Biology subjects

Maitra, A.

Publications and source records attributed to Maitra, A..

6 recordsLinked to original sources

bioRxiv2018

A pipeline for rapidly generating genetically engineered mouse models of pancreatic cancer using in vivo CRISPR-Cas9 mediated somatic recombination

Genetically engineered mouse models (GEMMs) that recapitulate the major genetic drivers in pancreatic ductal adenocarcinoma (PDAC) have provided unprecedented insights into the pathogenesis of this lethal neoplasm. Nonetheless, generating an autochthonous model is an expensive, time consuming and labor intensive process, particularly when tissue specific expression or deletion of compound alleles are involved. In addition, many of the current PDAC GEMMs cause embryonic, pancreas-wide activation or loss of driver alleles, neither of which reflects the cognate human disease scenario. The advent of CRISPR/Cas9 based gene editing can potentially circumvent many of the aforementioned shortcomings of conventional breeding schema, but ensuring the efficiency of gene editing in vivo remains a challenge. Here we have developed a pipeline for generating PDAC GEMMs of complex genotypes with high efficiency using a single \"workhorse\" mouse strain expressing Cas9 in the adult pancreas under a p48 promoter. Using adeno-associated virus (AAV) mediated delivery of multiplexed guide RNAs (sgRNAs) to the adult murine pancreas of p48-Cre; LSL-Cas9 mice, we confirm our ability to express an oncogenic Kras G12D allele through homology-directed repair (HDR), in conjunction with CRISPR-induced disruption of cooperating alleles (Trp53, Lkb1 and Arid1A). The resulting GEMMs demonstrate a spectrum of precursor lesions (pancreatic intraepithelial neoplasia [PanIN] or Intraductal papillary mucinous neoplasm [IPMN] with eventual progression to PDAC. Next generation sequencing of the resulting murine PDAC confirms HDR of oncogenic KrasG12D allele at the endogenous locus, and insertion deletion (\"indel\") and frameshift mutations of targeted tumor suppressor alleles. By using a single \"workhorse\" mouse strain and optimal AAV serotype for in vivo gene editing with combination of driver alleles, we have created a facile autochthonous platform for interrogation of the PDAC genome.

cancer biology↗

bioRxiv2018

GnasR201C Induces Murine Pancreatic Cystic Neoplasms through Suppression of YAP1 Signaling and Transcriptional Reprogramming

Background & AimsSomatic \"hotspot\" mutations of GNAS, which encodes for the alpha subunit of stimulatory G-protein, are present in ~60% of intraductal papillary mucinous neoplasms (IPMNs) of the pancreas. There are currently no cognate animal models that recapitulate the biology of mutant Gnas-induced IPMNs, and the underlying mechanisms that lead to the cystic pathway of neoplasia in the pancreas remain unknown.\n\nMethodsWe generated p48-Cre; LSL-KrasG12D; Rosa26R-LSL-rtTA-TetO-GnasR201C mice (Kras; Gnas mice) where pancreas-specific GnasR201C expression was induced by doxycycline administration. In this model, mutant Kras is constitutively expressed, and control mice were produced through absence of doxycycline. Separate cohorts of mice were utilized for timed necropsies and for Kaplan-Meier survival analysis. Isogenic cell lines (with doxycycline inducible mutant Gnas expression) were propagated from the resulting pancreatic ductal adenocarcinoma (PDAC).\n\nResultsCo-expression of KrasG12D and GnasR201C resulted in the development of pancreatic cystic lesions resembling human IPMNs in 100% of mice, with higher grades of epithelial dysplasia observed over time. Approximately one-third of Kras; Gnas mice developed PDAC at a median of 38 weeks post doxycycline induction. GnasR201C did not accelerate oncogenic transformation with KrasG12D, but rather, reprogrammed Ras-induced neoplasms towards a well-differentiated phenotype. GnasR201C induction led to activation of the inhibitory Hippo kinase cascade and cytoplasmic sequestration of phosphorylated YAP1 protein, a phenomenon that was also observed in human IPMN with GNAS mutations.\n\nConclusionsGNASR201C functions not as a traditional oncogene, but rather as an \"oncomodulator\" of KRAS-mediated pancreatic neoplasia, through suppression of YAP1 and transcriptional reprogramming towards a differentiated (large ductal) phenotype.

