PDCM Finder: an open global research platform for patient-derived cancer models

PDCM Finder (www.cancermodels.org) is a cancer research platform that aggregates clinical, genomic and functional data from patient-derived xenografts, organoids and cell lines. It was launched in April 2022 as a successor of the PDX Finder portal, which focused solely on patient-derived xenograft models. Currently the portal has over 6200 models across 13 cancer types, including rare paediatric models (17%) and models from minority ethnic backgrounds (33%), making it the largest free to consumer and open access resource of this kind. The PDCM Finder standardises, harmonises and integrates the complex and diverse data associated with PDCMs for the cancer community and displays over 90 million data points across a variety of data types (clinical metadata, molecular and treatment-based). PDCM data is FAIR and underpins the generation and testing of new hypotheses in cancer mechanisms and personalised medicine development.

Abstract 3105: PDCM Finder: An open global cancer research platform for patient-derived cancer models

PDCM Finder (https://cancermodels.org) is a new portal that aggregates patient-derived models (xenografts, cell lines and organoids) from 27 academic and commercial providers, enables users to search and compare over 6000 models and associated molecular data, and connects users with model providers to facilitate collaboration among researchers.

Users can search for models of interest by either (1) exploring molecular data summaries for models of specific cancer types, or (2) using the intuitive search and faceted filtering options of the web user interface or (3) access resource database via REST API to run their own analysis. The data includes gene expression, gene mutation, copy number alteration, cytogenetics, patient treatment and drug dosing studies. We link external resources like publication platforms and cancer specific annotation tools enabling exploration and prioritization of PDCM variation data (COSMIC, CIViC, OncoMX, OpenCRAVAT).

In addition to exploring PDCM metadata and data, the portal enables users to validate their PDX models against PDX MI standard, get a FAIRness score of the model of interest, explore originating resource data processing protocols, training materials and contact the model supplier/provider.

PDCM Finder builds on the success of the PDX Finder resource (PMID:30535239). Critical PDCM attributes, such as diagnosis, drug names and genes, are harmonized and integrated into a cohesive ontological model based on the PDX Minimal information standard (PDX MI, PMID: 29092942). PDX MI has become established in the community for data exchange, adopted by the PDX providers, consortia and informatics tools integrating PDX data. We are working with the community on the PDCM Minimal Information standard in an effort to make PDCM datasets adhere to the FAIR data principles. We are driving the development of and promoting the use of descriptive standards to facilitate data interoperability and promote global sharing of models. We provide expertise and software components to support several worldwide consortia including PDXNet, PDMR and EurOPDX. PDCM Finder is freely available under an Apache 2.0 license (https://github.com/PDCMFinder). This work is supported by NCI U24 CA204781 01, U24 CA253539, and R01 CA089713. We welcome feedback on the resource and are looking for participants for usability studies - please get in touch if interested.

Citation Format: Zinaida Perova, Csaba Halmagyi, Alex Follette, Mauricio Martinez, Federico Lopez-Gomez, Jeremy Mason, Abayomi Mosaku, Nathalie Conte, Ross Thorne, Steven Neuhauser, Dale Begley, Debra Krupke, Terrence Meehan, Carol Bult, Helen Parkinson. PDCM Finder: An open global cancer research platform for patient-derived cancer models [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 3105.

The EurOPDX Data Portal: an open platform for patient-derived cancer xenograft data sharing and visualization

Background

Patient-derived xenografts (PDX) mice models play an important role in preclinical trials and personalized medicine. Sharing data on the models is highly valuable for numerous reasons – ethical, economical, research cross validation etc. The EurOPDX Consortium was established 8 years ago to share such information and avoid duplicating efforts in developing new PDX mice models and unify approaches to support preclinical research. EurOPDX Data Portal is the unified data sharing platform adopted by the Consortium.

Main body

In this paper we describe the main features of the EurOPDX Data Portal (https://dataportal.europdx.eu/), its architecture and possible utilization by researchers who look for PDX mice models for their research. The Portal offers a catalogue of European models accessible on a cooperative basis. The models are searchable by metadata, and a detailed view provides molecular profiles (gene expression, mutation, copy number alteration) and treatment studies. The Portal displays the data in multiple tools (PDX Finder, cBioPortal, and GenomeCruzer in future), which are populated from a common database displaying strictly mutually consistent views.

(Short) Conclusion

EurOPDX Data Portal is an entry point to the EurOPDX Research Infrastructure offering PDX mice models for collaborative research, (meta)data describing their features and deep molecular data analysis according to users’ interests.

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer

Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of >530 PKs have been targeted by FDA-approved medications, and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to investigate the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on word and concept neighborhoods in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. Our results with historical data show that associations between PKs and specific cancers can be predicted years in advance with good accuracy. Our tool can be used to predict the relevance of inhibiting PKs for specific cancers and to support the design of well-focused clinical trials to discover novel PKIs for cancer therapy.

Abstract 3212: PDX Finder: Largest global catalog of patient tumor derived xenograft models

Patient-derived tumor xenograft (PDX) models are a critical oncology platform for cancer research, drug development and personalized medicine. Because of the heterogeneous nature of PDX repositories, finding models of interest is a challenge. The Jackson Laboratory and EMBL-EBI are developing PDX Finder, the world's largest open PDX database containing phenomic information of over 2900 models (www.pdxfinder.org, †). In support of this initiative, we developed the PDX Minimal Information standard (PDX-MI) which defines the minimal necessary metadata required to describe models (††). Within PDX Finder, critical attributes like diagnosis, drug names or genes are harmonized into a cohesive ontological data model based on PDX-MI. An intuitive, faceted search interface allows users to select models based on clinical/PDX attributes, tumor markers, dataset availability and/or drug dosing results. We provide PDX, patient, drug and molecular data details pages where all available information can be browsed and downloaded. To further facilitate users' model selection, we link key external resources like publication platforms and cancer specific annotation tools, enabling exploration and prioritisation of PDX variation data (COSMIC, CivicDb, OpenCRAVAT). Links to originating resource protocols and contact information are provided, facilitating data understanding and further collaboration.

Alongside database development activities, PDX Finder has undertaken activities to tackle areas of standards and tool development, data integration and outreach. PDX Finder provides key expertise and software components to support several worldwide consortia including PDXNet, PDMR and EurOPDX. We are driving the development of, and promoting the use of descriptive standards to facilitate data interoperability and promote global sharing of models. Our standard has become established in the community for data exchange, adopted by PDX providers, consortia, and informatic tools integrating PDX data. It has been re-used by different initiatives in the context of data collection and data modelling allowing adherence to the FAIR data principles - Findability, Accessibility, Interoperability and Reusability. PDX Finder is increasing awareness of PDX models, facilitating data integration, and enabling international collaboration, maximising the investment in, and translational capabilities of these important models of human cancer.

