scholarly journals Can Circulating Tumor DNA Support a Successful Screening Test for Early Cancer Detection? The Grail Paradigm

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2171
Author(s):  
Oscar D. Pons-Belda ◽  
Amaia Fernandez-Uriarte ◽  
Eleftherios P. Diamandis
Keyword(s):  
Cancer Detection ◽  
Early Cancer ◽  
Circulating Tumor Dna ◽  
Tumor Burden ◽  
Diagnostic Methods ◽  
Stage I ◽  
Early Cancer Detection ◽  
Early Cancer Diagnosis ◽  
Pan Cancer ◽  
Tumor Dna

Circulating tumor DNA (ctDNA) is a new pan-cancer tumor marker with important applications for patient prognosis, monitoring progression, and assessing the success of the therapeutic response. Another important goal is an early cancer diagnosis. There is currently a debate if ctDNA can be used for early cancer detection due to the small tumor burden and low mutant allele fraction (MAF). We compare our previous calculations on the size of detectable cancers by ctDNA analysis with the latest experimental data from Grail’s clinical trial. Current ctDNA-based diagnostic methods could predictably detect tumors of sizes greater than 10–15 mm in diameter. When tumors are of this size or smaller, their MAF is about 0.01% (one tumor DNA molecule admixed with 10,000 normal DNA molecules). The use of 10 mL of blood (4 mL of plasma) will likely contain less than a complete cancer genome, thus rendering the diagnosis of cancer impossible. Grail’s new data confirm the low sensitivity for early cancer detection (<30% for Stage I–II tumors, <20% for Stage I tumors), but specificity was high at 99.5%. According to these latest data, the sensitivity of the Grail test is less than 20% in Stage I disease, casting doubt if this test could become a viable pan-cancer clinical screening tool.

Can a Broad Molecular Screen Based on Circulating Tumor DNA Aid in Early Cancer Detection?

2020 ◽  
Vol 5 (6) ◽  
pp. 1372-1377
Author(s):  
Clare Fiala ◽  
Eleftherios P Diamandis
Keyword(s):  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Early Cancer ◽  
Circulating Tumor Dna ◽  
Genetic Alterations ◽  
Population Screening ◽  
Early Cancer Detection ◽  
Potential Applicability ◽  
American Society ◽  
Tumor Dna

Abstract Early detection of cancer has been a major research focus for almost a century. Current methods for early cancer detection suffer from suboptimal sensitivity and specificity, especially when used for population screening. For most major cancers, including breast, prostate, lung, ovarian, and pancreatic cancer, population screening is still controversial or is not recommended by expert bodies. Circulating tumor DNA (ctDNA) is an exciting new cancer biomarker with potential applicability to all cancer types. Recent investigations have shown that genetic alterations or epigenetic modifications in ctDNA could be used for cancer detection with a liquid biopsy (i.e., a tube of blood). Tests based on ctDNA have attracted considerable attention for various applications, such as patient management, prognosis, early diagnosis, and population screening. Recently, new biotechnology companies were founded, with the goal of revolutionizing early cancer detection by using ctDNA. We previously examined this technology, as published by various academic laboratories and of one leading company, Grail, and drew attention to potential obstacles. After 3 years of intense development, this technology seems to have made some progress. Here, we will analyze the latest clinical data presented by Grail in October 2019, during the inaugural American Society of Clinical Oncology (ASCO) 2019 Breakthrough Conference. Despite considerable technical improvements, it seems that the sensitivity and specificity of the Grail test as a pan-cancer screening tool are still too low for clinical use. The prospects that this test could be further improved are also discussed.

scholarly journals A mathematical model of ctDNA shedding predicts tumor detection size

2020 ◽  
Vol 6 (50) ◽  
pp. eabc4308
Author(s):  
Stefano Avanzini ◽  
David M. Kurtz ◽  
Jacob J. Chabon ◽  
Everett J. Moding ◽  
Sharon Seiko Hori ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Cancer Detection ◽  
Early Cancer ◽  
Circulating Tumor Dna ◽  
Early Cancer Detection ◽  
Tumor Evolution ◽  
Cancer Early Detection ◽  
Annual Screening ◽  
Detection Strategies ◽  
Tumor Dna

Early cancer detection aims to find tumors before they progress to an incurable stage. To determine the potential of circulating tumor DNA (ctDNA) for cancer detection, we developed a mathematical model of tumor evolution and ctDNA shedding to predict the size at which tumors become detectable. From 176 patients with stage I to III lung cancer, we inferred that, on average, 0.014% of a tumor cell’s DNA is shed into the bloodstream per cell death. For annual screening, the model predicts median detection sizes of 2.0 to 2.3 cm representing a ~40% decrease from the current median detection size of 3.5 cm. For informed monthly cancer relapse testing, the model predicts a median detection size of 0.83 cm and suggests that treatment failure can be detected 140 days earlier than with imaging-based approaches. This mechanistic framework can help accelerate clinical trials by precomputing the most promising cancer early detection strategies.

scholarly journals Can circulating tumor DNA be used for direct and early stage cancer detection?

