Association of the CD4+ /CD8+ ratio with response to PD-1 inhibitor-based combination therapy and dermatologic toxicities in patients with advanced gastric and esophageal cancer

doi:10.21203/rs.3.rs-2345882/v1

Background

The host immune system affects treatment response to immune checkpoint inhibitors (ICIs) and can be reflected by circulating immune cells. The aims of this study were to evaluate whether circulating T cells are correlated with clinical response and dermatologic toxicities in patients with advanced gastric and esophageal cancer receiving PD-1 inhibitor-based combination therapy.

Methods

Patients with advanced gastric and esophageal cancer who received PD-1 inhibitor-based combination therapy (n = 203) were enrolled. Cox regression model was used to investigate independent prognostic factors, which were applied to generate a nomogram. The nomogram was validated using calibration plots and validation cohort data. Kaplan-Meier method and log-rank test were subsequently conducted to evaluate the correlation between CD4⁺/CD8⁺ ratio and OS. Additionally, correlations between CD4⁺/CD8⁺ ratio and other clinicopathological characteristics were analyzed by Pearson Chi-Square test and Continuity Correction.

Results

In the training cohort, ECOG performance status (PS), PD-L1 expression, use of antibiotics, and CD4⁺/CD8⁺ ratio were identified as independent prognostic factors. A nomogram to predict OS and survival probabilities was constructed using these factors. The nomogram showed a good discrimination ability (C-index, 0.767) and good calibration, and was externally confirmed in the validation cohort (C-index, 0.791) and test cohort (C-index, 0.784). In subgroup analysis, CD4⁺/CD8⁺ ratio was significantly correlated with OS in patients stratified by age, sex, antibiotic use, and ICI treatment line. Kaplan-Meier analysis showed that median OS in patients with a CD4⁺/CD8⁺ ratio ≥ 1.10 was 6.2 months, which was significantly shorter than those of patients with a CD4⁺/CD8⁺ ratio < 1.10 (P < 0.001). Patients with CD4⁺/CD8⁺ ratio < 1.10 had superior objective response rate (43.8% vs. 23.1%) and disease control rate (72.9% vs. 59.0%) relative to those with a ratio ≥ 1.10. In addition, PD-L1 expression, corticosteroids use, and CD4⁺/CD8⁺ ratio can predict dermatologic toxicities independently.

Conclusions

Baseline CD4⁺/CD8⁺ ratio is a potential prognostic factor for patients with advanced gastric and esophageal cancer treated with PD-1 inhibitor-based combination therapy. Nomogram incorporating CD4⁺/CD8⁺ ratio, ECOG PS at ICI initiation, PD-L1 expression, and antibiotic use can predict OS with considerable accuracy. In addition, CD4⁺/CD8⁺ ratio can predict dermatologic toxicities independently.

gastric cancer

esophageal cancer

immunotherapy

CD4+/CD8+ ratio

survival

dermatologic toxicities

Gastric cancer (GC) and esophageal cancer (EC) account for 8.7% of emerging cancer cases and 13.2% of total cancer mortality globally [1], with approximately 1.3 million patients dying from these diseases worldwide. In China, GC ranked third in terms of cancer incidence rate, while EC ranked sixth in 2020 [2]. GC and EC are characterized by high mortality with an overall 5-year survival rate of < 20% [3]. In 2020, total deaths from GC and EC in China accounted for 12.4% of all cancer fatalities [2].

In recent years, in addition to chemotherapy and targeted therapies, more treatment options have emerged for patients with advanced GC or EC, due to the introduction of immune checkpoint inhibitors, such as programmed cell death protein 1 (PD-1)/programmed cell death receptor protein 1 (PD-L1) inhibitors, leading to clear improvements in overall survival (OS). Results from the Checkmate-649 trail confirmed the effectiveness of PD-1 inhibitors as first-line treatment for advanced GC and EC, through combined application of nivolumab and chemotherapy [4]; median OS for nivolumab plus chemotherapy was 13.1 months, which was significantly longer than that with chemotherapy alone (11.1 months). Further, OS reached 14.0 months in patients with a combined positive score (CPS) of ≥ 1. Subsequently, the KEYNOTE-811 trail investigated the effectiveness of first-line pembrolizumab plus trastuzumab and chemotherapy in patients with HER2-positive advanced GC [5]. The superiority of combined pembrolizumab and chemotherapy as first-line treatment for advanced EC was further confirmed in the KEYNOTE-590 trail [6]. Furthermore, the phase 3 randomized clinical trial, KEYNOTE-062, found that pembrolizumab alone was non-inferior to chemotherapy in terms of OS as a first-line treatment for GC with a CPS of ≥ 1 [7].

Although immunotherapy has radically changed the treatment paradigm for advanced GC and EC, there remains a proportion of patients who cannot benefit from this approach. Thus, identification of biomarkers is indispensable for predicting which patients with GC and EC can benefit from PD-L1 inhibitor-based therapy. To date, several biomarkers, including PD-L1 expression, microsatellite instability, tumor mutation burden (TMB), immune gene signatures, and tumor-infiltrating lymphocytes (TILs), have been proven to be correlated with the effect of immunotherapy and patients’ outcome [8–14]. For example, the subsets and localization of TILs, which vary between tumor types, are decisive for tumor control and progression [15]. High density of CD8⁺ T cells infiltration was shown to be associated with better prognosis in gastric and esophageal cancer [16, 17]. Further, there is increasing evidence that the gut microbiome, as well as antibiotic and corticosteroid use, can also influence the efficacy of immunotherapy [18–20]; however, most of these biomarkers are expensive and based on analysis of tumor specimens, which are often difficult to acquire, especially during dynamic supervision of treatment responses.

