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Abstract

To find out if the T cell repertoire is efficiently and specifically provoked in patients with breast cancer, we have investigated the clonotypes of main T cell subsets (based on V $\beta$ -Chain) in tumor draining lymph nodes. CD4+ helper, CD8+ cytotoxic and (CD4+CD127 ${}^{\text{dim}}$ CD25+) regulatory T cells, were negatively selected, and isolated, from lymph node mononuclear cells of 14 untreated patients with breast cancer. Four non-malignant patients, who underwent surgical operation, were also recruited as the control group. Based on sequences and new nomenclature of the T cell Receptor $\beta$ Variables (TRBVs) available in the international ImMunoGeneTics (IMGT) database, 28 TRBV specific forward primers and two TRB Constant region (TRBC) specific reverse primers were developed to amplify all functional alleles. Fluorescent-labeled PCR products were then run on an ABI PRISM 310 Genetic-Analyzer. The data was analyzed by GeneMapper software version 3.1. Clonotype analysis suggested that activated T cells are present in breast cancer. More TRBV usage were detected among CD4+ helper and regulatory subsets, with Gaussian-like pattern in the majority of functional TRBV families; whereas CD8+ cytotoxic T cells showed oligoclonality in almost all TRBV families with one or two dominant peaks in each family. Similar pattern in some of these TRBVs were also observed among controls. Having no expression or polyclonality in the controls, the oligoclonal pattern observed in the TRBV18, however, appears to be specific to breast cancer patients. This phenomenon may reflect the existence of new antigenic stimulation(s) in BC patients, preferentially activating those clones of T cells that express TRBV18.

Keywords

Breast cancer TCR clonotype Tregs cytotoxic T cells helper T cells

1. Introduction

Infiltration of T cells in tumor microenvironment is commonly deemed the patient’s ability to develop an immune response against tumor. In order to become activated, all T cells require antigen recognition via their T Cell Receptor (TCR), a heterodimer glycoprotein comprising either $\alpha$ / $\beta$ or $\gamma$ / $\delta$ chains. Genes encoding for V $\alpha$ and V $\beta$ are assembled by somatic recombination from gene segments called “Variable (V)”, “Diversity (D, only for $\beta$ chain)”, and “Joining (J)”. Random insertion and deletion of nucleotides into/from the V-D-J junctions of the $\beta$ -chain increase diversity in CDR3 which comes into direct contact with the antigenic peptides bound to major histocompatibility complex molecule. These recombinatorial events make it possible for the immune system to have a diverse repertoire ready so that it could respond to various antigens [including infection and tumor specific or associated antigens] [1, 2, 3].

Populations of newly generated naive T cells express a broad receptor repertoire. Following antigen recognition, specific clones from this repertoire are activated and expanded to contribute in forthcoming effector mechanisms, resulting in skewness in normal primary repertoire or even oligoclonal repertoires. The expanded T cell clones are believed to reflect the presence of immunodominant antigen(s), and can be detected on the molecular levels, applying analysis of clonality of the individual TCR repertoire [4, 5]. Determining the TCR repertoire clonality and diversity is one of the best ways to study how tumor cells modulate T cell repertoire. Such investigations could provide a global picture of T cell clonal expansion, representing the antigen/immunogens found in tumor microenvironment. Furthermore, this global picture not only sheds light on tumor-T cell dialog, but also provides an invaluable framework for further immune-gene or -cell therapy [6, 7, 8, 9]. In recent years, analysis of TCR repertoire clonality and diversity has been considered as a monitoring tool to identify the T cell populations of interest in a variety of abnormalities including cancers and autoimmunities [10, 11]. As for the analysis of the TCR repertoire, the TRB (T cell Receptor $\beta$ -chain) would generally be the preferable choice: not only because of the impact on CDR3 [as a result of possessing D gene segment], but also due to lack of allelic exclusion extant in the TRA locus, itself giving rise to two different TCRs expression among a considerable portion of mature T cells. It is to be noted that TRB chain would most likely have a (considerably) higher level of combinatorial and junctional diversity [2].

For purposes of finding out if the T cell repertoire is efficiently provoked in a specific manner in patients with breast cancer, in the present study, we have investigated the clonotypes of different T cell subsets, including effector (CD4+ and CD8+), as well as, regulatory (CD4+CD25+CD127 ${}^{\text{neg}}$ ) T cells, in Tumor Draining Lymph Nodes (TDLNs) derived from these patients. V $\beta$ fragment-based analysis was applied as a tool for clonotype analysis.

2. Materials and methods

2.1 Samples

Fourteen untreated patients with BC, who underwent surgical resection, were recruited. Only patients in TNM stages I and II were investigated in this study. The clinical and pathological characteristics of the patients were collected from their files. Subsequent to a pathologist’s assessments of the resected lymph nodes as a routine laboratory procedure for patients’ treatment plan, part of an axillary TDLN was collected and used as the source of lymphocytes. Lymph nodes from four non-malignant patients (3 women and one man, 30–40 years old), having undergone surgical operation, were recruited in the study as the control group (three from head and neck region and one from axilla). The study was approved by the ethics committee of Shiraz University of Medical Sciences, and the informed consent was obtained from all participants before sample collection (Ref. No. IR.SUMS.REC.1389.S5289).

