to determine the best dose/schedule combination based on time to toxicity, the authors investigated combinations of 5 daily azacitidine doses, 8, 16, 24, 32, and 40 mg/m2, and 4 schedules: 1, 2, 3, or 4 cycles, each with 5 days of drug and 25 days of rest. Cycle 1 started on Day +40.Results: Reversible thrombocytopenia was the dose-limiting toxicity. The optimal combination was 32 mg/m2 given for 4 cycles. Median follow-up was 20.5 months. One-year event-free and overall survival were 58% and 77%, justifying further studies to estimate long-term clinical benefit. No dose significantly affected DNA global methylation.Conclusions: Azacitidine at 32 mg/m2 given for 5 days is safe and can be administered after allogeneic transplant for at least 4 cycles to heavily pretreated AML/MDS patients. The trial also suggested that this treatment may prolong event-free and overall survival, and that more cycles may be associated with greater benefit. [ABSTRACT FROM AUTHOR]

Late-onset (LO) toxicities are a serious concern in many phase I trials. Since most dose-limiting toxicities occur soon after therapy begins, most dose-finding methods use a binary indicator of toxicity occurring within a short initial time period. If an agent causes LO toxicities, however, an undesirably large number of patients may be treated at toxic doses before any toxicities are observed. A method addressing this problem is the time-to-event continual reassessment method (TITE-CRM, Cheung and Chappell, 2000). We propose a Bayesian dose-finding method similar to the TITE-CRM in which doses are chosen using time-to-toxicity data. The new aspect of our method is a set of rules, based on predictive probabilities, that temporarily suspend accrual if the risk of toxicity at prospective doses for future patients is unacceptably high. If additional follow-up data reduce the predicted risk of toxicity to an acceptable level, then accrual is restarted, and this process may be repeated several times during the trial. A simulation study shows that the proposed method provides a greater degree of safety than the TITE-CRM, while still reliably choosing the preferred dose. This advantage increases with accrual rate, but the price of this additional safety is that the trial takes longer to complete on average. [ABSTRACT FROM AUTHOR]

This article reviews phase 2-3 clinical trial designs, including their genesis and the potential role of such designs in treatment evaluation. The paper begins with a discussion of the many scientific flaws in the conventional phase 2 --> phase 3 treatment evaluation process that motivate phase 2-3 designs. This is followed by descriptions of some particular phase 2-3 designs that have been proposed, including two-stage designs to evaluate one experimental treatment, a design that accommodates both frontline and salvage therapy in oncology, two-stage select-and-test designs that evaluate several experimental treatments, dose-ranging designs, and a seamless phase 2-3 design based on both early response-toxicity outcomes and later event times. A general conclusion is that, in many circumstances, a properly designed phase 2-3 trial utilizes resources much more efficiently and provides much more reliable inferences than conventional methods. [ABSTRACT FROM AUTHOR]

For phase II clinical trials that determine the acceptability of an experimental treatment based on ordinal toxicity and ordinal response, most monitoring methods require each ordinal outcome to be dichotomized using a selected cut-point. This allows two early stopping rules to be constructed that compare marginal probabilities of toxicity and response to respective upper and lower limits. Important problems with this approach are loss of information due to dichotomization, dependence of treatment acceptability decisions on precisely how each ordinal variable is dichotomized, and ignoring association between the two outcomes. To address these problems, we propose a new Bayesian method, which we call U-Bayes, that exploits elicited numerical utilities of the joint ordinal outcomes to construct one early stopping rule that compares the mean utility to a lower limit. U-Bayes avoids the problems noted above by using the entire joint distribution of the ordinal outcomes, and not dichotomizing the outcomes. A step-by-step algorithm is provided for constructing a U-Bayes rule based on elicited utilities and elicited limits on marginal outcome probabilities. A simulation study shows that U-Bayes greatly improves the probability of determining treatment acceptability compared to conventional designs that use two monitoring rules based on marginal probabilities., (© 2024 John Wiley & Sons Ltd.)

Refractory aggressive lymphomas can be treated with allo-SCT, pursuing a graft-vs-lymphoma effect. While reduced intensity conditioning is safe, tumors often progress rapidly, indicating the need for more active conditioning regimens. The preclinical synergy we saw between gemcitabine (Gem), clofarabine (Clo) and busulfan (Bu) against lymphoma cell lines led us to study Gem/Clo/Bu clinically. Eligibility: age 12-65, refractory aggressive B-NHL, T-NHL or Hodgkin, with a matched donor. Infusional Gem was dose-escalated on days (d) -6 and -4 (475-975 mg/m 2 /day), followed by Clo (40 mg/m 2 /day) and Bu (target AUC, 4000 μMol min/day) (d -6 to -3). CD20 + tumors received rituximab. GVHD prophylaxis included ATG (MUD), tacrolimus and MMF. We compared their outcomes to matched-pair concurrent controls receiving Flu/Mel + matched allo-SCT. We enrolled 64 patients, median age 46 (17-63), 31 B-NHL/22 T-NHL/11 Hodgkin, 36 MSD/28 MUD (all PBPC), median 4 (2-10) prior therapies; 18 prior auto-SCT, 42 active diseases at allo-SCT (12 PD). Toxicities (mucositis and transaminitis) were manageable. Gem/Clo/Bu was myeloablative yielding early full donor chimerism. Grades II-IV/III-IV acute GVHD rates of 37% and 18%; chronic GVHD of 33% (13% severe); NRM at D100/1 year was 7% and 18%. ORR/CR rates: 78%/71% (B-NHL), 93%/93% (T-NHL), 67%/67% (Hodgkin). At a median follow-up of 60 (12-110) months, EFS/OS rates: 36%/47%. Gem/Clo/Bu patients had better median EFS (12 vs. 3 months, P = 0.001) and OS (25 vs. 7 months, P = 0.003) than 113 Flu/Mel matched-pair controls. The new myeloablative regimen Gem/Clo/Bu has limited toxicity and high activity in allo-SCT for aggressive lymphomas, yielding better outcomes than concurrent matched-pair controls receiving Flu/Mel., Competing Interests: Competing interests: The authors declare no competing interests. Ethics approval and consent to participate: This research involving human subjects and human data is in accordance with the Declaration of Helsinki. This study was approved by local IRB and written informed consent was obtained from all subjects., (© 2024. The Author(s).)

