The Key Genes for Perineural Invasion in Pancreatic Ductal Adenocarcinoma Identified With Monte-Carlo Feature Selection Method

ATF3-induced activation of NF-κB pathway results in acquired PARP inhibitor resistance in pancreatic adenocarcinoma

PURPOSE

Olaparib, an inhibitor of poly-(adenosine diphosphate-ribose) polymerase (PARP), has been shown to have anticancer benefits in patients with pancreatic cancer who have a germline mutation in BRCA1/2. However, resistance acquired on long-term exposure to olaparib significantly impedes clinical efficacy.

METHODS

In this study, the chromatin accessibility and differentially expressed transcripts of parental and olaparib-resistant pancreatic cancer cell lines were assessed using the Assay for Transposase Accessible Chromatin with sequencing (ATAC-seq) and mRNA-seq. Detection of downstream genes regulated by transcription factors using ChIP (Chromatin immunoprecipitation assay).

RESULTS

According to pathway enrichment analysis, differentially expressed genes in olaparib-resistant cells were remarkably enriched in the NF-κB signaling pathway. With ATAC-seq, we identified chromatin regions with higher accessibility in olaparib-resistant cells and predicted a series of important transcription factors. Among them, activating transcription factor 3 (ATF3) was significantly highly expressed. Functional experiments verified that inhibition of ATF3 suppressed the NF-κB pathway significantly and restored olaparib sensitivity in olaparib-resistant cells.

CONCLUSION

Experiments in vitro and in vivo indicate ATF3 enhances olaparib resistance through the NF-κB signaling pathway, suggesting that ATF3 could be employed as an olaparib sensitivity and prognostic indicator in patients with pancreatic cancer.

Cuproptosis and its application in different cancers: an overview

Heavy metal ions are essential micronutrients for human health. They are also indispensable to maintaining health and regular operation of organs. Increasing or decreasing these metal ions will lead to cell death, such as ferroptosis. Tsvetkov et al. have recently proposed a novel cell death method called "Cuproptosis". Many researchers have linked this form of death to the diagnosis, prognosis, microenvironment infiltration, and prediction of immunotherapeutic efficacy of various tumors to better understand these tumors. Similarly, with the proposal of this mechanism, the killing effect of copper ionophores on cancer cells has come to our attention again. We introduced the mechanism of cuproptosis in detail and described the establishment of the corresponding prognostic model and risk score for uveal melanoma through cuproptosis. In addition, we describe the current progress in the study of cancer in other organs through cuproptosis and summarize the treatment of tumours by copper ionophore and its future research direction. With further research, the concept of cuproptosis may help us understand cancer and guide its clinical treatment.

Feature selection and association rule learning identify risk factors of malnutrition among Ethiopian schoolchildren

Introduction

Previous studies have sought to identify risk factors for malnutrition in populations of schoolchildren, depending on traditional logistic regression methods. However, holistic machine learning (ML) approaches are emerging that may provide a more comprehensive analysis of risk factors.

Methods

This study employed feature selection and association rule learning ML methods in conjunction with logistic regression on epidemiological survey data from 1,036 Ethiopian school children. Our first analysis used the entire dataset and then we reran this analysis on age, residence, and sex population subsets.

Results

Both logistic regression and ML methods identified older childhood age as a significant risk factor, while females and vaccinated individuals showed reduced odds of stunting. Our machine learning analyses provided additional insights into the data, as feature selection identified that age, school latrine cleanliness, large family size, and nail trimming habits were significant risk factors for stunting, underweight, and thinness. Association rule learning revealed an association between co-occurring hygiene and socio-economical variables with malnutrition that was otherwise missed using traditional statistical methods.

Discussion

Our analysis supports the benefit of integrating feature selection methods, association rules learning techniques, and logistic regression to identify comprehensive risk factors associated with malnutrition in young children.

DNA methylation-based classifier differentiates intrahepatic pancreato-biliary tumours

BACKGROUND

Differentiating intrahepatic cholangiocarcinomas (iCCA) from hepatic metastases of pancreatic ductal adenocarcinoma (PAAD) is challenging. Both tumours have similar morphological and immunohistochemical pattern and share multiple driver mutations. We hypothesised that DNA methylation-based machine-learning algorithms may help perform this task.

METHODS

We assembled genome-wide DNA methylation data for iCCA (n = 259), PAAD (n = 431), and normal bile duct (n = 70) from publicly available sources. We split this cohort into a reference (n = 399) and a validation set (n = 361). Using the reference cohort, we trained three machine learning models to differentiate between these entities. Furthermore, we validated the classifiers on the technical validation set and used an internal cohort (n = 72) to test our classifier.

FINDINGS

On the validation cohort, the neural network, support vector machine, and the random forest classifiers reached accuracies of 97.68%, 95.62%, and 96.5%, respectively. Filtering by anomaly detection and thresholds improved the accuracy to 99.07% (37 samples excluded by filtering), 96.22% (17 samples excluded), and 100% (44 samples excluded) for the neural network, support vector machine and random forest, respectively. Because of best balance between accuracy and number of predictable cases we tested the neural network with applied filters on the in-house cohort, obtaining an accuracy of 95.45%.

