Genome-wide DNA methylation profile analysis identifies an individualized predictive signature for melanoma immune response

Integrative analysis of epigenetic subtypes in acute myeloid Leukemia: A multi-center study combining machine learning for prognostic and therapeutic insights

BACKGROUND

Acute Myeloid Leukemia (AML) exhibits significant heterogeneity in clinical outcomes, yet current prognostic stratification systems based on genetic alterations alone cannot fully capture this complexity. This study aimed to develop an integrated epigenetic-based classification system and evaluate its prognostic value.

METHODS

We performed multi-omics analysis on five independent cohorts totaling 1,103 AML patients. The Cancer Genome Atlas-Acute Myeloid Leukemia (TCGA-LAML) cohort (n = 83) provided comprehensive multi-omics data including DNA methylation profiles (Illumina 450K platform), RNA sequencing (mRNA, lncRNA, and miRNA), and somatic mutation profiles. The BEAT (n = 649), TARGET (n = 156), GSE12417 (n = 79), and GSE37642 (n = 136) cohorts contributed transcriptome data. Molecular subtypes were identified using empirical Bayes-based clustering on the TCGA cohort. LSC17 scores were calculated using a validated 17-gene expression signature. A random survival forest model was developed integrating molecular features with LSC17 scores, validated across all cohorts. Immune microenvironment analysis employed multiple deconvolution methods (ESTIMATE, CIBERSORT, xCell) and pathway analysis (GSVA, GSEA). Drug sensitivity was predicted using the pRRophetic algorithm with GDSC database reference.

RESULTS

Multi-omics integration revealed two molecularly distinct AML subtypes with significant survival differences (CS2 vs CS1, P < 0.001). The random survival forest model, incorporating 20 key epigenetic features (including CPNE8, CD109, and CHRDL1) and LSC17 scores, achieved superior prognostic accuracy (C-index: 0.72-0.78) across validation cohorts. Both epigenetic risk score (HR = 2.45, 95%CI: 1.86-3.24) and LSC17 score (HR = 1.89, 95%CI: 1.42-2.51) maintained independent prognostic value in multivariate analysis. Integration of both scores in a nomogram improved 1-, 3-, and 5-year survival predictions (C-index: 0.81). High-risk patients exhibited distinct immune profiles with elevated M2 macrophages (1.8-fold) and Tregs (2.3-fold), while low-risk patients showed enhanced NK cell activity (2.1-fold). Drug sensitivity analysis identified differential responses to epigenetic regulators (LAQ824, P = 0.000139; MS-275, P = 0.00104) and proteasome inhibitors (Bortezomib, P = 0.00747; MG-132, P = 0.0106) between risk groups.

CONCLUSIONS

This integrated classification system combining epigenetic features and stem cell signatures provides new insights into AML heterogeneity and therapeutic targeting. The complementary nature of epigenetic and stem cell-related prognostic factors suggests potential for improved risk stratification in clinical practice. Future prospective validation studies are warranted to confirm these findings.

Tumor infiltrating lymphocytes (TILs) - Pathologia, quo vadis? - A global survey

Tumor-infiltrating lymphocytes (TILs) and the tumor microenvironment have become increasingly important in cancer research, and immunotherapy has achieved major breakthroughs in improving patient outcomes. Despite the significant role of the pathologist in identifying, subtyping and reporting TILs, the implementation and assessment of TILs in pathology routine remains vague. To assess the actual use of TILs in routine clinical practice, a formal standardized questionnaire was disseminated on two social media platforms ("X" and LinkedIn) and by email in June 2024. Based on the results, we conducted a literature review on TILs via Medline/Pubmed in the two most scored and reported entities, namely malignant melanoma and colorectal cancer (CRC). 77 participants from 24 different countries around the world, mostly pathologists (n = 63, 82.0 %), completed the survey. More than half of the participants do not assess or report TILs in their daily (clinical) practice, a trend consistent across the countries included in the study. A variety of methods are used to report TILs, ranging from Artificial Intelligence (AI)-based scoring algorithms to quantification by eyeballing. Despite recognizing the importance of TIL assessment in clinical routine, many participants find it time-consuming and express a strong preference for AI-based quantification. Our survey reflects the perspective of mostly early career pathologists who recognize the importance of TILs in cancer but face challenges in implementation. The development of AI tools and consensus guidelines could alleviate these barriers. In addition, increasing the visibility and understanding of the role of pathologists within the medical community remains critical.

