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IntroductionDeep learning (DL) models predicting biomarker expression in images of hematoxylin and eosin (H&E)-stained tissues can improve access to multi-marker immunophenotyping, crucial for therapeutic monitoring, biomarker discovery, and personalized treatment development. Conventionally, these models are trained on ground truth cell labels derived from IHC-stained tissue sections adjacent to H&E-stained ones, which might be less accurate than labels from the same section. Although many such DL models have been developed, the impact of ground truth cell label derivation methods on their performance has not been studied.MethodologyIn this study, we assess the impact of cell label derivation on H&E model performance, with CD3+ T-cells in lung cancer tissues as a proof-of-concept. We compare two Pix2Pix generative adversarial network (P2P-GAN)-based virtual staining models: one trained with cell labels obtained from the same tissue section as the H&E-stained section (the ‘same-section’ model) and one trained on cell labels from an adjacent tissue section (the ‘serial-section’ model).ResultsWe show that the same-section model exhibited significantly improved prediction performance compared to the ‘serial-section’ model. Furthermore, the same-section model outperformed the serial-section model in stratifying lung cancer patients within a public lung cancer cohort based on survival outcomes, demonstrating its potential clinical utility.DiscussionCollectively, our findings suggest that employing ground truth cell labels obtained through the same-section approach boosts immunophenotyping DL solutions.

Abstract 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

Abstract 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

Abstract 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.

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.

IntroductionImmune 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.MethodsHCC 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.ResultsImmunohistochemical 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.ConclusionImmunohistochemical 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.

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.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected half a billion people, including vulnerable populations such as cancer patients. While increasing evidence supports the persistence of SARS-CoV-2 months after a negative nasopharyngeal swab test, the effects on long-term immune memory and cancer treatment are unclear. In this report, we examined post-COVID-19 tissue-localized immune responses in a hepatocellular carcinoma (HCC) patient and a colorectal cancer (CRC) patient. Using spatial whole-transcriptomic analysis, we demonstrated spatial profiles consistent with a lymphocyte-associated SARS-CoV-2 response (based on two public COVID-19 gene sets) in the tumors and adjacent normal tissues, despite intra-tumor heterogeneity. The use of RNAscope and multiplex immunohistochemistry revealed that the spatial localization of B cells was significantly associated with lymphocyte-associated SARS-CoV-2 responses within the spatial transcriptomic (ST) niches showing the highest levels of virus. Furthermore, single-cell RNA sequencing data obtained from previous (CRC) or new (HCC) ex vivo stimulation experiments showed that patient-specific SARS-CoV-2 memory B cells were the main contributors to this positive association. Finally, we evaluated the spatial associations between SARS-CoV-2-induced immunological effects and immunotherapy-related anti-tumor immune responses. Immuno-predictive scores (IMPRES) revealed consistent positive spatial correlations between T cells/cytotoxic lymphocytes and the predicted immune checkpoint blockade (ICB) response, particularly in the HCC tissues. However, the positive spatial correlation between B cells and IMPRES score was restricted to the high-virus ST niche. In addition, tumor immune dysfunction and exclusion (TIDE) analysis revealed marked T cell dysfunction and inflammation, alongside low T cell exclusion and M2 tumor-associated macrophage infiltration. Our results provide in situ evidence of SARS-CoV-2-generated persistent immunological memory, which could not only provide tissue protection against reinfection but may also modulate the tumor microenvironment, favoring ICB responsiveness. As the number of cancer patients with COVID-19 comorbidity continues to rise, improved understanding of the long-term immune response induced by SARS-CoV-2 and its impact on cancer treatment is much needed.

The World Health Organization has defined long COVID-19 (LC) as a condition that occurs in individuals with a history of SARS-CoV-2 infection who exhibit persistent symptoms after its acute phase that last for at least two months and cannot be explained by an alternative diagnosis. Since we had previously reported residual viral antigens in tissues of convalescent patients, we aimed to assess the presence of such antigens in long COVID tissues. Here, we established the presence of the residual virus in the appendix, skin, and breast tissues of 2 patients who exhibited LC symptoms 163 and 426 days after symptom onset. With multiplex immunohistochemistry, we detected viral nucleocapsid protein in all three tissues. The nucleocapsid protein was further observed to colocalize with macrophage marker CD68, suggesting that immune cells were direct targets of SARS-CoV-2. Additionally, using RNAscope, the presence of viral RNA was also detected. Our positive finding in the breast tissue is corroborated by the recent reports of immunocompromised patients experiencing LC symptoms and persistent viral replication. Overall, our findings and emerging LC studies raise the possibility that the gastrointestinal tract may function as a reservoir for SARS-CoV-2.

