The prognostic significance of the absolute counts of peripheral blood lymphocyte subsets in patients with advanced gastric cancer

doi:10.21203/rs.3.rs-1020780/v2

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The prognostic significance of the absolute counts of peripheral blood lymphocyte subsets in patients with advanced gastric cancer

https://doi.org/10.21203/rs.3.rs-1020780/v2

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The percentages of lymphocyte subsets (PL) of peripheral blood which mainly include CD3⁺, CD3⁺CD4⁺, CD3⁺CD8⁺, B, and NK cells have been paid much attention in advanced gastric cancer (AGC), but PL is often inconsistent with disease severity and tumor progression, appear no significant changes even after chemotherapy, which often lead to clinical misjudgment. Clinic observation suggests that absolute counts of lymphocyte subsets (ACL) are more correlated to the tumor progression and prognosis.

The 291 patients with AGC including 93 who received chemotherapy and 63 normal controls (NCs) were recruited from the First Teaching Hospital of Tianjin University of Traditional Chinese Medicine. The PL and ACL of peripheral blood were detected by flow cytometry-based single-platform method. PL and ACL between AGC patients (AGCs) and NCs were compared. The primary endpoint was progression-free survival (PFS) and overall survival (OS), the second endpoint was complete response (CR), partial response (PR), stable disease (SD), Disease Control rate, and progressive disease (PD). Two independent t-tests were used to compare between groups. PFS was calculated by the Kaplan-Meier method. Univariate and multivariate analyses were used to analyze the variables that affect disease progression. Compared to NCs, the percentages of CD3⁺CD8⁺ and B cells were decreased only (P < 0.05), while the AC of CD3⁺, CD3⁺CD4⁺, CD3⁺CD8⁺, B and NK cells were significantly lower (P < 0.001). AGCs with high ACL had longer PFS and OS than those with low ACL (P < 0.0001). Multivariate analysis showed that when the AC of CD3⁺CD4⁺cells was more than 405 cells/μL, the PFS and OS of AGCs were significantly prolonged (P < 0.001), and the sensitivity and specificity were the most obvious. This study evaluated the prognosis of 93 AGCs received chemotherapy: the high ACL had significantly longer PFS and OS compared with low groups (P < 0.0001), excepted AC of NK cells in PFS; the AC of CD3⁺CD4⁺ > 405 cells/μL was an independent protective factor for PFS and OS in AGCs (P < 0.001); all ACL have greater disease control rate (DCR) than progressive disease (PD) rate at high ACL, in contrast to low ACL where PD rate is higher than DCR. The ACL was significantly impaired and closely associated with PFS and OS in AGCs, the same was true in patients receiving chemotherapy. Statistics suggested the AC of CD3⁺CD4⁺cells was the most sensitive parameter for the prognosis of AGCs.

Chinese Clinic Trial Registry number: ChiCTR-IOR-17014139; Registry date: 2017/12/25.

Advanced Gastric Cancer (AGC)

Absolute counts of Lymphocyte subsets (ACL)

Percentages of lymphocyte subsets (PL)

Absolute count (AC)

CD3+CD4+ T lymphocytes and prognostic significance.

As the fifth most common cancer and the third leading cause of cancer death in the world, the incidence of gastric cancer (GC) was gradually showing a younger trend, and the mortality rate was very high[1, 2]. Human immune function plays an important role in the occurrence and progression of cancer[3, 4]. In the process of tumor immunity, besides the coordination of many kinds of immune cells[5], a large number of immune cells is needed to fight against cancers. Tumor-infiltrating lymphocytes have been a topic of interest and attract much attention[6], but it is difficult to obtain and apply in clinical diagnosis and treatment. Peripheral blood circulating lymphocytes can not only reflect the general immune status and response of patients[7], but also easy to access. According to the biological function and the expression of cell surface antigens[8], human peripheral blood lymphocytes can be divided into three main groups, including T lymphocytes (CD3⁺), B lymphocytes (CD19⁺) and natural killer (NK) lymphocytes (CD16⁺CD56⁺). The T lymphocytes can be mainly divided into two subsets[9]: CD4⁺ and CD8⁺. Studies shown that the absolute count (AC) of CD3⁺CD4⁺ lymphocytes is mainly used for HIV virus monitoring, and HIV carriers show a continuous decline in the AC of CD3⁺CD4⁺ lymphocytes during infection[10]. Percentages or AC of CD3⁺CD4⁺ and total T and B lymphocytes are used to determine and monitor certain kinds of immunodeficiency or autoimmune diseases[11]. NK cells (CD16⁺CD56⁺) are cytotoxic to tumor cells and other cells infected by viruses[12]. The cytotoxicity mediated by NK cells doesn’t require the expression of major histocompatibility complex (MHC) class I or class II on the target cells[13]. However, the impact of the absolute counts of lymphocyte subsets (ACL) on the prognosis of patients with advanced gastric cancer (AGC) is not clear, and less attention has been paid to it. Our previous study suggested that the immune function of patients with non-small cell lung cancer was impaired, showing a decrease in the ACL significantly. Moreover, patients with high baseline AC of CD3⁺CD4⁺ cells (> 502 cells/µL) had longer progression-free survival (PFS)[14]. Hence, accurate assessment of the ACL in patients with AGC (AGCs) may have important reference significance for clinical diagnosis, treatment, and prognosis of patients.

Now, chemotherapy, immunotherapy, and immune response to the vaccine have been studied in AGCs[15–17]. Moreover, some AGCs had not benefited from these treatments[18]. Nevertheless, the value of ACL in AGCs in the clinical prognosis evaluation is unknown. The prognosis of GC is not only related to the pathological characteristics, tumor progression, metastasis, and treatment[19], but also related to the percentages and AC of peripheral blood lymphocyte subsets. In this study, the percentages of lymphocyte subsets (PL) and ACL in peripheral blood of AGCs and normal controls (NCs) were detected by flow cytometry using single platform technology and analyzed the prognosis relationship between lymphocyte subsets and AGCs received chemotherapy. The report is as follows.

