Citations

Chimeric antigen receptor (CAR) T cell immunotherapy for the treatment of neurological autoimmune diseases is promising, but CAR T cell kinetics and immune alterations after treatment are poorly understood. Here, we performed single-cell multi-omics sequencing of paired cerebrospinal fluid (CSF) and blood samples from patients with neuromyelitis optica spectrum disorder (NMOSD) treated with anti–B cell maturation antigen (BCMA) CAR T cells. Proliferating cytotoxic-like CD8 + CAR T cell clones were identified as the main effectors in autoimmunity. Anti-BCMA CAR T cells with enhanced features of chemotaxis efficiently crossed the blood-CSF barrier, eliminated plasmablasts and plasma cells in the CSF, and suppressed neuroinflammation. The CD44-expressing early memory phenotype in infusion products was potentially associated with CAR T cell persistence in autoimmunity. Moreover, CAR T cells from patients with NMOSD displayed distinctive features of suppressed cytotoxicity compared with those from hematological malignancies. Thus, we provide mechanistic insights into CAR T cell function in patients with neurological autoimmune disease.

Abstract Objective. 4D-CBCT provides phase-resolved images valuable for radiomics analysis for outcome prediction throughout treatment courses. However, 4D-CBCT suffers from streak artifacts caused by under-sampling, which severely degrades the accuracy of radiomic features. Previously we developed group-patient-trained deep learning methods to enhance the 4D-CBCT quality for radiomics analysis, which was not optimized for individual patients. In this study, a patient-specific model was developed to further improve the accuracy of 4D-CBCT based radiomics analysis for individual patients. Approach. This patient-specific model was trained with intra-patient data. Specifically, patient planning 4D-CT was augmented through image translation, rotation, and deformation to generate 305 CT volumes from 10 volumes to simulate possible patient positions during the onboard image acquisition. 72 projections were simulated from 4D-CT for each phase and were used to reconstruct 4D-CBCT using FDK back-projection algorithm. The patient-specific model was trained using these 305 paired sets of patient-specific 4D-CT and 4D-CBCT data to enhance the 4D-CBCT image to match with 4D-CT images as ground truth. For model testing, 4D-CBCT were simulated from a separate set of 4D-CT scan images acquired from the same patient and were then enhanced by this patient-specific model. Radiomics features were then extracted from the testing 4D-CT, 4D-CBCT, and enhanced 4D-CBCT image sets for comparison. The patient-specific model was tested using 4 lung-SBRT patients’ data and compared with the performance of the group-based model. The impact of model dimensionality, region of interest (ROI) selection, and loss function on the model accuracy was also investigated. Main results. Compared with a group-based model, the patient-specific training model further improved the accuracy of radiomic features, especially for features with large errors in the group-based model. For example, the 3D whole-body and ROI loss-based patient-specific model reduces the errors of the first-order median feature by 83.67%, the wavelet LLL feature maximum by 91.98%, and the wavelet HLL skewness feature by 15.0% on average for the four patients tested. In addition, the patient-specific models with different dimensionality (2D versus 3D) or loss functions (L1 versus L1 + VGG + GAN) achieved comparable results for improving the radiomics accuracy. Using whole-body or whole-body+ROI L1 loss for the model achieved better results than using the ROI L1 loss alone as the loss function. Significance. This study demonstrated that the patient-specific model is more effective than the group-based model on improving the accuracy of the 4D-CBCT radiomic features analysis, which could potentially improve the precision for outcome prediction in radiotherapy.

Abstract Mutant peptides presented in cancer are superior vaccine candidates than self peptides. The efficacy of mutant K-Ras, P53 and EFGR peptides have been tested as cancer vaccines in pancreatic cancer, colorectal cancer, and lung adenocarcinoma. However, the occurrence of these mutations in cancer is limited. Further more, these mutant peptides can only be presented by certain MHC alleles. Neoantigens expressed by passenger mutations which are not involved in common proliferative molecular pathways are potential candidates for personalized vaccine design. Such passenger mutations have drawn attention by the recent clinical data that colorectal cancer with a large number of somatic mutations due to mismatch-repair defects are more susceptible to immune checkpoint blockade by PD1 antibody therapy. In this study, we analyzed the next generation sequencing data of 147 lung adenocarcinoma patients from The Cancer Genome Atlas and predicted neoantigens presented by MHC Class I and Class II molecules. We found 18175 expressed clonal somatic mutations, with an average of 124 per patient. Based on such somatic mutations, we predicted 8731 neo-peptides, among which 3888 strong-binders and 4843 weak-binders to MHC Class I molecules were found by NetMHCPan. For HLA-A*02:01, 778 strong binding and 822 weak binding neo-peptides were found. In average, 11 neo-antigen peptides of mutations were predicted per individual patient with HLA-A*02:01 allele. Smokers expressed 5.73 fold higher mutant neo-antigen peptides than non-smokers. The 20 most commonly mutated genes with predicted neo-antigens are TTN, RYR2, MUC16, KRAS, ZFHX4, TP53, USH2A, NAV3, KEAP1, ANK2, DNAH9, ADAMTS12, CSMD1, CSMD3, SPTA1, FAT3, ZNF804A, PAPPA2, PCLO, EGFR. As a positive control, the frequency of known proliferation-related mutations (KRAS, P53, and EGFR) were determined in 147 patients, which are consistent with previously next generation sequencing studies of lung adenocarcinoma. Mutation hotspots were found in proliferation-related mutations (KRAS), but not in passenger mutations. 6950 neo-peptides, including 259 strong binders and 6691 weak binders were found for HLA DRB1, an MHC Class II molecule. INDEL mutation type of neo-epitopes were found in 119 of 147 patients, with an average of 4.34 INDEL mutations presented by MHC Class I and 0.86 INDEL mutations presented by MHC Class II molecule HLA DRB1 in every patient. Our results support the feasibility of discovering individualized mutant peptides as candidates for immunodiagnosis and immunotherapy of lung adenocarcinoma. Neo-epitopes which can be presented by both MHC Class I and MHC Class II molecules are attractive targets for designing long-peptide vaccines. Note: This abstract was not presented at the meeting. Citation Format: Dapeng Zhou, Weijing Cai, Deng Pan, Tan Wen Ling, Jiaqian Wang, Caichun Zhou, Yanyan Lou. Neoantigens predicted by clonal mutation analysis in lung adenocarcinoma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 628. doi:10.1158/1538-7445.AM2017-628

NAD + can influence DNA repair by modulating protein interactions.