Circulating Nucleic Acids Are Associated With Outcomes of Patients With Pancreatic Cancer.

BACKGROUND & AIMS: We aimed to investigate the clinical utility of circulating tumor cell DNA (ctDNA) and exosome DNA (exoDNA) in pancreatic cancer. METHODS: We collected liquid biopsy samples from 194 patients undergoing treatment for localized or metastatic pancreatic adenocarcinoma from April 7, 2015, through October 13, 2017 (425 blood samples collected before [baseline] and during therapy). Additional liquid biopsy samples were collected from 37 disease control individuals. Droplet digital polymerase chain reaction was used to determine KRAS mutant allele fraction (MAF) from ctDNA and exoDNA purified from plasma. For the longitudinal analysis, we analyzed exoDNA and ctDNA in 123 serial blood samples from 34 patients. We performed analysis including Cox regression, Fisher exact test, and Bayesian inference to associate KRAS MAFs in exoDNA and ctDNA with prognostic and predictive outcomes. RESULTS: In the 34 patients with potentially resectable tumors, an increase in exoDNA level after neoadjuvant therapy was significantly associated with disease progression (P = .003), whereas ctDNA did not show correlations with outcomes. Concordance rates of KRAS mutations present in surgically resected tissue and detected in liquid biopsy samples were greater than 95%. On univariate analysis, patients with metastases and detectable ctDNA at baseline status had significantly shorter times of progression-free survival (PFS) (hazard ratio [HR] for death, 1.8; 95% CI, 1.1-3.0; P = .019), and overall survival (OS) (HR, 2.8; 95% CI, 1.4-5.7; P = .0045) compared with patients without detectable ctDNA. On multivariate analysis, MAFs ≥5% in exoDNA were a significant predictor of PFS (HR, 2.28; 95% CI, 1.18-4.40; P = .014) and OS (HR, 3.46; 95% CI, 1.40-8.50; P = .007). A multianalyte approach showed detection of both ctDNA and exoDNA MAFs ≥5% at baseline status to be a significant predictor of OS (HR, 7.73, 95% CI, 2.61-22.91, P = .00002) on multivariate analysis. In the longitudinal analysis, an MAF peak above 1% in exoDNA was significantly associated with radiologic progression (P = .0003). CONCLUSIONS: In a prospective cohort of pancreatic cancer patients, we show how longitudinal monitoring using liquid biopsy samples through exoDNA and ctDNA provides both predictive and prognostic information relevant to therapeutic stratification.

Directional allelic imbalance profiling and visualization from multi-sample data with RECUR.

MOTIVATION: Genetic analysis of cancer regularly includes two or more samples from the same patient. Somatic copy number alterations leading to allelic imbalance (AI) play a critical role in cancer initiation and progression. Directional analysis and visualization of the alleles in imbalance in multi-sample settings allow for inference of recurrent mutations, providing insights into mutation rates, clonality and the genomic architecture and etiology of cancer. RESULTS: The REpeat Chromosomal changes Uncovered by Reflection (RECUR) is an R application for the comparative analysis of AI profiles derived from SNP array and next-generation sequencing data. The algorithm accepts genotype calls and 'B allele' frequencies (BAFs) from at least two samples derived from the same individual. For a predefined set of genomic regions with AI, RECUR compares BAF values among samples. In the presence of AI, the expected value of a BAF can shift in two possible directions, reflecting an increased or decreased abundance of the maternal haplotype, relative to the paternal. The phenomenon of opposite haplotype shifts, or 'mirrored subclonal allelic imbalance', is a form of heterogeneity, and has been linked to clinico-pathological features of cancer. RECUR detects such genomic segments of opposite haplotypes in imbalance and plots BAF values for all samples, using a two-color scheme for intuitive visualization. AVAILABILITY AND IMPLEMENTATION: RECUR is available as an R application. Source code and documentation are available at scheet.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Driver Mutations in Normal Airway Epithelium Elucidate Spatiotemporal Resolution of Lung Cancer.