Study design

All subjects were given informed consent following the Declaration of Helsinki and the clinical trial was approved by the Clinical Research Ethics Committee of the First Teaching Hospital of Tianjin University of Traditional Chinese Medicine (TYLL2017[K]002) and registered at the Chinese Clinic Trial Registry (ChiCTR-IOR-17014139). A total of 387 patients with GC and NCs were enrolled in the First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China, from January 1, 2016, to October 31, 2020. According to inclusion criteria, 291 AGCs and 63 age-matched NCs were included in the study finally, the flow diagram of subject inclusion and analysis procedures in the study showed in Fig. 1. Peripheral blood was collected from the subjects before the treatment, and the PL and ACL in AGCs and NCs were assayed by flow cytometry (BD FACS Canto II: U6573380-00541, USA) using a lyse/no-wash procedure based on a single-platform technique, the known number of fluorescence-labeled beads in BD Trucount tube was used as the internal reference for absolute count assay. The primary endpoints of outcome were PFS and overall survival (OS). The second endpoints were complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD) according to Response Evaluation Criteria in Solid Tumors (RECIST; version 1.1). There were no patients in this study whose efficacy was evaluated as CR. The disease control rate (DCR) was the sum of the PR rate and SD rate. PL and ACL in AGCs and NCs were compared. The characteristics of AGCs and NCs were shown in Table 1, there was no significant difference in gender and age between the AGCs cohort and the NCs cohort (P > 0.05). The 291 AGCs included 93 AGCs received chemotherapy (5 fluorouracil leucovorin and oxaliplatin, FOLFOX) (Table 3). Treatment will be discontinued in the event of severe adverse events or disease progression, or if the patient or investigator decides to discontinue treatment.

Patients

The AGCs recruited must meet the inclusion criteria: 1. had definitive pathological and immunohistochemical diagnosis[20]; 2. were not accompanied by other malignant tumors[21]; 3. had complete clinical and laboratory data; 4. had no serious diseases of heart, liver, kidney, brain, hematopoietic system and immune system; 5. had aged at least 18 years. The exclusion criteria were the following conditions: 1. with undetermined AGC diagnosis or GC with stage I or II; 2. with other malignant tumors; 3. with incomplete clinical and laboratory data (Fig. 1). All examinations of the NCs enrolled were normal, including physical examination, blood routine, liver function, renal function, and blood glucose.

GC, gastric cancer; AGCs, patients with advanced gastric cancer; ACL, absolute counts of lymphocyte subsets; PFS, progression-free survival; OS, overall survival.

Table 1

The baseline characteristics of NCs and AGCs
Characteristics	AGCs	NCs
N	291	63
Age (years), median, mean ± SD	66, 65.45 ± 9.41	64, 64.7 ± 6.96
Sex (Male / Female)	206 (70.8%) / 85 (29.2%)	44 (70%) / 19 (30%)
Family history (Yes)	185 (63.6%)	0
Smoking history (Yes)	188 (64.6%)	8 (12.7%)
Drinking history (Yes)	139 (47.8%)	3 (4.8%)
Adenocarcinoma (Yes)	236 (81.1%)	NA
Differentiated degree (Low / Medium or High)	252 (86.6%) / 39 (13.4%)	NA
Clinical stage (Ⅲ / Ⅳ)	124 (42.6%) / 167 (57.4%)	NA
Lymph node metastasis (Yes)	196 (67.4%)	NA
NCs, Normal Controls; SD, standard deviation; NA, Not Appliable.

Assay Of Lymphocyte Subsets By Flow Cytometry

The PL and ACL in the peripheral blood of the subjects were detected by a ten-color flow cytometer (BD FACS Canto II: U6573380-00541). The reagents were BD Multitest IMK kit (Catalog NO: 662965) containing CD3/CD8/CD45/CD4 antibody (FITC Mouse Anti-Human CD3 antibody, PE Mouse Anti-Human CD8 antibody, PerCP Mouse Anti-Human CD45 antibody, APC Mouse Anti-Human CD4 antibody), CD3/CD16⁺CD56/CD45/CD19 antibody (FITC Mouse Anti-Human CD3 antibody, PE Mouse Anti-Human CD16 antibody, PE Mouse Anti-Human CD56 antibody, PerCP Mouse Anti-Human CD45 antibody, and APC Mouse Anti-Human CD19 antibody) and BD Multitest IMK Kit Lysing Solution (Catalog NO: 91-1087). The EDTA blood collecting tubes and BD Trucount tubes (Catalog NO: 340334) were also purchased from BD Biosciences, USA.

Sample Collection, Cellular Staining And Analyzing

Two milliliters of fresh peripheral blood from 354 subjects were collected using a BD Vacutainer EDTA blood collection tube, and peripheral blood lymphocyte subsets were stained and analyzed according to the instructions of the BD Multitest IMK kit.

For each sample labeled A and B to two BD Trucount Tubes;

20 µL of BD Multitest CD3/CD8/CD45/CD4 and CD3/CD16⁺CD56/CD45/CD19 reagents were pipetted into the bottom of each tube labeled A and B without touching the bead pellet, respectively;

50 µL of well-mixed, anticoagulated whole blood was drawn by reverse pipetting method and pipetted into the tube just above the metal retainer;

Caped the tubes and gently rotated to mix the antibody and the sample;

The mixture was placed in a dark place for incubation for 15 minutes at a room temperature of 20℃-25℃;

Added 450 µL of 1× BD Multitest IMK Kit Lysing Solution to each tube, covered the tube, and gently shook the tube until the liquid was uniform;

Incubated the tube in the dark at a room temperature of 20–25℃ for 15 minutes;

Analyzed on a ten-color flow cytometer.

