Extracellular vesicles miR-574-5p and miR-181a-5p as prognostic markers in NSCLC patients treated with Nivolumab

doi:10.21203/rs.3.rs-4258337/v1

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Research Article

Extracellular vesicles miR-574-5p and miR-181a-5p as prognostic markers in NSCLC patients treated with Nivolumab

https://doi.org/10.21203/rs.3.rs-4258337/v1

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published 06 Aug, 2024

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Background

Immune checkpoint inhibitors (ICIs) have revolutionized the management of advanced non-small cell lung cancer (NSCLC), although patient survival is still unsatisfactory. Accurate predictive markers capable of personalizing the treatment of patients with NSCLC are still lacking. Circulating extracellular vesicles involved in cell-to-cell communications through miRNAs (EV-miRs) transfer are promising markers.

Methods

Plasma from 245 patients with advanced NSCLC who received nivolumab as second-line therapy was collected and analyzed. EV-miRnome was profiled on 174/245 patients by microarray platform and selected EV-miRs were validated by qPCR. A prognostic model combining EV-miR and clinical variables was built using stepwise Cox regression analysis and tested on an independent patient cohort (71/245). EV-PD-L1 gene copy number was assessed by digital PCR. For 54 patients with disease control, EV-miR changes at best response versus baseline were investigated by microarray and validated by qPCR.

Results

EV-miRNome profiling at baseline identified two EV-miR (miR-181a-5p, miR-574-5p) that, combined with performance status, are capable of discriminating patients unlikely from those that are likely to benefit from immunotherapy (median overall survival of 4 months or higher than 9 months, respectively). EV-PD-L1 digital evaluation reported higher baseline copy number in patients at increased risk of mortality, without improving the prognostic score. Best response EV-miRNome profiling selected six deregulated EV-miRs (miR19a-3p, miR-20a-5p, miR-142-3p, miR-1260a, miR-1260b, miR-5100) in responding patients. Their longitudinal monitoring highlighted a significant downmodulation already in the first treatment cycles, which lasted more than six months.

Conclusions

Our results demonstrate that EV-miRs are promising prognostic markers for NSCLC patients treated with nivolumab.

non-small cell lung cancer (NSCLC)

immune-checkpoint inhibitor

nivolumab

Extracellular vesicle miRNA

prognosis score

miR-574-5p

miR-181a-5p

pluripotency of stem cell

Toll-like receptor

In recent years, immunotherapy has emerged as a valid treatment option in different types of cancer, including non-small cell lung cancer (NSCLC) [1, 2]. Nivolumab, an immune check-point inhibitor (ICI) directed against the programmed cell death-1 (PD-1) protein, is currently available as further line of treatment for NSCLC patients with either squamous or non- squamous histology [1, 3]. However, in spite of the impressive results achieved by ICIs, a non-negligible fraction of patients does not benefit from immunotherapy; indeed, with specific regards to pre-treated patients, single-agent ICIs achieve objective response only in 20% of cases. The tumor expression of programmed cell death-1 ligand (PD-L1) has predictive role in patients with NSCLC treated with ICIs, although its strength in second-line settings is less defined [1, 4, 5]. Therefore, the identification of more robust biomarkers for ICI is of the utmost importance. Circulating molecules are a promising source of non-invasive prognostic markers, especially in ICI-based regimes where immunocompetent cells participate in the response to the drug [6, 7]. To this regard, a number a blood-miRNA based signatures have been proposed as response predictors [8–10]. However, circulating extracellular vesicles microRNAs (EV-miRs) appear to be a better biomarker source than their cell-free counterparts, due to their stability, quantity and quality [11]. Furthermore, evidence also indicates that tumor-derived EV-miRs can modulate the behavior of recipient cells [12]. In particular, EV-miRs have also been described to play a relevant role in the antitumor immune response, by immune cells modulation, as well as tumor antigen processing [13]. All previous evidences demonstrate that miRs, trapped in EVs, represent a surrogate for the tumor microenvironment and are promising prognostic markers particularly in patients receiving ICIs, where both the immune system and the features of the tumor influence response to therapy. To date, few studies have reported on EV-miRs as predictors of response to ICIs in NSCLC [14–16], mainly including small cohorts of patients treated with both PD1 or PD-L1-based therapies as first or second line, with consequent limited clinical application. Recently, EV PD-L1 (EV-PD-L1) mRNA copy number (CN) has also been described as an unfavorable prognostic marker in ICIs [17]. However, similar to previous EV-miR studies, the NSCLC cohort was too limited to provide reliable results.

Here, we developed a robust EV-miR prognostic score in a large cohort of patients treated with nivolumab as second-line treatment for advanced NSCLC to select patients who would benefit most from treatment with this immunomodulator (Fig. 1a). We also evaluated the prognostic role of EV-PD-L1 CN mRNA assessed by digital PCR in the plasma of these patients.

