Browsing by Author "Wallace, Paul K."

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    Augmenting antibody response to EGF-depleting immunotherapy: Findings from a phase I trial of CIMAvax-EGF in combination with nivolumab in advanced stage NSCLC
    (Frontiers Media, 2022-08-03) Evans, Rachel; Lee, Kelvin; Wallace, Paul K.; Reid, Mary; Muhitch, Jason; Dozier, Askia; Mesa, Circe; Luaces, Patricia L.; Santos-Morales, Orestes; Groman, Adrienne; Cedeno, Carlos; Cinquino, Aileen; Fisher, Daniel T.; Puzanov, Igor; Opyrchal, Mateusz; Fountzilas, Christos; Dai, Tong; Ernstoff, Marc; Attwood, Kristopher; Hutson, Alan; Johnson, Candace; Mazorra, Zaima; Saavedra, Danay; Leon, Kalet; Lage, Agustin; Crombet, Tania; Dy, Grace K.; Medicine, School of Medicine
    Background: CIMAvax-EGF is an epidermal growth factor (EGF)-depleting immunotherapy which has shown survival benefit as a switch maintenance treatment after platinum-based chemotherapy in advanced non-small cell lung cancer (NSCLC). The primary objective of this trial is to establish the safety and recommended phase II dose (RP2D) of CIMAvax-EGF in combination with nivolumab as second-line therapy for NSCLC. Methods: Patients with immune checkpoint inhibitor-naive metastatic NSCLC were enrolled using a "3+3" dose-escalation design. Toxicities were graded according to CTCAE V4.03. Thirteen patients (one unevaluable), the majority with PD-L1 0%, were enrolled into two dose levels of CIMAvax-EGF. Findings: The combination was determined to be safe and tolerable. The recommended phase 2 dose of CIMAvax-EGF was 2.4 mg. Humoral response to CIMAvax-EGF was achieved earlier and in a greater number of patients with the combination compared to historical control. Four out of 12 evaluable patients had an objective response.
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    Automated Assessment of Disease Progression in Acute Myeloid Leukemia by Probabilistic Analysis of Flow Cytometry Data
    (Institute of Electrical and Electronics Engineers, 2017-05) Rajwa, Bartek; Wallace, Paul K.; Griffiths, Elizabeth A.; Dundar, Murat; Computer and Information Science, School of Science
    OBJECTIVE: Flow cytometry (FC) is a widely acknowledged technology in diagnosis of acute myeloid leukemia (AML) and has been indispensable in determining progression of the disease. Although FC plays a key role as a posttherapy prognosticator and evaluator of therapeutic efficacy, the manual analysis of cytometry data is a barrier to optimization of reproducibility and objectivity. This study investigates the utility of our recently introduced nonparametric Bayesian framework in accurately predicting the direction of change in disease progression in AML patients using FC data. METHODS: The highly flexible nonparametric Bayesian model based on the infinite mixture of infinite Gaussian mixtures is used for jointly modeling data from multiple FC samples to automatically identify functionally distinct cell populations and their local realizations. Phenotype vectors are obtained by characterizing each sample by the proportions of recovered cell populations, which are, in turn, used to predict the direction of change in disease progression for each patient. RESULTS: We used 200 diseased and nondiseased immunophenotypic panels for training and tested the system with 36 additional AML cases collected at multiple time points. The proposed framework identified the change in direction of disease progression with accuracies of 90% (nine out of ten) for relapsing cases and 100% (26 out of 26) for the remaining cases. CONCLUSIONS: We believe that these promising results are an important first step toward the development of automated predictive systems for disease monitoring and continuous response evaluation. SIGNIFICANCE: Automated measurement and monitoring of therapeutic response is critical not only for objective evaluation of disease status prognosis but also for timely assessment of treatment strategies.