Utility of the JAX Clinical Knowledgebase in capture and assessment of complex genomic cancer data

Patterson, Sara E.; Statz, Cara; Yin, Taofei; Mockus, Susan M.

doi:10.1038/s41698-018-0073-y

Cited by 35 publications

(30 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A low copy number change threshold (|log 2 (CN ratio) change| > 0.5) was selected to include genes with subclonal alterations. To test for functional biases, we compared CNA-altered genes to gene sets with known cancer- and treatment-related functions, notably genes in TCGA oncogenic signaling pathways 49 ; genes with copy number and expression changes associated with therapeutic sensitivity, resistance or changes in drug response from the JAX Clinical Knowledgebase 50,51 ; and genes with frequent amplifications or deletions in the Cancer Gene Census 52 (Cosmic version 89). We calculated the proportion of altered genes for sample pairs from each model across all platforms and tumor types.…”

Section: Resultsmentioning

confidence: 99%

“…( a ) Distribution of proportion of altered genes for pairwise comparisons of PDX samples for various gene sets: Protein-coding genes annotated by Ensembl; Oncogenic signaling pathways identified by TCGA 49 ; JAX CKB 50,51 Amp indicates genes with copy number gain or over-expression associated with therapeutic sensitivity or resistance; JAX CKB Del indicates genes with copy number loss or under-expression associated with therapeutic sensitivity or resistance; Census Amp Del indicates genes with frequent amplifications or deletions in the Cancer Gene Census 52 . CNA genes were identified by |residual| > 0.5 from linear regression model.…”

Section: Resultsmentioning

confidence: 99%

“…Genes with gain in copy number or expression, or loss in copy number or expression that conferred therapeutic sensitivity, resistance or increase/decrease in drug response from the JAX Clinical Knowledgebase 45,46 (JAX-CKB) based on literature curation (https://ckbhome.jax.org/, as of 06-18-2019).…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

Woo¹,

Giordano

Srivastava³

et al. 2019

Preprint

View full text Add to dashboard Cite

41 a PDXNET consortium 42 b EurOPDX consortium 43 # These authors contributed equally to this work.44 § These authors jointly supervised this work. ABSTRACT 107Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical 108 studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution 109 during PDX engraftment and propagation, impacting the accuracy of PDX modeling of human 110 cancer. Here we exhaustively analyze copy number alterations (CNAs) in 1451 PDX and matched 111 patient tumor (PT) samples from 509 PDX models. CNA inferences based on DNA sequencing 112 and microarray data displayed substantially higher resolution and dynamic range than gene 113 expression-based inferences, and they also showed strong CNA conservation from PTs through 114 late-passage PDXs. CNA recurrence analysis of 130 colorectal and breast PT/PDX-early/PDX-115 late trios confirmed high-resolution CNA retention. We observed no significant enrichment of 116 cancer-related genes in PDX-specific CNAs across models. Moreover, CNA differences between 117 patient and PDX tumors were comparable to variations in multi-region samples within patients. 118Our study demonstrates the lack of systematic copy number evolution driven by the PDX mouse 119 host. 121 MAIN 122A variety of models of human cancer have been used to study basic biological processes and 123 predict responses to treatment. For example, mouse models with genetically engineered 124 mutations in oncogenes and tumor suppressor genes have clarified the genetic and molecular 125 basis of tumor initiation and progression 1,2 , though responses sometimes differ between human 126 and mouse 3 . Cell lines have also been widely used to study cancer cells, but they lack the 127 heterogeneity and microenvironment of in vivo tumors and have shown limitations for predicting 128 clinical response 4 . Human tumors engrafted into transplant-compliant recipient mice (patient-129 derived xenografts, PDX) have advantages over prior systems for preclinical drug efficacy studies 130 because they allow researchers to directly study human cells and tissues in vivo 5-8 . Comparisons131 of genome characteristics and histopathology of primary tumors and xenografts of human breast 132 cancer 9-13 , ovarian cancer 14 , colorectal cancer 15 and lung cancer 16-18 , have demonstrated that the 133 biological properties of patient-derived tumors are largely preserved in xenografts. A growing body 134 of literature supports their use in cancer drug discovery and development 19-21 . 135A caveat to PDX models is that intratumoral evolution can occur during engraftment and 136 passaging 11,22-25 . Such evolution could potentially modify treatment response of PDXs with 137 respect to the patient tumors 23,26,27 , particularly if the evolution were to systematically alter cancer-138 related genes. This issue is related to multi-region comparisons of patient tumors 28-31 , for which 139 local mutational and immune infiltration variations have sugg...

