Decreased Expression of T-Cell-Associated Immune Markers Predicts Poor Prognosis in Patients with Advanced Follicular Lymphoma Received Rituximab Plus Bendamustine

doi:10.21203/rs.3.rs-648976/v1

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Decreased Expression of T-Cell-Associated Immune Markers Predicts Poor Prognosis in Patients with Advanced Follicular Lymphoma Received Rituximab Plus Bendamustine

https://doi.org/10.21203/rs.3.rs-648976/v1

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Background: Several clinical risk stratification models have been proposed to predict the clinical outcomes of follicular lymphoma (FL) cases, however, few reports are available to predict prognosis of FL cases receiving bendamustine-based regimens. We previously examined the utility of rituximab-bendamustine (RB) treatment for newly diagnosed advanced FL, who showed non-optimal responses to two cycles of R-CHOP therapy.

Methods: In this study, we explored the biomarkers that could influence outcomes for the RB-treated FL cases in the context of the prospective cohort by target capture and sanger sequencing, and gene-expression profiling analyses using 50 diagnostic biopsies.

Results: We first examined the mutational status of 410 genes in tumor specimens derived from RB-treated cases. As reported before, CREBBP, KMT2D, MEF2B, BCL2, EZH2, CARD11, TNFRSF14, EP300, and APC were recurrently mutated, however, none of which was predictive for progression-free survival (PFS) in RB-treated cases. Similarly, the m7-FLIPI did not correlate with PFS or progression of disease within 24 months (POD24). A gene expression analysis using a panel of 770 genes associated with carcinogenesis and/or immune response showed that the expression of CD8+ T-cell markers (GZMM, FLT3LG, CD8A, CD8B, GZMK) and half of the genes regulating Th1 and Th2 responses were significantly lower in the POD24 group than in the noPOD24 group. Finally, we selected 10 genes (TBX21, CXCR3, CCR4, CD8A, CD8B, GZMM, FLT3LG, CD3E, EOMES, GZMK), and dichotomized RB-treated cases into immune infiltration^high (infil^high) and infiltration^low (infil^low) clusters. The 3-years PFS rate was lower in the infil^low cluster than in the infil^high cluster (50.0% [95% CI: 27.1–69.2%] vs. 84.2% [95% CI: 58.7–94.6%], p=0.0237). Of note, the proportion of cases with peripheral lymphopenia (<869/mL) at diagnosis was higher in the infil^low cluster than in the infil^high cluster (38.5% vs. 9.09%, OR: 6.25 [95%CI, 1.20-32.7], p=0.0235).

Conclusion: These results suggest that the T-cell-associated immune markers could be useful to predict prognosis in RB-treated FL cases.

Trial registration: This trial was retrospectively registered in UMIN on April. 24, 2014 (UMIN000013795; http://www.umin.ac.jp/icdr/index-j.html).

Oncology

Follicular lymphoma

Bendamustine

Lymphopenia

T-cell

Tumor microenvironment

POD24

Follicular lymphoma (FL) is the most common indolent subtype of B-cell non-Hodgkin lymphomas¹. The majority of FL patients present with advanced stages¹, and those with high tumor burden and/or disease-related symptoms require immunochemotherapy.

To date, several chemotherapy regimens, including CHOP (cyclophosphamide, doxorubicin, vincristine, and prednisone), CVP (cyclophosphamide, vincristine, and prednisone), and bendamustine (B) in combination with anti-CD20 monoclonal antibodies such as rituximab (R) or obinutuzumab (G)^2,3 have been recommended as front-line therapies in several guidelines^4–8. Most of the FL patients receiving these front-line immunochemotherapy regimens can obtain long-term survival benefit⁹. However, about 10–20% of the patients experience progression of disease within 24 months of diagnosis or treatment initiation (which is called POD24), and these patients have a substantially increased risk of death within 5 years after diagnosis^9–12.

