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BACKGROUND: Multiplexed immunofluorescence (mIF) staining, such as CODEX and MIBI, holds significant clinical value for various fields, such as disease diagnosis, biological research, and drug development. However, these techniques are often hindered by high time and cost requirements. METHODS: Here we present a Multimodal-Attention-based virtual mIF Staining (MAS) system that utilises a deep learning model to extract potential antibody-related features from dual-modal non-antibody-stained fluorescence imaging, specifically autofluorescence (AF) and DAPI imaging. The MAS system simultaneously generates predictions of mIF with multiple survival-associated biomarkers in gastric cancer using self- and multi-attention learning mechanisms. FINDINGS: Experimental results with 180 pathological slides from 94 patients with gastric cancer demonstrate the efficiency and consistent performance of the MAS system in both cancer and noncancer gastric tissues. Furthermore, we showcase the prognostic accuracy of the virtual mIF images of seven gastric cancer related biomarkers, including CD3, CD20, FOXP3, PD1, CD8, CD163, and PD-L1, which is comparable to those obtained from the standard mIF staining. INTERPRETATION: The MAS system rapidly generates reliable multiplexed staining, greatly reducing the cost of mIF and improving clinical workflow. FUNDING: Stanford 2022 HAI Seed Grant; National Institutes of Health 1R01CA256890.

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Reliable large-scale cell detection and segmentation is the fundamental first step to understanding biological processes in the brain. The ability to phenotype cells at scale can accelerate preclinical drug evaluation and system-level brain histology studies. The impressive advances in deep learning offer a practical solution to cell image detection and segmentation. Unfortunately, categorizing cells and delineating their boundaries for training deep networks is an expensive process that requires skilled biologists. This paper presents a novel self-supervised Dual-Loss Adaptive Masked Autoencoder (DAMA) for learning rich features from multiplexed immunofluorescence brain images. DAMA's objective function minimizes the conditional entropy in pixel-level reconstruction and feature-level regression. Unlike existing self-supervised learning methods based on a random image masking strategy, DAMA employs a novel adaptive mask sampling strategy to maximize mutual information and effectively learn brain cell data. To the best of our knowledge, this is the first effort to develop a self-supervised learning method for multiplexed immunofluorescence brain images. Our extensive experiments demonstrate that DAMA features enable superior cell detection, segmentation, and classification performance without requiring many annotations. In addition, to examine the generalizability of DAMA, we also experimented on TissueNet, a multiplexed imaging dataset comprised of two-channel fluorescence images from six distinct tissue types, captured using six different imaging platforms. Our code is publicly available at https://github.com/hula-ai/DAMA.

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Efforts to understand the complexities of human biology encompass multidimensional aspects, with proteins emerging as crucial components. However, studying the human ovary introduces unique challenges due to its complex dynamics and changes over a lifetime, varied cellular composition, and limited sample access. Here, four new RNA-seq samples of ovarian cortex spanning ages of 7 to 32 were sequenced and added to the existing data in the Human Protein Atlas (HPA) database www.proteinatlas.org, opening the doors to unique possibilities for exploration of oocyte-specific proteins. Based on transcriptomics analysis of the four new tissue samples representing both prepubertal girls and women of fertile age, we selected 20 protein candidates that lacked previous evidence at the protein level, so-called "missing proteins" (MPs). The proteins were validated using high-resolution antibody-based profiling and single-cell transcriptomics. Fourteen proteins exhibited consistent single-cell expression patterns in oocytes and granulosa cells, confirming their presence in the ovary and suggesting that these proteins play important roles in ovarian function, thus proposing that these 14 proteins should no longer be classified as MPs. This research significantly advances the understanding of MPs, unearthing fresh avenues for prospective exploration. By integrating innovative methodologies and leveraging the wealth of data in the HPA database, these insights contribute to refining our understanding of protein roles within the human ovary and opening the doors for further investigations into missing proteins and human reproduction.

