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Genetic ancestry–oriented cancer research requires the ability to perform accurate and robust genetic ancestry inference from existing cancer-derived data, including whole-exome sequencing, transcriptome sequencing, and targeted gene panels, very often in the absence of matching cancer-free genomic data. Here we examined the feasibility and accuracy of computational inference of genetic ancestry relying exclusively on cancer-derived data. A data synthesis framework was developed to optimize and assess the performance of the ancestry inference for any given input cancer-derived molecular profile. In its core procedure, the ancestral background of the profiled patient is replaced with one of any number of individuals with known ancestry. The data synthesis framework is applicable to multiple profiling platforms, making it possible to assess the performance of inference specifically for a given molecular profile and separately for each continental-level ancestry; this ability extends to all ancestries, including those without statistically sufficient representation in the existing cancer data. The inference procedure was demonstrated to be accurate and robust in a wide range of sequencing depths. Testing of the approach in four representative cancer types and across three molecular profiling modalities showed that continental-level ancestry of patients can be inferred with high accuracy, as quantified by its agreement with the gold standard of deriving ancestry from matching cancer-free molecular data. This study demonstrates that vast amounts of existing cancer-derived molecular data are potentially amenable to ancestry-oriented studies of the disease without requiring matching cancer-free genomes or patient self-reported ancestry. Significance: The development of a computational approach that enables accurate and robust ancestry inference from cancer-derived molecular profiles without matching cancer-free data provides a valuable methodology for genetic ancestry–oriented cancer research.

Comparison of IGO- and OGO-derived lesions

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

Supplemental Figures

Confocal z-stack imaging of immunofluorescent (IF) images of mStrawberry-hT3 grafts 4 weeks after IGO transplantation

Table S3

Supplementary Table S1 Survival times of IGO and OGO mice Supplementary Table S2 Characteristics of patient-derived organoids, including KRAS, TP53, SMAD4, CDKN2A mutation status and patient stage Supplementary Table S3 Summary of Survival Data, including engraftment rate, mean survival, metastatic frequency Supplementary Table S4 Primers for quantitative PCR

Method Used to Define the Thresholds for Fast- and Slow-Progressing IGO-Derived Tumors

Supplemental Table Legends

Supplementary methods; supplementary figures; supplementary tables 1 - 18

Genetic ancestry–oriented cancer research requires the ability to perform accurate and robust genetic ancestry inference from existing cancer-derived data, including whole-exome sequencing, transcriptome sequencing, and targeted gene panels, very often in the absence of matching cancer-free genomic data. Here we examined the feasibility and accuracy of computational inference of genetic ancestry relying exclusively on cancer-derived data. A data synthesis framework was developed to optimize and assess the performance of the ancestry inference for any given input cancer-derived molecular profile. In its core procedure, the ancestral background of the profiled patient is replaced with one of any number of individuals with known ancestry. The data synthesis framework is applicable to multiple profiling platforms, making it possible to assess the performance of inference specifically for a given molecular profile and separately for each continental-level ancestry; this ability extends to all ancestries, including those without statistically sufficient representation in the existing cancer data. The inference procedure was demonstrated to be accurate and robust in a wide range of sequencing depths. Testing of the approach in four representative cancer types and across three molecular profiling modalities showed that continental-level ancestry of patients can be inferred with high accuracy, as quantified by its agreement with the gold standard of deriving ancestry from matching cancer-free molecular data. This study demonstrates that vast amounts of existing cancer-derived molecular data are potentially amenable to ancestry-oriented studies of the disease without requiring matching cancer-free genomes or patient self-reported ancestry.Significance:The development of a computational approach that enables accurate and robust ancestry inference from cancer-derived molecular profiles without matching cancer-free data provides a valuable methodology for genetic ancestry–oriented cancer research.

