Malignant Cell Populations Research Articles

Flow Sorting, Whole Genome Amplification and Next-Generation Sequencing as Combined Tools to Study Heterogeneous Acute Lymphoblastic Leukemia.

Next-generation sequencing (NGS) methods have been introduced for immunoglobulin (IG)/T-cell receptor (TR) gene rearrangement analysis in acute lymphoblastic leukemia (ALL) and lymphoma (LBL). These methods likely constitute faster and more sensitive approaches to analyze heterogenous cases of ALL/LBL, yet it is not known whether gene rearrangements constituting low percentages of the total sequence reads represent minor subpopulations of malignant cells or background IG/TR gene rearrangements in normal B-and T-cells. In a comparison of eight cases of B-cell precursor ALL (BCP-ALL) using both the EuroClonality NGS method and the IdentiClone multiplex-PCR/gene-scanning method, the NGS method identified between 29% and 139% more markers than the gene-scanning method, depending on whether the NGS data analysis used a threshold of 5% or 1%, respectively. As an alternative to using low thresholds, we show that IG/TR gene rearrangements in subpopulations of cancer cells can be discriminated from background IG/TR gene rearrangements in normal B-and T-cells through a combination of flow cytometry cell sorting and multiple displacement amplification (MDA)-based whole genome amplification (WGA) prior to the NGS. Using this approach to investigate the clonal evolution in a BCP-ALL patient with double relapse, clonal TR rearrangements were found in sorted leukemic cells at the time of second relapse that could be identified at the time of diagnosis, below 1% of the total sequence reads. These data emphasize that caution should be exerted when interpreting rare sequences in NGS experiments and show the advantage of employing the flow sorting of malignant cell populations in NGS clonality assessments.

Single-cell transcriptomic analysis of gingivo-buccal oral cancer reveals two dominant cellular programs.

Oral squamous cell carcinoma of the gingivo-buccal region (OSCC-GB) is the most common cancer among men in India, and is associated with poor prognosis and frequent recurrence. Cellular heterogeneity in OSCC-GB was investigated by single-cell RNA sequencing of tumors derived from the oral cavity of 12 OSCC-GB patients, 3 of whom had concomitant presence of a precancerous lesion (oral submucous fibrosis [OSMF]). Unique malignant cell types, features, and phenotypic shifts in the stromal cell population were identified in oral tumors with associated submucous fibrosis. Expression levels of FOS, ATP1A, and DUSP1 provided robust discrimination between tumors with or without the concomitant presence of OSMF. Malignant cell populations shared between tumors with and without OSMF were enriched with the expression of partial epithelial-mesenchymal transition (pEMT) or fetal cell type signatures indicative of two dominant cellular programs in OSCC-GB-pEMT and fetal cellular reprogramming. Malignant cells exhibiting fetal cellular and pEMT programs were enriched with the expression of immune-related pathway genes known to be involved in antitumor immune response. In the tumor microenvironment, higher infiltration of immune cells than the stromal cells was observed. The T cell population was large in tumors and diverse subtypes of T cells with varying levels of infiltration were found. We also detected double-negative PLCG2+ T cells and cells with intermediate M1-M2 macrophage polarization. Our findings shed light on unique aspects of cellular heterogeneity and cell states in OSCC-GB.

Molecular and immunological mechanisms of clonal evolution in multiple myeloma.

Multiple myeloma (MM) is a hematologic malignancy characterized by the proliferation of clonal plasma cells in the bone marrow (BM). It is known that early genetic mutations in post-germinal center B/plasma cells are the cause of myelomagenesis. The acquisition of additional chromosomal abnormalities and distinct mutations further promote the outgrowth of malignant plasma cell populations that are resistant to conventional treatments, finally resulting in relapsed and therapy-refractory terminal stages of MM. In addition, myeloma cells are supported by autocrine signaling pathways and the tumor microenvironment (TME), which consists of diverse cell types such as stromal cells, immune cells, and components of the extracellular matrix. The TME provides essential signals and stimuli that induce proliferation and/or prevent apoptosis. In particular, the molecular pathways by which MM cells interact with the TME are crucial for the development of MM. To generate successful therapies and prevent MM recurrence, a thorough understanding of the molecular mechanisms that drive MM progression and therapy resistance is essential. In this review, we summarize key mechanisms that promote myelomagenesis and drive the clonal expansion in the course of MM progression such as autocrine signaling cascades, as well as direct and indirect interactions between the TME and malignant plasma cells. In addition, we highlight drug-resistance mechanisms and emerging therapies that are currently tested in clinical trials to overcome therapy-refractory MM stages.

Single-cell RNA sequencing captures patient-level heterogeneity and associated molecular phenotypes in breast cancer pleural effusions.

