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Intratumor heterogeneity (ITH) represents a major challenge for anticancer therapies. An integrated, multidimensional, multiregional approach dissecting ITH of the clear cell renal cell carcinoma (ccRCC) tumor microenvironment (TME) is employed at the single-cell level with mass cytometry (CyTOF), multiplex immunofluorescence (MxIF), and single-nucleus RNA sequencing (snRNA-seq) and at the bulk level with whole-exome sequencing (WES), RNA-seq, and methylation profiling. Multiregional analyses reveal unexpected conservation of immune composition within each individual patient, with profound differences among patients, presenting patient-specific tumor immune microenvironment signatures despite underlying genetic heterogeneity from clonal evolution. Spatial proteogenomic TME analysis using MxIF identifies 14 distinct cellular neighborhoods and, conversely, demonstrated architectural heterogeneity among different tumor regions. Tumor-expressed cytokines are identified as key determinants of the TME and correlate with clinical outcome. Overall, this work signifies that spatial ITH occurs in ccRCC, which may drive clinical heterogeneity and warrants further interrogation to improve patient outcomes.

Despite the breast cancer microenvironment being recognized as a critical participant in tumor progression and therapeutic response, current technologies for tumor cell-to-cell interaction analyses are limited to qualitative and descriptive histological methods. Here, we utilized multiplex immunofluorescence (MxIF), RNA sequencing (RNA-seq), and microarray gene expression to quantitatively assess, using the BostonGene automated pipeline, the immune microenvironment composition of clinical stage 2 or 3 primary estrogen receptor-positive (ER+) HER2-negative (HER-) breast tumors (n = 29) from patients treated with neoadjuvant palbociclib in combination with anastrozole. Microenvironment characteristics of pretreatment tumor biopsies were associated with molecular subtype, cytokine signatures, and Ki67 response. MxIF analysis (21 markers, 2-13 ROIs per slide) of the tumor immune composition revealed that while the cellular composition was similar for each tumor region of the same patient, significant cellular heterogeneity was observed in tumor biopsies from different patients. The spatial heterogeneity was found in the distribution of both immune infiltrate cells and stromal components. T cells, CD68+ and CD163+ macrophages, and B cells were the predominant cell populations in the stromal component. Further MxIF analysis showed an increase in Ki67+ tumor cells in nonresponders (NR) versus responders (R). The number of immune cells was similar between Luminal A and B breast cancer molecular subtypes. A lower number of Ki67+ tumor cells was found in Luminal A compared to Luminal B tumors, as expected. MxIF analysis of cell-to-cell interactions revealed that distinct groups of cellular neighborhoods were significantly increased in NR compared with R: the immune inflammation cluster with a high density of CD4 T cells, Tregs, and CD8 T cells in the stromal component (p = 0.03); CD163+ macrophage-enriched cluster (p < 0.001); and tertiary lymphoid structures cluster (p < 0.001). Transcriptomic analysis showed that while CXCL1, CCL17, CCL13, CXCL9, and CCL5 expression correlated with the high density of CD4 and CD8 T cells in the stromal component cluster, VEGFA, IL1B, CXCL16, CCL3, and CCL7 were increased in the CD163+ and CD68+ macrophage-enriched clusters, indicating that differential cytokine expression is associated with the tumor architecture.Comprehensively characterizing, via MxIF, the immune microenvironment of ER+ HER2- breast cancer provided resolution of cellular architecture and elucidated spatial relationships among the immune and stromal cells of the tumor. Quantitatively assessing breast tumor cell-to-cell interactions has the potential to identify imaging markers of response, ultimately leading to the development of effective therapy options. Citation Format: Maria Bruttan, Souzan Sanati, Anna Belozerova, Ilia Galkin, Akshaya Ramachandran, Pavel Ovcharov, Grigory Perelman, Viktor Svekolkin, Ekaterina Postovalova, Alexander Bagaev, Krystle Nomie, Shana Thomas, Jeremy Hoog, Ravshan Ataullakhanov, James Hsieh, Cynthia Ma. Tumor microenvironment heterogeneity identifies potential biomarkers of response in ER-positive breast cancers treated with palbociclib [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6151.