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Introduction: Outcomes following tumor resection vary dramatically among patients with pancreatic ductal adenocarcinoma (PDAC). A challenge in defining predictive biomarkers is to discern within the complex tumor tissue the specific subpopulations and relationships that drive recurrence. Multiplexed immunofluorescence is valuable for such studies when supplied with markers of relevant subpopulations and analysis methods to sort out the intra-tumor relationships that are informative of tumor behavior. We hypothesized that the glycan biomarkers CA19-9 and STRA, which detect separate subpopulations of cancer cells, define intra-tumoral features associated with recurrence. Methods: We probed this question using automated signal thresholding and spatial cluster analysis applied to the immunofluorescence images of the STRA and CA19-9 glycan biomarkers in whole-block sections of PDAC tumors collected from curative resections. Results: The tumors (N = 22) displayed extreme diversity between them in the amounts of the glycans and in the levels of spatial clustering, but neither the amounts nor the clusters of the individual and combined glycans associated with recurrence. The combined glycans, however, marked divergent types of spatial clusters, alternatively only STRA, only CA19-9, or both. The co-occurrence of more than one cluster type within a tumor associated significantly with disease recurrence, in contrast to the independent occurrence of each type of cluster. In addition, intra-tumoral regions with heterogeneity in biomarker clusters spatially aligned with pathology-confirmed cancer cells, whereas regions with homogeneous biomarker clusters aligned with various non-cancer cells. Conclusion: Thus, the STRA and CA19-9 glycans are markers of distinct and co-occurring subpopulations of cancer cells that in combination are associated with recurrence. Furthermore, automated signal thresholding and spatial clustering provides a tool for quantifying intra-tumoral subpopulations that are informative of outcome.

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Profiling molecular expression in situ allows the integration of biomolecular and cellular features, enabling an in-depth understanding of biological systems. Multiplexed immunofluorescence methods can visualize tens to hundreds of proteins from individual tissue samples, but their application is usually limited to thin tissue sections. Multiplexed immunofluorescence of thick tissues or intact organs will enable high-throughput profiling of cellular protein expression within 3D tissue architectures (e.g., blood vessels, neural projections, tumors), opening a new dimension in diverse biological research and medical applications. We will review current multiplexed immunofluorescence methods and discuss possible approaches and challenges to achieve 3D multiplexed immunofluorescence.

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Multiplexed immunofluorescence imaging of formalin-fixed, paraffin-embedded (FFPE) specimens mounted on glass slides allow the identification of multiple cell phenotypes while retaining spatial and morphological context. Multiplex immunofluorescence protocols have already been developed and validated for mouse tissues. Immunophenotyping analysis reliably depicts the immune landscape of cancer tissues that has been demonstrated to influence cancer development and progression as well as to have an impact on therapy responsiveness and resistance. Here, we describe a method for multiplexed fluorescence image analysis, enabling analysis of mouse cancer morphology and cell phenotypes in FFPE sections.

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Traditional immunofluorescence (IF) imaging assays are limited to the detection of just a few markers due to spectral overlap of fluorescent emission bands. Furthermore, standard fluorescent imaging instruments have a dynamic range that is too narrow to capture the full range of expression values seen in biology, precluding the accurate quantification of single-cell target expression. Here we describe a protocol for detection and quantification of dozens of protein targets with single-cell quantitative precision using an iterative staining approach called ChipCytometry™.

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Immune checkpoint modulation has been a vital therapeutic option in many malignancies, and targeting of novel immune checkpoints, including OX40/OX40L costimulatory signaling, is being assessed in clinical trials. However, little is known about the role of OX40 and OX40L in pancreatic ductal adenocarcinoma (PDAC). Thus, we investigated the clinical significance of OX-40 and OX40L and their associations with alternative immune checkpoints, immune infiltrates, clinicopathological features, and clinical outcomes. We performed multiplexed immunofluorescence staining for OX40, OX40L, CD8, and CD68 using tissue microarrays from 255 patients. Immunohistochemistry data for PD-L1, B7-H3, B7-H4, CD3, and Foxp3 were analyzed. And the RNA sequencing data of OX40/OX40L in The Cancer Genome Atlas and International Cancer Genome Consortium databases were also evaluated. The positive rates for OX40 on tumor cells (TCs) and immune cells (ICs) were 8.6% and 10.2%, respectively, and the positive rates for OX40L on TCs, ICs, and macrophages were 20%, 40.4%, and 12.9%, respectively. OX40 was associated with favorable clinicopathological features. OX40+ on ICs, OX40L+ on TCs, or OX40L+ on macrophages, rather than the total gene and protein levels of OX40/OX40L, were associated with improved survival. OX40+ on ICs and OX40L+ on macrophages were independent factors of clinical outcomes. Moreover, we could more accurately stratify patients through the combination of OX40 on ICs and OX40L on TCs, and patients with OX40+ ICs and OX40L+CK+ showed the best outcome. And we demonstrated that patients with OX40-ICs and low CD8+ T cells infiltration had unfavorable survival. Intriguingly, OX40+ ICs or OX40L+ macrophages demonstrated superior survival in patients with PD-L1 negativity than in those with PD-L1 positivity. Furthermore, OX40+ ICs were correlated with negative B7-H4 on TCs, high densities of CD3 T cells, and high densities of Foxp3 T cells; OX40+ TCs and OX40L+ TCs were associated with low densities of Foxp3 T cells. We identified OX40 and OX40L as promising predictors for prognosis in PDAC.