Finally, we demonstrated that some EV-miRs are deregulated in patients with disease control at the time of best response (BR). Notably, their modulation occurs at the first assessment, remaining deregulated six months after initiation of therapy, confirming a potential role in the immune cell modulation (Fig. 1B).

Study Population

Patients’ eligibility criteria were: i) histologically confirmed diagnosis of advanced NSCLC; ii) disease progression after at least one line of platinum-based chemotherapy; iii) no previous treatment with other ICIs; iv) no corticosteroid treatment at dose > 10 mg/day. The enrolled patients received nivolumab according to the indications based on the CheckMate 017 [1] and CheckMate 057 [3] studies and underwent computed tomography scan (CT-scan) assessment every 4 cycles of therapy. Initially, nivolumab was administered at the dose of 3 mg/Kg every 2 weeks. However, starting from March 2nd 2018, the dosing schedule was modified to a flat dose of 240 mg every two weeks, for the patients already on treatment as well as for new patients starting treatment thereafter. Nivolumab was administered until disease progression, onset of unacceptable toxicities, patient’s refusal, death, or up to 96 weeks of treatment. Treatment beyond tumor progression was allowed based on investigators’ judgment as long as clinical benefit was perceived. Disease progression (PD), stable disease (SD) and partial response (PR) were determined based on the Response Evaluation Criteria in Solid Tumors (RECIST) v.1.1. For each patient, a peripheral blood sample was collected at baseline and at every 4 cycles of treatment, based on the patient’s status. In addition, 6 mL of peripheral blood sample in EDTA tube were also collected from 24 healthy individuals (18 males and 6 females, median age 55, 25–69 years). Plasma samples were isolated within 1 hour from sampling by two consecutive rounds of centrifugation and stored at -80°C.

EV characterization and EV-miR profiling

EV-RNA, including miRs, was purified from plasma by Exo-RNeasy Midi Kit (Qiagen, Hilden, Germany) adding the spike-In miR control (UniSP6, Qiagen). To confirm the isolation of pure EVs, their size and concentration were investigated by Nanoparticle Tracking Analysis (NTA) (NanoSight LM10, Malvern Instru-ments Ltd, Malvern, UK) and confirmed by both non-conventional cytofluorimetry (CFDA-SE Vybrant™ CFDA SE Cell Tracer Kit, ThermoFisher Scientific, San Jose, CA, USA; APC Mouse Anti-Human CD9, Clone HI9a, 312108; BioLegend, Saa Diego, CA, USA; PE-Cy7 Mouse Anti-Human CD63, Clone H5C6, 561982; BD Biosciences), and western blot (anti-flotillin-1 1:10,000, ab41927; anti-CD9-1:1000 EPR23105-121, Abcam, Cambridge, UK), as already described [18]. In addition, the protein contamination (i.e., lipoproteins), was also investigated by ELISA using Human ApoA1 and Human apoB Kits (Mabtech, Inc. Cincinnati, USA); specifically, in each plate, 100 µl of plasma (1:5000), isolated plasma EVs (1:2) and standard samples were run in duplicate and analyzed according to the manufacturer’s instructions. The EV-miRNome was profiled by microarray using SurePrint Human miRNA 8X60K (Agilent Technologies, Santa Clara, CA, USA; AMADID: 070156). Briefly, 6 µl of EV-RNA were processed as already described [19], as well as image processing with Feature Extraction v.9.5.3.1 (Agilent Technologies) and data preprocessing with LIMMA package for microarray, available within R statistical software. Background correction and between array normalization were carried out using the normexp method, with an offset = 20, and the scale method, respectively. Probes non-detected in more than 50% of the short survivors (SS, survival time < 9 months) and long survivors (LS, survival time ≥ 9 months) were filtered out. Then replicated probes and replicated miRs were averaged.

EV-miRNA validation

Four µl of EV-RNA were reverse-transcribed using the miRCURY LNA RT Kit (Qiagen). Then, 2–4 µl of diluted complementary DNA (cDNA, 1:20–30) were amplified using iTaq Univer SYBR Green Supermix (Bio-Rad, Hercules, CA, USA) with 1 µl of specific miR primers (Supplementary Table S1). The relative concentration was calculated as follows: 2^-(EV-miR-Ct-Mean - UniSP6-Ct-Mean) X 1000 (Ct: threshold cycles).

