Organoids for functional precision medicine in advanced pancreatic cancer.

BACKGROUND: Patient-derived organoids (PDO) are promising tumor avatars that could enable ex vivo drug tests to personalize patients' treatment in the frame of functional precision oncology (FPM). Yet, clinical evidence remain scarce. This study aims to evaluate whether PDO can be implemented in clinical practice to benefit patients with advanced refractory pancreatic adenocarcinoma (PDAC). METHODS: During 2021-2022, 87 patients were prospectively enrolled in an IRB-approved protocol. Inclusion criteria were: histologically-confirmed PDAC, tumor site accessible. A panel of 25 approved antitumor therapies (chemogram) was tested and compared to patient responses to assess PDO predictive values and map the drug sensitivity landscape in PDAC. RESULTS: Fifty-four PDOs were generated from 87 pretreated patients (take-on rate 62%). The main PDO mutations were KRAS (96%), TP53 (88%) and CDKN2A/B (22%), with 91% concordance rate with their tumor of origin. The mean turnaround-time to chemogram was 6.8 weeks. In 91% of cases, ≥1 hit was identified (gemcitabine (n=20/54), docetaxel (n=18/54) and vinorelbine (n=17/54) with a median of 3 hits/patient [range:0-12]). Our cohort included 34 evaluable patients with full clinical follow-up. We report a chemogram sensitivity of 83.3% and specificity of 92.9%. The overall-response rate and progression-free survival were higher when patients received a "hit" treatment as compared to patients that received a "non-hit" drug (as part of routine management). Finally, we leveraged our PDO collection as a platform for drug validation and combo identification. We tested the anti-KRASG12D (MRTX1133), alone or combined, and identified a specific synergy with anti-EGFR therapies in KRASG12D variants. CONCLUSION: We report the largest prospective study aiming at implementing PDO-based FPM and identify very robust predictive values in this clinical setting. In a clinically relevant turnaround-time, we identify putative hits for 91% of patients, providing unexpected potential survival benefits in this very aggressive indication. While this remains to be confirmed in interventional precision oncology trials, PDO collection already provide powerful opportunities for drugs and combinatorial treatment development.

High-resolution and quantitative spatial analysis reveal intra-ductal phenotypic and functional diversification in pancreatic cancer.

A 'classical' and a 'basal-like' subtype of pancreatic cancer have been reported, with differential expression of GATA6 and different dosages of mutant KRAS. We established in situ detection of KRAS point mutations and mRNA panels for the consensus subtypes aiming to project these findings to paraffin-embedded clinical tumour samples for spatial quantitative analysis. We unveiled that, next to inter-patient and intra-patient inter-ductal heterogeneity, intraductal spatial phenotypes exist with anti-correlating expression levels of GATA6 and KRASG12D . The basal-like mRNA panel better captured the basal-like cell states than widely used protein markers. The panels corroborated the co-existence of the classical and basal-like cell states in a single tumour duct with functional diversification, i.e. proliferation and epithelial-to-mesenchymal transition respectively. Mutant KRASG12D detection ascertained an epithelial origin of vimentin-positive cells in the tumour. Uneven spatial distribution of cancer-associated fibroblasts could recreate similar intra-organoid diversification. This extensive heterogeneity with functional cooperation of plastic tumour cells poses extra challenges to therapeutic approaches. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Molecular deciphering of primary liver neuroendocrine neoplasms confirms their distinct existence with foregut-like profile.

Isolated hepatic localizations of neuroendocrine tumors (NETs) are generally considered as metastatic NETs of unknown primary but could correspond to primary hepatic NETs (PHNETs), a poorly explored entity. We aimed to describe the clinicopathological and molecular features of PHNETs and compare them with other primary NETs. We assembled a retrospective cohort of patients managed for hepatic localization of NET without extra-hepatic primary tumor after exhaustive clinical, imaging, and immunohistochemical characterization. We performed whole-exome sequencing with mutational and copy number analysis. Transcriptomic profiles were compared with pancreatic (n = 31), small-bowel (n = 22), and lung (n = 15) NETs using principal component analysis, unsupervised clustering, and gene set enrichment analysis. Among 27 screened patients, 16 had PHNET (solitary tumor in 63%, median size 11 cm, G2 NETs in 81%) following clinical and pathological review. DNA analyses showed 'foregut-like' genomic profiles with frequent alterations in pathways of Fanconi DNA repair (75%), histone modifiers (58%), adherens junctions (58%), and cell cycle control (50%). The most frequently involved genes were KMT2A (58%), ATM (42%), CDH1, CDKN2C, FANCF, and MEN1 (33% each). Transcriptomic analyses showed that PHNETs clustered closer to foregut (pancreatic, lung) NETs than to midgut (small-bowel) NETs, while remaining a distinct entity with a specific profile. Assessment of potentially predictive biomarkers suggested efficacy of treatments usually active in foregut NETs. In conclusion, PHNETs display a foregut-like molecular profile distinct from other types of NETs, with recurrent molecular alterations. Upon exhaustive work-up to exclude an unrecognized primary tumor, PHNETs should not be considered metastatic NETs from an unknown primary. © 2022 The Pathological Society of Great Britain and Ireland.

