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Detection of minimal residual disease in patients with cancer, who are in complete remission with no cancer cells detectable, has the potential to improve recurrence-free survival through treatment selection. Studies analyzing circulating tumor DNA (ctDNA) in patients with solid tumors suggest the potential to accurately predict and detect relapse, enabling treatment strategies that may improve clinical outcomes. Over the past decade, assays for ctDNA detection in plasma samples have steadily increased in sensitivity and specificity. These are applied for the detection of residual disease after treatment and for earlier detection of recurrence. Novel clinical trials are now assessing how assays for "residual disease and recurrence" (RDR) may influence current treatment paradigms and potentially change the landscape of risk classification for cancer recurrence. In this review, we appraise the progress of RDR detection using ctDNA and consider the emerging role of liquid biopsy in the monitoring and management of solid tumors., Competing Interests: Declaration of interests J.C.M.W. and N.R. are inventors/contributors on patents covering methods for ctDNA detection (including WO/2020/104670). P.R. reports institutional research funding from Novartis, Grail/Illumina, ArcherDx/Invitae, Inivata, Epic Sciences, and Tempus; advisory board/honoraria from Novartis, AstraZeneca, Foundation Medicine, Pfizer, Epic Sciences, Inivata, and Natera; and consulting for Tempus. Y.-S.Y. reports honoraria/consultancy from Novartis, Lilly, Pfizer, AstraZeneca, Eisai, MSD, and Specialised Therapeutics and travel support from Pfizer, Eisai, and Roche. S.-J.D. has received research funding from Roche Genentech and Cancer Therapeutics (CTx) CRC and has been an advisory board member for AstraZeneca and Inivata. S.K. serves on the advisory board and is a consultant for Inivata and Natera. N.R. and C.D.M. are officers of Inivata. T.I.M., P.R., E.L.M., R.G., I.T., Y.-S.Y., W.A.R., B.B., A.B., and J.T. served on the advisory board and consulted for Inivata., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be improved by analyzing hundreds to thousands of mutations that are first identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) pipeline, which combines custom error-suppression methods and signal-enrichment approaches based on biological features of ctDNA. With this approach, the detection limit in each sample can be estimated independently based on the number of informative reads sequenced across multiple patient-specific loci. We applied INVAR to custom hybrid-capture sequencing data from 176 plasma samples from 105 patients with melanoma, lung, renal, glioma, and breast cancer across both early and advanced disease. By integrating signal across a median of >10 5 informative reads, ctDNA was routinely quantified to 1 mutant molecule per 100,000, and in some cases with high tumor mutation burden and/or plasma input material, to parts per million. This resulted in median area under the curve (AUC) values of 0.98 in advanced cancers and 0.80 in early-stage and challenging settings for ctDNA detection. We generalized this method to whole-exome and whole-genome sequencing, showing that INVAR may be applied without requiring personalized sequencing panels so long as a tumor mutation list is available. As tumor sequencing becomes increasingly performed, such methods for personalized cancer monitoring may enhance the sensitivity of cancer liquid biopsies., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Background: Recent advances in the study and clinical applications of circulating tumor DNA (ctDNA) are limited by practical considerations of sample collection. Whole-genome sequencing (WGS) is increasingly used for analysis of ctDNA, identifying copy-number alterations and fragmentation patterns. We hypothesized that low-depth/shallow WGS (sWGS) data may be generated from minute amounts of cell-free DNA, and that fragment-size selection may remove contaminating genomic DNA from small blood volumes. Dried blood spots have practical advantages for sample collection, may facilitate serial sampling, and could support novel study designs in humans and animal models., Methods: We developed a protocol for the isolation and analysis of cell-free DNA from dried blood spots using filter paper cards and bead-based size selection. DNA extracted and size-selected from dried spots was analyzed using sWGS and polymerase chain reaction (PCR)., Results: Analyzing a 50 μL dried blood spot from frozen whole blood of a patient with melanoma, we identified ctDNA based on the presence of tumor-specific somatic copy-number alterations, and found a fragment-size profile similar to that observed in plasma DNA. We found alterations in different chromosomes in blood spots from 2 patients with high-grade serous ovarian carcinoma. Extending this approach to serial dried blood spots from mouse xenograft models, we detect tumor-derived cell-free DNA and identified ctDNA from the originally grafted ascites., Conclusion: Our data suggest that ctDNA can be detected and monitored in dried blood spots from archived and fresh blood samples, enabling new approaches for sample collection and novel study/trial designs for both patients and in vivo models., (© American Association for Clinical Chemistry 2020.)

