Bayesian modelling of response to therapy and drug-sensitivity in acute lymphoblastic leukemia.

Acute lymphoblastic leukemia (ALL) is a heterogeneous haematologic malignancy involving the abnormal proliferation of immature lymphocytes and accounts for most paediatric cancer cases. The management of ALL in children has seen great improvement in the last decades thanks to greater understanding of the disease leading to improved treatment strategies evidenced through clinical trials. Common therapy regimens involve a first course of chemotherapy (induction phase), followed by treatment with a combination of anti-leukemia drugs.

Pharmacotypes across the genomic landscape of pediatric acute lymphoblastic leukemia and impact on treatment response.

Contemporary chemotherapy for childhood acute lymphoblastic leukemia (ALL) is risk-adapted based on clinical features, leukemia genomics and minimal residual disease (MRD); however, the pharmacological basis of these prognostic variables remains unclear. Analyzing samples from 805 children with newly diagnosed ALL from three consecutive clinical trials, we determined the ex vivo sensitivity of primary leukemia cells to 18 therapeutic agents across 23 molecular subtypes defined by leukemia genomics. There was wide variability in drug response, with favorable ALL subtypes exhibiting the greatest sensitivity to L-asparaginase and glucocorticoids.

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

Purpose: As immune checkpoint inhibitors (ICI) become increasingly used in frontline settings, identifying early indicators of response is needed. Recent studies suggest a role for circulating tumor DNA (ctDNA) in monitoring response to ICI, but uncertainty exists in the generalizability of these studies. Here, the role of ctDNA for monitoring response to ICI is assessed through a standardized approach by assessing clinical trial data from five independent studies.

A Bayesian model with application for adaptive platform trials having temporal changes.

Temporal changes exist in clinical trials. Over time, shifts in patients' characteristics, trial conduct, and other features of a clinical trial may occur. In typical randomized clinical trials, temporal effects, that is, the impact of temporal changes on clinical outcomes and study analysis, are largely mitigated by randomization and usually need not be explicitly addressed.

A Bayesian decision-theoretic design for simultaneous biomarker-based subgroup selection and efficacy evaluation.

The success of drug development of targeted therapy often hinges on an appropriate selection of the sensitive patient population, mostly based on patients' biomarker levels. At the planning stage of a phase II study, although a potential biomarker may have been identified, a threshold value for defining sensitive patient population is often unavailable for adopting many existing biomarker-guided designs. To address this issue, we propose a two-stage design that allows for simultaneous biomarker threshold selection and efficacy evaluation while accommodating situations where the drug is efficacious in the entire patient population.

Feature engineering and machine learning for causality assessment in pharmacovigilance: Lessons learned from application to the FDA Adverse Event Reporting System.

Background: Our objective was to support the automated classification of Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) reports for their usefulness in assessing the possibility of a causal relationship between a drug product and an adverse event.

Method: We used a data set of 326 redacted FAERS reports that was previously annotated using a modified version of the World Health Organization-Uppsala Monitoring Centre criteria for drug causality assessment by a group of SEs at the FDA and supported a similar study on the classification of reports using supervised machine learning and text engineering methods. We explored many potential features, including the incorporation of natural language processing on report text and information from external data sources, for supervised learning and developed models for predicting the classification status of reports.

A feasibility study of combined epigenetic and vaccine therapy in advanced colorectal cancer with pharmacodynamic endpoint.

Epigenetic therapies may modulate the tumor microenvironment. We evaluated the safety and optimal sequence of combination DNA methyltransferase inhibitor guadecitabine with a granulocyte macrophage-colony-stimulating-factor (GM-CSF) secreting colon cancer (CRC) vaccine (GVAX) using a primary endpoint of change in CD45RO + T cells. 18 patients with advanced CRC enrolled, 11 underwent paired biopsies and were evaluable for the primary endpoint.

Meta-analysis of rare adverse events in randomized clinical trials: Bayesian and frequentist methods.

Background/aims: Regulatory approval of a drug or device involves an assessment of not only the benefits but also the risks of adverse events associated with the therapeutic agent. Although randomized controlled trials (RCTs) are the gold standard for evaluating effectiveness, the number of treated patients in a single RCT may not be enough to detect a rare but serious side effect of the treatment. Meta-analysis plays an important role in the evaluation of the safety of medical products and has advantage over analyzing a single RCT when estimating the rate of adverse events.

The precision interventions for severe and/or exacerbation-prone asthma (PrecISE) adaptive platform trial: statistical considerations.

