RESUMO
ABSTRACT: Patients with multiple myeloma (MM) treated with B-cell maturation antigen (BCMA)-specific chimeric antigen receptor (CAR) T cells usually relapse with BCMA+ disease, indicative of CAR T-cell suppression. CD200 is an immune checkpoint that is overexpressed on aberrant plasma cells (aPCs) in MM and is an independent negative prognostic factor for survival. However, CD200 is not present on MM cell lines, a potential limitation of current preclinical models. We engineered MM cell lines to express CD200 at levels equivalent to those found on aPCs in MM and show that these are sufficient to suppress clinical-stage CAR T-cells targeting BCMA or the Tn glycoform of mucin 1 (TnMUC1), costimulated by 4-1BB and CD2, respectively. To prevent CD200-mediated suppression of CAR T cells, we compared CRISPR-Cas9-mediated knockout of the CD200 receptor (CD200RKO), to coexpression of versions of the CD200 receptor that were nonsignaling, that is, dominant negative (CD200RDN), or that leveraged the CD200 signal to provide CD28 costimulation (CD200R-CD28 switch). We found that the CD200R-CD28 switch potently enhanced the polyfunctionality of CAR T cells, and improved cytotoxicity, proliferative capacity, CAR T-cell metabolism, and performance in a chronic antigen exposure assay. CD200RDN provided modest benefits, but surprisingly, the CD200RKO was detrimental to CAR T-cell activity, adversely affecting CAR T-cell metabolism. These patterns held up in murine xenograft models of plasmacytoma, and disseminated bone marrow predominant disease. Our findings underscore the importance of CD200-mediated immune suppression in CAR T-cell therapy of MM, and highlight a promising approach to enhance such therapies by leveraging CD200 expression on aPCs to provide costimulation via a CD200R-CD28 switch.
Assuntos
Imunoterapia Adotiva , Mieloma Múltiplo , Humanos , Camundongos , Animais , Mieloma Múltiplo/metabolismo , Antígenos CD28/metabolismo , Linfócitos T , Antígeno de Maturação de Linfócitos B/metabolismo , Recidiva Local de Neoplasia/metabolismoRESUMO
Precise detection of invasive cancer on whole-slide images (WSI) is a critical first step in digital pathology tasks of diagnosis and grading. Convolutional neural network (CNN) is the most popular representation learning method for computer vision tasks, which have been successfully applied in digital pathology, including tumor and mitosis detection. However, CNNs are typically only tenable with relatively small image sizes (200 × 200 pixels). Only recently, Fully convolutional networks (FCN) are able to deal with larger image sizes (500 × 500 pixels) for semantic segmentation. Hence, the direct application of CNNs to WSI is not computationally feasible because for a WSI, a CNN would require billions or trillions of parameters. To alleviate this issue, this paper presents a novel method, High-throughput Adaptive Sampling for whole-slide Histopathology Image analysis (HASHI), which involves: i) a new efficient adaptive sampling method based on probability gradient and quasi-Monte Carlo sampling, and, ii) a powerful representation learning classifier based on CNNs. We applied HASHI to automated detection of invasive breast cancer on WSI. HASHI was trained and validated using three different data cohorts involving near 500 cases and then independently tested on 195 studies from The Cancer Genome Atlas. The results show that (1) the adaptive sampling method is an effective strategy to deal with WSI without compromising prediction accuracy by obtaining comparative results of a dense sampling (â¼6 million of samples in 24 hours) with far fewer samples (â¼2,000 samples in 1 minute), and (2) on an independent test dataset, HASHI is effective and robust to data from multiple sites, scanners, and platforms, achieving an average Dice coefficient of 76%.
Assuntos
Neoplasias da Mama/diagnóstico , Neoplasias da Mama/patologia , Feminino , Humanos , Processamento de Imagem Assistida por Computador/métodos , Redes Neurais de ComputaçãoRESUMO
With the increasing ability to routinely and rapidly digitize whole slide images with slide scanners, there has been interest in developing computerized image analysis algorithms for automated detection of disease extent from digital pathology images. The manual identification of presence and extent of breast cancer by a pathologist is critical for patient management for tumor staging and assessing treatment response. However, this process is tedious and subject to inter- and intra-reader variability. For computerized methods to be useful as decision support tools, they need to be resilient to data acquired from different sources, different staining and cutting protocols and different scanners. The objective of this study was to evaluate the accuracy and robustness of a deep learning-based method to automatically identify the extent of invasive tumor on digitized images. Here, we present a new method that employs a convolutional neural network for detecting presence of invasive tumor on whole slide images. Our approach involves training the classifier on nearly 400 exemplars from multiple different sites, and scanners, and then independently validating on almost 200 cases from The Cancer Genome Atlas. Our approach yielded a Dice coefficient of 75.86%, a positive predictive value of 71.62% and a negative predictive value of 96.77% in terms of pixel-by-pixel evaluation compared to manually annotated regions of invasive ductal carcinoma.