Extracellular vesicle-derived lncRNA-GC1 serves as a novel biomarker for predicting and monitoring the immunotherapeutic outcomes of patients with gastric cancer

doi:10.21203/rs.3.rs-4981595/v1

Download PDF

Research Article

Extracellular vesicle-derived lncRNA-GC1 serves as a novel biomarker for predicting and monitoring the immunotherapeutic outcomes of patients with gastric cancer

https://doi.org/10.21203/rs.3.rs-4981595/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

Efforts to predict the outcomes of patients with gastric cancer (GC) following immune checkpoint inhibitor (ICI) treatments remain limited, owing to a lack of reliable biomarkers. Studies have found that extracellular vesicle (EV)-derived lncRNA-GC1 may serve as a GC-specific biomarker. This study was designed to expand on these previous results by estimating the usefulness of EV-derived lncRNA-GC1 as a predictive indicator for patients with GC who undergo ICI treatments.

Methods

EV-derived lncRNA-GC1 levels were measured using quantitative polymerase chain reaction (qPCR) in patients with unresectable or metastatic GC who were receiving ICI treatments. Correlations between this biomarker and ICI treatment outcomes were analyzed in a training cohort (n = 136), three external validation cohorts (n = 188, n = 214, and n = 30), and one prospective cohort (n = 192).

Results

Circulating EVs exhibited a lncRNA-GC1 expression profile that was distinct from that of tissues or circulating cells. EV-derived lncRNA-GC1 levels were found to be independent of PD-L1 expression status or the density of CD8⁺ T cell infiltration. EV-derived lncRNA-GC1 could be used to effectively predict ICI-related patient outcomes, and could be used for dynamic monitoring throughout treatments. Lower levels of EV-derived lncRNA-GC1 were associated with tumor microenvironmental characteristics such as more robust antitumor immunity—including higher levels of activated CD8⁺ T/NK cells and an increased TH1/TH2 ratio. Such biomarkers can be stably detected in clinical practice. These results were consistent in both the two external validation cohorts and the one prospective cohort.

Conclusion

EV-derived lncRNA-GC1 can be used to reliably predict immunotherapeutic outcomes in patients with GC who undergo ICI treatments, suggesting that targeted analyses of this lncRNA may be useful for guiding treatment planning, monitoring, and associated decision-making processes.

Extracellular vesicle

LncRNA-GC1

Immunotherapeutic outcome

Gastric cancer

While significant improvements have been made in the prognoses of patients with gastric cancer (GC) in recent years, GC remains the second most common cause of cancer-related death in China. ^{[1, 2]}Upon initial diagnosis, 20–40% of patients with GC already exhibit metastatic or unresectable disease.^{[3, 4]}These patients are unlikely to achieve significant survival benefits from traditional treatments, and thus urgently require access to appropriate precision therapies. Although molecular subtyping efforts have revolutionized immune checkpoint inhibitor (ICI) treatment for GC, most patients still fail to benefit from this treatment approach.^{[5, 6]}Current biomarkers that can predict the efficacy of ICI treatments include PD-L1 expression status, tumor mutational burden (TMB), and microsatellite instability (MSI) status. ^[7]However, the efficacy of predictions based on these biomarkers is low, which highlights the need to define novel robust molecular biomarkers that can be reliably used to monitor responses to immunotherapy in patients with GC, as well as predict associated prognostic outcomes.^{[8, 9]}

Liquid biopsies have recently been developed as a way to detect disease-related biomaterials—including circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and extracellular vesicles (EVs). ^{[10, 11]}However, while directly relevant to the underlying malignancy of interest, CTCs are extremely rare, fragile, and heterogeneous, which limits their value as therapeutic biomarkers.^{[12, 13]} Moreover, cell-free ctDNA is extremely scarce, and the sensitivity needed to reliably detect it thus remains difficult to achieve.^{[14, 15]}Conversely, EVs are readily secreted by various physiological and pathological cell types, offering some insights into heterogeneous biological alterations within the tumor microenvironment.

EVs can contain macromolecular cargos such as non-coding RNAs (ncRNAs), piwi-interacting RNAs (piRNAs), and transfer RNAs (tRNAs). These EV-derived contents have been identified to offer particular value as biomarkers associated with oncological outcomes.^[16–18]In a previous study, we determined that circulating EV-derived lncRNA-GC1 can be readily analyzed via noninvasive liquid biopsy to diagnose and monitor patients with GC.^[19]As a key oncogenic ncRNA, lncRNA-GC1 (also known as GClnc1) can determine the therapeutic resistance and progression of GC cells.^[20]Abnormally elevated levels of lncRNA-GC1 are evident in tumor tissues and circulating EVs of patients with GC, and may increase GC cell metastasis and resistance to treatment.^[21] However, the utility of EV-derived lncRNA-GC1 as a biomarker to predict ICI treatment outcomes in patients with GC remains to be clarified.