Purpose:Napabucasin (2-acetylfuro-1,4-naphthoquinone or BBI-608) is a small molecule currently being clinically evaluated in various cancer types. It has mostly been recognized for its ability to inhibit STAT3 signaling. However, based on its chemical structure, we hypothesized that napabucasin is a substrate for intracellular oxidoreductases and therefore may exert its anticancer effect through redox cycling, resulting in reactive oxygen species (ROS) production and cell death.Experimental Design:Binding of napabucasin to NAD(P)H:quinone oxidoreductase-1 (NQO1), and other oxidoreductases, was measured. Pancreatic cancer cell lines were treated with napabucasin, and cell survival, ROS generation, DNA damage, transcriptomic changes, and alterations in STAT3 activation were assayed in vitro and in vivo. Genetic knockout or pharmacologic inhibition with dicoumarol was used to evaluate the dependency on NQO1.Results:Napabucasin was found to bind with high affinity to NQO1 and to a lesser degree to cytochrome P450 oxidoreductase (POR). Treatment resulted in marked induction of ROS and DNA damage with an NQO1- and ROS-dependent decrease in STAT3 phosphorylation. Differential cytotoxic effects were observed, where NQO1-expressing cells generating cytotoxic levels of ROS at low napabucasin concentrations were more sensitive. Cells with low or no baseline NQO1 expression also produced ROS in response to napabucasin, albeit to a lesser extent, through the one-electron reductase POR.Conclusions:Napabucasin is bioactivated by NQO1, and to a lesser degree by POR, resulting in futile redox cycling and ROS generation. The increased ROS levels result in DNA damage and multiple intracellular changes, one of which is a reduction in STAT3 phosphorylation.

Supplementary Figures 1,2,3,4,5,6,7

Supplementary Table 19

Table listing the differentially expressed genes between napabucasin treated cells compared to DMSO as vehicle control

Supplementary Figure Legends

BackgroundMolecular profiling of pancreatic adenocarcinoma (PDAC) demonstrates that genomic and transcriptomic features are associated with prognosis and chemosensitivity. We evaluated treatment outcomes by genetic alterations inTP53andGATA6to determine the prognostic and predictive impact of co-mutations, among patients with pancreatic cancer in New York’s largest healthcare system.MethodsRetrospective analysis was performed of patients at Northwell Health diagnosed with PDAC between 2014 to 2022. Surgical status was used to segregate patients into two groups: resected and unresected.TP53genotype andGATA6amplification status were compared for overall survival (OS) as measured from time of diagnosis. Additionally, patient survival by chemotherapy regimen administered was evaluated. The Kaplan–Meier method was used to determine overall survival (OS) and the Wilcoxon test was used to compare survival curves. Previously established and published patient-derived organoids [1] were used to investigateGATA6expression, genetic status, and chemotherapy drug sensitivity.ResultsTumor mutation status was available for 128 patients.TP53mutations were found in 104 patients (81.3%),GATA6amplifications were found in 18 patients (14.0%), and 16 (12.5%) patients had mutations in both genes. Patients withTP53mutations had worse OS compared to the wild-typeTP53population (n = 22) (median OS 22.4 months, 95% CI 12.5 to 41.1, vs. 44.3 months, 95% CI 24.0 to 82.0, HR 2.03, p = 0.038). Among patients with aTP53mutation, a survival advantage was observed in those who had aGATA6amplification (n=16) compared to those who did not (n=86) (median OS 25.5 months vs. 19.4 months, HR 1.82, p = 0.027). Among patients with unresected PDAC who wereTP53-mutant, the presence ofGATA6amplification (n=11) was associated with a substantial survival advantage compared toGATA6wildtype (n=52) (median OS 25.5 months, vs. 10.1 months, HR 0.35, p = 0.004). In theTP53mutation group, among 33 patients who received gemcitabine and nab-paclitaxel as the first-line palliative chemotherapy, patients withGATA6amplification (n = 8) had significantly improved survival compared to those withoutGATA6amplification (n = 25) (mean OS 23.1 months vs 9.4 months, HR 0.52, p = 0.017). However, pancreatic cancer organoids withTP53mutation (n=34) did not exhibit increased drug sensitivity to GnP withGATA6amplification.ConclusionsGenetic mutations inTP53were associated with shorter OS than wild-typeTP53. We found thatGATA6amplification appeared to attenuate poor prognosis observed inTP53-mutant patients regardless of type of standard chemotherapy received.

