Immuno-profiling and cellular spatial analysis using five immune oncology multiplex immunofluorescence panels for paraffin tumor tissue

Spatial Dissection of the Immune Landscape of Solid Tumors to Advance Precision Medicine

Chemotherapeutics, radiation, targeted therapeutics, and immunotherapeutics each demonstrate clinical benefits for a small subset of patients with solid malignancies. Immune cells infiltrating the tumor and the surrounding stroma play a critical role in shaping cancer progression and modulating therapy response. They do this by interacting with the other cellular and molecular components of the tumor microenvironment. Spatial multi-omics technologies are rapidly evolving. Currently, such technologies allow high-throughput RNA and protein profiling and retain geographical information about the tumor microenvironment cellular architecture and the functional phenotype of tumor, immune, and stromal cells. An in-depth spatial characterization of the heterogeneous tumor immune landscape can improve not only the prognosis but also the prediction of therapy response, directing cancer patients to more tailored and efficacious treatments. This review highlights recent advancements in spatial transcriptomics and proteomics profiling technologies and the ways these technologies are being applied for the dissection of the immune cell composition in solid malignancies in order to further both basic research in oncology and the implementation of precision treatments in the clinic.

Optimizing immunofluorescence with high-dynamic-range imaging to enhance PD-L1 expression evaluation for 3D pathology assessment from NSCLC tumor tissue

Assessing programmed death ligand 1 (PD-L1) expression through immunohistochemistry (IHC) is the golden standard in predicting immunotherapy response of non-small cell lung cancer (NSCLC). However, observation of heterogeneous PD-L1 distribution in tumor space is a challenge using IHC only. Meanwhile, immunofluorescence (IF) could support both planar and three-dimensional (3D) histological analyses by combining tissue optical clearing with confocal microscopy. We optimized clinical tissue preparation for the IF assay focusing on staining, imaging, and post-processing to achieve quality identical to traditional IHC assay. To overcome limited dynamic range of the fluorescence microscope's detection system, we incorporated a high dynamic range (HDR) algorithm to restore the post imaging IF expression pattern and further 3D IF images. Following HDR processing, a noticeable improvement in the accuracy of diagnosis (85.7%) was achieved using IF images by pathologists. Moreover, 3D IF images revealed a 25% change in tumor proportion score for PD-L1 expression at various depths within tumors. We have established an optimal and reproducible process for PD-L1 IF images in NSCLC, yielding high quality data comparable to traditional IHC assays. The ability to discern accurate spatial PD-L1 distribution through 3D pathology analysis could provide more precise evaluation and prediction for immunotherapy targeting advanced NSCLC.

Deciphering the tumor immune microenvironment from a multidimensional omics perspective: insight into next-generation CAR-T cell immunotherapy and beyond

Tumor immune microenvironment (TIME) consists of intra-tumor immunological components and plays a significant role in tumor initiation, progression, metastasis, and response to therapy. Chimeric antigen receptor (CAR)-T cell immunotherapy has revolutionized the cancer treatment paradigm. Although CAR-T cell immunotherapy has emerged as a successful treatment for hematologic malignancies, it remains a conundrum for solid tumors. The heterogeneity of TIME is responsible for poor outcomes in CAR-T cell immunotherapy against solid tumors. The advancement of highly sophisticated technology enhances our exploration in TIME from a multi-omics perspective. In the era of machine learning, multi-omics studies could reveal the characteristics of TIME and its immune resistance mechanism. Therefore, the clinical efficacy of CAR-T cell immunotherapy in solid tumors could be further improved with strategies that target unfavorable conditions in TIME. Herein, this review seeks to investigate the factors influencing TIME formation and propose strategies for improving the effectiveness of CAR-T cell immunotherapy through a multi-omics perspective, with the ultimate goal of developing personalized therapeutic approaches.

Immune profiling of mouse lung adenocarcinoma paraffin tissues using multiplex immunofluorescence panel: a pilot study

BACKGROUND

Immune profiling has become an important tool for identifying predictive, prognostic and response biomarkers for immune checkpoint inhibitors from tumor microenvironment (TME). We aimed to build a multiplex immunofluorescence (mIF) panel to apply to formalin-fixed and paraffin-embedded tissues in mice tumors and to explore the programmed cell death protein 1/ programmed cell death 1 ligand 1 (PD-1/PD-L1) axis.

RESULTS

An automated eight-color mIF panel was evaluated to study the TME using seven antibodies, including cytokeratin 19, CD3e, CD8a, CD4, PD-1, PD-L1, F4-80 and DAPI, then was applied in six mice lung adenocarcinoma samples. Cell phenotypes were quantified by software to explore the co-localization and spatial distribution between immune cells within the TME. This mice panel was successfully optimized and applied to a small cohort of mice lung adenocarcinoma cases. Image analysis showed a sparse degree of immune cell expression pattern in this cohort. From the spatial analysis we found that T cells and macrophages expressing PD-L1 were close to the malignant cells and other immune cells.

CONCLUSIONS

Comprehensive immune profiling using mIF in translational studies improves our ability to correlate the PD-1/PD-L1 axis and spatial distribution of lymphocytes and macrophages in mouse lung cancer cells to provide new cues for immunotherapy, that can be translated to human tumors for cancer intervention.

