Non-invasive Cancer Screening Research Articles

Ultrasensitive In2O3-Based Nanoflakes for Lung Cancer Diagnosis and the Sensing Mechanism Investigated by Operando Spectroscopy.

Rapid gas sensing with high sensitivity and selectivity is pivotal in advanced production, in smart living, and increasingly in medical health applications. This study presents a novel Pt@InNiOx nanoflake isoprene sensor that achieves an exceptionally low limit of detection (LOD) at 2 ppb, the lowest reported for isoprene sensors to date. Notably, it exhibits high selectivity and remarkable antihumidity capacity, thus meeting the stringent requirements for lung cancer screening. To unravel the sensing mechanism, we fabricate an operando DRIFTS-Raman cell coupled to online electrical measurements. It reveals that the ultrasensitive performance of Pt@InNiOx nanoflakes stems from the activated conjugated structure of isoprene by Pt nanoclusters and from the enhanced isoprene adsorption and electron interaction due to the nanoflake morphology. The p-n junction constructed by doping Ni maintains Fermi level equilibrium, shielding it from humidity interference. Practically, we integrate these ultrasensitive Pt@InNiOx nanoflakes into a miniaturized portable electronic device that successfully distinguishes lung cancer patients with expiratory isoprene below 40 ppb, from the healthy population with isoprene above 60 ppb, enabling an accurate diagnosis in clinics. Our work not only provides a breakthrough in low-cost, noninvasive cancer screening through breath analysis but also advances the rational design of cutting-edge gas sensing materials.

Cancer screening through surface-enhanced Raman spectroscopy fingerprinting analysis of urinary metabolites using surface-carbonized silver nanowires on a filter membrane

BackgroundLabel-free surface-enhanced Raman spectroscopy (SERS)-based metabolic profiling has great potential for early cancer diagnosis, but further advancements in analytical methods and clinical evidence studies are required for clinical applications. To improve the cancer diagnostic accuracy of label-free SERS spectral analysis of complex biological fluids, it is necessary to obtain specifically enhanced SERS signals of cancer-related metabolites present at low concentrations. ResultsThis study presents a novel 3D SERS sensor, comprising a surface-carbonized silver nanowire (AgNW)-stacked filter membrane, alongside an optimized urine/methanol/chloroform extraction technique, which specifically changes the molecular adsorption and orientation of aromatic metabolites onto SERS substrates. By analyzing the pretreated urine samples on the surface-carbonized AgNW 3D SERS sensor, distinct and highly enhanced SERS peaks derived from semi-polar aromatic metabolites were observed for pancreatic cancer and prostate cancer samples compared with normal controls. Urine metabolite analysis using SERS fingerprinting successfully differentiated pancreatic cancer and prostate cancer groups from normal control group: normal control (n = 56), pancreatic cancer (n = 40), and prostate cancer (n = 39). Significance and noveltyWe confirmed the clinical feasibility of performing fingerprint analysis of urinary metabolites based on the surface-carbonized AgNW 3D SERS sensor and methanol/chloroform extraction for noninvasive cancer screening. This technology holds potential for large-scale screening owing to its high accuracy, and cost effective, simple and rapid detection method.

Apo10 and TKTL1 in blood macrophages as biomarkers for differentiating lung cancer from benign lung lesions: a comparative study with conventional biomarkers.

The detection of biomarkers in blood macrophages is a new non-invasive cancer screening method, but its performance in early stage lung cancer screening remains undetermined. We evaluated the Apo10 and TKTL1 levels in blood macrophages of 156 early-stage lung cancer patients and 153 controls. APT (combination of Apo10 and TKTL1) level was significantly higher in the lung cancer group than that in the control group (P < 0.001). AUROC analysis showed that APT has high diagnostic value in differentiating early-stage lung cancer (AUC = 0.9132) and can be considered a biomarker for screening lung cancer patients from individuals with lung nodules.

Pan-cancer detection and tissue-of-origin identification by whole-genome sequencing of plasma cell-free DNA.

