Patient characteristics
A total of nine patients were enrolled and treated on trial, three in the 500µg cohort and six in the 2500µg cohort. The baseline characteristics of patients are described in Table 1. The median age of all patients was 67, and the majority were male (78%). Of eight patients with baseline gene mutation data available, six had MYD88 mutations; of these, one had a CXCR4 WHIM mutation. The median time from diagnosis of asymptomatic LPL to first vaccination was 2.2 years.
Table 1
Baseline Patient Characteristics
Characteristic (range)
|
Cohort 1 500µg (n = 3)
|
Cohort 2 2500µg (n = 6)
|
Median Age
|
65 (56–71)
|
69 (61–78)
|
Male Sex
|
100%
|
67%
|
ECOG performance status 0–1
|
100%
|
100%
|
Time from diagnosis of SWM to 1st vaccination (yrs.)
|
8.1 (1.4–8.8)
|
2.0 (0.7–10.8)
|
Genotype* (no. of patients)
|
|
|
MYD88WT/CXCR4WT
|
1
|
1
|
MYD88 L265P/CXCR4WT
|
2
|
3
|
MYD88 L265P/CXCR4WHIM
|
0
|
1
|
Bone Marrow infiltration (%)
|
30 (25–40)
|
30 (10–50)
|
Serum IgM (mg/dL)
|
2900 (814–3150)
|
3255 (473–7210)
|
Monoclonal Protein (g/dL)
|
2.3 (0.9–2.8)
|
2.5 (0.4–6.3)
|
Hemoglobin (g/dL)
|
13.6 (12.2–14.9)
|
12.3 (137–353)
|
Platelet count (K/µL)
|
204 (189–372)
|
278 (137–353)
|
Beta 2 microglobulin (mg/L)
|
2.5 (2.0-3.7)
|
2.5 (1.7–3.7)
|
Albumin (g/dL)
|
77.7 (76.9–80)
|
4.1 (3.7–4.4)
|
LDH (U/L) (normal range: 313–618)**
|
378 (376–405)
|
337 (201–613)
|
*Genotype not available for 1 patient in Cohort 2 |
* LDH not available for 1 patient in Cohort 2 |
Safety, tolerability, and response assessment
All patients successfully completed planned therapy. No patients in either cohort experienced dose limiting toxicities (DLTs) or Grade 4 adverse events (AEs). Ten months after the last vaccination, LPL-005 developed a grade 3 non-malignant pleural effusion, grade 1 pericardial effusion, and leukocytopenia, accompanied by an increase in rheumatoid factor (23.1 IU/mL [normal range 0.0-15.9]) and an ANA titer of 1:80; all findings resolved within 2 months. Grade 1–2 AEs occurring in 3 or more patients were leukopenia, nausea, myalgias, fatigue, diarrhea, anemia, hyperglycemia, and increased creatinine. Details are provided in Table 2.
Table 2
|
Total No. of patients
(n = 9)
|
|
Most common adverse events
|
Any Grade
|
≥ Grade 3
|
Hematologic
|
|
|
Leukocytopenia
|
6
|
|
Anemia
|
4
|
|
Gastrointestinal
|
Nausea
|
5
|
|
Diarrhea
|
3
|
|
General
|
Fatigue
|
4
|
|
Myalgia
|
3
|
|
Respiratory
|
Dyspnea
|
3
|
|
Pleural Effusion
|
1
|
1
|
Cardiac
|
Pericardial Effusion
|
1
|
|
Dermatologic
|
Injection Site Reaction
|
3
|
|
Lab Abnormalities
|
Creatinine increase
|
4
|
|
Hyperglycemia
|
6
|
|
Using response criteria from the 6th International WM Workshop Consensus Panel, LPL-003 achieved a minor response (MR). The best response for the remaining eight patients was stable disease (SD) (Fig. 1B). After a median follow-up period of 77 months for all patients, four patients have experienced progression to symptomatic WM, requiring initiation of systemic therapy (LPL-005, -006, -007, and − 009) at 29, 8, 32, and 25 months, respectively. LPL-006 experienced early disease progression and was lost to follow up 8.8 months after last vaccination, before a post-vaccine bone marrow sample could be obtained. All remaining patients are known to be alive.
