Quality control for single-cell analysis of high-plex tissue profiles using CyLinter.

Tumors are complex assemblies of cellular and acellular structures patterned on spatial scales from microns to centimeters. Study of these assemblies has advanced dramatically with the introduction of high-plex spatial profiling. Image-based profiling methods reveal the intensities and spatial distributions of 20-100 proteins at subcellular resolution in 103-107 cells per specimen. Despite extensive work on methods for extracting single-cell data from these images, all tissue images contain artifacts such as folds, debris, antibody aggregates, optical aberrations and image processing errors that arise from imperfections in specimen preparation, data acquisition, image assembly and feature extraction. Here we show that these artifacts dramatically impact single-cell data analysis, obscuring meaningful biological interpretation. We describe an interactive quality control software tool, CyLinter, that identifies and removes data associated with imaging artifacts. CyLinter greatly improves single-cell analysis, especially for archival specimens sectioned many years before data collection, such as those from clinical trials.

SpatialCells: automated profiling of tumor microenvironments with spatially resolved multiplexed single-cell data.

Cancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal and other cells within the tumor microenvironment (TME). Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize molecular, cellular and spatial properties of TMEs for various malignancies. This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of TMEs using multiplexed single-cell data. The source code and tutorials are available at https://semenovlab.github.io/SpatialCells. SpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion and metastasis.

18F-FDG-PET/CT based total metabolic tumor volume change during neoadjuvant chemotherapy predicts outcome in advanced epithelial ovarian cancer.

OBJECTIVE: To evaluate the predictive potential of total metabolic tumor volume (MTV) reduction during neoadjuvant chemotherapy (NACT) with 18F-FDG-PET/CT in an advanced FIGO stage III/IV epithelial ovarian cancer (EOC) patient cohort. METHODS: Twenty-nine primarily inoperable EOC patients underwent 18F-FDG-PET/CT before and after NACT. The pre- and post-NACT total MTV, in addition to the percentage MTV reduction during NACT, were compared with primary therapy outcome and progression-free survival (PFS). ROC-analysis determined an optimal threshold for MTV reduction identifying patients with progressive or stable disease (PD/SD) at the end of primary therapy. A multivariate analysis with residual tumor (0/>0), FIGO stage (III/IV) and MTV reduction compared to PFS was performed. The association between MTV reduction and overall survival (OS) was evaluated. RESULTS: The median pre- and post-NACT total MTV were 352 cm3 (range 150 to 1322 cm3) and 51 cm3 (range 0 to 417 cm3), respectively. The median MTV reduction during NACT was 89% (range 24% to 100%). Post-NACT MTV and MTV reduction associated with primary therapy outcome (MTV post-NACT p = 0.007, MTV reduction p = 0.001) and PFS (MTV post-NACT p = 0.005, MTV reduction p = 0.005). MTV reduction <85% identified the PD/SD patients (sensitivity 70%, specificity 78%, AUC 0.79). In a multivariate analysis, MTV reduction (p = 0.002) and FIGO stage (p = 0.003) were statistically significant variables associated with PFS. MTV reduction during NACT corresponded to OS (p = 0.05). CONCLUSION: 18F-FDG-PET/CT is helpful in NACT response evaluation. Patients with total MTV reduction <85% during NACT might be candidates for second-line chemotherapy and clinical trials, instead of interval debulking surgery.

Postoperative human epididymis protein 4 predicts primary therapy outcome in advanced epithelial ovarian cancer.

