Deconstructing Pancreatic Cancer Using Next Generation-Omic Technologies-From Discovery to Knowledge-Guided Platforms for Better Patient Management

Spatial transcriptomics and the anatomical pathologist: Molecular meets morphology

In recent years anatomical pathology has been revolutionised by the incorporation of molecular findings into routine diagnostic practice, and in some diseases the presence of specific molecular alterations are now essential for diagnosis. Spatial transcriptomics describes a group of technologies that provide up to transcriptome-wide expression profiling while preserving the spatial origin of the data, with many of these technologies able to provide these data using a single tissue section. Spatial transcriptomics allows expression profiling of highly specific areas within a tissue section potentially to subcellular resolution, and allows correlation of expression data with morphology, tissue type and location relative to other structures. While largely still research laboratory-based, several spatial transcriptomics methods have now achieved compatibility with formalin-fixed paraffin-embedded tissue (FFPE), allowing their use in diagnostic tissue samples, and with further development potentially leading to their incorporation in routine anatomical pathology practice. This mini review provides an overview of spatial transcriptomics methods, with an emphasis on platforms compatible with FFPE tissue, approaches to assess the data and potential applications in anatomical pathology practice.

An alternative splicing signature defines the basal-like phenotype and predicts worse clinical outcome in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is characterized by extremely poor prognosis. PDAC presents with molecularly distinct subtypes, with the basal-like one being associated with enhanced chemoresistance. Splicing dysregulation contributes to PDAC; however, its involvement in subtype specification remains elusive. Herein, we uncover a subtype-specific splicing signature associated with prognosis in PDAC and the splicing factor Quaking (QKI) as a determinant of the basal-like signature. Single-cell sequencing analyses highlight QKI as a marker of the basal-like phenotype. QKI represses splicing events associated with the classical subtype while promoting basal-like events associated with shorter survival. QKI favors a plastic, quasi-mesenchymal phenotype that supports migration and chemoresistance in PDAC organoids and cell lines, and its expression is elevated in high-grade primary tumors and metastatic lesions. These studies identify a splicing signature that defines PDAC subtypes and indicate that QKI promotes an undifferentiated, plastic phenotype, which renders PDAC cells chemoresistant and adaptable to environmental changes.

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.

Overcoming Microbiome-Acquired Gemcitabine Resistance in Pancreatic Ductal Adenocarcinoma

Gastrointestinal cancers (GICs) are one of the most recurrent diseases in the world. Among all GICs, pancreatic cancer (PC) is one of the deadliest and continues to disrupt people's lives worldwide. The most frequent pancreatic cancer type is pancreatic ductal adenocarcinoma (PDAC), representing 90 to 95% of all pancreatic malignancies. PC is one of the cancers with the worst prognoses due to its non-specific symptoms that lead to a late diagnosis, but also due to the high resistance it develops to anticancer drugs. Gemcitabine is a standard treatment option for PDAC, however, resistance to this anticancer drug develops very fast. The microbiome was recently classified as a cancer hallmark and has emerged in several studies detailing how it promotes drug resistance. However, this area of study still has seen very little development, and more answers will help in developing personalized medicine. PC is one of the cancers with the highest mortality rates; therefore, it is crucial to explore how the microbiome may mold the response to reference drugs used in PDAC, such as gemcitabine. In this article, we provide a review of what has already been investigated regarding the impact that the microbiome has on the development of PDAC in terms of its effect on the gemcitabine pathway, which may influence the response to gemcitabine. Therapeutic advances in this type of GIC could bring innovative solutions and more effective therapeutic strategies for other types of GIC, such as colorectal cancer (CRC), due to its close relation with the microbiome.