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Background: To study neoadjuvant chemoradiotherapy (nCRT) and potential predictive factors for response in locally advanced oral cavity cancer (LA-OCC). Methods: The INVERT trial is an ongoing single-center, prospective phase 2, proof-of-principle trial. Operable patients with stage III-IVA squamous cell carcinomas of the oral cavity were eligible and received nCRT consisting of 60 Gy with concomitant cisplatin and 5-fluorouracil. Surgery was scheduled 6-8 weeks after completion of nCRT. Explorative, multiplex immunohistochemistry (IHC) was performed on pretreatment tumor specimen, and diffusion-weighted magnetic resonance imaging (DW-MRI) was conducted prior to, during nCRT (day 15), and before surgery to identify potential predictive biomarkers and imaging features. Primary endpoint was the pathological complete response (pCR) rate. Results: Seventeen patients with stage IVA OCC were included in this interim analysis. All patients completed nCRT. One patient died from pneumonia 10 weeks after nCRT before surgery. Complete tumor resection (R0) was achieved in 16/17 patients, of whom 7 (41%, 95% CI: 18-67%) showed pCR. According to the Clavien-Dindo classification, grade 3a and 3b complications were found in 4 (25%) and 5 (31%) patients, respectively; grade 4-5 complications did not occur. Increased changes in the apparent diffusion coefficient signal intensities between MRI at day 15 of nCRT and before surgery were associated with better response (p=0.022). Higher abundances of programmed cell death protein 1 (PD1) positive cytotoxic T-cells (p=0.012), PD1+ macrophages (p=0.046), and cancer-associated fibroblasts (CAFs, p=0.036) were associated with incomplete response to nCRT. Conclusion: nCRT for LA-OCC followed by radical surgery is feasible and shows high response rates. Larger patient cohorts from randomized trials are needed to further investigate nCRT and predictive biomarkers such as changes in DW-MRI signal intensities, tumor infiltrating immune cells, and CAFs.

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The epithelial-to-mesenchymal transition (EMT) and its reversal, the mesenchymal-to-epithelial transition (MET) are critical components of the metastatic cascade in breast cancer and many other solid tumor types. Recent work has uncovered the presence of a variety of states encompassed within the EMT spectrum, each of which may play unique roles or work collectively to impact tumor progression. However, defining EMT status is not routinely carried out to determine patient prognosis or dictate therapeutic decision-making in the clinic. Identifying and quantifying the presence of various EMT states within a tumor is a critical first step to scoring patient tumors to aid in determining prognosis. Here, we review the major strides taken towards translating our understanding of EMT biology from bench to bedside. We review previously used approaches including basic immunofluorescence staining, flow cytometry, single-cell sequencing, and multiplexed tumor mapping. Future studies will benefit from the consideration of multiple methods and combinations of markers in designing a diagnostic tool for detecting and measuring EMT in patient tumors.

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BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a formidable challenge for patients and clinicians. OBJECTIVE: To analyze the distribution of 31 different markers in tumor and stromal portions of the tumor microenvironment (TME) and identify immune cell populations to better understand how neoplastic, non-malignant structural, and immune cells, diversify the TME and influence PDAC progression. METHODS: Whole slide imaging (WSI) and cyclic multiplexed-immunofluorescence (MxIF) was used to collect 31 different markers over the course of nine distinctive imaging series of human PDAC samples. Image registration and machine learning algorithms were developed to largely automate an imaging analysis pipeline identifying distinct cell types in the TME. RESULTS: A random forest algorithm accurately predicted tumor and stromal-rich areas with 87% accuracy using 31 markers and 77% accuracy using only five markers. Top tumor-predictive markers guided downstream analyses to identify immune populations effectively invading into the tumor, including dendritic cells, CD4+ T cells, and multiple immunoregulatory subtypes. CONCLUSIONS: Immunoprofiling of PDAC to identify differential distribution of immune cells in the TME is critical for understanding disease progression, response and/or resistance to treatment, and the development of new treatment strategies.

