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BACKGROUND: Despite the improved survival observed in PD-1/PD-L1 blockade therapy, a substantial proportion of cancer patients, including those with non-small cell lung cancer (NSCLC), still lack a response. METHODS: Transcriptomic profiling was conducted on a discovery cohort comprising 100 whole blood samples, as collected multiple times from 48 healthy controls (including 43 published data) and 31 NSCLC patients that under treatment with a combination of anti-PD-1 Tislelizumab and chemotherapy. Differentially expressed genes (DEGs), simulated immune cell subsets, and germline DNA mutational markers were identified from patients achieved a pathological complete response during the early treatment cycles. The predictive values of mutational markers were further validated in an independent immunotherapy cohort of 1661 subjects, and then confirmed in genetically matched lung cancer cell lines by a co-culturing model. RESULTS: The gene expression of hundreds of DEGs (FDR p < 0.05, fold change < -2 or > 2) distinguished responders from healthy controls, indicating the potential to stratify patients utilizing early on-treatment features from blood. PD-1-mediated cell abundance changes in memory CD4 + and regulatory T cell subset were more significant or exclusively observed in responders. A panel of top-ranked genetic alterations showed significant associations with improved survival (p < 0.05) and heightened responsiveness to anti-PD-1 treatment in patient cohort and co-cultured cell lines. CONCLUSION: This study discovered and validated peripheral blood-based biomarkers with evident predictive efficacy for early therapy response and patient stratification before treatment for neoadjuvant PD-1 blockade in NSCLC patients.
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The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.
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[This corrects the article DOI: 10.18632/oncotarget.23253.].
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Multidrug resistance is a great obstacle in successful chemotherapy of colorectal cancer. However, the molecular mechanism underlying multidrug resistance is not fully understood. Dishevelled, a pivot in Wnt signaling, has been linked to cancer progression, while its role in chemoresistance remains unclear. Here, we found that Dishevelled1-3 was over-expressed in multidrug-resistant colorectal cancer cells (HCT-8/VCR) compared to their parental cells. Silencing Dishevelled1-3 resensitized HCT-8/VCR cells to multiple drugs including vincristine, 5-fluorouracil and oxaliplatin. Moreover, Dishevelled1-3 increased the protein levels of multidrug resistance protein 1 (P-gp/MDR1), multidrug resistance-associated protein 2 (MRP2), and breast cancer resistance protein (BCRP), Survivin and Bcl-2 which are correlated with multidrug resistance. shß-catenin abolished Dishevelled-mediated these protein expressions. Unexpectedly, none of Dishevelled1-3 controlled ß-catenin accumulation and nuclear translocation. Furthermore, the nuclear translocations of Dishevelled1-3 were promoted in HCT-8/VCR cells compared to HCT-8. Dishevelled1-3 bound to ß-catenin in nucleus, and promoted nuclear complex formation and transcription activity of ß-catenin/TCF. Taken together, Dishevelled1-3 contributed to multidrug resistance in colorectal cancer via activating Wnt/ß-catenin signaling and inducing the expressions of P-gp, MRP2, BCRP, Survivin and Bcl-2, independently of ß-catenin accumulation and nuclear translocation. Silencing Dishevelled1-3 resensitized multidrug-resistant colorectal cancer cells, providing a novel therapeutic target for successful chemotherapy of colorectal cancer.
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Cisplatin (CDDP) is currently recommended as the front-line chemotherapeutic agent for lung cancer. However, the resistance to cisplatin is widespread in patients with advanced lung cancer, and the molecular mechanism of such resistance remains incompletely understood. Disheveled (DVL), a key mediator of Wnt/ß-catenin, has been linked to cancer progression, while the role of DVL in cancer drug resistance is not clear. Here, we found that DVL2 was over-expressed in cisplatin-resistant human lung cancer cells A549/CDDP compared to the parental A549 cells. Inhibition of DVL2 by its inhibitor (3289-8625) or shDVL2 resensitized A549/CDDP cells to cisplatin. In addition, over-expression of DVL2 in A549 cells increased the protein levels of BCRP, MRP4, and Survivin, which are known to be associated with chemoresistance, while inhibition of DVL2 in A549/CDDP cells decreased these protein levels, and reduced the accumulation and nuclear translocation of ß-catenin. In addition, shß-catenin abolished the DVL2-induced the expression of BCRP, MRP4, and Survivin. Furthermore, our data showed that GSK3ß/ß-catenin signals were aberrantly activated by DVL2, and inactivation of GSK3ß reversed the shDVL2-induced down-regulation of ß-catenin. Taken together, these results suggested that inhibition of DVL2 can sensitize cisplatin-resistant lung cancer cells through down-regulating Wnt/ß-catenin signaling and inhibiting BCRP, MRP4, and Survivin expression. It promises a new strategy to chemosensitize cisplatin-induced cytotoxicity in lung cancer.