Periostin- and podoplanin-positive cancer-associated fibroblast subtypes cooperate to shape the inflamed tumor microenvironment in aggressive pancreatic adenocarcinoma.

Cancer-associated fibroblasts (CAFs) are orchestrators of the pancreatic ductal adenocarcinoma (PDAC) microenvironment. Previously we described four CAF subtypes with specific molecular and functional features. Here, we have refined our CAF subtype signatures using RNAseq and immunostaining with the goal of defining bioinformatically the phenotypic stromal and tumor epithelial states associated with CAF diversity. We used primary CAF cultures grown from patient PDAC tumors, human data sets (in-house and public, including single-cell analyses), genetically engineered mouse PDAC tissues, and patient-derived xenografts (PDX) grown in mice. We found that CAF subtype RNAseq signatures correlated with immunostaining. Tumors rich in periostin-positive CAFs were significantly associated with shorter overall survival of patients. Periostin-positive CAFs were characterized by high proliferation and protein synthesis rates and low α-smooth muscle actin expression and were found in peri-/pre-tumoral areas. They were associated with highly cellular tumors and with macrophage infiltrates. Podoplanin-positive CAFs were associated with immune-related signatures and recruitment of dendritic cells. Importantly, we showed that the combination of periostin-positive CAFs and podoplanin-positive CAFs was associated with specific tumor microenvironment features in terms of stromal abundance and immune cell infiltrates. Podoplanin-positive CAFs identified an inflammatory CAF (iCAF)-like subset, whereas periostin-positive CAFs were not correlated with the published myofibroblastic CAF (myCAF)/iCAF classification. Taken together, these results suggest that a periostin-positive CAF is an early, activated CAF, associated with aggressive tumors, whereas a podoplanin-positive CAF is associated with an immune-related phenotype. These two subpopulations cooperate to define specific tumor microenvironment and patient prognosis and are of putative interest for future therapeutic stratification of patients. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Whole-Transcriptome Profiling on Small FFPE Samples: Which Sequencing Kit Should Be Used?

RNA sequencing (RNA-Seq) appears as a great tool with huge clinical potential, particularly in oncology. However, sufficient sample size is often a limiting factor and the vast majority of samples from patients with cancer are formalin-fixed paraffin-embedded (FFPE). To date, several sequencing kits are proposed for FFPE samples yet no comparison on low quantities were performed. To select the most reliable, cost-effective, and relevant RNA-Seq approach, we applied five FFPE-compatible kits (based on 3' capture, exome-capture and ribodepletion approaches) using 8 ng to 400 ng of FFPE-derived RNA and compared them to Nanostring on FFPE samples and to a reference PolyA (Truseq) approach on flash-frozen samples of the same tumors. We compared gene expression correlations and reproducibility. The Smarter Pico V3 ribodepletion approach appeared systematically the most comparable to Nanostring and Truseq (p < 0.001) and was a highly reproducible technique. In comparison with exome-capture and 3' kits, the Smarter appeared more comparable to Truseq (p < 0.001). Overall, our results suggest that the Smarter is the most robust RNA-Seq technique to study small FFPE samples and 3' Lexogen presents an interesting quality−price ratio for samples with less limiting quantities.

Performance of Next-Generation Sequencing for the Detection of Microsatellite Instability in Colorectal Cancer With Deficient DNA Mismatch Repair.

