Dynamic peripheral blood immune cell markers for predicting the response of patients with metastatic cancer to immune checkpoint inhibitors

Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

BACKGROUND

The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology.

MATERIALS AND METHODS

We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data.

RESULTS

A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data.

CONCLUSION

AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.

Integration of liquid biopsy and immunotherapy: opening a new era in colorectal cancer treatment

Immunotherapy has revolutionized the conventional treatment approaches for colorectal cancer (CRC), offering new therapeutic prospects for patients. Liquid biopsy has shown significant potential in early screening, diagnosis, and postoperative monitoring by analyzing circulating tumor cells (CTC) and circulating tumor DNA (ctDNA). In the era of immunotherapy, liquid biopsy provides additional possibilities for guiding immune-based treatments. Emerging technologies such as mass spectrometry-based detection of neoantigens and flow cytometry-based T cell sorting offer new tools for liquid biopsy, aiming to optimize immune therapy strategies. The integration of liquid biopsy with immunotherapy holds promise for improving treatment outcomes in colorectal cancer patients, enabling breakthroughs in early diagnosis and treatment, and providing patients with more personalized, precise, and effective treatment strategies.

Blood immune cells as potential biomarkers predicting relapse-free survival of stage III/IV resected melanoma patients treated with peptide-based vaccination and interferon-alpha

Introduction

Despite the recent approval of several therapies in the adjuvant setting of melanoma, tumor relapse still occurs in a significant number of completely resected stage III-IV patients. In this context, the use of cancer vaccines is still relevant and may increase the response to immune checkpoint inhibitors. We previously demonstrated safety, immunogenicity and preliminary evidence of clinical efficacy in stage III/IV resected melanoma patients subjected to a combination therapy based on peptide vaccination together with intermittent low-dose interferon-α2b, with or without dacarbazine preconditioning (https://www.clinicaltrialsregister.eu/ctr-search/search, identifier: 2008-008211-26). In this setting, we then focused on pre-treatment patient immune status to highlight possible factors associated with clinical outcome.

Methods

Multiparametric flow cytometry was used to identify baseline immune profiles in patients' peripheral blood mononuclear cells and correlation with the patient clinical outcome. Receiver operating characteristic curve, Kaplan-Meier survival and principal component analyses were used to evaluate the predictive power of the identified markers.

Results

We identified 12 different circulating T and NK cell subsets with significant (p ≤ 0.05) differential baseline levels in patients who later relapsed with respect to patients who remained free of disease. All 12 parameters showed a good prognostic accuracy (AUC>0.7, p ≤ 0.05) and 11 of them significantly predicted the relapse-free survival. Remarkably, 3 classifiers also predicted the overall survival. Focusing on immune cell subsets that can be analyzed through simple surface staining, three subsets were identified, namely regulatory T cells, CD56^dimCD16^- NK cells and central memory γδ T cells. Each subset showed an AUC>0.8 and principal component analysis significantly grouped relapsing and non-relapsing patients (p=0.034). These three subsets were used to calculate a combination score that was able to perfectly distinguish relapsing and non-relapsing patients (AUC=1; p=0). Noticeably, patients with a combined score ≥2 demonstrated a strong advantage in both relapse-free (p=0.002) and overall (p=0.011) survival as compared to patients with a score <2.

Discussion

Predictive markers may be used to guide patient selection for personalized therapies and/or improve follow-up strategies. This study provides preliminary evidence on the identification of peripheral blood immune biomarkers potentially capable of predicting the clinical response to combined vaccine-based adjuvant therapies in melanoma.

Cytokine profiling identifies circulating IL-6 and IL-15 as prognostic stratifiers in patients with non-small cell lung cancer receiving anti-PD-1/PD-L1 blockade therapy

Whether circulating levels of specific cytokines at baseline link with treatment efficacy of immune checkpoint blockade (ICB) therapy in patients with non-small cell lung cancer remains unknown. In this study, serum samples were collected in two independent, prospective, multicenter cohorts before the initiation of ICB. Twenty cytokines were quantified, and cutoff values were determined by receiver operating characteristic analyses to predict non-durable benefit. The associations of each dichotomized cytokine status with survival outcomes were assessed. In the discovery cohort (atezolizumab cohort; N = 81), there were significant differences in progression-free survival (PFS) in accordance with the levels of IL-6 (log-rank test, P = 0.0014), IL-15 (P = 0.00011), MCP-1 (P = 0.013), MIP-1β (P = 0.0035), and PDGF-AB/BB (P = 0.016). Of these, levels of IL-6 and IL-15 were also significantly prognostic in the validation cohort (nivolumab cohort, N = 139) for PFS (log-rank test, P = 0.011 for IL-6 and P = 0.00065 for IL-15) and overall survival (OS; P = 3.3E-6 for IL-6 and P = 0.0022 for IL-15). In the merged cohort, IL-6^high and IL-15^high were identified as independent unfavorable prognostic factors for PFS and OS. The combined IL-6 and IL-15 status stratified patient survival outcomes into three distinct groups for both PFS and OS. In conclusion, combined assessment of circulating IL-6 and IL-15 levels at baseline provides valuable information to stratify the clinical outcome of patients with non-small cell lung cancer treated with ICB. Further studies are required to decipher the mechanistic basis of this finding.