Wang, Panwen

Abstract Background: Although CAR T-cell therapy has achieved remarkable response rates in adult patients (pts) with relapsed and refractory B-cell non-Hodgkin’s lymphoma (NHL), 20-30% of pts still have primary refractory disease, and 30-40% of pts relapse, often within 6 months of therapy. Given that durable clinical response can be achieved without CAR T cell persistence, we hypothesized that durable remission is correlated with the expansion of tumor-reactive T-cell receptor (TCR) clones following CAR T-cell infusion. To assess this, we examined the TCR repertoire of adult pts with B-NHL who received CD19 CAR T-cell (axi-cel) therapy to identify correlates of clinical response. Methods: Single-cell (sc) RNA sequencing (seq) and scTCR-seq were performed on peripheral blood mononuclear cells from B-NHL pts before lymphodepletion chemotherapy (pre), at peak CAR-T expansion (peak), and one month following CAR-T (M1). scRNAseq and scTCR-seq data were processed using CellRanger v7.0.1, which were then analyzed using Immunarch (v0.9.0) for TCR clonality and sample diversity. Diversity was assessed using the Gini coefficient to indicate clone size inequality (0 = maximum diversity). TCR diversity and phenotype of expanded and emerged TCR clones post CAR-T were compared across clinical response categories: CR (complete remission for at least 6 months), PD1 (primary refractory), and PD2 (relapsed after partial or complete response). Results: We analyzed 107,035 T cells from 77 serial samples of 32 pts (16 CR, 4 PD1, 12 PD2) and 5 healthy controls. Compared to pts with PD1 and PD2, pts with CR had a larger Gini coefficient for TCR clones at M1 relative to Pre and Peak. The Gini coefficient was the largest for tumor recirculating CD8 T effector memory (Tem) and activated CD8 peripheral memory (Tpm) cells at M1 for pts with CR. Examining the repertoire of emerged and expanded TCR clones at Peak and M1 from Pre, pts with PD2, compared to pts with CR, had a higher percentage of activated CD8 T peripheral memory (Tpm) and T regulatory cells (Treg) among the emerged clones and activated CD8 Tpm among the expanded clones. Patients with PD1, relative to patients with CR, had more proliferating and non-activated CD8 Tpm among the expanded and emerged clones. Conclusion: NHL pts with CR after CAR-T had more robust clonal expansion of T cells, particularly tumor recirculating CD8 Tem and activated CD8 Tpm, compared to pts with relapsed or refractory disease. In contrast, pts with relapsed or refractory disease had an expansion of TCR clones with a larger proportion of CD8 Tpm and Tregs. Endogenous tumor-reactive TCR expansion may help predict favorable clinical response following CAR T-cell therapy. Citation Format: Aiman J. Faruqi, Panwen Wang, Radhika Bansal, Andre De Menezes Silva Corraes, Henan Zhang, Zuoyi Shao, Kevin Regan, Eider Moreno Cortes, Arushi Khurana, Nora Bennani, Yucai Wang, Paul Hampel, Jonas Paludo, Patrick Johnston, Hemant Murthy, Madiha Iqbal, Stephen Ansell, Javier Munoz, Allison Rosenthal, Mohamed Kharfan Dabaja, Januario E. Castro, Saad Kenderian, Haidong Dong, Ying Li, Yi Lin. T-cell receptor repertoire changes associated with clinical response in patients with B-cell non-Hodgkin’s lymphoma receiving CD19 CAR-T therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 53.