3055 Background: Despite notable advances in cancer therapeutics, much of the mortality of human cancers results from late diagnosis. The early detection of cancer remains an unmet need that can benefit the effective treatments and better prognosis. Cell-free DNA (cfDNA) in the circulation provides an emerging diagnostic method for non-invasive cancer screening. The fragmentation signatures of cfDNA originating from different cell types or malignancies display distinct patterns, suggesting the potential as cancer biomarkers. This study aims to investigate the fragmentation profiles of cfDNA in several cancer types, and to develop a new approach for pan-cancer detection and tissue-of-origin identification. Methods: Plasma samples from 739 patients with lung (N=577), colorectal (N=98), breast (N=51) and liver (N=13) cancers, as well as 716 healthy individuals were collected in this study. Cell-free DNA was isolated and used to generate the whole-genome sequencing data. We developed an approach called PatternWGS (Pan-cancer detection and tissue-of-origin identification by whole-genome sequencing) to analyze the DNA fragmentation patterns comprehensively. Fragmentation features integrating cfDNA fragment size distribution, frequency of end motif sequence, and copy number variation were utilized to construct the optimal prediction model. We presented a multi-cancer detection model for distinguishing individuals with and without cancer using Generalized Linear Models (glmnet), and further built a cancer-origin model to predict cancer types using Random Forest algorithm. Training and testing occurred on randomly selected sampling splits of 60% and 40% of the data, respectively. Features selection and model construction were performed by only training cohort, and the test set was solely used for performance evaluation. Results: A larger proportion of shorter cfDNA fragments in cancers was observed. We proposed a fragment score indicating the degree of short fragmentation, and this fragment score was significantly higher in cancers (p&lt;0.001). Several end motifs were identified to be specifically enriched in cancer patients. The multi-cancer detection model reached an area under the curve (AUC) of 0.98, with the sensitivity of 93% at 95% specificity. The sensitivity of detecting lung, colorectal, breast, and liver cancers reached 94.2%, 87%, 100% and 100% respectively, at 95% specificity. The cancer-origin model demonstrated an overall accuracy of 81.7% for the identification of tissue of origin. Conclusions: Our prediction model demonstrated a superior performance for multi-cancer detection and cancer origin prediction, suggesting the potential of plasma cfDNA fragmentation signatures as non-invasive biomarkers for early cancer detection in clinical. The distinct fragmentation patterns in cancer provide new insight to understand the mechanism of tumorigenesis.

Abstract 2203: Circulating rare events inform disease progression in patients with upper tract urothelial carcinoma undergoing surgery

Abstract The liquid biopsy presents potential as a non-invasive cancer screening tool, and its role in diagnosing and monitoring the progression of primary upper tract urothelial carcinoma (UTUC) to reveal the tumor’s kinetics remains to be fully understood. This study includes 22 patients diagnosed with UTUC, from which we collected 1 peripheral blood (PB) sample prior to surgery (pre-op; n = 22) and 1 sample at follow-up (post-op; n = 11). Each patient’s sample was analyzed via our enrichment-free third generation comprehensive High-Definition Single Cell Assay (HDSCA3.0) workflow to detect rare events in the PB. Rare events include circulating tumor cells (CTCs) and large extracellular vesicles (LEVs) and are identified via a 4-channel immunofluorescence assay that captures the following morphometric expressions: pan cytokeratin (CK), vimentin (V), CD45/CD31 (CD), and cellular nucleation (which is absent in LEVs) through DAPI (D). Through a matched sample analysis approach via a Wilcoxon signed-rank test, pre-op and post-op liquid biopsy profiles were compared to each other as well as to samples from 50 normal donors (NDs) who had no known pathology. The HDSCA3.0 analysis discovered a highly heterogeneous spread of rare events in PB samples collected from patients with UTUC in both pre-op and post-op time periods and in ND samples. For pre-op vs ND samples, significant differences were found in total rare events, total rare cells, events expressing D|V, D|CK|V|CD, D|V|CD, total LEVs, total CK+ cells, CK LEVs, and events expressing D and D|CK. For post-op vs ND samples, significant differences were in total rare events, total rare cells, events expressing D|V, D|CK|V|CD, total CK+ cells, and events expressing D|CK|V. For pre-op vs post-op samples, significant differences manifested in total LEVs, CK LEVs, and CK|CD LEVs. At follow-up, 3 patients had an increase in rare events, and 8 patients had a decrease in rare events, 4 of the 8 presenting with ND rare event levels. This study has the potential to inform how primary UTUC evolves in patients over time as well as elucidate if rare event expressions may correlate with specific disease states. By analyzing circulating rare events through channel-type classifications, different frequencies at various time points compared to ND samples can assist in clinical decision making for treating UTUC. Citation Format: Salmaan Sayeed, George Courcoubetis, Alireza Ghoreifi, Amy Huang, Jeremy Mason, Inderbir S. Gill, Peter Kuhn, Hooman Djaladat, Stephanie N. Shishido. Circulating rare events inform disease progression in patients with upper tract urothelial carcinoma undergoing surgery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2203.

