ctDNA monitoring using patient-specific sequencing and integration of variant reads

Beyond basics: Key mutation selection features for successful tumor-informed ctDNA detection

Tumor-informed mutation-based approaches are frequently used for detection of circulating tumor DNA (ctDNA). Not all mutations make equally effective ctDNA markers. The objective was to explore if prioritizing mutations using mutational features-such as cancer cell fraction (CCF), multiplicity, and error rate-would improve the success rate of tumor-informed ctDNA analysis. Additionally, we aimed to develop a practical and easily implementable analysis pipeline for identifying and prioritizing candidate mutations from whole-exome sequencing (WES) data. We analyzed WES and ctDNA data from three tumor-informed ctDNA studies, one on bladder cancer (Cohort A) and two on colorectal cancer (Cohorts I and N). The studies included 390 patients. For each patient, a unique set of mutations (median mutations/patient: 6, interquartile 13, range: 1-46, total n = 4023) were used as markers of ctDNA. The tool PureCN was used to assess the CCF and multiplicity of each mutation. High-CCF mutations were detected more frequently than low-CCF mutations (Cohort A: odds ratio [OR] 20.6, 95% confidence interval [CI] 5.72-173, p = 1.73e-12; Cohort I: OR 2.24, 95% CI 1.44-3.52, p = 1.66e-04; and Cohort N: OR 1.78, 95% CI 1.14-2.79, p = 7.86e-03). The detection-likelihood was additionally improved by selecting mutations with multiplicity of two or above (Cohort A: OR 1.55, 95% CI 1. 14-2.11, p = 3.85e-03; Cohort I: OR 1.78, 95% CI 1.23-2.56, p = 1.34e-03; and Cohort N: OR 1.94, 95% CI 1.63-2.31, p = 2.83e-14). Furthermore, selecting the mutations for which the ctDNA detection method had the lowest error rates, additionally improved the detection-likelihood, particularly evident when plasma cell-free DNA tumor fractions were below 0.1% (p = 2.1e-07). Selecting mutational markers with high CCF, high multiplicity, and low error rate significantly improve ctDNA detection likelihood. We provide free access to the analysis pipeline enabling others to perform qualified prioritization of mutations for tumor-informed ctDNA analysis.

Use of ctDNA in early breast cancer: analytical validity and clinical potential

Circulating free tumor DNA (ctDNA) analysis is gaining popularity in precision oncology, particularly in metastatic breast cancer, as it provides non-invasive, real-time tumor information to complement tissue biopsies, allowing for tailored treatment strategies and improved patient selection in clinical trials. Its use in early breast cancer has been limited so far, due to the relatively low sensitivity of available techniques in a setting characterized by lower levels of ctDNA shedding. However, advances in sequencing and bioinformatics, as well as the use of methylome profiles, have led to an increasing interest in the application of ctDNA analysis in early breast cancer, from screening to curative treatment evaluation and minimal residual disease (MRD) detection. With multiple prospective clinical trials in this setting, ctDNA evaluation may become useful in clinical practice. This article reviews the data regarding the analytical validity of the currently available tests for ctDNA detection and the clinical potential of ctDNA analysis in early breast cancer.

Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment

In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole-genome sequencing (WGS). Here we now introduce MRD-EDGE, a machine-learning-guided WGS ctDNA single-nucleotide variant (SNV) and copy-number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGESNV uses deep learning and a ctDNA-specific feature space to increase SNV signal-to-noise enrichment in WGS by ~300× compared to previous WGS error suppression. MRD-EDGECNV also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1 Gb to 200 Mb, vastly expanding its applicability within solid tumors. We harness the improved performance to identify MRD following surgery in multiple cancer types, track changes in TF in response to neoadjuvant immunotherapy in lung cancer and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal-to-noise enrichment in MRD-EDGESNV enables plasma-only (non-tumor-informed) disease monitoring in advanced melanoma and lung cancer, yielding clinically informative TF monitoring for patients on immune-checkpoint inhibition.