BACKGROUND & AIMS: Next-generation sequencing (NGS) was recently approved by the United States Food and Drug Administration to detect microsatellite instability (MSI) arising from defective mismatch repair (dMMR) in patients with metastatic colorectal cancer (mCRC) before treatment with immune checkpoint inhibitors (ICI). In this study, we aimed to evaluate and improve the performance of NGS to identify MSI in CRC, especially dMMR mCRC treated with ICI. METHODS: CRC samples used in this post hoc study were reassessed centrally for MSI and dMMR status using the reference methods of pentaplex polymerase chain reaction and immunohistochemistry. Whole-exome sequencing (WES) was used to evaluate MSISensor, the Food and Drug Administration-approved and NGS-based method for assessment of MSI. This was performed in (1) a prospective, multicenter cohort of 102 patients with mCRC (C1; 25 dMMR/MSI, 24 treated with ICI) from clinical trials NCT02840604 and NCT033501260, (2) an independent retrospective, multicenter cohort of 113 patients (C2; 25 mCRC, 88 non-mCRC, all dMMR/MSI untreated with ICI), and (3) a publicly available series of 118 patients with CRC from The Cancer Genome Atlas (C3; 51 dMMR/MSI). A new NGS-based algorithm, namely MSICare, was developed. Its performance for assessment of MSI was compared with MSISensor in C1, C2, and C3 at the exome level or after downsampling sequencing data to the MSK-IMPACT gene panel. MSICare was validated in an additional retrospective, multicenter cohort (C4) of 152 patients with new CRC (137 dMMR/MSI) enriched in tumors deficient in MSH6 (n = 35) and PMS2 (n = 9) after targeted sequencing of samples with an optimized set of microsatellite markers (MSIDIAG). RESULTS: At the exome level, MSISensor was highly specific but failed to diagnose MSI in 16% of MSI/dMMR mCRC from C1 (4 of 25; sensitivity, 84%; 95% confidence interval [CI], 63.9%-95.5%), 32% of mCRC (8 of 25; sensitivity, 68%; 95% CI, 46.5%-85.1%), and 9.1% of non-mCRC from C2 (8 of 88; sensitivity, 90.9%; 95% CI, 82.9%-96%), and 9.8% of CRC from C3 (5 of 51; sensitivity, 90.2%; 95% CI, 78.6%-96.7%). Misdiagnosis included 4 mCRCs treated with ICI, of which 3 showed an overall response rate without progression at this date. At the exome level, reevaluation of the MSI genomic signal using MSICare detected 100% of cases with true MSI status among C1 and C2. Further validation of MSICare was obtained in CRC tumors from C3, with 96.1% concordance for MSI status. Whereas misdiagnosis with MSISensor even increased when analyzing downsampled WES data from C1 and C2 with microsatellite markers restricted to the MSK-IMPACT gene panel (sensitivity, 72.5%; 95% CI, 64.2%-79.7%), particularly in the MSH6-deficient setting, MSICare sensitivity and specificity remained optimal (100%). Similar results were obtained with MSICare after targeted NGS of tumors from C4 with the optimized microsatellite panel MSIDIAG (sensitivity, 99.3%; 95% CI, 96%-100%; specificity, 100%). CONCLUSIONS: In contrast to MSISensor, the new MSICare test we propose performs at least as efficiently as the reference method, MSI polymerase chain reaction, to detect MSI in CRC regardless of the defective MMR protein under both WES and targeted NGS conditions. We suggest MSICare may rapidly become a reference method for NGS-based testing of MSI in CRC, especially in mCRC, where accurate MSI status is required before the prescription of ICI.

Immunophenotypic and molecular characterization of pancreatic neuroendocrine tumors producing serotonin.

Serotonin producing pancreatic neuroendocrine tumors (SP-PanNET) account for 0.58-1.4% of all pancreatic neuroendocrine tumors (PanNET). They may present with atypical symptoms, such as acute pancreatitis and are often radiologically characterized by main pancreatic duct dilatation. SP-PanNET are well differentiated neuroendocrine tumors (NET) distinct from classical PanNET by atypical serotonin secretion and abundant dense stroma deposition, like serotonin producing ileal NET leading in some cases to difficulties to reliably distinguish SP-PanNET from ileal NET metastases. The biology and molecular profile of SP-PanNET remain poorly characterized and the cell of origin within the pancreas is unclear. To address these questions, we analyzed a large cohort of SP-PanNET by immunohistochemistry (n = 29; ATRX, DAXX, MENIN, Islet1, PAX6, PDX1, ARX, CDX2), whole genome copy number array (Oncoscan™) and a large NGS panel (NovoPM™) (n = 10), FISH (n = 13) and RNA sequencing (n = 24) together with 21 ileal NET and 29 nonfunctioning PanNET (NF-PanNET). These analyses revealed a unique genomic profile with frequent isolated loss of chromosome 1 (14 cases-61%) and few pathogenic mutations (KMT2C in 2 cases, ARID1A in 1 case). Unsupervised RNAseq-based clustering showed that SP-PanNET were closer to NF-PanNET than ileal NET with an exclusive beta cell-like signature. SP-PanNET showed TGF-ß pathway activation signatures associated with extracellular matrix remodeling and similar signature were reproduced in vitro when pancreatic stellate cells were exposed to serotonin. SP-PanNET immunohistochemical profile resemble that of ileal NET except for PDX1 and PAX6 expression to a lesser extend suggesting that these two markers may be useful to diagnose SP-PanNET. Taken together, this suggests that SP-PanNET are a very specific PanNET entity with a peculiar biology leading to the characteristic fibrotic aspect.

DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification.

BACKGROUND: Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data. RESULTS: We present DECONbench, a standardized unbiased benchmarking resource, applied to the evaluation of computational methods quantifying cell-type heterogeneity in cancer. DECONbench includes gold standard simulated benchmark datasets, consisting of transcriptome and methylome profiles mimicking pancreatic adenocarcinoma molecular heterogeneity, and a set of baseline deconvolution methods (reference-free algorithms inferring cell-type proportions). DECONbench performs a systematic performance evaluation of each new methodological contribution and provides the possibility to publicly share source code and scoring. CONCLUSION: DECONbench allows continuous submission of new methods in a user-friendly fashion, each novel contribution being automatically compared to the reference baseline methods, which enables crowdsourced benchmarking. DECONbench is designed to serve as a reference platform for the benchmarking of deconvolution methods in the evaluation of cancer heterogeneity. We believe it will contribute to leverage the benchmarking practices in the biomedical and life science communities. DECONbench is hosted on the open source Codalab competition platform. It is freely available at: https://competitions.codalab.org/competitions/27453 .

Basal-like and classical cells coexist in pancreatic cancer revealed by single-cell analysis on biopsy-derived pancreatic cancer organoids from the classical subtype.

Pancreatic ductal adenocarcinoma (PDAC) is composed of stromal, immune, and cancerous epithelial cells. Transcriptomic analysis of the epithelial compartment allows classification into different phenotypic subtypes as classical and basal-like. However, little is known about the intra-tumor heterogeneity particularly in the epithelial compartment. Growing evidences suggest that this phenotypic segregation is not so precise and different cancerous cell types may coexist in a single tumor. To test this hypothesis, we performed single-cell transcriptomic analyses using combinational barcoding exclusively on epithelial cells from six different classical PDAC patients obtained by Endoscopic Ultrasound (EUS) with Fine Needle Aspiration (FNA). To purify the epithelial compartment, PDAC were grown as biopsy-derived pancreatic cancer organoids. Single-cell transcriptomic analysis allowed the identification of four main cell clusters present in different proportions in all tumors. Remarkably, although all these tumors were classified as classical, one cluster present in all corresponded to a basal-like phenotype. These results reveal an unanticipated high heterogeneity of pancreatic cancers and demonstrate that basal-like cells, which have a highly aggressive phenotype, are more widespread than expected.

Specific Genomic Alterations in High-Grade Pulmonary Neuroendocrine Tumours with Carcinoid Morphology.

INTRODUCTION: High-grade lung neuroendocrine tumours with carcinoid morphology have been recently reported; they may represent the thoracic counterparts of grade 3 digestive neuroendocrine tumours. We aimed to study their genetic landscape including analysis of tumoral heterogeneity. METHODS: Eleven patients with high-grade (>20% Ki-67 and/or >10 mitoses) lung neuroendocrine tumours with a carcinoid morphology were included. We analysed copy number variations, somatic mutations, and protein expression in 16 tumour samples (2 samples were available for 5 patients allowing us to study spatial and temporal heterogeneity). RESULTS: Genomic patterns were heterogeneous ranging from "quiet" to tetraploid, heavily rearranged genomes. Oncogene mutations were rare and most genetic alterations targeted tumour suppressor genes. Chromosomes 11 (7/11), 3 (6/11), 13 (4/11), and 6-17 (3/11) were the most frequently lost. Altered tumour suppressor genes were common to both carcinoids and neuroendocrine carcinomas, involving different pathways including chromatin remodelling (KMT2A, ARID1A, SETD2, SMARCA2, BAP1, PBRM1, KAT6A), DNA repair (MEN1, POLQ, ATR, MLH1, ATM), cell cycle (RB1, TP53, CDKN2A), cell adhesion (LATS2, CTNNB1, GSK3B) and metabolism (VHL). Comparative spatial/temporal analyses confirmed that these tumours emerged from clones of lower aggressivity but revealed that they were genetically heterogeneous accumulating "neuroendocrine carcinoma-like" genetic alterations through progression such as TP53/RB1 alterations. CONCLUSION: These data confirm the importance of chromatin remodelling genes in pulmonary carcinoids and highlight the potential role of TP53 and RB1 to drive the transformation in more aggressive high-grade tumours.