PDX Finder is freely available under an Apache 2 license (github.com/pdxfinder). Work supported by NCI U24 CA204781 01, R01 CA089713 and the European Molecular Biology Laboratory.

† Conte et al, 2019. PDX Finder: A Portal for Patient-Derived tumor Xenograft Model Discovery. NAR, 2019 Jan.†† Meehan, Conte et al, 2017. PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models. Cancer Res. 2017 Nov.PDXNet: www.pdxnetwork.org, PDMR: pdmr.cancer.gov, EUROPDX: www.europdx.eu

Citation Format: Nathalie Conte, Csaba Halmagyi, Abayomi Mosaku, Jeremy C. Mason, Alex W. Follette, Ross Thorne, Steven Neuhauser, Dale Begley, Debbie M. Krupke, Helen Parkinson, Terrence Meehan, Carol Bult. PDX Finder: Largest global catalog of patient tumor derived xenograft models [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3212.

Innovations, challenges, and minimal information for standardization of humanized mice

Mice xenotransplanted with human cells and/or expressing human gene products (also known as "humanized mice") recapitulate the human evolutionary specialization and diversity of genotypic and phenotypic traits. These models can provide a relevant in vivo context for understanding of human-specific physiology and pathologies. Humanized mice have advanced toward mainstream preclinical models and are now at the forefront of biomedical research. Here, we considered innovations and challenges regarding the reconstitution of human immunity and human tissues, modeling of human infections and cancer, and the use of humanized mice for testing drugs or regenerative therapy products. As the number of publications exploring different facets of humanized mouse models has steadily increased in past years, it is becoming evident that standardized reporting is needed in the field. Therefore, an international community-driven resource called "Minimal Information for Standardization of Humanized Mice" (MISHUM) has been created for the purpose of enhancing rigor and reproducibility of studies in the field. Within MISHUM, we propose comprehensive guidelines for reporting critical information generated using humanized mice.

Opportunities, resources, and techniques for implementing genomics in clinical care

Advances in technologies for assessing genomic variation and an increasing understanding of the effects of genomic variants on health and disease are driving the transition of genomics from the research laboratory into clinical care. Genomic medicine, or the use of an individual's genomic information as part of their clinical care, is increasingly gaining acceptance in routine practice, including in assessing disease risk in individuals and their families, diagnosing rare and undiagnosed diseases, and improving drug safety and efficacy. We describe the major types and measurement tools of genomic variation that are currently of clinical importance, review approaches to interpreting genomic sequence variants, identify publicly available tools and resources for genomic test interpretation, and discuss several key barriers in using genomic information in routine clinical practice.

Abstract 1074: The PDX Data Commons and Coordinating Center (PDCCC) for PDXNet in support of preclinical research

Patient-Derived Xenografts (PDX) are proven models to study novel drugs or drug combinations and test hypothesis in preclinical studies. The overarching goal of the PDXNet is to coordinate the development of appropriate PDX models and methods for preclinical drug testing to advance CTEP clinical development of new cancer agents.

The PDXNet is an NCI-funded consortium of six PDX Development and Trial Centers (PDTCs) and one PDCCC. Four PDTCs are responsible for developing PDXs and executing specific preclinical trials focused on cancer types including breast cancer, melanoma, and lung cancer. The other two recently awarded centers are specifically focused on minority PDX models and preclinical trials. Besides the PDTCs, the NCI Patient-Derived Models Repository (PDMR) at the Frederick National Laboratory for Cancer Research (FNLCR) is also providing models and data to the PDXNet. The PDCCC is responsible for coordination and developing standards for PDX generation as well as data analysis and metadata harmonization. The PDX Data Commons is built on top of existing NCI resources, leveraging the Cancer Genomics Cloud maintained by Seven Bridges Genomics, where PDXNet data is co-located with TCGA and other large-scale datasets. The PDCCC is co-led by experts from the Jackson Laboratory, providing scientific leadership in xenograft methods and cancer biology to ensure the promulgation of standards that are well-suited for the PDX community.

A new portal has been set up at https://www.pdxnetwork.org/ to serve as the point of access to PDXNet resources. In addition, we established ongoing network-wide meetings to facilitate knowledge exchange, held PDXNet portal trainings, and set up working groups to tackle specific challenges. For instance, the Data Ontology working group has been working towards building a common data ontology model specifically for PDX datasets. We are in the process of annotating the very first dataset using this new ontology on the PDXNet portal. Also, the Workflows working group has been working on building and benchmarking various RNA-seq and whole exome sequencing analysis workflows to standardize data processing between PDXNet grantees and create a harmonized PDXNet dataset. These PDX models and the accompanying data will be opened to the community for data mining and/or preclinical research.

The PDXNet is a strong step toward building a consensus around PDX models, so that the power for discovery can be expanded by making multi-institutional PDX cohorts a reality. As the coordination center, we are also working closely with the EuroPDX project to exchange standards and knowledge to support the PDX community with a set of standards going forward. The PDCCC is a central part of this process to systematically capture and analyze the variables most influential to PDX models and share protocols and tools to make PDXs an interchangeable research currency for preclinical discovery.

Citation Format: Jacqueline Rosains, Anuj Srivastava, Wingyi Woo, Vishal Sarsani, ZiMing Zhao, Javad Noorbakhsh, Ogan D. Abaan, Christian Frech, Jack DiGiovanna, Ryan Jeon, Steve Neuhauser, Peter Robinson, Yvonne A. Evrard, Carol Bult, Jeffrey A. Moscow, Brandi Davis-Dusenbery, Jeffrey H. Chuang. The PDX Data Commons and Coordinating Center (PDCCC) for PDXNet in support of preclinical research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1074.

Abstract 3842: Comprehensive genomic analysis demonstrates concordance of PDX models and patient tumor cohorts

The Jackson Laboratory has established more than 400 unique patient-derived xenograft (PDX) cancer models from patient tumors in the immunocompromised NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (aka, NSGTM) mouse strain, spanning across more than 30 tumor types. At low passages, these engrafted models are known to retain similar molecular characteristics and heterogeneity to the originating human tumor. As such, PDX models offer an excellent preclinical platform to test drug responses of novel cancer therapeutics and a powerful resource for conducting preclinical cancer pharmacogenomic studies. To aid the selection of suitable PDX models for preclinical studies and for the research purpose to understand tumor biology and response or resistance to a given treatment, we have characterized the PDX models for their transcriptomic, mutational and copy number profiles using sequencing and array approaches. We have established a compendium of PDX-tailored computational pipelines as the analysis of genomic data from PDX models could be challenging due to a) the contamination of PDX sample with mouse stroma, which complicates downstream bioinformatics analyses as mouse genome is almost 90% homologous to the human genome, and b) the lack of matched normal material to call somatic events. Our pipelines incorporate various filters to identify tumor specific single nucleotide variants, indels, copy number changes and expression profile in the PDX model. For the purpose of validating the accuracy of our analysis pipelines and demonstrating that the JAX PDX models are indeed representative of patient tumors, we compared JAX’s PDX cohort with patient cohorts in TCGA for mutations, copy number aberrations and RNA expression concordance. Using gene sets representative of each tumor type, we found that the overall genomic profile of each PDX tumor type is more correlated to the same tumor type in TCGA than other tumor types. In addition, an integrative analysis across all data types reveals that there are more common affected pathways between the same tumor type in PDX and TCGA. This comprehensive analysis revealed that the PDX and patient cohorts exhibit similar molecular characteristics, hence establishing the suitability of JAX PDX models as in vivo models to study fundamental tumor biology as well as to carry out preclinical studies of cancer drugs, including identification of biomarkers of response or resistance.

