Quantitative analysis and clonal characterization of T-cell receptor β repertoires in patients with advanced non-small cell lung cancer treated with cancer vaccine

  • Authors:
    • Tu Mai
    • Atsushi Takano
    • Hiroyuki Suzuki
    • Takashi Hirose
    • Takahiro Mori
    • Koji Teramoto
    • Kazuma Kiyotani
    • Yusuke Nakamura
    • Yataro Daigo
  • View Affiliations

  • Published online on: May 5, 2017     https://doi.org/10.3892/ol.2017.6125
  • Pages: 283-292
Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

With the development of cancer immunotherapy that may activate T cells, a practical and quantitative method to improve monitoring and/or prediction of immunological response of patients as a predictive biomarker is of importance. To examine possible biomarkers for a therapeutic cancer vaccine containing a mixture of three epitope peptides derived from cell division‑associated 1, lymphocyte antigen 6 complex locus K and insulin‑like growth factor‑II mRNA‑binding protein 3, T‑cell receptor β (TCRβ) repertoires of blood samples from 24 patients with human leukocyte antigen‑A*2402‑positive non‑small cell lung cancer were characterized prior to and following 8 weeks of the cancer vaccine treatment, by applying a next‑generation sequencing method. It was identified that 14 patients with overall survival (OS) times of ≥12 months had significantly lower TCRβ diversity indexes in samples prior to treatment, compared with 10 patients who succumbed within 1 year (P=0.03). In addition, patients with a high level of activated CD8+ T cells that are defined by a high granzyme A/CD8 ratio had favorable OS rates (log‑rank test, P=0.04). The TCRβ diversity index and immunogenic gene markers following vaccine administration may serve as predictive or monitoring biomarkers for cancer vaccine treatment.

Introduction

Despite increasing knowledge about lung cancer and its treatment modalities over the last few decades, this disease continues to be responsible for the largest number of mortalities in males and females worldwide (1). Lung cancer was estimated to account for 27% of all cancer-associated mortalities in 2014 (2). There are two major histological types of lung cancer: Small cell lung cancer (SCLC) and non-SCLC (NSCLC) (3). Standard treatments for NSCLC include combinations of surgery, chemotherapy and radiotherapy (4). Although most advanced-stage patients receive chemotherapy and achieve clinical responses to a certain extent, the majority eventually experience relapse (5,6). The 5-year survival rate of patients with NSCLC only marginally improved from 15.9 in 2008 to 18.0% in 2014 (2,7,8). Therefore, novel therapies are required to ensure improved management of NSCLC.

Immunotherapy is a novel therapeutic strategy that is currently being evaluated for the treatment of NSCLC (4). Until recently, studies of vaccine treatment for patients with lung cancer have not yielded very positive results either due to non-specific immune system activation or due to toxicity (913). The lung, which has a high level of environmental exposure, has long been considered a poorly immunogenic tumor site (14). Therefore, an effective vaccine against NSCLC should contain antigens that are specific to the tumor cells and have the ability to generate immunogenicity following administration (15). Previously, cDNA microarray and laser microdissection were used to identify three genes for which transcripts were observed at high levels in cells from lung cancer, esophageal cancer, testis and placenta, but not in normal cells: Cell division-associated 1 (CDCA1) (16,17), lymphocyte antigen 6 complex locus K (LY6K) (17,18) and insulin-like growth factor-II mRNA-binding protein 3 (IMP-3) (17,19) Accordingly, a vaccine comprising three human leukocyte antigen (HLA)-A24-restricted epitope peptides derived from these genes was developed for NSCLC and esophageal cancer (20). A Phase I clinical study of a combination of three peptides, including LY6K and IMP-3, in patients with advanced esophageal squamous cell carcinoma demonstrated that the vaccine was well tolerated and that strong T-cell responses to these specific antigens were induced following vaccination (21). Previously, a Phase II clinical study of a combination of three peptides (LY6K, IMP-3 and CDCA1) in patients with advanced head and neck squamous cell carcinoma also reported peptide-specific cytotoxic T lymphocyte (CTL) responses in the majority of the HLA-A2402-positive patients (22). In the present study, a Phase II clinical trial with exploratory investigations was conducted using a vaccine comprising these three peptides in patients with advanced NSCLC who were refractory to standard therapies (15).

One important predictive biomarker of vaccine therapy efficacy is the ability to induce an immunogenic response against specific cancer cells (23). Traditionally, enzyme-linked immune-spot and HLA-multimer assays have been used to measure CTL responses, since a high level of CTL infiltration into a tumor was reported to be associated with a good response to treatment (24). However, these assays require the ex vivo expansion of peripheral blood lymphocytes (PBLs) via stimulation and are not practical in clinical settings (25). In addition, lung tumor biopsy to examine intratumoral CTL infiltration is invasive (26). Therefore, it may be preferable to identify biomarkers from easily available human materials, including blood, using the predictive power that has been demonstrated to associate well with overall survival (OS) rate (27). The expansion and activation of certain T-cell populations, including cytotoxic CD8+ T cells, has been reported to be beneficial for the recognition and elimination of cancer cells (28,29). By contrast, the expansion of regulatory T cells may be harmful, as these T cells protect cancer cells by suppressing tumor-specific CD8+ cytotoxic T cells (3033). Therefore, it is important to quantitatively characterize the T-cell receptor (TCR) repertoires of patients with cancer prior to and following immunotherapy, including cancer vaccine treatment, to improve the understanding of the molecular mechanism underlying treatment efficacy.

In total ~95% of T cells express TCR, which is a hetero-dimer of the TCRα and TCRβ chains, a signature of each T lymphocyte (34). To date, the TCRα gene on chromosome 14 has been reported to comprise a total of 70 variable (V) exons, 61 joining (J) exons and 1 constant (C) exon, whereas the TCRβ gene on chromosome 7 comprises 60 V exons, 15 J exons and 2 C exons (35). In addition, TCRβ contains two diversity (D) exons; accordingly, the TCR genes undergo somatic V(D)J recombination, resulting in a significant increase in TCR diversity (3638). This V(D)J segment rearrangement results in the highly variable complementary-determining region (CDR3), thus allowing the recognition of any possible antigens presented by HLA molecules (39). It has been estimated that ~1018 different TCRs are generated in humans (25).

