Robust immunoscore model to predict the response to anti-PD1 therapy in melanoma

Targeted demethylation and activation of NLRC5 augment cancer immunogenicity through MHC class I

Impaired expression of MHC (major histocompatibility complex) class I in cancers constitutes a major mechanism of immune evasion. It has been well documented that the low level of MHC class I is associated with poor prognosis and resistance to checkpoint blockade therapies. However, there is lmited approaches to specifically induce MHC class I to date. Here, we show an approach for robust and specific induction of MHC class I by targeting an MHC class I transactivator (CITA)/NLRC5, using a CRISPR/Cas9-based gene-specific system, designated TRED-I (Targeted reactivation and demethylation for MHC-I). The TRED-I system specifically recruits a demethylating enzyme and transcriptional activators on the NLRC5 promoter, driving increased MHC class I antigen presentation and accelerated CD8+ T cell activation. Introduction of the TRED-I system in an animal cancer model exhibited tumor-suppressive effects accompanied with increased infiltration and activation of CD8+ T cells. Moreover, this approach boosted the efficacy of checkpoint blockade therapy using anti-PD1 (programmed cell death protein) antibody. Therefore, targeting NLRC5 by this strategy provides an attractive therapeutic approach for cancer.

The T Cell Immunoscore as a Reference for Biomarker Development Utilizing Real-World Data from Patients with Advanced Malignancies Treated with Immune Checkpoint Inhibitors

BACKGROUND

We aimed to determine the prognostic value of an immunoscore reflecting CD3+ and CD8+ T cell density estimated from real-world transcriptomic data of a patient cohort with advanced malignancies treated with immune checkpoint inhibitors (ICIs) in an effort to validate a reference for future machine learning-based biomarker development.

METHODS

Transcriptomic data was collected under the Total Cancer Care Protocol (NCT03977402) Avatar® project. The real-world immunoscore for each patient was calculated based on the estimated densities of tumor CD3+ and CD8+ T cells utilizing CIBERSORTx and the LM22 gene signature matrix. Then, the immunoscore association with overall survival (OS) was estimated using Cox regression and analyzed using Kaplan-Meier curves. The OS predictions were assessed using Harrell's concordance index (C-index). The Youden index was used to identify the optimal cut-off point. Statistical significance was assessed using the log-rank test.

RESULTS

Our study encompassed 522 patients with four cancer types. The median duration to death was 10.5 months for the 275 participants who encountered an event. For the entire cohort, the results demonstrated that transcriptomics-based immunoscore could significantly predict patients at risk of death (p-value < 0.001). Notably, patients with an intermediate-high immunoscore achieved better OS than those with a low immunoscore. In subgroup analysis, the prediction of OS was significant for melanoma and head and neck cancer patients but did not reach significance in the non-small cell lung cancer or renal cell carcinoma cohorts.

CONCLUSIONS

Calculating CD3+ and CD8+ T cell immunoscore using real-world transcriptomic data represents a promising signature for estimating OS with ICIs and can be used as a reference for future machine learning-based biomarker development.

A Comprehensive Benchmark of Transcriptomic Biomarkers for Immune Checkpoint Blockades

Immune checkpoint blockades (ICBs) have revolutionized cancer therapy by inducing durable clinical responses, but only a small percentage of patients can benefit from ICB treatments. Many studies have established various biomarkers to predict ICB responses. However, different biomarkers were found with diverse performances in practice, and a timely and unbiased assessment has yet to be conducted due to the complexity of ICB-related studies and trials. In this study, we manually curated 29 published datasets with matched transcriptome and clinical data from more than 1400 patients, and uniformly preprocessed these datasets for further analyses. In addition, we collected 39 sets of transcriptomic biomarkers, and based on the nature of the corresponding computational methods, we categorized them into the gene-set-like group (with the self-contained design and the competitive design, respectively) and the deconvolution-like group. Next, we investigated the correlations and patterns of these biomarkers and utilized a standardized workflow to systematically evaluate their performance in predicting ICB responses and survival statuses across different datasets, cancer types, antibodies, biopsy times, and combinatory treatments. In our benchmark, most biomarkers showed poor performance in terms of stability and robustness across different datasets. Two scores (TIDE and CYT) had a competitive performance for ICB response prediction, and two others (PASS-ON and EIGS_ssGSEA) showed the best association with clinical outcome. Finally, we developed ICB-Portal to host the datasets, biomarkers, and benchmark results and to implement the computational methods for researchers to test their custom biomarkers. Our work provided valuable resources and a one-stop solution to facilitate ICB-related research.

