Cancer screening: a mathematical model relating secreted blood biomarker levels to tumor sizes

Proteomic and lipidomic analysis of low-density lipoprotein identifies potential biomarkers of early estrogen receptor-positive breast cancer

Estrogen receptor (ER)-positive breast cancer (BC) is a prevalent and fatal cancer among women, and there is a need to identify molecules involved in the disease pathophysiology which could also serve as biomarkers for early detection. Detection of cancer markers in whole plasma produces excessive information, and identifying important markers involved in cancer progression is challenging. We identified a BC-specific low-density lipoprotein (LDL) particle isolated by ultracentrifugation from the plasma of ER-positive BC patients. This LDL has an aberrant proteome and lipidome, significantly different from that of LDL from healthy women, including a high association with the pro-tumor chemokines CXCL4 and CXCL7, and an enrichment with the lipid subclasses phosphatidylethanolamine, ceramide, triglycerides, lysophosphatidylcholine, phosphatidylserine, phosphatidic acid, and sphingomyelin. In contrast, phosphatidylinositol species were significantly less abundant in LDL from tumor patients than in control. Moreover, BC-associated LDL has a distinct effect on macrophage phenotype, inducing an increased gene expression of IL1β, IL8 and CD206 and decreased gene expression of TNFα, a gene signature characteristic of tumor-associated macrophages (TAMs). This suggests that this formerly unrecognized form of LDL may represent LDL particles that are recruited by the tumor microenvironment to support tumor progression by inducing discrete subsets of TAMs. In conclusion, these data offer BC-associated LDL as an early biomarker detection platform for ER-positive BC. Furthermore, LDL-associated proteins and lipids that promote BC progression may also serve in the future as novel targets for BC therapies.

Estimating the ovarian cancer CA-125 preclinical detectable phase, in-vivo tumour doubling time, and window for detection in early stage: an exploratory analysis of UKCTOCS

BACKGROUND

The ovarian cancer (OC) preclinical detectable phase (PCDP), defined as the interval during which cancer is detectable prior to clinical diagnosis, remains poorly characterised. We report exploratory analyses from the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS).

METHODS

In UKCTOCS between Apr-2001 and Sep-2005, 101,314 postmenopausal women were randomised to no screening (NS) and 50,625 to annual multimodal screening (MMS) (until Dec-2011) using serum CA-125 interpreted by the Risk of Ovarian Cancer Algorithm (ROCA). All provided a baseline blood sample. Women with invasive epithelial OC diagnosed between randomisation and trial censorship (Dec-2014) in the MMS and NS arms with two or more CA-125 measurements, including one within two years of diagnosis were included. OC-free women (2:1 to cases) from the MMS arm provided information on baseline CA-125 distribution. CA-125 measurements were obtained from MMS results, secondary analysis of baseline samples, and medical records. PCDP duration and in-vivo tumour doubling time were estimated using the change-point model underlying ROCA. Early-stage (Stage I and II) PCDP was estimated from a Bayesian model for the probability of early stage given a CA-125 measurement.

FINDINGS

Of 541 women (2371 CA-125 measurements) with high-grade serous cancer (HGSC), 93% (504/541) secreted CA-125 into the circulation. Median CA-125 PCDP duration for clinically-diagnosed HGSC was 15.2 (IQR 13.1-16.9, 95% IPR 9.6-21.8) months, of which 11.9 (IQR 10.5-13.1, 95% IPR 7.5-16.5) months was in early stage. The median HGSC in-vivo tumour doubling time for cancers secreting CA-125 was 2.9 (IQR 2.3-3.7, 95% IPR 1.5-7.6) months.

INTERPRETATION

We report a comprehensive characterisation of the OC CA-125 PCDP. The 12-month window for early-stage detection and short tumour doubling time of HGSC provide a benchmark for researchers evaluating novel screening approaches including need to reduce diagnostic workup interval. Equally the findings provide urgent impetus for clinicians to reduce intervals from presentation to treatment onset.

FUNDING

NCI Early Detection Research Network, Concord (MA) Detect Ovarian Cancer Early Fund, MRC Clinical Trials Unit at UCL Core Funding.

A Monte-Carlo planning strategy for medical follow-up optimization: Illustration on multiple myeloma data

Designing patient-specific follow-up strategies is key to personalized cancer care. Tools to assist doctors in treatment decisions and scheduling follow-ups based on patient preferences and medical data would be highly beneficial. These tools should incorporate realistic models of disease progression under treatment, multi-objective optimization of treatment strategies, and efficient algorithms to personalize follow-ups by considering patient history. We propose modeling cancer evolution using a Piecewise Deterministic Markov Process, where patients alternate between remission and relapse phases, and control the model via long-term cost function optimization. This considers treatment side effects, visit burden, and quality of life, using noisy blood marker measurements for feedback. Instead of discretizing the problem with a discrete Markov Decision Process, we apply the Partially-Observed Monte-Carlo Planning algorithm to solve the continuous-time, continuous-state problem, leveraging the near-deterministic nature of cancer progression. Our approach, tested on multiple myeloma patient data, outperforms exact solutions of the discrete model and allows greater flexibility in cost function modeling, enabling patient-specific follow-ups. This method can also be adapted to other diseases.

A proactive/reactive mass screening approach with uncertain symptomatic cases

We study the problem of mass screening of heterogeneous populations under limited testing budget. Mass screening is an essential tool that arises in various settings, e.g., the COVID-19 pandemic. The objective of mass screening is to classify the entire population as positive or negative for a disease as efficiently and accurately as possible. Under limited budget, testing facilities need to allocate a portion of the budget to target sub-populations (i.e., proactive screening) while reserving the remaining budget to screen for symptomatic cases (i.e., reactive screening). This paper addresses this decision problem by taking advantage of accessible population-level risk information to identify the optimal set of sub-populations for proactive/reactive screening. The framework also incorporates two widely used testing schemes: Individual and Dorfman group testing. By leveraging the special structure of the resulting bilinear optimization problem, we identify key structural properties, which in turn enable us to develop efficient solution schemes. Furthermore, we extend the model to accommodate customized testing schemes across different sub-populations and introduce a highly efficient heuristic solution algorithm for the generalized model. We conduct a comprehensive case study on COVID-19 in the US, utilizing geographically-based data. Numerical results demonstrate a significant improvement of up to 52% in total misclassifications compared to conventional screening strategies. In addition, our case study offers valuable managerial insights regarding the allocation of proactive/reactive measures and budget across diverse geographic regions.