Urinary analysis based on surface-enhanced Raman scattering for the noninvasive screening of lung cancer

• The AuNPs-based SERS probes were developed for the detection of adenosine • The platform can identify adenosine, uric acid, guanine and creatinine in urine • The assay was successfully applied for the noninvasive screening of lung cancer We developed a reliable simple analysis of adenosine, creatinine and other nucleosides (cytidine and guanosine) with single measurement based on surface-enhanced Raman spectroscopy (SERS) using DSP-AuNPs with Cu 2+ ions probe and applied it to achieve noninvasive screening of lung cancer. The gold nanoparticles (AuNPs) were modified with 3,3′-dithiodipropionic acid di (N-hydroxysuccinimide ester) (DSP), which could react with above mentioned molecules followed with the aggregation of AuNPs at the presence of Cu 2+ , thus form the plasmonic hot spots to dramatically increase their fingerprint Raman signal. The probe of DSP-AuNPs was applied for the detail analysis of urinary adenosine of healthy people and lung cancer patients. The SERS measurement indicated a higher concentration of urinary adenosine for lung cancer patients than that of healthy people, which was consistent with the results measured by high-performance liquid chromatography (HPLC). In combination of Raman spectroscopy with the orthogonal partial least squares discriminant analysis (OPLS-DA) model, we are able to discriminate the lung cancer patients from healthy people with the urinary test. The results indicated that besides of adenosine, other metabolites including uric acid, guanine and creatinine may also be the potential tumor markers in urine for the noninvasive lung cancer diagnosis. Such a method paves a way for the noninvasive cancer screening and it can be further modified for the detection of other molecules on medical diagnosis.

Non-invasive cancer detection in canine urine through Caenorhabditis elegans chemotaxis.

Cancer is the leading cause of death in companion animals, and successful early treatment has been a challenge in the veterinary field. We have developed the Non-Invasive Cancer Screening (N.C.S.) Study to perform cancer detection through the analysis of canine urine samples. The test makes use of the strong olfactory system of the nematode Caenorhabditis elegans, which was previously shown to positively respond to urine samples from human cancer patients. We performed a proof-of-concept study to optimize the detection capability in urine samples obtained from dogs with naturally occurring cancers. In this study, we established a scale for identifying the cancer risk based on the magnitude of the chemotaxis index of C. elegans toward a canine urine sample. Through validation, the N.C.S. Study achieved a sensitivity of 85%, showing that it is highly sensitive to indicate the presence of cancer across multiple types of common canine cancers. The test also showed a 90% specificity to cancer samples, indicating a low rate of over-identifying cancer risk. From these results, we have demonstrated the ability to perform low-cost, non-invasive cancer detection in companion animals—a method that can increase the ability to perform cancer diagnosis and treatment.

Pan-Cancer Detection and Typing by Mining Patterns in Large Genome-Wide Cell-Free DNA Sequencing Datasets.

Cell-free DNA (cfDNA) analysis holds great promise for non-invasive cancer screening, diagnosis, and monitoring. We hypothesized that mining the patterns of cfDNA shallow whole-genome sequencing datasets from patients with cancer could improve cancer detection. By applying unsupervised clustering and supervised machine learning on large cfDNA shallow whole-genome sequencing datasets from healthy individuals (n = 367) and patients with different hematological (n = 238) and solid malignancies (n = 320), we identified cfDNA signatures that enabled cancer detection and typing. Unsupervised clustering revealed cancer type-specific sub-grouping. Classification using a supervised machine learning model yielded accuracies of 96% and 65% in discriminating hematological and solid malignancies from healthy controls, respectively. The accuracy of disease type prediction was 85% and 70% for the hematological and solid cancers, respectively. The potential utility of managing a specific cancer was demonstrated by classifying benign from invasive and borderline adnexal masses with an area under the curve of 0.87 and 0.74, respectively. This approach provides a generic analytical strategy for non-invasive pan-cancer detection and cancer type prediction.