Citation Format: Xing Yi Woo, Vinod Yadav, Al Simons, Anuj Srivastava, Guruprasad Ananda, Vishal Kumar Sarsani, Roger Liu, Grace Stafford, Joel Graber, Krishna Karuturi, Susie Airhart, Joshy George, Carol Bult. Comprehensive genomic analysis demonstrates concordance of PDX models and patient tumor cohorts [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3842. doi:10.1158/1538-7445.AM2017-3842

Bedside Back to Bench: Building Bridges between Basic and Clinical Genomic Research

Genome sequencing has revolutionized the diagnosis of genetic diseases. Close collaborations between basic scientists and clinical genomicists are now needed to link genetic variants with disease causation. To facilitate such collaborations, we recommend prioritizing clinically relevant genes for functional studies, developing reference variant-phenotype databases, adopting phenotype description standards, and promoting data sharing.

Abstract IA13: The Patient-Derived Xenograft Program at The Jackson Laboratory

The Patient Derived Xenograft (PDX) Program at The Jackson Laboratory have as its focus, the development of quality surrogates of primary human tumors for the research community, the construction and optimization of murine hosts for human tumor xenografts, and the conduct of research into cancer biology using these PDX models. Begun in 2010, the PDX Program is based on the foundational murine host, the NOD-scid IL2rgnull or NSG (NOD.Cg-Prkdcscid IL2rgtm1wjl/SzJIL2rgtm1wjl) mouse, which lacks T, B, and NK cells and harbors deficiencies in innate immune response due to defects in multiple cytokine receptors including the IL2. 4, 7, 9, 15, and 21 receptors1,2. NSG mice have superior engraftment profiles as compared to other immunodeficient strains. These tumor xenografts in NSG hosts maintain the features of primary of engrafted human tumors including, tumor heterogeneity, three-dimensional architecture, and tumor biology including response to therapeutics. The engraftment rates vary according to tumor types with prostate cancers being among the lowest (3%), and colorectal cancers (66%), melanomas, and glioblastomas being among the best, according to whether the tumors are from metastatic sites, show a higher tumor grade, or have larger amounts of starting tumor material.

We currently have 446 PDX models in stock with ~100 in the pipeline, and when coupled with 218 models from our Korean cooperative institution (Ewha University), our total collection is ~664 covering 15 tumor types with none beyond passage number 4 (P4). Most have genomic data available and the Korean PDXs have matching normal tissue and DNA sequence data. Importantly, the human immune system can be engrafted in NSG mice and in the presence of a human tumor, can be used to study tumor-immune cell interactions including response to immune therapeutics. In addition, our colleagues and we have developed genetic modifications of the NSG to provide unique engraftment options: MHC class I and II knockouts to reduce xenogeneic graft-vs.-host disease, expressing HLA-A2 and other HLA class I and II transgenes to develop HLA-restricted human cytotoxic T cells, and IL-6 and prolactin to support engraftment of tumors needing these factors. NSG mice expressing human IL3/GM-CSF/SCF supports the immune reconstitution of myeloid progenitors providing a more complete immune system and has been useful in testing cancer immune therapeutics. With 26 staff dedicated to PDX and NSG experimentation, we provide strains of NSG mice with or without human immune cell reconstitution, and conduct large-scale pharmacological studies in cohorts of PDX models.

1Hidalgo M, Amant F, Biankin AV, Budinská E, Byrne AT, Caldas C, Clarke RB, de Jong S, Jonkers J, Mælandsmo GM, Roman-Roman S, Seoane J, Trusolino L, Villanueva A. Patient-derived xenograft models: an emerging platform for translational cancer research. Cancer Discov. 2014 Sep;4(9):998-1013.

2Shultz LD, Goodwin N, Ishikawa F, Hosur V, Lyons BL, Greiner DL. Human cancer growth and therapy in immunodeficient mouse models. Cold Spring Harbor Protoc. 2014 Jul 1;2014(7):694-708.

Citation Format: Edison T. Liu, James Keck, Susie Airhart, Lenny Shultz, Charles Lee, Carol Bult. The Patient-Derived Xenograft Program at The Jackson Laboratory. [abstract]. In: Proceedings of the AACR Special Conference: Patient-Derived Cancer Models: Present and Future Applications from Basic Science to the Clinic; Feb 11-14, 2016; New Orleans, LA. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(16_Suppl):Abstract nr IA13.

Abstract IA29: Mice host selection for patient-derived xenograft (PDX) model development and other critical factors for success

The Jackson Laboratory PDX program engages 26 staff members, 5 PIs, and runs thousands of PDX models per year in pre-clinical experiments both for internal scientific experiments and external projects. Over the last 6 years of operation of the PDX program, we have gained a significant amount of experience in all aspects of the process. This experience includes the genomic analysis of the tumors and the establishment of an extensive database annotating many of the 446 PDX models in our US inventory. Herein, we describe some of the characteristics of the system that enhances successful experimentation in this platform.

Several factors significantly improve the engraftment rate of tumors1: 1) the degree of immunodeficiency the host mouse - the NOD.Cg-Prkdcscid IL2rgtm1wjl/SzJ (aka, NSG), the NOD.CB17-Prkdcscid/J (aka,NOD-scid), or the NOD.Cg-Rag1tm1momIL2rgtm1wjl/SzJ (aka, NRG) engrafting better than the beige-scid and athymic nude mice; 2) the greater the amount of tissue engrafted, 3) the late or metastatic nature of the tumor, 4) the shorter time from surgery to implantation, 5) the absence of enzymatic dissociation, and 6) orthotopic implantation. Sequencing and expression analysis show the maintenance of the core genomic configuration between PDX and the primary tumors though some genetic differences are noted of indeterminate significance. Human stromal cells, however, tend to be replaced by murine stroma after the first passage. Tumor genetic heterogeneity is maintained through the fourth passage in NSG PDX models as confirmed by deep sequencing of bulk tumor and of individual progenitor clones. Most importantly, however, the tumor responses to systemic therapies appear to reflect the patient response2. Therefore, the use of PDX models to test novel agents may speed pre-clinical drug development.