This study used a novel, noninvasive approach for clinically validating EV-derived lncRNA-GC1 through four independent retrospective and prospective multicenter patient cohorts. This strategy revealed that this biomarker offers significant value as a tool that is capable of directing efforts in clinical decision-making and predicting patients’ immunotherapeutic responses to GC, as well as survival outcomes, independently of traditional biomarkers such as PD-L1 and CD8⁺ T cell infiltration. Accordingly, applying EV-derived lncRNA-GC1 as a biomarker for monitoring ICI-related responses in patients with GC may help to protect responders from excess treatment-related toxicity while ensuring that non-responders can more readily access alternative treatments.

Ethical considerations

The study was conducted in accordance with the Declaration of Helsinki and registered with clinicaltrials.gov. The Institutional Ethics Committees and Review Board of the A/B/C hospital approved this study. Diagnostic accuracy (STARD) guidelines were used in this study, which was conducted in accordance with the International Ethical Guidelines for the Reporting Recommendations for Tumor Marker Prognostic Studies guidelines. The study was reported according to the STROCSS criteria.^[22]

Study design and patient cohort

This was an explorative biomarker study of a three-phase analysis that included a retrospective training phase, a retrospective validation phase, and a prospective validation phase. During the retrospective training phase, 136 patients with GC were enrolled (cohort 1) from A Hospital between 2020–2022. Quantitative polymerase chain reaction (qPCR) was used to assess lncRNA-GC1 expression levels in matched circulating EV and tissue samples, in order to determine specifically how EV-derived lncRNA-GC1 could be used as a tool to monitor patient immunotherapy responses by evaluating its expression in tissue vs serum samples. During the retrospective validation phase, two external cohorts (cohorts 2 and 3) were established, involving 188 patients with GC from B Hospital between 2018–2021 and 214 patients with GC from C Hospital between 2017–2020. Another cohort (cohort 4), consisting of 30 patients with GC who were enrolled from B Hospital between 2021–2022, was used to study the stability of circulating EV-derived lncRNA-GC1 in clinical settings. The purpose of this phase was to externally confirm the role of EV-derived lncRNA-GC1 as a companion biomarker during ICI treatments, and whether the stability of circulating EV-derived lncRNA-GC1 was sufficient to meet the practical constraints and needs of clinical practice. Finally, during the prospective validation phase, another 192 patients with GC (cohort 5) were prospectively enrolled from the C Hospital between 2021–2022. The goal of this phase was to validate the clinical utility of EV-derived lncRNA-GC1 as a tool for monitoring the treatment responses of patients with GC, as well as predicting patient survival. An overall workflow for the study is presented in Fig. 1, and the characteristics of the enrolled patients are compiled in eTable 1.

This study comprised nearly 1,000 tissue and serum samples from patients with GC. The eligible patients were individuals who met the following criteria: ≥ 18 years of age; histologically confirmed GC; radiographically or histologically diagnosed unresectable or metastatic GC; at least one measurable tumor lesion; an Eastern Cooperative Oncology Group (ECOG) score of 0–3 over previous 1-week treatments; treatment-naïve for four weeks before ICI treatments; and free of any major complications such as digestive tract obstruction, hemorrhage, or perforation. Patients were also excluded if they (1) exhibited histologically confirmed squamous carcinoma or neuroendocrine carcinoma; (2) had been diagnosed with GC alongside other comorbid cancers; (3) or underwent ICI treatment alongside other forms of therapy—including chemotherapy, radiotherapy, or cytotoxic therapy. Initial treatment regimens were defined as first-line therapy (line 1), while all subsequent sequentially-numbered regimens were collectively referred to as line > 1 therapies.

Sample preparation

Baseline blood samples were collected 3–5 days before the initiation of the first treatment cycle, with samples subsequently being collected at intervals of three cycles until disease progression was observed. Vacuette EDTA-coated tubes collected up to 10 mL of whole blood per sample, followed by centrifugation (30 min, 3,000 ×g) within 2 h following collection. Tubes were labeled with unique identifiers and stored at − 80°C for further analysis. All tissue samples were collected using needle or endoscopic biopsy procedures.

Measurement of EV-derived lncRNA-GC1

EV-derived lncRNA-GC1 concentrations were measured by isolating EVs from the patients’ plasma samples, characterizing these EVs, and detecting lncRNA-GC1 concentrations via qPCR. EVs were isolated using a previously described protocol.^[23]Briefly, the plasma samples were passed through 0.20 µm membrane filters (Millipore), after which the EVs were concentrated via ultracentrifugation. Transmission electron microscopy (TEM; Thermo Scientific) and nanoparticle tracking analysis (NTA; NanoSight NS300, UK) were used to assess the sizes and distributions of the particles. Western immunoblotting was used to confirm the surface marker expression patterns of these EVs. Briefly, the EVs were lysed using RIPA buffer, separated via SDS-PAGE, transferred to polyvinylidene fluoride (PVDF) membranes, and blots were probed using primary anti-CD9 (1:2,000; CST, USA), anti-CD63 (1:1,000; CST, USA), and anti-CD81 (1:2,000; CST, USA) at 4°C overnight.