Multimodal CTC detection using stem cell antigen-specific immunosilica particles and immunofluorescent quantum dots

Amid the COVID-19 pandemic, cancer continues to be the most devastating disease worldwide. Liquid biopsy of circulating tumor cells (CTCs) has recently become a painless and noninvasive tool for obtaining carcinoma cell samples for molecular profiling. Here, we report efficient detection and collection of cancer cells in blood samples by combining stem cell antigen (CD44)-specific immunosilica particles and immunofluorescent quantum dots with spectrally and temporally resolved single-photon counting. We accurately detect 1–10 cells among 100 cancer cells of the breast, lungs, or cervix in 1 mL blood samples. In addition, the bright and narrowband emission of CdSe/ZnS quantum dots enables temporally and spectrally resolved photon counting for multiplexed cancer cell detection. The cancer cell-specific and large immunosilica particles helped us collect the specific cells. We validate the detection efficiency and multimodality of this strategy by time-stamped and energy-dispersed single-photon counting of orange- and red-emitting quantum dots and green-fluorescing nuclei stained with Syto-13/25 dye. Thus, the present work highlights the prospects of multimodal CTC detection for noninvasive cancer screening and postsurgical or therapeutic follow-up.

A Novel Method for Colorectal Cancer Screening Based on Circulating Tumor Cells and Machine Learning.

Colorectal cancer is one of the most common types of cancer, and it can have a high mortality rate if left untreated or undiagnosed. The fact that CRC becomes symptomatic at advanced stages highlights the importance of early screening. The reference screening method for CRC is colonoscopy, an invasive, time-consuming procedure that requires sedation or anesthesia and is recommended from a certain age and above. The aim of this study was to build a machine learning classifier that can distinguish cancer from non-cancer samples. For this, circulating tumor cells were enumerated using flow cytometry. Their numbers were used as a training set for building an optimized SVM classifier that was subsequently used on a blind set. The SVM classifier’s accuracy on the blind samples was found to be 90.0%, sensitivity was 80.0%, specificity was 100.0%, precision was 100.0% and AUC was 0.98. Finally, in order to test the generalizability of our method, we also compared the performances of different classifiers developed by various machine learning models, using over-sampling datasets generated by the SMOTE algorithm. The results showed that SVM achieved the best performances according to the validation accuracy metric. Overall, our results demonstrate that CTCs enumerated by flow cytometry can provide significant information, which can be used in machine learning algorithms to successfully discriminate between healthy and colorectal cancer patients. The clinical significance of this method could be the development of a simple, fast, non-invasive cancer screening tool based on blood CTC enumeration by flow cytometry and machine learning algorithms.

Cell-Main Spectra Profile Screening Technique in Simulation of Circulating Tumour Cells Using MALDI-TOF Mass Spectrometry.

Simple SummaryCancer cells can detach from a primary tumour and present in peripheral blood as circulating tumour cells, or in the widest sense, as circulating atypical cells (CAC). Although CAC are a promising biomarker for non-invasive cancer screening, they occur at very low frequency and their detection and characterization remains challenging. We here validated isolation and concentration of untouched CAC from spiked cancer cell blood samples and combined this with matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS). This workflow was optimised to detect as little as six cancer cells per 5000 white blood cells. Future development of our workflow may cover a larger range of cancer types and further improvements to enable the use of MALDI-TOF MS as a cancer-screening platform in clinical settings.Circulating atypical cells (CAC) are released from a primary tumour site into peripheral blood and are indicators of cancer metastasis. CAC occur at very low frequency in circulating blood, and their detection remains challenging. Moreover, white blood cells (WBC) are the major contaminant in enriched CAC samples. Here, we developed matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) as a novel CAC characterization platform. Main spectra profiles (MSP) of normal and cancer cells were generated by MALDI-TOF MS, and a cell-main spectra database was then compiled and analysed using the MALDI Biotyper software. Logarithmic scores accurately predicted distinct cell types. The feasibility of this workflow was then validated using simulated samples, which were prepared by 5000 WBC of three healthy individuals spiked with varying numbers (3, 6, 12, 25, 50, and 100) of lung, colon, or prostate cancer cells. MALDI-TOF MS was able to detect cancer cells down to six cells over the background noise of 5000 WBC with significantly higher predictive scores as compared to WBC alone. Further development of cell-MSP database to cover all cancer types sourced from cell lines and patient tumours may enable the use of MALDI-TOF MS as a cancer-screening platform in clinical settings in the future.