Immunoscore Signatures in Surgical Specimens and Tumor-Infiltrating Lymphocytes in Pretreatment Biopsy Predict Treatment Efficacy and Survival in Esophageal Cancer

OBJECTIVES

Tumor-infiltrating lymphocytes (TILs) have long been recognized as playing an important role in tumor immune microenvironment. Lately, the Immunoscore (IS) has been proposed as a new method of quantifying the number of TILs in association with patient survival in several cancer types.

METHODS

In 300 preoperatively untreated esophageal cancer (EC) patients who underwent curative resection at two different institutes, immunohistochemical staining using CD3 and CD8 antibodies was performed to evaluate IS, as objectively scored by auto-counted TILs in the tumor core and invasive margin. In addition, in pre-neoadjuvant chemotherapy (pre-NAC) endoscopic biopsies of a different cohort of 146 EC patients who received NAC, CD3, and CD8 were immunostained to evaluate TIL density.

RESULTS

In all cases, the IS-high (score 3-4) group tended to have better survival [5-year overall survival (OS) of the IS-high vs low group: 77.6 vs 65.8%, P = 0.0722] than the IS-low (score 1-2) group. This trend was more remarkable in cStage II-IV patients (70.2 vs 54.5%, P = 0.0208) and multivariate analysis of OS further identified IS (hazard ratio 2.07, P = 0.0043) to be an independent prognostic variable. In preNAC biopsies, NAC-responders had higher densities than non-responders of both CD3 + ( P = 0.0106) and CD8 + cells ( P = 0.0729) and, particularly CD3 + cell density was found to be an independent prognostic factor (hazard ratio 1.75, P = 0.0169).

CONCLUSIONS

The IS signature in surgical specimens and TIL density in preNAC- biopsies could be predictive markers of clinical outcomes in EC patients.

Integration of transcriptomics data into agent-based models of solid tumor metastasis

Recent progress in our understanding of cancer mostly relies on the systematic profiling of patient samples with high-throughput techniques like transcriptomics. With this approach, one can find gene signatures and networks underlying cancer aggressiveness and therapy resistance. However, omics data alone cannot generate insights into the spatiotemporal aspects of tumor progression. Here, multi-level computational modeling is a promising approach that would benefit from protocols to integrate the data generated by the high-throughput profiling of patient samples. We present a computational workflow to integrate transcriptomics data from tumor patients into hybrid, multi-scale cancer models. In the method, we conduct transcriptomics analysis to select key differentially regulated pathways in therapy responders and non-responders and link them to agent-based model parameters. We then determine global and local sensitivity through systematic model simulations that assess the relevance of parameter variations in triggering therapy resistance. We illustrate the methodology with a de novo generated agent-based model accounting for the interplay between tumor and immune cells in a melanoma micrometastasis. The application of the workflow identifies three distinct scenarios of therapy resistance.

Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence

We live in an unprecedented time in oncology. We have accumulated samples and cases in cohorts larger and more complex than ever before. New technologies are available for quantifying solid or liquid samples at the molecular level. At the same time, we are now equipped with the computational power necessary to handle this enormous amount of quantitative data. Computational models are widely used helping us to substantiate and interpret data. Under the label of systems and precision medicine, we are putting all these developments together to improve and personalize the therapy of cancer. In this review, we use melanoma as a paradigm to present the successful application of these technologies but also to discuss possible future developments in patient care linked to them. Melanoma is a paradigmatic case for disruptive improvements in therapies, with a considerable number of metastatic melanoma patients benefiting from novel therapies. Nevertheless, a large proportion of patients does not respond to therapy or suffers from adverse events. Melanoma is an ideal case study to deploy advanced technologies not only due to the medical need but also to some intrinsic features of melanoma as a disease and the skin as an organ. From the perspective of data acquisition, the skin is the ideal organ due to its accessibility and suitability for many kinds of advanced imaging techniques. We put special emphasis on the necessity of computational strategies to integrate multiple sources of quantitative data describing the tumour at different scales and levels.

