Search

Both tumor-infiltrating lymphocytes (TIL) and PD-1+ peripheral blood lymphocytes (PBL) are enriched for tumor-reactive clones recognizing known and unknown tumor antigens. However, the relationship between the T-cell receptor-β (TCRβ) repertoires of the TILs and T cells expanded from paired PD-1+ PBLs, and whether T cells expanded from PD-1+ PBLs can be used to treat patients with cancer as TIL substitutes remain unclear. Here, we established a highly efficient protocol to prepare polyclonal T cells from PD-1+ PBLs. A functional T-cell assay and tetramer staining revealed that cells from PD-1+ PBLs were relatively enriched for tumor-reactive T cells. Furthermore, deep TCRβ sequencing data revealed that an average of 11.29% (1.32%–29.06%; P = 0.015; n = 8) tumor-resident clonotypes were found in T cells expanded from paired PD-1+ PBLs, and the mean accumulated frequency of TIL clones found in T cells expanded from PD-1+ PBLs was 35.11% (7.23%–78.02%; P = 0.017; n = 8). Moreover, treatment of four patients, who failed multiline therapy and developed acquired resistance to anti-PD-1, with autologous T cells expanded from PD-1+ PBLs combined with anti-PD-1 antibody elicited objective responses from three of them. These results indicate that T cells expanded from PD-1+ PBLs share more clones with paired TILs and could be used to treat patients with cancer as TIL substitutes. Significance: This study harnesses the tumor reactivity of PD-1+ PBLs, developing a method to expand T cells from these clones as a potential therapeutic strategy and TIL substitute in patients with cancer. See related commentary by Ladle, p. 1940

IEEE 802.11p standard, operating over the 75-MHz spectrum at 5.9-GHz band with one control channel (CCH) and six service channels (SCHs), has been poised to provide V2X services over vehicular ad hoc networks (VANETs). However, due to the absence of central coordinator and the nature of high vehicular mobility, it is difficult to achieve reliable multichannel coordination and adaptive resource reservation to make full use of SCHs, resulting in dramatic throughput degradation. To mitigate this, in this article, we propose an adaptive high-throughput multichannel medium access control (MAC) protocol, namely, AHT-MAC, which can effectively handle the data transmissions over SCHs. With AHT-MAC, the data transmission range (TR) is adjusted according to the beacon TR over the CCH so that a transmitting node can determine proper communication candidates and prepare available resources for both communication nodes before transmissions. Moreover, the communication coordination is done through a two-way handshake. During the handshake, adaptive resource reservation is realized following the proposed resource sharing mechanism, where nodes first utilize as much resource as possible and then share them with others proactively. To increase the success probability of the communication handshake, a request conflict resolution mechanism is also designed to nullify improper handshakes. Therefore, AHT-MAC can reduce the resource wastage due to handshake failures and extra overheads for retransmission requests. Our performance analysis shows that AHT-MAC can significantly improve the system throughput and reduce the channel access period.

Background Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma and accounts for cancer-related deaths. Survival rates are very low when the tumor is discovered in the late-stage. Thus, developing an efficient strategy to stratify patients by the stage of the cancer and inner mechanisms that drive the development and progression of cancers is critical in early prevention and treatment. Results In this study, we developed new strategies to extract important gene features and trained machine learning-based classifiers to predict stages of ccRCC samples. The novelty of our approach is that (i) We improved the feature preprocessing procedure by binning and coding, and increased the stability of data and robustness of the classification model. (ii) We proposed a joint gene selection algorithm by combining the Fast-Correlation-Based Filter (FCBF) search with the information value, the linear correlation coefficient, and variance inflation factor, and removed irrelevant/redundant features. Then the logistic regression-based feature selection method was used to determine influencing factors. (iii) Classification models were developed using machine learning algorithms. This method is evaluated on RNA expression value of clear cell renal cell carcinoma derived from The Cancer Genome Atlas (TCGA). The results showed that the result on the testing set (accuracy of 81.15% and AUC 0.86) outperformed state-of-the-art models (accuracy of 72.64% and AUC 0.81) and a gene set FJL-set was developed, which contained 23 genes, far less than 64. Furthermore, a gene function analysis was used to explore molecular mechanisms that might affect cancer development. Conclusions The results suggested that our model can extract more prognostic information, and is worthy of further investigation and validation in order to understand the progression mechanism.

Background Transcriptome data generates massive amounts of information that can be used for characterization and prognosis of patient outcomes for many diseases. The goal of our research is to predict the survival time of lung adenocarcinoma patients and improve the accuracy of classifying the long-survival cohort and short-survival cohort. Methods We filtered prognostic features related with survival time of lung adenocarcinoma patients by the method of Relief and predicted whether survival time of the patient is >3 years or not—using eight machine learning algorithms (Support Vector Machines, Random Forests, Logistic Regression, Naïve Bayes, Linear Regression, Support Vector Regression (kernel Poly), Support Vector Regression (kernel Linear), and Ridge Regression). Then the best-performed algorithm was chosen to build a predictive model of survival time of lung adenocarcinoma patients. Further, another dataset was used to verify the stability and suitability of this model. We explored the underlying mechanisms of RNA expression changes with the corresponding DNA mutations and DNA methylation patterns in the 22 selected genetic features. Results The best machine learning algorithm was Naïve Bayes (accuracy=75%, AUC =0.81) using the top 22 genetic features, and this algorithm had the stable and great performance on another dataset as well. The coupled mutation number of the long-survival group (>6 years) was less than the short-survival group (

Background There is no standard neoadjuvant therapy for locally advanced esophageal cancer in China. The role of neoadjuvant chemotherapy plus immunotherapy for locally advanced esophageal cancer is still being explored. Methods This open-label, randomized phase II study was conducted at a single center between July 2019 and September 2020, 30 patients with locally advanced ESCC (T3, T4, or lymph-node positive) were enrolled. Patients were randomized according to the enrollment order at a 1:1 ratio to receive chemotherapy on day 1 and toripalimab on day 3 (experiment group) or chemotherapy and toripalimab on day 1 (control group). The chemotherapeutic regimen was paclitaxel and cisplatin. Surgery was performed 4 to 6 weeks after the second cycle of chemoimmunotherapy. The primary endpoint was pCR rate, the secondary endpoint were safety and disease-free survival. Results Thirty patients completed at least one cycle of chemoimmunotherapy; 11 in the experimental group and 13 in the control group received surgery. R0 resection was got in all these 24 patients. Four patients (36%) in the experimental group and one (7%) in the control group achieved pCR. The experimental group showed a statistically non-significant higher pCR rate (P = 0.079). PD-L1 combined positive score (CPS) examination was performed in 14 patients; one in control group had a PD-L1 CPS of 10 and pCR was achieved, while the left 13 were all ≤ 1, 11 of the 13 patients received surgery in which two (in the experimental group) got pCR. Two patients endured from ≥ grade 3 adverse events, one suffered from grade 3 immune-related enteritis after one cycle of chemoimmunotherapy and dropped off the study. Another patient died from severe pulmonary infection and troponin elevation after surgery. CONCLUSIONS Although the primary endpoint was not met, initial results of this study showed that delaying toripalimab to day 3 in chemoimmunotherapy might achieve a higher pCR rate than that on same day, and further large-sample clinical trials are needed to verify this. Trial registration: NCT 03985670, registered 10 June 2019.