This data deposit contains the supplementary files for the University of Liverpool thesis titled "Global (phospho)proteomics analysis of EGFR inhibitor drug tolerance and resistance mechanisms in cell-based models of non-small cell lung cancer", by Nefeli Boni-Kazantzidou, 2024.

Thesis abstract: 
​Lung cancer is the leading cause of cancer mortality in the world, and a major target for pharmacological intervention. A significant percentage of non-small cell lung cancers (NSCLCs) possess ‘activating’ mutations in the Epidermal Growth Factor Receptor (EGFR) gene, which make the encoded protein susceptible to targeting with small molecule EGFR tyrosine kinase inhibitors (TKIs). The third-generation covalent inhibitor osimertinib (Tagrisso ®) was originally designed to target a common mechanism of TKI resistance, the T790M ‘gatekeeper’ EGFR mutation, but this drug is now first-line treatment for EGFR-mutant NSCLC.  However, many patients remain susceptible to drug-induced resistance and the molecular mechanisms driving this process are often obscure.  
Therefore, the aim of my thesis was to investigate acute and long-term adaptation mechanisms to osimertinib exposure using a global quantitative proteomic and phospho-proteomic analysis in a cell-based model of NSCLC in which resistance to osimertinib could be modelled. I developed a sensitive and in-depth quantitative workflow based on isobaric peptide labelling and tandem mass spectrometry (MS/MS). I used statistical and computational tools to associate changes in protein and phosphopeptide levels with regulatory pathways that generate adaptive responses during osimertinib exposure. 
In Chapter 3, I reveal the time and dose-dependent effects of acute exposure to osimertinib in the PC9 adenocarcinoma cell line, which is highly sensitive to EGFRi due to a deletion in exon 19 of the EGFR gene. The quantification of EGFR autophosphorylation by MS confirmed persistent dose-dependent blockade of EGFR throughout drug exposure. Phosphoproteomic profiling revealed responses of immediate activation of the PI3K-SGK1 axis and inhibition, followed by reactivation, of PI3K-AKT and MEK1/2-ERK1/2 signalling pathways. 
In Chapters 3 and 4, I discuss key changes in cellular protein profiles involved in components of kinase-based signalling over acute (Chapter 3) and long-term (Chapter 4) osimertinib exposure; specifically the Rho-GTPase cycle, and proteins centrally wired into cell organisation, apoptosis, cell cycle and transcriptional control. Quantified changes in phosphorylation dynamics were accompanied by corresponding changes in cell morphology and growth, with resistant cells showing increased attachment and slowed growth. Protein-level changes, starting at 4 h post-treatment, also confirm metabolic remodelling and switches in extracellular matrix markers and integrin signalling. 
I next evaluated consolidation of cellular adaptation mechanisms that drive signalling pathway-bypass mechanisms and pro-survival re-programming of PC9 cells (Chapter 5). I found that osimertinib-adapted cells showed sustained osimertinib-induced alterations in integrin signalling in favour of ITGAV-containing integrins and associated ligands. This was accompanied by changes in EMT markers and an overall phenotypic profile that favours increased cellular adhesion. Finally, I investigated resistance roles related to Ser/Thr kinases, including death-associated protein kinase 1 (DAPK1), doublecortin-like kinase (DCLK1), and Serum and Glucocorticoid-Kinase 1 (SGK1).  Combination targeting of DCLK1/SGK1 or DAPK1 and EGFR support varied roles for signaling networks controlled by these kinases in the development of osimertinib-induced phenotypic transformation linked to drug tolerance. 
In conclusion, the longitudinal study of osimertinib exposure reported in this thesis represents an in-depth profile of cellular response and adaptation to EGFR inhibition, and represents an advanced data resource for the future study of therapy-based resistance mechanisms in NSCLC

The experimental design, data quality control and statistical analysis steps are outlined below. Full LC-MS acquisition settings and bioinformatics analysis steps are included at the end of the file.  