Primary chemotherapy treatment response monitoring in advanced epithelial ovarian cancer (EOC) is currently based on CT-imaging and serum CA125 values. Serum HE4 profile during first line chemotherapy has not been previously studied. We evaluated the HE4 profile during first line chemotherapy after primary (PDS) and interval debulking surgery (IDS). In total, 49 FIGO stage III/IV EOC patients were included in the study. 22 patients underwent PDS and 27 patients neoadjuvant chemotherapy (NACT) followed by IDS. Serial HE4 and CA125 serum samples were taken during first line chemotherapy. The association of postoperative tumor markers to surgery outcome, primary therapy outcome and progression free survival (PFS) were determined. The lowest HE4 and CA125 values during chemotherapy were compared to primary therapy outcome and PFS. The postoperative HE4 was associated to residual tumor after surgery (p = 0.0001), primary therapy outcome (p = 0.004) and PFS (p = 0.03) in all patients (n = 40). The postoperative CA125 was associated to PFS after IDS (n = 26, p = 0.006), but not after PDS. In multivariate analysis with FIGO stage (III/IV), residual tumor (0/>0) and postoperative CA125, the postoperative HE4 was the only statistically significant prognostic variable predicting PFS. Both HE4 and CA125 nadir corresponded to primary therapy outcome (HE4 p < 0.0001, CA125 p < 0.0001) and PFS (HE4 p = 0.009, CA125 p < 0.0001). HE4 is a promising candidate for EOC response monitoring. In our study, the performance of HE4 in response monitoring of first line chemotherapy was comparable to that of CA125. Of the postoperative values, only HE4 was statistically significantly associated to primary therapy outcome.

18F-FDG-PET/CT can identify histopathological non-responders to platinum-based neoadjuvant chemotherapy in advanced epithelial ovarian cancer.

OBJECTIVE: The aim of this study was to examine the relationship between the reduction of maximum standardized uptake values (SUVmax) in 18F-FDG-PET/CT to histopathological changes obtained with neoadjuvant chemotherapy (NACT) in advanced epithelial ovarian cancer (EOC). We wanted to evaluate whether 18F-FDG-PET/CT is useful for identifying patients who will not respond to NACT and would therefore benefit from second-line chemotherapy instead of interval debulking surgery (IDS). METHODS: Twenty-six primarily inoperable EOC patients treated with NACT were enrolled in this study. 18F-FDG-PET/CT imaging was performed before diagnostic laparoscopy and after three to four NACT cycles. The relationship between the decrease in omental SUVmax from before to after NACT with omental histopathological response was examined in samples taken from the corresponding anatomical sites during IDS. Patients were divided into three groups according to chemotherapy-induced histopathological changes. Serum CA125 and HE4 halftimes during NACT as well as Ki-67 antigen expression in IDS samples were determined. RESULTS: The median omental SUVmax change during NACT was -64% (range-16% to -84%), and it was associated with histopathological response (p=0.004, OR 0.9, CI 0.84-0.97). A SUVmax decrease of less than 57% identified histopathological non-responders. Progression-free survival (PFS) differed between the poor, moderate and good histopathological response groups (0.9 year vs. 1.2 years vs. 1.4 years, respectively, p=0.05). The SUVmax change was not associated with PFS. CONCLUSION: 18F-FDG-PET/CT was able to identify patients who would not respond to NACT. To obtain a histopathological response in EOC, a substantial metabolic response in 18F-FDG-PET/CT is necessary.

Serum HE4 and CA125 as predictors of response and outcome during neoadjuvant chemotherapy of advanced high-grade serous ovarian cancer.

Human epididymis protein 4 (HE4) is a novel tumour marker in epithelial ovarian cancer (EOC). Data on its profile and predictive potential for subsequent outcome after neoadjuvant chemotherapy (NACT) are still under investigation. The aim of this study was to compare CA125 and HE4 profiles with radiologic response after NACT and to evaluate their potential as predictors of clinical outcome in a primarily inoperable EOC patient cohort. Twenty-five EOC patients of high-grade subtype (HGSC) treated with NACT were enrolled in the study. Serum HE4 and CA125 samples were taken at the time of diagnosis and before interval debulking surgery (IDS). Pre-NACT and pre-IDS HE4 and CA125 and their percentage changes were compared with NACT response seen on CT and surgical outcome in IDS. We also evaluated the biomarkers' abilities to predict platinum-free interval (PFI), progression-free survival (PFS) and overall survival (OS). All 25 patients were considered inoperable in laparoscopy at the time of diagnosis. HE4 and CA125 changes during NACT did not correlate with the changes seen on CT. Surgical outcome in IDS was associated with pre-IDS biomarker values but not with those taken before diagnosis. In IDS, 87 % had <1-cm residual tumour. In patients with HE4 change >80 and <80 % during NACT, the median OS was 3.38 and 1.60 years (p = 0.01), respectively. Serum HE4 is a promising additional tool when evaluating advanced HGSC patient's response to NACT. It may be helpful when deciding whether to proceed to IDS or to second-line chemotherapy.