Alteration of functionality and differentiation directed by changing gene expression patterns in myeloid-derived suppressor cells (MDSCs) in tumor microenvironment and bone marrow through early to terminal phase of tumor progression

Myeloid-derived suppressor cells are heterogenous immature myeloid lineage cells that can differentiate into neutrophils, monocytes, and dendritic cells as well. These cells have been characterized to have potent immunosuppressive capacity in neoplasia and a neoplastic chronic inflammatory microenvironment. Increased accumulation of myeloid-derived suppressor cells was reported with poor clinical outcomes in patients. They support neoplastic progression by abrogating antitumor immunity through inhibition of lymphocyte functions and directly by facilitating tumor development. Yet the shifting genetic signatures of this myeloid lineage cell toward immunosuppressive functionality in progressive tumor development remain elusive. We have attempted to identify the gene expression profile using lineage-specific markers of these unique myeloid lineage cells in a tumor microenvironment and bone marrow using a liquid transplantable mice tumor model to trace the changing influence of the tumor microenvironment on myeloid-derived suppressor cells. We analyzed the phenotype, functional shift, suppressive activity, differentiation status, and microarray-based gene expression profile of CD11b+Gr1+ lineage-specific cells isolated from the tumor microenvironment and bone marrow of 4 stages of tumor-bearing mice and compared them with control counterparts. Our analysis of differentially expressed genes of myeloid-derived suppressor cells isolated from bone marrow and the tumor microenvironment reveals unique gene expression patterns in the bone marrow and tumor microenvironment-derived myeloid-derived suppressor cells. It also suggests T-cell suppressive activity of myeloid-derived suppressor cells progressively increases toward the mid-to-late phase of the tumor and a significant differentiation bias of tumor site myeloid-derived suppressor cells toward macrophages, even in the presence of differentiating agents, indicating potential molecular characteristics of myeloid-derived suppressor cells in different stages of the tumor that can emerge as an intervention target.

Surface-enhanced Raman scattering sensing for detection and mapping of key cellular biomarkers

Cellular biomarkers mainly contain proteins, nucleic acids, glycans and many small molecules including small biomolecule metabolites, reactive oxygen species and other cellular chemical entities. The detection and mapping of the key cellular biomarkers can effectively help us to understand important cellular mechanisms associated with physiological and pathological processes, which greatly promote the development of clinical diagnosis and disease treatment. Surface-enhanced Raman scattering (SERS) possesses high sensitivity and is free from the influence of strong self-fluorescence in living systems as well as the photobleaching of the dyes. It exhibits rich and narrow chemical fingerprint spectra for multiplexed detection, and has become a powerful tool to detect and map cellular biomarkers. In this review, we present an overview of recent advances in the detection and mapping of different classes of cellular biomarkers based on SERS sensing. These advances fully confirm that the SERS-based sensors and sensing methods have great potential for the exploration of biological mechanisms and clinical applications. Additionally, we also discuss the limitations of present research and the future developments of the SERS technology in this field.

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.

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.

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.

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.

Mathematical modeling and dynamical analysis of anti-tumor drug dose-response

Cancer is a serious threat to human health and life. Using anti-tumor drugs is one of the important ways for treating cancer. A large number of experiments have shown that the hormesis appeared in the dose-response relationship of various anti-tumor drugs. Modeling this phenomenon will contribute to finding the appropriate dose. However, few studies have used dynamical models to quantitatively explore the hormesis phenomenon in anti-tumor drug dose-response. In this study, we present a mathematical model and dynamical analysis to quantify hormesis of anti-tumor drugs and reveal the critical threshold of antibody dose. Firstly, a dynamical model is established to describe the interactions among tumor cells, natural killer cells and M2-polarized macrophages. Model parameters are fitted through the published experimental data. Secondly, the positivity of solution and bounded invariant set are given. The stability of equilibrium points is proved. Thirdly, through bifurcation analysis and numerical simulations, the hormesis phenomenon of low dose antibody promoting tumor growth and high dose antibody inhibiting tumor growth is revealed. Furthermore, we fit out the quantitative relationship of the dose-response of antibodies. Finally, the critical threshold point of antibody dose changing from promoting tumor growth to inhibiting tumor growth is obtained. These results can provide suggestions for the selection of appropriate drug dosage in the clinical treatment of cancer.

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.

Reducing Ovarian Cancer Mortality Through Early Detection: Approaches Using Circulating Biomarkers

More than two-thirds of all women diagnosed with epithelial ovarian cancer (EOC) will die from the disease (>14,000 deaths annually), a fact that has not changed considerably in the last three decades. Although the 5-year survival rates for most other solid tumors have improved steadily, ovarian cancer remains an exception, making it the deadliest of all gynecologic cancers and five times deadlier than breast cancer. When diagnosed early, treatment is more effective, with a 5-year survival rate of up to 90%. Unfortunately, most cases are not detected until after the cancer has spread, resulting in a dismal 5-year survival rate of less than 30%. Current screening methods for ovarian cancer typically use a combination of a pelvic examination, transvaginal ultrasonography, and serum cancer antigen 125 (CA125), but these have made minimal impact on improving mortality. Thus, there is a compelling unmet need to develop new molecular tools that can be used to diagnose early-stage EOC and/or assist in the clinical management of the disease after a diagnosis, given that more than 220,000 women are living with ovarian cancer in the United States and are at risk of recurrence. Here, we discuss the state of advancing liquid-based approaches for improving the early detection of ovarian cancer.See all articles in this Special Collection Honoring Paul F. Engstrom, MD, Champion of Cancer Prevention.

Multicompartment modeling of protein shedding kinetics during vascularized tumor growth

Identification of protein biomarkers for cancer diagnosis and prognosis remains a critical unmet clinical need. A major reason is that the dynamic relationship between proliferating and necrotic cell populations during vascularized tumor growth, and the associated extra- and intra-cellular protein outflux from these populations into blood circulation remains poorly understood. Complementary to experimental efforts, mathematical approaches have been employed to effectively simulate the kinetics of detectable surface proteins (e.g., CA-125) shed into the bloodstream. However, existing models can be difficult to tune and may be unable to capture the dynamics of non-extracellular proteins, such as those shed from necrotic and apoptosing cells. The models may also fail to account for intra-tumoral spatial and microenvironmental heterogeneity. We present a new multi-compartment model to simulate heterogeneously vascularized growing tumors and the corresponding protein outflux. Model parameters can be tuned from histology data, including relative vascular volume, mean vessel diameter, and distance from vasculature to necrotic tissue. The model enables evaluating the difference in shedding rates between extra- and non-extracellular proteins from viable and necrosing cells as a function of heterogeneous vascularization. Simulation results indicate that under certain conditions it is possible for non-extracellular proteins to have superior outflux relative to extracellular proteins. This work contributes towards the goal of cancer biomarker identification by enabling simulation of protein shedding kinetics based on tumor tissue-specific characteristics. Ultimately, we anticipate that models like the one introduced herein will enable examining origins and circulating dynamics of candidate biomarkers, thus facilitating marker selection for validation studies.

