Cancer Liquid Biopsy Research Articles

Assessment of Early Detection by Multi-omics-Based Liquid Biopsy in Lung Cancer: A Prospective Study (ASCEND-LUNG)

Assessment of Early Detection by Multi-omics-Based Liquid Biopsy in Lung Cancer: A Prospective Study (ASCEND-LUNG)

Advances in analytical microfluidic workflows for differential cancer diagnosis

AbstractEarly detection and functional analysis of circulating tumor cells (CTCs) in liquid biopsy rather than counting them offer great promise as physicians can improve differential diagnosis for second‐ or third‐line treatment beyond today's genotype information from next‐generation sequencing and the limited statistical power of histopathology analysis. Microfluidics holds a great potential for an accurate and reliable isolation and analytical examination of cancer liquid biopsy. This review describes fundamentals and technological breakthroughs of immunolabeled and label‐free analytical microfluidic chips (µ‐chips), developed to screen and examine CTCs. The impacts of highly‐selective biomarkers and their features on the assembly and renovation of high‐tech immunolabeled and label‐free µ‐chips are discussed. Owing to significant impacts of multifunctional nanostructures on the performance of analytical µ‐chips based on engineering‐driven workflows, this review also focuses on their critical roles for screening CTCs benefited from the nanoscale physicochemical features. The advantages of integrated analytical µ‐chips are then identified and examined regarding their dominated sorting technologies. Furthermore, this review explores innovative technologies incorporating downstream cellular‐/molecular analysis into integrative analytical µ‐chips to pave the way for non‐invasive point‐of‐care diagnostics.

910P Assessment of early detection by multi-omics-based liquid biopsy in lung cancer: A prospective study (ASCEND-LUNG)

Liquid biopsy offers an optimal approach for the early detection of lung cancer that is the leading cause of cancer mortality worldwide. Here, we report the results of AssesSment of early-deteCtion basEd oN liquiD biopsy in LUNG cancer (ASCEND-LUNG, NCT04817046), a prospective study designed to develop early detection models for lung cancer based on multi-omics assays including cell-free DNA (cfDNA) methylation, mutation, and tumor proteins.

Introduction of Nanomaterials to Biosensors for Exosome Detection: Case Study for Cancer Analysis.

Exosomes have been gaining attention for early cancer diagnosis owing to their biological functions in cells. Several studies have reported the relevance of exosomes in various diseases, including pancreatic cancer, retroperitoneal fibrosis, obesity, neurodegenerative diseases, and atherosclerosis. Particularly, exosomes are regarded as biomarkers for cancer diagnosis and can be detected in biofluids, such as saliva, urine, peritoneal fluid, and blood. Thus, exosomes are advantageous for cancer liquid biopsies as they overcome the current limitations of cancer tissue biopsies. Several studies have reported methods for exosome isolation, and analysis for cancer diagnosis. However, further clinical trials are still required to determine accurate exosome concentration quantification methods. Recently, various biosensors have been developed to detect exosomal biomarkers, including tumor-derived exosomes, nucleic acids, and proteins. Among these, the exact quantification of tumor-derived exosomes is a serious obstacle to the clinical use of liquid biopsies. Precise detection of exosome concentration is difficult because it requires clinical sample pretreatment. To solve this problem, the use of the nanobiohybrid material-based biosensor provides improved sensitivity and selectivity. The present review will discuss recent progress in exosome biosensors consisting of nanomaterials and biomaterial hybrids for electrochemical, electrical, and optical-based biosensors.

Recent advances in nanotechnology-enabled biosensors for detection of exosomes as new cancer liquid biopsy.

Cancer liquid biopsy detects circulating biomarkers in body fluids, provides information that complements medical imaging and tissue biopsy, allows sequential monitoring of cancer development, and, therefore, has shown great promise in cancer screening, diagnosis, and prognosis. Exosomes (also known as small extracellular vesicles) are cell-secreted, nanosized vesicles that transport biomolecules such as proteins and RNAs for intercellular communication. Exosomes are actively involved in cancer development and progression and have become promising circulating biomarkers for cancer liquid biopsy. Conventional exosome characterization methods such as quantitative reverse transcription polymerase chain reaction (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA) are limited by low sensitivity, tedious process, large sample volume, and high cost. To overcome these challenges, new biosensors have been developed to offer sensitive, simple, fast, high throughput, low sample consumption, and cost-effective detection of exosomal biomarkers. In this review, we summarized recent advances in nanotechnology-enabled biosensors that detect exosomal RNAs (both microRNAs and mRNAs) and proteins for cancer screening, diagnosis, and prognosis. The biosensors were grouped based on their sensing mechanisms, including fluorescence-based biosensors, colorimetric biosensors, electrical/electrochemical biosensors, plasmonics-based biosensors, surface-enhanced Raman spectroscopy (SERS)-based biosensors, and inductively coupled plasma mass spectrometry (ICP-MS) and photothermal biosensors. The future directions for the development of exosome-based biosensors were discussed.

