Longitudinal High-Throughput Sequencing of the T-Cell Receptor Repertoire Reveals Dynamic Change and Prognostic Significance of Peripheral Blood TCR Diversity in Metastatic Colorectal Cancer During Chemotherapy

Colorectal cancer (CRC) is a major cause of cancer mortality and morbidity. Despite advances in chemotherapy and targeted therapy, unsustainable clinical benefit was noted due to recurrence and therapy resistance. The immune status of the cancer patient may affect the effectiveness of disease treatments. The dynamic change in the T-cell receptor (TCR) repertoire might be a clinical parameter for monitoring treatment responses. In this study, we aimed to determine the characteristics and clinical significance of the TCR repertoire in patients with unresectable metastatic colorectal cancer (mCRC). Herein, we comprehensively profile 103 peripheral blood samples from 20 healthy controls and 16 CRC patients with a follow-up of 98 to 452 days to identify hypervariable rearrangements of the TCRα and TCRβ repertoires using high-throughput sequencing. We found that TCRα repertoires, TCRβ repertoires, and CDR3 clonotypes were altered in mCRC patients compared with healthy controls. The diversity of TCR repertoires and CDR3 clonotypes decreased in most mCRC patients after therapy. Furthermore, compared with baseline TCR diversity, patients whose TCR diversity dropped considerably during therapy had better treatment responses, including lower CEA and CA19-9 levels and smaller tumor sizes. TCR baseline diversity was also significantly associated with partial response (PR) status (odds ratio: 5.29, p = 0.04). In conclusion, the present study demonstrated the association between dynamic changes in TCR diversity during chemotherapy and clinical outcomes as well as the potential utility of the TCR repertoire in predicting the prognosis of cancer treatment.

Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients

Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. The tumor microenvironment (TME) plays a vital role in HCC progression. Thus, this research was designed to analyze the correlation between the TME and the prognosis of HCC patients and to construct a TME-related long noncoding RNA (lncRNA) signature to determine HCC patients’ prognosis and response to immunotherapy.Methods: We assessed the stromal–immune–estimate scores within the HCC microenvironment using the ESTIMATE (Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data) algorithm based on The Cancer Genome Atlas database, and their associations with survival and clinicopathological parameters were also analyzed. Thereafter, differentially expressed lncRNAs were filtered out according to the immune and stromal scores. Cox regression analysis was performed to build a TME-related lncRNA risk signature. Kaplan–Meier analysis was used to explore the prognostic value of the risk signature. Furthermore, we explored the biological functions and immune microenvironment features in the high- and low-risk groups. Lastly, we probed the association of the risk model with treatment responses to immune checkpoint inhibitors (ICIs) in HCC.Results: The stromal, immune, and estimate scores were obtained utilizing the ESTIMATE algorithm for patients with HCC. Kaplan–Meier analysis showed that high scores were significantly correlated with better prognosis in HCC patients. Six TME-related lncRNAs were screened to construct the prognostic model. The Kaplan–Meier curves suggested that HCC patients with low risk had better prognosis than those with high risk. Receiver operating characteristic (ROC) curve and Cox regression analyses indicated that the risk model could predict HCC survival exactly and independently. Functional enrichment analysis revealed that some tumor- and immune-related pathways were activated in the high-risk group. We also revealed that some immune cells, which were important in enhancing immune responses toward cancer, were significantly increased in the low-risk group. In addition, there was a close correlation between ICIs and the risk signature, which can be used to predict the treatment responses of HCC patients.Conclusion: We analyzed the influence of the stromal, immune, and estimate scores on the prognosis of HCC patients. A novel TME-related lncRNA risk model was established, which could be effectively applied as an independent prognostic biomarker and predictor of ICIs for HCC patients.

