Single-cell transcriptome analysis profiling lymphatic invasion-related TME in colorectal cancer.

Lymphatic invasion (LI) is extremely aggressive and induces worse prognosis among patients with colorectal cancer (CRC). Thus, it is critical to characterize the cellular and molecular mechanisms underlying LI in order to establish novel and efficacious therapeutic targets that enhance the prognosis of CRC patients. RNA-seq data, clinical and survival information of colon adenocarcinoma (COAD) patients were obtained from the TCGA database. In addition, three scRNA-seq datasets of CRC patients were acquired from the GEO database. Data analyses were conducted with the R packages. We assessed the tumor microenvironment (TME) differences between LI+ and LI- based scRNA-seq data, LI+ cells exhibited augmented abundance of immunosuppression and invasive subset. Marked extracellular matrix network activation was also observed in LI+ cells within SPP1+ macrophages. We revealed that an immunosuppressive and pro-angiogenic TME strongly enhanced LI, as was evidenced by the CD4+ Tregs, CD8+ GZMK+, SPP1+ macrophages, e-myCAFs, and w-myCAFs subcluster infiltrations. Furthermore, we identified potential LI targets that influenced tumor development, metastasis, and immunotherapeutic response. Finally, a novel LIRS model was established based on the expression of 14 LI-related signatures, and in the two testing cohorts, LIRS was also proved to have accurate prognostic predictive ability. In this report, we provided a valuable resource and extensive insights into the LI of CRC. Our conclusions can potentially benefit the establishment of highly efficacious therapeutic targets as well as diagnostic biomarkers that improve patient outcomes.

Short-Term Photovoltaic Power Forecasting Based on Historical Information and Deep Learning Methods.

The accurate prediction of photovoltaic (PV) power is essential for planning power systems and constructing intelligent grids. However, this has become difficult due to the intermittency and instability of PV power data. This paper introduces a deep learning framework based on 7.5 min-ahead and 15 min-ahead approaches to predict short-term PV power. Specifically, we propose a hybrid model based on singular spectrum analysis (SSA) and bidirectional long short-term memory (BiLSTM) networks with the Bayesian optimization (BO) algorithm. To begin, the SSA decomposes the PV power series into several sub-signals. Then, the BO algorithm automatically adjusts hyperparameters for the deep neural network architecture. Following that, parallel BiLSTM networks predict the value of each component. Finally, the prediction of the sub-signals is summed to generate the final prediction results. The performance of the proposed model is investigated using two datasets collected from real-world rooftop stations in eastern China. The 7.5 min-ahead predictions generated by the proposed model can reduce up to 380.51% error, and the 15 min-ahead predictions decrease by up to 296.01% error. The experimental results demonstrate the superiority of the proposed model in comparison to other forecasting methods.

Identification of KRAS G12V associated clonal neoantigens and immune microenvironment in long-term survival of pancreatic adenocarcinoma.

OBJECTIVE: To investigate the molecular characteristics in tumor immune microenvironment that affect long-term survival of patients with pancreatic adenocarcinoma (PAAD). METHODS: The tumor related genetic features of a female PAAD patient (over 13-year survival) who suffered from multiple recurrences and metastases, and six operations over one decade were investigated deeply. Genomic features and immune microenvironment signatures of her primary lesion as well as six metastatic tumors at different time-points were characterized. RESULTS: High-frequency clonal neoantigenic mutations identified in these specimens revealed the significant associations between clonal neoantigens with her prognosis after each surgery. Meanwhile, the TCGA and ICGC databases were employed to analyse the function of KRAS G12V in pancreatic cancer. CONCLUSIONS: The genomic analysis of clonal neoantigens combined with tumor immune microenvironment could promote the understandings of personalized prognostic evaluation and the stratification of resected PAAD individuals with better outcome.

Characterization of evolution trajectory and immune profiling of brain metastasis in lung adenocarcinoma.