PD-L1 gene absolute quantification

The EV-PD-L1 mRNA copy number (CN/1 mL) was assessed by QX200 droplet digital PCR (ddPCR) system (Bio-Rad). Briefly, 2 µl of EV-RNA were reverse transcribed using SuperScript™ IV VILO™ Master Mix (ThermoFisher Scientific). Then, 10 µl of cDNA were amplified using the ddPCR Supermix for Probes (No dUTP) (Bio-Rad) with the PD-L1 FAM-labeled assay (dHsaCPE5058502). Quantification was assessed using the QuantaSoft software (Bio-Rad) in bidimensional visualization, applying the threshold based on the negative template control signal. Samples with a droplet number < 10,000 were repeated.

Functional enrichment and pathway analyses

The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, gene ontology (GO) enrichments were performed by DIANA-miRPath v.3.0 [20] and MIcroRNAENrichmentTURnedNETwork (MIENTURNET) [21] (June 2022) free web tools. Outputs were selected based on enrichment False Discovery Rate < 0.05.

Statistical Analysis

To build the prognostic score, we used 9-month cutoff based on the CheckMate 017 results [1] splitting the training set (n = 174) in SS and LS patients. Class comparison was performed using the R limma function between: i) SS (n = 96) vs LS (n = 78); ii) BR vs baseline (n = 54 SD/PR patients). To select EV-miRs associated with overall survival (OS), the patient cohort was randomly split for 10 times in subgroups made up of 70% of the patients (125/174, with every patient selected at least once), maintaining the same proportion of SS and LS patients in each subset [22]. A penalized Cox regression model using the LASSO method was built on each subgroup using the cv.glmnet function of the glmnet R package, performing a 10-fold cross validation step to fit the model, and the EV-miRs with non-zero coefficients were retained. The EV-miRs retained by at least eight out of ten models were chosen for the validation step. In addition, the EV-miRs retained from five to seven models were further selected based on the model coefficients and available literature. Specifically, their potential role in cancer was assessed using PubMed and the following keywords: “miR-ID AND Cancer” or “miR-ID AND Lung cancer” (December 2021). Hence, for each miR, the impact was calculated by multiplying the number of papers by its regression coefficient; EV-miRs with absolute impact greater than 1 were selected for validation. Spearman correlation between microarray and qPCR data was tested using the cor.test function of the stats R package. The coxph function of the survival R package was applied on the samples with qPCR data available (n = 104) to perform univariable and multivariable Cox regression. Stepwise Cox regression was carried out to find the EV-miR combination (with or without clinical variables) with best prediction accuracy, using the step function of the stats R package. Collinearity between selected variables was assessed through the vif R function in the caret Package. Risk scores were calculated by means of a weighted sum of the variables in the model, where the weights are the Cox regression coefficients (logHR). Predictor values were centered using their overall means. The best model was then tested on an independent cohort of patients with the predict R function. Kaplan-Meier curves and UNO’s area under the curve (AUC) were calculated to assess the performance of the models, using the ggsurvplot (survminer package) and timeROC R functions, respectively. To test the differences between time points (TPs) during therapy, Wilcoxon signed rank sum test was performed, and the pairwise.Wilcoxon.test R function was used to obtain the p-values from the comparison between groups. P-values were adjusted following the Benjamini-Hockberg procedure.

Study population

Two hundred and forty-five patients (174 training set, 71 test set) with advanced NSCLC were enrolled to receive nivolumab in second line setting (Table 1, Supplementary Table S2). The median age was 67.5 (range: 37–88). Most of the patients were male (70%) with a history of smoking habit (87%). Tumor histology was adenocarcinoma in 76%. At the time of data analysis, 212 patients (86%) had died. Median progression-free survival (PFS) and OS were 3 and 7.5 months, respectively. Notably, 56 patients experienced early death (ED), defined as event that occurred before undergoing the first CT. Among the remaining 190 patients, 96 (50%) experienced PD as BR, while 41 patients (22%) achieved PR, 49 (26%) SD, and 4 (2%) did not undergo further assessments due to worsened clinical conditions. Overall, both training and test cohorts were similar in terms of clinical features, although slightly more patients of the training set (50% vs 8.5% of test set) received more than one-line therapy prior to nivolumab administration.

