Mathematical model identifies blood biomarker-based early cancer detection strategies and limitations

Enhancing Tumor Targeted Therapy: The Role of iRGD Peptide in Advanced Drug Delivery Systems

Chemotherapy remains the primary therapeutic approach in treating cancer. The tumor microenvironment (TME) is the complex network surrounding tumor cells, comprising various cell types, such as immune cells, fibroblasts, and endothelial cells, as well as ECM components, blood vessels, and signaling molecules. The often stiff and dense network of the TME interacts dynamically with tumor cells, influencing cancer growth, immune response, metastasis, and resistance to therapy. The effectiveness of the treatment of solid tumors is frequently reduced due to the poor penetration of the drug, which leads to attaining concentrations below the therapeutic levels at the site. Cell-penetrating peptides (CPPs) present a promising approach that improves the internalization of therapeutic agents. CPPs, which are short amino acid sequences, exhibit a high ability to pass cell membranes, enabling them to deliver drugs efficiently with minimal toxicity. Specifically, the iRGD peptide, a member of CPPs, is notable for its capacity to deeply penetrate tumor tissues by binding simultaneously integrins ανβ3/ανβ5 and neuropilin receptors. Indeed, ανβ3/ανβ5 integrins are characteristically expressed by tumor cells, which allows the iRGD peptide to home onto tumor cells. Notably, the respective dual-receptor targeting mechanism considerably increases the permeability of blood vessels in tumors, enabling an efficient delivery of co-administered drugs or nanoparticles into the tumor mass. Therefore, the iRGD peptide facilitates deeper drug penetration and improves the efficacy of co-administered therapies. Distinctively, we will focus on the iRGD mechanism of action, drug delivery systems and their application, and deliberate future perspectives in developing iRGD-conjugated therapeutics. In summary, this review discusses the potential of iRGD in overcoming barriers to drug delivery in cancer to maximize treatment efficiency while minimizing side effects.

From Proteomics to the Analysis of Single Protein Molecules

Limit of detection (LoD) is a term that is used to characterize the sensitivity of an analytical method. The existing limitation of the sensitivity of analysis using modern mass spectrometry methods has been experimentally shown to be a limiting factor in the application of proteomic technologies in medicine. This article proposes a concept of a new technology that will set a new vector of development in the development of systems for solving problems of medical diagnostics and deals with theoretical and practical aspects of creating a new technology for the detection of single biomacromolecules (in particular, proteins) in biological samples. Such technology should be based on the principle of signal registration similar to that used in a Geiger counter (also known as a Geiger-Müller counter or G-M counter), a device that automatically counts the number of ionizing particles that hit it. This counter is free from probabilistic components; it registers a signal if there is at least one target molecule in the analysis chamber. Predictive medical diagnostics require technology based on methods where sensitivity allows for the detection of single marker molecules in a biological sample volume of 1-10 µL, the smallest volume of biomaterial used in laboratory diagnostics. Creation of a detector with a sensitivity of 10-18 M would allow for the detection of one molecule in 1 µL of the sample, which fundamentally makes this approach analogous to a G-M counter for solutions. To date, bioanalytical methods are limited to a sensitivity of 10-12 M (which is approximately 1 million molecules per 1 μL), which is insufficient to capture the early stages of pathological processes.

AFM-fishing technology for protein detection in solutions

The review considers the possibility of using atomic force microscopy (AFM) as a basic method for protein detection in solutions with low protein concentrations. The demand for new bioanalytical approaches is determined by the problem of insufficient sensitivity of systems used in routine practice for protein detection. Special attention is paid to demonstration of the use in bioanalysis of a combination of AFM and fishing methods as an approach of concentrating biomolecules from a large volume of the analyzed solution on a small surface area.

Description of an activity-based enzyme biosensor for lung cancer detection

BACKGROUND

Lung cancer is associated with the greatest cancer mortality as it typically presents with incurable distributed disease. Biomarkers relevant to risk assessment for the detection of lung cancer continue to be a challenge because they are often not detectable during the asymptomatic curable stage of the disease. A solution to population-scale testing for lung cancer will require a combination of performance, scalability, cost-effectiveness, and simplicity.

METHODS

One solution is to measure the activity of serum available enzymes that contribute to the transformation process rather than counting biomarkers. Protease enzymes modify the environment during tumor growth and present an attractive target for detection. An activity based sensor platform sensitive to active protease enzymes is presented. A panel of 18 sensors was used to measure 750 sera samples from participants at increased risk for lung cancer with or without the disease.

RESULTS

A machine learning approach is applied to generate algorithms that detect 90% of cancer patients overall with a specificity of 82% including 90% sensitivity in Stage I when disease intervention is most effective and detection more challenging.

CONCLUSION

This approach is promising as a scalable, clinically useful platform to help detect patients who have lung cancer using a simple blood sample. The performance and cost profile is being pursued in studies as a platform for population wide screening.

Autoantibodies, antigen-autoantibody complexes and antigens complement CA125 for early detection of ovarian cancer

BACKGROUND

Multiple antigens, autoantibodies (AAb), and antigen-autoantibody (Ag-AAb) complexes were compared for their ability to complement CA125 for early detection of ovarian cancer.

METHODS

Twenty six biomarkers were measured in a single panel of sera from women with early stage (I-II) ovarian cancers (n = 64), late stage (III-IV) ovarian cancers (186), benign pelvic masses (200) and from healthy controls (502), and then split randomly (50:50) into a training set to identify the most promising classifier and a validation set to compare its performance to CA125 alone.

RESULTS

Eight biomarkers detected ≥ 8% of early stage cases at 98% specificity. A four-biomarker panel including CA125, HE4, HE4 Ag-AAb and osteopontin detected 75% of early stage cancers in the validation set from among healthy controls compared to 62% with CA125 alone (p = 0.003) at 98% specificity. The same panel increased sensitivity for distinguishing early-stage ovarian cancers from benign pelvic masses by 25% (p = 0.0004) at 95% specificity. From 21 autoantibody candidates, 3 AAb (anti-p53, anti-CTAG1 and annt-Il-8) detected 22% of early stage ovarian cancers, potentially lengthening lead time prior to diagnosis.

CONCLUSION

A four biomarker panel achieved greater sensitivity at the same specificity for early detection of ovarian cancer than CA125 alone.

Advances in three dimensional metal enhanced fluorescence based biosensors using metal nanomaterial and nano-patterned surfaces

Metal enhanced fluorescence (MEF) is a phenomenon that increases fluorescence signal through placement of metal near a fluorophore. For biosensing applications, MEF-based biosensors are becoming increasingly popular as it enables highly sensitive detection of molecules, important for early diagnosis. The structure and size of the metal influence the optical properties through enhancing the fluorophore photostability and light absorption and emission. In recent years, many metal nanostructures have been fabricated and examined for their effectiveness in developing MEF-based biosensors. This review focuses on the latest applications of three-dimensional nanostructures and nano-patterned surfaces used to develop and improve fluorescence sensing via MEF. Current reviews mostly discussed the applications of two dimensional MEF and metal-nanoparticles-based MEF with a focus on fabrication of nanoparticles and metal substrates. In this article, we focused more on the effect of the metal nanostructure and size on MEF and then provided an in-depth summary of the performance of the state-of-the-art three dimensional MEF-based biosensors. While more work is needed to demonstrate applicability for complex samples, it is evident that with the use of metal nanoparticles and three dimensional nano-patterns, the assay sensitivity of fluorescence-based detection can be greatly improved, making it suitable for use in early disease diagnostics.