Modern advances in systemic and localized therapies for patients with renal cell carcinoma (RCC) have significantly improved patients' outcomes. If disease progression occurs after initial treatment, clinicians often have multiple options for a first salvage therapy. Because salvage and initial treatments both may affect overall survival time, and they may interact in unanticipated ways, there is a growing need to determine sequences of initial therapy and first salvage therapy that maximize overall survival while maintaining quality of life. The complexity of this problem grows if a second salvage therapy must be chosen for patients with treatment-resistant disease or a second progression occurs following first salvage. On November 9, 2023, a think tank was convened during the International Kidney Cancer Symposium (IKCS) North America to discuss challenges in accounting for postprogression therapies when estimating overall survival (OS) time based on randomized controlled trial (RCT) data. The present manuscript summarizes the topics discussed, with the aim to encourage adoption of statistical methods that account for salvage therapy effects to obtain scientifically valid OS estimation. We highlight limitations of traditional methods for estimating OS that account for initial treatments while ignoring salvage therapy effects and discuss advantages of applying more sophisticated statistical methods for estimation and trial design. These include identifying multistage treatment strategies, correcting for confounding due to salvage therapy effects, and conducting Sequentially Multiple Assignment Randomized Trials (SMARTs) to obtain unbiased comparisons between multistage strategies. We emphasize the critical role of patient input in trial design, and the potential for information technology (IT) advances to support complex trial designs and real-time data analyses. By addressing these challenges, future RCTs can better inform clinical decision-making and improve patient outcomes in RCC., Competing Interests: Declaration of competing interest Pavlos Msaouel reports honoraria for scientific advisory board membership for Mirati Therapeutics, Bristol Myers Squibb, and Exelixis; consulting fees from Axiom Healthcare; nonbranded educational programs supported by Exelixis, Pfizer, and DAVA Oncology; leadership or fiduciary roles as a Medical Steering Committee member for the Kidney Cancer Association and a Kidney Cancer Scientific Advisory Board member for KCCure; and research funding from Takeda, Bristol Myers Squibb, Mirati Therapeutics, Regeneron Pharmaceuticals, and Gateway for Cancer Research. Eric A. Singer reports honoraria for data safety monitoring board membership for Aura Biosciences and leadership or fiduciary roles as a Medical Steering Committee member for the Kidney Cancer Association. Kathryn E Beckermann received funding to the institution for preclinical awards including BMS-IASLC-LCFA, Pionyr, Arsenal Bio, and Aravive, as well as funds as a consultant to Aravive, Aveo, Arcus, Alpine Bioscience, BMS, Exelixis, Merck, Eisai, Nimbus, and Adicet. Bradley A. McGregor reports consulting fees from Arcus, BMS, Daiichi, Eisai, Exelixis, Gilead, Lily, Pfizer as well as research funding to institution from BMS, Exelixis, Gilead, and Pfizer. Ulka N. Vaishampayan reports consulting fees from BMS. Novartis, Merck, Exelixis, Pfizer, Glilead and Alkermes, as well as speaker fees from Bayes and Exelixis. Eric Jonasch reports research support from Abbvie, Arrowhead, Aveo, BMS, Corvus, Merck, NiKang, ProFoundBio, Telix, honoraria from Aveo, Clinical Care Options, DAVA, EBSCO, Eisai, Exelixis, GSK, IDEO Oncology, i3 Health, Ipsen, Kaplan, Merck, Novartis, NiKang, Petra Drugstore, Plexus, leadership position in NCCN Guidelines (Vice-Chair Kidney Cancer Panel), member of the NCI RCC Steering Committee, and board Member of the International Kidney Cancer Coalition. Katie Coleman is employed by the Rare Cancer Research Foundation and has received honoraria from Exelixis for a speaking engagement related to patient advocacy. Nizar M. Tannir reported receiving and personal fees (honoraria) from Calithera Biosciences during the conduct of the study; and grants (sponsored trial) from Calithera Biosciences, Bristol Myers Squibb (BMS), Nektar Therapeutics, Arrowhead Pharmaceuticals, and Novartis, as well as personal fees (honoraria) from Calithera Biosciences, BMS, Eisai Medical Research, Merck Sharp & Dohme (MSD), Deka Biosciences, Neoleukin Therapeutics, Exelixis, and Ono Pharmaceutical outside the submitted work. Daniel M Geynisman reports honoraria from Targeted Oncology, consulting fees from Pfizer, Exelixis, AstraZeneca, Seattle Genetics/Astellas, Merck, and Bristol-Myers Squibb, as well as research funding to institution from Merck, Astellas Pharma, Harpoon Therapeutics, Arvinas, and CG Oncology. Walter M. Stadler reports consulting fees for participation in Data and Safety Monitoring Board from Astra-Zeneca, Merck, Pfizer, and Treadwell Therapeutics, other consulting fees from Astra-Zeneca, Aveo, Caremark/CVS, EMA Wellness, Fortress Biotech, and Xencor, speakers bureau for CME programs from Dava Oncology, Global Academyc for Medical Education, OncLive, PeerView, Research to Practice, and Vindico, research funding to institution from Abbvie, Amgen, Astra-Zeneca, Astellas (Medivation), Bayer, Bristol-Myers-Squibb, Boehringer-Ingelheim, Calithera, Corvus, Eisai, Exilixis, Genentech (Roche), Johnson & Johnson (Janssen), Merck, Novartis, Pfizer, Seattle Genetics, X4Pharmaceuticals, Xencor, stock holdings from Fortress Biotech, Expert Witness for Sandoz, as well as miscellaneous fees from Cancer (ACS), Up-To-Date, and the Kidney Cancer Journal. Yousef Zakharia reports advisory board / consulting fees from Bristol Myers Squibb, Amgen, Janssen, Eisai, Exelixis, Genzyme Corporation, Pfizer, EMD Serono, Astellas, and Seagen, Grant/research institutional support from Pfizer, Exelixis, and Eisai, as well as honoraria for data safety monitoring board membership from Janssen Research and Development. Daniel D. Shapiro has no disclosures., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Breast cancer is the most frequent type of cancer and the second leading cause of cancer-related mortality in women. Mastectomies remain a key component of the treatment of non-metastatic breast cancer, and strategies to treat acute postoperative pain, a complication affecting nearly all patients undergoing surgery, continues to be an important clinical challenge. This study aimed to determine the impact of intraoperative methadone administration compared to conventional short-acting opioids on pain-related perioperative outcomes in women undergoing a mastectomy., Methods: This single-center retrospective study included adult women undergoing total mastectomy. The primary outcome of this study was postoperative pain intensity on day 1 after surgery. Secondary outcomes included perioperative opioid consumption, perioperative non-opioid analgesics use, duration of surgery and anesthesia, time to extubation, pain intensity in the postanesthesia care unit (PACU), anti-emetic use in PACU, and length of stay in hospital. We used the propensity score-based nearest matching with a 1:3 ratio to balance the patient baseline characteristics., Results: 133 patients received methadone, and 2192 patients were treated with short-acting opioids. The analysis demonstrated that methadone was associated with significantly lower intraoperative and postoperative opioid consumption as measured by oral morphine equivalents and lower average pain intensity scores in the postanesthesia care unit. Moreover, methadone was also shown to reduce the use of non-opioid analgesia during surgery., Conclusion: Our study suggests that the unique pharmacological properties of methadone, including a short onset of action when given intravenously, long-acting pharmacokinetics, and multimodal effects, are associated with better acute pain management after a total mastectomy., Competing Interests: Declaration of competing interest Evan D. Kharasch reports financial support was provided by National Institutes of Health. Peter F. Thall and Ruitao Lin report financial support to NIH/NCI under award number P30CA016672. If there are other authors, they 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 © 2024 Elsevier Inc. All rights reserved.)

Optimizing doses for multiple indications is challenging. The pooled approach of finding a single optimal biological dose (OBD) for all indications ignores that dose-response or dose-toxicity curves may differ between indications, resulting in varying OBDs. Conversely, indication-specific dose optimization often requires a large sample size. To address this challenge, we propose a Randomized two-stage basket trial design that Optimizes doses in Multiple Indications (ROMI). In stage 1, for each indication, response and toxicity are evaluated for a high dose, which may be a previously obtained maximum tolerated dose, with a rule that stops accrual to indications where the high dose is unsafe or ineffective. Indications not terminated proceed to stage 2, where patients are randomized between the high dose and a specified lower dose. A latent-cluster Bayesian hierarchical model is employed to borrow information between indications, while considering the potential heterogeneity of OBD across indications. Indication-specific utilities are used to quantify response-toxicity trade-offs. At the end of stage 2, for each indication with at least one acceptable dose, the dose with highest posterior mean utility is selected as optimal. Two versions of ROMI are presented, one using only stage 2 data for dose optimization and the other optimizing doses using data from both stages. Simulations show that both versions have desirable operating characteristics compared to designs that either ignore indications or optimize dose independently for each indication., (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)

Background and Methods: Although signet ring cell (SRC) histology is associated with resistance to neoadjuvant chemoradiotherapy and worse overall survival (OS) in esophageal adenocarcinoma (EAC), its prognostic relationship among patients who survive the early period following resection is unknown. EAC patients who underwent trimodality therapy at a single institution (2006-2018) were identified. Bayesian multivariable regression (BMR) analyses of OS and additional OS from a 3-year landmark were performed., Results: Of 631 patients, SRCs were present in 16.0% (N = 101). SRC was associated with shorter median OS (45.8 [95% confidence interval: 31.0-96.7] vs. 79.8 [63.0-107.2] months; p = 0.014). In BMR analysis, the absence of an SRC component was moderately associated with improved OS (probability of beneficial effect, PBE = 0.879). Three-year conditional BMR analysis of additional OS (N = 357) showed that SRC status no longer had a prognostic effect (PBE = 0.546); higher pathological stage was strongly associated with worse additional OS (PBE < 0.001)., Conclusions: The presence of SRC portends worse OS following trimodality therapy for EAC. However, this prognostic impact is dynamic and abates by 3 years postoperatively. In contrast, a higher pathological stage is strongly associated with poor overall and 3-year conditional survival., Discussion: These findings may inform postoperative patient counseling and surveillance protocols., (© 2024 Wiley Periodicals LLC.)

