In this study, a combination of image analysis and spatially resolved multi-omics was applied to determine frequencies and molecular profiles of tumor cells, along with early- and late- stage differentiated T-helper and T-cytotoxic subsets (TH,57−, TC,57−, TH,57+, TC,57+) in a population-based cohort of 102 MCL patients (Fig.1). Fine-tuning of deep learning-based image analysis models is required to retrieve accurate measurements of cell frequencies in MCL. Cell frequencies are an important metrics to describe the biology of the MCL TIME. mIF (DNA, CD3, CD8 and CD57) was used to determine the abundance and diversity of the four subsets of T-cells in 102 MCL patients in tumor-rich and tumor-sparse regions, respectively (Fig.2A, Additional file 1: Fig. S2A). The fine-tuned Cellpose model provided improved accuracy in nuclei identification and cell segmentation/identification compared to untuned models (Fig.2B and 2C). Compared to a random forest (RF) based cell classifier, our workflow also showed better accuracy in cell identification, particularly in cases with variable staining background (Fig.2C). Overall, this optimized workflow handled varying background, staining artifacts, and signal bleed over in most tissue cores (Fig.2C) and accurately identified and classified cells (Fig.2D). CD57 + T-cells constitute a significant proportion of infiltrating T-cells in MCL. In tumor-rich regions, total CD3 frequency varied from 1.7–16.8%. The median (± SD) frequencies of the four T-cell subsets investigated were 4.9 ± 4.6% (TH,57-), 2.5 ± 2.9% (TC,57−), 0.5 ± 0.4% (TH,57+) and 0.7 ± 1.5% (TC,57+) (Fig.3A). Thus, T-cells in tumor-rich regions were dominated by TH,57− (56.6 ± 16.4% out of all CD3 + cells), and TC,57− (28.7 ± 10.6%) while the CD57 positive subsets constituted 4.6 ± 3.1% and 8.3 ± 8.9%, respectively (TH,57+ and TC,57+) (Additional file 2: Table S5). The frequency of T-cell infiltration in the tumor-rich regions was highly variable between patients (Fig.3C and Additional file 1: Fig. S2E). Of interest, no correlation was observed between the T57+/T57− ratio and CD3 + cell frequency (R: -0.094, p ~ ns) indicating that infiltration of highly differentiated sub-sets is independent on total T-cell infiltration (Additional file 1: Fig. S2B and S2C). TC and late-stage differentiated, CD57 + T-cell subsets are enriched among MCL-infiltrating T-cells. Differences in T-cell subtypes between tumor-rich and tumor-sparse regions were compared in a subset of patients (n = 39) were both regions were available (Fig.3B and Additional file 2: Table S5). The ratio of TH/TC (CD4/CD8) was lower in tumor-rich regions, suggesting a higher percentage of TC among tumor infiltrating T-cells in MCL. In paired analysis, the percentage of TC among TH (irrespective of CD57 status) was 34.7% in tumor-rich vs 25.3% in tumor-sparse regions Additional file 2: Table S5). Regarding cell differentiation stage, the T57+/T57- cell percentage (16.5 vs 4.2%) was significantly higher in the tumor-rich compared to tumor-sparse regions (Additional file 2: Table S5, Fig.3). Thus, TC and CD57 + T-cell subsets are highly enriched among infiltrating T-cells in tumor-rich compared to tumor-sparse regions. Diversity among T-cell sub-types is not associated with total CD3 infiltration. The Shannon-Wiener Diversity Index (SDI), allowed us to explore the complexity of the T-cell populations, including the investigated four subsets. A low SDI score indicates that one of the T-cell subsets are dominating among the total T-cells, while a high score indicates that multiple T-cell sub-types contribute to the total number of T-cells. We show that T-cell subset complexity (SDI score) was independent of total CD3 frequency (Fig.3C and 3D) but associated to the presence of late T-cell differentiation stages (Fig.3E). Thus, increased T-cell infiltration per see will not automatically lead to the presence of both TH and TC, or well differentiated T-cells. TC,57+ cells are associated with highly proliferative MCL, while total CD3 + T-cell infiltration is positively associated with favourable prognosis. We investigated whether the abundance of the four T-cell subsets in tumor-rich regions were associated to established clinicopathological parameters, including