Aptamer-based gold nanoparticle aggregates for ultrasensitive amplification-free detection of PSMA

Early diagnosis is one of the most important factors in determining the prognosis in cancer. Sensitive detection and quantification of tumour-specific biomarkers have the potential to improve significantly our diagnostic capability. Here, we introduce a triggerable aptamer-based nanostructure based on an oligonucleotide/gold nanoparticle architecture that selectively disassembles in the presence of the biomarker of interest; its optimization is based also on in-silico determination of the aptamer nucleotides interactions with the protein of interest. We demonstrate this scheme for the case of Prostate Specific Membrane Antigen (PSMA) and PSMA derived from PSMA-positive exosomes. We tested the disassembly of the system by diameter and count rate measurements in dynamic light scattering, and by inspection of its plasmon resonance shift, upon addition of PSMA, finding appreciable differences down to the sub-picomolar range; this points towards the possibility that this approach may lead to sensors competitive with diagnostic biochemical assays that require enzymatic amplification. More generally, this scheme has the potential to be applied to a broad range of pathologies with specific identified biomarkers.

Activatable Nanoreporters for Real-Time Tracking of Macrophage Phenotypic States Associated with Disease Progression

Diagnosis of inflammatory diseases is characterized by identifying symptoms, biomarkers, and imaging. However, conventional techniques lack the sensitivities and specificities to detect disease early. Here, it is demonstrated that the detection of macrophage phenotypes, from inflammatory M1 to alternatively activated M2 macrophages, corresponding to the disease state can be used to predict the prognosis of various diseases. Activatable nanoreporters that can longitudinally detect the presence of the enzyme Arginase 1, a hallmark of M2 macrophages, and nitric oxide, a hallmark of M1 macrophages are engineered, in real-time. Specifically, an M2 nanoreporter enables the early imaging of the progression of breast cancer as predicted by selectively detecting M2 macrophages in tumors. The M1 nanoreporter enables real-time imaging of the subcutaneous inflammatory response that rises from a local lipopolysccharide (LPS) administration. Finally, the M1-M2 dual nanoreporter is evaluated in a muscle injury model, where an initial inflammatory response is monitored by imaging M1 macrophages at the site of inflammation, followed by a resolution phase monitored by the imaging of infiltrated M2 macrophages involved in matrix regeneration and wound healing. It is anticipated that this set of macrophage nanoreporters may be utilized for early diagnosis and longitudinal monitoring of inflammatory responses in various disease models.

Caged Luciferase Inhibitor-Based Bioluminescence Switching Strategy Enables Efficient Detection of Serum APN Activity and the Identification of Its Roles in Metastasis of Non-Small Cell Lung Cancer

Bioluminogenic probes emerged as powerful tools for imaging and analysis of various bioanalyses, but traditional approaches would be limited to the low sensitivity during determine the low activity of protease in clinical specimens. Herein, we proposed a caged luciferase inhibitor-based bioluminescence-switching strategy (CLIBS) by using a cleavable luciferase inhibitor to modulate the activity of luciferase reporter to amplify the detective signals, which led to the enhancement of detection sensitivity, and enabled the determination of circulating Aminopeptidase N (APN) activity in thousands of times diluted serum. By applying the CLIBS to serum samples in non-small cell lung cancer (NSCLC) patients from two clinical cohorts, we revealed that, for the first time, higher circulating APN activities but not its concentration, were associated with more NSCLC metastasis or higher metastasis stages by subsequent clinical analysis, and can serve as an independent factor for forecasting NSCLC patients' risk of metastasis.

Plasma proteome of growing tumors

Early detection of cancer is vital for the best chance of successful treatment, but half of all cancers are diagnosed at an advanced stage. A simple and reliable blood screening test applied routinely would therefore address a major unmet medical need. To gain insight into the value of protein biomarkers in early detection and stratification of cancer we determined the time course of changes in the plasma proteome of mice carrying transplanted human lung, breast, colon, or ovarian tumors. For protein measurements we used an aptamer-based assay which simultaneously measures ~ 5000 proteins. Along with tumor lineage-specific biomarkers, we also found 15 markers shared among all cancer types that included the energy metabolism enzymes glyceraldehyde-3-phosphate dehydrogenase, glucose-6-phophate isomerase and dihydrolipoyl dehydrogenase as well as several important biomarkers for maintaining protein, lipid, nucleotide, or carbohydrate balance such as tryptophanyl t-RNA synthetase and nucleoside diphosphate kinase. Using significantly altered proteins in the tumor bearing mice, we developed models to stratify tumor types and to estimate the minimum detectable tumor volume. Finally, we identified significantly enriched common and unique biological pathways among the eight tumor cell lines tested.

CRISPR-Cas-amplified urinary biomarkers for multiplexed and portable cancer diagnostics

Synthetic biomarkers, bioengineered sensors that generate molecular reporters in diseased microenvironments, represent an emerging paradigm in precision diagnostics. Despite the utility of DNA barcodes as a multiplexing tool, their susceptibility to nucleases in vivo has limited their utility. Here we exploit chemically stabilized nucleic acids to multiplex synthetic biomarkers and produce diagnostic signals in biofluids that can be 'read out' via CRISPR nucleases. The strategy relies on microenvironmental endopeptidase to trigger the release of nucleic acid barcodes and polymerase-amplification-free, CRISPR-Cas-mediated barcode detection in unprocessed urine. Our data suggest that DNA-encoded nanosensors can non-invasively detect and differentiate disease states in transplanted and autochthonous murine cancer models. We also demonstrate that CRISPR-Cas amplification can be harnessed to convert the readout to a point-of-care paper diagnostic tool. Finally, we employ a microfluidic platform for densely multiplexed, CRISPR-mediated DNA barcode readout that can potentially evaluate complex human diseases rapidly and guide therapeutic decisions.

Clinical Peptidomics: Advances in Instrumentation, Analyses, and Applications

Extensive effort has been devoted to the discovery, development, and validation of biomarkers for early disease diagnosis and prognosis as well as rapid evaluation of the response to therapeutic interventions. Genomic and transcriptomic profiling are well-established means to identify disease-associated biomarkers. However, analysis of disease-associated peptidomes can also identify novel peptide biomarkers or signatures that provide sensitive and specific diagnostic and prognostic information for specific malignant, chronic, and infectious diseases. Growing evidence also suggests that peptidomic changes in liquid biopsies may more effectively detect changes in disease pathophysiology than other molecular methods. Knowledge gained from peptide-based diagnostic, therapeutic, and imaging approaches has led to promising new theranostic applications that can increase their bioavailability in target tissues at reduced doses to decrease side effects and improve treatment responses. However, despite major advances, multiple factors can still affect the utility of peptidomic data. This review summarizes several remaining challenges that affect peptide biomarker discovery and their use as diagnostics, with a focus on technological advances that can improve the detection, identification, and monitoring of peptide biomarkers for personalized medicine.