A dynamic treatment regime (DTR) is a mathematical representation of a multistage decision process. When applied to sequential treatment selection in medical settings, DTRs are useful for identifying optimal therapies for chronic diseases such as AIDs, mental illnesses, substance abuse, and many cancers. Sequential multiple assignment randomized trials (SMARTs) provide a useful framework for constructing DTRs and providing unbiased between-DTR comparisons. A limitation of SMARTs is that they ignore data from past patients that may be useful for reducing the probability of exposing new patients to inferior treatments. In practice, this may result in decreased treatment adherence or dropouts. To address this problem, we propose a generalized outcome-adaptive (GO) SMART design that adaptively unbalances stage-specific randomization probabilities in favor of treatments observed to be more effective in previous patients. To correct for bias induced by outcome adaptive randomization, we propose G-estimators and inverse-probability-weighted estimators of DTR effects embedded in a GO-SMART and show analytically that they are consistent. We report simulation results showing that, compared to a SMART, Response-Adaptive SMART and SMART with adaptive randomization, a GO-SMART design treats significantly more patients with the optimal DTR and achieves a larger number of total responses while maintaining similar or better statistical power., (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)

With the advent of targeted agents and immunological therapies, the medical research community has become increasingly aware that conventional methods for determining the best dose or schedule of a new agent are inadequate. It has been well established that conventional phase I designs cannot reliably identify safe and effective doses. This problem applies, generally, for cytotoxic agents, radiation therapy, targeted agents, and immunotherapies. To address this, the US Food and Drug Administration's Oncology Center of Excellence initiated Project Optimus, with the goal "to reform the dose optimization and dose selection paradigm in oncology drug development." As a response to Project Optimus, the articles in this special issue of Clinical Trials review recent advances in methods for choosing the dose or schedule of a new agent with an overall objective of informing clinical trialists of these innovative designs. This introductory article briefly reviews problems with conventional methods, the regulatory changes that encourage better dose optimization designs, and provides brief summaries of the articles that follow in this special issue., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Identifying optimal doses in early-phase clinical trials is critically important. Therapies administered at doses that are either unsafe or biologically ineffective are unlikely to be successful in subsequent clinical trials or to obtain regulatory approval. Identifying appropriate doses for new agents is a complex process that involves balancing the risks and benefits of outcomes such as biological efficacy, toxicity, and patient quality of life., Purpose: While conventional phase I trials rely solely on toxicity to determine doses, phase I-II trials explicitly account for both efficacy and toxicity, which enables them to identify doses that provide the most favorable risk-benefit trade-offs. It is also important to account for patient covariates, since one-size-fits-all treatment decisions are likely to be suboptimal within subgroups determined by prognostic variables or biomarkers. Notably, the selection of estimands can influence our conclusions based on the prognostic subgroup studied. For example, assuming monotonicity of the probability of response, higher treatment doses may yield more pronounced efficacy in favorable prognosis compared to poor prognosis subgroups when the estimand is mean or median survival. Conversely, when the estimand is the 3-month survival probability, higher treatment doses produce more pronounced efficacy in poor prognosis compared to favorable prognosis subgroups., Methods and Conclusions: Herein, we first describe why it is essential to consider clinical practice when designing a clinical trial and outline a stepwise process for doing this. We then review a precision phase I-II design based on utilities tailored to prognostic subgroups that characterize efficacy-toxicity risk-benefit trade-offs. The design chooses each patient's dose to optimize their expected utility and allows patients in different prognostic subgroups to have different optimal doses. We illustrate the design with a dose-finding trial of a new therapeutic agent for metastatic clear cell renal cell carcinoma., Competing Interests: Declaration of conflicting interestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: P.M. reports honoraria for scientific advisory board membership for Mirati Therapeutics, Bristol Myers Squibb, and Exelixis; consulting fees from Axiom Healthcare; non-branded educational programs supported by Exelixis and Pfizer; leadership or fiduciary roles as a Medical Steering Committee member for the Kidney Cancer Association and a Kidney Cancer Scientific Advisory Board member for KCCure; and research funding from Takeda, Bristol Myers Squibb, Mirati Therapeutics, and Gateway for Cancer Research.

There is a pressing need for allogeneic chimeric antigen receptor (CAR)-immune cell therapies that are safe, effective and affordable. We conducted a phase 1/2 trial of cord blood-derived natural killer (NK) cells expressing anti-CD19 chimeric antigen receptor and interleukin-15 (CAR19/IL-15) in 37 patients with CD19 + B cell malignancies. The primary objectives were safety and efficacy, defined as day 30 overall response (OR). Secondary objectives included day 100 response, progression-free survival, overall survival and CAR19/IL-15 NK cell persistence. No notable toxicities such as cytokine release syndrome, neurotoxicity or graft-versus-host disease were observed. The day 30 and day 100 OR rates were 48.6% for both. The 1-year overall survival and progression-free survival were 68% and 32%, respectively. Patients who achieved OR had higher levels and longer persistence of CAR-NK cells. Receiving CAR-NK cells from a cord blood unit (CBU) with nucleated red blood cells ≤ 8 × 10 7 and a collection-to-cryopreservation time ≤ 24 h was the most significant predictor for superior outcome. NK cells from these optimal CBUs were highly functional and enriched in effector-related genes. In contrast, NK cells from suboptimal CBUs had upregulation of inflammation, hypoxia and cellular stress programs. Finally, using multiple mouse models, we confirmed the superior antitumor activity of CAR/IL-15 NK cells from optimal CBUs in vivo. These findings uncover new features of CAR-NK cell biology and underscore the importance of donor selection for allogeneic cell therapies. ClinicalTrials.gov identifier: NCT03056339 ., (© 2024. The Author(s).)

Relapse is the major cause of failure of high-dose chemotherapy (HDC) with autologous stem cell transplantation (ASCT) for B cell non-Hodgkin lymphomas (B-NHL). Improvement strategies include use in combination with effective immunotherapies. We hypothesized that the combination of rituximab/HDC/ASCT with expanded cord blood (CB)-derived natural killer (NK) cells is safe and active in B-NHL. Patients with B-NHL age 15 to 70 years and appropriate ASCT candidates were eligible for the study. The CB units were selected without considering HLA match with the recipient. The CB NK cells were expanded from day -19 to day -5. Treatment included rituximab on days -13 and -7, BEAM (carmustine/etoposide/cytarabine/melphalan) on days -13 to -7, lenalidomide on days -7 to -2, CB NK infusion (10 8 /kg) on day -5, and ASCT (day 0). The primary endpoint was 30-day treatment-related mortality (TRM); secondary endpoints included relapse-free survival (RFS), overall survival (OS), and persistence of CB NK cells. We enrolled 20 patients. CB NK cells were expanded a median of 1552-fold with >98% purity and >96% viability. We saw no adverse events attributable to the CB NK cells and 0% 30-day TRM. At median follow-up of 47 months, the RFS and OS rates were 53% and 74%, respectively. CB NK cells were detectable in blood for 2 weeks, independent of HLA-mismatch status. CD16 expression in donor NK cells was correlated favorably with outcome, and homozygosity for the high-affinity CD16 variant (158 V/V) in CB, but not recipient, NK cells was correlated with better outcomes. Our data indicate that the combination of expanded and highly purified CB-derived NK cells with HDC/ASCT for B-NHL is safe. CD16 expression in donor NK cells, particularly if homozygous for the high-affinity CD16 variant, was correlated with better outcomes., (Copyright © 2023 The American Society for Transplantation and Cellular Therapy. Published by Elsevier Inc. All rights reserved.)

A generalized phase 1-2-3 design, Gen 1-2-3, that includes all phases of clinical treatment evaluation is proposed. The design extends and modifies the design of Chapple and Thall (2019), denoted by CT. Both designs begin with a phase 1-2 trial including dose acceptability and optimality criteria, and both select an optimal dose for phase 3. The Gen 1-2-3 design has the following key differences. In stage 1, it uses phase 1-2 criteria to identify a set of candidate doses rather than 1 dose. In stage 2, which is intermediate between phase 1-2 and phase 3, it randomizes additional patients fairly among the candidate doses and an active control treatment arm and uses survival time data from both stage 1 and stage 2 patients to select an optimal dose. It then makes a Go/No Go decision of whether or not to conduct phase 3 based on the predictive probability that the selected optimal dose will provide a specified substantive improvement in survival time over the control. A simulation study shows that the Gen 1-2-3 design has desirable operating characteristics compared to the CT design and 2 conventional designs., (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)

