Extracellular vesicle PD‑L1 dynamics predict durable response to immune‑checkpoint inhibitors and survival in patients with non‑small cell lung cancer

deMiguel‑Perezetal. J Exp Clin Cancer Res (2022) 41:186 https://doi.org/10.1186/s13046‑022‑02379‑1 RESEARCH Extracellular vesicle PD‑L1 dynamics predict durable response toimmune‑checkpoint inhibitors andsurvival inpatients withnon‑small cell lung cancer Diego de Miguel‑Perez1,2, Alessandro Russo2,3, Oscar Arrieta4, Murat Ak5,6, Feliciano Barron4, Muthukumar Gunasekaran2, Priyadarshini Mamindla6, Luis Lara‑Mejia4, Christine B. Peterson7, Mehmet E. Er5,6, Vishal Peddagangireddy5, Francesco Buemi3, Brandon Cooper2, Paolo Manca8, Rena G. Lapidus2, Ru‑Ching Hsia2, Andres F. Cardona9, Aung Naing10, Sunjay Kaushal2, Fred R. Hirsch1, Philip C. Mack1, Maria Jose Serrano11, Vincenzo Adamo3, Rivka R. Colen5,6 and Christian Rolfo1,2* Abstract Background: Immune‑checkpoint inhibitors (ICIs) changed the therapeutic landscape of patients with lung cancer. However, only a subset of them derived clinical benefit and evidenced the need to identify reliable predictive bio‑ markers. Liquid biopsy is the non‑invasive and repeatable analysis of biological material in body fluids and a promis‑ ing tool for cancer biomarkers discovery. In particular, there is growing evidence that extracellular vesicles (EVs) play an important role in tumor progression and in tumor‑immune interactions. Thus, we evaluated whether extracellular vesicle PD‑L1 expression could be used as a biomarker for prediction of durable treatment response and survival in patients with non‑small cell lung cancer (NSCLC) undergoing treatment with ICIs. Methods: Dynamic changes in EV PD‑L1 were analyzed in plasma samples collected before and at 9 ± 1 weeks dur‑ ing treatment in a retrospective and a prospective independent cohorts of 33 and 39 patients, respectively. Results: As a result, an increase in EV PD‑L1 was observed in non‑responders in comparison to responders and was an independent biomarker for shorter progression‑free survival and overall survival. To the contrary, tissue PD‑L1 expression, the commonly used biomarker, was not predictive neither for durable response nor survival. Conclusion: These findings indicate that EV PD‑L1 dynamics could be used to stratify patients with advanced NSCLC who would experience durable benefit from ICIs. Keywords: Extracellular vesicles, PD‑L1, Biomarkers, Immunotherapy, NSCLC © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Background Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of several malignancies, including non-small cell lung cancer (NSCLC). Programmed cell death protein ligand-1 (PD-L1) expression in cancer cells is one of the inhibitory mechanisms involved in tumor immune evasion by PD-1 binding and subsequent T cell impairment [1]. Several trials have reported impressive Open Access *Correspondence: christian.rolf[email protected] 1 Center for Thoracic Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA Full list of author information is available at the end of the article Page 2 of 14 deMiguel‑Perezetal. J Exp Clin Cancer Res (2022) 41:186 activity of anti-PD-(L)1 monoclonal antibodies alone or in combination with chemotherapy or other immunotherapeutic drugs such as anti-CTLA-4 in patients withNSCLC [2–4]. Consequently, the FDA approved the firstor second-line use of drugs such as pembrolizumab (anti-PD-1), atezolizumab (anti-PD-L1), nivolumab (antiPD-1), or recently cemiplimab (anti-PD-1) [5] in several tumors including lung cancer. Significant advances have been made in the search for the ideal predictive biomarker, including the recent acceptance of tumor mutational burden (TMB) and microsatellite instability-high (MSI-H) as predictive biomarkers in the tumor-agnostic use of pembrolizumab [6]. However, PD-L1 detection by immunohistochemistry (IHC) is the FDA-approved and most commonly used predictive biomarker in these patients. Nevertheless, many patients expressing high PD-L1 did not benefit from the treatment and a considerable percentage of those with low/negative PD-L1 expression did, which might be caused by its high variability [7, 8]. Along with inter-tumor variability, patients withNSCLC show substantial intra-tumoral heterogeneity and changes in PD-L1 expression can