Oral Presentation 29th Annual Lorne Proteomics Symposium 2024

Synergistic proteomic signatures underlie drug sensitivity and resistance in cancer (#16)

Priya Ramarao-Milne 1 , Roc Reguant 1 , Hawlader Al-Mamun 2 , Lujain Elazab 1 , Qing Zhong 3 , Julika Wenzel 1 , Roger Reddell 3 , Natalie Twine 1 4 , Denis Bauer 1 4 5
  1. Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Sydney, NSW, Australia
  2. Data 61, Commonwealth Scientific and Industrial Research Organisation, Sydney, NSW, Australia
  3. ProCan, Children's Medical Research Institute , Sydney, New South Wales, Australia
  4. Applied BioSciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, Australia
  5. Department of Biomedical Sciences, Macquarie University, Sydney, NSW, Australia

Introduction: Proteomics is rapidly as a promising field for discovering cancer biomarkers predicting treatment efficacy. Recently, ProCan and the Wellcome Sanger Institute published the world’s largest pan-cancer proteomic dataset of 949 cell lines, treated with 625 anti-cancer drugs. This dataset is a powerful tool for identifying proteomic biomarkers of drug sensitivity and resistance, which can be used to build drug susceptibility profiles for tumours. This capability could be useful as a clinical adjunct to determine the optimal treatment for a patient, while avoiding administration of ineffective and toxic drugs.

Methods: Recent studies have identified single proteins in cell line proteomic data correlating with drug susceptibility. However, due to computational demand and lack of suitable algorithms, identifying pair-wise (doublet), and higher-order (triplet and quadruplet) combinations that synergistically modulate drug susceptibility are beyond the scope of current methods. Here, we use a novel machine learning algorithm to identify pathway synergies, uncovering higher-order proteomic signatures underlying drug response.

Results: We present a comprehensive catalogue of proteomic signatures in cancer correlating with drug susceptibility, enabling insight into biologically relevant pathways with predictive value. Our method uncovers “global” baseline signatures predicting drug susceptibility that recurrently appear across all cell lines, and “local” signatures that exclusively predict susceptibility to specific drug classes. For example, high baseline expressions of MCM family proteins are highly predictive of low IC50 values, or increased sensitivity to microtubule inhibitors only. On the other hand, we identify protein hubs centred around PAIRB and LMNB2 which confer “global” resistance to most drug classes.

Conclusion: This study lays the foundation for developing diagnostic and predictive panels which can help to identify signatures of sensitivity and resistance in a patient’s tumour. Our findings contribute towards the goal of leveraging ‘omic data to guide cancer precision medicine, leading to more effective, personalised treatments for cancer patients.