Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey.
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ABSTRACT:
SUBMITTER: Liu L
PROVIDER: S-EPMC8308091 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Cancer is a leading cause of mortality worldwide. Over 50% of cancers are diagnosed late, rendering many treatments ineffective. Existing liquid biopsy studies demonstrate a minimally invasive and inexpensive approach for disease detection but lack parsimonious biomarker selection, exhibit poor cancer detection performance and lack appropriate validation and testing. We established a tailored machine learning pipeline, DEcancer, for liquid biopsy analysis that addresses these limitations and improved performance. In a test set from a published cohort of 1,005 patients including 8 cancer types and 812 cancer-free individuals, DEcancer increased stage 1 cancer detection sensitivity across cancer types from 48 to 90%. In addition, with a test set cohort of patients from a high dimensional proteomics dataset of 61 lung cancer patients and 80 cancer-free individuals, DEcancer's performance using a 14-43 protein panel was comparable to 1,000 original proteins. DEcancer is a promising tool which may facilitate improved cancer detection and management.
| S-EPMC10165183 | biostudies-literature
Project description:Molecularly targeted therapies and immune checkpoint inhibitors have markedly improved the therapeutic management of advanced lung cancer. However, it still remains the leading cause of cancer-related mortality worldwide, with disease stage at diagnosis representing the main prognostic factor. Detection of lung cancer at an earlier stage of disease, potentially susceptible of curative resection, can be critical to improve patients survival. Low-dose computed tomography (LDCT) screening of high-risk patients has been demonstrated to reduce mortality from lung cancer, but can be also associated with high false-positive rate, thus often resulting in unnecessary interventions for patients. Novel sensitive and specific biomarkers for identification of high-risk subjects and early detection that can be used alternatively and/or complement current routine diagnostic procedures are needed. Liquid biopsy has recently demonstrated its clinical usefulness in advanced NSCLC as a surrogate of tissue biopsy for noninvasive assessment of specific genomic alterations, thereby providing prognostic and predictive information. Different biosources from liquid biopsy, including cell free circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), exosomes and tumor-educated platelets (TEPs), have also been widely investigated for their potential role in lung cancer diagnosis. This review will provide an overview on the circulating biomarkers being evaluated for lung cancer detection, mainly focusing on results from most recent studies, the techniques developed to perform their assessment in blood and other biologic fluids and challenges in their clinical applications.
| S-EPMC5945693 | biostudies-other
Project description:Introduction:Mammography is the gold standard for early breast cancer detection, but shows important limitations. Blood-based approaches on basis of cell-free DNA (cfDNA) provide minimally invasive screening tools to characterize epigenetic alterations of tumor suppressor genes and could serve as a liquid biopsy, complementing mammography. Methods:Potential biomarkers were identified from The Cancer Genome Atlas (TCGA), using HumanMethylation450-BeadChip data. Promoter methylation status was evaluated quantitatively by pyrosequencing in a serum test cohort (n = 103), a serum validation cohort (n = 368) and a plasma cohort (n = 125). Results:SPAG6, NKX2-6 and PER1 were identified as novel biomarker candidates. ITIH5 was included on basis of our previous work. In the serum test cohort, a panel of SPAG6 and ITIH5 showed 63% sensitivity for DCIS and 51% sensitivity for early invasive tumor (pT1, pN0) detection at 80% specificity. The serum validation cohort revealed 50% sensitivity for DCIS detection on basis of NKX2-6 and ITIH5. Furthermore, an inverse correlation between methylation frequency and cfDNA concentration was uncovered. Therefore, markers were tested in a plasma cohort, achieving a 64% sensitivity for breast cancer detection using SPAG6, PER1 and ITIH5. Conclusions:Although liquid biopsy remains challenging, a combination of SPAG6, NKX2-6, ITIH5 and PER1 (SNiPER) provides a promising tool for blood-based breast cancer detection.
| S-EPMC6849652 | biostudies-literature
Project description:[This corrects the article DOI: 10.18632/oncotarget.27303.].
| S-EPMC7392623 | biostudies-literature
Project description: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).
| S-EPMC9224706 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is a highly prevalent and lethal cancer globally. Over 90% of HCC cases arise in the context of liver cirrhosis, and the severity of the underlying liver disease or advanced tumor stage at diagnosis significantly limits treatment options. Early diagnosis is crucial, and all guidelines stress the importance of screening protocols for HCC early detection as a public health objective. As serum biomarkers are not optimal for early diagnosis, liquid biopsy has emerged as a promising tool for diagnosis, prognostication, and patients' stratification for personalized therapy in various solid tumors, including HCC. While circulating tumor cells (CTCs) are better suited for personalized therapy and prognosis, cell-free DNA (cfDNA) and extracellular vesicle-based technologies show potential for early diagnosis, HCC screening, and surveillance protocols. Evaluating the added value of liquid biopsy genetic and epigenetic biomarkers for HCC screening is a key goal in translational research. Somatic mutations commonly found in HCC can be investigated in cfDNA and plasma exosomes as genetic biomarkers. Unique methylation patterns in cfDNA or cfDNA fragmentome features have been suggested as innovative tools for early HCC detection. Likewise, extracellular vesicle cargo biomarkers such as miRNAs and long non-coding RNAs may serve as potential biomarkers for early HCC detection. This review will explore recent findings on the utility of liquid biopsy for early HCC diagnosis. Combining liquid biopsy methods with traditional serological biomarkers could improve the overall diagnostic accuracy for early HCC detection.