Malignant pleural effusions (MPEs) are a common complication of advanced cancers, particularly those adjacent to the pleura, such as lung and breast cancer. The pathophysiology of MPE formation remains poorly understood, and although MPEs are routinely used for the diagnosis of breast cancer patients, their composition and biology are poorly understood. It is difficult to distinguish invading malignant cells from resident mesothelial cells and to identify the directionality of interactions between these populations in the pleura. There is a need to characterize the phenotypic diversity of breast cancer cell populations in the pleural microenvironment, and investigate how this varies across patients. Here, we used single-cell RNA-sequencing to study the heterogeneity of 10 MPEs from seven metastatic breast cancer patients, including three Miltenyi-enriched samples using a negative selection approach. This dataset of almost 65000 cells was analysed using integrative approaches to compare heterogeneous cell populations and phenotypes. We identified substantial inter-patient heterogeneity in the composition of cell types (including malignant, mesothelial and immune cell populations), in expression of subtype-specific gene signatures and in copy number aberration patterns, that captured variability across breast cancer cell populations. Within individual MPEs, we distinguished mesothelial cell populations from malignant cells using key markers, the presence of breast cancer subtype expression patterns and copy number aberration patterns. We also identified pleural mesothelial cells expressing a cancer-associated fibroblast-like transcriptomic program that may support cancer growth. Our dataset presents the first unbiased assessment of breast cancer-associated MPEs at a single cell resolution, providing the community with a valuable resource for the study of MPEs. Our work highlights the molecular and cellular diversity captured in MPEs and motivates the potential use of these clinically relevant biopsies in the development of targeted therapeutics for patients with advanced breast cancer.

Spatial whole transcriptome profiling of primary tumor from patients with metastatic prostate cancer.

Prostate cancer (PCa) is a highly heterogeneous disease in terms of its molecular makeup and clinical prognosis. The prostate tumor microenvironment (TME) is hypothesized to play an important role in driving disease aggressiveness, but precise mechanisms remain elusive. In our study, we used spatial transcriptomics to explore for the first time the spatial gene expression heterogeneity within primary prostate tumors from patients with metastatic disease. In total, we analyzed 5459 tissue spots from three PCa patients comprising castration-resistant (CRPC) and neuroendocrine (NEPC) disease stages. Within CRPC, we identified a T cell cluster whose activity might be impaired by nearby regulatory T cells, potentially mediating the aggressive disease phenotype. Moreover, we identified Hallmark signatures of epithelial-mesenchymal transition in a cancer-associated fibroblast (CAF) cluster, indicating the aggressive characteristic of the primary TME leading to metastatic dissemination. Within NEPC, we identified active immune-stroma cross-talk exemplified by significant ligand-receptor interactions between CAFs and M2 macrophages. Further, we identified a malignant cell population that was associated with the down-regulation of an immune-related gene signature. Lower expression of this signature was associated with higher levels of genomic instability in advanced PCa patients (SU2C cohort, n = 99) and poor recurrence free survival in early-stage PCa patients (TCGA cohort, n = 395), suggesting prognostic biomarker potential. Taken together, our study reveals the importance of whole transcriptome profiling at spatial resolution for biomarker discovery and for advancing our understanding of tumor biology.

Direct early growth response-1 knockdown decreases melanoma viability independent of mitogen-activated extracellular signal-related kinase inhibition.

To investigate downstream molecular changes caused by mitogen-activated protein kinase (MEK) inhibitor treatment and further explore the impact of direct knockdown of early growth response-1 ( EGR1 ) in melanoma cell culture. RNA-sequencing (RNA-Seq) was performed to determine gene expression changes with MEK inhibitor treatment. Treatment with MEK inhibitor (trametinib) was then assessed in two cutaneous (MEL888, MEL624) and one conjunctival (YUARGE 13-3064) melanoma cell line. Direct knockdown of EGR1 was accomplished using lentiviral vectors containing shRNA. Cell viability was measured using PrestoBlueHS Cell Viability Reagent. Total RNA and protein were assessed by qPCR and SimpleWestern. RNA-Seq demonstrated a profound reduction in EGR1 with MEK inhibitor treatment, prompting further study of melanoma cell lines. Following trametinib treatment of melanoma cells, viability was reduced in both cutaneous (MEL888 26%, P < 0.01; MEL624 27%, P < 0.001) and conjunctival (YUARGE 13-3064 33%, P < 0.01) melanoma compared with DMSO control, with confirmed EGR1 knockdown to 0.04-, 0.01-, and 0.16-fold DMSO-treated levels (all P < 0.05) in MEL888, MEL624, and YUARGE 13-3064, respectively. Targeted EGR1 knockdown using shRNA reduced viability in both cutaneous (MEL624 78%, P = 0.05) and conjunctival melanoma (YUARGE-13-3064 67%, P = 0.02). RNA-Sequencing in MEK inhibitor-treated cells identified EGR1 as a candidate effector molecule of interest. In a malignant melanoma cell population, MEK inhibition reduced viability in both cutaneous and conjunctival melanoma with a profound downstream reduction in EGR1 expression. Targeted knockdown of EGR1 reduced both cutaneous and conjunctival melanoma cell viability independent of MEK inhibition, suggesting a key role for EGR1 in melanoma pathobiology.