Analysis of cell free DNA to predict outcome to bevacizumab therapy in colorectal cancer patients

To predict outcome to combination bevacizumab (BVZ) therapy, we employed cell-free DNA (cfDNA) to determine chromosomal instability (CIN), nucleosome footprints (NF) and methylation profiles in metastatic colorectal cancer (mCRC) patients. Low-coverage whole-genome sequencing (LC-WGS) was performed on matched tumor and plasma samples, collected from 74 mCRC patients from the AC-ANGIOPREDICT Phase II trial (NCT01822444), and analysed for CIN and NFs. A validation cohort of plasma samples from the University Medical Center Mannheim (UMM) was similarly profiled. 61 AC-ANGIOPREDICT plasma samples collected before and following BVZ treatment were selected for targeted methylation sequencing. Using cfDNA CIN profiles, AC-ANGIOPREDICT samples were subtyped with 92.3% accuracy into low and high CIN clusters, with good concordance observed between matched plasma and tumor. Improved survival was observed in CIN-high patients. Plasma-based CIN clustering was validated in the UMM cohort. Methylation profiling identified differences in CIN-low vs. CIN high (AUC = 0.87). Moreover, significant methylation score decreases following BVZ was associated with improved outcome (p = 0.013). Analysis of CIN, NFs and methylation profiles from cfDNA in plasma samples facilitates stratification into CIN clusters which inform patient response to treatment.

Extracellular Vesicles for Childhood Cancer Liquid Biopsy

Liquid biopsy involves the utilization of minimally invasive or noninvasive techniques to detect biomarkers in biofluids for disease diagnosis, monitoring, or guiding treatments. This approach is promising for the early diagnosis of childhood cancer, especially for brain tumors, where tissue biopsies are more challenging and cause late detection. Extracellular vesicles offer several characteristics that make them ideal resources for childhood cancer liquid biopsy. Extracellular vesicles are nanosized particles, primarily secreted by all cell types into body fluids such as blood and urine, and contain molecular cargos, i.e., lipids, proteins, and nucleic acids of original cells. Notably, the lipid bilayer-enclosed structure of extracellular vesicles protects their cargos from enzymatic degradation in the extracellular milieu. Proteins and nucleic acids of extracellular vesicles represent genetic alterations and molecular profiles of childhood cancer, thus serving as promising resources for precision medicine in cancer diagnosis, treatment monitoring, and prognosis prediction. This review evaluates the recent progress of extracellular vesicles as a liquid biopsy platform for various types of childhood cancer, discusses the mechanistic roles of molecular cargos in carcinogenesis and metastasis, and provides perspectives on extracellular vesicle-guided therapeutic intervention. Extracellular vesicle-based liquid biopsy for childhood cancer may ultimately contribute to improving patient outcomes.

Imaging and Liquid Biopsy for Distinguishing True Progression From Pseudoprogression in Gliomas, Current Advances and Challenges

RATIONALE AND OBJECTIVES

Gliomas are aggressive brain tumors with a poor prognosis. Assessing treatment response is challenging because magnetic resonance imaging (MRI) may not distinguish true progression (TP) from pseudoprogression (PsP). This review aims to discuss imaging techniques and liquid biopsies used to distinguish TP from PsP.

MATERIALS AND METHODS

This review synthesizes existing literature to examine advances in imaging techniques, such as magnetic resonance diffusion imaging (MRDI), perfusion-weighted imaging (PWI) MRI, and liquid biopsies, for identifying TP or PsP through tumor markers and tissue characteristics.

RESULTS

Advanced imaging techniques, including MRDI and PWI MRI, have proven effective in delineating tumor tissue properties, offering valuable insights into glioma behavior. Similarly, liquid biopsy has emerged as a potent tool for identifying tumor-derived markers in biofluids, offering a non-invasive glimpse into tumor evolution. Despite their promise, these methodologies grapple with significant challenges. Their sensitivity remains inconsistent, complicating the accurate differentiation between TP and PSP. Furthermore, the absence of standardized protocols across platforms impedes the reliability of comparisons, while inherent biological variability adds complexity to data interpretation.

CONCLUSION

Their potential applications have been highlighted, but gaps remain before routine clinical use. Further research is needed to develop and validate these promising methods for distinguishing TP from PsP in gliomas.