Advances in next-generation sequencing technology have made it possible to sequence millions of TCR cDNAs, and thus characterize patient TCR repertoires in a single experiment (4042). In the present study, it was hypothesized that the expansion and activation of a large number of T-cell populations, particularly the cytotoxic T-cell population, may be used as predictive biomarkers in response to vaccine treatment to assess the outcomes of patients with NSCLC who had received cancer vaccine therapy. In the present study, T-cell repertoires and certain immune-associated molecules in patients with advanced NSCLC with an HLA-A*2402 who received the cancer vaccine treatment were characterized using cDNA-sequencing technology and a gene expression assay.

Materials and methods

Vaccines and patients

A total of 53 patients with advanced NSCLC resistant to standard therapies were enrolled between 21 May 2012 and 4 April 2013 in a Phase II open-label multicenter non-randomized clinical cancer vaccination trial conducted in an exploratory setting. Patients were vaccinated with a mixture of 1 mg each of three HLA-A24-restricted epitope peptides derived from CDCA1, LY6K and IMP-3 mixed with incomplete Freund's adjuvant (Montanide ISA 51; SEPPIC, Puteaux, France) (trial no. NCT01592617). The clinical characteristics and treatment information for all 53 patients enrolled in the clinical trial are summarized in Table I. Patients received weekly subcutaneous injections of the peptides into the axillary region until disease progression was observed or the patient declined continued vaccine treatment. Written informed consent was obtained from all individuals enrolled in the trial. The trials were carried out in accordance with The Declaration of Helsinki on experimentation on human subjects, under the approval of the institutional ethics committees of the individual institutes. TCRβ sequencing and gene expression analysis were performed for blood samples from 24/35 HLA-A*2402-positive patients obtained.

Table I.

Background of patients.

Table I.

Background of patients.

CharacteristicsTotal, nHLA-A*2402(+), nHLA-A*2402(−), n
Total533518
  Median age ± SD, years64.0±7.764.1±7.563.9±8.2
Sex
  Female21165
  Male321913
Location of primary lesion
  Pulmonary hilum550
  Lung field432815
  Pleural effusion101
  Missing422
Histological type
  Adenocarcinoma432815
  Squamous cell carcinoma972
  Pleomorphic carcinoma101
T factor
  TX19910
  T0211
  T1a330
  T1b110
  T2a440
  T2b101
  T3761
  T416115
N factor
  NX422
  N018117
  N1220
  N218144
  N31165
M factor
  MX000
  M0981
  M1a20137
  M1b1798
  M1a+1b752
ECOG performance status score
  0382513
  11183
  2422
Smoking status
  Current or former smoker302010
  Never smoked23158
EGFR mutation status
  Positive1367
  Not detected36279
  Not reported422

[i] HLA, human leukocyte antigen; EGFR, epidermal growth factor receptor; T, tumor; N, node; M, metastasis; ECOG, Eastern Cooperative Oncology Group.

Peripheral blood mononuclear cell collection

Blood samples were collected from patients prior to and following 8 weeks of vaccine treatment. Blood was drawn into BD Vacutainer® CPT™ cell preparation tubes containing sodium citrate (CPT; BD Biosciences, Franklin Lakes, NJ, USA). Samples were immediately centrifuged at 400 × g for 20 min at room temperature. Peripheral blood mononuclear cells (PBMCs) were collected from the second layer of the column and washed with PBS. Cell numbers were determined using a hemocytometer, and cell viability was assessed via trypan blue staining. Following treatment with trypan blue, cells inside the four large corner squares were counted at ×100 magnification under the light microscope (CKX41; Olympus Corporation, Tokyo, Japan). A total of ~8×106 viable cells/sample were used for RNA isolation.

RNA isolation and polymerase chain reaction (PCR) amplification

An RNeasy mini kit (Qiagen, Inc., Valencia, CA, USA) was used to isolate total RNA from PBMCs. In total 10 µl DNase I (from the RNeasy mini kit) treatment was applied to remove undesirable genomic DNA contamination. A SMART cDNA library construction kit (Clontech Laboratories, Inc., Mountain View, CA, USA) was used to synthesize cDNA with a common adaptor (SMART IV oligonucleotide) at the 5′-ends. PCRs were performed to amplify TCRβ cDNAs. All possible TCRβ combinations were captured using a common forward primer (5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGTATCAACGCAGAGTGGCCAT-3′) complementary to the SMART IV adaptor and a reverse primer specific for the constant region (5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGDVHDVTCTGATGGCTCAAACACAGC-3′). The PCR protocol was as follows: 94°C for 3 min; followed by 30 cycles of 94°C for 30 sec, 65°C for 30 sec and 72°C for 1 min. Size selection (between 300 and 950 base pairs) was conducted using Pippin Prep (Sage Science, Beverly, MA, USA) to collect products known to be of the expected size (43). This experiment was performed once with the patient blood sample.

Library preparation and sequencing

A Nextera XT DNA library kit (Illumina, Inc., San Diego, CA, USA) was used to add adapter sequences onto template DNA to generate multiplexed sequencing libraries, allowing the sequencing and distinction of multiple samples in a single experiment. The PCR protocol was as follows: 95°C for 3 min; 8 cycles of 95°C for 30 sec, 55°C for 30 sec and 72°C for 30 sec; and a hold step at 72°C for 5 min. Multiple dual index-encoded samples were combined in a single sequencing library. This library was loaded onto the MiSeq Reagent kit (version 3; Illumina, Inc.) and sequenced using an Illumina MiSeq Desktop Sequencer (Illumina, Inc.).

TCR sequence analysis

FASTQ files containing sequencing reads were generated using the MiSeq sequencer and mapped to the reference sequences derived from IMGT/GENE-DB (www.imgt.org) with the Bowtie2 alignment program (version 2.1.0; http://bowtie-bio.sourceforge.net/bowtie2/index.shtml) (44). To determine CDR3 in TCRβ, a conserved cysteine residue encoded in the 3′ end of the V segment and a conserved phenylalanine residue encoded in the 5′ end of the J segment, which signal CDR3, were identified as described previously (25). Amino acid sequences were determined using the nucleotide sequences between the conserved TCR V cysteine residue and TCR J phenylalanine residue.