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

Woo¹,

Giordano

Srivastava³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…On the JAX CKB website, users can search by gene, gene variants, drug, drug class, indication, and clinical trials. Notably, JAX CKB can build complex molecular profiles that allow for the association of therapeutic efficacy to multiple genetic alterations simultaneously instead of each variant in isolation (Patterson et al, 2019). JAX CKB is hosted at The Jackson Laboratory.…”

Section: Jackson Laboratory Clinical Knowledge Base (Jax Ckb)mentioning

confidence: 99%

A Review of Precision Oncology Knowledgebases for Determining the Clinical Actionability of Genetic Variants

Warner

2020

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

The increased availability of tumor genetic testing and targeted cancer therapies contributes to the advancement of precision medicine in the field of oncology. Precision oncology knowledgebases provide a way of organizing clinically relevant genetic information in a way that is easily accessible for both oncologists and patients, facilitating the genetic-based clinical decision making. Many organizations and companies have built precision oncology knowledgebases, intended for multiple users. In general, these knowledgebases offer information on cancer-related genetic variants as well as their associated diagnostic, prognostic, and therapeutic implications, but they often differ in their information curations, designs, and user experiences. It is advisable that oncologists use multiple knowledgebases during their practice to have them complement each other. In the future, convergence toward common standards and formats is needed to ensure that the comprehensive knowledge across all sources can be unified to bring the oncology community closer to the achievement of the goal of precision oncology.

show abstract

“…When curating this field, the most specific Variant Name described by the source should be used (e.g., KRAS G12/G13 rather than KRAS Exon 2 Mutation). The Variant Name can be very specific [e.g., VHL R176fs (c.528delG)], or can refer to a collection of variants fitting a named category (i.e., categorical variants [15]). Examples of categorical variants include KRAS G12/G13, EGFR Exon 20 Insertion, and PIK3CA Mutation (Supplementary Figure 4).…”

Section: Curating Within the Variant Knowledge Modelmentioning

confidence: 99%

The CIViC knowledge model and standard operating procedures for curation and clinical interpretation of variants in cancer

Danos

Krysiak

Barnell

et al. 2019

Preprint

View full text Add to dashboard Cite

Manually curated variant knowledgebases and their associated knowledge models are serving an increasingly important role in distributing and interpreting variants in cancer. These knowledgebases vary in their level of public accessibility, and the complexity of the models used to capture clinical knowledge. CIViC (Clinical Interpretations of Variants in Cancer -www.civicdb.org) is a fully open, free-to-use cancer variant interpretation knowledgebase that incorporates highly detailed curation of evidence obtained from peer-reviewed publications. Currently, the CIViC knowledge model consists of four main components: Genes, Variants, Evidence Items, and Assertions. Each component has an associated knowledge model and methods for curation. Gene and Variant data contextualize the genomic region(s) involved in the clinical statement. Evidence Items provide structured associations between variants and their clinically predictive/therapeutic, prognostic, diagnostic, predisposing, and functional implications. Finally, CIViC Assertions summarize collections of CIViC Evidence Items for a specific Disease, Variant, and Clinical Significance with incorporation of clinical and technical guidelines. Here we present the CIViC knowledge model, curation standard operating procedures, and detailed examples to support community-driven curation of cancer variants.relevant variants, as well as open distribution of a standardized cancer variant knowledgebase [8,9]. The Clinical Interpretations of Variants in Cancer (CIViC) knowledgebase (www.civicdb.org) was developed to address the challenges outlined above [10].CIViC is a fully open, free-to-use knowledgebase, which incorporates clinical evidence associated with a biomedical publication. Evidence supporting specific clinical interpretations is gathered via crowdsourced curation followed by expert review and moderation. All submissions, revisions, moderations, and comments on CIViC entries are tracked and displayed through the CIViC web interface, providing transparency and clear provenance of all content in the knowledgebase.Here we describe the CIViC knowledge model, and provide a standard operating procedure for clinical interpretation of variants in cancer using the CIViC platform. We first provide details on how the knowledge model permits consumption as well as curation of clinical information. We then describe each of the four components (Genes, Variants, Evidence Items, and Assertions) by outlining associated features, detailing methods for moderation, and providing curation examples. Further details on the CIViC knowledge model, standards and guidelines for curation and moderation, and details on the CIViC project are available in the CIViC help documents (https://civicdb.org/help/introduction).

show abstract

Utility of the JAX Clinical Knowledgebase in capture and assessment of complex genomic cancer data

Cited by 35 publications

References 43 publications

Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

A Review of Precision Oncology Knowledgebases for Determining the Clinical Actionability of Genetic Variants

The CIViC knowledge model and standard operating procedures for curation and clinical interpretation of variants in cancer

Contact Info

Product

Resources

About