Circulating tumor DNA as an early cancer detection tool

Circulating tumor DNA holds substantial promise as an early detection biomarker, particularly for cancers that do not have currently accepted screening methodologies, such as ovarian, pancreatic, and gastric cancers. Many features intrinsic to ctDNA analysis may be leveraged to enhance its use as an early cancer detection biomarker: including ctDNA fragment lengths, DNA copy number variations, and associated patient phenotypic information. Furthermore, ctDNA testing may be synergistically used with other multi-omic biomarkers to enhance early detection. For instance, assays may incorporate early detection proteins (i.e., CA-125), epigenetic markers, circulating tumor RNA, nucleosomes, exosomes, and associated immune markers. Many companies are currently competing to develop a marketable early cancer detection test that leverages ctDNA. Although some hurdles (like early stage disease assay accuracy, high implementation costs, confounding from clonal hematopoiesis, and lack of clinical utility studies) need to be addressed before integration into healthcare, ctDNA assays hold substantial potential as an early cancer screening test.

Novel Approaches to Ovarian Cancer Screening

PURPOSE OF REVIEW

Both conventional and novel approaches to early detection of ovarian cancer are reviewed in the context of new developments in our understanding of ovarian cancer biology.

RECENT FINDINGS

While CA125 as a single value lacks adequate specificity or sensitivity for screening, large studies have shown that a 2-stage strategy which tracks CA125 change over time and prompts transvaginal ultrasound (TVS) for a small subset of women with abnormally rising biomarker values achieves adequate specificity and detects a higher fraction of early-stage disease. Sensitivity could clearly be improved in both blood tests and in imaging. Metastasis can occur from ovarian cancers too small to increase blood levels of protein antigens and a significant fraction of ovarian cancers arise from the fimbriae of fallopian tubes that cannot be imaged with TVS. Autoantibodies, miRNA, ctDNA, DNA methylation in blood, and cervical mucus might improve sensitivity of the initial phase and magnetic relaxometry and autofluorescence could improve imaging in the second phase. Enhancing the sensitivity of two-stage strategies for early detection could reduce mortality from ovarian cancer.

Upregulation of lncRNA GATA6-AS suppresses the migration and invasion of cervical squamous cell carcinoma by downregulating MTK-1

The diagnosis and treatment of cervical squamous cell carcinoma is challenged by difficulties in the determination of tumor invasion. GATA binding protein 6 antisense (GATA6-AS) is a recently identified long non-coding RNA that exhibits critical functions in the growth of endothelial cells; however, to the best of our knowledge, its involvement in other physiological and pathological processes is unknown. By reverse transcription-quantitative polymerase chain reaction, the present study examined the expression of GATA6-AS in tumor tissues and adjacent healthy tissues, and the serum from patients with cervical squamous cell carcinoma and healthy controls. The diagnostic and prognostic potentials of GATA6-AS for cervical squamous cell carcinoma were analyzed by receiver operating characteristic curve analysis and survival curve analysis, respectively. Associations between the serum levels of GATA6-AS and clinical characteristics of patients with cervical squamous cell carcinoma were analyzed by a χ² test. A GATA6-AS overexpression vector was transfected into cervical squamous cell carcinoma cells, and the effects on cell migration and invasion were investigated by Transwell migration and invasion assays, respectively. The expression of mitogen-activated protein kinase kinase 4 (MTK-1) following transfection with the GATA6-AS overexpression vector was detected by western blot analysis. It was identified that the expression levels of GATA6-AS were lower in tumor tissues compared with healthy tissues. In addition, serum levels of GATA6-AS were higher in patients compared with healthy controls. The serum levels of GATA6-AS were associated with tumor metastasis, and may serve as a potential diagnostic and prognostic marker for cervical squamous cell carcinoma. Furthermore, GATA6-AS overexpression inhibited cancer cell migration and invasion. The expression levels of MTK-1 were also reduced following GATA6-AS overexpression. Therefore, the present study proposed that downregulated GATA6-AS expression was associated with tumor metastasis in cervical squamous cell carcinoma, and that GATA6-AS expression may inhibit cancer cell migration and invasion by downregulating MTK-1.

Conserved serum protein biomarkers associated with growing early colorectal adenomas

A major challenge for the reduction of colon cancer is to detect patients carrying high-risk premalignant adenomas with minimally invasive testing. As one step, we have addressed the feasibility of detecting protein signals in the serum of patients carrying an adenoma as small as 6-9 mm in maximum linear dimension. Serum protein biomarkers, discovered in two animal models of early colonic adenomagenesis, were studied in patients using quantitative mass-spectrometric assays. One cohort included patients bearing adenomas known to be growing on the basis of longitudinal computed tomographic colonography. The other cohort, screened by optical colonoscopy, included both patients free of adenomas and patients bearing adenomas whose risk status was judged by histopathology. The markers F5, ITIH4, LRG1, and VTN were each elevated both in this patient study and in the studies of the Pirc rat model. The quantitative study in the Pirc rat model had demonstrated that the elevated level of each of these markers is correlated with the number of colonic adenomas. However, the levels of these markers in patients were not significantly correlated with the total adenoma volume. Postpolypectomy blood samples demonstrated that the elevated levels of these four conserved markers persisted after polypectomy. Two additional serum markers rapidly renormalized after polypectomy: growth-associated CRP levels were enhanced only with high-risk adenomas, while PI16 levels, not associated with growth, were reduced regardless of risk status. We discuss biological hypotheses to account for these observations, and ways for these signals to contribute to the prevention of colon cancer.

Non-invasive early detection of acute transplant rejection via nanosensors of granzyme B activity

The early detection of the onset of transplant rejection is critical for the long-term survival of patients. The diagnostic gold standard for detecting transplant rejection involves a core biopsy, which is invasive, has limited predictive power and carries a morbidity risk. Here, we show that nanoparticles conjugated with a peptide substrate specific for the serine protease granzyme B, which is produced by recipient T cells during the onset of acute cellular rejection, can serve as a non-invasive biomarker of early rejection. When administered systemically in mouse models of skin graft rejection, these nanosensors preferentially accumulate in allograft tissue, where they are cleaved by granzyme B, releasing a fluorescent reporter that filters into the recipient's urine. Urinalysis then discriminates the onset of rejection with high sensitivity and specificity before features of rejection are apparent in grafted tissues. Moreover, in mice treated with subtherapeutic levels of immunosuppressive drugs, the reporter signals in urine can be detected before graft failure. This method may enable routine monitoring of allograft status without the need for biopsies.

