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Predicting patient outcome based on gene-expression biomarkers learned from colorectal cancer organoids and cell lines

ABSTRACT: Colorectal cancer is characterized by an extremely high mortality rate, mainly caused by the high metastatic potential of this type of cancer. To date chemotherapy remains the backbone of the treatment of metastatic colorectal cancer. Three main chemotherapeutic drugs used for treatment of metastatic colorectal cancer are 5-FU, oxaliplatin and irinotecan which is metabolized to an active compound SN-38. The main goal of this study was to find the genes connected to the resistance to the aforementioned drugs and to construct predictive gene expression-based classifier to separate responders and non-responders. In this study we used 7 established colorectal cancer organoids to conduct correlational analysis of the expression and drug resistance. We also included in the study publicly available datasets of colorectal cancer cell lines. The obtained results demonstrated that the expression of just a small group of genes consistently correlate with resistance to standard chemotherapeutic drugs in different datasets. Some of these genes have been previously connected to prognosis or response to anticancer drugs, but some of them were linked to the drug resistance for the first time. The classifier constructed on the expression levels of some of these genes allows to discriminate between patients with favorable and unfavorable 5-year OS prognosis and can be further validated in clinic to improve the results of the treatment.

ORGANISM(S): Homo sapiens

PROVIDER: GSE251958 | GEO | 2025/01/08

REPOSITORIES: GEO

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Project description:Purpose: Irinotecan (SN38) and oxaliplatin are chemotherapeutic agents used in the treatment of colorectal cancer. However, the frequent development of resistance to these drugs represents a considerable challenge in the clinic. Alus as retrotransposons comprise 11% of the human genome. Genomic toxicity induced by carcinogens or drugs can reactivate Alus by altering DNA methylation. Whether or not reactivation of Alus occurs in SN38 and oxaliplatin resistance remains unknown. Methods: We applied reduced representation bisulfite sequencing (RRBS) to investigate the DNA methylome in SN38- or oxaliplatin-resistant colorectal cancer cell line models. Moreover, we extended the RRBS analysis to tumor tissue from 14 patients with colorectal cancer who either did or did not benefit from capecitabine + oxaliplatin treatment. For the clinical samples, we applied a concept of DNA methylation entropy to estimate the diversity of DNA methylation states of the identified resistance phenotype-associated methylation loci observed in the cell line models. Results: We identified different loci being characteristic for the different resistant cell lines. Interestingly, 53% of the identified loci were Alu sequences -- especially the Alu Y subfamily. Furthermore, we identified an enrichment of Alu Y sequences that likely results from increased integration of new copies of Alu Y sequence in the drug-resistant cell lines. In the clinical samples, SOX1 and other SOX gene family members were shown to display variable DNA methylation states in their gene regions. The Alu Y sequences showed remarkable variation in DNA methylation states across the clinical samples. Our findings imply a crucial role of Alu Y in colorectal cancer drug resistance. Our study underscores the complexity of colorectal cancer aggravated by mobility of Alu elements and stresses the importance of personalized strategies, using a systematic and dynamic view, for effective cancer therapy. Investigation of the representive methylome of well-established SN38 and Oxaliplatin resistant cell line models and 14 clinical colorectal metastatic samples that have developed resistance to XELOX to review the epigenetic mechnism of the drug resistance.

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.

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Predicting patient outcome based on gene-expression biomarkers learned from colorectal cancer organoids and cell lines

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