To advance preclinical testing of novel targeted drugs in colorectal cancer (CRC) we established a panel of 133 mouse xenograft models from fresh tumor specimens of 239 patients with CRC of all four UICC stages. A subgroup of 67 xenograft models was treated with cetuximab, bevacizumab and oxaliplatin as single agents. Mutation status of KRAS (G12, G13, A146T), BRAF (V600E) and PIK3CA (E542K, E545K, H1047R) was assessed in all xenografts by allelespecific real-time PCR. KRAS codon 61 was assessed by conventional sequencing. AREG and EREG expression levels were analyzed by real-time PCR expression assays. In the treatment experiment we observed response rates of 27% (18/67) for cetuximab, 3% (2/67) for bevacizumab, and 6% (4/67) for oxaliplatin. Classification based on KRAS, BRAF and PIK3CA mutation status identified 15 of the responders (sensitivity 83%, confidence interval at p = 0.05 (CI): 59% -96%), and 38 nonresponders (specificity 78%, CI: 63% -88%). If any mutation except in KRAS codon 13 were considered, the classifier reached sensitivity of 94% and specificity of 69%. We improved specificity of the classifiers to 90% and 86% respectively by adding AREG and EREG RNA expression thresholds retrospectively. In patient-derived xenograft models, we found a predictive classifier for response to cetuximab that is more accurate than established biomarkers. We confirmed its potential performance in primary human tumors. For patients, the classifier's sensitivity promises increased response rates and its specificity limits unnecessary toxicity. Given the scope of our xenograft models across all UICC stages, this applies not only to mCRC but also to the adjuvant setting of earlier stages. The xenograft collection allows to mimic randomized phase II trials and to test novel drugs effectively as single agents or in combinations. It also enables the development of highly accurate companion diagnostics as demonstrated by us for cetuximab.
393 Background: We previously reported on the discovery and prospective validation of a blood based test (Detector C) for early detection of colorectal cancer (CRC). Detector C measures 202 RNA markers in white blood cells as a response of the host to tumor formation and growth. Detector C was validated using a prospective, multicenter case-control study with 343 patients (pts), 210 cases with confirmed CRC and 133 controls undergoing a complete screening colonoscopy. Detector C has a validated sensitivity (S+) of 90% (95% CI 0.851-0.937) and specificity (S-) of 88% (95% CI 0.812-0.930) (Rosental A. et al, J Clin Oncol 28:7s, 2010 (suppl; abstr 3580)). We now present the discovery of Detector C 2.0 based on 445 samples representing most of the pts previously used in the discovery and validation sets of Detector C. Methods: We used Affymetrix U133 plus 2.0 expression data of 291 CRC cases and 154 controls for discovery of Detector C 2.0. Random forest was used for feature (gene) selection and the support vector machine algorithm was employed as classifier in 600 repetitions of double-nested bootstraps to discriminate between cases and controls. Within each repetition, randomly chosen 23 controls and 160 CRC cases served as prospective validation set. The most frequent chosen genes for discrimination between cases and controls formed the consensus signature, namely Detector C 2.0. Results: Choosing a signature length of 1000 genes resulted in the following second order unbiased prospective performance estimates: S+=0.916 (95% CI 0.863-0.954) and S-= 0.948 (95% CI 0.773-0.994). S+ for UICC stage I and stage II cases were 0.93 and 0.94. S+ for high-grade intraepithelial neoplasia was ∼ 0.67 and S+ for adenoma ≥ 10 mm was ∼ 0.45. Conclusions: Using a three times larger discovery set for Detector C 2.0 than for Detector C we improved S+ (cancer detection rate) by 1.6% to 91.6%. The most important enhancement is the high S- of 94.8% of Detector C 2.0 which is six percent higher than the S- of Detector C. Detector C 2.0 is based on a much larger pts set and should be even more robust than Detector C. We will prospectively validate this test in the largest case-control study ever performed in early detection of CRC.
Nosocomial and other severe infections are able to cause life-threatening complications in organ transplant recipients. Unter such conditions the treatment will be successful only in early status of infection. Fluconazol (Diflucan) has been used in the treatment of candidosis in cardiac and renal transplant patients with good results, and lack of adverse effects.
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