Rationale: Uninvolved normal-appearing airway epithelium has been shown to exhibit specific mutations characteristic of nearby non-small cell lung cancers (NSCLCs). Yet, its somatic mutational landscape in patients with early-stage NSCLC is unknown.Objectives: To comprehensively survey the somatic mutational architecture of the normal airway epithelium in patients with early-stage NSCLC.Methods: Multiregion normal airways, comprising tumor-adjacent small airways, tumor-distant large airways, nasal epithelium and uninvolved normal lung (collectively airway field), matched NSCLCs, and blood cells (n = 498) from 48 patients were interrogated for somatic single-nucleotide variants by deep-targeted DNA sequencing and for chromosomal allelic imbalance events by genome-wide genotype array profiling. Spatiotemporal relationships between the airway field and NSCLCs were assessed by phylogenetic analysis.Measurements and Main Results: Genomic airway field carcinogenesis was observed in 25 cases (52%). The airway field epithelium exhibited a total of 269 somatic mutations in most patients (n = 36) including key drivers that were shared with the NSCLCs. Allele frequencies of these acquired variants were overall higher in NSCLCs. Integrative analysis of single-nucleotide variants and allelic imbalance events revealed driver genes with shared "two-hit" alterations in the airway field (e.g., TP53, KRAS, KEAP1, STK11, and CDKN2A) and those with single hits progressing to two in the NSCLCs (e.g., PIK3CA and NOTCH1).Conclusions: Tumor-adjacent and tumor-distant normal-appearing airway epithelia exhibit somatic driver alterations that undergo selection-driven clonal expansion in NSCLC. These events offer spatiotemporal insights into the development of NSCLC and, thus, potential targets for early treatment.

Strategies for identification of somatic variants using the Ion Torrent deep targeted sequencing platform.

BACKGROUND: 'Next-generation' (NGS) sequencing has wide application in medical genetics, including the detection of somatic variation in cancer. The Ion Torrent-based (IONT) platform is among NGS technologies employed in clinical, research and diagnostic settings. However, identifying mutations from IONT deep sequencing with high confidence has remained a challenge. We compared various computational variant-calling methods to derive a variant identification pipeline that may improve the molecular diagnostic and research utility of IONT. RESULTS: Using IONT, we surveyed variants from the 409-gene Comprehensive Cancer Panel in whole-section tumors, intra-tumoral biopsies and matched normal samples obtained from frozen tissues and blood from four early-stage non-small cell lung cancer (NSCLC) patients. We used MuTect, Varscan2, IONT's proprietary Ion Reporter, and a simple subtraction we called "Poor Man's Caller." Together these produced calls at 637 loci across all samples. Visual validation of 434 called variants was performed, and performance of the methods assessed individually and in combination. Of the subset of inspected putative variant calls (n=223) in genomic regions that were not intronic or intergenic, 68 variants (30%) were deemed valid after visual inspection. Among the individual methods, the Ion Reporter method offered perhaps the most reasonable tradeoffs. Ion Reporter captured 83% of all discovered variants; 50% of its variants were visually validated. Aggregating results from multiple packages offered varied improvements in performance. CONCLUSIONS: Overall, Ion Reporter offered the most attractive performance among the individual callers. This study suggests combined strategies to maximize sensitivity and positive predictive value in variant calling using IONT deep sequencing.

Genetic risk factors for the development of osteonecrosis in children under age 10 treated for acute lymphoblastic leukemia.

Osteonecrosis is a dose-limiting toxicity in the treatment of pediatric acute lymphoblastic leukemia (ALL). Prior studies on the genetics of osteonecrosis have focused on patients ≥10 years of age, leaving the genetic risk factors for the larger group of children <10 years incompletely understood. Here, we perform the first evaluation of genetic risk factors for osteonecrosis in children <10 years. The discovery cohort comprised 82 cases of osteonecrosis and 287 controls treated on Children's Oncology Group (COG) standard-risk ALL protocol AALL0331 (NCT00103285, https://clinicaltrials.gov/ct2/show/NCT00103285), with results tested for replication in 817 children <10 years treated on COG protocol AALL0232 (NCT00075725, https://clinicaltrials.gov/ct2/show/NCT00075725). The top replicated single nucleotide polymorphisms (SNPs) were near bone morphogenic protein 7 [BMP7: rs75161997, P = 5.34 × 10(-8) (odds ratio [OR] 15.0) and P = .0498 (OR 8.44) in the discovery and replication cohorts, respectively] and PROX1-antisense RNA1 (PROX1-AS1: rs1891059, P = 2.28 × 10(-7) [OR 6.48] and P = .0077 [OR 3.78] for the discovery and replication cohorts, respectively). The top replicated nonsynonymous SNP, rs34144324, was in a glutamate receptor gene (GRID2, P = 8.65 × 10(-6) [OR 3.46] and P = .0136 [OR 10.8] in the discovery and replication cohorts, respectively). In a meta-analysis, the BMP7 and PROX1-AS1 variants (rs75161997 and rs1891059, respectively) met the significance threshold of <5 × 10(-8). Top replicated SNPs were enriched in enhancers active in mesenchymal stem cells, and analysis of annotated genes demonstrated enrichment in glutamate receptor and adipogenesis pathways. These data may provide new insights into the pathophysiology of osteonecrosis.