Given the heterogeneity of disease courses, several clinical risk stratification models, including Follicular lymphoma International Prognostic Index (FLIPI-1 and FLIPI-2), PRIMA-PI, and FLEX were proposed to predict the clinical outcomes of FL patients such as progression-free survival (PFS) and overall survival (OS)^13–16. However, all of these models have limited value to predict POD24. Meanwhile, accumulated findings from genetic and gene-expression profiling analyses showed that several molecular risk models mainly based on the intrinsic tumor properties, such as m7-FLIPI¹⁷, POD24-PI¹⁸ and 23-gene-expression profiling (GEP) score^19,20, were shown to be a useful tool to identify FL patients at high- or low-risk of progression after treatment with immunochemotherapy. In addition, a gene expression signature based on the extrinsic tumor environment, particularly focusing on the tumor microenvironment (TME) of FL, was established to predict POD24¹¹. However, these models are mainly based on the data from the patients who received R-CHOP or R-CVP, and few reports are available to predict the prognosis of FL patients receiving B-based regimens.

We previously conducted a multicenter prospective phase 2 (CONVERT) trial, and demonstrated the utility of RB for treatment-naive advanced FL showing non-optimal responses (nOR) to the initial two cycles of R-CHOP therapy²¹. In addition, we reported that peripheral lymphopenia (< 869/uL) before treatment was an independent poor prognostic factor for RB-treated FL patients in both our trial and validation cohorts²¹. Thus, the main object of this study is to identify molecular biomarker(s) that could predict outcomes of RB-treated FL patients by genetic and gene-expression profiling analyses.

Study design and patients

The design and characteristics of the registered patients in the CONVERT trial were described previously²¹. In brief, newly diagnosed adult patients with advanced (stage III/ IV or stage II with bulky disease), CD20-positive FL, with histologic grade 1-3a (excepting low FLIPI-2) were eligible for this trial. All patients initially received two cycles of standard R-CHOP. Patients who achieved complete response (CR) /complete response unconfirmed (CRu) according to Cheson’s morphological criteria 1999²² (defined as an optimal response [OR] group) further received 4-6 cycles of R-CHOP treatment. The remaining patients who failed to achieve CR/CRu (defined as a non-optimal response [nOR] group), subsequently received six cycles of RB (R 375 mg/m² d1, B 90 mg/m² d1-2) every 28 days.

This study was designed and conducted according to the Declaration of Helsinki and was registered in UMIN (000013795) and jRCT (051180181). All patients provided written informed consent. The protocol, informed consent forms, and any amendments were approved by the institutional review boards of each hospital and certified review board.

Target capture sequencing assay

DNA was extracted from formalin-fixed paraffin-embedded (FFPE) samples using a DNA storm kit (Cell Data Sciences, Inc). The targeted DNA library for panel sequencing that comprises approximately 1.65 Mb coding regions of all exons in 409 genes (Supplementary Table 1) was constructed using an Oncomine Tumor Mutation Load Assay (Thermo Fisher Scientific) according to the manufacturer’s protocol. In brief, 20 ng DNA was subjected to multiplex PCR amplification with an Ion AmpliSeq Library Kit 2.0. After multiplex PCR, Ion Xpress Barcode Adapters (Thermo Fisher Scientific) were ligated to the PCR products, which were then purified with the use of AMPure XP beads (Beckman Coulter, Brea, CA). The purified libraries were pooled and then sequenced with an Ion Torrent S5 instrument and Ion 550 Chip Kit (Thermo Fisher Scientific). DNA sequencing data were accessed through the Torrent Suite ver. 5.10 program (Thermo Fisher Scientific). Reads were aligned against the hg19 human reference genome, and variants were analyzed with the use of Ion reporter ver. 5.10 (Thermo Fisher Scientific). Raw variant calls were manually checked using the integrative genomics viewer (IGV, Broad Institute). In this analysis, we defined the following criteria for the sequence quality (1) the uniformity of amplicon coverage of >60%, (2) the minimum quality criterion was 80% of target bases with ≥ 100x sequencing coverage. The mutations were filtered with the exclusion of (1) Fisher’s exact P-value <0.01, (2) Phred QUAL score of <99, (3) allele frequency in tumor <0.025, and missense SNVs with an allele frequency of 0.45–0.55 in copy-neutral regions, unless they were listed in the COSMIC database (v70) or reported to be mutated in FL. Germline mutations were excluded with the NCBI dbSNP build 131 and UCSC genome browser database^23,24.

