McGrath, Kaitlyn

Background The importance of the immune system in modulating the trajectory of lymphoma outcomes has been increasingly recognized. We recently showed that CD4+ cells are associated with clinical outcomes in a prospective cohort of almost 500 patients with follicular lymphoma (FL). Specifically, we showed that the absence of CD4+ cells inside follicles was independently associated with increased risk of early clinical failure. These data suggest that the composition, as well as the spatial distribution of immune cells within the tumor microenvironment (TME), play an important role in FL. To further define the architecture of the TME in FL we analyzed a FL tumor section using the Co-Detection by Indexing (CODEX) multiplex immunofluorescence system. Methods An 8-micron section from a formalin-fixed paraffin-embedded block containing a lymph node specimen from a patient with FL was stained with a cocktail of 15 CODEX antibodies. Five regions of interest (ROIs) were imaged using a 20X air objective. Images underwent single-cell segmentation using a Unet neural network, trained on manually segmented cells (Fig 1A). Cell type assignment was done after scaling marker expression and clustering using Phenograph. Each ROI was manually masked to indicate areas inside follicles (IF) and outside follicles (OF). Relative and absolute frequencies of cell types were calculated for each region. Cellular contacts were measured as number and types of cell-cell contacts within two cellular diameters. To identify proximity communities, we clustered cells based on number and type of neighboring masks using Phenograph. The number of cell types and cellular communities were calculated inside and outside follicles after adjustment for total IF and OF areas. The significance of cell contact was measured using a random permutation test. Results We identified 13 unique cell subsets (11 immune, 1 endothelial, 1 unclassified) in the TME of our FL section (Fig. 1A). The unique phenotype of each subset was confirmed using a dimensionality reduction tool (t-SNE). The global composition of the TME varied minimally across ROIs and consisted primarily of B cells, T cells, and macrophages subsets - in decreasing order of frequency. Higher spatial heterogeneity across ROIs was observed in the frequency of T cell subsets in comparison to B cells subsets. Inspecting the spatial distribution of T cell subsets (Fig. 1B), we observed that cytotoxic T cells were primarily located in OF areas, whereas CD4+ T cells were found in both IF and OF areas. Notably, the majority of CD4+ T cells inside the follicles expressed CD45RO (memory phenotype), while most of the CD4+ T cells outside the follicles did not. Statistical analysis of the spatial distribution of CD4+ memory T cell subsets confirmed a significant increase in their frequency inside follicles compared to outside (20.4% vs 11.2%, p < 0.001; Fig. 1D). Cell-cell contact analysis (Fig 1C) showed increased homotypic contact for all cell types. We also found a higher frequency of heterotypic contact between Ki-67+CD4+ memory T cells and Ki-67+ B cells. Pairwise analysis showed these findings were statistically significant, indicating these cells are organized in niches rather than randomly distributed across image. Analysis of cellular communities (Fig. 1C) identified 13 niches, named according to the most frequent type of cell-cell contact. All CD4+ memory T cell subsets were found to belong to the same neighborhood (CD4 Memory community). Analysis of the spatial distribution of this community confirmed that these niches were more frequently located inside follicles rather than outside (26.3±4% vs 0.004%, p < 0.001, Fig. 1D). Conclusions Analysis of the TME using CODEX provides insights on the complex composition and unique architecture of this FL case. Cells were organized in a pattern characterized by (1) high degree of homotypic contact and (2) increased heterotypic interaction between activated B cells and activated CD4+ memory T cells. Spatial analysis of both individual cell subsets and cellular neighborhoods demonstrate a statistically significant increase in CD4+ memory T cells inside malignant follicles. This emerging knowledge about the specific immune-architecture of FL adds mechanistic details to our initial observation around the prognostic value of the TME in this disease. These data support future studies using modulation of the TME as a therapeutic target in FL. Figure 1 Disclosures Galkin: BostonGene: Current Employment, Patents & Royalties. Svekolkin:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Postovalova:BostonGene: Current Employment, Current equity holder in private company. Bagaev:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Ovcharov:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Varlamova:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Novak:Celgene/BMS: Research Funding. Witzig:AbbVie: Consultancy; MorphSys: Consultancy; Incyte: Consultancy; Acerta: Research Funding; Karyopharm Therapeutics: Research Funding; Immune Design: Research Funding; Spectrum: Consultancy; Celgene: Consultancy, Research Funding. Nowakowski:Nanostrings: Research Funding; Seattle Genetics: Consultancy; Curis: Consultancy; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other; Kymera: Consultancy; Denovo: Consultancy; Kite: Consultancy; Celgene/BMS: Consultancy, Research Funding; Roche: Consultancy, Research Funding; MorphoSys: Consultancy, Research Funding. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding. Ansell:Trillium: Research Funding; Takeda: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Bristol Myers Squibb: Research Funding; AI Therapeutics: Research Funding; ADC Therapeutics: Research Funding.