INTERPRETATION

We developed a classifier that can differentiate between iCCAs, intrahepatic metastases of a PAAD, and normal bile duct tissue with high accuracy. This tool can be used for improving the diagnosis of pancreato-biliary cancers of the liver.

FUNDING

This work was supported by Berlin Institute of Health (JCS Program), DKTK Berlin (Young Investigator Grant 2022), German Research Foundation (493697503 and 314905040 - SFB/TRR 209 Liver Cancer B01), and German Cancer Aid (70113922).

Establishment of a Machine Learning Model for the Risk Assessment of Perineural Invasion in Head and Neck Squamous Cell Carcinoma

Perineural invasion is a prevalent pathological finding in head and neck squamous cell carcinoma and a risk factor for unfavorable survival. An adequate diagnosis of perineural invasion by pathologic examination is limited due to the availability of tumor samples from surgical resection, which can arise in cases of definitive nonsurgical treatment. To address this medical need, we established a random forest prediction model for the risk assessment of perineural invasion, including occult perineural invasion, and characterized distinct cellular and molecular features based on our new and extended classification. RNA sequencing data of head and neck squamous cell carcinoma from The Cancer Genome Atlas were used as a training cohort to identify differentially expressed genes that are associated with perineural invasion. A random forest classification model was established based on these differentially expressed genes and was validated by inspection of H&E-stained whole image slides. Differences in epigenetic regulation and the mutational landscape were detected by an integrative analysis of multiomics data and single-cell RNA-sequencing data were analyzed. We identified a 44-gene expression signature related to perineural invasion and enriched for genes mainly expressed in cancer cells according to single-cell RNA-sequencing data. A machine learning model was trained based on the expression pattern of the 44-gene set with the unique feature to predict occult perineural invasion. This extended classification model enabled a more accurate analysis of alterations in the mutational landscape and epigenetic regulation by DNA methylation as well as quantitative and qualitative differences in the cellular composition in the tumor microenvironment between head and neck squamous cell carcinoma with or without perineural invasion. In conclusion, the newly established model could not only complement histopathologic examination as an additional diagnostic tool but also guide the identification of new drug targets for therapeutic intervention in future clinical trials with head and neck squamous cell carcinoma patients at a higher risk for treatment failure due to perineural invasion.

Vasculitis, CA19-9, and Perineural Invasion Differentially Predict Response and Surgical Outcome in Pancreatic Ductal Adenocarcinoma

PURPOSE

Curative intent treatment of pancreatic adenocarcinoma (PDAC) relies on surgical resection. Modern treatment protocols focus on optimizing neoadjuvant therapy to increase resectability and improve oncologic outcomes. To elucidate differences in outcomes, we investigated the relationship between neoadjuvant chemotherapy (NAC), either with or without stereotactic body radiation therapy (SBRT), and vascular inflammation, surgical outcomes, and the resultant transcriptomic changes.

METHODS AND MATERIALS

Clinical data were collected from patients with borderline resectable PDAC (clinical T3-T4N0-1) who underwent NAC or NAC-SBRT followed by curative intent resection between 2014 and 2019. Vascular structures on surgical specimens were histologically evaluated for vasculitis. RNA sequencing was used to evaluate differential gene expression and to generate enrichment maps. Multivariate analysis was used to analyze the relationship between patient characteristics and oncological outcome.

RESULTS

In total, 46 patients met inclusion criteria (n = 12 NAC, n = 34 NAC-SBRT) with a median follow-up of 20.1 months. All patients underwent curative resection, with 91.3% achieving R0. There was no significant difference in patterns of failure, overall survival, or progression-free survival between NAC and NAC-SBRT groups. Patients with vasculitis had a lower median overall survival compared with those without (14.5 vs 28.3 months; hazard ratio, 12.96; 95% confidence interval, 3.55-47.28; P < .001). There was no significant correlation between inflammation and surgical complications or pathologic response. Neoadjuvant therapy did not have a significant effect on development of vasculitis (odds radio, 1.64 for NAC-SBRT; 95% confidence interval, 0.40-8.43; P = .52). Predictors of poor survival included perineural invasion and high baseline carbohydrate antigen 19-9 (CA19-9) (>191 U/mL). Patients with robust CA19-9 (>20% decrease) responses to neoadjuvant therapy had enrichment in immune response, chemotaxis, and cytotoxic T-cell and natural killer-cell proliferation.

CONCLUSIONS

Vasculitis predicts for poor survival outcomes in patients with PDAC; NAC-SBRT did not increase the rate of vasculitis compared with NAC. Perineural invasion and CA19-9 remain strong prognosticators. Understanding and optimizing immune interactions remain a crucial hurdle in achieving response in pancreatic cancer.