Sanger sequencing analysis

Exon 2 and 3 of MEF2B were analyzed using the PCR primers designed with Primer3 (Supplementary Table 2). The MEF2B coding exons 2 and 3 were amplified by PCR as described previously²⁵. After denaturation step at 94°C for 5 min, PCR consisting of denaturation (94°C for 30 sec) annealing (56°C for 30 sec), and extension steps (72°C for 30 sec) were repeated until 35 cycles, followed by final extension step at 72°C for 7 min on a GeneAmp PCR system 9700 (Thermo Fisher Scientific). After visual confirmation of amplification, the PCR product was submitted for sanger sequence analysis as previously described²⁵.

The m7-FLIPI score calculation

The m7-FLIPI score was calculated by using the calculator presented at the German Low-Grade Lymphoma Study Group official internet site (www.glsg.de/m7-flipi/). The same cut-off level as used in the original publication was applied to determine high- and low-risk categories (m7-FLIPI score ≥0.8 and <0.8, respectively)¹⁷.

Gene-expression profiling analysis

RNA was extracted from FFPE samples using an RNA storm kit (Cell Data Sciences, Inc). Details of the nCounter assay (NanoString Technologies, Seattle, WA, USA) have been reported previously^24,26. In this study, the customized PanCancer immune-profiling panel (NanoString Technologies), which consists of 770 genes related to cancer or immune cells and additional 30 genes (Supplementary Table 3), was used for nCounter-based gene-expression measurements. The data were analyzed by using nSolver 4.0 software (NanoString) and JMP version 13.0 software (SAS Institute).

Statistical analysis

The cutoff date for this analysis was December 31, 2019. The survival analysis was performed by the Kaplan-Meier method, which was evaluated by a log-rank test. Fisher’s exact test was used for between-categorical data comparisons. For the comparison of two continuous variables data were tested by Wilcoxon rank sum test. Statistical analyses were performed with R 4.1.0 software (The R Foundation for Statistical Computing), and p value <0.05 was considered statistically significant.

Patient characteristics and clinical outcomes

A total of fifty-six patients initially received two cycles of R-CHOP. Then, 13 patients (23.2%), who achieved CR/CRu (optimal response [OR]-group), continued R-CHOP for further 4-6 cycles. The remaining 43 patients (76.8%), who were judged as nOR, received RB as the protocol treatment. The FFPE samples were available for extracting sufficient and undegraded DNA and/or RNA in 50/56 cases (89.3%) for this study. The baseline characteristics of these 50 cases were shown in Table 1. At a median follow-up time of 47.4 months (range: 3.20–79.3), PFS and OS rates at 3 years in all cases (n=50) were 71.0% (95% CI: 56.0-81.7%) and 93.5% (95% CI: 81.2-97.9%), respectively (Supplementary Fig. 1a, b). The 3-years PFS rates were 87.5% in the OR-group (95% CI: 38.7-98.1%) and 67.5% in the nOR-group (95% CI: 50.7-9.7%), respectively, with no statistically significant difference (p=0.213). The 3-years OS rates were 100% in the OR-group (95% CI: not applicable) and 92.2% in the nOR-group (95% CI: 77.6-97.4%, p=0.283) (Supplementary Fig. 1c,1d).

Mutational landscape of the whole population

To investigate the mutational landscape in this cohort, we performed the target capture sequence using tumor samples from the above described 50 cases. Eight cases were excluded from this analysis because of their poor DNA qualities. Thus, 42 cases (OR n=8; nOR n=34) were analyzed on the targeted panel for 409 genes, in which most of the recurrently mutated genes in FL were included excepting MEF2B. MEF2B encodes a transcriptional activator and is mutated in 10∼20% of FL and its mutational status is applied in m7-FLIPI²⁷. Thus, we analyzed its mutation status by sanger sequencing independently. In this analysis, we focused on the exon 2 and exon3 of MEF2B, because most of MEF2B mutations in FL were scattered around these regions²⁸. As a result, MEF2B mutations were identified in 13/42 cases (31.0%), all consisting of L67R in the exon 3, which mutated site was reported to play an important role in the interaction with EP300 proteins²⁸. The double mutations with L67R and G2R in exon 2 were identified in one case (Fig. 1a).