Pancreatic ductal adenocarcinoma (PDA) samples reveal extensive cellular heterogeneity. Using single-cell RNA sequencing, we uncover multiple tumor cell populations distinguished by their differentiation state and associated with different stages of tumor progression in a mouse model of PDA. We identify Spdef as a factor required for tumorigenesis in pancreatic cancer cells of epithelial and mucinous nature. By comparative analysis of cell differentiation states in mice and humans, we find that the Spdef program is highly expressed by human PDAs of the classical subtype. Mouse and human PDA cells expressing elevated levels of Spdef are dependent upon this transcription factor for tumor progression in vivo. The tumor-promoting function of Spdef is recapitulated by two Spdef target genes that regulate protein folding and endoplasmic reticulum activity, Agr2 and Ern2/Ire1β. These findings offer insights into the factors controlling differentiation states in PDA and identify new vulnerabilities in the most common subtype of pancreatic cancer.

Genetic ancestry-oriented cancer research requires the ability to perform accurate and robust genetic ancestry inference from existing cancer-derived data, including whole exomes, transcriptomes and targeted gene panels, very often in the absence of matching cancer-free genomic data. Here we examine the feasibility and accuracy of such computation. In order to optimize and assess the performance of the ancestry inference for any given input cancer-derived molecular profile, we have developed a data synthesis framework. In its core procedure, the ancestral background of the profiled patient is replaced with one of any number of individuals with known ancestry. Data synthesis is applicable to multiple profiling platforms and makes it possible to assess the performance of inference specifically for a given molecular profile, and separately for each continental-level ancestry. This ability extends to all ancestries, including those without statistically sufficient representation in the existing cancer data. We further show that our inference procedure is accurate and robust in a wide range of sequencing depths. Testing our approach for three representative cancer types, and across three molecular profiling modalities, we demonstrate that global, continental-level ancestry of the patient can be inferred with high accuracy, as quantified by its agreement with the golden standard of the ancestry derived from matching cancer-free molecular data. Our study demonstrates that vast amounts of existing cancer-derived molecular data potentially are amenable to ancestry-oriented studies of the disease, without recourse to matching cancer-free genomes or patients’ self-identification by ancestry.

Due to the grasshopper effect, the Arctic food chain in Canada is contaminated with persistent organic pollutants (POPs) of industrial origin, including polychlorinated biphenyls and organochlorine pesticides. Exposure to POPs may be a contributor to the greater incidence of poor fetal growth, placental abnormalities, stillbirths, congenital defects and shortened lifespan in the Inuit population compared to non-Aboriginal Canadians. Although maternal exposure to POPs is well established to harm pregnancy outcomes, paternal transmission of the effects of POPs is a possibility that has not been well investigated. We used a rat model to test the hypothesis that exposure to POPs during gestation and suckling leads to developmental defects that are transmitted to subsequent generations via the male lineage. Indeed, developmental exposure to an environmentally relevant Arctic POPs mixture impaired sperm quality and pregnancy outcomes across two subsequent, unexposed generations and altered sperm DNA methylation, some of which are also observed for two additional generations. Genes corresponding to the altered sperm methylome correspond to health problems encountered in the Inuit population. These findings demonstrate that the paternal methylome is sensitive to the environment and that some perturbations persist for at least two subsequent generations. In conclusion, although many factors influence health, paternal exposure to contaminants plays a heretofore-underappreciated role with sperm DNA methylation contributing to the molecular underpinnings involved.