Multi-omics Analysis Reveals Immune Features Associated with Immunotherapy Benefit in Patients with Squamous Cell Lung Cancer from Phase III Lung-MAP S1400I Trial

PURPOSE

Identifying molecular and immune features to guide immune checkpoint inhibitor (ICI)-based regimens remains an unmet clinical need.

EXPERIMENTAL DESIGN

Tissue and longitudinal blood specimens from phase III trial S1400I in patients with metastatic squamous non-small cell carcinoma (SqNSCLC) treated with nivolumab monotherapy (nivo) or nivolumab plus ipilimumab (nivo+ipi) were subjected to multi-omics analyses including multiplex immunofluorescence (mIF), nCounter PanCancer Immune Profiling Panel, whole-exome sequencing, and Olink.

RESULTS

Higher immune scores from immune gene expression profiling or immune cell infiltration by mIF were associated with response to ICIs and improved survival, except regulatory T cells, which were associated with worse overall survival (OS) for patients receiving nivo+ipi. Immune cell density and closer proximity of CD8+GZB+ T cells to malignant cells were associated with superior progression-free survival and OS. The cold immune landscape of NSCLC was associated with a higher level of chromosomal copy-number variation (CNV) burden. Patients with LRP1B-mutant tumors had a shorter survival than patients with LRP1B-wild-type tumors. Olink assays revealed soluble proteins such as LAMP3 increased in responders while IL6 and CXCL13 increased in nonresponders. Upregulation of serum CXCL13, MMP12, CSF-1, and IL8 were associated with worse survival before radiologic progression.

CONCLUSIONS

The frequency, distribution, and clustering of immune cells relative to malignant ones can impact ICI efficacy in patients with SqNSCLC. High CNV burden may contribute to the cold immune microenvironment. Soluble inflammation/immune-related proteins in the blood have the potential to monitor therapeutic benefit from ICI treatment in patients with SqNSCLC.

A randomized, non-comparative phase 2 study of neoadjuvant immune-checkpoint blockade in retroperitoneal dedifferentiated liposarcoma and extremity/truncal undifferentiated pleomorphic sarcoma

Based on the demonstrated clinical activity of immune-checkpoint blockade (ICB) in advanced dedifferentiated liposarcoma (DDLPS) and undifferentiated pleomorphic sarcoma (UPS), we conducted a randomized, non-comparative phase 2 trial ( NCT03307616 ) of neoadjuvant nivolumab or nivolumab/ipilimumab in patients with resectable retroperitoneal DDLPS (n = 17) and extremity/truncal UPS (+ concurrent nivolumab/radiation therapy; n = 10). The primary end point of pathologic response (percent hyalinization) was a median of 8.8% in DDLPS and 89% in UPS. Secondary end points were the changes in immune infiltrate, radiographic response, 12- and 24-month relapse-free survival and overall survival. Lower densities of regulatory T cells before treatment were associated with a major pathologic response (hyalinization > 30%). Tumor infiltration by B cells was increased following neoadjuvant treatment and was associated with overall survival in DDLPS. B cell infiltration was associated with higher densities of regulatory T cells before treatment, which was lost upon ICB treatment. Our data demonstrate that neoadjuvant ICB is associated with complex immune changes within the tumor microenvironment in DDLPS and UPS and that neoadjuvant ICB with concurrent radiotherapy has significant efficacy in UPS.

Stem-like CD8 T cells in stage I lung adenocarcinoma as a prognostic biomarker: A preliminary study

OBJECTIVES

This study aimed to investigate the presence of stem-like CD8 T (CD8 TSL) cells in lung adenocarcinoma (LUAD) and explore their relationships with the clinical outcomes.

METHODS

Multiplex immunofluorescence (mIF) was performed to identify CD8 TSL and antigen-presenting cells (APC) in 76 LUAD patients. Differences in the number of CD8 TSL cells based on tumor stage and the spatial relationships between CD8 TSL cells and APC niches were determined. The optimal cutoff value of CD8 TSL cells for predicting survival in patients with stage I LUAD was calculated.

RESULTS

CD8 TSL cells were present in all tumors, and their numbers were significantly higher in stage I patients than in stage III patients (P = 0.010); CD8 TSL cells located in the APC niches accounted for 69.7% (53/76) of the hotspot fields. The optimal cutoff value for the number of CD8 TSL cells required to predict the overall survival (OS) in patients with stage I LUAD was 2.5 per 10000 μm2. The median OS and progression-free survival (PFS) in the high-level group (>2.5) were significantly (P < 0.001) longer than those in the low-level group (≤2.5). The number of CD8 TSL cells was an independent prognostic factor for stage I LUAD. Patients with more CD8 TSL cells had a lower risk of death and disease progression than those with less CD8 TSL cells.

CONCLUSION

CD8 TSL cells were observed in patients with stages I-III LUAD and might serve as prognostic biomarkers for stage I LUAD.