Assessing inter-component heterogeneity of biphasic uterine carcinosarcomas.

OBJECTIVE: Uterine carcinosarcoma (UCS) is a rare and aggressive form of uterine cancer. It is bi-phasic, exhibiting histological features of both malignant epithelial (carcinoma) and mesenchymal (sarcoma) elements, reflected in ambiguity in accepted treatment guidelines. We sought to study the genomic and transcriptomic profiles of these elements individually to gain further insights into the development of these tumors. METHODS: We macro-dissected carcinomatous, sarcomatous, and normal tissues from formalin fixed paraffin embedded uterine samples of 10 UCS patients. Single nucleotide polymorphism microarrays, targeted DNA sequencing and whole-transcriptome RNA-sequencing were performed. Somatic chromosomal alterations (SCAs), point mutation and gene expression profiles were compared between carcinomatous and sarcomatous components. RESULTS: In addition to TP53, other recurrently mutated genes harboring putative driver or loss-of-function mutations included PTEN, FBXW7, FGFR2, KRAS, PIK3CA and CTNNB1, genes known to be involved in UCS. Intra-patient somatic mutation and SCA profiles were highly similar between paired carcinoma and sarcoma samples. An epithelial-mesenchymal transition (EMT) signature tended to differentiate components, with EMT-like status more common in advanced-stage patients exhibiting higher inter-component SCA heterogeneity. CONCLUSIONS: From DNA analysis, our results indicate a monoclonal disease origin for this cohort. Yet expression-derived EMT statuses of the carcinomatous and sarcomatous components were often discrepant, and advanced cases displayed greater genomic heterogeneity. Therefore, separately-profiled components of UCS tumors may better inform disease progression or potential.

Rapid and powerful detection of subtle allelic imbalance from exome sequencing data with hapLOHseq.

MOTIVATION: The detection of subtle genomic allelic imbalance events has many potential applications. For example, identifying cancer-associated allelic imbalanced regions in low tumor-cellularity samples or in low-proportion tumor subclones can be used for early cancer detection, prognostic assessment and therapeutic selection in cancer patients. We developed hapLOHseq for the detection of subtle allelic imbalance events from next-generation sequencing data. RESULTS: Our method identified events of 10 megabases or greater occurring in as little as 16% of the sample in exome sequencing data (at 80×) and 4% in whole genome sequencing data (at 30×), far exceeding the capabilities of existing software. We also found hapLOHseq to be superior at detecting large chromosomal changes across a series of pancreatic samples from TCGA. AVAILABILITY AND IMPLEMENTATION: hapLOHseq is available at scheet.org/software, distributed under an open source MIT license. CONTACT: pscheet@alum.wustl.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Improving the sensitivity of sample clustering by leveraging gene co-expression networks in variable selection.

BACKGROUND: Many variable selection techniques have been proposed for the clustering of gene expression data. While these methods tend to filter out irrelevant genes and identify informative genes that contribute to a clustering solution, they are based on criteria that do not consider the potential interactive influence among individual genes. Motivated by ensemble clustering, there is a strong interest in leveraging the structure of gene networks for gene selection, so that the relationship information between genes can be effectively utilized, while the selected genes are expected to preserve all the possible clustering structures in the data. RESULTS: We present a new filter method that uses the gene connectivity in the gene co-expression network as the evaluation criteria for variable selection. The gene connectivity measures the importance of the genes in term of their expression similarity with others in the co-expression network. The hard threshold and soft threshold transformations are employed to construct the gene co-expression networks. Both simulation studies and real data analysis have shown that the network based on soft thresholding is more effective in selecting relevant variables and provides better clustering results compared to the hard thresholding transformation and two other canonical filter methods for variable selection. Furthermore, a new module analysis approach is proposed to reveal the higher order organization of the gene space, where the genes of a module share significant topological similarity and are associated with a consensus partition of the sample space. We demonstrate that the identified modules can lead to biologically meaningful sample partitions that might be missed by other methods. CONCLUSIONS: By leveraging the structure of gene co-expression network, first we propose a variable selection method that selects individual genes with top connectivity. Both simulation studies and real data application have demonstrated that our method has better performance in terms of the reliability of the selected genes and sample clustering results. In addition, we propose a module recovery method that can help discover novel sample partitions that might be hidden when performing clustering analyses using all available genes. The source code of our program is available at http://nba.uth.tmc.edu/homepage/liu/netVar/.