For our qPCR analyses, Trizol was used to isolate total EV-derived RNA, after which an MMLV kit (Takara, Japan) was used to synthesize cDNA from 30 ng of total RNA. Subsequent qPCR reactions were performed under the following conditions: 95°C for 5 min, followed by 40 cycles of 95°C for 10 s and 60°C for 30 s. Relative circulating EV-derived lncRNA-GC1 concentrations were normalized using the 2^−ΔΔCt method. All isolated lncRNA-GC1 was amplified using the following primers: F-TGGGGTAACTTAGCAGTTTCAAT-R (sense); F-GGCAAGCAGTAATCTTACATGCAC-R (antisense).

Radiographic analyses and survival outcomes

The enrolled patients underwent baseline computed tomography (CT) of the chest, abdomen, and pelvis within seven days prior to treatment initiation. Scanning was repeated every 8–10 weeks, or according to clinical indication. One radiologist, who was blinded to the study, evaluated all of the CT analyses. The patients’ responses to immunotherapy were evaluated according to the RECIST v 1.1 criteria. Radiographic tumor burdens were determined using the sum of the longest values of the diameter of the unidimensional tumor. Treatment response was defined according to standard criteria—including complete response (CR; tumor not detectable via imaging), partial response (PR, ≥ 30% reduction in tumor burden), progressive disease (PD; ≥ 20% increase in tumor burden or the development of new lesions), or stable disease (SD; none of the above criteria were met). Patients who never achieved an objective response were considered non-responders, with the rest being considered responders. Durable clinical benefit (DCB) was defined based on the proportion of patients that achieved an objective response or SD for > 12 months. Overall survival (OS) was defined as the interval between surgery and the final follow-up or death. Conversely, progression-free survival (PFS) was defined as the interval between surgery and disease progression or death.

Statistical analysis

Correlations between serological and clinical variables were analyzed using Mann-Whitney U tests. Spearman’s rank correlation analyses were used to test for consistency among the various indexes evaluated. Clinical parameters were compared among patient subgroups using Chi-squared tests. Kaplan–Meier curves and the log-rank test were used to compare the OS and PFS of patient subgroups who exhibited different levels of circulating EV-derived lncRNA-GC1. All data were compared using SPSS 18.0 (IBM) and GraphPad Prism 8.0 (GraphPad), with P < 0.05 set as the threshold for statistical significance.

Analyses of lncRNA-GC1 expression profiles in patients with GC undergoing ICI treatments

Initially, differential centrifugation was used to purify EVs from patient plasma samples. TEM and NTA analyses then confirmed that the particles were 30–200 nm in size (eFigures 1A–B). Western immunoblotting also confirmed the particles to be positive for the EV surface biomarkers CD9, CD63, and CD81, confirming the high quality of the isolated EVs (eFigures 1C). To exclude potential interference caused by variable EV content concentrations on our findings, absolute lncRNA-GC1 expression was normalized to λ polyA⁺ RNA for establishing relative expression levels. For patients in cohort 1, no significant changes in the expression of the EV-related biomarkers CD9/CD63/CD81 were observed when comparing baseline samples with those obtained during treatment (eFigure 2), suggesting that EV content remained stable throughout ICI treatments. These findings thus confirmed that EV-derived lncRNA-GC1 can be reliably quantified in patients undergoing ICI treatments.

The concordance between lncRNA-GC1 expression in EV and tissue samples was then examined. EV-derived lncRNA-GC1 and PD-L1 both showed no significant differences between CPS negative (CPS^neg, combined positive score, CPS = 0) and CPS positive (CPS^pos, CPS ≥ 1) tissue samples (eFigures 3A–B). Moreover, no significant correlations were observed between EV-derived expression of the T cell marker CD3 or the B cell marker CD19 and circulating T or B cell populations (eFigures 3C–D). These results suggest that circulating EVs exhibit a transcriptional spectrum that is distinct from those of tissues or circulating cells in patients with GC.

Baseline lncRNA-GC1 levels predict the survival outcomes of patients with GC receiving ICI treatments independent of PD-L1 expression status and CD8 ⁺ T cell infiltration

In a previous study, we identified a 5.6-fold EV-derived lncRNA-GC1 cutoff value as a reliable threshold for the detection of GC in a relatively large patient cohort.²⁹ This cutoff value was also used for all subsequent analyses in the current study, as it was derived from a GC patient population of almost 1,000—which was considered to be a sufficiently large population to allow for the minimization of batch effects between cohorts, thus ensuring that we could perform simple and reliable predictive analyses.

Based on this cutoff value, the patients in cohort 1 were assigned to either the lncRNA-GC1^high group (> 5.6-fold, n = 41, 30.1%) or lncRNA-GC1^low group (≤ 5.6-fold, n = 95, 69.9%). Correlations between EV-derived lncRNA-GC1 and ICI treatment responses were then assessed. Using the RECIST criteria, patients were classified as either responders (CR + PR) or non-responders (SD + PD). EV-derived lncRNA-GC1 levels were found to be significantly lower in the responders vs the non-responders (P = 0.0002, Fig. 2A). In addition, the proportion of responders in the lncRNA-GC1^high group was significantly lower than that among the lncRNA-GC1^low patients (16.0% vs. 52.5%, P < 0.0001, Fig. 2B). The patients who had high baseline levels of EV-derived lncRNA-GC1 had poor PFS (hazard ratio [HR], 2.628; 95% confidence interval [CI], 1.523–4.534; P < 0.0001; Fig. 2C) and OS (HR, 3.432; 95% CI; 1.894–6.217, P < 0.0001; Fig. 2D).

