Ted Gooley

Bio: Ted Gooley is an academic researcher from Fred Hutchinson Cancer Research Center. The author has contributed to research in topics: Transplantation & Hematopoietic stem cell transplantation. The author has an hindex of 101, co-authored 437 publications receiving 36647 citations. Previous affiliations of Ted Gooley include Leipzig University & Pacific Northwest National Laboratory.

Estimation of failure probabilities in the presence of competing risks: new representations of old estimators

Abstract: A topic that has received attention in both the statistical and medical literature is the estimation of the probability of failure for endpoints that are subject to competing risks. Despite this, it is not uncommon to see the complement of the Kaplan-Meier estimate used in this setting and interpreted as the probability of failure. If one desires an estimate that can be interpreted in this way, however, the cumulative incidence estimate is the appropriate tool to use in such situations. We believe the more commonly seen representations of the Kaplan-Meier estimate and the cumulative incidence estimate do not lend themselves to easy explanation and understanding of this interpretation. We present, therefore, a representation of each estimate in a manner not ordinarily seen, each representation utilizing the concept of censored observations being 'redistributed to the right.' We feel these allow a more intuitive understanding of each estimate and therefore an appreciation of why the Kaplan-Meier method is inappropriate for estimation purposes in the presence of competing risks, while the cumulative incidence estimate is appropriate.

CD19 CAR–T cells of defined CD4+:CD8+ composition in adult B cell ALL patients

Abstract: BACKGROUND. T cells that have been modified to express a CD19-specific chimeric antigen receptor (CAR) have antitumor activity in B cell malignancies; however, identification of the factors that determine toxicity and efficacy of these T cells has been challenging in prior studies in which phenotypically heterogeneous CAR–T cell products were prepared from unselected T cells. METHODS. We conducted a clinical trial to evaluate CD19 CAR–T cells that were manufactured from defined CD4+ and CD8+ T cell subsets and administered in a defined CD4+:CD8+ composition to adults with B cell acute lymphoblastic leukemia after lymphodepletion chemotherapy. RESULTS. The defined composition product was remarkably potent, as 27 of 29 patients (93%) achieved BM remission, as determined by flow cytometry. We established that high CAR–T cell doses and tumor burden increase the risks of severe cytokine release syndrome and neurotoxicity. Moreover, we identified serum biomarkers that allow testing of early intervention strategies in patients at the highest risk of toxicity. Risk-stratified CAR–T cell dosing based on BM disease burden decreased toxicity. CD8+ T cell–mediated anti-CAR transgene product immune responses developed after CAR–T cell infusion in some patients, limited CAR–T cell persistence, and increased relapse risk. Addition of fludarabine to the lymphodepletion regimen improved CAR–T cell persistence and disease-free survival. CONCLUSION. Immunotherapy with a CAR–T cell product of defined composition enabled identification of factors that correlated with CAR–T cell expansion, persistence, and toxicity and facilitated design of lymphodepletion and CAR–T cell dosing strategies that mitigated toxicity and improved disease-free survival. TRIAL REGISTRATION. ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT01865617","term_id":"NCT01865617"}}NCT01865617. FUNDING. R01-CA136551; Life Science Development Fund; Juno Therapeutics; Bezos Family Foundation.

Reduced mortality after allogeneic hematopoietic-cell transplantation.

Abstract: BACKGROUND Over the past decade, advances have been made in the care of patients undergoing transplantation. We conducted a study to determine whether these advances have improved the outcomes of transplantation. METHODS We analyzed overall mortality, mortality not preceded by relapse, recurrent malignant conditions, and the frequency and severity of major complications of transplantation, including graft-versus-host disease (GVHD) and hepatic, renal, pulmonary, and infectious complications, among 1418 patients who received their first allogeneic transplants at our center in Seattle in the period from 1993 through 1997 and among 1148 patients who received their first allogeneic transplants in the period from 2003 through 2007. Components of the Pretransplant Assessment of Mortality (PAM) score were used in regression models to adjust for the severity of illness at the time of transplantation. RESULTS In the 2003-2007 period, as compared with the 1993-1997 period, we observed significant decreases in mortality not preceded by relapse, both at day 200 (by 60%) and overall (by 52%), the rate of relapse or progression of a malignant condition (by 21%), and overall mortality (by 41%), after adjustment for components of the PAM score. The results were similar when the analyses were limited to patients who received myeloablative conditioning therapy. We also found significant decreases in the risk of severe GVHD; disease caused by viral, bacterial, and fungal infections; and damage to the liver, kidneys, and lungs. CONCLUSIONS We found a substantial reduction in the hazard of death related to allogeneic hematopoietic-cell transplantation, as well as increased long-term survival, over the past decade. Improved outcomes appear to be related to reductions in organ damage, infection, and severe acute GVHD. (Funded by the National Institutes of Health.).

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Abstract: Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Axicabtagene Ciloleucel CAR T-Cell Therapy in Refractory Large B-Cell Lymphoma

Abstract: BackgroundIn a phase 1 trial, axicabtagene ciloleucel (axi-cel), an autologous anti-CD19 chimeric antigen receptor (CAR) T-cell therapy, showed efficacy in patients with refractory large B-cell lymphoma after the failure of conventional therapy. MethodsIn this multicenter, phase 2 trial, we enrolled 111 patients with diffuse large B-cell lymphoma, primary mediastinal B-cell lymphoma, or transformed follicular lymphoma who had refractory disease despite undergoing recommended prior therapy. Patients received a target dose of 2×106 anti-CD19 CAR T cells per kilogram of body weight after receiving a conditioning regimen of low-dose cyclophosphamide and fludarabine. The primary end point was the rate of objective response (calculated as the combined rates of complete response and partial response). Secondary end points included overall survival, safety, and biomarker assessments. ResultsAmong the 111 patients who were enrolled, axi-cel was successfully manufactured for 110 (99%) and administered to 101 (91%)....