2.2 Cell preparation and sorting

To obtain the single-cell suspension, fresh lymph nodes were mechanically minced into small pieces in complete culture medium, containing 10% FBS and filtered through a 40 $\mu$ m cell strainer (BD Bioscience, Rockville, MD, USA). Lymph node mononuclear cells were then, isolated, applying Ficoll-Hypaque gradient centrifugation in 1000 g at 20 ${}^{\circ}$ C for 20 min. Harvested cells were, later, washed and resuspended in MACS buffer (Miltenyi Biotec, Germany) followed by negative selection using antibody-coated magnetic beads and Miltenyi Mini and Midi MACS systems (Miltenyi Biotec, Germany). CD4 ${}^{+}$ T cells isolating kit (Order No. 130-096-533, Miltenyi Biotec, Germany), CD8 ${}^{+}$ T cells isolating kit (Order No. 130-096-495, Miltenyi Biotec, Germany), and CD4 ${}^{+}$ CD127 ${}^{\text{dim}}$ CD25 ${}^{+}$ regulatory T cells isolating Kit (Order No. 130-094-775, Miltenyi Biotec, Germany) were used according to manufacturer’s instructions to collect the cognate cell subsets. The purity of the separated cells having been checked by using analysis on a FACSCalibur flow cytometer (BD Bioscience, USA), CD4+ and CD8+ T lymphocytes with more than 90% purity and less than 5% contamination with other CD3+ lymphocytes were selected for spectratyping. The purity was determined to be in between 75% and 95% in the case of Treg cells.

2.3 RNA extraction and cDNA synthesis

Total RNA was extracted from cell subsets applying appropriate RNA extraction kit according to the manufacturer’s instructions (High Pure RNA Isolation kit, Roche Applied Science, USA and PicoPure RNA Isolation Kit, ABI, Sunnyvale, CA, USA), followed by the first strand synthesis of cDNA, using random hexamer and oligo-dT primers (High Capacity cDNA Reverse Transcription Kits, ABI, Sunnyvale, CA, USA). The integrity of cDNA preparation was checked by assessing TRBC gene expression by means of three TRBC specific primers (Table 1). Two bright bands with 438 and 70 base pair length should, thus, appear on the gel. The cDNA samples were stored at $-$ 80 ${}^{\circ}$ C until use.

Table 1
Forward TRBV primers developed through sequences and new nomenclature of the TRBVs in IMGT database

TRBV	TRBV primers	Tm	Amplicon (bp)
			Min	Max
TRBV1 (Arden: 27)	R-T-P-
TRBV2 (Arden: 22)	TGAAAGCAGGACTCACAGAACCT	55.75	407	413
TRBV3 (Arden: 9)	TCTCACCTAAATCTCCAGACAAAGCT	55.62	185	212
TRBV4 (Arden: 7)	CCAAGACACCTGGTCATGGGA	55.74	352	382
TRBV5 (Arden: 5)	CTGATCAAAACGAGAGGACAGCA	55.01	349	373
TRBV6 (Arden: 13)	1–9: GGTATCGACAAGACCCAGGCA	55.90	272	305
	4: CTGGTATAGACAAGATCTAGGACTGG	53.57	281	301
TRBV7 (Arden: 6)	2/3: TGTGATCCAATTTCAGGTCATACTGC	55.58	314	347
	6/7/8/9: CAGGG(C/G)CCAGAGTTTCTGAC	54.90	265	276
TRBV8 (Arden: 30, 32)	R-T-P-
TRBV9 (Arden: 1)	ATCACAGCAACTGGACAGCGA	56.40	346	373
TRBV10 (Arden: 12)	1/3: ACTGACAAAGGAGAAGTCTCAGATG	54.31	211	244
	2: ACAGATAAAGGAGAAGTCCCCGATG	55.88	211	244
TRBV11 (Arden: 21)	TGTGGCTTTTTGGTGCAATCCT	55.57	325	358
TRBV12 (Arden: 8)	CTGGTACAGACAGACCATGATGC	55.12	283	311
TRBV13 (Arden: 23)	TCGTTTTATGAAAAGATGCAGAGCGA	55.86	236	257
TRBV14 (Arden: 16)	GCCTGCAGAACTGGAGGATTCT	56.36	128	155
TRBV15 (Arden: 24)	TCCAGGAGGCCGAACACTTC	55.96	204	228
TRBV16 (Arden: 25)	AAACAGGTATGCCCAAGGAAAGA	54.13	–	–
TRBV17 (Arden: 26)	R-T-P-
TRBV18 (Arden: 18)	CAGCCCAATGAAAGGACACAGT	55.15	312	339
TRBV19 (Arden: 17)	AGGGCAAGGGCTGAGATTGAT	55.48	254	287
TRBV20 (Arden: 2)	CACAACTATGTTTTGGTATCGTCAGTTC	55.14	292	322
TRBV21 (Arden: 10)	GTTATGTTTACTGGTATCGTAAGAAGC	52.61	–	–
TRBV22 (Arden: 29)	R-T-P-
TRBV23 (Arden: 19)	TGTACCCCCGAAAAAGGACATAC	54.35	–	–
TRBV24 (Arden: 15)	CAGTGTCTCTCGACAGGCACA	56.20	182	212
TRBV25 (Arden: 11)	TGAGTCAACAGTCTCCAGAATAAGGA	55.12	194	215
TRBV26 (Arden: 28)	R-T-P-
TRBV27 (Arden: 14)	GCTGGGCTTAAGGCAGATCTAC	54.95	259	286
TRBV28 (Arden: 3)	GGCTACGGCTGATCTATTTCTCAT	54.39	254	278
TRBV29 (Arden: 4)	CATGATGTTCTGGTACCGTCAGC	55.72	293	320
TRBV30 (Arden: 20)	ACCTATACTGGTACCGACAGGCT	56.22	273	308
TRBVA, B, C	R-T-P-
TRBC reverse primer	GCCTTTTGGGTGTGGGAGATC
TRBC-FAM labeled GeneScan	FAM-ATGGCTCAAACACAGCGACCTC

R-: no rearrangement, T-: no Transcript, P-: no protein.