Background: Cell-free tumor-derived DNA (ctDNA) allows non-invasive monitoring of cancers, but its utility in renal cell cancer (RCC) has not been established., Methods: Here, a combination of untargeted and targeted sequencing methods, applied to two independent cohorts of patients (n = 91) with various renal tumor subtypes, were used to determine ctDNA content in plasma and urine., Results: Our data revealed lower plasma ctDNA levels in RCC relative to other cancers of similar size and stage, with untargeted detection in 27.5% of patients from both cohorts. A sensitive personalized approach, applied to plasma and urine from select patients (n = 22) improved detection to ~ 50%, including in patients with early-stage disease and even benign lesions. Detection in plasma, but not urine, was more frequent amongst patients with larger tumors and in those patients with venous tumor thrombus. With data from one extensively characterized patient, we observed that plasma and, for the first time, urine ctDNA may better represent tumor heterogeneity than a single tissue biopsy. Furthermore, in a subset of patients (n = 16), longitudinal sampling revealed that ctDNA can track disease course and may pre-empt radiological identification of minimal residual disease or disease progression on systemic therapy. Additional datasets will be required to validate these findings., Conclusions: These data highlight RCC as a ctDNA-low malignancy. The biological reasons for this are yet to be determined. Nonetheless, our findings indicate potential clinical utility in the management of patients with renal tumors, provided improvement in isolation and detection approaches.

Existing methods to improve detection of circulating tumor DNA (ctDNA) have focused on genomic alterations but have rarely considered the biological properties of plasma cell-free DNA (cfDNA). We hypothesized that differences in fragment lengths of circulating DNA could be exploited to enhance sensitivity for detecting the presence of ctDNA and for noninvasive genomic analysis of cancer. We surveyed ctDNA fragment sizes in 344 plasma samples from 200 patients with cancer using low-pass whole-genome sequencing (0.4×). To establish the size distribution of mutant ctDNA, tumor-guided personalized deep sequencing was performed in 19 patients. We detected enrichment of ctDNA in fragment sizes between 90 and 150 bp and developed methods for in vitro and in silico size selection of these fragments. Selecting fragments between 90 and 150 bp improved detection of tumor DNA, with more than twofold median enrichment in >95% of cases and more than fourfold enrichment in >10% of cases. Analysis of size-selected cfDNA identified clinically actionable mutations and copy number alterations that were otherwise not detected. Identification of plasma samples from patients with advanced cancer was improved by predictive models integrating fragment length and copy number analysis of cfDNA, with area under the curve (AUC) >0.99 compared to AUC <0.80 without fragmentation features. Increased identification of cfDNA from patients with glioma, renal, and pancreatic cancer was achieved with AUC > 0.91 compared to AUC < 0.5 without fragmentation features. Fragment size analysis and selective sequencing of specific fragment sizes can boost ctDNA detection and could complement or provide an alternative to deeper sequencing of cfDNA., (Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Mutational signatures accumulate in somatic cells as an admixture of endogenous and exogenous processes that occur during an individual's lifetime. Since dividing cells release cell-free DNA (cfDNA) fragments into the circulation, we hypothesize that plasma cfDNA might reflect mutational signatures. Point mutations in plasma whole genome sequencing (WGS) are challenging to identify through conventional mutation calling due to low sequencing coverage and low mutant allele fractions. In this proof of concept study of plasma WGS at 0.3–1.5x coverage from 215 patients and 227 healthy individuals, we show that both pathological and physiological mutational signatures may be identified in plasma. By applying machine learning to mutation profiles, patients with stage I-IV cancer can be distinguished from healthy individuals with an Area Under the Curve of 0.96. Interrogating mutational processes in plasma may enable earlier cancer detection, and might enable the assessment of cancer risk and etiology. Detection of mutational signatures in cell-free DNA (cfDNA) is challenging due to low sequence coverage and low mutant allele fractions. Here, the authors identify mutational signatures in plasma whole genome sequencing of cancer patients and use machine learning to distinguish them from healthy individuals. [ABSTRACT FROM AUTHOR]