Remodeling the tumor-immune microenvironment by anti-CTLA4 blockade enhanced subsequent anti-PD-1 efficacy in advanced nasopharyngeal carcinoma

Sequential immunotherapy has shown certain advantages in malignancy. Here, we aim to evaluate the efficacy of sequential anti-CTLA-4 and anti-PD-1 treatment for recurrent or metastatic nasopharyngeal carcinoma patients (R/M NPC). We retrospectively analysis 2 phase I trial of ipilimumab and camrelizumab in Chinese R/M NPC patients. These patients were initially treated with ipilimumab, a CTLA4 blockade, followed by anti-PD-1 treatment. We observed a durable tumor remission in these patients (mPFS: 12.3 months; mDoR: 20.9 months). Multimodal investigations of biopsy samples disclosed remodeling of tumor-immune microenvironment triggered by ipilimumab. In responders, we found increased tumoral PD-L1/PD-L2 expression and T-cell infiltration after ipilimumab treatment, accompanied by reduced stroma and malignant cell components. In contrast, non-responders exhibited increased B-cell infiltration and increased peripheral CD19 + B cells, suggesting a defective transition from memory B cells to plasma cells. This study proposes that sequential therapy can potentially enhance treatment efficacy in chemotherapy-resistant NPC patients and provides insights into how preexisting anti-CTLA4 blockade can influence subsequent anti-PD-1 efficacy by remodeling the TME. Additionally, our results highlight the need for therapeutic strategies targeting naïve/memory B cells.

The Immunological Landscape of M1 and M2 Macrophages and Their Spatial Distribution in Patients with Malignant Pleural Mesothelioma

BACKGROUND

Several tumor-associated macrophages (TAMs) have shown promise as prognosticators in cancer. Our aim was to validate the importance of TAMs in malignant pleural mesothelioma (MPM) using a two-stage design.

METHODS

We explored The Cancer Genome Atlas (TCGA-MESO) to select immune-relevant macrophage genes in MPM, including M1/M2 markers, as a discovery cohort. This computational cohort was used to create a multiplex immunofluorescence panel. Moreover, a cohort of 68 samples of MPM in paraffin blocks was used to validate the macrophage phenotypes and the co-localization and spatial distribution of these immune cells within the TME and the stromal or tumor compartments.

RESULTS

The discovery cohort revealed six immune-relevant macrophage genes (CD68, CD86, CD163, CD206, ARG1, CD274), and complementary genes were differentially expressed by M1 and M2 phenotypes with distinct roles in the tumor microenvironment and were associated with the prognosis. In addition, immune-suppressed MPMs with increased enrichment of CD68, CD86, and CD163 genes and high densities of M2 macrophages expressing CD163 and CD206 proteins were associated with worse overall survival (OS). Interestingly, below-median distances from malignant cells to specific M2a and M2c macrophages were associated with worse OS, suggesting an M2 macrophage-driven suppressive component in these tumors.

CONCLUSIONS

The interactions between TAMs in situ and, particularly, CD206+ macrophages are highly relevant to patient outcomes. High-resolution technology is important for identifying the roles of macrophage populations in tissue specimens and identifying potential therapeutic candidates in MPM.

New Insights into Macrophage Polarization and its Prognostic Role in Patients with Colorectal Cancer Liver Metastasis

Background

As liver metastasis is the most common cause of mortality in patients with colorectal cancer, studying colorectal cancer liver metastasis (CLM) microenvironment is essential for improved understanding of tumor biology and to identify novel therapeutic targets.

Methods

We used multiplex immunofluorescence platform to study tumor associated macrophage (TAM) polarization and adaptive T cell subtypes in tumor samples from 105 CLM patients (49 without and 56 with preoperative chemotherapy).

Results

CLM exhibited M2 macrophage polarization, and helper T cells were the prevalent adaptive T cell subtype. The density of total, M2 and TGFβ-expressing macrophages, and regulatory T cells was lower in CLM treated with preoperative chemotherapy. CLM with right-sided primary demonstrated enrichment of TGFβ-expressing macrophages, and with left-sided primary had higher densities of helper and cytotoxic T cells. In multivariate analysis, high density of M2 macrophages correlated with longer recurrence-free survival (RFS) in the entire cohort [hazard ratio (HR) 0.425, 95% CI 0.219-0.825, p=0.011) and in patients without preoperative chemotherapy (HR 0.45, 95% CI 0.221-0.932, p=0.032). High pSMAD3-expressing macrophages were associated with shorter RFS in CLM after preoperative chemotherapy.

Conclusions

Our results highlight the significance of a multi-marker approach to define the macrophage subtypes and identify M2 macrophages as a predictor of favorable prognosis in CLM.

Exploratory study of macrophage polarization and spatial distribution in colorectal cancer liver metastasis: a pilot study

Background

The liver is the most typical site of metastatic disease for patients with colorectal cancer (CRC), and up to half the patients with CRC will develop colorectal liver metastasis (CLM). Studying the tumor microenvironment, particularly macrophages and their spatial distribution, can give us critical insight into treatment.

Methods

Ten CLMs (five treatment-naïve and five post-neoadjuvant chemotherapy) were stained with multiplex immunofluorescence panels against cytokeratins, CD68, Arg1, CD206, CD86, CD163, PD-L1, and MRP8-14. Densities of cell phenotypes and their spatial distribution in the tumor center and the normal liver-tumor interface were correlated with clinicopathological variables.

