Genomic Variations in Pancreatic Cancer and Potential Opportunities for Development of New Approaches for Diagnosis and Treatment

The Molecular Twin artificial-intelligence platform integrates multi-omic data to predict outcomes for pancreatic adenocarcinoma patients

Contemporary analyses focused on a limited number of clinical and molecular biomarkers have been unable to accurately predict clinical outcomes in pancreatic ductal adenocarcinoma. Here we describe a precision medicine platform known as the Molecular Twin consisting of advanced machine-learning models and use it to analyze a dataset of 6,363 clinical and multi-omic molecular features from patients with resected pancreatic ductal adenocarcinoma to accurately predict disease survival (DS). We show that a full multi-omic model predicts DS with the highest accuracy and that plasma protein is the top single-omic predictor of DS. A parsimonious model learning only 589 multi-omic features demonstrated similar predictive performance as the full multi-omic model. Our platform enables discovery of parsimonious biomarker panels and performance assessment of outcome prediction models learning from resource-intensive panels. This approach has considerable potential to impact clinical care and democratize precision cancer medicine worldwide.

Identification of key biomarkers and associated pathways of pancreatic cancer using integrated transcriptomic and gene network analysis

Pancreatic cancer shows malignancy around the world standing in 4th position for causing death globally. This cancer is majorly divided into exocrine and neuroendocrine where exocrine pancreatic ductal adenocarcinoma is observed to be nearly 85% of cases. The lack of diagnosis of pancreatic cancer is considered to be one of the major drawbacks to the prognosis and treatment of pancreatic cancer patients. The survival rate after diagnosis is very low, due to the higher incidence of drug resistance to cancer which leads to an increase in the mortality rate. The transcriptome analysis for pancreatic cancer involves dataset collection from the ENA database, incorporating them into quality control analysis to the quantification process to get the summarized read counts present in collected samples and used for further differential gene expression analysis using the DESeq2 package. Additionally, explore the enriched pathways using GSEA software and represented them by utilizing the enrichment map finally, the gene network has been constructed by Cytoscape software. Furthermore, explored the hub genes that are present in the particular pathways and how they are interconnected from one pathway to another has been analyzed. Finally, we identified the CDKN1A, IL6, and MYC genes and their associated pathways can be better biomarker for the clinical processes to increase the survival rate of of pancreatic cancer.

Agrimoniin is a dual inhibitor of AKT and ERK pathways that inhibit pancreatic cancer cell proliferation

Molecular-targeted therapy has shown its effectiveness in pancreatic cancer, while single-targeted drug often cannot provide long-term benefit because of drug resistance. Fortunately, multitarget combination therapy can reverse drug resistance and achieve better efficacy. The typical treatment characteristics of traditional Chinese medicine monomer on tumor are multiple targets, with small side effects, low toxicity, and so forth. Agrimoniin has been reported to be effective on some cancers, while the mechanism still needs to be clarified. In this study, we used 5-ethynyl-2'-deoxyuridine, cell counting kit-8, flow cytometry, and western blot experiments to confirm that agrimoniin can significantly inhibit the proliferation of pancreatic cancer cell PANC-1 by inducing apoptosis and cell cycle arrest. In addition, by using SC79, LY294002 (the agonist or inhibitor of AKT pathway), and U0126 (the inhibitor of ERK pathway), we found that agrimoniin inhibited cell proliferation by simultaneously inhibiting AKT and ERK pathways. Moreover, agrimoniin could significantly increase the inhibitory effect of LY294002 and U0126 on pancreatic cancer cells. Meanwhile, in vivo experiments also supported the above results. In general, agrimoniin is a double-target inhibitor of AKT and ERK pathways in pancreatic cancer cells; it is expected to be used as a resistance reversal agent of targeted drugs or a synergistic drug of the inhibitor of AKT pathway or ERK pathway.