An Inflection Point in Cancer Protein Biomarkers: What Was and What's Next

Biomarkers remain the highest value proposition in cancer medicine today - especially protein biomarkers. Yet despite decades of evolving regulatory frameworks to facilitate the review of emerging technologies, biomarkers have been mostly about promise with very little to show for improvements in human health. Cancer is an emergent property of a complex system and deconvoluting the integrative and dynamic nature of the overall system through biomarkers is a daunting proposition. The last two decades have seen an explosion of multi-omics profiling and a range of advanced technologies for precision medicine, including the emergence of liquid biopsy, exciting advances in single cell analysis, artificial intelligence (machine and deep learning) for data analysis and many other advanced technologies that promise to transform biomarker discovery. Combining multiple omics modalities to acquire a more comprehensive landscape of the disease state, we are increasingly developing biomarkers to support therapy selection and patient monitoring. Furthering precision medicine, especially in oncology, necessitates moving away from the lens of reductionist thinking towards viewing and understanding that complex diseases are, in fact, complex adaptive systems. As such, we believe it is necessary to re-define biomarkers as representations of biological system states at different hierarchical levels of biological order. This definition could include traditional molecular, histologic, radiographic, or physiological characteristics, as well as emerging classes of digital markers and complex algorithms. To succeed in the future, we must move past purely observational individual studies and instead start building a mechanistic framework to enable integrative analysis of new studies within the context of prior studies. Identifying information in complex systems and applying theoretical constructs, such as information theory, to study cancer as a disease of dysregulated communication could prove to be "game changing" for the clinical outcome of cancer patients.

Quantifying the Contributions of Environmental Factors to Prostate Cancer and Detecting Risk-Related Diet Metrics and Racial Disparities

The relevance of nongenetic factors to prostate cancer (PCa) has been elusive. We aimed to quantify the contributions of environmental factors to PCa and identify risk-related diet metrics and relevant racial disparities. We performed a unique analysis of the Diet History Questionnaire data of 41 830 European Americans (EAs) and 1282 African Americans (AAs) in the PLCO project. The independent variables in the regression models consisted of age at trial entry, race, family history of prostate cancer (PCa-fh), diabetes history, body mass index (BMI), lifestyle (smoking and coffee consumption), marital status, and a specific nutrient/food factor (X). P < .05 and a 95% confidence interval excluding zero were adopted as the criteria for determining a significant difference (effect). We established a priority ranking among PCa risk-related genetic and environmental factors according to the deviances explained by them in the multivariate Cox-PH regression analysis: age > PCa-fh > diabetes ⩾ race > lifestyle ⩾marital-status ⩾BMI > X. We confirmed previous studies showing that (1) high protein and saturated fat levels in diet were related to increased PCa risk, (2) high-level supplementary selenium intake was harmful rather than beneficial for preventing PCa, and (3) supplementary vitamin B6 was beneficial for preventing benign PCa. We obtained the following novel findings: high-level organ meat intake was an independent predictor for increased aggressive PCa risk; supplementary iron, copper and magnesium increased benign PCa risk; and the AA diet was "healthy" in terms of the relatively lower protein and fat levels and was "unhealthy" in that it more commonly contained organ meat. In conclusion, we established a priority ranking among the contributing factors for PCa and identified several risk-related diet metrics and the racial disparities. Our findings suggested some new approaches to prevent PCa such as restriction of organ meat intake and supplementary microminerals.

Glutathione-Activated Emission of Ultrasmall Gold Nanoparticles in the Second Near-Infrared Window for Imaging of Early Kidney Injury

Biomarker-activatable luminescent probes with high sensitivity and specificity show great promise in advanced bioimaging applications. However, the lack of stable biomarkers at an early stage is currently a major obstacle for sensitive early disease imaging. Herein, we develop a facile in vivo ligand exchange strategy to achieve renal-clearable activatable luminescent gold nanoparticles (AuNPs), which are independent of biomarkers for sensitive and long-time imaging of early kidney injury. Significantly activated emission in the second near-infrared region (∼1026 nm) is realized from the ligand exchange of triphenylphosphine-3,3',3″-trisulfonic acid (TPPTS)-coated AuNPs (∼1.4 nm, TPPTS-AuNPs) with quantitative amounts of glutathione (GSH). The abundant GSH in cells, particularly in liver sinusoids, is then demonstrated successfully to activate the emission of TPPTS-AuNPs with an extremely low background for both cell imaging and in vivo visualization of visceral organs (e.g., liver and kidneys). In addition, the in vivo GSH-exchanged TPPTS-AuNPs show enhanced interactions with acidic renal tubular epithelial cells, resulting in sensitive (contrast index, ∼3.9) and long-time (>6.5 h) noninvasive monitoring of acidosis-induced early kidney injury. This facile ligand exchange strategy opens new possibilities for designing activatable luminescent probes independent of biomarkers for earlier disease diagnosis and treatment.

Recent advances in plasmon-enhanced luminescence for biosensing and bioimaging

Plasmon-enhanced luminescence (PEL) is a unique photophysical phenomenon in which the interaction between luminescent moieties and metal nanostructures results in a marked luminescence enhancement. PEL offers several advantages and has been extensively used to design robust biosensing platforms for luminescence-based detection and diagnostics applications, as well as for the development of many efficient bioimaging platforms, enabling high-contrast non-invasive real-time optical imaging of biological tissues, cells, and organelles with high spatial and temporal resolution. This review summarizes recent progress in the development of various PEL-based biosensors and bioimaging platforms for diverse biological and biomedical applications. Specifically, we comprehensively assessed rationally designed PEL-based biosensors that can efficiently detect biomarkers (proteins and nucleic acids) in point-of-care tests, highlighting significant improvements in the sensing performance upon the integration of PEL. In addition to discussing the merits and demerits of recently developed PEL-based biosensors on substrates or in solutions, we include a brief discussion on integrating PEL-based biosensing platforms into microfluidic devices as a promising multi-responsive detection method. The review also presents comprehensive details about the recent advances in the development of various PEL-based multi-functional (passive targeting, active targeting, and stimuli-responsive) bioimaging probes, highlighting the scope of future improvements in devising robust PEL-based nanosystems to achieve more effective diagnostic and therapeutic insights by enabling imaging-guided therapy.

Serum biomarker-based early detection of pancreatic ductal adenocarcinomas with ensemble learning

BACKGROUND

Earlier detection of pancreatic ductal adenocarcinoma (PDAC) is key to improving patient outcomes, as it is mostly detected at advanced stages which are associated with poor survival. Developing non-invasive blood tests for early detection would be an important breakthrough.

METHODS

The primary objective of the work presented here is to use a dataset that is prospectively collected, to quantify a set of cancer-associated proteins and construct multi-marker models with the capacity to predict PDAC years before diagnosis. The data used is part of a nested case-control study within the UK Collaborative Trial of Ovarian Cancer Screening and is comprised of 218 samples, collected from a total of 143 post-menopausal women who were diagnosed with pancreatic cancer within 70 months after sample collection, and 249 matched non-cancer controls. We develop a stacked ensemble modelling technique to achieve robustness in predictions and, therefore, improve performance in newly collected datasets.