Table 1

Clinical and pathological characteristics of training set and test set. Abbreviation: N.A.: not available.
Training Set characteristics	n.	%	Test Set Characteristics	n.	%
Patients	174	-	Patients	71	-
Age			Age
< 70	104	59.8%	< 70	37	52.1%
≥ 70	70	40.2%	≥ 70	34	47.9%
Gender			Gender
Male	123	70.7%	Male	48	67.6%
Female	51	29.3%	Female	23	32.4%
Histotype			Histotype
No Squamous cell carcinoma	134	77.0%	No Squamous cell carcinoma	53	74.6%
Squamous cell carcinoma	40	23.0%	Squamous cell carcinoma	18	25.4%
ECOG PS			ECOG PS
1	148	85.1%	1	58	81.7%
2	26	14.9%	2	8	11.3%
Not available	0	0.0%	Not available	5	7.0%
Smoking status			Smoking status
Never	15	8.6%	Never	12	16.9%
Former and current smoker	159	91.4%	Former and current smoker	52	73.2%
N.A.	0	0.0%	N.A.	7	9.9%
Metastasis			Metastasis
Only brain	21	12.1%	Only brain	13	18.1%
Only liver	38	21.8%	Only liver	11	15.3%
Brain and liver	7	4.0%	Brain and liver	2	2.8%
Other metastasis sites	122	70.1%	Other metastasis sites	49	69.0%
Prior lines of treatment			Prior lines of treatment
1	87	50.0%	1	60	84.5%
> 1	87	50.0%	> 1	6	8.5%
N.A.	0	0.0%	N.A.	5	7.0%

Construction and validation of a prognostic EV-miR and clinical data score

The prognostic score was obtained through four consecutive steps (Fig. 2).

1 Discovery phase

Initially, the purity and the EV enrichment were assessed on a pool of plasma from 10 healthy individuals (HIs: 5 males + 5 females). Notably, we found a high enrichment of small and large EVs (mean: 196 nm, 87 − 32 nm) with a weak contamination of albumine/IgG (~ 1%) and almost total absence of Apo-B100 lipoprotein (0.003%) and in the isolated EVs compared the non-processed plasma (Supplementary Fig. 1). The whole EV-miRNome of 174 patients reported a median number of 375 detected EV-miRs (range: 100–785). When looking at the difference between SS and LS, the median number of detected EV-miRs was significantly higher in the first group (390 in SS versus 347 in LS; p-value = 0.04). For the prognostic EV-miR-based score, the training cohort was randomly split 10 times in subgroups made up of 70% of the patients, with every patient selected at least once (Fig. 2A; Supplementary Table S3) [22], and a penalized Cox regression model was built on each subgroup identifying 45 EV-miRs retained by at least one model. Among these we selected 6 EV-miRs as follows: 4 (miR-150-5p, miR-208a-5p, miR-6510-5p, miR-574-5p) retained from eight out of ten models and 2 further EV-miRs (EV-miR-181a-5p and EV-miR-486-3p) with absolute impact > 1 (Supplementary Table S3).

2. Technical validation

The expression of the six selected EV-miRs was assessed by qPCR on a subset of 104/174 samples with available plasma (Fig. 2B; Supplementary Table S4). For the qPCR normalization step, we initially selected miR-1228-3p as the most stable EV-miR revealed by microarray, which was previously proposed as EV-miR reference [23]. However, the qPCR did not detect its expression in any samples. Hence, we decided to normalize qPCR data using an internal miR Spike-in Control (UniSp6) added to our samples prior to RNA isolation. Two EV-miRs were not validated for the following reasons: EV-miR-6510-5p failed the qPCR amplification, suggesting a non-specific hybridization problem in microarrays, and EV-miR-208a-5p showed an inverse correlation between qPCR and microarray data (Supplementary Fig. S2). The other four EV-miRs showed a positive correlation between qPCR and microarray data (p-value < 0.03). The expression levels of these EV-miRs were also evaluated in healthy individuals (i.e., six pools including three males and 1 female in each mixture), confirming a deregulation between controls and SS/LS NSCLC patients (Fig. 3). Then, the four EV-miRs were used for the model construction.

Figure 3. Box-plots of EV-miR expression assessed by qPCR on cancer patients and healthy controls. A) Box-plots of the four EV-miRs in the training set: 55 Short Survivors (SS: OS < 9 months; light blues); 49 Long Survivors (LS: OS ≥ 9 months; blues); 24 healthy individuals mixed in 6 pools (HI, green). B) Box-plots of the four EV-mRs in the test set: 42 SS (orange); 29 LS (red); 24 HI (green).

3. Model construction

Ninety-six of 104 patients with qPCR data for all the 4 EV-miRs and clinical data available were used for score construction by step-wise Cox regression model (Fig. 2C). Two models were built: one with EV-miRs only and another one including also clinical variables. In both cases only EV-miR-181a-5p, EV-miR-574-5p were selected but the best combination to predict OS was the one with the 2 EV-miRs plus performance status (PS). No collinearity between the three predictors was observed, as confirmed by the very low variance inflation factors (EV-miR-574-5p VIF = 1.082; EV-miR-181a-5p VIF = 1.001; PS VIF = 1.0761). The combined prognostic score showed a good prediction of survival at 9 months (time dependent UNO’s AUC = 0.76). High-risk subjects showed a median OS of 4 months compared with low-risk patients who did not reach the median within 9 months, both in the model with two EV-miRs (Fig. 4A; log-rank test p-value = 0.0019) and in the combined model that also included PS (Fig. 4B; log-rank test p-value = 0.00012).