Results

M2 macrophages were the predominant subtype in both the tumor center and the periphery, with a relatively higher density at the periphery. The larger tumors, more than 3.9 cm, were associated with higher densities of total CD68+ macrophages and CD68+CD163+ CD206neg and CD68+CD206+ CD163neg M2 macrophage subtypes. Total macrophages in the tumor periphery demonstrated significantly greater proximity to malignant cells than did those in the tumor center (p=0.0371). The presence of higher than median CD68+MRP8-14+CD86neg M1 macrophages in the tumor center was associated with poor overall survival (median 2.34 years) compared to cases with lower than median M1 macrophages at the tumor center (median 6.41 years) in univariate analysis.

Conclusion

The dominant polarization of the M2 macrophage subtype could drive new therapeutic approaches in CLM patients.

Personal immune profiles: Diversity and prognostic value for oral tongue squamous cell carcinoma evaluated by comprehensive immune parameter analyses with multiplex immunofluorescence

OBJECTIVES

Understanding the tumor immune microenvironment is becoming increasingly necessary for risk prediction and treatment selection. In particular, oral cancer has various immunosuppressive characteristics in the tumor microenvironment. Therefore, we comprehensively assessed the immune profiles of oral tongue squamous cell carcinoma (OTSCC).

MATERIALS AND METHODS

Multiplex immunofluorescence and tissue imaging analyses were performed to evaluate immune profiles at the invasive tumor front of 60 OTSCC surgical specimens. We analyzed 58 immune parameters including the density and proportion (%) of total leukocytes (Leu) and T cells, six subsets of T and myeloid cells, and the expression of programmed cell death-1 (PD-1) and PD-1 ligand 1 (PD-L1).

RESULTS

The density, proportion, and location of CD45⁺ Leu, three T cell subsets (CD8⁺, Foxp3^-CD4⁺ conventional, and Foxp3⁺CD4⁺ regulatory T cells), CD163^-CD68⁺ M1 and CD163⁺CD68⁺ M2 macrophages, and neutrophils were highly variable at the individual level. The density and proportion of M2 macrophages were significantly lower in the T1 stage group. Risk prediction analyses for recurrence and/or metastasis (R/M) showed that R/M (+) T1 cases had significantly higher M2 density and percentages.

CONCLUSIONS

The immune profiles of OTSCC patients are diverse and cannot be predicted from clinicopathological information alone. The M2 macrophage abundance is a potential candidate biomarker for R/M in the early stage of OTSCC. Personal immune profiling may provide beneficial information for risk prediction and treatment selection.

Microbiome alteration via fecal microbiota transplantation is effective for refractory immune checkpoint inhibitor-induced colitis

Immune checkpoint inhibitors (ICIs) target advanced malignancies with high efficacy but also predispose patients to immune-related adverse events like immune-mediated colitis (IMC). Given the association between gut bacteria with response to ICI therapy and subsequent IMC, fecal microbiota transplantation (FMT) represents a feasible way to manipulate microbial composition in patients, with a potential benefit for IMC. Here, we present a large case series of 12 patients with refractory IMC who underwent FMT from healthy donors as salvage therapy. All 12 patients had grade 3 or 4 ICI-related diarrhea or colitis that failed to respond to standard first-line (corticosteroids) and second-line immunosuppression (infliximab or vedolizumab). Ten patients (83%) achieved symptom improvement after FMT, and three patients (25%) required repeat FMT, two of whom had no subsequent response. At the end of the study, 92% achieved IMC clinical remission. 16S rRNA sequencing of patient stool samples revealed that compositional differences between FMT donors and patients with IMC before FMT were associated with a complete response after FMT. Comparison of pre- and post-FMT stool samples in patients with complete responses showed significant increases in alpha diversity and increases in the abundances of Collinsella and Bifidobacterium, which were depleted in FMT responders before FMT. Histologically evaluable complete response patients also had decreases in select immune cells , including CD8+ T cells, in the colon after FMT when compared with non-complete response patients (n = 4). This study validates FMT as an effective treatment strategy for IMC and gives insights into the microbial signatures that may play a critical role in FMT response.

Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition

Generating realistic virtual patients from a limited amount of patient data is one of the major challenges for quantitative systems pharmacology modeling in immuno-oncology. Quantitative systems pharmacology (QSP) is a mathematical modeling methodology that integrates mechanistic knowledge of biological systems to investigate dynamics in a whole system during disease progression and drug treatment. In the present analysis, we parameterized our previously published QSP model of the cancer-immunity cycle to non-small cell lung cancer (NSCLC) and generated a virtual patient cohort to predict clinical response to PD-L1 inhibition in NSCLC. The virtual patient generation was guided by immunogenomic data from iAtlas portal and population pharmacokinetic data of durvalumab, a PD-L1 inhibitor. With virtual patients generated following the immunogenomic data distribution, our model predicted a response rate of 18.6% (95% bootstrap confidence interval: 13.3-24.2%) and identified CD8/Treg ratio as a potential predictive biomarker in addition to PD-L1 expression and tumor mutational burden. We demonstrated that omics data served as a reliable resource for virtual patient generation techniques in immuno-oncology using QSP models.