Background: Stereotactic body radiotherapy (SBRT) has the potential to ablate localised pancreatic ductal adenocarcinoma. Selective dismutase mimetics sensitise tumours while reducing normal tissue toxicity. This trial was designed to establish the efficacy and toxicity afforded by the selective dismutase mimetic avasopasem manganese when combined with ablative SBRT for localised pancreatic ductal adenocarcinoma., Methods: In this adaptive, randomised, double-blind, placebo-controlled, phase 1b/2 trial, patients aged 18 years or older with borderline resectable or locally advanced pancreatic cancer who had received at least 3 months of chemotherapy and had an Eastern Cooperative Oncology Group performance status of 0-2 were enrolled at six academic sites in the USA. Eligible patients were randomly assigned (1:1), with block randomisation (block sizes of 6-12) with a maximum of 24 patients per group, to receive daily avasopasem (90 mg) or placebo intravenously directly before (ie, within 180 min) SBRT (50, 55, or 60 Gy in five fractions, adaptively assigned in real time by Bayesian estimates of 90-day safety and efficacy). Patients and physicians were masked to treatment group allocation, but not to SBRT dose. The primary objective was to find the optimal dose of SBRT with avasopasem or placebo as determined by the late onset EffTox method. All analyses were done on an intention-to-treat basis. This study is registered with ClinicalTrials.gov, NCT03340974, and is complete., Findings: Between Jan 25, 2018, and April 29, 2020, 47 patients were screened, of whom 42 were enrolled (median age was 71 years [IQR 63-75], 23 [55%] were male, 19 [45%] were female, 37 [88%] were White, three [7%] were Black, and one [2%] each were unknown or other races) and randomly assigned to avasopasem (n=24) or placebo (n=18); the placebo group was terminated early after failing to meet prespecified efficacy parameters. At data cutoff (June 28, 2021), the avasopasem group satisfied boundaries for both efficacy and toxicity. Late onset EffTox efficacy response was observed in 16 (89%) of 18 patients at 50 Gy and six (100%) of six patients at 55 Gy in the avasopasem group, and was observed in three (50%) of six patients at 50 Gy and nine (75%) of 12 patients at 55 Gy in the placebo group, and the Bayesian model recommended 50 Gy or 55 Gy in five fractions with avasopasem for further study. Serious adverse events of any cause were reported in three (17%) of 18 patients in the placebo group and six (25%) of 24 in the avasopasem group. In the placebo group, grade 3 adverse events within 90 days of SBRT were abdominal pain, acute cholangitis, pyrexia, increased blood lactic acid, and increased lipase (one [6%] each); no grade 4 events occurred. In the avasopasem group, grade 3-4 adverse events within 90 days of SBRT were acute kidney injury, increased blood alkaline phosphatase, haematoma, colitis, gastric obstruction, lung infection, abdominal abscess, post-surgical atrial fibrillation, and pneumonia leading to respiratory failure (one [4%] each).There were no treatment-related deaths but one late death in the avasopasem group due to sepsis in the setting of duodenal obstruction after off-study treatment was reported as potentially related to SBRT., Interpretation: SBRT that uses 50 or 55 Gy in five fractions can be considered for patients with localised pancreatic ductal adenocarcinoma. The addition of avasopasem might further enhance disease outcomes. A larger phase 2 trial (GRECO-2, NCT04698915) is underway to validate these results., Funding: Galera Therapeutics., Competing Interests: Declaration of interests CMT reports grant or contracts with US National Institutes of Health (NIH and CPRIT), licenses with Xerient Pharmaceuticals for licensed or individual patent for radioprotection of the upper gastrointestinal tract, and consulting fees with Phebry. JMF reports consultant and advisory board roles for Viewray, and payment for presentations for Boston Scientific. TAA reports an unfunded study steering committee role for Galera Therapeutics (Galera). MP reports research support to their institution from Merck and Varian, royalties to serve as section editor for cholangiocarcinoma, consulting fees from Voxelmetric and Syntactx, a speakers honorarium for Oakstone CME, advisory board participation for Varian, and an unpaid ASTRO Education Committee role. BC reports royalties from Springer, honoraria for Varian-Educational and Oakstone, and a leadership or fiduciary role with the National Comprehensive Cancer Network. MSB reports funding from Galera, institution grants or contracts from Nanobiotix, Silenseed, Trisalis, Artidis, SUC2, CPRIT, AstraZeneca, Merck, Siemens Medical, and Boston Scientific (Augmenix) and consulting fees from Oncosil and Strapax. SP reports consulting or advisory roles for Zymeworks, Ipsen, Novartis, Janssen, Boehringer Ingelheim, and AskGene Pharma, and institutional research funding from Mirati Therapeutics, Lilly, Xencor, Novartis, Rgenix, Bristol-Myers Squibb, Astellas Pharma, Framewave, 4D Pharma, Boehringer Ingelheim, NGM Biopharmaceuticals, Janssen, Arcus Biosciences, Elicio Therapeutics, Bionte, Ipsen, Zymeworks, Pfizer, ImmunoMET, Immuneering, and Amal Therapeutics. C-WDT reports an institution-sponsored research agreement with Sirtex, and individual and institutional honoraria from PanTher. DWK reports grants or contracts from Bold Therapeutics, and payment or honoraria from AstraZeneca. JC reports travel expense money from Galera. EJK reports research grants and contracts from NIH, US Department of Defense, Cancer Prevention and Research Institute of Texas, and Artidis, royalties or licenses from Taylor and Francis, and Kallisio, consulting fees from Kallisio, AstraZeneca, RenovoRx, MD Anderson Physician Network, MJH Life Sciences, and Quantum Aurea Capital, honoraria from Northwestern University and Amplity, a patent pending for 3D oral stents, scientific and medical advisory board roles for Cholangiocarcinoma Foundation, and stock or stock options with Quantum Aurea Capital. PD reports consulting fees from the American Society for Radiation Oncology, payment or honoraria from American Society for Clinical Oncology, Conveners, Physicians Education Resource, Imedex, and Bayer. RAW reports royalties from McGraw-Hill. GOS reports a research agreement with Artios Pharma. RSST reports institutional funding from Galera and NIH National Cancer Institute (NCI). YY reports personal fees from AbbVie, Amgen, Bexion Pharmaceuticals, BeyondSpring Pharmaceuticals, Boehringer Ingelheim Pharmaceuticals, Bristol Myers Squibb, Century Therapeutics, Enliven Therapeutics, Repare Therapeutics, Servier Pharmaceuticals, Starpax Pharmaceuticals, and Vertex Pharmaceuticals during the conduct of the study. PFT reports research funding from NIH NCI. RAB is an employee and officer of, reports receiving compensation from, and holds equity in the sponsor, Galera, residual royalties resulting from the acquisition of Confluence from Aclaris Therapeutics, stock or stock options with Galera, Euclises, Aclaris Therapeutics, and Novocure, and patents for methods for treatment of diseases, (US Pat 10,493,081, 2019 issued to Galera); a patent methods for treatment of diseases (US Pat 9,149,483, 2015 issued to Galera). JH was an employee and officer of, reports receiving compensation from, and holds equity in the sponsor, Galera. JMH reports research funding to their institution and support for attending a meeting from Canopy Cancer Collective, royalties or licenses from Springer, stock or stock options with Histosonics, and consulting fees from Histosonics and Boston Scientific. SEH is an employee of MyCareGorithm and has her own limited liability company, Beyond the White Coat. She has received travel assistance from ViewRay, stock options from Rittenhouse, and her institution received research funding from ViewRay and Galera. LEC, JAJ, VB, MM, GM, NR, EBL, MHGK, SB, and SM declare no competing interests., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Conventional designs for choosing a dose for a new therapy may select doses that are unsafe or ineffective and fail to optimize progression-free survival time, overall survival time, or response/remission duration. We explain and illustrate limitations of conventional dose-finding designs and make four recommendations to address these problems. When feasible, a dose-finding design should account for long-term outcomes, include screening rules that drop unsafe or ineffective doses, enroll an adequate sample size, and randomize patients among doses. As illustrations, we review three designs that include one or more of these features. The first illustration is a trial that randomized patients among two cell therapy doses and standard of care in a setting where it was assumed on biological grounds that dose toxicity and dose-response curves did not necessarily increase with cell dose. The second design generalizes phase I-II by first identifying a set of candidate doses, rather than one dose, randomizing additional patients among the candidates, and selecting an optimal dose to maximize progression-free survival over a longer follow-up period. The third design combines a phase I-II trial and a group sequential randomized phase III trial by using survival time data available after the first stage of phase III to reoptimize the dose selected in phase I-II. By incorporating one or more of the recommended features, these designs improve the likelihood that a selected dose or schedule will be optimal, and thus will benefit future patients and obtain regulatory approval., (©2023 American Association for Cancer Research.)