RESULTS

Here we show that with ensemble learning we can predict PDAC status with an AUC of 0.91 (95% CI 0.75-1.0), sensitivity of 92% (95% CI 0.54-1.0) at 90% specificity, up to 1 year prior to diagnosis, and at an AUC of 0.85 (95% CI 0.74-0.93) up to 2 years prior to diagnosis (sensitivity of 61%, 95% CI 0.17-0.83, at 90% specificity).

CONCLUSIONS

The ensemble modelling strategy explored here outperforms considerably biomarker combinations cited in the literature. Further developments in the selection of classifiers balancing performance and heterogeneity should further enhance the predictive capacity of the method.

Analysis of Single Biomacromolecules and Viruses: Is It a Myth or Reality?

The beginning of the twenty-first century witnessed novel breakthrough research directions in the life sciences, such as genomics, transcriptomics, translatomics, proteomics, metabolomics, and bioinformatics. A newly developed single-molecule approach addresses the physical and chemical properties and the functional activity of single (individual) biomacromolecules and viral particles. Within the alternative approach, the combination of "single-molecule approaches" is opposed to "omics approaches". This new approach is fundamentally unique in terms of its research object (a single biomacromolecule). Most studies are currently performed using postgenomic technologies that allow the properties of several hundreds of millions or even billions of biomacromolecules to be analyzed. This paper discusses the relevance and theoretical, methodological, and practical issues related to the development potential of a single-molecule approach using methods based on molecular detectors.

Endogenous incretin levels and risk of first incident cancer: a prospective cohort study

Concerns have been raised regarding a potentially increased risk of cancer associated with treatment with glucagon-like peptide-1 (GLP-1) receptor agonists. Here, we explored whether fasting and oral glucose tolerance test post-challenge glucose-dependent insulinotropic peptide (GIP) and GLP-1 levels were associated with incident first cancer. Within the cardiovascular re-examination arm of the population-based Malmö Diet Cancer study (n = 3734), 685 participants with a previous cancer diagnosis were excluded, resulting in 3049 participants (mean age 72.2 ± 5.6 years, 59.5% women), of whom 485 were diagnosed with incident first cancer (median follow-up time 9.9 years). Multivariable Cox-regression and competing risk regression (death as competing risk) were used to explore associations between incretin levels and incident first cancer. Higher levels of fasting GLP-1 (462 incident first cancer cases/2417 controls) showed lower risk of incident first cancer in competing risk regression (sub-hazard ratio 0.90; 95% confidence interval 0.82-0.99; p = 0.022). No association was seen for fasting GIP, post-challenge GIP, or post-challenge GLP-1 and incident first cancer. In this prospective study, none of the fasting and post-challenge levels of GIP and GLP-1 were associated with higher risk of incident first cancer; by contrast, higher levels of fasting GLP-1 were associated with lower risk of incident first cancer.

Simultaneous analysis of mutations and methylations in circulating cell-free DNA for hepatocellular carcinoma detection

Cell-free DNA (cfDNA)-based liquid biopsy is a promising approach for the early detection of cancer. A major hurdle is the limited yield of cfDNA from one blood draw, limiting the use of most samples to one test of either mutation or methylation. Here, we develop a technology, Mutation Capsule Plus (MCP), which enables multiplex profiling of one cfDNA sample, including simultaneous detection of genetic and epigenetic alterations and genome-wide discovery of methylation markers. With this technology, we performed de novo screening of methylation markers on cfDNA samples from 30 hepatocellular carcinoma (HCC) cases and 30 non-HCC controls. The methylation markers enriched in HCC cfDNA were further profiled in parallel with a panel of mutations on a training cohort of 60 HCC and 60 non-HCC cases, resulting in an HCC detection model. We validated the model in an independent retrospective cohort with 58 HCC and 198 non-HCC cases and got 90% sensitivity with 94% specificity. Furthermore, we applied the model to a prospective cohort of 311 asymptomatic hepatitis B virus carriers with normal liver ultrasonography and serum AFP concentration. The model detected four of the five HCC cases in the cohort, showing 80% sensitivity and 94% specificity. These findings demonstrate that the MCP technology has potential for the discovery and validation of multiomics biomarkers for the noninvasive detection of cancer. This study also provides a comprehensive database of genetic and epigenetic alterations in the cfDNA of a large cohort of HCC cases and high-risk non-HCC individuals.

Assay Performance of a Label-Free, Solution-Phase CYFRA 21-1 Determination

CYFRA 21.1, a cytokeratin fragment of epithelial origin, has long been a valuable blood-based biomarker. As with most biomarkers, the clinical diagnostic value of CYFRA 21.1 is dependent on the quantitative performance of the assay. Looking toward translation, it is shown here that a free-solution assay (FSA) coupled with a compensated interferometric reader (CIR) can be used to provide excellent analytical performance in quantifying CYFRA 21.1 in patient serum samples. This report focuses on the analytical performance of the high-sensitivity (hs)-CYFRA 21.1 assay in the context of quantifying the biomarker in two indeterminate pulmonary nodule (IPN) patient cohorts totaling 179 patients. Each of the ten assay calibrations consisted of 6 concentrations, each run as 7 replicates (e.g., 10 × 6 × 7 data points) and were performed on two different instruments by two different operators. Coefficients of variation (CVs) for the hs-CYFRA 21.1 analytical figures of merit, limit of quantification (LOQ) of ca. 60 pg/mL, B _max, initial slope, probe-target binding affinity, and reproducibility of quantifying an unknown were found to range from 2.5 to 8.3%. Our results demonstrate the excellent performance of our FSA-CIR hs-CYFRA 21-1 assay and a proof of concept for potentially redefining the performance characteristics of this existing important candidate biomarker.

Future of Digital Assays to Resolve Clinical Heterogeneity of Single Extracellular Vesicles

Extracellular vesicles (EVs) are complex lipid membrane vehicles with variable expressions of molecular cargo, composed of diverse subpopulations that participate in the intercellular signaling of biological responses in disease. EV-based liquid biopsies demonstrate invaluable clinical potential for overhauling current practices of disease management. Yet, EV heterogeneity is a major needle-in-a-haystack challenge to translate their use into clinical practice. In this review, existing digital assays will be discussed to analyze EVs at a single vesicle resolution, and future opportunities to optimize the throughput, multiplexing, and sensitivity of current digital EV assays will be highlighted. Furthermore, this review will outline the challenges and opportunities that impact the clinical translation of single EV technologies for disease diagnostics and treatment monitoring.

