Comparison of Biomarker Modalities for Predicting Response to PD-1/PD-L1 Checkpoint Blockade: A Systematic Review and Meta-analysis

Digital analysis of the prostate tumor microenvironment with high-order chromogenic multiplexing

As our understanding of the tumor microenvironment grows, the pathology field is increasingly utilizing multianalyte diagnostic assays to understand important characteristics of tumor growth. In clinical settings, brightfield chromogenic assays represent the gold-standard and have developed significant trust as the first-line diagnostic method. However, conventional brightfield tests have been limited to low-order assays that are visually interrogated. We have developed a hybrid method of brightfield chromogenic multiplexing that overcomes these limitations and enables high-order multiplex assays. However, how compatible high-order brightfield multiplexed images are with advanced analytical algorithms has not been extensively evaluated. In the present study, we address this gap by developing a novel 6-marker prostate cancer assay that targets diverse aspects of the tumor microenvironment such as prostate-specific biomarkers (PSMA and p504s), immune biomarkers (CD8 and PD-L1), a prognostic biomarker (Ki-67), as well as an adjunctive diagnostic biomarker (basal cell cocktail) and apply the assay to 143 differentially graded adenocarcinoma prostate tissues. The tissues were then imaged on our spectroscopic multiplexing imaging platform and mined for proteomic and spatial features that were correlated with cancer presence and disease grade. Extracted features were used to train a UMAP model that differentiated healthy from cancerous tissue with an accuracy of 89% and identified clusters of cells based on cancer grade. For spatial analysis, cell-to-cell distances were calculated for all biomarkers and differences between healthy and adenocarcinoma tissues were studied. We report that p504s positive cells were at least 2× closer to cells expressing PD-L1, CD8, Ki-67, and basal cell in adenocarcinoma tissues relative to the healthy control tissues. These findings offer a powerful insight to understand the fingerprint of the prostate tumor microenvironment and indicate that high-order chromogenic multiplexing is compatible with digital analysis. Thus, the presented chromogenic multiplexing system combines the clinical applicability of brightfield assays with the emerging diagnostic power of high-order multiplexing in a digital pathology friendly format that is well-suited for translational studies to better understand mechanisms of tumor development and growth.

Spatial heterogeneity of infiltrating immune cells in the tumor microenvironment of non-small cell lung cancer

Tumor-infiltrating lymphocytes (TILs) are essential components of the tumor microenvironment (TME) of non-small cell lung cancer (NSCLC). Still, it is difficult to describe due to their heterogeneity. In this study, five cell markers from NSCLC patients were analyzed. We segmented tumor cells (TCs) and TILs using Efficientnet-B3 and explored their quantitative information and spatial distribution. After that, we simulated multiplex immunohistochemistry (mIHC) by overlapping continuous single chromogenic IHCs slices. As a result, the proportion and the density of programmed cell death-ligand 1 (PD-L1)-positive TCs were the highest in the core. CD8+ T cells were the closest to the tumor (median distance: 41.71 μm), while PD-1+T cells were the most distant (median distance: 62.2μm), and our study found that most lymphocytes clustered together within the peritumoral range of 10-30 μm where cross-talk with TCs could be achieved. We also found that the classification of TME could be achieved using CD8+ T-cell density, which is correlated with the prognosis of patients. In addition, we achieved single chromogenic IHC slices overlap based on CD4-stained IHC slices. We explored the number and spatial distribution of cells in heterogeneous TME of NSCLC patients and achieved TME classification. We also found a way to show the co-expression of multiple molecules economically.

Pan-cancer evaluation of tumor-infiltrating lymphocytes and programmed cell death protein ligand-1 in metastatic biopsies and matched primary tumors

Tumor immunological characterization includes evaluation of tumor-infiltrating lymphocytes (TILs) and programmed cell death protein ligand-1 (PD-L1) expression. This study investigated TIL distribution, its prognostic value, and PD-L1 expression in metastatic and matched primary tumors (PTs). Specimens from 550 pan-cancer patients of the SHIVA01 trial (NCT01771458) with available metastatic biopsy and 111 matched PTs were evaluated for TILs and PD-L1. Combined positive score (CPS), tumor proportion score (TPS), and immune cell (IC) score were determined. TILs and PD-L1 were assessed according to PT organ of origin, histological subtype, and metastatic biopsy site. We found that TIL distribution in metastases did not vary according to PT organ of origin, histological subtype, or metastatic biopsy site, with a median of 10% (range: 0-70). TILs were decreased in metastases compared to PT (20% [5-60] versus 10% [0-40], p < 0.0001). CPS varied according to histological subtype (p = 0.02) and biopsy site (p < 0.02). TPS varied according to PT organ of origin (p = 0.003), histological subtype (p = 0.0004), and metastatic biopsy site (p = 0.00004). TPS was higher in metastases than in PT (p < 0.0001). TILs in metastases did not correlate with overall survival. In conclusion, metastases harbored fewer TILs than matched PT, regardless of PT organ of origin, histological subtype, and metastatic biopsy site. PD-L1 expression increased with disease progression. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Immune Checkpoint Inhibitor Score Predicts Survival Benefit of Immunotherapy in Patients with Non-small Cell Lung Cancer

BACKGROUND

The use of immune checkpoint inhibitors (ICIs) in patients with advanced lung cancer is increasing. Despite ongoing studies to predict the efficacy of ICIs, its use in clinical practice remains difficult. Thus, we aimed to discover a predictive marker by analyzing blood cell characteristics and developing a scoring system for patients treated with ICIs.

METHODS

This was a prospective multicenter study in patients with advanced nonsmall cell lung cancer (NSCLC) who received ICIs as second-line treatment from June 2021 to November 2022. Blood cell parameters in routine blood samples were evaluated using an automated hematology analyzer. Immune checkpoint inhibitor score (IChIS) was calculated as the sum of neutrophil count score and immature granulocyte score.

RESULTS

A total of 143 patients from four institutions were included. The treatment response was as follows: partial response, 8.4%; stable disease, 37.1%; and progressive disease, 44.8%. Median progression-free survival and overall survival after ICI treatment was 3.0 and 8.3 months, respectively. Median progression-free survival in patients with an IChIS of 0 was 4.0 months, which was significantly longer than 1.9 months in patients with an IChIS of 1 and 1.0 month in those with an IChIS of 2 (p=0.001). The median overall survival in patients with an IChIS of 0 was 10.2 months, which was significantly longer than 6.8 and 1.8 months in patients with an IChIS of 1 and 2, respectively (p<0.001).

CONCLUSION

Baseline IChIS could be a potential biomarker for predicting survival benefit of immunotherapy in NSCLC.

AI-powered genomic mutation signature for predicting immune checkpoint inhibitor therapy outcomes in gastroesophageal cancer: a multi-cohort analysis

BACKGROUND

Immune checkpoint inhibitors (ICIs) have significantly transformed the treatment of gastroesophageal cancer (GEC). However, the lack of reliable prognostic biomarkers hinders the ability to predict patient response to ICI therapy.

METHODS

In this study, we engineered and validated a genomic mutation signature (GMS) utilizing an innovative artificial intelligence (AI) algorithm to forecast ICI therapy outcomes in GEC patients. We further explored immune profiles across subtypes through comprehensive multiomics analysis. Our investigation of drug sensitivity data from the Genomics of Drug Sensitivity in Cancer (GDSC) database led to the identification of trametinib as a potential therapeutic agent. We subsequently evaluated trametinib's efficacy in AGS and MKN45 cell lines using Cell Counting Kit-8 (CCK8) assays and clonogenic experiments.