Despite a characteristic indolent course, a substantial subset of follicular lymphoma (FL) patients has an early relapse with a poor outcome. Cells in the microenvironment may be a key contributor to treatment failure. We used a discovery and validation study design to identify microenvironmental determinants of early failure and then integrated these results into the FLIPI. In total, 496 newly diagnosed FL grade 1–3 A patients who were prospectively enrolled into the MER cohort from 2002 to 2012 were evaluated. Tissue microarrays were stained for CD4, CD8, FOXP3, CD32b, CD14, CD68, CD70, SIRP-α, TIM3, PD-1, and PD-L1. Early failure was defined as failing to achieve event-free survival at 24 months (EFS24) in immunochemotherapy-treated patients and EFS12 in all others. CyTOF and CODEX analysis were performed to characterize intratumoral immunophenotypes. Lack of intrafollicular CD4 expression was the only predictor of early failure that replicated with a pooled OR 2.37 (95%CI 1.48–3.79). We next developed a bio-clinical risk model (BioFLIPI), where lack of CD4 intrafollicular expression moved patients up one FLIPI risk group, adding a new fourth high-risk group. Compared with BioFLIPI score of 1, patients with a score of 2 (OR 2.17; 95% CI 1.08–4.69), 3 (OR 3.53; 95% CI 1.78–7.54), and 4 (OR 8.92; 95% CI 4.00–21.1) had increasing risk of early failure. The favorable intrafollicular CD4 T cells were identified as activated central memory T cells, whose prognostic value was independent from genetic features. In conclusion, lack of intrafollicular CD4 expression predicts early failure in FL and combined with FLIPI improves identification of high-risk patients; however, independent validation is warranted.

Diagnosis of low-grade myelodysplastic syndromes (LG-MDS) is one of the most challenging in hematopathology as it relies predominantly on morphologic assessment of dysplasia. Prior studies have demonstrated poor interobserver agreement among pathologists. Histomorphological evaluation of bone marrow core biopsy samples remains the gold standard for diagnostic workup of LG-MDS, including myelodysplastic syndromes (MDS) and other myeloid neoplasms. However, this approach may be subjective, and cannot quantitatively assess subtle differences in marrow topography and the cellular microenvironment. Multiparametric in situ imaging (MISI) through various techniques enables multiple biomarker detection in a single tissue. BostonGene has developed an AI-based image analysis platform to reveal spatial information and subtle histomorphologic features in an objective, quantitative fashion. Here, we demonstrate the potential for automated AI-based imaging analysis of MISI to assist in the differentiation of LG-MDS samples from normal marrow tissues (NBM). Decalcified human bone marrow core biopsy tissues from LG-MDS (n=6) and uninvolved staging marrows (NBM, n=4) were first prepared by immunofluorescence-based MISI via staining with DAPI and CD34, CD38, CD117, CD71, CD15, and CD61 antibodies. BostonGene analyzed the resulting images (fig.1) using a proprietary AI-based imaging platform to identify cells by segmentation performed with the pre-trained instance segmentation neural network. Cell types were identified with mean marker expression values using an accelerated version of BostonGene's phenograph clustering algorithm. Pathologists manually masked fat and bone trabeculae. Using a combination of cell size/shape parameters and antigen expression levels, the following unique cell types were identified: myeloblasts, proerythroblasts, erythroid normoblasts, maturing granulocytes, megakaryocytes, mast cells, plasma cells, and B-cell precursors (hematogones). Data revealed differences in the cellular content of NBM and LG-MDS samples, and separation of LG-MDS samples with the del(5q) subtype (n=2). While linear slender islet-like small clusters of erythroid normoblasts were detected in NBM, we observed a chaotic arrangement of them in LG-MDS samples. In LG-MDS samples, we found an increase in the total number of erythroid normoblasts from 17% to 31%, LG-MDS-del(5q) had 14%. The ratio of maturing granulocytes to erythroid normoblasts (M:E ratio) was significantly lower in LG-MDS (0.63) than in NBM (1.95). The M:E ratio generated by MISI strongly correlated to the M:E ratio produced by manual differential count of bone marrow aspirate samples (R=0.83, p < 0.003). Additionally, fewer hematogones were identified in LG-MDS marrows compared to NBM samples, as reported by others using orthogonal methods. Topographic analysis showed the fat to cellular tissue area ratio was higher in NBM (0.73) than LG-MDS (0.41), but the ratio of trabecular area to total tissue area was higher in LG-MDS (1.67) than NMB (0.74). Spatially, myeloblasts and megakaryocytes were found closer to trabeculae in NBM than LG-MDS;12 different cell communities were identified;2 of them (cluster 3 - erythroid normoblasts enriched, cluster 5 - erythroid normoblasts contacting proerythroblasts) were distributed statistically significantly differently in NBM and LG-MDS samples, indicating the use of MISI with AI-based imaging to distinguish LG-MDS from NBM. Patients of MDS-del(5q) subtype differ significantly from other MDS samples and are more similar to NBM. AI-based image analysis applied to MISI of bone marrow tissue revealed multiple cell types in single tissue sections, along with histologically subtle differences in topography between NBM and LG-MDS samples. These results