A known volume of sample was stained directly in a BD Trucount Tube. The lyophilized pellet in the tube dissolves, releasing a known number of fluorescent beads. During analysis, the absolute number (cells/µL) of gated cells in the sample can be determined using BD FACS Canto-specific BD clinical software[22, 23]. This is the absolute count formula of lymphocyte subsets.

$$\text{c}\text{e}\text{l}\text{l} \text{a}\text{b}\text{s}\text{o}\text{l}\text{u}\text{t}\text{e} \text{c}\text{o}\text{u}\text{n}\text{t} \left(\text{c}\text{e}\text{l}\text{l}\text{s}/{\mu }\text{L}\right) =\frac{Acquired cells events\times Total Beads}{Acquired beads\times Voulme of sample}$$

Statistical analysis

The comparisons between groups were analyzed by two independent sample t-tests. The median values and frequencies of the continuous and categorical variables were calculated. The survival rate was calculated by the Kaplan-Meier method. PFS was defined as the time from the onset of the study to the disease progression or death in AGCs. OS was defined as the time from the start of the study to death from any cause. The cut-off value was calculated by the ROC curve. Univariate analysis and multivariate analysis were used to analyze the factors affecting disease progression. Variables with P < 0.05 in univariate analysis were entered for multivariate analysis. Log-rank test was used for univariate analysis and the proportional hazards regression model (COX model) was used for multivariate analysis. P < 0.05 was considered statistically significant. The data were analyzed by SPSS 25. 0 software and plotted by GraphPad Prism 9. 00 software.

Comparison of lymphocyte subsets between AGCs and NCs

The following comparison was performed to analyze the injury status of PL and ACL in AGCs. The PL and ACL in AGCs and NCs were detected and compared. Compared with NCs, the PL of AGCs were decreased only in CD3⁺CD8⁺ and B cells (P = 0.022; P = 0.004, respectively) (Fig. 2A), while the ACL in AGCs were all significantly decreased (P < 0.001) (Fig. 2B).

Compared to NCs, we found a more regular pattern of impairment in ACL than PL in AGCs.

Comparison Of Pl And Acl In Agcs Between Stage Ⅲ And Stage Ⅳ

We further analyzed the changes in the PL and ACL at different stages of AGCs.

More importantly, when analyzing the relationship between PL, ACL, and clinical stage, only the percentage of CD3⁺CD8⁺ and B cells were different in AGCs with stage III compared with NCs (P = 0.032; P = 0.036, respectively) (Fig. 2C), while the ACL in AGCs with stage III were all significantly different from that of NCs (P < 0.001) (Fig. 2D). When comparing AGCs with stage Ⅳ to NCs, only the percentage of B cells in AGCs was different (P = 0.005) (Fig. 2E), and the ACL of AGCs was all significantly different from that of NCs too (P < 0.001) (Fig. 2F). The PL had no significant difference in AGCs between stage Ⅲ and stage Ⅳ (P > 0.05) (Fig. 2G), however, the ACL in AGCs with stage Ⅳ decreased significantly (CD3⁺, P < 0.001; CD4⁺, P < 0.001; CD8⁺, P < 0.001; B, P < 0.001; NK, P = 0.001, respectively) (Fig. 2H).

This result suggested that the decreases of ACL in AGCs were more significant and sensitive to reflect the injury of lymphocyte subsets than PL. Only attention was paid to PL in clinical practice, it could lead to clinical misjudgment and affect treatment. The PL represents the proportion and composition of lymphocyte subsets[24], which directly reflects their development and differentiation function, while ACL was an exact number, direct reflecting their proliferation ability[25]. These results suggested that the impaired proliferation of lymphocyte subsets in peripheral blood of AGCs may be an important reason for the progression of GC.

Prognostic Impact Of Acl On Pfs

We analyzed the relationship between ACL and PFS of AGCs. The PFS of AGCs with stage III was significantly higher than that of AGCs with stage IV (HR 0.26; 95%CI [0.18–0.38]; P < 0.0001) (Fig. 3A). To further analyze the effect of ACL on PFS, the cut-off points of ACL were analyzed and calculated by the ROC curve (Table 2). The results suggested that the AC of each subgroup of lymphocytes was higher than the cut-off point, and the PFS of AGCs was significantly longer. The AC was lower than the cut-off point, and the PFS was significantly shorter, which showed a significant positive correlation. The related results of CD3⁺, CD3⁺CD4⁺, CD3⁺CD8⁺, B and NK cells were shown in Fig. 3B (HR 5.04; 95% CI [3.47–7.31]; P < 0.0001), Fig. 3C (HR 9.25; 95% CI [6.44–13.26]; P < 0.0001), Fig. 3D (HR 2.16; 95% CI [1.51–3.10]; P < 0.0001), Fig. 3E (HR 2.05; 95% CI [1.43–2.95]; P < 0.0001) and Fig. 3F (HR 2.39; 95% CI [1.67–3.43]; P < 0.0001), respectively.

Thus, the PFS of AGCs reduced as GC progressed and distant metastasis occurred. Peripheral blood ACL was closely related to the prognosis of PFS in AGCs. The higher the ACL, the better the prognosis, otherwise, the poorer the prognosis. This suggested that the number of lymphocyte subsets was the basis for normal immune function and it showed that high ACL was conducive to improving the PFS of AGCs.

Prognostic Impact Of Acl On Os

We also analyzed the relationship between ACL and OS of AGCs. Figure 4A showed that OS was significantly higher with stage III than that in AGCs with stage IV (HR 0.25; 95%CI [0.18 to 0.36]; P < 0.0001). Similar to PFS, AGCs with CD3⁺ > 652 cells/µL (Fig. 4B, HR5.13; 95%CI [3.53 to 7.45]; P < 0.0001), CD3⁺CD4⁺ > 405 cells/µL ( Fig. 4C, HR9.61; 95%CI [6.70 to 13.80]; P < 0.0001), CD3⁺CD8⁺ > 215 cells/µL (Fig. 4D, HR2.41; 95%CI [1.68 to 3.46]; P < 0.0001), B > 77 cells/µL (Fig. 4E, HR2.19; 95%CI [1.52 to 3.14]; P < 0.0001) and NK > 140 cells/µL (Fig. 4F, HR2.57; 95%CI [1.79 to 3.69]; P < 0.0001), their OS were significantly longer than those of AGCs with lower ACL than cut-off point of their itself, respectively. Prognostic impacts of PL on PFS and OS had no significant difference (P > 0.05) (Supplement Fig. 1), except for the percentage of CD3⁺CD8⁺ (Supplement Fig. 1E and 1G).