Aging is associated with immune attrition that may impact the effectiveness of the immune system to protect the host from pathogens. Here the authors show that immune aging is associated with alterations in the Wnt/β-catenin signaling and reduced stem cell memory T lymphocytes, hinting the Wnt/β-catenin pathway as a potential therapy target.

BackgroundThe relationships between tumor stromal features (such as desmoplastic reaction, myxoid stroma, and keloid-like collagen bundles) and immune cells in the colorectal carcinoma microenvironment have not yet been fully characterized.MethodsIn 908 tumors with available tissue among 4,465 incident colorectal adenocarcinoma cases in two prospective cohort studies, we examined desmoplastic reaction, myxoid stroma, and keloid-like collagen bundles. We conducted multiplex immunofluorescence for T cells [CD3, CD4, CD8, CD45RO (PTPRC), and FOXP3] and for macrophages [CD68, CD86, IRF5, MAF, and MRC1 (CD206)]. We used the inverse probability weighting method and the 4,465 incident cancer cases to adjust for selection bias.ResultsImmature desmoplastic reaction was associated with lower densities of intraepithelial CD3+CD8+CD45RO+ cells [multivariable odds ratio (OR) for the highest (vs. lowest) density category, 0.43; 95% confidence interval (CI), 0.29–0.62; Ptrend

Summary: Clinically important broadly reactive B cells evolve during multiple infections, with B cells re-activated after secondary infection differing from B cells activated after a primary infection. Here we studied CD27highCD38high plasmablasts from patients with a primary or secondary dengue virus infection. Three transcriptionally and functionally distinct clusters were identified. The largest cluster 0/1 was plasma cell-related, with cells coding for serotype cross-reactive antibodies of the IgG1 isotype, consistent with memory B cell activation during an extrafollicular response. Cells in clusters 2 and 3 expressed low levels of antibody genes and high levels of genes associated with oxidative phosphorylation, EIF2 pathway, and mitochondrial dysfunction. Clusters 2 and 3 showed a transcriptional footprint of T cell help, in line with activation from naive B cells or memory B cells. Our results contribute to the understanding of the parallel B cell activation events that occur in humans after natural primary and secondary infection.

Background Myeloid cells represent an abundant yet heterogeneous cell population in the colorectal cancer microenvironment, and their roles remain poorly understood.Methods We used multiplexed immunofluorescence combined with digital image analysis to identify CD14+ monocytic and CD15+ granulocytic cells and to evaluate their maturity (HLA-DR and CD33), immunosuppressive potential (ARG1) and proximity to cytokeratin (KRT)-positive tumor cells in 913 colorectal carcinomas. Using covariate data of 4465 incident colorectal cancers in two prospective cohort studies, the inverse probability weighting method was used with multivariable-adjusted Cox proportional hazards models to assess cancer-specific mortality according to ordinal quartiles (Q1–Q4) of myeloid cell densities. Immune cell–tumor cell proximity was measured with the nearest neighbor method and the G-cross function, which determines the likelihood of any tumor cell having at least one immune cell of the specified type within a certain radius.Results Higher intraepithelial (Ptrend=0.0002; HR for Q4 (vs Q1), 0.48, 95% CI 0.31 to 0.76) and stromal (Ptrend