Reduction in clonal tumor subpopulations and their gene expression pathways after vaccination in the mature B-cell, but not in the LPL plasma cell-like compartment
To interrogate vaccine-induced changes directly in the tumor microenvironment, bone marrow samples were obtained a median of 3 months (range 1–13 months) after vaccine treatment from all nine patients, except patient LPL-006. We performed single cell RNA-seq analysis on matched pre- and post-vaccine bone marrow samples, paired with matched single cell BCR and TCR sequencing (Fig. S1B-F). To analyze specific changes in LPL cells following vaccination we separated and re-clustered heterogeneous B-lineage populations and obtained a total of 12 clusters based on differential gene expression (Fig. 1C, Fig. S2A). To specifically identify clonal tumor cells across various clusters we matched single cell BCRs with the previously identified unique tumor idiotype (Ig VH and VL CDR3) sequences used for manufacturing individualized therapeutic vaccines for each patient. LPL is known to consist of distinct clonal B-cell- and plasma cell-like subpopulations18,19. Clusters 0, 1 and 2, representing mature B cells, were comprised almost entirely of the tumor clonotype, with cluster 1 being the most abundant (Fig. 1C, right). Plasmablast-like and mature plasma cells (clusters 5 and 10, respectively) were less abundant but also contained relatively high proportions of tumor clonotypes (Fig. 1C, right). Analysis of paired total B-lineage cells pre- vs. post-vaccine showed significantly reduced frequencies post-vaccine for B-cell cluster 1 but not for the plasma cell-like clusters (Fig. 1D). This reduction in B-cell frequencies in cluster 1 was entirely attributable to a specific reduction in the tumor clonotype. This reduction in tumor cell clonotype frequencies was observed in all except for three evaluable patients (Fig. 1E).
Concomitant changes in global gene expression patterns in tumor cells were associated with the reduction of the tumor mature B-cell compartment post-vaccine. Differential gene expression analysis of each of the relevant B-lineage cell clusters revealed a pattern of significant gene downregulation following vaccine in clusters 0, 1 and 2 (Fig. 1F, top). Among the top downregulated genes were FOS, JUN, ATF3, ATF4, NFKBIA and MAP3K8 which are essential for the growth of B lymphocytes 20,21 as well as proteins of the EIF (eukaryotic initiation factor) family (EIF4A1 22, EIF4A2, EIF4A3), GADD34 23, ribosomal protein L family (RPL4, RPL9, RPL13, RPL21, RP23, PRL27, RPL37, RPL38, RPL10A) and ribosomal proteins family (RPS2, RPS6, RPS9, RPS11, RPS16, RPS20, RPS26, RPS27) which are essential for lymphoma cell proliferation and protein synthesis 24 (Fig. 1F, top). Furthermore, pathway analysis based on differentially expressed genes identified signaling pathways significantly reduced post vaccine known to be critical for B-cell survival including IL-125, IL-626, IGF-127 and APRIL 28 (Fig. 1F, bottom left). BCR 29, PI3K/AKT 30,31 and ERK/MAPK, which are involved in survival-promoting signaling by mutant MYD88 in WM cells, were also significantly downregulated 32,33. Conversely, PPAR signaling, which is known to promote tumor cell apoptosis 34, and ferroptosis cell death pathways 35 were both upregulated by these clusters. Finally, the analysis predicted overall downregulation of biological processes (z-score > 2, adjusted log p-value > 1.3) including cell survival, viability, proliferation, protein synthesis and RNA transcription and upregulation of necrosis (Fig. 1F, bottom right). Notably, no global changes were inferred for corresponding plasma cell-like clusters. These observations suggest that tumor subpopulations of LPL within a single patient may be dichotomous in their response to therapeutic vaccine treatment, with mature B-cell subpopulations more susceptible than plasma cell-like cells.