Quality Control for Single Cell Analysis of High-plex Tissue Profiles using CyLinter.

Tumors are complex assemblies of cellular and acellular structures patterned on spatial scales from microns to centimeters. Study of these assemblies has advanced dramatically with the introduction of high-plex spatial profiling. Image-based profiling methods reveal the intensities and spatial distributions of 20-100 proteins at subcellular resolution in 103-107 cells per specimen. Despite extensive work on methods for extracting single-cell data from these images, all tissue images contain artefacts such as folds, debris, antibody aggregates, optical aberrations and image processing errors that arise from imperfections in specimen preparation, data acquisition, image assembly, and feature extraction. We show that these artefacts dramatically impact single-cell data analysis, obscuring meaningful biological interpretation. We describe an interactive quality control software tool, CyLinter, that identifies and removes data associated with imaging artefacts. CyLinter greatly improves single-cell analysis, especially for archival specimens sectioned many years prior to data collection, such as those from clinical trials.

PRAME expression in melanoma is negatively regulated by TET2-mediated DNA hydroxymethylation.

Preferentially Expressed Antigen in Melanoma (PRAME) and Ten-Eleven Translocation (TET) dioxygenase-mediated 5-hydroxymethylcytosine (5hmC) are emerging melanoma biomarkers. We observed an inverse correlation between PRAME expression and 5hmC levels in benign nevi, melanoma in situ, primary invasive melanoma, and metastatic melanomas via immunohistochemistry and multiplex immunofluorescence: nevi exhibited high 5hmC and low PRAME, whereas melanomas showed the opposite pattern. Single-cell multiplex imaging of melanoma precursors revealed that diminished 5hmC coincides with PRAME upregulation in premalignant cells. Analysis of TCGA and GTEx databases confirmed a negative relationship between TET2 and PRAME mRNA expression in melanoma. Additionally, 5hmC levels were reduced at the PRAME 5' promoter in melanoma compared to nevi, suggesting a role for 5hmC in PRAME transcription. Restoring 5hmC levels via TET2 overexpression notably reduced PRAME expression in melanoma cell lines. These findings establish a function of TET2-mediated DNA hydroxymethylation in regulating PRAME expression and demonstrate epigenetic reprogramming as pivotal in melanoma tumorigenesis. Teaser: Melanoma biomarker PRAME expression is negatively regulated epigenetically by TET2-mediated DNA hydroxymethylation.

Chemotherapy induces myeloid-driven spatial T-cell exhaustion in ovarian cancer.

To uncover the intricate, chemotherapy-induced spatiotemporal remodeling of the tumor microenvironment, we conducted integrative spatial and molecular characterization of 97 high-grade serous ovarian cancer (HGSC) samples collected before and after chemotherapy. Using single-cell and spatial analyses, we identify increasingly versatile immune cell states, which form spatiotemporally dynamic microcommunities at the tumor-stroma interface. We demonstrate that chemotherapy triggers spatial redistribution and exhaustion of CD8+ T cells due to prolonged antigen presentation by macrophages, both within interconnected myeloid networks termed "Myelonets" and at the tumor stroma interface. Single-cell and spatial transcriptomics identifies prominent TIGIT-NECTIN2 ligand-receptor interactions induced by chemotherapy. Using a functional patient-derived immuno-oncology platform, we show that CD8+T-cell activity can be boosted by combining immune checkpoint blockade with chemotherapy. Our discovery of chemotherapy-induced myeloid-driven spatial T-cell exhaustion paves the way for novel immunotherapeutic strategies to unleash CD8+ T-cell-mediated anti-tumor immunity in HGSC.

SpatialCells: Automated Profiling of Tumor Microenvironments with Spatially Resolved Multiplexed Single-Cell Data.

