CYP3A5 mediates basal and acquired therapy resistance in different subtypes of pancreatic ductal adenocarcinoma

Dual and triple gene combinations of KRT5, KRT17, and S100A2 identify basal-like subtype of pancreatic ductal adenocarcinoma and correlate with survival outcome

There is a significant difference in prognosis and response to chemotherapy between basal and classical subtypes of pancreatic ductal adenocarcinoma (PDAC). Further biomarkers are required to identify subtypes of PDAC. We selected candidate biomarkers via review articles. Correlations between these candidate markers and the PDAC molecular subtype gene sets were analyzed using bioinformatics, confirming the biomarkers for identifying classical and basal subtypes. Subsequently, 298 PDAC patients were included, and their tumor tissues were immunohistochemically stratified using these biomarkers. Survival data underwent analysis, including Cox proportional hazards modeling. Our results indicate that the pairwise and triple combinations of KRT5/KRT17/S100A2 exhibit a higher correlation coefficient with the basal-like subtype gene set, whereas the corresponding combinations of GATA6/HNF4A/TFF1 show a higher correlation with the classical subtype gene set. Whether analyzing unmatched or propensity-matched data, the overall survival time was significantly shorter for the basal subtype compared with the classical subtype (p < .001), with basal subtype patients also facing a higher risk of mortality (HR = 4.017, 95% CI 2.675-6.032, p < .001). In conclusion, the combined expression of KRT5, KRT17, and S100A2, in both pairwise and triple combinations, independently predicts shorter overall survival in PDAC patients and likely identifies the basal subtype. Similarly, the combined expression of GATA6, HNF4A, and TFF1, in the same manner, may indicate the classical subtype. In our study, the combined application of established biomarkers offers valuable insights for the prognostic evaluation of PDAC patients.

A Novel AMPK Inhibitor Sensitizes Pancreatic Cancer Cells to Ferroptosis Induction

Cancer cells must develop strategies to adapt to the dynamically changing stresses caused by intrinsic or extrinsic processes, or therapeutic agents. Metabolic adaptability is crucial to mitigate such challenges. Considering metabolism as a central node of adaptability, it is focused on an energy sensor, the AMP-activated protein kinase (AMPK). In a subtype of pancreatic ductal adenocarcinoma (PDAC) elevated AMPK expression and phosphorylation is identified. Using drug repurposing that combined screening experiments and chemoproteomic affinity profiling, it is identified and characterized PF-3758309, initially developed as an inhibitor of PAK4, as an AMPK inhibitor. PF-3758309 shows activity in pre-clinical PDAC models, including primary patient-derived organoids. Genetic loss-of-function experiments showed that AMPK limits the induction of ferroptosis, and consequently, PF-3758309 treatment restores the sensitivity toward ferroptosis inducers. The work established a chemical scaffold for the development of specific AMPK-targeting compounds and deciphered the framework for the development of AMPK inhibitor-based combination therapies tailored for PDAC.

Pancreatic cancer organoid-screening captures personalized sensitivity and chemoresistance suppression upon cytochrome P450 3A5-targeted inhibition

Cytochrome P450 3A5 (CYP3A5) has been proposed as a predictor of therapy response in subtypes of pancreatic ductal adenocarcinoma cancer (PDAC). To validate CYP3A5 as a therapeutic target, we developed a high-content image organoid-based screen to quantify the phenotypic responses to the selective inhibition of CYP3A5 enzymatic activity by clobetasol propionate (CBZ), using a cohort of PDAC-derived organoids (PDACOs). The chemoresistance of PDACOs to a panel of standard-of-care drugs, alone or in combination with CBZ, was investigated. PDACO pharmaco-profiling revealed CBZ to have anti-cancer activity that was dependent on the CYP3A5 level. In addition, CBZ restored chemo-vulnerability to cisplatin in a subset of PDACOs. A correlative proteomic analysis established that CBZ caused the suppression of multiple cancer pathways sustained by or associated with a mutant form of p53. Limiting the active pool of CYP3A5 enables targeted and personalized therapy to suppress pro-oncogenic mechanisms that fuel chemoresistance in some PDAC tumors.

Transcriptomic subtyping of gastrointestinal malignancies

Gastrointestinal (GI) cancers are highly heterogeneous at multiple levels. Tumor heterogeneity can be captured by molecular profiling, such as genetic, epigenetic, proteomic, and transcriptomic classification. Transcriptomic subtyping has the advantage of combining genetic and epigenetic information, cancer cell-intrinsic properties, and the tumor microenvironment (TME). Unsupervised transcriptomic subtyping systems of different GI malignancies have gained interest because they reveal shared biological features across cancers and bear prognostic and predictive value. Importantly, transcriptomic subtypes accurately reflect complex phenotypic states varying not only per tumor region, but also throughout disease progression, with consequences for clinical management. Here, we discuss methodologies of transcriptomic subtyping, proposed taxonomies for GI malignancies, and the challenges posed to clinical implementation, highlighting opportunities for future transcriptomic profiling efforts to optimize clinical impact.

Stromal Rigidity Stress Accelerates Pancreatic Intraepithelial Neoplasia Progression and Chromosomal Instability via Nuclear Protein Tyrosine Kinase 2 Localization

Because the mechanotransduction by stromal stiffness stimulates the rupture and repair of the nuclear envelope in pancreatic progenitor cells, accumulated genomic aberrations are under selection in the tumor microenvironment. Analysis of cell growth, micronuclei, and phosphorylated Ser-139 residue of the histone variant H2AX (γH2AX) foci linked to mechanotransduction pressure in vivo during serial orthotopic passages of mouse KrasLSL-G12D/+;Trp53flox/flox;Pdx1-Cre (KPC) cancer cells in the tumor and in migrating through the size-restricted 3-μm micropores. To search for pancreatic cancer cell-of-origin, analysis of single-cell data sets revealed that the extracellular matrix shaped an alternate route of acinar-ductal transdifferentiation of acinar cells into topoisomerase II α (TOP2A)-overexpressing cancer cells and derived subclusters with copy number amplifications in MYC-PTK2 (protein tyrosine kinase 2) locus and PIK3CA. High-PTK2 expression is associated with 171 differentially methylated CpG loci, 319 differentially expressed genes, and poor overall survival in The Cancer Genome Atlas-Pancreatic Adenocarcinoma cohort. Abolished RGD-integrin signaling by disintegrin KG blocked the PTK2 phosphorylation, increased cancer apoptosis, decreased vav guanine nucleotide exchange factor 1 (VAV1) expression, and prolonged overall survival in the KPC mice. Reduction of α-smooth muscle actin deposition in the CD248 knockout KPC mice remodeled the tissue stroma and down-regulated TOP2A expression in the epithelium. In summary, stromal stiffness induced the onset of cancer cells-of-origin by ectopic TOP2A expression, and the genomic amplification of MYC-PTK2 locus via alternative transdifferentiation of pancreatic progenitor cells is the vulnerability useful for disintegrin KG treatment.

CYP3A5 unexpectedly regulates glucose metabolism through the AKT-TXNIP-GLUT1 axis in pancreatic cancer

CYP3A5 is a cytochrome P450 (CYP) enzyme that metabolizes drugs and contributes to drug resistance in cancer. However, it remains unclear whether CYP3A5 directly influences cancer progression. In this report, we demonstrate that CYP3A5 regulates glucose metabolism in pancreatic ductal adenocarcinoma. Multi-omics analysis showed that CYP3A5 knockdown results in a decrease in various glucose-related metabolites through its effect on glucose transport. A mechanistic study revealed that CYP3A5 enriches the glucose transporter GLUT1 at the plasma membrane by restricting the translation of TXNIP, a negative regulator of GLUT1. Notably, CYP3A5-generated reactive oxygen species were proved to be responsible for attenuating the AKT-4EBP1-TXNIP signaling pathway. CYP3A5 contributes to cell migration by maintaining high glucose uptake in pancreatic cancer. Taken together, our results, for the first time, reveal a role of CYP3A5 in glucose metabolism in pancreatic ductal adenocarcinoma and identify a novel mechanism that is a potential therapeutic target.

