Molecular Subtypes Of Pancreatic Cancer Research Articles

Integrated chromosomal instability and tumor microbiome redefined prognosis-related subtypes of pancreatic cancer

BackgroundPancreatic cancer is a common malignancy with poor prognosis and limited treatment. Here we aimed to investigate the role of host chromosomal instability (CIN) and tumor microbiome in the prognosis of pancreatic cancer patients. MethodsOne hundred formalin-fixed paraffin-embedded (FFPE) pancreatic cancer samples were collected. DNA extracted from FFPE samples were analyzed by low-coverage whole-genome sequencing (WGS) via a customized bioinformatics workflow named ultrasensitive chromosomal aneuploidy detector. ResultsSamples are tested according to the procedure of ultrasensitive chromosomal aneuploidy detector (UCAD). We excluded 2 samples with failed quality control, 1 patient lost to follow-up and 6 dead in the perioperative period. The final 91 patients were admitted for the following analyses. Thirteen (14.3%) patients with higher CIN score had worse overall survival (OS) than those with lower CIN score. The top 20 microbes in pancreatic cancer samples included 15 species of bacteria and 5 species of viruses. Patients with high human herpesvirus (HHV)-7 and HHV-5 DNA reads exhibited worse OS. Furthermore, we classified 91 patients into 3 subtypes. Patients with higher CIN score (n =13) had the worst prognosis (median OS 6.9 mon); patients with lower CIN score but with HHV-7/5 DNA load (n = 24) had worse prognosis (median OS 10.6 mon); while patients with lower CIN score and HHV-7/5 DNA negative (n = 54) had the best prognosis (median OS 21.1 mon). ConclusionsHigh CIN and HHV-7/5 DNA load were associated with worse survival of pancreatic cancer. The novel molecular subtypes of pancreatic cancer based on CIN and microbiome had prognostic value.

Improving the prognosis of pancreatic cancer: insights from epidemiology, genomic alterations, and therapeutic challenges.

Pancreatic cancer, notorious for its late diagnosis and aggressive progression, poses a substantial challenge owing to scarce treatment alternatives. This review endeavors to furnish a holistic insight into pancreatic cancer, encompassing its epidemiology, genomic characterization, risk factors, diagnosis, therapeutic strategies, and treatment resistance mechanisms. We delve into identifying risk factors, including genetic predisposition and environmental exposures, and explore recent research advancements in precursor lesions and molecular subtypes of pancreatic cancer. Additionally, we highlight the development and application of multi-omics approaches in pancreatic cancer research and discuss the latest combinations of pancreatic cancer biomarkers and their efficacy. We also dissect the primary mechanisms underlying treatment resistance in this malignancy, illustrating the latest therapeutic options and advancements in the field. Conclusively, we accentuate the urgent demand for more extensive research to enhance the prognosis for pancreatic cancer patients.

Abstract 1064: Cancer-specific molecular subtypes of pancreatic cancer reveal clinically relevant molecular dependency

Abstract Introduction: Pancreatic cancer is lethal, showing dismal prognosis and therapeutic resistance. Previous molecular subtypes from genome or transcriptome did not show clinical relevance regarding precision strategy for optimal therapeutic options followed by precise patient classification. This study aims to uncover consensus molecular subtypes from cancer-specific multi-omics data showing clinically relevant therapeutic opportunities. Methods: We performed comprehensive analyses using the dataset from the cancer dependency map (DepMap) project, including cancer-specific molecular characterization with multi-omics data, genome-wide loss-of-function screening using the CRISPR-Cas9 system, and cancer drug sensitivity. The subtype-specific molecular signatures were validated in independent translational cohorts (TCGA-PAAD; n=150, ICGC-PACA-AU; n=461, ICGC-PACA-CA; n=317). Results: Integrative profiling of multi-omics molecular layers (Mutational signature, Copy number alteration, Transcriptome, MicroRNA, Chromatic profile, Proteome, and Metabolome) from pancreatic cancer cell lines (n=59) from the Cancer Cell Line Encyclopedia (CCLE) revealed a total of three cancer-specific molecular subtypes showing distinct tumor biology through all omics layer as well as clinical relevance with unique molecular dependency. Major molecular features of each subtype were reproducible in the validation cohorts. Subtype-specific molecular biomarkers, including mutational signatures and metabolites, were identified. Finally, the target drug with subtype-specific genetic dependency was analyzed to provide a precision strategy according to distinct subtypes’ molecular characterization. Conclusions: Integrative profiling from multi-omics molecular layers revealed precision strategies based on cancer-specific molecular subtypes of pancreatic cancer in terms of tumor classification and discriminative therapeutic opportunities. Prospective translational studies companion with clinical trials based on cancer-specific molecular subtypes is mandatory to establish the precision strategy for managing pancreatic cancer. Citation Format: Sung Hwan Lee, Jiyeon Park, Sunyoung Lee, Ju-Seog Lee. Cancer-specific molecular subtypes of pancreatic cancer reveal clinically relevant molecular dependency [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1064.

