Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.

MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging.

Highly multiplexed tissue imaging makes detailed molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of large multichannel images poses a substantial computational challenge. Here, we describe a modular and open-source computational pipeline, MCMICRO, for performing the sequential steps needed to transform whole-slide images into single-cell data. We demonstrate the use of MCMICRO on tissue and tumor images acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software.

Loss of myoepithelial calponin-1 characterizes high-risk ductal carcinoma in situ cases, which are further stratified by T cell composition.

A hallmark of ductal carcinoma in situ (DCIS) progression is a loss of the surrounding ductal myoepithelium. However, whether compromise in myoepithelial differentiation, rather than overt cellular loss, can be used to predict the risk of DCIS progression is unknown. Here we address this question utilizing pure and mixed DCIS cases (N = 30) as surrogates for DCIS at low and high risk for progression, respectively. We used multiplex immunohistochemical staining to evaluate the relationship between myoepithelial cell differentiation and lymphoid immune cell types associated with poor prognostic DCIS. Our results show that myoepithelial calponin-1 discriminates between pure and mixed DCIS lesions better than histological subtype, presence of necrosis, or nuclear grade. Additionally, focal loss of myoepithelial cells associated with increased PD-1+CD8+ T cells, which suggests a link between the myoepithelium and immune surveillance. To identify associations between calponin-1 expression and immune response, we performed unsupervised hierarchical clustering of myoepithelial and immune cell biomarkers on 219 DCIS lesions from 30 cases. Notably, the majority of pure (low-risk) DCIS lesions clustered in a high calponin-1, T cell low group, whereas the majority of mixed (high-risk) DCIS lesions clustered in a low calponin-1, T cell high group, specifically with CD8+ and PD-1+CD8+ T cells. However, a subset of pure DCIS lesions had a similar calponin-1 and immune signature as the majority of mixed DCIS lesions, which have low calponin-1 and T cell enrichment-raising the possibility that these pure DCIS lesions might be at a high risk for progression.

An adaptable chromosome preparation methodology for use in invertebrate research organisms.

BACKGROUND: The ability to efficiently visualize and manipulate chromosomes is fundamental to understanding the genome architecture of organisms. Conventional chromosome preparation protocols developed for mammalian cells and those relying on species-specific conditions are not suitable for many invertebrates. Hence, a simple and inexpensive chromosome preparation protocol, adaptable to multiple invertebrate species, is needed. RESULTS: We optimized a chromosome preparation protocol and applied it to several planarian species (phylum Platyhelminthes), the freshwater apple snail Pomacea canaliculata (phylum Mollusca), and the starlet sea anemone Nematostella vectensis (phylum Cnidaria). We demonstrated that both mitotically active adult tissues and embryos can be used as sources of metaphase chromosomes, expanding the potential use of this technique to invertebrates lacking cell lines and/or with limited access to the complete life cycle. Simple hypotonic treatment with deionized water was sufficient for karyotyping; growing cells in culture was not necessary. The obtained karyotypes allowed the identification of differences in ploidy and chromosome architecture among otherwise morphologically indistinguishable organisms, as in the case of a mixed population of planarians collected in the wild. Furthermore, we showed that in all tested organisms representing three different phyla this protocol could be effectively coupled with downstream applications, such as chromosome fluorescent in situ hybridization. CONCLUSIONS: Our simple and inexpensive chromosome preparation protocol can be readily adapted to new invertebrate research organisms to accelerate the discovery of novel genomic patterns across the branches of the tree of life.

Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer.

Tumor molecular data sets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demonstrate the power of using machine learning (ML) to analyze a single-cell, spatial, and highly multiplexed proteomic data set from human pancreatic cancer and reveal underlying biological mechanisms that may contribute to clinical outcomes. We designed a multiplex immunohistochemistry antibody panel to compare T-cell functionality and spatial localization in resected tumors from treatment-naïve patients with localized pancreatic ductal adenocarcinoma (PDAC) with resected tumors from a second cohort of patients treated with neoadjuvant agonistic CD40 (anti-CD40) monoclonal antibody therapy. In total, nearly 2.5 million cells from 306 tissue regions collected from 29 patients across both cohorts were assayed, and over 1,000 tumor microenvironment (TME) features were quantified. We then trained ML models to accurately predict anti-CD40 treatment status and disease-free survival (DFS) following anti-CD40 therapy based on TME features. Through downstream interpretation of the ML models' predictions, we found anti-CD40 therapy reduced canonical aspects of T-cell exhaustion within the TME, as compared with treatment-naïve TMEs. Using automated clustering approaches, we found improved DFS following anti-CD40 therapy correlated with an increased presence of CD44+CD4+ Th1 cells located specifically within cellular neighborhoods characterized by increased T-cell proliferation, antigen experience, and cytotoxicity in immune aggregates. Overall, our results demonstrate the utility of ML in molecular cancer immunology applications, highlight the impact of anti-CD40 therapy on T cells within the TME, and identify potential candidate biomarkers of DFS for anti-CD40-treated patients with PDAC.