Potential of osteopontin in the management of epithelial ovarian cancer

Background Osteopontin (sOPN) is a promising blood tumour marker for detecting epithelial ovarian cancer (EOC). However, other clinical uses of sOPN as a tumour marker in EOC are still lacking. Since sOPN concentrations in serum are not associated with those in ascites, we compared clinical value of sOPN concentrations in the two body fluids. Patients and methods The study included 31 women with advanced EOC and 34 women with benign gynaecological pathology. In the EOC group, serum for sOPN analysis was obtained preoperatively, after primary debulking surgery and after chemotherapy. In the control group, serum was obtained before and after surgery. Ascites and peritoneal fluid were obtained during surgery. sOPN concentrations were determined by flow cytometry bead-based assay. Results The sensitivity and specificity of sOPN in detecting EOC was 91.2% and 90.3% (cut-off = 47.4 ng/ml) in serum, and 96.8% and 100% (cut-off = 529.5 ng/ml) in ascites. Kaplan-Meier analysis showed a significant association between higher serum sOPN concentration and overall survival (p = 0.018) or progression free survival (p = 0.008). Higher ascites sOPN concentrations were associated with suboptimally debulked tumour and unresectable disease. Higher serum sOPN concentrations were associated with refractory disease or incomplete response to platinum-based chemotherapy. Conclusions The study showed that ascites sOPN level mirrors present disease and is superior to serum level for diagnostic purposes and surgical planning, although the end result of treatment is the response of the whole body in fighting the disease. The preoperative sOPN concentration in serum thus better reflects disease outcome.

A two-drug combination simulation study for metastatic castrate resistant prostate cancer

BACKGROUND

Prostate cancer often evolves resistance to androgen deprivation therapy leading to a lethal metastatic castrate-resistant form. Besides androgen independence, subpopulations of the tumor are genetically heterogeneous. With the advent of tumor genome sequencing we asked which has the greater influence on reducing tumor size: genetic background, heterogeneity, or drug potency?

METHODS

A previously developed theoretical evolutionary dynamics model of stochastic branching processes is applied to compute the probability of tumor eradication with two targeted drugs. Publicly available data sets were surveyed to parameterize the model.

RESULTS

Our calculations reveal that the greatest influence on successful treatment is the genetic background including the number of mutations overcoming resistance. Another important criteria is the tumor size at which it is still possible to achieve tumor eradication, for example, 2-4 cm large tumors have at best a 10% probability to be eradicated when 50 mutations can confer resistance to each drug.

CONCLUSION

Overall, this study finds that genetic background and tumor heterogeneity are more important than drug potency in treating mCRPC. It also points toward identifying metastatic sites early using biochemical assays and/or dPET.

Beta-galactosidase-responsive synthetic biomarker for targeted tumor detection

Tumor biomarkers are highly desirable for the screening of patients with a risk of tumor development and progression. Here, we report a beta-galactosidase (β-gal)-responsive acetaminophen (β-GR-APAP) as a synthetic plasma biomarker for targeted tumor detection. Tumor β-gal labeling via the recognition of tumor-related antigen enabled the detection of a tumor using β-GR-APAP.

Affinity enrichment for mass spectrometry: improving the yield of low abundance biomarkers

INTRODUCTION

Mass spectrometry (MS) is the premier tool for discovering novel disease-associated protein biomarkers. Unfortunately, when applied to complex body fluid samples, MS has poor sensitivity for the detection of low abundance biomarkers (≪10 ng/mL), derived directly from the diseased tissue cells or pathogens. Areas covered: Herein we discuss the strengths and drawbacks of technologies used to concentrate low abundance analytes in body fluids, with the aim to improve the effective sensitivity for MS discovery. Solvent removal by dry-down or dialysis, and immune-depletion of high abundance serum or plasma proteins, is shown to have disadvantages compared to positive selection of the candidate biomarkers by affinity enrichment. A theoretical analysis of affinity enrichment reveals that the yield for low abundance biomarkers is a direct function of the binding affinity (Association/Dissociation rates) used for biomarker capture. In addition, a high affinity capture pre processing step can effectively dissociate the candidate biomarker from partitioning with high abundance proteins such as albumin. Expert commentary: Properly designed high affinity capture materials can enrich the yield of low abundance (0.1-10 picograms/mL) candidate biomarkers for MS detection. Affinity capture and concentration, as an upfront step in sample preparation for MS, combined with MS advances in software and hardware that improve the resolution of the chromatographic separation can yield a transformative new class of low abundance biomarkers predicting disease risk or disease latency.

Improving cancer detection through combinations of cancer and immune biomarkers: a modelling approach

Background

Early cancer diagnosis is one of the most important challenges of cancer research, since in many cancers it can lead to cure for patients with early stage diseases. For epithelial ovarian cancer (which is the leading cause of death among gynaecologic malignancies) the classical detection approach is based on measurements of CA-125 biomarker. However, the poor sensitivity and specificity of this biomarker impacts the detection of early-stage cancers.

Methods

Here we use a computational approach to investigate the effect of combining multiple biomarkers for ovarian cancer (e.g., CA-125 and IL-7), to improve early cancer detection.

Results

We show that this combined biomarkers approach could lead indeed to earlier cancer detection. However, the immune response (which influences the level of secreted IL-7 biomarker) plays an important role in improving and/or delaying cancer detection. Moreover, the detection level of IL-7 immune biomarker could be in a range that would not allow to distinguish between a healthy state and a cancerous state. In this case, the construction of solution diagrams in the space generated by the IL-7 and CA-125 biomarkers could allow us predict the long-term evolution of cancer biomarkers, thus allowing us to make predictions on cancer detection times.

Conclusions

Combining cancer and immune biomarkers could improve cancer detection times, and any predictions that could be made (at least through the use of CA-125/IL-7 biomarkers) are patient specific.