Background: Cancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal, and other cells within the tumor microenvironment. Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize the molecular, cellular, and spatial properties of tumor microenvironments for various malignancies. Results: This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of tumor microenvironments using multiplexed single-cell data. Conclusions: SpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion, and metastasis.

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues.

Upcoming technologies enable routine collection of highly multiplexed (20-60 channel), subcellular resolution images of mammalian tissues for research and diagnosis. Extracting single cell data from such images requires accurate image segmentation, a challenging problem commonly tackled with deep learning. In this paper, we report two findings that substantially improve image segmentation of tissues using a range of machine learning architectures. First, we unexpectedly find that the inclusion of intentionally defocused and saturated images in training data substantially improves subsequent image segmentation. Such real augmentation outperforms computational augmentation (Gaussian blurring). In addition, we find that it is practical to image the nuclear envelope in multiple tissues using an antibody cocktail thereby better identifying nuclear outlines and improving segmentation. The two approaches cumulatively and substantially improve segmentation on a wide range of tissue types. We speculate that the use of real augmentations will have applications in image processing outside of microscopy.

The Spatial Landscape of Progression and Immunoediting in Primary Melanoma at Single-Cell Resolution.

Cutaneous melanoma is a highly immunogenic malignancy that is surgically curable at early stages but life-threatening when metastatic. Here we integrate high-plex imaging, 3D high-resolution microscopy, and spatially resolved microregion transcriptomics to study immune evasion and immunoediting in primary melanoma. We find that recurrent cellular neighborhoods involving tumor, immune, and stromal cells change significantly along a progression axis involving precursor states, melanoma in situ, and invasive tumor. Hallmarks of immunosuppression are already detectable in precursor regions. When tumors become locally invasive, a consolidated and spatially restricted suppressive environment forms along the tumor-stromal boundary. This environment is established by cytokine gradients that promote expression of MHC-II and IDO1, and by PD1-PDL1-mediated cell contacts involving macrophages, dendritic cells, and T cells. A few millimeters away, cytotoxic T cells synapse with melanoma cells in fields of tumor regression. Thus, invasion and immunoediting can coexist within a few millimeters of each other in a single specimen. SIGNIFICANCE: The reorganization of the tumor ecosystem in primary melanoma is an excellent setting in which to study immunoediting and immune evasion. Guided by classic histopathology, spatial profiling of proteins and mRNA reveals recurrent morphologic and molecular features of tumor evolution that involve localized paracrine cytokine signaling and direct cell-cell contact. This article is highlighted in the In This Issue feature, p. 1397.

The Immunology of Hormone Receptor Positive Breast Cancer.

Immune checkpoint blockade (ICB) has revolutionized the treatment of cancer patients. The main focus of ICB has been on reinvigorating the adaptive immune response, namely, activating cytotoxic T cells. ICB has demonstrated only modest benefit against advanced breast cancer, as breast tumors typically establish an immune suppressive tumor microenvironment (TME). Triple-negative breast cancer (TNBC) is associated with infiltration of tumor infiltrating lymphocytes (TILs) and patients with TNBC have shown clinical responses to ICB. In contrast, hormone receptor positive (HR+) breast cancer is characterized by low TIL infiltration and minimal response to ICB. Here we review how HR+ breast tumors establish a TME devoid of TILs, have low HLA class I expression, and recruit immune cells, other than T cells, which impact response to therapy. In addition, we review emerging technologies that have been employed to characterize components of the TME to reveal that tumor associated macrophages (TAMs) are abundant in HR+ cancer, are highly immune-suppressive, associated with tumor progression, chemotherapy and ICB-resistance, metastasis and poor survival. We reveal novel therapeutic targets and possible combinations with ICB to enhance anti-tumor immune responses, which may have great potential in HR+ breast cancer.

Clinical Efficacy and Molecular Response Correlates of the WEE1 Inhibitor Adavosertib Combined with Cisplatin in Patients with Metastatic Triple-Negative Breast Cancer.