Background: Randomized controlled trials are considered the gold standard for evaluating experimental treatments but often require large sample sizes. Single-arm trials require smaller sample sizes but are subject to bias when using historical control data for comparative inferences. This article presents a Bayesian adaptive synthetic-control design that exploits historical control data to create a hybrid of a single-arm trial and a randomized controlled trial., Methods: The Bayesian adaptive synthetic control design has two stages. In stage 1, a prespecified number of patients are enrolled in a single arm given the experimental treatment. Based on the stage 1 data, applying propensity score matching and Bayesian posterior prediction methods, the usefulness of the historical control data for identifying a pseudo sample of matched synthetic-control patients for making comparative inferences is evaluated. If a sufficient number of synthetic controls can be identified, the single-arm trial is continued. If not, the trial is switched to a randomized controlled trial. The performance of The Bayesian adaptive synthetic control design is evaluated by computer simulation., Results: The Bayesian adaptive synthetic control design achieves power and unbiasedness similar to a randomized controlled trial but on average requires a much smaller sample size, provided that the historical control data patients are sufficiently comparable to the trial patients so that a good number of matched controls can be identified in the historical control data. Compared to a single-arm trial, The Bayesian adaptive synthetic control design yields much higher power and much smaller bias., Conclusion: The Bayesian adaptive synthetic-control design provides a useful tool for exploiting historical control data to improve the efficiency of single-arm phase II clinical trials, while addressing the problem of bias when comparing trial results to historical control data. The proposed design achieves power similar to a randomized controlled trial but may require a substantially smaller sample size.

This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative random sample from a patient population is impossible for a clinical trial because patients are accrued sequentially over time and thus comprise a convenience sample, subject only to protocol entry criteria. Consequently, the trial's sample is unlikely to represent a definable patient population. We use causal diagrams to illustrate the difference between random allocation of interventions within a clinical trial sample and true simple or stratified random sampling, as executed in surveys. We argue that group-specific statistics, such as a median survival time estimate for a treatment arm in an RCT, have limited meaning as estimates of larger patient population parameters. In contrast, random allocation between interventions facilitates comparative causal inferences about between-treatment effects, such as hazard ratios or differences between probabilities of response. Comparative inferences also require the assumption of transportability from a clinical trial's convenience sample to a targeted patient population. We focus on the consequences and limitations of randomization procedures in order to clarify the distinctions between pairs of complementary concepts of fundamental importance to data science and RCT interpretation. These include internal and external validity, generalizability and transportability, uncertainty and variability, representativeness and inclusiveness, blocking and stratification, relevance and robustness, forward and reverse causal inference, intention to treat and per protocol analyses, and potential outcomes and counterfactuals.

A Bayesian design is proposed for randomized phase II clinical trials that screen multiple experimental treatments compared to an active control based on ordinal categorical toxicity and response. The underlying model and design account for patient heterogeneity characterized by ordered prognostic subgroups. All decision criteria are subgroup specific, including interim rules for dropping unsafe or ineffective treatments, and criteria for selecting optimal treatments at the end of the trial. The design requires an elicited utility function of the two outcomes that varies with the subgroups. Final treatment selections are based on posterior mean utilities. The methodology is illustrated by a trial of targeted agents for metastatic renal cancer, which motivated the design methodology. In the context of this application, the design is evaluated by computer simulation, including comparison to three designs that conduct separate trials within subgroups, or conduct one trial while ignoring subgroups, or base treatment selection on estimated response rates while ignoring toxicity., (© 2022 The International Biometric Society.)

Designs for early phase dose finding clinical trials typically are either phase I based on toxicity, or phase I-II based on toxicity and efficacy. These designs rely on the implicit assumption that the dose of an experimental agent chosen using these short-term outcomes will maximize the agent's long-term therapeutic success rate. In many clinical settings, this assumption is not true. A dose selected in an early phase oncology trial may give suboptimal progression-free survival or overall survival time, often due to a high rate of relapse following response. To address this problem, a new family of Bayesian generalized phase I-II designs is proposed. First, a conventional phase I-II design based on short-term outcomes is used to identify a set of candidate doses, rather than selecting one dose. Additional patients then are randomized among the candidates, patients are followed for a predefined longer time period, and a final dose is selected to maximize the long-term therapeutic success rate, defined in terms of duration of response. Dose-specific sample sizes in the randomization are determined adaptively to obtain a desired level of selection reliability. The design was motivated by a phase I-II trial to find an optimal dose of natural killer cells as targeted immunotherapy for recurrent or treatment-resistant B-cell hematologic malignancies. A simulation study shows that, under a range of scenarios in the context of this trial, the proposed design has much better performance than two conventional phase I-II designs., (© 2023 John Wiley & Sons Ltd.)

Purpose: Dose constraints for reirradiation of recurrent primary brain tumors are not well-established. This study was conducted to prospectively evaluate composite dose constraints for conventionally fractionated brain reirradiation., Methods and Materials: A single-institution, prospective study of adults with previously irradiated, recurrent brain tumors was performed. For 95% of patients, electronic dosimetry records from the first course of radiation (RT1) were obtained and deformed onto the simulation computed tomography for the second course of radiation (RT2). Conventionally fractionated treatment plans for RT2 were developed that met protocol-assigned dose constraints for RT2 alone and the composite dose of RT1 + RT2. Prospective composite dose constraints were based on histology, interval since RT1, and concurrent bevacizumab. Patients were followed with magnetic resonance imaging including spectroscopy and perfusion studies. Primary endpoint was the rate of symptomatic brain necrosis at 6 months after RT2., Results: Patients were enrolled from March 2017 to May 2018; 20 were evaluable. Eighteen had glioma, 1 had atypical choroid plexus papilloma, and 1 had hemangiopericytoma. Nineteen patients were treated with volumetric modulated arc therapy, and one was treated with protons. Median RT1 dose was 57 Gy (range, 50-60 Gy). Median RT1-RT2 interval was 49 months (range, 9-141 months). Median RT2 dose was 42.4 Gy (range, 36-60 Gy). Median planning target volume was 186 cc (range, 8-468 cc). Nineteen of 20 patients (95%) were free of grade 3+ central nervous system necrosis. One patient had grade 3+ necrosis 2 months after RT2; the patient recovered fully and lived another 18 months until dying of disease progression. Median overall survival from RT2 start for all patients was 13.3 months (95% credible interval, 6.3-20.7); for patients with glioblastoma, 11.5 months (95% credible interval, 6.1-20.1)., Conclusions: Brain reirradiation can be safely performed with conventionally fractionated regimens tailored to previous dose distributions. The prospective composite dose constraints described here are a starting point for future studies of conventionally fractionated reirradiation., (Copyright © 2023 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.)

A Bayesian feature allocation model (FAM) is presented for identifying cell subpopulations based on multiple samples of cell surface or intracellular marker expression level data obtained by cytometry by time of flight (CyTOF). Cell subpopulations are characterized by differences in marker expression patterns, and cells are clustered into subpopulations based on their observed expression levels. A model-based method is used to construct cell clusters within each sample by modeling subpopulations as latent features, using a finite Indian buffet process. Non-ignorable missing data due to technical artifacts in mass cytometry instruments are accounted for by defining a static missingship mechanism. In contrast with conventional cell clustering methods, which cluster observed marker expression levels separately for each sample, the FAM-based method can be applied simultaneously to multiple samples, and also identify important cell subpopulations likely to be otherwise missed. The proposed FAM-based method is applied to jointly analyse three CyTOF datasets to study natural killer (NK) cells. Because the subpopulations identified by the FAM may define novel NK cell subsets, this statistical analysis may provide useful information about the biology of NK cells and their potential role in cancer immunotherapy which may lead, in turn, to development of improved NK cell therapies., Competing Interests: Conflict of interest: The authors have no conflicts of interest to declare., (© (RSS) Royal Statistical Society 2023. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

A robust Bayesian design is presented for a single-arm phase II trial with an early stopping rule to monitor a time to event endpoint. The assumed model is a piecewise exponential distribution with non-informative gamma priors on the hazard parameters in subintervals of a fixed follow up interval. As an additional comparator, we also define and evaluate a version of the design based on an assumed Weibull distribution. Except for the assumed models, the piecewise exponential and Weibull model based designs are identical to an established design that assumes an exponential event time distribution with an inverse gamma prior on the mean event time. The three designs are compared by simulation under several log-logistic and Weibull distributions having different shape parameters, and for different monitoring schedules. The simulations show that, compared to the exponential inverse gamma model based design, the piecewise exponential design has substantially better performance, with much higher probabilities of correctly stopping the trial early, and shorter and less variable trial duration, when the assumed median event time is unacceptably low. Compared to the Weibull model based design, the piecewise exponential design does a much better job of maintaining small incorrect stopping probabilities in cases where the true median survival time is desirably large., (© 2022 John Wiley & Sons Ltd.)

We propose a curve-free random-effects meta-analysis approach to combining data from multiple phase I clinical trials to identify an optimal dose. Our method accounts for between-study heterogeneity that may stem from different study designs, patient populations, or tumor types. We also develop a meta-analytic-predictive (MAP) method based on a power prior that incorporates data from multiple historical studies into the design and conduct of a new phase I trial. Performances of the proposed methods for data analysis and trial design are evaluated by extensive simulation studies. The proposed random-effects meta-analysis method provides more reliable dose selection than comparators that rely on parametric assumptions. The MAP-based dose-finding designs are generally more efficient than those that do not borrow information, especially when the current and historical studies are similar. The proposed methodologies are illustrated by a meta-analysis of five historical phase I studies of Sorafenib, and design of a new phase I trial.