PD-1 or PD-L1 inhibitors impede tumor immune escape by blocking the binding of T-cell inhibitory receptor PD-1 and its ligand PD-L1 expressed on tumor or host cells, thereby enhancing the killing effect of T cells, particularly CD8⁺ T cells. Tumor infiltrating lymphocytes (TILs), particularly CD8⁺ TILs, in the tumor microenvironment, have potential predictive value for the prognosis and curative effect of immune checkpoint inhibitors (ICIs) [21, 22]; however, patient’s tumor tissues are also required for evaluation of TILs. Considering the difficulty of repeated sampling tumor tissue and the substantial trauma it causes, TILs are unlikely to be clinically applied for dynamic supervision of curative effects in patients. Peripheral blood neutrophil lymphocyte ratio (NLR), absolute monocyte count (AMC), absolute lymphocyte count (ALC), and absolute neutrophil count (ANC) also have some value for the prognosis of immunotherapy in various types of cancer [23–27]. To date, whether there is a correlation between peripheral T lymphocytes or their subtypes and immunotherapy efficacy in patients with GC and EC remains to be elucidated. Hence, the aims of this research were to investigate whether peripheral T lymphocytes and their subtypes can be used to predict the response to PD-1 inhibitor-based combination therapy in patients with advanced GC and EC.

Dermatologic toxicities appear to be one of most prevalent immnue-related adverse events (irAEs) (incidence, 34–42%), both with anti-PD-1 and anti-PD-L1agents [28]. The add of PD-1 inhibitor to chemotherapy will greatly increase the incidence of dermatologic toxicities than chemotherapy alone. For example, the odds ratio of rash for all grades in PD-1/PD-L1 plus Chemotherapy versus Chemotherapy was approximately 1.87 (95%CI: 1.59–2.20) in all type of cancer [29]. In gastric cancer, the reported odds ratio was 2.83 (95%CI: 1.34–5.99) while the odds ratio was 1.66 (95%CI: 0.91–3.05) in esophageal cancer [29]. The pathophysiologic mechanisms governing cutaneous irAEs have not been established. They are, however, clearly related to T-cell activation mediated by blockade of PD-1 or the PD-L1 ligand [30]. Prediction and early recognition of dermatologic toxicities are of great significance in mitigating the severity of the lesions. Thus, it is also very important to identify biomarkers which can predict dermatologic toxicities.

Patients

Patients with advanced GC or EC who had received PD-1 inhibitor-based combination therapy from January 2019 to March 2021 in Changzhou Second People’s Hospital (n = 151) were selected. They were randomly divided into the training (n = 87) and validation (n = 64) cohort with a ratio of 4 to 3. Another 52 patients received PD-1 inhibitor-based combination therapy from April 2021 to December 2021 were selected as the test cohort. All patients were > 18 years old and were diagnosed with GC or EC by histopathology. Other key inclusion criteria were evaluable lesions per Response Evaluation Criteria in Solid Tumors (RECIST), version 1.1; and an Eastern Cooperative Oncology Group (ECOG) performance status score of 0 to 2. The exclusion criteria were history of autoimmune diseases or active autoimmune diseases; congenital or acquired immunodeficiency; receving ongoing systemic immunosuppressive therapy; and other types of concurrent cancer. Ethical approval was obtained from the ethics committees of Changzhou No. 2 People’s Hospital. The study was conducted in accordance with the principles of the Declaration of Helsinki.

Flow Cytometry

A routine clinical flow cytometry test protocol was followed for evaluation of lymphocytes in the peripheral blood as described previously. Briefly, 100 µL whole blood was added into a tube and cells were blocked with 2.5 µg of Human BD Fc Block for 10 min, then stained with antibodies in PBS containing 0.1% (wt/vol) BSA and 0.1% NaN₃. The following antibodies were purchased from BD Biosciences (San Jose, CA, USA): anti-CD3 (SK7), anti-CD4 (RPA-T4), and anti-CD8 (RPA-T8). Then, 2mL 1×RBC Lysis Buffer (Biolegend, 420301) was added into the samples. After incubation for 10–15 min at room temperature, cells were collected by centrifugation at 1200 rpm for 5 min and resuspended in FACS staining buffer. Flow cytometry data were acquired on a BD FACSCanto and analyzed using BD FACSDiva flow cytometry analysis software.

Pd-l1 Immunohistochemical Assays

The PD-L1 assays (22C3) was performed by its corresponding autostainer (Dako Autostainer Link48 for 22C3 assays) according to the manufacturers’ instructions. The combined positive score (CPS) was calculated by dividing the number of PD-L1-stained cells (tumor cells, lymphocytes, macrophages) by the total number of viable tumor cells, multiplied by 100. The maximum combined positive score was defined as 100. The areas with tumor necrosis were exempted for the assessment.