Detection of circulating tumor cells and evaluation of epithelial-mesenchymal transition patterns of circulating tumor cells in ovarian cancer.

BackgroundCirculating tumor cells (CTCs) have considered to be promising liquid biopsy in cancer due to the intact information of whole cells and the potential to reflect micrometastasis. However, CTCs research are extremely limited in ovarian cancer, probably due to their rarity. The predictive value of CTCs and circulating tumor microemboli (CTM) in metastasis remains to be elucidated in ovarian cancer. This study tried to identify CTCs/CTM in ovarian cancer with considerably positive rate. To preliminarily identify the invasive capacity of CTCs/CTM, the epithelial-mesenchymal transition (EMT) patterns of CTCs/CTM was evaluated. Moreover, for comprehensive understanding of invasiveness of disseminated cells in ovarian cancer, EMT pattern of exfoliated tumor cells in ascites were also confirmed in this study.MethodsPeripheral blood samples and ascites samples were collected from 22 ovarian cancer patients. The Microfiltration combined with morphological analysis was used to detect CTC single cells or cell clusters. Microfiltration combined with morphological analysis was applied in the CTC isolation and identification. EMT was evaluated by immunofluorescence via markers including vimentin and cytokeratin.ResultsMicrofiltration combined with morphological analysis was introduced to detect CTCs/CTM with a positivity rate of 40.9% in ovarian cancer patients. The number of CTC varied from 1 to 8, with CTM number from 4 to 30. CTCs/CTM of all samples have experienced EMT process. Vimentin was expressed in all CTC samples and all tumor cells in ascites, while cytokeratin was expressed in 44.4% (4/9) of CTC samples. There were no significant differences of the clinical parameters between the CTC-positive and CTC-negative patients.ConclusionsThis study showed that both CTCs/CTM and detached tumor cells in ascites might have undergone complete or partial EMT in ovarian cancer. Moreover, microfiltration combined with cytomorphological analysis showed a considerable CTC detection rate.

Abstract 5920: Multi-cancer early detection with a spectroscopic liquid biopsy platform

Abstract The development of a robust multi-cancer early detection (MCED) test would be transformational within the cancer diagnostics field, as earlier detection is vital to inhibit disease progression, improve patient prognosis and reduce mortality rates. In this large-scale spectroscopic study, we present the Dxcover® Cancer Liquid Biopsy, the world’s first infrared spectroscopy-based blood test for the early detection of multiple cancers. The full cohort (n=2094) was comprised of patients with a confirmed cancer diagnosis (n=1543), symptomatic non-cancer patients (n=460) and healthy volunteers (n=91). For the cancer versus non-cancer classification, receiver operating characteristic analysis produced a mean curve with an area under the curve (AUC) value of 0.86. When adjusted for greater sensitivity, our model achieved 90% sensitivity with 61% specificity, and yielded a sensitivity of 56% with 91% specificity when tailored for higher specificity. We also tuned for maximized sensitivity or specificity, whilst the other parameter was fixed above a minimum value of 45% for the cross-validation result, based upon performance levels of currently available clinically benchmarked tests. This resulted in a 94% sensitivity where specificity was 47%, and a 94% specificity with 48% sensitivity. The overall detection rates were 93% for stage I, 84% for stage II, 92% for stage III and 95% for stage IV for the sensitivity-tuned model. The specificity-tuned model detected 90% of non-cancer patients accurately, resulting in few false negatives (54/551). For the binary classifiers of individual cancer types against symptomatic control patients, promising AUC values were reported for all cancers: brain (0.90), breast (0.74), colorectal (0.91), kidney (0.91), lung (0.90), ovarian (0.85), pancreatic (0.81) and prostate (0.85). Importantly, the detection rates did not vary significantly according to cancer stage, suggesting the technology is very effective at detecting early-stage tumors, which is a necessity for MCED tests. Citation Format: James M. Cameron, Alexandra Sala, Georgios Antoniou, Paul M. Brennan, Justin J. A. Conn, Siobhan Connal, David S. Palmer, Benjamin R. Smith, Matthew J. Baker. Multi-cancer early detection with a spectroscopic liquid biopsy platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5920.