Gene expression assay

First-strand cDNA products as aforementioned were used in a gene expression assay to analyze the expression of CD4, CD8 and granzyme A (GZMA) transcripts. A PCR TaqMan gene expression assay was performed on an ABI ViiA™ 7 system (Applied Biosystems; Thermo Fisher Scientific, Inc.), according to the manufacturer's protocol. All CD4, CD8 and GZMA transcript expression levels were normalized to the transcript expression of the housekeeping gene GAPDH.

Statistical analysis

OS rates were analyzed using the Kaplan-Meier estimator method, and survival was measured in days between the first vaccination and mortality. Progression-free survival (PFS) was measured in days from the first vaccination to the date of first documented disease progression or the date of mortality from any cause. The statistical significance of the survival period was analyzed using the Harrington-Fleming test.

The diversity indexes (DIs) of CDR3 sequences were calculated using inverse Simpson's index formula as previously reported (25). The DI reflects the total number and also the evenness of the identified clonotypes.

The Mann-Whitney test was used to compare the DIs between the long-survival and short-survival groups. A paired Student's t-test was performed to compare the DI prior to and following vaccine treatment within each group. These statistical tests were conducted using Prism software (version 6.0; GraphPad, Inc., La Jolla, CA, USA). The median was used as a threshold point to divide into two groups: High GZMA/CD8 and low GZMA/CD8 or high CD8/CD4 and low CD8/CD4. A log-rank test was performed using R software (version 3.2.0; The R Project for Statistical Computing, Vienna, Austria) to compare the percentage survival in these two groups. Data are presented as the mean ± standard error. P<0.05 was considered to indicate a statistically significant difference.

Results

Association between the TCRβ DI and survival status of patients with NSCLC treated with cancer peptide vaccines

Patients with NSCLC (35 HLA-A*2402-positive and 18 others) who had previously failed standard therapies were recruited, as summarized in Table I. The median OS time of these patients was 400 days, whereas the PFS was 57 days. No significant difference in the clinical outcomes between the HLA-A*2402-positive and -negative groups was identified. None of the patients exhibited a complete response according to the Response Evaluation Criteria in Solid Tumors (45). The most common adverse events of any grades observed during the present study are described in Table II.

Table II.

Treatment-associated adverse effects.

Table II.

Treatment-associated adverse effects.

HLA-A*2402(+) (n=35)HLA-A*2402(−) (n=18)Total (n=53)