Immunotherapy targeting the CD274 (PD-L1)/PDCD1 (PD-1) immune checkpoint axis has emerged as a promising treatment strategy for various cancers. Experimental evidence suggests that phosphatidylinositol-4,5-bisphosphonate 3-kinase (PI3K) signaling may upregulate CD274 expression. Thus, we hypothesized that PIK3CA mutation, PTEN loss, or their combined status might be associated with CD274 overexpression in colorectal carcinoma. We assessed tumor CD274 and PTEN expression by immunohistochemistry and assessed PIK3CA mutation by pyrosequencing in 753 patients among 4,465 incident rectal and colon cancer cases that had occurred in two U.S.-wide prospective cohort studies. To adjust for potential confounders and selection bias due to tissue availability, inverse probability weighted multivariable ordinal logistic regression analyses used the 4,465 cases and tumoral data including microsatellite instability, CpG island methylator phenotype, KRAS and BRAF mutations. PIK3CA mutation and loss of PTEN expression were detected in 111 of 753 cases (15%) and 342 of 585 cases (58%), respectively. Tumor CD274 expression was negative in 306 (41%), low in 195 (26%), and high in 252 (33%) of 753 cases. PTEN loss was associated with CD274 overexpression [multivariable odds ratio (OR) 1.83; 95% confidence interval (CI), 1.22–2.75; P = .004]. PIK3CA mutation was statistically-insignificantly (P = .036 with the stringent alpha level of 0.005) associated with CD274 overexpression (multivariable OR, 1.54; 95% CI, 1.03–2.31). PIK3CA-mutated PTEN-lost tumors (n = 33) showed higher prevalence of CD274-positivity (82%) than PIK3CA-wild-type PTEN-lost tumors (n = 204; 70% CD274-positivity) and PTEN-expressed tumors (n = 147; 50% CD274-positivity) (P = .003). Our findings support the role of PI3K signaling in the CD274/PDCD1 pathway.

Background: Tumour budding and poorly differentiated clusters (PDC) represent forms of tumour invasion. We hypothesised that T-cell densities (reflecting adaptive anti-tumour immunity) might be inversely associated with tumour budding and PDC in colorectal carcinoma. Methods: Utilising 915 colon and rectal carcinomas in two U.S.-wide prospective cohort studies, and multiplex immunofluorescence combined with machine learning algorithms, we assessed CD3, CD4, CD8, CD45RO (PTPRC), and FOXP3 co-expression patterns in lymphocytes. Tumour budding and PDC at invasive fronts were quantified by digital pathology and image analysis using the International tumour Budding Consensus Conference criteria. Using covariate data of 4,420 incident colorectal cancer cases, inverse probability weighting (IPW) was integrated with multivariable logistic regression analysis that assessed the association of T-cell subset densities with tumour budding and PDC while adjusting for selection bias due to tissue availability and potential confounders, including microsatellite instability status. Findings: Tumour budding counts were inversely associated with density of CD3+CD8+ [lowest vs. highest: multivariable odds ratio (OR), 0.50; 95% confidence interval (CI), 0.35–0.70; Ptrend < 0.001] and CD3+CD8+CD45RO+ cells (lowest vs. highest: multivariable OR, 0.44; 95% CI, 0.31–0.63; Ptrend < 0.001) in tumour epithelial region. Tumour budding levels were associated with higher colorectal cancer-specific mortality (multivariable hazard ratio, 2.13; 95% CI, 1.57–2.89; Ptrend < 0.001) in Cox regression analysis. There were no significant associations of PDC with T-cell subsets. Interpretation: Tumour epithelial naïve and memory cytotoxic T cell densities are inversely associated with tumour budding at invasive fronts, suggesting that cytotoxic anti-tumour immunity suppresses tumour microinvasion.

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.

Introduction: Deep learning (DL) models predicting biomarker expression in images of hematoxylin and eosin (H&E)-stained tissues can improve access to multi-marker immunophenotyping, crucial for therapeutic monitoring, biomarker discovery, and personalized treatment development. Conventionally, these models are trained on ground truth cell labels derived from IHC-stained tissue sections adjacent to H&Estained ones, which might be less accurate than labels from the same section. Although many such DL models have been developed, the impact of ground truth cell label derivation methods on their performance has not been studied. Methodology: In this study, we assess the impact of cell label derivation on H&E model performance, with CD3+ T-cells in lung cancer tissues as a proof-ofconcept. We compare two Pix2Pix generative adversarial network (P2P-GAN)- based virtual staining models: one trained with cell labels obtained from the same tissue section as the H&E-stained section (the 'same-section' model) and one trained on cell labels from an adjacent tissue section (the 'serial-section' model). Results: We show that the same-section model exhibited significantly improved prediction performance compared to the 'serial-section' model. Furthermore, the same-section model outperformed the serial-section model in stratifying lung cancer patients within a public lung cancer cohort based on survival outcomes, demonstrating its potential clinical utility. Discussion: Collectively, our findings suggest that employing ground truth cell labels obtained through the same-section approach boosts immunophenotyping DL solutions. [ABSTRACT FROM AUTHOR]