A highly-entangled single-wire buckypaper with superior mechanical properties and excellent impact resistance is constructed.

BackgroundDCLRE1B is a 5'-to-3' exonuclease, which is involved in repairing ICL-related DNA damage. DCLRE1B has been reported to cause poor prognosis in a variety of cancers. Nonetheless, there is no research on DCLRE1B's biological role in pan-cancer datasets. Thus, ascertaining the processes via which DCLRE1B modulates tumorigenesis was the goal of the extensive bioinformatics investigation of pan-cancer datasets in the present research.MethodsIn our research, employing internet websites and databases including TIMER, GEPIA, TISIDB, Kaplan-Meier Plotter, SangerBox, cBioPortal, and LinkedOmics, DCLRE1B-related data in numerous tumors were extracted. To ascertain the association among DCLRE1B expression, prognosis, genetic changes, and tumor immunity, the pan-cancer datasets were examined. The DCLRE1B's biological roles in pancreatic cancer cells were ascertained by employing wound healing, in vitro CCK-8, and MeRIP-qPCR assays.ResultAccording to the pan-cancer analysis, in numerous solid tumors, DCLRE1B upregulation was observed. Expression of DCLRE1B was found to be substantially related to the cancer patients' prognoses. Similarly, expression of DCLRE1B exhibited substantial association with immune cells in several cancer types. DCLRE1B expression correlated with immune checkpoint (ICP) gene expression and impacted immunotherapy sensitivity. According to in vitro trials, DCLRE1B promoted PC cells' proliferation and migration capacities. Also, according to GSEA enrichment analysis, DCLRE1B might participate in the JAK-STAT signaling pathway, which was confirmed by western blotting. In addition, we also found that the downregulation of DCLRE1B may be regulated by METTL3-mediated m6A modification.ConclusionsIn human cancer, the overexpression of DCLRE1B was generally observed, which aided cancer onset and advancement via a variety of processes comprising control of the immune cells' tumor infiltration. According to this study's findings, in a few malignant tumors, DCLRE1B is a candidate immunotherapeutic and prognostic biomarker.

Abstract Background Approximately 90% of pancreatic ductal adenocarcinoma (PDAC) cases are driven by the untargetable non‐G12C KRAS mutations, and only a small subset of patients are eligible for FDA‐approved precision therapies. The practice of precision therapy in pancreatic cancer was limited by the paucity of targetable genetic alterations, especially in the Asian population. Methods To explore therapeutic targets in 499 Chinese PDAC patients, a deep sequencing panel (OncoPanscan™, Genetron health) was used to characterize somatic alterations including point mutations, indels, copy number alterations, gene fusions as well as pathogenic germline variants. Results We performed genomic profiling in 499 Chinese PDAC patients, which revealed somatic driver mutations in KRAS, TP53, CDKN2A, SMAD4, ARID1A, RNF43, and pathogenic germline variants (PGVs) in cancer predisposition genes including BRCA2, PALB2, and ATM. Overall, 20.4% of patients had targetable genomic alterations. About 8.4% of patients carried inactivating germline and somatic variants in BRCA1/2 and PALB2, which were susceptible to platinum and PARP inhibitors therapy. Patients with KRAS wild‐type disease and early‐onset pancreatic cancer (EOPC) harbored actionable mutations including BRAF, EGFR, ERBB2, and MAP2K1/2. Compared to PGV‐negative patients, PGV‐positive patients were younger and more likely to have a family history of cancer. Furthermore, PGVs in PALB2, BRCA2, and ATM were associated with high PDAC risk in the Chinese population. Conclusions Our results demonstrated that a genetic screen of actionable genomic variants could facilitate precision therapy and cancer risk reduction in pancreatic cancer patients of Asian ethnicity.