Supplementary file 1: Proteins_24h_osimertinib_timecourse

Experimental design	
Model system:	
PC9 lung adenocarcinoma cell-line, purchased from Sigma-Aldrich (#90071810), authenticated by Eurofins Genomics	
Treatment design	Cells were treated with osimertinib 10nM or 1 μM (dilution in cell culture media from 10 mM stock in DMSO), by replacing their cell culture media with osimertinib-supplemented media for the indicated time intervals. Samples exposed to each concentration were collected after 5 min, 20 min, 1 h, 4 h, 8 h and 24 h of drug exposure. 
Treatment controls	
control 0h_DMSO	0h_DMSO: Cells exposed to media supplemented with DMSO (0.01% v/v) for 5 min. 
control 24h_DMSO	24h_DMSO: Cells exposed to media supplemented with DMSO (0.01% v/v) for 24 h. 
Input data	
Total protein analysis, performed in four experimental batches, with 10 fractions each (20 MS raw files in total), performed on a Thermo Fisher Fusion Lumos™ (in-line with Dionex U3000 LC system), 2h MS acquisition per fraction
Batch 1: 	Replicate 1
Batch 2: 	Replicate 2
Batch 3:	Replicate 3
Batch 4:	Replicate 4
Each batch contained 2 x 24h_DMSO controls (this condition had 8 replicates vs 4 for all other conditions)	
Data searching and quantification	Proteome Discoverer 2.4, Mascot 2.7
Search parameters	Tryptic digestion, 2 missed cleavages, TMTpro as N-terminal and K- static modification, phospho S/T/Y as dynamic modifications
This file includes the protein results tab of the Proteome Discoverer output	
Data processing prior to statistical analysis: 	
1) Abundance normalisation	median normalisation, on all averages, Proteome Discoverer 2.4
1)Phosphopeptide isoforms with valid quantitative values in 2 out 4 replicates were retained	
2) Batch correction step	function RemoveBatchEffect, package limma
3) Missing values imputation 	knn imputation, function knn.impute, package bnstruct
	
Statistical analysis 	Perseus 2.07
Analysis parameters	Student's two-tailed t test, assuming equal variances (5 min, 20 min, 1 h, 4 h , 8 h), Welch's two-tailed t test, assuming unequal variances (24 h)
Benjamini-Hochberg correction for multiple comparisons
Adjusted p value (q value) threshold for significance = 0.05
Comparisons performed:	
Treated 5 min (10 nM, 1uM) vs 0h_DMSO	
Treated 20 min (10 nM, 1uM) vs 0h_DMSO	
Treated 1h (10 nM, 1uM) vs 0h_DMSO	
Treated 4h (10 nM, 1uM) vs 0h_DMSO	
Treated 8h (10 nM, 1uM) vs 0h_DMSO	
Treated 24h (10 nM, 1uM) vs 24h_DMSO	

Supplementary file 2: Phosphopeptide_isoforms_24h_osimertinib_timecourse
Experimental design	
Model system:	
PC9 lung adenocarcinoma cell-line, purchased from Sigma-Aldrich (#90071810), authenticated by Eurofins Genomics	
Treatment design	Cells were treated with osimertinib 10nM or 1 μM (dilution in cell culture media from 10 mM stock in DMSO), by replacing their cell culture media with osimertinib-supplemented media for the indicated time intervals. Samples exposed to each concentration were collected after 5 min, 20 min, 1 h, 4 h, 8 h and 24 h of drug exposure. 
Treatment controls	
control 0h_DMSO	0h_DMSO: Cells exposed to media supplemented with DMSO (0.01% v/v) for 5 min. 
control 24h_DMSO	24h_DMSO: Cells exposed to media supplemented with DMSO (0.01% v/v) for 24 h. 

Input data	
Titanium dioxide-enriched phosphopeptide analysis, performed in four experimental batches, with 10 fractions each (20 MS raw files in total), performed on a Thermo Fisher Fusion Lumos™ (in-line with Dionex U3000 LC system), 2h MS acquisition per fraction

