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Individualized multi-omic pathway deviation scores using multiple factor analysis.

Andrea Rau1, Regina Manansala2, Michael J Flister3

  • 1Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France.

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Summary
This summary is machine-generated.

This study introduces padma, a novel tool for analyzing multi-omic data in tumors. Padma identifies pathway-specific molecular changes linked to patient outcomes in breast and lung cancers.

Keywords:
Cancer genomicsMulti-omic dataMultiple factor analysisPathways

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Area of Science:

  • Oncology
  • Bioinformatics
  • Genomics

Background:

  • Malignant progression involves complex somatic changes across genetic, epigenetic, and transcriptional levels.
  • Understanding these multi-omic alterations is crucial for correlating tumor features with clinical outcomes.

Purpose of the Study:

  • To present padma, a novel pathway-centric tool for analyzing multi-omic tumor data.
  • To quantify and characterize individualized, pathway-specific multi-omic deviations and their drivers.

Main Methods:

  • Utilized a multiple factor analysis framework to develop the padma tool.
  • Employed a multi-omic consensus representation to integrate diverse data types.
  • Applied padma to analyze genetic, epigenetic, and transcriptomic data in cancer.

Main Results:

  • padma successfully quantifies individualized pathway-specific multi-omic deviations.
  • Demonstrated the tool's utility in correlating molecular perturbations with patient outcomes.
  • Identified clinically actionable pathways perturbed in breast and lung cancer.

Conclusions:

  • padma provides a novel approach to dissecting complex multi-omic alterations in cancer.
  • The tool facilitates the identification of molecular drivers impacting patient prognosis.
  • padma aids in understanding cancer progression and developing targeted therapeutic strategies.