Subtype-WGME enables whole-genome-wide multi-omics cancer subtyping

We present an innovative strategy for integrating whole-genome-wide multi-omics data, which facilitates adaptive amalgamation by leveraging hidden layer features derived from high-dimensional omics data through a multi-task encoder. Empirical evaluations on eight benchmark cancer datasets substantiated that our proposed framework outstripped the comparative algorithms in cancer subtyping, delivering superior subtyping outcomes. Building upon these subtyping results, we establish a robust pipeline for identifying whole-genome-wide biomarkers, unearthing 195 significant biomarkers. Furthermore, we conduct an exhaustive analysis to assess the importance of each omic and non-coding region features at the whole-genome-wide level during cancer subtyping. Our investigation shows that both omics and non-coding region features substantially impact cancer development and survival prognosis. This study emphasizes the potential and practical implications of integrating genome-wide data in cancer research, demonstrating the potency of comprehensive genomic characterization. Additionally, our findings offer insightful perspectives for multi-omics analysis employing deep learning methodologies.

KRT81 and HNF1A expression in pancreatic ductal adenocarcinoma: investigation of predictive and prognostic value of immunohistochemistry-based subtyping

Even after decades of research, pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal disease and responses to conventional treatments remain mostly poor. Subclassification of PDAC into distinct biological subtypes has been proposed by various groups to further improve patient outcome and reduce unnecessary side effects. Recently, an immunohistochemistry (IHC)-based subtyping method using cytokeratin-81 (KRT81) and hepatocyte nuclear factor 1A (HNF1A) could recapitulate some of the previously established molecular subtyping methods, while providing significant prognostic and, to a limited degree, also predictive information. We refined the KRT81/HNF1A subtyping method to classify PDAC into three distinct biological subtypes. The prognostic value of the IHC-based method was investigated in two primary resected cohorts, which include 269 and 286 patients, respectively. In the second cohort, we also assessed the predictive effect for response to erlotinib + gemcitabine. In both PDAC cohorts, the new HNF1A-positive subtype was associated with the best survival, the KRT81-positive subtype with the worst, and the double-negative with an intermediate survival (p < 0.001 and p < 0.001, respectively) in univariate and multivariate analyses. In the second cohort (CONKO-005), the IHC-based subtype was additionally found to have a potential predictive value for the erlotinib-based treatment effect. The revised IHC-based subtyping using KRT81 and HNF1A has prognostic significance for PDAC patients and may be of value in predicting treatment response to specific therapeutic agents.

Characterization of CYP3A5 Selective Inhibitors for Reaction Phenotyping of Drug Candidates

CYP3A is one of the most important classes of enzymes and is involved in the metabolism of over 70% drugs. While several selective CYP3A4 inhibitors have been identified, the search for a selective CYP3A5 inhibitor has turned out to be rather challenging. Recently, several selective CYP3A5 inhibitors have been identified through high-throughput screening of ~ 11,000 compounds and hit expansion using human recombinant enzymes. We set forth to characterize the three most selective CYP3A5 inhibitors in a more physiologically relevant system of human liver microsomes to understand if these inhibitors can be used for reaction phenotyping studies in drug discovery settings. Gomisin A and T-5 were used as selective substrate reactions for CYP3A4 and CYP3A5 to determine IC50 values of the two enzymes. The results showed that clobetasol propionate and loteprednol etabonate were potent and selective CYP3A5 reversible inhibitors with selectivity of 24-fold against CYP3A4 and 39-fold or more against the other major CYPs. The selectivity of difluprednate in HLM is much weaker than that in the recombinant enzymes due to hydrolysis of the acetate group in HLM. Based on the selectivity data, loteprednol etabonate can be utilized as an orthogonal approach, when experimental fraction metabolized of CYP3A5 is greater than 0.5, to understand CYP3A5 contribution to drug metabolism and its clinical significance. Future endeavors to identify even more selective CYP3A5 inhibitors are warranted to enable accurate determination of CYP3A5 contribution to metabolism versus CYP3A4.

Regulation of PXR in drug metabolism: chemical and structural perspectives

INTRODUCTION

Pregnane X receptor (PXR) is a master xenobiotic sensor that transcriptionally controls drug metabolism and disposition pathways. PXR activation by pharmaceutical drugs, natural products, environmental toxins, etc. may decrease drug efficacy and increase drug-drug interactions and drug toxicity, indicating a therapeutic value for PXR antagonists. However, PXR's functions in physiological events, such as intestinal inflammation, indicate that PXR activators may be useful in certain disease contexts.

AREAS COVERED

We review the reported roles of PXR in various physiological and pathological processes including drug metabolism, cancer, inflammation, energy metabolism, and endobiotic homeostasis. We then highlight specific cellular and chemical routes that modulate PXR activity and discuss the functional consequences. Databases searched and inclusive dates: PubMed, 1 January 1980 to 10 January 2024.

EXPERT OPINION

Knowledge of PXR's drug metabolism function has helped drug developers produce small molecules without PXR-mediated metabolic liabilities, and further understanding of PXR's cellular functions may offer drug development opportunities in multiple disease settings.

Research progress regarding CYP3A gene family in gastric cancer

Cytochrome P450 family 3 subfamily A (CYP3A), a major member of cytochrome P450 (CYP) family, is one of the most important drug metabolizing enzymes in human. CYP3A includes 4 gene subtypes (CYP3A4, CYP3A5, CYP3A7, and CYP3A43), which is involved in 60% of drug metabolism in the human. It is not only widely distributed in normal tissues, but also significantly overexpressed in various tumor tissues. Recently, CYP3A has attracted great attention due to its involvement in the progression from chronic atrophic gastritis to gastric cancer, as well as the differential metabolism and resistance of chemotherapeutic drugs. Targeting CYP3A gene mediated-prodrug provides new ideas for the treatment of gastric cancer and is expected to become a new target for the diagnosis and treatment of gastric cancer.

Alteration in the expression of the chemotherapy resistance‑related genes in response to chronic and acute hypoxia in pancreatic cancer

Pancreatic cancer is currently one of the least curable types of human cancer and remains a key health problem. One of the most important characteristics of pancreatic cancer is its ability to grow under hypoxic conditions. Hypoxia is associated with resistance of cancer cells to radiotherapy and chemotherapy. It is a major contributor to pancreatic cancer genetic instability, which local and systemic resistance that may result in poor clinical outcome. Accordingly, identifying gene expression changes in cancer resistance genes that occur under hypoxic conditions may identify a new therapeutic target. The aim of the present study was to explore the association between hypoxia and resistance to chemotherapy and determine the alteration in the expression of cancer resistance-related genes in the presence of hypoxia. Pancreatic cancer cells (PANC-1) were exposed to 8 h hypoxic episodes (<1% oxygen) three times/week for a total of 20 episodes (chronic hypoxia) or 72 h hypoxic episodes twice/week for a total of 10 episodes (acute hypoxia). The alterations in gene expression were examined using reverse transcription-quantitative PCR array compared with normoxic cells. Chemoresistance of hypoxic cells toward doxorubicin was assessed using MTT cell proliferation assay. Both chronic and acute hypoxia induced chemoresistance toward doxorubicin in PANC-1 pancreatic cancer cell line. The greatest changes occurred in estrogen Receptor Alpha Gene (ESR1) and ETS Like-1 protein (ELK1) pathways, in nucleic transcription factor Peroxisome proliferator-activated receptors (PPARs) and in a cell cycle inhibitor cyclin dependent kinase inhibitor 1A (CDKN1A). The present study demonstrated that exposing cells to prolonged hypoxia results in different gene expression changes involving pleotropic pathways that serve a role in inducing resistance in pancreatic cancer.