Adverse eventsCases (incidence rate), n (%)Events, nCases (incidence rate), n (%)Events, nCases (incidence rate), n (%)Events, n
Total34 (97.1)15916 (88.9)5651 (94.3)215
Infectious disease13 (37.1)203 (16.7)416 (30.2)24
  Nasopharyngitis6 (17.1)71 (5.6)17 (13.2)8
  Herpes zoster2 (5.7)20 (0.0)02 (3.8)2
  Periodontitis1 (2.9)11 (5.6)12 (3.8)2
  Bronchitis1 (2.9)10 (0.0)01 (1.9)1
  Cellulitis1 (2.9)10 (0.0)01 (1.9)1
  Influenza1 (2.9)10 (0.0)01 (1.9)1
  Esophageal candidiasis1 (2.9)10 (0.0)01 (1.9)1
  Paronychia0 (0.0)01 (5.6)11 (1.9)1
  Pneumonitis0 (0.0)01 (5.6)11 (1.9)1
  Pulpitis1 (2.9)10 (0.0)01 (1.9)1
  Rhinitis1 (2.9)10 (0.0)01 (1.9)1
  Foot tinea pedis1 (2.9)10 (0.0)01 (1.9)1
  Tonsillitis1 (2.9)10 (0.0)01 (1.9)1
  Urinary tract infection1 (2.9)10 (0.0)01 (1.9)1
  Device related infection1 (2.9)10 (0.0)01 (1.9)1
Malignant neoplasm9 (25.7)93 (16.7)412 (22.6)13
  Malignant pleuritis2 (5.7)22 (11.1)24 (7.5)4
  Central nervous system metastasis2 (5.7)20 (0.0)02 (3.8)2
  NSCLC2 (5.7)20 (0.0)02 (3.8)2
  Malignant ascites1 (2.9)10 (0.0)01 (1.9)1
  Esophageal cancer1 (2.9)10 (0.0)01 (1.9)1
  Tumor infiltration of bone marrow1 (2.9)10 (0.0)01 (1.9)1
  Meningeal metastasis0 (0.0)01 (5.6)11 (1.9)1
  Brain tumor0 (0.0)01 (5.6)11 (1.9)1
Metabolic disease2 (5.7)20 (0.0)02 (3.8)2
  Hyperuricemia1 (2.9)10 (0.0)01 (1.9)1
  Hyperlipidemia1 (2.9)10 (0.0)01 (1.9)1
Mental disease1 (2.9)11 (5.6)12 (3.8)2
  Insomnia1 (2.9)11 (5.6)12 (3.8)2
Nervous system disorder6 (17.1)81 (5.6)17 (13.2)9
  Headache3 (8.6)30 (0.0)03 (5.7)3
  Brain compression1 (2.9)10 (0.0)01 (1.9)1
  Convulsion1 (2.9)20 (0.0)01 (1.9)2
  Dizziness1 (2.9)10 (0.0)01 (1.9)1
  Hypoesthesia0 (0.0)01 (5.6)11 (1.9)1
  Drowsiness1 (2.9)10 (0.0)01 (1.9)1
Eye disease1 (2.9)11 (5.6)12 (3.8)2
  Cataract1 (2.9)10 (0.0)01 (1.9)1
  Dry eye0 (0.0)01 (5.6)11 (1.9)1
Ear disease and labyrinthine disturbance1 (2.9)10 (0.0)01 (1.9)1
  Rotary vertigo1 (2.9)10 (0.0)01 (1.9)1
Heart disease2 (5.7)40 (0.0)02 (3.8)4
  Arrhythmia1 (2.9)10 (0.0)01 (1.9)1
  Pericardial effusion collection1 (2.9)10 (0.0)01 (1.9)1
  Angina1 (2.9)10 (0.0)01 (1.9)1
  Supraventricular tachycardia1 (2.9)10 (0.0)01 (1.9)1
Angiopathy0 (0.0)01 (5.6)11 (1.9)1
  Hypertension0 (0.0)01 (5.6)11 (1.9)1
Respiratory disease15 (42.9)184 (22.2)719 (35.8)25
  Upper respiratory infection11 (31.4)132 (11.1)413 (24.5)17
  Allergic rhinitis1 (2.9)11 (5.6)12 (3.8)2
  Atelectasis0 (0.0)01 (5.6)11 (1.9)1
  Voice disturbance1 (2.9)10 (0.0)01 (1.9)1
  Dyspnea0 (0.0)01 (5.6)11 (1.9)1
  Pleuritis1 (2.9)10 (0.0)01 (1.9)1
  Sneezing1 (2.9)10 (0.0)01 (1.9)1
  Hypertrophic rhinitis1 (2.9)10 (0.0)01 (1.9)1
Gastrointestinal injury13 (37.1)155 (27.8)718 (34.0)22
  Constipation5 (14.3)52 (11.1)27 (13.2)7
  Diarrhea3 (8.6)30 (0.0)03 (5.7)3
  Vomiting1 (2.9)12 (11.1)33 (5.7)4
  Nausea2 (5.7)20 (0.0)02 (3.8)2
  Abdominal discomfort1 (2.9)10 (0.0)01 (1.9)1
  Enteritis0 (0.0)01 (5.6)11 (1.9)1
  Gastritis1 (2.9)10 (0.0)01 (1.9)1
  Hemorrhoid0 (0.0)01 (5.6)11 (1.9)1
  Esophageal stenosis1 (2.9)10 (0.0)01 (1.9)1
  Stomatitis1 (2.9)10 (0.0)01 (1.9)1
Biliary system disorders1 (2.9)10 (0.0)01 (1.9)1
  Cholecystitis1 (2.9)10 (0.0)01 (1.9)1
Skin disease7 (20.0)91 (5.6)18 (15.1)10
  Rash2 (5.7)20 (0.0)02 (3.8)2
  Alopecia0 (0.0)01 (5.6)11 (1.9)1
  Xerosis cutis1 (2.9)10 (0.0)01 (1.9)1
  Asteatotic eczema1 (2.9)10 (0.0)01 (1.9)1
  Erythema1 (2.9)30 (0.0)01 (1.9)3
Skeletal muscle or soft tissue disorder5 (14.3)51 (5.6)26 (11.3)7
  Back pain1 (2.9)10 (0.0)01 (1.9)1
  Fasciitis0 (0.0)01 (5.6)21 (1.9)2
  Melalgia1 (2.9)10 (0.0)01 (1.9)1
  Periarthritis1 (2.9)10 (0.0)01 (1.9)1
  Spondylosis deformans1 (2.9)10 (0.0)01 (1.9)1
  Synovial cyst1 (2.9)10 (0.0)01 (1.9)1
Kidney or urinary disorder0 (0.0)01 (5.6)11 (1.9)1
  Strangury0 (0.0)01 (5.6)11 (1.9)1
Body or injection site disorder34 (97.1)5114 (77.8)2248 (90.6)73
  Injection site reaction33 (94.3)4113 (72.2)2046 (86.8)61
  High fever2 (5.7)51 (5.6)13 (5.7)6
  Disease progression2 (5.7)21 (5.6)13 (5.7)3
  General fatigue1 (2.9)10 (0.0)01 (1.9)1
  Injection site bleeding1 (2.9)10 (0.0)01 (1.9)1
  Pain1 (2.9)10 (0.0)01 (1.9)1
Laboratory examination7 (20.0)92 (11.1)39 (17.0)12
  Elevated GTPγ2 (5.7)21 (5.6)13 (5.7)3
  Lymphocytopenia2 (5.7)20 (0.0)02 (3.8)2
  Elevated hepatic transaminase0 (0.0)01 (5.6)11 (1.9)1
  Elevated CPK1 (2.9)10 (0.0)01 (1.9)1
  Elevated serum potassium level0 (0.0)01 (5.6)11 (1.9)1
  Elevated hepatic transaminase0 (0.0)01 (5.6)11 (1.9)1
  Increases in urine glucose levels1 (2.9)10 (0.0)01 (1.9)1
  Abnormal liver function test1 (2.9)10 (0.0)01 (1.9)1
  Albuminuria1 (2.9)10 (0.0)01 (1.9)1
  Weight loss1 (2.9)10 (0.0)01 (1.9)1
Injury, toxicosis and procedural complication5 (14.3)51 (5.6)16 (11.3)6
  Fractured sacrum0 (0.0)01 (5.6)11 (1.9)1
  Lacerated wound1 (2.9)10 (0.0)01 (1.9)1
  Ligament sprain1 (2.9)10 (0.0)01 (1.9)1
  Ecchymoma1 (2.9)10 (0.0)01 (1.9)1
  Bruise1 (2.9)10 (0.0)01 (1.9)1
  Radiation dermatitis1 (2.9)10 (0.0)01 (1.9)1

[i] HLA, human leukocyte antigen; CPK, creatine phosphokinase; NSCLC, non-small cell lung cancer.

Blood samples from 24 of 35 HLA-A*2402-positive patients were obtained, 14 of which achieved stable disease and/or remained alive >12 months after enrollment, and possible predictive immune biomarkers, including TCRβ analysis, were examined. The TCRβ DIs were compared between long-term survivors (lived for >12 months after enrollment) and short-term survivors (died within 12 months of enrollment) by calculating an inverse Simpson's DI (1/D) of the TCRβ repertoire, as described in the Materials and methods section. The present data revealed that prior to vaccine treatment, patients in the long-term survival group exhibited significantly lower TCRβ DIs compared with short-term survivors (63±61 vs. 259±307; P=0.03; Fig. 1A). However, the difference between the two groups was not significant in blood samples following 8 weeks of treatment (107±108 vs. 188±165; P=0.12; Fig. 1B) although a trend in the increase in DI was observed in the long-term survival group (mean of differences, 44±7; P=0.07; Fig. 1C). By contrast, no significant change in DI between samples prior to and following the treatment was observed in the short-term survival group (Fig. 1D). The difference in the changes in DI in the long-term and short-term groups is presented in Fig. 1E.