Alignment, segmentation and neighborhood analysis in cyclic immunohistochemistry data using CASSATT.

Cyclic immunohistochemistry (cycIHC) uses sequential rounds of colorimetric immunostaining and imaging for quantitative mapping of location and number of cells of interest. Additionally, cycIHC benefits from the speed and simplicity of brightfield microscopy, making the collection of entire tissue sections and slides possible at a trivial cost compared to other high dimensional imaging modalities. However, large cycIHC datasets currently require an expert data scientist to concatenate separate open-source tools for each step of image pre-processing, registration, and segmentation, or the use of proprietary software. Here, we present a unified and user-friendly pipeline for processing, aligning, and analyzing cycIHC data - Cyclic Analysis of Single-Cell Subsets and Tissue Territories (CASSATT). CASSATT registers scanned slide images across all rounds of staining, segments individual nuclei, and measures marker expression on each detected cell. Beyond straightforward single cell data analysis outputs, CASSATT explores the spatial relationships between cell populations. By calculating the log odds of interaction frequencies between cell populations within tissues and tissue regions, this pipeline helps users identify populations of cells that interact-or do not interact-at frequencies that are greater than those occurring by chance. It also identifies specific neighborhoods of cells based on the assortment of neighboring cell types that surround each cell in the sample. The presence and location of these neighborhoods can be compared across slides or within distinct regions within a tissue. CASSATT is a fully open source workflow tool developed to process cycIHC data and will allow greater utilization of this powerful staining technique.

Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer.

Tumor molecular datasets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demonstrate the power of using machine learning to analyze a single-cell, spatial, and highly multiplexed proteomic dataset from human pancreatic cancer and reveal underlying biological mechanisms that may contribute to clinical outcome. A novel multiplex immunohistochemistry antibody panel was used to audit T cell functionality and spatial localization in resected tumors from treatment-naive patients with localized pancreatic ductal adenocarcinoma (PDAC) compared to a second cohort of patients treated with neoadjuvant agonistic CD40 (αCD40) monoclonal antibody therapy. In total, nearly 2.5 million cells from 306 tissue regions collected from 29 patients across both treatment cohorts were assayed, and more than 1,000 tumor microenvironment (TME) features were quantified. We then trained machine learning models to accurately predict αCD40 treatment status and disease-free survival (DFS) following αCD40 therapy based upon TME features. Through downstream interpretation of the machine learning models' predictions, we found αCD40 therapy to reduce canonical aspects of T cell exhaustion within the TME, as compared to treatment-naive TMEs. Using automated clustering approaches, we found improved DFS following αCD40 therapy to correlate with the increased presence of CD44+ CD4+ Th1 cells located specifically within cellular spatial neighborhoods characterized by increased T cell proliferation, antigen-experience, and cytotoxicity in immune aggregates. Overall, our results demonstrate the utility of machine learning in molecular cancer immunology applications, highlight the impact of αCD40 therapy on T cells within the TME, and identify potential candidate biomarkers of DFS for αCD40-treated patients with PDAC.

Modulation of myeloid and T cells in vivo by Bruton's tyrosine kinase inhibitor ibrutinib in patients with metastatic pancreatic ductal adenocarcinoma.