Stratification of Pancreatic Ductal Adenocarcinomas Based on Tumor and Microenvironment Features.

BACKGROUND & AIMS: Genomic studies have revealed subtypes of pancreatic ductal adenocarcinoma (PDA) based on their molecular features, but different studies have reported different classification systems. It is a challenge to obtain high-quality, freshly frozen tissue for clinical analysis and determination of PDA subtypes. We aimed to redefine subtypes of PDA using a large number of formalin-fixed and paraffin-embedded PDA samples, which are more amenable to routine clinical evaluation. METHODS: We collected PDA samples from 309 consecutive patients who underwent surgery from September 1996 through December 2010 at 4 academic hospitals in Europe; nontumor tissue samples were not included. Samples were formalin fixed and paraffin embedded. DNA and RNA were isolated; gene expression, targeted DNA sequencing, and immunohistochemical analyses were performed. We used independent component analysis to deconvolute normal, tumor, and microenvironment transcriptome patterns in samples. We devised classification systems from an unsupervised analysis using a consensus clustering approach of our data set after removing normal contamination components. We associated subtypes with overall survival and disease-free survival of patients using Cox proportional hazards regression with estimation of hazard ratios and 95% confidence interval. We used The Cancer Genome Consortium and International Cancer Genome Consortium PDA data sets as validation cohorts. RESULTS: We validated the previously reported basal-like and classical tumor-specific subtypes of PDAs. We identified features of the PDA, including microenvironment gene expression patterns, that allowed tumors to be categorized into 5 subtypes, called pure basal like, stroma activated, desmoplastic, pure classical, and immune classical. These PDA subtypes have features of cancer cells and immune cells that could be targeted by pharmacologic agents. Tumor subtypes were associated with patient outcomes, based on analysis of our data set and the International Cancer Genome Consortium and The Cancer Genome Consortium PDA data sets. We also observed an exocrine signal associated with acinar cell contamination (from pancreatic tissue). CONCLUSIONS: We identified a classification system based on gene expression analysis of formalin-fixed PDA samples. We identified 5 PDA subtypes, based on features of cancer cells and the tumor microenvironment. This system might be used to select therapies and predict patient outcomes. We found evidence that the previously reported exocrine-like (called ADEX) tumor subtype resulted from contamination with pancreatic acinar cells. ArrayExpress accession number: E-MTAB-6134.

CoRegNet: reconstruction and integrated analysis of co-regulatory networks.

UNLABELLED: CoRegNet is an R/Bioconductor package to analyze large-scale transcriptomic data by highlighting sets of co-regulators. Based on a transcriptomic dataset, CoRegNet can be used to: reconstruct a large-scale co-regulatory network, integrate regulation evidences such as transcription factor binding sites and ChIP data, estimate sample-specific regulator activity, identify cooperative transcription factors and analyze the sample-specific combinations of active regulators through an interactive visualization tool. In this study CoRegNet was used to identify driver regulators of bladder cancer. AVAILABILITY: CoRegNet is available at http://bioconductor.org/packages/CoRegNet CONTACT: remy.nicolle@issb.genopole.fr or mohamed.elati@issb.genopole.fr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PreCisIon: PREdiction of CIS-regulatory elements improved by gene's positION.