The OS of AGCs was closely related to the ACL, so high numbers of lymphocytes could prolong the OS of AGCs.

Effect Of Variables On The Pfs And The Os Of Agcs

As there were five subsets of ACL that affected PFS and OS of patients, it was necessary to analyze and evaluate which ACL was the best prognostic biomarker for AGCs. Univariate analysis and Log-rank test were used to analyze the variables (Table 2), and then the variables of P < 0.05 were input to multivariate analysis for COX model analysis (Fig. 5). In Fig. 5A, higher AC of CD3⁺CD4⁺ cells (> 405 cells/µL) (HR 0.192; 95%CI [0.092 to 0.398]; P < 0.001) was an independent protective factor for PFS, while clinical stage IV (HR 2.433; 95%CI [1.513to3.911]; P < 0.001) and family history (HR 1.603; 95%CI [1.053 to 2.441]; P = 0.028) was an independent risk factor for PFS. In Fig. 5B, higher AC of CD3⁺CD4⁺ cells (> 405 cells/µL) (HR 0.196; 95%CI [0.093 to 0.411]; P < 0.001) was an independent protective factor for OS, while clinical stage IV (HR 2.288; 95%CI [1.411 to 3.71]; P = 0.001) was an independent risk factor for OS.

This indicates that the AC of CD3⁺CD4⁺ cells is the best prognostic biomarker of PFS and OS of AGCs.

Table 2

Univariate analysis of PFS and OS of AGCs
Univariate viable Cutoff point PFS OS
		p-value	HR	p-value	HR
Age (≥ 66 vs < 66) (years)	66	0.271	1.225	0.380	1.176
Sex (Male vs Female)		0.308	0.806	0.177	0.751
Family history (Yes vs No)		0.025	1.581	0.061	1.465
Past medical history (Yes vs No)		0.960	1.009	0.429	1.158
Smoking history (Yes vs No)		0.726	1.071	0.503	1.141
Drinking history (Yes vs No)		0.936	0.985	0.779	0.949
Adenocarcinoma (Yes vs No)		0.772	0.935	0.872	1.038
Differentiated degree (Moderate or High vs Low)		0.100	0.615	0.105	0.618
Clinical stage (Ⅳ vs Ⅲ)		< 0.001	3.934	< 0.001	4.360
Lymph node metastasis (Yes vs No)		0.289	1.241	0.232	1.280
CD3 + cell (%) (High vs Low)	69	0.375	1.178	0.187	1.279
CD4 + cell (%) (High vs Low)	40	0.189	0.784	0.657	0.920
CD8 + cell (%) (High vs Low)	23	0.004	1.712	0.014	1.578
B cell (%) (High vs Low)	9	0.490	0.879	0.421	0.860
NK cell (%) (High vs Low)	15	0.509	0.885	0.267	0.813
AC of CD3 + cell (cells/µL) (High vs Low)	625	< 0.001	0.192	< 0.001	0.190
AC of CD4 + cell (cells/µL) (High vs Low)	405	< 0.001	0.100	< 0.001	0.098
AC of CD8 + cell (cells/µL) (High vs Low)	215	< 0.001	0.462	< 0.001	0.411
AC of B cell (cells/µL) (High vs Low)	77	< 0.001	0.484	< 0.001	0.447
AC of NK cell (cells/µL) (High vs Low)	140	< 0.001	0.415	< 0.001	0.385

Prognosis Of Acl On Pfs And Os In Agcs Received Chemotherapy

To evaluate the prognosis of lymphocyte subsets on PFS and OS in AGCs received chemotherapy, we analyzed the prognosis between lymphocyte subsets and PFS and OS of 93 AGCs who received chemotherapy (Table 3).

Similar to the previous results, AGCs received chemotherapy with higher AC of CD3⁺, CD3⁺CD4⁺, CD3⁺CD8⁺, B, and NK cells had significantly longer PFS and OS than those of AGCs with lower ACL than the cut-off point of their itself. The related PFS and OS results of CD3⁺, CD3⁺CD4⁺, CD3⁺CD8⁺, B and NK cells were shown in Fig. 6A, HR 4.268, 95%CI [0.231 to 8.166], P < 0.0001; Fig. 6B, HR 5.337, 95%CI [2.884 to 9.877], P < 0.0001; Fig. 6C, HR 2.612, 95%CI [1.419 to 4.808], P = 0.0016; Fig. 6D, HR 1.896, 95%CI [1.026 to 3.505], P = 0.03; Fig. 6F, HR 4.657, 95%CI [2.408 to 9.007], P < 0.0001; Fig. 6G, HR 5.337, 95%CI [2.884 to 9.877], P < 0.0001; Fig. 6H, HR 3.228, 95%CI [1.733 to 6.015], P < 0.0001; Fig. 6I, HR 2.215, 95%CI [1.141 to 3.955], P = 0.01; Fig. 6J, HR 1.802, 95%CI [0.972 to 3.339], P = 0.0473, respectively; excepted AC of NK cells in PFS (Fig. 6E, HR 1.657, 95%CI [0.9 to 3.06], P = 0.09). And there was no significant difference in the prognostic effect of PL on PFS and OS in AGCs received chemotherapy (P > 0.05) (Supplement Fig. 2).

Given this, to determine which ACL has the most significant prognosis on the PFS and OS of AGCs received chemotherapy, we conducted univariate (Table 4) and multivariate analyses (Fig. 7). Higher CD3⁺CD4⁺ cells (> 405 cells/µL) (HR 0.272; 95%CI [0.086 to 0.861]; P = 0.027) was an independent protective factor for PFS and OS in AGCs.