RESULTS

We developed a GMS by integrating 297 algorithms, enabling autonomous prognosis prediction for GEC patients. The GMS demonstrated consistent performance across three public cohorts, exhibiting high sensitivity and specificity for overall survival (OS) at 6, 12, and 18 months, as shown by Receiver Operator Characteristic Curve (ROC) analysis. Notably, the GMS surpassed traditional clinical and molecular features, including tumor mutational burden (TMB), programmed death-ligand 1 (PD-L1) expression, and microsatellite instability (MSI), in predictive accuracy. Low-risk samples exhibited elevated levels of cytolytic immune cells and heightened immunogenic potential compared to high-risk samples. Our investigation identified trametinib as a potential therapeutic agent. An inverse correlation was observed between GMS and trametinib IC50. Moreover, the high-risk-derived AGS cell line showed increased sensitivity to trametinib compared to the low-risk-derived MKN45 cell line.

CONCLUSION

The GMS utilized in this study successfully demonstrated the ability to reliably predict the survival advantage for patients with GECs undergoing ICI therapy.

The spatial multi-omics revolution in cancer therapy: Precision redefined

Spatially resolved multi-omics revolutionizes cancer therapy by decoding the cellular and molecular heterogeneity of the tumor microenvironment through spatial coordinates. This commentary discusses the roles of spatial multi-omics in identifying precise therapeutic targets and predicting treatment responses while also highlighting the challenges that impede its integration into precision medicine.

A Phase 1a/1b Dose Escalation/Expansion Study of the Anti-PD-1 Monoclonal Antibody Nofazinlimab in Chinese Patients with Solid Tumors or Lymphoma

BACKGROUND

Immune checkpoint blockade with anti-programmed cell death 1 (PD-1) antibodies has demonstrated efficacy in multiple tumor types. Nofazinlimab is a humanized rat antibody targeting PD-1. A first-in-human study of nofazinlimab conducted in Australia found no dose-limiting toxicities (DLTs) and the maximum tolerated dose (MTD) was not reached in the range of 1-10 mg/kg.

OBJECTIVE

We evaluated nofazinlimab for multiple advanced malignancies in Chinese patients.

PATIENTS AND METHODS

This was a phase 1a/1b, open-label, multicenter, dose-escalation/expansion trial. In phase 1a, patients received an abbreviated dose escalation of nofazinlimab at 60 mg and 200 mg every 3 weeks (Q3W) to determine DLTs and the recommended phase 2 dose (RP2D). In phase 1b, patients received the RP2D (monotherapy/combination) in six arms by tumor type; DLTs were evaluated for nofazinlimab plus lenvatinib in the unresectable hepatocellular carcinoma (uHCC) arm. Safety (continuously monitored in patients who received nofazinlimab) and efficacy (patients with measurable baseline disease) were assessed.

RESULTS

Overall, 107 patients were eligible and received nofazinlimab. In phase 1a, no DLTs were observed; the RP2D was 200mg Q3W. In phase 1b, no DLTs were observed with nofazinlimab plus lenvatinib. The safety profile was consistent with that observed in the first-in-human study (NCT03475251). In phase 1b, 21/88 (23.9%) patients achieved confirmed objective responses, 26 (29.5%) had stable disease, and 9/20 (45.0%) patients with uHCC achieved confirmed objective responses to nofazinlimab plus lenvatinib.

CONCLUSIONS

Nofazinlimab was well tolerated in Chinese patients. Preliminary efficacy was encouraging, particularly for nofazinlimab plus lenvatinib in uHCC, which is being studied in an ongoing phase 3 trial.

CLINICAL TRIAL REGISTRATION

NCT03809767; registered 18 January 2019.

Spatial landscapes of cancers: insights and opportunities

Solid tumours comprise many different cell types organized in spatially structured arrangements, with substantial intratumour and intertumour heterogeneity. Advances in spatial profiling technologies over the past decade hold promise to capture the complexity of these cellular architectures to build a holistic view of the intricate molecular mechanisms that shape the tumour ecosystem. Some of these mechanisms act at the cellular scale and are controlled by cell-autonomous programmes or communication between nearby cells, whereas other mechanisms result from coordinated efforts between large networks of cells and extracellular molecules organized into tissues and organs. In this Review we provide insights into the application of single-cell and spatial profiling tools, with a focus on spatially resolved transcriptomic tools developed to understand the cellular architecture of the tumour microenvironment and identify opportunities to use them to improve clinical management of cancers.

Advances in spatial multi-omics in tumors

Single-cell techniques have convincingly demonstrated that tumor tissue usually contains multiple genetically defined cell subclones with different gene mutation sets as well as various transcriptional profiles, but the spatial heterogeneity of the microenvironment and the macrobiological characteristics of the tumor ecosystem have not been described. For the past few years, spatial multi-omics technologies have revealed the cellular interactions, microenvironment, and even systemic tumor-host interactions in the tumor ecosystem at the spatial level, which can not only improve classical therapies such as surgery, radiotherapy, and chemotherapy but also promote the development of emerging targeted therapies in immunotherapy. Here, we review some emerging spatial omics techniques in cancer research and therapeutic applications and propose prospects for their future development.

Spatial Immunophenotyping from Whole-Slide Multiplexed Tissue Imaging Using Convolutional Neural Networks

The multiplexed immunofluorescence (mIF) platform enables biomarker discovery through the simultaneous detection of multiple markers on a single tissue slide, offering detailed insights into intratumor heterogeneity and the tumor-immune microenvironment at spatially resolved single cell resolution. However, current mIF image analyses are labor-intensive, requiring specialized pathology expertise which limits their scalability and clinical application. To address this challenge, we developed CellGate, a deep-learning (DL) computational pipeline that provides streamlined, end-to-end whole-slide mIF image analysis including nuclei detection, cell segmentation, cell classification, and combined immuno-phenotyping across stacked images. The model was trained on over 750,000 single cell images from 34 melanomas in a retrospective cohort of patients using whole tissue sections stained for CD3, CD8, CD68, CK-SOX10, PD-1, PD-L1, and FOXP3 with manual gating and extensive pathology review. When tested on new whole mIF slides, the model demonstrated high precision-recall AUC. Further validation on whole-slide mIF images of 9 primary melanomas from an independent cohort confirmed that CellGate can reproduce expert pathology analysis with high accuracy. We show that spatial immuno-phenotyping results using CellGate provide deep insights into the immune cell topography and differences in T cell functional states and interactions with tumor cells in patients with distinct histopathology and clinical characteristics. This pipeline offers a fully automated and parallelizable computing process with substantially improved consistency for cell type classification across images, potentially enabling high throughput whole-slide mIF tissue image analysis for large-scale clinical and research applications.

Cancer organoids 2.0: modelling the complexity of the tumour immune microenvironment

The development of neoplasia involves a complex and continuous interplay between malignantly transformed cells and the tumour microenvironment (TME). Cancer immunotherapies targeting the immune TME have been increasingly validated in clinical trials but response rates vary substantially between tumour histologies and are often transient, idiosyncratic and confounded by resistance. Faithful experimental models of the patient-specific tumour immune microenvironment, capable of recapitulating tumour biology and immunotherapy effects, would greatly improve patient selection, target identification and definition of resistance mechanisms for immuno-oncology therapeutics. In this Review, we discuss currently available and rapidly evolving 3D tumour organoid models that capture important immune features of the TME. We highlight diverse opportunities for organoid-based investigations of tumour immunity, drug development and precision medicine.

PD-1/PD-L1 axis: implications in immune regulation, cancer progression, and translational applications

The PD-1/PD-L1 axis is a complex signaling pathway that has an important role in the immune system cells. Programmed cell death protein 1 (PD-1) acts as an immune checkpoint on the T lymphocytes, B lymphocytes, natural killer (NK), macrophages, dendritic cells (DCs), monocytes, and myeloid cells. Its ligand, the programmed cell death 1 ligand (PD-L1), is expressed in the surface of the antigen-presenting cells (APCs). The binding of both promotes the downregulation of the T cell response to ensure the activation to prevent the onset of chronic immune inflammation. This axis in the tumor microenvironment (TME) performs a crucial role in the tumor progression and the escape of the tumor by neutralizing the immune system, the engagement of PD-L1 with PD-1 in the T cell causes dysfunctions, neutralization, and exhaustion, providing the tumor mass production. This review will provide a comprehensive overview of the functions of the PD-1/PD-L1 system in immune function, cancer, and the potential therapeutic implications of the PD-1/PD-L1 pathway for cancer management.