The determination of pathological grading has a guiding significance for the treatment of pancreatic ductal adenocarcinoma (PDAC) patients. However, there is a lack of an accurate and safe method to obtain pathological grading before surgery. The aim of this study is to develop a deep learning (DL) model based on 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG-PET/CT) for a fully automatic prediction of preoperative pathological grading of pancreatic cancer. A total of 370 PDAC patients from January 2016 to September 2021 were collected retrospectively. All patients underwent 18F-FDG-PET/CT examination before surgery and obtained pathological results after surgery. A DL model for pancreatic cancer lesion segmentation was first developed using 100 of these cases and applied to the remaining cases to obtain lesion regions. After that, all patients were divided into training set, validation set, and test set according to the ratio of 5:1:1. A predictive model of pancreatic cancer pathological grade was developed using the features computed from the lesion regions obtained by the lesion segmentation model and key clinical characteristics of the patients. Finally, the stability of the model was verified by sevenfold cross-validation. The Dice score of the developed PET/CT-based tumor segmentation model for PDAC was 0.89. The area under curve (AUC) of the PET/CT-based DL model developed on the basis of the segmentation model was 0.74, with an accuracy, sensitivity, and specificity of 0.72, 0.73, and 0.72, respectively. After integrating key clinical data, the AUC of the model improved to 0.77, with its accuracy, sensitivity, and specificity boosted to 0.75, 0.77, and 0.73, respectively. To the best of our knowledge, this is the first deep learning model to end-to-end predict the pathological grading of PDAC in a fully automatic manner, which is expected to improve clinical decision-making.

Recently, multi-functional MXene-based composite fibers have attracted much attention in fabricating smart textiles, owing to their outstanding electrical properties. However, the poor mechanical properties of such fibers usually make it difficult to spin continuously and limit their practical applications. Herein, a novel immersion Rotary Jet Spinning (iRJS) process was developed to produce high-strength continuous MXene/sodium alginate (SA) composite fibers (MSCFs). The prepared MSCFs exhibit superior fracture strength of 145.2 MPa and electrical conductivity of 479.2 S cm-1, with high MXene loading up to 75 wt%, respectively. In addition, they present linear-elastic and elastic-plastic behaviors, depending on the MXene content. The molecular dynamics simulation reveals that different tensile behaviors originate from the self-bending of SA chains and sliding between SA chains. Furthermore, one kind of non-woven MSCF felt structure was fabricated by a pressurized densification strategy. The electromagnetic interference (EMI) shielding effectiveness of this felt with a relatively thin thickness of-0.14 mm can reach-65.4 dB in the X-band. This work opens new avenues for fabricating high-performance multi-functional MXene-based hierarchical nanomaterials.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with aggressive biological behaviour. Its rapid proliferation and tumour growth require reprogramming of glucose metabolism or the Warburg effect. However, the association between glycolysis‐related genes with clinical features and prognosis of PDAC is still unknown. Here, we used the meta‐analysis to correlate the hazard ratios (HR) of 106 glycolysis genes from MSigDB by the cox proportional hazards regression analysis in 6 clinical data sets of PDAC patients to form a training cohort, and a single group of PDAC patients from the TCGA, ICGC, Arrayexpress and GEO databases to form the validation cohort. Then, a glycolysis‐related prognosis (GRP) score based on 29 glycolysis prognostic genes was established in 757 PDAC patients from the training composite cohort and validated in 267 ICGC‐CA validation cohort (all P < .05). In addition, including PADC, the prognostic value was also confirmed in other 7 out of 30 pan‐cancer cohorts. The GRP score was significantly related to specific metabolism pathways, immune genes and immune cells in the patients with PADC (all P < .05). Finally, by combining with immune cells, the GRP score also well‐predicted the chemosensitivity of patients with PADC in the TCGA cohort (AUC = 0.709). In conclusion, this study developed a GRP score for patients with PDAC in predicting prognosis and chemosensitivity for PDAC.

