Cancer biomarkers: Emerging trends and clinical implications for personalized treatment

Passaro, Antonio; Al Bakir, Maise; Hamilton, Emily G.; Diehn, Maximilian; André, Fabrice; Roy-Chowdhuri, Sinchita; Mountzios, Giannis; Wistuba, Ignacio I.; Swanton, Charles; Peters, Solange

doi:10.1016/j.cell.2024.02.041

Cited by 22 publications

(1 citation statement)

References 215 publications

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“…Cancer biomarkers encompass a wide array of tissue and liquid-based biochemical substances, including DNA, RNA, intracellular/secreted proteins, extracellular vesicles and circulating tumour cells. Over recent decades, there has been a significant push towards achieving personalized cancer treatment through the discovery and use of cancer-specific biomarkers [ 4 ].…”

mentioning

confidence: 99%

Special Issue “Cancer Biomarker Research and Personalized Medicine 2.0”

Meehan,

Gray

2024

JPM

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mentioning

confidence: 99%

Special Issue “Cancer Biomarker Research and Personalized Medicine 2.0”

Meehan,

Gray

2024

JPM

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Precision Cancer Medicine 2.0—Oncology in the postgenomic era

Masina,

Caldas

2024

Molecular Oncology

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Genomic medicine has transformed the lives of patients with cancer by enabling individualised and evidence‐based clinical decision‐making. Despite this progress, the implementation of precision cancer medicine is limited by its dependence on isolated biomarkers. The development of bulk and single‐cell multiomic technologies has revealed the enormous complexity of the cancer ecosystem. Beyond the cancer cell, the tumour microenvironment, macroenvironment and host factors, including the microbiome, profoundly influence the cancer phenotype, and accounting for these enhances the resolution of precision medicine. The advent of robust multiomic profiling and interpretable machine learning algorithms mark the dawn of a new postgenomic era of personalised cancer medicine. In Precision Cancer Medicine 2.0, high‐resolution personalised clinical decision‐making is informed by the comprehensive multiomic profiling of tumour and host, integrated using artificial intelligence.

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Radiogenomics: bridging the gap between imaging and genomics for precision oncology

He,

Huang,

Zhang

et al. 2024

MedComm

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Genomics allows the tracing of origin and evolution of cancer at molecular scale and underpin modern cancer diagnosis and treatment systems. Yet, molecular biomarker‐guided clinical decision‐making encounters major challenges in the realm of individualized medicine, consisting of the invasiveness of procedures and the sampling errors due to high tumor heterogeneity. By contrast, medical imaging enables noninvasive and global characterization of tumors at a low cost. In recent years, radiomics has overcomes the limitations of human visual evaluation by high‐throughput quantitative analysis, enabling the comprehensive utilization of the vast amount of information underlying radiological images. The cross‐scale integration of radiomics and genomics (hereafter radiogenomics) has the enormous potential to enhance cancer decoding and act as a catalyst for digital precision medicine. Herein, we provide a comprehensive overview of the current framework and potential clinical applications of radiogenomics in patient care. We also highlight recent research advances to illustrate how radiogenomics can address common clinical problems in solid tumors such as breast cancer, lung cancer, and glioma. Finally, we analyze existing literature to outline challenges and propose solutions, while also identifying future research pathways. We believe that the perspectives shared in this survey will provide a valuable guide for researchers in the realm of radiogenomics aiming to advance precision oncology.

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Cancer biomarkers: Emerging trends and clinical implications for personalized treatment

Cited by 22 publications

References 215 publications

Special Issue “Cancer Biomarker Research and Personalized Medicine 2.0”

Special Issue “Cancer Biomarker Research and Personalized Medicine 2.0”

Precision Cancer Medicine 2.0—Oncology in the postgenomic era

Radiogenomics: bridging the gap between imaging and genomics for precision oncology

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