Multi-Tissue Controls and Multiplex Immunocytochemistry in Pulmonary Cytology

INTRODUCTION

The World Health Organization 2021 lung cancer classification highlights the central role of immunohistochemistry (IHC) in diagnostic pathology. Despite traditional IHC being essential, its limitation to one marker per tissue section brings challenges, particularly when facing cytological limitedly sized samples. To overcome these challenges, multiplex immunocytochemistry (mICC) techniques offer the simultaneous detection of multiple markers from a single section. These advances complement the highly complex imaging techniques that enable additional analyses of cellular interactions.

METHODS

The present study outlines a comprehensive mICC methodology of an automated multiplex immunoperoxidase staining method and multiple tissue hybrid controls for ICC/mICC. Protocols are presented in detail and demonstrate a careful approach to optimizing various markers for diagnostic workup including immunotherapy.

CONCLUSION

Multiplex IHC/ICC emerges as a transformative force in biomedical diagnostics and research. Beyond simultaneous marker detection, it unravels complexities within tissues - unveiling co-localization nuances, deciphering expression patterns, and enhancing understanding of cellular populations. As personalized treatments gain prominence, the study emphasizes the heightened importance of diagnostic tools and sample adequacy. The present methodological study, encapsulating an automated multiplex immunoperoxidase staining method, symbolizes a stride towards precision in pulmonary carcinoma diagnosis. Multi-tissue controls represent a key element in quality assurance in pathology laboratories.

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

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

Biomarker development for PD-(L)1 axis inhibition: a consensus view from the SITC Biomarkers Committee

Therapies targeting the programmed cell death protein-1/programmed death-ligand 1 (PD-L1) (abbreviated as PD-(L)1) axis are a significant advancement in the treatment of many tumor types. However, many patients receiving these agents fail to respond or have an initial response followed by cancer progression. For these patients, while subsequent immunotherapies that either target a different axis of immune biology or non-immune combination therapies are reasonable treatment options, the lack of predictive biomarkers to follow-on agents is impeding progress in the field. This review summarizes the current knowledge of mechanisms driving resistance to PD-(L)1 therapies, the state of biomarker development along this axis, and inherent challenges in future biomarker development for these immunotherapies. Innovation in the development and application of novel biomarkers and patient selection strategies for PD-(L)1 agents is required to accelerate the delivery of effective treatments to the patients most likely to respond.

AUNP-12 Near-Infrared Fluorescence Probes across NIR-I to NIR-II Enable In Vivo Detection of PD-1/PD-L1 Axis in the Tumor Microenvironment

The innovative PD-1/PD-L1 pathway strategy is gaining significant traction in cancer therapeutics. However, fluctuating response rates of 20-40% to PD-1/PD-L1 inhibitors, coupled with the risk of hyperprogression after immunotherapy, underscore the need for accurate patient selection and the identification of more beneficiaries. Molecular imaging, specifically near-infrared (NIR) fluorescence imaging, is a valuable alternative for real-time, noninvasive visualization of dynamic PD-L1 expression in vivo. This research introduces AUNP-12, a novel PD-L1-targeting peptide antagonist conjugated with Cy5.5 and CH1055 for first (NIR-I) and second near-infrared (NIR-II) imaging. These probes have proven to be effective in mapping PD-L1 expression across various mouse tumor models, offering insights into tumor-immune interactions. This study highlights the potential of AUNP-12-Cy5.5 and AUNP-12-CH1055 for guiding clinical immunotherapy through precise patient stratification and dynamic monitoring, supporting the shift toward molecular imaging for personalized cancer care.

RNA M6A modification shaping cutaneous melanoma tumor microenvironment and predicting immunotherapy response

Recent years have seen rising mortality rates linked to cutaneous melanoma (SKCM), despite advances in immunotherapy. Understanding RNA N6-methyladenosine (M6A) significance in SKCM is crucial for prognosis, tumor microenvironment (TME), immune cell presence, and immunotherapy efficacy. We analyzed 23 M6A regulators using SKCM samples from TCGA and GEO databases, identifying three M6A modification patterns linked to TME cell infiltration. Principal component analysis (PCA) yielded an M6A score for individual tumors, utilizing patient gene expression profiles and CNV data from TCGA. M6A modification patterns play a crucial role in SKCM development and progression, influencing tumor attributes such as inflammatory stage, subtype, TME interstitial activity, and genetic mutations. The M6A score independently predicts patient outcomes and correlates with improved response to immunotherapy, validated across anti-PD-1 and anti-PD-L1 therapy cohorts. M6A modifications significantly impact the TME landscape, with the M6A score serving as a predictive marker for immunotherapy response. Integrating M6A-related information into clinical practice could revolutionize SKCM management and treatment strategies.

Automated immunoassay of serum NY-ESO-1 and XAGE1 antibodies for predicting clinical benefit with immune checkpoint inhibitor (ICI) in advanced non-small cell lung cancer

BACKGROUND

NY-ESO-1 and XAGE1 cancer/testis antigens elicit humoral and cellular immune responses in NSCLC patients. We aimed to predict clinical benefit with ICI monotherapy, using an automated immunoassay of NY-ESO-1/XAGE1 antibodies (Abs).

METHODS

This study enrolled 99 NSCLC patients who received nivolumab after chemotherapy, including 21 patients harboring EGFR, ALK, or KRAS alterations. The cutoff value (10 units/mL) of NY-ESO-1 and XAGE1 Ab was determined based on Ab levels in non-malignant controls, and NY-ESO-1/XAGE1 Abs in NSCLC were measured before nivolumab. Differences in PFS and OS between the Ab-positive and Ab-negative groups were retrospectively analyzed using Cox regression analysis after applying inverse probability of treatment weighting (IPTW).

RESULTS

NY-ESO-1/XAGE1 Abs were positive in 28 NSCLC, who responded more highly to nivolumab than the Ab-negatives (response rate 50.0% vs. 15.5 %, p < 0.0007). The IPTW-adjusted positives and negatives for NY-ESO-1/XAGE1 Abs were 24.5 and 70.2, respectively. The Ab-positives showed longer IPTW-adjusted PFS (HR = 0.59, 95 % CI: 0.39-0.90, p = 0.014) and IPTW-adjusted OS (HR = 0.51, 95 % CI: 0.32-0.81, p = 0.004) than the Ab-negatives. Among NSCLC harboring driver genes, the Ab-positives (n = 10) showed longer PFS (HR = 0.34, 95 % CI: 0.13-0.89, p = 0.029) and OS (HR = 0.27, 95 % CI: 0.098-0.75, p = 0.012) than the Ab-negatives (n = 11).

CONCLUSION

Our immunoassay of NY-ESO-1/XAGE1 Abs is probably useful for predicting the clinical benefit with nivolumab in NSCLC, including those harboring driver genes. These results suggest that our immunoassay may be useful in ICI monotherapy for NSCLC.

A novel artificial intelligence network to assess the prognosis of gastrointestinal cancer to immunotherapy based on genetic mutation features

Background

Immune checkpoint inhibitors (ICIs) have revolutionized gastrointestinal cancer treatment, yet the absence of reliable biomarkers hampers precise patient response prediction.

