Abstract 1319: Establishment and characterization of fresh primary human colorectal tumor implants
Abstract A personalized approach towards the therapy of colorectal carcinoma is critical for a successful outcome. In recent years, direct transfer xenograft models (or the “tumor graft”) have proven to be highly predictive and are being adapted in clinical practice. However, direct drug evaluation...
Saved in:
Published in: | Cancer research (Chicago, Ill.) Vol. 72; no. 8_Supplement; p. 1319 |
---|---|
Main Authors: | , , , , , , , , , , , , , |
Format: | Journal Article |
Language: | English |
Published: |
15-04-2012
|
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | Abstract
A personalized approach towards the therapy of colorectal carcinoma is critical for a successful outcome. In recent years, direct transfer xenograft models (or the “tumor graft”) have proven to be highly predictive and are being adapted in clinical practice. However, direct drug evaluation in tumor graft models for each individual patient is not feasible due to time and cost constraints. Predictive biomarker discovery is an extremely promising application of the tumor graft model, since the discovered biomarker signatures will direct therapy choice in a much broader patient population. In collaboration with the Catholic Health Initiatives Center for Translational Research we at Caliper Life Sciences have established a collection of tumor graft samples of primary human colorectal carcinomas. All samples were collected fresh from consenting patients following an IRB-approved protocol and implanted in vivo on the day of surgical tumor resection. Tumors were grafted either orthotopically or subcutaneously in female NIH-III mice. We present here the results of a comparative study of gene and protein expression profiles for the primary tumor (clinical samples) and the direct transfer xenografts. These data will be correlated with drug sensitivity profiles generated by using the same samples. The resulting data sets will be used to characterize predictive biomarker signatures that will be a valuable tool for selection of the most effective therapy tailored to the individual patient.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1319. doi:1538-7445.AM2012-1319 |
---|---|
ISSN: | 0008-5472 1538-7445 |
DOI: | 10.1158/1538-7445.AM2012-1319 |