Robust Cell Detection and Segmentation for Image Cytometry Reveal Th17 Cell Heterogeneity

Particle sorting method based on swirl induction

Fluid-based methods for particle sorting demonstrate increasing appeal in many areas of biosciences due to their biocompatibility and cost-effectiveness. Herein, we construct a microfluidic sorting system based on a swirl microchip. The impact of microchannel velocity on the swirl stagnation point as well as particle movement is analyzed through simulation and experiment. Moreover, the quantitative mapping relationship between flow velocity and particle position distribution is established. With this foundation established, a particle sorting method based on swirl induction is proposed. Initially, the particle is captured by a swirl. Then, the Sorting Region into which the particle aims to enter is determined according to the sorting condition and particle characteristic. Subsequently, the velocities of the microchannels are adjusted to control the swirl, which will induce the particle to enter its corresponding Induction Region. Thereafter, the velocities are adjusted again to change the fluid field and drive the particle into a predetermined Sorting Region, hence the sorting is accomplished. We have extensively conducted experiments taking particle size or color as a sorting condition. An outstanding sorting success rate of 98.75% is achieved when dealing with particles within the size range of tens to hundreds of micrometers in radius, which certifies the effectiveness of the proposed sorting method. Compared to the existing sorting techniques, the proposed method offers greater flexibility. The adjustment of sorting conditions or particle parameters no longer requires complex chip redesign, because such sorting tasks can be successfully realized through simple microchannel velocities control.

Sequential Chromogenic IHC: Spatial Analysis of Lymph Nodes Identifies Contact Interactions between Plasmacytoid Dendritic Cells and Plasmablasts

Recent clinical observations have emphasized the critical role that the spatial organization of immune cells in lymphoid structures plays in the success of cancer immunotherapy and patient survival. However, implementing sequential chromogenic IHC (scIHC) to analyze multiple biomarkers on a single tissue section has been limited because of a lack of a standardized, rigorous guide to the development of customized biomarker panels and a need for user-friendly analysis pipelines that can extract meaningful data. In this context, we provide a comprehensive guide for the development of novel biomarker panels for scIHC, using practical examples and illustrations to highlight the most common complications that can arise during the setup of a new biomarker panel, and provide detailed instructions on how to prevent and detect cross-reactivity between secondary reagents and carryover between detection antibodies. We also developed a novel analysis pipeline based on non-rigid tissue deformation correction, Cellpose-inspired automated cell segmentation, and computational network masking of low-quality data. We applied this biomarker panel and pipeline to study regional lymph nodes from patients with head and neck cancer, identifying novel contact interactions between plasmablasts and plasmacytoid dendritic cells in vivo. Given that Toll-like receptors, which are highly expressed in plasmacytoid dendritic cells, play a key role in vaccine efficacy, the significance of this cell-cell interaction decisively warrants further studies. In summary, this work provides a streamlined approach to the development of customized biomarker panels for scIHC that will ultimately improve our understanding of immune responses in cancer.

Significance

We present a comprehensive guide for developing customized biomarker panels to investigate cell-cell interactions in the context of immune responses in cancer. This approach revealed novel contact interactions between plasmablasts and plasmacytoid dendritic cells in lymph nodes from patients with head and neck cancer.

SEG: Segmentation Evaluation in absence of Ground truth labels

Identifying individual cells or nuclei is often the first step in the analysis of multiplex tissue imaging (MTI) data. Recent efforts to produce plug-and-play, end-to-end MTI analysis tools such as MCMICRO1- though groundbreaking in their usability and extensibility - are often unable to provide users guidance regarding the most appropriate models for their segmentation task among an endless proliferation of novel segmentation methods. Unfortunately, evaluating segmentation results on a user's dataset without ground truth labels is either purely subjective or eventually amounts to the task of performing the original, time-intensive annotation. As a consequence, researchers rely on models pre-trained on other large datasets for their unique tasks. Here, we propose a methodological approach for evaluating MTI nuclei segmentation methods in absence of ground truth labels by scoring relatively to a larger ensemble of segmentations. To avoid potential sensitivity to collective bias from the ensemble approach, we refine the ensemble via weighted average across segmentation methods, which we derive from a systematic model ablation study. First, we demonstrate a proof-of-concept and the feasibility of the proposed approach to evaluate segmentation performance in a small dataset with ground truth annotation. To validate the ensemble and demonstrate the importance of our method-specific weighting, we compare the ensemble's detection and pixel-level predictions - derived without supervision - with the data's ground truth labels. Second, we apply the methodology to an unlabeled larger tissue microarray (TMA) dataset, which includes a diverse set of breast cancer phenotypes, and provides decision guidelines for the general user to more easily choose the most suitable segmentation methods for their own dataset by systematically evaluating the performance of individual segmentation approaches in the entire dataset.