Conventional approaches to predict transcriptional regulatory interactions usually rely on the definition of a shared motif sequence on the target genes of a transcription factor (TF). These efforts have been frustrated by the limited availability and accuracy of TF binding site motifs, usually represented as position-specific scoring matrices, which may match large numbers of sites and produce an unreliable list of target genes. To improve the prediction of binding sites, we propose to additionally use the unrelated knowledge of the genome layout. Indeed, it has been shown that co-regulated genes tend to be either neighbors or periodically spaced along the whole chromosome. This study demonstrates that respective gene positioning carries significant information. This novel type of information is combined with traditional sequence information by a machine learning algorithm called PreCisIon. To optimize this combination, PreCisIon builds a strong gene target classifier by adaptively combining weak classifiers based on either local binding sequence or global gene position. This strategy generically paves the way to the optimized incorporation of any future advances in gene target prediction based on local sequence, genome layout or on novel criteria. With the current state of the art, PreCisIon consistently improves methods based on sequence information only. This is shown by implementing a cross-validation analysis of the 20 major TFs from two phylogenetically remote model organisms. For Bacillus subtilis and Escherichia coli, respectively, PreCisIon achieves on average an area under the receiver operating characteristic curve of 70 and 60%, a sensitivity of 80 and 70% and a specificity of 60 and 56%. The newly predicted gene targets are demonstrated to be functionally consistent with previously known targets, as assessed by analysis of Gene Ontology enrichment or of the relevant literature and databases.

PPAPDC1B and WHSC1L1 are common drivers of the 8p11-12 amplicon, not only in breast tumors but also in pancreatic adenocarcinomas and lung tumors.

Amplification of the 8p11-12 chromosomal region is a common genetic event in many epithelial cancers. In breast cancer, several genes within this region have been shown to display oncogenic activity. Among these genes, the enzyme-encoding genes, PPAPDC1B and WHSC1L1, have been identified as potential therapeutic targets. We investigated whether PPAPDC1B and WHSC1L1 acted as general driver genes, thereby serving as therapeutic targets in other tumors with 8p11-12 amplification. By using publicly available genomic data from a panel of 883 cell lines derived from different cancers, we identified the cell lines presenting amplification of both WHSC1L1 and PPAPDC1B. In particular, we focused on cell lines derived from lung cancer and pancreatic adenocarcinoma and found a correlation between the amplification of PPAPDC1B and WHSC1L1 with their overexpression. Loss-of-function studies based on the use of siRNA and shRNA demonstrated that PPAPDC1B and WHSC1L1 played a major role in regulating the survival of pancreatic adenocarcinoma and small-cell lung cancer-derived cell lines, both in anchorage-dependent and anchorage-independent conditions, displaying amplification and overexpression of these genes. We also demonstrated that PPAPDC1B and WHSC1L1 regulated xenograft growth in these cell lines. Finally, quantitative RT-PCR experiments after PPAPDC1B and WHSC1L1 knockdown revealed exclusive PPAPDC1B and WHSC1L1 gene targets in small-cell lung cancer and pancreatic adenocarcinoma-derived cell lines compared with breast cancer.

Role of the fatty pancreatic infiltration in pancreatic oncogenesis.

Although pancreatic precancerous lesions are known to be related to obesity and fatty pancreatic infiltration, the mechanisms remain unclear. We assessed the role of fatty infiltration in the process of pancreatic oncogenesis and obesity. A combined transcriptomic, lipidomic and pathological approach was used to explore neoplastic transformations. Intralobular (ILF) and extralobular (ELF) lipidomic profiles were analyzed to search for lipids associated with pancreatic intraepithelial neoplasia (PanINs) and obesity; the effect of ILF and ELF on acinar tissue and the histopathological aspects of pancreatic parenchyma changes in obese (OB) and non-obese patients. This study showed that the lipid composition of ILF was different from that of ELF. ILF was related to obesity and ELF-specific lipids were correlated to PanINs. Acinar cells were shown to have different phenotypes depending on the presence and proximity to ILF in OB patients. Several lipid metabolic pathways, oxidative stress and inflammatory pathways were upregulated in acinar tissue during ILF infiltration in OB patients. Early acinar transformations, called acinar nodules (AN) were linked to obesity but not ELF or ILF suggesting that they are the first reversible precancerous pancreatic lesions to occur in OB patients. On the other hand, the number of PanINs was higher in OB patients and was positively correlated to ILF and ELF scores as well as to fibrosis. Our study suggests that two types of fat infiltration must be distinguished, ELF and ILF. ILF plays a major role in acinar modifications and the development of precancerous lesions associated with obesity, while ELF may play a role in the progression of PDAC.

Proteogenomic Characterization of Bladder Cancer Reveals Sensitivity to Apoptosis Induced by Tumor Necrosis Factor-related Apoptosis-inducing Ligand in FGFR3-mutated Tumors.