A Bayesian method is proposed for personalized treatment selection in settings where data are available from a randomized clinical trial with two or more outcomes. The motivating application is a randomized trial that compared letrozole plus bevacizimab to letrozole alone as first-line therapy for hormone receptor positive advanced breast cancer. The combination treatment arm had larger median progression-free survival time, but also a higher rate of severe toxicities. This suggests that the risk-benefit trade-off between these two outcomes should play a central role in selecting each patient's treatment, particularly since older patients are less likely to tolerate severe toxicities. To quantify the desirability of each possible outcome combination for an individual patient, we elicited from breast cancer oncologists a utility function that varied with age. The utility was used as an explicit criterion for quantifying risk-benefit trade-offs when making personalized treatment selections. A Bayesian nonparametric multivariate regression model with a dependent Dirichlet process prior was fit to the trial data. Under the fitted model, a new patient's treatment can be selected based on the posterior predictive utility distribution. For the breast cancer trial dataset, the optimal treatment depends on the patient's age, with the combination preferable for patients 70 years or younger and the single agent preferable for patients older than 70.

We discuss how causal diagrams can be used by clinicians to make better individualized treatment decisions. Causal diagrams can distinguish between settings where clinical decisions can rely on a conventional additive regression model fit to data from a historical randomized clinical trial (RCT) to estimate treatment effects and settings where a different approach is needed. This may be because a new patient does not meet the RCT's entry criteria, or a treatment's effect is modified by biomarkers or other variables that act as mediators between treatment and outcome. In some settings, the problem can be addressed simply by including treatment-covariate interaction terms in the statistical regression model used to analyze the RCT dataset. However, if the RCT entry criteria exclude a new patient seen in the clinic, it may be necessary to combine the RCT data with external data from other RCTs, single-arm trials, or preclinical experiments evaluating biological treatment effects. For example, external data may show that treatment effects differ between histological subgroups not recorded in an RCT. A causal diagram may be used to decide whether external observational or experimental data should be obtained and combined with RCT data to compute statistical estimates for making individualized treatment decisions. We use adjuvant treatment of renal cell carcinoma as our motivating example to illustrate how to construct causal diagrams and apply them to guide clinical decisions.

Conditioning regimens play a major role in determining disease outcomes following allogeneic hematopoietic stem cell transplantation (allo-HSCT). The use of i.v. busulfan (Bu) as part of conditioning chemotherapy has been shown to be effective in controlling disease relapse; however, disease relapse remains a major cause of death following allo-HSCT. This study was conducted to determine the long-term outcomes of vorinostat with i.v. Bu plus dual nucleoside analogs clofarabine (Clo) and fludarabine (Flu) in the conditioning regimen for patients undergoing allo-HSCT. This was a rapid dose escalation phase I/II study designed to determine whether the addition of vorinostat would improve the efficacy of standard i.v. Bu/Flu/Clo conditioning regimen. This report presents the long-term disease outcomes of this combination in 68 patients with high-risk leukemia, including 31 (46%) with acute lymphoblastic leukemia (ALL) and 37 (54%) with acute myelogenous leukemia (AML) or myelodysplastic syndrome (MDS). Fifty-eight patients (85%) were in morphologic complete remission at time of transplantation, and 38 (56%) received a matched unrelated donor graft. Over the median follow-up of 37.6 months, 29 of the 68 patients died (43%), and the nonrelapse mortality (NRM) rate was 22% (n = 15). The median overall survival and median NRM were not reached. Nineteen patients (28%) experienced disease progression. The median progression-free survival was 36.8 months. Thirty-seven patients (57%) developed grade II-IV acute graft-versus-host disease (GVHD), and 20 patients (31%) developed chronic GVHD. Our results suggest a lack of benefit from adding a short course of vorinostat to i.v. Bu/Flu/Clo conditioning regimens for leukemia patients undergoing allo- HSCT., (Copyright © 2022 The American Society for Transplantation and Cellular Therapy. Published by Elsevier Inc. All rights reserved.)

Pretransplant conditioning with Fludarabine (Flu)-Busulfan (Bu) is safe, but clofarabine (Clo) has improved antileukemic activity. Hypothesis: Flu+Clo-Bu (FCB) yields superior progression-free survival (PFS) after allogeneic transplantation. We randomized 250 AML/MDS patients aged 3-70, Karnofsky Score ≥80, with matched donors, to FCB (n = 120) or Flu-Bu (n = 130), stratifying complete remission (CR) vs. No CR, (NCR). HCT-CI scores varied, from 0 to 10. All evaluable patients engrafted. Median follow-up was 66 months (interquartile range: 58-80). Three-year relapse incidence (RI), 25% with FCB, vs. 39% with Flu-Bu (p = 0.018), offset by higher non-relapse mortality, 22.6% (95%CI: 16-30.2%) vs. 12.3% (95%CI: 6.5-19%). Three-year PFS was 52% (95%CI: 44-62%) (FCB), vs. 48% (95%CI: 41-58%) (Flu-Bu). FCB benefited CR patients less, NCR patients age ≤ 60 had 3-year 34% RI (95%CI: 19-49%) (FCB) vs. 56% (95%CI: 38-70%) after Flu-Bu (p = 0.037). NCR patients >60 years had 3-year RI 10.0% (FCB), vs. 56.0%, after Flu-Bu (p = 0.003). Bayesian regression analysis including treatment-covariate interactions showed FCB superiority in NCR patients with low HCT-CI (0-2). Serious adverse event profiles were similar for the regimens. Conditioning with FCB did not improve PFS overall, but improved disease control in NCR patients, mandating confirmatory trials. Remission status and HCT-CI should be considered when using FCB., (© 2022. The Author(s).)

The accumulation of immune-suppressive myeloid cells is a critical determinant of resistance to anti-programmed death-1 (PD-1) therapy in advanced clear cell renal cell carcinoma (ccRCC). In preclinical models, the tyrosine kinase inhibitor sitravatinib enhanced responses to anti-PD-1 therapy by modulating immune-suppressive myeloid cells. We conducted a phase 1-2 trial to choose an optimal sitravatinib dose combined with a fixed dose of nivolumab in 42 immunotherapy-naïve patients with ccRCC refractory to prior antiangiogenic therapies. The combination demonstrated no unexpected toxicities and achieved an objective response rate of 35.7% and a median progression-free survival of 11.7 months, with 80.1% of patients alive after a median follow-up of 18.7 months. Baseline peripheral blood neutrophil-to-lymphocyte ratio correlated with response to sitravatinib and nivolumab. Patients with liver metastases showed durable responses comparable to patients without liver metastases. In addition, correlative studies demonstrated reduction of immune-suppressive myeloid cells in the periphery and tumor microenvironment following sitravatinib treatment. This study provides a rationally designed combinatorial strategy to improve outcomes of anti-PD-1 therapy in advanced ccRCC.

Precision medicine relies on the idea that, for a particular targeted agent, only a subpopulation of patients is sensitive to it and thus may benefit from it therapeutically. In practice, it is often assumed based on preclinical data that a treatment-sensitive subpopulation is known, and moreover that the agent is substantively efficacious in that subpopulation. Due to important differences between preclinical settings and human biology, however, data from patients treated with a new targeted agent often show that one or both of these assumptions are false. This paper provides a Bayesian randomized group sequential enrichment design that compares an experimental treatment to a control based on survival time and uses early response as an ancillary outcome to assist with adaptive variable selection and enrichment. Initially, the design enrolls patients under broad eligibility criteria. At each interim decision, submodels for regression of response and survival time on a baseline covariate vector and treatment are fit; variable selection is used to identify a covariate subvector that characterizes treatment-sensitive patients and determines a personalized benefit index, and comparative superiority and futility decisions are made. Enrollment of each cohort is restricted to the most recent adaptively identified treatment-sensitive patients. Group sequential decision cutoffs are calibrated to control overall type I error and account for the adaptive enrollment restriction. The design provides a basis for precision medicine by identifying a treatment-sensitive subpopulation, if it exists, and determining whether the experimental treatment is superior to the control in that subpopulation. A simulation study shows that the proposed design reliably identifies a sensitive subpopulation, yields much higher generalized power compared to several existing enrichment designs and a conventional all-comers group sequential design, and is robust., (© 2021 The International Biometric Society.)