Screening for ovarian cancer: imaging challenges and opportunities for improvement

The United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) recently reported a reduction in the average overall mortality among ovarian cancer patients screened with an annual sequential, multimodal strategy that tracked biomarker CA125 over time, where increasing serum CA125 levels prompted ultrasound. However, multiple cases were documented wherein serum CA125 levels were rising, but ultrasound screens were normal, thus delaying surgical intervention. A significant factor which could contribute to false negatives is that many aggressive ovarian cancers are believed to arise from epithelial cells on the fimbriae of the fallopian tubes, which are not readily imaged. Moreover, because only a fraction of metastatic tumors may reach a sonographically-detectable size before they metastasize, annual screening with ultrasound may fail to detect a large fraction of early-stage ovarian cancers. The ability to detect ovarian carcinomas before they metastasize is critical and future efforts towards improving screening should focus on identifying unique features specific to aggressive, early-stage tumors, as well as improving imaging sensitivity to allow for detection of tubal lesions. Implementation of a three-stage multimodal screening strategy in which a third modality is employed in cases where the first-line blood-based assay is positive and the second-line ultrasound exam is negative may also prove fruitful in detecting early-stage cases missed by ultrasound.

Assessing the Diagnostic Value of Plasma-Free DNA in Prostate Cancer Screening

Background:

Prostate cancer is the second form of cancer among men worldwide. For early cancer detection, we should identify tumors in initial stages before the physical signs become visible. The present study aims to evaluate the diagnostic value of cell-free DNA (cfDNA), its comparison with prostate-specific antigen (PSA) level in prostate cancer screening and also in patients with localized prostate cancer, metastatic form, and benign prostatic hyperplasia (BPH).

Methods:

The participants of this study were selected from 126 patients with genitourinary symptoms suspected prostate cancer, rising PSA, and/or abnormal rectal examination results and 10 healthy subjects as controls. Peripheral blood plasma before any treatment measures was considered. cfDNA was extracted using a commercial kit, and PSA levels were measured by ELISA. The ANOVA test was used to compare the average serum level of PSA and plasma concentration of cfDNA between the groups. The correlation between variables was measured by the Pearson test.

Results:

The subgroups consisted of 50 patients with localized prostate cancer, 26 patients with metastatic prostate cancer, 50 patients with BPH, and 10 healthy subjects; the average concentrations of cfDNA in these subgroups were 15.04, 19.62, 9.51, and 8.7 ng/μl, respectively. According to p < 0.0001 obtained from multivariate test, there was a significant difference between all the groups.

Conclusion:

Our findings indicated significant differences between cfDNA levels of patients with localized and metastatic prostate cancer, and differences of these two groups from BPH and healthy cases show the importance of this biomarker in non-invasive diagnostic procedures.

Synthetic Biomarker Design by Using Analyte-Responsive Acetaminophen

The use of synthetic biomarkers is an emerging technique to improve disease diagnosis. Here, we report a novel design strategy that uses analyte-responsive acetaminophen (APAP) to expand the catalogue of analytes available for synthetic biomarker development. As proof-of-concept, we designed hydrogen peroxide (H₂ O₂ )-responsive APAP (HR-APAP) and succeeded in H₂ O₂ detection with cellular and animal experiments. In fact, for blood samples following HR-APAP injection, we demonstrated that the plasma concentration ratio [APAP+APAP conjugates]/[HR-APAP] accurately reflects in vivo differences in H₂ O₂ levels. We anticipate that our practical methodology will be broadly useful for the preparation of various synthetic biomarkers.

A Model-Based Personalized Cancer Screening Strategy for Detecting Early-Stage Tumors Using Blood-Borne Biomarkers

An effective cancer blood biomarker screening strategy must distinguish aggressive from nonaggressive tumors at an early, intervenable time. However, for blood-based strategies to be useful, the quantity of biomarker shed into the blood and its relationship to tumor growth or progression must be validated. To study how blood biomarker levels correlate with early-stage viable tumor growth in a mouse model of human cancer, we monitored early tumor growth of engineered human ovarian cancer cells (A2780) implanted orthotopically into nude mice. Biomarker shedding was monitored by serial blood sampling, whereas tumor viability and volume were monitored by bioluminescence imaging and ultrasound imaging. From these metrics, we developed a mathematical model of cancer biomarker kinetics that accounts for biomarker shedding from tumor and healthy cells, biomarker entry into vasculature, biomarker elimination from plasma, and subject-specific tumor growth. We validated the model in a separate set of mice in which subject-specific tumor growth rates were accurately predicted. To illustrate clinical translation of this strategy, we allometrically scaled model parameters from mouse to human and used parameters for PSA shedding and prostate cancer. In this manner, we found that blood biomarker sampling data alone were capable of enabling the detection and discrimination of simulated aggressive (2-month tumor doubling time) and nonaggressive (18-month tumor doubling time) tumors as early as 7.2 months and 8.9 years before clinical imaging, respectively. Our model and screening strategy offers broad impact in their applicability to any solid cancer and associated biomarkers shed, thereby allowing a distinction between aggressive and nonaggressive tumors using blood biomarker sampling data alone. Cancer Res; 77(10); 2570-84. ©2017 AACR.

Design and development of a microarray processing station (MPS) for automated miniaturized immunoassays

Here we describe the design and evaluation of a fluidic device for the automatic processing of microarrays, called microarray processing station or MPS. The microarray processing station once installed on a commercial microarrayer allows automating the washing, and drying steps, which are often performed manually. The substrate where the assay occurs remains on place during the microarray printing, incubation and processing steps, therefore the addressing of nL volumes of the distinct immunoassay reagents such as capture and detection antibodies and samples can be performed on the same coordinate of the substrate with a perfect alignment without requiring any additional mechanical or optical re-alignment methods. This allows the performance of independent immunoassays in a single microarray spot.

Comparative Study of Autoantibody Responses between Lung Adenocarcinoma and Benign Pulmonary Nodules

INTRODUCTION

The reduction in lung cancer mortality associated with computed tomography (CT) screening has led to its increased use and a concomitant increase in the detection of benign pulmonary nodules. Many individuals found to have benign nodules undergo unnecessary, costly, and invasive procedures. Therefore, there is a need for companion diagnostics that stratify individuals with pulmonary nodules into high-risk or low-risk groups. Lung cancers can trigger host immune responses and elicit antibodies against tumor antigens. The identification of these autoantibodies (AAbs) and their corresponding antigens may expand our knowledge of cancer immunity, leading to early diagnosis or even benefiting immunotherapy. Previous studies were performed mostly in the context of comparing cancers and healthy (smoker) controls. We have performed one of the first studies to understand humoral immune response in patients with cancer, patients with benign nodules, and healthy smokers.