We then proceeded to evaluate the correlations between EV-derived lncRNA-GC1 and patient survival outcomes as a function of the PD-L1 expression status and CD8⁺ T cell infiltration density. In total, 57.4% of the samples were negative for PD-L1 (CPS = 0; 78/136; PD-L1^neg), while 42.6% were PD-L1-positive (CPS ≥ 1; 58/136; PD-L1^pos). The levels of EV-derived lncRNA-GC1 showed no significant differences between PD-L1^neg and PD-L1^pos GC (P = 0.6347, Fig. 3A). The correlations between EV-derived lncRNA-GC1 and survival outcomes as a function of CD8⁺ T cell infiltration (CD8^high vs. CD8^low) were then assessed. In total, 36.0% had high CD8⁺ T cell infiltration density (49/136; CD8^high), while 64.0% had low CD8⁺ T cell infiltration density (89/136; CD8^low). No significant differences in EV-derived lncRNA-GC1 expression were observed when comparing patients with low vs high levels of CD8⁺ T cell infiltration (P = 0.6311; Fig. 3B).

Among the patients who were PD-L1^pos, the median PFSs in the lncRNA-GC1^high and lncRNA-GC1^low groups were 14.30 and 24.30 months, respectively (P = 0.0007), while the median OSs were 17.60 and 27.30 months, respectively (P = 0.0102, Fig. 3C). Among the PD-L1^neg patients, the median PFSs in the lncRNA-GC1^low and lncRNA-GC1^high groups were 11.40 and 18.70 months, respectively (P = 0.0076, Fig. 3D), while the median OSs were 16.70 and 29.80 months, respectively (P = 0.0002, Fig. 3D). These results suggest that the patients with low EV-derived lncRNA-GC1 levels exhibited better survival outcomes than those with high levels, independent of PD-L1 expression status. The patients with low EV-derived lncRNA-GC1 levels were associated with superior PFS and OS outcomes than those with high levels, independently of the density of CD8⁺ T cell infiltration (all P < 0.05, Figs. 3E–F). These data suggest that EV-derived lncRNA-GC1 can be used to predict the survival outcomes of patients with GC receiving ICI treatments, independently of PD-L1 expression status or CD8⁺ T cell infiltration density.

Dynamic changes of EV-derived lncRNA-GC1 enable the monitoring of ICI treatment responses

Based on the observed predictive value of baseline EV-derived lncRNA levels, we then assessed the monitoring role of this biomarker throughout ICI treatments. By comparing EV-derived lncRNA-GC1 levels at baseline and one month, the patients in cohort 1 were classified into four groups: group low (from baseline-high to one month-low), group high (from baseline-low to one month-high), group remain-low (from baseline-low to one month-low), and group remain-high (from baseline-high to one month-high). During the first month of ICI treatments, EV-derived lncRNA-GC1 was able to effectively predict the survival outcomes of these patients (PFS: HR, 3.104; 95% CI, 1.612–5.976; P < 0.0001; OS: HR, 2.818; 95% CI, 1.473–5.391; P < 0.0001; Fig. 4A).

Considering all available time points, EV-derived lncRNA-GC1 levels were found to be lower in responders (CR + PR) vs non-responders (PD + SD; P < 0.05, Fig. 4B), supporting the prognostic relevance of this biomarker. When assessing EV-derived lncRNA-GC1 levels at baseline, between 1–3, 4–6, and ≥ 6 months following the start of treatment, these levels were lower in the patients who achieved overall response (ORR), disease control (DCR), non-progression (PFS), or remained alive (OS) relative to those that did not meet these endpoints (Figs. 4C–F). Notably, timeline-scaled EV-derived lncRNA-GC1 levels remained high in the high and remain-high groups while remaining low in the low and remain-low groups, which highlighted the ability of EV-derived lncRNA-GC1 to represent a stable indicator that can be used to stratify patients throughout treatment (Fig. 4G). These results confirm the value of EV-derived lncRNA-GC1 as a powerful biomarker capable of dynamically monitoring the response to treatment of patients with GC who are treated via ICI therapy.

Retrospective and prospective validation confirms the value of lncRNA-GC1 as a biomarker for predicting and monitoring ICI treatment response

To confirm the reliability of the predictive value of EV-derived lncRNA-GC1 in patients with GC who undergo ICI treatment, two retrospective cohorts and one prospective cohort were formed, which were then used for external validation using the same criteria and cutoff threshold used for cohort 1. For both retrospective validation cohorts (cohorts 2 and 3), baseline EV-derived lncRNA-GC1 levels were negatively correlated with patient PFS (eFigures 4A and 5A) and OS (eFigures 4B and 5B). In addition, patients who achieved ORR (eFigures 4C and 5C), DCR (eFigures 4D and 5D), non-progression (eFigures 4E and 5E), or remained alive (eFigures 4F and 5F) exhibited lower levels of EV-derived lncRNA-GC1 relative to those that did not. Notably, timeline-scaled EV-derived lncRNA-GC1 levels remained high for patients in the high and remain-high groups, and low for those in the low and remain-low groups (eFigures 4G and 5G). Furthermore, the proportion of patients with DCB in the lncRNA-GC1^low group was significantly higher than that in the lncRNA-GC1^high group (eFigures 4H and 5H). The area under the Kaplan-Meier curve (AUC) values for EV-derived lncRNA-GC1 in these two retrospective cohorts were 0.8821 and 0.8748 (eFigures 4I and 5I).

