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Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogeneous disease. Transcriptomic and genetic characterization of DLBCL has increased the understanding of its intrinsic pathogenesis and provided potential therapeutic targets. However, the role of the microenvironment in DLBCL biology remains less understood. Here, we performed a transcriptomic analysis of the microenvironment of 4,655 DLBCLs from multiple independent cohorts and described four major lymphoma microenvironment categories that associate with distinct biological aberrations and clinical behavior. We also found evidence of genetic and epigenetic mechanisms deployed by cancer cells to evade microenvironmental constraints of lymphoma growth, supporting the rationale for implementing DNA hypomethylating agents in selected patients with DLBCL. In addition, our work uncovered new therapeutic vulnerabilities in the biochemical composition of the extracellular matrix that were exploited to decrease DLBCL proliferation in preclinical models. This novel classification provides a road map for the biological characterization and therapeutic exploitation of the DLBCL microenvironment.Significance:In a translational relevant transcriptomic-based classification, we characterized the microenvironment as a critical component of the B-cell lymphoma biology and associated it with the DLBCL clinical behavior establishing a novel opportunity for targeting therapies.This article is highlighted in the In This Issue feature, p. 1307

Supplementary Figures

Supplementary Tables

Supplementary Table S1. Clinicopathologic characteristics of each mpMRI-invisible and -visible PCa tumor.

Purpose:Multiparametric MRI (mpMRI) has become an indispensable radiographic tool in diagnosing prostate cancer. However, mpMRI fails to visualize approximately 15% of clinically significant prostate cancer (csPCa). The molecular, cellular, and spatial underpinnings of such radiographic heterogeneity in csPCa are unclear.Experimental Design:We examined tumor tissues from clinically matched patients with mpMRI-invisible and mpMRI-visible csPCa who underwent radical prostatectomy. Multiplex immunofluorescence single-cell spatial imaging and gene expression profiling were performed. Artificial intelligence–based analytic algorithms were developed to examine the tumor ecosystem and integrate with corresponding transcriptomics.Results:More complex and compact epithelial tumor architectures were found in mpMRI-visible than in mpMRI-invisible prostate cancer tumors. In contrast, similar stromal patterns were detected between mpMRI-invisible prostate cancer and normal prostate tissues. Furthermore, quantification of immune cell composition and tumor-immune interactions demonstrated a lack of immune cell infiltration in the malignant but not in the adjacent nonmalignant tissue compartments, irrespective of mpMRI visibility. No significant difference in immune profiles was detected between mpMRI-visible and mpMRI-invisible prostate cancer within our patient cohort, whereas expression profiling identified a 24-gene stromal signature enriched in mpMRI-invisible prostate cancer. Prostate cancer with strong stromal signature exhibited a favorable survival outcome within The Cancer Genome Atlas prostate cancer cohort. Notably, five recurrences in the 8 mpMRI-visible patients with csPCa and no recurrence in the 8 clinically matched patients with mpMRI-invisible csPCa occurred during the 5-year follow-up post-prostatectomy.Conclusions:Our study identified distinct molecular, cellular, and structural characteristics associated with mpMRI-visible csPCa, whereas mpMRI-invisible tumors were similar to normal prostate tissue, likely contributing to mpMRI invisibility.

Supplementary Figure S2. The mpMRI-visible PCa tumors show marked differences between the malignant and non-malignant compartments. A, Box plots showing WD per mpMRI-invisible and -visible PCa tumors. P-values were calculated with the Mann-Whitney U test. B, Combination of WD plotted against the malignant cell density per mpMRI-visible and -invisible PCa tumors and patient relapse.