Efficacy Evaluation And Grouping

All patients underwent CT examination after receiving immunotherapy. Efficacy was evaluated by using Response Evaluation Criteria in Solid Tumors 1.1 (RECIST 1.1) criteria and classified as complete response (CR), partial response (PR), stable disease, and progressive disease (PD).

Statistical analysis

Statistical analyses were conducted using R 3.3.1 software (Institute for Statistics and Mathematics, Vienna, Austria) and SPSS statistical software (version 21.0, SPSS Inc, IBM, Armonk, NY, USA). The optimal cutoff value for CD4⁺/CD8⁺ T cells was identified by generating receiver operating characteristics (ROC) curves. Univariate and multivariate Cox proportional hazard models were applied to investigate independent prognostic factors. Identified significant prognostic factors were further used to generate a nomogram, and the discrimination power of the nomogram evaluated by calculating the C-index. In addition, calibration plots were used to assess the probability of concordance between predicted and actual survival. The discriminatory ability of the nomogram was also externally validated using data from a separate validation patient cohort. The Kaplan-Meier method and log-rank test were subsequently applied to evaluate correlations between CD4⁺/CD8⁺ ratio and OS. Correlations between baseline CD4⁺/CD8⁺ ratio and other clinicopathological characteristics were analyzed using the Pearson Chi-Square test and Continuity Correction. The logistic regression model was used to estimate the effects of these factors on risk for dermatologic toxicities. For each factor, we calculated the HRs, ORs and corresponding 95%CIs. Two-sided P < 0.05 was considered statistically significant.

Baseline clinicopathological characteristics

Patients with GC or EC who received PD-1 inhibitor-based combination therapy (n = 203) were enrolled in the study. In the training cohort (n = 87), median age at ICI initiation was 67 years. 27 patients had high blood pressure (HBP), and 10 had diabetes mellitus (DM). Tumor histology types were adenocarcinoma (n = 45) and squamous carcinoma (n = 42), with 42 patients having EC and 45 GC. Distant metastases were detected in 69 patients and 25 patients were positive for PD-L1 expression (CPS ≥ 1%). Antibiotic (n = 14) and corticosteriods (n = 30) use were recorded during or before ICI treatment. ICI treatment was initiated in the 1st (n = 58), 2nd (n = 23), or 3rd to 4th (n = 6) lines. Overall incidence of dermatologic toxicities was 33.3% (29/87) in the training cohort. Measured laboratory variables included ALC, AMC, and ANC, among others. Baseline clinicopathlogical characteristics of the validation cohort were also presented in Table 1. All these characteristics were comparable between the two cohorts (P > 0.05). The baseline clinicopathlogical characteristics of the test cohort were presented in the supplementary Table 1.

Table 1. Baseline clinicopathological characteristics of patients in the training and validation cohort
Characteristics	Training cohort (n=87)	Validation cohort (n=64)	P-value
Demographic characteristics
Age at ICI initiation (years)	67 (27-86)	65 (45-89)	0.630
Sex
Male	67 (77.0%)	50 (78.1%)	0.871
Female	20 (23.0%)	14 (21.9%)
ECOG PS at ICI initiation
0	4 (4.6%)	3 (4.7%)	0.993
1	34 (39.1%)	25 (39.1%)
2	49 (56.3%)	36 (56.3%)
HBP
Yes	27 (31.0%)	12 (18.8%)	0.088
No	60 (69.0%)	52 (81.3%)
DM
Yes	10 (11.5%)	6 (9.4%)	0.676
No	77 (88.5%)	58 (90.6%)
Tumor variables
Histology
Adenocarcinoma	45 (51.7%)	33 (51.6%)	0.984
Squamous carcinoma	42 (48.3%)	31 (48.4%)
Tumor type
Esophageal cancer	42 (48.3%)	31 (48.4%)	0.984
Gastric cancer	45 (51.7%)	33 (51.6%)
Distant metastasis
Yes	69 (79.3%)	48 (75.0%)	0.531
No	18 (20.7%)	16 (25.0%)
PD-L1 expression (CPS)
>1%	25 (28.7%)	27 (42.2%)	0.215
<1%	37 (42.5%)	25 (39.1%)
Missing	25 (28.7%)	12 (18.8%)
ICI treatment variables
Antibiotic use
Yes	14 (16.1%)	12 (18.8%)	0.669
No	73 (83.9%)	52 (81.3%)
Corticosteroids use
Yes	30 (34.5%)	23 (35.9%)	0.853
No	57 (65.5%)	41 (64.1%)
ICI treatment line
1st-line	58 (66.7%)	53 (82.8%)	0.113
2nd-line	23 (26.4%)	7 (10.9%)
3rd, or 4th-line	6 (6.9%)	4 (6.3%)
Dermatologic toxicities
Yes	29 (33.3%)	20 (31.3%)	0.787
No	58 (66.7%)	44 (68.8%)
Laboratory variables
ALC (*10^9/L)	1.18 (0.33-4.53)	1.10 (0.19-2.46)	0.121
AMC (*10^9/L)	0.44 (0.09-1.89)	0.46 (0.14-1.10)	0.920
ANC (*10^9/L)	3.12 (1.02-15.04)	3.58 (1.65-15.50)	0.920
HGB (g/L)	114 (54-167)	119 (72-150)	0.238
LDH (IU/L)	169 (94-3187)	170 (21-1540)	0.291
CEA (ng/mL)	3.20 (0.27-645.20)	2.27 (0.22-481.70)	0.432
CA 19-9 (U/mL)	16.28 (0.01-883.10)	7.44 (0.69-1000)	0.111
CD3+	656 (318-2760)	784 (296-2664)	0.078
CD3+CD4+CD8-	339 (104-1592)	418 (137-1572)	0.100
CD3+CD4-CD8+	296 (114-1024)	352 (124-1120)	0.176
CD4+/CD8+ ratio	1.06 (0.25-5.60)	1.29 (0.46-3.00)	0.904

