Advances in Melanoma: From Genetic Insights to Therapeutic Innovations DOI Creative Commons
Fernando Valdez-Salazar,

Luis Alberto Jiménez-Del Río,

Jorge Ramón Padilla‐Gutiérrez

et al.

Biomedicines, Journal Year: 2024, Volume and Issue: 12(8), P. 1851 - 1851

Published: Aug. 14, 2024

Advances in melanoma research have unveiled critical insights into its genetic and molecular landscape, leading to significant therapeutic innovations. This review explores the intricate interplay between alterations, such as mutations BRAF, NRAS, KIT, pathogenesis. The MAPK PI3K/Akt/mTOR signaling pathways are highlighted for their roles tumor growth resistance mechanisms. Additionally, this delves impact of epigenetic modifications, including DNA methylation histone changes, on progression. microenvironment, characterized by immune cells, stromal soluble factors, plays a pivotal role modulating behavior treatment responses. Emerging technologies like single-cell sequencing, CRISPR-Cas9, AI-driven diagnostics transforming research, offering precise personalized approaches treatment. Immunotherapy, particularly checkpoint inhibitors mRNA vaccines, has revolutionized therapy enhancing body’s response. Despite these advances, mechanisms remain challenge, underscoring need combined therapies ongoing achieve durable comprehensive overview aims highlight current state transformative impacts advancements clinical practice.

Language: Английский

Integrated analysis of single-cell and bulk RNA sequencing data reveals a pan-cancer stemness signature predicting immunotherapy response DOI Creative Commons
Zhen Zhang, Zixian Wang,

Yan‐Xing Chen

et al.

Genome Medicine, Journal Year: 2022, Volume and Issue: 14(1)

Published: April 29, 2022

Although immune checkpoint inhibitor (ICI) is regarded as a breakthrough in cancer therapy, only limited fraction of patients benefit from it. Cancer stemness can be the potential culprit ICI resistance, but direct clinical evidence lacking.

Language: Английский

Citations

175
Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review DOI
Arsela Prelaj, Vanja Mišković,

Michele Zanitti

et al.

Annals of Oncology, Journal Year: 2023, Volume and Issue: 35(1), P. 29 - 65

Published: Oct. 23, 2023

Language: Английский

Citations

117
Hallmarks of cancer stemness DOI Creative Commons

Jia-Jian Loh,

Stephanie Ma

Cell stem cell, Journal Year: 2024, Volume and Issue: 31(5), P. 617 - 639

Published: May 1, 2024

Language: Английский

Citations

71
Interferon-stimulated neutrophils as a predictor of immunotherapy response DOI Creative Commons

Madeleine Benguigui,

Tim J. Cooper,

Prajakta Kalkar

et al.

Cancer Cell, Journal Year: 2024, Volume and Issue: 42(2), P. 253 - 265.e12

Published: Jan. 4, 2024

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

Language: Английский

Citations

64
Beyond CTLA-4 and PD-1 Inhibition: Novel Immune Checkpoint Molecules for Melanoma Treatment DOI Open Access
Dimitrios C. Ziogas, Charalampos Theocharopoulos, Panagiotis-Petros Lialios

et al.

Cancers, Journal Year: 2023, Volume and Issue: 15(10), P. 2718 - 2718

Published: May 11, 2023

More than ten years after the approval of ipilimumab, immune checkpoint inhibitors (ICIs) against PD-1 and CTLA-4 have been established as most effective treatment for locally advanced or metastatic melanoma, achieving durable responses either monotherapies in combinatorial regimens. However, a considerable proportion patients do not respond experience early relapse, due to multiple parameters that contribute melanoma resistance. The expression other checkpoints beyond molecules remains major mechanism evasion. recent anti-LAG-3 ICI, relatlimab, combination with nivolumab disease, has capitalized on extensive research field highlighted potential further improvement prognosis by synergistically blocking additional targets new ICI-doublets, antibody-drug conjugates, novel modalities. Herein, we provide comprehensive overview presently published molecules, including LAG-3, TIGIT, TIM-3, VISTA, IDO1/IDO2/TDO, CD27/CD70, CD39/73, HVEM/BTLA/CD160 B7-H3. Beginning from their immunomodulatory properties co-inhibitory co-stimulatory receptors, present all therapeutic modalities targeting these tested preclinical clinical settings. Better understanding checkpoint-mediated crosstalk between effector cells is essential generating more strategies augmented response.

Language: Английский

Citations

48
Pan‐Cancer Single‐Cell and Spatial‐Resolved Profiling Reveals the Immunosuppressive Role of APOE+ Macrophages in Immune Checkpoint Inhibitor Therapy DOI Creative Commons
Chuan Liu, Jindong Xie, Bo Lin

et al.