Introduction: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal common malignancies, with little improvement in patient outcomes over the past decades. We have developed a novel methodology to culture organoids from both human healthy pancreatic ductal epithelial tissues and PDAC. A collection of patient-derived organoids (PDO), grown using this protocol, numbers over 100 models to date. The 100% neoplastic purity of the organoid cultures facilitates molecular characterization that has been traditionally challenging in the pauci-cellular state of primary pancreatic tumors. These PDO open new opportunities for deep genomic and transcriptomic studies of the disease, and for individualized drug screens. Here we demonstrate that accurate predictive models of response to pharmacological treatments of PDAC can be developed using data from such screens alongside molecular profiles of the PDO. Methods: Molecular analysis of the PDO library yielded genomic and transcriptional profiles of the cultures, including those of copy number variation (CNV), mutations in the exome and mRNA expression. From these, we drew features for prediction of drug responses. Using molecular features drawn from these profiles, we developed a panel of predictive models for response to standard-of-care cytotoxic agents and a number of targeted treatments. We employed Random Forest (RF) regression as a machine-learning tool for this purpose. Results: PDO are faithful models of PDAC, whose molecular features closely resemble those of PDAC tumor specimens. Using a subset of these features, we were able to accurately learn PDO responses to cytotoxic agents: for each of the five agents considered, the predicted drug response correlated strongly (p < 10-7) with the observed value. A similar accuracy of prediction was achieved for a number of targeted agents. Conclusion: PDOs are a valuable resource for molecular and pharmacological characterization of PDAC, with a potential to guide clinical decisions with regard to treatment. Citation Format: Astrid Deschênes, Pascal Belleau, Dennis Plenker, Amber Habowski, Hardik Patel, Youngkyu Park, Hervé Tiriac, Lindsey A. Baker, Alexander Krasnitz, David A. Tuveson. Genomic and pharmaco-genomic profiling of pancreatic cancer using patient-derived organoids [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4042.

Pancreatic ductal adenocarcinoma (PDA) is characterized by intra-tumoral heterogeneity. Using single-cell RNA sequencing, we reveal multiple tumor subpopulations distinguished by their differentiation state and associated with different stages of tumor progression. We identify SPDEF as a factor required for tumorigenesis in pancreatic cancer cells of epithelial and mucinous nature. SPDEF levels correlate with glandular differentiation and high secretory activity, which are characteristic features of human PDA of the classical subtype. SPDEF can drive mucus production in pancreatic cancer cells and is essential for the growth of mucus-secreting tumors by regulating endoplasmic reticulum (ER) activity. These findings offer insights into the factors controlling differentiation in PDA and may inform new therapeutic strategies with improved efficacy. Citation Format: Claudia Tonelli, Georgi N. Yordanov, Yuan Hao, Astrid Deschênes, Erin Brosnan, Abishek Doshi, Youngkyu Park, Jonathan Preall, David A. Tuveson. The transcription factor SPDEF promotes the survival of mucinous pancreatic ductal adenocarcinoma of the classical subtype [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6081.

Introduction: For multiple cancer types, epidemiological data exhibit strong correlations between, on the one hand, the incidence of the disease, its severity when diagnosed, and its clinical outcome, and, on the other hand, the ancestral background of the patient. Recent studies point to genetic and phenotypic differences between tumors in patient populations with differing genetic ancestries, and to the need for more data collection to power research in this area. We sought to facilitate such analysis by developing computational tools for genetic ancestry inference from cancer-derived molecular data, without the need for the patient’s cancer-free genotype or self-declared race or ethnicity. The ability to perform such inference accurately would unlock vast amounts of such data for ancestry-oriented studies of cancer from two major sources. One is the body of data stored by similar massive digital repositories. The other is the body of archival tumor tissues from which molecular data may be generated. Methods: We developed methods for genetic ancestry inference from cancer-derived whole exomes, transcriptomes and targeted gene panels, in the absence of matching cancer-free genomic data. These are adaptive, endowed with the ability to optimize their performance for each input cancer-derived molecular profile. As a result, these inference methods perform consistently, and with quantifiable accuracy, across a range of profiling depths and qualities, and mitigate cancer-related damage to the genome, such as somatic copy-number variation. Results: We examined the performance of these tools with molecular data from three cancer types: pancreatic and ovarian cancers as representative of epithelial tumor types, and acute myeloid leukemia as an example of hematopoietic malignancy. Three molecular data types were considered: whole-exome sequences, exome sequences targeting a panel of cancer-related genes and RNA sequences. The inference accuracy was found to be consistently above 97% across the three cancers and the three data types. Conclusion: Our study demonstrates the feasibility of accurate inference of genetic ancestry from cancer-derived data, with no need for matching cancer-free genotypes. Computational tools for this purpose will be made available as open-source software. Citation Format: Pascal Belleau, Astrid Deschênes, David A. Tuveson, Alexander Krasnitz. Accurate inference of genetic ancestry from cancer-derived data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1223.