Supplementary Data file with Methods and each Supplementary figure/table linked to legend

Although there are immune checkpoint inhibitors (ICIs) available for the treatment of renal cell carcinoma (RCC), the utility of PD-L1 detection by immunohistochemistry (IHC) as a predictive biomarker in clear cell RCC (ccRCC) remains controversial. Nevertheless, alternative methods for PD-L1 detection, such as RNA sequencing (RNA-Seq), may be clinically useful in ccRCC; therefore, we sought to determine the ability of RNA-Seq to accurately and sensitively detect PD-L1 expression across different ccRCC clinical samples in comparison with IHC.Patients with ccRCC (n=127) who received treatment from Washington University in St. Louis between 2018 and 2020 were identified. Tumors from these patients were analyzed using RNA-Seq and IHC.PD-L1 detection by RNA-Seq strongly correlated with IHC (P.001), which was further validated using two independent datasets. Furthermore, RNA-Seq analysis identified an immune-enriched (higher PD-L1 positivity) and an immune-desert (lower PD-L1 positivity) microenvironment of ccRCC, which also correlated with IHC (P.00001).The results demonstrate the ability of RNA-Seq to detect PD-L1 in various ccRCC clinical samples compared to IHC. Ultimately, these findings suggest that PD-L1 detection by RNA-Seq can be further developed to determine the clinical utility of this methodology in ccRCC.

Severe and prolonged lymphopenia frequently occurs in patients with glioblastoma after standard chemoradiotherapy and has been associated with worse survival, but its underlying biological mechanism is not well understood. To address this, we performed a correlative study in which we collected and analyzed peripheral blood of patients with glioblastoma ( n = 20) receiving chemoradiotherapy using genomic and immune monitoring technologies. RNA sequencing analysis of the peripheral blood mononuclear cells (PBMC) showed an elevated concentration of myeloid-derived suppressor cell (MDSC) regulatory genes in patients with lymphopenia when compared with patients without lymphopenia after chemoradiotherapy. Additional analysis including flow cytometry and single-cell RNA sequencing further confirmed increased numbers of circulating MDSC in patients with lymphopenia when compared with patients without lymphopenia after chemoradiotherapy. Preclinical murine models were also established and demonstrated a causal relationship between radiation-induced MDSC and systemic lymphopenia using transfusion and depletion experiments. Pharmacological inhibition of MDSC using an arginase-1 inhibitor (CB1158) or phosphodiesterase-5 inhibitor (tadalafil) during radiation therapy (RT) successfully abrogated radiation-induced lymphopenia and improved survival in the preclinical models. CB1158 and tadalafil are promising drugs in reducing radiation-induced lymphopenia in patients with glioblastoma. These results demonstrate the promise of using these classes of drugs to reduce treatment-related lymphopenia and immunosuppression.

BackgroundNeoantigen (NeoAg) peptides displayed at the tumor cell surface by human leukocyte antigen molecules show exquisite tumor specificity and can elicit T cell mediated tumor rejection. However, few NeoAgs are predicted to be shared between patients, and none to date have demonstrated therapeutic value in the context of vaccination.MethodsWe report here a phase I trial of personalized NeoAg peptide vaccination (PPV) of 24 stage III/IV non-small cell lung cancer (NSCLC) patients who had previously progressed following multiple conventional therapies, including surgery, radiation, chemotherapy, and tyrosine kinase inhibitors (TKIs). Primary endpoints of the trial evaluated feasibility, tolerability, and safety of the personalized vaccination approach, and secondary trial endpoints assessed tumor-specific immune reactivity and clinical responses. Of the 16 patients with epidermal growth factor receptor (EGFR) mutations, nine continued TKI therapy concurrent with PPV and seven patients received PPV alone.ResultsOut of 29 patients enrolled in the trial, 24 were immunized with personalized NeoAg peptides. Aside from transient rash, fatigue and/or fever observed in three patients, no other treatment-related adverse events were observed. Median progression-free survival and overall survival of the 24 vaccinated patients were 6.0 and 8.9 months, respectively. Within 3–4 months following initiation of PPV, seven RECIST-based objective clinical responses including one complete response were observed. Notably, all seven clinical responders had EGFR-mutated tumors, including four patients that had continued TKI therapy concurrently with PPV. Immune monitoring showed that five of the seven responding patients demonstrated vaccine-induced T cell responses against EGFR NeoAg peptides. Furthermore, two highly shared EGFR mutations (L858R and T790M) were shown to be immunogenic in four of the responding patients, all of whom demonstrated increases in peripheral blood neoantigen-specific CD8+ T cell frequencies during the course of PPV.ConclusionsThese results show that personalized NeoAg vaccination is feasible and safe for advanced-stage NSCLC patients. The clinical and immune responses observed following PPV suggest that EGFR mutations constitute shared, immunogenic neoantigens with promising immunotherapeutic potential for large subsets of NSCLC patients. Furthermore, PPV with concurrent EGFR inhibitor therapy was well tolerated and may have contributed to the induction of PPV-induced T cell responses.