gender, age, Ki-67, TP53 mutational status and p53 protein level (Additional file 2: Table S6). Female gender was shown to be positively correlated to high Tc cell frequency (median cut-off, chi-square p = 0.05). High Ki-67 expression in MCL cells (> 30%) was shown to be positively correlated with high TC,57+ frequency (median cut-off, chi-square p = 0.0051), and group-wise comparison supported the trend but was not significant (Additional file 1: Fig. S2D). Survival analysis showed that higher infiltration of total T-cells, and individual T-cell subsets including TC,57−, and TH,57+ were associated with favorable prognosis in unadjusted models (Additional file 2: Table S7, Additional file 1: Fig. S4). The optimal cut-off (8.4%) for CD3 was in line with our previous study cohort (10.1%) [26], but prognostic information on CD57 + T-cells have not previously been reported in MCL. We further compared the median overall survival for patient where either both tumor-sparse and tumor-rich regions (5.69 years), or only tumor-rich regions (4.22 years) were available, but the difference was not significant using log-rank statistics (Additional file 1: Fig S2D). We conclude that the enrichment of infiltrating TC and CD57 + sub-sets indicates that they mediate important function in the MCL TIME. The tentative functionality of the individual subsets was further investigated using molecular profiling as described below. Spatially guided comparison of T H and TC subtypes in tumor-rich versus tumor-sparse regions of MCL tissue. The molecular profile of TH- and TC− cells was compared in tumor-rich versus tumor-sparse regions. Targeted proteomic (n = 63) (Additional file 2: Table S3) and transcriptomic panel (n = 1482) were explored (Additional file 2: Table S4). Of note, too few AOIs from CD57 + T-cells were available to allow analysis of such subsets in tumor-sparse regions, and analysis was focused on CD57- TH and TC subsets. Deconvolution analysis supports data on well differentiated T-cells of memory type among infiltrating T-cells in MCL. Cells in the dense MCL tissue are in direct cell-to-cell contact, leading to AOIs including information also from the most adjacent cells, beyond targeted cell-type. Despite being a technical drawback, this can be explored biologically. Considering this, the difference in cell type abundance between tumor-rich and tumor-sparse regions was assessed using deconvolution of the transcriptome data from 25 patients. This showed that macrophages, myeloid dendritic cells and plasmacytoid dendritic cells were more abundant in tumor-sparse compared to tumor-rich regions, indicating close contact between T-cells and antigen presenting cells (APC) in the tumor-sparse region (Fig.4B). Furthermore, regulatory T-cells (Tregs) were predicted to be of higher proportion in the Tc compartments in tumor-sparse vs tumor-rich regions. As expected, Tregs were predicted to be more common among TH compared to Tc in tumor-rich regions. However, it should be pin-pointed that no difference in Treg proportion was seen comparing TH and TC in tumor-sparse regions. In addition, CD4 + T-cell memory cells were predicted to be more abundant in tumor-sparse while CD8 + T-cell memory cells were more abundant in tumor-rich regions. This suggests that CD8 + T-cells not only are more abundant among infiltrating cells as reported above, but also more differentiated which is in line with the reported CD57 + enrichment. T-cells in tumor-sparse regions show increased use of TNF-related pathways in T H cells and higher levels of T-cell suppressive proteins such as VISTA, TIM3, LAG3, and IDO1. Gene set enrichment analysis identified common and unique pathways activated in TC and TH collected in tumor-sparse regions, when compared to tumor-rich (Fig.5A). Of note, TNF-related signalling in Tc and PD-L1/PD-1 checkpoint pathway was increased in TH collected in tumor-sparse regions. Transcripts associated with the pathways were validated by differential gene expression analysis (Fig.5B-C,Additional file 2: Table S8). For the TNF signalling pathway, 15/23 transcripts were confirmed to be individually de-regulated when comparing the expression in tumor-rich versus tumor-sparse regions. These included