When the results from target capture sequencing and sanger sequencing were combined, a total of 34 genes was found to be recurrently mutated (in ≥2 cases), including 107 non-synonymous single nucleotide variants (SNVs), 19 multiple mutations, 16 frameshift insertions or deletions, 13 stop-gain mutations, and 7 non-frameshift insertions or deletions. Overall, 37/42 cases (88.1%) had at least one coding mutation within 410 genes with a median of 4 mutations per patient (range 0–10) (Fig. 1a), and the average depth of sequencing coverage in the 37 cases was 882X (103-3,999X). The most recurrently mutated genes were CREBBP (59.5%), KMT2D (38.1%), MEF2B (31.0%), BCL2 (28.6%), EZH2 (21.4%), CARD11 (16.7%), TNFRSF14, and EP300 (14.3% each), APC (11.9%), with the landscape closely resembling the previous reports^1,29,30 (Fig. 1b). Positions and types of somatic mutations in these genes were shown in Supplementary Fig. 2.

Association of mutation profile with clinical outcomes in the nOR-group

Among recurrently mutated genes, their mutation frequencies (in ≥10%) in the OR- and nOR-groups were shown in Table 2. Next, we analyzed whether mutation status influenced the outcomes in the nOR-group (n=34). As shown in Fig. 2a, the mutation status of the specific gene did not influence PFS significantly in univariate analyses. We also evaluated the influence of gene mutations on POD24. Among 7 cases with POD24 and 27 cases without POD24, 28.6% (2/7) of POD24 cases harbored PTEN or CIC mutations as compared with 0% (0/27) in cases without POD24, with a statistical difference (p=0.0374) (Fig. 2b). Interestingly, no POD24 event occurred in RB-treated cases with CARD11 mutations in the nOR-group. In addition, the cases with CARD11 mutations tended to have better 3-years PFS rates than unmutated cases but with no statistically significant difference (100% vs. 59.3%, p=0.174) (Fig. 2c).

Association of m7-FLIPI with clinical outcomes

We next investigated whether m7-FLIPI could be applied to this total cohort (n=42). As expected, no cases (0/8) in the OR-group were classified into the high-risk category in the m7-FLIPI as compared with 29.4% (10/34) in the nOR-group but without a significant difference (p=0.165) (Table 2). The high-risk group tended to have an inferior 3-years PFS rate than the low-risk group (76.8% [95% CI: 57.4%–88.2%] vs. 60.0% [95% CI: 25.3%–82.7%]) but without a significant difference (p=0.104) (Fig. 3a).

Next, we focused on RB-treated cases (n=34) in the nOR-group. In this group, the proportion of cases in the low- and high-risk category in m7-FLIPI was 70.6% (24/34) and 29.4% (10/34), respectively (Table 2). At a median follow-up time of 48.7 months (4.07-79.3), the 3-years PFS rates were 70.8% in the low-risk group (95% CI: 48.4-84.9%) and 60.0% in the high-risk group (95% CI: 25.3-82.7%), respectively, without a significant difference (p=0.264) (Fig. 3b). Among 24 cases in the low-risk group, 4 cases experienced POD24 (16.7%), while 3 from 10 cases in high-risk group experienced POD24 (30%) without a significant difference (OR: 0.467, [95%CI: 0.0830-2.62], p=0.394) (Fig. 3c).

Association of the 23-gene expressions with clinical outcomes

We next analyzed gene expression levels by nCounter system using the FFPE samples from 48 cases. Sample quality was assessed by mRNA levels of 40 housekeeping genes in each sample (Supplementary Table 3). It was previously reported that the GEP score based on the expression of 23 genes was associated with clinical outcomes of FL cases¹⁹, which was confirmed by the following study²⁰. Furthermore, the 23 genes could identify two main clusters characterized by high- or low-expression associated with favorable and poor outcomes²⁰. So, we subjected our 48 cases to hierarchical clustering using these 23 genes. Consistent with the previous reports^19,20, we could identify two main clusters (cluster 1 and cluster 2) (Fig. 4a), however, at a median follow-up time of 47.4 months (3.2-79.3), 3-years PFS rates did not differ significantly between the two clusters (74.7% [95% CI: 55.6%–86.5%] vs. 60.0% [95% CI: 31.8%–79.7%], p=0.436) (Fig. 4b).