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.

Checkpoint immunotherapy (CPI) and its combination with tyrosine kinase inhibitor (TKI) therapy are widely used in the treatment of many cancers, including clear cell renal cell carcinoma (ccRCC). Although recent advances in these therapies demonstrate effectiveness, a subset of patients are unresponsive. In this study, using deep immune profiling by mass cytometry (CyTOF), we analyzed peripheral blood mononuclear cell (PBMC) samples from 86 ccRCC patients pre- and post-treatment. The cohort included patients treated with first-line CPI (n = 43), TKI (n = 15) and combinatorial CPI+TKI (n = 28), with PBMC samples collected pre- (n = 41) and post- (n = 69) therapy (n = 24 patients total with paired samples). Samples were characterized by CyTOF analysis of 30 different cell surface markers. Using t-distributed stochastic neighbor embedding (tSNE) and Phenograph clustering, the presence of immune populations and subpopulations were characterized and associated with therapeutic response as well as longitudinally tracked. While most cell types did not differ between pre- and post-treatment states, a decrease in CD8 and CD4 PD1+ T cell subsets was observed in patients treated with CPI and CPI+TKI (p < 0.001). Also, increased CD8 effector memory T cells were observed in post-treatment samples in those treated with CPI or CPI+TKI (p < 0.001).Subsequent analysis of pre-treatment samples in the context of clinical outcome revealed specific features associated with favorable response to immunotherapy. Responders (R) in comparison with non-responders (NR) were characterized by an increased number of naive CD4 T cells (p = 0.03), CD8 T cells (p = 0.038), immature NK cells (p = 0.005) and lower percentage of granulocytes (p = 0.012). Using unsupervised hierarchical clustering, 8 patient groups were identified with a unique immune profile enriched by a certain PBMC population. Three groups (G1-G3), which included a high ratio of R, were enriched by naive T cells, immature NK cells and non-classical monocytes, respectively. The G4 group, enriched with B cells, consisted of R and NR in equal numbers. The G5-G8 groups, enriched with granulocytes, classical monocytes, mature NK-cells and effector CD8 T cells, respectively, were primarily comprised of NR. Patients with favorable pre-treatment immune profiles also belonged to favorable groups post-treatment, with a similar trend observed with unfavorable profiles. In conclusion, this analysis revealed a remarkable association between the initial immune status or “portrait” of the patient before therapy and subsequent therapeutic outcome as well as the development of therapeutic resistance. Citation Format: Svetlana Shishkova, Dmitry Tabakov, Evgeny Egorov, Akshaya Ramachandran, Yang Lyu, Krystle Nomie, Jessica Brown, Vladimir Zyrin, Alexander Zaytcev, Natalia Miheecheva, Ekaterina Postovalova, Alexander Bagaev, Ravshan Ataullakhanov, James Hsieh. Deep immune profiling by mass cytometry revealed an association between the state of immune system before treatment and response to checkpoint inhibitor therapy in clear cell renal cell carcinoma [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 2061.