Integrated Workflow for the Label-Free Isolation and Genomic Analysis of Single Circulating Tumor Cells in Pancreatic Cancer

As pancreatic cancer is the third deadliest cancer in the U.S., the ability to study genetic alterations is necessary to provide further insight into potentially targetable regions for cancer treatment. Circulating tumor cells (CTCs) represent an especially aggressive subset of cancer cells, capable of causing metastasis and progressing the disease. Here, we present the Labyrinth-DEPArray pipeline for the isolation and analysis of single CTCs. Established cell lines, patient-derived CTC cell lines and freshly isolated CTCs were recovered and sequenced to reveal single-cell copy number variations (CNVs). The resulting CNV profiles of established cell lines showed concordance with previously reported data and highlight several gains and losses of cancer-related genes such as FGFR3 and GNAS. The novel sequencing of patient-derived CTC cell lines showed gains in chromosome 8q, 10q and 17q across both CTC cell lines. The pipeline was used to process and isolate single cells from a metastatic pancreatic cancer patient revealing a gain of chromosome 1q and a loss of chromosome 5q. Overall, the Labyrinth-DEPArray pipeline offers a validated workflow combining the benefits of antigen-free CTC isolation with single cell genomic analysis.

Identification of LncRNA Prognostic Signature Associated With Genomic Instability in Pancreatic Adenocarcinoma

Background

Genomic instability (GI) is a critical feature of cancer which plays a key role in the occurrence and development of pancreatic adenocarcinoma (PAAD). Long non-coding RNA (LncRNA) is an emerging prognostic biomarker because it is involved in regulating GI. Recently, researchers used such GI-related LncRNAs (GILncRNAs) to establish a prognostic signature for patients with cancer and helped in predicting the overall prognosis of the patients. However, it is evident that patients with PAAD still lack such prognostic signature constructed with GILncRNA.

Methods

The present study screened GILncRNAs from 83 patients with PAAD. Prognosis-related GILncRNAs were identified by univariate Cox regression analysis. The correlation coefficients of these GILncRNAs were obtained by multivariate Cox regression analysis and used to construct a signature. The signature in the present study was then assessed through survival analysis, mutation correlation analysis, independent prognostic analysis, and clinical stratification analysis in the training set and validated in the testing as well as all TCGA set. The current study performed external clinical relevance validation of the signature and validated the effect of AC108134.2 in GILncSig on PAAD using in vitro experiments. Finally, the function of GILncRNA signature (GILncSig) dependent on Gene Ontology enrichment analysis was explored and chemotherapeutic drug sensitivity analysis was also performed.

Results

Results of the present study found that a total of 409 GILncRNAs were identified, 5 of which constituted the prognostic risk signature in this study, namely, AC095057.3, AC108134.2, AC124798.1, AL606834.1, and AC104695.4. It was found that the signature of the present study was better than others in predicting the overall survival and applied to patients with PAAD of all ages, genders, and tumor grades. Further, it was noted that the signature of the current study in the GSE102238, was correlated with tumor length, and tumor stage of patients with PAAD. In vitro, functional experiments were used in the present study to validate that AC108134.2 is associated with PAAD genomic instability and progression. Notably, results of the pRRophetic analysis in the current study showed that the high-risk group possessed reverse characteristics and was sensitive to chemotherapy.

Conclusions

In conclusion, it was evident that the GILncSig used in the present study has good prognostic performance. Therefore, the signature may become a potential sensitive biological indicator of PAAD chemotherapy, which may help in clinical decision-making and management of patients with cancer.

Opportunities and challenges in targeted therapy and immunotherapy for pancreatic cancer

Pancreatic cancer is one of the most malignant tumours with a poor prognosis. In recent years, the incidence of pancreatic cancer is on the rise. Traditional chemotherapy and radiotherapy for pancreatic cancer have been improved, first-line and second-line palliative treatments have been developed, and adjuvant treatments have also been used in clinical. However, the 5-year survival rate is still less than 10% and new treatment methods such as targeted therapy and immunotherapy need to be investigated. In the past decades, many clinical trials of targeted therapies and immunotherapies for pancreatic cancer were launched and some of them showed an ideal prospect in a subgroup of pancreatic cancer patients. The experience of both success and failure of these clinical trials will be helpful to improve these therapies in the future. Therefore, the current research progress and challenges of selected targeted therapies and immunotherapies for pancreatic cancer are reviewed.