CancerEMC: frontline non-invasive cancer screening from circulating protein biomarkers and mutations in cell-free DNA.

The early detection of cancer through accessible blood tests can foster early patient interventions. Although there are developments in cancer detection from cell-free DNA (cfDNA), its accuracy remains speculative. Given its central importance with broad impacts, we aspire to address the challenge. A bagging Ensemble Meta Classifier (CancerEMC) is proposed for early cancer detection based on circulating protein biomarkers and mutations in cfDNA from blood. CancerEMC is generally designed for both binary cancer detection and multi-class cancer type localization. It can address the class imbalance problem in multi-analyte blood test data based on robust oversampling and adaptive synthesis techniques. Based on the clinical blood test data, we observe that the proposed CancerEMC has outperformed other algorithms and state-of-the-arts studies (including CancerSEEK) for cancer detection. The results reveal that our proposed method (i.e. CancerEMC) can achieve the best performance result for both binary cancer classification with 99.17% accuracy (AUC = 0.999) and localized multiple cancer detection with 74.12% accuracy (AUC = 0.938). Addressing the data imbalance issue with oversampling techniques, the accuracy can be increased to 91.50% (AUC = 0.992), where the state-of-the-art method can only be estimated at 69.64% (AUC = 0.921). Similar results can also be observed on independent and isolated testing data. https://github.com/saifurcubd/Cancer-Detection. Supplementary data are available at Bioinformatics online.

Identifying the tissues-of-origin of circulating cell-free DNAs is a promising way in noninvasive diagnostics.

Advances in sequencing technologies facilitate personalized disease-risk profiling and clinical diagnosis. In recent years, some great progress has been made in noninvasive diagnoses based on cell-free DNAs (cfDNAs). It exploits the fact that dead cells release DNA fragments into the circulation, and some DNA fragments carry information that indicates their tissues-of-origin (TOOs). Based on the signals used for identifying the TOOs of cfDNAs, the existing methods can be classified into three categories: cfDNA mutation-based methods, methylation pattern-based methods and cfDNA fragmentation pattern-based methods. In cfDNA mutation-based methods, the SNP information or the detected mutations in driven genes of certain diseases are employed to identify the TOOs of cfDNAs. Methylation pattern-based methods are developed to identify the TOOs of cfDNAs based on the tissue-specific methylation patterns. In cfDNA fragmentation pattern-based methods, cfDNA fragmentation patterns, such as nucleosome positioning or preferred end coordinates of cfDNAs, are used to predict the TOOs of cfDNAs. In this paper, the strategies and challenges in each category are reviewed. Furthermore, the representative applications based on the TOOs of cfDNAs, including noninvasive prenatal testing, noninvasive cancer screening, transplantation rejection monitoring and parasitic infection detection, are also reviewed. Moreover, the challenges and future work in identifying the TOOs of cfDNAs are discussed. Our research provides a comprehensive picture of the development and challenges in identifying the TOOs of cfDNAs, which may benefit bioinformatics researchers to develop new methods to improve the identification of the TOOs of cfDNAs.

Application of C. elegans cancer screening test for the detection of pancreatic tumor in genetically engineered mice.

Pancreatic ductal adenocarcinoma (PDAC) exhibits a very early onset of metastasis. Thus, early detection and treatment are pivotal to successful eradication of pancreatic cancers. Economical and non-invasive cancer screening systems is indispensable for this purpose. Previously our group developed a novel method to detect various kinds of human cancer using nematode Caenorhabditis elegans (C. elegans) that respond to cancer odor in urine; however, whether this method is useful for non-human species remains to be understood. In this study, we examined its effectiveness in the detection of murine pancreatic tumor spontaneously generated in genetically-engineered mice. We generated pancreas-specific KrasG12D and/or c-Met deletion mutant mice and measured the probability of spontaneous tumor generation in these mice. The chemotactic indexes of C. elegans to the urine samples of these mutant mice were measured. As previously described, oncogenic KrasG12D was necessary to induce pancreatic intraepithelial neoplasia in this mouse model, while c-Met mutation did not show further effect. The chemotactic analysis indicated that C. elegans avoids urine of healthy recipient mice, while they tended to be attracted to urine of mice with KrasG12D. Our study demonstrated that C. elegans can recognize the odor of pancreatic cancer in urine of KrasG12D model mouse, suggesting the similarity of cancer odor between species. Our result facilitates further studies on mechanism of cancer detection by C. elegans.