The most important use of PDX systems may be in immuno-oncology given that “humanized” PDX models where implantation of a primary human tumor is implanted in a mouse with a reconstituted human cellular immune system provides a powerful preclinical experimental platform to test new immune modulators in human cancers. We, and others, have shown that humanized NSG mice bearing human tumor xenografts exhibit dramatic responses to immune checkpoint inhibitors that are immune cell and drug dependent3. Immune reconstitution is more complex because of the requirement of human cytokines that are not substituted by their murine counterparts. Engineered mice expressing human cytokines, e.g., IL3, CSF2 (GM-CSF), and KITLG (stem cell factor) in the NSG background (aka, NSG-SGM3 mice), and those expressing CSF1 (M-CSF), IL3, CSF2, and THPO in the C;129S4-Rag2tm1.1Flv IL2rgtm1.1Flv/J background (aka, MITRG mice) after engraftment with human hematopoietic stem cells have been shown to support myeloid cells4 including macrophages absent in standard NSG5. It is anticipated that these “next generation” humanized mice will provide a more nuanced picture of the tumor-immune system interaction6.

It is important to understand the challenges and limitations of the PDX platform that can be mitigated to a degree by quality control and study design. There are simple caveats. ~5 % of engrafted solid tumors give rise to EBV positive lymphomas and not the primary tumor. Moreover, ~5-10% of PDX tumors are overgrown by a transformed murine cell especially in late passaged. Thus, stringent histological quality control is necessary which includes the assessment of human cytokeratin, which provides an assessment of murine cancer incursion of solid human cancers. Another concern is that drug dosing for PDX experiments is often very different from that used in human studies. The NSG mice are more sensitive to certain DNA damaging agents and to radiation than NRG or Rag1null mice. Therefore, the structuring of combination studies using genotoxic agents is complicated and should be interpreted with appropriate care.

In terms of study design, we have found that each PDX model from an individual patient will give rise to individual tumors in an NSG cohort with significant growth and response variations. Thus, for each treatment arm we have calculated that between 6-8 animals is the minimal number required to attain statistical power of 95-99% to identify efficacy between arms. Moreover, in any treatment arm, it is necessary to assess the response of each individual PDX bearing mouse since a few individual tumors in a cohort may be resistant to a drug whereas the average of the arm shows an overall response. Recently, Gao, et al7 presented an alternative way to conduct PDX studies for drug development where only one mouse per PDX model was used per drug. The overall data (not whether a drug specifically was efficacious in a specific disease type) provided important strategic information in development. They raised an important point and that is that PDX preclinical studies should be structured differently from classical clinical trials to make best use of the platform. Not only the trial design, but even how to call a response should be reexamined. The partial responses seen in PDX experiments that can be precisely quantified but that do not qualify using RECIST criteria provide potentially important information about drug efficacy.

Taken together, the PDX platform using severely immunodeficient mice is a powerful tool that can significantly accelerate the development of new therapeutics by dramatically facilitating the advancement of innovative therapies with a high likelihood for success8.

References:

1Shultz LD, Goodwin N, Ishikawa F, Hosur V, Lyons BL, Greiner DL. Human cancer growth and therapy in immunodeficient mouse models. Cold Spring Harbor Protoc. 2014 Jul 1;2014(7):694-708.

2Garralda E, Paz K, López-Casas PP, Jones S, Katz A, Kann LM, López- Rios F, Sarno F, Al-Shahrour F, Vasquez D, Bruckheimer E, Angiuoli SV, Calles A, Diaz LA, Velculescu VE, Valencia A, Sidransky D, Hidalgo M. Integrated next-generation sequencing and avatar mouse models for personalized cancer treatment. Clin Cancer Res. 2014 May 1;20(9):2476-84 Epub 2014 Mar 14.

3Wang M, Keck JG, Cheng M, Cai D, Shultz L, Palucka K, Banchereau J, Bult C, Huntress R. Patient-derived tumor xenografts in humanized NSG mice: a model to study immune responses in cancer therapy. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-050.

4Billerbeck E, Barry WT, Mu K, Dorner M, Rice CM, Ploss A. Development of human CD4+FoxP3+ regulatory T cells in human stem cell factor-, granulocyte-macrophage colony-stimulating factor-, and interleukin-3- expressing NOD-SCID IL2Rγ(null) humanized mice. Blood. 2011 Mar 17;117(11):3076-86.

5Rongvaux A, Willinger T, Martinek J, Strowig T, Gearty SV, Teichmann LL, Saito Y, Marches F, Halene S, Palucka AK, Manz MG, Flavell RA. Development and function of human innate immune cells in a humanized mouse model. Nat Biotechnol. 2014 Apr;32(4):364-72.

6Shultz LD, Brehm MA, Garcia-Martinez JV, Greiner DL. Humanized mice for immune system investigation: progress, promise and challenges. Nat Rev Immunol. 2012 Nov;12(11):786-98.

7Gao H, Korn JM, Ferretti S, Monahan JE, Wang Y, Singh M, Zhang C, Schnell C, Yang G, Zhang Y, Balbin OA, Barbe S, Cai H, Casey F, Chatterjee S, Chiang DY, Chuai S, Cogan SM, Collins SD, Dammassa E, Ebel N, Embry M, Green J, Kauffmann A, Kowal C, Leary RJ, Lehar J, Liang Y, Loo A, Lorenzana E, Robert McDonald E 3rd, McLaughlin ME, Merkin J, Meyer R, Naylor TL, Patawaran M, Reddy A, Röelli C, Ruddy DA, Salangsang F, Santacroce F, Singh AP, Tang Y, Tinetto W, Tobler S, Velazquez R, Venkatesan K, Von Arx F, Wang HQ, Wang Z, Wiesmann M, Wyss D, Xu F, Bitter H, Atadja P, Lees E, Hofmann F, Li E, Keen N, Cozens R, Jensen MR, Pryer NK, Williams JA, Sellers WR. High- throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat Med. 2015 Nov;21(11):1318-25.

8Gandara DR, Mack PC, Bult C, Li T, Lara PN Jr, Riess JW, Astrow SH, Gandour-Edwards R, Cooke DT, Yoneda KY, Moore EH, Pan CX, Burich RA, David EA, Keck JG, Airhart S, Goodwin N, de Vere White RW, Liu ET. Bridging tumor genomics to patient outcomes through an integrated patient-derived xenograft platform. Clin Lung Cancer. 2015 May;16(3):165- 72.

Citation Format: Edison T. Liu, Carol Bult, Jeff Chuang, Pooja Kumar, Mingshan Cheng, R. Krishna Murthy Karuturi, Vivek Philip, James Keck, Karolina Palucka, Larry Shultz. Mice host selection for patient-derived xenograft (PDX) model development and other critical factors for success. [abstract]. In: Proceedings of the AACR Special Conference: Patient-Derived Cancer Models: Present and Future Applications from Basic Science to the Clinic; Feb 11-14, 2016; New Orleans, LA. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(16_Suppl):Abstract nr IA29.