Phase I trial of KN046, a novel bispecific antibody targeting PD-L1 and CTLA-4 in patients with advanced solid tumors

BACKGROUND

KN046 is a novel bispecific antibody targeting programmed death ligand 1 (PD-L1) and cytotoxic T lymphocyte-associated protein 4 (CTLA-4). This multicenter phase I trial investigated the safety, tolerability, pharmacokinetics (PK), and efficacy of KN046 in patients with advanced solid tumors.

METHODS

Patients who failed standard treatment were included. KN046 was administered at doses of 1, 3, and 5 mg/kg every 2 weeks (Q2W), 5 mg/kg every 3 weeks (Q3W), and 300 mg Q3W based on the modified toxicity probability interval method in the dose-escalation phase; the recommended dose was used in the expansion phase. Primary objectives were maximum tolerated dose (MTD) and recommended phase II dose (RP2D) in escalation and preliminary efficacy in expansion. Secondary objectives included PK, pharmacodynamics, safety, and tolerability of KN046. We also explored biomarkers based on PD-L1 expression, multiplex immunofluorescence (mIF) staining, and RNAseq-derived nCounter platform.

RESULTS

Totally, 100 eligible patients were enrolled, including 59 with nasopharyngeal carcinoma (NPC), 36 with epidermal growth factor receptor (EGFR)-mutated non-small-cell lung cancer (NSCLC), and those with other advanced solid tumors. The most common treatment-related adverse events (TRAEs) were rash (33.0%), pruritus (31.0%), and fatigue (20.0%). Grade ≥3 TRAEs were observed in 14.0% of participants. No dose-limiting toxicity occurred in the dose-escalation phase, and the MTD was not reached. The RP2D was determined as 5 mg/kg Q2W according to the pharmacokinetic-pharmacodynamic model, the preliminary exposure-response analysis, and the overall safety profile. Among 88 efficacy-evaluable participants, the objective response rate (ORR) was 12.5%, and the median duration of response was 16.6 months. In the NPC subgroup, the ORR was 15.4%, and the median overall survival (OS) was 24.7 (95% CI 16.3 to not estimable) months. In the EGFR-mutant NSCLC subgroup, the ORR was 6.3%. mIF analysis results showed patients with high CD8 expression showed longer median OS (27.1 vs 9.2 months, p=0.02); better prognosis was observed in patients with high CD8 and PD-L1 expression.

CONCLUSIONS

KN046 was well tolerated and showed promising antitumor efficacy in advanced solid tumors, especially in patients with NPC. The combination of both CD8 and PD-L1 expression improved the prediction of KN046 response.

TRIAL REGISTRATION NUMBERS

NCT03733951 .

High density and proximity of CD8+ T cells to tumor cells are correlated with better response to nivolumab treatment in metastatic pleural mesothelioma

BACKGROUND

The efficacy of immune checkpoint inhibitors (ICIs) in pleural mesothelioma has recently been established. The response to ICIs can be predicted by quantitative analysis of cells and their spatial distribution in the tumor microenvironment (TME). However, the detailed composition of the TME in pleural mesothelioma has not been reported. We evaluated the association between the TME and response to ICIs in this cancer.

METHODS

A retrospective analysis of 22 pleural mesothelioma patients treated with nivolumab in different centers was performed using surgical specimens. Four patients had a partial response to nivolumab (response group) and 18 patients had stable or progressive disease (nonresponse group). The number of CD4, CD8, FoxP3, CK, and PD-L1 positive cells, cell density, and cell-to-cell distance were analyzed by multiplex immunofluorescence.

RESULTS

PD-L1 expression did not differ significantly between the response and nonresponse groups. The density of total T cells and of CD8⁺ T cells was significantly higher in the response than in the nonresponse group. CD8⁺ T cells were more clustered and located closer to tumor cells, whereas regulatory T cells were located further from tumor cells in the response than in the nonresponse group.

CONCLUSIONS

High density and spatial proximity of CD8⁺ T cells to tumor cells were associated with better response to nivolumab, whereas the proximity of regulatory T cells to tumor cells was associated with worse response, suggesting that the distinct landscape of the TME could be a potential predictor of ICI efficacy in pleural mesothelioma.

Cellular and Molecular Mechanisms of Liver Fibrosis in Patients with NAFLD

The expression of immune- and cancer-related genes was measured in liver biopsies from 107 NAFLD patients. The strongest difference in overall gene expression was between liver fibrosis stages F3 and F4, with 162 cirrhosis-associated genes identified. Strong correlations with fibrosis progression from F1 to F4 were observed for 91 genes, including CCL21, CCL2, CXCL6, and CCL19. In addition, the expression of 21 genes was associated with fast progression to F3/F4 in an independent group of eight NAFLD patients. These included the four chemokines, SPP1, HAMP, CXCL2, and IL-8. A six-gene signature including SOX9, THY-1, and CD3D had the highest performance detecting the progressors among F1/F2 NAFLD patients. We also characterized immune cell changes using multiplex immunofluorescence platforms. Fibrotic areas were strongly enriched in CD3+ T cells compared to CD68+ macrophages. While the number of CD68+ macrophages increased with fibrosis severity, the increase in CD3+ T-cell density was more substantial and progressive from F1 to F4. The strongest correlation with fibrosis progression was observed for CD3+CD45R0+ memory T cells, while the most significant increase in density between F1/F2 and F3/F4 was for CD3+CD45RO+FOXP3+CD8- and CD3+CD45RO-FOXP3+CD8- regulatory T cells. A specific increase in the density of CD68+CD11b+ Kupffer cells with liver fibrosis progression was also observed.

Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition

Generating realistic virtual patients from a limited amount of patient data is one of the major challenges for quantitative systems pharmacology modeling in immuno-oncology. Quantitative systems pharmacology (QSP) is a mathematical modeling methodology that integrates mechanistic knowledge of biological systems to investigate dynamics in a whole system during disease progression and drug treatment. In the present analysis, we parameterized our previously published QSP model of the cancer-immunity cycle to non-small cell lung cancer (NSCLC) and generated a virtual patient cohort to predict clinical response to PD-L1 inhibition in NSCLC. The virtual patient generation was guided by immunogenomic data from iAtlas portal and population pharmacokinetic data of durvalumab, a PD-L1 inhibitor. With virtual patients generated following the immunogenomic data distribution, our model predicted a response rate of 18.6% (95% bootstrap confidence interval: 13.3-24.2%) and identified CD8/Treg ratio as a potential predictive biomarker in addition to PD-L1 expression and tumor mutational burden. We demonstrated that omics data served as a reliable resource for virtual patient generation techniques in immuno-oncology using QSP models.

Immune cellular patterns of distribution affect outcomes of patients with non-small cell lung cancer

Studying the cellular geographic distribution in non-small cell lung cancer is essential to understand the roles of cell populations in this type of tumor. In this study, we characterize the spatial cellular distribution of immune cell populations using 23 makers placed in five multiplex immunofluorescence panels and their associations with clinicopathologic variables and outcomes. Our results demonstrate two cellular distribution patterns-an unmixed pattern mostly related to immunoprotective cells and a mixed pattern mostly related to immunosuppressive cells. Distance analysis shows that T-cells expressing immune checkpoints are closer to malignant cells than other cells. Combining the cellular distribution patterns with cellular distances, we can identify four groups related to inflamed and not-inflamed tumors. Cellular distribution patterns and distance are associated with survival in univariate and multivariable analyses. Spatial distribution is a tool to better understand the tumor microenvironment, predict outcomes, and may can help select therapeutic interventions.

A Low-Cost Modular Imaging System for Rapid, Multiplexed Immunofluorescence Detection in Clinical Tissues

To image 4-plex immunofluorescence-stained tissue samples at a low cost with cellular level resolution and sensitivity and dynamic range required to detect lowly and highly abundant targets, here we describe a robust, inexpensive (<$9000), 3D printable portable imaging device (Tissue Imager). The Tissue Imager can immediately be deployed on benchtops for in situ protein detection in tissue samples. Applications for this device are broad, ranging from answering basic biological questions to clinical pathology, where immunofluorescence can detect a larger number of markers than the standard H&E or chromogenic immunohistochemistry (CIH) staining, while the low cost also allows usage in classrooms. After characterizing our platform's specificity and sensitivity, we demonstrate imaging of a 4-plex immunology panel in human cutaneous T-cell lymphoma (CTCL) formalin-fixed paraffin-embedded (FFPE) tissue samples. From those images, positive cells were detected using CellProfiler, a popular open-source software package, for tumor marker profiling. We achieved a performance on par with commercial epifluorescence microscopes that are >10 times more expensive than our Tissue Imager. This device enables rapid immunofluorescence detection in tissue sections at a low cost for scientists and clinicians and can provide students with a hands-on experience to understand engineering and instrumentation. We note that for using the Tissue Imager as a medical device in clinical settings, a comprehensive review and approval processes would be required.

Analysis of Immune Intratumor Heterogeneity Highlights Immunoregulatory and Coinhibitory Lymphocytes as Hallmarks of Recurrence in Stage I Non-Small Cell Lung Cancer

Our understanding of the molecular mechanisms underlying postsurgical recurrence of non-small cell lung cancer (NSCLC) is rudimentary. Molecular and T cell repertoire intratumor heterogeneity (ITH) have been reported to be associated with postsurgical relapse; however, how ITH at the cellular level impacts survival is largely unknown. Here we report the analysis of 2880 multispectral images representing 14.2% to 27% of tumor areas from 33 patients with stage I NSCLC, including 17 cases (relapsed within 3 years after surgery) and 16 controls (without recurrence ≥5 years after surgery) using multiplex immunofluorescence. Spatial analysis was conducted to quantify the minimum distance between different cell types and immune cell infiltration around malignant cells. Immune ITH was defined as the variance of immune cells from 3 intratumor regions. We found that tumors from patients having relapsed display different immune biology compared with nonrecurrent tumors, with a higher percentage of tumor cells and macrophages expressing PD-L1 (P =.031 and P =.024, respectively), along with an increase in regulatory T cells (Treg) (P =.018), antigen-experienced T cells (P =.025), and effector-memory T cells (P =.041). Spatial analysis revealed that a higher level of infiltration of PD-L1+ macrophages (CD68+PD-L1+) or antigen-experienced cytotoxic T cells (CD3+CD8+PD-1+) in the tumor was associated with poor overall survival (P =.021 and P =.006, respectively). A higher degree of Treg ITH was associated with inferior recurrence-free survival regardless of tumor mutational burden (P =.022), neoantigen burden (P =.021), genomic ITH (P =.012) and T cell repertoire ITH (P =.001). Using multiregion multiplex immunofluorescence, we characterized ITH at the immune cell level along with whole exome and T cell repertoire sequencing from the same tumor regions. This approach highlights the role of immunoregulatory and coinhibitory signals as well as their spatial distribution and ITH that define the hallmarks of tumor relapse of stage I NSCLC.

