A Subset of Type I Conventional Dendritic Cells Controls Cutaneous Bacterial Infections through VEGFα-Mediated Recruitment of Neutrophils.

Skin conventional dendritic cells (cDCs) exist as two distinct subsets, cDC1s and cDC2s, which maintain the balance of immunity to pathogens and tolerance to self and microbiota. Here, we examined the roles of dermal cDC1s and cDC2s during bacterial infection, notably Propionibacterium acnes (P. acnes). cDC1s, but not cDC2s, regulated the magnitude of the immune response to P. acnes in the murine dermis by controlling neutrophil recruitment to the inflamed site and survival and function therein. Single-cell mRNA sequencing revealed that this regulation relied on secretion of the cytokine vascular endothelial growth factor α (VEGF-α) by a minor subset of activated EpCAM+CD59+Ly-6D+ cDC1s. Neutrophil recruitment by dermal cDC1s was also observed during S. aureus, bacillus Calmette-Guérin (BCG), or E. coli infection, as well as in a model of bacterial insult in human skin. Thus, skin cDC1s are essential regulators of the innate response in cutaneous immunity and have roles beyond classical antigen presentation.

The Prognostic Value of CD39 as a Marker of Tumor-Specific T Cells in Triple-Negative Breast Cancer in Asian Women.

Triple-negative breast cancer (TNBC) has a poor prognosis with limited therapeutic options available for affected patients. Efforts are ongoing to identify surrogate markers for tumor-specific CD8+ T cells that can predict the response to immune checkpoint inhibitor (ICI) therapies, such as programmed cell death protein 1 or programmed cell death ligand-1 blockade. We have previously identified tumor-specific CD39+CD8+ T cells in non-small cell lung cancer that might help predict patient responses to programmed cell death protein 1 or programmed cell death ligand-1 blockade. Based on this finding, we conducted a comparative interrogation of TNBC in an Asian cohort to evaluate the potential of CD39 as a surrogate marker of tumor-specific CD8+ T cells. Using ICI-treated TNBC mouse models (n = 24), flow cytometric analyses of peripheral blood mononuclear cells and tumor-infiltrating lymphocytes revealed that >99% of tumor-specific CD8+ T cells also expressed CD39. To investigate the relationship between CD39+CD8+ T-cell density and CD39 expression with disease prognosis, we performed multiplex immunohistochemistry staining on treatment-naive human TNBC tissues (n = 315). We saw that the proportion of CD39+CD8+ T cells in human TNBC tumors correlated with improved overall survival, as did the densities of other CD39+ immune cell infiltrates, such as CD39+CD68+ macrophages. Finally, increased CD39 expression on CD8+ T cells was also found to predict the response to ICI therapy (pembrolizumab) in a separate cohort of 11 TNBC patients. These findings support the potential of CD39+CD8+ T-cell density as a prognostic factor in Asian TNBC patients.

Quantitative Multiplexed Analysis of Indoleamine 2,3-Dioxygenase (IDO) and Arginase-1 (ARG1) Expression and Myeloid Cell Infiltration in Colorectal Cancer.

Indoleamine 2,3-dioxygenase (IDO) and arginase-1 (ARG1) are amino acid-metabolizing enzymes, frequently highly expressed in cancer. Their expression may deplete essential amino acids, lead to immunosuppression, and promote cancer growth. Still, their expression patterns, prognostic significance, and spatial localization in the colorectal cancer microenvironment are incompletely understood. Using a custom 10-plex immunohistochemistry assay and supervised machine learning-based digital image analysis, we characterized IDO and ARG1 expression in monocytic cells, granulocytes, mast cells, and tumor cells in 833 colorectal cancer patients. We evaluated the prognostic value and spatial arrangement of IDO- and ARG1-expressing myeloid and tumor cells. IDO was mainly expressed not only by monocytic cells but also by some tumor cells, whereas ARG1 was predominantly expressed by granulocytes. Higher density of IDO+ monocytic cells was an independent prognostic factor for improved cancer-specific survival both in the tumor center (Ptrend = .0002; hazard ratio [HR] for the highest ordinal category Q4 [vs Q1], 0.51; 95% CI, 0.33-0.79) and the invasive margin (Ptrend = .0015). Higher density of granulocytes was associated with prolonged cancer-specific survival in univariable models, and higher FCGR3+ARG1+ neutrophil density in the tumor center also in multivariable analysis (Ptrend = .0020). Granulocytes were, on average, located closer to tumor cells than monocytic cells. Furthermore, IDO+ monocytic cells and ARG1- granulocytes were closer than IDO- monocytic cells and ARG1+ granulocytes, respectively. The mRNA expression of the IDO1 gene was assessed in myeloid and tumor cells using publicly available single-cell RNA sequencing data for 62 colorectal cancers. IDO1 was mainly expressed in monocytes and dendritic cells, and high IDO1 activity in monocytes was associated with enriched immunostimulatory pathways. Our findings provided in-depth information about the infiltration patterns and prognostic value of cells expressing IDO and/or ARG1 in the colorectal cancer microenvironment, highlighting the significance of host immune response in tumor progression.