Therefore, a higher ACL was a prerequisite and basis for chemotherapy to prolong the survival of AGCs, especially a higher AC of CD3⁺CD4⁺ cells.

Table 3

The baseline characteristics of AGCs received chemotherapy.
Characteristics	N = 93	%
Age (years)
≤ 66	51	55
> 66	42	45
Sex
Male	66	71
Female	27	29
Family history
Yes	22	23.7
No	71	76.3
Smoking history
Yes	46	49.5
No	47	50.5
Drinking history
Yes	20	21.5
No	73	78.5
Adenocarcinoma
Yes	55	59.1
No	38	40.9
Differentiated degree
Low	77	82.8
Medium or High	16	17.2
Clinical stage
Ⅲ	35	37.6
Ⅳ	58	62.4
Lymph node metastasis
Yes	25	26.9
No	68	73.1
Response evaluation
Complete response (CR)	0	0
Partial response (PR)	11	11.8
Stable disease (SD)	40	43
Progressive disease (PD)	42	45.2

Table 4

Univariate analysis of PFS and OS of AGCs received chemotherapy
Univariate viable Cutoff point PFS OS
		p-value	HR	p-value	HR
CD3 + cell (%) (High vs Low)	69	0.911	1.035	0.891	0.959
CD4 + cell (%) (High vs Low)	40	0.068	0.555	0.113	0.609
CD8 + cell (%) (High vs Low)	23	0.628	1.162	0.688	1.133
B cell (%) (High vs Low)	9	0.678	1.137	0.503	1.231
NK cell (%) (High vs Low)	15	0.715	1.121	0.635	1.161
AC of CD3 + cell (cells/µL) (High vs Low)	625	< 0.001	0.212	< 0.001	0.195
AC of CD4 + cell (cells/µL) (High vs Low)	405	< 0.001	0.178	< 0.001	0.156
AC of CD8 + cell (cells/µL) (High vs Low)	215	0.003	0.380	< 0.001	0.285
AC of B cell (cells/µL) (High vs Low)	77	0.04	0.526	0.015	0.460
AC of NK cell (cells/µL) (High vs Low)	140	0.102	0.601	0.053	0.541

Based on previous analysis, the ACL was divided into high and low groups according to their cutoff point (Table 5). In 93 patients, 47 (51%) had a high AC of CD3⁺CD4⁺ (> 405 cells/µL), of them 11 (24%) were PR, 26 (55%) were SD, 10 (21%) were PD, the DCR reached 79%, while 46 (49%) had a low AC of CD3⁺CD4⁺ (≤ 405 cells/µL), of them 0 were PR, 14 (30%) were SD, 32 (70%) were PD, the DCR was 30% only. The AC of CD3⁺, CD3⁺CD8⁺, B, and NK cells showed similar characteristics to the AC of CD3⁺CD4⁺. The higher ACL (> cutoff-point) had better DCR than the lower ACL (≤ cutoff-point), which means the lower ACL had a higher PD rate.

Table 5

Evaluation response of ACL of AGCs received chemotherapy
Cells	Groups	n	Disease control		Progressive Disease
		93	PR = 11	SD = 40	PD = 42
		93	n (%)	n (%)	n (%)
CD3⁺	> 625 cells/µL	56	11 (20%)	32 (57%)	13 (23%)
CD3⁺	≤ 625 cells/µL	37	0	8 (22%)	29 (78%)
CD3⁺CD4⁺	> 405 cells/µL	47	11 (24%)	26 (55%)	10 (21%)
CD3⁺CD4⁺	≤ 405 cells/µL	46	0	14 (30%)	32 (70%)
CD3⁺CD8⁺	> 215 cells/µL	48	10 (21%)	22 (46%)	16 (33%)
CD3⁺CD8⁺	≤ 215 cells/µL	45	1 (2%)	18 (40%)	26 (58%)
B	> 77 cells/µL	52	8 (16%)	23 (44%)	21 (40%)
B	≤ 77 cells/µL	41	3 (7%)	17 (42%)	21 (51%)
NK	> 140 cells/µL	51	10 (20%)	23 (45%)	18 (35%)
NK	≤ 140 cells/µL	42	1 (2%)	17 (41%)	24 (57%)

In this study, peripheral blood PL and ACL of AGCs were analyzed by flow cytometry using a single platform technique to provide evidence for the prognostic value of ACL in AGC. The results showed that the PL was not a sensitive indicator of immune damage in AGC progression, whereas ACL was significantly impaired in AGCs (Fig. 2), and that ACL was correlated with disease progression and had a good prognosis for PFS (Fig. 3) and OS (Fig. 4), especially the AC of CD3⁺CD4⁺ cells (Fig. 5). It indicated that innate and adaptive immunity plays a crucial role in the anti-tumor process[26], and the ACL plays a pivotal role in this process.