4. Test on an independent cohort

Finally, four EV-miRs were tested by qPCR on an independent set of patients never tested (n = 71) confirming similar trends in SS, LS and HI (Fig. 2; Supplementary Table S5). Patient stratification according to the combined score including EV-miRs and PS [(-0.174 * miR-181a-5p exp) + (0.249 * miR-574-5p exp) + (1.096 * PS)] yielded a good separation (Fig. 4C; log-rank test p-value = 0.0012), with high-risk patients having a median OS of approximately four months; in contrast, low-risk patients did not reach the median within nine months after nivolumab start. Notably, the combined score reported a similar survival predictive ability in the test set (UNO’s AUC = 0.77).

EV-PD-L1 mRNA copy number evaluation

PD-L1 EV-mRNA copy number (CN/1 mL) on 185 patients showed an average of 81.7 (0.0–1,440). When we estimated its prognostic effect, we found a generally higher mean value in SS (107.1) than in LS (54.7) patients (Fig. 5A). Notably, the highest average EV-PD-L1 CN was observed in ED patients (202.8) compared to PD (68.5) or SD (68.7), while few copies (42.6) were found in PR (Fig. 5B). However, when we evaluated EV-PD-L1 with the two prognostic EV-miRs and PS in multivariable Cox regression, no statistical significance was obtained (p-value = 0.296) nor EV-PD-L1 was selected in the stepwise multivariable Cox regression approach.

Figure 5. Box-plots of the EV-PD-L1 mRNA copy number (A) Box-plots of the EV-PD-L1 mRNA in Long Survivors (LS; OS ≥ 9 months; 90 patients, pts; Red) and Short Survivors (SS OS < 9 months; 95 pts; Light Blue). (B) Box-plots of the EV-PD-L1 in early death (ED; 26 pts; Red), Progression Disease (PD; 90 pts; Green), Stable disease (SD; 27 pts; Light Blue), Partial Response (PR; 34 pts; Violet) patients. *p-value < 0.05.

Modulation of EV-miRs during therapy in patients with a disease control

Finally, to investigate the potential role of EV-miRs in modulating the immune system during treatment, we profiled the EV-miRNome in a subset of disease-controlled patients (31 stable disease, SD; 23 partial response, PR) and compared expression levels at BR versus baseline. The analysis identified 11 deregulated EV-miRs (i.e., 8 miRs in SD + PR and 3 miRs in PR only) that were then validated by qPCR on 47 pts 27 SD and 20 PR, confirming 9 significantly downmodulated EV-miRs (Table 2; Supplementary Table S6; Pearson correlation coefficient = 0.89, p-value = 0.005).

Table 2

**List of EV-miRs differentially expressed between BR time** **vs.** **baseline.** The table reports the logarithmic fold changes (logFC) and p-values for 11 EV-miRs obtained by microarray (N = 54) and qPCR (N = 47) (*p-value < 0.05).
EV-miRs	BR	logFC (microarray)	p-value (microarray)	logFC (qPCR)	p-value (q-PCR)
hsa-miR-7977	SD + PR	-0.58	1.60E-05*	-2.16	1.58E-03*
hsa-miR-7975	SD + PR	-0.56	3.15E-05*	-2.08	5.59E-06*
hsa-miR-142-3p	SD + PR	-0.56	3.07E-03*	-2.02	2.30E-06*
hsa-miR-19a-3p	SD + PR	-0.53	2.72E-04*	-1.92	3.54E-04*
hsa-miR-20a-5p	SD + PR	-0.51	1.09E-03*	-1.66	5.55E-04*
hsa-miR-5100	SD + PR	-0.48	7.76E-05*	-1.30	2.52E-04*
hsa-miR-7641	SD + PR	0.45	2.54E-03*	-0.08	0.8455
hsa-miR-3610	SD + PR	0.50	1.62E-04*	Failed	-
hsa-miR-1260b	PR	-0.52	5.91E-04*	-1.72	1.48E-03*
hsa-miR-1260a	PR	-0.56	1.63E-03*	-1.84	1.78E-04*
hsa-miR-26a-5p	PR	-0.52	3.16E-02*	-1.52	8.91E-03*

Then, the downregulated EV-miRs were monitored at three consecutive CT-scans (T1: 2 months; T2: 4 months; T3: 6 months) (Supplementary Table S7). Notably, 6/9 EV-miRs (miR19a-3p, miR-20a-5p, miR-142-3p, EV-miR-1260a, miR-1260b, miR-5100) were significantly downmodulated at all time-points in PR patients, whereas no miR was deregulated in SD patients (Fig. 6A; Supplementary Table S8).