Spatial downregulation of CD74 signatures may drive invasive component development in part-solid lung adenocarcinoma

Pulmonary nodules with part-solid imaging features manifest during the progression from preinvasive to invasive lung adenocarcinoma. To define the spatial composition and evolutionary trajectories of early-stage lung adenocarcinoma, we combined spatial transcriptomics (ST) and pathological annotations from 20 part-solid nodules (PSNs), four of which were matched with single-cell RNA sequencing. Two malignant cell populations (MC1 and MC2) were identified, and a linear evolutionary relationship was observed. Compared to MC2, the pre-existing malignant MC1 exhibited a lower metastatic signature, corresponding to the preinvasive component (lepidic) on pathology and the ground glass component on PSN imaging. Higher immune infiltration was observed among MC1 regions in ST profiles, and further analysis revealed that macrophages may be involved in this process through the CD74 axis. This work provides deeper insights into the evolutionary process and spatial immune cell composition behind PSNs and highlights the mechanisms of immune escape behind this adenocarcinoma trajectory.

ATRT-10. SINGLE-CELL TRANSCRIPTOME ANALYSIS REVEALS CELLULAR HIERARCHIES AND THERAPEUTIC VULNERABILITIES OF ETMR

Abstract Embryonal tumors with multilayered rosettes (ETMR) are malignant brain tumors that occur predominantly in infants and young children. Most patients die within two years of diagnosis, and more effective, targeted therapies are urgently needed. To better characterize the oncogenic mechanisms of key driver alterations and to identify novel therapeutic targets, we set out to study the cellular heterogeneity of ETMR using single-cell RNA sequencing. Analyses conducted on &amp;gt;4,000 high-quality cells collected from eleven primary and relapse specimens revealed a common cellular hierarchy across all tumors: A highly proliferative neural stem cell-like population (SOX2+) that gives rise to intermediate progenitors (NEUROD1/NEUROG1+) and more differentiated neuron-like cells (STMN2/4+). These malignant cell populations closely overlap with histological patterns of ETMRs, as confirmed by multiplexed immunofluorescence microscopy on patients’ tumors. Comparison to single-cell datasets from human fetuses indicated high resemblance to normal cortical neurogenesis but also revealed key tumor-specific differences. These include expression of the chromosome 19 miRNA cluster (C19MC, the presumed driver in ~90% of ETMRs), which was restricted to the malignant stem cell-like population. Investigating oncogenic mechanisms of C19MC (comprising 46 miRNA genes) through transcriptome-wide RNA immunoprecipitation analysis, we identified extensive target gene regulation for most C19MC members, including distinct regulators of cell cycle, pluripotency, and neuronal differentiation. Silencing of C19MC families with antisense oligonucleotides resulted in pronounced reduction of ETMR cell line growth, indicating potential avenues for therapeutic targeting in the future. To identify more immediately actionable targets, we investigated inter-cellular signaling between malignant cell populations of ETMRs. Interestingly, we identified marked FGFR and NOTCH receptor-ligand interactions common to all tumors. An in vitro screen of experimental and approved small molecule inhibitors designed to target these interactions nominated several promising candidates for clinical evaluation. Our unpublished results provide much needed insight into targeting ETMR cellular states using multiple modes of action.

Efficient chimeric antigen receptor targeting of a central epitope of CD22

Chimeric antigen receptor (CAR) T-cell therapy has had considerable success in the treatment of B-cell malignancies. Targeting the B-lineage marker CD19 has brought great advances to the treatment of acute lymphoblastic leukemia and B-cell lymphomas. However, relapse remains an issue in many cases. Such relapse can result from downregulation or loss of CD19 from the malignant cell population or expression of alternate isoforms. Consequently, there remains a need to targetalternative B-cell antigens and diversify the spectrum of epitopes targeted within the same antigen. CD22 has been identified as a substitute target in cases of CD19-negative relapse. One anti-CD22 antibody-clone m971-targets a membrane-proximal epitope of CD22 and has been widely validated and used in the clinic. Here, we have compared m971-CAR with a novel CAR derived from IS7, an antibody that targets a central epitope on CD22. The IS7-CAR has superior avidity and is active and specific against CD22-positive targets, including B-acute lymphoblastic leukemia patient-derived xenograft samples. Side-by-side comparisons indicated that while IS7-CAR killed less rapidly than m971-CAR invitro, it remains efficient in controlling lymphoma xenograft models invivo. Thus, IS7-CAR presents a potential alternative candidate for the treatment of refractory B-cell malignancies.