592 Background: Immune responses in the tumor microenvironment have prognostic and predictive value in BC. However, the potential of immune responses observed in peripheral blood as biomarkers in BC remains unclear. We have shown that a higher frequency of circulating monocytes and a lower frequency of antigen-experienced memory CD8+ T cells are associated with response to NAC in triple negative BC (Leon-Ferre et al SABCS 2019). Here, we used cytometry by time-of-flight (CyTOF) to evaluate associations between circulating immune cells, clinical features and response to T-based NAC in HER2+ BC. Methods: PBMC suspensions from 36 pts with stage I-III HER2+ BC were prospectively collected prior to initiation of T-based NAC, stained with 29 metal-tagged antibodies optimized to identify major human immune cell subsets, and acquired in the Helios CyTOF instrument. Differential abundance analysis of immune cells by clinical characteristics and by NAC response was evaluated using Wilcoxon rank sum test. % of immune cell subsets is presented as % of all PBMCs. Results: Most pts presented with ER- tumors (56%), measuring > 5cm (64%) and with nodal metastases (78%). After NAC, 16 pts (44%) achieved pathologic complete response (pCR). Analysis of preNAC PBMCs demonstrated a significantly higher number of B cells (8% vs 5%, p = 0.05) and effector memory CD8+ T cells (CD45RA-/CCR7-, 3 vs 1%, p = 0.02) in pts with pCR compared to those with residual disease. Of the B cell subsets, naïve B cells (CD24-/CD27-) were higher in pts who achieved pCR vs not (7% vs 4%, 0 = 0.04). Regarding clinical characteristics, cN+ pts at presentation exhibited a lower number of peripheral blood T cells compared to cN- pts (47% vs 63%, p = 0.03). Of the T cell subsets, overall CD4+ and naïve CD4+ T cells (CD45RA+/CCR7+) were lower in cN+ vs cN- pts (31% vs 45%, p = 0.05; and 11% vs 24%, p = 0.04). We also observed differences in CD56+/CD16- NK cells by ER status (ER- 1% vs ER+ 3%, p = 0.01), and a moderate negative correlation between age and % circulating CD8+ T cells (rho -0.4669, p = 0.004). Conclusions: Distinct peripheral blood immune cell profiles are observed in HER2+ BC at diagnosis, and are associated with response to T-based NAC and initial clinical characteristics. Notably, pts who later achieved pCR had a relative abundance of B cells and effector memory CD8+ T cells at diagnosis. These data suggest that immune cell phenotyping in peripheral blood may have potential as a biomarker to predict response to NAC in BC.

Abstract Background: Local antitumor immunity—as assessed by quantifying tumor-infiltrating immune cells—is increasingly recognized as a critical factor influencing prognosis and therapy response in TNBC. However, an understanding of systemic antitumor immune responses occurring in peripheral blood, and their influence on prognosis and chemotherapy response has not been rigorously studied. Methods: Cytometry by time-of-flight (CyTOFTM, Fluidigm) was used to examine viably cryopreserved peripheral blood mononuclear cell (PBMC) suspensions prospectively collected from patients with early-stage TNBC prior to initiation of standard neoadjuvant paclitaxel followed by doxorubicin and cyclophosphamide (NACT) as part of the BEAUTY study [1]. Samples were stained using a panel of metal-tagged antibodies, recognizing 30 surface proteins optimized for immune monitoring of human peripheral blood. Differential abundance analysis of immune cell subsets was carried out to evaluate differences between patients who achieved pCR versus those with residual disease after NACT, and between patients with known luminal androgen receptor (LAR) versus basal TNBC subtypes defined by bulk tumor RNA sequencing. Results: Viably cryopreserved PBMC samples from 40 treatment-naive TNBC patients were available for analysis. The median age was 52 years (range 32 - 73), with 6 (15%) patients having tumors classified as LAR TNBC, and the remaining 34 (85%) as basal TNBC. Overall, 21 (53%) patients achieved pCR after NACT. After acquisition on the mass cytometer, the median yield per sample was 626,815 single-cell events (range 42,786 - 1,035,575), with a median percent debris of 13.7% (range 14 - 58). Across the 40 PBMC samples, the total yield was 23,507,094 single-cell events. The median frequencies of major circulating immune cell subsets across the 40 TNBC patients were: T cells 53.9% (range 25.4 - 71.3), with 33.4% CD4+ T cells (range 11.4 - 46.7) and 10.3% CD8+ T cells (range 5.8 - 19.9); B cells 10.8% (3.3 - 32.6), NK cells 8.6% (1.7 - 17.0) and monocytes 10.6% (2.7 - 29.8). Examining pre-treatment blood samples, patients with residual disease after NACT exhibited a higher median frequency of baseline CD14+CD16- classical monocytes (7.5% vs. 4.1%, p=0.025) and a lower frequency of terminally-differentiated effector memory cytotoxic (CD8+) T cells (0.6% vs. 1.7%, p=0.038) compared to patients who achieved pCR. Patients with LAR TNBC also exhibited a higher frequency of CD14+CD16- classical monocytes (11.5% vs 4.3%, p=0.058), and in addition exhibited a lower frequency of central memory CD4+ T cells (10.4% vs 15.2%, p=0.048). No difference in CD8+ T cells was seen by LAR status. Additional associations of peripheral blood immune cell subsets and classic tumor pathological features will be presented at the meeting. Conclusion: To our knowledge, this is the first study focused on TNBC to demonstrate variation in peripheral blood immune cell populations by molecular TNBC subtype (LAR vs. basal), and by chemotherapy response. A higher frequency of circulating classical monocytes—which can infiltrate into tissues and give rise to macrophages—appears to be detrimental; whereas a higher frequency of circulating antigen-experienced memory CD8+ T cells seems to be protective, suggesting a putative role of this cell subset in TNBC anti-tumoral immunity. Reference: [1] Goetz MP et al. JNCI 2017, PMID:28376176 Citation Format: Roberto A Leon-Ferre, Kaitlyn McGrath, Jodi M Carter, Krishna R Kalari, Vera J Suman, Richard Weinshilboum, Liewei Wang, Keith L Knutson, Stephen M Ansell, Judy C Boughey, J C Villasboas, Matthew P Goetz. Deep phenotyping using CyTOF identifies peripheral blood immune signatures associated with clinical outcomes and molecular subtypes in patients with early-stage triple negative breast cancer (TNBC) [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-04-09.