Error-Corrected Deep Targeted Sequencing of Circulating Cell-Free DNA from Colorectal Cancer Patients for Sensitive Detection of Circulating Tumor DNA

Circulating tumor DNA (ctDNA) is a promising biomarker, reflecting the presence of tumor cells. Sequencing-based detection of ctDNA at low tumor fractions is challenging due to the crude error rate of sequencing. To mitigate this challenge, we developed ultra-deep mutation-integrated sequencing (UMIseq), a fixed-panel deep targeted sequencing approach, which is universally applicable to all colorectal cancer (CRC) patients. UMIseq features UMI-mediated error correction, the exclusion of mutations related to clonal hematopoiesis, a panel of normal samples for error modeling, and signal integration from single-nucleotide variations, insertions, deletions, and phased mutations. UMIseq was trained and independently validated on pre-operative (pre-OP) plasma from CRC patients (n = 364) and healthy individuals (n = 61). UMIseq displayed an area under the curve surpassing 0.95 for allele frequencies (AFs) down to 0.05%. In the training cohort, the pre-OP detection rate reached 80% at 95% specificity, while it was 70% in the validation cohort. UMIseq enabled the detection of AFs down to 0.004%. To assess the potential for detection of residual disease, 26 post-operative plasma samples from stage III CRC patients were analyzed. From this we found that the detection of ctDNA was associated with recurrence. In conclusion, UMIseq demonstrated robust performance with high sensitivity and specificity, enabling the detection of ctDNA at low allele frequencies.

Subclonal Cancer Driver Mutations Are Prevalent in the Unresected Peritumoral Edema of Adult Diffuse Gliomas

Adult diffuse gliomas commonly recur regardless of therapy. As recurrence typically arises from the peritumoral edema adjacent to the resected bulk tumor, the profiling of somatic mutations from infiltrative malignant cells within this critical, unresected region could provide important insights into residual disease. A key obstacle has been the inability to distinguish between next-generation sequencing (NGS) noise and the true but weak signal from tumor cells hidden among the noncancerous brain tissue of the peritumoral edema. Here, we developed and validated True2 sequencing to reduce NGS-associated errors to <1 false positive/100 kb panel positions while detecting 97.6% of somatic mutations with an allele frequency ≥0.1%. True2 was then used to study the tumor and peritumoral edema of 22 adult diffuse gliomas including glioblastoma, astrocytoma, oligodendroglioma, and NF1-related low-grade neuroglioma. The tumor and peritumoral edema displayed a similar mutation burden, indicating that surgery debulks these cancers physically but not molecularly. Moreover, variants in the peritumoral edema included unique cancer driver mutations absent in the bulk tumor. Finally, analysis of multiple samples from each patient revealed multiple subclones with unique mutations in the same gene in 17 of 22 patients, supporting the occurrence of convergent evolution in response to patient-specific selective pressures in the tumor microenvironment that may form the molecular foundation of recurrent disease. Collectively, True2 enables the detection of ultralow frequency mutations during molecular analyses of adult diffuse gliomas, which is necessary to understand cancer evolution, recurrence, and individual response to therapy.

SIGNIFICANCE

True2 is a next-generation sequencing workflow that facilitates unbiased discovery of somatic mutations across the full range of variant allele frequencies, which could help identify residual disease vulnerabilities for targeted adjuvant therapies.

Mutation detection in saliva from oral cancer patients

OBJECTIVES

The incidence of head and neck squamous cell carcinoma (HNSCC) continues to increase and although advances have been made in treatment, it still has a poor overall survival with local relapse being common. Conventional imaging methods are not efficient at detecting recurrence at an early stage when still potentially curable. The aim of this study was to test the feasibility of using saliva to detect the presence of oral squamous cell carcinoma (OSCC) and to provide additional evidence for the potential of this approach.

MATERIALS AND METHODS

Fresh tumor, whole blood and saliva were collected from patients with OSCC before treatment. Whole exome sequencing (WES) or gene panel sequencing of tumor DNA was performed to identify somatic mutations in tumors and to select genes for performing gene panel sequencing on saliva samples.

RESULTS

The most commonly mutated genes identified in primary tumors by DNA sequencing were TP53 and FAT1. Gene panel sequencing of paired saliva samples detected tumor derived mutations in 9 of 11 (82%) patients. The mean variant allele frequency for the mutations detected in saliva was 0.025 (range 0.004 - 0.061).

CONCLUSION

Somatic tumor mutations can be detected in saliva with high frequency in OSCC irrespective of site or stage of disease using a limited panel of genes. This work provides additional evidence for the suitability of using saliva as liquid biopsy in OSCC and has the potential to improve early detection of recurrence in OSCC. Trials are currently underway comparing this approach to standard imaging techniques.