Next, we compared 3-years PFS between the cluster 1 and 2 in the RB-treated 39 cases. At a median follow-up time of 46.1 months (3.2-79.3), the cases in cluster 2 tended to have an inferior 3-years PFS rate compared with those in cluster 1 (50.0% [95% CI: 18.4%–75.3%] vs. 72.4% [95% CI: 52.3%–85.1%]) but without a significant difference (p=0.263) (Fig. 4c). We further compared the expression of 23 genes between 8 cases with POD24 and 31 cases without POD24. Although these genes were reported to portend good or poor risk, the expression levels of these genes were almost equivalent in the two groups (Supplementary Fig. 3).

Association of the T-cell-associated gene expressions with clinical outcomes

In this trial, we previously reported that a low absolute lymphocyte count (ALC) was an independent poor prognostic factor for RB-treated patients²¹. Because lymphopenia was considered to reflect an immune-suppressive state, we applied the customized pan-cancer immune-profiling panel, which consists of 770 genes related to cancer development and/or immune reactions, to the RB-treated cases in the nOR-group (n=39). As a result, a total of 33 genes, including 2 upregulated (CD79a and POU2F2) and 31 downregulated genes (each with log2 fold change > |0.5|, and corrected [-log10P] >1.5) were differentially expressed in the POD24 group (n=8) compared with in the noPOD24 group (n=31). Of interest, the top genes downregulated in the POD24 group were markedly enriched with the T-cell-associated genes (Fig. 5a).

Thus, we further compared the expression levels of molecules specific for various types of T cells (Th1, Th2, and Th17, T follicular helper [Tfh], regulatory T [Treg], NK, and CD8⁺ T cells) and of immune checkpoint markers between the two groups. Interestingly, the expression of CD8⁺ T cell markers (GZMM, FLT3LG, CD8A, CD8B, GZMK) except for PRF1 was significantly lower in the POD24 group than in the noPOD24 group (Fig. 5b). Furthermore, about half of the genes related to Th1 and Th2 responses in T cells were downregulated in the POD24 group compared to those in the noPOD24 group. In contrast, Treg, NK, and immune checkpoint markers were almost equivalently expressed in the two groups (Supplementary Fig. 4).

Next, we selected 10 genes (TBX21, CXCR3, CCR4, CD8A, CD8B, GZMM, FLT3LG, CD3E, EOMES, GZMK) by manual curation to integrate biological aspects mainly focused on the CD8⁺ T cell, and performed unsupervised hierarchical clustering using these genes. As a result, we could dichotomize 39 RB-treated cases into immune infiltration^high (infil^high) and infiltration^low (infil^low) clusters (Fig. 5c). The 3-years PFS rate was significantly lower in the infil^low cluster than in the infil^high cluster (50.0% [95% CI: 27.1%–69.2%] vs. 84.2% [95% CI: 58.7%–94.6%], p=0.0237) (Fig. 5d). Of note, POD24 was observed in 8/20 (40%) cases in the infil^low cluster, and no POD24 event occurred in the infil^high cluster with a significant difference (p=0.0084) (Fig. 5e).

Association of the T-cell-associated gene expressions with their mutation profile

We finally analyzed whether gene mutations influence the expression of T-cell-associated genes in all cases. However, the frequencies of mutations were roughly the same between infil^low and infil^high clusters in most genes (Fig. 6a). For example, the mutation frequencies in CREBBP³¹, EZH2³², and T/NFSF14³³, which are supposed to regulate the tumor microenvironment, did not differ significantly between the two clusters. In contrast, a significant difference was noticed for the CARD11 mutations. In the infil^high cluster, 31.2% (6/19) of the cases harbored CARD11 mutations compared to 4.76% (1/21) in the infil^low cluster (p=0.0395) (Fig. 6b). Of note, the proportion of cases with low ALCs was higher in the infil^low cluster than in the infil^high cluster (38.5% vs. 9.09%, OR: 6.25 [95%CI, 1.20-32.7], p=0.0235) (Fig. 6c).