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2129 ◽  
Author(s):  
Eleftherios P. Diamandis ◽  
Clare Fiala
Keyword(s):  
Patient Outcomes ◽  
Cancer Detection ◽  
Early Cancer ◽  
Early Stage ◽  
Circulating Tumor Dna ◽  
Major Advance ◽  
Early Cancer Diagnosis ◽  
Asymptomatic Individuals ◽  
Stage 1 ◽  
Tumor Dna

In the August 16th issue of Science Translational Medicine, Phallen et al propose a method for early cancer diagnosis by using circulating tumor DNA (1). One major advance of this paper includes optimized sequencing of cell-free/circulating tumor DNA (ctDNA) without knowledge of tumor mutations. Evaluation of 200 patients with colorectal, breast, lung and ovarian cancer revealed mutations in ctDNA in approx. 60-70% of all patients, including stage 1 and stage 2 disease. If this data can be reproduced in asymptomatic individuals, they will likely have a major impact on early cancer detection and patient outcomes. In this commentary, we examine the feasibility of this approach for detecting small, asymptomatic tumors, based on previously published empirical data.

scholarly journals Circulating Tumor DNA for Early Cancer Detection

2018 ◽  
Vol 3 (2) ◽  
pp. 300-313 ◽  
Author(s):  
Clare Fiala ◽  
Vathany Kulasingam ◽  
Eleftherios P Diamandis
Keyword(s):  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
High Throughput Sequencing ◽  
Early Cancer ◽  
Genetic Material ◽  
Circulating Tumor Dna ◽  
Early Cancer Detection ◽  
Blood Draw ◽  
Early Stage Disease ◽  
Tumor Dna

AbstractBackgroundCancer cells release circulating tumor DNA (ctDNA) into the bloodstream, which can now be quantified and examined using novel high-throughput sequencing technologies. This has led to the emergence of the “liquid biopsy,” which proposes to analyze this genetic material and extract information on a patient's cancer using a simple blood draw.ContentctDNA has been detected in many advanced cancers. It has also been proven to be a highly sensitive indicator of relapse and prognosis. Sequencing the genetic material has also led to the discovery of mutations targetable by existing therapies. Although ctDNA screening is more expensive, it is showing promise against circulating tumor cells and traditional cancer biomarkers. ctDNA has also been detected in other bodily fluids, including cerebrospinal fluid, urine, saliva, and stool. The utility of ctDNA for early cancer detection is being studied. However, a blood test for cancer faces heavy obstacles, such as extremely low ctDNA concentrations in early-stage disease and benign mutations caused by clonal hematopoiesis, causing both sensitivity and specificity concerns. Nonetheless, companies and academic laboratories are highly active in developing such a test.ConclusionCurrently, ctDNA is unlikely to perform at the high level of sensitivity and specificity required for early diagnosis and population screening. However, ctDNA in blood and other fluids has important clinical applications for cancer monitoring, prognosis, and selection of therapy that require further investigation.

scholarly journals Circulating tumor DNA as an early cancer detection tool

2020 ◽  
Vol 207 ◽  
pp. 107458 ◽  
Author(s):  
Andrea Campos-Carrillo ◽  
Jeffrey N. Weitzel ◽  
Prativa Sahoo ◽  
Russell Rockne ◽  
Janet V. Mokhnatkin ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Early Cancer ◽  
Circulating Tumor Dna ◽  
Early Cancer Detection ◽  
Detection Tool ◽  
Tumor Dna

scholarly journals Frequent tumor burden monitoring of esophageal squamous cell carcinoma with circulating tumor DNA using individually designed digital PCR

2020 ◽  
Author(s):  
T. Iwaya ◽  
F. Endo ◽  
M. Yaegashi ◽  
N. Sasaki ◽  
R. Fujisawa ◽  
...  
Keyword(s):  
Squamous Cell ◽  
Mutation Screening ◽  
Cell Cancer ◽  
Daily Practice ◽  
Digital Pcr ◽  
Circulating Tumor Dna ◽  
Tumor Burden ◽  
Stage I ◽  
Positive Rate ◽  
Tumor Dna

