Patients with resected stage II-III melanoma have approximately a 35% chance of death from their disease. A deeper understanding of the tumor immune microenvironment (TIME) is required to stratify patients and identify factors leading to therapy resistance. We previously identified that the melanoma immune profile (MIP), an IFN-based gene signature, and the ratio of CD8 cytotoxic T lymphocytes (CTL) to CD68 macrophages both predict disease-specific survival (DSS). Here, we compared primary with metastatic tumors and found that the nuclei of tumor cells were significantly larger in metastases. The CTL/macrophage ratio was significantly different between primary tumors without distant metastatic recurrence (DMR) and metastases. Patients without DMR had higher degrees of clustering between tumor cells and CTLs, and between tumor cells and HLA-DR macrophages, but not HLA-DR macrophages. The HLA-DR subset coexpressed CD163CSF1R at higher levels than CD68HLA-DR macrophages, consistent with an M2 phenotype. Finally, combined transcriptomic and multiplex data revealed that densities of CD8 and M1 macrophages correlated with their respective cell phenotype signatures. Combination of the MIP signature with the CTL/macrophage ratio stratified patients into three risk groups that were predictive of DSS, highlighting the potential use of combination biomarkers for adjuvant therapy. SIGNIFICANCE: These findings provide a deeper understanding of the tumor immune microenvironment by combining multiple modalities to stratify patients into risk groups, a critical step to improving the management of patients with melanoma.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7112471PMC
http://dx.doi.org/10.1158/0008-5472.CAN-19-2039DOI Listing

Publication Analysis

Top Keywords

tumor immune
12
immune microenvironment
12
tumor cells
12
deeper understanding
8
understanding tumor
8
stratify patients
8
ctl/macrophage ratio
8
hla-dr macrophages
8
macrophages hla-dr
8
risk groups
8

Similar Publications

Introduction: Immune checkpoint inhibitors (ICI) have improved the therapeutic arsenal in outpatient oncology care; however, data on necessity of hospitalizations associated with immune-related adverse events (irAEs) are scarce. Here, we characterized hospitalizations of patients undergoing ICI, from the prospective cohort study of the immune cooperative oncology group (ICOG) Hannover.

Methods: Between 12/2019 and 06/2022, 237 patients were included.

View Article and Find Full Text PDF

Background: To correlate between immunohistochemical expression of tumor-infiltrating lymphocytes (TILs), tumor-associated macrophages (TAMs), and natural killer (NK) cells with the AJCC 8th edition TNM staging system and other disease-modifying clinico-pathological variables.

Methods: The representative histology sections of tumor invasive margin (IM) and tumor core (TC) were selected according to the International Immuno-Oncology Biomarker Working Group and were subjected to immunohistochemistry with antibodies for TILs (CD3, CD8, FOXP3), NK Cells (CD57), TAMs (CD68, CD163) and pan-leukocyte marker (CD45). Histo-immuno-density-intensity (HIDI) scoring was calculated as a product of the proportion and intensity of staining.

View Article and Find Full Text PDF

Background: Immunotherapy is a significant risk factor for severe COVID-19 in multiple myeloma (MM) patients. Understanding how immunotherapies lead to severe COVID-19 is crucial for improving patient outcomes.

Methods: Human protein microarrays were used to examine the expression of 440 protein molecules in MM patients treated with bispecific T-cell engagers (BiTe) (n = 9), anti-CD38 monoclonal antibodies (mAbs) (n = 10), and proteasome inhibitor (PI)-based regimens (n = 10).

View Article and Find Full Text PDF

An essential task in spatial transcriptomics is identifying spatially variable genes (SVGs). Here, we present Celina, a statistical method for systematically detecting cell type-specific SVGs (ct-SVGs)-a subset of SVGs exhibiting distinct spatial expression patterns within specific cell types. Celina utilizes a spatially varying coefficient model to accurately capture each gene's spatial expression pattern in relation to the distribution of cell types across tissue locations, ensuring effective type I error control and high power.

View Article and Find Full Text PDF

Integrating mitochondrial and lysosomal gene analysis for breast cancer prognosis using machine learning.

Sci Rep

January 2025

Departments of Breast Surgery, First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, People's Republic of China.

The impact of mitochondrial and lysosomal co-dysfunction on breast cancer patient outcomes is unclear. The objective of this study is to develop a predictive machine learning (ML) model utilizing mitochondrial and lysosomal co-regulators in order to provide a foundation for future studies focused on breast cancer (BC) patients' stratification and personalized interventions. Firstly, Differences and correlations of mitochondrial and lysosome related genes were screened and validated by differential analysis, copy number variation (CNV), single nucleotide polymorphism (SNPs) and correlation analysis.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!