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Additional File 4: Fig. S3. Differential phosphorylation of Ser, Thr, and Tyr in recurrent NF-PitNETs and reproducibility of data. a–c Fisher’s test shows significantly high number of hyper and hypo phosphorylated Ser (a, p< 0.0001) and Thr (b, p = 0.01) phosphopeptides in R as compared to I. c No difference in number of phosphotyrosine was found. d, e Pearson’s correlation coefficient for invasive (d, r = 0.78) and recurrent (e, r = 0.73) indicate strong reproducibility among replicates (p < 0.0001). I Invasive; R Recurrent; Ser Serine; Thr Threonine; Tyr Tyrosine.

Additional File 5: Fig. S4. Increased upregulated phosphopeptides in the recurrent NF-PitNET subgroup. a, b Graphical representation showing the number of phosphopeptides with the Log2-fold change in the various NF-PitNET groups with red lines indicating hypo- and hyper-phosphorylated peptides. c Principle component (PC) analysis of NF-PitNET phosphoproteome reveal disease subgroup segregation and replicates group together. Singular value decomposition (SVD) with imputation of fold change of phosphopeptides of each NF-PitNET subgroup is used to calculate principal components. X and Y axis show principal component 1(PC1) and principal component 2 (PC2) that explain 63.3% and 82.8% of the total variance, respectively. Each grey dots represents an NF-PitNET subgroup. Abbreviations: NI/NR, non-invasive/non-recurrent; I, Invasive; R, Recurrent; PC Principle component. Green colour represents invasive (I) subgroups while blue colour represents recurrent (R).

Additional file 2: Fig. S1. Classification of the NF-PitNET’s subgroups based on the expression of transcription factors. a–d Immunohistochemistry against PIT1 (a), TPIT (b), and SF1 (c). Tumours negative for all three PIT1, TPIT, and SF1 were classified as null cell tumours (d). Positive immunostaining is shown by brown colour marked by black arrows. Scale bar in H represents 100 µm.

Additional File 6: Fig. S5. Gene ontology (GO) pathway analyses showing enriched (red) or depleted (green) hyperphosphorylated proteins in R NF-PitNET. a–d Phosphoproteins exclusively overphosphorylated in recurrent PitNETs were used for gene ontology analysis using FunRich (version 3.1.3) software. X axis represents fold change enrichment of GO categories in recurrent NF-PitNETs as compared to non-invasive/non-recurrent and Y axis represents the GO categories. a Cell component analysis showed endoplasmic reticular membrane proteins were most enriched followed by cell cortex, ribosome, nuclear speck, and cytoplasmic microtubules. b Graphical representation of molecular function revealed proteins with ATPase activity were most enriched while caspase activator proteins were most depleted in recurrent NF-PitNETs. c Graphical representation of biological process showed proteins involved in cell proliferation, regulation of cell proliferation and migration were most enriched. d Graphical representation of biological pathways showed glypican-3 signalling and overall high enrichment of Wnt signalling and regulation of nuclear β-catenin signalling as the most enriched pathways in recurrent NF-PitNETs. R Recurrence; GO Gene ontology

Additional file 1: Supplementary Tables.

Additional File 3: Fig. S2. Experimental workflow of phosphoproteomic analysis of NF-PitNETs. Patients were divided into three groups: non-invasive/non-recurrent (NI/NR), invasive (I), and recurrent (R). For quantification peptides were labelled with tandem mass tags (TMT). Phosphopeptide enrichment was done by titanium dioxide (TiO2) and fractionated by basic reverse phase liquid chromatography (bRPLC) prior to LC–MS/MS analysis on two different mass spectrometrs, Orbitrap Velos and Orbitarp Fusion Tribrid mass spectrometer (ThermoScientific). Peptides identified in triplicates were further used for in-silico functional analysis and validation on large cohort (n = 200) by immunohistochemistry on tissue microarray (TMA).

Additional File 7: Fig. S6. Protein-protein interaction network of phosphoproteins exclusively overphosphorylated in recurrent NFPTs showed upregulation of β-catenin signalling. Phosphoproteins only overphosphorylated in recurrent subgroup (R) were mapped using FunRich (version 3.1.3) software. Each red node represents a hyperphosphorylated protein and blue line indicates interactions. EGFR as found to be in the center of interaction hub while β-catenin was found to be part of most of the upregulated pathways. Blue arrows indicate β-catenin and MYH9, which are significantly hyperphosphorylated in R. Nodes with green circles represents proteins involved in regulation of nuclear β-catenin signaling, while nodes with yellow circles represents proteins involved in developmental pathways. PSMD2 and PSMA3 (indicated by black arrows) represents proteins involved in cell cycle regulation.

Supplementary Table 1. Summary of clinical characteristics of patient 1 in the follow up Supplementary Table 2. Summary of clinical characteristics of patient 2 in the follow up

Aspirin prevents thrombosis by inhibiting platelet cyclooxygenase (COX)-1 activity and the production of thromboxane (Tx)A2 , a pro-thrombotic eicosanoid. However, the non-platelet actions of aspirin limit its antithrombotic effects. Here, we used platelet-COX-1-ko mice to define the platelet and non-platelet eicosanoids affected by aspirin. Mass-spectrometry analysis demonstrated blood from platelet-COX-1-ko and global-COX-1-ko mice produced similar eicosanoid profiles in vitro: for example, formation of TxA2 , prostaglandin (PG) F2α , 11-hydroxyeicosatraenoic acid (HETE), and 15-HETE was absent in both platelet- and global-COX-1-ko mice. Conversely, in vivo, platelet-COX-1-ko mice had a distinctly different profile from global-COX-1-ko or aspirin-treated control mice, notably significantly higher levels of PGI2 metabolite. Ingenuity Pathway Analysis (IPA) predicted that platelet-COX-1-ko mice would be protected from thrombosis, forming less pro-thrombotic TxA2 and PGE2 . Conversely, aspirin or lack of systemic COX-1 activity decreased the synthesis of anti-aggregatory PGI2 and PGD2 at non-platelet sites leading to predicted thrombosis increase. In vitro and in vivo thrombosis studies proved these predictions. Overall, we have established the eicosanoid profiles linked to inhibition of COX-1 in platelets and in the remainder of the cardiovascular system and linked them to anti- and pro-thrombotic effects of aspirin. These results explain why increasing aspirin dosage or aspirin addition to other drugs may lessen antithrombotic protection.