Project description:Fluoropyrimidines (FP) are the backbone chemotherapy in colorectal cancer (CRC) treatment; however, their use is associated with cardiotoxicity, which is underreported. In the present study, we aimed to prospectively determining the incidence rates and related risk factors of FP-induced cardiotoxicity (FIC) in CRC patients and at identifying predictive biomarkers. One hundred and twenty-nine consecutive previously untreated CRC patients underwent active cardiological monitoring, including 5-items simplified questionnaire on symptoms, electrocardiogram (ECG) and plasma sample collection during FP chemotherapy. FIC was defined as the presence of ECG alterations and/or the arising of at least one symptom of chest pain, dyspnoea, palpitations or syncope. The primary objective was the evaluation of FIC incidence. Secondary objectives were the correlation of FIC with well-known cardiological risk factors and the identification of circulating biomarkers (serum levels of TnI, proBNP; miRNA analysis) as predictors of FIC. Twenty out of 129 (15.5%) patients experienced FIC. The most common symptoms were dyspnoea (60%) and chest pain (40%), while only 15% of patients presented ECG alterations, including one acute myocardial infarction. Retreatment with FP was attempted in 90% of patients with a favourable outcome. Despite 48% of patients having cardiological comorbidities, we did not observe an increased FIC in this subgroup. Only the subgroup of females with the habit of alcohol consumption showed an increased risk of FIC. None of the circulating biomarkers evaluated demonstrated a clinical utility as FIC predictors. FIC can be an unexpected, life-threatening adverse event that can limit the subsequent treatment choices in CRC patients. In this prospective study, well-known cardiological comorbidities were not related to higher FIC risk and circulating biomarkers predictive of toxicity could not be found. With careful monitoring, mainly based on symptoms, almost all patients completed the FP treatment.

Project description:Expression profiling of breast cancer tumours, comparing 10 year survivors to deceased patients Background It is of great significance to find better markers to correctly distinguish between high-risk and low-risk breast cancer patients since the majority of breast cancer cases are at present being overtreated. Methods 46 tumours from node-negative breast cancer patients were studied with gene expression microarrays. A t-test was carried out in order to find a set of genes where the expression might predict clinical outcome. Two classifiers were used to evaluate the gene lists on the different data sets, a correlation-based classifier and a VFI (Voting Features Interval) classifier. We then evaluated the predictive accuracy of this expression signature on tumour sets from two similar studies on lymph-node negative patients which had developed gene expression signatures superior to current methods in classifying node-negative breast tumours. These two signatures were also tested on our material. Results A list of 51 genes whose expression profiles could predict clinical outcome with high accuracy in our material (96% or 89% accuracy in cross-validation, depending on type of classifier) was developed. When tested on two independent data sets, the expression signature based on the 51 identified genes had good predictive qualities in one of the data sets (74% accuracy), whereas their predictive value on the other data set were poor, presumably due to the fact that only 23 of the 51 genes were found in that material. We also found that previously developed expression signatures could predict clinical outcome well to moderately well in our material (72% and 61%, respectively). Conclusion The list of 51 genes derived in this study might have potential for clinical utility as a prognostic gene set, and may include candidate genes of potential relevance for clinical outcome in breast cancer. According to the predictions by this expression signature, 30 of the 46 patients should have had different adjuvant treatment than they did. Keywords: Expression Microarray, Lymph-node-negative Breast Cancer, Clinical Outcome, Classification

Project description:Background: Validated markers to predict outcome in rectal cancer patients treated with multimodal therapy remain elusive. Identifying molecular profiles for disease prognosis would be required for the design of clinical trials aimed at optimizing risk-adapted therapies. We have therefore used whole genome expression profiling of tumors of a large cohort of patients enrolled in the multicenter trials of the German Rectal Cancer Study Group (GRCSG) to identify molecular profiles for individualized therapy. Methods: We prospectively collected pretherapeutic biopsies from patients (n=300) treated according to the GRCSG trial guidelines from seven different German surgical departments using rigid quality controls. These samples were profiled by global gene expression analysis and a classifier developed to predict postoperative lymph node status and Disease Free Survival (DFS). The performance of the classifier was validated with an independent, prospectively collected set of samples. Findings: The final training and test set included 198 patients. Analyzes for postoperative nodal status and DFS revealed 69 and 674 differentially regulated genes, respectively. Depending on the classifier the accuracy to predict lymph node status ranged from 64% to 69% with negative predictive values between 72% and 74%. Stratification according to DFS resulted in a good (n=99), bad (n=96) and a small very bad prognosis group (n=3). Based on linear discriminant analysis the classifier for positive lymph node status was validated in 47 independent patients and revealed an accuracy of 72% and a positive predictive value of 100%. Thereby, prediction based on molecular profiling is superior to prediction based on conventional clinical markers. Interpretation: Whole genome expression analysis of pretherapeutical biopsies resulted in a molecular classifier of disease prognosis. This classifier was successfully validated. The high positive predictive value for post therapeutic lymph node status allows identification of patients requiring alternative or intensified treatment protocols. These data are currently validated for their clinical applicability and should be taken as basis for future molecular driven clinical trials to develop risk-adapted treatments.

