No abstract

The advent of personalised medicine promises a deeper understanding of mechanisms and therefore therapies. However, the connection between genomic sequences and clinical treatments is often unclear. We studied 50 breast cancer patients belonging to a population-cohort in the state of Qatar. From Sanger sequencing, we identified several new deleterious mutations in the estrogen receptor 1 gene (ESR1). The effect of these mutations on drug treatment in the protein target encoded by ESR1, namely the estrogen receptor, was achieved via rapid and accurate protein–ligand binding affinity interaction studies which were performed for the selected drugs and the natural ligand estrogen. Four nonsynonymous mutations in the ligand-binding domain were subjected to molecular dynamics simulation using absolute and relative binding free energy methods, leading to the ranking of the efficacy of six selected drugs for patients with the mutations. Our study shows that a personalised clinical decision system can be created by integrating an individual patient’s genomic data at the molecular level within a computational pipeline which ranks the efficacy of binding of particular drugs to variant proteins.

Immune checkpoint inhibitors (ICIs) including anti-PD-1 and anti-PD-L1 antibodies, have significantly changed the treatment outcomes of NSCLC patients with better overall survival. However, 15-40% of the patients still fail to respond to ICIs therapy. Identification of biomarkers associated with responses are mandated in order to increase the efficacy of such therapy. In this study we evaluated 27 serum-derived exosomal immuno-oncological proteins and 44 cytokines/chemokines before and after ICIs therapy in 17 NSCLC patients to identify surrogate biomarkers for treatment/monitoring patient stratification for maximum therapeutic benefit. We first confirmed the identity of the isolated exosomes to have their specific markers (CD63, CD81, HSP70 and CD91). We have demonstrated that baseline concentration of exosomal-PD-L1 (p<0.0001), exosomal-PD-L2 (p=0.0413) and exosomal-PD-1 (p=0.0131) from NSCLC patients were significantly higher than their soluble-free forms. Furthermore, the exosomal-PD-L1 was present in all the patients (100%), while only 71% of patients expressed tissue PD-L1. This indicates that exosomal-PD-L1 is a more reliable diagnostic biomarker. Interestingly, exosomal-PD-L2 expression was significantly higher (p=0.0193) in tissue PD-L1-negative patients compared to tissue PD-L1-positive patients. We have also shown that immuno-oncological proteins isolated from pre-ICIs treated patients were significantly higher in exosomes compared to their soluble-free counterparts (CD152, p=0.0008; CD80, p=0.0182; IDO, p=0.0443; Arginase, p<0.0001; Nectin-2, p<0.0001; NT5E, p<0.0001; Siglec-7, p<0.0001; Siglec-9, p=0.0335; CD28, p=0.0092; GITR, p<0.0001; MICA, p<0.0001). Finally, the changes in the expression levels of exosomal immuno-oncological proteins/cytokines and their correlation with tumor response to ICIs treatment were assessed. There was a significant downregulation of exosomal PD-L1 (p=0.0156), E-Cadherin (p=0.0312), ULBP1 (p=0.0156), ULBP3 (p=0.0391), MICA (p=0.0391), MICB (p=0.0469), Siglec7 (p=0.0078) and significant upregulation of exosomal PD-1 (p=0.0156) and IFN- γ (p=0.0156) in responding patients. Non-responding patients showed a significant increase in exosomal-PD-L1 (p=0.0078). Furthermore, responding-patients without liver-metastasis showed significant-upregulation of PD-1 (p=0.0070), and downregulation of ULBP1 (p=0.0137) and Siglec-7 (p=0.0037). Non-responding patients had significant-downregulation of ULBP3 (p=0.0317) in patient without brain-metastasis and significant-upregulation/downregulation of PD-L1 and ULBP3 (p=0.0262/0.0286) in patients with pulmonary-metastasis. We demonstrated for the first time that exosomal immuno-oncological proteins/cytokines are potential biomarkers to monitor response to ICIs therapy and can predict the clinical outcomes in NSCLC patients.