Tm of each primers was calculated using online PubMed primer design software.

3. TRBV expression repertoire analysis by GeneScan method

3.1 Primer design

The forward primers were designed by the instrumentality of sequences and new nomenclature of the TRBVs in IMGT database (http://imgt.cines.fr/). According to this classification system, TRBV genes are categorized into 33 families, of which 23 are functional. TRBV1, TRBV8, TRBV17, TRBV22, TRBV26, TRBVA, TRBVB, and TRBVC families were excluded from this panel, as these only contain non-functional orphans or pseudo-genes. The reverse TRBC primer was designed for a location in the identical sequence of TRBC1 and TRBC2 genes, one of which was labeled with 6-carboxyfluorescein (FAM) at its 5’ end. Finally, a panel of 28 TRBV and two TRBC gene specific primers were used. Primer sequences were, then, checked for cross-reactivity in “nr” database of NCBI (http://www.ncbi.nlm.nih.gov), as well as for suitable chemical properties especially having the similar melting temperature. PCR products from this set of primers range from 128 to 413 bp, and may be easily detected by capillary electrophoresis. To check the accuracy and specific binding of each TRBV primer, cDNA from available T cell lines [including Jurkat expressing TRBV12 and HPB-ALL expressing TRBV5] (Pasteur Institute of Iran, Cell Bank, Tehran, Iran) as well as 28 carriers cDNA constructs were brought into use as positive and negative controls for each TRBV primer. Each construct which contains a rearranged TCR with the relevant TRBV gene was cloned using pGEM-FT TA cloning vector) gifted from Avicenna institute, Tehran, Iran). Separate PCR reactions were performed to assess the sensitivity, as well as the specificity of all 28 designed forward primers. Based on the results (data not shown), we indicated that each specific primer (by simultaneous use with common revers primer) only amplifies its cognate target TRBV fragment, but no other 24 cDNA constructs. During this assessment, we also adjusted optimal annealing temperature at which all the primers keep this sensitivity and specificity. We further provided supporting data for the accuracy of PCR primers by applying the same procedure on cDNA extracted from abovementioned cell lines (data not shown). Both primers’ sequence and the equivalent TRBV gene in the old nomenclature ofArden et al. [12] are available in Table 1.

Table 2
Mixtures of the PCR products for GeneScan analysis

Group	TRBVs
1	2, 10, 19, 23, 24, 29
2	4, 7.2, 12, 14, 16, 28
3	3, 6.1, 7.6, 9, 18
4	5, 6.4, 20, 25, 27
5	11, 13, 15, 21, 30

3.2 First round PCR

TRBV-specific PCR amplifications were performed using a panel of 28 BV-specific forward and a common TRBC-specific reverse primer. PCR was conducted as follows: 5 min at 95 ${}^{\circ}$ C; followed by 30–40 cycles of 30 sec at 95 ${}^{\circ}$ C, 45 s at 66 ${}^{\circ}$ C, and 45 s at 72 ${}^{\circ}$ C; with a final extension of 72 ${}^{\circ}$ C for 5 min. PCR products were stored at $-$ 20 ${}^{\circ}$ C, or continued immediately to run-off labeling reaction.

3.3 Run-off labeling reaction

A fluorescent TRBC reverse primer labeled at their 5’ end with FAM was used in the labeling master mix for global analysis of the T cell repertoire by GeneScan method. The run-off mixture with a TRBC primer was prepared, applying 3 picomoles of FAM labeled TRBC primer and 2 $\mu$ l of first round PCR products as the template. Amplification was performed on a DNA thermal cycler (Eppendorf, Germany) with the following thermal program: 30 sec at 95 ${}^{\circ}$ C; followed by 10 cycles of 25 sec at 95 ${}^{\circ}$ C, 45 sec at 60 ${}^{\circ}$ C, and 45 sec at 72 ${}^{\circ}$ C; with a final extension of 72 ${}^{\circ}$ C for 5 min. The number of amplification cycles could be increased to 15 if it were necessary to improve the signal-to-noise ratio. The products were stored at $-$ 20 ${}^{\circ}$ C, or run immediately on a Genetics Analyzer (ABI 310 Genetic Analyzer, Sunnyvale, CA, USA).

3.4 Spectratype visualization

Based on their size, TRBVs PCR products were distributed into five groups per reaction (Table 2). One microliter of each TRBV labeling mixture was loaded onto 13 $\mu$ l HiDi formamide (ABI, Sunnyvale, CA, USA) containing 0.5 $\mu$ l of ROX400 internal size standard (ABI, Sunnyvale, CA, USA) in a sequencing tube with septa (ABI, Sunnyvale, CA, USA). The samples were denatured at 95 ${}^{\circ}$ C for 5 min to be rapidly cooled to 4 ${}^{\circ}$ C for inhibiting renaturing; they were, later on, separated on POP4 in an ABI 310 Genetic Analyzer. The run temperature was specified at 60 ${}^{\circ}$ C while injection time was adjusted to be 10 sec.