| S-EPMC10556479 | biostudies-literature
Project description:High-grade ovarian cancer (HGOC) is the leading cause of mortality from gynecological malignancies, because of diagnosis at a metastatic stage. Current screening options fail to improve mortality because of the absence of early-stage-specific biomarkers. We postulated that a liquid biopsy, such as utero-tubal lavage (UtL), may identify localized lesions better than systemic approaches of serum/plasma analysis. Further, while mutation-based assays are challenged by the rarity of tumor DNA within nonmutated DNA, analyzing the proteomic profile, is expected to enable earlier detection, as it reveals perturbations in both the tumor as well as in its microenvironment. To attain deep proteomic coverage and overcome the high dynamic range of this body fluid, we applied our method for microvesicle proteomics to the UtL samples. Liquid biopsies from HGOC patients (n = 49) and controls (n = 127) were divided into a discovery and validation sets. Data-dependent analysis of the samples on the Q-Exactive mass spectrometer provided depth of 8578 UtL proteins in total, and on average ∼3000 proteins per sample. We used support vector machine algorithms for sample classification, and crossed three feature-selection algorithms, to construct and validate a 9-protein classifier with 70% sensitivity and 76.2% specificity. The signature correctly identified all Stage I lesions. These results demonstrate the potential power of microvesicle-based proteomic biomarkers for early cancer diagnosis.
| S-EPMC6495259 | biostudies-literature
Project description:Background: Most pancreatic cancers are found at progressive stages when they cannot be surgically removed. Therefore, a highly accurate early detection method is urgently needed. Methods: This study analyzed serum from Japanese patients who suffered from pancreatic ductal adenocarcinoma (PDAC) and aimed to establish a PDAC-diagnostic system with metabolites in serum. Two groups of metabolites, primary metabolites (PM) and phospholipids (PL), were analyzed using liquid chromatography/electrospray ionization mass spectrometry. A support vector machine was employed to establish a machine learning-based diagnostic algorithm. Results: Integrating PM and PL databases improved cancer diagnostic accuracy and the area under the receiver operating characteristic curve. It was more effective than the algorithm based on either PM or PL database, or single metabolites as a biomarker. Subsequently, 36 statistically significant metabolites were fed into the algorithm as a collective biomarker, which improved results by accomplishing 97.4% and was further validated by additional serum. Interestingly, specific clusters of metabolites from patients with preoperative neoadjuvant chemotherapy (NAC) showed different patterns from those without NAC and were somewhat comparable to those of the control. Conclusion: We propose an efficient screening system for PDAC with high accuracy by liquid biopsy and potential biomarkers useful for assessing NAC performance.
| S-EPMC8734412 | biostudies-literature
Project description:High-grade ovarian cancer (HGOC) is the leading cause of mortality from gynecological malignancies, due to diagnosis at a metastatic stage. Current screening options fail to improve mortality due to the absence of early-stage-specific biomarkers. We postulated that a liquid biopsy, such as utero-tubal lavage (UtL), may identify localized lesions better than systemic approaches of serum/plasma analysis. Furthermore, while mutation-based assays are challenged by the rarity of tumor DNA within non-mutated DNA, analyzing the proteomic profile, is expected to enable earlier detection, as it reveals perturbations in both the tumor as well as in its microenvironment. To attain deep proteomic coverage and overcome the high dynamic range of this body fluid, we applied our method for microvesicle proteomics to the UtL samples. Liquid biopsies from HGOC patients (n=49), controls (n=127) and healthy BRCA mutation carriers (n=25), were divided into a discovery and validation sets. Data-dependent analysis of the samples on the Q-Exactive mass spectrometer provided depth of 8,578 UtL proteins in total, and on average ~3,000 proteins per sample. We used support vector machine algorithms for sample classification, and crossed three feature-selection algorithms, to construct and validate a 9-protein classifier with 70% sensitivity and 76.2% specificity. The signature correctly identified all Stage I lesions and highlighted increased risk in healthy BRCA carriers. These results demonstrate the potential power of microvesicle-based proteomic biomarkers for early cancer diagnosis.
2019-02-16 | PXD009655 | Pride
Project description:The emerging field of liquid biopsy stands at the forefront of novel diagnostic strategies for cancer and other diseases. Liquid biopsy allows minimally invasive molecular characterization of cancers for diagnosis, patient stratification to therapy, and longitudinal monitoring. Liquid biopsy strategies include detection and monitoring of circulating tumor cells, cell-free DNA, and extracellular vesicles. In this review, we address the current understanding and the role of existing liquid-biopsy-based modalities in cancer diagnostics and monitoring. We specifically focus on the technical and clinical challenges associated with liquid biopsy and biomarker development being addressed by the Liquid Biopsy Consortium, established through the National Cancer Institute. The Liquid Biopsy Consortium has developed new methods/assays and validated existing methods/technologies to capture and characterize tumor-derived circulating cargo, as well as addressed existing challenges and provided recommendations for advancing biomarker assays.
| S-EPMC10591039 | biostudies-literature