Urinary T cells are detected in patients with immune checkpoint inhibitor-associated immune nephritis that are clonotypically identical to kidney T cell infiltrates

Acute kidney injury (AKI) occurs in ~20% of patients receiving immune checkpoint inhibitor (ICI) therapy; however, only 2-5% will develop ICI-mediated immune nephritis. Conventional tests are nonspecific in diagnosing disease pathology and invasive procedures (i.e. kidney biopsy) may not be feasible. In other autoimmune renal diseases, urinary immune cells correlated with the pathology or were predictive of disease activity. Corresponding evidence and analysis are absent for ICI-mediated immune nephritis. We report the first investigation analyzing immune cell profiles of matched kidney biopsies and urine of patients with ICI-AKI. We demonstrated the presence of urinary T cells in patients with immune nephritis by flow cytometry analysis. Clonotype analysis of T cell receptor (TCR) sequences confirmed enrichment of kidney TCRs in urine. As ICI therapies become standard of care for more cancers, noninvasively assessing urinary immune cells of ICI therapy recipients can facilitate clinical management and an opportunity to tailor ICI-nephritis treatment.

Identification of Novel Tumor Microenvironment Regulating Factor That Facilitates Tumor Immune Infiltration in Cervical Cancer

Cervical cancer is one of the most common gynecologic malignancies and one of the leading causes of cancer-related deaths in women worldwide. There are more than 30 categories of human papillomavirus infections in the genital tract. The recently discovered immune checkpoint suppression is a potential approach to improve clinical outcomes in these patients by altering immune cell function. However, many questions remain unanswered in terms of this method. For example, the proportion of responders is limited and the exact mechanism of action is uncertain. The tumor microenvironment (TME) has long been regarded as having nonnegligible influence on effectiveness of immunotherapy. The programmed cell death protein 1 (PD-1) pathway has received much attention due to its involvement in activating T-cell immune checkpoint responses. Since tumor cells may evade immune detection and become highly resistant to conventional treatments, anti-PD-1/PD-L1 antibodies are preferred as a kind of cancer treatment and many have just been licensed. To provide a theoretical basis for the development of new therapies, investigating the effect of tumor microenvironment on the prognosis of cervical cancer is necessary. In this work, immunological scores obtained from the ESTIMATE algorithm were used to differentiate between patients with high and low immune cell infiltration. We identified 11 immunologically significant differentially expressed genes (DEGs). For example, CXCR3 was found to be an important factor in CD8⁺ T cell recruitment and tumor immunological infiltration in cervical cancer. These results may lead to novel directions of understanding complex interactions between cancer cells and the tumor microenvironment, as well as new treatment options for cervical cancer.

Multiplex Immunofluorescence and the Digital Image Analysis Workflow for Evaluation of the Tumor Immune Environment in Translational Research

A robust understanding of the tumor immune environment has important implications for cancer diagnosis, prognosis, research, and immunotherapy. Traditionally, immunohistochemistry (IHC) has been regarded as the standard method for detecting proteins in situ, but this technique allows for the evaluation of only one cell marker per tissue sample at a time. However, multiplexed imaging technologies enable the multiparametric analysis of a tissue section at the same time. Also, through the curation of specific antibody panels, these technologies enable researchers to study the cell subpopulations within a single immunological cell group. Thus, multiplexed imaging gives investigators the opportunity to better understand tumor cells, immune cells, and the interactions between them. In the multiplexed imaging technology workflow, once the protocol for a tumor immune micro environment study has been defined, histological slides are digitized to produce high-resolution images in which regions of interest are selected for the interrogation of simultaneously expressed immunomarkers (including those co-expressed by the same cell) by using an image analysis software and algorithm. Most currently available image analysis software packages use similar machine learning approaches in which tissue segmentation first defines the different components that make up the regions of interest and cell segmentation, then defines the different parameters, such as the nucleus and cytoplasm, that the software must utilize to segment single cells. Image analysis tools have driven dramatic evolution in the field of digital pathology over the past several decades and provided the data necessary for translational research and the discovery of new therapeutic targets. The next step in the growth of digital pathology is optimization and standardization of the different tasks in cancer research, including image analysis algorithm creation, to increase the amount of data generated and their accuracy in a short time as described herein. The aim of this review is to describe this process, including an image analysis algorithm creation for multiplex immunofluorescence analysis, as an essential part of the optimization and standardization of the different processes in cancer research, to increase the amount of data generated and their accuracy in a short time.

