Prospective high-throughput genome profiling in advanced cancers:
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ABSTRACT:
PROVIDER: EGAS00001004554 | EGA |
REPOSITORIES: EGA
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Project description:We conducted a prospective monocenter clinical trial called PERMED01 to evaluate the number patients with locally advanced or metastatic cancer for whom identification of molecular alterations in tumor samples could lead to the delivery of a targeted therapy. Patients accessible to tumor biopsy were prospectively enrolled at the Paoli-Calmettes Institute in the PERMED01 study (ClinicalTrials.gov, NCT02342158). Genomic profiling of frozen tissue was established by whole-genome array comparative genomic hybridization (aCGH)
2021-03-01 | E-MTAB-9998 | biostudies-arrayexpress
Project description:Background: Colorectal cancer is the third most common and the fourth most lethal cancer in the world. In the majority of cases, patients are diagnosed at an advanced stage or even metastatic, thus explaining the high mortality. The standard protocol for treating patients with locally advanced non-metastatic colorectal cancer (CRC) is neoadjuvant radio-chemotherapy (NRCT) with 5-fluorouracil (5-FU), but the resistance rate to this treatment remains high with approximately 30% of non-responders. The lack of evidence available in clinical practice to predict NRCT resistance to 5-FU and to guide clinical practice therefore encourages the search for biomarkers of this resistance. Methods: From twenty-three formalin-fixed paraffin-embedded (FFPE) biopsies performed before NRCT with 5-FU of locally advanced non-metastatic CRC patients, we extracted and analysed the tumor proteome of these patients. From clinical data, we were able to classify the twenty-two patients in our cohort into three treatment response groups: non-responders (NR), partial responders (PR) and total responders (TR), and to compare the proteomes of these different groups. Results: We have highlighted 384 differentially expressed proteins between NR and PR, 248 between NR and TR and 417 between PR and TR. Among these proteins, we have identified many differentially expressed proteins identified as having a role in cancer (IFIT1, FASTKD2, PIP4K2B, ARID1B, SLC25A33: overexpressed in TR; CALD1, CPA3, B3GALT5, CD177, RIPK1: overexpressed in NR). We have also identified that DPYD, the main degradation enzyme of 5-FU, was overexpressed in NR, as well as several ribosomal and mitochondrial proteins also overexpressed in NR. Conclusions: From these retrospective study, we implemented a protein extraction protocol from FFPE biopsy to highlight protein differences between different response groups to RCTN with 5-FU in patients with locally advanced non-metastatic CRC. These results will pave the way for a larger cohort for better sensitivity and specificity of the signature to guide decisions in the choice of treatment.
2018-06-25 | PXD008440 | Pride
Project description:PURPOSE: The IRSN-23 model uses DNA microarray analysis of tumor tissue to personalize patients into highly sensitive chemotherapy (Gp-R) or less sensitive (Gp-NR) based on the expression of immune-related genes. This study assessed the reproducibility of the IRSN-23 in prospective independent validation sets and investigated its clinical significance and impact on breast cancer subtype classification. METHODS: Tumour tissues were collected from 146 breast cancer patients undergoing preoperative chemotherapy (paclitaxel-FEC) ± trastuzumab in Osaka University Hospital (OUH). Patients were classified into Gp-R or Gp-NR using IRSN-23, and the model was examined for its ability to predict the pathological complete response (pCR). RESULTS: In the OUH prospective dataset, the pCR rate was significantly higher in the Gp-R group (29·3% (11/41)) than in the Gp-NR group (1·4% (1/71)) not using trastuzumab (P = 1·70E-5). CONCLUSION: This study provides prospective validation of the IRSN-23 in predicting chemotherapy efficacy, demonstrating a high degree of reproducibility.
2023-05-19 | GSE230881 | GEO
Project description:The treatment strategy of rectal cancer has substantially changed in recent decades. Historically postoperative chemoradiotherapy (CRT) was considered to be the first-line therapy for stage II and III rectal cancers. However, the preoperative CRT is now considered to be the optimal therapy regimen for locally advanced rectal ancer due to its improved local control, reduced toxicity, and increased rate of sphincter preservation. Our study established a clinically practical multi-class prediction model by adopting a novel strategy that applies two separate prediction models (MI and TO predictor) sequentially to a patient to predict the response. For promising clinical practice, we validated our model in a published dataset, which is completely independent dataset from ours. This study suggests a clinically plausible prediction model that correctly infers the preoperative CRT response of patients with high accuracy based on 163 gene signatures we identified. Total RNAs were isolated from primary rectal tumor tissues of 69 patients who underwent chemoradiation therapy (CRT). These patients are classified into four different CRT responses: minimal response (MI), moderate response (MO), near total response (NT) and total response (TO). All the RNAs were subjected to microarray analysis using Affymetrix GenChip arrays.