Abbreviations: ICI, immune checkpoint inhibitors, HBP, high blood pressure, DM, diabetes mellitus, ALC, absolute lymphocyte count, AMC, absolute monocyte count, ANC, absolute neutrophil count, HGB, hemoglobin, LDH, lactate dehydrogenase, CEA, Carcinoma Embryonic Antigen, CA19-9, carbohydrate antigen 19-9.

Prognostic factors for OS

Univariate analysis identified ECOG PS at ICI initiation, distant metastasis, PD-L1 expression, antibiotic use, corticosteroids use, dermatologic toxicities, NLR, LMR, and CD4⁺/CD8⁺ ratio as potentially correlated with patients’ prognosis (P < 0.05). These factors were then incorporated into a multivariate Cox regression model for further analysis. The results demonstrated that ECOG PS at ICI initiation (hazard ratio (HR), 2.698, 95% confidence interval (CI), 1.189 - 6.122), PD-L1 expression (HR, 0.439, 95% CI, 0.203 - 0.949), antibiotic use (HR, 3.301, 95%CI, 1.416 - 7.696), and CD4⁺/CD8⁺ ratio (HR, 1.636, 95% CI, 1.129 - 2.370) were independent prognostic variables (P < 0.05, Table 2). The prognostic value of these factors was verified by analysis of validation group and test group data. Among them, ECOG PS at ICI initiation (HR, 2.519, 95% CI, 1.036-6.125, P = 0.042), antibiotic use (HR, 3.948, 95% CI, 1.044-14.929, P = 0.043), and CD4⁺/CD8⁺ ratio (HR, 2.170, 95% CI, 1.119-4.209, P = 0.022) remained as independent prognostic factors in the validation cohort (Table 3).

Table 2. Univariate and multivariate analysis of prognostic factors in the training cohort
	Univariate analysis			Multivariate analysis
Characteristics	HR	95%CI	P-value	HR	95%CI	P-value
Age
<65	1.014	0.599-1.718	0.958
>65	Reference
Sex
Male	1.552	0.835-2.885	0.165
Female	Reference
ECOG PS at ICI initiation
2	2.163	1.253-3.735	0.006	2.698	1.189-6.122	0.018
0-1	Reference			Reference
HBP
Yes	0.895	0.512-1.564	0.697
No	Reference
DM
Yes	0.493	0.231-1.053	0.068
No	Reference
Tumor type
Esophageal cancer	1.345	0.807-2.243	0.255
Gastric cancer	Reference
Distant metastases
Yes	2.057	1.076-3.933	0.029	1.238	0.405-3.788	0.708
No	Reference			Reference
PD-L1 expression
>1%	0.424	0.217-0.831	0.012	0.439	0.203-0.949	0.036
<1%	Reference			Reference
Antibiotic use
Yes	2.119	1.139-3.943	0.018	3.301	1.416-7.696	0.006
No	Reference			Reference
Corticosteroids use
Yes	1.741	1.008-3.006	0.047	2.120	0.957-4.696	0.064
No	Reference			Reference
Dermatologic toxicities
Yes	0.399	0.222-0.719	0.002	0.745	0.328-1.690	0.481
No	Reference			Reference
HGB (g/L)	1.000	0.988-1.012	0.987
NLR	1.074	1.023-1.128	0.004	0.969	0.900-1.043	0.404
LMR	0.764	0.624-0.935	0.009	0.820	0.622-1.081	0.160
LDH (IU/L)	1.000	1.000-1.001	0.932
CEA (ng/ml)	1.001	0.998-1.004	0.499
CA19-9 (U/mL)	1.000	0.998-1.002	0.958
CD3+	1.000	0.999-1.001	0.722
CD3+CD4+CD8-	1.000	0.999-1.001	0.388
CD3+CD4-CD8+	0.998	0.997-1.000	0.068
CD4+/CD8+ ratio	1.423	1.140-1.775	0.002	1.636	1.129-2.370	0.009

Abbreviations: ICI, immune checkpoint inhibitors, HBP, high blood pressure, DM, diabetes mellitus, NLR, neutrophil-to-lymphocyte ratio, LMR, lymphocyte-to-monocyte ratio, HGB, hemoglobin, LDH, lactate dehydrogenase, CEA, Carcinoma Embryonic Antigen, CA19-9, carbohydrate antigen 19-9.