Therefore, detection of the ACL of patients will help clinicians to understand the overall immune function of patients, facilitate clinical decision-making and predict efficacy. The decrease in ACL in AGCs, resulting in reduced anti-tumor activity, indicated that impaired immune function could not prevent the progression of tumors. Thereby, increasing the number of lymphocytes in AGCs is significant in clinical treatment, which may help to control or even delay the progression of solid tumors and prolong the survival of patients. B cells mediate humoral immune response[27, 28] and are involved in regulating the functions of various immune cells, such as macrophages, dendritic cells, NK cells, and T cells[29–31]. B cells also act as antigen-presenting cells and can process and present soluble antigens[32]. Tumor cells are directly recognized and killed by NK cells [33], T cells mediate cellular immunity[34], and TCR-CD3⁺ is a specific marker on the surface of T cells[35]. According to the difference in TCR, T cells can be divided into αβ T cells and γδ T cells[36]. According to the difference in function, αβ T cells can be divided into CD3⁺CD4⁺ helper T cells (Th) and CD8⁺ cytotoxic T cells (CTL)[37]. Th1 cells secrete IL-2, IFN-γ, and other cytokines to mediate the cellular immune response[38]. Th2 secretes IL-4, IL-5, IL-6, IL-10, IL-13, and other cytokines to assist the humoral immune response[39]. CTL induces lysis and apoptosis of tumor cells by secreting perforin, granzyme, lymphotoxin, and expressing Fas ligand[40]. Our study showed that CD3⁺CD4⁺ cells > 405 cells/µL was the independent positive factor of PFS (Fig. 5). Therefore, we should pay more attention to the AC of CD3⁺CD4⁺ cells in the clinical prognosis of AGCs. CD3⁺CD4⁺ cells exert their anti-tumor effects by dominating cellular immune response, assisting humoral immune response, and promoting the activation, proliferation, and effector function of CTL and B cells in secondary lymphoid organs[41–43]. CD3⁺CD4⁺ cells can directly proliferate and differentiate into CD3⁺CD4⁺ effector cells[44]. They can also eliminate tumor cells by regulating the tumor microenvironment[45]. Tumor antigens are taken up by dendritic cells (DC) and presented by MHC class I and MHC class II molecules on the surface of DC to the TCR of CD4⁺ and CD8⁺ Tn[24], respectively. The TCR activates CTL precursor cells and Th cells[46]. CTL precursor cells proliferate and differentiate into CTL cells under the combination of P-MHC I specific activation signals and cytokines released from Th cells[47]. Therefore, CD3⁺CD4⁺ cells play a commander role in the anti-tumor immune response, and their number may be the key to the anti-tumor immune response.

We performed a detailed analysis to investigate the prognosis of ACL on PFS and OS of AGCs received chemotherapy. Chemotherapy can shrink tumors that are sensitive to cytotoxic drugs. However, chemotherapy can cause adverse effects, such as bone marrow suppression that impairs the proliferative function of hematopoietic stem cells in the bone marrow and T cells in the peripheral blood[48–50]. As a result, immune dysfunction occurs when immune cells can’t complete the anti-tumor immune response. The study showed that high ACL was associated with good prognosis while low ACL with poor prognosis in AGCs received chemotherapy (Fig. 6), which verified the importance of AC of CD3⁺, CD3⁺CD4⁺, CD3⁺CD8⁺, B, and NK cells. Moreover, multivariate analysis showed that AC of CD3⁺CD4⁺ cells was an independent protective factor for the PFS and OS and was the best prognostic marker in patients who received chemotherapy (Fig. 7). This results further suggested that high ACL was important in prolonging the PFS and OS of AGCs. What’s really interesting is that all ACL have greater DCR than PD rate at high ACL, in contrast to low ACL where PD rate is higher than DCR (Table 5). It suggests that the immune status of AGCs with low ACL needs to be carefully assessed when using chemotherapy in clinical treatment, and ACL can be used as a good reference in the prognosis of AGCs received chemotherapy, especially the AC of CD3⁺CD4⁺ cell (Fig. 7).

As a result, in the long-term anti-tumor stress state of AGCs, it is vital to maintain the corresponding proportion of different types of immune cells in the body. On this basis, an adequate cell number of CD3⁺, CD3⁺CD4⁺, CD3⁺CD8⁺, B, and NK cells facilitates prolonged patient survival and the efficacy of chemotherapy, notably high AC of CD3⁺CD4⁺ cells.

The study showed that the ACL in AGCs was significantly lower than that in NCs and was closely related to the impaired immune function of AGCs. The ACL had considerable prognostic value for OS and PFS in AGGs and those receiving chemotherapy. In addition, it was an independent protective factor for AGCs when the AC of CD3⁺CD4⁺ cells > 405 cells/µL. These results suggest that the AC of CD3⁺CD4⁺ cells is a significant clinical prognostic biomarker for AGCs, providing the vital theoretical basis for the clinical diagnosis and chemotherapy of AGCs.

Acknowledgments

The authors thank the Specialized Research Fund for the Doctoral Program of Higher Education (No.20121210110010) and the Scientific Research Plan Project of the Tianjin Education Commission (No. 2018KJ034).

Author’s contributions

Guan Zhang drafted the manuscript. Jianchun Yu conceived and designed manuscript. Ying Xia were responsible for data acquisition and interpretation. Aqing Liuperformed the statistical analysis. Yanjie Yang, Wentao Li, Yunhe Liu and Jing Zhang verified the contents and revised the manuscript., Qian Cui, Dong Wang, Xu Liu, Yongtie Guo, Huayu Chen critically revised the manuscript. All authors reviewed and approved the final manuscript.

Availability of data and materials

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

All enrolled participants signed the informed consent. The present study involving human samples was approved by the Ethics Committee of the First Teaching Hospital, Tianjin University of Traditional Chinese Medicine (Tianjin, China) (TYLL2017 [K] 002, 25 December 2017) and was conducted following the Declaration of Helsinki. The clinical trial was registered at the Chinese Clinic Trial Registry (ChiCTR-IOR-17014139).

Competing interests

The authors declare that they have no conflict of interest.