Protocol of the IntenSify-Trial: An open-label phase I trial of the CYP3A inhibitor cobicistat and the cytostatics gemcitabine and nab-paclitaxel in patients with advanced stage or metastatic pancreatic ductal adenocarcinoma to evaluate the combination's pharmacokinetics, safety, and efficacy

Expression of CYP3A5 protein is a basal and acquired resistance mechanism of pancreatic ductal adenocarcinoma cells conferring protection against the CYP3A and CYP2C8 substrate paclitaxel through metabolic degradation. Inhibition of CYP3A isozymes restores the cells sensitivity to paclitaxel. The combination of gemcitabine and nab-paclitaxel is an established regimen for the treatment of metastasized or locally advanced inoperable pancreatic cancer. Cobicistat is a CYP3A inhibitor developed for the pharmacoenhancement of protease inhibitors. The addition of cobicistat to gemcitabine and nab-paclitaxel may increase the antitumor effect. We will conduct a phase I dose escalation trial with a classical 3 + 3 design to investigate the safety, tolerability, and pharmacokinetics (PKs) of gemcitabine, nab-paclitaxel, and cobicistat. Although the doses of gemcitabine (1000 mg/m2 ) and cobicistat (150 mg) are fixed, three dose levels of nab-paclitaxel (75, 100, and 125 mg/m2 ) will be explored to account for a potential PK drug interaction. After the dose escalation phase, we will set the recommended dose for expansion (RDE) and treat up to nine patients in an expansion part of the trial. The trial is registered under the following identifiers EudraCT-Nr. 2019-001439-29, drks.de: DRKS00029409, and ct.gov: NCT05494866. Overcoming resistance to paclitaxel by CYP3A5 inhibition may lead to an increased efficacy of the gemcitabine and nab-paclitaxel regimen. Safety, efficacy, PK, and RDE data need to be acquired before investigating this combination in a large-scale clinical study.

The F-box-only protein 44 regulates pregnane X receptor protein level by ubiquitination and degradation

Pregnane X receptor (PXR) is a ligand-activated nuclear receptor that transcriptionally upregulates drug-metabolizing enzymes [e.g., cytochrome P450 3A4 (CYP3A4)] and transporters. Although the regulation of PXR target genes is well-characterized, less is known about the regulation of PXR protein level. By screening an RNAi library, we identified the F-box-only protein 44 (FBXO44) as a novel E3 ligase for PXR. PXR abundance increases upon knockdown of FBXO44, and, inversely, decreases upon overexpression of FBXO44. Further analysis revealed that FBXO44 interacts with PXR, leading to its ubiquitination and proteasomal degradation, and we determined that the F-box associated domain of FBXO44 and the ligand binding domain of PXR are required for the functional interaction. In summary, FBXO44 regulates PXR protein abundance, which has downstream consequences for CYP3A4 levels and drug-drug interactions. The results of this study provide new insight into the molecular mechanisms that regulate PXR protein level and activity and suggest the importance of considering how modulating E3 ubiquitin ligase activities will affect PXR-mediated drug metabolism.

Persister cell phenotypes contribute to poor patient outcomes after neoadjuvant chemotherapy in PDAC

Neoadjuvant chemotherapy can improve the survival of individuals with borderline and unresectable pancreatic ductal adenocarcinoma; however, heterogeneous responses to chemotherapy remain a significant clinical challenge. Here, we performed RNA sequencing (n = 97) and multiplexed immunofluorescence (n = 122) on chemo-naive and postchemotherapy (post-CTX) resected patient samples (chemoradiotherapy excluded) to define the impact of neoadjuvant chemotherapy. Transcriptome analysis combined with high-resolution mapping of whole-tissue sections identified GATA6 (classical), KRT17 (basal-like) and cytochrome P450 3A (CYP3A) coexpressing cells that were preferentially enriched in post-CTX resected samples. The persistence of GATA6hi and KRT17hi cells post-CTX was significantly associated with poor survival after mFOLFIRINOX (mFFX), but not gemcitabine (GEM), treatment. Analysis of organoid models derived from chemo-naive and post-CTX samples demonstrated that CYP3A expression is a predictor of chemotherapy response and that CYP3A-expressing drug detoxification pathways can metabolize the prodrug irinotecan, a constituent of mFFX. These findings identify CYP3A-expressing drug-tolerant cell phenotypes in residual disease that may ultimately inform adjuvant treatment selection.

Bile Acids and Microbiota Interplay in Pancreatic Cancer

Evidence suggests the involvement of the microbiota, including oral, intra-tumoral and gut, in pancreatic cancer progression and response to therapy. The gut microbiota modulates the bile acid pool and is associated with maintaining host physiology. Studies have shown that the bile acid/gut microbiota axis is dysregulated in pancreatic cancer. Bile acid receptor expression and bile acid levels are dysregulated in pancreatic cancer as well. Studies have also shown that bile acids can cause pancreatic cell injury and facilitate cancer cell proliferation. The microbiota and its metabolites, including bile acids, are also altered in other conditions considered risk factors for pancreatic cancer development and can alter responses to chemotherapeutic treatments, thus affecting patient outcomes. Altogether, these findings suggest that the gut microbial and/or bile acid profiles could also serve as biomarkers for pancreatic cancer detection. This review will discuss the current knowledge on the interaction between gut microbiota interaction and bile acid metabolism in pancreatic cancer.

Urokinase-Type Plasminogen Activator Receptor (uPAR) Cooperates with Mutated KRAS in Regulating Cellular Plasticity and Gemcitabine Response in Pancreatic Adenocarcinomas

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal cancers. Given the currently limited therapeutic options, the definition of molecular subgroups with the development of tailored therapies remains the most promising strategy. Patients with high-level gene amplification of urokinase plasminogen activator receptor (uPAR/PLAUR) have an inferior prognosis. We analyzed the uPAR function in PDAC to understand this understudied PDAC subgroup's biology better.

METHODS

A total of 67 PDAC samples with clinical follow-up and TCGA gene expression data from 316 patients were used for prognostic correlations. Gene silencing by CRISPR/Cas9, as well as transfection of uPAR and mutated KRAS, were used in PDAC cell lines (AsPC-1, PANC-1, BxPC3) treated with gemcitabine to study the impact of these two molecules on cellular function and chemoresponse. HNF1A and KRT81 were surrogate markers for the exocrine-like and quasi-mesenchymal subgroup of PDAC, respectively.

RESULTS

High levels of uPAR were correlated with significantly shorter survival in PDAC, especially in the subgroup of HNF1A-positive exocrine-like tumors. uPAR knockout by CRISPR/Cas9 resulted in activation of FAK, CDC42, and p38, upregulation of epithelial makers, decreased cell growth and motility, and resistance against gemcitabine that could be reversed by re-expression of uPAR. Silencing of KRAS in AsPC1 using siRNAs reduced uPAR levels significantly, and transfection of mutated KRAS in BxPC-3 cells rendered the cell more mesenchymal and increased sensitivity towards gemcitabine.

CONCLUSIONS

Activation of uPAR is a potent negative prognostic factor in PDAC. uPAR and KRAS cooperate in switching the tumor from a dormant epithelial to an active mesenchymal state, which likely explains the poor prognosis of PDAC with high uPAR. At the same time, the active mesenchymal state is more vulnerable to gemcitabine. Strategies targeting either KRAS or uPAR should consider this potential tumor-escape mechanism.

Complex glandular pattern is an aggressive morphology that predicts poor prognosis of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignant neoplasm with various morphologies. Recognition of histological patterns that can predict prognosis is important in pathological examination. Recently, the complex glandular pattern was defined as a morphology associating the poor prognosis in lung adenocarcinoma. We investigated the significance of the complex glandular pattern in PDAC by performing a retrospective analysis. Among 240 consecutive cases of conventional PDACs, 21 cases in which complex glandular pattern constituted >50 % of the total tumor volume (CG-PDACs) were identified. The prevalence of CG-PDAC was 8.8 % among all preoperative therapy-naïve and surgically resected conventional PDACs. Compared to the control PDACs (n = 95), the CG-PDACs were characterized by significantly higher prevalence of small- to medium-sized artery invasion (71.4 % vs. 14.7 %, p < 0.0001), intratumoral necrosis (59.1 % vs. 16.8 %, p < 0.0001), tumor budding (mean: 15.5 vs. 12.5 per 0.785 mm², p = 0.04), significantly higher Ki67 proliferative index (mean: 75.0 % vs. 54.7 %, p < 0.0001), and the HNF1α-/KRT81+ (quasi-mesenchymal) immunophenotype (42.9 % vs. 19.0 %, p = 0.004). In Kaplan-Meier analyses, the CG-PDAC patients achieved significantly worse disease-free survival (DFS) and overall survival (OS) compared to the control PDAC patients; the respective median DFS and OS were 6.3 and 17.7 months for CG-PDACs, and 22.6 and 52.8 months for control PDACs. A multivariate Cox regression analysis showed that predominance of complex glandular pattern was an independent prognostic factor (hazard ratio: 2.95; 95 % confidence interval: 1.46-5.98; p = 0.003). Our results provide new insights into the complex glandular pattern in conventional PDACs as a novel and potentially useful prognostic factor.