Quantifying and correcting slide-to-slide variation in multiplexed immunofluorescence images

MOTIVATION

Multiplexed imaging is a nascent single-cell assay with a complex data structure susceptible to technical variability that disrupts inference. These in situ methods are valuable in understanding cell-cell interactions, but few standardized processing steps or normalization techniques of multiplexed imaging data are available.

RESULTS

We implement and compare data transformations and normalization algorithms in multiplexed imaging data. Our methods adapt the ComBat and functional data registration methods to remove slide effects in this domain, and we present an evaluation framework to compare the proposed approaches. We present clear slide-to-slide variation in the raw, unadjusted data and show that many of the proposed normalization methods reduce this variation while preserving and improving the biological signal. Furthermore, we find that dividing multiplexed imaging data by its slide mean, and the functional data registration methods, perform the best under our proposed evaluation framework. In summary, this approach provides a foundation for better data quality and evaluation criteria in multiplexed imaging.

AVAILABILITY AND IMPLEMENTATION

Source code is provided at: https://github.com/statimagcoll/MultiplexedNormalization and an R package to implement these methods is available here: https://github.com/ColemanRHarris/mxnorm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An omic and multidimensional spatial atlas from serial biopsies of an evolving metastatic breast cancer

Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata that includes treatment times and doses, anatomic imaging, and blood-based response measurements to clinical and exploratory analyses, which includes comprehensive DNA, RNA, and protein profiles; images of multiplexed immunostaining; and 2- and 3-dimensional scanning electron micrographs. These data report aspects of heterogeneity and evolution of the cancer genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples of how integrative analyses of these data reveal potential mechanisms of response and resistance and suggest novel therapeutic vulnerabilities.

Characterization of the tumor immune microenvironment of sinonasal squamous-cell carcinoma

BACKGROUND

Treatment and prognosis of sinonasal squamous-cell carcinoma (SNSCC) have not significantly improved despite improvements in radical therapy. Characterization of the tumor immune microenvironment (TiME) may identify patient subgroups associated with disease recurrence, and provide new biomarkers for improved patient stratification and treatment.

METHODS

The TiME was quantitatively evaluated by multiplex immunohistochemistry (mIHC) in archived tissue sections from 38 patients with SNSCC, and were assessed for differences between recurrent (n = 20) and nonrecurrent (n = 18) groups. Hierarchical clustering analyses were performed to identify phenotypic TiME subgroups within the cohort and were used to compare survival outcomes.

RESULTS

Our mIHC analysis revealed increased T-cell populations and decreased myeloid-cell populations in SNSCC patients without recurrent disease, as compared with patients with recurrent disease. Within T-cell subsets, there was a significantly higher percentage of granzyme B⁺ , T-bet⁺ , Eomes⁺ T cells, as well as higher proliferation of CD8⁺ T cells within the nonrecurrent group relative to the recurrent group. Furthermore, immune-cell complexity profiles of SNSCC revealed hyper- and hypo-T-cell-inflamed, myeloid-inflamed, B-cell-inflamed, and broadly hypoinflamed subtypes not previously identified by gene expression analyses. Our study revealed that presence of either hyper- or hypo-T-cell-inflamed TiME subtypes were associated with increased survival outcomes as compared with broadly hypoinflamed TiME subtypes (p = 0.035 and 0.0376, respectively).

CONCLUSIONS

The TiME of SNSCC reveals distinct subtypes, which may correlate with recurrence and survival outcomes.