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The spatial organization of immune cells within the tumor microenvironment (TME) largely determines the anti-tumor immunity and also highly predicts tumor progression and therapeutic response. Tim-3 is a well-accepted immune checkpoint and plays multifaceted immunoregulatory roles via interaction with distinct Tim-3 ligands (Tim-3L), showing great potential as an immunotherapy target. However, the cell sociology mediated by Tim-3/Tim-3L and their contribution to tumor development remains elusive. Here, we analyzed the spatial distribution of Tim-3/Tim-3L in TME using multiplex fluorescence staining and revealed that despite the increased Tim-3 expression in various tumor-infiltrated lymphocytes, Tim-3+CD4+ cells were more accumulated in parenchymal/tumor region compared with stromal region and exhibited more close association with patient survival. Strikingly, CD4 T cells surrounding Tim-3L+ cells expressed higher Tim-3 than other cells in cancerous tissues. In vivo studies confirmed that depletion of CD4 T cells completely abrogated the inhibition of tumor growth and metastasis, as well as the functional improvement of CD8 T and NK, mediated by Tim-3 blockade, which was further validated in peripheral lymphocytes from patients with hepatocellular carcinoma. In conclusion, our findings unravel the importance of CD4 T cells in Tim-3/Tim-3L-mediated immunosuppression and provide new thoughts for Tim-3 targeted cancer immunotherapy.

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BACKGROUND: An elevated peripheral blood derived neutrophil-to-lymphocyte ratio (dNLR) is a negative prognostic marker for patients with non-small cell lung cancer (NSCLC) receiving chemotherapy and immune checkpoint inhibitors. Whether dNLR is also associated with clinical outcomes to first-line pembrolizumab among patients with NSCLC and a programmed cell death ligand 1 (PD-L1) Tumor Proportion Score (TPS) of ≥50% is uncertain. How dNLR relates to the tumor immune microenvironment is also unclear. METHODS: In two participating academic centers, we retrospectively analyzed the dNLR (defined as the absolute neutrophil count/white cell count - absolute neutrophil count) prior to initiation of first-line pembrolizumab in patients with metastatic NSCLC and a PD-L1 TPS ≥50% and lacking genomic alterations in EGFR and ALK. An unbiased recursive partitioning algorithm was used to investigate an optimal dNLR cut-off with respect to objective response rate (ORR). Multiplexed immunofluorescence for CD8+, FOXP3+, PD-1+, and PD-L1 was performed on a separate cohort of NSCLCs to determine the immunophenotype associated with dNLR. RESULTS: A total of 221 patients treated with first-line pembrolizumab were included in this study. The optimal dNLR cut-off to differentiate treatment responders from non-responders was 2.6. Compared with patients with a dNLR ≥2.6 (n=97), patients with dNLR <2.6 (n=124) had a significantly higher ORR (52.4% vs 24.7%, p<0.001), a significantly longer median progression-free survival (mPFS 10.4 vs 3.4 months, HR 0.48, 95% CI 0.35 to 0.66, p<0.001), and a significantly longer median overall survival (mOS 36.6 vs 9.8 months, HR 0.34, 95% CI 0.23 to 0.49, p<0.001). After adjusting for age, sex, tobacco use, performance status, histology, serum albumin level, oncogenic driver status, and PD-L1 distribution (50%-89% vs ≥90%), a dNLR <2.6 was confirmed to be an independent predictor of longer mPFS (HR 0.47, 95% CI 0.33 to 0.67, p<0.001) and mOS (HR 0.32, 95% CI 0.21 to 0.49, p<0.001). Among advanced NSCLC samples with a PD-L1 TPS of ≥50%, those with a dNLR <2.6 had significantly higher numbers of tumor-associated CD8+, FOXP3+, PD-1 +immune cells, and PD-1 +CD8+T cells than those with a dNLR ≥2.6. CONCLUSIONS: Among patients with NSCLC and a PD-L1 TPS ≥50%, a low dNLR has a distinct immune tumor microenvironment and more favorable outcomes to first-line pembrolizumab.