Anti-Cancer Activity of Verteporfin in Cholangiocarcinoma.

Cholangiocarcinoma (CCA) is a heterogenous malignancy that arises from the biliary epithelium and has a poor clinical prognosis. The Hippo/yes-associated protein (YAP) pathway has been reported to affect various aspects of tumorigenesis, with high expression of YAP1 being negatively associated with survival in CCA patients. Thus, we investigated the antitumoral effect of verteporfin, a YAP1 pathway inhibitor, in YAP1/AKT hydrodynamic tail vein injected murine models. We also used flow cytometry and single-cell RNA sequencing (scRNA-seq) to analyze the change in the immune cell profile and malignant cell stemness following verteporfin treatment. Our results demonstrated reduced liver weight and tumor formation in verteporfin-treated groups compared to that of a vehicle-treated group. Immune cell profiling through flow cytometry showed that relative to the vehicle, verteporfin induced a higher ratio of tumor-associated macrophage (TAM) M1/M2 and increased the percentage of activated CD8 T cell population (CD8+CD25+ and CD8+CD69+). scRNA-seq analysis showed significantly increased TAM M1 populations following verteporfin treatment and decreased proportions of stem-like cells within the malignant cell population. In summary, this study indicates that in CCA YAP/AKT murine models, verteporfin reduces tumorigenesis by polarizing anti-tumoral TAM and activating CD8 T cells and decreasing stem-like malignant cell proportions in the tumor microenvironment.

Design, Synthesis, and Investigation of Cytotoxic Effects of 5-Hydroxyindole-3-Carboxylic Acid and Ester Derivatives as Potential Anti-breast Cancer Agents.

Breast cancer is a deadly disease with a high prevalence rate among females. Despite several treatments, scientists are still engaged in finding less invasive treatments for this disease. The cellular proliferation rate and cell viability survey are critical to assess the drug's effect on both normal and malignant cell populations. Indole derivatives are promising candidates for their cytotoxic effect causing on breast cancer cells; however, they are less toxic on normal cells. This study synthesized 23 novel 5-hydroxyindole-3-carboxylic acids and related esters featuring various linear, cyclic, and primary aromatic amines. The MTT assay indicated the cytotoxicity of all acid and ester derivatives against the MCF-7 cells with no significant cytotoxicity on normal human dermal fibroblasts cells. Compound 5d, an ester derivative possessing a 4-methoxy group, was the most potent compound, with a half-maximal effective concentration of 4.7 µM. Compounds 5a, 5d, and 5l bearing ester group in their structure demonstrated cytotoxicity values < 10 µM against the MCF-7 cell line and were safe for advanced screening.

Computational modeling of locoregional recurrence with spatial structure identifies tissue-specific carcinogenic profiles.

Local and regional recurrence after surgical intervention is a significant problem in cancer management. The multistage theory of carcinogenesis precisely places the presence of histologically normal but mutated premalignant lesions surrounding the tumor - field cancerization, as a significant cause of cancer recurrence. The relationship between tissue dynamics, cancer initiation and cancer recurrence in multistage carcinogenesis is not well known. This study constructs a computational model for cancer initiation and recurrence by combining the Moran and branching processes in which cells requires 3 or more mutations to become malignant. In addition, a spatial structure-setting is included in the model to account for positional relativity in cell turnover towards malignant transformation. The model consists of a population of normal cells with no mutation; several populations of premalignant cells with varying number of mutations and a population of malignant cells. The model computes a stage of cancer detection and surgery to eliminate malignant cells but spares premalignant cells and then estimates the time for malignant cells to re-emerge. We report the cellular conditions that give rise to different patterns of cancer initiation and the conditions favoring a shorter cancer recurrence by analyzing premalignant cell types at the time of surgery. In addition, the model is fitted to disease-free clinical data of 8,957 patients in 27 different cancer types; From this fitting, we estimate the turnover rate per month, relative fitness of premalignant cells, growth rate and death rate of cancer cells in each cancer type. Our study provides insights into how to identify patients who are likely to have a shorter recurrence and where to target the therapeutic intervention.