BACKGROUND: In preclinical studies of pancreatic ductal adenocarcinoma (PDAC), ibrutinib improved the antitumor efficacy of the standard of care chemotherapy. This led to a phase 1b clinical trial to determine the safety, tolerability, and immunologic effects of ibrutinib treatment in patients with advanced PDAC. METHODS: Previously untreated patients with PDAC were enrolled in a phase 1b clinical trial (ClinicalTrials.gov) to determine the safety, toxicity, and maximal tolerated dose of ibrutinib when administered with the standard regimen of gemcitabine and nab-paclitaxel. To study the immune response to ibrutinib alone, the trial included an immune response arm where patients were administered with ibrutinib daily for a week followed by ibrutinib combined with gemcitabine and nab-paclitaxel. Endoscopic ultrasonography-guided primary PDAC tumor biopsies and blood were collected before and after ibrutinib monotherapy. Changes in abundance and functional state of immune cells in the blood was evaluated by mass cytometry by time of flight and statistical scaffold analysis, while that in the local tumor microenvironment (TME) were assessed by multiplex immunohistochemistry. Changes in B-cell receptor and T-cell receptor repertoire were assessed by sequencing and analysis of clonality. RESULTS: In the blood, ibrutinib monotherapy significantly increased the frequencies of activated inducible T cell costimulator+(ICOS+) CD4+ T cells and monocytes. Within the TME, ibrutinib monotherapy led to a trend in decreased B-cell abundance but increased interleukin-10+ B-cell frequency. Monotherapy also led to a trend in increased mature CD208+dendritic cell density, increased late effector (programmed cell death protein 1 (PD-1-) eomesodermin (EOMES+)) CD8+ T-cell frequency, with a concomitantly decreased dysfunctional (PD-1+ EOMES+) CD8+ T-cell frequency. When ibrutinib was combined with chemotherapy, most of these immune changes were not observed. Patients with partial clinical responses had more diverse T and B cell receptor repertoires prior to therapy initiation. CONCLUSION: Ibrutinib monotherapy skewed the immune landscape both in the circulation and TME towards activated T cells, monocytes and DCs. These effects were not observed when combining ibrutinib with standard of care chemotherapy. Future studies may focus on other therapeutic combinations that augment the immunomodulatory effects of ibrutinib in solid tumors. TRIAL REGISTRATION NUMBER: NCT02562898.

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.

Single-cell spatial architectures associated with clinical outcome in head and neck squamous cell carcinoma.

There is increasing evidence that the spatial organization of cells within the tumor-immune microenvironment (TiME) of solid tumors influences survival and response to therapy in numerous cancer types. Here, we report results and demonstrate the applicability of quantitative single-cell spatial proteomics analyses in the TiME of primary and recurrent human papillomavirus (HPV)-negative head and neck squamous cell carcinoma (HNSCC) tumors. Single-cell compositions of a nine patient, primary and recurrent (n = 18), HNSCC cohort is presented, followed by deeper investigation into the spatial architecture of the TiME and its relationship with clinical variables and progression free survival (PFS). Multiple spatial algorithms were used to quantify the spatial landscapes of immune cells within TiMEs and demonstrate that neoplastic tumor-immune cell spatial compartmentalization, rather than mixing, is associated with longer PFS. Mesenchymal (αSMA+) cellular neighborhoods describe distinct immune landscapes associated with neoplastic tumor-immune compartmentalization and improved patient outcomes. Results from this investigation are concordant with studies in other tumor types, suggesting that trends in TiME cellular heterogeneity and spatial organization may be shared across cancers and may provide prognostic value in multiple cancer types.

Quantitative Spatial Profiling of Immune Populations in Pancreatic Ductal Adenocarcinoma Reveals Tumor Microenvironment Heterogeneity and Prognostic Biomarkers.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive disease with poor 5-year survival rates, necessitating identification of novel therapeutic targets. Elucidating the biology of the tumor immune microenvironment (TiME) can provide vital insights into mechanisms of tumor progression. In this study, we developed a quantitative image processing platform to analyze sequential multiplexed IHC data from archival PDAC tissue resection specimens. A 27-plex marker panel was employed to simultaneously phenotype cell populations and their functional states, followed by a computational workflow to interrogate the immune contextures of the TiME in search of potential biomarkers. The PDAC TiME reflected a low-immunogenic ecosystem with both high intratumoral and intertumoral heterogeneity. Spatial analysis revealed that the relative distance between IL10+ myelomonocytes, PD-1+ CD4+ T cells, and granzyme B+ CD8+ T cells correlated significantly with survival, from which a spatial proximity signature termed imRS was derived that correlated with PDAC patient survival. Furthermore, spatial enrichment of CD8+ T cells in lymphoid aggregates was also linked to improved survival. Altogether, these findings indicate that the PDAC TiME, generally considered immuno-dormant or immunosuppressive, is a spatially nuanced ecosystem orchestrated by ordered immune hierarchies. This new understanding of spatial complexity may guide novel treatment strategies for PDAC. SIGNIFICANCE: Quantitative image analysis of PDAC specimens reveals intertumoral and intratumoral heterogeneity of immune populations and identifies spatial immune architectures that are significantly associated with disease prognosis.

Deciphering the Immune Complexity in Esophageal Adenocarcinoma and Pre-Cancerous Lesions With Sequential Multiplex Immunohistochemistry and Sparse Subspace Clustering Approach.