Genomic Profiling of Bronchoalveolar Lavage Fluid in Lung Cancer

Genomic profiling of bronchoalveolar lavage (BAL) samples may be useful for tumor profiling and diagnosis in the clinic. Here, we compared tumor-derived mutations detected in BAL samples from subjects with non-small cell lung cancer (NSCLC) to those detected in matched plasma samples. Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) was used to genotype DNA purified from BAL, plasma, and tumor samples from patients with NSCLC. The characteristics of cell-free DNA (cfDNA) isolated from BAL fluid were first characterized to optimize the technical approach. Somatic mutations identified in tumor were then compared with those identified in BAL and plasma, and the potential of BAL cfDNA analysis to distinguish lung cancer patients from risk-matched controls was explored. In total, 200 biofluid and tumor samples from 38 cases and 21 controls undergoing BAL for lung cancer evaluation were profiled. More tumor variants were identified in BAL cfDNA than plasma cfDNA in all stages (P < 0.001) and in stage I to II disease only. Four of 21 controls harbored low levels of cancer-associated driver mutations in BAL cfDNA [mean variant allele frequency (VAF) = 0.5%], suggesting the presence of somatic mutations in nonmalignant airway cells. Finally, using a Random Forest model with leave-one-out cross-validation, an exploratory BAL genomic classifier identified lung cancer with 69% sensitivity and 100% specificity in this cohort and detected more cancers than BAL cytology. Detecting tumor-derived mutations by targeted sequencing of BAL cfDNA is technically feasible and appears to be more sensitive than plasma profiling. Further studies are required to define optimal diagnostic applications and clinical utility.

SIGNIFICANCE

Hybrid-capture, targeted deep sequencing of lung cancer mutational burden in cell-free BAL fluid identifies more tumor-derived mutations with increased allele frequencies compared with plasma cell-free DNA. See related commentary by Rolfo et al., p. 2826.

Liquid biopsies for early diagnosis of brain tumours: in silico mathematical biomarker modelling

Brain tumours are the biggest cancer killer in those under 40 and reduce life expectancy more than any other cancer. Blood-based liquid biopsies may aid early diagnosis, prediction and prognosis for brain tumours. It remains unclear whether known blood-based biomarkers, such as glial fibrillary acidic protein (GFAP), have the required sensitivity and selectivity. We have developed a novel in silico model which can be used to assess and compare blood-based liquid biopsies. We focused on GFAP, a putative biomarker for astrocytic tumours and glioblastoma multi-formes (GBMs). In silico modelling was paired with experimental measurement of cell GFAP concentrations and used to predict the tumour volumes and identify key parameters which limit detection. The average GBM volumes of 449 patients at Leeds Teaching Hospitals NHS Trust were also measured and used as a benchmark. Our model predicts that the currently proposed GFAP threshold of 0.12 ng ml-1 may not be suitable for early detection of GBMs, but that lower thresholds may be used. We found that the levels of GFAP in the blood are related to tumour characteristics, such as vasculature damage and rate of necrosis, which are biological markers of tumour aggressiveness. We also demonstrate how these models could be used to provide clinical insight.

In vivo computation with sensor fusion and search acceleration for smart tumor homing

BACKGROUND AND OBJECTIVE

Motivated by the advancements on bioresorbable nanoswimmers, this paper considers the advantages of direct targeting over systemic targeting for smart tumor homing under the general framework of computational nanobiosensing. Nanoswimmers assembled by magnetic nanoparticles can be used as contrast agents to estimate the locations of tumors inside the human body.

METHODS

Closely observing the response of nanoswimmers (which act as in vivo biosensors) to the tumor-triggered biological gradients and then guiding them through external manipulation, can result in a higher accumulation at the diseased location. Sensor informatics along with data fusion can play a crucial role in such a knowledge-aided targeting process. Specifically, built upon our previous work on direct targeting inspired by the gradient descent optimization, this work is focused on resolving the real-life constraints of in vivo natural computation such as uniformity of the magnetic field and finite life span of the nanoswimmers. To overcome these challenges, we propose a multi-estimate-fusion strategy to obtain a common steering direction for the swarm of nanoswimmers.

RESULTS

We show through computational experiments (1) that the mean of individual gradient estimations provides the best choice for symmetrical conditions (tumor location in line with the direction of blood flow) while leader-based swarm steering gives the best results for non-symmetrical search space, and (2) that the iterative memory-driven gradient descent optimization detects the target faster compared to the classical memory-less gradient descent and knowledge-less systemic targeting.

CONCLUSION

Our proposed strategies demonstrate that a clear demarcation between malignant tumors and healthy tissues can be visualized before nanoswimmers are consumed in human vasculature. We believe that our work will help in overcoming the challenges posed by natural in vivo computation for tumor diagnosis at its early stage.

Tumor volume doubling time estimated from digital breast tomosynthesis mammograms distinguishes invasive breast cancers from benign lesions

OBJECTIVES

The aim of this study was to determine whether lesion size metrics on consecutive screening mammograms could predict malignant invasive carcinoma versus benign lesion outcome.

METHODS

We retrospectively reviewed suspicious screen-detected lesions confirmed by biopsy to be invasive breast cancers or benign that were visible on current and in-retrospect prior screening mammograms performed with digital breast tomosynthesis from 2017 to 2020. Four experienced radiologists recorded mammogram dates, breast density, lesion type, lesion diameter, and morphology on current and prior exams. We used logistic regression models to evaluate the association of invasive breast cancer outcome with lesion size metrics such as maximum dimension, average dimension, volume, and tumor volume doubling time (TVDT).

RESULTS

Twenty-eight patients with invasive ductal carcinoma or invasive lobular carcinoma and 40 patients with benign lesions were identified. The mean TVDT was significantly shorter for invasive breast cancers compared to benign lesions (0.84 vs. 2.5 years; p = 0.0025). Patients with a TVDT of less than 1 year were shown to have an odds ratio of invasive cancer of 6.33 (95% confidence interval, 2.18-18.43). Logistic regression adjusted for age, lesion maximum dimension, and lesion volume demonstrated that shorter TVDT was the size variable significantly associated with invasive cancer outcome.

CONCLUSION

Invasive breast cancers detected on current and in-retrospect prior screening mammograms are associated with shorter TVDT compared to benign lesions. If confirmed to be sufficiently predictive of benignity in larger studies, lesions visible on mammograms which in comparison to prior exams have longer TVDTs could potentially avoid additional imaging and/or biopsy.

KEY POINTS

• We propose tumor volume doubling time as a measure to distinguish benign from invasive breast cancer lesions. • Logistic regression results summarized the utility of the odds ratio in retrospective clinical mammography data.

Magnetic supercluster particles for highly sensitive magnetic biosensing of proteins

A strategy is reported to improve the detection limits of current giant magnetoresistance (GMR) biosensors by augmenting the effective magnetic moment that the magnetic tags on the biosensors can exert. Magnetic supercluster particles (MSPs), each of which consists of ~ 1000 superparamagnetic cores, are prepared by a wet-chemical technique and are utilized to improve the limit of detection of GMR biosensors down to 17.6 zmol for biotin as a target molecule. This value is more than four orders of magnitude lower than that of the conventional colorimetric assay performed using the same set of reagents except for the signal transducer. The applicability of MSPs in immunoassay is further demonstrated by simultaneously detecting vascular endothelial growth factor (VEGF) and C-reactive protein (CRP) in a duplex assay format. MSPs outperform commercially available magnetic nanoparticles in terms of signal intensity and detection limit.