MAPK3K5, MAPK14, BIRC3, CASP8, TNFRSF1B, AKT3, CASP10, IL18R1, CYLD, PIK3R1, TNFRSF1A, TRAF1, FOS, SOCS3 and CCL2. Key transcripts include CCL2, which is involved in leukocyte recruitment, regulators of PI3K-AKT signalling (TRAF1, PIK3R1, AKT3) and MAPK signalling (MAPK14, MAP3K5, CYLD, TNFRSF1A/B, CASP8/10). As expected, signals to attract and retain T-cells were enriched in these tumor-sparse regions, that are abundant in T-cells. These included, for example, CCL21, CCL19, SELL (selectin L) and CCR7 (Fig.5B-C, Additional file 2: Table S8), which are all mediators of T-cell/APCs recruitment and T-cell adhesion [27, 28, 29, 30]. Differential protein expression in tumor-sparse compared to tumor-rich regions reveal both common and uniquely regulated proteins in TH,57− and TC,57− cells (Additional file 2: Table S9, Fig.5D-F). For example, CD45RO was enriched in TH,57− in tumor-sparse regions, which was validated using Wilcoxon t-test (data not shown). This is in line with the predicted higher abundance of memory TH cells in tumor-sparse regions. Of note, the T-cell suppressive proteins IDO1, VISTA and TIM-3 also had higher expression in tumor-sparse regions. T-cell subsets in the tumor-sparse region also showed higher relative abundance of Fibronectin, SMA, and CD34, which are markers of neovascularization and angiogenesis. Deconvolution predicted increased abundance of fibroblasts in the tumor-sparse region, and the proteins may also derive from such cells. Additionally, we observe increased expression of STING in tumor-sparse regions, which is known to increase IDO1 activity [31]. Other proteins were only differentially regulated in either TH,57− or TC,57− cells. These included LAG3, which was significantly higher on TH,57− cells in tumor-sparse regions. LAG3 is an inhibitory receptor known to be highly expressed on exhausted T-cells [32]. ICOS, which is a hallmark of CD8 + tissue-resident memory T-cells [33], had higher expression in TC,57− in tumor-sparse regions. Thus, CD57- subsets in the tumor-sparse region showed markers of exhaustion together with expression of the protein memory marker CD45RO. High expression of CD163 in tumor-sparse suggested potential presence of CD163 + M2 type macrophages/monocytes, which supports the results from the deconvolution analysis. For the tumor-rich regions, gene set enrichment analysis showed that T-cells were in close contact with tumor cells, with upregulation of pathways associated with tumorigenesis including B-cell receptor signalling and DNA replication (Fig.5A). In summary, comparison of tumor-sparse and tumor-rich regions showed that memory CD8 + T-cells were predicted to be more abundant among infiltrating T-cells. Furthermore, the infiltrating TC had higher expression of transcripts involved in TNF signalling compared to tumor-adjacent T-cells in tumor-sparse regions, indicating that they are activated. Based on that several tumor suppressive proteins (IDO1, VISTA, TIM-3, LAG-3, and ICOS) had a higher expression in the tumor-sparse region we conclude that a wide range of T-cell suppressive features may hamper expansion of T-cells in regions also adjacent to the tumor. Infiltrating T-cells in tumor-rich regions: Identification of unique analytes among early and late T H and TC subsets in MCL. The molecular features of infiltrating (sampled in the tumor-rich region) early and late TH and TC subsets have not been studied in MCL and such information can provide insight in lymphoma-related immune dysregulation. Transcriptional analysis of infiltrating TH and TC cell subsets were performed as well as further subset analysis of the four T-cell subsets, TC,57−, TH,57−, TC,57+ and TH,57+, using proteomic profiling. Transcriptional analysis reveals co-regulation of Treg markers in T H cells and expression of antigens associated to late differentiation in TC cells. Expression of 58 transcripts were associated with TH cells and expression of 62 transcripts were associated with TC cells (Additional file 2: Table S10, Additional file 1: Fig. S5A). In TH cells, co-regulation was observed between IL7R, LEF1, TCF7, IL6ST, CD4 and TECR (Fig.6A). TCF7 and LEF1 are known co-regulators for various signalling mechanisms including maintenance of CD4 T-cell receptor expression [34], activation of CD4 T-cells [35] and immune suppressive functions by Tregs [36]. In addition, higher expression of FOXP3, CTLA4 and IL6R, which are all important for Treg function [37] were observed in TH vs TC, as expected (Additional file 2: Table S10). In TC, differentially expressed transcripts that were co-regulated included CD8A, CD8B, GZMA, GZMK, GZMH, PRF1, LAG3, NKG7, TNFRSF9, CCL4, CCL5, EOMES, CCR5, CTSW, and FASLG (Fig.6A). Chemokines, CCL5 and CCL4 have been associated with modulation of TIME particularly with respect to immune cell infiltration and tumor progression [38]. In addition, SLAMF7, TARP, TRGC1.2, CD2, SH2D1A and CCL3.L1, KLRG1, KLRK1, XCL1.2 and IL2RB (CD122) had higher expression in TC than TH (Additional file 2: Table S10). XCL1 is a chemokine mainly produced by activated TC or NK cells and has been correlated to PD-L1 expression on tumor cells [39]. EOMES have been shown to separate between CD57 + cells with low cytotoxicity but proliferative capacity and IL7R expression (EOMEShigh), and CD57 + cells with pronounced cytotoxic functions but more terminally differentiated phenotype (EOMESint) [40]. Several of the markers are consistent with a late-stage differentiated CD8 effector cell phenotype, including KLRG1 [41], in line with the observed increased frequency of CD57 + Tc among infiltrating T-cells as reported above. Proteomic analysis of T C,57− , TH,57−, TC,57+ and TH,57+ subsets to identify unique expression profiles, shows that inhibitory and stimulatory receptors are co-expressed and that CD57 + is associated with LAG3, 4-1BB, PD-L1 and PD-L2 expression. Unique proteomic characteristics of the each of the T-cell subsets, TC,57−, TH,57−, TC,57+ and TH,57+, were determined using multi group comparisons. In total, 26 differentially expressed proteins were identified (Fig.6B and Additional file 2: Table S11, Additional file 1: Fig. S5B-F). PCA biplot shows the contribution of the 26 proteins in segregating the four T-cell subtypes, particularly the overall TC and TH subsets (Fig.6C). Early (CD57-) and late (CD57+) differentiation stages of TC and TH subtypes were compared (Fig.6B, right panel). CD27, PD-L1, and 4-1BB were more abundant in both TC,57+ and TH,57+ compared to CD57- subsets, indicating that these cell types both express markers of T-cell expansion (4-1BB, CD27) and inhibitory molecules (PD-L1) at the same time (Fig.6B and D). T-cells with dual expression of both co- and inhibitory molecules have previously been described as polyfunctional, although the exact role of such cells is unclear [42]. In TC,57+, uniquely differentially regulated proteins compared to the other T-cell subsets included relative higher expression of, among others, LAG3, ICOS, and CD68. The expression of LAG3 indicates T-cell exhaustion [43] and ICOS has been associated with IL-10 production and CD8 + tissue-resident memory T-cells [33]. The relatively increased levels of CD68 indicate that macrophages are proximal to TC,57+. TC,57− had uniquely higher expression of, among others, GITR. TH,57+ cells, had higher levels of PD-L2, GZMA, GITR and CD45RO, while CD57- subset had higher levels of FOXP3, CD25 and ARG1 (Fig.6B, right panel). The expression of PD-L2 is in line with the definition of CD57 + T-cells as suppressive T-cell subsets. The increased expression of FOXP3 and CD25 in TH,57− indicate that Tregs mainly are of low differentiation stage and lack CD57 expression. The multi-group differential expression analysis was validated using pair-wise comparisons, which also identified additional regulated proteins comparing early and late differentiation stage, such as VISTA, which was enriched in TH,57− compared to TH,57+. TIM-3 was enriched in TC,57+ compared to TC,57− (Additional file 1: Fig. S5F). The expression levels of PD-1, PD-L1 and PD-L2 were visualized separately for the four subsets of T-cells (Fig.6D). Increased expression of PD-L1 and PD-L2 was associated with the CD57 + subtypes of both TH and TC and confirmed the immune suppressive features of these late differentiated subtypes. No difference in PD-1 was detected, in contrast to a few previous reports on other cancers [44, 45]. Furthermore, differences between cytotoxic and helper T-cells were determined. Both early-stage