Esophageal adenocarcinoma (EAC) develops from a chronic inflammatory environment across four stages: intestinal metaplasia, known as Barrett's esophagus, low- and high-grade dysplasia, and adenocarcinoma. Although the genomic characteristics of this progression have been well defined via large-scale DNA sequencing, the dynamics of various immune cell subsets and their spatial interactions in their tumor microenvironment remain unclear. Here, we applied a sequential multiplex immunohistochemistry (mIHC) platform with computational image analysis pipelines that allow for the detection of 10 biomarkers in one formalin-fixed paraffin-embedded (FFPE) tissue section. Using this platform and quantitative image analytics, we studied changes in the immune landscape during disease progression based on 40 normal and diseased areas from endoscopic mucosal resection specimens of chemotherapy treatment- naïve patients, including normal esophagus, metaplasia, low- and high-grade dysplasia, and adenocarcinoma. The results revealed a steady increase of FOXP3+ T regulatory cells and a CD163+ myelomonocytic cell subset. In parallel to the manual gating strategy applied for cell phenotyping, we also adopted a sparse subspace clustering (SSC) algorithm allowing the automated cell phenotyping of mIHC-based single-cell data. The algorithm successfully identified comparable cell types, along with significantly enriched FOXP3 T regulatory cells and CD163+ myelomonocytic cells as found in manual gating. In addition, SCC identified a new CSF1R+CD1C+ myeloid lineage, which not only was previously unknown in this disease but also increases with advancing disease stages. This study revealed immune dynamics in EAC progression and highlighted the potential application of a new multiplex imaging platform, combined with computational image analysis on routine clinical FFPE sections, to investigate complex immune populations in tumor ecosystems.

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.

Beyond Programmed Death-Ligand 1: B7-H6 Emerges as a Potential Immunotherapy Target in SCLC.

INTRODUCTION: The programmed death-ligand 1 (PD-L1) immune checkpoint inhibitors, atezolizumab and durvalumab, have received regulatory approval for the first-line treatment of patients with extensive-stage SCLC. Nevertheless, when used in combination with platinum-based chemotherapy, these PD-L1 inhibitors only improve overall survival by 2 to 3 months. This may be due to the observation that less than 20% of SCLC tumors express PD-L1 at greater than 1%. Evaluating the composition and abundance of checkpoint molecules in SCLC may identify molecules beyond PD-L1 that are amenable to therapeutic targeting. METHODS: We analyzed RNA-sequencing data from SCLC cell lines (n = 108) and primary tumor specimens (n = 81) for expression of 39 functionally validated inhibitory checkpoint ligands. Furthermore, we generated tissue microarrays containing SCLC cell lines and patient with SCLC specimens to confirm expression of these molecules by immunohistochemistry. We annotated patient outcomes data, including treatment response and overall survival. RESULTS: The checkpoint protein B7-H6 (NCR3LG1) exhibited increased protein expression relative to PD-L1 in cell lines and tumors (p < 0.05). Higher B7-H6 protein expression correlated with longer progression-free survival (p = 0.0368) and increased total immune infiltrates (CD45+) in patients. Furthermore, increased B7-H6 gene expression in SCLC tumors correlated with a decreased activated natural killer cell gene signature, suggesting a complex interplay between B7-H6 expression and immune signature in SCLC. CONCLUSIONS: We investigated 39 inhibitory checkpoint molecules in SCLC and found that B7-H6 is highly expressed and associated with progression-free survival. In addition, 26 of 39 immune checkpoint proteins in SCLC tumors were more abundantly expressed than PD-L1, indicating an urgent need to investigate additional checkpoint targets for therapy in addition to PD-L1.

Leukocyte Heterogeneity in Pancreatic Ductal Adenocarcinoma: Phenotypic and Spatial Features Associated with Clinical Outcome.

Immunotherapies targeting aspects of T cell functionality are efficacious in many solid tumors, but pancreatic ductal adenocarcinoma (PDAC) remains refractory to these treatments. Deeper understanding of the PDAC immune ecosystem is needed to identify additional therapeutic targets and predictive biomarkers for therapeutic response and resistance monitoring. To address these needs, we quantitatively evaluated leukocyte contexture in 135 human PDACs at single-cell resolution by profiling density and spatial distribution of myeloid and lymphoid cells within histopathologically defined regions of surgical resections from treatment-naive and presurgically (neoadjuvant)-treated patients and biopsy specimens from metastatic PDAC. Resultant data establish an immune atlas of PDAC heterogeneity, identify leukocyte features correlating with clinical outcomes, and, through an in silico study, provide guidance for use of PDAC tissue microarrays to optimally measure intratumoral immune heterogeneity. Atlas data have direct applicability as a reference for evaluating immune responses to investigational neoadjuvant PDAC therapeutics where pretherapy baseline specimens are not available. SIGNIFICANCE: We provide a phenotypic and spatial immune atlas of human PDAC identifying leukocyte composition at steady state and following standard neoadjuvant therapies. These data have broad utility as a resource that can inform on leukocyte responses to emerging therapies where baseline tissues were not acquired.This article is highlighted in the In This Issue feature, p. 1861.