Advanced Science, Journal Year: 2024, Volume and Issue: 11(23)

Published: April 3, 2024

Abstract The heterogeneity of macrophages influences the response to immune checkpoint inhibitor (ICI) therapy. However, few studies explore impact APOE + on ICI therapy using single‐cell RNA sequencing (scRNA‐seq) and machine learning methods. scRNA‐seq bulk RNA‐seq data are Integrated construct an M.Sig model for predicting based distinct molecular signatures macrophage algorithms. Comprehensive analysis as well in vivo vitro experiments applied potential mechanisms affecting response. shows clear advantages efficacy prognosis pan‐cancer patients. proportion is higher non‐responders triple‐negative breast cancer compared with responders, interaction longer distance between CD8 exhausted T (Tex) cells confirmed by multiplex immunohistochemistry. In a 4T1 tumor‐bearing mice model, combined treatment best efficacy. real‐world immunotherapy accurately predicts pan‐cancer, which may be associated Tex cells.

Language: Английский

Citations

32
Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection DOI Creative Commons
Hongwei Liu, Wei Zhang, Yihao Zhang

et al.

Computational and Structural Biotechnology Journal, Journal Year: 2024, Volume and Issue: 23, P. 2798 - 2810

Published: June 29, 2024

The widespread use of high-throughput sequencing technologies has revolutionized the understanding biology and cancer heterogeneity. Recently, several machine-learning models based on transcriptional data have been developed to accurately predict patients' outcome clinical response. However, an open-source R package covering state-of-the-art algorithms for user-friendly access yet be developed. Thus, we proposed a flexible computational framework construct machine learning-based integration model with elegant performance (Mime). Mime streamlines process developing predictive high accuracy, leveraging complex datasets identify critical genes associated prognosis. An in silico combined de novo PIEZO1-associated signatures constructed by demonstrated accuracy predicting outcomes patients compared other published models. Furthermore, could also precisely infer immunotherapy response applying different Mime. Finally, SDC1 selected from potential as glioma target. Taken together, our provides solution constructing will greatly expanded provide valuable insights into current fields. is available GitHub (https://github.com/l-magnificence/Mime).

Language: Английский

Citations

30
Informing immunotherapy with multi-omics driven machine learning DOI Creative Commons
Yawei Li, Wu Xin, Deyu Fang

et al.

npj Digital Medicine, Journal Year: 2024, Volume and Issue: 7(1)

Published: March 14, 2024

Abstract Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid hematologic malignancies. However, the benefits of are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict response is crucial. Machine learning (ML) play a pivotal role harnessing multi-omic cancer datasets unlocking new insights into immunotherapy. This review provides an overview cutting-edge ML models applied omics data analysis, including prediction immunotherapy-relevant tumor microenvironment identification. We elucidate how leverages diverse types identify significant biomarkers, enhance our understanding mechanisms, optimize decision-making process. Additionally, we discuss current limitations this rapidly evolving field. Finally, outline future directions aimed at overcoming these barriers improving efficiency research.

Language: Английский

Citations

28
Deciphering the tumor immune microenvironment from a multidimensional omics perspective: insight into next-generation CAR-T cell immunotherapy and beyond DOI Creative Commons
Zhaokai Zhou, Jiahui Wang, Jiaojiao Wang

et al.

Molecular Cancer, Journal Year: 2024, Volume and Issue: 23(1)

Published: June 26, 2024

Abstract Tumor immune microenvironment (TIME) consists of intra-tumor immunological components and plays a significant role in tumor initiation, progression, metastasis, response to therapy. Chimeric antigen receptor (CAR)-T cell immunotherapy has revolutionized the cancer treatment paradigm. Although CAR-T emerged as successful for hematologic malignancies, it remains conundrum solid tumors. The heterogeneity TIME is responsible poor outcomes against advancement highly sophisticated technology enhances our exploration from multi-omics perspective. In era machine learning, studies could reveal characteristics its resistance mechanism. Therefore, clinical efficacy tumors be further improved with strategies that target unfavorable conditions TIME. Herein, this review seeks investigate factors influencing formation propose improving effectiveness through perspective, ultimate goal developing personalized therapeutic approaches.

Language: Английский

Citations

19
Single-cell sequencing technology applied to epigenetics for the study of tumor heterogeneity DOI Creative Commons

Yuhua Hu,

Feng Shen, Xi Yang

et al.

Clinical Epigenetics, Journal Year: 2023, Volume and Issue: 15(1)

Published: Oct. 11, 2023

Previous studies have traditionally attributed the initiation of cancer cells to genetic mutations, considering them as fundamental drivers carcinogenesis. However, recent research has shed light on crucial role epigenomic alterations in various cell types present within tumor microenvironment, suggesting their potential contribution formation and progression. Despite these significant findings, progress understanding epigenetic mechanisms regulating heterogeneity been impeded over past few years due lack appropriate technical tools methodologies.The emergence single-cell sequencing enhanced our governing by revealing distinct layers individual (chromatin accessibility, DNA/RNA methylation, histone modifications, nucleosome localization) diverse omics (transcriptomics, genomics, multi-omics) at level. These technologies provide us with new insights into molecular basis intratumoral help uncover key events driving development.This paper provides a comprehensive review emerging analytical experimental approaches omics, focusing specifically epigenomics. capture integrate multiple dimensions cells, thereby features. Additionally, this outlines future trends current limitations.

Language: Английский

Citations

29