Abstract 2791: Integrating biospecimen science approaches into clinical assay development-PAR 22-049

Abstract Introduction to the problem: Biospecimens are the essential starting materials for the biomarker assays that enable precision medicine. Biospecimen preanalytical factors can directly influence molecular results from assays conducted for basic research, biomarker discovery, and biomarker validation, and can also influence the development of validated clinical assays. The U.S. National Cancer Institute (NCI) Biorepositories and Biospecimen Research Branch supports extramural research as a part of this FOA to investigate and mitigate preanalytical challenges facing clinical assay development and analytical validation in a variety of biospecimen types. The overall goal of this initiative is to expedite biomarker clinical assay development through evidence based standardization of biospecimen handling practices. The work is centered on emerging and clinically relevant biomarker assays conducted on a variety of molecular platforms. Proposed solution and methods: Researchers may investigate tissue and fluid biospecimens including core biopsies, fine needle aspirates, tissue exudates, blood, urine, saliva, breast milk, feces, sweat, CSF, and pleural and esophageal aspirates. Investigator designed experiments explore how different biospecimen preanalytical conditions affect biomarkers in a clinical setting. To date, ten grants have been funded. The grants address important areas in biospecimen science in both liquid and solid biopsy biopsies in breast cancer, glioblastomas, lung cancer, bladder cancer, prostate cancer, cutaneous and peripheral T cell lymphoma. Investigators can collaborate with each other and with the NCI, FDA and NIST through the U01 funding mechanism. Conclusions: The results from this research program will elucidate how analytical quantification of clinically relevant biomarkers is affected by patient context factors, variation in biospecimen collection, processing, and storage procedures, and how such effects can be mitigated to improve the development, performance and accuracy of clinical biomarker assays utilized in clinical research and patient care. The receipt dates for this program, PAR 22 049 in 2022, are January 11, 2022, June 7th, 2022, and September 13th, 2022. Citation Format: Lokesh Agrawal, Abhi Rao, Ping Guan, Miguel Ossandon, Helen Moore. Integrating biospecimen science approaches into clinical assay development-PAR 22-049 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2791.

Abstract 5922: Clinical validation of a spectroscopic liquid biopsy for early detection of brain cancer

Abstract Diagnostic delays impact the quality of life and survival of patients with brain tumors. Earlier and expeditious diagnoses in these patients are crucial to reducing the morbidities and mortalities associated with brain tumors. A simple, rapid blood test that can be administered easily in a primary care setting to efficiently identify symptomatic patients who are most likely to have a brain tumor would enable quicker referral to brain imaging for those who need it most. Blood serum samples from 603 patients were prospectively collected and analyzed. Patients either had non-specific symptoms that could be indicative of a brain tumor on presentation to the Emergency Department, or a new brain tumor diagnosis and referral to the neurosurgical unit, NHS Lothian, Scotland. Patient blood serum samples were analyzed using the Dxcover® Brain Cancer liquid biopsy. This technology utilizes infrared spectroscopy combined with a diagnostic algorithm to predict the presence of intracranial disease. Our liquid biopsy approach reported an area under the receiver operating characteristic curve of 0.8. The sensitivity-tuned model achieves a 96% sensitivity with 45% specificity (NPV 99.3%) and identified 100% of glioblastoma multiforme patients. When tuned for a higher specificity, the model yields sensitivity of 47% with 90% specificity (PPV 28.4%). This simple, non-invasive blood test facilitates the triage and radiographic diagnosis of brain tumor patients, while providing reassurance to healthy patients. Minimizing time to diagnosis would facilitate identification of brain tumor patients at an earlier stage, enabling more effective, less morbid surgical and adjuvant care. Citation Format: James M. Cameron, Paul M. Brennan, Georgios Antoniou, Holly J. Butler, Loren Christie, Justin J.A. Conn, Tom Curran, Ewan Gray, Mark G. Hegarty, Michael Jenkinson, Daniel Orringer, David S. Palmer, Alexandra Sala, Benjamin R. Smith, Matthew J. Baker. Clinical validation of a spectroscopic liquid biopsy for early detection of brain cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5922.