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The high mortality of COVID-19 is mostly attributed to acute respiratory distress syndrome (ARDS), whose histopathological correlate is diffuse alveolar damage (DAD). Furthermore, severe COVID-19 is often accompanied by a cytokine storm and a disrupted response of the adaptive immune system. Studies aiming to depict this dysregulation have mostly investigated the peripheral cell count as well as the functionality of immune cells. We investigated the impact of SARS-CoV-2 on antigen-presenting cells using multiplexed immunofluorescence. Similar to MERS-CoV and SARS-CoV, SARS-CoV-2 appears to be impairing the maturation of dendritic cells (DCs). DC maturation involves a switch in surface antigen expression, which enables the cells' homing to lymph nodes and the subsequent activation of T-cells. As quantitative descriptions of the local inflammatory infiltrate are still scarce, we compared the cell population of professional antigen-presenting cells (APC) in the lungs of COVID-19 autopsy cases in different stages of DAD. We found an increased count of myeloid dendritic cells (mDCs) in later stages. Interestingly, mDCs also showed no significant upregulation of maturation markers in DAD-specimens with high viral load. Accumulation of immature mDCs, which are unable to home to lymph nodes, ultimately results in an inadequate T-cell response.

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Over the last 15 years, advances in immunofluorescence-imaging based cycling methods, antibody conjugation methods, and automated image processing have facilitated the development of a high-resolution, multiplexed tissue immunofluorescence (MxIF) method with single cell-level quantitation termed Cell DIVETM. Originally developed for fixed oncology samples, here it was evaluated in highly fixed (up to 30 days), archived monkeypox virus-induced inflammatory skin lesions from a retrospective study in 11 rhesus monkeys to determine whether MxIF was comparable to manual H-scoring of chromogenic stains. Six protein markers related to immune and cellular response (CD68, CD3, Hsp70, Hsp90, ERK1/2, ERK1/2 pT202_pY204) were manually quantified (H-scores) by a pathologist from chromogenic IHC double stains on serial sections and compared to MxIF automated single cell quantification of the same markers that were multiplexed on a single tissue section. Overall, there was directional consistency between the H-score and the MxIF results for all markers except phosphorylated ERK1/2 (ERK1/2 pT202_pY204), which showed a decrease in the lesion compared to the adjacent non-lesioned skin by MxIF vs an increase via H-score. Improvements to automated segmentation using machine learning and adding additional cell markers for cell viability are future options for improvement. This method could be useful in infectious disease research as it conserves tissue, provides marker colocalization data on thousands of cells, allowing further cell level data mining as well as a reduction in user bias.

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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.

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BACKGROUND: Immunohistochemistry (IHC) for programmed cell death ligand 1 (PD-L1) displays staining diversity. We compared IHC staining of PD-L1 in gastric cancer (GC) by using three commercially available antibody clones, and analyzed the correlation with the prognosis. METHODS: IHC using PD-L1 antibodies (clones SP142, 28-8 and E1L3N) in 315 formalin-fixed paraffin-embedded samples was qualitatively compared at the 1, 5 and 10% cut-off by two pathologists on total, tumor and immune/stromal cells. We used computer - assisted scoring to quantitatively analyze and compare the "H-score" of PD-L1 expression in 66 samples on total cells. The antibody clone SP142 was selected to investigate the infiltration of PD-L1+CD8+ T cells using automated quantitative immunofluorescence analyses (n = 50) and the prognostic significance. The prognoses were assessed by log-rank test. RESULTS: PD-L1 clones SP142 and 28-8 displayed great concordance by qualitative (κ = 0.816, 0.810 for total cells and tumor cells at the 5% cut-off) and quantitative analyses (R2 = 0.7991, 0.8187 for positive percentage and "H-score"). PD-L1 clone SP142 showed the highest positivity in immune/stromal cells staining (18.41%) compared to 28-8 (7.62%), while clone E1L3N showed poor staining in both tumor and immune/stromal cells. Clone SP142, but not 28-8 and E1L3N, predicted a worse prognosis at the 5% cut-off (p = 0.0243). Both the clone SP142 and 28-8 had high inter-pathologist correlation for tumor staining (R2 = 0.9805 and R2 = 0.9853), but a moderate correlation for stromal/immune cell staining (R2 = 0.5653 and R2 = 0.5745). Furthermore, a higher density of PD-L1+CD8+ T cells was correlated with a shorter survival time (R2 = 0.0909, p = 0.0352). CONCLUSIONS: PD-L1 antibody clone SP142 was superior in cell staining, particularly in immune/stromal cell and prognosis. These findings are important for selection of PD-L1 antibody clones in the future diagnostic test.