and late-stage differentiated TH cells were compared to their TC counterparts, respectively. The comparison mostly highlighted expected differences such as higher CD4 and lower CD8, GZMA and GZMB in both TH,57− and TH,57+ compared to the respective TC counterpart. (Fig.6B, Additional file 1: Fig. S5F). Uniquely differentially expressed proteins TH,57− compared to TC,57− include higher levels of CD25, FOXP3, ICOS, CD45, STING [46] and lower levels of 4-1BB (Fig.6B, left panel). Differentially expressed proteins in TH,57+ compared to TC,57+ include higher levels of GITR (TNFRSF18) (Fig.5B, left). GITR is a co-stimulatory surface receptor on T-cells and shapes humoral immunity by controlling the balance between follicular T helper cells and regulatory T follicular cells [47]. In TC,57+, LAG3 and OX40L were more abundant. LAG3, as previously described, is a known check-point inhibitor and normally expressed by activated T-cells [48], while OX40 signalling enhances primary and memory T-cell response and promote antitumor activity [49]. Thus, we conclude that LAG3 is higher on TH in tumor-sparse compared to tumor-rich regions as reported above, but that the relative expression is highest on TC, 57+ cells in tumor-rich regions (Additional file 1: Fig. S6). We conclude that, both markers of expansion and inhibitory molecules, such as, PD-L1, PD-L2, ICOS and, LAG3 and were positively associated with the late differentiated CD57 + T-cell subtypes compared to CD57- T-cells. CD47 don’t eat me signals are associated with high total CD3 while CXCL9 is associated with high T C, 57+ frequency The intra-patient variation in measured CD3 + T-cell frequency was assessed and found to be acceptable, and aggregated mean values were used in subsequent analysis (Additional file 1: Fig. S7). T-cell frequency was positively associated with outcome as shown above (Additional file 1: Fig. S4). Data integration was performed to identify transcripts/proteins that may explain the connection between outcome and T-cell frequency. The analysis identifies differentially expressed transcripts and proteins in both tumor cells and T-cell subsets that are predictive of high (> 8.4%) or low (< 8.4%) T-cell infiltration. High level of total CD3 + T-cell infiltration is associated with increased levels of CD47, IL7R and key components of antigen presentation. DIABLO (permutation test, p < 0.005) identified 15 transcripts in the CD20 + tumor cells (AOIs) to be positively associated with T-cell rich MCLs (Fig.7, Additional file 1: Fig. S8A), including IL7R, CD47, CD80, CD84, and NLRC5. IL7R is associated with the attraction of T-cell to the TIME. CD47, is an important inhibitory molecule involved in evasion of immune clearance by macrophages that carry the ligand SIRPα [50]. CD80 is a co-stimulatory molecule essential for T-cell activation. CD84, which is a self-binding immunoreceptor belonging to the signalling lymphocyte activation molecule (SLAM) family, is determinative of high T-cell infiltration and activity. Lastly, NLRC5/CITA induces expression of genes encoding critical components of the MHC class I pathway, which is essential for the cancer antigen presentation and recruitment/activation of cytotoxic T-cells [51]. Increased expression of ICOS, indicate that T-cells are involved in antigen recognition, but the function can be dual with either anti-tumor or tumor promotions responses [52]. In CD20 + AOIs, 15 transcripts were negatively associated to T-cell rich MCL and included genes related to cell cycling, DNA repair and mobility, all associated with aggressive disease. For example, TUBB, SPOP, LAMA3, CDC20, BIRC5, H2AX and the transcription factor FOXM1 (Fig.7, Additional file 1: Fig S8A). In addition to analysis of CD20 + tumor cells, T-cells were also investigated to increase our understanding of how the infiltrating T-cells differed on the molecular level in tumors with T-cell rich or T-cell sparse TIMEs. As expected, few analytes differed, as identical cell types were compared only based on differences in the MCL TIME composition. Results showed that several markers associated with CD8 + T-cell response was positively associated with T-cell rich TIME including IDO1 (both TH and TC), CD45RO, ECSIT, and co-stimulatory