Abstract 3129: Single-cell RNA sequencing of early-stage to refractory metastatic ER+ breast cancers reveal phenotypic states underlying resistance to multiple lines of therapy

Abstract Breast cancer is a leading cause of global cancer-related mortality. About 75% of breast tumors are estrogen receptor-positive (ER+). Nearly 30-50% of ER+ breast cancers eventually become refractory to multiple lines of therapy and progress to an uncurable metastatic stage despite positive 5-year results on primary endocrine therapy. To uncover the malignant phenotypes underlying the development of therapeutic resistance, we performed single-cell RNA sequencing (scRNA-seq) of 219,532 cells from 80 ER+ breast cancer patients. This included 28 solid tumors and 52 metastatic fluid samples (pleural effusions and ascites). We grouped samples in three groups to study the evolution of biological phenotypes based on the number of lines of therapy administered to a patient: 1. Early (non-metastatic), 2. Advanced (metastatic with less than 3 lines of therapy), and Refractory (metastatic with 3 or more lines of therapy). We used Pareto task inference analysis to model evolutionary task trade-offs across malignant cell populations from the early, advanced, and refractory tumors. Our model revealed 5 unique phenotypic states or “archetypes” that existed across ER+ cancer cells. A multitask learning model based on group lasso revealed the specialized biological tasks associated with each archetype, including 1. Metabolism and self-renewal, 2. EGF/STAT3 signaling, 3. Estrogen signaling, 4. Apoptosis, and 5. MYC activation with rapid proliferation. We found the estrogen signaling archetype was progressively lost in advanced and refractory tumors (P=7.2x10-110), while the MYC archetype was progressively gained in the advanced and refractory tumors (P=2.45x10-9). The EGF/STAT3 archetype was unique to refractory tumors (P= 1.42x10-37). We further investigated the evolution of resistant traits by modeling the pathway phenotypes in malignant cells against the number of lines of therapy received by the patients. Patients refractive to multiple lines of therapy progressively gained mTOR signaling pathway activity concurrent with loss of estrogen-signaling. Multiple pathways that crosstalk with the mTOR pathway were activated in the tumors receiving multiple lines of therapy, including IGFR, and EGF/ERBB signaling. In comparison to early and advanced tumors, refractory tumors also gained JNK/p38 signaling (P=0.006) and activated glycolysis (P=5x10-6). This study reveals how metastatic ER+ breast cancer cells acquire capabilities for resistance to multiple lines of therapy, thus serving as a unique resource for understanding refractory ER+ breast cancer biology. Citation Format: Aritro Nath, Patrick Cosgrove, Jason Griffiths, Jeffrey Chang, Adam Cohen, Andrea Bild. Single-cell RNA sequencing of early-stage to refractory metastatic ER+ breast cancers reveal phenotypic states underlying resistance to multiple lines of therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3129.

Abstract 2333: Verteporfin demonstrates antitumoral effect in murine cholangiocarcinoma models

Abstract Cholangiocarcinoma (CCA) is a heterogenous malignancy that is derived from the biliary epithelium. There is growing concern as CCA incidence in United States has steadily increased. Additionally, CCA disproportionately affects different populations, and overall prognosis remains poor with a 5-year survival rate of 10%. Moreover, the Hippo/yes-associated protein (YAP) pathway has been reported to affect various aspects of tumorigenesis and recent reports showed that higher expression of YAP is negatively associated with the survival rate in CCA patients. Thus, we investigated the antitumoral effect of verteporfin, a YAP pathway inhibitor, in YAP/Akt and Notch/Akt hydrodynamic tail vein injected murine models. Our results demonstrated reduced liver weight in verteporfin treated groups compared to the vehicle treated group. Single cell RNA sequence analysis noted the verteporfin treated group had reduced stemness-related expressing cells in the malignant cell population. Furthermore, B cell, CD8+ T cell, and Tumor-Associated Macrophages M1 (TAM M1) populations increased following verteporfin treatment. In summary, this study indicates that verteporfin reduces tumorigenesis in CCA by the regulation of cancer stemness and modulation of immune cells, which includes anti-tumoral/pro-inflammatory TAM M1 in CCA animal models. Citation Format: Jihye Lee, Xin Wang, Jing Bian, Maggie Cam, Changqing Xie. Verteporfin demonstrates antitumoral effect in murine cholangiocarcinoma models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2333.