Table 3
Clinical and pathological characteristics of patients

Characteristics	Value
Age (years)	46.93 $\pm$ 13.12
Tumor type
Infiltrative ductal carcinoma (IDC)	11 (78.6%)
Medullary carcinomas (MC)	3 (21.4%)
Lymph node status
Free	10 (71.4%)
Involved	4 (28.6%)
Stage
I	4 (28.6%)
II	10 (71.4%)
Tumor size (cm)
T1 ( $\leqslant$ 2)	6 (42.9%)
T2 (2–5)	8 (57.1%)
Estrogen receptor (ER)
Positive	8 (61.5)
Negative	5 (38.5)
Unreported	1
Progesterone receptor (PR)
Positive	8 (61.5)
Negative	5 (38.5)
Unreported	1
Her2 expression
Positive	9 (69.2)
Negative	4 (30.8)
Unreported	1

3.5 Analysis

Data analysis was performed by GeneMapper 3.1 software. Scanning results of fluorescence-dye-labeled PCR products by GeneScan analysis were interpreted as follows: one peak representing a mono-clone; one or two striking peaks indicating dominant clones, and designated as oligoclonal expansion, while poly-clonality was characterized by a number of peaks arranged in a normal distribution. SPSS software version 15 (SPSS GmbH Software, Germany), was also used to compare the means of expressed clones among different T cell subsets.

Figure 1.

TRBV repertoire profiles from T cell subsets in one breast cancer patient. A: Spectratypes of different TRBV families in CD4+ helper subset demonstrate polyclonal distribution across most functional TRBV gene families; B: Several oligoclonal peaks could be observed in the most of the TRBV families in CD8+ cytotoxic subset, with monoclonal like band in some TRBVs i.e. TRBV25 and TRBV30; C: Polyclonal patterns and several oligoclonal peaks in most of TRBV families are observed in Treg subset. *TRBV designations are according to IMGT nomenclature; TRBV: T cell Receptor Beta Variable.

4. Results

Clinical and pathological characteristics of the patients are summarized in Table 3. As illustrated, 14 untreated women with BC aging from 32 to 69 years (46.93 $\pm$ 13.12 years) were enrolled into spectratype analysis. Ten out of 14 patients (71.4%) were in TNM stage II, and the rest were in stage I (28.6%). According to the pathological reports, majority of the patients had Infiltrating Ductal Carcinoma (IDC) (11/14, 78.6%) and four patients had at least one lymph node involved by tumor cells (4/14, 28.6%).

4.1 TCR repertoire qualitative analysis of different T cell subsets in TDLNs of breast cancer patients

As represented in Figs 1 and 2, diverse profiles of TCR gene rearrangement were observed in the patients and in the controls. TRBV16, 21, 23 families could be detected neither in patients nor in controls as for all subsets of T cells. TRBV2, 4, 6.4, 7.6, 25, 28 and 30 families were undetectable or poorly expressed in different subpopulations of both groups. Although the slightly higher number of families were expressed in the CD4+ helper subset of the patients and the controls, statistical analysis did not reveal any difference in the number of observed clones among different subsets.

Figure 2.

TRBV repertoire profiles from T cell subsets of one non-malignant control. A: Spectratypes of different TRBV families in CD4+ helper subset with polyclonal distribution in most functional TRBV gene families and B: CD8+ cytotoxic subset with more oligoclonal peaks in the most of the TRBV families. *TRBV designations are according to IMGT nomenclature; TRBV: T cell Receptor Beta Variable.

4.2 Cases of CD4+ T cell TRBV usage in TDLNs of BC patients and controls

The mean value for the number of clones expressed in the CD4+ helper subset of both patients and controls was 19.71 (min: 15, max: 24) and 18.75 (min: 17, max: 21), respectively; however, no significant difference was observed. As demonstrated in Figs 1A and 2A, spectratypes of different TRBV families in CD4+ helper subset demonstrated polyclonal distribution across most functional TRBV gene families, figuring out much like Gaussian distribution both in patients and controls. Although some TRBV families such as TRBV4, 5, 7.2, 9, 11, and 14 showed oligoclonal patterns in most BC patients’ CD4+ T cells, such oligoclonality was also observed in CD4+ population isolated from non-malignant controls. Additionally, no expression of TRBV30 could be detected in CD4+ subset of BC patients, whereas it was expressed in all non-malignant controls.

4.3 CD8+ T cell TRBV usage in TDLNs of BC patients and controls

In the case of CD8+ subset, the mean number of expressed clones was 17.46 $\pm$ 2.40 – significantly lower in comparison to CD4+ subset (19.71 $\pm$ 2.49, $P=$ 0.021). As laid out in Figs 1B and 2B, several oligoclonal peaks could be observed in the most of the TRBV families in CD8+ cytotoxic subset of BC patients and controls, with monoclonal represented band in some TRBVs, as is apparent in TRBV25 and TRBV30 in Fig. 1B. Over and above TRBV16, 21, and 23, TRBV2 was also not detected in the CD8+ T cells of both patients and controls. The control group had also no expression of TRBV4, 6.4 and 25 families, whereas they were weakly expressed in 4, 6 and 5 BC patients, respectively. As investigated, these patients did not have in common any special clinico-pathological features. Further investigation of the clonality pattern of TRBV families in CD8+ T cells from patients revealed that TRBV18 showed relatively unanimous oligoclonality in the most of patients, whereas this family showed polyclonal distribution in two controls and did not expressed in the others. In addition, one monoclonal like peak could be detected in TRBV12, 10, 20, 25, 28, 29 and 30 in 1 to 3 BC patients.

4.4 TCR repertoire of Treg subset in TDLNs of BC patients

Within the Treg subpopulation, the mean number of expressed clones in BC patients was 18.10 $\pm$ 3.21. Except for TRBV16, 21 and 23, which were absent in Treg subpopulation – just like CD8 and CD4 subpopulations – other TRBV families were detected at least in one patient. Polyclonal patterns, as well as several oligoclonal peaks in most of TRBV families were observed in Treg subset of the patients (Fig. 1C). No data were available for the control group.