Abstract 2332: Patient-derived tumor xenografts in humanized NSG-SGM3 mice: a new immuno-oncology platform

Humanized mice engrafted with tumors enable in vivo investigation of the interactions between the human immune system and human cancer. We have recently found that humanized NOD-scid IL2Rγnull (NSG) mice bearing patient-derived xenografts (PDX) allow efficacy studies of check-point inhibitors. Next generation NSG strains include triple transgenic NSG mice expressing human cytokines KITLG, CSF2, and IL-3 (NSG-SGM3). Here we provide a direct comparison of check-point inhibitors evaluation in NSG and NSG-SGM3 mice engrafted with CD34+ human hematopoietic progenitor cells (HPCs) from the same donor and implanted with PDX tumors. Corroborating earlier studies, reconstitution of human immune system in the blood was faster and more robust in NSG-SGM3 compared to NSG recipients throughout the course of the study (18 weeks). Human CD45+ cells reached 25% of total blood cells at week 4 in hu-NSG-SGM3 mice and at week 9 in hu-NSG mice. A majority of blood hCD45+ cells in hu-NSG-SGM3 at week 4 were CD33+ myeloid cells. Circulating hCD3+ T cells reached 10% at week 9 and included regulatory T cells (Tregs), consistent with earlier studies. Hu-NSG mice displayed comparable hCD3+ T cells in the blood only at 12-15 weeks and did not contain Tregs. PDX tumors were then engrafted into partially HLA-matched hu-NSG-SGM3 mice at 9 weeks post engraftment. Two PDX models previously shown to respond to anti-PD1 therapy in hu-NSG mice, BR1126 and LG1306, were used. Treatment with the anti-PD-1 receptor antibody pembrolizumab (Keytruda) significantly reduced tumor growth in both models. Thus, PDX-bearing hu-NSG-SGM3 mice might serve as a new and improved platform for preclinical immuno-oncology efficacy studies.

Citation Format: Li-Chin Yao, Mingshan Cheng, Minan Wang, Jacques Banchereau, Karolina Palucka, Leonard Shultz, Carol Bult, James G. Keck. Patient-derived tumor xenografts in humanized NSG-SGM3 mice: a new immuno-oncology platform. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2332.

Abstract 2411: Evolution during propagation and treatment of patient-derived triple negative breast cancer xenografts

Individual tumors, including the aggressive and difficult to treat triple-negative (ER-/PR-/HER2-) breast cancers (TNBCs) are heterogeneous collections of cells with multiple subclonal populations each contributing to the tumor. While subclonal heterogeneity is likely responsible for the development of drug resistance, identification of how tumor cell populations change over time has been difficult, largely because of the challenges in resampling tumor tissue at close time points. Here we quantify tumor evolution in human patient-derived xenografts implanted into NSG mice, which we use to test subclonal heterogeneity as a function of location within a tumor, propagation time, and drug treatment. We used high-depth (∼400x) sequencing of a targeted panel of 358 genes to quantify somatic mutation allele frequencies from 6 spatially-separated and 8 temporally-propagated xenograft samples derived from the same TNBC patient tumors. Samples ranged in age from 2-4 months post-engraftment. Although we observed a few low frequency mutations distinguishing samples, overall we found that allele frequencies of somatic mutations were well-preserved on this time scale. We then generated replicate xenografts from the same patient tumor and treated them respectively with cisplatin, doxorubicin, cyclophosphamide, docetaxel, or vehicle control for 25 days. Although again somatic mutations showed few differences in allele frequency across samples, substantial variations were seen when data were analyzed for copy number alterations. To confirm these effects we repeated the treatments for xenografts derived from two additional TNBC patients. Again we observed strong changes in tumor heterogeneity at the copy number level. This effect was particularly, but not exclusively, apparent in tumors with the greatest response to therapy. We further verified these measurements through Sanger and digital PCR sequencing on the treated mice and other mice in the same cohorts. Using a multi-sample xenograft propagation, dissection, sequencing, and computational analysis protocol, we have shown that tumor subpopulation changes in response to treatment can be quantified and distinguished from spatial or temporal effects, even for treatment time courses as short as 1 month. In triple negative breast cancer these variations are most apparent at the level of copy number variation. Our study demonstrates how patient-derived xenografts can provide detailed resolution of tumor population evolution during the manifestation of resistance.

Citation Format: Hyunsoo Kim, Pooja Kumar, Francesca Menghi, Joshy George, Guru Ananda, Susan Mockus, Chengsheng Zhang, Nicholas Larson, Henry C. Chen, Yan Yang, James Keck, R. Krishnamurthy Karuturi, Charles Lee, Carol Bult, Edison Liu, Jeffrey H. Chuang. Evolution during propagation and treatment of patient-derived triple negative breast cancer xenografts. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2411.

Development and Characterization of Bladder Cancer Patient-Derived Xenografts for Molecularly Guided Targeted Therapy

BACKGROUND

The overarching goal of this project is to establish a patient-derived bladder cancer xenograft (PDX) platform, annotated with deep sequencing and patient clinical information, to accelerate the development of new treatment options for bladder cancer patients. Herein, we describe the creation, initial characterization and use of the platform for this purpose.

METHODS AND FINDINGS

Twenty-two PDXs with annotated clinical information were established from uncultured unselected clinical bladder cancer specimens in immunodeficient NSG mice. The morphological fidelity was maintained in PDXs. Whole exome sequencing revealed that PDXs and parental patient cancers shared 92-97% of genetic aberrations, including multiple druggable targets. For drug repurposing, an EGFR/HER2 dual inhibitor lapatinib was effective in PDX BL0440 (progression-free survival or PFS of 25.4 days versus 18.4 days in the control, p = 0.007), but not in PDX BL0269 (12 days versus 13 days in the control, p = 0.16) although both expressed HER2. To screen for the most effective MTT, we evaluated three drugs (lapatinib, ponatinib, and BEZ235) matched with aberrations in PDX BL0269; but only a PIK3CA inhibitor BEZ235 was effective (p<0.0001). To study the mechanisms of secondary resistance, a fibroblast growth factor receptor 3 inhibitor BGJ398 prolonged PFS of PDX BL0293 from 9.5 days of the control to 18.5 days (p<0.0001), and serial biopsies revealed that the MAPK/ERK and PIK3CA-AKT pathways were activated upon resistance. Inhibition of these pathways significantly prolonged PFS from 12 day of the control to 22 days (p = 0.001). To screen for effective chemotherapeutic drugs, four of the first six PDXs were sensitive to the cisplatin/gemcitabine combination, and chemoresistance to one drug could be overcome by the other drug.