Objective: This phase I trial (NCT01912625) evaluated the safety and pharmacokinetics of definitive concurrent chemoradiotherapy (cCRT) and the radiosensitizer trametinib (MEK1/2 inhibitor) for KRAS-mutated nonmetastatic non-small cell lung cancer (NSCLC)., Methods: Patients received cCRT (carboplatin/paclitaxel and 60 Gy/30 fractions radiotherapy); oral trametinib (7 days/week) commenced on day 1 and completed on the final day of radiotherapy. Dose-finding of trametinib was done using the time-to-event continual reassessment method (TiTE-CRM); dose levels were 0.5mg (level -1), 1mg (initial, level 1), 1.5mg (level 2), and 2mg (level 3). Progression-free (PFS) and overall survival (OS) times were also recorded., Results: Fifteen patients (stage III, variety of KRAS mutations) were treated, with 1/5/4/5 at dose levels -1/1/2/3, respectively. Five patients received dose reductions (n=2, levels 2 and 3; n=1, level 1). Twelve patients completed the full cCRT course. One patient (following 12d trametinib) was taken off protocol for an unrelated/unresolved grade 1 event and later experienced grade 5 sepsis/respiratory failure. There was one grade 4 retinal detachment; grade 3 events included skin rash (n=2) and ventricular dysfunction, pneumonitis, pain, fatigue, and diarrhea (n=1 each). The final dose selected by the TiTE-CRM of trametinib was 1.5 mg. Pharmacokinetic profiles were elucidated and extensively described. At median follow-up of 70 months, median PFS was 11 months and median OS was 38 months., Conclusions: The MTD for trametinib when combined with cCRT is 1.5 mg, with encouraging preliminary outcomes. This combination merits further study to combine with consolidation durvalumab in non-metastatic KRAS mutant NSCLC., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Purpose: Novel Bayesian adaptive designs provide an effective way to improve clinical trial efficiency. These designs are superior to conventional methods, but implementing them can be challenging. The aim of this article was to describe what we learned while applying a novel Bayesian phase I-II design in a recent trial., Methods: The primary goal of the trial was to optimize radiation therapy (RT) dose among three levels (low, standard, and high), given either with placebo (P) or an investigational agent (A), for treating locally advanced, radiation-naive pancreatic cancer, deemed appropriate for RT rather than surgery. Up to 48 patients were randomly assigned fairly between RT plus P and RT plus A, with RT dose-finding done within each arm using the late-onset efficacy-toxicity design on the basis of two coprimary end points, tumor response and dose-limiting toxicity, both evaluated at up to 90 days. The random assignment was blinded, but within each arm, unblinded RT doses were chosen adaptively using software developed within the institution., Results: Implementing the design involved double-blind balance-restricted random assignment, real-time assessment of patient outcomes to evaluate the efficacy-toxicity trade-off for each RT dose in each arm to optimize each patient's RT dose adaptively, and transition from a single-center trial to a multicenter trial. We present lessons learned and illustrative documentation., Conclusion: Implementing novel Bayesian adaptive trial designs requires close collaborations between physicians, pharmacists, statisticians, data managers, and sponsors. The process is difficult but manageable and essential for efficient trial conduct. Close collaboration during trial conduct is a key component of any trial that includes real-time adaptive decision rules., Competing Interests: Rebecca S. S. Tidwell Research Funding: Galera Therapeutics (Inst) Ying Yuan Honoraria: Ono Pharmaceutical Consulting or Advisory Role: Boehringer Ingelheim, Amgen, AbbVie, Servier, Starpax Medical, Vertex, MicuRx Pharmaceuticals, BeyondSpring Pharmaceuticals, Bristol Myers Squibb/Celgene/Juno No other potential conflicts of interest were reported. Rebecca S. S. Tidwell Research Funding: Galera Therapeutics (Inst) Ying Yuan Honoraria: Ono Pharmaceutical Consulting or Advisory Role: Boehringer Ingelheim, Amgen, AbbVie, Servier, Starpax Medical, Vertex, MicuRx Pharmaceuticals, BeyondSpring Pharmaceuticals, Bristol Myers Squibb/Celgene/Juno No other potential conflicts of interest were reported., (© 2021 by American Society of Clinical Oncology.)

A Bayesian phase I-II design is presented that optimizes the dose of a new agent within predefined prognostic subgroups. The design is motivated by a trial to evaluate targeted agents for treating metastatic clear cell renal carcinoma, where a prognostic risk score defined by clinical variables and biomarkers is well established. Two clinical outcomes are used for dose-finding, time-to-toxicity during a prespecified follow-up period, and efficacy characterized by ordinal disease status evaluated at the end of follow-up. A joint probability model is constructed for these outcomes as functions of dose and subgroup. The model performs adaptive clustering of adjacent subgroups having similar dose-outcome distributions to facilitate borrowing information across subgroups. To quantify toxicity-efficacy risk-benefit trade-offs that may differ between subgroups, the objective function is based on outcome utilities elicited separately for each subgroup. In the context of the renal cancer trial, a design is constructed and a simulation study is presented to evaluate the design's reliability, safety, and robustness, and to compare it to designs that either ignore subgroups or run a separate trial within each subgroup., (© 2021 John Wiley & Sons Ltd.)

This paper proposes randomized controlled clinical trial design to evaluate external cooling as a means to control fever and thereby reduce mortality in patients with septic shock. The trial will include concurrent external cooling and control arms while adaptively incorporating historical control arm data. Bayesian group sequential monitoring will be done using a posterior comparative test based on the 60-day survival distribution in each concurrent arm. Posterior inference will follow from a Bayesian discrete time survival model that facilitates adaptive incorporation of the historical control data through an innovative regression framework with a multivariate spike-and-slab prior distribution on the historical bias parameters. For each interim test, the amount of information borrowed from the historical control data will be determined adaptively in a manner that reflects the degree of agreement between historical and concurrent control arm data. Guidance is provided for selecting Bayesian posterior probability group-sequential monitoring boundaries. Simulation results elucidating how the proposed method borrows strength from the historical control data are reported. In the absence of historical control arm bias, the proposed design controls the type I error rate and provides substantially larger power than reasonable comparators, whereas in the presence bias of varying magnitude, type I error rate inflation is curbed.