Unlocking Precision Medicine: Liquid Biopsy Advancements in Renal Cancer Detection and Monitoring

Renal cell carcinoma (RCC) remains a formidable diagnostic challenge, especially in the context of small renal masses. The quest for non-invasive screening tools and biomarkers has steered research towards liquid biopsy, focusing on microRNAs (miRNAs), exosomes, and circulating tumor cells (CTCs). MiRNAs, small non-coding RNAs, exhibit notable dysregulation in RCC, offering promising avenues for diagnosis and prognosis. Studies underscore their potential across various biofluids, including plasma, serum, and urine, for RCC detection and subtype characterization. Encouraging miRNA signatures show correlations with overall survival, indicative of their future relevance in RCC management. Exosomes, with their diverse molecular cargo, including miRNAs, emerge as enticing biomarkers, while CTCs, emanating from primary tumors into the bloodstream, provide valuable insights into cancer progression. Despite these advancements, clinical translation necessitates further validation and standardization, encompassing larger-scale studies and robust evidence generation. Currently lacking approved diagnostic assays for renal cancer, the potential future applications of liquid biopsy in follow-up care, treatment selection, and outcome prediction in RCC patients are profound. This review aims to discuss and highlight recent advancements in liquid biopsy for RCC, exploring their strengths and weaknesses in the comprehensive management of this disease.

Chromatin- and nucleosome-associated features in liquid biopsy: implications for cancer biomarker discovery

Cell-free DNA (cfDNA) from the bloodstream has been studied for cancer biomarker discovery, and chromatin-derived epigenetic features have come into the spotlight for their potential to expand clinical applications. Methylation, fragmentation, and nucleosome positioning patterns of cfDNA have previously been shown to reveal epigenomic and inferred transcriptomic information. More recently, histone modifications have emerged as a tool to further identify tumor-specific chromatin variants in plasma. A number of sequencing methods have been developed to analyze these epigenetic markers, offering new insights into tumor biology. Features within cfDNA allow for cancer detection, subtype and tissue of origin classification, and inference of gene expression. These methods provide a window into the complexity of cancer and the dynamic nature of its progression. In this review, we highlight the array of epigenetic features in cfDNA that can be extracted from chromatin- and nucleosome-associated organization and outline potential use cases in cancer management.

Priming agents transiently reduce the clearance of cell-free DNA to improve liquid biopsies

Liquid biopsies enable early detection and monitoring of diseases such as cancer, but their sensitivity remains limited by the scarcity of analytes such as cell-free DNA (cfDNA) in blood. Improvements to sensitivity have primarily relied on enhancing sequencing technology ex vivo. We sought to transiently augment the level of circulating tumor DNA (ctDNA) in a blood draw by attenuating its clearance in vivo. We report two intravenous priming agents given 1 to 2 hours before a blood draw to recover more ctDNA. Our priming agents consist of nanoparticles that act on the cells responsible for cfDNA clearance and DNA-binding antibodies that protect cfDNA. In tumor-bearing mice, they greatly increase the recovery of ctDNA and improve the sensitivity for detecting small tumors.

Multi-modal cell-free DNA genomic and fragmentomic patterns enhance cancer survival and recurrence analysis

The structure of cell-free DNA (cfDNA) is altered in the blood of patients with cancer. From whole-genome sequencing, we retrieve the cfDNA fragment-end composition using a new software (FrEIA [fragment end integrated analysis]), as well as the cfDNA size and tumor fraction in three independent cohorts (n = 925 cancer from >10 types and 321 control samples). At 95% specificity, we detect 72% cancer samples using at least one cfDNA measure, including 64% early-stage cancer (n = 220). cfDNA detection correlates with a shorter overall (p = 0.0086) and recurrence-free (p = 0.017) survival in patients with resectable esophageal adenocarcinoma. Integrating cfDNA measures with machine learning in an independent test set (n = 396 cancer, 90 controls) achieve a detection accuracy of 82% and area under the receiver operating characteristic curve of 0.96. In conclusion, harnessing the biological features of cfDNA can improve, at no extra cost, the diagnostic performance of liquid biopsies.

A randomised phase 2 study of intermittent versus continuous dosing of dabrafenib plus trametinib in patients with BRAFV600 mutant advanced melanoma (INTERIM)

BACKGROUND

BRAF+MEK inhibitors extend life expectancy of patients with BRAFV600 mutant advanced melanoma. Acquired resistance limits duration of benefit, but preclinical and case studies suggest intermittent dosing could overcome this limitation. INTERIM was a phase 2 trial evaluating an intermittent dosing regimen.

