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Background <p>Chromosomal instability (CIN) is involved in about 70% of colorectal cancers (CRCs) and is associated with poor prognosis and drug resistance. From a clinical perspective, a better knowledge of these tumour’s biology will help to guide therapeutic strategies more effectively.</p> Methods <p>We used high-density chromosomal microarray analysis to evaluate CIN level of patient-derived organoids (PDOs) and their original mCRC tissues. We integrated the RNA-seq and mass spectrometry-based proteomics data from PDOs in a functional interaction network to identify the significantly dysregulated processes in CIN. This was followed by a proteome-wGII Pearson correlation analysis and an in silico validation of main findings using functional genomic databases and patient-tissues datasets to prioritize the high-confidence CIN features.</p> Results <p>By applying the weighted Genome Instability Index (wGII) to identify CIN, we classified PDOs and demonstrated a good correlation with tissues. Multi-omics analysis showed that our organoids recapitulated genomic, transcriptomic and proteomic CIN features of independent tissues cohorts. Thanks to proteotranscriptomics, we uncovered significant associations between mitochondrial metabolism and epithelial-mesenchymal transition in CIN CRC PDOs. Correlating PDOs wGII with protein abundance, we identified a subset of proteins significantly correlated with CIN. Co-localisation analysis in PDOs strengthened the putative role of IPO7 and YAP, and, through in silico analysis, we found that some of the targets give significant dependencies in cell lines with CIN compatible status.</p> Conclusions <p>We first demonstrated that PDO models are a faithful reflection of CIN tissues at the genetic and phenotypic level. Our new findings prioritize a subset of genes and molecular processes putatively required to cope with the burden on cellular fitness imposed by CIN and associated with disease aggressiveness.</p>

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Decoding chromosomal instability insights in CRC by integrating omics and patient-derived organoids

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月28日

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  • Federica Papaccio,
  • Manuel Cabeza-Segura,
  • Blanca García-Micó,
  • Francisco Gimeno-Valiente,
  • Sheila Zúñiga-Trejos,
  • Valentina Gambardella,
  • María Fernanda Gutiérrez‐Bravo,
  • Carolina Martinez‐Ciarpaglini,
  • Pilar Rentero‐Garrido,
  • Tania Fleitas,
  • Susana Roselló,
  • Juan Antonio Carbonell-Asins,
  • Marisol Huerta,
  • David Moro-Valdezate,
  • Desamparados Roda,
  • Noelia Tarazona,
  • Manuel M. Sánchez del Pino,
  • Andrés Cervantes,
  • Josefa Castillo

摘要

Background

Chromosomal instability (CIN) is involved in about 70% of colorectal cancers (CRCs) and is associated with poor prognosis and drug resistance. From a clinical perspective, a better knowledge of these tumour’s biology will help to guide therapeutic strategies more effectively.

Methods

We used high-density chromosomal microarray analysis to evaluate CIN level of patient-derived organoids (PDOs) and their original mCRC tissues. We integrated the RNA-seq and mass spectrometry-based proteomics data from PDOs in a functional interaction network to identify the significantly dysregulated processes in CIN. This was followed by a proteome-wGII Pearson correlation analysis and an in silico validation of main findings using functional genomic databases and patient-tissues datasets to prioritize the high-confidence CIN features.

Results

By applying the weighted Genome Instability Index (wGII) to identify CIN, we classified PDOs and demonstrated a good correlation with tissues. Multi-omics analysis showed that our organoids recapitulated genomic, transcriptomic and proteomic CIN features of independent tissues cohorts. Thanks to proteotranscriptomics, we uncovered significant associations between mitochondrial metabolism and epithelial-mesenchymal transition in CIN CRC PDOs. Correlating PDOs wGII with protein abundance, we identified a subset of proteins significantly correlated with CIN. Co-localisation analysis in PDOs strengthened the putative role of IPO7 and YAP, and, through in silico analysis, we found that some of the targets give significant dependencies in cell lines with CIN compatible status.

Conclusions

We first demonstrated that PDO models are a faithful reflection of CIN tissues at the genetic and phenotypic level. Our new findings prioritize a subset of genes and molecular processes putatively required to cope with the burden on cellular fitness imposed by CIN and associated with disease aggressiveness.