4591 Background: UC is associated with high recurrence rates, progression, and resistance to platinum-based therapy. Checkpoint inhibitors (CPIs) are often used for treating UC, but predictive biomarkers that characterize response are lacking in the majority of patients. Defining the TME is essential to understanding patient response to CPIs. Employing a transcriptome-based classification platform, we sought to identify predictive and prognostic subtypes of UC using malignant cell and TME features. Methods: We collected a metacohort of 2,418 UC samples from 14 publicly available datasets, one of which had atezolizumab response data (IMvigor210). Using the methodology described in Bagaev et al. 2021, we selected 28 signatures composed of specific gene sets reflecting distinct cellular processes. Analysis of signature expression and unsupervised clustering was performed. Results: We identified 7 recurring novel UC subtypes (Table 1) with unique genomic and molecular characteristics. The subtypes and key findings include: an immune desert (D) subtype characterized by genomic instability high HER2 expression; an immune desert, FGFR-altered (D-FGFR) subtype with FGFR alterations; an immune enriched (IE) subtype with an enriched TME and high CPI response; a fibrotic (F) subtype with a mesenchymal TME, and strong TGFβ signaling; an immune enriched, fibrotic (IE-F) subtype with a mixed TME and a high CPI response rate; a fibrotic, basal (F-B) subtype with a mesenchymal TME and minimal genomic targets, and a neuroendocrine-like (NE-L) subtype with a high CPI response rate. Conclusions: UC can be classified into 7 subtypes with distinct prognoses, CPI response rates, and druggable targets using malignant cell and TME profiling. Patients with IE, IE-F, and NE-L UCs may be good candidates for CPIs. D UCs may benefit from HER2-i and PARP-i, while FGFR-i might be more suitable for D-FGFR UCs. TGFβi and PARPi may be effective for F UCs, but F-B UCs have no targetable findings. These findings warrant additional investigation for clinical translation. [Table: see text]

Purpose: Multiparametric MRI (mpMRI) has become an indispensable radiographic tool in diagnosing prostate cancer. However, mpMRI fails to visualize approximately 15% of clinically significant prostate cancer (csPCa). The molecular, cellular, and spatial underpinnings of such radiographic heterogeneity in csPCa are unclear. Experimental Design: We examined tumor tissues from clinically matched patients with mpMRI-invisible and mpMRI-visible csPCa who underwent radical prostatectomy. Multiplex immunofluorescence single-cell spatial imaging and gene expression profiling were performed. Artificial intelligence–based analytic algorithms were developed to examine the tumor ecosystem and integrate with corresponding transcriptomics. Results: More complex and compact epithelial tumor architectures were found in mpMRI-visible than in mpMRI-invisible prostate cancer tumors. In contrast, similar stromal patterns were detected between mpMRI-invisible prostate cancer and normal prostate tissues. Furthermore, quantification of immune cell composition and tumor-immune interactions demonstrated a lack of immune cell infiltration in the malignant but not in the adjacent nonmalignant tissue compartments, irrespective of mpMRI visibility. No significant difference in immune profiles was detected between mpMRI-visible and mpMRI-invisible prostate cancer within our patient cohort, whereas expression profiling identified a 24-gene stromal signature enriched in mpMRI-invisible prostate cancer. Prostate cancer with strong stromal signature exhibited a favorable survival outcome within The Cancer Genome Atlas prostate cancer cohort. Notably, five recurrences in the 8 mpMRI-visible patients with csPCa and no recurrence in the 8 clinically matched patients with mpMRI-invisible csPCa occurred during the 5-year follow-up post-prostatectomy. Conclusions: Our study identified distinct molecular, cellular, and structural characteristics associated with mpMRI-visible csPCa, whereas mpMRI-invisible tumors were similar to normal prostate tissue, likely contributing to mpMRI invisibility.

Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogeneous disease. Transcriptomic and genetic characterization of DLBCL has increased the understanding of its intrinsic pathogenesis and provided potential therapeutic targets. However, the role of the microenvironment in DLBCL biology remains less understood. Here, we performed a transcriptomic analysis of the microenvironment of 4,655 DLBCLs from multiple independent cohorts and described four major lymphoma microenvironment categories that associate with distinct biological aberrations and clinical behavior. We also found evidence of genetic and epigenetic mechanisms deployed by cancer cells to evade microenvironmental constraints of lymphoma growth, supporting the rationale for implementing DNA hypomethylating agents in selected patients with DLBCL. In addition, our work uncovered new therapeutic vulnerabilities in the biochemical composition of the extracellular matrix that were exploited to decrease DLBCL proliferation in preclinical models. This novel classification provides a road map for the biological characterization and therapeutic exploitation of the DLBCL microenvironment.Significance:In a translational relevant transcriptomic-based classification, we characterized the microenvironment as a critical component of the B-cell lymphoma biology and associated it with the DLBCL clinical behavior establishing a novel opportunity for targeting therapies.This article is highlighted in the In This Issue feature, p. 1307

The emergence of immune checkpoint inhibitors (ICI) have revolutionized treatment of many cancer types, including renal cell carcinoma (RCC). Although there are currently three ICI combination regimens approved as standard of care for the first-line treatment of advanced RCC, the utility of PD-L1 detected by immunohistochemistry (IHC) as a predictive biomarker remains somewhat controversial. In this study, we sought to determine the ability to detect PD-L1 expression accurately and sensitively in comparison with IHC as well as whether RNA-seq-based cellular deconvolution potentially correlates with PD-L1 IHC for the development of a deconvolution-based biomarker. Tumors from 103 clear cell RCC (ccRCC) patients were collected to compare sensitivity and clinical utility of RNA-seq and IHC in PD-L1 detection. Additionally, we used gene expression signatures to comprehensively characterize the ccRCC tumor microenvironments (TMEs) from RNA-seq to examine PD-L1 expression levels across varied TMEs. This classification system revealed that the ccRCC samples clustered into an immune-enriched microenvironment with high lymphocyte infiltration and an immune desert microenvironment predominantly composed of malignant cells. For RNA-seq-based cellular deconvolution, a unique and robust machine learning algorithm, named Kassandra, was developed using over 8,000 RNA profiles of sorted cells that outperformed all current cellular deconvolution algorithms. The RNA-seq of ccRCC clinical tumors (n =89) were analyzed by Kassandra. Our RNA-seq analysis identified ccRCC tumors with high (maximum), intermediate and low (minimum) PD-L1 expression, which were then validated with IHC. Among these, 5/5, 10/10 and 87/88 ccRCC tumors with high, medium and low expression, respectively, detected via RNA-seq matched with the PD-L1 levels detected with IHC, suggesting a high degree of concordance (p < 0.01) between the two methods. Notably, the immune-enriched ccRCC clusters had a higher percentage of PD-L1-positive malignant and immune cells. PD1+ CD8 T cells, identified via RNAseq-based cellular deconvolution (Kassandra), most strongly correlated with PD-L1 IHC (p < 0.05). PD-L1 status was predicted with an 0.82 AUC performance score using a regression model. In conclusion, our results indicated that PD-L1 expression detected by RNA-seq as well as the presence of PD1+ CD8 T cells determined by applying Kassandra strongly correlated with the PD-L1 values obtained via IHC, potentially providing a less costly and more robust alternative to the conventional methods. In addition, an increased number of PD-L1-positive tumor and immune cells within the immune-enriched TME may suggest the significance of the TME composition as a predictive factor in response to ICIs. Citation Format: Natalia Miheecheva, Maria Sorokina, Akshaya Ramachandran, Yang Lyu, Danil Stupichev, Alexander Bagaev, Ekaterina Postovalova, Krystle Nomie, Felix Frenkel, Maria Tsiper, Nathan Fowler, Ravshan Ataullakhanov, James J. Hsieh. Evaluating the clinical utility of RNA-seq-based PD-L1 expression and cellular deconvolution as alternatives to conventional immunohistochemistry in clear cell renal cell carcinoma [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 161.

