Quantitative Serum Glycomics of Esophageal Adenocarcinoma, and Other Esophageal Disease Onsets

Aberrant glycosylation has been implicated in various types of cancers and changes in glycosylation may be associated with signaling pathways during malignant transformation. Glycomic profiling of blood serum, in which cancer cell proteins or their fragments with altered glycosylation patterns are shed, could reveal the altered glycosylation. We performed glycomic profiling of serum from patients with no known disease (N=18), patients with high grade dysplasia (HGD, N=11) and Barrett’s (N=5), and patients with esophageal adenocarcinoma (EAC, N=50) in an attempt to delineate distinct differences in glycosylation between these groups. The relative intensities of 98 features were significantly different among the disease onsets; 26 of these correspond to known glycan structures. The changes in the relative intensities of three of the known glycan structures predicted esophageal adenocarcinoma with 94% sensitivity and better than 60% specificity as determined by receiver operating characteristic (ROC) analysis. We have demonstrated that comparative glycomic profiling of EAC reveals a subset of glycans that can be selected as candidate biomarkers. These markers can differentiate disease-free from HGD, disease-free from EAC, and HGD from EAC. The clinical utility of these glycan biomarkers requires further validation

The altered transcriptome and DNA methylation profiles of docetaxel resistance in breast cancer PDX models

Taxanes are standard therapy in clinical practice for metastatic breast cancer; however, primary or acquired chemoresistance are a common cause of mortality. Breast cancer patient-derived xenografts (PDX) are powerful tools for the study of cancer biology and drug treatment response. Specific DNA methylation patterns have been associated to different breast cancer subtypes but its association with chemoresistance remains unstudied. Aiming to elucidate docetaxel resistance mechanisms, we performed genome-wide DNA methylation in breast cancer PDX models, including luminal and triple-negative breast cancer (TNBC) models sensitive to docetaxel, their matched models after emergence of chemoresistance and residual disease after short-term docetaxel treatment. We found that DNA methylation profiles from breast cancer PDX models maintain the subtype-specific methylation patterns of clinical samples. Two main DNA methylation clusters were found in TNBC PDX and remain stable during the emergence of docetaxel resistance; however, some genes/pathways were differentially methylated according to docetaxel response. A DNA methylation signature of resistance able to segregate TNBC based on chemotherapy response was identified. Transcriptomic profiling of selected sensitive/resistant pairs and integrative analysis with methylation data demonstrated correlation between some differentially methylated and expressed genes in docetaxel-resistant TNBC PDX models. Multiple gene expression changes were found after the emergence of docetaxel resistance in TNBC. DNA methylation and transcriptional changes identified between docetaxel-sensitive and -resistant TNBC PDX models or residual disease may have predictive value for chemotherapy response in TNBC. IMPLICATIONS: Subtype-specific DNA methylation patterns are maintained in breast cancer PDX models. While no global methylation changes were found, we uncovered differentially DNA methylated and expressed genes/pathways associated with the emergence of docetaxel resistance in TNBC

PDXNet portal: patient-derived Xenograft model, data, workflow and tool discovery.

We created the PDX Network (PDXNet) portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources, to facilitate collaboration among researchers and to make these data easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols and training materials for processing PDX data. Currently, the portal contains PDXNet model information and data resources from 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCI\u27s Patient-Derived Model Repository (PDMR) for public access. These models have 2134 associated sequencing files from 873 samples across 308 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal includes results from freely available, robust, validated and standardized analysis workflows on PDXNet sequencing files and PDMR data (3857 samples from 629 patients across 85 disease types). The PDXNet portal is continuously updated with new data and is of significant utility to the cancer research community as it provides a centralized location for PDXNet resources, which support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials

RANK is an independent biomarker of poor prognosis in estrogen receptor-negative breast cancer and a therapeutic target in patient-derived xenografts

Despite strong preclinical data, the therapeutic benefit of the RANKL inhibitor denosumab in BC patients, beyond its bone-related effects, is unclear. Here, we investigated the prognostic value of RANK expression and its functionality in human BC. We analyzed RANK and RANKL expression in more than 1500 BC cases (777 being estrogen receptor-negative (ER-)) from four independent cohorts. We confirmed that RANK is more frequently expressed in ER- tumors, but it is also found in a subset of ER+ tumors. In ER- BC, RANK expression was independently associated with poor outcome, especially in postmenopausal patients and those who received adjuvant chemotherapy. Gene expression analyses unraveled distinct biology associated with RANK in relation to ER expression and menopause, and evidenced enhanced RANK activation in ER- postmenopausal tumors, together with regulation of metabolic pathways. Functional studies and transcriptomic analyses in ER- RANK+ patients-derived orthoxenografts demonstrated that activation of RANK signaling pathway promotes tumor cell proliferation and stemness, and regulates multiple biological processes including tumor immune surveillance and metabolism. Our results demonstrate that RANK expression is an independent poor prognosis biomarker in postmenopausal ER- BC patients and support the rational of using RANK pathway inhibitors in combination with chemotherapy in ER- BC.N

A Renewable Tissue Resource of Phenotypically Stable, Biologically and Ethnically Diverse, Patient-Derived Human Breast Cancer Xenograft Models