Integrative multiomics enhancer activity profiling identifies therapeutic vulnerabilities in cholangiocarcinoma of different etiologies.

OBJECTIVES: Cholangiocarcinoma (CCA) is a heterogeneous malignancy with high mortality and dismal prognosis, and an urgent clinical need for new therapies. Knowledge of the CCA epigenome is largely limited to aberrant DNA methylation. Dysregulation of enhancer activities has been identified to affect carcinogenesis and leveraged for new therapies but is uninvestigated in CCA. Our aim is to identify potential therapeutic targets in different subtypes of CCA through enhancer profiling. DESIGN: Integrative multiomics enhancer activity profiling of diverse CCA was performed. A panel of diverse CCA cell lines, patient-derived and cell line-derived xenografts were used to study identified enriched pathways and vulnerabilities. NanoString, multiplex immunohistochemistry staining and single-cell spatial transcriptomics were used to explore the immunogenicity of diverse CCA. RESULTS: We identified three distinct groups, associated with different etiologies and unique pathways. Drug inhibitors of identified pathways reduced tumour growth in in vitro and in vivo models. The first group (ESTRO), with mostly fluke-positive CCAs, displayed activation in estrogen signalling and were sensitive to MTOR inhibitors. Another group (OXPHO), with mostly BAP1 and IDH-mutant CCAs, displayed activated oxidative phosphorylation pathways, and were sensitive to oxidative phosphorylation inhibitors. Immune-related pathways were activated in the final group (IMMUN), made up of an immunogenic CCA subtype and CCA with aristolochic acid (AA) mutational signatures. Intratumour differences in AA mutation load were correlated to intratumour variation of different immune cell populations. CONCLUSION: Our study elucidates the mechanisms underlying enhancer dysregulation and deepens understanding of different tumourigenesis processes in distinct CCA subtypes, with potential significant therapeutics and clinical benefits.

Spatially resolved multimarker evaluation of CD274 (PD-L1)/PDCD1 (PD-1) immune checkpoint expression and macrophage polarisation in colorectal cancer.

BACKGROUND: The CD274 (PD-L1)/PDCD1 (PD-1) immune checkpoint interaction may promote cancer progression, but the expression patterns and prognostic significance of PD-L1 and PD-1 in the colorectal cancer microenvironment are inadequately characterised. METHODS: We used a custom 9-plex immunohistochemistry assay to quantify the expression patterns of PD-L1 and PD-1 in macrophages, T cells, and tumour cells in 910 colorectal cancer patients. We evaluated cancer-specific mortality according to immune cell subset densities using multivariable Cox regression models. RESULTS: Compared to PD-L1- macrophages, PD-L1+ macrophages were more likely M1-polarised than M2-polarised and located closer to tumour cells. PD-L1+ macrophage density in the invasive margin associated with longer cancer-specific survival [Ptrend = 0.0004, HR for the highest vs. lowest quartile, 0.52; 95% CI: 0.34-0.78]. T cell densities associated with longer cancer-specific survival regardless of PD-1 expression (Ptrend < 0.005 for both PD-1+ and PD-1- subsets). Higher densities of PD-1+ T cell/PD-L1+ macrophage clusters associated with longer cancer-specific survival (Ptrend < 0.005). CONCLUSIONS: PD-L1+ macrophages show distinct polarisation profiles (more M1-like), spatial features (greater co-localisation with tumour cells and PD-1+ T cells), and associations with favourable clinical outcome. Our comprehensive multimarker assessment could enhance the understanding of immune checkpoints in the tumour microenvironment and promote the development of improved immunotherapies.