molecule JAML in TC [53]. A few TC transcripts were associated with T-cell sparse MCL and included transcripts associated with transcription (GTF3C1) and alternative splicing (MBNL3) (Fig.7, Additional file 1: Fig. S8A). The protein GITR, markers of Tregs was also higher in regions of low T-cell infiltration. Studies have shown that reverse signalling via GITR can induce IDO1 expression [54]. Correlation analysis reveal an inverse relationship between IDO1 and GITR in MCL (Additional file 1: Fig. S9). Differences in T C, 57+ infiltration are associated with increased proliferation, and secretion of CXCL9 in tumor cells and expression of CD45RO, TIGIT, PD-L1 and 4-1BB in TC cells. As reported above, infiltration of TC,57+ cells vary across patients with a positive association to proliferation index. To understand what the specific drivers of such late differentiation stage Tc infiltration are, DIABLO (permutation test < 0.007) was used to define transcripts and proteins associated with and predicted TC,57+ infiltration above or below the median value (0.69%, Additional file 2: Table S6). Focusing on the CD20 + tumor cells, the transcriptomic analysis showed that CXCL9 was most important for the classification of patients with either high or low TC, 57+ infiltration. CXCL9 is important for recruitment of T-cells and has been shown to correlate with anti-tumor activity [55]. Other differentially regulated transcripts were involved in cell-cycling, DNA replication and repair such as PARP9, MCM2 and STAT1 (Fig.8, Additional file 1: Fig. S8B). In the T-cells, proteomic analysis showed that, similar to total CD3 + T-cells, IDO1 was associated with high infiltration of TC, 57+. Most relevant differentially regulated transcripts/proteins in T-cell subsets reflected the higher content of cytotoxic T-cells such as PRF1, LYZ, EOMES, CCR5 and NKG7 (Fig.8, Additional file 1: Fig. S8B). Of interest, the immune-check point inhibitors TIGIT and PD-L1 were positively associated with high TC, 57+ frequency. In line with the well differentiated stage of an increased promotion of T-cells, CD45RO, 4-1BB and the transcription factor PRDM1 was positively associated with high TC, 57+ frequency. In summary, combined analysis of the level of T-cell infiltration and associated molecular profiles in tumor cells showed that the CD47 immune evasion marker was up-regulated by tumor cells in T-cell-rich TIMEs, while CXCL9 was specifically associated with high degree of Tc of late differentiation stage. The summary of all the main findings and results of this study is provided in Fig.9.Image analysis to retrieve information on T-cell subset composition in MCL TIME
See Also
Frontiers | ALK-rearranged and EGFR wild-type lung adenocarcinoma transformed to small cell lung cancer: a case reportTissue expression of H3-4 - Staining in colonOcular Manifestations of Cutaneous T-Cell LymphomaT-cell / histiocyte-rich B cell lymphoma mimicking... : Indian Journal of Cancer
FAQs
What is the T cell treatment for mantle cell lymphoma? ›
CAR T-cell (chimeric antigen receptor T-cell) has shown encouraging outcomes in the treatment of several cancers, including mantle cell lymphoma (MCL) that is a rare type of non-Hodgkin lymphoma (NHL) which begins in the lymph nodes.
What are prognostic markers in mantle cell lymphoma? ›The Mantle Cell Lymphoma International Prognostic Index (MIPI) considers 4 factors: age, ECOG performance status, serum LDH level and serum white blood cell count.
What are the biomarkers for mantle cell lymphoma? ›B-cell specific biomarkers
As a B cell lymphoma, MCL is characterized by the presence of characteristic B cell expression patterns with high (> 90%) expression of CD5, CD19, CD20, CD21, CD22, CD43, CD79a, sIg, and cIg, and low (< 10%) expression of CD10 and CD23.
Single IHC stains for cyclin D1 and CD5 are typically used for MCL diagnosis. However, cyclin D1 is expressed in histiocytes and endothelial cells,8,9 and CD5 also stains T cells.
How serious is mantle cell lymphoma? ›How serious is mantle cell lymphoma? This is a serious illness because it can spread very quickly to become an advanced form of cancer. Mantle cell lymphoma is not a curable lymphoma. In most cases, treatment can put the condition into remission.