Therapy resistance mechanisms in hematological malignancies

Hematologic malignancies are model diseases for understanding neoplastic transformation and serve as prototypes for developing effective therapies. Indeed, the concept of systemic cancer therapy originated in hematologic malignancies and has guided the development of chemotherapy, cellular therapies, immunotherapy and modern precision oncology. Despite significant advances in the treatment of leukemias, lymphomas and multiple myelomas, treatment resistance associated with molecular and clinical relapse remains very common. Therapy of relapsed and refractory disease remains extremely difficult, and failure of disease control at this stage remains the leading cause of mortality in patients with hematologic malignancies. In recent years, many efforts have been made to identify the genetic and epigenetic mechanisms that drive the development of hematologic malignancies to the stage of full-blown disease requiring clinical intervention. In contrast, the mechanisms responsible for treatment resistance in hematologic malignancies remain poorly understood. For example, the molecular characteristics of therapy-resistant persisting cells in minimal residual disease (MRD) remain rather elusive. In this mini-review we want to discuss that cellular heterogeneity and plasticity, together with adaptive genetic and epigenetic processes, lead to reduced sensitivity to various treatment regimens such as chemotherapy and pathway inhibitors such as tyrosine kinase inhibitors. However, resistance mechanisms may be conserved across biologically distinct cancer entities. Recent technological advances have made it possible to explore the underlying mechanisms of therapy resistance with unprecedented resolution and depth. These include novel multi-omics technologies with single cell resolution combined with advanced biocomputational approaches, along with artificial intelligence (AI) and sophisticated disease models for functional validation.

Dysregulation of HNF1B/Clusterin axis enhances disease progression in a highly aggressive subset of pancreatic cancer patients

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy and is largely refractory to available treatments. Identifying key pathways associated with disease aggressiveness and therapeutic resistance may characterize candidate targets to improve patient outcomes. We used a strategy of examining the tumors from a subset of PDAC patient cohorts with the worst survival to understand the underlying mechanisms of aggressive disease progression and to identify candidate molecular targets with potential therapeutic significance. Non-negative matrix factorization (NMF) clustering, using gene expression profile, revealed three patient subsets. A 142-gene signature specific to the subset with the worst patient survival, predicted prognosis and stratified patients with significantly different survival in the test and validation cohorts. Gene-network and pathway analysis of the 142-gene signature revealed dysregulation of Clusterin (CLU) in the most aggressive patient subset in our patient cohort. Hepatocyte nuclear factor 1 b (HNF1B) positively regulated CLU, and a lower expression of HNF1B and CLU was associated with poor patient survival. Mechanistic and functional analyses revealed that CLU inhibits proliferation, 3D spheroid growth, invasiveness and epithelial-to-mesenchymal transition (EMT) in pancreatic cancer cell lines. Mechanistically, CLU enhanced proteasomal degradation of EMT-regulator, ZEB1. In addition, orthotopic transplant of CLU-expressing pancreatic cancer cells reduced tumor growth in mice. Furthermore, CLU enhanced sensitivity of pancreatic cancer cells representing aggressive patient subset, to the chemotherapeutic drug gemcitabine. Taken together, HNF1B/CLU axis negatively regulates pancreatic cancer progression and may potentially be useful in designing novel strategies to attenuate disease progression in PDAC patients.

Functional noninvasive detection of glycolytic pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) lacks effective treatment options beyond chemotherapy. Although molecular subtypes such as classical and QM (quasi-mesenchymal)/basal-like with transcriptome-based distinct signatures have been identified, deduced therapeutic strategies and targets remain elusive. Gene expression data show enrichment of glycolytic genes in the more aggressive and therapy-resistant QM subtype. However, whether the glycolytic transcripts are translated into functional glycolysis that could further be explored for metabolic targeting in QM subtype is still not known.

METHODS

We used different patient-derived PDAC model systems (conventional and primary patient-derived cells, patient-derived xenografts (PDX), and patient samples) and performed transcriptional and functional metabolic analysis. These included RNAseq and Illumina HT12 bead array, in vitro Seahorse metabolic flux assays and metabolic drug targeting, and in vivo hyperpolarized [1-¹³C]pyruvate and [1-¹³C]lactate magnetic resonance spectroscopy (HP-MRS) in PDAC xenografts.

RESULTS

We found that glycolytic metabolic dependencies are not unambiguously functionally exposed in all QM PDACs. Metabolic analysis demonstrated functional metabolic heterogeneity in patient-derived primary cells and less so in conventional cell lines independent of molecular subtype. Importantly, we observed that the glycolytic product lactate is actively imported into the PDAC cells and used in mitochondrial oxidation in both classical and QM PDAC cells, although more actively in the QM cell lines. By using HP-MRS, we were able to noninvasively identify highly glycolytic PDAC xenografts by detecting the last glycolytic enzymatic step and prominent intra-tumoral [1-¹³C]pyruvate and [1-¹³C]lactate interconversion in vivo.

CONCLUSION

Our study adds functional metabolic phenotyping to transcriptome-based analysis and proposes a functional approach to identify highly glycolytic PDACs as candidates for antimetabolic therapeutic avenues.

Mechanisms of PDAC subtype heterogeneity and therapy response

Pancreatic ductal adenocarcinoma (PDAC) is clinically challenging due to late diagnosis and resistance to therapy. Two major PDAC subtypes have been defined based on malignant epithelial cell gene expression profiles; the basal-like/squamous subtype is associated with a worse prognosis and therapeutic resistance as opposed to the classical subtype. Subtype specification is not binary, consistent with plasticity of malignant cell phenotype. PDAC heterogeneity and plasticity reflect partly malignant cell-intrinsic transcriptional and epigenetic regulation. However, the stromal and immune compartments of the tumor microenvironment (TME) also determine disease progression and therapy response. It is evident that integration of intrinsic and extrinsic factors can dictate subtype heterogeneity, and thus, delineating the pathways involved can help to reprogram PDAC towards a classical/druggable subtype.

PharmVar GeneFocus: CYP3A5

The Pharmacogene Variation Consortium (PharmVar) catalogs star (*) allele nomenclature for the polymorphic human CYP3A5 gene. Genetic variation within the CYP3A5 gene locus impacts the metabolism of several clinically important drugs, including the immunosuppressants tacrolimus, sirolimus, cyclosporine, and the benzodiazepine midazolam. Variable CYP3A5 activity is of clinical importance regarding tacrolimus metabolism. This GeneFocus provides a CYP3A5 gene summary with a focus on aspects regarding standardized nomenclature. In addition, this review also summarizes recent changes and updates, including the retirement of several allelic variants and provides an overview of how PharmVar CYP3A5 star allele nomenclature is utilized by the Pharmacogenomics Knowledgebase (PharmGKB) and the Clinical Pharmacogenetics Implementation Consortium (CPIC).

The molecular landscape of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is predicted to become the second leading cause of cancer-related mortality within the next decade, with limited effective treatment options and a dismal long-term prognosis for patients. Surgical resection of early, localised disease provides the only chance for potentially curative treatment; however, most patients with PDAC present with advanced disease and are not suitable for surgery. Genomic analyses of PDAC tumour lesions have identified a small number of recurrent alterations that are detected across most tumours, and beyond that a large number that either occur at a low (<5%) prevalence or are patient-specific in nature. This molecular heterogeneity has presented a significant challenge for the characterisation of tumour subtypes and effective molecular biomarkers, which have not yet manifested clinical benefits for diagnosis, treatment or prognosis in PDAC. These challenges are compounded by the overall lack of tumour biopsies for sequencing, the invasive nature of tissue sampling and the confounding effects of low tumour cellularity in many PDAC biopsy specimens, which have limited the applications of molecular profiling in unresectable patients and for longitudinal tumour monitoring. Further investigation into alternative sources of tumour analytes that can be sampled using minimally invasive methods and used to complement molecular analyses from tissue sequencing are required.

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.