occur after first-line treatments, hindering the accurate classification of PD-L1 status [9]. Therefore, aside from the lack of “real-time” information, a single tissue biopsy may not be able to recapitulate the exact status of the tumor microenvironment at the time of treatment that, in some cases, can be months or even years after tumor collection. Thus, there is a huge need to identify reliable predictive biomarkers for anti-PD(L)1 agents that can reflect the status of the tumor microenvironment in real-time. PD-L1 protein expression can also be found in extracellular vesicles (EVs) [10]. These vesicles are double‐membrane structures of 20 – 2000nm involved in intercellular communication and found in body fluids such as blood [11]. EVs are involved in the cross-talk within the tumor microenvironment and play a role in the inhibition of the anti-tumor immune response and metastasis, in particular by PD-L1 presentation [12–15] So far, only few studies have evaluated the potential of PD-L1 expression inEVs as a predicting biomarker in patients withlung cancer undergoing ICIs and they showed conflicting results that warrant further investigation [16, 17]. On the other hand, radiomics is a rapidly growing field in imaging, which can convert a patient’s imaging scans into mineable quantitative data to better understand the tumor heterogeneity and microenvironment [18]. In particular, radiomics may predict immunotherapy response and outcome in multiple cancers, including NSCLC and other advanced solid tumors [18–21]. Although combining independent predictive markers has been recommended to improve accuracy for treatment response prediction, to the best of our knowledge, only one study has attempted to combine radiomics and liquid biopsy data to predict the response to immunotherapy in patients withNSCLC [16]. Considering the limitations of that study regarding a very small cohort, using non-contrast computed tomography (CT), and most likely model overfitting, we believe that further clinical evaluation of these combined markers is needed. There is compelling evidence that the anti-tumor immune response is a complex process regulated by the interaction between the tumor, the immune system, and multiple host factors in which EVs play a critical role. Thus, we aimed to identify and validate the predictive role of EV PD-L1 dynamics in patients withadvanced/ metastatic NSCLC treated with ICIs compared to the standard-of-care tissue PD-L1. Additionally, we aimed to create and evaluate the performance of a multiparametric predictive model with the inclusion of radiomics analysis in our initial cohort. Materials andmethods Study design andpatients We conducted the retrospective analysis of blood samples and CT scan images from patients withadvanced/ metastatic NSCLC treated with anti-PD-1 antibodies at the Medical Oncology Unit of A.O. Papardo of Messina, Italy, between May 2018 and November 2019 with follow-up until August 2021 (Training cohort A). Then, we prospectively analyzed blood samples frompatients with advanced/metastatic NSCLC enrolled in the phase 2 PROLUNG clinical trial [22], undergoing Pembrolizumab + Docetaxel or Docetaxel alone at the National Cancer Institute, Mexico, with follow-up until August 2021 (Validation cohort B) (Fig.1). All patients provided written informed consent and the study was approved by each institutional review board. Inclusion criteria considered patients older than 18years old at the time of diagnosis, stage IIIB or IV according to the 8th edition of the American Joint Committee on Cancer TNM manual [23], and immune-naïve patients treated with anti-PD(L)-1 (nivolumab, pembrolizumab) inhibitors in the first, second, or third line. Histological PD-L1 expression was assessed by immunohistochemistry using SP263, 22C3, or 28–8 clones as per clinical practice in the most recent formalin-fixed paraffin-embedded tissue block from the primary tissue or metastasis and classified according to the tumor proportion score (TPS). Tumor assessment during treatment was evaluated by CT scan after 9 ± 1weeks and at 21 ± 3weeks of treatment according to the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 [24]. Additionally, an independent institution review of the CT scan from the discovery cohort was conducted Page 3 of 14 