Methods

We developed and validated a genomic mutation signature (GMS) employing a novel artificial intelligence network to forecast the prognosis of gastrointestinal cancer patients undergoing ICIs therapy. Subsequently, we explored the underlying immune landscapes across different subtypes using multiomics data. Finally, UMI-77 was pinpointed through the analysis of drug sensitization data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. The sensitivity of UMI-77 to the AGS and MKN45 cell lines was evaluated using the cell counting kit-8 (CCK8) assay and the plate clone formation assay.

Results

Using the artificial intelligence network, we developed the GMS that independently predicts the prognosis of gastrointestinal cancer patients. The GMS demonstrated consistent performance across three public cohorts and exhibited high sensitivity and specificity for 6, 12, and 24-month overall survival (OS) in receiver operating characteristic (ROC) curve analysis. It outperformed conventional clinical and molecular features. Low-risk samples showed a higher presence of cytolytic immune cells and enhanced immunogenic potential compared to high-risk samples. Additionally, we identified the small molecule compound UMI-77. The half-maximal inhibitory concentration (IC50) of UMI-77 was inversely related to the GMS. Notably, the AGS cell line, classified as high-risk, displayed greater sensitivity to UMI-77, whereas the MKN45 cell line, classified as low-risk, showed less sensitivity.

Conclusion

The GMS developed here can reliably predict survival benefit for gastrointestinal cancer patients on ICIs therapy.

Primary Tumor Characteristics as Biomarkers of Immunotherapy Response in Advanced Melanoma: A Retrospective Cohort Study

Identifying patients likely to benefit from immune checkpoint inhibitor (ICI) treatment remains a crucial goal for melanoma. The objective of this study is to assess the association between primary tumor features and immunotherapy response and survival in advanced melanoma patients. In this single-center retrospective cohort study, disease characteristics, response to immunotherapy, PFS, and OS were assessed among melanoma patients (excluding mucosal and uveal primaries) treated with ICI. Among 447 patients, 300 (67.1%) received anti-PD-1 monotherapy and 147 (32.9%) received ipilimumab/nivolumab. A total of 338 (75.6%) had cutaneous melanoma, 29 (6.5%) had acral melanoma, and 80 (17.9%) had melanoma of unknown primary. Ulceration and stage at initial presentation were associated with inferior outcomes on univariate analysis. However, on multivariate analysis, this result was not observed, but cutaneous melanoma and each of its subtypes (superficial spreading, nodular, other, unknown) were positively associated with response, longer PFS, and longer OS. Metastatic stage (M1c, M1d) at presentation (OR = 1.8, p < 0.05) and BRAFV600E mutation status (OR = 1.6, p < 0.001) were associated with shorter PFS. This study is limited by its retrospective and single-center design. Cutaneous melanoma and its subtypes were significantly associated with response, PFS, and OS compared with acral or unknown primary melanoma.

The combination of immune checkpoint inhibitors and antibody-drug conjugates in the treatment of urogenital tumors: a review insights from phase 2 and 3 studies

With the high incidence of urogenital tumors worldwide, urinary system tumors are among the top 10 most common tumors in men, with prostate cancer ranking first and bladder cancer fourth. Patients with resistant urogenital tumors often have poor prognosis. In recent years, researchers have discovered numerous specific cancer antigens, which has led to the development of several new anti-cancer drugs. Using protein analysis techniques, researchers developed immune checkpoint inhibitors (ICIs) and antibody-conjugated drugs (ADCs) for the treatment of advanced urogenital tumors. However, tumor resistance often leads to the failure of monotherapy. Therefore, clinical trials of the combination of ICIs and ADCs have been carried out in numerous centers around the world. This article reviewed phase 2 and 3 clinical studies of ICIs, ADCs, and their combination in the treatment of urogenital tumors to highlight safe and effective methods for selecting individualized therapeutic strategies for patients. ICIs activate the immune system, whereas ADCs link monoclonal antibodies to toxins, which can achieve a synergistic effect when the two drugs are combined. This synergistic effect provides multiple advantages for the treatment of urogenital tumors.

High-throughput transcriptome profiling indicates ribosomal RNAs to be associated with resistance to immunotherapy in non-small cell lung cancer (NSCLC)

BACKGROUND

Despite the impressive outcomes with immune checkpoint inhibitor (ICI) in non-small cell lung cancer (NSCLC), only a minority of the patients show long-term benefits from ICI. In this study, we used retrospective cohorts of ICI treated patients with NSCLC to discover and validate spatially resolved protein markers associated with resistance to programmed cell death protein-1 (PD-1) axis inhibition.

METHODS

Pretreatment samples from 56 patients with NSCLC treated with ICI were collected and analyzed in a tissue microarray (TMA) format in including four different tumor regions per patient using the GeoMx platform for spatially informed transcriptomics. 34 patients had assessable tissue with tumor compartment in all 4 TMA spots, 22 with leukocyte compartment and 12 with CD68 compartment. The patients' tissue that was not assessable in fourfold redundancy in each compartment was designated as the validation cohort; cytokeratin (CK) (N=22), leukocytes CD45 (N=31), macrophages, CD68 (N=43). The human whole transcriptome, represented by~18,000 individual genes assessed by oligonucleotide-tagged in situ hybridization, was sequenced on the NovaSeq platform to quantify the RNAs present in each region of interest.

RESULTS

54,000 gene variables were generated per case, from them 25,740 were analyzed after removing targets with expression lower than a prespecified frequency. Cox proportional-hazards model analysis was performed for overall and progression-free survival (OS, PFS, respectively). After identifying genes significantly associated with limited survival benefit (HR>1)/progression per spot per patient, we used the intersection of them across the four TMA spots per patient. This resulted in a list of 12 genes in the tumor-cell compartment (RPL13A, GNL3, FAM83A, CYBA, ACSL4, SLC25A6, EPAS1, RPL5, APOL1, HSPD1, RPS4Y1, ADI1). RPL13A, GNL3 in tumor-cell compartment were also significantly associated with OS and PFS, respectively, in the validation cohort (CK: HR, 2.48; p=0.02 and HR, 5.33; p=0.04). In CD45 compartment, secreted frizzled-related protein 2, was associated with OS in the discovery cohort but not in the validation cohort. Similarly, in the CD68 compartment ARHGAP and PNN interacting serine and arginine rich protein were significantly associated with PFS and OS, respectively, in the majority but not all four spots per patient.

CONCLUSION

This work highlights RPL13A and GNL3 as potential indicative biomarkers of resistance to PD-1 axis blockade that might help to improve precision immunotherapy strategies for lung cancer.

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.

Preclinical imaging evaluation of a bispecific antibody targeting hPD1/CTLA4 using humanized mice

BACKGROUND

The lack of an efficient way to screen patients who are responsive to immunotherapy challenges PD1/CTLA4-targeting cancer treatment. Immunotherapeutic efficacy cannot be clearly determined by peripheral blood analyses, tissue gene markers or CT/MR value. Here, we used a radionuclide and imaging techniques to investigate the novel dual targeted antibody cadonilimab (AK104) in PD1/CTLA4-positive cells in vivo.

METHODS

First, humanized PD1/CTLA4 mice were purchased from Biocytogen Pharmaceuticals (Beijing) Co., Ltd. to express hPD1/CTLA4 in T-cells. Then, mouse colon cancer MC38-hPD-L1 cell xenografts were established in humanized mice. A bispecific antibody targeting PD1/CTLA4 (AK104) was labeled with radio-nuclide iodine isotopes. Immuno-PET/CT imaging was performed using a bispecific monoclonal antibody (mAb) probe 124I-AK104, developed in-house, to locate PD1+/CTLA4+ tumor-infiltrating T cells and monitor their distribution in mice to evaluate the therapeutic effect.

RESULTS

The 124I-AK104 dual-antibody was successfully constructed with ideal radiochemical characteristics, in vitro stability and specificity. The results of immuno-PET showed that 124I-AK104 revealed strong hPD1/CTLA4-positive responses with high specificity in humanized mice. High uptake of 124I-AK104 was observed not only at the tumor site but also in the spleen. Compared with PD1- or CTLA4-targeting mAb imaging, 124I-AK104 imaging had excellent standard uptake values at the tumor site and higher tumor to nontumor (T/NT) ratios.