Assessing equivalent and inverse change in genes between diverse experiments

Background: It is important to identify when two exposures impact a molecular marker (e.g., a gene's expression) in similar ways, for example, to learn that a new drug has a similar effect to an existing drug. Currently, statistically robust approaches for making comparisons of equivalence of effect sizes obtained from two independently run treatment vs. control comparisons have not been developed. Results: Here, we propose two approaches for evaluating the question of equivalence between effect sizes of two independent studies: a bootstrap test of the Equivalent Change Index (ECI), which we previously developed, and performing Two One-Sided t-Tests (TOST) on the difference in log-fold changes directly. The ECI of a gene is computed by taking the ratio of the effect size estimates obtained from the two different studies, weighted by the maximum of the two p-values and giving it a sign indicating if the effects are in the same or opposite directions, whereas TOST is a test of whether the difference in log-fold changes lies outside a region of equivalence. We used a series of simulation studies to compare the two tests on the basis of sensitivity, specificity, balanced accuracy, and F1-score. We found that TOST is not efficient for identifying equivalently changed gene expression values (F1-score = 0) because it is too conservative, while the ECI bootstrap test shows good performance (F1-score = 0.95). Furthermore, applying the ECI bootstrap test and TOST to publicly available microarray expression data from pancreatic cancer showed that, while TOST was not able to identify any equivalently or inversely changed genes, the ECI bootstrap test identified genes associated with pancreatic cancer. Additionally, when investigating publicly available RNAseq data of smoking vs. vaping, no equivalently changed genes were identified by TOST, but ECI bootstrap test identified genes associated with smoking. Conclusion: A bootstrap test of the ECI is a promising new statistical approach for determining if two diverse studies show similarity in the differential expression of genes and can help to identify genes which are similarly influenced by a specific treatment or exposure. The R package for the ECI bootstrap test is available at https://github.com/Hecate08/ECIbootstrap.

MALDI-MSI: A Powerful Approach to Understand Primary Pancreatic Ductal Adenocarcinoma and Metastases

Cancer-related deaths are very commonly attributed to complications from metastases to neighboring as well as distant organs. Dissociate response in the treatment of pancreatic adenocarcinoma is one of the main causes of low treatment success and low survival rates. This behavior could not be explained by transcriptomics or genomics; however, differences in the composition at the protein level could be observed. We have characterized the proteomic composition of primary pancreatic adenocarcinoma and distant metastasis directly in human tissue samples, utilizing mass spectrometry imaging. The mass spectrometry data was used to train and validate machine learning models that could distinguish both tissue entities with an accuracy above 90%. Model validation on samples from another collection yielded a correct classification of both entities. Tentative identification of the discriminative molecular features showed that collagen fragments (COL1A1, COL1A2, and COL3A1) play a fundamental role in tumor development. From the analysis of the receiver operating characteristic, we could further advance some potential targets, such as histone and histone variations, that could provide a better understanding of tumor development, and consequently, more effective treatments.

Subtyping for pancreatic cancer precision therapy

The cases of pancreatic cancer and associated deaths are increasing consistently and have become a global health concern. Prevalent intratumoral and intertumoral heterogeneity in pancreatic cancer has been revealed as an important cause of its poor prognosis. However, few precision management strategies have been formulated to treat this complex disease. There is growing evidence supporting the significance of subtyping pancreatic tumors on the basis of their molecular characteristics for improving the accuracy of clinical decision-making on treatment. Here, we summarize the current approaches to classification of pancreatic cancer, and highlight the feasibility and potential defects of their application in precision therapy.

Machine-Learning-Based Bibliometric Analysis of Pancreatic Cancer Research Over the Past 25 Years

Machine learning and semantic analysis are computer-based methods to evaluate complex relationships and predict future perspectives. We used these technologies to define recent, current and future topics in pancreatic cancer research. Publications indexed under the Medical Subject Headings (MeSH) term 'Pancreatic Neoplasms' from January 1996 to October 2021 were downloaded from PubMed. Using the statistical computing language R and the interpreted, high-level, general-purpose programming language Python, we extracted publication dates, geographic information, and abstracts from each publication's metadata for bibliometric analyses. The generative statistical algorithm "latent Dirichlet allocation" (LDA) was applied to identify specific research topics and trends. The unsupervised "Louvain algorithm" was used to establish a network to identify relationships between single topics. A total of 60,296 publications were identified and analyzed. The publications were derived from 133 countries, mostly from the Northern Hemisphere. For the term "pancreatic cancer research", 12,058 MeSH terms appeared 1,395,060 times. Among them, we identified the four main topics "Clinical Manifestation and Diagnosis", "Review and Management", "Treatment Studies", and "Basic Research". The number of publications has increased rapidly during the past 25 years. Based on the number of publications, the algorithm predicted that "Immunotherapy", Prognostic research", "Protein expression", "Case reports", "Gemcitabine and mechanism", "Clinical study of gemcitabine", "Operation and postoperation", "Chemotherapy and resection", and "Review and management" as current research topics. To our knowledge, this is the first study on this subject of pancreatic cancer research, which has become possible due to the improvement of algorithms and hardware.

A GATA6-centred gene regulatory network involving HNFs and ΔNp63 controls plasticity and immune escape in pancreatic cancer

OBJECTIVE

Molecular taxonomy of tumours is the foundation of personalised medicine and is becoming of paramount importance for therapeutic purposes. Four transcriptomics-based classification systems of pancreatic ductal adenocarcinoma (PDAC) exist, which consistently identified a subtype of highly aggressive PDACs with basal-like features, including ΔNp63 expression and loss of the epithelial master regulator GATA6. We investigated the precise molecular events driving PDAC progression and the emergence of the basal programme.

DESIGN

We combined the analysis of patient-derived transcriptomics datasets and tissue samples with mechanistic experiments using a novel dual-recombinase mouse model for Gata6 deletion at late stages of KRasG12D-driven pancreatic tumorigenesis (Gata6LateKO).

RESULTS

This comprehensive human-to-mouse approach showed that GATA6 loss is necessary, but not sufficient, for the expression of ΔNp63 and the basal programme in patients and in mice. The concomitant loss of HNF1A and HNF4A, likely through epigenetic silencing, is required for the full phenotype switch. Moreover, Gata6 deletion in mice dramatically increased the metastatic rate, with a propensity for lung metastases. Through RNA-Seq analysis of primary cells isolated from mouse tumours, we show that Gata6 inhibits tumour cell plasticity and immune evasion, consistent with patient-derived data, suggesting that GATA6 works as a barrier for acquiring the fully developed basal and metastatic phenotype.

CONCLUSIONS

Our work provides both a mechanistic molecular link between the basal phenotype and metastasis and a valuable preclinical tool to investigate the most aggressive subtype of PDAC. These data, therefore, are important for understanding the pathobiological features underlying the heterogeneity of pancreatic cancer in both mice and human.

Single-cell Transcriptomic Architecture Unraveling the Complexity of Tumor Heterogeneity in Distal Cholangiocarcinoma

BACKGROUND & AIMS

Distal cholangiocarcinoma (dCCA) are a group of epithelial cell malignancies that occurs at the distal common bile duct, and account for approximately 40% of all cholangiocarcinoma cases. dCCA remains a highly lethal disease as it typically features remarkable cellular heterogeneity. A comprehensive exploration of cellular diversity and the tumor microenvironment is essential to depict the mechanisms driving dCCA progression.

METHODS

Single-cell RNA sequencing was used here to dissect the heterogeneity landscape and tumor microenvironment composition of human dCCAs. Seven human dCCAs and adjacent normal bile duct samples were included in the current study for single-cell RNA sequencing and subsequent validation approaches. Additionally, the results of the analyses were compared with bulk transcriptomic datasets from extrahepatic cholangiocarcinoma and single-cell RNA data from intrahepatic cholangiocarcinoma.

RESULTS

We sequenced a total of 49,717 single cells derived from human dCCAs and adjacent tissues, identifying 11 distinct cell types. Malignant cells displayed remarkable inter- and intra-tumor heterogeneity with 5 distinct subsets were defined in tumor samples. The malignant cells displayed variable degree of aneuploidy, which can be classified into low- and high-copy number variation groups based on either amplification or deletion of chr17q12 - chr17q21.2. Additionally, we identified 4 distinct T lymphocytes subsets, of which cytotoxic CD8+ T cells predominated as effectors in tumor tissues, whereas tumor infiltrating FOXP3+ CD4+ regulatory T cells exhibited highly immunosuppressive characteristics.