Incorporating prior knowledge into Gene Network Study.

MOTIVATION: A major goal in genomic research is to identify genes that may jointly influence a biological response. From many years of intensive biomedical research, a large body of biological knowledge, or pathway information, has accumulated in available databases. There is a strong interest in leveraging these pathways to improve the statistical power and interpretability in studying gene networks associated with complex phenotypes. This prior information is a valuable complement to large-scale genomic data such as gene expression data generated from microarrays. However, it is a non-trivial task to effectively integrate available biological knowledge into gene expression data when reconstructing gene networks. RESULTS: In this article, we developed and applied a Lasso method from a Bayesian perspective, a method we call prior Lasso (pLasso), for the reconstruction of gene networks. In this method, we partition edges between genes into two subsets: one subset of edges is present in known pathways, whereas the other has no prior information associated. Our method assigns different prior distributions to each subset according to a modified Bayesian information criterion that incorporates prior knowledge on both the network structure and the pathway information. Simulation studies have indicated that the method is more effective in recovering the underlying network than a traditional Lasso method that does not use the prior information. We applied pLasso to microarray gene expression datasets, where we used information from the Pathway Commons (PC) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as prior information for the network reconstruction, and successfully identified network hub genes associated with clinical outcome in cancer patients. AVAILABILITY: The source code is available at http://nba.uth.tmc.edu/homepage/liu/pLasso.

Haploscope: a tool for the graphical display of haplotype structure in populations.

Patterns of linkage disequilibrium are often depicted pictorially by using tools that rely on visualizations of raw data or pairwise correlations among individual markers. Such approaches can fail to highlight some of the more interesting and complex features of haplotype structure. To enable natural visual comparisons of haplotype structure across subgroups of a population (e.g. isolated subpopulations or cases and controls), we propose an alternative visualization that provides a novel graphical representation of haplotype frequencies. We introduce Haploscope, a tool for visualizing the haplotype cluster frequencies that are produced by statistical models for population haplotype variation. We demonstrate the utility of our technique by examining haplotypes around the LCT gene, an example of recent positive selection, in samples from the Human Genome Diversity Panel. Haploscope, which has flexible options for annotation and inspection of haplotypes, is available for download at http://scheet.org/software.

Integrated annotation and analysis of genetic variants from next-generation sequencing studies with variant tools.

MOTIVATION: Storing, annotating and analyzing variants from next-generation sequencing projects can be difficult due to the availability of a wide array of data formats, tools and annotation sources, as well as the sheer size of the data files. Useful tools, including the GATK, ANNOVAR and BEDTools can be integrated into custom pipelines for annotating and analyzing sequence variants. However, building flexible pipelines that support the tracking of variants alongside their samples, while enabling updated annotation and reanalyses, is not a simple task. RESULTS: We have developed variant tools, a flexible annotation and analysis toolset that greatly simplifies the storage, annotation and filtering of variants and the analysis of the underlying samples. variant tools can be used to manage and analyze genetic variants obtained from sequence alignments, and the command-line driven toolset could be used as a foundation for building more sophisticated analytical methods. AVAILABILITY AND IMPLEMENTATION: variant tools consists of two command-line driven programs vtools and vtools_report. It is freely available at http://varianttools.sourceforge.net, distributed under a GPL license. CONTACT: bpeng@mdanderson.org.

Evaluation of the Aggressive-Variant Prostate Cancer Molecular Signature in Clinical Laboratory Improvement Amendments (CLIA) Environments.