Wong M, Huang J, Chan P, et al. Global Incidence and Mortality of Gastric Cancer, 1980-2018. JAMA Netw Open 2021;4:e2118457
Brauer DG, Wu N, Keller MR, et al. Care Fragmentation and Mortality in Readmission after Surgery for Hepatopancreatobiliary and Gastric Cancer: A Patient-Level and Hospital-Level Analysis of the Healthcare Cost and Utilization Project Administrative Database. J Am Coll Surg 2021;232:921-932
Postow MA, Callahan MK, Wolchok JD. Immune Checkpoint Blockade in Cancer Therapy. J CLIN ONCOL 2015;33:1974-1982
Kim-Hellmuth S, Bechheim M, Putz B, et al. Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations. NAT COMMUN 2017;8:266
Campbell C, Rudensky A. Roles of Regulatory T Cells in Tissue Pathophysiology and Metabolism. CELL METAB 2020;31:18-25
Sarnaik AA, Hamid O, Khushalani NI, et al. Lifileucel, a Tumor-Infiltrating Lymphocyte Therapy, in Metastatic Melanoma. J CLIN ONCOL 2021;39:2656-2666
Hill DL, Carr EJ, Rutishauser T, et al. Immune system development varies according to age, location, and anemia in African children. SCI TRANSL MED 2020;12
Wu SY, Fu T, Jiang YZ, Shao ZM. Natural killer cells in cancer biology and therapy. MOL CANCER 2020;19:120
Wu Z, Wu H. Accounting for cell type hierarchy in evaluating single cell RNA-seq clustering. GENOME BIOL 2020;21:123
Fox MP, Rosen S. A new cascade of HIV care for the era of "treat all". PLOS MED 2017;14:e1002268
Schade AE, Schieven GL, Townsend R, et al. Dasatinib, a small-molecule protein tyrosine kinase inhibitor, inhibits T-cell activation and proliferation. BLOOD 2008;111:1366-1377
Marcus A, Gowen BG, Thompson TW, et al. Recognition of tumors by the innate immune system and natural killer cells. ADV IMMUNOL 2014;122:91-128
Hammer Q, Ruckert T, Borst EM, et al. Peptide-specific recognition of human cytomegalovirus strains controls adaptive natural killer cells. NAT IMMUNOL 2018;19:453-463
Xia Y, Li W, Li Y, et al. The clinical value of the changes of peripheral lymphocyte subsets absolute counts in patients with non-small cell lung cancer. TRANSL ONCOL 2020;13:100849
Kawazoe A, Ando T, Hosaka H, et al. Safety and activity of trifluridine/tipiracil and ramucirumab in previously treated advanced gastric cancer: an open-label, single-arm, phase 2 trial. Lancet Gastroenterol Hepatol 2021;6:209-217
Wiedermann U, Garner-Spitzer E, Chao Y, et al. Clinical and Immunologic Responses to a B-Cell Epitope Vaccine in Patients with HER2/neu-Overexpressing Advanced Gastric Cancer-Results from Phase Ib Trial IMU.ACS.001. CLIN CANCER RES 2021;27:3649-3660
Li J, Deng Y, Zhang W, et al. Subcutaneous envafolimab monotherapy in patients with advanced defective mismatch repair/microsatellite instability high solid tumors. J HEMATOL ONCOL 2021;14:95
Zhang X, Liang H, Li Z, et al. Perioperative or postoperative adjuvant oxaliplatin with S-1 versus adjuvant oxaliplatin with capecitabine in patients with locally advanced gastric or gastro-oesophageal junction adenocarcinoma undergoing D2 gastrectomy (RESOLVE): an open-label, superiority and non-inferiority, phase 3 randomised controlled trial. LANCET ONCOL 2021;22:1081-1092
Zhang P, Zheng Z, Ling L, et al. w09, a novel autophagy enhancer, induces autophagy-dependent cell apoptosis via activation of the EGFR-mediated RAS-RAF1-MAP2K-MAPK1/3 pathway. AUTOPHAGY 2017;13:1093-1112
Janjigian YY, Shitara K, Moehler M, et al. First-line nivolumab plus chemotherapy versus chemotherapy alone for advanced gastric, gastro-oesophageal junction, and oesophageal adenocarcinoma (CheckMate 649): a randomised, open-label, phase 3 trial. LANCET 2021;398:27-40
Wang F, Wei XL, Wang FH, et al. Safety, efficacy and tumor mutational burden as a biomarker of overall survival benefit in chemo-refractory gastric cancer treated with toripalimab, a PD-1 antibody in phase Ib/II clinical trial NCT02915432. ANN ONCOL 2019;30:1479-1486
Nicholson JK, Jones BM, Hubbard M. CD4 T-lymphocyte determinations on whole blood specimens using a single-tube three-color assay. Cytometry 1993;14:685-689
Nicholson J, Kidd P, Mandy F, Livnat D, Kagan J. Three-color supplement to the NIAID DAIDS guideline for flow cytometric immunophenotyping. Cytometry 1996;26:227-230
Do JS, Min B. IL-15 produced and trans-presented by DCs underlies homeostatic competition between CD8 and {gamma}{delta} T cells in vivo. BLOOD 2009;113:6361-6371
Sun C, Nierman P, Kendall EK, et al. Clinical and biological implications of target occupancy in CLL treated with the BTK inhibitor acalabrutinib. BLOOD 2020;136:93-105
Boasso A, Royle CM, Doumazos S, et al. Overactivation of plasmacytoid dendritic cells inhibits antiviral T-cell responses: a model for HIV immunopathogenesis. BLOOD 2011;118:5152-5162
Pollinger B, Krishnamoorthy G, Berer K, et al. Spontaneous relapsing-remitting EAE in the SJL/J mouse: MOG-reactive transgenic T cells recruit endogenous MOG-specific B cells. J EXP MED 2009;206:1303-1316
Roth GA, Gale EC, Alcantara-Hernandez M, et al. Injectable Hydrogels for Sustained Codelivery of Subunit Vaccines Enhance Humoral Immunity. ACS Cent Sci 2020;6:1800-1812
Xi K, Gu Y, Tang J, et al. Microenvironment-responsive immunoregulatory electrospun fibers for promoting nerve function recovery. NAT COMMUN 2020;11:4504
Chen J, Petrus M, Bryant BR, et al. Autocrine/paracrine cytokine stimulation of leukemic cell proliferation in smoldering and chronic adult T-cell leukemia. BLOOD 2010;116:5948-5956
Deng C, Zhang Q, He P, et al. Targeted apoptosis of macrophages and osteoclasts in arthritic joints is effective against advanced inflammatory arthritis. NAT COMMUN 2021;12:2174
Salzer E, Zoghi S, Kiss MG, et al. The cytoskeletal regulator HEM1 governs B cell development and prevents autoimmunity. Sci Immunol 2020;5
Zhu EF, Gai SA, Opel CF, et al. Synergistic innate and adaptive immune response to combination immunotherapy with anti-tumor antigen antibodies and extended serum half-life IL-2. CANCER CELL 2015;27:489-501
Ward BJ, Gobeil P, Seguin A, et al. Phase 1 randomized trial of a plant-derived virus-like particle vaccine for COVID-19. NAT MED 2021;27:1071-1078
Pillarisetti K, Edavettal S, Mendonca M, et al. A T-cell-redirecting bispecific G-protein-coupled receptor class 5 member D x CD3 antibody to treat multiple myeloma. BLOOD 2020;135:1232-1243
Wang GC, Dash P, McCullers JA, Doherty PC, Thomas PG. T cell receptor alphabeta diversity inversely correlates with pathogen-specific antibody levels in human cytomegalovirus infection. SCI TRANSL MED 2012;4:128r-142r
Loyal L, Warth S, Jurchott K, et al. SLAMF7 and IL-6R define distinct cytotoxic versus helper memory CD8(+) T cells. NAT COMMUN 2020;11:6357
Yu F, Sharma S, Jankovic D, et al. The transcription factor Bhlhe40 is a switch of inflammatory versus antiinflammatory Th1 cell fate determination. J EXP MED 2018;215:1813-1821
Sokol CL, Barton GM, Farr AG, Medzhitov R. A mechanism for the initiation of allergen-induced T helper type 2 responses. NAT IMMUNOL 2008;9:310-318
Akalay I, Janji B, Hasmim M, et al. EMT impairs breast carcinoma cell susceptibility to CTL-mediated lysis through autophagy induction. AUTOPHAGY 2013;9:1104-1106
Martinez RJ, Andargachew R, Martinez HA, Evavold BD. Low-affinity CD4+ T cells are major responders in the primary immune response. NAT COMMUN 2016;7:13848
Bourges C, Groff AF, Burren OS, et al. Resolving mechanisms of immune-mediated disease in primary CD4 T cells. EMBO MOL MED 2020;12:e12112
Guimond M, Veenstra RG, Grindler DJ, et al. Interleukin 7 signaling in dendritic cells regulates the homeostatic proliferation and niche size of CD4+ T cells. NAT IMMUNOL 2009;10:149-157
Tian Y, Babor M, Lane J, et al. Unique phenotypes and clonal expansions of human CD4 effector memory T cells re-expressing CD45RA. NAT COMMUN 2017;8:1473
Rakhra K, Bachireddy P, Zabuawala T, et al. CD4(+) T cells contribute to the remodeling of the microenvironment required for sustained tumor regression upon oncogene inactivation. CANCER CELL 2010;18:485-498
Fulton RB, Hamilton SE, Xing Y, et al. The TCR's sensitivity to self peptide-MHC dictates the ability of naive CD8(+) T cells to respond to foreign antigens. NAT IMMUNOL 2015;16:107-117
Wu T, Guan J, Handel A, et al. Quantification of epitope abundance reveals the effect of direct and cross-presentation on influenza CTL responses. NAT COMMUN 2019;10:2846
Abdelfattah N, Rajamanickam S, Panneerdoss S, et al. MiR-584-5p potentiates vincristine and radiation response by inducing spindle defects and DNA damage in medulloblastoma. NAT COMMUN 2018;9:4541
Demaria M, O'Leary MN, Chang J, et al. Cellular Senescence Promotes Adverse Effects of Chemotherapy and Cancer Relapse. CANCER DISCOV 2017;7:165-176
Islam KM, Anggondowati T, Deviany PE, et al. Patient preferences of chemotherapy treatment options and tolerance of chemotherapy side effects in advanced stage lung cancer. BMC CANCER 2019;19:835