deMiguel‑Perezetal. J Exp Clin Cancer Res (2022) 41:186 using RECIST v1.1 and Immune-related Response Evaluation Criteria In Solid Tumors (irRECIST) [25] for the radiomics analysis. Durable responders included those patients demonstrating complete response (CR), partial response (PR), or stable disease (SD) at the 21 ± 3 week evaluation while non-responders included those with progressive disease (PD). Progression-free survival (PFS) was defined as the time elapsed between the start of the treatment to the first radiological or clinical progression and overall survival (OS) as the time from the start of the therapy till exitus. Blood samples Three mL of peripheral blood were collected into EDTA Vacutainer® tubes at baseline (T1) and at the first response evaluation (T2). Blood samples were centrifuged at 2,000 × g for 15min and plasma was isolated and frozen at -80°C. Extracellular vesicle isolation Extracellular vesicles were isolated according to standard protocols from our group [26, 27]. Briefly, plasma aliquots were thawed and each500µl were diluted into 1mL of 1X PBS and centrifuged at 3,000 × g for 20min at 4°C and later at 10,000 × g for 30min at 4°C to remove cell debris. Supernatants were recovered, diluted with 1X PBS, and centrifuged at 100,000 × g for 70min at 4°C into 6.5mL, Open-Top Thickwall Polycarbonate Tubes (Beckman Coulter) in an Optima MAX Ultracentrifuge (Beckman Coulter). Finally, EVs pellets were resuspended in 1X PBS or lysed with 1X RIPA lysis buffer (Cell Signaling) and sonicated for 2min for further analysis. Extracellular vesicle PD‑L1 characterization Following the last recommendations of the International Society of Extracellular Vesicles (ISEV) [28], EVs were characterized by nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), and western blot following our standardized methodology [27]. In addition, immunogold TEM characterization of PD-L1 expression in EVs was performed. EV PD-L1 expression was evaluated by immunoblot and EV PD-L1 dynamics (ΔEV PD-L1) were calculated as the expression of PD-L1 normalized against CD9 in the second time point divided by the same value in the paired baseline sample [(PD-L1/CD9) T2 / (PD-L1/CD9) T1]. A full description of the EV characterization methodology can be found in the Supplementary Methods. Increase in EV PD-L1 was defined as patients with ΔEV PD-L1 > 1 and decrease in those with ΔEV PD-L1 < 1. Patients with lower volume or quality of available plasma were excluded from the study (Cohort B: 2 patients in the Pembrolizumab + Docetaxel and 4 in the Docetaxel group). Radiomics imaging analysis Radiomics analysis of target and non-target lesions was executed according to our established methodology [21]. Briefly, all lesions were segmented using 3D Slicer 4.10.1 module (Slicer 4.10.1: Summary, Highlights and Changelog—Announcements / Release Notes—3D Slicer Community) by a different color label. Additional volumes of interest (VOI) of the normal pectoralis major muscle were segmented for within-phase normalization. Ten intensity-level histogram features and 195Gy level co-occurrence matrix (GLCM) features were extracted [29, 30]. We calculated the average, range, and angular variance of each feature for different angles, resulting in 39 rotation-invariant texture features calculated for each VOI for five gray levels. Additionally, we computed 195 volume-dependent second-order features by dividing each GLCM feature by the volume of the segmented lesions; therefore, a total of 400 radiomics features were Fig. 1 Study design: Graphical scheme of patient accrual, follow‑up, and biomarker analysis [created with Biorender.com] Page 4 of 14 deMiguel‑Perezetal. J Exp Clin Cancer Res (2022) 41:186 acquired. Radiomics analysis was performed using our in-house pipeline in Matlab (version 2017b; MathWorks Inc) and Phyton Programming Language (version Phyton 3.7). Bioinformatics andstatistical analysis Statistical analysis and graphs were done using SPSS [SPSS Statistics for Windows, Version 22.0 (IBM Corp., Armonk NY, US), GraphPad Prism Version 8.4 (GraphPad Software Inc., San Diego CA, US) and R software (version 3.4.0, R Foundation for Statistical Computing, Vienna, Austria). Non-parametric test evaluated differences between variables. Univariate predictive models were generated with logistic