Purpose: BK virus-associated hemorrhagic cystitis (BKV-HC) is a common complication of allogenic hematopoietic stem cell transplantation (AHSCT), particularly in recipients of alternative donor transplants, which are being performed in increasing numbers. BKV-HC typically results in painful hematuria, urinary obstruction, and renal dysfunction, without a definitive therapeutic option., Methods: We performed a clinical trial (ClinicalTrials.gov identifier: NCT02479698) to assess the feasibility, safety, and efficacy of administering most closely HLA-matched third-party BKV-specific cytotoxic T lymphocytes (CTLs), generated from 26 healthy donors and banked for off-the-shelf use. The cells were infused into 59 patients who developed BKV-HC following AHSCT. Comprehensive clinical assessments and correlative studies were performed., Results: Response to BKV-CTL infusion was rapid; the day 14 overall response rate was 67.7% (40 of 59 evaluable patients), which increased to 81.6% among evaluable patients at day 45 (40 of 49 evaluable patients). No patient lost a previously achieved response. There were no cases of de novo grade 3 or 4 graft-versus-host disease, graft failure, or infusion-related toxicities. BKV-CTLs were identified in patient blood samples up to 3 months postinfusion and their in vivo expansion predicted for clinical response. A matched-pair analysis revealed that, compared with standard of care, after accounting for prognostic covariate effects, treatment with BKV-CTLs resulted in higher probabilities of response at all follow-up timepoints as well as significantly lower transfusion requirement., Conclusion: Off-the-shelf BKV-CTLs are a safe and effective therapy for the management of patients with BKV-HC after AHSCT., Competing Interests: David MarinHonoraria: TakedaResearch Funding: TakedaPatents, Royalties, Other Intellectual Property: Licensing of cell therapy product to Takeda Hind RafeiPatents, Royalties, Other Intellectual Property: Pending patent: United States Provisional Appl. No. 62/963,121, Ref.: UTSC.P1172US.P1-1001106067 Rafet BasarResearch Funding: TakedaPatents, Royalties, Other Intellectual Property: Institutional financial conflict of interest with Takeda Pharmaceutical for the licensing of the technology related to CAR NK cells research. MD Anderson has implemented an Institutional Conflict of Interest Management and Monitoring Plan to manage and monitor the conflict of interest with respect to MDACC's conduct of any other ongoing or future research related to this relationship. MD declares no competing financial interest Pinaki BanerjeePatents, Royalties, Other Intellectual Property: Pinaki Banerjee and The University of Texas MD Anderson Cancer Center (MDACC) have an institutional financial conflict of interest with Takeda Pharmaceutical for the licensing of the technology related to CAR-NK cell research. MD Anderson has implemented an Institutional Conflict of Interest Management and Monitoring Plan to manage and monitor the conflict of interest with respect to MDACC's conduct of any other ongoing or future research related to this relationship Fleur M. AungHonoraria: ALX Oncology Roy ChemalyHonoraria: Merck Sharp & Dohme, Oxford Immunotec, GenentechConsulting or Advisory Role: Partner Therapeutics, Merck, Ansun Biopharma, Shiniogi, Pulmotect, Paratek, ADMA Biologics, Cidara Therapeutics, Xenex, Kyorin, Janssen, Wockhardt Pharmaceuticals, ReViral, AdagioResearch Funding: Merck, Chimerix, Viracor Eurofins, Ansun Biopharma, Karius, Gilead Sciences, XenexTravel, Accommodations, Expenses: Merck, Oxford Immunotec Victor MulanovichConsulting or Advisory Role: Legend Biotech, Swedish Orphan Biovitrum Amin M. AlousiHonoraria: Generon, Genentech, Kadmon, Prolacta Bioscience Qaiser BashirConsulting or Advisory Role: Takeda, Spectrum Pharmaceuticals, Kite Pharma, Amgen, Purdue PharmaResearch Funding: Takeda, Celgene, Acrotech Biopharma, Stemline Therapeutics May DaherResearch Funding: TakedaPatents, Royalties, Other Intellectual Property: Institutional financial conflict of interest with Takeda Pharmaceutical for the licensing of the technology related to CAR-NK cell research. MD Anderson has implemented an Institutional Conflict of Interest Management and Monitoring Plan to manage and monitor the conflict of interest with respect to MDACC's conduct of any other ongoing or future research related to this relationship Chitra HosingHonoraria: SanofiConsulting or Advisory Role: Sanofi, Alexion Pharmaceuticals, NKARTAResearch Funding: CelgeneTravel, Accommodations, Expenses: Celgene Roy B. JonesEmployment: Stafa, Bayer, NovartisTravel, Accommodations, Expenses: Velos Partow KebriaeiHonoraria: Kite Pharma, Novartis, PfizerConsulting or Advisory Role: Jazz PharmaceuticalsResearch Funding: ZIOPHARM Oncology, AmgenTravel, Accommodations, Expenses: Kite Pharma, Novartis, Pfizer Issa KhouriResearch Funding: Pfizer, Bristol Myers Squibb Rohtesh MehtaResearch Funding: Kadmon, CSL Behring, Incyte Yago NietoResearch Funding: Affimed Therapeutics, AstraZeneca, Secura Bio Betul OranResearch Funding: Arog Phamarceuticals, Astex Pharmaceuticals Uday PopatResearch Funding: Abbvie, Novartis, Bayer Muzaffar QazilbashSpeakers' Bureau: Merck, SanofiResearch Funding: Amgen, BiolineRx, Angiocrine Bioscience, Janssen Samer SrourConsulting or Advisory Role: Celgene Richard E. ChamplinConsulting or Advisory Role: Johnson & Johnson/Janssen, Omeros, Actinium Pharmaceuticals, Kadmon, ArogPatents, Royalties, Other Intellectual Property: Royalty from Takeda Corporation Elizabeth J. ShpallHonoraria: Magenta Therapeutics, Novartis, Partner Therapeutics, BayerConsulting or Advisory Role: Novartis, Magenta Therapeutics, Adaptimmune, Partner Therapeutics, Mesoblast, AXIO Research, Bayer HealthCare PharmaceuticalsPatents, Royalties, Other Intellectual Property: TakedaTravel, Accommodations, Expenses: Magenta Therapeutics, Novartis Katayoun RezvaniConsulting or Advisory Role: Adicet Bio, ViroGen, GemoAb, TakedaResearch Funding: Affimed Therapeutics, Pharmacyclics, TakedaPatents, Royalties, Other Intellectual Property: Patent on generation of BKV CTLs for the treatment of HC or PML, Patent on generation of CAR NK cells, License agreement and research agreement with Takeda to develop CB-CAR NK cells for the treatment of B-cell malignancies and other cancers, which creates an institutional conflict of interest under MD Anderson policyNo other potential conflicts of interest were reported.

We argue that well-informed patient-specific decision-making may be carried out as three consecutive tasks: (1) estimating key parameters of a statistical model, (2) using prognostic information to convert these parameters into clinically interpretable values, and (3) specifying joint utility functions to quantify risk-benefit trade-offs between clinical outcomes. Using the management of metastatic clear cell renal cell carcinoma as our motivating example, we explain the role of prognostic covariates that characterize between-patient heterogeneity in clinical outcomes. We show that explicitly specifying the joint utility of clinical outcomes provides a coherent basis for patient-specific decision-making.

Background: COVID-19 Convalescent plasma (CCP) is safe and effective, particularly if given at an early stage of the disease. Our study aimed to identify an association between survival and specific antibodies found in CCP., Patients and Methods: Patients ≥18 years of age who were hospitalized with moderate to severe COVID-19 infection and received CCP at the MD Anderson Cancer Center between 4/30/2020 and 8/20/2020 were included in the study. We quantified the levels of anti-SARS-CoV-2 antibodies, as well as antibodies against antigens of other coronavirus strains, in the CCP units and compared antibody levels with patient outcomes. For each antibody, a Bayesian exponential survival time regression model including prognostic variables was fit, and the posterior probability of a beneficial effect (PBE) of higher antibody level on survival time was computed., Results: CCP was administered to 44 cancer patients. The median age was 60 years (range 37-84) and 19 (43%) were female. Twelve patients (27%) died of COVID-19-related complications. Higher levels of two non-SARS-CoV-2-specific antibodies, anti-HCoV-OC43 spike IgG and anti-HCoV-HKU1 spike IgG, had PBE = 1.00, and 4 SARS-CoV-2-specific antibodies had PBEs between 0.90 and 0.95. Other factors associated with better survival were shorter time to CCP administration, younger age, and female sex., Conclusions: Common cold coronavirus spike IgG antibodies anti-HCoV-OC43 and anti-HCoV-HKU1 may target a common domain for SARS-CoV-2 and other coronaviruses. They provide a promising therapeutic target for monoclonal antibody production., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Greenbaum, Klein, Martinez, Song, Thall, Ramdial, Knape, Aung, Scroggins, Knopfelmacher, Mulanovich, Borjan, Adachi, Muthu, Leung, Medina, Champlin, Olson, Alousi, Rezvani and Shpall.)

Background: Risk of recurrence among patients with oropharyngeal cancer (OPC) who survive 5 years is low. The goal of this study was to assess long-term survival of patients with OPC alive without recurrence 5 years after diagnosis., Methods: This study included newly diagnosed patients with OPC, who had been treated with radiation and were alive without recurrence 5 years after diagnosis. Overall survival (OS) probabilities beyond 5 years were estimated using the Kaplan-Meier method. Factors associated with OS were determined using Bayesian piecewise exponential survival regression. Standardized mortality ratios for all-cause death were estimated controlling for study year, age, and sex in the US general population., Results: Among 1699 patients, the additional 2-year, 5-year, and 10-year OS probabilities were 94%, 83%, and 63%, respectively, and were lower than those in the general population. Patients who were older, were current or former smokers, had other than tonsil or base of tongue tumors, or had T4 tumors had a higher risk of death. Patients who had base of tongue tumors and had received intensity-modulated radiation therapy (IMRT) or lower-radiation doses had a lower risk of death. Standardized mortality ratios were higher among current and heavy smokers and lower among recipients of IMRT and lower radiation doses., Conclusions: In this large cohort, long-term survival among patients with OPC was good but lower than predicted for the general population. Patients treated with IMRT and those with less tobacco exposure had better outcomes., (© 2020 American Cancer Society.)

Adaptive enrichment designs for clinical trials may include rules that use interim data to identify treatment-sensitive patient subgroups, select or compare treatments, or change entry criteria. A common setting is a trial to compare a new biologically targeted agent to standard therapy. An enrichment design's structure depends on its goals, how it accounts for patient heterogeneity and treatment effects, and practical constraints. This article first covers basic concepts, including treatment-biomarker interaction, precision medicine, selection bias, and sequentially adaptive decision making, and briefly describes some different types of enrichment. Numerical illustrations are provided for qualitatively different cases involving treatment-biomarker interactions. Reviews are given of adaptive signature designs; a Bayesian design that uses a random partition to identify treatment-sensitive biomarker subgroups and assign treatments; and designs that enrich superior treatment sample sizes overall or within subgroups, make subgroup-specific decisions, or include outcome-adaptive randomization.