Multi-target combinatory strategy to overcome tumor immune escape

Immune therapy has become the fourth approach after surgery, chemotherapy, and radiotherapy in cancer treatment. Many immune checkpoints were identified in the last decade since ipilimumab, which is the first immune checkpoint inhibitor to cytotoxic T-lymphocyte associated protein 4, had been approved by the US Food and Drug Administration (FDA) for the treatment of unresectable or metastatic melanoma in 2011. The use of several antibody drugs that target PD1/PD-L1 for various cancer treatments has been approved by the FDA. However, fewer people are benefitting from immune checkpoint inhibitor treatment in solid cancers. Approximately 80% of patients do not respond appropriately because of primary or acquired therapeutic resistance. Along with the characterization of more immune checkpoints, the combinatory treatment of multiimmune checkpoint inhibitors becomes a new option when monotherapy could not receive a good response. In this work, the author focuses on the combination therapy of multiple immune checkpoints (does not include targeted therapy of oncogenes or chemotherapy), introduces the current progression of multiple immune checkpoints and their related inhibitors, and discusses the advantages of combination therapy, as well as the risk of immune-related adverse events.

Best Practices for Technical Reproducibility Assessment of Multiplex Immunofluorescence

Multiplex immunofluorescence (mIF) tyramide signal amplification is a new and useful tool for the study of cancer that combines the staining of multiple markers in a single slide. Several technical requirements are important to performing high-quality staining and analysis and to obtaining high internal and external reproducibility of the results. This review manuscript aimed to describe the mIF panel workflow and discuss the challenges and solutions for ensuring that mIF panels have the highest reproducibility possible. Although this platform has shown high flexibility in cancer studies, it presents several challenges in pre-analytic, analytic, and post-analytic evaluation, as well as with external comparisons. Adequate antibody selection, antibody optimization and validation, panel design, staining optimization and validation, analysis strategies, and correct data generation are important for reproducibility and to minimize or identify possible issues during the mIF staining process that sometimes are not completely under our control, such as the tissue fixation process, storage, and cutting procedures.

Pathology Quality Control for Multiplex Immunofluorescence and Image Analysis Assessment in Longitudinal Studies

Immune profiling of formalin-fixed, paraffin-embedded tissues using multiplex immunofluorescence (mIF) staining and image analysis methodology allows for the study of several biomarkers on a single slide. The pathology quality control (PQC) for tumor tissue immune profiling using digital image analysis of core needle biopsies is an important step in any laboratory to avoid wasting time and materials. Although there are currently no established inclusion and exclusion criteria for samples used in this type of assay, a PQC is necessary to achieve accurate and reproducible data. We retrospectively reviewed PQC data from hematoxylin and eosin (H&E) slides and from mIF image analysis samples obtained during 2019. We reviewed a total of 931 reports from core needle biopsy samples; 123 (13.21%) were excluded during the mIF PQC. The most common causes of exclusion were the absence of malignant cells or fewer than 100 malignant cells in the entire section (n = 42, 34.15%), tissue size smaller than 4 × 1 mm (n = 16, 13.01%), fibrotic tissue without inflammatory cells (n = 12, 9.76%), and necrotic tissue (n = 11, 8.94%). Baseline excluded samples had more fibrosis (90 vs 10%) and less necrosis (5 vs 90%) compared with post-treatment excluded samples. The most common excluded organ site of the biopsy was the liver (n = 19, 15.45%), followed by soft tissue (n = 17, 13.82%) and the abdominal region (n = 15, 12.20%). We showed that the PQC is an important step for image analysis and that the absence of malignant cells is the most limiting sample characteristic for mIF image analysis. We also discuss other challenges that pathologists need to consider to report reliable and reproducible image analysis data.

Methods to Determine and Analyze the Cellular Spatial Distribution Extracted From Multiplex Immunofluorescence Data to Understand the Tumor Microenvironment

Image analysis using multiplex immunofluorescence (mIF) to detect different proteins in a single tissue section has revolutionized immunohistochemical methods in recent years. With mIF, individual cell phenotypes, as well as different cell subpopulations and even rare cell populations, can be identified with extraordinary fidelity according to the expression of antibodies in an mIF panel. This technology therefore has an important role in translational oncology studies and probably will be incorporated in the clinic. The expression of different biomarkers of interest can be examined at the tissue or individual cell level using mIF, providing information about cell phenotypes, distribution of cells, and cell biological processes in tumor samples. At present, the main challenge in spatial analysis is choosing the most appropriate method for extracting meaningful information about cell distribution from mIF images for analysis. Thus, knowing how the spatial interaction between cells in the tumor encodes clinical information is important. Exploratory analysis of the location of the cell phenotypes using point patterns of distribution is used to calculate metrics summarizing the distances at which cells are processed and the interpretation of those distances. Various methods can be used to analyze cellular distribution in an mIF image, and several mathematical functions can be applied to identify the most elemental relationships between the spatial analysis of cells in the image and established patterns of cellular distribution in tumor samples. The aim of this review is to describe the characteristics of mIF image analysis at different levels, including spatial distribution of cell populations and cellular distribution patterns, that can increase understanding of the tumor microenvironment.