Background: A malignant tumor's immune environment, including infiltrating immune cell status, can be critical to patient outcomes. Recent studies have shown that immune cell infiltration (ICI) in pancreatic cancer (PC) is highly correlated with the response to immunotherapy and patient prognosis. Therefore, we aimed to create an ICI score that accurately predicts patient outcomes and immunotherapeutic efficacy. Methods: The ICI statuses of patients with PC were estimated from the publicly available The Cancer Genome Atlas (TCGA) pancreatic ductal adenocarcinoma and GSE57495 gene expression datasets using two computational algorithms (CIBERSORT and ESTIMATE). ICI and transcriptome subsets were defined using a clustering algorithm, and survival analysis was also performed. Principal component analysis was used to calculate the novel ICI score, and gene set enrichment analysis was performed to identify the pathways underlying the defined clusters. The tumor mutational burden (TMB) was further explored in TCGA cohort, and survival analysis was used to assess the capability of the ICI and TMB scores to predict overall survival. Additionally, common driver gene mutations and their differential expression in the different ICI score group were investigated. Results: The ICI landscapes of 240 patients were generated using the devised algorithm, revealing three ICI and three gene clusters whose use improved the prediction of overall survival (p = 0.019 and p < 0.001, respectively). Crucial immune checkpoint genes were differentially expressed among these subtypes; the RIG-I-LIKE and NOD-LIKE receptor signaling pathways were enriched in samples with low ICI scores (p < 0.05). We also found that the TMB scores could predict survival outcomes, whereas the ICI scores also could predict prognoses independent of TMB. Notably, ICI scores could effectively predict responses to immunotherapy. KRAS, TP53, CDKN2A, SMAD4 and TTN remained the most commonly mutated genes in PC; moreover, KRAS and TP53 mutation rates were significantly different between the two ICI score groups. Conclusions: We developed a novel ICI score that could independently predict the response to immunotherapy and survival of patients with PC. Evaluation of the ICI landscape in a larger cohort could clarify the interactions between these infiltrating cells, the tumor microenvironment and response to immunotherapy. Lay abstract Pancreatic cancer (PC) is a lethal malignancy with a higher mortality rate. Currently, immunotherapy is increasingly interesting to clinical researchers and considered a novel and efficient treatment. However, in clinical practice, immunotherapy has not demonstrated consistent therapeutic responses across all patients. Thus, to identify the immunotherapy-sensitive subgroup of advanced PC patients is important based on immune cell infiltration. In this study, we downloaded and processed transcriptomic data from TCGA-PAAD and GEO databases and used CIBERSORT and ESTIMATE algorithms to reveal the immune cell infiltration landscape of pancreatic cancer. According to consensus clustering results, we identified three ICI and gene clusters for guiding the identification of immune-subtype in future clinical treatments. Finally, we calculated an ICI score for each subject to describe their tumor immune landscape and performed the risk grouping for all patients and multiomics analysis. In sum, the ICI score and clusters could be used in the future to assist clinicians in identifying patients with the greatest chance of responding to immunotherapy.

Abstract Background: Small bowel adenocarcinoma (SBA) is a rare malignancy of the gastrointestinal (GI) tract without approved targeted therapy. Because the treatment approaches for colorectal cancer showed limited efficacy in SBA, the NCCN guide strongly encourages SBA patients to participate in clinical trials. The identification of targetable mutations through genomic profiling will gain new insights into the etiology of SBA and enable physicians to select the matched trials for patients. Methods: To characterize actionable targets in 50 Chinese SBA patients, a 830-gene next-generation sequencing panel was applied to assess the mutational status of their tumor tissues, including SNV, insertions/deletions, CNV and re-arrangements. Also, paired genomic DNA sample was extracted and analyzed by a 139-gene NGS panel to identify pathogenic germline variants. Results: The most frequently mutated genes in this SBA cohort were TP53 (68%), KRAS (56%), APC (38%), SMAD4 (20%), CTNNB1 (16%), ARID2 (14%), FAT3 (12%), FBXW7 (12%), KMT2C (12%) and PIK3CA (12%). Potentially targetable alterations include BRAF (10%), MDM2 (10%), ERBB2/HER2 (8%), PIK3CA (4%) and MAP2K1 (4%). Except one V600E mutation, all mutations in BRAF are either type 2 (L597R) or type 3 (N581S and D594N) which were sensitive to MEK inhibitor trametinib. We also found two MAP2K1 activating mutations (K57E and K57N) which can be targeted by trametinib. ERBB2/HER2 was highly amplified in one patient (copy number 25) and ERBB2/HER2 driver mutations were seen in 3 (6%) patients including V842I (n =2) and V777L (n =1). These four patients were good candidates for HER2-targeted therapy clinical trials. We also observed two activating PIK3CA mutations (R108H and G364R) which may be sensitive to PIK3CA inhibitor alpelisib. In addition, we observed targetable gene amplification of MDM2 (n =1) and FGF3/4/19 (n =2). Interestingly, one patient carried an activating ZKSCAN1-MET fusion gene which can be targeted by FDA-approved MET inhibitor carbozantinib. Among 46 patients with available MSI status, two were designated as microsatellite instability. The median TMB of microsatellite stable patients was 3.7 mutations/Mb. Lastly, we observed five deleterious germline mutations, two for SBDS, one each for APC, ERCC5 and MLH1. Overall, at least 18 (36%) patients harbored potentially actionable genetic mutations. Conclusions: The mutational landscape of our SBA cohort provided compelling evidence that multiple signaling pathways play a role in the pathogenesis of SBA and many driver mutations in these pathways are targetable. Our findings indicated that SBA patients should participate in matched clinical trials for better management of this disease. Citation Format: Rong Liu, Xiaomo Li, Zhiming Zhao, Tonghui Ma, Fei Wang, Si Liu. Genomic profiling of small bowl adenocarcinoma reveals targetable mutations in multiple signaling pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2183.