Aggressive-variant prostate cancers (AVPCs) are a subset of metastatic castrate-resistant prostate cancers (mCRPCs) characterized by defects in ≥ two of three of TP53, RB1, and PTEN (AVPCm), a profile linked to lineage plasticity, androgen indifference, and platinum sensitivity. Men with mCRPC undergoing biopsies for progression were assessed for AVPCm using immunohistochemistry (IHC), next-generation sequencing (NGS) of solid tumor DNA (stDNA), and NGS of circulating tumor DNA (ctDNA) assays in CLIA-certified labs. Biopsy characteristics, turnaround times, inter-reader concordance, and inter-assay concordance were assessed. AVPCm was detected in 13 (27%) patients via IHC, two (6%) based on stDNA, and seven (39%) based on ctDNA. The concordance of the IHC reads between pathologists was variable. IHC had a higher detection rate of AVPCm+ tumors with the shortest turnaround times. stDNA had challenges with copy number loss detection, limiting its detection rate. ctDNA detected the greatest proportion of AVPCm+ tumors but had a low tumor content in two thirds of patients. These data show the operational characteristics of AVPCm detection using various assays, and inform trial design using AVPCm as a criterion for patient selection or stratification.

Intragenic EGFR::EGFR.E1E8 Fusion (EGFRvIII) in 4331 Solid Tumors.

Epidermal growth factor receptor variant III (EGFRvIII, the deletion of exons 2-7) is a recurrent intragenic EGFR::EGFR.E1E8 fusion that occurs in high-grade gliomas. The presence of EGFRvIII in other solid tumors has not been well characterized. We retrospectively reviewed advanced malignant solid tumor cases tested by a custom hybrid capture 610-gene next-generation sequencing platform from 2021 to 2022. EGFRvIII was identified in 17 of 4331 (0.4%) cases, including 16 of 238 (7%) brain tumors and 1/301 (0.3%) breast tumors. EGFRvIII-positive brain tumors were all glioblastoma IDH-wildtype, most with concurrent TERT promoter mutation (14 of 16), EGFR amplification (13 of 16), and EGFR mutation (8 of 16). The only EGFRvIII-positive breast lesion was a sarcomatoid neoplasm in a young female patient. A separate breast case tested outside our institution with reported EGFRvIII was noted in a young female patient with a malignant phyllodes tumor with stromal overgrowth. Microscopically, both EGFRvIII-positive breast tumors showed high-grade sarcomatoid morphology with brisk mitotic activity. In summary, EGFRvIII is rare, occurring primarily in glioblastoma and rarely in breast sarcomatoid neoplasm, with no instances identified in other tumor types in our series. This select group of patients may benefit from chemotherapy and/or targeted anti-EGFR therapy.

An S/T-Q cluster domain census unveils new putative targets under Tel1/Mec1 control.

BACKGROUND: The cellular response to DNA damage is immediate and highly coordinated in order to maintain genome integrity and proper cell division. During the DNA damage response (DDR), the sensor kinases Tel1 and Mec1 in Saccharomyces cerevisiae and ATM and ATR in human, phosphorylate multiple mediators which activate effector proteins to initiate cell cycle checkpoints and DNA repair. A subset of kinase substrates are recognized by the S/T-Q cluster domain (SCD), which contains motifs of serine (S) or threonine (T) followed by a glutamine (Q). However, the full repertoire of proteins and pathways controlled by Tel1 and Mec1 is unknown. RESULTS: To identify all putative SCD-containing proteins, we analyzed the distribution of S/T-Q motifs within verified Tel1/Mec1 targets and arrived at a unifying SCD definition of at least 3 S/T-Q within a stretch of 50 residues. This new SCD definition was used in a custom bioinformatics pipeline to generate a census of SCD-containing proteins in both yeast and human. In yeast, 436 proteins were identified, a significantly larger number of hits than were expected by chance. These SCD-containing proteins did not distribute equally across GO-ontology terms, but were significantly enriched for those involved in processes related to the DDR. We also found a significant enrichment of proteins involved in telophase and cytokinesis, protein transport and endocytosis suggesting possible novel Tel1/Mec1 targets in these pathways. In the human proteome, a wide range of similar proteins were identified, including homologs of some SCD-containing proteins found in yeast. This list also included high concentrations of proteins in the Mediator, spindle pole body/centrosome and actin cytoskeleton complexes. CONCLUSIONS: Using a bioinformatic approach, we have generated a census of SCD-containing proteins that are involved not only in known DDR pathways but several other pathways under Tel1/Mec1 control suggesting new putative targets for these kinases.