HNF1B-driven three-dimensional chromatin structure for molecular classification in pancreatic cancers.

The molecular subtypes of pancreatic cancer (PC), either classical/progenitor-like or basal/squamous-like, are currently a major topic of research because of their direct association with clinical outcomes. Some transcription factors (TFs) have been reported to be associated with these subtypes. However, the mechanisms by which these molecular signatures of PCs are established remain unknown. Epigenetic regulatory processes, supported by dynamic changes in the chromatin structure, are essential for transcriptional profiles. Previously, we reported the importance of open chromatin profiles in the biological features and transcriptional status of PCs. Here, we aimed to analyze the relationships between three-dimensional (3D) genome structures and the molecular subtypes of human PCs using Hi-C analysis. We observed a correlation of the specific elements of 3D genome modules, including compartments, topologically associating domains, and enhancer-promoter loops, with the expression of related genes. We focused on HNF1B, a TF that is implicated in the progenitor subtype. Forced expression of HNF1B in squamous-type PC organoids induced the upregulation and downregulation of genes associated with progenitor and squamous subtypes, respectively. Long-range genomic interactions induced by HNF1B were accompanied by compartment modulation and H3K27ac redistribution. We also found that these HNF1B-induced changes in subtype-related gene expression required an intrinsically disordered region, suggesting a possible involvement of phase separation in compartment modulation. Thus, mapping of 3D structural changes induced by TFs, such as HNF1B, may become a useful resource for further understanding the molecular features of PCs.

Systematic Analysis of Molecular Subtypes Based on the Expression Profile of Immune-Related Genes in Pancreatic Cancer.

Immunotherapy has a good therapeutic effect and provides a new approach for cancer treatment. However, only limited studies have focused on the use of molecular typing to construct an immune characteristic index for gene expression in pancreatic adenocarcinoma (PAAD) and to assess the effectiveness of immunotherapy in patients with PAAD. Clinical follow-up data and gene expression profile of PAAD patients were retrieved from The Cancer Genome Atlas (TCGA) database. Based on 184 immune features, molecular subtypes of pancreatic cancer were found by the "ConsensusClusterPlus" package, and the association between clinical features and immune cell subtype distribution was analysed. In addition, the relationship between the proportion of immune subtypes and the expression of immune checkpoints was analysed. The CIBERSORT algorithm was introduced to evaluate the immune scores of different molecular subtypes. We used the tumor immune dysfunction and exclusion (TIDE) algorithm to assess the potential clinical effect of immunotherapy interventions on single-molecule subtypes. In addition, the oxidative stress index was constructed by linear discriminant analysis DNA (LDA), and weighted correlation network analysis was performed to identify the core module of the index and its characteristic genes. Expression of hub genes was verified by immunohistochemical analysis in an independent PAAD cohort. Pancreatic cancer is divided into three molecular subtypes (IS1, IS2, and IS3), with significant differences in prognosis between multiple cohorts. Expression of immune checkpoint-associated genes was significantly reduced in IS3 and higher in IS1 and IS2, suggesting that the three subgroups have different responsiveness to immunotherapy interventions. The results of the CIBERSORT analysis showed that IS1 exhibited the highest levels of immune infiltration, whereas the results of the TIDE analysis showed that the T-cell dysfunction score of IS1 was higher than that of IS2 and IS3. Furthermore, IS3 was found to be more sensitive to 5-FU and to have a higher immune signature index than IS1 and IS2. Based on WGCNA analysis, 10 potential gene markers were identified, and their expression at the protein level was verified by immunohistochemical analysis. Specific molecular expression patterns in pancreatic cancer can predict the efficacy of immunotherapy and influence the prognosis of patients.