METHODS

We first profiled seroreactivity to 10,000 full-length human proteins in 40 patients with early-stage lung cancer and 40 smoker controls by using nucleic acid programmable protein arrays to identify candidate cancer-specific AAbs. Enzyme-linked immunosorbent assays of promising candidates were performed on 137 patients with lung cancer and 127 smoker controls, as well as on 170 subjects with benign pulmonary nodules.

RESULTS

From protein microarray screening experiments using a discovery set of 40 patients and 40 smoker controls, 17 antigens showing higher reactivity in lung cancer cases relative to the controls were subsequently selected for evaluation in a large sample set (n = 264) by using enzyme-linked immunosorbent assay. A five-AAb classifier (tetratricopeptide repeat domain 14 [TTC14], B-Raf proto-oncogene, serine/threonine kinase [BRAF], actin like 6B [ACTL6B], MORC family CW-type zinc finger 2 [MORC2], and cancer/testis antigen 1B [CTAG1B]) that can differentiate lung cancers from smoker controls with a sensitivity of 30% at 89% specificity was developed. We further tested AAb responses in subjects with CT-positive benign nodules (n = 170), and developed a five-AAb panel (keratin 8, type II, TTC14, Kruppel-like factor 8, BRAF, and tousled like kinase 1) with a sensitivity of 30% at 88% specificity. Interestingly, messenger RNA levels of six AAb targets (TTC14, BRAF, MORC family CW-type zinc finger 2, cancer/testis antigen 1B, keratin 8, type II, and tousled like kinase 1) were also found to increase in lung adenocarcinoma tissues based on The Cancer Genome Atlas data set.

CONCLUSION

We discovered AAbs associated with lung adenocaricnoma that have the potential to differentiate cancer from CT-positive benign diseases. We believe that these antibodies warrant future validation using a larger sample set and/or longitudinal samples individually or as a panel. They could potentially be part of companion molecular diagnostic modalities that will benefit subjects undergoing CT screening for lung cancer.

A cancer treatment based on synergy between anti-angiogenic and immune cell therapies

A mathematical model integrating tumor angiogenesis and tumor-targeted cytotoxicity by immune cells was developed to identify the therapeutic window of two distinct modes to treat cancer: (1) an anti-angiogenesis treatment based on the monoclonal antibody bevacizumab that targets tumor vasculature, and (2) immunotherapy involving the injection of unlicensed dendritic cells to boost the anti-tumor adaptive response. The angiogenic cytokine Vascular Endothelial Growth Factor (VEGF) contributes to the immunosuppressive tumor microenvironment, which is responsible for the short-lived therapeutic effect of cancer-targeted immunotherapy. The effect of immunosuppression on the width of the therapeutic window of each treatment was quantified. Experimental evidence has shown that neutralizing immunosuppressive cytokines results in an enhanced immune response against infections and chronic diseases. The model was used to determine treatment protocols involving the combination of anti-VEGF and unlicensed dendritic cell injections that enhance tumor regression. The model simulations predicted that the most effective method to treat tumors involves administering a series of biweekly anti-VEGF injections to disrupt angiogenic processes and limit tumor growth. The simulations also verified the hypothesis that reducing the concentration of the immunosuppressive factor VEGF prior to an injection of unlicensed dendritic cells enhances the cytotoxicity of CD8+ T cells and results in complete tumor elimination. Feasible treatment protocols for tumors that are diagnosed late and have grown to a relatively large size were identified.

Simulation of the Protein-Shedding Kinetics of a Fully Vascularized Tumor

Circulating biomarkers are of significant interest for cancer detection and treatment personalization. However, the biophysical processes that determine how proteins are shed from cancer cells or their microenvironment, diffuse through tissue, enter blood vasculature, and persist in circulation remain poorly understood. Since approaches primarily focused on experimental evaluation are incapable of measuring the shedding and persistence for every possible marker candidate, we propose an interdisciplinary computational/experimental approach that includes computational modeling of tumor tissue heterogeneity. The model implements protein production, transport, and shedding based on tumor vascularization, cell proliferation, hypoxia, and necrosis, thus quantitatively relating the tumor and circulating proteomes. The results highlight the dynamics of shedding as a function of protein diffusivity and production. Linking the simulated tumor parameters to clinical tumor and vascularization measurements could potentially enable this approach to reveal the tumor-specific conditions based on the protein detected in circulation and thus help to more accurately manage cancer diagnosis and treatment.

Mathematical framework for activity-based cancer biomarkers

Advances in nanomedicine are providing sophisticated functions to precisely control the behavior of nanoscale drugs and diagnostics. Strategies that coopt protease activity as molecular triggers are increasingly important in nanoparticle design, yet the pharmacokinetics of these systems are challenging to understand without a quantitative framework to reveal nonintuitive associations. We describe a multicompartment mathematical model to predict strategies for ultrasensitive detection of cancer using synthetic biomarkers, a class of activity-based probes that amplify cancer-derived signals into urine as a noninvasive diagnostic. Using a model formulation made of a PEG core conjugated with protease-cleavable peptides, we explore a vast design space and identify guidelines for increasing sensitivity that depend on critical parameters such as enzyme kinetics, dosage, and probe stability. According to this model, synthetic biomarkers that circulate in stealth but then activate at sites of disease have the theoretical capacity to discriminate tumors as small as 5 mm in diameter-a threshold sensitivity that is otherwise challenging for medical imaging and blood biomarkers to achieve. This model may be adapted to describe the behavior of additional activity-based approaches to allow cross-platform comparisons, and to predict allometric scaling across species.

Serial analysis of 38 proteins during the progression of human breast tumor in mice using an antibody colocalization microarray

Proteins in serum or plasma hold great potential for use in disease diagnosis and monitoring. However, the correlation between tumor burden and protein biomarker concentration has not been established. Here, using an antibody colocalization microarray, the protein concentration in serum was measured and compared with the size of mammary xenograft tumors in 11 individual mice from the time of injection; seven blood samples were collected from each tumor-bearing mouse as well as control mice on a weekly basis. The profiles of 38 proteins detected in sera from these animals were analyzed by clustering, and we identified 10 proteins with the greatest relative increase in serum concentration that correlated with growth of the primary mammary tumor. To evaluate the diagnosis of cancer based on these proteins using either an absolute threshold (i.e. a concentration cutoff) or self-referenced differential threshold based on the increase in concentration before cell injection, receiver operating characteristic curves were produced for 10 proteins with increased concentration, and the area under curve was calculated for each time point based on a single protein or on a panel of proteins, in each case showing a rapid increase of the area under curve. Next, the sensitivity and specificity of individual and optimal protein panels were calculated, showing high accuracy as early as week 2. These results provide a foundation for studies of tumor growth through measuring serial changes of protein concentration in animal models.