Systemic lymphopenia is common among glioblastoma (GBM) patients after chemoradiotherapy and is observed up to six months to 1 year after irradiation. Systemic lymphopenia is associated with a worse survival outcome in GBM. To delineate the underlying mechanisms of lymphopenia in GBM, we prospectively evaluated peripheral blood of GBM patients treated with standard radiation therapy (RT) and concurrent temozolomide as well as conducted in vivo experiments using a syngeneic murine GBM model. Among 20 patients prospectively evaluated, 10 patients (50%) developed lymphopenia and the other 10 did not. The RNA sequencing analysis of the peripheral blood mononuclear cell (PBMC) showed an elevated level of myeloid-derived suppressor cells (MDSCs) regulatory genes including Arg-1 in lymphopenic patients compared to non-lymphopenic patients after chemoradiotherapy. The single-cell RNA sequencing (scRNA-Seq) analysis of two selected phenotypical patients also demonstrated that a severe lymphopenic patient had elevated levels of two types of monocytic MDSCs (M-MDSCs) after RT compared to a non-lymphopenic patient. Immunophenotyping analysis of the PBMCs using flow cytometry confirmed reductions of CD4 and CD8 T cells as well as increased polymorphonuclear MDSCs (PMN-MDSCs) and M-MDSCs in lymphopenic patients compared to non-lymphopenic patients. The in vitro T cell proliferation and functional assays showed that MDSCs from lymphopenic patients exhibited were more suppressive compared to MDSCs from non-lymphopenic patients. Using a syngeneic orthotopic mouse (C57BL/6) GBM (GL261) model, we demonstrated that fractionated RT (2 Gy/day x 5) reduced CD4/CD8 cells and increased MDSCs compared to sham irradiated mice. The MDSCs from the irradiated mice also had increased Arg-1 expression. The addition of Arg-1 inhibitor (Arg1i) or phosphodiesterase type 5 inhibitor (PDE5i) to RT blocked the radiation-induced increase of MDSCs and reduction of CD4/CD8 cells. The combination of either Arg1i or PDE5i also enhanced the survival of mice with orthotopic tumors when compared to mice treated with RT alone or drug alone. In conclusion, we found that RT can induce an increase in circulating MDSCs in both patients and mice and the resulting circulating MDSCs can suppress T-cell proliferation and function. Inhibition of MDSCs using either Arg1i or PDE5i during RT may prevent systemic lymphopenia and affect disease control, which should warrant further investigation in clinical trials. Citation Format: Subhajit Ghosh, Matthew Inkman, Jin Zhang, Sukrutha Thotala, Ekaterina Tikhonova, Natalia Miheecheva, Felix Frenkel, Ravshan Ataullakhanov, Xiaowei Wang, Dennis Hallahan, Jiayi Huang, Dinesh Thotala. Association of circulating myeloid derived suppressor cells with systemic lymphopenia following chemoradiotherapy in glioblastoma [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 507.

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.