Enrichment analysis of the predicted targets of the six silenced EV-miRs identified signaling pathways previously described in the response to ICI, such as stem cell pluripotency, an approach exploited to enhance the antitumor response [24] (Fig. 6B; Supplementary Table S9A; Supplementary Fig. S3). Gene Ontology analysis showed a significant enrichment of toll-like receptor (TLR) signaling pathways (Fig. 2C; Supplementary Fig. S4; Supplementary Table S9B).

Here we performed an extensive EV-miRNome profiling in a large cohort of advanced NSCLC patients who received nivolumab after failure of previous therapies. The analysis showed a higher enrichment of EV-miRs in the plasma of patients with shorter OS (p-value = 0.04); this result is not surprising, indeed a higher EV-miR enrichment has already been positively associated to cancer progression [25]. Prognostic EV-miRs were initially obtained applying a penalized Cox regression model on microarray data and then further selected after qPCR technical validation, yielding four EV-miRs (EV-miR-150-5p, EV-miR-181a-5p, EV-miR-486-3p, EV-miR-574-5p) able to discriminate short from long survivors. All these miRs have been described in lung carcinogenesis [26–29] as well as in the EV-trapped forms [30–34]. In particular, EV-miR-150-5p has been defined as an immune-modulator [35] that targets regulator genes (i.e., IL-10 and PIM1) of the Myeloid-Derived Suppressor Cells (MDSC) which are potent suppressors of immune responses mediated by T lymphocytes and NK cells [36]. Indeed, miR-150-5p reported a negative coefficient in Cox regression analysis, confirming its immune modulator role. A combination of EV-miR-181a-5p and EV-miR-574-5p together with performance status resulted in the best prognostic model associated with OS in the training set, and the ability to predict survival was confirmed in an independent cohort of 71 patients with similar characteristics. Interestingly, miR-181a-5p has been linked to T cell activation [37–39], as well as interferon gamma (IFNG) overexpression and natural killer (NK) cell maturation [40, 41]. In addition, miR-181a has also been demonstrated to inhibit NSCLC cell lines [42] and the circulating form has been described as a marker for diagnosis and good prognosis in NSCLC [43, 35–37, 44, 45], also confirmed by its negative Cox regression coefficient. An increasing number of studies have also demonstrated that miR-574-5p overexpression correlates with lung cancer progression and metastasis [46–48], whereas its soluble form has been described as a promising marker for patient stratification with NSCLC [33, 47, 49, 50]. The positive Cox regression coefficient of miR-574-5p in our model supports its association with poor prognosis/response to ICIs. Overall, our findings support the hypothesis that evaluation of these EV-miRs in the plasma of patients receiving ICIs may help in predicting response to immunotherapy.

In addition, we also confirmed that higher EV-PD-L1 CN was linked to worse outcome, particularly in patients with ED, but addition of EV-PD-L1 CN to the EV-miR based score did not improve the significance of the prognostic prediction model. Currently, growing evidence demonstrates that EV-mRNA molecules, contrarily to small RNAs, can be found either as functionally mRNA source or non-functional fragmented form [51]. Hence, we can speculate that EV-PD-L1 mRNA may primarily be a non-functional degradation product linked to an aggressive disease, rather than a source for protein translation in recipient cells.

Concomitantly, we investigated the EV-miR changes at BR in patients experiencing disease control disclosing a significantly downmodulation of 9 EV-miRs at BR vs baseline. Notably, in patients experiencing a PR, 6 out of 9 EV-miRs (i.e., miR19a-3p, miR-20a-5p, miR-142-3p, miR-1260a, miR-1260b, miR-5100) were already downregulated at the time of the first CT-scan evaluation and remained silent over six months of therapy. Currently, accumulating evidence has demonstrated an oncogenic role of these circulating miRs in lung cancer progression [52–57]. In addition, previous studies have shown that EV-miR-20-5p is a negative T-cell regulator [58], and that miR-19a-3p is able to target major histocompatibility complex (MHC) class I genes [59]. In particular, MHC genes are involved in the adaptive immune response and their downmodulation has been described as a mechanism of resistance to ICI [60]. Concerning that point, Jiang and colleagues recently demonstrated that EVs deriving from proinflammatory macrophages (i.e., M1 phenotype), were able to silent miR-19a-3p through a long non-coding RNA (lncRNA: HOXA transcript at the distal tip). This leads to an upregulation of the toll like receptor 5 (TLR) signaling which, in turn, activates the polarization of the circulating monocytes into M1 macrophages [61]. Similarly, functional enrichment analysis on the predicted targets of the EV-miR downmodulated in responding patients showed an over-representation of TLR signaling (e.g., 1, 2, 3, 4, 5, 6, 9, 10). TLRs play a dual regulatory role in cancer with both anti-tumor and pro-tumor effects, depending on their class and cancer type [62]. In particular, TLR3 expression on NSCLC cells has been described associated with apoptosis activation, induced by caspase-3. Moreover, TLR3-mediated apoptosis, also increased the activation of immune response in NSCLC through CD103 + dendritic cells [63]. In this regard, TLR3 agonists have been proposed in clinical use as adjuvant to overcome the resistance to ICIs [64]. Enrichment analysis also showed an activation of cellular pathways such as induction of pluripotency of stem cells (PSCs), a reprogramming phenomenon from a somatic cell. To date, numerous in vivo studies have shown that induced PSCs might be exploited to improve the anti-tumor response, for example by generating T lymphocytes with a wide variation of T cell receptor rearrangement patterns [65]. More recently, Cichocki et al., reported that the activation of induced NK cells, derived from PSCs, may overcome the resistance of PD1 blockade, by recruiting T cells with increased production of inflammatory cytokines [66]. Altogether these results support a role in the activation of immune pathways, strengthening the immune system against cancer cells and inducing tumor suppression.