CONCLUSIONS

The results demonstrated the potential of translating 124I-AK104 into a method for screening patients who benefit from immunotherapy and the efficacy, as well as the feasibility, of this method was verified by immuno-PET imaging of humanized mice.

Diffuse intrinsic pontine glioma (DIPG): A review of current and emerging treatment strategies

Diffuse intrinsic pontine glioma (DIPG) is a childhood malignancy of the brainstem with a dismal prognosis. Despite recent advances in its understanding at the molecular level, the prognosis of DIPG has remained unchanged. This article aims to review the current understanding of the genetic pathophysiology of DIPG and to highlight promising therapeutic targets. Various DIPG treatment strategies have been investigated in pre-clinical studies, several of which have shown promise and have been subsequently translated into ongoing clinical trials. Ultimately, a multifaceted therapeutic approach that targets cell-intrinsic alterations, the micro-environment, and augments the immune system will likely be necessary to eradicate DIPG.

IFN-α/β/IFN-γ/IL-15 pathways identify GBP1-expressing tumors with an immune-responsive phenotype

Immunotherapy is widely used in cancer treatment; however, only a subset of patients responds well to it. Significant efforts have been made to identify patients who will benefit from immunotherapy. Successful anti-tumor immunity depends on an intact cancer-immunity cycle, especially long-lasting CD8+ T-cell responses. Interferon (IFN)-α/β/IFN-γ/interleukin (IL)-15 pathways have been reported to be involved in the development of CD8+ T cells. And these pathways may predict responses to immunotherapy. Herein, we aimed to analyze multiple public databases to investigate whether IFN-α/β/IFN-γ/IL-15 pathways could be used to predict the response to immunotherapy. Results showed that IFN-α/β/IFN-γ/IL-15 pathways could efficiently predict immunotherapy response, and guanylate-binding protein 1 (GBP1) could represent the IFN-α/β/IFN-γ/IL-15 pathways. In public and private cohorts, we further demonstrated that GBP1 could efficiently predict the response to immunotherapy. Functionally, GBP1 was mainly expressed in macrophages and strongly correlated with chemokines involved in T-cell migration. Therefore, our study comprehensively investigated the potential role of GBP1 in immunotherapy, which could serve as a novel biomarker for immunotherapy and a target for drug development.

Efficacy and safety of PD-1/PD-L1 inhibitor-based immune combination therapy versus sorafenib in the treatment of advanced hepatocellular carcinoma: a meta-analysis

Objective

To systematically evaluate the safety and efficacy of PD-1/PD-L1 inhibitor-based immunotherapy (hereafter referred to as "combination immunotherapy") compared with that of sorafenib in the treatment of hepatocellular carcinoma (HCC).

Methods

Databases such as PubMed, Embase, and the Cochrane Library were searched from the date of their establishment to September 2023 to identify randomized controlled trials (RCTs) of combination immunotherapy versus sorafenib for the treatment of advanced HCC. Two reviewers independently evaluated the quality of the included studies, extracted the data, and cross-checked the information. The meta-analysis was performed using RevMan 5.3 software.

Results

A total of 5 RCTs were included. The results of the meta-analysis showed the following: (1) Effectiveness. Compared to sorafenib, combination immunotherapy significantly improved overall survival (OS, HR = 0.69, 95% CI: 0.58 ~ 0.82, p < 0.01) and progression-free survival (PFS, HR = 0.62, 95% CI: 0.50 ~ 0.78, p < 0.001) in patients with advanced HCC. (2) Safety. Both groups had comparatively high incidences of adverse events (AEs), but the difference in any treatment-related adverse events was not significant between the two arms (OR = 0.98, 95% CI: 0.95 ~ 1.02, p = 0.34). The difference in the incidence of grade 1-2 adverse reactions was statistically significant (OR = 0.66, 95% CI = 0.49-0.90, p = 0.001). There were no differences in grade 3/4 TRAEs or grade 5 TRAEs (OR = 1.46, 95% CI = 0.78 ~ 2.71, p = 0.24; OR = 1.08, 95% CI = 0.73 ~ 1.58, p = 0.71).

Conclusion

Combined immunotherapy can significantly prolong the OS and PFS of patients with advanced HCC without increasing the incidence of adverse effects in terms of safety, but the incidence of AEs in different systems is different.

Prediction performance comparison of biomarkers for response to immune checkpoint inhibitors in advanced non-small cell lung cancer

BACKGROUND

The aim of the present study was to compare the predictive accuracy of PD-L1 immunohistochemistry (IHC), tissue or blood tumor mutation burden (tTMB, bTMB), gene expression profile (GEP), driver gene mutation, and combined biomarkers for immunotherapy response of advanced non-small cell lung cancer (NSCLC).

METHODS

In part 1, clinical trials involved with predictive biomarker exploration for immunotherapy in advanced NSCLC were included. The area under the curve (AUC) of the summary receiver operating characteristic (SROC), sensitivity, specificity, likelihood ratio and predictive value of the biomarkers were evaluated. In part 2, public datasets of immune checkpoint inhibitor (ICI)-treated NSCLC involved with biomarkers were curated (N = 871). Odds ratio (OR) of the positive versus negative biomarker group for objective response rate (ORR) was measured.

RESULTS

In part 1, the AUC of combined biomarkers (0.75) was higher than PD-L1 (0.64), tTMB (0.64), bTMB (0.68), GEP (0.67), and driver gene mutation (0.51). Combined biomarkers also had higher specificity, positive likelihood ratio and positive predictive value than single biomarkers. In part 2, the OR of combined biomarkers of PD-L1 plus TMB (PD-L1 cutoff 1%, 0.14; cutoff 50% 0.13) was lower than that of PD-L1 (cutoff 1%, 0.33; cutoff 50% 0.24), tTMB (0.28), bTMB (0.48), EGFR mutation (0.17) and KRAS mutation (0.47), for distinguishing ORR of patients after immunotherapy. Furthermore, positive PD-L1, tTMB-high, wild-type EGFR, and positive PD-L1 plus TMB were associated with prolonged progression-free survival (PFS).

CONCLUSION

Combined biomarkers have superior predictive accuracy than single biomarkers for immunotherapy response of NSCLC. Further investigation is warranted to select optimal biomarkers for various clinical settings.

Prostate cancer therapy using immune checkpoint molecules to target recombinant dendritic cells

PURPOSE

We developed immune checkpoint molecules to target recombinant dendritic cells (DCs) and verified their anti-tumor efficacy and immune response against prostate cancer.

MATERIALS AND METHODS

DCs were generated from mononuclear cells in the tibia and femur bone marrow of mice. We knocked down the programmed death ligand 1 (PD-L1) on monocyte-derived DCs through siRNA PD-L1. Cell surface antigens were immune fluorescently stained through flow cytometry to analyze cultured cell phenotypes. Furthermore, we evaluated the efficacy of monocyte-derived DCs and recombinant DCs in a prostate cancer mouse model with subcutaneous TRAMP-C1 cells. Lastly, DC-induced mixed lymphocyte and lymphocyte-only proliferations were compared to determine cultured DCs' function.

RESULTS

Compared to the control group, siRNA PD-L1 therapeutic DC-treated mice exhibited significantly inhibited tumor volume and increased tumor cell apoptosis. Remarkably, this treatment substantially augmented interferon-gamma and interleukin-2 production by stimulating T-cells in an allogeneic mixed lymphocyte reaction. Moreover, we demonstrated that PD-L1 gene silencing improved cell proliferation and cytokine production.