5. Discussion

T cells isolated from patients’ axillary lymph nodes have been commonly used as a source of tumor infiltrating lymphocytes, since these lymph nodes directly drain the tumor, and accordingly, would be expected to be enriched for specific reactive lymphocytes. The development of tumor immunity suggests that new antigen determinants might have been emerged as results of gene mutation, or that self-antigens overexpress during the transformation from normal to malignant cells [13]. In the present study, using spectratyping method, we investigated the clonotype of three main subsets of T cells in draining lymph nodes of BC patients, where a diverse T cell profiles were observed. However, more TRBV usage were detected in the CD4+ helper and regulatory subsets, with Gaussian-like pattern in the majority of functional TRBV families: whereas CD8+ cytotoxic T cells showed oligoclonality in almost all TRBV families with one or two dominant peaks in each family.

Previous investigations indicated that neoplastic transformations are able to shape T cell repertoire expansion based on the antigen they produced. Oligoclonality of T cells has been reported in the peripheral blood and within tumor infiltrating lymphocytes in patients with solid tumors as well as hematological malignancies [14, 15, 16, 17]. In the case of breast cancer most studies reported that the level of clonality in TILs, measured by PCR-based or flow cytometric analyses, remains unrestricted [18, 19, 20]. No correlation has been found, either, between the expression of specific TRAV/TRBV segments and cytotoxicity against allogeneic tumor cells [18]. Such a pattern in our study was only observed in the CD4+ T helper and regulatory subsets, which represent random rearrangement of TRBV genes. Beside non-specific activation, the polyclonality may also arise from heterogeneity that exists in helper population and that each subset renders different effector functions [21] and, consequently, may have different clonotypes in the setting of cancer. However, there are some reports which reveal limited heterogeneity of TRBV chain gene rearrangements in BC patients, irrespective of T cell subset [22, 23], almost all of the previous studies assessed the clonality of total T cells in BC, regardless of the subsets.

We also determined unanimous oligoclonal patterns in TRBV5, 7.2, 9 and 11 families among all three investigated subsets in the BC patients. A biased expression of TRBV7 (old: 6) was, moreover, observed in Chin et al. study on TIL clones derived from breast tumors [22]. A similar result was observed in Munsona et al. study in which a predominant expression of TRBV7 family members was observed in the BC TILs and PB; however, it was not mentioned which nomenclature was used in their study [24]. They suggested that this V-beta family is responsible for specific tumor recognition, but the same pattern was once more detected in our non-malignant controls. These observations collectively imply that there is a non-specific activation, maybe due to encountering common environmental antigen(s) or receiving a similar bystander stimulus.

As for CD8+ subset, spectratype analysis indicated that usage of this subset is more restricted than helper and regulatory subsets. Yet further analysis revealed that most of these TRBV families contained, at least, one or two dominant peaks showing their activation and more expansion in comparison to other clones. It is unlikely that the clonal expansion of CD8+ T cells is a result of conventional super-antigen stimulation or polyclonal bystander activation. On the other hand, compared to CD8+ T cells obtained from non-malignant LNs, the number of families containing dominant clones obviously increased in BC patients. Similar results were also reported by Ito et al. as well as Munson et al. in CD8+ T cells, isolated from peripheral blood and lymph node of BC patients [23, 24]. Munsona et al. showed that TCR repertoires of CD8+ T cells in HLA-A2+ tumors, LNs, and PB lymphocytes from patients with BC contain multiple shared alpha/beta pairs. Whereas these common TCRs were scarcely found in CD8+ T cells isolated from PB of HLA-A2+ healthy women as well as HLA-A2 negative donors. This might imply that these common TCRs may be recolonized tumor specific antigens.

Over on above unanimous oligoclonality which was observed in TRBV5, 7.2, 9, 11 families in both patients and controls, we also found a similar pattern in TRBV18 family. This oligoclonal pattern in the TRBV18 appears to be specific to BC patients due to no expression or polyclonality of this family in the controls. This phenomenon may reflect the existence of new antigenic stimulation(s) in BC patients, preferentially activating those clones of T cells that express TRBV18. Concordant to our results, Ito et al. found an increased incidence of oligoclonal expansion of TRBV18 in CD8+ T cells in draining LNs of BC patients. These having been said and done, the CDR3 region sequence of all clones was completely heterogeneous. We observed oligoclonality in all patients with one or two dominant peaks – three patients represented a monoclonal like band in this family. This difference may arise from the technique applied in Ito et al. study (radiolabeled probe on the gel) in which – because of low sensitivity of the method – a number of oligoclonal rearrangements were not detectable. However, further analyses with additional age-matched female samples are required to determine whether the oligoclonal expansion in TRBV18 CD8+ T cells in BC patients is tumor-specific or not. Also, we observed a monoclonal-like peak in some TRBV families. Usually, but not always, a dominant peak is deemed the result of some increase in expression of a single gene; however, the monoclonality of these bands ought to be confirmed by direct sequence analysis.

According to our best knowledge, this is the first study which – beside CD4+ and CD8+ T subsets – has investigated Tregs repertoire with CD4+CD25+ CD127 ${}^{\text{neg/low}}$ phenotype in TDLNs of patients with BC. Our results showed that the number of TRBV families expressed in Tregs is slightly lower than CD4+ helper T cells, but similar to CD4+ subset; expression pattern in the most of TRBV families is polyclonal. However, oligoclonality could also be seen in some families but no homogeneity was observed among patients. As the number of lymphocytes obtained from LNs of control group was not adequate for isolating regulatory cells, no comparison was made under non-malignant circumstances in relation to finding whether this activation is peculiar to BC or not. As mentioned before, some of these oligoclonalities and polyclonalities are similar to other subtypes, in particular CD4+ T cells. In addition to similar mechanisms, specific or nonspecific, which are involved in the amplification of these families or their specific clones, these similarities may also indicate that some of Treg cells shift tracks from CD4+CD25- effector T cells in tumor microenvironment.

