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Molecular dissection of colorectal cancer in pre-clinical models identifies biomarkers predicting sensitivity to EGFR inhibitors.

ABSTRACT: Colorectal carcinoma represents a heterogeneous entity, with only a fraction of the tumours responding to available therapies, requiring a better molecular understanding of the disease in precision oncology. To address this challenge, the OncoTrack consortium recruited 106 CRC patients (stages I-IV) and developed a pre-clinical platform generating a compendium of drug sensitivity data totalling >4,000 assays testing 16 clinical drugs on patient-derived in vivo and in vitro models. This large biobank of 106 tumours, 35 organoids and 59 xenografts, with extensive omics data comparing donor tumours and derived models provides a resource for advancing our understanding of CRC. Models recapitulate many of the genetic and transcriptomic features of the donors, but defined less complex molecular sub-groups because of the loss of human stroma. Linking molecular profiles with drug sensitivity patterns identifies novel biomarkers, including a signature outperforming RAS/RAF mutations in predicting sensitivity to the EGFR inhibitor cetuximab.

SUBMITTER: Schutte M

PROVIDER: S-EPMC5309787 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Molecular dissection of colorectal cancer in pre-clinical models identifies biomarkers predicting sensitivity to EGFR inhibitors.

Schütte Moritz M Risch Thomas T Abdavi-Azar Nilofar N Boehnke Karsten K Schumacher Dirk D Keil Marlen M Yildiriman Reha R Jandrasits Christine C Borodina Tatiana T Amstislavskiy Vyacheslav V Worth Catherine L CL Schweiger Caroline C Liebs Sandra S Lange Martin M Warnatz Hans-Jörg HJ Butcher Lee M LM Barrett James E JE Sultan Marc M Wierling Christoph C Golob-Schwarzl Nicole N Lax Sigurd S Uranitsch Stefan S Becker Michael M Welte Yvonne Y Regan Joseph Lewis JL Silvestrov Maxine M Kehler Inge I Fusi Alberto A Kessler Thomas T Herwig Ralf R Landegren Ulf U Wienke Dirk D Nilsson Mats M Velasco Juan A JA Garin-Chesa Pilar P Reinhard Christoph C Beck Stephan S Schäfer Reinhold R Regenbrecht Christian R A CR Henderson David D Lange Bodo B Haybaeck Johannes J Keilholz Ulrich U Hoffmann Jens J Lehrach Hans H Yaspo Marie-Laure ML

Nature communications 20170210

Colorectal carcinoma represents a heterogeneous entity, with only a fraction of the tumours responding to available therapies, requiring a better molecular understanding of the disease in precision oncology. To address this challenge, the OncoTrack consortium recruited 106 CRC patients (stages I-IV) and developed a pre-clinical platform generating a compendium of drug sensitivity data totalling >4,000 assays testing 16 clinical drugs on patient-derived in vivo and in vitro models. This large bio ...[more]

PMID: 28186126

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Project description:Abstract Background Early diagnosis of liver metastasis is of great importance for enhancing the survival of colorectal adenocarcinoma (CAD) patients, and the combined use of a single biomarker in a classier model has shown great improvement in predicting the metastasis of several types of cancers. However, it is little reported for CAD. This study therefore aimed to screen an optimal classier model of CAD with liver metastasis and explore the metastatic mechanisms of genes when applying this classier model. Methods The differentially expressed genes between primary CAD samples and CAD with metastasis samples were screened from the Moffitt Cancer Center (MCC) dataset GSE131418. The classification performances of six selected algorithms, namely, LR, RF, SVM, GBDT, NN, and CatBoost, for classification of CAD with liver metastasis samples were compared using the MCC dataset GSE131418 by detecting their classification test accuracy. In addition, the consortium datasets of GSE131418 and GSE81558 were used as internal and external validation sets to screen the optimal method. Subsequently, functional analyses and a drug‐targeted network construction of the feature genes when applying the optimal method were conducted. Results The optimal CatBoost model with the highest accuracy of 99%, and an area under the curve of 1, was screened, which consisted of 33 feature genes. A functional analysis showed that the feature genes were closely associated with a “steroid metabolic process” and “lipoprotein particle receptor binding” (eg APOB and APOC3). In addition, the feature genes were significantly enriched in the “complement and coagulation cascade” pathways (eg FGA, F2, and F9). In a drug‐target interaction network, F2 and F9 were predicted as targets of menadione. Conclusion The CatBoost model constructed using 33 feature genes showed the optimal classification performance for identifying CAD with liver metastasis. APOB, APOC3, FGA, F2, F9, and NKX2‐3 were potential biomarkers for classification of CAD with liver metastasis. Menadione might be a promising anti‐metastatic drug of CAD cells through functioning its role at sites of F2 and F9. CatBoost model constructed by 33 feature genes showed the optimal classification performance for identifying CAD liver metastasis.

Project description:BackgroundEpidermal growth factor receptor (EGFR) inhibitors, including cetuximab and panitumumab, are valuable therapeutics for colorectal cancer (CRC), but resistance to these inhibitors is common. The reason for such resistance is not well understood, which hampers development of better therapeutic strategies. Although activating mutations in KRAS, BRAF and PIK3CA are considered major drivers of CRC resistance to EGFR inhibitors, therapeutic targeting of these drug resistance drivers has not produced substantial clinical benefit.MethodsWe exploited cell lines and mouse tumor models (cell line xenografts and patient derived xenografts) for experiments of genetic and pharmacologic depletion of EGFR and/or its family member HER2, including EGFR mutants, inhibition of EGFR ligand shedding, and biochemical analysis of signaling proteins, to delineate the mechanism of CRC resistance to EGFR inhibitors and to assess the therapeutic activity of PEPDG278D, which is a recombinant human protein that induces the degradation of both EGFR and HER2.ResultsThe sensitivity of CRC cells to cetuximab and panitumumab correlates with the ability of these drugs to induce EGFR downregulation. PEPDG278D strongly inhibits oncogenic signaling and growth of CRC cells by causing profound depletion of EGFR and HER2, regardless of activating mutations of KRAS, BRAF and PIK3CA. siRNA knockdown of EGFR or HER2 also inhibits CRC cells resistant to EGFR inhibitors. Tumors harboring mutated KRAS, BRAF and/or PIK3CA also overexpress EGFR ligands, further suggesting that EGFR signaling remains important to the tumors. While excessive tumor-generated high-affinity EGFR ligands block target engagement by PEPDG278D, aderbasib, an inhibitor of ADAM10 and ADAM17, enables PEPDG278D to exert strong antitumor activity by inhibiting ligand shedding. Moreover, adding fluorouracil, which is commonly used in CRC treatment, to the combination of PEPDG278D and aderbasib further enhances tumor inhibition.ConclusionsOur study shows that CRC resistance to EGFR inhibitors results primarily from the inability of the inhibitors to downregulate their target and that a PEPDG278D-based combination treatment overcomes the resistance.

Dataset Information

Molecular dissection of colorectal cancer in pre-clinical models identifies biomarkers predicting sensitivity to EGFR inhibitors.

Publications

Molecular dissection of colorectal cancer in pre-clinical models identifies biomarkers predicting sensitivity to EGFR inhibitors.

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