A well-described mechanism of tumor cell resistance to T-cell-mediated killing is the downregulation of expression of HLA family genes, particularly HLA class II genes 36–40. To investigate this possibility, we compared expression of HLA family genes in tumor cells in relevant B- and plasma-cell clusters. Consistent with previous reports we observed downregulation of HLA class II family (HLA-DMA, HLA-DMB, HLA-DPA1, HLA-DPB1, HLA-DQA2) gene expression in clusters 5 and 10 containing plasmablast-like and plasma cells in both pre- and post-vaccine samples, but not in B-cell clusters 0 and 1 (Fig. 1G) 41,42. Interestingly, there was also a trend towards downregulation of expression of HLA class II (but not HLA class I) genes post-vaccine, compared with pre-vaccine in B-cell cluster 2 tumor cells (Fig. 1GH). In contrast, no significant changes were observed in tumor expression of T-cell checkpoint ligands, including PDL1 (CD274), and PDL2 (PDCD1LG2) (Fig. 1H and not shown). We also observed no significant differences in expression of genes of the death receptor family among clonal tumor B-cell or plasma cell-like clusters post-vaccine (Fig S1G) 43. Taken together these observations suggest that plasma-cell subpopulations of clonotypic tumor cells in LPL may exhibit immune evasion to our vaccine therapy by downregulating expression of HLA genes, rather than by activation of T-cell immune checkpoints.
Paired single cell transcriptomics reveals dynamic changes in T cells in the tumor microenvironment following vaccine treatment.
To investigate vaccine-induced changes in normal immune cells in the bone marrow microenvironment we re-clustered T-cell populations separately and obtained a total of 12 clusters that we identified based on differential gene expression, SingleR software analysis 44. and the expression of defined gene markers (Fig. 2A and Fig. S2B-C). These T-cell subpopulations were consistent across all patient samples (not shown). We analyzed changes in T-cell frequencies within each cluster, comparing paired pre- vs. post-vaccine bone marrow samples and observed statistically significant decrease in cell frequencies of naïve CD4 T cells (cluster 0) and trends toward increases in effector memory and terminal effector T cells (clusters 1 and 3, respectively) (Fig. 2B). We also performed differential gene expression analysis on each T-cell cluster, followed by pathway enrichment analysis pre- vs. post-vaccine using IPA software (Qiagen). We observed significant upregulation (adjusted log p-value > 1.3) of pathways involved in T cell activation, including TCR signaling, PI3K/AKT signaling, integrin signaling and leukocyte extravasation and down-regulation of PD-1/PD-L1 pathway following vaccination in effector T cells (Fig. 2C). Notably, there were no obvious changes in frequencies or signaling pathways in Treg (Fig. 2B cluster 9, and 2C).