Project description:KRAS mutation is a negative predictive factor for treatment with anti-epidermal growth factor receptor (EGFR) antibodies in metastatic colorectal cancer (mCRC). Novel predictive markers are required to further improve the selection of patients for this treatment. Here, we assessed the influence of modification of KRAS by gene copy number aberration (CNA) and microRNAs (miRNAs) in correlation to clinical outcome in mCRC patients treated with cetuximab in combination with chemotherapy and bevacizumab. Formalin-fixed paraffin-embedded primary tumour tissue was used from 34 mCRC patients in a phase III trial, who were selected based upon their good (n=17) or poor (n=17) progression-free survival (PFS) upon treatment with cetuximab in combination with capecitabine, oxaliplatin, and bevacizumab. Gene copy number at the KRAS locus was assessed using high resolution genome-wide array CGH and the expression levels of 17 miRNAs targeting KRAS were determined by real-time PCR. Good response was associated with 12p12.1 copy number loss, even in patients with a KRAS mutation, while copy number gain in wild-type KRAS patients was correlated with a poor response. In KRAS mutated tumours increased miR-200b and decreased miR-143 expression were associated with a good response. In wild-type KRAS patients, miRNA expression did not predict response in a multivariate model. Thus, assessment of KRAS CNA and miRNAs targeting KRAS might further optimize the selection of patients eligible for anti-EGFR therapy. Copy number detection was performed using NimbleScan and Nexus software Formalin-fixed paraffin-embedded primary tumour tissue was used from 34 metastisized colorectal cancer patients in a phase III trial (CKTO 2005-02; ClinTrials.gov NCT00208546) of the Dutch Colorectal Cancer Group (DCCG), who were selected based upon their good (n=17) or poor (n=17) progression-free survival (PFS) upon treatment with cetuximab in combination with capecitabine, oxaliplatin, and bevacizumab.

Project description:Expression profiling of breast cancer tumours, comparing 10 year survivors to deceased patients Background It is of great significance to find better markers to correctly distinguish between high-risk and low-risk breast cancer patients since the majority of breast cancer cases are at present being overtreated. Methods 46 tumours from node-negative breast cancer patients were studied with gene expression microarrays. A t-test was carried out in order to find a set of genes where the expression might predict clinical outcome. Two classifiers were used to evaluate the gene lists on the different data sets, a correlation-based classifier and a VFI (Voting Features Interval) classifier. We then evaluated the predictive accuracy of this expression signature on tumour sets from two similar studies on lymph-node negative patients which had developed gene expression signatures superior to current methods in classifying node-negative breast tumours. These two signatures were also tested on our material. Results A list of 51 genes whose expression profiles could predict clinical outcome with high accuracy in our material (96% or 89% accuracy in cross-validation, depending on type of classifier) was developed. When tested on two independent data sets, the expression signature based on the 51 identified genes had good predictive qualities in one of the data sets (74% accuracy), whereas their predictive value on the other data set were poor, presumably due to the fact that only 23 of the 51 genes were found in that material. We also found that previously developed expression signatures could predict clinical outcome well to moderately well in our material (72% and 61%, respectively). Conclusion The list of 51 genes derived in this study might have potential for clinical utility as a prognostic gene set, and may include candidate genes of potential relevance for clinical outcome in breast cancer. According to the predictions by this expression signature, 30 of the 46 patients should have had different adjuvant treatment than they did. Keywords: Expression Microarray, Lymph-node-negative Breast Cancer, Clinical Outcome, Classification 23 fresh frozen primary node negative breast cancer tumours from 10 year survivors were compared to 23 tumours from deceased patients in order to find genes where the expression differed between the two groups Microarrays were produced at the Swegene DNA Microarray Resource Center, Department of Oncology, Lund University, Sweden (http://swegene.onk.lu.se). Frozen tumours were homogenized together with TRIzol Reagent (Invitrogen, Carlsbad, CA, USA). From the cell-suspension total RNA was extracted using RNeasy mini kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s protocol. The quality of the RNA was evaluated with the Agilent 2100 BioAnalyser (Agilent Technologies, Palo Alto, CA, USA). Specimens where the 28S/16S ratio was lower than 1.0 or the RIN-value was lower than 6.7 were excluded from the study. For each sample, Cy3-dCTP (Amersham Biosciences, Buckinghamshire, UK) labelled probes were synthesized from 5 µg of the total tumour RNA and Cy5-dCTP (Amersham Biosciences) labelled reference were synthesized from 5 µg of commercial reference RNA (Universal Human Reference RNA, Stratagene, La Jolla, CA, USA) by reverse transcription. The probes were purified using ChipShot™ labelling cleanup system (Promega, Madison, WI, USA). The hybridizations were carried out using Pronto! Micro Array reagent systems (Corning Inc., Corning, NY, USA). For each sample, labelled tumour cDNA and reference cDNA were co-precipitated and hybridised to the microarray slide. The microarray slides were scanned with Agilent microarray scanner G2565AA (Agilent Technologies) and image analysis was performed using the Genepix 6.0.0.45 software (Axon Instruments, Union City, CA, USA).

Dataset Information

Metabolomic analysis of colorectal cancer patients.

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