highlight the importance of integrating in situ tissue analysis with techniques that examine single cell characteristics for a more comprehensive picture of the differences between normal tissue and tumor samples. Coupling sophisticated imaging analytics with this imaging method may provide a more powerful tool for novel biomarker discovery of prognostic and therapeutic significance in the management of MDS and other marrow-based disorders. Figure 1 Figure 1. Disclosures Svekolkin: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Varlamova: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Galkin: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Akaeva: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Smirnova: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Ovcharov: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Polyakova: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Tabakov: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company. Postovalova: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties: BostonGene. Koltakova: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company. Gunn: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company. Bagaev: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties: BostonGene.

BACKGROUND: Follicular lymphoma (FL) is an indolent malignancy of germinal center B-cell origin. FL patients experience remarkable heterogeneity in their disease trajectory, with many patients slowly progressing over several years, and a subset of patients experiencing an aggressive clinical course. Uncovering the cell-intrinsic and -extrinsic factors that govern differential progression and outcome in FL patients is thus essential. Beyond the genetic and epigenetic aberrations that contribute to FL oncogenesis, the tumor microenvironment (TME) plays an integral role in supporting the proliferation and survival of malignant cells. In a process described as "re-education", FL tumor cells may actively subvert the normal functions of non-malignant cells present in the TME, including T cells, follicular dendritic cells (FDCs), macrophages, dendritic cells, and stromal cells to support their survival and growth. METHODS: To address the importance of the TME in FL, we molecularly profiled excisional lymph node biopies from untreated FL patients using multiple platforms: bulk RNA sequencing (RNAseq), single-cell RNA sequencing (scRS), and a unique high content imaging method, Iterative Bleaching Extends MultipleXity (IBEX), which utilizes chemical bleaching to image 40+ proteins in the same tissue section by antibody staining. In combination with advanced computational tools for the quantitative analysis of cell types and distribution in tissues, we have used this approach to evaluate the TME:FL interaction within 8 FL samples, with 4 normal lymph nodes as controls. RESULTS: Both FL and TME components reconstructed from bulk RNA-seq were similar to the cellular composition revealed by scRS and IBEX analyses. Moreover, the bulk RNAseq and scRS identified the expression of genes involved in tumorigenesis and oncogenic signaling, often unique to each case. However, RNAseq-based approaches often miss important cellular and acellular components not readily extracted from dense tissues. For example, IBEX imaging can trace the pattern of blood vessels within a section, which cannot be achieved with non-imaging methods. In one case, our multi-parameter imaging studies revealed the close spatial interaction between clonal FL B cells, expressing a B-cell receptor (BCR) possessing a de novo N-linked glycosylation site introduced by somatic hypermutation, and cells expressing dendritic cell-specific intercellular adhesion molecule-3-grabbing non-integrin (DC-SIGN). This contact may activate pro-survival signaling in the malignant B cells. CONCLUSIONS: Integration of bulk RNAseq, scRS, clonotype analysis, and IBEX reveals both shared and unique aspects of different FL tumors. These data highlight the importance of integrating direct tissue analysis by high-content imaging with methods examining aspects of isolated cells. This approach may provide a more complete understanding of tumor biology, which in turn will identify patients at risk for developing aggressive disease and rationally improve treatment strategies for FL. This research was supported in part by the Intramural Research Program of the NIH, NIAID and NCI Figure Disclosures Bagaev: BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Plotnikova:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Galkin:BostonGene: Current Employment, Patents & Royalties. Postovalova:BostonGene: Current Employment, Current equity holder in private company. Svekolkin:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Isaev:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Lozinsky:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Meerson:BostonGene: Current Employment. Varlamova:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Ovcharov:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Polyakova:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Nomie:BostonGene: Current Employment, Current equity holder in private company. Kotlov:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Tsiper:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Frenkel:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Attaulakhanov:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Fowler:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties.