In our prospective validation analyses (cohort 5), compared to the lncRNA-GC1^high group, PFS and OS improved in patients with low levels of EV-derived lncRNA-GC1 (Figs. 5A–B). In addition, patients who achieved ORR (Fig. 5C), DCR (Figs. 5D and 5H), non-progression (Fig. 5E), or remained alive (Fig. 5F) exhibited lower levels of EV-derived lncRNA-GC1 for treatment. The timeline-scaled EV-derived lncRNA-GC1 remained high in patients in the high and remain-high groups, and low in those in the low and remain-low groups (Fig. 5G). Critically, EV-derived lncRNA-GC1 remained independently associated with patient PFS and OS (eTable 2), with an AUC of 0.8778, confirming its predictive value (Fig. 5I).

EV-derived lncRNA-GC1 is associated with unique tumor microenvironmental features

Given that EV-derived lncRNA-GC1 was found to be related to the response of our patients with GC to ICI treatments, correlations between EV-derived lncRNA-GC1 levels and the intratumoral immune landscape were further evaluated. Tertiary lymphoid structures (TLS), T follicular helper cells, and B cells were found to be significantly correlated with EV-derived lncRNA-GC1 (eFigures 6A–B). Moreover, low levels of EV-derived lncRNA-GC1 were significantly correlated with high levels of activated CD8⁺ T/NK cells and an increased TH1/TH2 ratio, thus indicating robust antitumor immunity (eFigures 6A–B). In addition, significantly more TLS-positive patients were observed in the lncRNA-GC1^low group vs the lncRNA-GC1^high one (50% vs. 24%, P = 0.003, eFigure 6C). The levels of EV-derived lncRNA-GC1 may correspond to the unique features of the tumor microenvironment that are associated with ICI treatment outcomes.

EV-derived lncRNA-GC1 remains stable under routine clinical testing conditions

To further establish the potential real-world clinical utility of monitoring dynamic changes of EV-derived lncRNA-GC1 as a means of guiding clinical ICI treatment planning, the stability of this biomarker was then assessed—under the presumption that poor stability would severely restrict any potential for the clinical implementation of these results. A retrospective expanding cohort (cohort 4) comprised of 30 patients was enrolled for the purpose. No significant changes in EV-derived lncRNA-GC1 levels were observed when these samples were treated with RNase (P = 0.524, eFigure 7A), incubated at room temperature for prolonged periods (P = 0.353, eFigure 7B), or subjected to repeated freeze-thaw cycles (P = 0.413, eFigure 7C). Notably, circulating lncRNA-GC1 levels were primarily derived from EVs, as was confirmed by the absence of EV-depleted sera (P < 0.0001, eFigure 7D). A significant correlation between EV-derived lncRNA-GC1 and total circulating lncRNA-GC1 levels was also observed (r = 0.7615, P = 0.0011, eFigure 7E). Together, these data indicate that EV encapsulation can shield lncRNA-GC1 and protect it from degradation, providing the levels of stability necessary for effective clinical use.

Advances in genomic technologies have highlighted the value of EVs as targets for the real-time monitoring of tumor biology, offering a means of readily bridging the genotype-phenotype gap. Notably, EV-based analyses offer particular value in precision medicine-based clinical applications.^[24] Herein, a multi-phase, multi-cohort study was used to systematically assess the value of EV-derived lncRNA-GC1 as a target for monitoring and predicting immunotherapeutic treatment outcomes in patients with GC. Our analyses yielded several novel findings. First, the expression profiles of circulating EVs remained stable throughout the treatment, in contrast to the expressional landscapes within tissues or CTCs. Furthermore, EV-derived lncRNA-GC1 levels could be used to effectively predict the outcomes of patients with GC before the initiation of ICI treatments, and changed dynamically throughout treatment—as was confirmed through both retrospective and prospective validation. Moreover, the low levels of EV-derived lncRNA-GC1 were associated with unique microenvironmental features that promote robust antitumor immunity, including higher levels of activated CD8⁺ T/NK cells and a higher TH1/TH2 ratio. Finally, the stability of EV-derived lncRNA-GC1 met the clinical requirements for future use. EV-derived lncRNA-GC1 has the potential to be used as a clinical biomarker that is capable of predicting and monitoring immunotherapy response outcomes in patients with GC.