CONCLUSION

The PDX models described here show good correlation with the patient at the genomic level and known patient response to treatment. This supports further evaluation of the PDXs for their ability to accurately predict a patient's response to new targeted and combination strategies for bladder cancer.

Abstract 4816: Design, validation, and interpretation of an NGS assay for actionable variants in solid tumors

The Jackson Laboratory Cancer Treatment Profile™ (JAX-CTP™) is a next generation sequencing (NGS)-based molecular diagnostic assay that detects actionable gene variants in solid tumors to inform the selection of targeted therapeutics for cancer treatment. We will describe the design of the 358- gene panel, analytical validation, and the curation and clinical reporting of actionable variants. Selection of the gene panel was based on known drug targets, casual implications in cancer, and a thorough pathway analysis. DNA is extracted from FFPE tumor samples and using hybrid capture, the genes of interest are enriched and sequenced on Illumina HiSeq 2500 or MiSeq sequencers. FASTQ files generated from Illumina's CASAVA software are submitted to the JAX Clinical Genome Analytics (CGA) data analysis pipeline to perform automated read quality assessment, alignment, and variant calling. Identified variants are then submitted for clinical curation using a combination of the in-house JAX Clinical Knowledgebase (CKB) and the external Genetic Variant Annotation (GVA) from CollabRx. Once clinically annotated, the variants are graded relative to their clinical utility for the specific tumor type and compiled into a clinical report to inform patient treatment. Extensive analytical validation, following CAP guidelines, was conducted to assess limit of detection, accuracy, precision, sensitivity, and specificity of the assay. The summarized optimized sensitivity of the assay is a minimum coverage of samples at 300X, a limit of detection of 10% for SNP’s/indels and ≥6 copies for CNV’s, and an average of 3-4 actionable variants per sample. The challenges of interpreting gene variants for clinical actionability and for establishing an analytically valid bioinformatic pipeline will be discussed in-depth.

Citation Format: Susan M. Mockus, Guruprasad Ananda, Micaela Lundquist, Vanessa Spotlow, Al Simons, Talia Mitchell, Grace A. Stafford, Christopher S. Potter, Vivek Philip, Timothy Stearns, Anuj Srivastava, Mary Barter, Lucy Rowe, Joan Malcolm, Carol Bult, Radha Krishna Murthy Katuturi, Karen Rasmussen, Douglas Hinerfeld. Design, validation, and interpretation of an NGS assay for actionable variants in solid tumors. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4816. doi:10.1158/1538-7445.AM2015-4816

Abstract LB-050: Patient-derived tumor xenografts in humanized NSG mice: a model to study immune responses in cancer therapy

Mouse models are frequently used to test the therapeutic efficacy of anti-cancer drugs. However, the translation of murine experimental data to treatments for patients with cancer often fails due to significant differences between the species, including the differences in the immune system. Our goal is to bridge this gap and to establish an in vivo preclinical model of human tumor immunotherapy by engrafting immunodeficient mice expressing a partial human immune system with human tumor implants. Humanized NOD-scid IL2Rγ (null) (hu-NSG) mice were initially generated by transplanting NSG mice with human CD34+ hematopoietic stem and progenitor cells (HSPCs) which support human hematopoietic and immune system development. Hu-NSG mice develop functional human T cells and B cells with high levels of TCR excision circles, complex TCR repertoire diversity and antigen-specific T cell proliferative responses. Several types of patient-derived tumors (non small cell lung cancer, sarcoma, triple negative breast cancer and invasive bladder cancer) were successfully implanted into HLA mismatched hu-NSG mice. Tumor growth curves show a delay in tumor growth in hu-NSG compared to non-humanized NSG mice. In a colon cancer xenograft model, treatment with chemotherapy agent (5-FU) or with a therapeutic antibody directed against VEGF (Avastin) resulted in decreased tumor growth. In addition to PDX tumors we have also tested human cancer cell lines. Tumor growth was observed in all hu-NSG mice implanted with human ovarian tumor cell line SKOV3-Luc-D3 cells at different time points post HSPC engraftment, showing no evidence of tumor rejection. Thus, our model of humanized mice bearing tumor-derived xenografts provides opportunities to study both the safety and efficacy of current cancer therapies.

Citation Format: Minan Wang, James G. Keck, Mingshan Cheng, Danying Cai, Leonard Shultz, Karolina Palucka, Jacques Banchereau, Carol Bult, Rick Huntress. Patient-derived tumor xenografts in humanized NSG mice: a model to study immune responses in cancer therapy. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-050. doi:10.1158/1538-7445.AM2015-LB-050

Abstract 318: Enhancing drug discovery and development throughput without sacrificing predictivity: ex vivo 3D drug response profiling (DRP) using patient-derived xenografts (PDX)

Background: PDX have become critical elements of preclinical drug development as they better reflect the heterogeneity, molecular and histopathologic signatures of the original tumor than cell lines or genetically engineered mouse models, and their drug response profiles correlate with clinical response. While PDX models have become a powerful tool in drug discovery and development, limitations include low throughput for broad drug screening, lack of dose-response curves, high cost and progressive loss of human-derived stromal elements over serial passages, restricting utility for certain therapeutic classes. A potential mechanism to overcome the low throughput and high cost of PDX models is the incorporation of ex vivo 3D (EV3D) DRP on cells isolated from early passage PDX models. Thus, we correlated DRP results using PDX with genetic mutations and drug response of PDX tested in vivo. Materials & Methods: Cells were isolated from low-passage triple negative breast, invasive bladder, and non-small cell lung PDX tumors propagated in NSG mice and cultured as 3D spheroids. 3D spheroid cultures were exposed to 15 clinically-relevant chemotherapy and targeted agents and assayed for cell viability over a range of concentrations. Non-linear regression curves were generated and relative IC50s estimated. In vivo response with limited numbers of agents at clinically relevant concentrations (3 including controls) was assessed. Results: 3D cultures and testing were successfully established across all PDX and IC50s were successfully generated in 98% of drugs tested. EV3D DRP of PDX tumors differentiated activity of cytotoxic and targeted agents across tumors of similar histologic site of origin. Gemcitabine (IC50 = .007 versus 27 uM) and docetaxel (0.2 versus 40uM) activity was highly correlated with in vivo response in bladder and breast cancers, respectively, whereas cisplatin was equally active across all tumor types (IC50 = 3-8uM). hENT1 mRNA expression was not predictive of gemcitabine activity. EV3D DRP data correlated with PDX and clinical outcome. It identified Erlotinib as being relatively inactive (3 uM) against lung cancer PDX with an EGFR e19del, T790M mutation which correlated with the outcome seen in the PDX mouse and the clinical patient outcome in which the patient became nonresponsive to erlotinib. Trametinib was highly active against lung cancer PDX with a KRAS G12C mutation (IC50 6.7 × 10-6 versus 1.1 × 10-3) and will be used to perform efficacy studies in the KRAS mutant lung PDX model Conclusions: EV3D DRP predicts in vivo response and correlates with pathway activating mutations. EV3D DRP using PDX may represent a novel high throughput and predictive drug response platform that enables compound ranking for preclinical and clinical applications.