Immunoscore Combining CD8, FoxP3, and CD68-Positive Cells Density and Distribution Predicts the Prognosis of Head and Neck Cancer Patients

We assessed immune cell infiltrates to develop an immunoscore for prognosis and to investigate its correlation with the clinical data of patients with head and neck cancer. CD8, FoxP3, and CD68 markers were evaluated by immunohistochemistry in 258 carcinoma samples and positive cells were counted in stromal and intra-tumoral compartments. The RStudio software was used to assess optimal cut-offs to divide the population according to survival while the prognostic value was established by using Kaplan–Meier curves and Cox regression models for each immune marker alone and in combination. We found with univariate analysis that the infiltration of immune cells in both compartments was predictive for recurrence-free survival and overall survival. Multivariate analysis revealed that CD8+ density was an independent prognostic marker. Additionally, the combination of CD8, FoxP3, and CD68 in an immunoscore provided a significant association with overall survival (p = 0.002, HR = 9.87). Such an immunoscore stayed significant (p = 0.018, HR = 11.17) in a multivariate analysis in comparison to tumor stage and histological grade, which had lower prognostic values. Altogether, our analysis indicated that CD8, FoxP3, and CD68 immunoscore was a strong, independent, and significant prognostic marker that could be introduced into the landscape of current tools to improve the clinical management of head and neck cancer patients.

Clinical Categorization Algorithm (CLICAL) and Machine Learning Approach (SRF-CLICAL) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-World Evidence from the Istituto Nazionale Tumori IRCCS Fondazione Pascale, Napoli, Italy

The real-life application of immune checkpoint inhibitors (ICIs) may yield different outcomes compared to the benefit presented in clinical trials. For this reason, there is a need to define the group of patients that may benefit from treatment. We retrospectively investigated 578 metastatic melanoma patients treated with ICIs at the Istituto Nazionale Tumori IRCCS Fondazione "G. Pascale" of Napoli, Italy (INT-NA). To compare patients' clinical variables (i.e., age, lactate dehydrogenase (LDH), neutrophil-lymphocyte ratio (NLR), eosinophil, BRAF status, previous treatment) and their predictive and prognostic power in a comprehensive, non-hierarchical manner, a clinical categorization algorithm (CLICAL) was defined and validated by the application of a machine learning algorithm-survival random forest (SRF-CLICAL). The comprehensive analysis of the clinical parameters by log risk-based algorithms resulted in predictive signatures that could identify groups of patients with great benefit or not, regardless of the ICI received. From a real-life retrospective analysis of metastatic melanoma patients, we generated and validated an algorithm based on machine learning that could assist with the clinical decision of whether or not to apply ICI therapy by defining five signatures of predictability with 95% accuracy.

The Ratio of GrzB+ - FoxP3+ over CD3+ T Cells as a Potential Predictor of Response to Nivolumab in Patients with Metastatic Melanoma

Simple Summary

Despite the recent success of immunotherapy in the treatment of malignant melanoma, many patients still do not benefit from these treatments, due to their failure to activate an antitumor immune response them. There is therefore a need to select patients who can truly benefit from these treatments. We have focused our study on immune cells present in the tumor microenvironment, and we have developed a formula (ratio) that correlates with the response to anti-PD1 therapy and progression-free and overall survival, based on the numerical difference between GRZB⁺ and FOXP3⁺ cells over the total CD3⁺ lymphocytes. This developed ratio could be useful to better select patients that may or may not benefit from anti-PD-1 treatment.

Abstract

The understanding of the molecular pathways involved in the dynamic modulation of the tumor microenvironment (TME) has led to the development of innovative treatments for advanced melanoma, including immune checkpoint blockade therapies. These approaches have revolutionized the treatment of melanoma, but are not effective in all patients, resulting in responder and non-responder populations. Physical interactions among immune cells, tumor cells and all the other components of the TME (i.e., cancer-associated fibroblasts, keratinocytes, adipocytes, extracellular matrix, etc.) are essential for effective antitumor immunotherapy, suggesting the need to define an immune score model which can help to predict an efficient immunotherapeutic response. In this study, we performed a multiplex immunostaining of CD3, FOXP3 and GRZB on both primary and unmatched in-transit metastatic melanoma lesions and defined a novel ratio between different lymphocyte subpopulations, demonstrating its potential prognostic role for cancer immunotherapy. The application of the suggested ratio can be useful for the stratification of melanoma patients that may or may not benefit from anti-PD-1 treatment.