A 14-Marker Multiplexed Imaging Panel for Prognostic Biomarkers and Tumor Heterogeneity in Head and Neck Squamous Cell Carcinoma

Recent advances made in treatment for head and neck squamous cell carcinoma (HNSCC) highlight the need for new prediction tools to guide therapeutic strategies. In this study, we aimed to develop a HNSCC-targeting multiplex immunohistochemical (IHC) panel that can evaluate prognostic factors and the intratumor heterogeneity of HNSCC. To identify IHC-based tissue biomarkers that constitute new multiplex IHC panel, a systematic review and meta-analysis were performed to analyze reported IHC biomarkers in laryngeal and pharyngeal SCC in the period of 2008–2018. The Cancer Genome Atlas (TCGA) and Reactome pathway databases were used to validate the prognostic and functional significance of the identified biomarkers. A 14-marker chromogenic multiplex IHC panel including identified biomarkers was used to analyze untreated HNSCC tissue. Forty-five high-quality studies and thirty-one candidate tissue biomarkers were identified (N = 7062). Prognostic validation in TCGA laryngeal and pharyngeal SCC cohort (N = 205) showed that β-catenin, DKK1, PINCH1, ADAM10, and TIMP1 were significantly associated with poor prognosis, which were related to functional categories such as immune system, cellular response, cell cycle, and developmental systems. Selected biomarkers were assembled to build a 14-marker panel, evaluating heterogeneity and polarized expression of tumor biomarkers in the tissue structures, which was particularly related to activation of Wnt/β-catenin pathway. Integrated IHC analysis based on a systemic review and meta-analysis provides an in situ proteomics tool to assess the aggressiveness and intratumor heterogeneity of HNSCC.

Neoadjuvant Selicrelumab, an Agonist CD40 Antibody, Induces Changes in the Tumor Microenvironment in Patients with Resectable Pancreatic Cancer

PURPOSE

CD40 activation is a novel clinical opportunity for cancer immunotherapy. Despite numerous active clinical trials with agonistic CD40 monoclonal antibodies (mAb), biological effects and treatment-related modulation of the tumor microenvironment (TME) remain poorly understood.

PATIENTS AND METHODS

Here, we performed a neoadjuvant clinical trial of agonistic CD40 mAb (selicrelumab) administered intravenously with or without chemotherapy to 16 patients with resectable pancreatic ductal adenocarcinoma (PDAC) before surgery followed by adjuvant chemotherapy and CD40 mAb.

RESULTS

The toxicity profile was acceptable, and overall survival was 23.4 months (95% confidence interval, 18.0-28.8 months). Based on a novel multiplexed immunohistochemistry platform, we report evidence that neoadjuvant selicrelumab leads to major differences in the TME compared with resection specimens from treatment-naïve PDAC patients or patients given neoadjuvant chemotherapy/chemoradiotherapy only. For selicrelumab-treated tumors, 82% were T-cell enriched, compared with 37% of untreated tumors (P = 0.004) and 23% of chemotherapy/chemoradiation-treated tumors (P = 0.012). T cells in both the TME and circulation were more active and proliferative after selicrelumab. Tumor fibrosis was reduced, M2-like tumor-associated macrophages were fewer, and intratumoral dendritic cells were more mature. Inflammatory cytokines/sec CXCL10 and CCL22 increased systemically after selicrelumab.

CONCLUSIONS

This unparalleled examination of CD40 mAb therapeutic mechanisms in patients provides insights for design of subsequent clinical trials targeting CD40 in cancer.

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.