Deciphering the Increased Prevalence of TP53 Mutations in Metastatic Prostate Cancer

The prevalence of TP53 mutations in advanced prostate cancers (PCa) is 3 to 5 times of the quantity in primary PCa. By an integrative analysis of the Cancer Genome Atlas and Catalogue of Somatic Mutations in Cancer data, we revealed the supporting evidence for 2 complementary hypotheses: H₁ - TP53 abnormalities promote metastasis or therapy-resistance of PCa cells, and H₂—part of TP53 mutations in PCa metastases occur after the diagnosis of original cancers. The plausibility of these hypotheses can explain the increased prevalence of TP53 mutations in PCa metastases. With H₁ and H₂ as the general assumptions, we developed mathematical models to decipher the change of the percentage frequency (prevalence) of TP53 mutations from primary tumors to metastases. The following results were obtained. Compared to TP53-normal patients, TP53-mutated patients had poorer biochemical relapse-free survival, higher Gleason scores, and more advanced t-stages (P < .01). Single-nucleotide variants in metastases more frequently occurred on G bases of the coding sequence than those in primary cancers (P = .03). The profile of TP53 hotspot mutations was significantly different between primary and metastatic PCa as demonstrated in a set of statistical tests (P < .05). By the derived formulae, we estimated that about 40% TP53 mutation records collected from metastases occurred after the diagnosis of the original cancers. Our study provided significant insight into PCa progression. The proposed models can also be applied to decipher the prevalence of mutations on TP53 (or other driver genes) in other cancer types.

Methods for Enhanced Fluorescence Detection of Proteins by using Entrapped Gold Nanoparticles in Membranes

Measuring protein levels from biofluids can provide important insight into human health and disease during various physiological and pathological conditions. In many situations, sensitive methods are required for protein quantification because at the early stages of many diseases, proteins in biofluids are present at very low concentrations. Here, a new and simple method is presented in the form of Basic and Alternative Protocols for an immunoassay performed on a nitrocellulose membrane, followed by the addition of gold nanoparticles prior to measuring fluorescence with a microscope. The assay protocol was optimized to achieve 3D metal-enhanced fluorescence (MEF) with increased antibody-binding capacity and enhanced fluorescence signals, improving assay sensitivity. Using different concentrations of spiked fluorescently labeled IgGs in pooled normal human plasma, a lower detection limit of 29 ng/ml was achieved. This limit of detection was found to be a thousand-fold lower than the conventional 2D assay and one order of magnitude lower than when the assay was performed on a 3D membrane without MEF. This method provides an easy way to improve immunoassay sensitivity, and it can be simply transferred to other labs. It also can extend to fluorescence detection of other analytes beyond proteins. © 2022 Wiley Periodicals LLC. Basic Protocol: Assay in nitrocellulose membrane with entrapped AuNPs using commercially available AuNPs Alternative Protocol: Assay in nitrocellulose membrane with entrapped AuNPs using lab-made AuNPs.

Quantitative predictive approaches for Dupuytren disease: a brief review and future perspectives

In this study we review the current state of the art for Dupuytren's disease (DD), while emphasising the need for a better integration of clinical, experimental and quantitative predictive approaches to understand the evolution of the disease and improve current treatments. We start with a brief review of the biology of this disease and current treatment approaches. Then, since certain aspects in the pathogenesis of this disorder have been compared to various biological aspects of wound healing and malignant processes, next we review some in silico (mathematical modelling and simulations) predictive approaches for complex multi-scale biological interactions occurring in wound healing and cancer. We also review the very few in silico approaches for DD, and emphasise the applicability of these approaches to address more biological questions related to this disease. We conclude by proposing new mathematical modelling and computational approaches for DD, which could be used in the absence of animal models to make qualitative and quantitative predictions about the evolution of this disease that could be further tested in vitro.

Sonobiopsy for minimally invasive, spatiotemporally-controlled, and sensitive detection of glioblastoma-derived circulating tumor DNA

Though surgical biopsies provide direct access to tissue for genomic characterization of brain cancer, they are invasive and pose significant clinical risks. Brain cancer management via blood-based liquid biopsies is a minimally invasive alternative; however, the blood-brain barrier (BBB) restricts the release of brain tumor-derived molecular biomarkers necessary for sensitive diagnosis.

Methods: A mouse glioblastoma multiforme (GBM) model was used to demonstrate the capability of focused ultrasound (FUS)-enabled liquid biopsy (sonobiopsy) to improve the diagnostic sensitivity of brain tumor-specific genetic mutations compared with conventional blood-based liquid biopsy. Furthermore, a pig GBM model was developed to characterize the translational implications of sonobiopsy in humans. Magnetic resonance imaging (MRI)-guided FUS sonication was performed in mice and pigs to locally enhance the BBB permeability of the GBM tumor. Contrast-enhanced T₁-weighted MR images were acquired to evaluate the BBB permeability change. Blood was collected immediately after FUS sonication. Droplet digital PCR was used to quantify the levels of brain tumor-specific genetic mutations in the circulating tumor DNA (ctDNA). Histological staining was performed to evaluate the potential for off-target tissue damage by sonobiopsy.

Results: Sonobiopsy improved the detection sensitivity of EGFRvIII from 7.14% to 64.71% and TERT C228T from 14.29% to 45.83% in the mouse GBM model. It also improved the diagnostic sensitivity of EGFRvIII from 28.57% to 100% and TERT C228T from 42.86% to 71.43% in the porcine GBM model.

Conclusion: Sonobiopsy disrupts the BBB at the spatially-targeted brain location, releases tumor-derived DNA into the blood circulation, and enables timely collection of ctDNA. Converging evidence from both mouse and pig GBM models strongly supports the clinical translation of sonobiopsy for the minimally invasive, spatiotemporally-controlled, and sensitive molecular characterization of brain cancer.

Recent Advances in Device Engineering and Computational Analysis for Characterization of Cell-Released Cancer Biomarkers

During cancer progression, tumors shed different biomarkers into the bloodstream, including circulating tumor cells (CTCs), extracellular vesicles (EVs), circulating cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA). The analysis of these biomarkers in the blood, known as 'liquid biopsy' (LB), is a promising approach for early cancer detection and treatment monitoring, and more recently, as a means for cancer therapy. Previous reviews have discussed the role of CTCs and ctDNA in cancer progression; however, ctDNA and EVs are rapidly evolving with technological advancements and computational analysis and are the subject of enormous recent studies in cancer biomarkers. In this review, first, we introduce these cell-released cancer biomarkers and briefly discuss their clinical significance in cancer diagnosis and treatment monitoring. Second, we present conventional and novel approaches for the isolation, profiling, and characterization of these markers. We then investigate the mathematical and in silico models that are developed to investigate the function of ctDNA and EVs in cancer progression. We convey our views on what is needed to pave the way to translate the emerging technologies and models into the clinic and make the case that optimized next-generation techniques and models are needed to precisely evaluate the clinical relevance of these LB markers.

Microenvironment-triggered multimodal precision diagnostics

Therapeutic outcomes in oncology may be aided by precision diagnostics that offer early detection, localization and the opportunity to monitor response to therapy. Here, we report a multimodal nanosensor engineered to target tumours through acidosis, respond to proteases in the microenvironment to release urinary reporters and (optionally) carry positron emission tomography probes to enable localization of primary and metastatic cancers in mouse models of colorectal cancer. We present a paradigm wherein this multimodal sensor can be employed longitudinally to assess burden of disease non-invasively, including tumour progression and response to chemotherapy. Specifically, we showed that acidosis-mediated tumour insertion enhanced on-target release of matrix metalloproteinase-responsive reporters in urine. Subsequent on-demand loading of the radiotracer ⁶⁴Cu allowed pH-dependent tumour visualization, enabling enriched microenvironmental characterization when compared with the conventional metabolic tracer ¹⁸F-fluorodeoxyglucose. Through tailored target specificities, this modular platform has the capacity to be engineered as a pan-cancer test that may guide treatment decisions for numerous tumour types.