DNA Methylation Classes of Stage II and III Primary Melanomas and Their Clinical and Prognostic Significance

PURPOSE

Patients with stage II and III cutaneous primary melanoma vary considerably in their risk of melanoma-related death. We explore the ability of methylation profiling to distinguish primary melanoma methylation classes and their associations with clinicopathologic characteristics and survival.

MATERIALS AND METHODS

InterMEL is a retrospective case-control study that assembled primary cutaneous melanomas from American Joint Committee on Cancer (AJCC) 8th edition stage II and III patients diagnosed between 1998 and 2015 in the United States and Australia. Cases are patients who died of melanoma within 5 years from original diagnosis. Controls survived longer than 5 years without evidence of melanoma recurrence or relapse. Methylation classes, distinguished by consensus clustering of 850K methylation data, were evaluated for their clinicopathologic characteristics, 5-year survival status, and differentially methylated gene sets.

RESULTS

Among 422 InterMEL melanomas, consensus clustering revealed three primary melanoma methylation classes (MethylClasses): a CpG island methylator phenotype (CIMP) class, an intermediate methylation (IM) class, and a low methylation (LM) class. CIMP and IM were associated with higher AJCC stage (both P = .002), Breslow thickness (CIMP P = .002; IM P = .006), and mitotic index (both P < .001) compared with LM, while IM had higher N stage than CIMP (P = .01) and LM (P = .007). CIMP and IM had a 2-fold higher likelihood of 5-year death from melanoma than LM (CIMP odds ratio [OR], 2.16 [95% CI, 1.18 to 3.96]; IM OR, 2.00 [95% CI, 1.12 to 3.58]) in a multivariable model adjusted for age, sex, log Breslow thickness, ulceration, mitotic index, and N stage. Despite more extensive CpG island hypermethylation in CIMP, CIMP and IM shared similar patterns of differential methylation and gene set enrichment compared with LM.

CONCLUSION

Melanoma MethylClasses may provide clinical value in predicting 5-year death from melanoma among patients with primary melanoma independent of other clinicopathologic factors.

Emerging prognostic biomarkers in advanced cutaneous melanoma: a literature update

INTRODUCTION

Over the past two years, the scientific community has witnessed an exponential growth in research focused on identifying prognostic biomarkers for melanoma, both in pre-clinical and clinical settings. This surge in studies reflects the need of developing effective prognostic indicators in the field of melanoma.

AREAS COVERED

The aim of this work is to review the scientific literature on the most recent findings on the development or validation of prognostic biomarkers in melanoma, in the attempt of providing both clinicians and researchers with an updated broad synopsis of prognostic biomarkers in cutaneous melanoma.

EXPERT OPINION

While the field of prognostic biomarkers in melanoma appears promising, there are several complexities and limitations to address. The interdependence of clinical, histological, and molecular features requires accurate classification of different biomarker families. Correlation does not imply causation, and adjustments for confounding factors are often overlooked. In this scenario, large-scale studies based on high-quality clinical trial data can provide more reliable evidence. It is essential to avoid oversimplification by focusing on a single biomarker, as the interactions among multiple factors contribute to define the disease course and patient's outcome. Furthermore, implementing well-supported evidence in real-life settings can help advance prognostic biomarker research in melanoma.

Biomarkers and experimental models for cancer immunology investigation

The rapid advancement of tumor immunotherapies poses challenges for the tools used in cancer immunology research, highlighting the need for highly effective biomarkers and reproducible experimental models. Current immunotherapy biomarkers encompass surface protein markers such as PD-L1, genetic features such as microsatellite instability, tumor-infiltrating lymphocytes, and biomarkers in liquid biopsy such as circulating tumor DNAs. Experimental models, ranging from 3D in vitro cultures (spheroids, submerged models, air-liquid interface models, organ-on-a-chips) to advanced 3D bioprinting techniques, have emerged as valuable platforms for cancer immunology investigations and immunotherapy biomarker research. By preserving native immune components or coculturing with exogenous immune cells, these models replicate the tumor microenvironment in vitro. Animal models like syngeneic models, genetically engineered models, and patient-derived xenografts provide opportunities to study in vivo tumor-immune interactions. Humanized animal models further enable the simulation of the human-specific tumor microenvironment. Here, we provide a comprehensive overview of the advantages, limitations, and prospects of different biomarkers and experimental models, specifically focusing on the role of biomarkers in predicting immunotherapy outcomes and the ability of experimental models to replicate the tumor microenvironment. By integrating cutting-edge biomarkers and experimental models, this review serves as a valuable resource for accessing the forefront of cancer immunology investigation.