Abstract 3924: Deep, unbiased multi-omics approach for identification of pancreatic cancer biomarkers from blood

Abstract Pancreatic cancer is the seventh leading cause of cancer-related death worldwide and the third leading cause of cancer-related death in the USA. The low survival rate of pancreatic cancer is due to the challenges in early detection of disease, highlighting the need for early diagnostic test development. While cancer signatures are less challenging to identify at the localized pancreatic tumor via biopsy, cancer signals found in the bloodstream due to cellular leakage, metastasis, signaling, innate immune response, remain of key interest due to reduced invasive sampling. The key challenges encountered in liquid biopsy cancer biomarker discovery studies are analyte degradation and dilution in a complex biological matrix, which limits high specificity and sensitivity measurements. To overcome these challenges, PrognomiQ has developed a comprehensive multi-omics platform that facilitates uncovering previously untapped information to gain a more holistic biological perspective at unprecedented depths and integrate molecular signatures across complex levels of biology. Implementation of this approach has led to the discovery of new pancreatic cancer-specific biomarkers and a deeper understanding of the integrated pathways of pancreatic cancer. In this case-control study, the plasma proteome and metabolome data were collected from 193 human plasma samples comprising 92 pancreatic cancer and 104 healthy subjects utilizing liquid chromatography-mass spectrometry. Subject samples were collected post-diagnosis, but pre-treatment for cancer subjects versus non-cancer controls. Sample collection and handling were the same for all samples. In our initial analysis, we detected 3,381 proteins in all samples (minimum of 3 samples per class), and utilizing a Bonferroni correction (FDR = 0.05) we showed 124 proteins to be statistically significant. We also determined ~200 lipids out of 678 total lipids and 49 of 299 metabolites present in all samples (minimum of 3 samples per class) to be statistically significant with a Bonferroni correction (FDR = 0.05). The detected analytes (proteins, lipids, and metabolites) are both known, and unknown, to have an association with pancreatic cancer. Analysis of the data collected from the described cohort will continue to determine Analysis is ongoing to integrate the multi-omics datasets and determine multivariate statistical performance to detect pancreatic cancer. Conclusion: The intent of this cohort and study was to detect biological signal for pancreatic cancer, and this preliminary analysis suggests there are significant differences between classes in the samples as collected. It remains to be seen if combining features within and across analyte classes improve detection. This is a case-control study, not an intent to test study but shows promise for the detection of pancreatic cancer across a multitude of analyte classes. Citation Format: Bruce Wilcox, John Blume, Kavya Swaminathan, Preston Williams, Manoj Khadka, Jared Deyarmin, Saividya Ramaswamy, Yuya Kodama, Brian Young, Chinmay Belthangady, Manway Liu, Mi Yang, Philip Ma. Deep, unbiased multi-omics approach for identification of pancreatic cancer biomarkers from blood [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3924.

Machine Learning-Assisted FTIR Analysis of Circulating Extracellular Vesicles for Cancer Liquid Biopsy.

Extracellular vesicles (EVs) are abundantly released into the systemic circulation, where they show remarkable stability and harbor molecular constituents that provide biochemical information about their cells of origin. Due to this characteristic, EVs are attracting increasing attention as a source of circulating biomarkers for cancer liquid biopsy and personalized medicine. Despite this potential, none of the discovered biomarkers has entered the clinical practice so far, and novel approaches for the label-free characterization of EVs are highly demanded. In this regard, Fourier Transform Infrared Spectroscopy (FTIR) has great potential as it provides a quick, reproducible, and informative biomolecular fingerprint of EVs. In this pilot study, we investigated, for the first time in the literature, the capability of FTIR spectroscopy to distinguish between EVs extracted from sera of cancer patients and controls based on their mid-IR spectral response. For this purpose, EV-enriched suspensions were obtained from the serum of patients diagnosed with Hepatocellular Carcinoma (HCC) of nonviral origin and noncancer subjects. Our data point out the presence of statistically significant differences in the integrated intensities of major mid-IR absorption bands, including the carbohydrate and nucleic acids band, the protein amide I and II bands, and the lipid CH stretching band. Additionally, we used Principal Component Analysis combined with Linear Discriminant Analysis (PCA-LDA) for the automated classification of spectral data according to the shape of specific mid-IR spectral signatures. The diagnostic performances of the proposed spectral biomarkers, alone and combined, were evaluated using multivariate logistic regression followed by a Receiving Operator Curve analysis, obtaining large Areas Under the Curve (AUC = 0.91, 95% CI 0.81–1.0). Very interestingly, our analyses suggest that the discussed spectral biomarkers can outperform the classification ability of two widely used circulating HCC markers measured on the same groups of subjects, namely alpha-fetoprotein (AFP), and protein induced by the absence of vitamin K or antagonist-II (PIVKA-II).