2013-05-14 | E-GEOD-46862 | biostudies-arrayexpress
Project description:The treatment strategy of rectal cancer has substantially changed in recent decades. Historically postoperative chemoradiotherapy (CRT) was considered to be the first-line therapy for stage II and III rectal cancers. However, the preoperative CRT is now considered to be the optimal therapy regimen for locally advanced rectal ancer due to its improved local control, reduced toxicity, and increased rate of sphincter preservation. Our study established a clinically practical multi-class prediction model by adopting a novel strategy that applies two separate prediction models (MI and TO predictor) sequentially to a patient to predict the response. For promising clinical practice, we validated our model in a published dataset, which is completely independent dataset from ours. This study suggests a clinically plausible prediction model that correctly infers the preoperative CRT response of patients with high accuracy based on 163 gene signatures we identified.
2013-05-14 | GSE46862 | GEO
Project description:In this study, we use cancer cell lines to investigate the molecular differences between resistant and responsive colorectal cancer cells. Since the treatment of patients with locally advanced rectal cancers involves chemoradiotherapy (CT/RT), we specifically implemented an in vitro protocol that mimics this clinical setting. We anticipate that this analysis will unveil relevant pathways underlying the resistance of rectal cancers to CT/RT, and enable the identification of novel therapeutic target genes.
2011-02-10 | GSE20298 | GEO
Project description:Prospective cohort carriage study
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Project description:Genomic Grade Index (GGI) is a 97-gene signature that improves histologic grade (HG) classification in invasive breast carcinoma. In this prospective study we sought to evaluate the feasibility of performing GGI in routine clinical practice and its impact on treatment recommendations. Patients with pT1pT2 or operable pT3, N0-3 invasive breast carcinoma were recruited from 8 centers in Belgium. Fresh surgical samples were sent at room temperature in the MapQuant DxM-bM-^DM-" PathKit for centralized genomic analysis. Genomic profiles were determined using Affymetrix U133 Plus 2.0 and GGI calculated using the MapQuant DxM-bM-^DM-" protocol, which defines tumors as low or high Genomic Grade (GG-1 and GG-3 respectively). 180 pts were recruited and 155 were eligible. The MapQuant test was performed in 142 cases and GGI was obtained in 78% of cases (n=111). Reasons for failures were 15 samples with <30% of invasive tumor cells (11%), 15 with insufficient RNA quality (10%), and 1 failed hybridization (<1%). For tumors with an available representative sample (M-bM-^IM-% 30% inv. tumor cells) (n=127), the success rate was 87.5 %. GGI reclassified 69% of the 54 HG2 tumors as GG-1 (54%) or GG-3 (46%). Changes in treatment recommendations occurred mainly in the subset of HG2 tumors reclassified into GG-3, with increased use of chemotherapy in this subset. The use of GGI is feasible in routine clinical practice and impacts treatment decisions in early-stage breast cancer. Total RNA was extracted from fresh tumor tissues comprising M-bM-^IM-%30% invasive tumor cells and hybridized on Affymetrix microarrays if the RIN was M-bM-^IM-% 7.
2013-06-03 | E-GEOD-43365 | biostudies-arrayexpress
Project description:Urothelial carcinoma (UC) of the urinary bladder has significant challenges in treatment due to its diverse genetic landscape and variable response to systemic therapy. In recent years, patient-derived organoids (PDOs) emerged as a novel tool to model primary tumors with higher resemblance than conventional 2D cell culture approaches. However, the potential of organoids to predict therapy response in a clinical setting remains to be evaluated. This study explores the clinical feasibility of PDOs for pharmacotyping in UC. Initially, we subjected tumor tissue specimens from 50 patients undergoing transurethral resection or radical cystectomy to organoid propagation, of whom 19 (38%) yielded PDOs suitable for drug sensitivity assessment. Notably, whole transcriptome-based analysis indicated that PDOs may show phenotypes distinct from their parental tumor tissue. Pharmacotyping within a clinically relevant timeframe [mean of 35.44 and 55 days for non-muscle invasive bladder cancer (NMIBC) and muscle invasive bladder cancer (MIBC), respectively] was achieved. Drug sensitivity analyses revealed marked differences between NMIBC and MIBC, with MIBC-derived organoids demonstrating higher chemosensitivity toward clinically relevant drugs. A case study correlating organoid response with patient treatment outcome illustrated the complexity of predicting chemotherapy efficacy, especially considering the rapid acquisition of drug resistance. We propose a workflow of prospective organoid-based pharmacotyping in UC, enabling further translational research and integration of this approach into clinical practice.
2024-11-03 | GSE280749 | GEO