METHODS

Patients with BRAFV600 mutant advanced melanoma due to start dabrafenib+trametinib were randomised to receive either continuous (CONT), or intermittent (INT; dabrafenib d1-21, trametinib d1-14 every 28 days) dosing. A composite primary endpoint included progression-free survival (PFS) and quality of life (QoL). Secondary endpoints included response rate (ORR), overall survival (OS) and adverse events (AEs). Mutant BRAFV600E ctDNA was measured by droplet digital PCR (ddPCR), using mutant allele frequency of > 1 % as the detection threshold.

RESULTS

79 patients (39 INT, 40 CONT) were recruited; median age 67 years, 65 % AJCC (7th ed) stage IV M1c, 29 % had brain metastases. With 19 months median follow-up, INT was inferior in all efficacy measures: median PFS 8.5 vs 10.7mo (HR 1.39, 95 %CI 0.79-2.45, p = 0.255); median OS 18.1mo vs not reached (HR 1.69, 95 %CI 0.87-3.28, p = 0.121), ORR 57 % vs 77 %. INT patients experienced fewer treatment-related AEs (76 % vs 88 %), but more grade > 3 AEs (53 % vs 42 %). QoL favoured CONT. Detection of BRAFV600E ctDNA prior to treatment correlated with worse OS (HR 2.55, 95 %CI 1.25-5.21, p = 0.01) in both arms. A change to undetected during treatment did not significantly predict better OS.

CONCLUSION

INTERIM findings are consistent with other recent clinical trials reporting that intermittent dosing does not improve efficacy of BRAF+MEK inhibitors.

A Novel Tissue-Free Method to Estimate Tumor-Derived Cell-Free DNA Quantity Using Tumor Methylation Patterns

Estimating the abundance of cell-free DNA (cfDNA) fragments shed from a tumor (i.e., circulating tumor DNA (ctDNA)) can approximate tumor burden, which has numerous clinical applications. We derived a novel, broadly applicable statistical method to quantify cancer-indicative methylation patterns within cfDNA to estimate ctDNA abundance, even at low levels. Our algorithm identified differentially methylated regions (DMRs) between a reference database of cancer tissue biopsy samples and cfDNA from individuals without cancer. Then, without utilizing matched tissue biopsy, counts of fragments matching the cancer-indicative hyper/hypo-methylated patterns within DMRs were used to determine a tumor methylated fraction (TMeF; a methylation-based quantification of the circulating tumor allele fraction and estimate of ctDNA abundance) for plasma samples. TMeF and small variant allele fraction (SVAF) estimates of the same cancer plasma samples were correlated (Spearman's correlation coefficient: 0.73), and synthetic dilutions to expected TMeF of 10-3 and 10-4 had estimated TMeF within two-fold for 95% and 77% of samples, respectively. TMeF increased with cancer stage and tumor size and inversely correlated with survival probability. Therefore, tumor-derived fragments in the cfDNA of patients with cancer can be leveraged to estimate ctDNA abundance without the need for a tumor biopsy, which may provide non-invasive clinical approximations of tumor burden.

Real-time analysis of the cancer genome and fragmentome from plasma and urine cell-free DNA using nanopore sequencing

Cell-free DNA (cfDNA) can be isolated and sequenced from blood and/or urine of cancer patients. Conventional short-read sequencing lacks deployability and speed and can be biased for short cfDNA fragments. Here, we demonstrate that with Oxford Nanopore Technologies (ONT) sequencing we can achieve delivery of genomic and fragmentomic data from liquid biopsies. Copy number aberrations and cfDNA fragmentation patterns can be determined in less than 24 h from sample collection. The tumor-derived cfDNA fraction calculated from plasma of lung cancer patients and urine of bladder cancer patients was highly correlated (R = 0.98) with the tumor fraction calculated from short-read sequencing of the same samples. cfDNA size profile, fragmentation patterns, fragment-end composition, and nucleosome profiling near transcription start sites in plasma and urine exhibited the typical cfDNA features. Additionally, a high proportion of long tumor-derived cfDNA fragments (> 300 bp) are recovered in plasma and urine using ONT sequencing. ONT sequencing is a cost-effective, fast, and deployable approach for obtaining genomic and fragmentomic results from liquid biopsies, allowing the analysis of previously understudied cfDNA populations.