Cyclic Multiplexed-Immunofluorescence (cmIF), a Highly Multiplexed Method for Single-Cell Analysis

Immunotherapy harnesses the power of the adaptive immune system and has revolutionized the field of oncotherapy, as novel therapeutic strategies have been introduced into clinical use. The development of immune checkpoint inhibitors has led to durable control of disease in a subset of advanced cancer patients, such as those with melanoma and non-small cell lung cancer. However, predicting patient responses to therapy remains a major challenge, due to the remarkable genomic, epigenetic, and microenvironmental heterogeneity present in each tumor. Breast cancer (BC) is the most common cancer in women, where hormone receptor-positive (HR+; estrogen receptor and/or progesterone receptor) BC comprises the majority (>50%) and has better prognosis, while a minority (<20%) are triple negative BC (TNBC), which has an aggressive phenotype. There is a clinical need to identify predictors of late recurrence in HR+ BC and predictors of immunotherapy outcomes in advanced TNBC. Tumor-infiltrating lymphocytes (TILs) have recently been shown to predict late recurrence in HR+, counter to the findings that TILs confer good prognosis in TNBC and human epidermal growth factor receptor 2 positive (HER2+) subtypes. Furthermore, the spatial arrangement of TILs also appears to have prognostic value, with dense clusters of immune cells predicting poor prognosis in HR+ and good prognosis in TNBC. Whether TIL clusters in different breast cancer subtypes represent the same or different landscapes of TILs is unknown and may have treatment implications for a significant portion of breast cancer patients. Current histopathological staining technology is not sufficient for characterizing the ensembles of TILs and their spatial patterns, in addition to tumor and microenvironmental heterogeneity. However, recent advances in cyclic immunofluorescence enable differentiation of the subsets based on TILs, tumor heterogeneity, and microenvironment composition between good and poor responders. A computational framework for understanding the importance of the spatial relationships between TILs and tumor cells in cancer tissues, which will allow for quantitative interpretation of cyclic immunostaining, is also under development. This chapter will explore the workflow for a newly developed cyclic multiplexed-immunofluorescence (cmIF) assay, which has been optimized for formalin-fixed. paraffin-embedded tissues and developed to process digital images for quantitative single-cell based spatial analysis of tumor heterogeneity and microenvironment, including immune cell composition.

Prognostic significance of spatial immune profiles in human solid cancers

Immune-based tumor characteristics in the context of tumor heterogeneity are associated with suppression as well as promotion of cancer progression in various tumor types. As immunity typically functions based on intercellular contacts and short-distance cytokine communications, the location and spatial relationships of the tumor immune microenvironment can provide a framework to understand the biology and potential predictive biomarkers related to disease outcomes. Immune spatial analysis is a newly emerging form of cancer research based on recent methodological advances in in situ single-cell analysis, where cell-cell interaction and the tissue architecture can be analyzed in relation to phenotyping the tumor immune heterogeneity. Spatial characteristics of tumors can be stratified into the tissue architecture level and the single-cell level. At the tissue architecture level, the prognostic significance of the density of immune cell lineages, particularly T cells, is leveraged by understanding longitudinal changes in cell distribution in the tissue architecture such as intra-tumoral and peri-tumoral regions, and invasive margins. At the single-cell level, the proximity of the tumor to the immune cells correlates with disease aggressiveness and therapeutic resistance, providing evidence to understand biological interactions and characteristics of the tumor immune microenvironment. In this review, we summarize recent findings regarding spatial information of the tumor immune microenvironment and review advances and challenges in spatial single-cell analysis toward developing tissue-based biomarkers rooted in the immune spatial landscape.

The tumour immune landscape and its implications in cutaneous melanoma

The field of tumour immunology has rapidly advanced in the last decade, leading to the advent of effective immunotherapies for patients with advanced cancers. This highlights the critical role of the immune system in determining tumour development and outcome. The tumour immune microenvironment (TIME) is highly heterogeneous, and the interactions between tumours and the immune system are vastly complex. Studying immune cell function in the TIME will provide an improved understanding of the mechanisms underpinning these interactions. This review examines the role of immune cell populations in the TIME based on their phenotype, function and localisation, as well as contextualising their position in the dynamic relationship between tumours and the immune system. We discuss the function of immune cell populations, examine their impact on patient outcome and highlight gaps in current understanding of their roles in the TIME, both in cancers in general and specifically in melanoma. Studying the TIME by evaluating both pro-tumour and anti-tumour effects may elucidate the conditions which lead to tumour growth and metastasis or immune-mediated tumour regression. Moreover, an in-depth understanding of these conditions could contribute to improved prognostication, more effective use of current immunotherapies and guide the development of novel treatment strategies and therapies.