Abstract 4342: Predictive precision medicine platform accurately predicts individual patient response to AML treatments to maximize outcomes

Abstract Introduction: Offering the optimal frontline treatment to a patient with acute myeloid leukemia (AML) requires trading off expected benefit and risk. Typical standard of care intensive induction chemotherapy (e.g., cytarabine plus idarubicin (7+3)) results in high clinical response rates. However, many patients receive a less intensive regimen (e.g., venetoclax plus decitabine (VenDec)) because their individual toxicity risk is high based on lack of medical fitness. Predicting an individual patient’s clinical response prior to treatment has the potential to increase the benefit/risk ratio (therapeutic index) for some patients and optimize their treatment selection. Here, we demonstrate the ability of an automated high-throughput, multi-color flow cytometry predictive precision medicine platform (PPMP) to predict response to 7+3 or VenDec. Methods: To assess correlation between PPMP-predicted and actual clinical response to 7+3 or VenDec in clinical trial NCT04263181, pre-induction blood samples were collected from 31 patients of which 18 received 7+3 (all newly diagnosed (ND) AML) and 13 VenDec (7 ND AML, 5 secondary AML, 1 MDS). We measured drug effects on leukemic blasts by applying a cutoff at the total blast count that optimizes separation between predicted responders and non-responders (“conventional approach”) or by a machine learning (ML) approach considering multiple cell populations. For the former approach, training sets represented 13 patients for 7+3 and 8 for VenDec. Both 7+3 and VenDec models were validated with 5 patients. For the ML approach, the model was trained on all 13 VenDec patients and monitored using leave-one-out cross-validation. Results: For the conventional approach, predicted and true clinical responses were highly correlated for 7+3 (AUC = 0.91) and VenDec (AUC = 0.81), with 100% precision (positive predictive values (PPV)) for both, i.e., all predicted responders were true clinical responders. Some true clinical responders were not identified (negative predictive value (NPV) = 67% for 7+3 and 57% for VenDec), resulting in an accuracy of 94% (7+3) and 77% (VenDec). To maximize NPV and accuracy for predicting VenDec clinical outcomes, we applied a novel ML-based algorithm to integrate the behavior of malignant and non-malignant cell populations, yielding a model with 100% accuracy (100% PPV and NPV). Additional outcome data, including overall survival, are under evaluation. Summary: Total blast-based predictions yielded accuracies of 94% for 7+3 and 77% for VenDec. An ML algorithm for VenDec considering additional cell populations increased the accuracy to 100%. Further studies will expand patient numbers. We plan to use this platform to inform our frontline decision making with the goal to maximize the therapeutic benefit/risk ratio and ensure that the most appropriate frontline therapy is used for each individual patient. Citation Format: Meagan A. Jacoby, John S. Welch, Peter Westervelt, Matthew Christopher, Geoffrey L. Uy, Ravi Vij, Keith E. Stockerl-Goldstein, Brad S. Kahl, Iskra Pusic, John F. DiPersio, Mark A. Schroeder, Miriam Y. Kim, Todd A. Fehniger, Armin Ghobadi, Christine J. Gu, Wade Anderson, Kathryn Vanderlaag, Kamran Ali, Camille Pataki, Markus D. Lacher. Predictive precision medicine platform accurately predicts individual patient response to AML treatments to maximize outcomes. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4342.

Abstract 5633: Analysis of the immune microenvironment and tumor-infiltrating immune cells across different solid tumors by combined spatial transcriptomics and proteomics

Abstract With the advent of immunotherapies such as immune checkpoint inhibitors and CAR T cells, the interest in the tumor microenvironment and tumor-infiltrating lymphocytes (TILs) cells has increased in recent years as these play a critical role in predicting response to therapy and clinical outcomes. We have reported on the identification of potential targets for CAR T cell therapies by analyzing a multitude of tumor samples with hundreds of antibodies by flow cytometry and a new spatial proteomics platform, the MACSima Imaging Platform (Schäfer et al. Nat. Commun., 2021; Kinkhabwala et.al. Sci. Rep., 2022). Here we report on the analysis of tumor microenvironment and tumor infiltrating immune cells using a novel combined spatial transcriptomics and proteomics approach. For protein expression analysis we generated a panel of 60 fluorochrome conjugated antibodies (REAscreen Immuno-oncology, human, FFPE, version 01) selected to identify immune cells, cancer associated fibroblasts, matrix, blood vessels, lymphatics and malignant epithelial cell populations. RNA analysis was based on a novel in situ hybridization and amplification method followed by a cyclic spatial decoding of transcripts (RNAsky). To standardize and further ease the process of protein and RNA detection we generated the antigen and RNA detection probes in a ready-to-use format, i.e., dried and sealed in 96 well plates ready to be inserted into the MACSima. FFPE specimens of different tumor entities reviewed by a pathologist were first hybridized to probes, which were amplified, and spatially decoded by fluorochrome conjugated oligonucleotides. Subsequently the same specimen was exposed to fluorescently labeled antibodies by cycles of antibody reaction, image acquisition and erasure of signal. Finally, specimens were stained with H&amp;E to report on tissue integrity and correlate spatial orientation. The 2D image stacks were analyzed for transcript and antigen quantification and pattern recognition using both, pixel and segmented single-cell data (MACS iQ View Analysis Software). Across the specimens we identified more than 20 cell types including 12 immune cell subsets. The comparison of the tumor microenvironment, tumor-infiltrating immune cells (TILs) and their spatial organization in relation to the tumor cells allowed to identify differences and similarities across the tumor types. In summary we report here on a novel standardized and automated combined spatial RNA and protein expression analysis of the tumor microenvironment and tumor infiltrating immune cells across tumor types. The characterization dataset demonstrates the ability of our platform to perform standardized in-depth phenotyping of sample cohorts. This will enable discovery and further development of predictive and prognostic biomarkers critical to patient stratification for immunotherapy. Citation Format: Julia Femel, Emily Neil, Dongju Park, Fabio El Yassouri, Anijutta Appelshoffer, Erica Lloyd, Michael DiBuono, Henry Sauer, Hanna Lafayette, Hsinyi Smith, Jinling Wang, Dominic Mangiardi, Alex Makrigiorgos, Paurush Praveen, Silvia Rüberg, Werner Müller, Tanya Wantenaar, Robert Pinard, Andreas Bosio. Analysis of the immune microenvironment and tumor-infiltrating immune cells across different solid tumors by combined spatial transcriptomics and proteomics. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5633.