Abstract 2273: Unsupervised clustering of multi-omics molecular layers reveals consensus molecular subtypes showing potential therapeutic opportunities for pancreatic cancer

Abstract Introduction: Pancreatic cancer is a lethal disease showing dismal prognosis and therapeutic resistance. Previous molecular subtypes from genome or transcriptome did not show clinical relevance regarding precision strategy for optimal therapeutic options followed by precise patient classification. This study aims to uncover consensus molecular subtypes from cancer-specific multi-omics data showing clinically relevant therapeutic opportunities. Methods: We performed comprehensive analyses using the dataset from the cancer dependency map (DepMap) project, including cancer-specific molecular characterization with multi-omics data, genome-wide loss-of-function screening using the CRISPR-Cas9 system, and cancer drug sensitivity. The subtype-specific molecular signatures were validated in independent translational cohorts (TCGA-PAAD; n=150, ICGC-PACA-AU; n=461, ICGC-PACA-CA; n=317). Results: Integrative profiling of multi-omics molecular layers (Mutational signature, Copy number alteration, Transcriptome, MicroRNA, Chromatic profile, Proteome, and Metabolome) from pancreatic cancer cell lines (n=59) from the Cancer Cell Line Encyclopedia (CCLE) revealed a total of three cancer-specific molecular subtypes showing distinct tumor biology through all omics layer as well as clinical relevance with unique molecular dependency. Major molecular features of each subtype were reproducible in the validation cohorts. Subtype-specific molecular biomarkers, including mutational signature and metabolites, were identified. Finally, the target drug with subtype-specific genetic dependency was analyzed to provide precision strategy according to distinct subtypes’ molecular characterization. Conclusions: Integrative profiling from multi-omics molecular layers revealed precision strategies based on cancer-specific molecular subtypes of pancreatic cancer in terms of tumor classification and discriminative therapeutic opportunities. Prospective translational studies companion with clinical trials based on cancer-specific molecular subtypes is mandatory to establish the precision strategy for managing pancreatic cancer. Citation Format: Sung Hwan Lee, Jiyeon Park. Unsupervised clustering of multi-omics molecular layers reveals consensus molecular subtypes showing potential therapeutic opportunities for pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2273.

Predictive Biomarkers for a Personalized Approach in Resectable Pancreatic Cancer.

The mainstay treatment for patients with immediate resectable pancreatic cancer remains upfront surgery, which represents the only potentially curative strategy. Nevertheless, the majority of patients surgically resected for pancreatic cancer experiences disease relapse, even when a combination adjuvant therapy is offered. Therefore, aiming at improving disease free survival and overall survival of these patients, there is an increasing interest in evaluating the activity and efficacy of neoadjuvant and perioperative treatments. In this view, it is of utmost importance to find biomarkers able to select patients who may benefit from a preoperative therapy rather than upfront surgical resection. Defined genomic alterations and a dynamic inflammatory microenvironment are the major culprits for disease recurrence and resistance to chemotherapeutic treatments in pancreatic cancer patients. Signal transduction pathways or tumor immune microenvironment could predict early recurrence and response to chemotherapy. In the last decade, distinct molecular subtypes of pancreatic cancer have been described, laying the bases to a tailored therapeutic approach, started firstly in the treatment of advanced disease. Patients with homologous repair deficiency, in particular with mutant germline BRCA genes, represent the first subgroup demonstrating to benefit from specific therapies. A fraction of patients with pancreatic cancer could take advantage of genome sequencing with the aim of identifying possible targetable mutations. These genomic driven strategies could be even more relevant in a potentially curative setting. In this review, we outline putative predictive markers that could help in the next future in tailoring the best therapeutic strategy for pancreatic cancer patients with a potentially curable disease.

Sample-Specific Perturbation of Gene Interactions Identifies Pancreatic Cancer Subtypes.

Pancreatic cancer is a highly fatal disease and an increasing common cause of cancer mortality. Mounting evidence now indicates that molecular heterogeneity in pancreatic cancer significantly impacts its clinical features. However, the dynamic nature of gene expression pattern makes it difficult to rely solely on gene expression alterations to estimate disease status. By contrast, biological networks tend to be more stable over time under different situations. In this study, we used a gene interaction network from a new point of view to explore the subtypes of pancreatic cancer based on individual-specific edge perturbations calculated by relative gene expression value. Our study shows that pancreatic cancer patients from the TCGA database could be separated into four subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of pancreatic cancer exhibited substantial heterogeneity in many aspects, including prognosis, phenotypic traits, genetic mutations, the abundance of infiltrating immune cell, and predictive therapeutic efficacy (chemosensitivity and immunotherapy efficacy). The new network-based subtypes were closely related to previous reported molecular subtypes of pancreatic cancer. This work helps us to better understand the heterogeneity and mechanisms of pancreatic cancer from a network perspective.