Pan cancer patterns of allelic imbalance from chromosomal alterations in 33 tumor types.

Somatic copy number alterations (SCNAs) serve as hallmarks of tumorigenesis and often result in deviations from one-to-one allelic ratios at heterozygous loci, leading to allelic imbalance (AI). The Cancer Genome Atlas (TCGA) reports SCNAs identified using a circular binary segmentation algorithm, providing segment mean copy number estimates from single-nucleotide polymorphism DNA microarray total intensities (log R ratio), but not allele-specific intensities ("B allele" frequencies) that inform of AI. Our approach provides more sensitive identification of SCNAs by modeling the "B allele" frequencies jointly, thereby bolstering the catalog of chromosomal alterations in this widely utilized resource. Here we present AI summaries for all 33 tumor sites in TCGA, including those induced by SCNAs and copy-neutral loss-of-heterozygosity (cnLOH). We identified AI in 94% of the tumors, higher than in previous reports. Recurrent events included deletions of 17p, 9q, 3p, amplifications of 8q, 1q, 7p, as well as mixed event types on 8p and 13q. We also observed both site-specific and pan-cancer (spanning 17p) cnLOH, patterns which have not been comprehensively characterized. The identification of such cnLOH events elucidates tumor suppressors and multi-hit pathways to carcinogenesis. We also contrast the landscapes inferred from AI- and total intensity-derived SCNAs and propose an automated procedure to improve and adjust SCNAs in TCGA for cases where high levels of aneuploidy obscured baseline intensity identification. Our findings support the exploration of additional methods for robust automated inference procedures and to aid empirical discoveries across TCGA.

HIF2 Regulates Intestinal Wnt5a Expression.

Radiation therapy for abdominal tumors is challenging because the small intestine is exquisitely radiosensitive. Unfortunately, there are no FDA-approved therapies to prevent or mitigate GI radiotoxicity. The EGLN protein family are oxygen sensors that regulate cell survival and metabolism through the degradation of hypoxia-inducible factors (HIFs). Our group has previously shown that stabilization of HIF2 through genetic deletion or pharmacologic inhibition of the EGLNs mitigates and protects against GI radiotoxicity in mice by improving intestinal crypt stem cell survival. Here we aimed to elucidate the molecular mechanisms by which HIF2 confers GI radioprotection. We developed duodenal organoids from mice, transiently overexpressed non-degradable HIF2, and performed bulk RNA sequencing. Interestingly, HIF2 upregulated known radiation modulators and genes involved in GI homeostasis, including Wnt5a. Non-canonical Wnt5a signaling has been shown by other groups to improve intestinal crypt regeneration in response to injury. Here we show that HIF2 drives Wnt5a expression in multiple duodenal organoid models. Luciferase reporter assays performed in human cells showed that HIF2 directly activates the WNT5A promoter via a hypoxia response element. We then evaluated crypt regeneration using spheroid formation assays. Duodenal organoids that were pre-treated with recombinant Wnt5a had a higher cryptogenic capacity after irradiation, compared to vehicle-treated organoids. Conversely, we found that Wnt5a knockout decreased the cryptogenic potential of intestinal stem cells following irradiation. Treatment with recombinant Wnt5a prior to irradiation rescued the cryptogenic capacity of Wnt5a knockout organoids, indicating that Wnt5a is necessary and sufficient for duodenal radioprotection. Taken together, our results suggest that HIF2 radioprotects the GI tract by inducing Wnt5a expression.

DNA Sequencing of Small Bowel Adenocarcinomas Identifies Targetable Recurrent Mutations in the ERBB2 Signaling Pathway.

