Platinum-based chemotherapy remains the cornerstone of treatment for most people with non-small cell lung cancer (NSCLC), either as adjuvant therapy in combination with a second cytotoxic agent or in combination with immunotherapy. Resistance to therapy, either in the form of primary refractory disease or evolutionary resistance, remains a significant issue in the treatment of NSCLC. Hence, predictive biomarkers and novel combinational strategies are required to improve the effectiveness and durability of treatment response for people with NSCLC. The aim of this study was to identify novel biomarkers and/or druggable proteins from deregulated protein networks within non-oncogene driven disease that are involved in the cellular response to cisplatin. Following exposure of NSCLC cells to cisplatin, in vitro quantitative mass spectrometry was applied to identify altered protein response networks. A total of 65 proteins were significantly deregulated following cisplatin exposure. These proteins were assessed to determine if they are druggable targets using novel machine learning approaches and to identify whether these proteins might serve as prognosticators of platinum therapy. Our data demonstrate novel candidates and drug-like molecules warranting further investigation to improve response to platinum agents in NSCLC.

All cell lines were purchased from the American Type Culture Collection (ATCC) and maintained at 37°C in a humidified atmosphere containing 5% CO2 in RPMI 1640 medium supplemented with 1% L-Glutamine and 1% non-essential amino acids (NEAA). Cell culture media were supplemented with 10% (v/v) heat-inactivated fetal bovine serum (FBS). For differential mass spectrometry-based quantitative proteomics, H460 cells were treated cisplatin (7.5 µM) for 24 h. For in vitro drug treatments prior to western blot analysis, cells were treated with cisplatin (5 µM) for 12 h.

MS analysis was performed using an AB Sciex 5600+ TripleTOF mass spectrometer interfaced to an EkspertTM NanoLC system. Sample preparation and library construction for differential quantitative liquid chromatography (LC)-MS/MS was performed and analysed as previously described37. Briefly, equal amounts of sample in triplicate were prepared by filter-aided sample preparation (FASP)38 and tryptic digested peptides were analysed by LC-MS/MS. For each biological replicate, a spectral library was generated with a traditional data- dependent acquisition (DDA) approach. The DDA data was searched against a human proteome library (UP000005640; UniProt.org) using ProteinPilot 5.0 (SCIEX). Following generation of the spectral library, the identification and quantification of cisplatin-treated and untreated H460 triplicate cell lysate derived peptides were quantified using an LC-MS/MS approach known as variable sequential window acquisition of all theoretical mass spectra (SWATH-MS) with a targeted data extraction strategy to mine the resulting fragment ion data set. SWATH-MS transition, peptide, and protein level summarisation was performed using Skyline39 by normalization at the peptide level using median normalization and tukey median polish for summarization over all features in a run. Log2 transformation was performed prior to further statistical analysis. Differentially regulated proteins were identified by applying empirical Bayes moderated t-statistics tests40,41 implemented in the limma package using R statistical environment (version 3.5.2) and the Benjamini-Hochberg correction42 was applied to control the false discovery rate (FDR (or q-value)). List of all quantified proteins (including differentially regulated proteins) by LC-MS/MS are listed in Supplemental Table 1. Sample Key: Untreated 454 482 583 Cisplatin 455 483 584