Abstract Background: Our knowledge of the prevalence of germline mutations associated with pancreatic cancer risk largely came from studies conducted in the Caucasian population. Therefore, there is an urgent need to characterize germline mutations of pancreatic cancer susceptibility genes in the non-Caucasian populations. In this study, we comprehensively analyzed germline mutations in known and candidate pancreatic cancer susceptibility genes in a large Chinese pancreatic duct adenocarcinoma (PDAC) cohort. Methods: Unselected PDAC patients were recruited from 2017 to 2020 and their genomic DNA sample was profiled by a 139-gene NGS panel. We quantified the prevalence of deleterious germline mutations in this Chinese cohort and compared it with published PDAC germline mutation profiling data. Results: In this 546 Chinese PDAC patient cohort, 64 deleterious germline mutations were found in 63 (11.5%) patients. Thirty-one (5.7%) patients carried a deleterious mutation in a pancreatic cancer susceptibility gene: 15 (2.7%) with BRCA1 (n =3) or BRCA2 (n =12) mutations, five with ATM, seven with PALB2, two with MSH6 and one each with a mutation in CDKN2A or TP53. Moreover, 24 (4.4%) patients had deleterious mutations in additional DNA damage repair (DDR) genes including BLM (n =1), NBN (n =1), RAD50 (n =1), RAD51D (n =2), RECQL4 (n =1), WRN (n =2) of the homologous recombination repair (HRR) pathway, ERCC2 (n =1), ERCC4 (n =1), ERCC5 (n =1) of the nucleotide excision repair (NER) pathway, BRIP1 (n =2), FANCC (n =1), FANCD2 (n =2), FANCM (n =2) of the Fanconi anemia (FA) pathway, MSH3 (n =2), PMS2 (n =1) of the mismatch repair (MMR) pathway, MUTYH (n =3) and MRE11A (n =1). The average age at diagnosis of patients with PDAC identified as having a germline mutation in a known (ATM, BRCA1, BRCA2, CDKN2A, MSH6, PALB2 and TP53) pancreatic cancer susceptibility gene was 54.2 ± 10.0 years, significantly lower than the average age of the patients without an identifiable susceptibility gene mutation (60.3 ± 10.6 years; P =0.002). We also compared PDAC-associated germline mutation frequency between our cohort (n =546) and the Mayo Clinic cohort (n =3030) to determine the similarity and difference of PDAC germline mutation landscape between Chinese and Caucasian populations. Of note, the mutation frequencies of ATM and PALB2 were 0.91% and 1.28% in our cohort, significantly lower and higher than 2.28% of ATM (P<0.05) and 0.40% of PALB2 (P =0.02) in the Mayo Clinic cohort, respectively. Conclusions: We found that nearly 12% patients in this Chinese PDAC cohort carry deleterious germline mutations, mainly in established pancreatic cancer susceptibility genes and DDR genes. Our data indicated that the PDAC germline mutation landscape of Chinese population is largely similar to that of the Caucasian population and the NCCN PDAC genetic screen guideline can be applied to the clinical management of PDAC patients of Asian descent. Citation Format: Zhiming Zhao, Xiaomo Li, Fei Wang, Tonghui Ma, Rong Liu. Germline mutation landscape in a large cohort of Chinese pancreatic cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2557.