It is to be noted that, regardless of primary stimulation, it has been shown that (following the activation), Tregs are able to suppress the immune response through specific or non-specific pathways and dampen the effective immune responses against tumors. It is, therefore, important to understand the mechanisms underlying the regulation of activation and function of these cells. Among parameters affecting the central and environmental selection of Tregs, the TCR repertoires received much attention, probably because the binding avidity of TCR to its ligand is one of the key checkpoints in establishing and maintaining tolerance as well as in functioning of these cells. Despite the fact that many questions remain to be answered, the general consensus is that both mouse- and human-Tregs require the signal through their TCR and cell-cell contacts for activation [25, 26]. As a result, evaluation of TCR diversity could be one of the useful tools that may help us to understand how Tregs modulate the immune response in cancer.

However, some studies suggest that in the healthy individuals, Tregs with CD4+CD25+ phenotype, express diverse TCR repertoire and a polymorphic array of CDR3 length, which – to some extent – overlaps with effectors T cells usage [27, 28]; there are, nevertheless, several reports displayed that the repertoire of these cells could be changed under different environmental conditions and/or after viral infections [29]. It has also been demonstrated that naïve and regulatory T cells’ repertoires are apparently distinct from one another [30, 31]. As far as we know, there are no reports on Treg clonality in human tumors; however, two recent studies in mice indicated that tumor infiltrating Tregs displayed biased repertoires, implying strong T cell responses with marked proliferation of a few dominant Tregs clones in tumors. This is notwithstanding the fact these Tregs displayed public TCR sequences that are common to many individuals but not specific to tumors [32, 33].

Taken together, our results suggest that responsive T cells are present in breast cancer draining lymph nodes. To the best of our knowledge, this is the first study investigating the clonotype of T cell for dealing with their effector and regulatory subsets, including CD4+ helper, CD8+ cytotoxic, and in particular, regulatory T cells in draining lymph nodes of BC patients. However, we cannot find any uniformity in expressed clones among different T cell subsets. This may imply that breast tumors possess fewer or a more restricted set of antigenic determinants than do other types of tumors as mentioned in previous studies [23]. It may also suggest that, during antigen specific immune response, limited T cell clone(s) generated as a reaction to eliciting antigen(s), and their proliferation or activation, may not necessarily be prominent enough to be detected by mRNA analysis method. On the other hand, we preferentially studied stage II patients, whose immune system may not have had enough time to activate especial clones or represent oligoclonality in T cells. Nonetheless, to identify T cells important for, or related to, a specific tumor immune response, it should be advantageous to analyze T cells infiltrating into the tumor microenvironment at a resolution permitting the detection of potential tumor-specific clonal expansions in BC patients: as the clonotype analysis in several cancers have been proven to show an increase in the oligoclonality of T cells at tumor sites [34, 35]. Moreover, as our previous studies revealed, CD4+ T cells and CD8+ T lymphocytes differentiate to different effector subtypes [21, 36]. Analysis of the total CD4+ and CD8+ T cell populations alone would have concealed the clonotype of a diverse repertoire in each compartments as shown in the Memon et al. study [37]. Therefore, we highly recommend that the clonotype of each subset be determined separately. Nevertheless, we detect a drastically increased expression of one or more TRBV transcripts such as TRBV18 in most of patients, particularly in CD8+ subset. These TRBVs should be cloned and sequenced to verify whether they have the same sequence or not. In addition, since oligoclonality in LNs of healthy controls has not been previously examined, and no information is available thereof, we need to analyze additional samples to determine if restricted oligoclonality in CD8+ T cells is an LN-specific phenomenon. Isolation of these clones particularly TRBV18 usage, subsequent analysis of their TCR sequence, and investigation of the target antigens may provide new clues in BC immunology and immunotherapy.

Footnotes

Acknowledgments

The present study was a part of the PhD thesis written by Zahra Faghih, financially supported by grants from Shiraz University of Medical Sciences, Shiraz, Iran [Grant No. 89-5289] and Shiraz Institute for Cancer Research [ICR-100-500].

Conflict of interest

None of the authors have any competing interests.

Appendix

Suppl. Table 1

The median and mean total frequency of TRBV families in breast cancer CD4+ helper cells ( $n=$ 14)

TRBV	Non-expressed	Expressed	Statistics in expressed families
			Min	Max	Median	Mean	SD
TRBV2	12	2	1.37	1.56	1.47	1.47	0.13
TRBV3	3	11	1.35	15.01	7.74	7.39	4.98
TRBV4	8	6	0.96	6.43	4.43	3.95	2.13
TRBV5	1	13	6.09	49.86	10.84	16.03	14.04
TRBV6.1	0	14	6.86	59.1	33.16	34.71	15.60
TRBV6.4	8	6	0.96	17.74	3.47	5.81	6.25
TRBV7.2	1	13	5.23	20.61	9.47	10.80	4.61
TRBV7.6	10	4	0.4	8.86	2.43	3.53	3.88
TRBV9	0	14	2.93	41.11	23.55	23.34	9.20
TRBV10	0	14	6.07	61.42	28.78	30.01	17.05
TRBV11	0	14	3.89	27.21	14.55	15.70	7.64
TRBV12	0	14	8.97	44.36	14.72	17.62	8.88
TRBV13	3	11	0.6	31.37	4.17	7.06	8.94
TRBV14	1	13	2.34	54.03	13.10	18.81	13.53
TRBV15	3	11	2.97	55.26	12.62	17.51	15.64
TRBV16	14	0	–	–	–	–	–
TRBV18	0	14	9.11	27.25	14.20	15.66	5.01
TRBV19	0	14	9.36	29.41	22.68	21.66	6.44
TRBV20	1	13	2.15	43.93	19.33	18.44	13.37
TRBV21	14	0	–	–	–	–	–
TRBV23	14	0	–	–	–	–	–
TRBV24	0	14	6.14	48.38	16.90	18.71	11.93
TRBV25	6	8	1.78	13.2	7.39	6.87	3.99
TRBV27	0	14	4.02	56.47	20.43	24.80	13.99
TRBV28	1	13	3.41	51.97	8.27	13.45	13.02
TRBV29	0	14	12.58	40.88	19.35	20.28	7.43
TRBV30	12	2	1.37	1.56	1.47	1.47	0.13
No. of expressed clones			15	24	20	19.71	2.49