Abstract 6781: Spatial omics using spatially-resolved laser-activated cell sorting for cancer biomarker discovery

Abstract Cancer is spatially heterogeneous in terms of genetic molecules. Revealing genomic, epigenomic, transcriptomic, and epitranscriptomic features that are unique to the malignant cell populations within cancer lead to cancer biomarker discoveries that can be translated into diagnostic or therapeutic tools. Here, we introduce spatially-resolved laser-activated cell sorting (SLACS) technology coupled to next generation sequencing and mass spectrometry that analyzes genetic molecules from regions of interest within spatial context. Specifically, we demonstrate application of SLACS in various cancers such as breast cancer, glioblastoma, glioma, meningioma, multiple myeloma, and leukemia. Examples of discovering cancer biomarker and their mechanism of action within spatial context are also provided. Specifically, we describe Select-seq which isolates the regions of interest as small as single cells from the immunofluorescence stained tissue and obtains the full-length transcriptome data. We demonstrated Select-seq on tumor tissue section from triple negative breast cancer patient who have received neoadjuvant chemotherapy. Select-seq produced full-length spatial transcriptome data, with which we analyzed the transcriptomic and epitranscriptomic features including alternative splicing variant, complementarity-determining region analysis of B cells, and even adenosine-to-inosine base editing. We were also able to analyze the immune- and stem-cell-like microniches, in which the complex mechanisms behind ferroptosis inhibition might suggest therapeutic options for the TNBC patients. Bridging spatial technologies to omics technologies, the discovery of specific markers within spatial context will provide insights into the next generation diagnostics and cancer therapeutics such as cancer vaccines. Citation Format: Amos C. Lee, Sumin Lee, Sunghoon Kwon. Spatial omics using spatially-resolved laser-activated cell sorting for cancer biomarker discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6781.

Abstract 5644: Spatially resolved transcriptomics points to distinct malignant cell populations within primary and castration resistant prostate cancer

Abstract Background: Prostate cancer (PCa) is the second most common cancer in men. Despite its high prevalence, many patients carry an indolent form of the disease and are thus suspect to overtreatment. Conversely, some cases treated with androgen deprivation therapy can develop into castration resistant prostate cancer (CRPC), for which there is no curative treatment. Understanding why some tumors are more aggressive than others could lead to more accurate patient risk stratification. Here we characterize cancer and normal cell subpopulations within prostate tissue in their spatial context using a multimodal integrative approach. Methods: We performed spatially resolved transcriptomics (ST) on a set of primary tumor PCa, CRPC and benign prostatic hyperplasia (BPH) patient samples. In addition to ST, we produced RNA-seq, DNA-seq and assay for transposase accessible chromatin using sequencing (ATAC-seq) data, allowing for multiomic integration within and across sample categories. We performed extensive analysis of ST data, employing unsupervised clustering, spot expression signal deconvolution, differential gene expression analysis and copy number variation (CNV) inference. Main Results: The systematic analysis of spot expression profiles revealed a high degree of variation in nearby tissue regions, as we found up to three unique luminal cell populations inside a one millimeter radius in PCa. Similarly in locally recurrent CRPC, we identified cumulative CNVs in proximal luminal cell populations, with the inferred CNV profiles validated through DNA-seq. A set of marker genes was calculated for each unique cell population, with multiple PCa associated genes found to be differentially expressed. Although we observed significant variation in the luminal cell populations, the stromal gene expression was markedly similar across all samples. Conclusions: We discovered shared, similar and unique cell populations both within and across different PCa and CRPC sections. We observed various luminal cell populations with distinct gene expression profiles in samples from both progression stages. The close spatial proximity of these cell clusters suggests that ST can be used to discover and examine finely detailed populations in their original spatial environment. Citation Format: Antti Kiviaho, Heini M. Kallio, Sini K. Eerola, Elisa M. Vuorinen, Tomi Häkkinen, Sinja Taavitsainen, Ebrahim Afyounian, Teemu Tolonen, Juha Kesseli, Alfonso Urbanucci, Kirsi J. Rautajoki, Teuvo L. Tammela, Tapio Visakorpi, Matti Nykter. Spatially resolved transcriptomics points to distinct malignant cell populations within primary and castration resistant prostate cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5644.