Abstract Background: Ampullary carcinoma (AC) is a rare gastrointestinal cancer with few effective standard treatment options. For patients with unresectable diseases, the response rates for radiotherapy, chemotherapy, and chemoradiotherapy are low. Evaluation of the actionable mutations through genomic profiling will enable patients to enroll in matched clinical trials. Methods: To characterize actionable targets in 27 Chinese AC patients, a 500-gene next-generation sequencing panel was applied to assess the mutational status of their tumor tissues, including SNV, insertions/deletions, CNV and re-arrangements. Also, paired genomic DNA sample was extracted and analyzed by a 93-gene NGS panel to characterize germline variants. Results: Recurrent somatic mutated genes were KRAS (63%), TP53 (59%), APC (26%), SMAD4 (19%), CDKN2A (15%), ELF3 (11%), ERBB2 (11%), ERBB3 (11%) and MAP2K4 (11%). Consistent with previous studies, all mutations in AC driver gene ELF3 (n =3) were inactivating (three indels and one in splicing-site). The median tumor mutation burden (TMB) of our cohort was 1.6 (0-6.1) mutations/Mb and none of our patients had high microsatellite instability (MSI-H). Overall, we observed a wide spectrum of hotspot mutations in cancer driver genes including ERBB3 (n =2), FBXW7 (n =2), U2AF1 (n =2), BRAF (n =1), ERBB2/HER2 (n =1) and PIK3CA (n =1). The HER2 R678Q mutation, located in the transmembrane domain (TMD), activated HER2 signaling by improving the active dimer interface or stabilizing an activating conformation of HER2. Interestingly, both ERBB3 mutations (G284R and D297Y) were located in the extracellular domain (ECD) and their oncogenic activity also depended on the kinase-activity of ERBB2. These three mutations, together with one case of ERBB2 amplification, can be targeted by anti-HER2 antibodies and small-molecule HER2 kinase inhibitors. The BRAF N581I mutation is resistant to BRAF inhibitor vemurafenib but sensitive to MEK inhibitor trametinib. The PIK3CA N1044K mutation is oncogenic but its sensitivity to PIK3CA inhibitor alpelisib is unknown. Additionally, one patient had high level of FGFR2 amplification which may be suitable for the clinical trials of FGFR2 inhibitors pemigatnib and erdafitnib. Furthermore, two patients carried loss-of-function germline mutations in DNA repair genes PALB2 and CHEK2 which may confer synthetic lethality with PARP inhibitors. Taken together, at least eight (29.6%) patients in our cohort had actionable therapeutic targets. Conclusions: Through comprehensive genomic characterization of Chinese AC patients, we identified multiple actionable mutations in multiple signaling pathways. Our findings indicated that molecular profiling can provide clinical benefits to a significant portion of AC patients. Citation Format: Fei Wang, Si Liu, Xiaomo Li, Zhiming Zhao, Tonghui Ma, Hongling Yuan, Rong Liu. Evaluation of somatic and germline mutations in ampullary carcinoma reveals actionable targets in multiple signaling pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2182.