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.

Although genomic analyses of clear cell renal cell carcinoma (ccRCC) patients have revealed targetable pathways that have led to FDA-approved therapies, the responses to these therapies remain limited. The significant success of immune checkpoint inhibitors and anti-angiogenic agents suggest that ccRCC has a unique tumor microenvironment composition and tumor behavior that influences therapeutic response. Here, we describe an integrated proteogenomic method to study intratumoral heterogeneity (ITH), microenvironment composition, tumor spatial behavior, and cellular communities in ccRCC. A unique AI-based segmentation platform for multiplex immunofluorescence (MxIF) was developed to analyze an entire tissue slide at single-cell resolution, including 70 regions of interest per slide, providing significant information regarding spatial architecture. Primary ccRCC tumors collected from patients were biopsied at multiple locations and subjected to MxIF (20 markers, n = 10 sites, 4 pts, ~1,000,000 cells), RNA-seq (n = 8 sites, 3 pts) and CyTOF (n = 21 sites, 6 pts), allowing integrated multi-omics analysis at the single-cell level. Integrated analysis showed that genomic intratumor heterogeneity (ITH) was remarkably similar across all regions biopsied from the same patient, and the cellular populations present within each region of the same patient were alike. However, some cell types such as TCM CD4 T cells and Tem CD38 and Tem PD1+CD69+CD38- CD8 T cells, and CD163+PDL1+LAMP+ showed great inter-patient differences in the proportion of these cell populations. Notably, 14 CD4 T cell, 13 CD8 T cell, and 10 macrophage subpopulations were identified across the ccRCC tumors. Moreover, 14 microenvironment proximity communities based on MxIF imaging analysis of ~1 million cells were identified. The presence of B cell- and T cell-enriched communities (e.g., CD8 T cell enrichment and T-cell enrichment at the tumor border) correlated with the expression of interferon-gamma, PD1, IL-6, IL-10, PD-L1, CXCL13, and others. Macrophage-enriched communities correlated with the expression of CXCL12 and PDGFRB. Finally, tertiary lymphoid structures within the “B cell-enriched” communities correlating with the expression of CXCL13 were found in two tumors collected from one patient, with subsequent validation via H&E staining. Further, B cell repertoire (BCR) analysis of RNA-seq of the tumors from this patient showed the presence of a large B cell clone in the tumors. In conclusion, via MxIF, 14 distinct spatial microenvironment communities with unique cytokine expression patterns were identified in ccRCC. Uncovering the spatial behavior of tumors can lead to the development of effective therapies personalized for each patient based on microenvironment composition and architecture. Citation Format: Natalia Miheecheva, Akshaya Ramachandran, Yang Lyu, Ekaterina Postovalova, Viktor Svekolkin, Ilia Galkin, Pavel Ovcharov, Diana Shamsutdinova, Vladimir Zyrin, Alexander Bagaev, Krystle Nomie, Felix Frenkel, Ravshan Ataullakhanov, James J. Hsieh. Integrated multiregional transcriptomic and multi-parameter single-cell imaging analysis of clear cell renal cell carcinoma elucidates diverse cellular communities present within the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2742.