Liquid biopsies and those three little words: finding the perfect match for the MTB

Monitoring ctDNA by liquid biopsies seems to represent the perfect match for precision oncology and its cornerstone clinical framework: the molecular tumour board (MTB). Detecting and scrutinising the success of targeted therapies or tracking and, for that matter, addressing the therapy with the evolutive nature of a tumour are some of the main advancements one considers to be important for the MTB. One challenge is correlating the estimated allele frequency of each identified genetic alteration determined by analysing the ctDNA sequencing results and matching these with the range of suitable drugs, which may limit the simultaneous treatment of all tumour variations. This limitation arises because a new biopsy would typically be required to evaluate the response to treatment. As a result, evaluating the success of MTB recommendations relies on traditional staging methods, highlighting an existing diagnostic gap. Thus, optimising liquid biopsy technology could enhance the efficacy of MTB treatment recommendations and ensuing tailored therapies. Herein, we discuss the prospect of ctDNA analyses in the molecular tumour board.

The changing face of circulating tumor DNA (ctDNA) profiling: Factors that shape the landscape of methodologies, technologies, and commercialization

Liquid biopsies, in particular the profiling of circulating tumor DNA (ctDNA), have long held promise as transformative tools in cancer precision medicine. Despite a prolonged incubation phase, ctDNA profiling has recently experienced a strong wave of development and innovation, indicating its imminent integration into the cancer management toolbox. Various advancements in mutation-based ctDNA analysis methodologies and technologies have greatly improved sensitivity and specificity of ctDNA assays, such as optimized preanalytics, size-based pre-enrichment strategies, targeted sequencing, enhanced library preparation methods, sequencing error suppression, integrated bioinformatics and machine learning. Moreover, research breakthroughs have expanded the scope of ctDNA analysis beyond hotspot mutational profiling of plasma-derived apoptotic, mono-nucleosomal ctDNA fragments. This broader perspective considers alternative genetic features of cancer, genome-wide characterization, classical and newly discovered epigenetic modifications, structural variations, diverse cellular and mechanistic ctDNA origins, and alternative biospecimen types. These developments have maximized the utility of ctDNA, facilitating landmark research, clinical trials, and the commercialization of ctDNA assays, technologies, and products. Consequently, ctDNA tests are increasingly recognized as an important part of patient guidance and are being implemented in clinical practice. Although reimbursement for ctDNA tests by healthcare providers still lags behind, it is gaining greater acceptance. In this work, we provide a comprehensive exploration of the extensive landscape of ctDNA profiling methodologies, considering the multitude of factors that influence its development and evolution. By illuminating the broader aspects of ctDNA profiling, the aim is to provide multiple entry points for understanding and navigating the vast and rapidly evolving landscape of ctDNA methodologies, applications, and technologies.

Current advances in detecting genetic and epigenetic biomarkers of colorectal cancer

Colorectal carcinoma (CRC) is the third most common cancer in terms of diagnosis and the second in terms of mortality. Recent studies have shown that various proteins, extracellular vesicles (i.e., exosomes), specific genetic variants, gene transcripts, cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and altered epigenetic patterns, can be used to detect, and assess the prognosis of CRC. Over the last decade, a plethora of conventional methodologies (e.g., polymerase chain reaction [PCR], direct sequencing, enzyme-linked immunosorbent assay [ELISA], microarray, in situ hybridization) as well as advanced analytical methodologies (e.g., microfluidics, electrochemical biosensors, surface-enhanced Raman spectroscopy [SERS]) have been developed for analyzing genetic and epigenetic biomarkers using both optical and non-optical tools. Despite these methodologies, no gold standard detection method has yet been implemented that can analyze CRC with high specificity and sensitivity in an inexpensive, simple, and time-efficient manner. Moreover, until now, no study has critically reviewed the advantages and limitations of these methodologies. Here, an overview of the most used genetic and epigenetic biomarkers for CRC and their detection methods are discussed. Furthermore, a summary of the major biological, technical, and clinical challenges and advantages/limitations of existing techniques is also presented.

The landscape of cell-free mitochondrial DNA in liquid biopsy for cancer detection

BACKGROUND

Existing methods to detect tumor signal in liquid biopsy have focused on the analysis of nuclear cell-free DNA (cfDNA). However, non-nuclear cfDNA and in particular mitochondrial DNA (mtDNA) has been understudied. We hypothesize that an increase in mtDNA in plasma could reflect the presence of cancer, and that leveraging cell-free mtDNA could enhance cancer detection.