Tag-femtosecond laser-induced breakdown spectroscopy for the sensitive detection of cancer antigen 125 in blood plasma

Successful treatment of cancers requires detecting early signs of the disease. One promising way to approach this is to develop minimally invasive tests for the sensitive and specific detection of biomarkers in blood. Irrespective of the detection approach one uses, this remains a challenging task because biomarkers are typically present in low concentrations and there are signals that interfere strongly with prevailing compounds of human fluids. In this paper, we show that elemental encoded particle assay coupled with femtosecond laser-induced breakdown spectroscopy for simultaneous multi-elemental analysis can significantly improve biomarker detectability. An estimated near single molecule per particle efficiency of this method leads to sensitive detection of ovarian cancer biomarker CA125 in human blood plasma. This work opens new ways for earlier detection of cancers and for multiplex assay developments in various analytical applications from proteomics, genomics, and neurology fields.

Disease detection by ultrasensitive quantification of microdosed synthetic urinary biomarkers

The delivery of exogenous agents can enable noninvasive disease monitoring, but existing low-dose approaches require complex infrastructure. In this paper, we describe a microdose-scale injectable formulation of nanoparticles that interrogate the activity of thrombin, a key regulator of clotting, and produce urinary reporters of disease state. We establish a customized single molecule detection assay that enables urinary discrimination of thromboembolic disease in mice using doses of the nanoparticulate diagnostic agents that fall under regulatory guidelines for "microdosing."

Control values of ovarian cancer tumor markers and standardisation of a protocol for sampling peritoneal fluid and performing washing during laparoscopy

BACKGROUND

Determination of the tumor marker concentration in peritoneal fluid (PF) may help to assess its potential to detect small concentration changes between benign ovarian pathology and early stage ovarian cancer. Peritoneal washing, which can also be obtained when PF is absent, is already included in the International Federation of Gynecology and Obstetrics (FIGO) staging classification for ovarian cancer but sampling has not yet been standardized. Since our aim was to evaluate the relationship between marker concentration in PF and washing, standardization of the sampling protocol was a prerequisite to ensure reliable results.

METHODS

Thirty-three women with non-malignant pathology of the reproductive organs were included in the study. We used three promising tumor markers for evaluation of the marker concentration in local fluid: osteopontin (sOPN), splice variant 6 of sCD44 (sCD44-v6) and vascular cell adhesion molecule-1 (sVCAM-1). After aspiration of PF, washing of the uterus, ovaries and pelvic peritoneum was performed with saline solution. Patients were divided into two groups based on the solution volume: A-20 ml and B-50 ml. To determine the efficiency of washing in relation to solution volume, washing was repeated three times. Concentrations of markers in samples were determined using flow cytometry.

RESULTS

Mean concentrations of markers were significantly higher (P <0.001) in PF than in the first washing. We demonstrated a significant positive correlation between marker concentrations in PF and first washing (sOPN: r = 0.447, P = 0.048; sCD44-v6: r = 0.660, P = 0.002; sVCAM-1: r = 0.526, P = 0.017). When using a smaller solution volume for washing, significantly higher (sVCAM-1: 2.5-fold, P = 0.021; sOPN: 3-fold, P = 0.024) or equal (sCD44-v6) mean concentrations of tumor markers were obtained.

CONCLUSIONS

Our work demonstrates for the first time that concentrations of sOPN, sCD44-v6 and sVCAM-1 in PF correlate with peritoneal washing in women with non-malignant pathology of the reproductive organs. This indicates that, for selected tumor markers, washing can replace PF when PF is absent. A standardized protocol for sampling PF and performing washing during laparoscopy was established.

Point-of-care diagnostics for noncommunicable diseases using synthetic urinary biomarkers and paper microfluidics

With noncommunicable diseases (NCDs) now constituting the majority of global mortality, there is a growing need for low-cost, noninvasive methods to diagnose and treat this class of diseases, especially in resource-limited settings. Molecular biomarkers combined with low-cost point-of-care assays constitute a potential solution for diagnosing NCDs, but the dearth of naturally occurring, predictive markers limits this approach. Here, we describe the design of exogenous agents that serve as synthetic biomarkers for NCDs by producing urinary signals that can be quantified by a companion paper test. These synthetic biomarkers are composed of nanoparticles conjugated to ligand-encoded reporters via protease-sensitive peptide substrates. Upon delivery, the nanoparticles passively target diseased sites, such as solid tumors or blood clots, where up-regulated proteases cleave the peptide substrates and release reporters that are cleared into urine. The reporters are engineered for detection by sandwich immunoassays, and we demonstrate their quantification directly from unmodified urine; furthermore, capture antibody specificity allows the probes to be multiplexed in vivo and quantified simultaneously by ELISA or paper lateral flow assay (LFA). We tailor synthetic biomarkers specific to colorectal cancer, a representative solid tumor, and thrombosis, a common cardiovascular disorder, and demonstrate urinary detection of these diseases in mouse models by paper diagnostic. Together, the LFA and injectable synthetic biomarkers, which could be tailored for multiple diseases, form a generalized diagnostic platform for NCDs that can be applied in almost any setting without expensive equipment or trained medical personnel.

Highlights of recent developments and trends in cancer nanotechnology research--view from NCI Alliance for Nanotechnology in Cancer

Although the incidence of cancer and cancer related deaths in the United States has decreased over the past two decades due to improvements in early detection and treatment, cancer still is responsible for a quarter of the deaths in this country. There is much room for improvement on the standard treatments currently available and the National Cancer Institute (NCI) has recognized the potential for nanotechnology and nanomaterials in this area. The NCI Alliance for Nanotechnology in Cancer was formed in 2004 to support multidisciplinary researchers in the application of nanotechnology to cancer diagnosis and treatment. The researchers in the Alliance have been productive in generating innovative solutions to some of the central issues of cancer treatment including how to detect tumors earlier, how to target cancer cells specifically, and how to improve the therapeutic index of existing chemotherapies and radiotherapy treatments. Highly creative ideas are being pursued where novelty in nanomaterial development enables new modalities of detection or therapy. This review highlights some of the innovative materials approaches being pursued by researchers funded by the NCI Alliance. Their discoveries to improve the functionality of nanoparticles for medical applications includes the generation of new platforms, improvements in the manufacturing of nanoparticles and determining the underlying reasons for the movement of nanoparticles in the blood.