Western-Style Diet, pks Island-Carrying Escherichia coli, and Colorectal Cancer: Analyses From Two Large Prospective Cohort Studies.

BACKGROUND & AIMS: Evidence supports a carcinogenic role of Escherichia coli carrying the pks island that encodes enzymes for colibactin biosynthesis. We hypothesized that the association of the Western-style diet (rich in red and processed meat) with colorectal cancer incidence might be stronger for tumors containing higher amounts of pks+E coli. METHODS: Western diet score was calculated using food frequency questionnaire data obtained every 4 years during follow-up of 134,775 participants in 2 United States-wide prospective cohort studies. Using quantitative polymerase chain reaction, we measured pks+E coli DNA in 1175 tumors among 3200 incident colorectal cancer cases that had occurred during the follow-up. We used the 3200 cases and inverse probability weighting (to adjust for selection bias due to tissue availability), integrated in multivariable-adjusted duplication-method Cox proportional hazards regression analyses. RESULTS: The association of the Western diet score with colorectal cancer incidence was stronger for tumors containing higher levels of pks+E coli (Pheterogeneity = .014). Multivariable-adjusted hazard ratios (with 95% confidence interval) for the highest (vs lowest) tertile of the Western diet score were 3.45 (1.53-7.78) (Ptrend = 0.001) for pks+E coli-high tumors, 1.22 (0.57-2.63) for pks+E coli-low tumors, and 1.10 (0.85-1.42) for pks+E coli-negative tumors. The pks+E coli level was associated with lower disease stage but not with tumor location, microsatellite instability, or BRAF, KRAS, or PIK3CA mutations. CONCLUSIONS: The Western-style diet is associated with a higher incidence of colorectal cancer containing abundant pks+E coli, supporting a potential link between diet, the intestinal microbiota, and colorectal carcinogenesis.

Immune cell profiles in the tumor microenvironment of early-onset, intermediate-onset, and later-onset colorectal cancer.

BACKGROUND: Despite heightened interest in early-onset colorectal cancer (CRC) diagnosed before age 50, little is known on immune cell profiles of early-onset CRC. It also remains to be studied whether CRCs diagnosed at or shortly after age 50 are similar to early-onset CRC. We therefore hypothesized that immune cell infiltrates in CRC tissue might show differential heterogeneity patterns between three age groups (< 50 "early onset," 50-54 "intermediate onset," ≥ 55 "later onset"). METHODS: We examined 1,518 incident CRC cases with available tissue data, including 35 early-onset and 73 intermediate-onset cases. To identify immune cells in tumor intraepithelial and stromal areas, we developed three multiplexed immunofluorescence assays combined with digital image analyses and machine learning algorithms, with the following markers: (1) CD3, CD4, CD8, CD45RO (PTPRC), and FOXP3 for T cells; (2) CD68, CD86, IRF5, MAF, and MRC1 (CD206) for macrophages; and (3) ARG1, CD14, CD15, CD33, and HLA-DR for myeloid cells. RESULTS: Although no comparisons between age groups showed statistically significant differences at the stringent two-sided α level of 0.005, compared to later-onset CRC, early-onset CRC tended to show lower levels of tumor-infiltrating lymphocytes (P = 0.013), intratumoral periglandular reaction (P = 0.025), and peritumoral lymphocytic reaction (P = 0.044). Compared to later-onset CRC, intermediate-onset CRC tended to show lower densities of overall macrophages (P = 0.050), M1-like macrophages (P = 0.062), CD14+HLA-DR+ cells (P = 0.015), and CD3+CD4+FOXP3+ cells (P = 0.039). CONCLUSIONS: This hypothesis-generating study suggests possible differences in histopathologic lymphocytic reaction patterns, macrophages, and regulatory T cells in the tumor microenvironment by age at diagnosis.

Association of autophagy status with amount of Fusobacterium nucleatum in colorectal cancer.