To analyze the clonal composition of T cells in the microenvironment we used matched single cell TCR-seq data (Fig. 2A, right panel). A mean of 750 unique T-cell clonotypes (range 141–1596) were identified pre- and post-vaccine each for each patient. Comparing post- vs. pre-vaccine samples, we observed expansion of existing clonotypes in all patients except for two of clinical progressors LPL-005 and − 009 (Fig. 2D). Furthermore, among the 20 most prevalent clonotypes detected post-vaccination the majority increased from low frequency clonotypes that were present before vaccination, except for clinical progressors LPL-005 and − 009 (Fig. 2E). New clonotypes were also detected post-vaccination (overall 4.4%), consistent with increased T-cell clonal diversity. Increasing clonal diversity post-vaccination was also suggested in most patients, as analyzed by individual Shannon entropy scores 45 (Fig. 2F). Phenotypically, unique or shared T-cell clonotypes expanded in post-vaccine samples localized primarily to clusters enriched for effector memory or effector T cells (not shown). This same pattern was observed for the 20 most abundant post-vaccine clonotypes (Fig. 2G, left) with cells localized to effector memory T cells and terminal effector T cell clusters. Post-vaccine clusters also showed increased frequencies of cells in the G2-M phase of the cell cycle, consistent with increased proliferation (Fig. 2G, right). Phenotypically these top 20 post-vaccine clonotypes were primarily CD8 T cells expressing markers affecting activation, differentiation, or proliferation, including CD27, CXCR446, HLA-DR47, PIK3RI48, REL49, and FKBP1A50 (Fig. 2H). In contrast, clonotypes detected only in a single T-cell or uniquely in pre-vaccine samples were distributed broadly across all T-cell subpopulations, including naïve CD4 and CD8 T cells, regulatory T cells, Th1/Th2 cells, Th17, and to lesser extent central memory T cells and effector memory phenotypes (not shown). Notably, the top 20 post-vaccine clonotypes showed a mixed pattern of up- and down-regulation of co-inhibitory molecules DUSP2 and TIGIT, respectively, with most, including PD-1, LAG3, and TIM3 (HAVCR2) showing no significant change post-vaccination (Fig. 2H). Taken together, these results suggest that vaccine therapy induced significant expansion and activation of terminal effector and effector memory T cells within the top 20 TCR clonotypes post-vaccine, with little activation of immune checkpoints.
Tumor idiotype-specific T-cell immune responses.
To detect idiotype-specific T cell responses elicited by the vaccine treatment, we analyzed T cells isolated directly from the bone marrow tumor microenvironment. T cells were enriched from each patient’s post vaccine sample by negative selection and then stimulated with autologous immortalized normal B cells (as antigen presenting cells, APCs) transfected with either Ig VH and VL sequences (expressed as sFv’s) derived from the respective patient-specific tumor idiotype (used previously for therapeutic vaccine production), or HIV Nef as a negative control, described previously 52. Multiplex cytokine analysis was performed on culture supernatants. Representative post-vaccination samples are shown from patients achieving minor response, stable disease, or progressive disease clinically (Fig. 2I). All patients T cells secreted cytokines in an antigen-specific manner, with the exception of the two patients who experienced progressive disease (LPL-005 and − 009). Taken together these functional data are consistent with T-cell clonal expansion post-vaccination detected by transcriptomic analysis above.
Vaccine-induced reduction in cross-talk between immune cell types and tumor cells in the microenvironment.
To infer and analyze global changes in cell-cell communications in the tumor microenvironment after vaccination, we employed comparative CellChat 53 software to analyze signaling interactions among all major cell types in pre- and post-vaccination bone marrow samples. A cell-cell interaction map was constructed using aggregate sc-RNAseq data from all evaluable patients with five major interaction populations: clonal LPL mature B cells, clonal LPL plasma cell-like cells, T/NK cells, myeloid cells, and normal B progenitor cells as controls. From this cell-cell interaction map, the total number of ligand-receptor pairs contributing to communication between any two interacting cell types was analyzed. We observed that the total number of inferred interactions between the five major cell types in the tumor microenvironment significantly decreased post- compared with pre-vaccine (Fig. 3A), with this same pattern consistently observed between individual pairs of cell types (Fig. S3A-C).
To investigate which cell populations contributed to the reduction in inferred interactions, we used network centrality analysis to compare incoming and outgoing interaction strengths (Fig. 3B). Interestingly, predicted interaction strengths for myeloid and LPL mature B-cell, but not LPL plasma cell-like populations, were most dramatically reduced post-vaccine.
We then analyzed the overall information flow for multiple specific signaling pathways across the pre and post-vaccine datasets 54. Multiple signaling pathways were implicated as active predominantly in pre- but not post-vaccine samples, including pathways such as APRIL 55, which is known to promote B- or plasma cell survival, and others with known roles supporting tumor cell proliferation in solid cancers, such as RESISTIN 56,57, VEGF 58, and IL-10 59, TGFβ and BMP60 (Fig. 3C). Moreover, the IL-6 signaling pathway, which promotes IgM secretion and LPL and plasma cell growth via the JAK/STAT pathway 26 was substantially reduced in post-vaccine samples.
