The field of precision oncology is rapidly progressing toward integrated “multiomics” analysis of multiple molecular species (such as DNA, RNA, or proteins) to provide a more complete profile of tumor heterogeneity. Micro/nanomaterial‐based systems, which leverage the unique properties of miniature materials, are currently well positioned to expand beyond rudimentary biomarker detection toward multiomics signature analysis. To enable clinical translation, the rational design and implementation of miniaturized systems should be driven by the unique clinical challenges present at various crucial cancer stages. This review features micro/nanomaterial‐based systems that are robustly tested on real patient samples for molecular biomarker detection at i) initial cancer screening and/or diagnosis, ii) cancer prognosis and risk stratification, and iii) longitudinal treatment/recurrence monitoring. Furthermore, this review discusses the use of micro/nanomaterials to facilitate sample preparation for different molecular biomarker species. Finally, this review deliberates on the recent paradigm shift of micro/nanomaterial‐based system innovation toward integrated multiomics cancer signature analysis and puts forth insights and perspectives on existing challenges. It is anticipated that this review could stimulate the propagation of new concepts and approaches to kick‐start a new generation of clinically translational technologies that capitalize on multiomics cancer signatures.

The implementation of accurate and sensitive molecular detection for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is paramount to effectively control the ongoing coronavirus disease 2019 (COVID-19) pandemic. In this regard, we herein propose the specific and highly sensitive SARS-CoV-2 detection based on nanoyeast single-chain-variable fragment (scFv) and ultrasensitive plasmonic nanobox-integrated nanomixing microassay. Importantly, this designed platform showcases the utility of nanoyeast-scFvs as specific capture reagents targeting the receptor-binding domain (RBD) of the virus and as monoclonal antibody alternatives suitable for cost-effective mass production and frequent testing. By capitalizing on single-particle active nanoboxes as plasmonic nanostructures for surface-enhanced Raman scattering (SERS), the microassay utilizes highly sensitive Raman signals to indicate virus infection. The developed microassay further integrated nanomixing for accelerating molecular collisions. Through the synergistic working of nanoyeast-scFv, plasmonic nanoboxes, and nanomixing, the highly specific and sensitive SARS-CoV-2 detection is achieved as low as 17 virus/μL without any molecular amplification. We successfully demonstrate SARS-CoV-2 detection in saliva samples of simulated patients at clinically relevant viral loads, suggesting the possibility of this platform for accurate and noninvasive patient screening.

Accurate identification of malignant lung lesions is a prerequisite for rational clinical management to reduce morbidity and mortality of lung cancer. However, classification of lung nodules into malignant and benign cases is difficult as they show similar features in computer tomography and sometimes positron emission tomography imaging, making invasive tissue biopsies necessary. To address the challenges in evaluating indeterminate nodules, the authors investigate the molecular profiles of small extracellular vesicles (sEVs) in differentiating malignant and benign lung nodules via a liquid biopsy‐based approach. Aiming to characterize phenotypes between malignant and benign groups, they develop a single‐molecule‐resolution‐digital‐sEV‐counting‐detection (DECODE) chip that interrogates three lung‐cancer‐associated sEV biomarkers and a generic sEV biomarker to create sEV molecular profiles. DECODE capturessEVs on a nanostructured pillar chip, confines individual sEVs, and profiles sEV biomarker expression through surface‐enhanced Raman scattering barcodes. The author utilize DECODE to generate a digitally acquired sEV molecular profiles in a cohort of 33 people, including patients with malignant and benign lung nodules, and healthy individuals. Significantly, DECODE reveals sEV‐specific molecular profiles that allow the separation of malignant from benign (area under the curve, AUC = 0.85), which is promising for non‐invasive characterisation of lung nodules found in lung cancer screening and warrants further clinincal validaiton with larger cohorts.

Immune checkpoint blockade (ICB) therapy has achieved remarkable success in many cancers including melanoma. However, ICB therapy benefits only a small proportion of patients and produces severe side effects for some patients. Thus, there is an urgent need to identify patients who are more likely to respond to ICB therapy to improve outcomes and minimize side effects. To predict ICB therapy responses, we design a surface-enhanced Raman scattering (SERS) assay for multiplex profiling of circulating tumor cells (CTCs) under basal and interferon-γ (IFN-γ) stimulation. Through simultaneous ensemble and single-cell measurements of CTCs, the SERS assay can reveal tumor heterogeneity and offer a comprehensive CTC phenotype for decision-making. Anisotropic gold-silver alloy nanoboxes are utilized as SERS plasmonic substrates for improved signal readouts of CTC surface biomarkers. By generating a unique CTC signature with four surface biomarkers, the developed assay enables the differentiation of CTCs from three different patient-derived melanoma cell lines. Significantly, in a cohort of 14 melanoma patients who received programmed cell death-1 blockade therapy, the changes of CTC signature induced by IFN-γ stimulation to CTCs show the potential to predict responders. We expect that the SERS assay can help select patients for receiving ICB therapy in other cancers.