Predicting pathogenic non-coding SVs disrupting the 3D genome in 1646 whole cancer genomes using multiple instance learning

Over the past years, large consortia have been established to fuel the sequencing of whole genomes of many cancer patients. Despite the increased abundance in tools to study the impact of SNVs, non-coding SVs have been largely ignored in these data. Here, we introduce svMIL2, an improved version of our Multiple Instance Learning-based method to study the effect of somatic non-coding SVs disrupting boundaries of TADs and CTCF loops in 1646 cancer genomes. We demonstrate that svMIL2 predicts pathogenic non-coding SVs with an average AUC of 0.86 across 12 cancer types, and identifies non-coding SVs affecting well-known driver genes. The disruption of active (super) enhancers in open chromatin regions appears to be a common mechanism by which non-coding SVs exert their pathogenicity. Finally, our results reveal that the contribution of pathogenic non-coding SVs as opposed to driver SNVs may highly vary between cancers, with notably high numbers of genes being disrupted by pathogenic non-coding SVs in ovarian and pancreatic cancer. Taken together, our machine learning method offers a potent way to prioritize putatively pathogenic non-coding SVs and leverage non-coding SVs to identify driver genes. Moreover, our analysis of 1646 cancer genomes demonstrates the importance of including non-coding SVs in cancer diagnostics.

Multi-Omic Biomarkers as Potential Tools for the Characterisation of Pancreatic Cystic Lesions and Cancer: Innovative Patient Data Integration

Pancreatic cancer (PC) is regarded as one of the most lethal malignant diseases in the world, with GLOBOCAN 2020 estimates indicating that PC was responsible for almost half a million deaths worldwide in 2020. Pancreatic cystic lesions (PCLs) are fluid-filled structures found within or on the surface of the pancreas, which can either be pre-malignant or have no malignant potential. While some PCLs are found in symptomatic patients, nowadays many PCLs are found incidentally in patients undergoing cross-sectional imaging for other reasons-so called 'incidentalomas'. Current methods of characterising PCLs are imperfect and vary hugely between institutions and countries. As such, there is a profound need for improved diagnostic algorithms. This could facilitate more accurate risk stratification of those PCLs that have malignant potential and reduce unnecessary surveillance. As PC continues to have such a poor prognosis, earlier recognition and risk stratification of PCLs may lead to better treatment protocols. This review will focus on the importance of biomarkers in the context of PCLs and PCand outline how current 'omics'-related work could contribute to the identification of a novel integrated biomarker profile for the risk stratification of patients with PCLs and PC.

Effects of ER-resident and secreted AGR2 on cell proliferation, migration, invasion, and survival in PANC-1 pancreatic cancer cells

Background

Anterior gradient-2 (AGR2) is a proto-oncogene involved in tumorigenesis and cancer progression. AGR2, predominantly localized in the endoplasmic reticulum (ER), is also a secreted protein detected in the extracellular compartment in multiple cancers. However, the biological functions of intracellular and extracellular AGR2 remain to be elucidated.

Methods

Based on the biochemical structure of AGR2 protein, PANC-1 pancreatic cancer cells stably expressing ER-resident or secreted AGR2 were generated by a lentivirus-mediated stable overexpression system. The capacities of cell proliferation, migration, invasion and survival were assessed in PANC-1 stable cells. Moreover, EGFR expression and activation were determined to explore the possible mechanism of AGR2 roles in pancreatic cancer tumorigenesis.