Bioinformatics Analysis for Circulating Cell-Free DNA in Cancer.

Molecular analysis of cell-free DNA (cfDNA) that circulates in plasma and other body fluids represents a “liquid biopsy” approach for non-invasive cancer screening or monitoring. The rapid development of sequencing technologies has made cfDNA a promising source to study cancer development and progression. Specific genetic and epigenetic alterations have been found in plasma, serum, and urine cfDNA and could potentially be used as diagnostic or prognostic biomarkers in various cancer types. In this review, we will discuss the molecular characteristics of cancer cfDNA and major bioinformatics approaches involved in the analysis of cfDNA sequencing data for detecting genetic mutation, copy number alteration, methylation change, and nucleosome positioning variation. We highlight specific challenges in sensitivity to detect genetic aberrations and robustness of statistical analysis. Finally, we provide perspectives regarding the standard and continuing development of bioinformatics analysis to move this promising screening tool into clinical practice.

Time-Dependent Diffusion MRI in Cancer: Tissue Modeling and Applications

In diffusion weighted imaging (DWI), the apparent diffusion coefficient has been recognized as a useful and sensitive surrogate for cell density, paving the way for non-invasive tumor staging, and characterization of treatment efficacy in cancer. However, microstructural parameters, such as cell size, density and/or compartmental diffusivities affect diffusion in various fashions, making of conventional DWI a sensitive but non-specific probe into changes happening at cellular level. Alternatively, tissue complexity can be probed and quantified using the time dependence of diffusion metrics, sometimes also referred to as temporal diffusion spectroscopy when only using oscillating diffusion gradients. Time-dependent diffusion (TDD) is emerging as a strong candidate for specific and non-invasive tumor characterization. Despite the lack of a general analytical solution for all diffusion times / frequencies, TDD can be probed in various regimes where systems simplify in order to extract relevant information about tissue microstructure. The fundamentals of TDD are first reviewed (a) in the short time regime, disentangling structural and diffusive tissue properties, and (b) near the tortuosity limit, assuming weakly heterogeneous media near infinitely long diffusion times. Focusing on cell bodies (as opposed to neuronal tracts), a simple but realistic model for intracellular diffusion can offer precious insight on diffusion inside biological systems, at all times. Based on this approach, the main three geometrical models implemented so far (IMPULSED, POMACE, VERDICT) are reviewed. Their suitability to quantify cell size, intra- and extracellular spaces (ICS and ECS) and diffusivities are assessed. The proper modeling of tissue membrane permeability – hardly a newcomer in the field, but lacking applications - and its impact on microstructural estimates are also considered. After discussing general issues with tissue modeling and microstructural parameter estimation (i.e. fitting), potential solutions are detailed. The in vivo applications of this new, non-invasive, specific approach in cancer are reviewed, ranging from the characterization of gliomas in rodent brains and observation of time-dependence in breast tissue lesions and prostate cancer, to the recent preclinical evaluation of new treatments efficacy. It is expected that clinical applications of TDD will strongly benefit the community in terms of non-invasive cancer screening.

Raman active components of skin cancer.

Raman spectroscopy (RS) has shown great potential in noninvasive cancer screening. Statistically based algorithms, such as principal component analysis, are commonly employed to provide tissue classification; however, they are difficult to relate to the chemical and morphological basis of the spectroscopic features and underlying disease. As a result, we propose the first Raman biophysical model applied to in vivo skin cancer screening data. We expand upon previous models by utilizing in situ skin constituents as the building blocks, and validate the model using previous clinical screening data collected from a Raman optical fiber probe. We built an 830nm confocal Raman microscope integrated with a confocal laser-scanning microscope. Raman imaging was performed on skin sections spanning various disease states, and multivariate curve resolution (MCR) analysis was used to resolve the Raman spectra of individual in situ skin constituents. The basis spectra of the most relevant skin constituents were combined linearly to fit in vivo human skin spectra. Our results suggest collagen, elastin, keratin, cell nucleus, triolein, ceramide, melanin and water are the most important model components. We make available for download (see supplemental information) a database of Raman spectra for these eight components for others to use as a reference. Our model reveals the biochemical and structural makeup of normal, nonmelanoma and melanoma skin cancers, and precancers and paves the way for future development of this approach to noninvasive skin cancer diagnosis.