No serum biomarkers that predict ICI-related therapeutic outcomes have yet been successfully translated into clinical practice. Several studies have demonstrated the promise of liquid biopsy-based biomarkers—including ctDNA, CTCs, and EVs—as tools for patient treatment monitoring, given that they can be routinely monitored in a largely noninvasive manner over the course of treatment.^{[25, 26]} However, CTCs and ctDNA are extremely rare, fragile, and heterogeneous, which restricts their practical value.^{[27, 28]} By contrast, EV-derived lncRNAs remain highly stable in the peripheral circulation ^[29] and can be dynamically monitored throughout treatment, even when samples have undergone prolonged or suboptimal storage. Using serum samples to detect EV-derived lncRNA-GC1 also obviates the spatial heterogeneity inherent when utilizing tissue samples. EV-derived lncRNA-GC1 represents an ideal target for liquid biopsy testing to predict and monitor immunotherapy responses.

As an oncogenic ncRNA, lncRNA-GC1 is upregulated in GC and many other tumor types, wherein it regulates diverse pathological processes.^{[30, 31]}At the mechanistic level, lncRNA-GC1 can promote tumor progression by acting as a modular scaffold that coordinates the localization of the WDR5 and KAT2A complexes, thereby affecting histone modification patterns and associated cellular physiology.^[20] However, a specific link between lncRNA-GC1 and the immune response has yet to be fully established. Here, EV-derived lncRNA-GC1 was found to be closely correlated with unique TME signatures that may indicate dynamic shifts in immune activity over the context of treatment. In addition, low EV-derived lncRNA-GC1 levels were correlated with more robust antitumor immunity, thus offering value as a biomarker capable of predicting and monitoring ICI-related therapeutic outcomes. However, additional work will be essential to fully detail the mechanisms whereby EV-derived lncRNA-GC1 shapes the composition of the tumor immune microenvironment.

This study had two major strengths. First, patients diagnosed with unresectable or metastatic GC often lack the energy or physical reserves necessary to explore a range of therapeutic interventions or tolerate treatment-related toxicity. At the same time, they often have only one chance to receive an effective treatment. Therefore, taking into account all available information relating to the patient, the most appropriate treatment must be identified and selected. The clinical dataset suggests that baseline circulating EV-derived lncRNA-GC1 levels can effectively predict patient immunotherapeutic outcomes before initiating ICI treatments. More critically, these EV-derived lncRNA-GC1 levels can also be leveraged for the dynamic monitoring of tumor progression throughout treatment. This possibility of using EV-derived lncRNA-GC1 as a biomarker for real-time patient monitoring is particularly important because clinicians are always willing to determine whether patients are responding after initiating ICI treatments. Accordingly, circulating EV-derived lncRNA-GC1 may offer value as a serum-based biomarker for prognostic evaluation and treatment monitoring in patients with GC.

This study is the first, to our knowledge, to examine the benefits of EV-derived lncRNA-GC1 as a prognostic biomarker or monitoring tool for patients with GC who are undergoing ICI treatments. However, although promising, our results are also subject to certain limitations worth noting. Most notably, the number of patients in this study was relatively limited. To mitigate this limitation, a systematic multi-phase implementation approach was applied to ensure that our findings were consistent across independent patient cohorts. In addition, since this was a nonrandomized study, the results may also be biased. To mitigate the potential for selection bias, our multidisciplinary team had no role in determining whether patients underwent ICI treatments. Critically, the prognostic relevance of circulating EV-derived lncRNA-GC1 was consistent and stable across cohorts from different medical centers. While further prospective randomized trials for diverse patient populations are warranted to validate the clinical relevance of these findings fully, this predictive biomarker holds great promise as a tool that will guide future therapeutic planning and decision-making efforts.

This study used systematic retrospective and prospective validation approaches to demonstrate the clinical utility of circulating EV-derived lncRNA-GC1 as a biomarker capable of reliably predicting the outcomes of patients with GC who undergo immunotherapeutic treatment. Patients with low EV-derived lncRNA-GC1 levels may have a more active tumor microenvironment, so they have a higher chance of benefitting from ICI treatments. Efforts to target the EV-derived lncRNA-GC1 pathway may also be helpful in improving therapeutic outcomes in patients with GC who undergo immunotherapeutic treatment—particularly in those with low circulating levels of this EV-derived biomarker.

gastric cancer

ICI

immune checkpoint inhibitor

extracellular vesicle

qPCR

quantitative polymerase chain reaction

TMB

tumor mutational burden

MSI.microsatellite instability

CTCs

circulating tumor cells

ctDNA circulating tumor DNA

ncRNAs

non-coding RNAs

piRNAs

piwi-interacting RNAs

tRNAs

and transfer RNAs

PD-L1

programmed cell death-ligand 1

ECOG

eastern cooperative oncology group

computed tomography

CPS

combined positive score

PFS

progression-free survival

overall survival

not significant

MSI

microsatellite instability

MSI-H

microsatellite instability high

MSI-L

microsatellite instability low

MSS

microsatellite stability

EBV

epstein-Barr virus

CTLA4

cytotoxic T lymphocyte-associated antigen-4

complete response

partial response

stable disease

progressive disease

DCB

Durable clinical benefit

hazard ratio

ORR

overall response

DCR

disease control rate.

Ethics approval:
The study was conducted in accordance with the Declaration of Helsinki and registered with Clinical Trials.gov, NCT05334849. All participants provided informed written consent. This study was approved by the Institutional Ethics Committees and Review Board of the Air Force 986th Hospital, Xijing Hospital, and the Chinese People’s Liberation Army General Hospital, respectively.