Batch 1: 	Replicate 1
Batch 2: 	Replicate 2
Batch 3:	Replicate 3
Batch 4:	Replicate 4
Each batch contained 2 x 24h_DMSO controls (this condition had 8 replicates vs 4 for all other conditions)	
Data searching and quantification	Proteome Discoverer 2.4, Mascot 2.7
Search parameters	Tryptic digestion, 2 missed cleavages, TMTpro as N-terminal and K- static modification, phospho S/T/Y as dynamic modifications
This file includes the phosphopeptide isoform results tab of the Proteome Discoverer output	
Data processing prior to statistical analysis: 	
1) Abundance normalisation	median normalisation, on all averages, Proteome Discoverer 2.4
1)Phosphopeptide isoforms with valid quantitative values in 2 out 4 replicates were retained	
2) Batch correction step	function RemoveBatchEffect, package limma
3) Missing values imputation 	knn imputation, function knn.impute, package bnstruct
Statistical analysis 	Perseus 2.07
Analysis parameters	Student's two-tailed t test, assuming equal variances (5 min, 20 min, 1 h, 4 h , 8 h), Welch's two-tailed t test, assuming unequal variances (24 h)
Benjamini-Hochberg correction for multiple comparisons
Adjusted p value (q value) threshold for significance = 0.05
Comparisons performed:	
Treated 5 min (10 nM, 1uM) vs 0h_DMSO	
Treated 20 min (10 nM, 1uM) vs 0h_DMSO	
Treated 1h (10 nM, 1uM) vs 0h_DMSO	
Treated 4h (10 nM, 1uM) vs 0h_DMSO	
Treated 8h (10 nM, 1uM) vs 0h_DMSO	
Treated 24h (10 nM, 1uM) vs 24h_DMSO	
Supplementary file 3: Complete_KSEA_Analysis_24h
Data input									
1)Results of titanium-dioxide enriched phosphopeptide analysis (Supplementary file 2), filtered for phosphosites with ptmRS localisation score of 99.8 or higher									
2) Results of total protein analysis (Supplementary file 1)									
Analysis_input: 									
log2FC	Log2FC of phosphopeptides was normalised against log2FC of their corresponding total protein: normalised log2FC= log2FC(phosphopeptide, vs control)-log2FC(total protein, vs control)		
p value	q value of phosphopeptide log2FC (prior to normalisation vs normal protein)		
each phosphosite is considered separately, associated with the log2FC and q value of its corresponding phosphopeptide isoform. Only phosphosites with ptmRS localisation score >99.8 have been retained. 								
Analysis performed									
Kinase Substrate Enrichment Analysis				function KSEA.Complete, KSEAapp package, R version 4.3.2			function settings:		NetworKIN = TRUE, NetworKIN.cutoff = 2, m.cutoff = 4, p.cutoff = 0.05

Supplementary file 4: Proteins_long-term_osimertinib_timecourse
Experimental design	
Model system:	
PC9 lung adenocarcinoma cell-line, purchased from Sigma-Aldrich (#90071810), authenticated by Eurofins Genomics	
Treatment design	Cells were treated continuously with osimertinib 1 μM (dilution in cell culture media from 10 mM stock in DMSO). Drug-supplemented media was changed every 3-4 days. 6 isogenic clones were treated and passaged indendently. Samples were collected after 8 (treated 8 days), 26 (treated 26 days) and 70 days (treated 70 days) of drug exposure. 
Treatment controls	
control t0 	untreated PC9 cells,  samples collected prior to osimertinib exposure from the same clonal population that was subsequently exposed to osimertinib
control t3 	DMSO-treated (0.01% v/v), continuously passaged cells that were cultured alongside osimertinib-treated cells and collected at 70 days simultaneously with osimertinib treated cells
Input data	
Total protein analysis, performed in two experimental batches, with 10 fractions each (20 MS raw files in total),), performed on a Thermo Fisher Fusion™ (in-line with Dionex U3000 LC system), 2h MS acquisition per fraction
Batch 1: 	control_t0 reps 1,2,3, control t3 reps 1,2,3,  treated reps 1 (clone B1), 2 (clone B2), 3 (clone B3)
Batch 2: 	control_t0 reps 4,5,6 control t3 reps 4,5,6,  treated reps 4 (clone B4), 5 (clone B5), 6 (clone B6)
Data searching and quantification	Proteome Discoverer 2.4, Mascot 2.7
Search parameters	Tryptic digestion, 2 missed cleavages, TMTpro as N-terminal and K- static modification
Data processing prior to statistical analysis: 	
1) Abundance normalisation	median normalisation, on all averages, Proteome Discoverer 2.4
1)Proteins with valid quantitative values in 4 out 6 replicates were retained	
2) Batch correction step	function RemoveBatchEffect, package limma
3) Missing values imputation 	knn imputation, function knn.impute, package bnstruct
4) Removal of outliers due to experimental error	control_t0_5, control_t3_4
This file includes the protein results tab of the Proteome Discoverer output	
Statistical analysis 	Perseus 2.07
Analysis parameters	Welch's two-tailed t test, assuming unequal variances
Benjamini-Hochberg correction for multiple comparisons
Adjusted p value (q value) threshold for significance = 0.05
Comparisons performed:	
Treated 8 days control t0	
Treated 26 days control t0	
Treated 70 days control t3