Integrated profiling of human pancreatic cancer organoids reveals chromatin accessibility features associated with drug sensitivity

Chromatin accessibility plays an essential role in controlling cellular identity and the therapeutic response of human cancers. However, the chromatin accessibility landscape and gene regulatory network of pancreatic cancer are largely uncharacterized. Here, we integrate the chromatin accessibility profiles of 84 pancreatic cancer organoid lines with whole-genome sequencing data, transcriptomic sequencing data and the results of drug sensitivity analysis of 283 epigenetic-related chemicals and 5 chemotherapeutic drugs. We identify distinct transcription factors that distinguish molecular subtypes of pancreatic cancer, predict numerous chromatin accessibility peaks associated with gene regulatory networks, discover regulatory noncoding mutations with potential as cancer drivers, and reveal the chromatin accessibility signatures associated with drug sensitivity. These results not only provide the chromatin accessibility atlas of pancreatic cancer but also suggest a systematic approach to comprehensively understand the gene regulatory network of pancreatic cancer in order to advance diagnosis and potential personalized medicine applications.

Clinical Impact of Molecular Subtyping of Pancreatic Cancer.

Pancreatic ductal adenocarcinoma is a highly lethal malignancy, which has now become the seventh most common cause of cancer death in the world, with the highest mortality rates in Europe and North America. In the past 30 years, there has been some progress in 5-year survival (rates increasing from 2.5 to 10%), but this is still extremely poor compared to all other common cancer types. Targeted therapies for advanced pancreatic cancer based on actionable mutations have been disappointing, with only 3–5% showing even a short clinical benefit. There is, however, a molecular diversity beyond mutations in genes responsible for producing classical canonical signaling pathways. Pancreatic cancer is almost unique in promoting an excess production of other components of the stroma, resulting in a complex tumor microenvironment that contributes to tumor development, progression, and response to treatment. Various transcriptional subtypes have also been described. Most notably, there is a strong alignment between the Classical/Pancreatic progenitor and Quasi-mesenchymal/Basal-like/Squamous subtype signatures of Moffit, Collinson, Bailey, Puleo, and Chan-Seng-Yue, which have potential clinical impact. Sequencing of epithelial cell populations enriched by laser capture microscopy combined with single-cell RNA sequencing has revealed the potential genomic evolution of pancreatic cancer as being a consequence of a gene expression continuum from mixed Basal-like and Classical cell populations within the same tumor, linked to allelic imbalances in mutant KRAS, with metastatic tumors being more copy number-unstable compared to primary tumors. The Basal-like subtype appears more chemoresistant with reduced survival compared to the Classical subtype. Chemotherapy and/or chemoradiation will also enrich the Basal-like subtype. Squamous/Basal-like programs facilitate immune infiltration compared with the Classical-like programs. The immune infiltrates associated with Basal and Classical type cells are distinct, potentially opening the door to differential strategies. Single-cell and spatial transcriptomics will now allow single cell profiling of tumor and resident immune cell populations that may further advance subtyping. Multiple clinical trials have been launched based on transcriptomic response signatures and molecular subtyping including COMPASS, Precision Promise, ESPAC6/7, PREDICT-PACA, and PASS1. We review several approaches to explore the clinical relevance of molecular profiling to provide optimal bench-to-beside translation with clinical impact.

Unsupervised clustering of multi-omics molecular layers reveals consensus molecular subtypes showing potential therapeutic opportunities for pancreatic cancer