Mass-encoded synthetic biomarkers for multiplexed urinary monitoring of disease

Biomarkers are becoming increasingly important in the clinical management of complex diseases, yet our ability to discover new biomarkers remains limited by our dependence on endogenous molecules. Here we describe the development of exogenously administered 'synthetic biomarkers' composed of mass-encoded peptides conjugated to nanoparticles that leverage intrinsic features of human disease and physiology for noninvasive urinary monitoring. These protease-sensitive agents perform three functions in vivo: they target sites of disease, sample dysregulated protease activities and emit mass-encoded reporters into host urine for multiplexed detection by mass spectrometry. Using mouse models of liver fibrosis and cancer, we show that these agents can noninvasively monitor liver fibrosis and resolution without the need for invasive core biopsies and substantially improve early detection of cancer compared with current clinically used blood biomarkers. This approach of engineering synthetic biomarkers for multiplexed urinary monitoring should be broadly amenable to additional pathophysiological processes and point-of-care diagnostics.

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

Most clinical blood biomarkers lack the necessary sensitivity and specificity to reliably detect cancer at an early stage, when it is best treatable. It is not yet clear how early a clinical blood assay can be used to detect cancer or how biomarker-based strategies can be improved to enable earlier detection of smaller tumors. To address these issues, we developed a mathematical model describing dynamic plasma biomarker kinetics in relation to the growth of a tumor, beginning with a single cancer cell. To exemplify a realistic scenario in which biomarker is shed by both cancerous and noncancerous cells, we primed the model on ovarian tumor growth and CA125 shedding data, for which tumor growth parameters and shedding rates are readily available in published literature. We found that a tumor could grow unnoticed for more than 10.1 years and reach a volume of about π/6(25.36 mm)(3), corresponding to a spherical diameter of about 25.36 mm, before becoming detectable by current clinical blood assays. Model parameters were perturbed over log orders of magnitude to quantify ideal shedding rates and identify other blood-based strategies required for early submillimeter tumor detectability. The detection times we estimated are consistent with recently published tumor progression time lines based on clinical genomic sequencing data for several cancers. Here, we rigorously showed that shedding rates of current clinical blood biomarkers are likely 10(4)-fold too low to enable detection of a developing tumor within the first decade of tumor growth. The model presented here can be extended to virtually any solid cancer and associated biomarkers.

Antibody colocalization microarray: a scalable technology for multiplex protein analysis in complex samples

DNA microarrays were rapidly scaled up from 256 to 6.5 million targets, and although antibody microarrays were proposed earlier, sensitive multiplex sandwich assays have only been scaled up to a few tens of targets. Cross-reactivity, arising because detection antibodies are mixed, is a known weakness of multiplex sandwich assays that is mitigated by lengthy optimization. Here, we introduce (1) vulnerability as a metric for assays. The vulnerability of multiplex sandwich assays to cross-reactivity increases quadratically with the number of targets, and together with experimental results, substantiates that scaling up of multiplex sandwich assays is unfeasible. We propose (2) a novel concept for multiplexing without mixing named antibody colocalization microarray (ACM). In ACMs, both capture and detection antibodies are physically colocalized by spotting to the same two-dimensional coordinate. Following spotting of the capture antibodies, the chip is removed from the arrayer, incubated with the sample, placed back onto the arrayer and then spotted with the detection antibodies. ACMs with up to 50 targets were produced, along with a binding curve for each protein. The ACM was validated by comparing it to ELISA and to a small-scale, conventional multiplex sandwich assay (MSA). Using ACMs, proteins in the serum of breast cancer patients and healthy controls were quantified, and six candidate biomarkers identified. Our results indicate that ACMs are sensitive, robust, and scalable.

Multifunctional core-shell nanoparticles: discovery of previously invisible biomarkers

Many low-abundance biomarkers for early detection of cancer and other diseases are invisible to mass spectrometry because they exist in body fluids in very low concentrations, are masked by high-abundance proteins such as albumin and immunoglobulins, and are very labile. To overcome these barriers, we created porous, buoyant, core-shell hydrogel nanoparticles containing novel high affinity reactive chemical baits for protein and peptide harvesting, concentration, and preservation in body fluids. Poly(N-isopropylacrylamide-co-acrylic acid) nanoparticles were functionalized with amino-containing dyes via zero-length cross-linking amidation reactions. Nanoparticles functionalized in the core with 17 different (12 chemically novel) molecular baits showed preferential high affinities (K(D) < 10(-11) M) for specific low-abundance protein analytes. A poly(N-isopropylacrylamide-co-vinylsulfonic acid) shell was added to the core particles. This shell chemistry selectively prevented unwanted entry of all size peptides derived from albumin without hindering the penetration of non-albumin small proteins and peptides. Proteins and peptides entered the core to be captured with high affinity by baits immobilized in the core. Nanoparticles effectively protected interleukin-6 from enzymatic degradation in sweat and increased the effective detection sensitivity of human growth hormone in human urine using multiple reaction monitoring analysis. Used in whole blood as a one-step, in-solution preprocessing step, the nanoparticles greatly enriched the concentration of low-molecular weight proteins and peptides while excluding albumin and other proteins above 30 kDa; this achieved a 10,000-fold effective amplification of the analyte concentration, enabling mass spectrometry (MS) discovery of candidate biomarkers that were previously undetectable.

Comparing the benefits of screening for breast cancer and lung cancer using a novel natural history model

To estimate the impact of early detection of cancer, knowledge of how quickly primary tumors grow and at what size they shed lethal metastases is critical. We developed a natural history model of cancer to estimate the probability of disease-specific cure as a function of tumor size, the tumor volume doubling time (TVDT), and disease-specific mortality reduction achievable by screening. The model was applied to non-small-cell lung carcinoma (NSCLC) and invasive ductal carcinoma (IDC), separately. Model parameter estimates were based on Surveillance Epidemiology and End Results (SEER) cancer registry datasets and validated on screening trials. Compared to IDC, NSCLC is estimated to have a lower probability of disease-specific cure at the same detected tumor size, shed lethal metastases at smaller sizes (median: 19 mm for IDC versus 8 mm for NSCLC), have a TVDT that is almost half as long (median: 252 days for IDC versus 134 days for NSCLC). Consequently, NSCLC is associated with a lower mortality reduction from screening at the same screen detection threshold and screening interval. In summary, using a similar natural history model of cancer, we quantify the disease-specific curability attributable to screening for breast cancer, and separately lung cancer, in terms of the TVDT and onset of lethal metastases.