AIMC Topic: Tumor Microenvironment

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Using machine learning to discover DNA metabolism biomarkers that direct prostate cancer treatment.

Scientific reports
DNA metabolism genes play pivotal roles in the regulation of cellular processes that contribute to cancer progression, immune modulation, and therapeutic response in prostate cancer (PC). Understanding the mechanisms by which these genes influence th...

Image-based inference of tumor cell trajectories enables large-scale cancer progression analysis.

Science advances
Current approaches to estimating cell trajectories, tumor progression dynamics, and cell population diversity of tumor microenvironment often depend on single-cell RNA sequencing, which is costly and resource intensive. To address this limitation, we...

Integrative radiomics of intra- and peri-tumoral features for enhanced risk prediction in thymic tumors: a multimodal analysis of tumor microenvironment contributions.

BMC medical imaging
OBJECTIVES: This study aims to explore the role of intra- and peri-tumoral radiomics features in tumor risk prediction, with a particular focus on the impact of peri-tumoral characteristics on the tumor microenvironment.

A perspective on integrating digital pathology, proteomics, clinical data and AI analytics in cancer research.

Journal of proteomics
Nearly 40 % of individuals will be diagnosed with cancer in their lifetime, translating to an estimated 20 million new cases annually. Despite remarkable therapeutic advances, only 15-20 % of patients achieve durable responses to immunotherapy, and t...

Innovative organ-on-a-chip platforms for exploring tumorigenesis and therapy in head and neck cancer.

Journal of translational medicine
BACKGROUND: Head and neck cancer (HNC) presents significant research challenges due to the complexity of its tumor microenvironment (TME) and the heterogeneity across different cancer subtypes. Recent advancements in three-dimensional (3D) culture mo...

Developing angiogenesis-related prognostic biomarkers and therapeutic strategies in bladder cancer using deep learning and machine learning.

Scientific reports
Bladder cancer (BLCA) is a prevalent urological malignancy that exhibits a high degree of tumor heterogeneity and morbidity. Tumor angiogenesis, a vital hallmark of cancer, greatly influences the tumor microenvironment (TME). The emergence of anti-an...

Integrative analysis of multi-omics data and gut microbiota composition reveals prognostic subtypes and predicts immunotherapy response in colorectal cancer using machine learning.

Scientific reports
Colorectal cancer (CRC) exhibits substantial heterogeneity in molecular subtypes and clinical outcomes. We performed an integrative analysis of multi-omics data from 274 CRC patients to investigate the impact of gut microbiota composition on prognosi...

Comprehensive multi-omics and machine learning framework for glioma subtyping and precision therapeutics.

Scientific reports
Glioma is a highly heterogeneous and aggressive brain tumour that demands an integrated understanding of its molecular and immunological landscape. We collected multi-omics data from 575 TCGA diffuse-glioma patients (156 IDH-wild-type WHO-grade 4 gli...

Novel molecular imaging approaches in oncology: towards a more accurate estimation of tumour response.

Current opinion in oncology
PURPOSE OF REVIEW: With novel therapeutics improving cancer survival rates, the need for accurate treatment response assessment has become increasingly crucial. The Response Evaluation Criteria in Solid Tumours remains the standard imaging method for...

Single-cell spatial transcriptomics reveals immunotherapy-driven bone marrow niche remodeling in AML.

Science advances
Given the graft-versus-leukemia effect observed with allogeneic hematopoietic stem cell transplantation in refractory or relapsed acute myeloid leukemia (AML), immunotherapies have been explored in nontransplant settings. We applied a multiomic appro...