Association between the immunogenic gene expression and survival status of patients with NSCLC treated with cancer peptide vaccines

To assess alterations in T-cell populations in response to treatment, TaqMan assays were performed to analyze the relative expression of CD8, CD4 and GZMA prior to the vaccine treatment. As presented in Fig. 2A, no significant differences were identified for CD8 and GZMA expression levels and the GZMA/CD8 ratio between the long-term and short-term survival groups. However, when the patients were separated into two groups by a median value of the GZMA/CD8 ratio, which may reflect the level of activated cytotoxic T cells, the survival curves were significantly different, favoring the group with a high GZMA/CD8 ratio (log-rank test, P=0.04; Fig. 2B). In addition, a tendency was observed for the CD8/CD4 ratio to be increased in the long-term survival group compared with the short-term survival group (this difference was not statistically significant, Fig. 2C). Notably, the median overall survival time in the low CD8/CD4 group was only 316 days compared with 598 days in the high CD8/CD4 group (Fig. 2D).

Discussion

Although numerous attempts have been made to develop an effective peptide cancer vaccine therapy to treat NSCLC, the results have not been positive (9,12,13). The present study aimed to screen possible predictive biomarkers that were able to distinguish responders from non-responders in patients with HLA-A24-positive NSCLC who received a cancer peptide vaccine treatment of a mixture of three peptides. Certain long-term survivors were observed among these patients who had previously failed standard therapies. Injection site reaction was the most common type of adverse event possibly associated with treatment. One patient in whom a coronary artery stent had been placed for angina succumbed to myocardial infarction as confirmed by autopsy, indicating a non-deniable causal association between treatment and this adverse event.

The immunogenic properties of this vaccine were evaluated through an analysis of the TCRβ repertoire in blood samples prior to and following 8 weeks of treatment. The main results included the following: i) A lower DI prior to vaccine treatment appeared to be beneficial, as it was associated with an improved survival status; and ii) a higher ratio of activated cytotoxic T cell at baseline, as indicated by GZMA/CD8 prior to treatment, appeared to be a favorable clinical outcome. A previous study demonstrated that cancer vaccination may cause an increase in circulating tumor antigen-specific T cells (46). However, the profiles of these T cells have not been well characterized with regard to their association with treatment outcomes. The results of the present study demonstrated that patients with a lower baseline DI tended to have a longer survival time. Notably, patients enrolled in the trial had advanced NSCLC refractory to standard chemotherapies. Prior treatment with cytotoxic chemotherapies may lead to generation of cancer-specific antigens that possibly generate immunological effects, although intensive chemotherapy may also kill these CTL clones (47). It was speculated that the residual immunogenic effects of prior chemotherapies led to the polyclonal expansion of T-cell clones and that this expansion, reflected as a lower DI of the TCRβ repertoire, contributed to the positive effect of the vaccine. Therefore, the pretreatment TCRβ DI may be used as a predictive marker of the ability of a patient to generate an immune response against either chemotherapeutic or vaccine treatment. Peptide vaccination was reported to boost a preexisting dominant clonotypic response (48). Therefore, a suggestion was made to administer vaccine therapy concomitantly with chemotherapy to generate the most effective immunological effects (49). A trend towards an increased TCRβ DI was observed in long-term survivors but not in short-term survivors, supporting earlier evidence that TCRβ repertoire diversification due to chemotherapy and vaccination is beneficial in the prevention of immune-resistant mutant cancer cells, since more cancer-specific T-cell clones; particularly cytotoxic T cells are generated to target cancer cells (49). This phenomenon may be explained as a secondary immune response to vaccine treatment. As the immune system (either cytotoxic T cells or macrophages) eliminated greater numbers of cancer cells, phagocytosis of these cells may result in the presentation of cancer-specific antigens by antigen-presenting cells. The results of the present study were consistent with those of a study by Fang et al (25), in which patients with NSCLC were demonstrated to benefit from chemotherapy prior to peptide vaccination due to an increase in the TCR repertoire diversity. Therefore, the TCRβ DI prior to treatment and the increase in the DI of PBLs following treatment may be used to monitor the responses of patients with NSCLC to the peptide vaccine.

A TaqMan gene expression assay was performed to quantify the relative presence of these two populations in the patient samples in the present study. GZMA has been proposed as a biomarker of activated cytolytic T lymphocytes (50). A high level of GZMA expression indicates a high level of inflammatory cells in allograft, autoimmune diabetes and chronic Chagas' myocardial lesions (51,52). Therefore, GZMA/CD8 ratios were calculated to study the activated subset of cytotoxic CD8 cells. Although no difference was observed in the expression of CD8 or GZMA between the long-term and short-term survivor groups, the results indicated that an increased level of GZMA/CD8 ratio was beneficial for the survival status. Although the difference was not statistically significant, the relative ratio of CD8+/CD4+ was increased prior to treatment in the long-term survival group. A limitation of the present study was that the limited number of available T lymphocytes did not allow for T-cell sorting prior to sequencing. Therefore, future studies are required to improve the characterization of the specific TCR repertoires of different T-cell populations (CD8+, CD4+CD25+ and CD4+CD25), and thus elucidate the specific immunogenic effect of the three-peptide cocktail vaccine.

In conclusion, TCRβ DI and the immunogenic markers, including GZMA, may serve as predictive biomarkers for successful cancer vaccine treatment. Limitations of the present study included the limited number of patients and the lack of placebo control arm. Future prospective studies, in which these markers are used to predict the outcomes of vaccine treatment, are warranted.

Acknowledgements

The present study was partly supported by a research grant from the Japanese Ministry of Health Labor and Welfare.