Breast cancer research is hampered by difficulties in obtaining and studying primary human breast tissue, and by the lack of in vivo preclinical models that reflect patient tumor biology accurately. To overcome these limitations, we propagated a cohort of human breast tumors grown in the epithelium-free mammary fat pad of SCID/Beige and NOD/SCID/IL2γ-receptor null (NSG) mice, under a series of transplant conditions. Both models yielded stably transplantable xenografts at comparably high rates (~21% and ~19%, respectively). Of the conditions tested, xenograft take rate was highest in the presence of a low-dose estradiol pellet. Overall, 32 stably transplantable xenograft lines were established, representing 25 unique patients. Most tumors yielding xenografts were “triple-negative” (ER-PR-HER2+) (n=19). However, we established lines from three ER-PR-HER2+ tumors, one ER+PR-HER2−, one ER+PR+HER2− and one “triple-positive” (ER+PR+HER2+) tumor. Serially passaged xenografts show biological consistency with the tumor of origin, are phenotypically stable across multiple transplant generations at the histologic, transcriptomic, proteomic, and genomic levels, and show comparable treatment responses as those observed clinically. Xenografts representing 12 patients, including two ER+ lines, showed metastasis to the mouse lung. These models thus serve as a renewable, quality-controlled tissue resource for preclinical studies investigating treatment response and metastasis

Patient-derived xenograft (PDX) models in basic and translational breast cancer research

Patient-derived xenograft (PDX) models of a growing spectrum of cancers are rapidly supplanting long-established traditional cell lines as preferred models for conducting basic and translational preclinical research. In breast cancer, to complement the now curated collection of approximately 45 long-established human breast cancer cell lines, a newly formed consortium of academic laboratories, currently from Europe, Australia, and North America, herein summarizes data on over 500 stably transplantable PDX models representing all three clinical subtypes of breast cancer (ER+, HER2+, and "Triple-negative" (TNBC)). Many of these models are well-characterized with respect to genomic, transcriptomic, and proteomic features, metastatic behavior, and treatment response to a variety of standard-of-care and experimental therapeutics. These stably transplantable PDX lines are generally available for dissemination to laboratories conducting translational research, and contact information for each collection is provided. This review summarizes current experiences related to PDX generation across participating groups, efforts to develop data standards for annotation and dissemination of patient clinical information that does not compromise patient privacy, efforts to develop complementary data standards for annotation of PDX characteristics and biology, and progress toward "credentialing" of PDX models as surrogates to represent individual patients for use in preclinical and co-clinical translational research. In addition, this review highlights important unresolved questions, as well as current limitations, that have hampered more efficient generation of PDX lines and more rapid adoption of PDX use in translational breast cancer research

Deconvolution of cancer cell states by the XDec-SM method.

Proper characterization of cancer cell states within the tumor microenvironment is a key to accurately identifying matching experimental models and the development of precision therapies. To reconstruct this information from bulk RNA-seq profiles, we developed the XDec Simplex Mapping (XDec-SM) reference-optional deconvolution method that maps tumors and the states of constituent cells onto a biologically interpretable low-dimensional space. The method identifies gene sets informative for deconvolution from relevant single-cell profiling data when such profiles are available. When applied to breast tumors in The Cancer Genome Atlas (TCGA), XDec-SM infers the identity of constituent cell types and their proportions. XDec-SM also infers cancer cells states within individual tumors that associate with DNA methylation patterns, driver somatic mutations, pathway activation and metabolic coupling between stromal and breast cancer cells. By projecting tumors, cancer cell lines, and PDX models onto the same map, we identify in vitro and in vivo models with matching cancer cell states. Map position is also predictive of therapy response, thus opening the prospects for precision therapy informed by experiments in model systems matched to tumors in vivo by cancer cell state

Human C6orf211 Encodes Armt1, a Protein Carboxyl Methyltransferase that Targets PCNA and Is Linked to the DNA Damage Response

Recent evidence supports the presence of an L-glutamyl methyltransferase(s) in eukaryotic cells, but this enzyme class has been defined only in certain prokaryotic species. Here, we characterize the human C6orf211 gene product as "acidic residue methyltransferase-1" (Armt1), an enzyme that specifically targets proliferating cell nuclear antigen (PCNA) in breast cancer cells, predominately methylating glutamate side chains. Armt1 homologs share structural similarities with the SAM-dependent methyltransferases, and negative regulation of activity by automethylation indicates a means for cellular control. Notably, shRNA-based knockdown of Armt1 expression in two breast cancer cell lines altered survival in response to genotoxic stress. Increased sensitivity to UV, adriamycin, and MMS was observed in SK-Br-3 cells, while in contrast, increased resistance to these agents was observed in MCF7 cells. Together, these results lay the foundation for defining the mechanism by which this post-translational modification operates in the DNA damage response (DDR)

Human C6orf211 Encodes Armt1, a Protein Carboxyl Methyltransferase that Targets PCNA and Is Linked to the DNA Damage Response

Recent evidence supports the presence of an L-glutamyl methyltransferase(s) in eukaryotic cells, but this enzyme class has been defined only in certain prokaryotic species. Here, we characterize the human C6orf211 gene product as “acidic residue methyltransferase-1” (Armt1), an enzyme that specifically targets proliferating cell nuclear antigen (PCNA) in breast cancer cells, predominately methylating glutamate side chains. Armt1 homologs share structural similarities with the SAM-dependent methyltransferases, and negative regulation of activity by automethylation indicates a means for cellular control. Notably, shRNA-based knockdown of Armt1 expression in two breast cancer cell lines altered survival in response to genotoxic stress. Increased sensitivity to UV, adriamycin, and MMS was observed in SK-Br-3 cells, while in contrast, increased resistance to these agents was observed in MCF7 cells. Together, these results lay the foundation for defining the mechanism by which this post-translational modification operates in the DNA damage response (DDR)