Supplementary file 5: Phosphopeptide_isoforms_long-term_osimertinib_timecourse
Experimental design	
Model system:	
PC9 lung adenocarcinoma cell-line, purchased from Sigma-Aldrich (#90071810), authenticated by Eurofins Genomics	
Treatment design	Cells were treated continuously with osimertinib 1 μM (dilution in cell culture media from 10 mM stock in DMSO). Drug-supplemented media was changed every 3-4 days. 6 isogenic clones were treated and passaged indendently. Samples were collected after 8 (treated 8 days), 26 (treated 26 days) and 70 days (treated 70 days) of drug exposure. 
Treatment controls	
control t0 	untreated PC9 cells,  samples collected prior to osimertinib exposure from the same clonal population that was subsequently exposed to osimertinib
control t3 	DMSO-treated (0.01% v/v), continuously passaged cells that were cultured alongside osimertinib-treated cells and collected at 70 days simultaneously with osimertinib treated cells
	
Input data	
Titanium dioxide-enriched phosphopeptide analysis, performed in two experimental batches, with 10 fractions each (20 MS raw files in total)	
Batch 1: 	control_t0 reps 1,2,3, control t3 reps 1,2,3,  treated reps 1 (clone B1), 2 (clone B2), 3 (clone B3)
Batch 2: 	control_t0 reps 4,5,6 control t3 reps 4,5,6,  treated reps 4 (clone B4), 5 (clone B5), 6 (clone B6)
Data searching and quantification	Proteome Discoverer 2.4, Mascot 2.7
Search parameters	Tryptic digestion, 2 missed cleavages, TMTpro as N-terminal and K- static modification, phospho S/T/Y as dynamic modifications
This file includes the phosphopeptide isoform results tab of the Proteome Discoverer output	
Data processing prior to statistical analysis: 	
1) Abundance normalisation	median normalisation, on all averages, Proteome Discoverer 2.4
1)Phosphopeptide isoforms with valid quantitative values in 4 out 6 replicates were retained	
2) Batch correction step	function RemoveBatchEffect, package limma
3) Missing values imputation 	knn imputation, function knn.impute, package bnstruct
4) Removal of outliers due to experimental error	control_t0_5, control_t3_4
	
Statistical analysis 	Perseus 2.07
Analysis parameters	Welch's two-tailed t test, assuming unequal variances
Benjamini-Hochberg correction for multiple comparisons
Adjusted p value (q value) threshold for significance = 0.05
Comparisons performed:	
Treated 8 days control t0	
Treated 26 days control t0	
Treated 70 days control t3	

Supplementary file 6: Complete_KSEA_Analysis_longterm
Data input									
1)Results of titanium-dioxide enriched phosphopeptide analysis (Supplementary file 5), filtered for phosphosites with ptmRS localisation score of 99.8 or higher (Supplementary file 6)									
2)Results of total protein analysis (Supplementary file 4)									
Analysis_input: 									
log2FC	Log2FC of phosphopeptides was normalised against log2FC of their corresponding total protein: normalised log2FC= log2FC(phosphopeptide, vs control)-log2FC(total protein, vs control)							
p value	q value of phosphopeptide log2FC (prior to normalisation vs normal protein)							
each phosphosite is considered separately, associated with the log2FC and q value of its corresponding phosphopeptide isoform. Only phosphosites with ptmRS localisation score >99.8 have been retained. 								
Analysis performed									
Kinase Substrate Enrichment Analysis				function KSEA.Complete, KSEAapp package, R version 4.3.2			function settings:		NetworKIN = TRUE, NetworKIN.cutoff = 2, m.cutoff = 4, p.cutoff = 0.05