cancer biology↗

bioRxiv2018

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

BackgroundEarly 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 heterogeneity that accompany multistep progression from non-invasive IPMNs to PDAC.\n\nMethodsSingle cell RNA-sequencing was performed through droplet-based sequencing on 5,403 cells from two low-grade IPMNs (LGD-IPMN), two high-grade IPMNs (HGD-IPMN), and two PDACs (all surgically resected).\n\nResultsAnalysis of single cell transcriptomes revealed heterogeneous alterations within the epithelium and the tumor microenvironment during the progression of non-invasive dysplasia to invasive cancer. While 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 pro-inflammatory immune component was readily seen in low-grade IPMNs, comprised 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.\n\nConclusionsThis 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 heterogeneity that accompany early cancer pathogenesis, and might be a useful substrate to identify targets for cancer interception.

pathology↗

bioRxiv2018

Mitochondrial Fusion Suppresses Pancreatic Cancer Growth via Reduced Oxidative Metabolism

Pancreatic cancer is a highly lethal disease whose aggressive biology that is driven by mitochondrial oxidative metabolism. Mitochondria normally form a network of fused organelles, but we find that patient-derived and genetically engineered murine pancreatic cancer cells exhibit highly fragmented mitochondria with robust oxygen consumption rates (OCR). When mitochondrial fusion was activated by the genetic or pharmacological inhibition Drp1, the morphology and metabolism of human and murine pancreatic cancer cells more closely resembled that of normal pancreatic epithelial cells. This reduced metabolism was correlated with slower tumor growth, fewer metastases, and enhanced survival in a syngeneic orthotopic model. Similarly, directly activating mitochondrial fusion by overexpression of Mfn2 also reduced tumor growth and metastases. Mitochondrial fusion in pancreatic cancer cells was associated with reduced mitochondrial mass and Complex I expression and function. Thus, these data suggest that enhancing mitochondrial fusion through Drp1 inhibition or enhanced Mfn2 expression or function has strong tumor suppressive activity against pancreatic cancer and may thus represent a highly novel and efficacious therapeutic target.

cancer biology↗

bioRxiv2018

YAP1 Oncogene is a Context-specific Driver for Pancreatic Ductal Adenocarcinoma

AbstractTranscriptomic profiling classifies pancreatic ductal adenocarcinoma (PDAC) into several molecular subtypes with distinctive histological and clinical characteristics. However, little is known about the molecular mechanisms that define each subtype and their correlation with clinical outcome. Mutant KRAS is the most prominent driver in PDAC, present in over 90% of tumors, but the dependence of tumors on oncogenic KRAS signaling varies between subtypes. In particular, squamous subtype are relatively independent of oncogenic KRAS signaling and typically display much more aggressive clinical behavior versus progenitor subtype. Here, we identified that YAP1 activation is enriched in the squamous subtype and associated with poor prognosis. Activation of YAP1 in progenitor subtype cancer cells profoundly enhanced malignant phenotypes and transformed progenitor subtype cells into squamous subtype. Conversely, depletion of YAP1 specifically suppressed tumorigenicity of squamous subtype PDAC cells. Mechanistically, we uncovered a significant positive correlation between WNT5A expression and the YAP1 activity in human PDAC, and demonstrated that WNT5A overexpression led to YAP1 activation and recapitulated YAP1-dependent but Kras-independent phenotype of tumor progression and maintenance. Thus, our study identifies YAP1 oncogene as a major driver of squamous subtype PDAC and uncovers the role of WNT5A in driving PDAC malignancy through activation of the YAP pathway.

cancer biology↗

bioRxiv2017

Computational discovery of tissue morphology biomarkers in very long-term survivors with pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest forms of cancer, with an average 5-year survival rate of only 8%. Within PDAC patients, however, there is a small subset of patients who survive >10 years. Deciphering underlying reasons behind prolonged survival could potentially provide new opportunities to treat PDAC; however, no genomic, transcriptomic, proteomic, or clinical signatures have been found to robustly separate this subset of patients. Digital pathology, in combination with machine learning, provides an opportunity to computationally search for tissue morphology patterns associated with disease outcomes. Here, we developed a computational framework to analyze whole-slide images (WSI) of PDAC patient tissue and identify tissue-morphology signatures for very long term surviving patients. Our results indicate that less tissue morphology heterogeneity is significantly linked to better patient survival and that the extra-tumoral space encodes prognostic information for survival. Based on information from morphological heterogeneity in the tumor and its adjacent area, we established a machine learning model with an AUC of 0.94. Our analysis workflow highlighted a quantitative visual-based tissue phenotype analysis that also allows direct interaction with pathology. This study demonstrates a pathway to accelerate the discovery of undetermined tissue morphology associated with pathogenesis states and prognosis and diagnosis of patients by utilizing new computational approaches.

bioinformatics↗