CONCLUSION

Our single-cell transcriptomic dataset depicts the inter- and intra-tumor heterogeneity of human dCCAs at the expression level.

Prognosis and tumor biology of pancreatic cancer patients with isolated lung metastases: translational results from the German multicenter AIO-YMO-PAK-0515 study

Background

Pulmonary metastasis (M1-PUL) as first site of dissemination in pancreatic ductal adenocarcinoma (PDAC) is a rare event and may define a distinct biological subgroup.

Patients and methods

Arbeitsgemeinschaft Internistische Onkologie-Young Medical Oncologists-Pankreas-0515 study (AIO-YMO-PAK-0515) was a retrospective German multicenter study investigating clinical and molecular characteristics of M1-PUL PDAC patients; 115 M1-PUL PDAC patients from 7 participating centers were included. Clinical characteristics and potential prognostic factors were defined within the M1-PUL cohort. Archival tumor samples were analyzed for Her2/neu, HNF1A and KRT81 expression. Additionally, messenger RNA (mRNA) expression analysis (using a 770-gene immune profiling panel) was carried out in the M1-PUL and in a control cohort (M1-ANY).

Results

Median overall survival in the entire M1-PUL cohort was 20 months; the most favorable prognosis (median survival: 28 months) was observed in the subgroup of 66 PDAC patients with metachronous lung metastases after previous curative-intent surgery. The number of metastatic lesions, uni- or bilateral lung involvement as well as metastasectomy were identified as potential prognostic factors. Her2/neu expression and PDAC subtyping (by HNF1A and KRT81) did not differ between the M1-PUL and the M1-ANY cohort. mRNA expression analysis revealed significant differentially expressed genes between both cohorts: CD63 and LAMP1 were among the top 20 differentially expressed genes and were identified as potential mediators of organotropism and favorable survival outcome of M1-PUL patients.

Conclusion

M1-PUL represents a clinically favorable cohort in PDAC patients. Site of relapse might already be predetermined at the time of surgery and could potentially be predicted by gene expression profiling.

•

The retrospective multicenter AIO-YMO-PAK-0515 study defines M1-PUL as a clinically favorable subgroup in PDAC.

•

The number of metastatic lesions, bilateral lung involvement and surgical metastasectomy may serve as prognostic factors.

•

Immune-related gene expression differs between patients with isolated pulmonary relapse versus other sites of relapse.

Metabolic reprogramming by driver mutation-tumor microenvironment interplay in pancreatic cancer: new therapeutic targets

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers globally with a mortality rate exceeding 95% and very limited therapeutic options. A hallmark of PDAC is its acidic tumor microenvironment, further characterized by excessive fibrosis and depletion of oxygen and nutrients due to poor vascularity. The combination of PDAC driver mutations and adaptation to this hostile environment drives extensive metabolic reprogramming of the cancer cells toward non-canonical metabolic pathways and increases reliance on scavenging mechanisms such as autophagy and macropinocytosis. In addition, the cancer cells benefit from metabolic crosstalk with nonmalignant cells within the tumor microenvironment, including pancreatic stellate cells, fibroblasts, and endothelial and immune cells. Increasing evidence shows that this metabolic rewiring is closely related to chemo- and radioresistance and immunosuppression, causing extensive treatment failure. Indeed, stratification of human PDAC tumors into subtypes based on their metabolic profiles was shown to predict disease outcome. Accordingly, an increasing number of clinical trials target pro-tumorigenic metabolic pathways, either as stand-alone treatment or in conjunction with chemotherapy. In this review, we highlight key findings and potential future directions of pancreatic cancer metabolism research, specifically focusing on novel therapeutic opportunities.

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.

Unraveling the Structural Basis of Selective Inhibition of Human Cytochrome P450 3A5

The human cytochrome P450 (CYP) CYP3A4 and CYP3A5 enzymes metabolize more than one-half of marketed drugs. They share high structural and substrate similarity and are often studied together as CYP3A4/5. However, CYP3A5 preferentially metabolizes several clinically prescribed drugs, such as tacrolimus. Genetic polymorphism in CYP3A5 makes race-based dosing adjustment of tacrolimus necessary to minimize acute rejection after organ transplantation. Moreover, the differential tissue distribution and expression levels of CYP3A4 and CYP3A5 can aggravate toxicity during treatment. Therefore, selective inhibitors of CYP3A5 are needed to distinguish the role of CYP3A5 from that of CYP3A4 and serve as starting points for potential therapeutic development. To this end, we report the crystal structure of CYP3A5 in complex with a previously reported selective inhibitor, clobetasol propionate (CBZ). This is the first CYP3A5 structure with a type I inhibitor, which along with the previously reported substrate-free and type II inhibitor-bound structures, constitute the main CYP3A5 structural modalities. Supported by structure-guided mutagenesis analyses, the CYP3A5-CBZ structure showed that a unique conformation of the F-F' loop in CYP3A5 enables selective binding of CBZ to CYP3A5. Several polar interactions, including hydrogen bonds, stabilize the position of CBZ to interact with this unique F-F' loop conformation. In addition, functional and biophysical assays using CBZ analogs highlight the importance of heme-adjacent moieties for selective CYP3A5 inhibition. Our findings can be used to guide further development of more potent and selective CYP3A5 inhibitors.

TNF-α-producing macrophages determine subtype identity and prognosis via AP1 enhancer reprogramming in pancreatic cancer

Large-scale genomic profiling of pancreatic cancer (PDAC) has revealed two distinct subtypes: 'classical' and 'basal-like'. Their variable coexistence within the stromal immune microenvironment is linked to differential prognosis; however, the extent to which these neoplastic subtypes shape the stromal immune landscape and impact clinical outcome remains unclear. By combining preclinical models, patient-derived xenografts, as well as FACS-sorted PDAC patient biopsies, we show that the basal-like neoplastic state is sustained via BRD4-mediated cJUN/AP1 expression, which induces CCL2 to recruit tumor necrosis factor (TNF)-α-secreting macrophages. TNF-α+ macrophages force classical neoplastic cells into an aggressive phenotypic state via lineage reprogramming. Integration of ATAC-, ChIP- and RNA-seq data revealed distinct JUNB/AP1 (classical) and cJUN/AP1 (basal-like)-driven regulation of PDAC subtype identity. Pharmacological inhibition of BRD4 led to suppression of the BRD4-cJUN-CCL2-TNF-α axis, restoration of classical subtype identity and a favorable prognosis. Hence, patient-tailored therapy for a cJUNhigh/TNF-αhigh subtype is paramount in overcoming highly inflamed and aggressive PDAC states.

Rationale for MYC imaging and targeting in pancreatic cancer

The incidence and lethality of pancreatic ductal adenocarcinoma (PDAC) will continue to increase in the next decade. For most patients, chemotherapeutic combination therapies remain the standard of care. The development and successful implementation of precision oncology in other gastrointestinal tumor entities point to opportunities also for PDAC. Therefore, markers linked to specific therapeutic responses and important subgroups of the disease are needed. The MYC oncogene is a relevant driver in PDAC and is linked to drug resistance and sensitivity. Here, we update recent insights into MYC biology in PDAC, summarize the connections between MYC and drug responses, and point to an opportunity to image MYC non-invasively. In sum, we propose MYC-associated biology as a basis for the development of concepts for precision oncology in PDAC.

The Diverse Applications of Pancreatic Ductal Adenocarcinoma Organoids

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal solid malignancies. While immortalized cancer cell lines and genetically engineered murine models have increased our understanding of PDAC tumorigenesis, they do not recapitulate inter- and intra-patient heterogeneity. PDAC patient derived organoid (PDO) biobanks have overcome this hurdle, and provide an opportunity for the high throughput screening of potential new therapies. This review provides a summary of the PDAC PDO biobanks established to date, and discusses how they have advanced our understanding of PDAC biology. Looking forward, the development of coculturing techniques for specific immune or stromal cell populations will enable a better understanding of the crosstalk that occurs within the tumor microenvironment, and the impact of this crosstalk on treatment response.