Pancreatic cancer is a lethal disease showing dismal prognosis and therapeutic resistance. Previous molecular subtypes from genome or transcriptome did not show clinical relevance regarding precision strategy for optimal therapeutic options followed by precise patient classification. This study aims to uncover consensus molecular subtypes from cancer-specific multi-omics data showing clinically relevant therapeutic opportunities. We performed comprehensive analyses using the dataset from the cancer dependency map (DepMap) project, including cancer-specific molecular characterization with multi-omics data, genome-wide loss-of-function screening using the CRISPR-Cas9 system, and cancer drug sensitivity. The subtype-specific molecular signatures were validated in independent translational cohorts (TCGA-PAAD; n = 150, ICGC-PACA-AU; n = 461, ICGC-PACA-CA; n = 317). Integrative profiling of multi-omics molecular layers (Mutational signature, Copy number alteration, Transcriptome, MicroRNA, Chromatic profile, Proteome, and Metabolome) from pancreatic cancer cell lines (n = 59) from the Cancer Cell Line Encyclopedia (CCLE) revealed a total of three cancer-specific molecular subtypes showing distinct tumor biology through all omics layer as well as clinical relevance with unique molecular dependency. Major molecular features of each subtype were reproducible in the validation cohorts. Subtype-specific molecular biomarkers, including mutational signature and metabolites, were identified. Finally, the target drug with subtype-specific genetic dependency was analyzed to provide precision strategy according to distinct subtypes' molecular characterization. Integrative profiling from multi-omics molecular layers revealed precision strategies based on cancer-specific molecular subtypes of pancreatic cancer in terms of tumor classification and discriminative therapeutic opportunities. Prospective translational studies companion with clinical trials based on cancer-specific molecular subtypes is mandatory to establish the precision strategy for managing pancreatic cancer.

Molecular Subtypes of Pancreatic Cancer: A Proteomics Approach.

Through molecular subtyping, therapeutic vulnerabilities can be exploited for developing personalized medicine. The real utility of molecular subtyping lies in the clinical translation for disease characterization. Proteomic methods with defined marker sets hold great promise for improving therapy outcomes by improving the speed of predictions for devising treatment strategies.See related article by Son et al., p. 3370.

Genomic analysis of pancreatic cancer reveals 3 molecular subtypes with different clinical outcomes.

Pancreatic cancer has a very high mortality with a 5-year survival of <5%. The purpose of this study was to classify specific molecular subtypes associated with prognosis of pancreatic cancer using The Cancer Genome Atlas (TCGA) multiplatform genomic data.Multiplatform genomic data (N = 178), including gene expression, copy number alteration, and somatic mutation data, were obtained from cancer browser (https://genome-cancer.ucsc.edu, cohort: TCGA Pancreatic Cancer). Clinical data including survival results were analyzed. We also used validation cohort (GSE50827) to confirm the robustness of these molecular subtypes in pancreatic cancer.When we performed unsupervised clustering using TCGA gene expression data, we found three distinct molecular subtypes associated with different survival results. Copy number alteration and somatic mutation data showed different genomic patterns for these three subtypes. Ingenuity pathway analysis revealed that each subtype showed differentially altered pathways. Using each subtype-specific genes (200 were selected), we could predict molecular subtype in another cohort, confirming the robustness of these molecular subtypes of pancreatic cancer. Cox regression analysis revealed that molecular subtype is the only significant prognostic factor for pancreatic cancer (P = .042, 95% confidence interval 0.523–0.98).Genomic analysis of pancreatic cancer revealed 3 distinct molecular subtypes associated with different survival results. Using these subtype-specific genes and prediction model, we could predict molecular subtype associated with prognosis of pancreatic cancer.

The Match between Molecular Subtypes, Histology and Microenvironment of Pancreatic Cancer and Its Relevance for Chemoresistance.

Simple SummaryBased on the four molecular subtypes of pancreatic cancer described by Bailey et al. 2016, in the present article we match the molecular, histology and microenvironment features of pancreatic cancer. This approach may help to understand the molecular basis of this kind of tumor, and how their microenvironment may affect treatment response. Moreover, we compile information about crucial factors that could serve as potential targets for drug design to achieve higher anti-tumor activity, and how histological evaluation of tumor microenvironment could provide first signs about treatment response.In the last decade, several studies based on whole transcriptomic and genomic analyses of pancreatic tumors and their stroma have come to light to supplement histopathological stratification of pancreatic cancers with a molecular point-of-view. Three main molecular studies: Collisson et al. 2011, Moffitt et al. 2015 and Bailey et al. 2016 have found specific gene signatures, which identify different molecular subtypes of pancreatic cancer and provide a comprehensive stratification for both a personalized treatment or to identify potential druggable targets. However, the routine clinical management of pancreatic cancer does not consider a broad molecular analysis of each patient, due probably to the lack of target therapies for this tumor. Therefore, the current treatment decision is taken based on patients´ clinicopathological features and performance status. Histopathological evaluation of tumor samples could reveal many other attributes not only from tumor cells but also from their microenvironment specially about the presence of pancreatic stellate cells, regulatory T cells, tumor-associated macrophages, myeloid derived suppressor cells and extracellular matrix structure. In the present article, we revise the four molecular subtypes proposed by Bailey et al. and associate each subtype with other reported molecular subtypes. Moreover, we provide for each subtype a potential description of the tumor microenvironment that may influence treatment response according to the gene expression profile, the mutational landscape and their associated histology.