Abstract Introduction: Single-cell RNA-sequencing (scRNA-seq) pairing with single-cell T-Cell-Receptor-sequencing (scTCR-seq) enables profiling of gene expression and TCR repertoires in individual T cells, elevating our understanding of T-cell-mediated immunity. Here, we present immunopipe, a comprehensive and flexible pipeline (https://github.com/pwwang/immunopipe) for analyzing the paired data. Besides the command-line tool, it offers a user-friendly web interface that allows users with varying programming expertise to configure, initiate, and monitor the pipeline. By combining extensive functionality with ease of use, immunopipe empowers users to unlock valuable insights from the data, which notably advances our understanding of immune response and fosters the development of innovative immunotherapies. Results: Immunopipe is a comprehensive analytical pipeline for scRNA-seq and scTCR-seq data, offering various modules leveraging existing tools and novel methods to ensure data quality and enable downstream analytics. It performs quality control for scRNA-seq data, clusters cells based on gene expression to facilitate the identification of cell populations and provides insights into T-cell heterogeneity and functional diversity through T-cell selection and clustering. Enrichment analysis identifies markers and pathways for T-cell characterization. Our automated T-cell annotation tool annotates cell types accurately and hierarchically. For scTCR-seq data, immunopipe analyzes TCR clones, including clonality, diversity, gene usage, and repertoire overlap. Clone residency analysis examines the presence of clones across samples. Integrative analyses combine scRNA-seq and scTCR-seq data, providing a comprehensive understanding of the cellular landscape. Immunopipe incorporates diverse cell group information to analyze T-cell populations, which enables granular perspectives of the data for comprehensive insights. Immunopipe offers a flexible platform, allowing users to customize the analyses as needed. It supports both core and optional analyses through configuration, where users have control over the adjustable parameters. The pipeline relies on cell grouping information that can be referenced from metadata or modified to generate new variables for analysis and data filtering. Immunopipe can be executed locally or on scheduler platforms, and a docker image is provided for compatibility and simplified installation. It also offers a web interface that facilitates configuration, execution monitoring, and access to results. Conclusion: Immunopipe is a flexible and user-friendly pipeline for analyzing scRNA-seq and scTCR-seq data. It empowers researchers to gain valuable insights that could enhance the understanding of immune responses and facilitate the development of innovative immunotherapies. Citation Format: Panwen Wang, Haidong Dong, Yue Yu, Shuwen Zhang, Zhifu Sun, Jean-Pierre A. Kocher, Junwen Wang, Lin Yi, Ying Li. Immunopipe: A comprehensive and flexible scRNA-seq and scTCR-seq data analysis pipeline [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4959.

Abstract High grade glioma (HGG) represents a group of devastating diseases with dismal prognosis. Surgical resection of the contrast enhancing (CE) region of HGG remains the mainstay of treatment, but recurrence inevitably arises from the unresected non-contrast enhancing (NE) region, surgically inaccessible due to cancer cell invasion into healthy brain tissue. Due to its critical role in recurrence, understanding of the NE region is central to the improvement of clinical outcomes. We reveal the biological characteristics of this region through image localized multi-regional sampling. We linked microenvironmental characteristics measured by multi-parametric MRI to genomic mutations and transcriptional phenotypes using mixed effect modeling which allowed us to control for individualized patient effects. We first confirmed that T2 is a significant indicator of IDH mutation status in the NE region, being the first description of such a relationship in a HGG cohort. We found the combination of EGFR amplification and CDKN2A homozygous loss was associated with a significantly lower mean diffusivity (MD) compared to double wild type tumors in the NE region, indicating the presence of greater cellular packing and proliferation in EGFR amplification/CDKN2A loss regions. Finally, using single cell pathway based tumor classifications, we showed that nK2, a DSC-MRI metric representing cell size heterogeneity, correlated positively with neuronal signature and negatively with glycolytic/plurimetabolic signature within the NE tumor, indicating that glycolytic/plurimetobolic tumors possessed a high amount of cell size heterogeneity compared to neuronal samples. This hypothesis was supported using digital reference object (DRO) modeling which confirmed that cell size and heterogeneity drove the differential nK2 signal between neuronal and glycolytic/plurimetabolic samples. We identified immune cell infiltrate as one possible mechanism of increased cell size heterogeneity using transcriptomic signature analysis which found more immune cell signatures within glycolytic/plurimetobolic tumors compared to neuronal. Collectively this study demonstrates the central role of multi-parametric MRI as a non-invasive measure of tumor biology and a tool for understanding the clinically critical NE region which can then inform new therapies targeting this region of HGG recurrence. Citation Format: Matthew Flick, Taylor Weiskittel, Kevin Meng-Lin, Fulvio D'Angelo, Francesca Caruso, Shannon Ensign, Mylan Blomquist, Luija Wang, Christopher Sereduk, Gustavo De Leon, Ashley Nespodzany, Javier Urcuyo, Ashlynn Gonzalez, Lee Curtin, Kyle Singleton, Aliya Anil, Natenael Simmineh, Erika Lewis, Teresa Noviello, Reyna Patel, Panwen Wang, Junwen Wang, Jennifer Eschbacher, Andrea Hawkins-Daarud, Pamela Jackson, Kris Smith, Peter Nakaji, Bernard Bendok, Richard Zimmerman, Chandan Krishna, Devi Patra, Naresh Patel, Mark Lyons, Matthew Neal, Kliment Donev, Maciej Mrugala, Alyx Porter, Scott Beeman, Yuxiang Zhou, Leslie Baxter, Christopher Plaisier, Jing Li, Hu Li, Anna Lasorella, Chad Quarles, Kristin Swanson, Michele Ceccarelli, Antonio Iavarone, Nhan Tran, Leland Hu. Multi-parametric MRI maps regional heterogeneity of high grade glioma phenotypes. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5621.