Suppl. Table 2

The median and mean total frequency of TRBV families in CD4+ helper cells of controls ( $n=$ 4)

TRBV	Non-expressed	Expressed	Statistics in expressed families
			Min	Max	Median	Mean	SD
TRBV2	4	0	–	–	–	–	–
TRBV3	1	3	0.88	5.42	1.70	2.67	2.42
TRBV4	1	3	0.68	9.54	5.03	5.08	4.43
TRBV5	0	4	6.68	11.15	7.81	8.36	1.95
TRBV6.1	0	4	15.39	29.29	18.11	20.23	6.19
TRBV6.4	3	1	2.04	2.04	2.04	2.04
TRBV7.2	0	4	3.47	9.09	4.96	5.62	2.65
TRBV7.6	4	0	–	–	–	–	–
TRBV9	0	4	8.86	25.74	15.86	16.58	8.36
TRBV10	0	4	8.85	23.64	17.61	16.93	7.16
TRBV11	0	4	5.74	15.23	8.67	9.58	4.45
TRBV12	0	4	13.99	17.85	14.60	15.26	1.77
TRBV13	3	1	–	–	–	2.77	–
TRBV14	1	3	2.15	7.89	7.68	5.91	3.26
TRBV15	0	4	2.87	18.88	9.86	10.37	8.27
TRBV16	4	0	–	–	–	–	–
TRBV18	0	4	6.78	14.2	7.85	9.17	3.45
TRBV19	0	4	10.43	22.79	16.84	16.73	6.44
TRBV20	0	4	2.91	6.77	6.13	5.49	1.79
TRBV21	4	0	–	–	–	–	–
TRBV23	4	0	–	–	–	–	–
TRBV24	0	4	11.44	17.87	13.75	14.20	2.68
TRBV25	3	1	–	–	0.78	0.78	–
TRBV27	0	4	3.35	29.63	17.20	16.85	12.23
TRBV28	0	4	1.15	12.7	8.32	7.62	5.40
TRBV29	1	3	9.79	12.13	11.40	11.11	1.20
TRBV30	0	4	2.67	15.71	2.72	5.96	6.50
No. of expressed clones			17	21	18.5	18.75	1.71

Suppl. Table 3

The median and mean of total frequency of TRBV families in breast cancer CD8+ cytotoxic cells ( $n=$ 13)

TRBV	Non-expressed	Expressed	Statistics in expressed families
			Min	Max	Median	Mean	SD
TRBV2	13	0	–	–	–	–	–
TRBV3	4	9	0.57	15.98	3.37	5.01	5.13
TRBV4	9	4	1.34	6.41	3.11	3.49	2.26
TRBV5	1	12	1.5	48.3	9.99	14.20	12.62
TRBV6.1	1	12	17.51	68.07	32.30	35.86	16.65
TRBV6.4	7	6	1.33	13.99	5.46	6.09	4.76
TRBV7.2	1	12	1.16	20.23	6.19	8.32	6.07
TRBV7.6	12	1	–	–	4.52	4.52	–
TRBV9	1	12	2.26	54.33	16.66	22.99	15.46
TRBV10	1	12	6.37	51.4	22.95	26.53	14.82
TRBV11	0	13	6.08	26.85	13.73	15.44	7.69
TRBV12	1	12	4.7	55.09	18.50	22.40	13.69
TRBV13	5	8	2.24	15.67	4.56	6.79	5.21
TRBV14	0	13	0.65	62.41	12.71	19.94	18.92
TRBV15	5	8	2.51	33.65	8.86	12.03	10.69
TRBV16	13	0	–	–	–	–	–
TRBV18	0	13	6.68	31.28	12.32	14.05	7.47
TRBV19	1	12	0.81	32.86	20.91	18.28	9.43
TRBV20	4	9	3.4	61.84	16.57	22.76	19.16
TRBV21	13	0	–	–	–	–	–
TRBV23	13	0	–	–	–	–	–
TRBV24	1	12	3.67	28.43	10.74	14.98	10.04
TRBV25	8	5	1.95	14.53	3.41	5.75	5.30
TRBV27	2	11	2.06	60.47	28.65	31.36	15.67

Suppl. Table 3, continued
TRBV	Non-expressed	Expressed	Statistics in expressed families
			Min	Max	Median	Mean	SD
TRBV28	3	10	0.83	27.4	3.33	7.85	9.15
TRBV29	2	11	3.83	31.59	13.81	15.08	8.37
TRBV30	3	10	1.12	26.96	3.52	7.63	9.10
No. of expressed clones			13	22	18	17.46	2.40