Long-Term Safety and Efficacy of Belimumab in Japanese Patients with SLE: a 7-Year Open-Label Continuation Study

Objectives Evaluate long-term safety, tolerability and efficacy of belimumab in Japanese patients with systemic lupus erythematosus (SLE). Methods This was a subgroup analysis of Japanese patients who completed studies BEL113750 or BEL112341 and were enrolled in a Phase 3, open-label extension study (BEL114333; NCT01597622). Eligible patients received intravenous belimumab 10 mg/kg every 28 days for ≤7 years. Primary endpoint: safety and tolerability. Secondary endpoints included SLE responder index (SRI)-4 response rate, SRI-4 components, severe SLE flare, use of corticosteroids/other SLE-related treatments. Analyses were based on observed data from first parent or current study belimumab dose through to study end. Results Of 71 Japanese patients enrolled, 69.0% completed the study. Overall, 98.6% patients had adverse events (AEs); 32.4% had serious AEs. The proportion of SRI-4 responders increased progressively (Year 1, Week 24: 40.9% [27/66]; Year 7, Week 48: 84.6% [11/13]) as did the proportion of SELENA-SLEDAI responders. The proportion of patients with no worsening in PGA (91.2−100.0%) and no new organ damage (92.6−100.0%) remained stable over time. Severe SLE flare was experienced by 11.3% (8/71) of patients. Corticosteroid and immunosuppressant use decreased over time. Conclusion : Favorable safety profile and treatment responses with belimumab were maintained for ≤7 years in Japanese patients with SLE.

Learning Single-Cell Perturbation Responses using Neural Optimal Transport

Understanding and predicting molecular responses towards external perturbations is a core question in molecular biology. Technological advancements in the recent past have enabled the generation of high-resolution single-cell data, making it possible to profile individual cells under different experimentally controlled perturbations. However, cells are typically destroyed during measurement, resulting in unpaired distributions over either perturbed or non-perturbed cells. Leveraging the theory of optimal transport and the recent advents of convex neural architectures, we learn a coupling describing the response of cell populations upon perturbation, enabling us to predict state trajectories on a single-cell level. We apply our approach, CellOT, to predict treatment responses of 21,650 cells subject to four different drug perturbations. CellOT outperforms current state-of-the-art methods both qualitatively and quantitatively, accurately capturing cellular behavior shifts across all different drugs.

Use of Artificial Intelligence as a Predictor of the Response to Treatment in Alopecia Areata (Preprint)

BACKGROUND Artificial intelligence (AI) has emerged in dermatology with some studies focusing on skin disorders such as skin cancer, atopic dermatitis, psoriasis, and onychomycosis. Alopecia areata (AA) is a dermatological disease whose prevalence is 0.7%-3% in the United States, and is characterized by oval areas of nonscarring hair loss of the scalp or body without evident clinical variables to predict its response to the treatment. Nonetheless, some studies suggest a predictive value of trichoscopic features in the evaluation of treatment responses. Assuming that black dots, broken hairs, exclamation marks, and tapered hairs are markers of negative predictive value of the treatment response, while yellow dots are markers of no response to treatment according to recent studies, the absence of these trichoscopic features could indicate favorable disease evolution without treatment or even predict its response. Nonetheless, no studies have reportedly evaluated the role of AI in AA on the basis of trichoscopic features. OBJECTIVE This study aimed to develop an AI algorithm to predict, using trichoscopic images, those patients diagnosed with AA with a better disease evolution. METHODS In total, 80 trichoscopic images were included and classified in those with or without features of negative prognosis. Using a data augmentation technique, they were multiplied to 179 images to train an AI algorithm, as previously carried out with dermoscopic images of skin tumors with a favorable response. Subsequently, 82 new images of AA were presented to the algorithm, and the algorithm classified these patients as responders and non-responders; this process was reviewed by an expert trichologist observer and presented a concordance higher than 90% with the algorithm identifying structures described previously. Evolution of the cases was followed up to truly determine their response to treatment and, therefore, to assess the predictive value of the algorithm. RESULTS In total, 32 of 40 (80%) images of patients predicted as nonresponders scarcely showed response to the treatment, while 34 of 42 (81%) images of those predicted as responders showed a favorable response to the treatment. CONCLUSIONS The development of an AI algorithm or tool could be useful to predict AA evolution and its response to treatment. However, further research is needed, including larger sample images or trained algorithms, by using images previously classified in accordance with the disease evolution and not with trichoscopic features.