Table 3. Multivariate analysis of OS in the validation cohort
	Multivariate analysis
Characteristics	HR	95%CI	P-value
ECOG PS at ICI initiation
2	2.519	1.036-6.125	0.042
0-1	Reference
Distant metastases
Yes	1.655	0.507-5.409	0.404
No	Reference
PD-L1 expression
>1%	0.609	0.245-1.516	0.287
<1%	Reference
Antibiotic use
Yes	3.948	1.044-14.929	0.043
No	Reference
Corticosteroids use
Yes	1.590	0.608-4.155	0.344
No	Reference
Dermatologic toxicities
Yes	0.520	0.154-1.755	0.292
No	Reference
NLR	1.018	0.897-1.154	0.787
LMR	0.623	0.356-1.091	0.098
CD4+/CD8+ ratio	2.170	1.119-4.209	0.022

Abbreviations: ICI, immune checkpoint inhibitors, NLR, neutrophil-to-lymphocyte ratio, LMR, lymphocyte-to-monocyte ratio.

Establishment and validation of the a nomogram

A nomogram was constructed based on the prognostic factors identified by multivariate analysis in the training cohort (Figure 1). The nomogram could predict median survival time and survival probabilities at 6, 12, and 24 months. Harrell’s C-index value of the nomogram was 0.767. After adjustment by bootstrapping with 1,000 re-samples, calibration plots, which showed concordance between actual and ideal survival predictions, were demonstrated for 6- and 12-month survival (Figure 2). Further, a nomogram comprising the same variables also showed a good discriminatory ability in the validation cohort (C-index, 0.791) and test cohort (C-index, 0.784).

Subgroup analysis for CD4⁺/CD8⁺ratio

In the subgroup analysis, CD4⁺/CD8⁺ ratio was significantly correlated with OS in patients stratified by age, sex, antibiotic use, and ICI treatment line. However, no correlations with OS were detected in patients without distant metastasis or with ECOG PS = 0-1 (Figure 3).

Value of CD4⁺/CD8⁺ ratio for predicting immunotherapy efficacy

Patients were divided into two groups, according to a CD4⁺/CD8⁺ratio cut-off value of 1.10. In the training cohort, Kaplan-Meier analysis showed that the median OS in patients with a CD4⁺/CD8⁺ratio > 1.10 was 6.2 months, which was significantly shorter than that of patients with CD4⁺/CD8⁺ratio< 1.10 (17.3 months, 95%CI 12.8-21.8) (P < 0.001, Figure 4A). For patients with EC, median OS was 7.6 (95%CI 3.7-11.5) months in those with a CD4⁺/CD8⁺ratio> 1.10 and 16.8 (95%CI 10.6-23.0) months in patients with CD4⁺/CD8⁺ratio < 1.10 (P = 0.002, Figure 4B). For patients with GC, the median OS was 5.2 (95%CI 0.7-8.7) and 17.5 (95%CI 10.2-24.8) months in those with CD4⁺/CD8⁺ ratio > 1.10 and < 1.10, respectively (P < 0.001, Figure 4C). Similar results were also observed in the validation cohort (Figure 4D-F).

Pie charts and histograms were constructed to illustrate the efficacy of immunotherapy in the training cohort. We found that patients with CD4⁺/CD8⁺ ratio < 1.10 had almost double the likelihood of good response to ICI treatment compared with those with ratio > 1.10 (CR 4.2% vs. 2.6%, PR, 39.6% vs. 20.5%, Figure 5A-B). On the contrast, patients with CD4⁺/CD8⁺ ratio > 1.10 had much higher probability of PD than those with ratio < 1.10 (41.0% vs. 27.1%). Overall, patients with CD4⁺/CD8⁺ratio < 1.10 had superior objective response rate (ORR, 43.8% vs. 23.1%) and disease control rate (DCR, 72.9% vs. 59.0%) than those with CD4⁺/CD8⁺ratio> 1.10, suggesting a significant difference in the efficacy of immunotherapy (Figure 5C).

Correlations between CD4⁺/CD8⁺ ratio and clinicopathological features

Next, we investigated correlations between CD4⁺/CD8⁺ ratio and other clinicopathological features in both the training and validation cohorts (Table 4). Age (P = 0.002), sex (P = 0.042), distant metastasis (P = 0.001), antibiotic use (P = 0.029), and dermatologic toxicities (P < 0.001) were found to be significantly correlated with CD4⁺/CD8⁺ ratio in the training cohort (Table 4). Significant correlations between CD4⁺/CD8⁺ratioand these factors, other than distant metastasis (P = 0.465), were further confirmed in the validation cohort. ECOG PS at ICI initiation (P = 0.080) and ICI treatment line (P = 0.067) were also potentially correlated with CD4⁺/CD8⁺ratio in the training cohort.