PURPOSE: Little is known about the genetic alterations characteristic of small bowel adenocarcinoma (SBA). Our purpose was to identify targetable alterations and develop experimental models of this disease.Experimental Design: Whole-exome sequencing (WES) was completed on 17 SBA patient samples and targeted-exome sequencing (TES) on 27 samples to confirm relevant driver mutations. Two SBA models with ERBB2 kinase activating mutations were tested for sensitivity to anti-ERBB2 agents in vivo and in vitro. Biochemical changes were measured by reverse-phase protein arrays. RESULTS: WES identified somatic mutations in 4 canonical pathways (WNT, ERBB2, STAT3, and chromatin remodeling), which were validated in the TES cohort. Although APC mutations were present in only 23% of samples, additional WNT-related alterations were seen in 12%. ERBB2 mutations and amplifications were present in 23% of samples. Patients with alterations in the ERBB2 signaling cascade (64%) demonstrated worse clinical outcomes (median survival 70.3 months vs. 109 months; log-rank HR = 2.4, P = 0.03). Two ERBB2-mutated (V842I and Y803H) cell lines were generated from SBA patient samples. Both demonstrated high sensitivity to ERBB2 inhibitor dacomitinib (IC50 < 2.5 nmol/L). In xenografts derived from these samples, treatment with dacomitinib reduced tumor growth by 39% and 59%, respectively, whereas it had no effect in an SBA wild-type ERBB2 model. CONCLUSIONS: The in vitro and in vivo models of SBA developed here provide a valuable resource for understanding targetable mutations in this disease. Our findings support clinical efforts to target activating ERBB2 mutations in patients with SBA that harbor these alterations.

Mitochondrial fusion exploits a therapeutic vulnerability of pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) requires mitochondrial oxidative phosphorylation (OXPHOS) to fuel its growth, however, broadly inhibiting this pathway might also disrupt essential mitochondrial functions in normal tissues. PDAC cells exhibit abnormally fragmented mitochondria that are essential to its oncogenicity, but it was unclear if this mitochondrial feature was a valid therapeutic target. Here, we present evidence that normalizing the fragmented mitochondria of pancreatic cancer via the process of mitochondrial fusion reduces OXPHOS, which correlates with suppressed tumor growth and improved survival in preclinical models. Mitochondrial fusion was achieved by genetic or pharmacologic inhibition of dynamin related protein-1 (Drp1) or through overexpression of mitofusin-2 (Mfn2). Notably, we found that oral leflunomide, an FDA-approved arthritis drug, promoted a two-fold increase in Mfn2 expression in tumors and was repurposed as a chemotherapeutic agent, improving the median survival of mice with spontaneous tumors by 50% compared to vehicle. We found that the chief tumor suppressive mechanism of mitochondrial fusion was enhanced mitophagy, which proportionally reduced mitochondrial mass and ATP production. These data suggest that mitochondrial fusion is a specific and druggable regulator of pancreatic cancer growth that could be rapidly translated to the clinic.

Single-Cell Transcriptomics of Pancreatic Cancer Precursors Demonstrates Epithelial and Microenvironmental Heterogeneity as an Early Event in Neoplastic Progression.

PURPOSE: Early detection of pancreatic ductal adenocarcinoma (PDAC) remains elusive. Precursor lesions of PDAC, specifically intraductal papillary mucinous neoplasms (IPMNs), represent a bona fide pathway to invasive neoplasia, although the molecular correlates of progression remain to be fully elucidated. Single-cell transcriptomics provides a unique avenue for dissecting both the epithelial and microenvironmental heterogeneities that accompany multistep progression from noninvasive IPMNs to PDAC. EXPERIMENTAL DESIGN: Single-cell RNA sequencing was performed through droplet-based sequencing on 5,403 cells from 2 low-grade IPMNs (LGD-IPMNs), 2 high-grade IPMNs (HGD-IPMN), and 2 PDACs (all surgically resected). RESULTS: Analysis of single-cell transcriptomes revealed heterogeneous alterations within the epithelium and the tumor microenvironment during the progression of noninvasive dysplasia to invasive cancer. Although HGD-IPMNs expressed many core signaling pathways described in PDAC, LGD-IPMNs harbored subsets of single cells with a transcriptomic profile that overlapped with invasive cancer. Notably, a proinflammatory immune component was readily seen in low-grade IPMNs, composed of cytotoxic T cells, activated T-helper cells, and dendritic cells, which was progressively depleted during neoplastic progression, accompanied by infiltration of myeloid-derived suppressor cells. Finally, stromal myofibroblast populations were heterogeneous and acquired a previously described tumor-promoting and immune-evading phenotype during invasive carcinogenesis. CONCLUSIONS: This study demonstrates the ability to perform high-resolution profiling of the transcriptomic changes that occur during multistep progression of cystic PDAC precursors to cancer. Notably, single-cell analysis provides an unparalleled insight into both the epithelial and microenvironmental heterogeneities that accompany early cancer pathogenesis and might be a useful substrate to identify targets for cancer interception.See related commentary by Hernandez-Barco et al., p. 2027.