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The complex network of the tissue system, in both pre-neoplastic tissues and tumors, demonstrates the need for a systems biology approach to cancer pathology, in which quantification of key tissue system processes is combined with informatics tools to produce actionable scores to aid clinical decision-making. A systems biology approach to cancer pathology enables integration of key system features that are relevant to diagnoses, patient outcomes, and responses to therapies. Key tissue system features relevant to cancer pathology include molecular and morphologic abnormalities in epithelia, cellular changes in the stroma such as immune infiltrates, and relationships between components of the system, such as interactions and spatial relationships between epithelial and stromal components, and also between specific immune cell subsets. Here, we describe a method for objective quantification of multiple epithelial and stromal biomarkers in the context of tissue architecture to generate a high dimensional tissue profile that can be used to build multivariable predictive models for cancer pathology.

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BACKGROUND: Measures of spatial intratumor heterogeneity are potentially important diagnostic biomarkers for cancer progression, proliferation, and response to therapy. Spatial relationships among cells including cancer and stromal cells in the tumor microenvironment (TME) are key contributors to heterogeneity. METHODS: We demonstrate how to quantify spatial heterogeneity from immunofluorescence pathology samples, using a set of 3 basic breast cancer biomarkers as a test case. We learn a set of dominant biomarker intensity patterns and map the spatial distribution of the biomarker patterns with a network. We then describe the pairwise association statistics for each pattern within the network using pointwise mutual information (PMI) and visually represent heterogeneity with a two-dimensional map. RESULTS: We found a salient set of 8 biomarker patterns to describe cellular phenotypes from a tissue microarray cohort containing 4 different breast cancer subtypes. After computing PMI for each pair of biomarker patterns in each patient and tumor replicate, we visualize the interactions that contribute to the resulting association statistics. Then, we demonstrate the potential for using PMI as a diagnostic biomarker, by comparing PMI maps and heterogeneity scores from patients across the 4 different cancer subtypes. Estrogen receptor positive invasive lobular carcinoma patient, AL13-6, exhibited the highest heterogeneity score among those tested, while estrogen receptor negative invasive ductal carcinoma patient, AL13-14, exhibited the lowest heterogeneity score. CONCLUSIONS: This paper presents an approach for describing intratumor heterogeneity, in a quantitative fashion (via PMI), which departs from the purely qualitative approaches currently used in the clinic. PMI is generalizable to highly multiplexed/hyperplexed immunofluorescence images, as well as spatial data from complementary in situ methods including FISSEQ and CyTOF, sampling many different components within the TME. We hypothesize that PMI will uncover key spatial interactions in the TME that contribute to disease proliferation and progression.

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BACKGROUND: Current histologic methods for diagnosis are limited by intra- and inter-observer variability. Immunohistochemistry (IHC) methods are frequently used to assess biomarkers to aid diagnoses, however, IHC staining is variable and nonlinear and the manual interpretation is subjective. Furthermore, the biomarkers assessed clinically are typically biomarkers of epithelial cell processes. Tumors and premalignant tissues are not composed only of epithelial cells but are interacting systems of multiple cell types, including various stromal cell types that are involved in cancer development. The complex network of the tissue system highlights the need for a systems biology approach to anatomic pathology, in which quantification of system processes is combined with informatics tools to produce actionable scores to aid clinical decision-making. AIMS: Here, we describe a quantitative, multiplexed biomarker imaging approach termed TissueCypher™ that applies systems biology to anatomic pathology. Applications of TissueCypher™ in understanding the tissue system of Barrett's esophagus (BE) and the potential use as an adjunctive tool in the diagnosis of BE are described. PATIENTS AND METHODS: The TissueCypher™ Image Analysis Platform was used to assess 14 epithelial and stromal biomarkers with known diagnostic significance in BE in a set of BE biopsies with nondysplastic BE with reactive atypia (RA, n = 22) and Barrett's with high-grade dysplasia (HGD, n = 17). Biomarker and morphology features were extracted and evaluated in the confirmed BE HGD cases versus the nondysplastic BE cases with RA. RESULTS: Multiple image analysis features derived from epithelial and stromal biomarkers, including immune biomarkers and morphology, showed significant differences between HGD and RA. CONCLUSIONS: The assessment of epithelial cell abnormalities combined with an assessment of cellular changes in the lamina propria may serve as an adjunct to conventional pathology in the assessment of BE.