Consent for publication:
I hereby consent to the publication of this article in the BMC Medicine

Competing interests:

All the authors declare no potential conflicts of interest.

Funding:

This study was funded by the National Natural Science Foundation of China with grant/award numbers No. 82002740 (to Xiaohui Lv) and No. 82073192 (to Bo Wei), the Shaanxi Science and Technology Innovation Team with grant/award Number 2021TD-43 (to Gang Ji), the Natural Science Foundation of Shaanxi Province with grant/award Number of 2023-YBSF-481 (to Xin Guo) and Shaanxi Provincial Youth Science and Technology Nova Cultivation Project with grant/award Number of 2021KJXX-25 (to Xin Guo). The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication.

Author Contribution

XG, XHL, and GJ contributed to the conception and design; JPW, XXW, DHD, YR, XC and LBC collected and assembled the data. XG, XHL, JPW,DHD and SHX contributed to data analysis and interpretation. XG and JPW wrote the manuscript. All authors finally reviewed and approved the manuscript.

Availability of data and materials:

The corresponding author Gang Ji had full access to all the data in the study and took responsibility for the data's integrity and the data analysis's accuracy.

Huang C, Liu H, Hu Y, et al. Laparoscopic vs Open Distal Gastrectomy for Locally Advanced Gastric Cancer: Five-Year Outcomes From the CLASS-01 Randomized Clinical Trial. JAMA Surg. 2022;157(1):9–17.
Wang Z, Han W, Xue F, et al. Nationwide gastric cancer prevention in China, 2021–2035: a decision analysis on effect, affordability and cost-effectiveness optimisation. Gut. 2022;71(12):2391–400.
Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49.
Dyba T, Randi G, Bray F, et al. The European cancer burden in 2020: Incidence and mortality estimates for 40 countries and 25 major cancers. Eur J Cancer. 2021;157:308–47.
Johnston FM, Beckman M. Updates on Management of Gastric Cancer. Curr Oncol Rep. 2019;21(8):67.
Joshi SS, Badgwell BD. Current treatment and recent progress in gastric cancer. CA Cancer J Clin. 2021;71(3):264–79.
Pereira MA, Ramos M, Dias AR, et al. Expression Profile of Markers for Targeted Therapy in Gastric Cancer Patients: HER-2, Microsatellite Instability and PD-L1. Mol Diagn Ther. 2019;23(6):761–71.
Zhao Q, Cao L, Guan L, et al. Immunotherapy for gastric cancer: dilemmas and prospect. Brief Funct Genomics. 2019;18(2):107–12.
Li K, Zhang A, Li X, Zhang H, Zhao L. Advances in clinical immunotherapy for gastric cancer. Biochim Biophys Acta Rev Cancer. 2021;1876(2):188615.
Han HS, Lee KW. Liquid Biopsy: An Emerging Diagnostic, Prognostic, and Predictive Tool in Gastric Cancer. J Gastric Cancer. 2024;24(1):4–28.
Lopes C, Chaves J, Ortigão R, Dinis-Ribeiro M, Pereira C. Gastric cancer detection by non-blood-based liquid biopsies: A systematic review looking into the last decade of research. United Eur Gastroenterol J. 2023;11(1):114–30.
Labib M, Kelley SO. Circulating tumor cell profiling for precision oncology. Mol Oncol. 2021;15(6):1622–46.
Chemi F, Mohan S, Guevara T, Clipson A, Rothwell DG, Dive C. Early Dissemination of Circulating Tumor Cells: Biological and Clinical Insights. Front Oncol. 2021;11:672195.
Stadler JC, Belloum Y, Deitert B, et al. Current and Future Clinical Applications of ctDNA in Immuno-Oncology. Cancer Res. 2022;82(3):349–58.
Kilgour E, Rothwell DG, Brady G, Dive C. Liquid Biopsy-Based Biomarkers of Treatment Response and Resistance. Cancer Cell. 2020;37(4):485–95.
Li C, Ni YQ, Xu H, et al. Roles and mechanisms of exosomal non-coding RNAs in human health and diseases. Signal Transduct Target Ther. 2021;6(1):383.
Tosar JP, Cayota A. Extracellular tRNAs and tRNA-derived fragments. RNA Biol. 2020;17(8):1149–67.
Hu W, Liu C, Bi ZY, et al. Comprehensive landscape of extracellular vesicle-derived RNAs in cancer initiation, progression, metastasis and cancer immunology. Mol Cancer. 2020;19(1):102.
Guo X, Lv X, Ru Y, et al. Circulating Exosomal Gastric Cancer-Associated Long Noncoding RNA1 as a Biomarker for Early Detection and Monitoring Progression of Gastric Cancer: A Multiphase Study. JAMA Surg. 2020;155(7):572–9.
Sun TT, He J, Liang Q, et al. LncRNA GClnc1 Promotes Gastric Carcinogenesis and May Act as a Modular Scaffold of WDR5 and KAT2A Complexes to Specify the Histone Modification Pattern. Cancer Discov. 2016;6(7):784–801.
Song Q, Lv X, Ru Y, et al. Circulating exosomal gastric cancer-associated long noncoding RNA1 as a noninvasive biomarker for predicting chemotherapy response and prognosis of advanced gastric cancer: A multi-cohort, multi-phase study. EBioMedicine. 2022;78:103971.
Mathew G, Agha R, Albrecht J et al. STROCSS. 2021: Strengthening the reporting of cohort, cross-sectional and case-control studies in surgery. Int J Surg. 2021. 96: 106165.
Cao M, Isaac R, Yan W, et al. Cancer-cell-secreted extracellular vesicles suppress insulin secretion through miR-122 to impair systemic glucose homeostasis and contribute to tumour growth. Nat Cell Biol. 2022;24(6):954–67.
Loriot Y, Marabelle A, Guégan JP, et al. Plasma proteomics identifies leukemia inhibitory factor (LIF) as a novel predictive biomarker of immune-checkpoint blockade resistance. Ann Oncol. 2021;32(11):1381–90.
Zhang Z, Wu H, Chong W, Shang L, Jing C, Li L. Liquid biopsy in gastric cancer: predictive and prognostic biomarkers. Cell Death Dis. 2022;13(10):903.
Ma S, Zhou M, Xu Y, et al. Clinical application and detection techniques of liquid biopsy in gastric cancer. Mol Cancer. 2023;22(1):7.
Del Díaz C, Fernández Aceñero MJ, Ortega Medina L. Liquid biopsy for gastric cancer: Techniques, applications, and future directions. World J Gastroenterol. 2024;30(12):1680–705.
Guo T, Tang XH, Gao XY, et al. A liquid biopsy signature of circulating exosome-derived mRNAs, miRNAs and lncRNAs predict therapeutic efficacy to neoadjuvant chemotherapy in patients with advanced gastric cancer. Mol Cancer. 2022;21(1):216.
Guo X, Peng Y, Song Q, et al. A Liquid Biopsy Signature for the Early Detection of Gastric Cancer in Patients. Gastroenterology. 2023;165(2):402–e41313.
Yang J, Gong Y, Lam VK, et al. Deep sequencing of circulating tumor DNA detects molecular residual disease and predicts recurrence in gastric cancer. Cell Death Dis. 2020;11(5):346.
Abdi E, Latifi-Navid S, Abdi F, Taherian-Esfahani Z. Emerging circulating MiRNAs and LncRNAs in upper gastrointestinal cancers. Expert Rev Mol Diagn. 2020;20(11):1121–38.