Citation Format: Tessa M. DesRochers, Christina Mattingly, Stephen Shuford, Matthew Gevaert, David Orr, Carol Bult, Susie Airhart, Mingshan Cheng, Minan Wang, James Keck, Howland Crosswell. Enhancing drug discovery and development throughput without sacrificing predictivity: ex vivo 3D drug response profiling (DRP) using patient-derived xenografts (PDX). [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 318. doi:10.1158/1538-7445.AM2015-318

Bridging tumor genomics to patient outcomes through an integrated patient-derived xenograft platform

New approaches to optimization of cancer drug development in the laboratory and the clinic will be required to fully achieve the goal of individualized, precision cancer therapy. Improved preclinical models that more closely reflect the now recognized genomic complexity of human cancers are needed. Here we describe a collaborative research project that integrates core resources of The Jackson Laboratory Basic Science Cancer Center with genomics and clinical research facilities at the UC Davis Comprehensive Cancer Center to establish a clinically and genomically annotated patient-derived xenograft (PDX) platform designed to enhance new drug development and strategies for targeted therapies. Advanced stage non-small-cell lung cancer (NSCLC) was selected for initial studies because of emergence of a number of "druggable" molecular targets, and recent recognition of substantial inter- and intrapatient tumor heterogeneity. Additionally, clonal evolution after targeted therapy interventions make this tumor type ideal for investigation of this platform. Using the immunodeficient NOD scid gamma mouse, > 200 NSCLC tumor biopsies have been xenotransplanted. During the annotation process, patient tumors and subsequent PDXs are compared at multiple levels, including histomorphology, clinically applicable molecular biomarkers, global gene expression patterns, gene copy number variations, and DNA/chromosomal alterations. NSCLC PDXs are grouped into panels of interest according to oncogene subtype and/or histologic subtype. Multiregimen drug testing, paired with next-generation sequencing before and after therapy and timed tumor pharmacodynamics enables determination of efficacy, signaling pathway alterations, and mechanisms of sensitivity-resistance in individual models. This approach should facilitate derivation of new therapeutic strategies and the transition to individualized therapy.

Development and validation of the JAX Cancer Treatment Profile™ for detection of clinically actionable mutations in solid tumors

BACKGROUND

The continued development of targeted therapeutics for cancer treatment has required the concomitant development of more expansive methods for the molecular profiling of the patient's tumor. We describe the validation of the JAX Cancer Treatment Profile™ (JAX-CTP™), a next generation sequencing (NGS)-based molecular diagnostic assay that detects actionable mutations in solid tumors to inform the selection of targeted therapeutics for cancer treatment.

METHODS

NGS libraries are generated from DNA extracted from formalin fixed paraffin embedded tumors. Using hybrid capture, the genes of interest are enriched and sequenced on the Illumina HiSeq 2500 or MiSeq sequencers followed by variant detection and functional and clinical annotation for the generation of a clinical report.

RESULTS

The JAX-CTP™ detects actionable variants, in the form of single nucleotide variations and small insertions and deletions (≤50 bp) in 190 genes in specimens with a neoplastic cell content of ≥10%. The JAX-CTP™ is also validated for the detection of clinically actionable gene amplifications.

CONCLUSIONS

There is a lack of consensus in the molecular diagnostics field on the best method for the validation of NGS-based assays in oncology, thus the importance of communicating methods, as contained in this report. The growing number of targeted therapeutics and the complexity of the tumor genome necessitate continued development and refinement of advanced assays for tumor profiling to enable precision cancer treatment.

Mutation discovery in mice by whole exome sequencing

We report the development and optimization of reagents for in-solution, hybridization-based capture of the mouse exome. By validating this approach in a multiple inbred strains and in novel mutant strains, we show that whole exome sequencing is a robust approach for discovery of putative mutations, irrespective of strain background. We found strong candidate mutations for the majority of mutant exomes sequenced, including new models of orofacial clefting, urogenital dysmorphology, kyphosis and autoimmune hepatitis.

Clusters of internally primed transcripts reveal novel long noncoding RNAs

Non-protein-coding RNAs (ncRNAs) are increasingly being recognized as having important regulatory roles. Although much recent attention has focused on tiny 22- to 25-nucleotide microRNAs, several functional ncRNAs are orders of magnitude larger in size. Examples of such macro ncRNAs include Xist and Air, which in mouse are 18 and 108 kilobases (Kb), respectively. We surveyed the 102,801 FANTOM3 mouse cDNA clones and found that Air and Xist were present not as single, full-length transcripts but as a cluster of multiple, shorter cDNAs, which were unspliced, had little coding potential, and were most likely primed from internal adenine-rich regions within longer parental transcripts. We therefore conducted a genome-wide search for regional clusters of such cDNAs to find novel macro ncRNA candidates. Sixty-six regions were identified, each of which mapped outside known protein-coding loci and which had a mean length of 92 Kb. We detected several known long ncRNAs within these regions, supporting the basic rationale of our approach. In silico analysis showed that many regions had evidence of imprinting and/or antisense transcription. These regions were significantly associated with microRNAs and transcripts from the central nervous system. We selected eight novel regions for experimental validation by northern blot and RT-PCR and found that the majority represent previously unrecognized noncoding transcripts that are at least 10 Kb in size and predominantly localized in the nucleus. Taken together, the data not only identify multiple new ncRNAs but also suggest the existence of many more macro ncRNAs like Xist and Air.

The human genome has been sequenced, and, intriguingly, less than 2% specifies the information for the basic protein building blocks of our bodies. So, what does the other 98% do? It now appears that the mammalian genome also specifies the instructions for many previously undiscovered “non protein-coding RNA” (ncRNA) genes. However, what these ncRNAs do is largely unknown. In recent years, strategies have been designed that have successfully identified hundreds of short ncRNAs—termed microRNAs—many of which have since been shown to act as genetic regulators. Also known to be functionally important are a handful of ncRNAs orders of magnitude larger in size than microRNAs. The availability of complete genome and comprehensive transcript sequences allows for the systematic discovery of more large ncRNAs. The authors developed a computational strategy to screen the mouse genome and identify large ncRNAs. They detected existing large ncRNAs, thus validating their approach, but, more importantly, discovered more than 60 other candidates, some of which were subsequently confirmed experimentally. This work opens the door to a virtually unexplored world of large ncRNAs and beckons future experimental work to define the cellular functions of these molecules.