Methodology
Liquid chromatography-tandem mass spectrometry analysis 
Prior to LC-MS/MS analysis, (phospho)peptides were re-solubilized in 96% (v/v) H2O, 3% (v/v) acetonitrile, 1% (v/v) TFA (1 μg/μL concentration for total proteome samples, up to 15 μL total volume for phospho-enriched samples). Samples were briefly sonicated in a bath sonicator and centrifuged (13000 x g for 15 min, 4 oC) to remove insoluble materials prior to LC, and the supernatant transferred to glass auto-sampler vials (Waters). Reversed-phase capillary HPLC separations were performed using an UltiMate 3000 nano system (Dionex) in-line with a High-Field Asymmetric Waveform Ion Mobility (FAIMS) unit and a Thermo Orbitrap Fusion™ or Fusion™ Lumos™ Tribrid mass spectrometer (Thermo Scientific, Bremen, Germany), using a protocol adapted from (Ferries et al., 2017). Approximately 2 μg of total protein samples were injected for each 2 h LC running method, and 75-90% of each phospho-enriched sample (to achieve the same sample load based on total ion chromatogram (TIC) area under the curve (AUC) per sample). Samples were loaded onto the trapping column (PepMap100, C18, 300 μm × 5 mm), using partial loop injection, for 7 min at a flow rate of 9 μL/min with 2% v/ v acetonitrile, 0.1% (v/v) TFA and then resolved on an analytical column (Easy-Spray C18 75 μm × 500 mm, 2 μm bead diameter column) using a gradient of 96.2% A (0.1% (v/v) formic acid (FA)): 3.8% B (80% (v/v) acetonitrile, 0.1% (v/v) FA) to 50% B over 97 min at a flow rate of 300 nL/min. Compensation voltages (CVs) of -40/-55/-70V were selected for the FAIMS unit. For each compensation voltage (cycle time 1.5 s), MS1 spectra were acquired over m/z 400-1600 in the orbitrap at 120k resolution; automatic gain control (AGC) was set to accumulate 4 × 105 ions, with a maximum injection time of 50 ms, minimum intensity threshold set to 25,000, charge states 2+ to 6+ and a dynamic exclusion of 60 s with a mass tolerance of 10 ppm. Data-dependent tandem MS analysis was performed using HCD-mediated fragmentation (normalised collision energy (NCE) of 38%), with MS2 spectra acquired at 50k resolution, with a fixed first m/z of 120, AGC target 1 x 105 ions (maximum injection time 200 ms).  

Mass-spectrometry data processing 
Data were processed using Thermo Proteome Discoverer (v. 2.4) in conjunction with MASCOT (MatrixScience, v. 2.7.0), using default settings, unless otherwise specified. Peak lists were searched against the human reviewed UniProt database (202010; 20,610 sequences). The following parameters were selected: MS1 tolerance of 10 ppm; MS2 tolerance of 0.01 Da; enzyme specificity set as trypsin permitting two missed cleavages; carbamidomethylation of cysteine was set as fixed modification. Methionine oxidation was selected as dynamic (variable modification), as well as the TMTpro moiety for TMT-labelled samples and S/T/Y phosphorylation for phospho-enriched samples. The ptmRS algorithm was run in PhosphoRS mode using diagnostic fragment ions and analyzer-specific fragment ion tolerances, as previously defined in the search. Data were filtered to a 1% false discovery rate (FDR) on peptide spectrum matches (PSMs) and proteins using the Percolator node. Protein contaminants were matched against the cRAP library database (compiled by the Global Proteome Machine Organization (Mellacheruvu et al., 2013)). TMT reporter ions were quantified with an integration tolerance of 20 ppm over the most confident centroid. Unique and razor peptides were used for protein quantification, with a co-isolation threshold of 50%, with normalisation based on total peptide amount (sum of peptide group abundance per sample/sum of peptide group abundance for all samples). 
Bioinformatics tools 
Normalised abundance values at the peptide isoform level (phospho-peptide analysis) and the protein level (total protein analysis) was filtered for >50-67% valid values per phospho-peptide/protein ( i.e. 2 out of 4 or 4 out 6 replicates in at least one condition), using PhosR function selectGrps (Kim et al., 2021). Remaining missing values were imputed using the bnstruct package (Franzin et al., 2017) knn.impute function. Batch correction was performed using the limma package (Ritchie et al., 2015), removeBatchEffect function, with the experimental TMTpro™ multiplexed batch specified as the single batch effect factor. Statistical analysis (two-sided, unpaired Student’s t tests and two-sided, unpaired Welch’s t tests, with Benjamini-Hochberg (Benjamini & Hochberg, 1995) correction for multiple comparisons) was performed in Perseus bioinformatics software (v. 1.6.14) (Tyanova et al., 2016) and further downstream analysis with in house R scripts (R Team (2022)). Kinase-substrate enrichment analysis was performed with KSEAApp R package (Wiredja et al., 2017). Input was q values and log2FC values for each phosphopeptide normalised against corresponding log2FC for total protein (log2FC(peptide)-log2FC(protein)). Kinases with >= 4 assigned substrates were selected.