Synthetic biomarkers: a twenty-first century path to early cancer detection

Detection of cancer at an early stage when it is still localized improves patient response to medical interventions for most cancer types. The success of screening tools such as cervical cytology to reduce mortality has spurred significant interest in new methods for early detection (for example, using non-invasive blood-based or biofluid-based biomarkers). Yet biomarkers shed from early lesions are limited by fundamental biological and mass transport barriers - such as short circulation times and blood dilution - that limit early detection. To address this issue, synthetic biomarkers are being developed. These represent an emerging class of diagnostics that deploy bioengineered sensors inside the body to query early-stage tumours and amplify disease signals to levels that could potentially exceed those of shed biomarkers. These strategies leverage design principles and advances from chemistry, synthetic biology and cell engineering. In this Review, we discuss the rationale for development of biofluid-based synthetic biomarkers. We examine how these strategies harness dysregulated features of tumours to amplify detection signals, use tumour-selective activation to increase specificity and leverage natural processing of bodily fluids (for example, blood, urine and proximal fluids) for easy detection. Finally, we highlight the challenges that exist for preclinical development and clinical translation of synthetic biomarker diagnostics.

Synthetic Circuit-Driven Expression of Heterologous Enzymes for Disease Detection

The integration of nanotechnology and synthetic biology could lay the framework for new classes of engineered biosensors that produce amplified readouts of disease states. As a proof-of-concept demonstration of this vision, here we present an engineered gene circuit that, in response to cancer-associated transcriptional deregulation, expresses heterologous enzyme biomarkers whose activity can be measured by nanoparticle sensors that generate amplified detection readouts. Specifically, we designed an AND-gate gene circuit that integrates the activity of two ovarian cancer-specific synthetic promoters to drive the expression of a heterologous protein output, secreted Tobacco Etch Virus (TEV) protease, exclusively from within tumor cells. Nanoparticle probes were engineered to carry a TEV-specific peptide substrate in order to measure the activity of the circuit-generated enzyme to yield amplified detection signals measurable in the urine or blood. We applied our integrated sense-and-respond system in a mouse model of disseminated ovarian cancer, where we demonstrated measurement of circuit-specific TEV protease activity both in vivo using exogenously administered nanoparticle sensors and ex vivo using quenched fluorescent probes. We envision that this work will lay the foundation for how synthetic biology and nanotechnology can be meaningfully integrated to achieve next-generation engineered biosensors.

Tumor Biomarker In-Solution Quantification, Standard Production, and Multiplex Detection

The diagnosis and monitoring of cancer have been facilitated by discovering tumor "biomarkers" and methods to detect their presence. Yet, for certain cancers, we still lack sensitive and specific biomarkers or the means to quantify subtle concentration changes successfully. The identification of new biomarkers of disease and improving the sensitivity of detection will remain key to changing clinical outcomes. Patient liquid biopsies (serum and plasma) are the most easily obtained sources for noninvasive analysis of proteins that tumor cells release directly and via extracellular microvesicles and tumor shedding. Therefore, an emphasis on creating reliable assays using serum/plasma and "direct, in-solution" ELISA approaches has built an industry centered on patient protein biomarker analysis. A need for improved dynamic range and automation has resulted in the application of ELISA principles to paramagnetic beads with chemiluminescent or fluorescent detection. In the clinical testing lab, chemiluminescent paramagnetic assays are run on automated machines that test a single analyte, minimize technical variation, and are not limited by serum sample volumes. This differs slightly from the R&D setting, where serum samples are often limiting; therefore, multiplexing antibodies to test multiple biomarkers in low serum volumes may be preferred. This review summarizes the development of historical biomarker "standards", paramagnetic particle assay principles, chemiluminescent or fluorescent biomarker detection advancements, and multiplexing for sensitive detection of novel serum biomarkers.

Pushing the detection limits: strategies towards highly sensitive optical-based protein detection

Proteins are one of the main constituents of living cells. Studying the quantities of proteins under physiological and pathological conditions can give valuable insights into health status, since proteins are the functional molecules of life. To be able to detect and quantify low-abundance proteins in biofluids for applications such as early disease diagnostics, sensitive analytical techniques are desired. An example of this application is using proteins as biomarkers for detecting cancer or neurological diseases, which can provide early, lifesaving diagnoses. However, conventional methods for protein detection such as ELISA, mass spectrometry, and western blotting cannot offer enough sensitivity for certain applications. Recent advances in optical-based micro- and nano-biosensors have demonstrated promising results to detect proteins at low quantities down to the single-molecule level, shining lights on their capacities for ultrasensitive disease diagnosis and rare protein detection. However, to date, there is a lack of review articles synthesizing and comparing various optical micro- and nano-sensing methods of enhancing the limits of detections of the antibody-based protein assays. The purpose of this article is to critically review different strategies of improving assay sensitivity using miniaturized biosensors, such as assay miniaturization, improving antibody binding capacity, sample purification, and signal amplification. The pros and cons of different methods are compared, and the future perspectives of this research field are discussed.

Ultrasensitive detection of circulating tumour DNA via deep methylation sequencing aided by machine learning

The low abundance of circulating tumour DNA (ctDNA) in plasma samples makes the analysis of ctDNA biomarkers for the detection or monitoring of early-stage cancers challenging. Here we show that deep methylation sequencing aided by a machine-learning classifier of methylation patterns enables the detection of tumour-derived signals at dilution factors as low as 1 in 10,000. For a total of 308 patients with surgery-resectable lung cancer and 261 age- and sex-matched non-cancer control individuals recruited from two hospitals, the assay detected 52-81% of the patients at disease stages IA to III with a specificity of 96% (95% confidence interval (CI) 93-98%). In a subgroup of 115 individuals, the assay identified, at 100% specificity (95% CI 91-100%), nearly twice as many patients with cancer as those identified by ultradeep mutation sequencing analysis. The low amounts of ctDNA permitted by machine-learning-aided deep methylation sequencing could provide advantages in cancer screening and the assessment of treatment efficacy.