Molecular Subtyping and Precision Medicine for Pancreatic Cancer.

Substantial progress in recent years has dramatically increased our knowledge of the molecular basis of cancer, revealing new potential therapeutic targets and paving the way for effective personalised medicine for the treatment of many tumour types. However, pancreatic cancer has been lagging behind in this success and continues to be one of the most lethal solid malignancies. Its molecular heterogeneity and the unselected design of the majority of clinical trials to date can in part explain the reason for our failure to make a significant change in the survival outcomes for patients with pancreatic cancer. A changing paradigm in drug development is required to validate the new molecular taxonomy and to rapidly translate preclinical discovery into clinical trials. Here, we review the molecular subtyping of pancreatic cancer, the challenges in identifying effective treatment regimens according to defined low-prevalence molecular subgroups and we illustrate a new model of translational therapeutic development that was established in the U.K. (Precision-Panc) as a potentially effective solution to improve outcomes for patients with pancreatic cancer.

Intraductal Transplantation Models of Human Pancreatic Ductal Adenocarcinoma Reveal Progressive Transition of Molecular Subtypes.

Pancreatic ductal adenocarcinoma (PDAC) is the most lethal common malignancy, with little improvement in patient outcomes over the past decades. Recently, subtypes of pancreatic cancer with different prognoses have been elaborated; however, the inability to model these subtypes has precluded mechanistic investigation of their origins. Here, we present a xenotransplantation model of PDAC in which neoplasms originate from patient-derived organoids injected directly into murine pancreatic ducts. Our model enables distinction of the two main PDAC subtypes: intraepithelial neoplasms from this model progress in an indolent or invasive manner representing the classical or basal-like subtypes of PDAC, respectively. Parameters that influence PDAC subtype specification in this intraductal model include cell plasticity and hyperactivation of the RAS pathway. Finally, through intratumoral dissection and the direct manipulation of RAS gene dosage, we identify a suite of RAS-regulated secreted and membrane-bound proteins that may represent potential candidates for therapeutic intervention in patients with PDAC. SIGNIFICANCE: Accurate modeling of the molecular subtypes of pancreatic cancer is crucial to facilitate the generation of effective therapies. We report the development of an intraductal organoid transplantation model of pancreatic cancer that models the progressive switching of subtypes, and identify stochastic and RAS-driven mechanisms that determine subtype specification.See related commentary by Pickering and Morton, p. 1448.This article is highlighted in the In This Issue feature, p. 1426.

Current research status and progress in molecular subty-ping of pancreatic cancer

Rational molecular subtyping of pancreatic cancer based on genome, transcriptome, proteomics and metabolomics data, in combination with systematic biological analysis, have greatly enriched the traditional histopathological typing methods based on morphology. The introduction of molecular subtyping reflects the progress in deep understanding of the essence of tumorigenesis of pancreatic cancer, providing a necessary foundation for the development of molecular targeted therapy and implementation of precision medicine. Therefore, molecular subtyping of pancreatic cancer has broad application prospects. The current article reviews the current research status and recent progress of molecular subtyping of pancreatic cancer, and discusses the clinical significance of different subtyping methods. Key words: Pancreatic neoplasms; Pancreatic can-cer; Molecular subtypes; Molecular targeted therapy; Progress

Molecular subtypes of pancreatic cancer.

Cancers that appear morphologically similar often have dramatically different clinical features, respond variably to therapy and have a range of outcomes. Compelling evidence now demonstrates that differences in the molecular pathology of otherwise indistinguishable cancers substantially impact the clinical characteristics of the disease. Molecular subtypes now guide preclinical and clinical therapeutic development and treatment in many cancer types. The ability to predict optimal therapeutic strategies ahead of treatment improves overall patient outcomes, minimizing treatment-related morbidity and cost. Although clinical decision making based on histopathological criteria underpinned by robust data is well established in many cancer types, subtypes of pancreatic cancer do not currently inform treatment decisions. However, accumulating molecular data are defining subgroups in pancreatic cancer with distinct biology and potential subtype-specific therapeutic vulnerabilities, providing the opportunity to define a de novo clinically applicable molecular taxonomy. This Review summarizes current knowledge concerning the molecular subtyping of pancreatic cancer and explores future strategies for using a molecular taxonomy to guide therapeutic development and ultimately routine therapy with the overall goal of improving outcomes for this disease.