Daily variation and effect of dietary folate on urinary pteridines

Introduction Urinary pteridines are putative molecular biomarkers for noninvasive cancer screening and prognostication. Central to their translational biomarker development is the need to understand the sources and extent of their non-epidemiological variation.

Abstract A32: Urine as a potential liquid biopsy for detecting tumor DNA in Wilms tumor patient: Detection of somatic mutations in urine opens perspectives of monitoring chemotherapy response in WT patients

Abstract Detection of tumor DNA in urine has been shown as viable method of diagnosis not only for urinary tract (e.g., kidney and bladder) cancers but also for other types, e.g. prostate. Applications of such method, resemble those which use plasma DNA and include cancer detection, monitoring of tumor growth or recurrence and response to chemotherapy or radiation therapy. As far as we know, there is a lack of studies applying urine DNA detection in Wilms tumor (WT), an embryonary kidney cancer type. Here we show evidences of detection for two somatic variants in the urine DNA of one WT patient. By using NGS exome sequencing of tumor tissue and leukocytes samples of the same patient we were able to find new somatic variants: a frame-shift indel in TNRC18 and a misssense SNV in INTS1 gene. Target sequencing applied to the DNA from the patient's urine revealed the presence of these two somatic variants. More interestingly, both variants could not be found in the urine DNA of this patient after treatment (chemotherapy and nephrectomy). Additionally, these genes are poorly characterized in WT contributing for the comprehension of the cellular processes that are operating in tumorigenesis of WT. Altogether, our findings contribute with the mutational repertoire of WT and reveals the potential of using urine DNA sequencing as a noninvasive cancer screening approach. Citation Format: Ana C. K. Miguez, Rodrigo F. Ramalho, Elisa N. Ferreira, Bruna D. F. Barros, Claudia A. A. de Paula, Renan Valieris, Louise D. C. Mota, Jorge E. Souza, Isabela W. Cunha, Cecília L. Costa, Sandro J. de Souza, Dirce M. Carraro. Urine as a potential liquid biopsy for detecting tumor DNA in Wilms tumor patient: Detection of somatic mutations in urine opens perspectives of monitoring chemotherapy response in WT patients. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Pediatric Cancer Research: From Mechanisms and Models to Treatment and Survivorship; 2015 Nov 9-12; Fort Lauderdale, FL. Philadelphia (PA): AACR; Cancer Res 2016;76(5 Suppl):Abstract nr A32.

Abstract 1578: Identification of type X collagen as a pan-cancer serum biomarker

Abstract We previously reported elevated expression of Type X Collagen (COL10A1) in solid tumors of diverse origin (Future Oncology 2012 Aug;8(8): 1031-40). This elevated expression in tumors was primarily localized to vasculature, while COL10A1 expression in most adult tissues was negligible. Given this pattern of expression, we hypothesized that COL10A1 might demonstrate utility as a blood-born tumor biomarker. Serum samples from healthy volunteers and from patients with active cancers of the breast, lung, pancreas, colon, kidney, and bladder were obtained from multiple existing bio-banks. COL10A1 serum levels were assessed in over 400 total patient samples using a polyclonal COL10A1 sandwich ELISA. The overall utility of COL10A1 levels in determining presence of cancer was judged by calculating ROC-AUC scores for each individual cancer type. In general, COL10A1 levels were increased in cancer patients, although this increase varied as a function of cancer type as well as by the bio-bank the sera was obtained from. Given the sensitivity and specificity limitations of polyclonal ELISA technology, even greater performance of COL10A1 as a tumor biomarker could be attainable using high-specificity monoclonal antibodies. We are currently characterizing such tests in prospectively collected sera samples from breast cancer patients. These data suggest that COL10A1 is a potential blood-born pan-cancer biomarker that could prove useful in non-invasive cancer screening as well as in monitoring tumor progression and treatment response. Citation Format: Maria J. Prendes, Karen B. Chapman, Orlena Tam, Joseph Wagner. Identification of type X collagen as a pan-cancer serum biomarker. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1578. doi:10.1158/1538-7445.AM2015-1578