A positive feedback loop of ARF6 activates ERK1/2 signaling pathway via DUSP6 silencing to promote pancreatic cancer progression

ERK1/2 are essential proteins mediating mitogen-activated protein kinase signaling downstream of RAS in pancreatic adenocarcinoma (PDAC). Our previous study reveals that ARF6 plays a positive regulatory role in ERK1/2 pathway in a feedback loop manner. A significant part of the literature on ARF6 has emphasized its oncogenic effect as an essential downstream molecule of ERK1/2, and no research has been done on the regulation mechanisms of the feedback loop between ARF6 and the ERK1/2 signaling pathway. In the present study, we explore the gene network downstream of ARF6 and find that DUSP6 may be the critical signal molecule in the positive feedback loop between ARF6 and ERK1/2. Specifically, to elucidate the negative correlations between ARF6 and DUSP6 in pancreatic cancer, we examine their expressions in pancreatic cancer tissues by immunohistochemical staining. Then the impact of DUSP6 on the proliferation and apoptosis of PDAC cells are investigated by gain-of-function and loss-of-function approaches. Mechanism explorations uncover that ARF6 suppresses the expression of DUSP6, which is responsible for the dephosphorylation of ERK1/2. Altogether, these results indicate that DUSP6 plays a tumor-suppressive role and acts as an intermediate molecule between ARF6 and ERK1/2 in PDAC cells, thereby forming a positive feedback loop.

TGF-β1-induced RAP2 regulates invasion in pancreatic cancer

Pancreatic cancer is highly lethal due to its aggressive invasive properties and capacity for metastatic dissemination. Additional therapeutic targets and effective treatment options for patients with tumours of high invasive capacity are required. Ras-related protein-2a (RAP2) is a member of the GTP-binding proteins. RAP2 has been reported to be widely upregulated in many types of cancers via regulating cytoskeleton reorganization, cell proliferation, migration, and adhesion, as well as inflammation. As a member of the RAS oncogene family, which has been demonstrated to drive pancreatic cancer oncogenesis and many other malignancies, the physiological roles of RAP2 in pancreatic cancer have seldom been discussed. In the present study, we explored the correlation between RAP2 expression and the prediction of overall survival of pancreatic cancer patients. Mechanistic studies were carried out to shed light on the role of RAP2 in pancreatic cancer invasion and how RAP2 is regulated in the invasive process. Our results demonstrated that patients with higher RAP2 expression showed unfavourable prognoses. studies demonstrated that silencing of inhibited the invasion of pancreatic cancer cells. Moreover, our results demonstrated that transforming growth factor-β1 (TGF-β1), an inducer of the metastatic potential of pancreatic cancer cells, regulates the expression of RAP2 via the transcription factor c-Myc. In conclusion, the present study uncovered RAP2 as a novel predictive marker and therapeutic target for pancreatic cancer.

Curing pancreatic cancer

The distinct biology of pancreatic cancer with aggressive and early invasive tumor cells, a tumor promoting microenvironment, late diagnosis, and high therapy resistance poses major challenges on clinicians, researchers, and patients. In current clinical practice, a curative approach for pancreatic cancer can only be offered to a minority of patients and even for those patients, the long-term outcome is grim. This bitter combination will eventually let pancreatic cancer rise to the second leading cause of cancer-related mortalities. With surgery being the only curative option, complete tumor resection still remains the center of pancreatic cancer treatment. In recent years, new developments in neoadjuvant and adjuvant treatment have emerged. Together with improved perioperative care including complication management, an increasing number of patients have become eligible for tumor resection. Basic research aims to further increase these numbers by new methods of early detection, better tumor modelling and personalized treatment options. This review aims to summarize the current knowledge on clinical and biologic features, surgical and non-surgical treatment options, and the improved collaboration of clinicians and basic researchers in pancreatic cancer that will hopefully result in more successful ways of curing pancreatic cancer.

Multiple Feature Selection Strategies Identified Novel Cardiac Gene Expression Signature for Heart Failure

Heart failure (HF) is a serious condition in which the support of blood pumped by the heart is insufficient to meet the demands of body at a normal cardiac filling pressure. Approximately 26 million patients worldwide are suffering from heart failure and about 17–45% of patients with heart failure die within 1-year, and the majority die within 5-years admitted to a hospital. The molecular mechanisms underlying the progression of heart failure have been poorly studied. We compared the gene expression profiles between patients with heart failure (n = 177) and without heart failure (n = 136) using multiple feature selection strategies and identified 38 HF signature genes. The support vector machine (SVM) classifier based on these 38 genes evaluated with leave-one-out cross validation (LOOCV) achieved great performance with sensitivity of 0.983 and specificity of 0.963. The network analysis suggested that the hub gene SMOC2 may play important roles in HF. Other genes, such as FCN3, HMGN2, and SERPINA3, also showed great promises. Our results can facilitate the early detection of heart failure and can reveal its molecular mechanisms.