e13588 Background: There is ample epidemiological evidence that, for multiple cancer types, incidence, stage at presentation and outcome correlate strongly with the patient’s race or ethnicity. Recent examination of cancer data repositories such as TCGA has revealed has revealed, for a number of cancers, ancestry dependence of somatic mutation spectrum and transcriptional phenotype. Further progress in genetic ancestry-oriented cancer research requires the ability to perform accurate and robust ancestry inference from existing cancer-derived data, including whole exomes, transcriptomes and targeted gene panels, very often in the absence of matching cancer-free genomic data. Such inference is potentially challenging, given widespread somatic alterations in tumor genomes, observed in multiple types of cancer. Methods: In order to optimize and assess the performance of the ancestry inference for any given input cancer-derived molecular profile, we develop a data synthesis framework. In its core procedure, the ancestral background of the profiled patient is replaced with one of any number of individuals with known ancestry. We then infer the substitute individual’s ancestry, using well-established methods of population genetics and relying on the 1000 Genomes collection as a population reference data set. Data synthesis is applicable to multiple profiling platforms and makes it possible to assess the performance of inference specifically for a given molecular profile, and separately for each continental-level ancestry. This ability extends to all ancestries, including those without statistically sufficient representation in the existing cancer data. Results: We test our approach for three representative cancer types: pancreatic and ovarian cancers as representative of epithelial tumor types, and acute myeloid leukemia as an example of hematopoietic malignancy. We consider tumor-derived data acquired by the whole-exome sequencing, by exome sequencing restricted to a panel of cancer-associated genes, and by RNA sequencing. For all three cancer types, and across the three sequencing platforms, we demonstrate that global, continental-level ancestry of the patient can be inferred accurately and robustly. Specifically, we find the inferred ancestries to be in at least 97% agreement with the golden standard of the ancestry derived from the matching cancer-free genomes. We further show that our inference procedure is accurate and robust in a wide range of sequencing depths. Conclusions: Our study demonstrates that vast amounts of existing cancer-derived molecular data are amenable to ancestry-oriented studies of the disease, without recourse to matching cancer-free genomes or patients' self-identification by ancestry. Furthermore, the procedure we developed facilitates ancestry-oriented research using archived tumor tissue specimens, for which matching cancer-free specimens may not be available.