Clonal hematopoiesis in lung cancer and its impact on liquid biopsy based on targeted deep sequencing.

e20532 Background: Blood-based liquid biopsy has proved its potential in enormous clinical applications, such as cancer screening, diagnosis, treatment guidance, disease tracking and monitoring. In certain scenarios (e.g., molecular residual disease), it requires the technique to be able to detect mutation with very low frequency (0.001% ̃ 1%). The major hurdle of ultra-sensitive circulating tumor DNA sequencing is the high background noise of plasma cell-free DNA (cfDNA) and clonal hematopoiesis (CH). Here in this study, we investigated the prevalence of CH in lung cancer patients and its interference with liquid biopsy. Methods: We retrospectively analyzed cfDNA and blood cell genomic DNA (gDNA) sequencing data sets (n = 1,261) from a group of Chinese lung cancer patients. Threshold (1%) and subthreshold (0.2%) for variant allele frequency were set and compared. We focused on 23 clonal hematopoiesis genes that were selected based on previous publications. Results: CH mutations were detected in 27.68% of all the patients at the threshold and 62.01% at the subthreshold, and the detection rate increased with age. DNMT3A was the most frequently mutated CH gene, accounting for more than half of the CH mutations. The CH mutations had a higher detection rate in smokers (72%) than non-smokers (59.4%) at subthreshold. VAFs of CH mutations in cell-free DNA strongly correlated with their VAFs in gDNA (Pearson’s R = 0.92, p < 2.2x10-16), while tumor-derived somatic mutations didn’t have such correlation. Conclusions: Our study showed that clonal hematopoiesis is very common in lung cancer patients, especially when examining low-frequency mutations. Sequencing of gDNA at equivalent depth is very important to filter out CH mutation in cancer liquid biopsy.

The Utility of Repetitive Cell-Free DNA in Cancer Liquid Biopsies.

Liquid biopsy is a broad term that refers to the testing of body fluids for biomarkers that correlate with a pathological condition. While a variety of body-fluid components (e.g., circulating tumor cells, extracellular vesicles, RNA, proteins, and metabolites) are studied as potential liquid biopsy biomarkers, cell-free DNA (cfDNA) has attracted the most attention in recent years. The total cfDNA population in a typical biospecimen represents an immensely rich source of biological and pathological information and has demonstrated significant potential as a versatile biomarker in oncology, non-invasive prenatal testing, and transplant monitoring. As a significant portion of cfDNA is composed of repeat DNA sequences and some families (e.g., pericentric satellites) were recently shown to be overrepresented in cfDNA populations vs their genomic abundance, it holds great potential for developing liquid biopsy-based biomarkers for the early detection and management of patients with cancer. By outlining research that employed cell-free repeat DNA sequences, in particular the ALU and LINE-1 elements, we highlight the clinical potential of the repeat-element content of cfDNA as an underappreciated marker in the cancer liquid biopsy repertoire.

Shedding of ctDNA, radiomics assessment of tumor disease volume (TDV), and concordance of mutations (mut) in synchronous liquid and tumor biopsies in metastatic breast cancer (MBC).