Background Intrahepatic cholangiocarcinoma (ICC) is a fatal primary liver cancer, and its long-term survival rate remains poor. RNA-binding proteins (RBPs) play an important role in critical cellular processes, failure of any one or more processes can lead to the development of multiple cancers. This study aimed to explore pivotal biomarkers and corresponding mechanisms to predict the prognosis of patients with ICC. Methods The transcriptomic and clinical information of patients were collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. Bioinformatic methods were used to identify survival-related and differentially-expressed biomarkers. Quantitative real-time PCR (qRT-PCR) and immunohistochemistry were used to detect the expression levels of key biomarkers in independent real-world cohorts. Subsequently, a prognostic signature was constructed that effectively distinguished patients in the high- and low-risk groups. Independent prognosis analysis was used to verify the signature’s independent predictive capabilities, and two nomograms were developed to predict survival. Results PIWIL4 and SUPT5H were identified and considered as pivotal biomarkers, and the same expression trends of upregulation in ICC were also validated via qRT-PCR and immunohistochemistry in the separate real-world sample cohorts. The prognostic signature showed good predictive capabilities according to the area under the curve. The correlation of the biomarkers with the tumour microenvironment suggested that the high riskScore was positively related to the enrichment of resting natural killer cells and activated memory CD4 + T cells. Conclusion In the present study, we demonstrated that PIWIL4 and SUPT5H could be used as novel prognostic biomarkers to develop a prognostic signature. This study provides potential biomarkers of prognostic value for patients with intrahepatic cholangiocarcinoma.

Pancreatic cancer is associated with a high mortality rate, and the prognosis is positively related to immune status. In this study, we constructed a prognostic signature from survival- and immune-related genes (IRGs) to guide treatment and assess prognosis of patients with pancreatic cancer. The transcriptomic data were obtained from The Cancer Genome Atlas (TCGA) database, and IRGs were extracted from the ImmPort database. Univariate and LASSO regression analysis were used to obtain survival-related IRGs. Finally, the prognostic signature was constructed using multivariate regression analysis. The laboratory experiments were conducted to verify the key IRG expression. Immune cells infiltration was analyzed using the CIBERSORT algorithm and TIMER database. Prognostic signature containing four IRGs (ADA2, TLR1, PTPN6, S100P) was constructed with good predictive performance; in particular, S100P played a significant role in the immune microenvironment, and tumorigenesis of pancreatic cancer. Moreover, we found that CD8+ T cell and activated CD4+ memory T cell tumor infiltration was lower in the high-risk group, while high-risk score correlated positively with higher tumor mutational burden, and the higher half inhibitory centration 50 of chemotherapeutic agents Docetaxel and Sunitinib. In summary, this study identified and constructed an immune-related prognostic signature that can predict overall survival, besides suggests that S100P was a novel immune-related biomarker. We hope that this signature will aid the identification of new biomarkers for the individualized immunotherapy of pancreatic cancer.

Rapid advances of big data and artificial intelligence (AI) techniques have pushed the envelope of traditional surgery to intelligent surgery, which revolutionizes the traditional medicine model dominated by the experiences of interveners and evidence-based medicine. Intelligent surgery requires to judiciously integrate the clinical experience of interveners and medical evidences with the new AI techniques, so as to achieve the best therapeutic effect for patients through effective disease risk control. This paper puts forward a new theory of prognostic control surgery in the long-term clinical practice of hepato-pancreato-biliary surgery. Our theory takes the optimal prognosis of patients as the goal, and pre-controls the disease risk through the best combination of the optimal intervener, intervention methods and intervention timing to achieve the maximum clinical benefits of patients with minimal medical trauma. This theory also takes full advantage of information technologies such as neural networks, deep learning, big data and imaging system, and uses high-dimensional network decision making to replace the traditional two-dimensional decision making tree model. A main technical challenge in the theory is how to effectively exploit the AI techniques in to improve the production efficiency of large hospitals, enhance the service level of primary medical care, and improve the diagnosis, prediction, treatment of diseases. Prognostic control surgery advocates the exploitation of emerging information technology methods, such as medical image recognition and prediction, 3D reconstruction based surgical planning, intraoperative navigation, and remote intelligent robotic surgical system, to promote the rapid and balanced surgical diagnosis and treatment. Guided by the theory of prognosis control surgery, our team successfully established minimally invasive anatomic hepatectomy, individualized pancreatic surgical approaches, modular hepatectomy, single-layer continuous suture of pancreaticojejunostomy, and end-to-end pancreatic anastomosis and advocated en-bloc resection of pancreatic cancer. This paper also provides the details of four core surgical strategies proposed in our prognosis control surgery, namely, blood control technique, surgical approach selection, resection technique and reconstruction technique, by which to achieve the minimally invasive surgery regularization and the complex surgery simplification.