RESULTS

We survey 203 healthy and 664 cancer plasma samples from three collection centers covering 12 cancer types with whole genome sequencing to catalogue the plasma mtDNA fraction. The mtDNA fraction is increased in individuals with cholangiocarcinoma, colorectal, liver, pancreatic, or prostate cancer, in comparison to that in healthy individuals. We detect almost no increase of mtDNA fraction in individuals with other cancer types. The mtDNA fraction in plasma correlates with the cfDNA tumor fraction as determined by somatic mutations and/or copy number aberrations. However, the mtDNA fraction is also elevated in a fraction of patients without an apparent increase in tumor-derived cfDNA. A predictive model integrating mtDNA and copy number analysis increases the area under the curve (AUC) from 0.73 when using copy number alterations alone to an AUC of 0.81.

CONCLUSIONS

The mtDNA signal retrieved by whole genome sequencing has the potential to boost the detection of cancer when combined with other tumor-derived signals in liquid biopsies.

Individualized tumor-informed circulating tumor DNA analysis for postoperative monitoring of non-small cell lung cancer

We report a personalized tumor-informed technology, Patient-specific pROgnostic and Potential tHErapeutic marker Tracking (PROPHET) using deep sequencing of 50 patient-specific variants to detect molecular residual disease (MRD) with a limit of detection of 0.004%. PROPHET and state-of-the-art fixed-panel assays were applied to 760 plasma samples from 181 prospectively enrolled early stage non-small cell lung cancer patients. PROPHET shows higher sensitivity of 45% at baseline with circulating tumor DNA (ctDNA). It outperforms fixed-panel assays in prognostic analysis and demonstrates a median lead-time of 299 days to radiologically confirmed recurrence. Personalized non-canonical variants account for 98.2% with prognostic effects similar to canonical variants. The proposed tumor-node-metastasis-blood (TNMB) classification surpasses TNM staging for prognostic prediction at the decision point of adjuvant treatment. PROPHET shows potential to evaluate the effect of adjuvant therapy and serve as an arbiter of the equivocal radiological diagnosis. These findings highlight the potential advantages of personalized cancer techniques in MRD detection.

Predicting tumour content of liquid biopsies from cell-free DNA

BACKGROUND

Liquid biopsy is a minimally-invasive method of sampling bodily fluids, capable of revealing evidence of cancer. The distribution of cell-free DNA (cfDNA) fragment lengths has been shown to differ between healthy subjects and cancer patients, whereby the distributional shift correlates with the sample's tumour content. These fragmentomic data have not yet been utilised to directly quantify the proportion of tumour-derived cfDNA in a liquid biopsy.

RESULTS

We used statistical learning to predict tumour content from Fourier and wavelet transforms of cfDNA length distributions in samples from 118 cancer patients. The model was validated on an independent dilution series of patient plasma.

CONCLUSIONS

This proof of concept suggests that our fragmentomic methodology could be useful for predicting tumour content in liquid biopsies.

Advances in novel strategies for isolation, characterization, and analysis of CTCs and ctDNA

Over the past decade, the detection and analysis of liquid biopsy biomarkers such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) have advanced significantly. They have received recognition for their clinical usefulness in detecting cancer at an early stage, monitoring disease, and evaluating treatment response. The emergence of liquid biopsy has been a helpful development, as it offers a minimally invasive, rapid, real-time monitoring, and possible alternative to traditional tissue biopsies. In resource-limited settings, the ideal platform for liquid biopsy should not only extract more CTCs or ctDNA from a minimal sample volume but also accurately represent the molecular heterogeneity of the patient's disease. This review covers novel strategies and advancements in CTC and ctDNA-based liquid biopsy platforms, including microfluidic applications and comprehensive analysis of molecular complexity. We discuss these systems' operational principles and performance efficiencies, as well as future opportunities and challenges for their implementation in clinical settings. In addition, we emphasize the importance of integrated platforms that incorporate machine learning and artificial intelligence in accurate liquid biopsy detection systems, which can greatly improve cancer management and enable precision diagnostics.