Despite several breakthroughs in our study, some limitations should be considered. Firstly, a relevant proportion of the enrolled patients experienced PD at the first response assessment. Furthermore, a non-negligible fraction of patients experienced worsened clinical condition and eventually died before undergoing the first response assessment. While this occurrence might appear unexpected, it depends on the fact that nivolumab was initially available through an expanded access program, which allowed to prescribe the agent to heavily pre-treated patients with increased tumor burden and more compromised performance status which limited life expectancy. The current trend in clinical practice includes the use of ICI in earlier settings compared to our study population, thus our score should be confirmed in treatment-naïve cohorts undergoing immunotherapy.

To the best of our knowledge, our study represents the largest EV-miR analysis, providing a robust and reliable prognostic score, based on the expression of two EV-miRs (EV-miR-181a-5p and EV-miR-574-5p) and clinical data (i.e., PS) able to identify NSCLC patients who could benefit from immunotherapy. EV-PD-L1 CN, on the other hand, although associated with progressive disease, did not improve the EV-miR score, leading to speculate that it can be a degradation product. In addition, the targets of the 6 EV-miRs downregulated at the time of BR are associated with the modulation of immune system cells towards a stem phenotype that might generate, for instance, lymphocytes with a wide variation of T cell receptor rearrangement patterns, a hypothesis also supported by TLR enrichment in responders. Our findings, if validated in independent cohorts, provide the basis for a better patient selection in line with immunotherapy personalization and open new avenues for the development of therapeutic strategies to improve response to nivolumab.

AUC: area under the curve;

BR: best response;

CN: copy number;

CT-scan: computed tomography scan;

ddPCR: droplet digital PCR;

ED: early death;

EV-miRs: EV microRNAs;

EV-PD-L1: EV PD-L1;

GO: gene ontology;

ICI: immune checkpoint inhibitor;

KEGG: Kyoto Encyclopedia of Genes and Genomes;

LS: long survivors;

NSCLC: non-small cell lung cancer;

OS: overall survival;

PD: disease progression;

PD-1: programmed cell death-1;

PD-L1: programmed cell death-1 ligand;

PFS: progression-free survival;

PR: partial response;

qPCR: quantitative PCR;

SD: stable disease;

SS: short survivors;

TLR: toll-like receptor;

TPs: time points;

Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Regional Ethics Committee (PR180REG2017; 13^th June 2017). Informed consent was obtained from all subjects involved in the study.

Consent for publication

Written informed consent has been obtained from the patients to publish this paper.

Availability of data and materials

All data generated during this study are included in this published article and its supplementary information files, whereas the datasets from microarray are available in Gene Expression Omnibus repository [http://www.ncbi.nlm.nih.gov/geo/, ID: GSE207715, released at the time of publication].