regression using glmnet function from the glmnet package in R software. Regression analysis was performed with the least absolute shrinkage and selection operator (LASSO) feature selection method to find the most relevant radiomics features associated with the response [31]. Selected features were entered into eXtreme Gradient Boosting (XGBoost) to build a classification model for predicting of tumor response to immunotherapy [32]. Finally, leave-one-out cross-validation (LOOCV) was applied to assess the robustness of our models. Feature selection, model building, and receiver operating characteristics (ROC) analyses were implemented using the R packages XGBoost (version 0.6.4.1), mlr (version 2.11), and pROC (version 1.9.1). The area under the curve (AUC) was calculated for each ROC curve and sensitivity and specificity values were shown for the optimal cut-point value from each curve, resulted by selecting the value providing higher overall sensitivity & specificity (Youden’s index). Survival analyses were performed by Kaplan–Meier (log‐rank test) and Cox Proportional‐Hazards Regression with backward stepwise selection for the multivariate model. Two-tailed p values < 0.05 were considered statistically significant. Results This study enrolled two independent cohorts of patients withadvanced/metastatic NSCLC. The training cohort (A) included 33 patients undergoing ICIs with a median follow-up of 12.4months (range 2.5 – 33.1). The validation cohort (B) enrolled 39 patients with median follow-up of 13.1months (range 3.5 – 56.5) from which 24 received Pembrolizumab + Docetaxel and 15 Docetaxel alone. Patients characteristics are summarized in Supplementary Table S1. EV PD‑L1 characterization Concentration, morphology, size, and specific markers in plasma EVs were analyzed to prove their nature and purity. First, the NTA showed that plasma EVs had a concentration of 2.15 × 108 particles/ mL and a mean diameter of 99.4nm (Fig.2A). Second, the immunogold TEM characterization depicted EVs of similar size with positive PD-L1 membranous expression (Fig.2B). Third, the western-blot revealed expression of PD-L1 and the EV markers CD9 and Flotillin-1 in EVs while absence of the non-EV marker GM130, commonly used as control Fig. 2 EVs characterization: (A) Nanoparticle tracking analysis (NTA) of EVs isolated from advanced NSCLC plasma samples showing a concentration of 2.15 × 108 particles/mL with a mode diameter of 68.4 nm. (B) The immunogold transmission electron microscopy (TEM) depicted EVs of similar size with expression of PD‑L1 in the membrane. (C) Western blot (WB) images revealed expression of PD‑L1, Flotillin‑1, and CD9 in the plasma EVs and lung cancer culture EVs, while low expression of GM130 Page 5 of 14 deMiguel‑Perezetal. J Exp Clin Cancer Res (2022) 41:186 for non-EV contamination (Fig.2C). Then, PD-L1 and CD9 expression were analyzed in paired samples from each patient, calculating the ΔEV PD-L1 (Supplementary Fig. S1). EV PD‑L1 dynamics are abetter predictive biomarker fordurable response toICIs thantissue PD‑L1 We analyzed the associations between the EV PD-L1 dynamics and the different clinical characteristics, including age, gender, line of treatment, histology, tissue PD-L1 (TPS), etc. in our three subgroups of patients with NSCLC, observing no statistical association with any of them (Supplementary Table S2). Then, we examined the potential role of this dynamic biomarker as a predictor of durable ICIs response compared to the standard-of-care biomarker, tissue PD-L1 (Representative images from responders and non-responders and the tissue and EV PD-L1 characterization are shown in Fig.3A-C). We found an increase in EV PD-L1 during treatment in non-responders in comparison with decreasing levels in responders in our cohort A of ICIs patients (p = 0.017) (Fig.3D). Similarly, in the Pembrolizumab + Docetaxel group, non-responders showed a trend towards increased EV PD-L1 in comparison to responders (p = 0.050) (Fig.3E) while no differences were observed in the Docetaxel treated patients (Fig.3F). No differences in these dynamics were found between the different treatments (Supplementary Fig. S2). Moreover, no association was found between the tissue PD-L1 expression and the durable response in any group. Thus, ΔEV PD-L1 outperformed