BACKGROUND: Molecular understanding of muscle-invasive (MIBC) and non-muscle-invasive (NMIBC) bladder cancer is currently based primarily on transcriptomic and genomic analyses. OBJECTIVE: To conduct proteogenomic analyses to gain insights into bladder cancer (BC) heterogeneity and identify underlying processes specific to tumor subgroups and therapeutic outcomes. DESIGN, SETTING, AND PARTICIPANTS: Proteomic data were obtained for 40 MIBC and 23 NMIBC cases for which transcriptomic and genomic data were already available. Four BC-derived cell lines harboring FGFR3 alterations were tested with interventions. INTERVENTION: Recombinant tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), second mitochondrial-derived activator of caspases mimetic (birinapant), pan-FGFR inhibitor (erdafitinib), and FGFR3 knockdown. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Proteomic groups from unsupervised analyses (uPGs) were characterized using clinicopathological, proteomic, genomic, transcriptomic, and pathway enrichment analyses. Additional enrichment analyses were performed for FGFR3-mutated tumors. Treatment effects on cell viability for FGFR3-altered cell lines were evaluated. Synergistic treatment effects were evaluated using the zero interaction potency model. RESULTS AND LIMITATIONS: Five uPGs, covering both NMIBC and MIBC, were identified and bore coarse-grained similarity to transcriptomic subtypes underlying common features of these different entities; uPG-E was associated with the Ta pathway and enriched in FGFR3 mutations. Our analyses also highlighted enrichment of proteins involved in apoptosis in FGFR3-mutated tumors, not captured through transcriptomics. Genetic and pharmacological inhibition demonstrated that FGFR3 activation regulates TRAIL receptor expression and sensitizes cells to TRAIL-mediated apoptosis, further increased by combination with birinapant. CONCLUSIONS: This proteogenomic study provides a comprehensive resource for investigating NMIBC and MIBC heterogeneity and highlights the potential of TRAIL-induced apoptosis as a treatment option for FGFR3-mutated bladder tumors, warranting a clinical investigation. PATIENT SUMMARY: We integrated proteomics, genomics, and transcriptomics to refine molecular classification of bladder cancer, which, combined with clinical and pathological classification, should lead to more appropriate management of patients. Moreover, we identified new biological processes altered in FGFR3-mutated tumors and showed that inducing apoptosis represents a new potential therapeutic option.

Prediction of Adjuvant Gemcitabine Sensitivity in Resectable Pancreatic Adenocarcinoma Using the GemPred RNA Signature: An Ancillary Study of the PRODIGE-24/CCTG PA6 Clinical Trial.

PURPOSE: GemPred, a transcriptomic signature predictive of the efficacy of adjuvant gemcitabine (GEM), was developed from cell lines and organoids and validated retrospectively. The phase III PRODIGE-24/CCTG PA6 trial has demonstrated the superiority of modified folinic acid, fluorouracil, irinotecan, and oxaliplatin (mFOLFIRINOX) over GEM as adjuvant therapy in patients with resected pancreatic ductal adenocarcinoma at the expense of higher toxicity. We evaluated the potential predictive value of GemPred in this population. PATIENTS AND METHODS: Routine formalin-fixed paraffin-embedded surgical specimens of 350 patients were retrieved for RNA sequencing and GemPred prediction (167 in the GEM arm and 183 in the mFOLFIRINOX [mFFX] arm). Survival analyses were stratified by resection margins, lymph node status, and cancer antigen 19-9 level. RESULTS: Eighty-nine patients' tumors (25.5%) were GemPred+ and were thus predicted to be gemcitabine-sensitive. In the GEM arm, GemPred+ patients (n = 50, 30%) had a significantly longer disease-free survival (DFS) than GemPred- patients (n = 117, 70%; median 27.3 v 10.2 months, hazard ratio [HR], 0.43 [95% CI, 0.29 to 0.65]; P < .001) and cancer-specific survival (CSS; median 68.4 v 28.6 months, HR, 0.42 [95% CI, 0.27 to 0.66]; P < .001). GemPred had no prognostic value in the mFFX arm. DFS and CSS were similar in GemPred+ patients who received adjuvant GEM and mFFX (median 27.3 v 24.0 months, and 68.4 v 51.4 months, respectively). The statistical interaction between GEM and GemPred+ status was significant for DFS (P = .008) and CSS (P = .004). GemPred+ patients had significantly more adverse events of grade ≥3 in the mFFX arm (76%) compared with those in the GEM arm (40%; P = .001). CONCLUSION: This ancillary study of a phase III randomized trial demonstrates that among the quarter of patients with a GemPred-positive transcriptomic signature, survival was comparable with that of mFOLFIRINOX, whereas those receiving adjuvant gemcitabine had fewer adverse events.