Glossary

Abbreviations

Abbreviations:

NSCLC

non-small-cell lung cancer

TCR

T-cell receptor

HLA

human leukocyte antigen

CDR3

complementary-determining region 3

PBMC

peripheral blood mononuclear cell

PBL

peripheral blood lymphocyte

DI

diversity index

References

1 

Global status report on noncommunicable diseases 2014. World Health Organization; Geneva: pp. 11–14. 2014

2 

Cancer Facts & Figures 2014. American Cancer Society; Atlanta, GA: pp. 14–15. 2014

3 

Travis WD, Brambilla E, Nicholson AG, Yatabe Y, Austin JH, Beasley MB, Chirieac LR, Dacic S, Duhig E, Flieder DB, et al: The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification. J Thorac Oncol. 10:1243–1260. 2015. View Article : Google Scholar : PubMed/NCBI

4 

Santana-Davila R and Martins R: Treatment of stage IIIA non-small-cell lung cancer: A concise review for the practicing oncologist. J Oncol Pract. 12:601–606. 2016. View Article : Google Scholar : PubMed/NCBI

5 

Katayama R, Shaw AT, Khan TM, Mino-Kenudson M, Solomon BJ, Halmos B, Jessop NA, Wain JC, Yeo AT, Benes C, et al: Mechanisms of acquired crizotinib resistance in ALK-rearranged lung Cancers. Sci Transl Med. 4:120ra172012. View Article : Google Scholar : PubMed/NCBI

6 

Kobayashi S, Boggon TJ, Dayaram T, Jänne PA, Kocher O, Meyerson M, Johnson BE, Eck MJ, Tenen DG and Halmos B: EGFR mutation and resistance of non-small-cell lung cancer to gefitinib. N Engl J Med. 352:786–792. 2005. View Article : Google Scholar : PubMed/NCBI

7 

Ettinger DS, Akerley W, Borghaei H, Chang AC, Cheney RT, Chirieac LR, D'Amico TA, Demmy TL, Govindan R, Grannis FW Jr, et al: Non-small cell lung cancer, version 2.2013. J Natl Compr Canc Netw. 11:645–653. 2013. View Article : Google Scholar : PubMed/NCBI

8 

Molina JR, Yang P, Cassivi SD, Schild SE and Adjei AA: Non-small cell lung cancer: Epidemiology, risk factors, treatment, and survivorship. Mayo Clin Proc. 83:pp. 584–594. 2008; View Article : Google Scholar : PubMed/NCBI

9 

Butts C, Socinski MA, Mitchell PL, Thatcher N, Havel L, Krzakowski M, Nawrocki S, Ciuleanu TE, Bosquée L, Trigo JM, et al: Tecemotide (L-BLP25) versus placebo after chemoradiotherapy for stage III non-small-cell lung cancer (START): A randomized, double-blind, phase 3 trial. Lancet Oncol. 15:59–68. 2014. View Article : Google Scholar : PubMed/NCBI

10 

Dessureault S, Noyes D, Lee D, Dunn M, Janssen W, Cantor A, Sotomayor E, Messina J and Antonia SJ: A phase-I trial using a universal GM-CSF-producing and CD40L-expressing bystander cell line (GM.CD40L) in the formulation of autologous tumor cell-based vaccines for cancer patients with stage IV disease. Ann Surg Oncol. 14:869–884. 2007. View Article : Google Scholar : PubMed/NCBI

11 

Nemunaitis J, Nemunaitis M, Senzer N, Snitz P, Bedell C, Kumar P, Pappen B, Maples PB, Shawler D and Fakhrai H: Phase II trial of Belagenpumatucel-L, a TGF-beta2 antisense gene modified allogeneic tumor vaccine in advanced non small cell lung cancer (NSCLC) patients. Cancer Gene Ther. 16:620–624. 2009. View Article : Google Scholar : PubMed/NCBI

12 

Palmer M, Parker J, Modi S, Butts C, Smylie M, Meikle A, Kehoe M, MacLean G and Longenecker M: Phase I study of the BLP25 (MUC1 peptide) liposomal vaccine for active specific immunotherapy in stage IIIB/IV non-small-cell lung cancer. Clin Lung Cancer. 3:49–58. 2001. View Article : Google Scholar : PubMed/NCBI

13 

Quoix E, Ramlau R, Westeel V, Papai Z, Madroszyk A, Riviere A, Koralewski P, Breton JL, Stoelben E, Braun D, et al: Therapeutic vaccination with TG4010 and first-line chemotherapy in advanced non-small-cell lung cancer: A controlled phase 2B trial. Lancet Oncol. 12:1125–1133. 2011. View Article : Google Scholar : PubMed/NCBI

14 

Zhang N, Zhong WZ and Wu YL: Special issue on personalized therapy in lung cancer. Transl Lung Cancer Res. 3:358–359. 2014.PubMed/NCBI

15 

Daigo Y, Takano A, Teramoto K, Chung S and Nakamura Y: A systematic approach to the development of novel therapeutics for lung cancer using genomic analyses. Clin Pharmacol Ther. 94:218–223. 2013. View Article : Google Scholar : PubMed/NCBI

16 

Hayama S, Daigo Y, Kato T, Ishikawa N, Yamabuki T, Miyamoto M, Ito T, Tsuchiya E, Kondo S and Nakamura Y: Activation of CDCA1-KNTC2, members of centromere protein complex, involved in pulmonary carcinogenesis. Cancer Res. 66:10339–10348. 2006. View Article : Google Scholar : PubMed/NCBI

17 

Kikuchi T, Daigo Y, Katagiri T, Tsunoda T, Okada K, Kakiuchi S, Zembutsu H, Furukawa Y, Kawamura M, Kobayashi K, et al: Expression profiles of non-small cell lung cancers on cDNA microarrays: Identification of genes for prediction of lymph-node metastasis and sensitivity to anti-cancer drugs. Oncogene. 22:2192–2205. 2003. View Article : Google Scholar : PubMed/NCBI

18 

Ishikawa N, Takano A, Yasui W, Inai K, Nishimura H, Ito H, Miyagi Y, Nakayama H, Fujita M, Hosokawa M, et al: Cancer-testis antigen lymphocyte antigen 6 complex locus K is a serologic biomarker and a therapeutic target for lung and esophageal carcinomas. Cancer Res. 67:11601–11611. 2007. View Article : Google Scholar : PubMed/NCBI