Abstract P2-18-01: Matched Single-Cell RNA-seq and Single-Cell ATAC-seq of Male Breast Cancers Nominate Salient Cancer-Specific Enhancers

Abstract Although male breast cancer accounts for less than 1% of all breast cancer cases, overall survival is worse for men compared to women. This is due, in part, to the older age of patients at the time of diagnosis and the increased presence of comorbidities in the male population. While male breast cancer presents with similarities to certain female breast cancers, the paucity of data available on male breast cancer makes it difficult to establish targeted therapies, and to date, most male breast cancers are treated according to the standards in place for female breast cancer. Thus, investigating the transcriptional and epigenetic landscape of male breast cancer with improved resolution is crucial for understanding the tumor heterogeneity of this rare cancer type and can lead to the discovery of new therapeutic targets. In this study, we present matched transcriptional (scRNA-seq) and epigenetic (scATAC-seq) profiles of two male breast cancer tumors at single-cell resolution. Using scRNA-seq, we determine differentially expressed genes between male and female breast cancer tumors, specifically in the malignant cell populations, and highlight several genes that may have therapeutic implications within male breast cancer. We then leverage the scATAC-seq data to link variation in chromatin accessibility to changes in gene expression and show that certain highly expressed genes in the tumor-epithelial cells of male breast tumors are linked to highly active enhancer elements that drive their expression. Finally, we examine transcription factor motifs in the tumor-epithelial regulatory regions of male breast cancer tumors to further investigate the regulatory logic of these cell types. Overall, this dataset exposes clinically relevant regulatory networks in male breast tumors, providing a useful resource to the field of breast oncology that expands on the current knowledge of male breast cancer genetics. Citation Format: Kamila Wisniewska, Hyunsoo Kim, Matthew J. Regner, Philip Spanheimer, Hector L. Franco. Matched Single-Cell RNA-seq and Single-Cell ATAC-seq of Male Breast Cancers Nominate Salient Cancer-Specific Enhancers [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-18-01.

Single-cell Deconvolution of a Specific Malignant Cell Population as a Poor Prognostic Biomarker in Low-risk Clear Cell Renal Cell Carcinoma Patients

BackgroundIntratumor heterogeneity (ITH) is a key feature in clear cell renal cell carcinomas (ccRCCs) that impacts outcomes such as aggressiveness, response to treatments, or recurrence. In particular, it may explain tumor relapse after surgery in clinically low-risk patients who did not benefit from adjuvant therapy. Recently, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to unravel expression ITH (eITH) and might enable better assessment of clinical outcomes in ccRCC. ObjectiveTo explore eITH in ccRCC with a focus on malignant cells (MCs) and assess its relevance to improve prognosis for low-risk patients. Design, setting, and participantsWe performed scRNA-seq on tumor samples from five untreated ccRCC patients ranging from pT1a to pT3b. Data were complemented with a published dataset composed of pairs of matched normal and ccRCC samples. InterventionRadical or partial nephrectomy on untreated ccRCC patients. Outcome measurements and statistical analysisViability and cell type proportions were determined by flow cytometry. Following scRNA-seq, a functional analysis was performed and tumor progression trajectories were inferred. A deconvolution approach was applied on an external cohort, and Kaplan-Meier survival curves were estimated with respect to the prevalence of malignant clusters. Results and limitationsWe analyzed 54 812 cells and identified 35 cell subpopulations. The eITH analysis revealed that each tumor contained various degrees of clonal diversity. The transcriptomic signatures of MCs in one particularly heterogeneous sample were used to design a deconvolution-based strategy that allowed the risk stratification of 310 low-risk ccRCC patients. ConclusionsWe described eITH in ccRCCs, and used this information to establish significant cell population–based prognostic signatures and better discriminate ccRCC patients. This approach has the potential to improve the stratification of clinically low-risk patients and their therapeutic management. Patient summaryWe sequenced the RNA content of individual cell subpopulations composed of clear cell renal cell carcinomas and identified specific malignant cells the genetic information of which can be used to predict tumor progression.