Hierarchical Clustering of Cutaneous Melanoma Based on Immunogenomic Profiling

Cutaneous melanoma is an aggressive malignancy with high heterogeneity. Several studies have been performed to identify cutaneous melanoma subtypes based on genomic profiling. However, few classifications based on assessments of immune-associated genes have limited clinical implications for cutaneous melanoma. Using 470 cutaneous melanoma samples from The Cancer Genome Atlas (TCGA), we calculated the enrichment levels of 29 immune-associated gene sets in each sample and hierarchically clustered them into Immunity High (Immunity_H, n=323, 68.7%), Immunity Medium (Immunity_M, n=135, 28.7%), and Immunity Low (Immunity_L, n=12, 2.6%) based on the ssGSEA score. The ESTIMATE algorithm was used to calculate stromal scores (range: -1,800.51-1,901.99), immune scores (range: -1,476.28-3,780.33), estimate scores (range: -2,618.28-5,098.14) and tumor purity (range: 0.216-0.976) and they were significantly correlated with immune subtypes (Kruskal-Wallis test, P < 0.001). The Immunity_H group tended to have higher expression levels of HLA and immune checkpoint genes (Kruskal-Wallis test, P < 0.05). The Immunity_H group had the highest level of naïve B cells, resting dendritic cells, M1 macrophages, resting NK cells, plasma cells, CD4 memory activated T cells, CD8 T cells, follicular helper T cells and regulatory T cells, and the Immunity_L group had better overall survival. The GO terms identified in the Immunity_H group were mainly immune related. In conclusion, immune signature-associated cutaneous melanoma subtypes play a role in cutaneous melanoma prognosis stratification. The construction of immune signature-associated cutaneous melanoma subtypes predicted possible patient outcomes and provided possible immunotherapy candidates.

Identification of an Immune-Related Prognostic Signature Associated With Immune Infiltration in Melanoma

Melanoma is the leading cause of cancer-related death among skin tumors, with an increasing incidence worldwide. Few studies have effectively investigated the significance of an immune-related gene (IRG) signature for melanoma prognosis. Here, we constructed an IRGs prognostic signature using bioinformatics methods and evaluated and validated its predictive capability. Then, immune cell infiltration and tumor mutation burden (TMB) landscapes associated with this signature in melanoma were analyzed comprehensively. With the 10-IRG prognostic signature, melanoma patients in the low-risk group showed better survival with distinct features of high immune cell infiltration and TMB. Importantly, melanoma patients in this subgroup were significantly responsive to MAGE-A3 in the validation cohort. This immune-related prognostic signature is thus a reliable tool to predict melanoma prognosis; as the underlying mechanism of this signature is associated with immune infiltration and mutation burden, it might reflect the benefit of immunotherapy to patients.

Role of the Neutrophil in the Pathogenesis of Advanced Cancer and Impaired Responsiveness to Therapy

Notwithstanding the well-recognized involvement of chronic neutrophilic inflammation in the initiation phase of many types of epithelial cancers, a growing body of evidence has also implicated these cells in the pathogenesis of the later phases of cancer development, specifically progression and spread. In this setting, established tumors have a propensity to induce myelopoiesis and to recruit neutrophils to the tumor microenvironment (TME), where these cells undergo reprogramming and transitioning to myeloid-derived suppressor cells (MDSCs) with a pro-tumorigenic phenotype. In the TME, these MDSCs, via the production of a broad range of mediators, not only attenuate the anti-tumor activity of tumor-infiltrating lymphocytes, but also exclude these cells from the TME. Realization of the pro-tumorigenic activities of MDSCs of neutrophilic origin has resulted in the development of a range of adjunctive strategies targeting the recruitment of these cells and/or the harmful activities of their mediators of immunosuppression. Most of these are in the pre-clinical or very early clinical stages of evaluation. Notable exceptions, however, are several pharmacologic, allosteric inhibitors of neutrophil/MDSC CXCR1/2 receptors. These agents have entered late-stage clinical assessment as adjuncts to either chemotherapy or inhibitory immune checkpoint-targeted therapy in patients with various types of advanced malignancy. The current review updates the origins and identities of MDSCs of neutrophilic origin and their spectrum of immunosuppressive mediators, as well as current and pipeline MDSC-targeted strategies as potential adjuncts to cancer therapies. These sections are preceded by a consideration of the carcinogenic potential of neutrophils.