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.

Multiomics analysis of serial PARP inhibitor treated metastatic TNBC inform on rational combination therapies.

In a pilot study, we evaluated the feasibility of real-time deep analysis of serial tumor samples from triple negative breast cancer patients to identify mechanisms of resistance and treatment opportunities as they emerge under therapeutic stress engendered by poly-ADP-ribose polymerase (PARP) inhibitors (PARPi). In a BRCA-mutant basal breast cancer exceptional long-term survivor, a striking tumor destruction was accompanied by a marked infiltration of immune cells containing CD8 effector cells, consistent with pre-clinical evidence for association between STING mediated immune activation and benefit from PARPi and immunotherapy. Tumor cells in the exceptional responder underwent extensive protein network rewiring in response to PARP inhibition. In contrast, there were minimal changes in the ecosystem of a luminal androgen receptor rapid progressor, likely due to indifference to the effects of PARP inhibition. Together, identification of PARPi-induced emergent changes could be used to select patient specific combination therapies, based on tumor and immune state changes.

High-dimensional multiplexed immunohistochemical characterization of immune contexture in human cancers.

Biomarker assessments of tumor specimens is widely used in cancer research to audit tumor cell intrinsic as well as tumor cell extrinsic features including the diversity of immune, stromal, and mesenchymal cells. To comprehensively and quantitatively audit the tumor-immune microenvironment (TiME), we developed a novel multiplex immunohistochemistry (mIHC) platform and computational image processing workflow using a single formalin-fixed paraffin-embedded (FFPE) tissue section. Herein, we validated this platform using nine matched primary newly diagnosed and recurrent head and neck squamous cell carcinoma (HNSCC) sections sequentially subjected to immunodetection with a panel of 29 antibodies identifying malignant tumor cells, and 17 distinct leukocyte lineages and their functional states. Image cytometric analysis was applied to interpret chromogenic signals from digitally scanned and coregistered light microscopy-based images enabling identification and quantification of individual tumor cells, structural features, immune cell phenotypes and their functional state. In agreement with our previous study via a 12-plex imaging mIHC platform, myeloid-inflamed status in newly diagnosed primary tumors associated with significantly short progression free survival, independent of lymphoid-inflamed status. Spatial distribution of tumor and immune cell lineages in TiME was also examined and revealed statistically significant CD8+ T cell exclusion from tumor nests, whereas regulatory T cells and myeloid cells, when present in close proximity to tumor cells, highly associated with rapid cancer recurrence. These findings indicate presence of differential immune-spatial profiles in newly diagnosed and recurrent HNSCC, and establish the robustness of the 29-plex mIHC platform and associated analytics for quantitative analysis of single tissue sections revealing longitudinal TiME changes.

Publisher Correction: The TLR7/8 agonist R848 remodels tumor and host responses to promote survival in pancreatic cancer.

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

The TLR7/8 agonist R848 remodels tumor and host responses to promote survival in pancreatic cancer.

A priority in cancer research is to innovate therapies that are not only effective against tumor progression but also address comorbidities such as cachexia that limit quality and quantity of life. We demonstrate that TLR7/8 agonist R848 induces anti-tumor responses and attenuates cachexia in murine models of pancreatic ductal adenocarcinoma (PDAC). In vivo, tumors from two of three cell lines were R848-sensitive, resulting in smaller tumor mass, increased immune complexity, increased CD8+ T-cell infiltration and activity, and decreased Treg frequency. R848-treated mice demonstrated improvements in behavioral and molecular cachexia manifestations, resulting in a near-doubling of survival duration. Knockout mouse studies revealed that stromal, not neoplastic, TLR7 is requisite for R848-mediated responses. In patient samples, we found Tlr7 is ubiquitously expressed in stroma across all stages of pancreatic neoplasia, but epithelial Tlr7 expression is relatively uncommon. These studies indicate immune-enhancing approaches including R848 may be useful in PDAC and cancer-associated cachexia.