Proteogenomic analysis of pancreatic cancer subtypes

Pancreatic cancer remains a significant public health problem with an ever-rising incidence of disease. Cancers of the pancreas are characterised by various molecular aberrations, including changes in the proteomics and genomics landscape of the tumour cells. Therefore, there is a need to identify the proteomic landscape of pancreatic cancer and the specific genomic and molecular alterations associated with disease subtypes. Here, we carry out an integrative bioinformatics analysis of The Cancer Genome Atlas dataset, including proteomics and whole-exome sequencing data collected from pancreatic cancer patients. We apply unsupervised clustering on the proteomics dataset to reveal the two distinct subtypes of pancreatic cancer. Using functional and pathway analysis based on the proteomics data, we demonstrate the different molecular processes and signalling aberrations of the pancreatic cancer subtypes. In addition, we explore the clinical characteristics of these subtypes to show differences in disease outcome. Using datasets of mutations and copy number alterations, we show that various signalling pathways previously associated with pancreatic cancer are altered among both subtypes of pancreatic tumours, including the Wnt pathway, Notch pathway and PI3K-mTOR pathways. Altogether, we reveal the proteogenomic landscape of pancreatic cancer subtypes and the altered molecular processes that can be leveraged to devise more effective treatments.

Genomic Heterogeneity of Pancreatic Ductal Adenocarcinoma and Its Clinical Impact

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer death due to limited advances in recent years in early diagnosis and personalized therapy capable of overcoming tumor resistance to chemotherapy. In the last decades, significant advances have been achieved in the identification of recurrent genetic and molecular alterations of PDAC including those involving the KRAS, CDKN2A, SMAD4, and TP53 driver genes. Despite these common genetic traits, PDAC are highly heterogeneous tumors at both the inter- and intra-tumoral genomic level, which might contribute to distinct tumor behavior and response to therapy, with variable patient outcomes. Despite this, genetic and genomic data on PDAC has had a limited impact on the clinical management of patients. Integration of genomic data for classification of PDAC into clinically defined entities-i.e., classical vs. squamous subtypes of PDAC-leading to different treatment approaches has the potential for significantly improving patient outcomes. In this review, we summarize current knowledge about the most relevant genomic subtypes of PDAC including the impact of distinct patterns of intra-tumoral genomic heterogeneity on the classification and clinical and therapeutic management of PDAC.

Temporal multi-omics identifies LRG1 as a vascular niche instructor of metastasis

Metastasis is the primary cause of cancer-related mortality. Tumor cell interactions with cells of the vessel wall are decisive and potentially rate-limiting for metastasis. The molecular nature of this cross-talk is, beyond candidate gene approaches, hitherto poorly understood. Using endothelial cell (EC) bulk and single-cell transcriptomics in combination with serum proteomics, we traced the evolution of the metastatic vascular niche in surgical models of lung metastasis. Temporal multiomics revealed that primary tumors systemically reprogram the body’s vascular endothelium to perturb homeostasis and to precondition the vascular niche for metastatic growth. The vasculature with its enormous surface thereby serves as amplifier of tumor-induced instructive signals. Comparative analysis of lung EC gene expression and secretome identified the transforming growth factor–β (TGFβ) pathway specifier LRG1, leucine-rich alpha-2-glycoprotein 1, as an early instructor of metastasis. In the presence of a primary tumor, ECs systemically up-regulated LRG1 in a signal transducer and activator of transcription 3 (STAT3)–dependent manner. A meta-analysis of retrospective clinical studies revealed a corresponding up-regulation of LRG1 concentrations in the serum of patients with cancer. Functionally, systemic up-regulation of LRG1 promoted metastasis in mice by increasing the number of prometastatic neural/glial antigen 2 (NG2)+ perivascular cells. In turn, genetic deletion of Lrg1 hampered growth of lung metastasis. Postsurgical adjuvant administration of an LRG1-neutralizing antibody delayed metastatic growth and increased overall survival. This study has established a systems map of early primary tumor-induced vascular changes and identified LRG1 as a therapeutic target for metastasis.

Identification of Novel Metabolism-Associated Subtypes for Pancreatic Cancer to Establish an Eighteen-Gene Risk Prediction Model

Pancreatic cancer (PanC) is an intractable malignancy with a high mortality. Metabolic processes contribute to cancer progression and therapeutic responses, and histopathological subtypes are insufficient for determining prognosis and treatment strategies. In this study, PanC subtypes based on metabolism-related genes were identified and further utilized to construct a prognostic model. Using a cohort of 171 patients from The Cancer Genome Atlas (TCGA) database, transcriptome data, simple nucleotide variants (SNV), and clinical information were analyzed. We divided patients with PanC into metabolic gene-enriched and metabolic gene-desert subtypes. The metabolic gene-enriched subgroup is a high-risk subtype with worse outcomes and a higher frequency of SNVs, especially in KRAS. After further characterizing the subtypes, we constructed a risk score algorithm involving multiple genes (i.e., NEU2, GMPS, PRIM2, PNPT1, LDHA, INPP4B, DPYD, PYGL, CA12, DHRS9, SULT1E1, ENPP2, PDE1C, TPH1, CHST12, POLR3GL, DNMT3A, and PGS1). We verified the reproducibility and reliability of the risk score using three validation cohorts (i.e., independent datasets from TCGA, Gene Expression Omnibus, and Ensemble databases). Finally, drug prediction was completed using a ridge regression model, yielding nine candidate drugs for high-risk patients. These findings support the classification of PanC into two metabolic subtypes and further suggest that the metabolic gene-enriched subgroup is associated with worse outcomes. The newly established risk model for prognosis and therapeutic responses may improve outcomes in patients with PanC.

A glycosyltransferase gene signature to detect pancreatic ductal adenocarcinoma patients with poor prognosis

Background

Pancreatic ductal adenocarcinoma (PDAC) is characterized by an important heterogeneity, reflected by different clinical outcomes and chemoresistance. During carcinogenesis, tumor cells display aberrant glycosylated structures, synthetized by deregulated glycosyltransferases, supporting the tumor progression. In this study, we aimed to determine whether PDAC could be stratified through their glycosyltransferase expression profiles better than the current binary classification (basal-like and classical) in order to improve detection of patients with poor prognosis.

Methods

Bioinformatic analysis of 169 glycosyltransferase RNA sequencing data were performed for 74 patient-derived xenografts (PDX) of resected and unresectable tumors. The Australian cohort of International Cancer Genome Consortium and the microarray dataset from Puleo patient's cohort were used as independent validation datasets.

Findings

New PDAC stratification based on glycosyltransferase expression profile allowed to distinguish different groups of patients with distinct clinical outcome (p-value = 0.007). A combination of 19 glycosyltransferases differentially expressed in PDX defined a glyco-signature, whose prognostic value was validated on datasets including resected whole tumor tissues. The glyco-signature was able to discriminate three clusters of PDAC patients on the validation cohorts, two clusters displaying a short overall survival compared to one cluster having a better prognosis. Both poor prognostic clusters having different glyco-profiles in Puleo patient's cohort were correlated with stroma activated or desmoplastic subtypes corresponding to distinct microenvironment features (p-value < 0.0001). Besides, differential expression and enrichment analyses revealed deregulated functional pathways specific to different clusters.

Interpretation

This study identifies a glyco-signature relevant for a prognostic use, potentially applicable to resected and unresectable PDAC. Furthermore, it provides new potential therapeutic targets.

Funding

This work was supported by INCa (Grants number 2018-078 and 2018-079), Fondation ARC (Grant number ARCPJA32020070002326), Cancéropôle PACA, DGOS (labelization SIRIC, Grant number 6038), Amidex Foundation and Ligue Nationale Contre le Cancer and by institutional fundings from INSERM and the Aix-Marseille Université.