Background: Identifying patients at a high risk of mortality post percutaneous coronary intervention (PCI) is of vital clinical importance. We investigated the utility of machine learning algorithms to predict short and intermediate-term risk of all-cause mortality in patients undergoing PCI. Methods: Patient-level demographics, clinical, electrocardiographic ,echocardiographic and angiographic data from January 2006 to December 2017 were extracted from the Mayo Clinic CathPCI registry and clinical records. For patients with multiple PCI events, data collected at the time of the index PCI was used for analysis. Patients who underwent bailout coronary artery bypass graft surgery (CABG) prior to discharge were excluded. 306 variables were incorporated into random forest machine learning model (RF) to predict all-cause mortality at 6 months and 1 year after PCI. Ten-fold cross-validation repeated five times was used to optimize the hyperparameters and estimate its external performance. The National Cardiovascular Data Registry (NCDR) based logistic regression model was used for comparison. The area under receiver operator characteristic curves (AUC) was calculated to assess the ability of the models to predict all-cause mortality. Results: A total of 17356 unique patients were included for the final analysis after excluding 165 patients who underwent CABG surgery during the index hospitalization. The mean age was 66.9 ± 12.5 years;71% were male. Indications for PCI were ST-elevation myocardial infarction (9.4%), non-ST elevation myocardial infarction (12.9%), unstable angina (17.7%), and stable angina (52.8%) in the cohort. In-hospital, 6-month & 1 year mortality rates were 1.9%,4.2% & 5.8% respectively. The RF model was superior to the NCDR model in predicting inhospital, 6-month, 1 year mortality (p<0.0001) ( Figure 1 ). Conclusion: Machine learning is superior to NCDR model in predicting short and intermediate risk of all-cause mortality post PCI.

AbstractIntroductionThe right ventricle (RV) strain measured by speckle tracking (RVS) is an echocardiographic parameter used to assess RV function. We compared RVS to RV fractional area change (FAC%), tricuspid annular plane systolic excursion (TAPSE) and Doppler tissue imaging‐derived peak systolic velocity (Sʹ) in the assessment of right ventricular (RV) systolic function measured using cardiac magnetic resonance imaging (MRI).MethodsWe enrolled consecutive patients who underwent cardiac MRI between Jan 2012 and Dec 2017 and a transthoracic echocardiogram (TTE) within 1 month of the MRI with no interval event. Baseline clinical characteristics and MRI parameters were extracted from chart review. Echocardiographic parameters were measured prospectively. TTE parameters including RVS, TAPSE, Sʹ, and FAC% were tested for accuracy to identify impaired RV EF (EF < 45% & <30%) using receiver operator curves.ResultsThe study cohort included 500 patients with mean age 55 years ± 18 and peak tricuspid regurgitation velocity 2.7 ± 1.4 m/s. The area under ROC for RVS was 0.69 (95% CI 0.63–0.75) and 0.78 (95% CI 0.70–0.88) to predict RVEF < 45% & RVEF < 30%, respectively. The RV FAC% had second highest accuracy of predicting RVEF among all the TTE parameters tested in study.ConclusionRight ventricular strain is the most accurate echocardiographic method to detect impaired right ventricular systolic function when using MRI as the gold standard.

Introduction: The right ventricle (RV) strain measured by speckle tracking (RVS) is a novel method of assessing RV function. We compared RVS to RV fractional area change (FAC%), tricuspid annular peak systolic excursion (TAPSE) and Doppler tissue imaging-derived peak systolic velocity (S’) in the assessment of right ventricular (RV) systolic function measured using cardiac magnetic resonance imaging (MRI). Methods: We enrolled consecutive patients who underwent cardiac MRI between Jan 2012- Dec 2017 and a transthoracic echocardiogram (TTE) within 1 month of the MRI with no interval event. Baseline clinical characteristics and MRI parameters were extracted from chart review. Echocardiographic parameters were measured prospectively. TTE parameters including RVS, TAPSE, S’ and FAC% were tested for accuracy to identify impaired RV EF (EF <45% & <30%) using receiver operator curves. Results: The study cohort included 500 patients with mean age 55 yr ± 18 and right ventricular systolic pressure 33.7 ± 13.6 mmHg. The area under ROC for RVS was 0.69 (95% CI 0.63 – 0.75) and 0.78 (95% CI 0.70 – 0.88) to predict RVEF <45% & RVEF <30% respectively. The RV FAC % had second highest accuracy of predicting RVEF among all the TTE parameters tested in study. Conclusion: Right ventricular strain is the most accurate echocardiographic method to detect impaired right ventricular systolic function when using MRI as the gold standard.