CONCLUSIONS

We developed monocyte-derived DCs transfected with PD-L1 siRNA from mouse bone marrow. Our study highlights that PD-L1 inhibition in DCs increases antigen-specific immune responses, corroborating previous immunotherapy methodology findings regarding castration-resistant prostate cancer.

Immunoradiotherapy for NSCLC: mechanisms, clinical outcomes, and future directions

Non-small-cell lung cancer (NSCLC) has an extremely low 5-year survival rate, with the only effective treatment being immunoradiotherapy (iRT). Here, we review the progress of clinical research on iRT for non-small-cell lung cancer (NSCLC) over 2018-2023, as well as the future directions. We first discuss the synergistic mechanisms of iRT, reflected in three aspects: immune regulation of RT, RT-activated immune-related pathways, and RT-related immune sensitization. iRT may include either external-beam or stereotactic-body RT combined with either immune checkpoint inhibitors (e.g., immunoglobulins against immune programmed cell death (PD) 1/PD ligand 1 or CD8+ T lymphocyte antigen 4) or traditional Chinese medicine drugs. Regarding clinical effectiveness and safety, iRT increases overall and progression-free survival and tumor control rate among patients with NSCLC but without a considerable increase in toxicity risk. We finally discuss iRT challenges and future directions reported over 2018-2023.

Multiplex Digital Spatial Profiling in Breast Cancer Research: State-of-the-Art Technologies and Applications across the Translational Science Spectrum

While RNA sequencing and multi-omic approaches have significantly advanced cancer diagnosis and treatment, their limitation in preserving critical spatial information has been a notable drawback. This spatial context is essential for understanding cellular interactions and tissue dynamics. Multiplex digital spatial profiling (MDSP) technologies overcome this limitation by enabling the simultaneous analysis of transcriptome and proteome data within the intact spatial architecture of tissues. In breast cancer research, MDSP has emerged as a promising tool, revealing complex biological questions related to disease evolution, identifying biomarkers, and discovering drug targets. This review highlights the potential of MDSP to revolutionize clinical applications, ranging from risk assessment and diagnostics to prognostics, patient monitoring, and the customization of treatment strategies, including clinical trial guidance. We discuss the major MDSP techniques, their applications in breast cancer research, and their integration in clinical practice, addressing both their potential and current limitations. Emphasizing the strategic use of MDSP in risk stratification for women with benign breast disease, we also highlight its transformative potential in reshaping the landscape of breast cancer research and treatment.

Multicellular ecotypes shape progression of lung adenocarcinoma from ground-glass opacity toward advanced stages

Lung adenocarcinoma is a type of cancer that exhibits a wide range of clinical radiological manifestations, from ground-glass opacity (GGO) to pure solid nodules, which vary greatly in terms of their biological characteristics. Our current understanding of this heterogeneity is limited. To address this gap, we analyze 58 lung adenocarcinoma patients via machine learning, single-cell RNA sequencing (scRNA-seq), and whole-exome sequencing, and we identify six lung multicellular ecotypes (LMEs) correlating with distinct radiological patterns and cancer cell states. Notably, GGO-associated neoantigens in early-stage cancers are recognized by CD8+ T cells, indicating an immune-active environment, while solid nodules feature an immune-suppressive LME with exhausted CD8+ T cells, driven by specific stromal cells such as CTHCR1+ fibroblasts. This study also highlights EGFR(L858R) neoantigens in GGO samples, suggesting potential CD8+ T cell activation. Our findings offer valuable insights into lung adenocarcinoma heterogeneity, suggesting avenues for targeted therapies in early-stage disease.

Cutting-edge technology and automation in the pathology laboratory

One of the goals of pathology is to standardize laboratory practices to increase the precision and effectiveness of diagnostic testing, which will ultimately enhance patient care and results. Standardization is crucial in the domains of tissue processing, analysis, and reporting. To enhance diagnostic testing, innovative technologies are also being created and put into use. Furthermore, although problems like algorithm training and data privacy issues still need to be resolved, digital pathology and artificial intelligence are emerging in a structured manner. Overall, for the field of pathology to advance and for patient care to be improved, standard laboratory practices and innovative technologies must be adopted. In this paper, we describe the state-of-the-art of automation in pathology laboratories in order to lead technological progress and evolution. By anticipating laboratory needs and demands, the aim is to inspire innovation tools and processes as positively transformative support for operators, organizations, and patients.

Current strategies for targeting HPK1 in cancer and the barriers to preclinical progress

INTRODUCTION

Hematopoietic progenitor kinase 1 (HPK1), a 97-kDa serine/threonine Ste20-related protein kinase, functions as an intracellular negative regulator, primarily in hematopoietic lineage cells, where it regulates T cells, B cells, dendritic cells, and other immune cells. Loss of HPK1 kinase activity results in exacerbated cytokine secretion, enhanced T cell signaling, improved viral clearance, and thus increased restraint of tumor growth. These findings highlight HPK1 as a promising target for immuno-oncology treatments, culminating in the advancement of candidate compounds targeting HPK1 to clinical trials by several biotech enterprises.

AREAS COVERED

Through searching PubMed, Espacenet-patent search, and clinicaltrials.gov, this review provides a comprehensive analysis of HPK1, encompassing its structure and roles in various downstream signaling pathways, the consequences of constitutive activation of HPK1, and potential therapeutic strategies to treat HPK1-driven malignancies. Moreover, the review outlines the patents issued for small molecule inhibitors and clinical investigations of HPK1.

EXPERT OPINION

To enhance the success of tumor immunotherapy in clinical trials, it is important to develop protein degraders, allosteric inhibitors, and antibody-drug conjugates based on the crystal structure of HPK1, and to explore combination therapy approaches. Although several challenges remain, the development of HPK1 inhibitors display promising in preclinical and clinical studies.

Redefining clinical practice through spatial profiling: a revolution in tissue analysis

Spatial biology, which combines molecular biology and advanced imaging, enhances our understanding of tissue cellular organisation. Despite its potential, spatial omics encounters challenges related to data complexity, computational requirements and standardisation of analysis. In clinical applications, spatial omics has the potential to revolutionise biomarker discovery, disease stratification and personalised treatments. It can identify disease-specific cell patterns, and could help risk stratify patients for clinical trials and disease-appropriate therapies. Although there are challenges in adopting it in clinical practice, spatial omics has the potential to significantly enhance patient outcomes. In this paper, we discuss the recent evolution of spatial biology, and its potential for improving our tissue level understanding and treatment of disease, to help advance precision and effectiveness in healthcare interventions.

Comparing deep learning and pathologist quantification of cell-level PD-L1 expression in non-small cell lung cancer whole-slide images

Programmed death-ligand 1 (PD-L1) expression is currently used in the clinic to assess eligibility for immune-checkpoint inhibitors via the tumor proportion score (TPS), but its efficacy is limited by high interobserver variability. Multiple papers have presented systems for the automatic quantification of TPS, but none report on the task of determining cell-level PD-L1 expression and often reserve their evaluation to a single PD-L1 monoclonal antibody or clinical center. In this paper, we report on a deep learning algorithm for detecting PD-L1 negative and positive tumor cells at a cellular level and evaluate it on a cell-level reference standard established by six readers on a multi-centric, multi PD-L1 assay dataset. This reference standard also provides for the first time a benchmark for computer vision algorithms. In addition, in line with other papers, we also evaluate our algorithm at slide-level by measuring the agreement between the algorithm and six pathologists on TPS quantification. We find a moderately low interobserver agreement at cell-level level (mean reader-reader F1 score = 0.68) which our algorithm sits slightly under (mean reader-AI F1 score = 0.55), especially for cases from the clinical center not included in the training set. Despite this, we find good AI-pathologist agreement on quantifying TPS compared to the interobserver agreement (mean reader-reader Cohen's kappa = 0.54, 95% CI 0.26-0.81, mean reader-AI kappa = 0.49, 95% CI 0.27-0.72). In conclusion, our deep learning algorithm demonstrates promise in detecting PD-L1 expression at a cellular level and exhibits favorable agreement with pathologists in quantifying the tumor proportion score (TPS). We publicly release our models for use via the Grand-Challenge platform.