Table 4. Correlations between CD4+/CD8+ ratio and clinicopathological variables
	Training cohort			Validation cohort
Characteristics	CD4+/CD8+ < 1.10	CD4+/CD8+ > 1.10	P-value	CD4+/CD8+ < 1.10	CD4+/CD8+ > 1.10	P-value
Age
<65	24 (77.4%)	7 (22.6%)	0.002	17 (53.1%)	15 (46.9%)	0.076
>65	24 (42.9%)	32 (57.1%)	0.002	10 (31.3%)	22 (68.8%)	0.076
Sex
Male	33 (49.3%)	34 (50.7%)	0.042	18 (36.0%)	32 (64.0%)	0.058
Female	15 (75.0%)	5 (25.0%)	0.042	9 (64.3%)	5 (35.7%)	0.058
ECOG PS at ICI initiation
0-1	25 (65.8%)	13 (34.2%)	0.080	15 (53.6%)	13 (46.4%)	0.104
2	23 (46.9%)	26 (53.1%)	0.080	12 (33.3%)	24 (66.7%)	0.104
HBP
Yes	16 (59.3%)	11 (40.7%)	0.607	3 (25.0%)	9 (75.0%)	0.181
No	32 (53.3%)	28 (46.7%)	0.607	24 (46.2%)	28 (53.8%)	0.181
DM
Yes	4 (40.0%)	6 (60.0%)	0.333	2 (33.3%)	4 (66.7%)	0.645
No	44 (57.1%)	33 (42.9%)	0.333	25 (43.1%)	33 (56.9%)	0.645
Tumor type
Esophageal cancer	25 (59.5%)	17 (40.5%)	0.430	10 (32.3%)	21 (67.7%)	0.119
Gastric cancer	23 (51.1%)	22 (48.9%)	0.430	17 (51.5%)	16 (48.%)	0.119
Distant metastases
Yes	32 (46.4%)	37 (53.6%)	0.001	19 (39.6%)	29 (60.4%)	0.465
No	16 (88.9%)	2 (11.1%)	0.001	8 (50.0%)	8 (50.0%)	0.465
PD-L1 expression
>1%	13 (52.0%)	12 (48.0%)	0.796	14 (51.9%)	13 (48.1%)	0.080
<1%	18 (48.6%)	19 (51.4%)	0.796	7 (28.0%)	18 (72.0%)	0.080
Antibiotic use
Yes	4 (28.6%)	10 (71.4%)	0.029	1 (8.3%)	11 (91.7%)	0.008
No	44 (60.3%)	29 (39.7%)	0.029	26 (50.0%)	26 (50.0%)	0.008
Corticosteroids use
Yes	14 (46.7%)	16 (53.3%)	0.247	7 (30.4%)	16 (69.6%)	0.154
No	34 (59.6%)	23 (40.4%)	0.247	20 (48.8%)	21 (51.2%)	0.154
Dermatologic toxicities
Yes	24 (82.8%)	5 (17.2%)	<0.001	13 (65.0%)	7 (35.0%)	0.013
No	24 (41.4%)	34 (58.6%)	<0.001	14 (31.8%)	30 (68.2%)	0.013
ICI treatment line
1st-line	28 (48.3%)	30 (51.7%)	0.067	17 (32.1%)	36 (67.9%)	<0.001
>2nd-line	20 (69.0%)	9 (31.0%)	0.067	10 (90.9%)	1 (9.1%)	<0.001

Abbreviations: ICI, immune checkpoint inhibitors, HBP, high blood pressure, DM, diabetes mellitus.

Value of CD4⁺/CD8⁺ratio in predicting dermatologic toxicities

Dermatologic toxicities, which mainly manifest in the form of pruritus and rash, appear to be one of the most prevalent immnue-related adverse events. Prediction and early recognition of dermatologic toxicities are of significant importance in mitigating the severity of the lesions. Although dermatologic toxicities is not an independent prognostic factor in multivariate Cox regression model, Kaplan-meier analysis showed that it is significantly correlated with OS. The median OS in patients without dermatologic toxicities was 9.2 (95%CI 6.9-11.5) months , which was significantly shorter than patients with dermatologic toxicities (17.5 months, 95%CI 8.0-27.0) (P < 0.001, Figure 6A). For patients with EC, median OS was 11.9 (95%CI 7.5-16.3) months in those without dermatologic toxicities and 23.3 (95%CI 15.9-30.7) months in patients with dermatologic toxicities (P = 0.003, Figure 6B). For patients with GC, the median OS was 8.5 (95%CI 6.6-10.4) and 14.3 (95%CI 10.4-18.1) months in those without or with dermatologic toxicities, respectively (P = 0.004, Figure 6C). In the logistic regression model, we found that ECOG PS at ICI initiation, PD-L1 expression, corticosteroids use, and CD4⁺/CD8⁺ ratio were potentially correlated with dermatologic toxicities (P <0.05, Table 5). Multivariate analysis further showed that PD-L1 expression (P < 0.001), corticosteroids use (P = 0.032), and CD4⁺/CD8⁺ ratio (P = 0.005) can predict dermatologic toxicities independently.

Table 5.Clinicopathological characteristics associated with the incidence of dermatologic toxicities
	Univariate analysis			Multivariate analysis
Characteristics	OR	95%CI	P-value	OR	95%CI	P-value
Age
<65	0.570	0.286-1.135	0.110
>65	Reference
Sex
Male	0.718	0.324-1.590	0.414
Female	Reference
ECOG PS at ICI initiation
0-1	0.445	0.223-0.891	0.022	0.655	0.253-1.697	0.384
2	Reference
HBP
Yes	1.549	0.684-3.508	0.294
No	Reference
DM
Yes	1.500	0.458-4.915	0.503
No	Reference
Tumor type
Esophageal cancer	0.853	0.432-1.687	0.648
Gastric cancer	Reference
Distant metastases
Yes	0.718	0.324-1.590	0.414
No	Reference
PD-L1 expression
>1%	7.091	2.964-16.962	<0.001	6.211	2.371-16.270	<0.001
<1%	Reference
Antibiotic use
Yes	0.728	0.284-1.869	0.509
No	Reference
Corticosteroids use
Yes	0.297	0.130-0.675	0.004	0.315	0.109-0.907	0.032
No	Reference
HGB (g/L)	1.004	0.988-1.021	0.620
NLR	1.000	0.923-1.084	0.995
LMR	1.031	0.822-1.293	0.792
LDH (IU/L)	0.999	0.998-1.001	0.233
CEA (ng/ml)	1.001	0.997-1.004	0.790
CA19-9 (U/mL)	1.000	0.998-1.002	0.835
CD4+/CD8+ ratio	0.257	0.129-0.515	<0.001	0.306	0.134-0.699	0.005