tissue PD-L1 as a predictive factor for identifying patients with non-durable clinical benefits from ICIs. ΔEV PD-L1 showed an area-underthe-curve (AUC) of 77.3% in cohort A (Fig. 3G) and 75% in the Pembrolizumab + Docetaxel group (Fig.3H), while the tissue PD-L1 showed only an AUC of 62.7% and 64.1%, respectively. Poor predictive values were observed for both biomarkers in the Docetaxel group (Fig. 3I). When considering the early response evaluated at the first CT scan, similar but not statistically significant differences were observed in Cohort A. However, as only one patient showed early PD in the Pembrolizumab + Docetaxel group, no significant differences were found (Supplementary Fig. S3). Furthermore, when the durable response was analyzed in all 57 patients undergoing treatment with ICIs, the dynamics of EV PD-L1 showed differences between patients with PR, SD, and PD (p = 0.009) since it was positively correlated with lesion size (p = 0.040) (Supplementary Fig. S4). Indeed, patients with increased EV PD-L1 showed an increase in lesion size (p = 0.036), but no association was found between the tissue PD-L1 TPS and tumor size (p = 0.330) or patients’ response (p = 0.561) (Fig. 4A). Moreover, increased EV PD-L1 identified non-responders with 73% sensitivity and 61% specificity (p = 0.009) (Fig.4B). On the other hand, high tissue PD-L1 was not associated with a durable response either when considering patients with TPS ≥ 50% (p = 0.192) or with TPS ≥ 1% (p = 0.370) (Fig.4B). Additional subgrouped analysis of the predictive performance of EV PD-L1 across different types and lines of therapy or TPS groups are shown in Supplementary Fig. S5. EV PD‑L1 dynamics are apredictive biomarker forsurvival During the follow-up of these cohorts, 28 (84.8%) patients progressed from Cohort A, 22 (91.7%) patients undergoing Pembrolizumab + Docetaxel, and 15 (100%) of those who underwent Docetaxel in cohort B (Supplementary Table S1). First, we analyzed the predictive value for PFS of the dynamics of EV PD-L1 in cohort A. Patients with EV PD-L1 decrease tend to experience longer PFS than those with increasing levels (Hazard ratio (HR) = 0.36; p = 0.097) (Fig.5A). This was validated in patients undergoing Pembrolizumab + Docetaxel, where those with EV PD-L1 decrease showed longer PFS (HR = 0.18; p = 0.020) (Fig. 5B). To the contrary, no differences in PFS were observed in the Docetaxel group (HR = 13.3; p = 0.784) (Fig.5C). The multivariate Cox’s regression analysis for the total 57 patients receiving ICIs revealed that ΔEV PD-L1 was an independent predictive biomarker for PFS, with decreased levels associated with longer PFS (HR) = 0.45; p = 0.008) while tissue PD-L1 expression was not (Supplementary Table S3) (Fig.5G). Regarding the mortality of these patients, 19 (57.6%) patients died in cohort A. In cohort B, 18 (75%) patients died in the Pembrolizumab + Docetaxel group and 12 (80%) in the Docetaxel group (Supplementary Table S1). As previously described for the PFS, we observed that patients with decreasedEV PD-L1 presented longer OS in cohort A (HR = 0.22; p = 0.031) (Fig.5D). As a validation, it was also associated with longer OS in patients undergoing Pembrolizumab + Docetaxel (HR = 0.23; p = 0.039) (Fig.5E) and no differences were observed in the Docetaxel group (HR = 1.63; p = 0.202) (Fig.5F). The multivariate Cox’s regression analysis for all 57 patients undergoing ICIs demonstrated that thedecrease in EV PD-L1 was an independent predictive biomarker for longer OS (HR = 0.35; p = 0.004); however, the tissue PD-L1 was not (Supplementary Table S4) (Fig.5G). Radiomic features complement EV PD‑L1 fortheprediction oftheresponse Additionally, we performed an exploratory analysis of radiomics data obtained from baseline CT scans from 27 patients from our training cohort (A). Among these patients, 11 (40.7%) were classified as durable responders Page 6 of 14 deMiguel‑Perezetal. J Exp Clin Cancer Res (2022) 41:186 Fig. 3 EV PD‑L1 dynamics outperformed tissue PD‑L1 as a predictor of ICIs response: (A) Representative axial section computed tomography (CT) images from a responder and a non‑responder at baseline and during ICIs treatment. (B) Examples of immunohistochemistry micrographs of positive and negative tissue PD‑L1 staining (scale bars 5 µm) and (C) EV