Defining DNA damage repair deficiency and replication stress in pancreatic cancer.

285 Background: Integrated multi-omic analyses revealed 24% of pancreatic cancer (PC) harbor defects in DNA damage response (DDR) and a subgroup demonstrate upregulation in replication stress pathways. DDR defective tumors preferentially respond to DNA damaging agents, and clinical responses to cell cycle inhibitors are seen in undefined subgroups, representing novel therapeutic strategies for PC. The aim of this study is to define and refine therapeutic segments for agents targeting DDR and replication stress in PC. Methods: We performed whole genome and RNA sequencing (RNAseq) on 48 patient-derived cell lines (PDCL) generated and characterized as part of the International Cancer Genome Initiative (ICGC). This identified increased replication stress in a sub-group of tumours, correlating with previously defined molecular subtypes of PC, irrespective of DDR status. Cytotoxic viability assays were performed using agents targeting the DDR pathway and cell cycle checkpoints, including Cisplatin, and inhibitors of PARP, ATR, WEE1, CHK1, CDK4/6 and PLK4. Subcutaneous patient derived xenografts (PDX) were generated to test therapeutic regimens in vivo. Results: DDR defective models, as defined by signatures of homologous recombination deficiency (HRD) were highly sensitive to Cisplatin and PARP inhibitors. Replication stress predicted differential responses to cell cycle inhibitors of WEE1, CHK1, CDK4/6 and PLK4. A novel mRNA signature of ATR inhibitor sensitivity was generated and correlated with response. Response to cell cycle checkpoint inhibitors were independent of DDR status, but strongly associated with replication stress. Conclusions: This proof of concept data demonstrates DDR deficiency and increased Replication Stress to be attractive targets in PC. Therapeutic vulnerabilities extend beyond platinum chemotherapy and can be targeted with novel small molecule inhibitors, with independent biomarkers predicting response to agents targeting either DDR or cell cycle checkpoints. This has led to the design and development of several personalized medicine trials via the Precision Panc platform targeting DDR and Replication stress, and will allow clinical testing of signatures of HRD and replication stress.

Molecular subtype specific efficacy of MEK inhibitors in pancreatic cancers.

Pancreatic cancer is an increasing cause of cancer related death worldwide. KRAS is the dominant oncogene in this cancer type and molecular rationale would indicate, that inhibitors of the downstream target MEK could be appropriate targeted agents, but clinical trials have failed so far to achieve statistically significant benefit in unselected patients. We aimed to identify predictive molecular biomarkers that can help to define subgroups where MEK inhibitors might be beneficial alone or in combination. Next-generation sequencing data of 50 genes in three pancreatic cancer cell lines (MiaPaCa2, BxPC3 and Panc1) were analyzed and compared to the molecular profile of 138 clinical pancreatic cancer samples to identify the molecular subtypes of pancreatic cancer these cell lines represent. Luminescent cell viability assay was used to determine the sensitivity of cell lines to kinase inhibitors. Western blot was used to analyze the pathway activity of the examined cell lines. According to our cell viability and pathway activity data on these model cell lines only cells harboring the rare G12C KRAS mutation and low EGFR expression are sensitive to single MEK inhibitor (trametinib) treatment. The common G12D KRAS mutation leads to elevated baseline Akt activity, thus treatment with single MEK inhibitors fails. However, combination of MEK and Akt inhibitors are synergistic in this case. In case of wild-type KRAS and high EGFR expression MEK inhibitor induced Akt phosphorylation leads to trametinib resistance which necessitates for MEK and EGFR or Akt inhibitor combination treatment. In all we provide strong preclinical rational and possible molecular mechanism to revisit MEK inhibitor therapy in pancreatic cancer in both monotherapy and combination, based on molecular profile analysis of pancreatic cancer samples and cell lines. According to our most remarkable finding, a small subgroup of patients with G12C KRAS mutation may still benefit from MEK inhibitor monotherapy.