Characterizing the evolutionary trajectory and immune profiling of brain metastasis (BM) may provide insights in the development of novel therapeutic strategies. Here, we performed whole-exome sequencing and multiplex immunofluorescence (MIF) of 40 samples from 12 lung adenocarcinoma (LUAD) patients with BM and compared to their paired primary tumors. We observed significantly higher intertumor heterogeneity between paired primary tumors and BMs, with only a median of 8.3% of genetic mutations identified as shared. Phylogenetic analysis revealed that BM-competent clones genetically diverged from their primary tumors at relatively early stage, suggesting that the parallel progression model is dominant. In cases with synchronous lymph node metastasis (LNM), phylogenetic analysis suggested that BM is a later event than LNM. MIF analysis found that BMs exhibited significantly lower CD8+ T cell infiltration (P = 0.048), and elevated CD4+Foxp3+ T cell infiltration (P = 0.036) and PD-1 expression (P = 0.047) in comparison to the matched primary tumors, indicating an immunosuppressive microenvironment in BMs. The current study revealed the discrepancy of mutational landscape as well as tumor immune microenvironment between BM and its primary tumor - such findings shall help us better understand the unique biological features of BM and develop innovative strategies accordingly for our patients with LUAD.

Mutational Landscape and Evolutionary Pattern of Liver and Brain Metastasis in Lung Adenocarcinoma.

INTRODUCTION: A comprehensive genomic analysis of paired primary tumors and their metastatic lesions may provide new insights into the biology of metastatic processes and therefore guide the development of novel strategies for intervention. To date, our knowledge of the genetic divergence and phylogenetic relationships among diverse metastatic lesions from cancer remains limited. METHODS: We performed whole-exome sequencing in 84 tissue and blood samples from 26 patients with lung adenocarcinoma having liver metastases (LiM) or brain metastases (BrM) before any systemic therapy, with the goal to molecularly characterize the metastatic process. Mutational landscape and evolutionary patterns were compared between paired primary lesions (primary lesion of LiM or BrM) and metastases (metastatic site of LiM or BrM). RESULTS: We found that common driver mutations, including TP53 and EGFR, were highly consistent between paired primary and metastatic tumors. Although tumor mutational burden was comparable among groups, the LiM group had significantly higher mutational and copy number variational similarity than the BrM group between paired primary lesions and metastases (p = 0.019 and p = 0.035, respectively). Phylogenetic analysis further revealed that LiM-competent disseminations had a higher level of genetic similarity to their paired primary lesions and were genetically diverged from their primary tumors at a relatively later stage than those of BrM. These results suggest that LiM favorably followed the linear progression model, whereas BrM was more consistent with the parallel progression model. CONCLUSIONS: This study suggests that the mutational landscape and evolutionary pattern was distinctly different between the LiM and BrM of lung adenocarcinoma.

Genomic landscape and its correlations with tumor mutational burden, PD-L1 expression, and immune cells infiltration in Chinese lung squamous cell carcinoma.

INTRODUCTION: To depict the genomic landscape of Chinese early-stage lung squamous cell carcinoma (LUSC) and investigate its correlation with tumor mutation burden (TMB), PD-L1 expression, and immune infiltrates. METHODS: Whole-exome sequencing was performed on 189 surgically resected LUSC. TMB was defined as the sum of nonsynonymous single nucleotide and indel variants. CD8+ tumor-infiltrating lymphocyte (TIL) density and PD-L1 expression were evaluated by immunohistochemistry. Six immune infiltrates were estimated using an online database. RESULTS: The median TMB was 9.43 mutations per megabase. Positive PD-L1 expression and CD8+ TILs density were identified in 24.3% and 78.8%. PIK3CA amplification was associated with significantly higher TMB (P = 0.036). Frequent genetic alterations had no impact on PD-L1 expression but PIK3CA amplification and KEAP1 mutation were independently associated with significantly lower CD8+ TIL density (P < 0.001, P = 0.005, respectively). Low TMB and high CD8+ TIL density were independently associated with longer disease-free survival (DFS) while none of them could individually predict the overall survival (OS). Combination of TMB and PD-L1 expression or TMB and CD8+ TIL density could stratify total populations into two groups with distinct prognosis. Classifying tumor-immune microenvironment based on PD-L1 expression and CD8+ TIL density showed discrepant genomic alterations but similar TMB, clinical features, and OS. Notably, patients with different smoking status had distinct prognostic factors. CONCLUSION: The combination of TMB, PD-L1 expression, immune infiltrates, and smoking status showed the feasibility to subgroup stratification in Chinese patients with early-stage LUSC, which might be helpful for future design of personalized immunotherapy trials in LUSC.