How long can a person live with mantle cell lymphoma? ›According to research, the median OS ranges from six years to nearly 10 years. This means that half of the people with MCL are alive six to 10 years after receiving their diagnosis. On a hopeful note, those diagnosed after 2000 showed an OS of 11.25 years.
Has anyone ever survived mantle cell lymphoma? ›Survival Analysis. For the whole cohort, median PFS was 60 months (95% CI 30–84 months) and we reported a 2-year, 5-year, and 10-year PFS of 63%, 50%, and 32%, respectively (Figure 1a).
Can you survive mantle cell lymphoma? ›Outlook. Most people respond well to their first round of chemotherapy. Often, they go an average of 20 months without their cancer getting worse. If you have mantle cell lymphoma, you can expect to live about 8 to 10 years, but you can live for 20 or more.
How bad is stage 4 mantle cell lymphoma? ›Stage 4 MCL is often difficult to treat, and it may relapse (return) after you complete your chemotherapy regimen. It can also become resistant or refractory to chemotherapy, meaning you don't go into remission (a period of having fewer or no symptoms).
Can mantle cell lymphoma be misdiagnosed? ›And, stage IV indicates bone marrow is affected. Patients are often diagnosed at stage IV, since mantle cell lymphoma symptoms can easily be confused with other less serious conditions, but Wang says there is hope: “Even patients with stage IV mantle cell lymphoma can live for many years to come.”
What is the most common site for mantle cell lymphoma? ›
Classical mantle cell lymphoma accounts for most cases. It affects lymph nodes but often spreads to other parts of the body, such as the bone marrow, spleen, bowel and liver. It is usually fast-growing, although it can sometimes have a slower course. Leukaemic non-nodal mantle cell lymphoma is less common.
How do you know if you have mantle cell lymphoma? ›The blood tests can reveal the number of blood cells you have, how well your kidneys and liver are working, and whether you have certain proteins in the blood that suggest you have mantle cell lymphoma. Biopsy. Your doctor may want to check a sample of the tissue in a lymph node.
What is the best chemotherapy for mantle cell lymphoma? ›It's a common treatment for mantle cell lymphoma. If you're relatively healthy and young, doctors will want to treat the disease aggressively with intense chemotherapy often with the immunotherapy drug, rituximab. Rituximab is a targeted drug that helps your body's own immune system kill off cancer cells.
What are the risk factors for mantle cell lymphoma? ›However, MCL most often affects patients older than age 60, especially men. Other risk factors shared by all types of NHL include being white and having a weakened immune system due to genetic syndromes, certain immune-suppressing medications or conditions such as human immunodeficiency virus (HIV).
Is mantle cell lymphoma same as leukemia? ›Mantle cell lymphoma (MCL) and chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) share many features and both arise from CD5+ B-cells; their distinction is critical as MCL is a more aggressive neoplasm. Rarely, cases of composite MCL and CLL/SLL have been reported.
What is the newest treatment for mantle cell lymphoma? ›Bispecific T-cell engagers (BiTEs) and chimeric antigen receptor (CAR) T-cell therapy are two examples of evolving immunotherapy options for MCL. BiTEs are engineered proteins that serve as a bridge between MCL cells and T cells, immune cells responsible for recognizing and neutralizing pathogens in the body.
What is the most successful treatment of mantle cell lymphoma? ›Over the past decade, several trials have demonstrated that many patients with mantle cell lymphoma (MCL) can be treated effectively in the frontline setting with rituximab and high-dose cytarabine- or bendamustine-based therapies.
What is the success rate of CAR T-cell therapy for mantle cell lymphoma? ›1, 2, 3, 4, 5 For relapsed/refractory mantle cell lymphoma (MCL), a single infusion of brexucabtagene autoleucel (brex-cel, KTE-X19), an anti-CD19 CAR T-cell therapy, was associated with an overall response rate of 93% and complete response (CR) rate of 67%.
What is the new pill for mantle cell lymphoma? ›On January 27, 2023, the Food and Drug Administration (FDA) granted accelerated approval to pirtobrutinib (Jaypirca, Eli Lilly and Company) for relapsed or refractory mantle cell lymphoma (MCL) after at least two lines of systemic therapy, including a BTK inhibitor.