In this study, we attempted to identify biomarker(s) that can predict clinical outcomes of RB-treated FL cases. At first, we examined gene mutational profiles in our cases. As a result, we found that types of mutated genes and their frequencies were roughly the same as previously reported^1,29,30, and we found that none of those mutations influenced PFS in univariate analyses. Among them, mutations of CARD11, which functions as a positive regulator of the NF-kB pathway in normal B and T lymphocytes, were detected in 16.7% of the cases (7/42). CARD11 mutation is more frequently observed in transformed FL than in untransformed FL^29,34 and counted as a poor prognostic factor in m7-FLIPI¹⁷. In accord with these results, all cases harboring CARD11 mutations didn’t show optimal responses to the initial 2 cycles of R-CHOP and were classified into the nOR-group. However, all these cases achieved and maintained CR at least for 3 years by the early conversion to RB treatment. These results raised a possibility that the negative impact of CARD11 mutations observed under R-CHOP treatment might be canceled by RB treatment. Meanwhile, 28.6% (2/7) of RB-treated cases with POD24 harbored PTEN or CIC mutations as compared with 0% (0/27) in cases without POD24 with a statistical difference (p = 0.0374). However, further studies are necessary to elucidate the clinical significance of PTEN or CIC mutations in RB-treated FL cases.

We also assessed whether m7-FLIPI is useful to predict prognosis of our RB-treated FL cases. Although the 3-years PFS tended to be higher in the m7-FLIPI low group than in the m7-FLIPI high group (70.8% vs. 60.0%), this difference was not significant (p = 0.264). Also, whereas m7-FLIPI was reported to be able to predict POD24 in R-CHOP-treated FL patients, POD24 was observed regardless of m7-FLIPI risk groups in our BR-treated cases (high 30% vs. low 16.7%, p = 0.394). It must be noted that although the results reported in this study may lack statistical power due to the small number of cases. In accord with our results, however, m7-FLIPI had no prognostic value in RB- or GB (G + B)-treated FL cases, as reported in a randomized phase III (GALLIUM) trial^3,35. Together, these results suggest that m7-FLIPI may not be an accurate prognostic factor in FL cases treated with B-based regimens.

The GEP score based on the expression of 23 genes was initially established to predict clinical outcomes in FL patients¹⁹, and based on the 23-gene expression panel, it was possible to identify two main clusters associated with favorable and poor outcome in the subsequent study²⁰. However, these studies included only a few cases treated with RB. Subsequently, this model was shown to have no prognostic value in RB- or GB-treated cohort in the GALLIUM study³⁶. In accord with this result, we found that the 3-years PFS rates didn’t differ between the cases in cluster 1 and cluster 2 (72.4% vs. 50.0%, p = 0.263). However, cluster 2 cases tended to have an inferior 3-years PFS rate compared with cluster 1 cases. Considering the small number of cases in this study, further studies are needed to draw a definite conclusion as to the efficacy of this model in predicting the prognosis of B -treated FL cases.

Several lines of evidence indicate that tumor cells depend on the interactions with non-malignant cells that constitute TME for their growth and survival^1,37. Particularly, because of the indolent feature of FL cells, the prognosis of FL is substantially affected by TME, which consists of various types of T lymphocytes (cytotoxic T cells [CTLs], Tregs, and so on), B lymphocytes, and tumor-associated macrophages (TAMs). These cells act as positive or negative regulators of immune response in FL, thereby influencing the clinical outcomes in this disease. Based on this concept, Tobin JWD, et al. analyzed the immune infiltration profile in FL cases with or without POD24, and observed that a low expression of immune effectors (TNFα, CD4), checkpoints [programmed death-ligand 2 (PD-L2)], or macrophage markers (CD68), were associated with POD24. Among them, PD-L2 was the most sensitive marker in identifying patients with poor prognosis in R-CHOP-treated patients¹¹.

To elucidate the roles of the immune microenvironment in RB-treated FL cases, we have applied the customized pan-cancer immune-profiling panel consisting of 770 genes to the RB-treated cases in the nOR-group. As a result, we found that CD8⁺ T cell markers such as CD8A, CD8B, FLT3LG, GZMM, and GZMK, were downregulated in the POD24 group, while markers for Treg and NK/T cells and immune checkpoint molecules were almost equivalently expressed in cases with or without POD24. Among the genes downregulated in the POD24 group, GZMM and GZMK, which are commonly expressed in CTLs and NK cells, have pro-apoptotic activities on tumor cells³⁸. A low expression of GZMM and GZMK in tumor tissues was associated with an unfavorable prognosis in cutaneous melanoma³⁹, and GZMK was also found to be downregulated in the process of FL transformation⁴⁰. Together, these results suggest that anti-FL immune responses may be weakened in RB-treated FL cases undergoing POD24. However, future studies using high throughput technologies, such as single cell transcriptome sequencing, multicolor immunohistochemistry, and imaging mass cytometry are needed to determine which T cell subpopulations indeed express each cytotoxic molecule.