In this study, the CD4⁺/CD8⁺ratio was identified as a potential prognostic biomarker for patients with advanced GC and EC treated with PD-1 inhibitor-based combination therapy. A nomogram incorporating CD4⁺/CD8⁺ratio, ECOG PS at ICI initiation, PD-L1 expression, and antibiotic use can predict OS with considerable accuracy. In addition, CD4⁺/CD8⁺ ratio can predict dermatologic toxicities independently.

Our study shows that lower CD4⁺/CD8⁺ ratio is associated with longer survival of patients with advanced GC and EC, and with greater probability that patients will benefit from PD-1 inhibitor-based combination therapy. In multivariate analysis, CD4⁺/CD8⁺ ratio was an independent prognostic factor for patients with advanced GC and EC, who had received immunotherapy. CD8⁺ T cells are primarily responsible for anti-tumor effects in tumor-associated immunity. Cytotoxic lymphocytes, which differentiate from CD8⁺ T cells, inhibit tumor progression through direct killing of tumor cells. There is evidences that levels of CD8⁺ effector T cells (CD8⁺CD45RO⁺) in peripheral blood are significantly correlated with the efficacy of immunotherapy and chemotherapy in various cancers [31-33]. On the contrast, CD4⁺ T cells have both pro-tumor and anti-tumor effects. For example, regulatory T cells (Tregs), an immunosuppressive subtype of CD4⁺ T cells, contribute to immune escape by secreting IL-10, IL-35, and TGF-β [34]. Notably, there is also evidence that ICIs may enhance the immunosuppressive function of Tregs, whereas cytotoxic T lymphocyte antigen 4 (CTLA-4) inhibitors could deplete these cells [35]. Furthermore, immunosuppression mediated by interactions between PD-1 on Tregs and PD-L1 on CD8⁺ T cells has been investigated and was attenuated by PD-1 inhibition [36]. T helper 17 (Th17) cells, another effector CD4⁺ T cell subset, play a controversial role in tumor immunity and their levels have been correlated with both unfavorable and favorable outcomes [37]. For example, Th17 cells were reported to significantly enhance breast cancer progression by secreting cytokines, including IL-22, IL-21, and IL-6 [38], while a clinical trial showed that CTLA4 inhibitors can induce Th17 cells in patients with melanoma and that levels of IL-17 detected in ascites can predict patient’s survival [39]. Accordingly, CD4⁺/CD8⁺ratio will alter with tumor progression and serve as a reliable biomarker to predict OS and immunotherapy efficacy.

PD-L1 is mainly expressed on tumor cells, as well as in lymphocytes and macrophages infiltrated in the tumor microenvironment, in an attempt to hide neoantigens from immune surveillance [40]. In this study, patients with positive expression of PD-L1 (CPS ≥ 1%) demonstrated better treatment effects than those negative for its expression (CPS < 1%) (HR, 0.404, 95% CI, 0.192–0.847). We also found that PD-L1 had independent prognostic value for patients with advanced GC and EC who received PD-1 inhibitor-based combination therapy. This result is consistent with previous reports. For example, the KEYNOTE-059 trial showed that, in metastatic GC or gastroesophageal junction cancer, the efficacy (ORR: 15.5% vs.6.4%) and the median duration of response (16.3 vs. 8.4 months) of patients with positive PD-L1 expression was far better than that of patients with negative PD-L1 expression [41]. Likewise, several studies have demonstrated that PD-L1 expression alone, or with other factors, is positively associated with the clinical benefits of PD-1/PD-L1 inhibitors [42-44].

In this study, patients who received antibiotics during treatment (within 1 months before or 1 month after) benefited less from PD-1 inhibitor-based combination therapy than those who did not (HR, 3.430, 95% CI, 1.479–7.955). Furthermore, antibiotic use was an independent prognostic factor for these patients. Pinato et al. also found that prior antibiotic treatment could impair response to ICIs and was associated with poor survival in patients with cancer [18]. This phenomenon may be explained by antibiotic-mediated alterations of gut microbiota. Recently, Routy et al. found that antibiotics can inhibit the clinical benefit of ICI treatment in patients with advanced cancers by transiently changing the gut microbiome composition [45]. For example, the abundance of Akkermansia muciniphila is positively correlated with the immunotherapy efficacy, and efficacy can be enhanced in an IL-12-dependent manner in non-responders to ICIs by fecal transplantation of Akkermansia muciniphila [45].

