From lab to life: technological innovations in transforming cancer metastasis detection and therapy

Abstract

Cancer metastasis remains the leading cause of cancer-related mortality and represents a major therapeutic bottleneck, primarily due to the limited availability of effective, targeted treatment strategies. While key oncogenic drivers such as HER2, EGFR, PIK3CA, KRAS, and BRAF activate critical pathways like PI3K/AKT and MAPK/ERK, promoting tumor proliferation and migration and metastasis. In addition, metastasis is also influenced by environmental factors, microbiomes, and genomic alterations. This complex interplay underscores the urgent need for comprehensive mechanistic insights into metastatic progression, alongside the development of innovative translational platforms. This review explores the external contributors to metastasis, including air and water pollution, chemical exposures, and microbiome dysbiosis, which impact tumor progression and immune evasion. It also discusses the roles of viral infections, organotropism, and genomic regulation in driving metastasis heterogeneity. To address these challenges, a novel integrative framework has been proposed that connects environmental modulators, tumor-associated microbiota, and oncogenic genomic alterations with cutting-edge methodologies such as 3D bioprinting, microphysiological systems, liquid biopsy, and advanced in vitro and in vivo models. High-resolution imaging and AI-driven multi-omics integration further enhance the precision of these approaches. By transcending traditional and reductionist, tumor-centric paradigms, this framework advocates for a systems-level, translational framework that bridges molecular insights with clinical applicability. Ultimately, this strategy seeks to resolve persistent therapeutic challenges in metastatic cancer management through interdisciplinary collaboration.

Keywords: Artificial intelligence; Cancer; Detection tools; Lab-on-a-chip; Metastasis diversity and organotropism.

Fig. 1

Metastatic diversity and organotropism. This figure depicts certain cancers and the organs to which they commonly metastasise. (a) Breast cancer usually metastasises to organs like the brain, lung, liver and bone, (b) Hepatocellular carcinoma typically metastasises to the lung, (c) Colorectal cancer metastasises to the liver and lung, (d) Prostate cancer commonly metastasises to the bone, (e) Melanoma can metastasise to the brain, lung and the liver, (f) Glioblastoma can metastasise to lung, liver and bone. [Created in https://BioRender.com]

Fig. 2

Conventional tools. The figure represents some conventional laboratory techniques and tests used to detect tumor metastasis (a) 2D monolayer culture, (b) 3D spheroid models, (c) Trans-well migration and invasion assay, (d) Hanging-drop assay, (e) In vivo animal models [Created in https://BioRender.com]

Fig. 3

Diagnostic tools for detection of metastasis. The figure represents for detection of metastasis (a) Imaging techniques which include ultrasound, bone scintigraphy, single-photon emission computed tomography (SPECT), computed tomography (CT), positron emission tomography (PET) and magnetic resonance imaging (MRI) (b) Biomarkers such as genetic-based markers and blood-based markers, (c) AI and deep learning tools such as AI-assisted metastasis detection, multi-omics based classification and AI-assisted mammograms, (d) Pathological and histopathological tools such as immunohistochemistry, liquid biopsy and tissue biopsy. [Created in https://BioRender.com]