BackgroundCirculating tumor DNA (ctDNA) test has not yet been an established tool for monitoring cancer. Sensitive, yet affordable methods should allow frequent ctDNA monitoring that can assist in clinical management.Patients and MethodsThis prospective observational study was conducted in a total of 36 patients with Stage I to IV esophageal squamous cell cancer (ESCC) were enrolled between September 1, 2015 and February 28, 2018. We investigated whether frequent ctDNA monitoring during treatment followed by routine surveillance by digital PCR (dPCR) using tumor-specific mutations offers clinical validity in daily practice for ESCC patients.ResultsMutation screening of tumors from analyzable 35 patients using a specifically-designed "SCC panel" revealed 221 mutations with variant allele frequency (VAF) >2%. VAF of ctDNA was informative in 34 patients surveillance by dPCR using 58 mutations (1-3 per patient). A total of 569 plasma samples at 332 time points for ctDNA testing were evaluated. In pretreatment plasma, the average VAF was higher in advanced stages than earlier stages (P < .0001); tumor volume was also higher for higher VAF (r = 0.71). The ctDNA-positive rate in the pretreatment plasma of stage II or higher was 85.2% (23/27) whereas 85.7% (6/7) stage I were below the detection limit. Ninety-one % (10/11) patients whose ctDNA increased during chemotherapy showed disease progression. Among patients who recurred, ctDNA elevated with a median lead time of 149 days to the imaging diagnosis. Patients with decreased ctDNA within 3 months of initial treatment (n = 10) showed significantly better outcomes than did patients with ctDNA-positive (n = 11; P < .0001, HR 0.10, 95% CI, 0.03-0.30).ConclusionsOur results indicate that frequent tumor burden monitoring using a small number of tumor-specific ctDNAs by dPCR enables prediction of relapse and chemotherapeutic efficacy, as well as relapse-free corroboration in management of ESCC patients.

scholarly journals A mathematical model of ctDNA shedding predicts tumor detection size

2020 ◽  
Author(s):  
Stefano Avanzini ◽  
David M. Kurtz ◽  
Jacob J. Chabon ◽  
Everett J. Moding ◽  
Sharon Seiko Hori ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Cancer Detection ◽  
Lung Tumor ◽  
Early Cancer ◽  
Early Stage ◽  
A Priori ◽  
Circulating Tumor Dna ◽  
Early Cancer Detection ◽  
Tumor Evolution ◽  
Early Stage Lung Cancer

AbstractEarly cancer detection aims to find tumors before they progress to an incurable stage. We developed a stochastic mathematical model of tumor evolution and circulating tumor DNA (ctDNA) shedding to determine the potential and the limitations of cancer early detection tests. We inferred normalized ctDNA shedding rates from 176 early stage lung cancer subjects and calculated that a 15 mL blood sample contains on average 1.7 genome equivalents of ctDNA for lung tumors with a volume of 1 cm3. For annual screening, the model predicts median detection sizes between 3.8 and 6.6 cm3 corresponding to lead times between 310 and 450 days compared to current lung tumor sizes at diagnosis. For monthly cancer relapse testing based on 20 a priori known mutations, the model predicts a median detection size of 0.26 cm3 corresponding to a lead time of 150 days. This mechanistic framework can help to optimize early cancer detection approaches.

Hybrid Deep Transfer Network and Rotational Sample Subspace Ensemble Learning for Early Cancer Detection

2020 ◽  
Vol 10 (10) ◽  
pp. 2289-2296
Author(s):  
Pin Wang ◽  
Shanshan Lv ◽  
Yongming Li ◽  
Qi Song ◽  
Linyu Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Ensemble Learning ◽  
Cancer Detection ◽  
Early Cancer ◽  
Early Cancer Detection ◽  
Mice Model ◽  
Early Cancer Diagnosis ◽  
Histopathology Images

Accurate histopathology cell image classification plays an important role in early cancer detection and diagnosis. Currently, Convolutional Neural Network is used to assist pathologists for histopathology image classification. In the paper, a Min mice model was applied to evaluate the capability of Convolutional Neural Network features for detecting early-stage carcinogenesis. However, due to the limited histopathology images of the mice model, it may cause overfitting for the classification. Hence, hybrid deep transfer network and rotational sample subspace ensemble learning is proposed for the histopathology image classification. First, deep features are obtained by deep transfer network based on regularized loss functions. Then, the rotational sample subspace sampling is applied to increase the diversity between training sets. Subsequently, subspace projection learning is introduced to achieve dimensionality reduction. Finally, the ensemble learning is used for histopathology image classification. The proposed method was tested on 126 histopathology images of the mouse model. The experimental results demonstrate that the proposed method has achieved a remarkable classification accuracy (99.39%, 99.74%, 100%). It has demonstrated that the proposed approach is promising for early cancer diagnosis.

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