Fusobacterium nucleatum (F. nucleatum), which has been associated with colorectal carcinogenesis, can impair anti-tumour immunity, and actively invade colon epithelial cells. Considering the critical role of autophagy in host defence against microorganisms, we hypothesised that autophagic activity of tumour cells might influence the amount of F. nucleatum in colorectal cancer tissue. Using 724 rectal and colon cancer cases within the Nurses' Health Study and the Health Professionals Follow-up Study, we evaluated autophagic activity of tumour cells by immunohistochemical analyses of BECN1 (beclin 1), MAP1LC3 (LC3), and SQSTM1 (p62) expression. We measured the amount of F. nucleatum DNA in tumour tissue by quantitative polymerase chain reaction (PCR). We conducted multivariable ordinal logistic regression analyses to examine the association of tumour BECN1, MAP1LC3, and SQSTM1 expression with the amount of F. nucleatum, adjusting for potential confounders, including microsatellite instability status; CpG island methylator phenotype; long-interspersed nucleotide element-1 methylation; and KRAS, BRAF, and PIK3CA mutations. Compared with BECN1-low cases, BECN1-intermediate and BECN1-high cases were associated with lower amounts of F. nucleatum with odds ratios (for a unit increase in three ordinal categories of the amount of F. nucleatum) of 0.54 (95% confidence interval, 0.29-0.99) and 0.31 (95% confidence interval, 0.16-0.60), respectively (Ptrend < 0.001 across ordinal BECN1 categories). Tumour MAP1LC3 and SQSTM1 levels were not significantly associated with the amount of F. nucleatum (Ptrend > 0.06). Tumour BECN1, MAP1LC3, and SQSTM1 levels were not significantly associated with patient survival (Ptrend > 0.10). In conclusion, tumour BECN1 expression is inversely associated with the amount of F. nucleatum in colorectal cancer tissue, suggesting a possible role of autophagy in the elimination of invasive microorganisms. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Targeting Glycolysis in Macrophages Confers Protection Against Pancreatic Ductal Adenocarcinoma.

Inflammation in the tumor microenvironment has been shown to promote disease progression in pancreatic ductal adenocarcinoma (PDAC); however, the role of macrophage metabolism in promoting inflammation is unclear. Using an orthotopic mouse model of PDAC, we demonstrate that macrophages from tumor-bearing mice exhibit elevated glycolysis. Macrophage-specific deletion of Glucose Transporter 1 (GLUT1) significantly reduced tumor burden, which was accompanied by increased Natural Killer and CD8+ T cell activity and suppression of the NLRP3-IL1ß inflammasome axis. Administration of mice with a GLUT1-specific inhibitor reduced tumor burden, comparable with gemcitabine, the current standard-of-care. In addition, we observe that intra-tumoral macrophages from human PDAC patients exhibit a pronounced glycolytic signature, which reliably predicts poor survival. Our data support a key role for macrophage metabolism in tumor immunity, which could be exploited to improve patient outcomes.

An integrated analysis of lymphocytic reaction, tumour molecular characteristics and patient survival in colorectal cancer.

BACKGROUND: Histological lymphocytic reaction is regarded as an independent prognostic marker in colorectal cancer. Considering the lack of adequate statistical power, adjustment for selection bias and comprehensive tumour molecular data in most previous studies, we investigated the strengths of the prognostic associations of lymphocytic reaction in colorectal carcinoma by utilising an integrative database of two prospective cohort studies. METHODS: We examined Crohn's-like reaction, intratumoural periglandular reaction, peritumoural reaction and tumour-infiltrating lymphocytes in 1465 colorectal carcinoma cases. Using covariate data of 4420 colorectal cancer cases in total, inverse probability-weighted Cox proportional hazard regression model was used to control for selection bias (due to tissue availability) and potential confounders, including stage, MSI status, LINE-1 methylation, PTGS2 and CTNNB1 expression, KRAS, BRAF and PIK3CA mutations, and tumour neoantigen load. RESULTS: Higher levels of each lymphocytic reaction component were associated with better colorectal cancer-specific survival (Ptrend < 0.002). Compared with cases with negative/low intratumoural periglandular reaction, multivariable-adjusted HRs were 0.55 (95% CI, 0.42-0.71) in cases with intermediate reaction and 0.20 (95% CI, 0.12-0.35) in cases with high reaction. These relationships were consistent in strata of MSI status or neoantigen loads (Pinteraction > 0.2). CONCLUSIONS: The four lymphocytic reaction components are prognostic biomarkers in colorectal carcinoma.

Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline.

Single-cell mass cytometry significantly increases the dimensionality of cytometry analysis as compared to fluorescence flow cytometry, providing unprecedented resolution of cellular diversity in tissues. However, analysis and interpretation of these high-dimensional data poses a significant technical challenge. Here, we present cytofkit, a new Bioconductor package, which integrates both state-of-the-art bioinformatics methods and in-house novel algorithms to offer a comprehensive toolset for mass cytometry data analysis. Cytofkit provides functions for data pre-processing, data visualization through linear or non-linear dimensionality reduction, automatic identification of cell subsets, and inference of the relatedness between cell subsets. This pipeline also provides a graphical user interface (GUI) for ease of use, as well as a shiny application (APP) for interactive visualization of cell subpopulations and progression profiles of key markers. Applied to a CD14-CD19- PBMCs dataset, cytofkit accurately identified different subsets of lymphocytes; applied to a human CD4+ T cell dataset, cytofkit uncovered multiple subtypes of TFH cells spanning blood and tonsils. Cytofkit is implemented in R, licensed under the Artistic license 2.0, and freely available from the Bioconductor website, https://bioconductor.org/packages/cytofkit/. Cytofkit is also applicable for flow cytometry data analysis.

Spatial omics techniques and data analysis for cancer immunotherapy applications.

In-depth profiling of cancer cells/tissues is expanding our understanding of the genomic, epigenomic, transcriptomic, and proteomic landscape of cancer. However, the complexity of the cancer microenvironment, particularly its immune regulation, has made it difficult to exploit the potential of cancer immunotherapy. High-throughput spatial omics technologies and analysis pipelines have emerged as powerful tools for tackling this challenge. As a result, a potential revolution in cancer diagnosis, prognosis, and treatment is on the horizon. In this review, we discuss the technological advances in spatial profiling of cancer around and beyond the central dogma to harness the full benefits of immunotherapy. We also discuss the promise and challenges of spatial data analysis and interpretation and provide an outlook for the future.

The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI.

Growing evidence supports the critical role of tumour microenvironment (TME) in tumour progression, metastases, and treatment response. However, the in-situ interplay among various TME components, particularly between immune and tumour cells, are largely unknown, hindering our understanding of how tumour progresses and responds to treatment. While mainstream single-cell omics techniques allow deep, single-cell phenotyping, they lack crucial spatial information for in-situ cell-cell interaction analysis. On the other hand, tissue-based approaches such as hematoxylin and eosin and chromogenic immunohistochemistry staining can preserve the spatial information of TME components but are limited by their low-content staining. High-content spatial profiling technologies, termed spatial omics, have greatly advanced in the past decades to overcome these limitations. These technologies continue to emerge to include more molecular features (RNAs and/or proteins) and to enhance spatial resolution, opening new opportunities for discovering novel biological knowledge, biomarkers, and therapeutic targets. These advancements also spur the need for novel computational methods to mine useful TME insights from the increasing data complexity confounded by high molecular features and spatial resolution. In this review, we present state-of-the-art spatial omics technologies, their applications, major strengths, and limitations as well as the role of artificial intelligence (AI) in TME studies.

Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST.

Spatially resolved transcriptomics involves a set of emerging technologies that enable the transcriptomic profiling of tissues with the physical location of expressions. Although a variety of methods have been developed for data integration, most of them are for single-cell RNA-seq datasets without consideration of spatial information. Thus, methods that can integrate spatial transcriptomics data from multiple tissue slides, possibly from multiple individuals, are needed. Here, we present PRECAST, a data integration method for multiple spatial transcriptomics datasets with complex batch effects and/or biological effects between slides. PRECAST unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, while requiring only partially shared cell/domain clusters across datasets. Using both simulated and four real datasets, we show improved cell/domain detection with outstanding visualization, and the estimated aligned embeddings and cell/domain labels facilitate many downstream analyses. We demonstrate that PRECAST is computationally scalable and applicable to spatial transcriptomics datasets from different platforms.