No competing interests reported.

SupplementalDigitalContentJournal.pdf

Download PDF

Reviews received at journal
02 Nov, 2024
Reviewers agreed at journal
28 Oct, 2024
Reviews received at journal
21 Oct, 2024
Reviewers agreed at journal
14 Oct, 2024
Reviewers agreed at journal
09 Sep, 2024
Reviewers invited by journal
09 Sep, 2024
Editor invited by journal
30 Aug, 2024
Editor assigned by journal
27 Aug, 2024
Submission checks completed at journal
27 Aug, 2024
First submitted to journal
27 Aug, 2024

You are reading this latest preprint version

Extracellular vesicle-derived lncRNA-GC1 serves as a novel biomarker for predicting and monitoring the immunotherapeutic outcomes of patients with gastric cancer

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Background

Methods

Ethical considerations

Study design and patient cohort

Sample preparation

Measurement of EV-derived lncRNA-GC1

Radiographic analyses and survival outcomes

Statistical analysis

Results

Analyses of lncRNA-GC1 expression profiles in patients with GC undergoing ICI treatments

Dynamic changes of EV-derived lncRNA-GC1 enable the monitoring of ICI treatment responses

EV-derived lncRNA-GC1 is associated with unique tumor microenvironmental features

EV-derived lncRNA-GC1 remains stable under routine clinical testing conditions

Discussion

Conclusions

Abbreviations

Declarations

Consent for publication:
I hereby consent to the publication of this article in the BMC Medicine

Competing interests:

Funding:

Author Contribution

Availability of data and materials:

References

Additional Declarations

Supplementary Files

Status:

Version 1

Extracellular vesicle-derived lncRNA-GC1 serves as a novel biomarker for predicting and monitoring the immunotherapeutic outcomes of patients with gastric cancer

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Background

Methods

Ethical considerations

Study design and patient cohort

Sample preparation

Measurement of EV-derived lncRNA-GC1

Radiographic analyses and survival outcomes

Statistical analysis

Results

Analyses of lncRNA-GC1 expression profiles in patients with GC undergoing ICI treatments

Dynamic changes of EV-derived lncRNA-GC1 enable the monitoring of ICI treatment responses

EV-derived lncRNA-GC1 is associated with unique tumor microenvironmental features

EV-derived lncRNA-GC1 remains stable under routine clinical testing conditions

Discussion

Conclusions

Abbreviations

Declarations

Consent for publication: I hereby consent to the publication of this article in the BMC Medicine

Competing interests:

Funding:

Author Contribution

Availability of data and materials:

References

Additional Declarations

Supplementary Files

Status:

Version 1

Consent for publication:
I hereby consent to the publication of this article in the BMC Medicine