S1.png
Supplement Fig. 1 Prognostic impact of PL on PFS and OS in AGCs. A Effect of the percentage of CD3⁺ cells on the prognosis of PFS. B Effect of the percentage of CD3⁺CD4⁺cells on the prognosis of PFS. C Effect of the percentage of CD3⁺ cells on the prognosis of OS. D Effect of the percentage of CD3⁺CD4⁺ cells on the prognosis of OS. E Effect of the percentage of CD3⁺CD8⁺ cells on the prognosis of PFS. F Effect of the percentage of B cells on the prognosis of PFS. G Effect of the percentage of CD3⁺CD8⁺ cells on the prognosis of OS. H Effect of the percentage of B cells on the prognosis of PFS. I Effect of the percentage of NK cells on the prognosis of PFS. J Effect of the percentage of NK cells on the prognosis of OS.
s2.png
Supplement Fig. 2 Prognostic impact of PL on PFS and OS in AGCs received chemotherapy. A Effect of the percentage of CD3⁺ cells on the prognosis of PFS. B Effect of the percentage of CD3⁺CD4⁺cells on the prognosis of PFS. C Effect of the percentage of CD3⁺ cells on the prognosis of OS. D Effect of the percentage of CD3⁺CD4⁺ cells on the prognosis of OS. E Effect of the percentage of CD3⁺CD8⁺ cells on the prognosis of PFS. F Effect of the percentage of B cells on the prognosis of PFS. G Effect of the percentage of CD3⁺CD8⁺ cells on the prognosis of OS. H Effect of the percentage of B cells on the prognosis of PFS. I Effect of the percentage of NK cells on the prognosis of PFS. J Effect of the percentage of NK cells on the prognosis of OS.

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The prognostic significance of the absolute counts of peripheral blood lymphocyte subsets in patients with advanced gastric cancer

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Abstract

Figures

Introduction

Materials And Methods

Study design

Patients

Assay Of Lymphocyte Subsets By Flow Cytometry

Sample Collection, Cellular Staining And Analyzing

Statistical analysis

Results

Comparison of lymphocyte subsets between AGCs and NCs

Comparison Of Pl And Acl In Agcs Between Stage Ⅲ And Stage Ⅳ

Prognostic Impact Of Acl On Pfs

Prognostic Impact Of Acl On Os

Effect Of Variables On The Pfs And The Os Of Agcs

Prognosis Of Acl On Pfs And Os In Agcs Received Chemotherapy

Discussion

Conclusion

Declarations

References

Supplementary Files

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