Neoadjuvant therapy for pancreatic cancer

Patients with localized pancreatic ductal adenocarcinoma (PDAC) are best treated with surgical resection of the primary tumour and systemic chemotherapy, which provides considerably longer overall survival (OS) durations than either modality alone. Regardless, most patients will have disease relapse owing to micrometastatic disease. Although currently a matter of some debate, considerable research interest has been focused on the role of neoadjuvant therapy for all forms of resectable PDAC. Whilst adjuvant combination chemotherapy remains the standard of care for patients with resectable PDAC, neoadjuvant chemotherapy seems to improve OS without necessarily increasing the resection rate in those with borderline-resectable disease. Furthermore, around 20% of patients with unresectable non-metastatic PDAC might undergo resection following 4-6 months of induction combination chemotherapy with or without radiotherapy, even in the absence of a clear radiological response, leading to improved OS outcomes in this group. Distinct molecular and biological responses to different types of therapies need to be better understood in order to enable the optimal sequencing of specific treatment modalities to further improve OS. In this Review, we describe current treatment strategies for the various clinical stages of PDAC and discuss developments that are likely to determine the optimal sequence of multimodality therapies by integrating the fundamental clinical and molecular features of the cancer.

The limits of molecular signatures for pancreatic ductal adenocarcinoma subtyping

Molecular signatures have been suggested as biomarkers to classify pancreatic ductal adenocarcinoma (PDAC) into two, three, four or five subtypes. Since the robustness of existing signatures is controversial, we performed a systematic evaluation of four established signatures for PDAC stratification across nine publicly available datasets. Clustering revealed inconsistency of subtypes across independent datasets and in some cases a different number of PDAC subgroups than in the original study, casting doubt on the actual number of existing subtypes. Next, we built sixteen classification models to investigate the ability of the signatures for tumor subtype prediction. The overall classification performance ranged from ∼35% to ∼90% accuracy, suggesting instability of the signatures. Notably, permuted subtypes and random gene sets achieved very similar performance. Cellular decomposition and functional pathway enrichment analysis revealed strong tissue-specificity of the predicted classes. Our study highlights severe limitations and inconsistencies that can be attributed to technical biases in sample preparation and tumor purity, suggesting that PDAC molecular signatures do not generalize across datasets. How stromal heterogeneity and immune compartment interplay in the diverging development of PDAC is still unclear. Therefore, a more mechanistic or a cross-platform multi-omic approach seems necessary to extract more robust and clinically exploitable insights.

Role of drug catabolism, modulation of oncogenic signaling and tumor microenvironment in microbe-mediated pancreatic cancer chemoresistance

Pancreatic ductal adenocarcinoma (PDAC) has one of the highest incidence/death ratios among all neoplasms due to its late diagnosis and dominant chemoresistance. Most PDAC patients present with an advanced disease characterized by a multifactorial, inherent and acquired resistance to current anticancer treatments. This remarkable chemoresistance has been ascribed to several PDAC features including the genetic landscape, metabolic alterations, and a heterogeneous tumor microenvironment that is characterized by dense fibrosis, and a cellular contexture including functionally distinct subclasses of cancer-associated fibroblasts, immune suppressive cells, but also a number of bacteria, shaping a specific tumor microbiome microenvironment. Thus, recent studies prompted the emergence of a new research avenue, by describing the role of the microbiome in gemcitabine resistance, while next-generation-sequencing analyses identified a specific microbiome in different tumors, including PDAC. Functionally, the contribution of these microbes to PDAC chemoresistance is only beginning to be explored. Here we provide an overview of the studies demonstrating that bacteria have the capacity to metabolically transform and hence inactivate anticancer drugs, as exemplified by the inhibition of the efficacy of 10 out of 30 chemotherapeutics by Escherichia coli. Moreover, a number of bacteria modulate specific oncogenic pathways, such as Fusobacterium nucleatum, affecting autophagy and apoptosis induction by 5-fluorouracil and oxaliplatin. We hypothesize that improved understanding of how chemoresistance is driven by bacteria could enhance the efficacy of current treatments, and discuss the potential of microbiome modulation and targeted therapeutic approaches as well as the need for more reliable models and biomarkers to translate the findings of preclinical/translational research to the clinical setting, and ultimately overcome PDAC chemoresistance, hence improving clinical outcome.

Molecular Pathology of Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is a biologically aggressive malignancy showing a remarkable resistance to existing therapies and is often diagnosed at an advanced stage, leaving only about 15-20% of patients with an option for surgical resection [...].