The transcriptional landscape of the mammalian genome

This study describes comprehensive polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5' and 3' boundaries of 181,047 transcripts with extensive variation in transcripts arising from alternative promoter usage, splicing, and polyadenylation. There are 16,247 new mouse protein-coding transcripts, including 5154 encoding previously unidentified proteins. Genomic mapping of the transcriptome reveals transcriptional forests, with overlapping transcription on both strands, separated by deserts in which few transcripts are observed. The data provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.

The Mouse Tumor Biology Database: integrated access to mouse cancer biology data

Mice have long been used as models for the study of human cancer. The National Cancer Institute has included among its research areas of extraordinary opportunity the development of new mouse genetic models of human cancer and the exploration of cancer imaging as a research tool. Because of the volume and interconnectedness of relevant data, the creation and maintenance of bioinformatics resources of mouse tumor biology is necessary to facilitate current and future cancer research. The Mouse Tumor Biology (MTB) Database provides electronic access to data generated through the study of spontaneous and induced tumors in genetically defined mice (inbred, hybrid, spontaneous and induced mutant, and genetically engineered strains of mice).

Large-scale mutagenesis of the mouse to understand the genetic bases of nervous system structure and function

N-ethyl-N-nitrosourea (ENU) mutagenesis is presented as a powerful approach to developing models for human disease. The efforts of three NIH Mutagenesis Centers established for the detection of neuroscience-related phenotypes are described. Each center has developed an extensive panel of phenotype screens that assess nervous system structure and function. In particular, these screens focus on complex behavioral traits from drug and alcohol responses to circadian rhythms to epilepsy. Each of these centers has developed a bioinformatics infrastructure to track the extensive number of transactions that are inherent in these large-scale projects. Over 100 new mouse mutant lines have been defined through the efforts of these three mutagenesis centers and are presented to the research community via the centralized Web presence of the Neuromice.org consortium (http://www.neuromice.org). This community resource provides visitors with the ability to search for specific mutant phenotypes, to view the genetic and phenotypic details of mutant mouse lines, and to order these mice for use in their own research program.

The Gene Ontology (GO) database and informatics resource

The Gene Ontology (GO) project (http://www. geneontology.org/) provides structured, controlled vocabularies and classifications that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences. Many model organism databases and genome annotation groups use the GO and contribute their annotation sets to the GO resource. The GO database integrates the vocabularies and contributed annotations and provides full access to this information in several formats. Members of the GO Consortium continually work collectively, involving outside experts as needed, to expand and update the GO vocabularies. The GO Web resource also provides access to extensive documentation about the GO project and links to applications that use GO data for functional analyses.

Development and evaluation of an automated annotation pipeline and cDNA annotation system

Manual curation has long been held to be the "gold standard" for functional annotation of DNA sequence. Our experience with the annotation of more than 20,000 full-length cDNA sequences revealed problems with this approach, including inaccurate and inconsistent assignment of gene names, as well as many good assignments that were difficult to reproduce using only computational methods. For the FANTOM2 annotation of more than 60,000 cDNA clones, we developed a number of methods and tools to circumvent some of these problems, including an automated annotation pipeline that provides high-quality preliminary annotation for each sequence by introducing an "uninformative filter" that eliminates uninformative annotations, controlled vocabularies to accurately reflect both the functional assignments and the evidence supporting them, and a highly refined, Web-based manual annotation tool that allows users to view a wide array of sequence analyses and to assign gene names and putative functions using a consistent nomenclature. The ultimate utility of our approach is reflected in the low rate of reassignment of automated assignments by manual curation. Based on these results, we propose a new standard for large-scale annotation, in which the initial automated annotations are manually investigated and then computational methods are iteratively modified and improved based on the results of manual curation.

Initial sequencing and comparative analysis of the mouse genome

The sequence of the mouse genome is a key informational tool for understanding the contents of the human genome and a key experimental tool for biomedical research. Here, we report the results of an international collaboration to produce a high-quality draft sequence of the mouse genome. We also present an initial comparative analysis of the mouse and human genomes, describing some of the insights that can be gleaned from the two sequences. We discuss topics including the analysis of the evolutionary forces shaping the size, structure and sequence of the genomes; the conservation of large-scale synteny across most of the genomes; the much lower extent of sequence orthology covering less than half of the genomes; the proportions of the genomes under selection; the number of protein-coding genes; the expansion of gene families related to reproduction and immunity; the evolution of proteins; and the identification of intraspecies polymorphism.

Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cDNAs

Only a small proportion of the mouse genome is transcribed into mature messenger RNA transcripts. There is an international collaborative effort to identify all full-length mRNA transcripts from the mouse, and to ensure that each is represented in a physical collection of clones. Here we report the manual annotation of 60,770 full-length mouse complementary DNA sequences. These are clustered into 33,409 'transcriptional units', contributing 90.1% of a newly established mouse transcriptome database. Of these transcriptional units, 4,258 are new protein-coding and 11,665 are new non-coding messages, indicating that non-coding RNA is a major component of the transcriptome. 41% of all transcriptional units showed evidence of alternative splicing. In protein-coding transcripts, 79% of splice variations altered the protein product. Whole-transcriptome analyses resulted in the identification of 2,431 sense-antisense pairs. The present work, completely supported by physical clones, provides the most comprehensive survey of a mammalian transcriptome so far, and is a valuable resource for functional genomics.

Functional annotation of a full-length mouse cDNA collection

The RIKEN Mouse Gene Encyclopaedia Project, a systematic approach to determining the full coding potential of the mouse genome, involves collection and sequencing of full-length complementary DNAs and physical mapping of the corresponding genes to the mouse genome. We organized an international functional annotation meeting (FANTOM) to annotate the first 21,076 cDNAs to be analysed in this project. Here we describe the first RIKEN clone collection, which is one of the largest described for any organism. Analysis of these cDNAs extends known gene families and identifies new ones.

Intraspecific variation in small-subunit rRNA sequences in GenBank: why single sequences may not adequately represent prokaryotic taxa

Small-subunit rRNA (SSU rRNA) sequencing is a powerful tool to detect, identify, and classify prokaryotic organisms, and there is currently an explosion of SSU rRNA sequencing in the microbiology community. We report unexpectedly high levels of intraspecific variation (within and between strains) of prokaryote SSU rRNA sequences deposited in GenBank. A total of 82% of the prokaryote species with two published SSU rRNA sequences had more variable positions than a 0.1% random sequencing error would predict, and 48% of these sequence pairs had more variable positions than predicted by a 1.0% random sequencing error. Other sources of sequence variability must account for some of this intraspecific variation. Given these results, phylogenetic studies and biodiversity estimates obtained by using prokaryotic SSU rRNA sequences cannot proceed under the assumption that rRNA sequences of single operons from single isolates adequately represent their taxa. Sequencing SSU rRNA molecules from multiple operons and multiple isolates is highly recommended to obtain meaningful phylogenetic hypotheses, as is careful attention to accurate strain identification.