Competing interests

C.G. declares: Honoraria from: Amgen, Astra Zeneca, Bristol-Myers-Squibb, Eli-Lilly, Merck-Sharp-Dohme, Novartis, Roche, Sanofi, Takeda, ThermoFisher. Research grants from: Bristol-Myers-Squibb; Italian Ministry of Health. G.R. declares: Honoraria from Bristol-Myers Squibb, Roche, MSD. M.T. declares: Honoraria from Travel, accommodation, expenses: Roche, Bristol-Myers Squibb, AstraZeneca, Takeda, Eli Lilly. Honoraria as medical writer: Novartis, Amgen, MSD. C.D. declares: Honoraria from Bristol-Myers Squibb, Roche, Astra Zeneca. F.P. de-clares: advisory/consultant's fee from Merck Sharp Dohme, Astra Zeneca, Janssen, Amgen, Beigene and Thermofisher Scientific. S.N. declares: speaker bureau/advisor’s fee from Eli Lilly, Merck Sharp Dohme, Roche, BMS, Takeda, Pfizer, Astra Zeneca, Amgen and Boehringer Ingel-heim. I.G.B declares: personal fees from BMS, MSD, Boehringer Ingelheim, Eli Lilly, and Roche. F.GR. declares: Advisory Role: Ad Hoc Advisory Boards/Consultations (last 3 years). Eli Lilly, Roche, Boehringer Ingelheim, AstraZeneca, Pierre Fabre, BMS, MSD, Novartis, Merck, Otsuka, Novartis, Takeda. Honoraria: Seminar/Talks to Industry (last 3 years). Eli Lilly, Roche, Boehringer Ingelheim, AstraZeneca, Pierre Fabre, AMGEN, Celgene, BMS, MSD. Research Funding (last 3 years) AstraZeneca, BMS, MSD. The other authors declare no conflict of interest.

Funding

This study was funded by Italian Ministry of Health (CO-2016-02361470; Ricerca Corrente 2022, RRC 2022; 5×1000 funds: 2018-2019; 5×1000 funds: 2022), Compagnia di San Paolo (2017-0529) and Bristol-Myers Squibb (CA209-828-BMS).

Authors' contributions

C.G., F.GR. and S.C. contributed to conception and design of the study; S.M., S.S., I.V., L.L., M.G.D.B., and S.C. performed the experiments; G.C., F.GU. and P.O. performed the statistical analysis; C.G., G.R., M.T., L.Z., C.D., E.R., V.L., G.B., F.P., G.M., S.B., R.C., L.R., F.B., I.B., I.G., S.N. involved in the collection of biological samples, clinical data and patient’s consent; C.G., S.M., G.R., and S.C. wrote the first draft of manuscript; G.C., F.GU., M.T., L.Z., and A.A. reviewed and edited the manuscript. All authors have read and agreed the version of the manuscript.

Acknowledgments

We thank Mrs. Zita Cavalieri and Mrs. Cristina Bruzzo for their invaluable technical support. We are also grateful for the “Corrado Toscano” donation that supports Dr. Marconi's scholarship.

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Competing interest reported. C.G. declares: Honoraria from: Amgen, Astra Zeneca, Bristol-Myers-Squibb, Eli-Lilly, Merck-Sharp-Dohme, Novartis, Roche, Sanofi, Takeda, ThermoFisher. Research grants from: Bristol-Myers-Squibb; Italian Ministry of Health. G.R. declares: Honoraria from Bristol-Myers Squibb, Roche, MSD. M.T. declares: Honoraria from Travel, accommodation, expenses: Roche, Bristol-Myers Squibb, AstraZeneca, Takeda, Eli Lilly. Honoraria as medical writer: Novartis, Amgen, MSD. C.D. declares: Honoraria from Bristol-Myers Squibb, Roche, Astra Zeneca. F.P. de-clares: advisory/consultant's fee from Merck Sharp Dohme, Astra Zeneca, Janssen, Amgen, Beigene and Thermofisher Scientific. S.N. declares: speaker bureau/advisor’s fee from Eli Lilly, Merck Sharp Dohme, Roche, BMS, Takeda, Pfizer, Astra Zeneca, Amgen and Boehringer Ingel-heim. I.G.B declares: personal fees from BMS, MSD, Boehringer Ingelheim, Eli Lilly, and Roche. F.GR. declares: Advisory Role: Ad Hoc Advisory Boards/Consultations (last 3 years). Eli Lilly, Roche, Boehringer Ingelheim, AstraZeneca, Pierre Fabre, BMS, MSD, Novartis, Merck, Otsuka, Novartis, Takeda. Honoraria: Seminar/Talks to Industry (last 3 years). Eli Lilly, Roche, Boehringer Ingelheim, AstraZeneca, Pierre Fabre, AMGEN, Celgene, BMS, MSD. Research Funding (last 3 years) AstraZeneca, BMS, MSD. The other authors declare no conflict of interest.

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Extracellular vesicles miR-574-5p and miR-181a-5p as prognostic markers in NSCLC patients treated with Nivolumab

Status:

Journal Publication

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Materials and Methods

Study Population

EV characterization and EV-miR profiling

EV-miRNA validation

PD-L1 gene absolute quantification

Functional enrichment and pathway analyses

Statistical Analysis

Results

Study population

Construction and validation of a prognostic EV-miR and clinical data score

1 Discovery phase

2. Technical validation

3. Model construction

4. Test on an independent cohort

EV-PD-L1 mRNA copy number evaluation

Modulation of EV-miRs during therapy in patients with a disease control

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1