We further classified RB-treated cases into infil^high and infil^low clusters using manually selected 10 genes (TBX21, CXCR3, CCR4, CD8A, CD8B, GZMM, FLT3LG, CD3E, EOMES, GZMK). The PFS rate at 3 years was significantly lower in the infil^low cluster than in the infil^high cluster (p = 0.0237). In addition, POD24 was observed in 8/20 (40%) cases in the infil^low cluster, while none of infil^high cases underwent POD24 (p = 0.0084). These results suggest that our classification would be useful to predict clinical outcomes including POD24 in RB-treated FL cases. Also, given the results presented here, the combination of RB with a novel therapy, which can restore the number and/or function of T cells such as lenalidomide^41,42, would be a promising strategy to improve prognosis of the cases with infil^low immune profile. However, it should be noted that our RB-treated cases constitute a unique population, namely those who received RB after two cycles of R-CHOP. Therefore, future studies are required to determine whether our results are applicable to de novo FL cases treated with a B-based regimen as the 1st -line treatment using larger prospective cohorts.

To clarify molecular mechanisms underlying the difference in infil^low and infil^high immune profiles, we compared the frequency of mutations in both clusters. However, the frequencies of mutations were roughly the same between infil^low and infil^high clusters in most genes, especially that regulate TME such as CREBBP, EZH2, and T/NFSF14, excepting CARD11. These results raise the possibility that epigenetic regulation might be different between the two clusters. Of importance, the only significant clinical parameter related to the infil^low immune profile was lymphopenia, which we previously identified as a poor prognostic marker for RB-treated cases ²¹. These results suggest that lymphopenia at diagnosis may reflect reduced T cell-mediated immune reactions in the TME and that it may be able to substitute for the immune profiling utilized in this study.

We here found that the expression of CD8⁺ T cell markers was significantly lower in RB-treated cases with POD24 than those without POD24. Furthermore, FL cases were classified into infil^low and infil^high clusters based on the expression of T cell-associated genes, which was useful to predict the prognosis of RB-treated FL cases.

RB: rituximab-bendamustine

FL: follicular lymphoma

B: bendamustine

R: rituximab

G: obinutuzumab

POD24: progression of disease within 24 months

FLIPI: Follicular lymphoma International Prognostic Index

PFS: progression-free survival

OS: overall survival

TME: tumor microenvironment

CR: complete response

Cru: complete response unconfirmed

FFPE: formalin-fixed paraffin-embedded

SNVs: single nucleotide variants

GEP: gene-expression profiling

ALC: absolute lymphocyte count

Tfh: T follicular helper

Treg; regulatory T

infil^high: immune infiltration^high

infil^low: immune infiltration^low

CTLs: cytotoxic T cells

TAMs: tumor-associated macrophages

FDCs: follicular dendritic cells

GELF: Groupe d'Etude des Lymphomes Folliculaires

Ethics approval and consent to participate

Consent for publication

Not applicable

Availability of data and materials

The datasets generated and/or analyzed during the current study are available in the source data files. (Persistent web link to datasets)

Competing interests

I.M. has received research funding from Chugai and Eizai Pharmaceuticals. S.R. has received payment for lectures from Chugai Pharmaceuticals.

No other potential conflicts of interest were reported.

Funding

Not applicable

Author contribution

S.R. and Y.T. designed and H.T., and I.M., supervised the research. S.R., H.I., H.H., M.M., Y.M., Y.W., C.H., T.H., T.K., K.S., Y.T., Y.M., T.A., K.S., K.N., and H.T. contributed to date collection and analysis. S.R., and H.T. performed statistical analysis. S.R., Y.W., K.S., K.N., H.T. and I.M. performed the research and prepared figures and tables. S.R., L.E. and H.T., and I.M. wrote the manuscript. And all authors read and approved the manuscript.

Acknowledgements

The authors thank the patients who participated in the study, their supporters, the investigators, and clinical research staff from the study centers.

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Decreased Expression of T-Cell-Associated Immune Markers Predicts Poor Prognosis in Patients with Advanced Follicular Lymphoma Received Rituximab Plus Bendamustine

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