We also investigated factors affecting CD4⁺/CD8⁺ratio, and found that age, sex, and antibiotic use were significantly correlated with CD4⁺/CD8⁺ratio. Further, ECOG PS at ICI initiation (P = 0.080) and ICI treatment line (P = 0.067) were also shown to be potentially correlated with CD4⁺/CD8⁺ ratio. With immune system aging, total T cells, and CD8⁺ T cells gradually decrease, while CD4⁺ T cells levels remained basically stable [46]. Thus, CD4⁺/CD8⁺ratio will exhibit an age-related increase. The underlying mechanism may be due to the fact that naïve CD4⁺ and CD8⁺ T cells mainly arise from the thymus, and the thymus decreases with aging. During this period, naïve CD4⁺ T cells show a modest age-related decline, while naïve CD8⁺ T cells decrease dramatically [47]. Sex was also correlated with CD4⁺/CD8⁺ ratio, with females tending to have higher CD4⁺/CD8⁺ratio, consistent with previous reports [39]. This phenomenon may be attributable to the metabolism and influence of sex hormones. For example, androgens accelerate thymocyte apoptosis, which can influence the levels of naïve CD4⁺ and CD8⁺ T cells to various degrees [48]. We also detected a positive correlation between antibiotic use and higher CD4⁺/CD8⁺ratio, which may be because of the distinct biological effects of antibiotics on different lymphocyte subsets. Josefsdottir et al. found that CD4⁺ and CD8⁺ T cells were both diminished in antibiotic-treated mice. Further, antibiotics have no direct inhibitory effects on bone marrow progenitors, but indirectly alter T-cell homeostasis by inhibiting STAT1 signaling, leading to impaired fecal microbiome and progenitor maintenance [49].

Among all the adverse events with a possible immune-related cause, dermatologic toxicities are very worthy to be investigated for their unique characteristics. Dermatologic toxicity is one of most common irAEs with an incidence of 34-42% and its main manifestations are pruritus and rash. In this study, 33.3% and 31.3% of patients were with dermatologic toxicities in the training cohort and validation cohort, respectively. Previous studies showed that dermatologic toxicities may be associated with positive anti-tumor response [50]. In this study, we found although dermatologic toxicities is not an independent prognostic factor for patients with advanced GC and EC treated with PD-1 inhibitor-based combination therapy, it is significantly correlated with their OS (P < 0.001). The mechanisms governing dermatologic toxicities have not been established, but they are potentially related to cytotoxic CD4⁺/CD8⁺ T-cell activation mediated by blockade of PD-1 or the PD-L1 ligand [28]. As expected, we also found that CD4⁺/CD8⁺ ratio (P = 0.005), as well as PD-L1 expression and corticosteroids use, can predict dermatologic toxicities independently.

This study has several limitations. First, the retrospective study design and relatively small sample size may have impacted the robustness of our findings. Second, some factors previously confirmed to be correlated with the efficacy of immunotherapy efficacy, such as TMB, were not included in the analysis because they were not measured. Third, patients received PD-1 inhibitor-based combination therapy in different treatment lines, which may have impacted the effects of immunotherapy and led to biased conclusions. Therefore, further study is needed to verify and expand data on the predictive value of the CD4⁺/CD8⁺ ratio forpatients with advanced GC and EC treated with PD-1 inhibitor-based combination therapy.

In conclusion, baseline CD4⁺/CD8⁺ ratio is a potential prognostic and predictive biomarker for patients with advanced GC and EC treated with PD-1 inhibitor-based combination therapy. Furthermore, a nomogram incorporating CD4⁺/CD8⁺ ratio, ECOG PS at ICI initiation, PD-L1 expression, and antibiotic use can predict OS with considerable accuracy in these patients.

DATA AVAILABILITY STATEMENT

The original contributions generated for the study are included in the article, further inquiries can be directed to the corresponding author.

ETHICS STATEMENT

Written informed consent was obtained from the individual for the publication of any potentially identifiable images or data included in this article.

AUTHOR CONTRIBUTIONS

Conceptualization, J.H., methodology, L.W. and J.H., software, S.X., Y.M. and J.H., validation, Y.M., Y.Z. and L.Z., formal analysis, L.W. and J.H., investigation, S.X. and J.H., resources, Y.M., Y.W., J.Y., J.W., and J.H., writing—original draft preparation, S.X. and J.H., writing—review and editing, W.L. and J.H., supervision, J.H, project administration, J.H., funding acquisition, L.W.and J.H.. All authors have read and agreed to the published version of the manuscript.

FUNDING

This research was funded by grants from the National Natural Science Foundation of China (81902955), Natural Science Foundation of Jiangsu Province (BK20190161), Project of Jiangsu Shuangchuang Doctor (QT201904), Foundation of Changzhou Medical Center of Nanjing Medical University (CMCB202203) and Youth Foundation from Health Committee of Shanghai Jing’an District (2021QN03).

ACKNOWLEDGMENTS

None.

Conflicts of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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No competing interests reported.

Association of the CD4+ /CD8+ ratio with response to PD-1 inhibitor-based combination therapy and dermatologic toxicities in patients with advanced gastric and esophageal cancer

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Patients And Methods

Patients

Flow Cytometry

Pd-l1 Immunohistochemical Assays

Efficacy Evaluation And Grouping

Statistical analysis

Results

Discussion

Declarations

References

Additional Declarations

Status:

Version 1