PD‑L1 blots from a responder with decreasing EV PD‑L1 (0.29) and a non‑responder showing an increase (1.55). (D) ICIs cohort A (n = 33), non‑responders (NR) showed increased EV PD‑L1 during treatment in comparison to responders (p = 0.017) (Mann–Whitney U test). (E) In the validation cohort, non‑responders undergoing Pembrolizumab + Docetaxel (n = 24) showed a trend towards increased EV PD‑L1 in comparison to responders (p = 0.050) while those treated with Docetaxel alone (n = 15) showed no differences (p = 0.794) (F) (Mann–Whitney U test). (G) As observed in the ROC curve, EV PD‑L1 dynamics was a better predictor than tissue PD‑L1 TPS with an AUC = 74.4% vs. 62.6% for the tissue (binary logistic regression). (H) This was also observed in the validation cohort of patients treated with ICIs with AUC = 75% for the EVs vs. 64.1% for the tissue. (I) In comparison, similar AUCs were observed in the Docetaxel treated group with 54.5% and 59.1%, respectively (binary logistic regression) Page 7 of 14 deMiguel‑Perezetal. J Exp Clin Cancer Res (2022) 41:186 by RECIST and 16 (59.3%) by irRECIST, while 15 (55.6%) patients were considered early responders by RECIST and 18 (66.7%) by irRECIST, in the first CT scan. Figure6A depicts the radiomics pipeline for feature extraction and model selection, where the most relevant features to predict RECIST durable response were selected and combined, resulting in a model of 6 LASSO features (Supplementary Table S5). We compared the predictive value of this signature with the tissue and EV PD-L1. We observed that for RECIST response, the combination of ΔEV PD-L1 and the radiomics signature was the best model, able to identify non-responders with an 81.5% accuracy. At the same time, for durable irRECIST, individual ΔEV PD-L1 was the best predictive model with 74.1% accuracy (Fig.6B & C). Similarly, when predicting early response, the combination with radiomics also improved the predictive accuracy of the dynamics of EV PD-L1 for early RECIST response but not for irRECIST response (Supplementary Fig. S6). Furthermore, these six features were used to predict survival, which showed that only low TL_FLV7 was associated with worse PFS (HR = 5.52, p = 0.019) (Supplementary Fig. S7 & S8). Discussion Advanced stages of NSCLC are characterized with a suppressed immune system with reduced counts of CD8 + T-cells, responsible for the immune response against tumors [33]. Consequently, ICIs have become one of the most promising therapeutic options, revolutionizing the therapeutic landscape of these patients. Fig. 4 Changes in lesion size of durable response correlated with EV PD‑L1 dynamics in patients undergoing ICIs. (A) As observed in the correlation matrix, larger increases in the tumor lesion were observed in patients with increased EV PD‑L1 (p = 0.036) (Mann–Whitney U test) but were independent of the levels of tissue PD‑L1 (p = 0.330) (Kruskal–Wallis test). No association was found between the tissue PD‑L1 TPS and the tumor response (p = 0.561) (Chi‑square test). (B) Increase in EV PD‑L1 identified non‑responders (p = 0.009), however, neither high tissue PD‑L1 TPS > 50% (p = 0.192) or TPS > 1% (p = 0.370) were associated with durable response (Chi‑square tests) Page 8 of 14 deMiguel‑Perezetal. J Exp Clin Cancer Res (2022) 41:186 Nevertheless, the efficacy of these treatments can still be primarily improved with the adequate use of reliable predictive biomarkers that could stratify which patients would benefit from them and avoid unnecessary adverse events for those who would not derive benefit. Nowadays, tissue PD-L1 is the standard-of-care for patient stratification, however, it fails at predicting the efficacy of ICIs due to several technical and biological issues associated with Fig. 5 EV PD‑L1 increase as a predictive biomarker for PFS and OS. (A) Patients with an increasing EV PD‑L1 (blue) showed a trend to shorter PFS (p = 0.097) in the ICIs cohort and demonstrated shorter PFS in the Pembrolizumab + Docetaxel treated group (p = 0.020). Still, no association with PFS was observed in the Docetaxel group (p = 0.784) (C). (D) Longer OS was depicted in patients with EV PD‑L1 increase (blue) in the ICIs cohort (p = 0.031) and the Pembrolizumab + Docetaxel group (p = 0.038) (E) while not in the Docetaxel control group (p = 