The analysis of individual cell types revealed that myeloid cells mainly contributed to the downregulation of information flow of these signaling pathways (Fig. 3D and Fig S3D-O). For example, we observed dramatic reductions in predicted outgoing signals provided by myeloid cells for both RESISTIN and APRIL pathways, as well as IL-6, associated with their respective ligand-receptor pairs (Fig. 3E).
Paradoxically, dichotomous upregulation of the insulin-like growth factor (IGF) signaling axis post-vaccine was inferred by plasma cell, but not mature B cell LPL subpopulations, including both autocrine and paracrine pathways, consistent with a potential mechanism of escape by the former (Fig. 3, D and E). Our scRNAseq data confirmed increased expression of IGF-1 among clonal tumor cells post-vaccine in both plasma cell-like clusters (clusters 5 and 10) but not in any B-cell clusters (0, 1, and 2). We also observed an increased proportion of clonal tumor cells expressing IGF-1 in cluster 10 (Fig. S3P).
Vaccine-induced changes in myeloid cell subpopulations in the tumor microenvironment
Given that vaccination was associated with significantly reduced cell-cell communication patterns in the tumor microenvironment, most pronounced in outgoing signals provided by myeloid cells to clonal LPL cells, we further analyzed subpopulations of myeloid cells by re-clustering them based on differential gene expression analysis from the combined datasets of pre- and post-vaccine bone marrow cells from all patients. We obtained a total of 9 clusters, based on the differential expression of established marker genes (Fig. 4. A, B and fig. S4A). Myeloid cell populations were consistent across all individual patient samples (not shown).
We analyzed changes in cell frequencies per cluster in paired pre- vs. post-vaccine patient samples and observed significant increases in frequencies of CD14−CD16+ non-classical monocytes (cluster 3) and concomitant significant decreases in the frequencies of CD14+CD16+ intermediate monocytes (cluster 4, Fig. 4C). Given that monocyte differentiation is believed to proceed from classical CD14+CD16− to non-classical CD14−CD16+ monocytes via intermediate CD14+CD16+ monocytes 61, these results may suggest that monocytes in the tumor microenvironment post vaccination undergo increased differentiation from intermediate to non-classical subpopulations, thereby causing a general skewing away from classical monocytes. This hypothesis was also supported by the trend towards decreasing CD14+CD16− classical monocyte frequencies (cluster 0) observed post- vs. pre-vaccination, although these differences did not reach statistical significance.
Next, we sought to identify the specific myeloid cell clusters which contributed to the dramatic reductions in outgoing predicted signals provided by myeloid cells to LPL cells by analyzing each of the individual signaling pathways in Fig. 3D. Comparing post- vs. pre-vaccine samples, most of the signaling pathways affected were associated with reductions in monocytic subpopulations, particularly cluster 3 non-classical monocytes, and to a lesser extent cluster 0 classical monocytes (Fig. 4D). Changes were also observed across many of the pathways for cluster 1 of mature neutrophils, but these were generally of lesser magnitudes.
Taken together, these data suggest that vaccination was associated with clear reductions in pro-tumoral outgoing signals provided by non-classical monocytes to LPL cells, but with a paradoxical expansion of this myeloid subpopulation.
Finally, because of the availability of potential therapeutic intervention, we also performed cell-cell communication analysis of the CD47-SIRPα pathway which predicted overall decreased signaling after vaccination (Fig. 3E), despite increased CD47 expression on at least one lymphoid (B-cell cluster 2) and one plasmacytoid (B-cell cluster 10) LPL tumor subpopulation. SIRPα was observed on cluster 0 classical monocytes pre-vaccination, with no significant change in expression post-vaccination (Fig. 4E).