PURPOSE: We report results from a phase II study assessing the efficacy of the WEE1 inhibitor adavosertib with cisplatin in metastatic triple-negative breast cancer (mTNBC). PATIENTS AND METHODS: Patients with mTNBC treated with 0-1 prior lines of chemotherapy received cisplatin 75 mg/m2 i.v. followed 21 days later by cisplatin plus adavosertib 200 mg oral twice daily for five doses every 21 days. The study had 90% power to detect the difference between null (20%) and alternative (40%) objective response rates (ORR) with a one-sided type I error of 0.1: an ORR >30% was predefined as making the regimen worthy of further study. RNA sequencing and multiplex cyclic immunofluorescence on pre- and post-adavosertib tumor biopsies, as well as targeted next-generation sequencing on archival tissue, were correlated with clinical benefit, defined as stable disease ≥6 months or complete or partial response. RESULTS: A total of 34 patients initiated protocol therapy; median age was 56 years, 2 patients (6%) had BRCA2 mutations, and 14 (41%) had one prior chemotherapy. ORR was 26% [95% confidence interval (CI), 13-44], and median progression-free survival was 4.9 months (95% CI, 2.3-5.7). Treatment-related grade 3-5 adverse events occurred in 53% of patients, most commonly diarrhea in 21%. One death occurred because of sepsis, possibly related to study therapy. Tumors from patients with clinical benefit demonstrated enriched immune gene expression and T-cell infiltration. CONCLUSIONS: Among patients with mTNBC treated with 0-1 prior lines, adavosertib combined with cisplatin missed the prespecified ORR cutoff of >30%. The finding of immune-infiltrated tumors in patients with clinical benefit warrants validation.

Author Correction: Virtual clinical trials identify effective combination therapies in ovarian cancer.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Virtual clinical trials identify effective combination therapies in ovarian cancer.

A major issue in oncology is the high failure rate of translating preclinical results in successful clinical trials. Using a virtual clinical trial simulations approach, we present a mathematical framework to estimate the added value of combinatorial treatments in ovarian cancer. This approach was applied to identify effective targeted therapies that can be combined with the platinum-taxane regimen and overcome platinum resistance in high-grade serous ovarian cancer. We modeled and evaluated the effectiveness of three drugs that target the main platinum resistance mechanisms, which have shown promising efficacy in vitro, in vivo, and early clinical trials. Our results show that drugs resensitizing chemoresistant cells are superior to those aimed at triggering apoptosis or increasing the bioavailability of platinum. Our results further show that the benefit of using biomarker stratification in clinical trials is dependent on the efficacy of the drug and tumor composition. The mathematical framework presented herein is suitable for systematically testing various drug combinations and clinical trial designs in solid cancers.

Mathematical Modeling Predicts Response to Chemotherapy and Drug Combinations in Ovarian Cancer.

Platinum-based chemotherapy constitutes the backbone of clinical care in advanced solid cancers such as high-grade serous ovarian cancer (HGSOC) and has prolonged survival of millions of patients with cancer. Most of these patients, however, become resistant to chemotherapy, which generally leads to a fatal refractory disease. We present a comprehensive stochastic mathematical model and simulator approach to describe platinum resistance and standard-of-care (SOC) therapy in HGSOC. We used pre- and posttreatment clinical data, including 18F-FDG-PET/CT images, to reliably estimate the model parameters and simulate "virtual patients with HGSOC." Treatment responses of the virtual patients generated by our mathematical model were indistinguishable from real-life patients with HGSOC. We demonstrated the utility of our approach by evaluating the survival benefit of combination therapies that contain up to six drugs targeting platinum resistance mechanisms. Several resistance mechanisms were already active at diagnosis, but combining SOC with a drug that targets the most dominant resistance subpopulation resulted in a significant survival benefit. This work provides a theoretical basis for a cancer treatment paradigm in which maximizing platinum's killing effect on cancer cells requires overcoming resistance mechanisms with targeted drugs. This freely available mathematical model and simulation framework enable rapid and rigorous evaluation of the benefit of a targeted drug or combination therapy in virtual patients before clinical trials, which facilitates translating preclinical findings into clinical practice.Significance: These findings present a comprehensive mathematical model for platinum resistance and standard-of-care therapy in a solid cancer, allowing virtual evaluation of novel therapy regimens. Cancer Res; 78(14); 4036-44. ©2018 AACR.