0.202) (F) (log‐rank tests). Number of patients at risk of the event is shown every 6 months and the percentage of free of event (progression or death) patients is shown at 12 and 24 months. (G) In the 57 patients undergoing ICIs, an EV PD‑L1 increase was observed in those with shorter PFS and OS while tissue PD‑L1 was not (tissue PD‑L1 TPS, dark red = > 50%, red = 1–49%, pink < 1%, white = unknown; arrow = ongoing treatment; black & white squares bar = OS after treatment discontinuation; x = exitus (death); orange circles = progressive disease; filled dark blue rectangles = EV PD‑L1 increase Page 9 of 14 deMiguel‑Perezetal. J Exp Clin Cancer Res (2022) 41:186 PD-L1 IHC [34]. Moreover, the high complexity of the immune landscape of NSCLC suggest that many markers might be involved in the response [35] and hence are needed for its prediction. In this scenario, liquid biopsy holds promise as the real-time characterization of tumors through the study of molecules found in human body fluids, able to track lung tumors evolution over time [11]. Tissue and blood TMB or dynamics of ctDNA have been proposed as potential biomarkers. However, they are still not widely used in clinical practice due to the lack of method standardization and uncertain predictive value. Indeed, recent analyses of pivotal studies, including KEYNOTE-189 [36], KEYNOTE-021 [37], or the recent results of the BFAST cohort C study [38], raised several concerns on the validity of these beforehand promising biomarkers, requiring the evaluation of other alternative circulating markers of efficacy. Therefore, we examined the predictive role of plasma EV PD-L1 expression in a retrospective cohort of patients withadvanced/ metastatic NSCLC undergoing treatment with ICIs and validated it in a prospective analysis of a sub-cohort from the phase 2 PROLUNG clinical trial [22]. Furthermore, in an exploratory analysis, we included radiomics data in a multiparametric predictive model in combination with the EV PD-L1. First, we demonstrated that PD-L1 can be expressed in EVs in plasma from patients with advancedNSCLC, confirming previous findings observed in other solid tumors [39]. Then, we analyzed the dynamics of EV PD-L1 levels from baseline to 8weeks of anti-PD-1 treatment in these patients. We observed that these dynamic changes were associated with durable responses to the treatment, since non-responders showed increased levels in comparison to a decrease in responders, even correlated with the change in lesion size. This concurs with previous studies on EV PD-L1 in patients and mice models receiving ICIs in which theincrease in EV PD-L1 correlated with poor response, disease activity, or lesion size [15, 39, 40]. Similarly, a smaller study analyzed the dynamics of PD-L1 expression but focused only on exosomes from 44 patients withmelanoma undergoing pembrolizumab [13]. Contrary to our results, this study described increased levels of exosomal PD-L1 in both responders and non-responders by irRECIST, observing a higher increase during the 6th week of treatment in responders but equal levels at the 9th week. The idea of increased levels being a predictor of better response contradicts their results and those from other studies which demonstrated that exosomal PD-L1 inhibited CD8 + T-cell function and facilitated tumor growth in invitro and invivo models [13, 15, 41]. This discrepancy could be caused by their use of the best response by irRECIST as classification for the clinical response, which would include as responders, patients with new metastasis or oligoprogression, or even those who shortly responded and then progressed. In addition, the difference in timepoints for second blood Fig. 6 Combination of radiomics and EV PD‑L1 dynamics for predicting durable response: (A) Characteristic pipeline for radiomic analysis including CT scan image segmentation, feature extraction, and feature and model selection by machine learning. (B) The introduction of the 6‑features radiomic signature into the ΔEV PD‑L1 predictive model for RECIST improved its performance as observed in the considerable increase of sensitivity and specificity, with an accuracy of 81.5%. (C) On the contrary, the best model for prediction of irRECIST only included the ΔEV PD‑L1 with an accuracy of 74.1% (binary logistic regression)