Results

It was discovered that secreted AGR2, but not ER-resident AGR2, promotes cell proliferation, migration and invasion of PANC-1 cells. Moreover, the data indicated that both the ER-resident and the secreted AGR2 enhance the survival capacity of PANC-1 cells after tunicamycin-induced ER stress and gemcitabine treatment. However, EGFR expression and activation were not found to be involved in AGR2-dependent oncogenic phenotypes in PANC-1 cells.

Conclusions

Secreted AGR2 is predominantly involved in cell proliferation, migration and invasion in PANC-1 pancreatic cancer cells. Both secreted and ER-resident AGR2 contribute to the survival of PANC-1 cells under the challenging conditions. These findings provide insight into how different localizations of AGR2 have contributed to pancreatic cancer growth, metastasis, and drug sensitivity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07743-y.

Identification of Cross Talk between FoxM1 and RASSF1A as a Therapeutic Target of Colon Cancer

Metastatic colorectal cancer (mCRC) is characterized by the expression of cellular oncogenes, the loss of tumor suppressor gene function. Therefore, identifying integrated signaling between onco-suppressor genes may facilitate the development of effective therapy for mCRC. To investigate these pathways we utilized cell lines and patient derived organoid models for analysis of gene/protein expression, gene silencing, overexpression, and immunohistochemical analyses. An inverse relationship in expression of oncogenic FoxM1 and tumor suppressor RASSF1A was observed in various stages of CRC. This inverse correlation was also observed in mCRC cells lines (T84, Colo 205) treated with Akt inhibitor. Inhibition of FoxM1 expression in mCRC cells as well as in our ex vivo model resulted in increased RASSF1A expression. Reduced levels of RASSF1A expression were found in normal cells (RWPE-1, HBEpc, MCF10A, EC) stimulated with exogenous VEGF₁₆₅. Downregulation of FoxM1 also coincided with increased YAP phosphorylation, indicative of tumor suppression. Conversely, downregulation of RASSF1A coincided with FoxM1 overexpression. These studies have identified for the first time an integrated signaling pathway between FoxM1 and RASSF1A in mCRC progression, which may facilitate the development of novel therapeutic options for advanced colon cancer therapy.

ClinGen Cancer Somatic Working Group - standardizing and democratizing access to cancer molecular diagnostic data to drive translational research

A growing number of academic and community clinics are conducting genomic testing to inform treatment decisions for cancer patients (1). In the last 3-5 years, there has been a rapid increase in clinical use of next generation sequencing (NGS) based cancer molecular diagnostic (MolDx) testing (2). The increasing availability and decreasing cost of tumor genomic profiling means that physicians can now make treatment decisions armed with patient-specific genetic information. Accumulating research in the cancer biology field indicates that there is significant potential to improve cancer patient outcomes by effectively leveraging this rich source of genomic data in treatment planning (3). To achieve truly personalized medicine in oncology, it is critical to catalog cancer sequence variants from MolDx testing for their clinical relevance along with treatment information and patient outcomes, and to do so in a way that supports large-scale data aggregation and new hypothesis generation. One critical challenge to encoding variant data is adopting a standard of annotation of those variants that are clinically actionable. Through the NIH-funded Clinical Genome Resource (ClinGen) (4), in collaboration with NLM's ClinVar database and >50 academic and industry based cancer research organizations, we developed the Minimal Variant Level Data (MVLD) framework to standardize reporting and interpretation of drug associated alterations (5). We are currently involved in collaborative efforts to align the MVLD framework with parallel, complementary sequence variants interpretation clinical guidelines from the Association of Molecular Pathologists (AMP) for clinical labs (6). In order to truly democratize access to MolDx data for care and research needs, these standards must be harmonized to support sharing of clinical cancer variants. Here we describe the processes and methods developed within the ClinGen's Somatic WG in collaboration with over 60 cancer care and research organizations as well as CLIA-certified, CAP-accredited clinical testing labs to develop standards for cancer variant interpretation and sharing.