e16285 Background: Recent investigation in pancreatic adenocarcinoma (PDAC) demonstrates an association of molecular subtype with chemosensitivity and prognosis. TP53 and GATA6 are two genes independently associated with worse survival in PDAC. Here, we evaluated treatment outcome by genetic alterations of TP53 and GATA6 among patients with pancreatic cancer across New York’s largest health care system. Methods: This is a retrospective analysis of patients at Northwell Health diagnosed with PDAC between 2014 to 2021. Surgical status was used to segregate patients into two groups: resected and unresected. TP53 and GATA6 mutation status was compared for disease-free survival (DFS) (resected) or progression-free survival (PFS) (unresected), and for overall survival (OS). Additionally, patient survival by type of chemotherapy administered was evaluated. The Kaplan–Meier method was used to determine overall survival (OS), and the Wilcoxon test was used to compare survival curves. Results: Tumor mutational profiling data were available for 129 patients. TP53 mutations were found in 105 patients (81.4%), GATA6 amplifications were found in 18 patients (14.0%), and 16 patients had both mutations. TP53 mutations were associated with a worse OS compared to the wild-type TP53 population (n = 24) (median OS 22.7 months, 95% CI 12.5 to 41.1, vs. 44.3 months, 95% CI 24.5 to 80.3, HR 1.97, p = 0.048). Among patients with a TP53 mutation, a survival advantage was observed in those who had a GATA6 amplification compared to those who did not (median OS 25.5 months, 95% CI 10.9 to 44.6, vs. 22.1 months, 95% CI 12.6 to 51.5, HR 1.68, p = 0.038). In patients with unresected PDAC, the survival advantage of GATA6 amplification within TP53 mutated group was more prominent than the absence of the amplification (median OS 23.8 months, 95% CI 10.0 to 56.5, n = 12 vs. 10.5 months, 95% CI 4.4 to 24.8, n = 55, HR 0.52, p = 0.008). In the TP53 mutation group, among 33 patients who received Gem/NabP as the first-line chemotherapy, patients with GATA6 amplification (n = 8) had significantly improved survival compared to those without GATA6 amplification (n = 25) (mean OS 23.1 months, 95% CI 8.5 to 63.0, vs. 9.4 months, 95% CI 3.44 to 26.6, HR 0.51, p = 0.027). Conclusions: Genetic mutations in TP53 were associated with shorter OS than wild-type TP53. Contrary to our initial expectation that co-mutation would be associated with even worse survival, we found that GATA6 amplification appeared to attenuate poor prognosis observed in TP53-mutant patients. Additionally, GATA6 amplification may be a predictive marker for better responses to Gem/NabP chemotherapy.

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

Pancreatic cancer is the most lethal common solid malignancy. Systemic therapies are often ineffective and predictive biomarkers to guide treatment are urgently needed. We generated a pancreatic cancer patient-derived organoid (PDO) library that recapitulates the mutational spectrum and transcriptional subtypes of primary pancreatic cancer. New driver oncogenes were nominated and transcriptomic analyses revealed unique clusters. PDOs exhibited heterogeneous responses to standard-of-care chemotherapeutics and investigational agents. In a case study manner, we find that PDO therapeutic profiles paralleled patient outcomes and that PDOs enable longitudinal assessment of chemosensitivity and evaluation of synchronous metastases. We derived organoid-based gene expression signatures of chemosensitivity that predicted improved responses for many patients to chemotherapy in both the adjuvant and advanced disease settings. Finally, we nominated alternative treatment strategies for chemorefractory PDOs using targeted agent therapeutic profiling. We propose that combined molecular and therapeutic profiling of PDOs may predict clinical response and enable prospective therapeutic selection. Citation Format: Herve Tiriac, Pascal Belleau, Dannielle Engle, Dennis Plenker, Astrid Deschenes, Tim Somerville, Fieke Froeling, Richard Moffitt, Jennifer Knox, Alexander Krasnitz, Steven Gallinger, David Tuveson. Organoid profiling identifies common responders to chemotherapy in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2019 Sept 6-9; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2019;79(24 Suppl):Abstract nr C57.

Network science is a cross-disciplinary domain that focuses on the representation, analysis and modeling of complex systems as networks or graphs.In computational biology, Cytoscape is a very popular open source software platform for network modeling and visualization. As an example, Cytoscape can be used to identify disease modules which are defined as connected subnetworks showing mechanistic evidence for a specific phenotype. In the context of a new subnetwork identification, it is of interest to have access to measures of connectivity that help to assess the probability that this subnetwork could arise by chance. The R subnetsim package uses bootstrapping to test the hypothesis that a specific subnetwork is more connected than randomly generated subnetworks with the same number of nodes. Three measures of connectivity are validated by the R subnetsim package: - the number of nodes in the subnetwork including all the first-degree neighbor nodes - the number of links in the subnetwork including all the links of the first-degree neighbor nodes - the number of links in the subnetwork TheR subnetsim packagealso offers the possibility to easily graph the results of the simulations. This protocol provides the detailed method of a simulation analysis done on a specific subnetwork. To do so, the global subnetwork, from which the subnetwork is extracted, is needed in SIF format file.