1086 Background: Genomic alterations driving MBC progression may be better captured by ctDNA reflecting clonal evolution, but it is currently unknow whether ctDNA analysis can replace tumor sequencing for clinical decision purposes. Aim: to study the concordance between mut in synchronous plasma and tumor samples prospectively collected from patients (pts) with MBC progressing on their last systemic therapy. Methods: MiSeq Amplicon-based NGS (custom panel of 60 cancer-related genes; BRCA1/2 and PALB2 not included) was performed in both tumor biopsy and plasma. The concordance of ESCAT Tier I and II mut ( PIK3CA, AKT1, ERBB2, ESR1, PTEN) was determined and correlated with mutant allele fraction (MAF), TDV, and clinical features. Findings from liquid biopsies were classified as true positive (TP-ctDNA) if a given mut was detected in both tumor and plasma and false negative (FN-ctDNA) if only in the tumor. TDV: all metastasis volume assessed by CT scan (excluding sclerotic bone metastasis), and analyzed by an experienced radiologist using the 3DSlicer semiautomatic segmentation tool (TDV = pixel size x number of pixels). Non-shedding cases were those where any mut was detected in tumor but none in plasma. Results: 88 cases were collected (luminal 64, HER2+ 17, triple negative 7). Median age at diagnosis 49 years (range 28-80). Radiomics assessment could be performed in 78/88 cases. The plasma/tissue concordance at case level was 74%. Discordance came from 23 cases; in 15 cases mut was only found in tissue and in 8 cases it was only detected in plasma. At gene level, PIK3CA had the highest concordance (79%); in ESR1 it was 52%. Higher concordance associated with non-luminal subtype (OR 0.08, 95%CI 0.002 – 0.59) and shorter interval between primary diagnosis and metastatic relapse (20.3 vs 51 months; p =.02), but not with MAF. FN-ctDNA occurred in 15/49 cases (31%) and associated with luminal subtype (p =.02), but not with other clinical variables. Non-shedding cases associated with older age (p =.03), luminal subtype (p =.007), low TDV (p =.0006) and < 3 metastatic sites (p =.05). In patients with visceral metastasis (n = 45), higher TDV associated with lower probability of FN-ctDNA (p =.03). All non-luminal subtypes were shedders and all but one were TP-ctDNA. In multivariate analysis, higher probability of TP-ctDNA in luminal tumors associated with tumor sampling from a progressing lesion (OR 10.8; 95% 1.5 – 122; p =.03) and shorter interval between diagnosis of metastatic disease and biopsy (OR 0.96, 95% CI 0.92 – 0.99; p =.03). Conclusions: Our results suggest that ctDNA can detect a significant proportion of clinically relevant mut in MBC. Patients’ characteristics, tumor subtype, type of gene, and tumor volume should be integrated with ctDNA results to better inform clinical decisions.

Machine learning based detection of pancreatic tumors using the Dxcover cancer platform.

e16275 Background: Pancreatic cancer is the 7th most deadly cancer worldwide with over 460,000 victims per year. In the current diagnostic pathway, carbohydrate antigen (CA) 19-9 serum test is the blood assessment used for detection of pancreatic cancer; although, with poor positive predictive values reported, it is not specific for pancreatic tumors as its levels can be raised in symptomatic patients with other benign comorbidities and/or because of other tumors in the surrounding area. Attenuated total reflection–Fourier transform infrared spectroscopy (ATR-FTIR) has demonstrated exceptional potential in human blood serum analysis for cancer diagnostics and its implementation in the clinical environment could represent a significant step forward in the early detection of pancreatic cancer. This proof-of-concept study aimed to investigate the use of the Dxcover cancer liquid biopsy as a novel approach for pancreatic cancer detection. Methods: The study was focused on the discrimination between both cancer (n = 100) versus healthy control samples (n = 100), and cancer (n = 35) versus symptomatic non-malignant control samples (n = 35) from patients with comorbidities and/or confounding diseases. Various machine learning algorithms were applied to discriminate between the classes: random forest (RF), partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM). Receiver operating characteristic (ROC) analysis was also employed to evaluate the classes’ degree of diagnostic separability. Permutation tests were performed for each classification in order to ensure their statistical significance. Results: Cancer (n = 100) and healthy control samples (n = 100) were distinguished with excellent results, achieving results up to a sensitivity of 91.0 %, specificity of 87.6 %, and accuracy of 89.3 % with PLS-DA. Moreover, an area under the curve (AUC) equal to 0.954 was obtained through ROC analysis. When discriminating between cancer (n = 35) and symptomatic control samples (n = 35), accuracy values were recorded in the range between 70.6 and 77.3 % with ROC analysis showing a balanced sensitivity and specificity over 75 % with an AUC of 0.844. Both discriminations were proven statistically significant. Conclusions: Pancreatic cancer detection in early stages would be key in improving prognosis and survival rates of patients, through targeted earlier surgery and treatments. The Dxcover cancer liquid biopsy could represent a powerful tool in the clinical environment as an easy-to-use, minimally invasive and reliable spectroscopic blood test for detection of pancreatic cancer.

Multicancer early detection with a spectroscopic liquid biopsy platform.

