Cancer Stem Cell DNA Enabled Real‐Time Genotyping with Self‐Functionalized Quantum Superstructures—Overcoming the Barriers of Noninvasive cfDNA Cancer Diagnostics

Abstract

efficacy and reduce mortality. [1] Unfortunately, diagnosis of cancer at advanced stage leading to poor survival could be attri buted to the molecular heterogeneity of the tumors [2] in addition to the clonal evolution during tumor progression. [3] The dynamically changing tumor landscape also poses a significant challenge for clinical management, necessitating a real-time assessment of tumor states for informed decision-making and treatment optimization. Although extensively used in the clinic, radiographic imaging techniques such as MRI, CT, PET often fail to detect the shifts in tumor burden. [4] Conventional tissue biopsy is highly invasive, restricted to a localized snapshot of the tumor and highly unrepeatable. Hence, new tools to supplement the existing clinical tools are the immediate necessity for accurate tracking of tumor dynamics to enhance cancer prognostic prediction for the best possible treatment.Liquid biopsy (LB) offers an excellent solution for cancer diagnosis and treatment optimization. [5,6] As circulating tumor DNA (ctDNA) can present multitude of relevant tumor information across a variety of clinical contexts [6] such as tumor burden, [7] tumor-specific genomic alterations, [1] tumor heterogeneity; [8] cell-free DNA (cfDNA) including circulating tumor DNA (ctDNA) is rapidly developing Cancer diagnosis and determining its tissue of origin are crucial for clinical implementation of personalized medicine. Conventional diagnostic techniques such as imaging and tissue biopsy are unable to capture the dynamic tumor landscape. Although circulating tumor DNA (ctDNA) shows promise for diagnosis, the clinical relevance of ctDNA remains largely undetermined due to several biological and technical complexities. Here, cancer stem cell-ctDNA is used to overcome the biological complexities like the inability for molecular analysis of ctDNA and dependence on ctDNA concentration rather than the molecular profile. Ultrasensitive quantum superstructures overcome the technical complexities of trace-level detection and rapid diagnosis to detect ctDNA within its short half-life. Activation of multiple surface enhanced Raman scattering mechanisms of the quantum superstructures achieved a very high enhancement factor (1.35 × 10 11 ) and detection at ultralow concentration (10 −15 M) with very high reliability (RSD: 3-12%). Pilot validation with clinical plasma samples from an independent validation cohort achieved a diagnosis sensitivity of ≈95% and specificity of 83%. Quantum superstructures identified the tissue of origin with ≈75-86% sensitivity and ≈92-96% specificity. With large scale clinical validation, the technology can develop into a clinically useful liquid biopsy tool improving cancer diagnostics.The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/smtd.202101467.

OncoProfiler - A Multi-Cancer Early Detection (MCED) Assay

“…The nanosensor demonstrated limit of detection down to femtomolar concentration with various tumor-associated markers, such as cancer cellular DNA and proteins [8,15]. The chemical stability of the nanosensor and high repeatability [10,[16][17][18] of test make it a clinically applicable tool to diagnosis cancer using patient blood as a primary model. Figure 1 illustrate the workflow of OncoProfiler.…”

“…The ultra-sensitivity of Onco Profiler enabled us to discover four tumor associated biomarkers that were hard-to-detect with any other methods, such as, global DNA methylation, extracellular vesicles and components of immune cells [12][13][14]17,[19][20]. The pilot validation of OncoProfiler was tested with a small set (22-75) of patient blood samples for 5 different cancer types with diverse tissues of origin.…”

“…The reported results are based on a single tumor-associated biomarker. If multiple biomarkers are used simultaneously, we can expect a holistic representation of the spatial and temporal heterogeneity of a tumor [16][17][18] as well as high diagnostic accuracy.…”