e17106 Background: Clear cell renal cell carcinoma (ccRCC) exhibits conspicuous intratumor heterogeneity (ITH) - a driver of tumor evolution and metastasis. ITH in RCC has been studied extensively with bulk tumor DNA sequencing, which lacks the ability to integrate single cell resolution data, spatial architecture, and microenvironment composition. Therefore, we analyzed primary ccRCC tumors at multiple biopsy sites with CyTOF, multiplex immunofluorescence (MxIF), whole exome sequencing (WES), RNA sequencing (RNA-seq), single nuclei RNA-seq (snRNA-seq), and whole genome bisulfite sequencing (WGBS). Methods: Primary ccRCC tumors collected from 6 patients (pts) were biopsied at multiple locations and subjected to CyTOF (n = 21 sites, 6 pts), MxIF (20 markers, n = 8 sites, 3 pts), WES (n = 8 sites, 3 pts), RNA-seq (n = 8 sites, 3 pts), snRNA-seq (n = 8 sites, 3 pts), and WGBS (n = 8 sites, 3 pts), enabling integrated multi-omics analysis. MxIF, CyTOF, and genomic/transcriptomic analyses were performed by BostonGene. Results: Genomic intratumor (IT) evolution of ccRCC cells was tracked with WES, and subclonal distribution of SETD2, STAG2, TSC2 and PBRM1 mutations was observed in different IT regions. Different regions of the same tumor were similar, whereas individual patient tumors were distinct according to tumor microenvironment cellular composition measured by CyTOF or deconvoluted from RNA-seq. The cellular deconvolution of the ccRCC tumors reconstructed from RNA-seq correlated with CyTOF, snRNA-seq and WGBS, showing high concordance among the methods. The promoter CpG island methylation levels, averaged across all genes, positively correlated with ccRCC grade. MxIF revealed spatial IT heterogeneity in the distribution of immune infiltrate components. Macrophages and T cells dispersed among malignant cells; whereas, T cells formed clusters at unique tumor margins. Conclusions: The utilization of multi-omics methods produced a high-resolution portrait of the ccRCC tumor composition and identified differential ITH among regions within the primary tumors or among individual primary tumors. This study demonstrated strong concordance among the different technologies, suggesting that tumor deconvolution by bulk RNA-seq might be clinically applicable for ccRCC tumors. MxIF analysis enabled a fine elucidation of the spatial relationships among the tumor and the immune and stromal cells, missed by common omic platforms. Integrated single cell multi-omics could render specific pathobiological and therapeutic insights that impact treatment decisions.

e17506 Background: The inclusion of multiparametric MRI (mpMRI) to prostate cancer (PCa) diagnostics has increased the detection rate and has improved the prediction rate of clinically significant PCa. Yet, mpMRI has a false negative rate of 12.6%, missing 6-13% of clinically significant PCa. The mechanisms underlying MRI visibility are poorly understood; therefore, to probe the molecular and cellular underpinnings of PCa MRI visibility, we profiled tissue from Gleason score and clinically matched patients with MRI-visible and MRI-invisible PCa who underwent radical prostatectomy. Methods: MRI-invisible (n = 7) and MRI-visible (n = 8) PCa tumors were evaluated with multiplex immunofluorescence (MxIF; 14 markers) and were subjected to gene expression profiling using the HTG EdgeSeq Oncology Biomarker Panel (2,549 genes). Gene expression analysis was also performed using The Cancer Genome Atlas (TCGA), including normal prostate (n = 52) and PCa (n = 387) tissue. Analyses were performed using the BostonGene automated pipeline. Results: MpMRI-visible PCa tumors (62.5%) displayed compact epithelial tumor architecture compared with mpMRI-invisible PCa tumors (28.5%). mpMRI-visible PCa had higher malignant cell density (p = 0.04) and increased neighboring malignant cells (p = 0.07), correlating with MRI-visible PCa complex tumor architecture (r = 0.49 for neighboring malignant cells vs tumor cell density). Tumor stromal organization differences were determined by measuring Wasserstein distances between distributions, and mpMRI-invisible PCa stroma appeared more similar to normal tissue. The visible group exhibited lower expression of stroma-enriched genes such as filamin C (FLNC) (FDR < 0.1) and cellular adhesion-related genes (FDR < 0.4), with gene expression signatures markedly different compared to normal prostate tissue. Higher malignant cell density, neighboring malignant cells, and Wasserstein distances, and low FLNC expression – all mpMRI visibility characteristics – were associated with patient relapse (p = 0.02). Low stroma signature expression in the TCGA cohort correlated with inferior PCa PFS (p = 0.005). Conclusions: This is the first integrated multi-omics analysis of clinically matched mpMRI-visible and -invisible PCa. mpMRI-invisible tumors exhibited molecular, cellular, and structural characteristics more akin to normal prostate tissue, which may render them undetectable by imaging. A stroma-associated gene signature, a mpMRI-invisible tumor feature, correlated with better PCa clinical outcomes.