Suppl. Table 4

The median and mean of total frequency of TRBV families in CD8+ cytotoxic cells of controls ( $n=$ 3)

TRBV	Non-expressed	Expressed	Statistics in expressed families
			Min	Max	Median	Mean	SD
TRBV2	3	0	–	–	–	–	–
TRBV3	2	1	–	–	3.08	3.08	–
TRBV4	3	0	–	–	–	–	–
TRBV5	0	3	1.41	11.19	3.63	5.41	5.13
TRBV6.1	1	2	12.66	73.26	42.96	42.96	42.85
TRBV6.4	3	0	–	–	–	–	–
TRBV7.2	0	3	1.84	37.01	2.33	13.73	20.17
TRBV7.6	2	1	–	–	6.11	6.11	–
TRBV9	1	2	11.84	49.49	30.67	30.67	26.62
TRBV10	0	3	8.42	18.42	17.16	14.67	5.45
TRBV11	0	3	1.78	14.64	8.10	8.17	6.43
TRBV12	0	3	5.6	58.32	14.32	26.08	28.26
TRBV13	2	1	–	–	3.47	3.47	–
TRBV14	0	3	1.24	25.94	5.02	10.73	13.30
TRBV15	1	2	1.2	11.43	6.32	6.32	7.23
TRBV16	3	0	–	–	–	–	–
TRBV18	0	3	2.44	27.27	9.35	13.02	12.82
TRBV19	0	3	7.25	17.39	14.59	13.08	5.24
TRBV20	0	3	0.39	16.93	11.49	9.60	8.43
TRBV21	3	0	–	–	–	–	–
TRBV23	3	0	–	–	–	–	–
TRBV24	0	3	7.67	14.72	9.96	10.78	3.60
TRBV25	3	0	–	–	–	–	–
TRBV27	0	3	14.41	28.88	25.24	22.84	7.53
TRBV28	0	3	4.65	44.18	11.63	20.15	21.10
TRBV29	0	3	9.71	11.51	10.96	10.73	0.92
TRBV30	2	1	–	–	5.64	5.64	–
No. of expressed clones			14	20	15	16.33	3.21

Suppl. Table 5

The median and mean total frequency of TRBV families in breast cancer Treg cells ( $n=$ 10)

TRBV	Non-expressed	Expressed	Statistics in expressed families
			Min	Max	Median	Mean	SD
TRBV2	9	1	–	–	2.86	2.86	–
TRBV3	2	8	0.77	25.96	3.58	6.53	8.54
TRBV4	6	4	0.75	13.09	2.65	4.78	5.61
TRBV5	2	8	6.32	13.48	12.78	10.74	3.30
TRBV6.1	0	10	18.48	63.38	36.52	38.04	13.28
TRBV6.4	3	7	0.91	2.7	1.60	1.77	0.66
TRBV7.2	0	10	3.31	20.56	8.92	11.32	6.66
TRBV7.6	9	1	–	–	2.23	2.23	–
TRBV9	1	9	4.43	40.86	24.16	23.35	13.15
TRBV10	1	9	6.85	59.64	19.96	23.29	17.90
TRBV11	1	9	10.35	30.69	17.61	18.21	6.70
TRBV12	1	9	9.09	34.09	20.71	20.47	9.93
TRBV13	5	5	2.17	8.06	3.51	3.93	2.41
TRBV14	0	10	5.29	37.93	9.60	14.33	9.95
TRBV15	2	8	4.4	28.84	9.82	13.56	9.25
TRBV16	10	0	–	–	–	–	–

Suppl. Table 5, continued
TRBV	Non-expressed	Expressed	Statistics in expressed families
			Min	Max	Median	Mean	SD
TRBV18	0	10	2.73	25.46	15.50	15.68	6.75
TRBV19	2	8	3.82	32.73	15.18	18.44	10.55
TRBV20	0	10	2.02	35.19	7.68	11.17	10.54
TRBV21	10	0	–	–	–	–	–
TRBV23	10	0	–	–	–	–	–
TRBV24	1	9	4.21	36.76	12.52	16.44	9.96
TRBV25	7	3	0.27	8.94	2.81	4.01	4.46
TRBV27	1	9	6.76	40.38	21.13	22.30	10.72
TRBV28	2	8	2.5	31.04	4.75	9.59	10.01
TRBV29	1	9	6.29	38.82	12.86	18.69	12.20
TRBV30	3	7	0.78	48.54	10.94	18.12	19.56
No. of expressed clones			12	24	18	18.10	3.21

Supplementary data

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Analysis of T cell receptor repertoire based on V β chain in patients with breast cancer

Abstract

Keywords

1. Introduction

2. Materials and methods

2.1 Samples

2.2 Cell preparation and sorting

2.3 RNA extraction and cDNA synthesis

Table 1 Forward TRBV primers developed through sequences and new nomenclature of the TRBVs in IMGT database

3.1 Primer design

Table 2 Mixtures of the PCR products for GeneScan analysis

3.3 Run-off labeling reaction

3.4 Spectratype visualization

Table 3 Clinical and pathological characteristics of patients

4.1 TCR repertoire qualitative analysis of different T cell subsets in TDLNs of breast cancer patients

4.3 CD8+ T cell TRBV usage in TDLNs of BC patients and controls

4.4 TCR repertoire of Treg subset in TDLNs of BC patients

5. Discussion

Footnotes

Acknowledgments

Conflict of interest

Appendix

Supplementary data

References

Table 1
Forward TRBV primers developed through sequences and new nomenclature of the TRBVs in IMGT database

Table 2
Mixtures of the PCR products for GeneScan analysis

Table 3
Clinical and pathological characteristics of patients