Application of Artificial Intelligence to In Vitro Tumor Modeling and Characterization of the Tumor Microenvironment.

In vitro tumor models have played vital roles in enhancing the understanding of the cellular and molecular composition of tumors, as well as their biochemical and biophysical characteristics. Advances in technology have enabled the evolution of tumor models from two-dimensional cell cultures to three-dimensional printed tumor models with increased levels of complexity and diverse output parameters. With the increase in complexity, the new generation of models is able to replicate the architecture and heterogeneity of the tumor microenvironment more realistically than their predecessors. In recent years, artificial intelligence (AI) has been used extensively in healthcare and research, and AI-based tools have also been applied to the precise development of tumor models. The incorporation of AI facilitates the use of high-throughput systems for real-time monitoring of tumorigenesis and biophysical tumor properties, raising the possibility of using AI alongside tumor modeling for personalized medicine. Here, the integration of AI tools within tumor modeling is reviewed, including microfluidic devices and cancer-on-chip models.

Immunohistochemical scoring of LAG-3 in conjunction with CD8 in the tumor microenvironment predicts response to immunotherapy in hepatocellular carcinoma.

Introduction: Immune checkpoint blockade (ICB) is a systemic therapeutic option for advanced hepatocellular carcinoma (HCC). However, low patient response rates necessitate the development of robust predictive biomarkers that identify individuals who will benefit from ICB. A 4-gene inflammatory signature, comprising CD8, PD-L1, LAG-3, and STAT1, was recently shown to be associated with a better overall response to ICB in various cancer types. Here, we examined whether tissue protein expression of CD8, PD-L1, LAG-3, and STAT1 predicts response to ICB in HCC. Methods: HCC samples from 191 Asian patients, comprising resection specimens from 124 patients (ICB-naïve) and pre-treatment specimens from 67 advanced HCC patients treated with ICB (ICB-treated), were analyzed for CD8, PD-L1, LAG-3, and STAT1 tissue expression using multiplex immunohistochemistry followed by statistical and survival analyses. Results: Immunohistochemical and survival analyses of ICB-naïve samples showed that high LAG-3 expression was associated with shorter median progression-free survival (mPFS) and overall survival (mOS). Analysis of ICB-treated samples revealed that high proportions of LAG-3+ and LAG-3+CD8+ cells pre-treatment were most closely associated with longer mPFS and mOS. Using a log-likelihood model, adding the total LAG-3+ cell proportion to the total CD8+ cell proportion significantly increased the predictive values for mPFS and mOS, compared with the total CD8+ cell proportion alone. Moreover, levels of CD8 and STAT1, but not PD-L1, were significantly correlated with better responses to ICB. After analyzing viral-related and non-viral HCC samples separately, only the LAG3+CD8+ cell proportion was significantly associated with responses to ICB regardless of viral status. Conclusion: Immunohistochemical scoring of pre-treatment levels of LAG-3 and CD8 in the tumor microenvironment may help predict ICB benefits in HCC patients. Furthermore, immunohistochemistry-based techniques offer the advantage of being readily translatable in the clinical setting.

Bayesian risk prediction model for colorectal cancer mortality through integration of clinicopathologic and genomic data.

Routine tumor-node-metastasis (TNM) staging of colorectal cancer is imperfect in predicting survival due to tumor pathobiological heterogeneity and imprecise assessment of tumor spread. We leveraged Bayesian additive regression trees (BART), a statistical learning technique, to comprehensively analyze patient-specific tumor characteristics for the improvement of prognostic prediction. Of 75 clinicopathologic, immune, microbial, and genomic variables in 815 stage II-III patients within two U.S.-wide prospective cohort studies, the BART risk model identified seven stable survival predictors. Risk stratifications (low risk, intermediate risk, and high risk) based on model-predicted survival were statistically significant (hazard ratios 0.19-0.45, vs. higher risk; P < 0.0001) and could be externally validated using The Cancer Genome Atlas (TCGA) data (P = 0.0004). BART demonstrated model flexibility, interpretability, and comparable or superior performance to other machine-learning models. Integrated bioinformatic analyses using BART with tumor-specific factors can robustly stratify colorectal cancer patients into prognostic groups and be readily applied to clinical oncology practice.

Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients.

Histopathologic assessment is indispensable for diagnosing colorectal cancer (CRC). However, manual evaluation of the diseased tissues under the microscope cannot reliably inform patient prognosis or genomic variations crucial for treatment selections. To address these challenges, we develop the Multi-omics Multi-cohort Assessment (MOMA) platform, an explainable machine learning approach, to systematically identify and interpret the relationship between patients' histologic patterns, multi-omics, and clinical profiles in three large patient cohorts (n = 1888). MOMA successfully predicts the overall survival, disease-free survival (log-rank test P-value<0.05), and copy number alterations of CRC patients. In addition, our approaches identify interpretable pathology patterns predictive of gene expression profiles, microsatellite instability status, and clinically actionable genetic alterations. We show that MOMA models are generalizable to multiple patient populations with different demographic compositions and pathology images collected from distinctive digitization methods. Our machine learning approaches provide clinically actionable predictions that could inform treatments for colorectal cancer patients.

Multimodal molecular landscape of response to Y90-resin microsphere radioembolization followed by nivolumab for advanced hepatocellular carcinoma.

BACKGROUND: Combination therapy with radioembolization (yttrium-90)-resin microspheres) followed by nivolumab has shown a promising response rate of 30.6% in a Phase II trial (CA209-678) for advanced hepatocellular carcinoma (HCC); however, the response mechanisms and relevant biomarkers remain unknown. METHODS: By collecting both pretreatment and on-treatment samples, we performed multimodal profiling of tissue and blood samples and investigated molecular changes associated with favorable responses in 33 patients from the trial. RESULTS: We found that higher tumor mutation burden, NCOR1 mutations and higher expression of interferon gamma pathways occurred more frequently in responders. Meanwhile, non-responders tended to be enriched for a novel Asian-specific transcriptomic subtype (Kaya_P2) with a high frequency of chromosome 16 deletions and upregulated cell cycle pathways. Strikingly, unlike other cancer types, we did not observe any association between T-cell populations and treatment response, but tumors from responders had a higher proportion of CXCL9+/CXCR3+ macrophages. Moreover, biomarkers discovered in previous immunotherapy trials were not predictive in the current cohort, suggesting a distinctive molecular landscape associated with differential responses to the combination therapy. CONCLUSIONS: This study unraveled extensive molecular changes underlying distinctive responses to the novel treatment and pinpointed new directions for harnessing combination therapy in patients with advanced HCC.

Spatially Resolved Single-Cell Assessment of Pancreatic Cancer Expression Subtypes Reveals Co-expressor Phenotypes and Extensive Intratumoral Heterogeneity.

Pancreatic ductal adenocarcinoma (PDAC) has been classified into classical and basal-like transcriptional subtypes by bulk RNA measurements. However, recent work has uncovered greater complexity to transcriptional subtypes than was initially appreciated using bulk RNA expression profiling. To provide a deeper understanding of PDAC subtypes, we developed a multiplex immunofluorescence (mIF) pipeline that quantifies protein expression of six PDAC subtype markers (CLDN18.2, TFF1, GATA6, KRT17, KRT5, and S100A2) and permits spatially resolved, single-cell interrogation of pancreatic tumors from resection specimens and core needle biopsies. Both primary and metastatic tumors displayed striking intratumoral subtype heterogeneity that was associated with patient outcomes, existed at the scale of individual glands, and was significantly reduced in patient-derived organoid cultures. Tumor cells co-expressing classical and basal markers were present in > 90% of tumors, existed on a basal-classical polarization continuum, and were enriched in tumors containing a greater admixture of basal and classical cell populations. Cell-cell neighbor analyses within tumor glands further suggested that co-expressor cells may represent an intermediate state between expression subtype poles. The extensive intratumoral heterogeneity identified through this clinically applicable mIF pipeline may inform prognosis and treatment selection for patients with PDAC. SIGNIFICANCE: A high-throughput pipeline using multiplex immunofluorescence in pancreatic cancer reveals striking expression subtype intratumoral heterogeneity with implications for therapy selection and identifies co-expressor cells that may serve as intermediates during subtype switching.