3034 Background: A rapid, low-cost, sensitive, multi-cancer early detection (MCED) test would be transformational in the diagnostics field. Earlier cancer detection and instigation of treatment can increase survival rates. An effective test must accurately identify the small proportion of patients with typically non-specific symptoms who actually have cancer. Such symptoms don’t easily segregate by organ system, necessitating a multi-cancer approach. Methods: In this large-scale study ( n = 2094 patients) we applied the Dxcover Cancer Liquid Biopsy to differentiate cancer against non-cancer, as well as organ specific tests to identify cancers of the brain, breast, colorectal, kidney, lung, ovary, pancreas, and prostate. The test uses Fourier transform infrared spectroscopy to analyze all macromolecules in a minute volume of patient serum, and machine learning to build a classifier of the resultant spectral profiles for calling the likelihood of cancer. Results: For the overall cancer classification, our model achieved 90% sensitivity with 61% specificity when tuned for sensitivity, with detection rates of 93% for stage I, 84% for stage II, 92% for stage III and 95% for stage IV. We also tuned for maximum sensitivity or specificity, whilst the other statistic was fixed above a minimum value of 45%. This resulted in 94% sensitivity with 47% specificity, and 94% specificity with 48% sensitivity, respectively. For organ specific cancer classifiers area under the curve values were calculated for all cancers: brain (0.90), breast (0.74), colorectal (0.91), kidney (0.91), lung (0.90), ovarian (0.85), pancreatic (0.81) and prostate (0.85). Conclusions: Cancer treatment is often more effective when given earlier and this low-cost strategy can facilitate the requisite earlier diagnosis. With further development, the Dxcover MCED test could have a significant impact on early detection of cancer, which is vital in the quest for improved survival and quality of life.

Extracellular Vesicle-Based Method for Detecting MYCN Amplification Status of Pediatric Neuroblastoma.

Simple SummaryMYCN gene amplification, the strongest prognostic marker of aggressive neuroblastoma, is detected on invasive biopsy tissues. This study aimed to establish a less invasive method to detect MYCN status based on MYCN mRNA contents in large extracellular vesicles or microvesicles. MYCN mRNA-containing microvesicles were detectable in three distinct MYCN-amplified neuroblastoma cell lines but absent in three neuroblastoma cells with MYCN-non-amplification. The feasibility of this EV-based workflow was successfully demonstrated by using the simulated samples (prepared by pulsing neuroblastoma MVs into the normal human serum) and bone marrow plasma specimens obtained from nine patients at various disease stages. Taken together, this study established the novel EV-based method for detecting MYCN status in pediatric neuroblastoma.MYCN amplification is the strongest predictor of high-risk neuroblastoma (NB). The standard procedure to detect MYCN status requires invasive procedures. Extracellular vesicles (EVs) contain molecular signatures of originated cells, present in biofluids, and serve as an invaluable source for cancer liquid biopsies. This study aimed to establish an EV-based method to detect the MYCN status of NB. Two EV subtypes, i.e., microvesicles (MVs) and exosomes, were sequentially isolated from the culture supernatant by step-wise centrifugation, ultrafiltration, and size-exclusion chromatography. Quantitative RT-PCR was performed to detect MYCN mRNA. As a result, MYCN mRNA was detectable in the MVs, but not exosomes, of MYCN-amplified NB cells. MYCN mRNA-containing MVs (MYCN-MV) were successfully detected in three distinct MYCN-amplified NB cell lines but absent in three MYCN non-amplification cells. The simulated samples were prepared by pulsing MVs into human serum. MYCN–MV detection in the simulated samples showed a less interfering effect from the human blood matrix. Validation using clinical specimens (2 mL bone marrow plasma) obtained from patients at various disease stages showed a promising result. Five out of six specimens of MYCN-amplified patients showed positive results, while there were no false positives in four plasma samples of the MYCN non-amplification group. This study communicated a novel EV-based method for detecting the MYCN status of pediatric NB based on MYCN mRNA contents in MVs. Future studies should be pursued in a prospective cohort to determine its true diagnostic performance.

Single-Molecule Sequencing Enables Long Cell-Free DNA Detection and Direct Methylation Analysis for Cancer Patients.