[Ductal adenocarcinoma of the pancreas: subtypes and molecular pathology]

Ductal adenocarcinoma is the most common tumor of the pancreas. Although relatively rare, it poses one of the greatest oncological challenges because of its poor prognosis, which has so far only slightly improved. Progress has been made in the more precise classification and standardization of the morphological assessment. In the current WHO classification, prognostically relevant subtypes are clearly delimited among themselves and from ductal adenocarcinoma not otherwise specified (NOS). In the recent TNM classification, a size-based T‑category was introduced. Diagnostically, the histological assessment of the resection specimen is relatively easy; on the other hand, assessment of the fine-needle biopsy from the primary tumor or a liver metastasis is still difficult. The molecular stratification of ductal adenocarcinoma and the other pancreatic neoplasms has made great progress. This not only defined the genetics of tumor entities, but also identified the prognosis and biology of tumor groups on the basis of RNA expression patterns. The range of treatment could be expanded by targeted molecular therapies (especially for patients with BRCA1/2 germline mutations, NTRK- or NRG1-fusions, or oncogenic BRAF and PIK3CA mutations as well as tumors with microsatellite instability (MSI)), even if targeted therapies are currently only available for a minority of patients (<10%).

Ginsenoside Rg3 increases gemcitabine sensitivity of pancreatic adenocarcinoma via reducing ZFP91 mediated TSPYL2 destabilization

Background

Ginsenoside Rg3 and gemcitabine have mutual enhancing antitumor effects. However, the underlying mechanisms are not clear. This study explored the influence of ginsenoside Rg3 on Zinc finger protein 91 homolog (ZFP91) expression in pancreatic adenocarcinoma (PAAD) and their regulatory mechanisms on gemcitabine sensitivity.

Methods

RNA-seq and survival data from The Cancer Genome Atlas (TCGA)-PAAD and Genotype-Tissue Expression (GTEx) were used for in-silicon analysis. PANC-1, BxPC-3, and PANC-1 gemcitabine-resistant (PANC-1/GR) cells were used for in vitro analysis. PANC-1 derived tumor xenograft nude mice model was used to assess the influence of ginsenoside Rg3 and ZFP91 on tumor growth in vivo.

Results

Ginsenoside Rg3 reduced ZFP91 expression in PAAD cells in a dose-dependent manner. ZFP91 upregulation was associated with significantly shorter survival of patients with PAAD. ZFP91 overexpression induced gemcitabine resistance, which was partly conquered by ginsenoside Rg3 treatment. ZFP91 depletion sensitized PANC-1/GR cells to gemcitabine treatment. ZFP91 interacted with Testis-Specific Y-Encoded-Like Protein 2 (TSPYL2), induced its poly-ubiquitination, and promoted proteasomal degradation. Ginsenoside Rg3 treatment weakened ZFP91-induced TSPYL2 poly-ubiquitination and degradation. Enforced TSPYL2 expression increased gemcitabine sensitivity of PAAD cells and partly reversed induced gemcitabine resistance in PANC-1/GR cells.

Conclusion

Ginsenoside Rg3 can increase gemcitabine sensitivity of pancreatic adenocarcinoma at least via reducing ZFP91 mediated TSPYL2 destabilization.

Combined tumor epithelial and stromal histopathology with keratin 81 expression predicts prognosis for pancreatic ductal adenocarcinoma

BACKGROUND

Development of a pragmatic pathologic classifier of pancreatic ductal adenocarcinoma (PDAC) that reflects biological behavior is needed.

METHODS

The tumor epithelial and stromal features of PDAC and molecular subtype-related markers were evaluated in three independent cohorts.

RESULTS

In the non-neoadjuvant therapy cohort (n = 108), regarding tumor-epithelial feature, non-gland-forming type showed worse prognosis compared to gland-forming type (P < .001). For tumor-stromal feature, in gland-forming type, the prognosis was good in order of inactivated stroma-rich, stroma-poor, and activated stroma-rich (P = .027). Whereas, non-gland-forming type revealed no difference of prognosis according to tumor stroma. Of molecular subtype-related markers, keratin 81 expression was correlated with non-gland-forming type and poor prognosis (P = .005 and P = .021, respectively). Other markers (HNF1A, c-MET, and p53) showed no significant differences in prognosis. In the neoadjuvant therapy cohort (n = 68), non-gland-forming type was correlated with high residual tumor volume (≥20%) (P < .001) and gland-forming/stroma-poor type was not present. In the next-generation sequencing cohort (n = 55), non-gland-forming type was correlated with a higher number of the KRAS, TP53, CDKN2A, and SMAD4 mutations (P = .038).

CONCLUSION

Combined tumor epithelial and stromal histopathology with keratin 81 expression is suggested to be useful for predicting prognosis of PDAC.

PXR Modulates the Prostate Cancer Cell Response to Afatinib by Regulating the Expression of the Monocarboxylate Transporter SLC16A1

Simple Summary

Many kinase inhibitors have been tested as potential alternatives for the treatment of castration-resistant prostate cancers. However, none of these clinical trials led to drug approval despite interesting responses. Our study reveals that genes involved in drug metabolism and their master regulator PXR (Pregnane X Receptor) could be responsible, at least in part, for these disappointing results as they can modulate tumor cell response to specific kinase inhibitors. We found that stable expression of PXR sensitized prostate cancer cells to erlotinib, dabrafenib, and afatinib, while it rendered cells resistant to dasatinib and had no effect for other inhibitors tested. We also report for the first time that sensitization to afatinib is due to an alteration in drug transport that involves the SLC16A1 monocarboxylate transporter. Together, our results further indicate that PXR might be considered as a biomarker of response to kinase inhibitors in castration-resistant prostate cancers.

Abstract

Resistance to castration is a crucial issue in the treatment of metastatic prostate cancer. Kinase inhibitors (KIs) have been tested as potential alternatives, but none of them are approved yet. KIs are subject of extensive metabolism at both the hepatic and the tumor level. Here, we studied the role of PXR (Pregnane X Receptor), a master regulator of metabolism, in the resistance to KIs in a prostate cancer setting. We confirmed that PXR is expressed in prostate tumors and is more frequently detected in advanced forms of the disease. We showed that stable expression of PXR in 22Rv1 prostate cancer cells conferred a resistance to dasatinib and a higher sensitivity to erlotinib, dabrafenib, and afatinib. Higher sensitivity to afatinib was due to a ~ 2-fold increase in its intracellular accumulation and involved the SLC16A1 transporter as its pharmacological inhibition by BAY-8002 suppressed sensitization of 22Rv1 cells to afatinib and was accompanied with reduced intracellular concentration of the drug. We found that PXR could bind to the SLC16A1 promoter and induced its transcription in the presence of PXR agonists. Together, our results suggest that PXR could be a biomarker of response to kinase inhibitors in castration-resistant prostate cancers.

The recurring features of molecular subtypes in distinct gastrointestinal malignancies-A systematic review

In colorectal cancer (CRC), pancreatic ductal adenocarcinoma (PDAC) and gastric cancer (GC) multiple studies of inter-tumor heterogeneity have identified molecular subtypes, which correlate with clinical features. Our aim was to investigate the attributes of molecular subtypes across three different gastrointestinal cancer types. We performed a systematic search for publications on molecular subtypes or classifications in PDAC and GC and compared the described subtypes with the established consensus molecular subtypes of CRC. Examining the characteristics of subtypes across CRC, PDAC and GC resulted in four categories of subtypes. We describe uniting and distinguishing features within a mesenchymal, an epithelial, an immunogenic and a metabolic and digestive subtype category. We conclude that molecular subtypes of CRC, PDAC and GC display relevant overlap in molecular features and clinical outcomes. This finding encourages quantitative studies on subtypes across different cancer types and could lead to a paradigm shift in future treatment strategies.

The molecular biology of pancreatic adenocarcinoma: translational challenges and clinical perspectives

Pancreatic cancer is an increasingly common cause of cancer mortality with a tight correspondence between disease mortality and incidence. Furthermore, it is usually diagnosed at an advanced stage with a very dismal prognosis. Due to the high heterogeneity, metabolic reprogramming, and dense stromal environment associated with pancreatic cancer, patients benefit little from current conventional therapy. Recent insight into the biology and genetics of pancreatic cancer has supported its molecular classification, thus expanding clinical therapeutic options. In this review, we summarize how the biological features of pancreatic cancer and its metabolic reprogramming as well as the tumor microenvironment regulate its development and progression. We further discuss potential biomarkers for pancreatic cancer diagnosis, prediction, and surveillance based on novel liquid biopsies. We also outline recent advances in defining pancreatic cancer subtypes and subtype-specific therapeutic responses and current preclinical therapeutic models. Finally, we discuss prospects and challenges in the clinical development of pancreatic cancer therapeutics.