Glioma‐derived cell‐free DNA (cfDNA) is challenging to detect using liquid biopsy because quantities in body fluids are low. We determined the glioma‐derived DNA fraction in cerebrospinal fluid (CSF), plasma, and urine samples from patients using sequencing of personalized capture panels guided by analysis of matched tumor biopsies. By sequencing cfDNA across thousands of mutations, identified individually in each patient's tumor, we detected tumor‐derived DNA in the majority of CSF (7/8), plasma (10/12), and urine samples (10/16), with a median tumor fraction of 6.4 × 10−3, 3.1 × 10−5, and 4.7 × 10−5, respectively. We identified a shift in the size distribution of tumor‐derived cfDNA fragments in these body fluids. We further analyzed cfDNA fragment sizes using whole‐genome sequencing, in urine samples from 35 glioma patients, 27 individuals with non‐malignant brain disorders, and 26 healthy individuals. cfDNA in urine of glioma patients was significantly more fragmented compared to urine from patients with non‐malignant brain disorders (P = 1.7 × 10−2) and healthy individuals (P = 5.2 × 10−9). Machine learning models integrating fragment length could differentiate urine samples from glioma patients (AUC = 0.80–0.91) suggesting possibilities for truly non‐invasive cancer detection. SYNOPSIS: Gliomas are challenging to detect using tumor derived cell‐free DNA (cfDNA) in body fluids. In this study, two novel analysis methods (tumor‐guided sequencing and sWGS) were developed to explore the potential of using plasma and urine cfDNA as a liquid biopsy for this malignancy. Multiple tumor regions were sequenced to recover a high number of mutations for designing tumor‐guided sequencing panels.Using tumor‐guided sequencing and the INVAR analysis approach, mutations were detected in 7/8 CSF, 10/12 plasma and 10/16 urine gliomas samples.Using low coverage whole genome sequencing, cfDNA fragmentation patterns were analysed in urine samples from 35 glioma patients, 27 individuals with non‐malignant brain disorders, and 26 healthy individuals.Fragment lengths differed significantly between these groups; Machine learning models (LR, SVM, RF, GLMEN) integrating fragment length could differentiate urine samples from glioma patients (AUC = 0.80–0.91). [ABSTRACT FROM AUTHOR]

Simple Summary: The analysis of circulating tumor DNA (ctDNA) concentrations in blood plasma and the radiomic analysis of tumor images (i.e., quantification of textural features on medical imaging) have both been used to provide information about cancer progression. The purpose of this study was to assess a link between these two different modalities in order to determine whether results from one can be used to predict outcomes from the other. The results show that radiomics features can predict ctDNA levels in patients with metastatic melanoma even when controlling for confounding influences such as tumor volume. This establishes the potential for new biomarkers of tumor progression that could combine the specificity of ctDNA assays with the high-resolution spatial information obtained by imaging. Clinical imaging methods, such as computed tomography (CT), are used for routine tumor response monitoring. Imaging can also reveal intratumoral, intermetastatic, and interpatient heterogeneity, which can be quantified using radiomics. Circulating tumor DNA (ctDNA) in the plasma is a sensitive and specific biomarker for response monitoring. Here we evaluated the interrelationship between circulating tumor DNA mutant allele fraction (ctDNAmaf), obtained by targeted amplicon sequencing and shallow whole genome sequencing, and radiomic measurements of CT heterogeneity in patients with stage IV melanoma. ctDNAmaf and radiomic observations were obtained from 15 patients with a total of 70 CT examinations acquired as part of a prospective trial. 26 of 39 radiomic features showed a significant relationship with log(ctDNAmaf). Principal component analysis was used to define a radiomics signature that predicted ctDNAmaf independent of lesion volume. This radiomics signature and serum lactate dehydrogenase were independent predictors of ctDNAmaf. Together, these results suggest that radiomic features and ctDNAmaf may serve as complementary clinical tools for treatment monitoring. [ABSTRACT FROM AUTHOR]