Analysis of circulating tumor DNA has become increasingly important as a tool for cancer care. However, the focus of previous studies has been on short fragments of DNA. Also, bisulfite sequencing, a conventional approach for methylation analysis, causes DNA degradation, which is not ideal for the assessment of long DNA properties and methylation patterns. This study attempted to overcome such obstacles by single-molecule sequencing. Single-molecule real-time (SMRT) sequencing was used to sequence plasma DNA. We performed fragment size and direct methylation analysis for each molecule. A methylation score concerning single-molecule methylation patterns was used for cancer detection. A substantial proportion of plasma DNA was longer than 1 kb with a median of 16% in hepatocellular carcinoma (HCC) patients, hepatitis B virus carriers, and healthy individuals. The longest plasma DNA molecule in the HCC patients was 39.8 kb. Tumoral cell-free DNA (cfDNA) was generally shorter than nontumoral cfDNA. The longest tumoral cfDNA was 13.6 kb. Tumoral cfDNA had lower methylation levels compared with nontumoral cfDNA (median: 59.3% vs 76.9%). We developed and analyzed a metric reflecting single-molecule methylation patterns associated with cancer, named the HCC methylation score. HCC patients displayed significantly higher HCC methylation scores than those without HCC. Interestingly, compared to using short cfDNA (area under the receiver operating characteristic [ROC] curve, AUC: 0.75), the use of long cfDNA molecules greatly enhanced the discriminatory power (AUC: 0.91). A previously unidentified long cfDNA population was revealed in cancer patients. The presence and direct methylation analysis of these molecules open new possibilities for cancer liquid biopsy.

Tumor-Originated Exosomal hsa-miR-3937 as a Minimally Invasive Early Biomarker for Liquid Biopsy of Colorectal Cancer.

Background Exosomal microRNAs (miRNAs) have been linked to the genesis and progression of certain cancers. The role and regulation mechanism of cancer-derived exosomal miRNAs in CRC, however, remain unknown. Methods To address this, we first used miRNA sequencing to describe the miRNA profiles of circulating exosomes in order to identify miRNAs that were differently expressed between patients with CRC and healthy controls. Transmission electron microscopy, nanoparticle tracking analysis (NTA), and western blot were used to analyze exosomes generated from CRC cells. CCK-8, wound healing, and Transwell tests were used to see whether exosomes affected CRC cell proliferation, metastasis, and apoptosis, respectively. We chose and identified hsa-miR-3937, which was abundant in tumor-generated exosomes, based on earlier RNA sequencing data of exosomes obtained and extracted from seven matched specimens of tumor tissues and surrounding normal tissues of CRC patients. Results The role of hsa-miR-3937 in CRC cells was found, and silencing of hsa-miR-3937 decreased CRC cell invasion and migration in a Transwell experiment. Furthermore, we discovered that there was no link between hsa-miR-3937 expression and CRC cell apoptosis. Initially, it was discovered that BCL2L12 was the target gene of hsa-miR-3937, and the TCGA database highlighted the potential therapeutic relevance of BCL2L12. Furthermore, to identify hsa-miR-3937 as a biomarker of CRC, we used peripheral blood samples rather than patient tissues and extracted exosomes from plasma samples. To assess the expression levels and predictive usefulness of plasma exosomal hsa-miR-3937 in CRC, we performed RT-qPCR to identify hsa-miR-3937 levels in all samples. We also gathered clinicopathological information in order to look for links between aberrant hsa-miR-3937 expression and clinicopathological characteristics. The pathologic stage of CRC patients was linked to hsa-miR-3937 expression levels, and the same was true for the T stage. ROC curve study revealed that hsa-miR-3937 outperforms CEA and CA199. The combination of hsa-miR-3937, CEA, and CA199 exhibited the highest sensitivity for CRC diagnosis. Conclusions Our findings show that the tumor-originated exosomal hsa-miR-3937 is a potential and effective liquid biopsy marker for colorectal cancer detection and therapy.

Fluorescence Analysis of Circulating Exosomes for Breast Cancer Diagnosis Using a Sensor Array and Deep Learning.

Emerging liquid biopsy methods for investigating biomarkers in bodily fluids such as blood, saliva, or urine can be used to perform noninvasive cancer detection. However, the complexity and heterogeneity of exosomes require improved methods to achieve the desired sensitivity and accuracy. Herein, we report our study on developing a breast cancer liquid biopsy system, including a fluorescence sensor array and deep learning (DL) tool AggMapNet. In particular, we used a 12-unit sensor array composed of conjugated polyelectrolytes, fluorophore-labeled peptides, and monosaccharides or glycans to collect fluorescence signals from cells and exosomes. Linear discriminant analysis (LDA) processed the fluorescence spectral data of cells and cell-derived exosomes, demonstrating successful discrimination between normal and different cancerous cells and 100% accurate classification of different BC cells. For heterogeneous plasma-derived exosome analysis, CNN-based DL tool AggMapNet was applied to transform the unordered fluorescence spectra into feature maps (Fmaps), which gave a straightforward visual demonstration of the difference between healthy donors and BC patients with 100% prediction accuracy. Our work indicates that our fluorescent sensor array and DL model can be used as a promising noninvasive method for BC diagnosis.