The Precision Interventions for Severe and/or Exacerbation-prone Asthma (PrecISE) study is an adaptive platform trial designed to investigate novel interventions to severe asthma. The study is conducted under a master protocol and utilizes a crossover design with each participant receiving up to five interventions and at least one placebo. Treatment assignments are based on the patients' biomarker profiles and precision health methods are incorporated into the interim and final analyses.

Bayesian copy number detection and association in large-scale studies.

Background: Germline copy number variants (CNVs) increase risk for many diseases, yet detection of CNVs and quantifying their contribution to disease risk in large-scale studies is challenging due to biological and technical sources of heterogeneity that vary across the genome within and between samples.

Methods: We developed an approach called CNPBayes to identify latent batch effects in genome-wide association studies involving copy number, to provide probabilistic estimates of integer copy number across the estimated batches, and to fully integrate the copy number uncertainty in the association model for disease.

Results: Applying a hidden Markov model (HMM) to identify CNVs in a large multi-site Pancreatic Cancer Case Control study (PanC4) of 7598 participants, we found CNV inference was highly sensitive to technical noise that varied appreciably among participants.

A batch-effect adjusted Simon's two-stage design for cancer vaccine clinical studies.

In the development of cancer treatment vaccines, phase II clinical studies are conducted to examine the efficacy of a vaccine in order to screen out vaccines with minimal activity. Immune responses are commonly used as the primary endpoint for assessing vaccine efficacy. With respect to study design, Simon's two-stage design is a popular format for phase II cancer clinical studies because of its simplicity and ethical considerations.

Shortened-Duration Immunosuppressive Therapy after Nonmyeloablative, Related HLA-Haploidentical or Unrelated Peripheral Blood Grafts and Post-Transplantation Cyclophosphamide.

With post-transplantation cyclophosphamide (PTCy) as graft-versus-host disease (GVHD) prophylaxis, nonmyeloablative (NMA) HLA-haploidentical (haplo) and HLA-matched blood or marrow transplantation (BMT) have comparable outcomes. Previous reports have shown that discontinuation of immunosuppression (IS) as early as day 60 after infusion of a bone marrow (BM) haplo allograft with PTCy is feasible. There are certain diseases in which peripheral blood (PB) may be favored over BM, but given the higher rates of GVHD with PB, excessive GVHD is of increased concern.

Phase I and Pharmacokinetic Study of Romidepsin in Patients with Cancer and Hepatic Dysfunction: A National Cancer Institute Organ Dysfunction Working Group Study.

Purpose: Romidepsin dosing recommendations for patients with malignancy and varying degrees of hepatic dysfunction was lacking at the time of regulatory approval for T-cell lymphoma. We conducted a multicenter phase I clinical trial (ETCTN-9008) via the NCI Organ Dysfunction Working Group to investigate safety, first cycle MTD, and pharmacokinetic profile of romidepsin in this setting.

Patients And Methods: Patients with select advanced solid tumors or hematologic malignancies were stratified according to hepatic function.

Bayesian Methods in Regulatory Science.

Regulatory science comprises the tools, standards, and approaches that regulators use to assess safety, efficacy, quality, and performance of drugs and medical devices. A major focus of regulatory science is the design and analysis of clinical trials. Clinical trials are an essential part of clinical research programs that aim to improve therapies and reduce the burden of disease.

Information Visualization Platform for Postmarket Surveillance Decision Support.

Introduction: The US FDA receives more than 2 million postmarket reports each year. Safety Evaluators (SEs) review these reports, as well as external information, to identify potential safety signals. With the increasing number of reports and the size of external information, more efficient solutions for data integration and decision making are needed.

Discussion on "Predictively consistent prior effective sample sizes," by Beat Neuenschwander, Sebastian Weber, Heinz Schmidli, and Anthony O'Hagan.

Neuenschwander et al. address a seemingly easy but often complicated problem in applied Bayesian methodology. We discuss some issues that relate to the question of why one might care about the effective sample size ( ) in a Bayesian model and the motivation for reporting the .

Clinico-pathologic features, treatment and outcomes of breast cancer during pregnancy or the post-partum period.

Purpose: Breast cancer during pregnancy (BC-P) or the first year post-partum (BC-PP) is rare and whether it differs from breast cancer (BC) in young women not associated with pregnancy is uncertain.

Methods: We queried our institutional database for BC-P and BC-PP cases and matched controls with BC not associated with pregnancy diagnosed between January 1, 1985 and December 31, 2013. We performed two parallel retrospective cohort studies evaluating clinico-pathologic features, treatment and outcomes for BC-P and BC-PP cases compared to their controls.