Precision medicine for KRAS wild-type pancreatic adenocarcinomas.

BACKGROUND: KRAS mutation is the most common molecular alteration in pancreatic adenocarcinoma (PDAC), and around 10% of patients harbor KRAS wild-type tumors (KRASWT). METHODS: A retrospective chart review of clinical/molecular data was performed including all PDAC patients with a determined KRAS status (tumor molecular profiling on tissue or liquid biopsy). RESULTS: 342 patients were included with 54 KRASWT PDAC (16%) compared to 288 patients with KRASm PDAC. Median age was 61 years [IQR:54.0;67.0] and 164 pts (48%) were female. At diagnosis, KRASWT patients (63%) were more frequently diagnosed at a non-metastatic stage compared to KRASm patients (41%) (p = 0.003). Regarding metastatic sites, liver was less frequent in KRASWT (39%, p < 0.0001). Median overall survival (mOS) from initial diagnosis was significantly higher in the KRASWT group compared to KRASm (50.8 months, CI95% [32.0-NR] vs 21.1 months, CI95% [18.9-23.4] (p < 0.004 after adjustment on age, ECOG and stage at diagnosis). In first-line systemic treatment, (mostly FOLFIRINOX) progression-free survival (PFS) was also higher in KRASWT. Based on ESCAT classification, a putative actionable alteration (ESCAT I-III) was identified in 19 (36%) KRASWT pts and 46 (16%) KRASm patients (p < 0.0001) with more alterations in FGFR2, BRAF(V600E), NRTK and more MSI tumors. KRASWT harbored also fewer alterations in TP53, CDKN2A, and SMAD4. 12 KRASWT patients received a molecularly-matched treatment with clinical benefit and improved outcomes compared to KRASm patients. CONCLUSIONS: KRASWT patients display distinct disease characteristics and outcomes with prolonged overall survival. KRASWT patients also harbor more actionable molecular alterations, leading to higher survival rates after receiving molecularly matched treatments.

Prospective observational study on biomarkers of response in pancreatic ductal adenocarcinoma.

Adjuvant chemotherapy benefits patients with resected pancreatic ductal adenocarcinoma (PDAC), but the compromised physical state of post-operative patients can hinder compliance. Biomarkers that identify candidates for prompt adjuvant therapy are needed. In this prospective observational study, 1,171 patients with PDAC who underwent pancreatectomy were enrolled and extensively followed-up. Proteomic profiling of 191 patient samples unveiled clinically relevant functional protein modules. A proteomics-level prognostic risk model was established for PDAC, with its utility further validated using a publicly available external cohort. More importantly, through an interaction effect regression analysis leveraging both clinical and proteomic datasets, we discovered two biomarkers (NDUFB8 and CEMIP2), indicative of the overall sensitivity of patients with PDAC to adjuvant chemotherapy. The biomarkers were validated through immunohistochemistry on an internal cohort of 386 patients. Rigorous validation extended to two external multicentic cohorts-a French multicentric cohort (230 patients) and a cohort from two grade-A tertiary hospitals in China (466 patients)-enhancing the robustness and generalizability of our findings. Moreover, experimental validation through functional assays was conducted on PDAC cell lines and patient-derived organoids. In summary, our cohort-scale integration of clinical and proteomic data demonstrates the potential of proteomics-guided prognosis and biomarker-aided adjuvant chemotherapy for PDAC.

The immunological landscape in pancreatic ductal adenocarcinoma and overcoming resistance to immunotherapy.

Pancreatic ductal adenocarcinoma is associated with a poor prognosis and there are few treatment options. The development of immunotherapy in pancreatic ductal adenocarcinoma has been difficult, and immune checkpoint inhibitors are only effective in a very small subset of patients. Most obstacles for treatment have been related to intertumoural and intratumoural heterogeneity, the composition of tumour stroma, and crosstalk with cancer cells. Improved molecular characterisation of pancreatic ductal adenocarcinoma and a better understanding of its microenvironment have paved the way for novel immunotherapy strategies, including the identification of predictive biomarkers, the development of rational combinations to optimise effectiveness, and the targeting of new mechanisms. Future immunotherapy strategies should consider individual characteristics to move beyond the traditional immune targets and circumvent the resistance to therapies that have been developed so far.