Minicircles for a two-step blood biomarker and PET imaging early cancer detection strategy

Early cancer detection can dramatically increase treatment options and survival rates for patients, yet detection of early-stage tumors remains difficult. Here, we demonstrate a two-step strategy to detect and locate cancerous lesions by delivering tumor-activatable minicircle (MC) plasmids encoding a combination of blood-based and imaging reporter genes to tumor cells. We genetically engineered the MCs, under the control of the pan-tumor-specific Survivin promoter, to encode: 1) Gaussia Luciferase (GLuc), a secreted biomarker that can be easily assayed in blood samples; and 2) Herpes Simplex Virus Type 1 Thymidine Kinase mutant (HSV-1 sr39TK), a PET reporter gene that can be used for highly sensitive and quantitative imaging of the tumor location. We evaluated two methods of MC delivery, complexing the MCs with the chemical transfection reagent jetPEI or encapsulating the MCs in extracellular vesicles (EVs) derived from a human cervical cancer HeLa cell line. MCs delivered by EVs or jetPEI yielded significant expression of the reporter genes in cell culture versus MCs delivered without a transfection reagent. Secreted GLuc correlated with HSV-1 sr39TK expression with R² = 0.9676. MC complexation with jetPEI delivered a larger mass of MC for enhanced transfection, which was crucial for in vivo animal studies, where delivery of MCs via jetPEI resulted in GLuc and HSV-1 sr39TK expression at significantly higher levels than controls. To the best of our knowledge, this is the first report of the PET reporter gene HSV-1 sr39TK delivered via a tumor-activatable MC to tumor cells for an early cancer detection strategy. This work explores solutions to endogenous blood-based biomarker and molecular imaging limitations of early cancer detection strategies and elucidates the delivery capabilities and limitations of EVs.

The Hallmarks of Cancer as Ecologically Driven Phenotypes

Ecological fitness is the ability of individuals in a population to survive and reproduce. Individuals with increased fitness are better equipped to withstand the selective pressures of their environments. This paradigm pertains to all organismal life as we know it; however, it is also becoming increasingly clear that within multicellular organisms exist highly complex, competitive, and cooperative populations of cells under many of the same ecological and evolutionary constraints as populations of individuals in nature. In this review I discuss the parallels between populations of cancer cells and populations of individuals in the wild, highlighting how individuals in either context are constrained by their environments to converge on a small number of critical phenotypes to ensure survival and future reproductive success. I argue that the hallmarks of cancer can be distilled into key phenotypes necessary for cancer cell fitness: survival and reproduction. I posit that for therapeutic strategies to be maximally beneficial, they should seek to subvert these ecologically driven phenotypic responses.

Automated fluorescence quantification of extracellular vesicles collected from blood plasma using dielectrophoresis

Tumor-secreted exosomes and other extracellular vesicles (EVs) in circulation contain valuable biomarkers for early cancer detection and screening. We have previously demonstrated collection of cancer-derived nanoparticles (NPs) directly from whole blood and plasma with a chip-based technique that uses a microelectrode array to generate dielectrophoretic (DEP) forces. This technique enables direct recovery of NPs from whole blood and plasma. The biomarker payloads associated with collected particles can be detected and quantified with immunostaining. Accurately separating the fluorescence intensity of stained biomarkers from background (BG) levels becomes a challenge when analyzing the blood from early-stage cancer patients in which biomarker concentrations are low. To address this challenge, we developed two complementary techniques to standardize the quantification of fluorescently immunolabeled biomarkers collected and concentrated at predictable locations within microfluidic chips. The first technique was an automated algorithm for the quantitative analysis of fluorescence intensity at collection regions within the chip compared to levels at adjacent regions. The algorithm used predictable locations of particle collection within the chip geometry to differentiate regions of collection and BG. We successfully automated the identification and removal of optical artifacts from quantitative calculations. We demonstrated that the automated system performs nearly the same as a human user following a standard protocol for manual artifact removal with Pearson's r-values of 0.999 and 0.998 for two different biomarkers (n = 36 patients). We defined a usable dynamic range of fluorescence intensities corresponding to 1 to 2000 arbitrary units (a.u.). Fluorescence intensities within the dynamic range increased linearly with respect to exposure time and particle concentration. The second technique was the implementation of an internal standard to adjust levels of biomarker fluorescence based on the relative collection efficiency of the chip. Use of the internal standard reduced variability in measured biomarker levels due to differences in chip-to-chip collection efficiency, especially at low biomarker concentrations. The internal standard did not affect linear trends between fluorescence intensity and exposure time. Adjustments using the internal standard improved linear trends between fluorescence intensity and particle concentration. The optical quantification techniques described in this paper can be easily adapted for other lab-on-a-chip platforms that have predefined regions of biomarker or particle collection and that rely on fluorescence detection.

A mathematical model of tumor regression and recurrence after therapeutic oncogene inactivation

The targeted inactivation of individual oncogenes can elicit regression of cancers through a phenomenon called oncogene addiction. Oncogene addiction is mediated by cell-autonomous and immune-dependent mechanisms. Therapeutic resistance to oncogene inactivation leads to recurrence but can be counteracted by immune surveillance. Predicting the timing of resistance will provide valuable insights in developing effective cancer treatments. To provide a quantitative understanding of cancer response to oncogene inactivation, we developed a new 3-compartment mathematical model of oncogene-driven tumor growth, regression and recurrence, and validated the model using a MYC-driven transgenic mouse model of T-cell acute lymphoblastic leukemia. Our mathematical model uses imaging-based measurements of tumor burden to predict the relative number of drug-sensitive and drug-resistant cancer cells in MYC-dependent states. We show natural killer (NK) cell adoptive therapy can delay cancer recurrence by reducing the net-growth rate of drug-resistant cells. Our studies provide a novel way to evaluate combination therapy for personalized cancer treatment.

Sensitivity-Enhancing Strategies in Optical Biosensing

High-sensitivity detection of minute quantities or concentration variations of analytes of clinical importance is critical for biosensing to ensure accurate disease diagnostics and reliable health monitoring. A variety of sensitivity-improving concepts have been proposed from chemical, physical, and biological perspectives. In this review, elements that are responsible for sensitivity enhancement are classified and discussed in accordance with their operating steps in a typical biosensing workflow that runs through sampling, analyte recognition, and signal transduction. With a focus on optical biosensing, exemplary sensitivity-improving strategies are introduced, which can be developed into "plug-and-play" modules for many current and future sensors, and discuss their mechanisms to enhance biosensing performance. Three major strategies are covered: i) amplification of signal transduction by polymerization and nanocatalysts, ii) diffusion-limit-breaking systems for enhancing sensor-analyte contact and subsequent analyte recognition by fluid-mixing and analyte-concentrating, and iii) combined approaches that utilize renal concentration at the sampling and recognition steps and chemical signal amplification at the signal transduction step.

Biomarkers and Strategies for Early Detection of Ovarian Cancer

Early detection of ovarian cancer remains an important unmet medical need. Effective screening could reduce mortality by 10%-30%. Used individually, neither serum CA125 nor transvaginal sonography (TVS) is sufficiently sensitive or specific. Two-stage strategies have proven more effective, where a significant rise above a woman's baseline CA125 prompts TVS and an abnormal sonogram prompts surgery. Two major screening trials have documented that this strategy has adequate specificity, but sensitivity for early-stage (I-II) disease must improve to have a greater impact on mortality. To improve the first stage, different panels of protein biomarkers have detected cases missed by CA125. Autoantibodies against TP53 have detected 20% of early-stage ovarian cancers 8 months before elevation of CA125 and 22 months before clinical diagnosis. Panels of autoantibodies and antigen-autoantibody complexes are being evaluated with the goal of detecting >90% of early-stage ovarian cancers, alone or in combination with CA125, while maintaining 98% specificity in control subjects. Other biomarkers, including micro-RNAs, ctDNA, methylated DNA, and combinations of ctDNA alterations, are being tested to provide an optimal first-stage test. New technologies are also being developed with greater sensitivity than TVS to image small volumes of tumor.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."