Reference Detail

Contact

Missing content? – Request curation!

Request curation for specific Genes, Variants, or PubMed publications.

Have questions, comments, or suggestions? - Let us know!

Email us at : ckbsupport@jax.org

Ref Type Journal Article
PMID (26479923)
Authors Gao 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, 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
Title High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response.
URL
Abstract Text Profiling candidate therapeutics with limited cancer models during preclinical development hinders predictions of clinical efficacy and identifying factors that underlie heterogeneous patient responses for patient-selection strategies. We established ∼1,000 patient-derived tumor xenograft models (PDXs) with a diverse set of driver mutations. With these PDXs, we performed in vivo compound screens using a 1 × 1 × 1 experimental design (PDX clinical trial or PCT) to assess the population responses to 62 treatments across six indications. We demonstrate both the reproducibility and the clinical translatability of this approach by identifying associations between a genotype and drug response, and established mechanisms of resistance. In addition, our results suggest that PCTs may represent a more accurate approach than cell line models for assessing the clinical potential of some therapeutic modalities. We therefore propose that this experimental paradigm could potentially improve preclinical evaluation of treatment modalities and enhance our ability to predict clinical trial responses.

Filtering

  • Case insensitive filtering will display rows if any text in any cell matches the filter term
  • Use simple literal full or partial string matches
  • Separate multiple filter terms with a space. Any order may be used (i. e. a b c and c b a are equivalent )
  • Filtering will only apply to rows that are already loaded on the page. Filtering has no impact on query parameters.
  • Use quotes to match on a longer phrase with spaces (i.e. "mtor c1483f")

Sorting

  • Generally, the default sort order for tables is set to be first column ascending; however, specific tables may set a different default sort order.
  • Click on any column header arrows to sort by that column
  • Hold down the Shift key and click multiple columns to sort by more than one column. Be sure to set ascending or descending order for a given column before moving on to the next column.

Molecular Profile Treatment Approach
Gene Name Source Synonyms Protein Domains Gene Description Gene Role
Therapy Name Drugs Efficacy Evidence Clinical Trials
Drug Name Trade Name Synonyms Drug Classes Drug Description
Gene Variant Impact Protein Effect Variant Description Associated with drug Resistance
Molecular Profile Indication/Tumor Type Response Type Therapy Name Approval Status Evidence Type Efficacy Evidence References