19 

Tomita Y, Harao M, Senju S, Imai K, Hirata S, Irie A, Inoue M, Hayashida Y, Yoshimoto K, Shiraishi K, et al: Peptides derived from human insulin-like growth factor-II mRNA binding protein 3 can induce human leukocyte antigen-A2-restricted cytotoxic T lymphocytes reactive to cancer cells. Cancer Sci. 102:71–78. 2011. View Article : Google Scholar : PubMed/NCBI

20 

Suda T, Tsunoda T, Daigo Y, Nakamura Y and Tahara H: Identification of human leukocyte antigen-A24-restricted epitope peptides derived from gene products upregulated in lung and esophageal cancers as novel targets for immunotherapy. Cancer Sci. 98:1803–1808. 2007. View Article : Google Scholar : PubMed/NCBI

21 

Kono K, Mizukami Y, Daigo Y, Takano A, Masuda K, Yoshida K, Tsunoda T, Kawaguchi Y, Nakamura Y and Fujii H: Vaccination with multiple peptides derived from novel cancer-testis antigens can induce specific T-cell responses and clinical responses in advanced esophageal cancer. Cancer Sci. 100:1502–1509. 2009. View Article : Google Scholar : PubMed/NCBI

22 

Yoshitake Y, Fukuma D, Yuno A, Hirayama M, Nakayama H, Tanaka T, Nagata M, Takamune Y, Kawahara K, Nakagawa Y, et al: Phase II clinical trial of multiple peptide vaccination for advanced head and neck cancer patients revealed induction of immune responses and improved OS. Clin Cancer Res. 21:312–321. 2015. View Article : Google Scholar : PubMed/NCBI

23 

Harrop R: Cancer vaccines: Identification of biomarkers predictive of clinical efficacy. Hum Vaccin Immunother. 9:800–804. 2013. View Article : Google Scholar : PubMed/NCBI

24 

Grivennikov SI, Greten FR and Karin M: Immunity, inflammation, and cancer. Cell. 140:883–899. 2010. View Article : Google Scholar : PubMed/NCBI

25 

Fang H, Yamaguchi R, Liu X, Daigo Y, Yew PY, Tanikawa C, Matsuda K, Imoto S, Miyano S and Nakamura Y: Quantitative T cell repertoire analysis by deep cDNA sequencing of T cell receptor α and β chains using next-generation sequencing (NGS). Oncoimmunology. 3:e9684672015. View Article : Google Scholar : PubMed/NCBI

26 

Wong AK and Walkey AJ: Open lung biopsy among critically Ill, mechanically ventilated patients: A Metaanalysis. Ann Am Thorac Soc. 12:1226–1230. 2015.PubMed/NCBI

27 

Mehta S, Shelling A, Muthukaruppan A, Lasham A, Blenkiron C, Laking G and Print C: Predictive and prognostic molecular markers for cancer medicine. Ther Adv Med Oncol. 2:125–148. 2010. View Article : Google Scholar : PubMed/NCBI

28 

Zhang G, Zhao H, Wu J, Li J, Xiang Y, Wang G, Wu L and Jiao S: Adoptive immunotherapy for non-small cell lung cancer by NK and cytotoxic T lymphocytes mixed effector cells: Retrospective clinical observation. Int Immunopharmacology. 21:396–405. 2014. View Article : Google Scholar

29 

Santegoets SJ, Turksma AW, Suhoski MM, Stam AG, Albelda SM, Hooijberg E, Scheper RJ, van den Eertwegh AJ, Gerritsen WR, Powell DJ Jr, et al: IL-21 promotes the expansion of CD27+ CD28+ tumor infiltrating lymphocytes with high cytotoxic potential and low collateral expansion of regulatory T cells. J Transl Med. 11:372013. View Article : Google Scholar : PubMed/NCBI

30 

Khazaie K and von Boehmer H: The impact of CD4+CD25+ Treg on tumor specific CD8+ T cell cytotoxicity and cancer. Semin Cancer Biol. 16:124–136. 2006. View Article : Google Scholar : PubMed/NCBI

31 

Erfani N, Mehrabadi SM, Ghayumi MA, Haghshenas MR, Mojtahedi Z, Ghaderi A and Amani D: Increase of regulatory T cells in metastatic stage and CTLA-4 over expression in lymphocytes of patients with non-small cell lung cancer (NSCLC). Lung Cancer. 77:306–311. 2012. View Article : Google Scholar : PubMed/NCBI

32 

Phillips JD, Knab LM, Blatner NR, Haghi L, DeCamp MM, Meyerson SL, Heiferman MJ, Heiferman JR, Gounari F, Bentrem DJ and Khazaie K: Preferential expansion of pro-inflammatory Tregs in human non-small cell lung cancer. Cancer Immunol Immunother. 64:1185–1191. 2015. View Article : Google Scholar : PubMed/NCBI

33 

Gounaris E, Blatner NR, Dennis K, Magnusson F, Gurish MF, Strom TB, Beckhove P, Gounari F and Khazaie K: T-regulatory cells shift from a protective anti-inflammatory to a cancer-promoting proinflammatory phenotype in polyposis. Cancer Res. 69:5490–5497. 2009. View Article : Google Scholar : PubMed/NCBI

34 

Glusman G, Rowen L, Lee I, Boysen C, Roach JC, Smit AF, Wang K, Koop BF and Hood L: Comparative genomics of the human and mouse T cell receptor loci. Immunity. 15:337–349. 2001. View Article : Google Scholar : PubMed/NCBI

35 

Litman GW, Rast JP and Fugmann SD: The origins of vertebrate adaptive immunity. Nat Rev Immunol. 10:543–553. 2010. View Article : Google Scholar : PubMed/NCBI

36 

Folch G and Lefranc MP: The human T cell receptor beta variable (TRBV) genes. Exp Clin Immunogenet. 17:42–54. 2000. View Article : Google Scholar : PubMed/NCBI

37 

Haynes MR and Wu GE: Gene discovery at the human T-cell receptor alpha/delta locus. Immunogenetics. 59:109–121. 2007. View Article : Google Scholar : PubMed/NCBI