Gene families differ in composition, expression, and chromosomal organization between conifers and angiosperms, but little is known regarding nucleotide polymorphism. Using various sequencing strategies, an atlas of 212k high-confidence single nucleotide polymorphisms (SNPs) with a validation rate of more than 92% was developed for the conifer white spruce (Picea glauca). Nonsynonymous and synonymous SNPs were annotated over the corresponding 13,498 white spruce genes representative of 2,457 known gene families. Patterns of nucleotide polymorphisms were analyzed by estimating the ratio of nonsynonymous to synonymous numbers of substitutions per site (A/S). A general excess of synonymous SNPs was expected and observed. However, the analysis from several perspectives enabled to identify groups of genes harboring an excess of nonsynonymous SNPs, thus potentially under positive selection. Four known gene families harbored such an excess: dehydrins, ankyrin-repeats, AP2/DREB, and leucine-rich repeat. Conifer-specific sequences were also generally associated with the highest A/S ratios. A/S values were also distributed asymmetrically across genes specifically expressed in megagametophytes, roots, or in both, harboring on average an excess of nonsynonymous SNPs. These patterns confirm that the breadth of gene expression is a contributing factor to the evolution of nucleotide polymorphism. The A/S ratios of Medicago truncatula genes were also analyzed: several gene families shared between P. glauca and M. truncatula data sets had similar excess of synonymous or nonsynonymous SNPs. However, a number of families with high A/S ratios were found specific to P. glauca, suggesting cases of divergent evolution at the functional level.

High-density SNP genotyping arrays can be designed for any species given sufficient sequence information of high quality. Two high-density SNP arrays relying on the Infinium iSelect technology (Illumina) were designed for use in the conifer white spruce (Picea glauca). One array contained 7338 segregating SNPs representative of 2814 genes of various molecular functional classes for main uses in genetic association and population genetics studies. The other one contained 9559 segregating SNPs representative of 9543 genes for main uses in population genetics, linkage mapping of the genome and genomic prediction. The SNPs assayed were discovered from various sources of gene resequencing data. SNPs predicted from high-quality sequences derived from genomic DNA reached a genotyping success rate of 64.7%. Nonsingleton in silico SNPs (i.e. a sequence polymorphism present in at least two reads) predicted from expressed sequenced tags obtained with the Roche 454 technology and Illumina GAII analyser resulted in a similar genotyping success rate of 71.6% when the deepest alignment was used and the most favourable SNP probe per gene was selected. A variable proportion of these SNPs was shared by other nordic and subtropical spruce species from North America and Europe. The number of shared SNPs was inversely proportional to phylogenetic divergence and standing genetic variation in the recipient species, but positively related to allele frequency in P. glauca natural populations. These validated SNP resources should open up new avenues for population genetics and comparative genetic mapping at a genomic scale in spruce species.

ChIP-Sequencing (ChIP-Seq) provides a vast amount of information regarding the localization of proteins across the genome. The aggregation of ChIP-Seq enrichment signal in a metagene plot is an approach commonly used to summarize data complexity and to obtain a high level visual representation of the general occupancy pattern of a protein. Here we present the R package metagene, the graphical interface Imetagene and the companion package similaRpeak. Together, they provide a framework to integrate, summarize and compare the ChIP-Seq enrichment signal from complex experimental designs. Those packages identify and quantify similarities or dissimilarities in patterns between large numbers of ChIP-Seq profiles. We used metagene to investigate the differential occupancy of regulatory factors at noncoding regulatory regions (promoters and enhancers) in relation to transcriptional activity in GM12878 B-lymphocytes. The relationships between occupancy patterns and transcriptional activity suggest two different mechanisms of action for transcriptional control: i) a "gradient effect" where the regulatory factor occupancy levels follow transcription and ii) a "threshold effect" where the regulatory factor occupancy levels max out prior to reaching maximal transcription. metagene, Imetagene and similaRpeak are implemented in R under the Artistic license 2.0 and are available on Bioconductor.