Deep learning on tertiary lymphoid structures in hematoxylin-eosin predicts cancer prognosis and immunotherapy response

Tertiary lymphoid structures (TLSs) have been associated with favorable immunotherapy responses and prognosis in various cancers. Despite their significance, their quantification using multiplex immunohistochemistry (mIHC) staining of T and B lymphocytes remains labor-intensive, limiting its clinical utility. To address this challenge, we curated a dataset from matched mIHC and H&E whole-slide images (WSIs) and developed a deep learning model for automated segmentation of TLSs. The model achieved Dice coefficients of 0.91 on the internal test set and 0.866 on the external validation set, along with intersection over union (IoU) scores of 0.819 and 0.787, respectively. The TLS ratio, defined as the segmented TLS area over the total tissue area, correlated with B lymphocyte levels and the expression of CXCL13, a chemokine associated with TLS formation, in 6140 patients spanning 16 tumor types from The Cancer Genome Atlas (TCGA). The prognostic models for overall survival indicated that the inclusion of the TLS ratio with TNM staging significantly enhanced the models' discriminative ability, outperforming the traditional models that solely incorporated TNM staging, in 10 out of 15 TCGA tumor types. Furthermore, when applied to biopsied treatment-naïve tumor samples, higher TLS ratios predicted a positive immunotherapy response across multiple cohorts, including specific therapies for esophageal squamous cell carcinoma, non-small cell lung cancer, and stomach adenocarcinoma. In conclusion, our deep learning-based approach offers an automated and reproducible method for TLS segmentation and quantification, highlighting its potential in predicting immunotherapy response and informing cancer prognosis.

Spatial relationships in the urothelial and head and neck tumor microenvironment predict response to combination immune checkpoint inhibitors

Immune checkpoint inhibitors (ICI) can achieve remarkable responses in urothelial cancer (UC), which may depend on tumor microenvironment (TME) characteristics. However, the relationship between the TME, usually characterized by immune cell density, and response to ICI is unclear. Here, we quantify the TME immune cell densities and spatial relationships (SRs) of 24 baseline UC samples, obtained before pre-operative combination ICI treatment, using multiplex immunofluorescence. We describe SRs by approximating the first nearest-neighbor distance distribution with a Weibull distribution and evaluate the association between TME metrics and ipilimumab+nivolumab response. Immune cell density does not discriminate between response groups. However, the Weibull SR metrics of CD8+ T cells or macrophages to their closest cancer cell positively associate with response. CD8+ T cells close to B cells are characteristic of non-response. We validate our SR response associations in a combination ICI cohort of head and neck tumors. Our data confirm that SRs, in contrast to density metrics, are strong biomarkers of response to pre-operative combination ICIs.

Unveiling spatial complexity in solid tumor immune microenvironments through multiplexed imaging

Deciphering cellular components and the spatial interaction network of the tumor immune microenvironment (TIME) of solid tumors is pivotal for understanding biologically relevant cross-talks and, ultimately, advancing therapies. Multiplexed tissue imaging provides a powerful tool to elucidate spatial complexity in a holistic manner. We established and cross-validated a comprehensive immunophenotyping panel comprising over 121 markers for multiplexed tissue imaging using MACSima™ imaging cyclic staining (MICS) alongside an end-to-end analysis workflow. Applying this panel and workflow to primary cancer tissues, we characterized tumor heterogeneity, investigated potential therapeutical targets, conducted in-depth profiling of cell types and states, sub-phenotyped T cells within the TIME, and scrutinized cellular neighborhoods of diverse T cell subsets. Our findings highlight the advantage of spatial profiling, revealing immunosuppressive molecular signatures of tumor-associated myeloid cells interacting with neighboring exhausted, PD1high T cells in the TIME of hepatocellular carcinoma (HCC). This study establishes a robust framework for spatial exploration of TIMEs in solid tumors and underscores the potency of multiplexed tissue imaging and ultra-deep cell phenotyping in unraveling clinically relevant tumor components.

Spatial insights into immunotherapy response in non-small cell lung cancer (NSCLC) by multiplexed tissue imaging

The spatial localisation of immune cells within tumours are key to understand the intercellular communications that can dictate clinical outcomes. Here, we demonstrate an analysis pipeline for highly multiplexed CODEX data to phenotype and profile spatial features and interactions in NSCLC patients that subsequently received PD1 axis immunotherapy. We found that regulatory T cells (Tregs) are enriched in non-responding patients and this was consistent with their localization within stromal and peripheral tumour-margins. Proximity-based interactions between Tregs and both monocytes (p = 0.009) and CD8+ T cells (p = 0.009) were more frequently found in non-responding patients, while macrophages were more frequently located in proximity to HLADR+ tumour cells (p = 0.01) within responding patients. Cellular neighbourhoods analysis indicated that both macrophages (p = 0.003) and effector CD4+ T cells (p = 0.01) in mixed tumour neighbourhoods, as well as CD8+ T cells (p = 0.03) in HLADR+ tumour neighbourhoods were associated with favorable clinical response. Evaluation of the inferred regulatory functions between immune cells relative to the tumour suggested that macrophages exhibit an immunosuppressive phenotype against both CD4+ and CD8+ T cells, and that this association scores more highly in ICI refractory patients. These spatial patterns are associated with overall survival in addition to ICI response and may thus indicate features for the functional understanding of the tumour microenvironment.

Immunosurveillance in clinical cancer management

The progression of cancer involves a critical step in which malignant cells escape from control by the immune system. Antineoplastic agents are particularly efficient when they succeed in restoring such control (immunosurveillance) or at least establish an equilibrium state that slows down disease progression. This is true not only for immunotherapies, such as immune checkpoint inhibitors (ICIs), but also for conventional chemotherapy, targeted anticancer agents, and radiation therapy. Thus, therapeutics that stress and kill cancer cells while provoking a tumor-targeting immune response, referred to as immunogenic cell death, are particularly useful in combination with ICIs. Modern oncology regimens are increasingly using such combinations, which are referred to as chemoimmunotherapy, as well as combinations of multiple ICIs. However, the latter are generally associated with severe side effects compared with single-agent ICIs. Of note, the success of these combinatorial strategies against locally advanced or metastatic cancers is now spurring successful attempts to move them past the postoperative (adjuvant) setting to the preoperative (neoadjuvant) setting, even for patients with operable cancers. Here, the authors critically discuss the importance of immunosurveillance in modern clinical cancer management.

Enhancing anti-CD274 (PD-L1) targeting through combinatorial immunotherapy with bispecific antibodies and fusion proteins: from preclinical to phase II clinical trials

INTRODUCTION

Immune checkpoint inhibitors have achieved great success in the treatment of many different types of cancer. Programmed cell death protein ligand 1 (PD-L1, CD274) is a major immunosuppressive immune checkpoint and a target for several already approved monoclonal antibodies. Despite this, novel strategies are under development, as the overall response remains low.

AREAS COVERED

In this review, an overview of the current biomarkers for response to PD-L1 inhibitor treatment is given, followed by a discussion of potential novel biomarkers, including tumor mutational burden and circulating tumor DNA. Combinatorial immunotherapy is a potential novel strategy to increase the response to PD-L1 inhibitor treatment and currently, several interesting bispecific antibodies as well as bispecific fusion proteins are undergoing early clinical investigation. We focus on substances targeting PD-L1 and a secondary target, and a secondary immunomodulatory target like CTLA-4, TIGIT, or CD47.

EXPERT OPINION

Overall, the presented studies show anti-tumor activity of these combinatorial immunotherapeutic approaches. However, still relatively low response rates suggest a need for better biomarkers.

Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

High-Plex Assessment of Biomarkers in Tumors

The assessment of biomarkers plays a critical role in the diagnosis and treatment of many cancers. Biomarkers not only provide diagnostic, prognostic, or predictive information but also can act as effective targets for new pharmaceutical therapies. As the utility of biomarkers increases, it becomes more important to utilize accurate and efficient methods for biomarker discovery and, ultimately, clinical assessment. High-plex imaging studies, defined here as assessment of 8 or more biomarkers on a single slide, have become the method of choice for biomarker discovery and assessment of biomarker spatial context. In this review, we discuss methods of measuring biomarkers in slide-mounted tissue samples, detail the various high-plex methods that allow for the simultaneous assessment of multiple biomarkers in situ, and describe the impact of high-plex biomarker assessment on the future of anatomic pathology.

Serum amyloid A promotes glycolysis of neutrophils during PD-1 blockade resistance in hepatocellular carcinoma

The response to programmed death-1 (PD-1) blockade varies in hepatocellular carcinoma (HCC). We utilize a panel of 16 serum factors to show that a circulating level of serum amyloid A (SAA) > 20.0 mg/L has the highest accuracy in predicting anti-PD-1 resistance in HCC. Further experiments show a correlation between peritumoral SAA expression and circulating SAA levels in patients with progressive disease after PD-1 inhibition. In vitro experiments demonstrate that SAA induces neutrophils to express PD-L1 through glycolytic activation via an LDHA/STAT3 pathway and to release oncostatin M, thereby attenuating cytotoxic T cell function. In vivo, genetic or pharmacological inhibition of STAT3 or SAA eliminates neutrophil-mediated immunosuppression and enhances antitumor efficacy of anti-PD-1 treatment. This study indicates that SAA may be a critical inflammatory cytokine implicated in anti-PD-1 resistance in HCC. Targeting SAA-induced PD-L1+ neutrophils through STAT3 or SAA inhibition may present a potential approach for overcoming anti-PD1 resistance.

Immunophenotyping of peripheral blood in NSCLC patients discriminates responders to immune checkpoint inhibitors

PURPOSE

Immune checkpoint inhibitors (ICIs) dramatically changed the prognosis of patients with NSCLC. Unfortunately, a reliable predictive biomarker is still missing. Commonly used biomarkers, such as PD-L1, MSI, or TMB, are not quite accurate in predicting ICI efficacy.

METHODS

In this prospective observational cohort study, we investigated the predictive role of erythrocytes, thrombocytes, innate and adaptive immune cells, complement proteins (C3, C4), and cytokines from peripheral blood of 224 patients with stage III/IV NSCLC treated with ICI alone (pembrolizumab, nivolumab, and atezolizumab) or in combination (nivolumab + ipilimumab) with chemotherapy. These values were analyzed for associations with the response to the treatment and survival endpoints.

RESULTS

Higher baseline Tregs, MPV, hemoglobin, and lower monocyte levels were associated with favorable PFS and OS. Moreover, increased baseline basophils and lower levels of C3 predicted significantly improved PFS. The levels of the baseline immature granulocytes, C3, and monocytes were significantly associated with the occurrence of partial regression at the first restaging. Multiple studied parameters (n = 9) were related to PFS benefit at the time of first restaging as compared to baseline values. In addition, PFS nonbenefit group showed a decrease in lymphocyte count after three months of therapy. The OS benefit was associated with higher levels of lymphocytes, erythrocytes, hemoglobin, MCV, and MPV, and a lower value of NLR after three months of treatment.

CONCLUSION

Our work suggests that parameters from peripheral venous blood may be potential biomarkers in NSCLC patients on ICI. The baseline values of Tregs, C3, monocytes, and MPV are especially recommended for further investigation.

Interferon-stimulated neutrophils as a predictor of immunotherapy response

Despite the remarkable success of anti-cancer immunotherapy, its effectiveness remains confined to a subset of patients-emphasizing the importance of predictive biomarkers in clinical decision-making and further mechanistic understanding of treatment response. Current biomarkers, however, lack the power required to accurately stratify patients. Here, we identify interferon-stimulated, Ly6Ehi neutrophils as a blood-borne biomarker of anti-PD1 response in mice at baseline. Ly6Ehi neutrophils are induced by tumor-intrinsic activation of the STING (stimulator of interferon genes) signaling pathway and possess the ability to directly sensitize otherwise non-responsive tumors to anti-PD1 therapy, in part through IL12b-dependent activation of cytotoxic T cells. By translating our pre-clinical findings to a cohort of patients with non-small cell lung cancer and melanoma (n = 109), and to public data (n = 1440), we demonstrate the ability of Ly6Ehi neutrophils to predict immunotherapy response in humans with high accuracy (average AUC ≈ 0.9). Overall, our study identifies a functionally active biomarker for use in both mice and humans.

Tumor mutational burden predictability in head and neck squamous cell carcinoma patients treated with immunotherapy: systematic review and meta-analysis

BACKGROUND

Tumor mutational burden (TMB) has been demonstrated to predict the response to immune checkpoint inhibitors (ICIs) in various cancers. However, the role of TMB in head and neck squamous cell carcinoma (HNSCC) has not yet been specifically addressed. Since HNSCC patients exhibit a rather limited response to ICIs, there is an unmet need to develop predictive biomarkers to improve patient selection criteria and the clinical benefit of ICI treatment.

METHODS

We conducted a systematic review and meta-analysis according to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines. HNSCC cohort studies were selected when TMB prior to ICI treatment was evaluated, TMB cutoff value was available, and the prognostic value of TMB was evaluated by time-to-event survival analysis. A total of 11 out of 1960 articles were analyzed, including 1200 HNSCC patients.

RESULTS

The results showed that those patients harboring high TMB exhibited a significantly superior overall response rate (OR = 2.62; 95% CI 1.74-3.94; p < 0.0001) and a survival advantage (HR = 0.53; 95% CI 0.39-0.71; p < 0.0001) after ICI treatment.

CONCLUSION

This is the first meta-analysis to demonstrate a higher response and clinical benefit from ICI therapy in HNSCC patients with high TMB.

Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data

PURPOSE

Although immune checkpoint inhibitors (ICIs) have improved outcomes in certain patients with cancer, they can also cause life-threatening immunotoxicities. Predicting immunotoxicity risks alongside response could provide a personalized risk-benefit profile, inform therapeutic decision making, and improve clinical trial cohort selection. We aimed to build a machine learning (ML) framework using routine electronic health record (EHR) data to predict hepatitis, colitis, pneumonitis, and 1-year overall survival.

METHODS

Real-world EHR data of more than 2,200 patients treated with ICI through December 31, 2018, were used to develop predictive models. Using a prediction time point of ICI initiation, a 1-year prediction time window was applied to create binary labels for the four outcomes for each patient. Feature engineering involved aggregating laboratory measurements over appropriate time windows (60-365 days). Patients were randomly partitioned into training (80%) and test (20%) sets. Random forest classifiers were developed using a rigorous model development framework.

RESULTS

The patient cohort had a median age of 63 years and was 61.8% male. Patients predominantly had melanoma (37.8%), lung cancer (27.3%), or genitourinary cancer (16.4%). They were treated with PD-1 (60.4%), PD-L1 (9.0%), and CTLA-4 (19.7%) ICIs. Our models demonstrate reasonably strong performance, with AUCs of 0.739, 0.729, 0.755, and 0.752 for the pneumonitis, hepatitis, colitis, and 1-year overall survival models, respectively. Each model relies on an outcome-specific feature set, though some features are shared among models.

CONCLUSION

To our knowledge, this is the first ML solution that assesses individual ICI risk-benefit profiles based predominantly on routine structured EHR data. As such, use of our ML solution will not require additional data collection or documentation in the clinic.