38 

Scaviner D and Lefranc MP: The human T cell receptor alpha variable (TRAV) genes. Exp Clin Immunogenet. 17:83–96. 2000. View Article : Google Scholar : PubMed/NCBI

39 

Robinson MW, Hughes J, Wilkie GS, Swann R, Barclay ST, Mills PR, Patel AH, Thomson EC and McLauchlan J: Tracking TCRβ sequence clonotype expansions during antiviral therapy using high-throughput sequencing of the hypervariable region. Front Immunol. 7:1312016. View Article : Google Scholar : PubMed/NCBI

40 

Britanova OV, Putintseva EV, Shugay M, Merzlyak EM, Turchaninova MA, Staroverov DB, Bolotin DA, Lukyanov S, Bogdanova EA, Mamedov IZ, et al: Age-related decrease in TCR repertoire diversity measured with deep and normalized sequence profiling. J Immunol. 192:2689–2698. 2014. View Article : Google Scholar : PubMed/NCBI

41 

Li Z, Liu G, Tong Y, Zhang M, Xu Y, Qin L, Wang Z, Chen X and He J: Comprehensive analysis of the T-cell receptor beta chain gene in rhesus monkey by high throughput sequencing. Sci Rep. 5:100922015. View Article : Google Scholar : PubMed/NCBI

42 

Li D, Gao G, Li Z, Sun W, Li X, Chen N, Sun J and Yang Y: Profiling the T-cell receptor repertoire of patient with pleural tuberculosis by high-throughput sequencing. Immunol Lett. 162:170–180. 2014. View Article : Google Scholar : PubMed/NCBI

43 

Yew PY, Alachkar H, Yamaguchi R, Kiyotani K, Fang H, Yap KL, Liu HT, Wickrema A, Artz A, van Besien K, et al: Quantitative characterization of T-cell repertoire in allogeneic hematopoietic stem cell transplant recipients. Bone Marrow Transplant. 50:1227–1234. 2015. View Article : Google Scholar : PubMed/NCBI

44 

Giudicelli V, Chaume D and Lefranc MP: IMGT/GENE-DB: A comprehensive database for human and mouse immunoglobulin and T cell receptor genes. Nucleic Acids Res. 33(Database Issue): D256–D261. 2005. View Article : Google Scholar : PubMed/NCBI

45 

Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M, et al: New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur J Cancer. 45:228–247. 2009. View Article : Google Scholar : PubMed/NCBI

46 

Spiotto MT and Schreiber H: Rapid destruction of the tumor microenvironment by CTLs recognizing cancer-specific antigens cross-presented by stromal cells. Cancer Immun. 5:82005.PubMed/NCBI

47 

Bonmassar E, Bonmassar A, Vadlamudi S and Goldin A: Immunological alteration of leukemic cells in vivo after treatment with an antitumor drug. Proc Natl Acad Sci USA. 66:pp. 1089–1095. 1970; View Article : Google Scholar : PubMed/NCBI

48 

Speiser DE, Baumgaertner P, Barbey C, Rubio-Godoy V, Moulin A, Corthesy P, Devevre E, Dietrich PY, Rimoldi D, Liénard D, et al: A novel approach to characterize clonality and differentiation of human melanoma-specific T cell responses: Spontaneous priming and efficient boosting by vaccination. J Immunol. 177:1338–1348. 2006. View Article : Google Scholar : PubMed/NCBI

49 

Palermo B, Del Bello D, Sottini A, Serana F, Ghidini C, Gualtieri N, Ferraresi V, Catricalà C, Belardelli F, Proietti E, et al: Dacarbazine treatment before peptide vaccination enlarges T-cell repertoire diversity of melan-a-specific, tumor-reactive CTL in melanoma patients. Cancer Res. 70:7084–7092. 2010. View Article : Google Scholar : PubMed/NCBI

50 

Cornberg M, Chen AT, Wilkinson LA, Brehm MA, Kim SK, Calcagno C, Ghersi D, Puzone R, Celada F, Welsh RM and Selin LK: Narrowed TCR repertoire and viral escape as a consequence of heterologous immunity. J Clin Invest. 116:1443–1456. 2006. View Article : Google Scholar : PubMed/NCBI

51 

Reis DD, Jones EM, Tostes S Jr, Lopes ER, Gazzinelli G, Colley DG and McCurley TL: Characterization of inflammatory infiltrates in chronic chagasic myocardial lesions: Presence of tumor necrosis factor-alpha+ cells and dominance of granzyme A+, CD8+ lymphocytes. Am J Trop Med Hyg. 48:637–644. 1993. View Article : Google Scholar : PubMed/NCBI

52 

Chen RH, Ivens KW, Alpert S, Billingham ME, Fathman CG, Flavin TF, Shizuru JA, Starnes VA, Weissman IL and Griffiths GM: The use of granzyme A as a marker of heart transplant rejection in cyclosporine or anti-CD4 monoclonal antibody-treated rats. Transplantation. 55:146–153. 1993. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

July 2017
Volume 14 Issue 1

Print ISSN: 1792-1074
Online ISSN:1792-1082

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
APA
Mai, T., Takano, A., Suzuki, H., Hirose, T., Mori, T., Teramoto, K. ... Daigo, Y. (2017). Quantitative analysis and clonal characterization of T-cell receptor β repertoires in patients with advanced non-small cell lung cancer treated with cancer vaccine. Oncology Letters, 14, 283-292. https://doi.org/10.3892/ol.2017.6125
MLA
Mai, T., Takano, A., Suzuki, H., Hirose, T., Mori, T., Teramoto, K., Kiyotani, K., Nakamura, Y., Daigo, Y."Quantitative analysis and clonal characterization of T-cell receptor β repertoires in patients with advanced non-small cell lung cancer treated with cancer vaccine". Oncology Letters 14.1 (2017): 283-292.
Chicago
Mai, T., Takano, A., Suzuki, H., Hirose, T., Mori, T., Teramoto, K., Kiyotani, K., Nakamura, Y., Daigo, Y."Quantitative analysis and clonal characterization of T-cell receptor β repertoires in patients with advanced non-small cell lung cancer treated with cancer vaccine". Oncology Letters 14, no. 1 (2017): 283-292. https://doi.org/10.3892/ol.2017.6125