High-throughput methylation sequencing enables genome-wide detection of differentially methylated sites (DMS) or regions (DMR). Increasing evidence suggests that treatment-induced DMS can be transmitted across generations, but the analysis of induced methylation changes across multiple generations is complicated by the lack of sound statistical methods to evaluate significance levels. Due to software design, DMS detection was usually made on each generation separately, thus disregarding stochastic effects expected when a large number of DMS is detected in each generation. Here, we present a novel method based on Monte Carlo sampling, methylInheritance, to evaluate that the number of conserved DMS between several generations is associated to an effect inherited from a treatment and not randomness. Moreover, we developed an inheritance simulation package, methInheritSim, to demonstrate the performance of the methylInheritance method and to evaluate the power of different experimental designs. Finally, we applied methylInheritance to a DNA methylation dataset obtained from early-life persistent organic pollutants (POPs) exposed Sprague-Dawley female rats and their descendants through a paternal transmission. The results show that metylInheritance can efficiently identify treatment-induced inherited methylation changes. Specifically, we identified two intergenerationally conserved DMS at transcription start site (TSS); one of those persisted transgenerationally. Three transgenerationally conserved DMR were found at intra or integenic regions.

Genome-wide mapping of nucleosomes has revealed a great deal about the relationships between chromatin structure and control of gene expression. Recent next generation CHIP-chip and CHIP-Seq technologies have accelerated our understanding of basic principles of chromatin organization. These technologies have taught us that nucleosomes play a crucial role in gene regulation by allowing physical access to transcription factors. Recent methods and experimental advancements allow the determination of nucleosome positions for a given genome area. However, most of these methods estimate the number of nucleosomes either by an EM algorithm using a BIC criterion or an effective heuristic strategy. Here, we introduce a Bayesian method for identifying nucleosome positions. The proposed model is based on a Multinomial-Dirichlet classification and a hierarchical mixture distributions. The number and the positions of nucleosomes are estimated using a reversible jump Markov chain Monte Carlo simulation technique. We compare the performance of our method on simulated data and MNase-Seq data from Saccharomyces cerevisiae against PING and NOrMAL methods.

Uncovering mechanisms that drive pancreatic cancer could lead to effective therapies for this lethal disease. This study reveals how pancreatic cancer cells hyperactivate a pathway to sustain their proliferation. Inhibiting this pathway affects cancer cells, but not normal cells, highlighting a new therapeutic opportunity., Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis, and new therapies are needed. Altered metabolism is a cancer vulnerability, and several metabolic pathways have been shown to promote PDAC. However, the changes in cholesterol metabolism and their role during PDAC progression remain largely unknown. Here we used organoid and mouse models to determine the drivers of altered cholesterol metabolism in PDAC and the consequences of its disruption on tumor progression. We identified sterol O-acyltransferase 1 (SOAT1) as a key player in sustaining the mevalonate pathway by converting cholesterol to inert cholesterol esters, thereby preventing the negative feedback elicited by unesterified cholesterol. Genetic targeting of Soat1 impairs cell proliferation in vitro and tumor progression in vivo and reveals a mevalonate pathway dependency in p53 mutant PDAC cells that have undergone p53 loss of heterozygosity (LOH). In contrast, pancreatic organoids lacking p53 mutation and p53 LOH are insensitive to SOAT1 loss, indicating a potential therapeutic window for inhibiting SOAT1 in PDAC.