Exploring the Role of Circulating Cell-Free RNA in the Development of Colorectal Cancer

Circulating tumor RNA (ctRNA) has recently emerged as a novel and attractive liquid biomarker. CtRNA is capable of providing important information about the expression of a variety of target genes noninvasively, without the need for biopsies, through the use of circulating RNA sequencing. The overexpression of cancer-specific transcripts increases the tumor-derived RNA signal, which overcomes limitations due to low quantities of circulating tumor DNA (ctDNA). The purpose of this work is to present an up-to-date review of current knowledge regarding ctRNAs and their status as biomarkers to address the diagnosis, prognosis, prediction, and drug resistance of colorectal cancer. The final section of the article discusses the practical aspects involved in analyzing plasma ctRNA, including storage and isolation, detection technologies, and their limitations in clinical applications.

Comparison of tumor-informed and tumor-naïve sequencing assays for ctDNA detection in breast cancer

Analysis of circulating tumor DNA (ctDNA) to monitor cancer dynamics and detect minimal residual disease has been an area of increasing interest. Multiple methods have been proposed but few studies have compared the performance of different approaches. Here, we compare detection of ctDNA in serial plasma samples from patients with breast cancer using different tumor-informed and tumor-naïve assays designed to detect structural variants (SVs), single nucleotide variants (SNVs), and/or somatic copy-number aberrations, by multiplex PCR, hybrid capture, and different depths of whole-genome sequencing. Our results demonstrate that the ctDNA dynamics and allele fractions (AFs) were highly concordant when analyzing the same patient samples using different assays. Tumor-informed assays showed the highest sensitivity for detection of ctDNA at low concentrations. Hybrid capture sequencing targeting between 1,347 and 7,491 tumor-identified mutations at high depth was the most sensitive assay, detecting ctDNA down to an AF of 0.00024% (2.4 parts per million, ppm). Multiplex PCR targeting 21-47 tumor-identified SVs per patient detected ctDNA down to 0.00047% AF (4.7 ppm) and has potential as a clinical assay.

Early serial circulating tumor DNA sequencing predicts the efficacy of chemohormonal therapy in patients with metastatic hormone-sensitive prostate cancer

Chemohormonal therapy is a standard treatment for metastatic hormone-sensitive prostate cancer (mHSPC); however, there are no biomarkers to guide clinical decisions regarding therapeutic options. We aimed to evaluate the clinical utility of serial circulating tumor DNA (ctDNA) sequencing in early prediction of the efficacy of chemohormonal therapy in patients with mHSPC. We conducted a retrospective observational study of 66 patients with mHSPC receiving chemohormonal therapy who underwent serial targeted gene-panel ctDNA sequencing. Peripheral blood samples were collected before treatment and after one cycle of chemotherapy. Kaplan-Meier and log-rank analyses were used to analyze the association between ctDNA status and disease progression-free survival. Serial changes in the ctDNA fraction and genetic alterations were also observed. After one cycle of chemotherapy, 23 (34.8%) patients displayed elevated ctDNA levels, whereas the other patients (65.2%, n = 43) did not. The median time to castration resistance in the group with reduced ctDNA levels was significantly longer than that in the group with increased ctDNA levels (17.70 vs. 8.43 months [mo], p < 0.001). Interestingly, patients with de novo alterations in homologous recombination pathway genes after treatment experienced a shorter time to castration resistance than that experienced by the remaining patients (8.02 vs. 13.20 mo, p = 0.011). The increased ctDNA levels or de novo alterations detected in homologous recombination pathway genes are a harbinger of disease progression. Early serial ctDNA sequencing could aid clinicians in making accurate treatment decisions.

DREAMS: deep read-level error model for sequencing data applied to low-frequency variant calling and circulating tumor DNA detection

Circulating tumor DNA detection using next-generation sequencing (NGS) data of plasma DNA is promising for cancer identification and characterization. However, the tumor signal in the blood is often low and difficult to distinguish from errors. We present DREAMS (Deep Read-level Modelling of Sequencing-errors) for estimating error rates of individual read positions. Using DREAMS, we develop statistical methods for variant calling (DREAMS-vc) and cancer detection (DREAMS-cc). For evaluation, we generate deep targeted NGS data of matching tumor and plasma DNA from 85 colorectal cancer patients. The DREAMS approach performs better than state-of-the-art methods for variant calling and cancer detection.

Bridging biological cfDNA features and machine learning approaches

Liquid biopsies (LBs), particularly using circulating tumor DNA (ctDNA), are expected to revolutionize precision oncology and blood-based cancer screening. Recent technological improvements, in combination with the ever-growing understanding of cell-free DNA (cfDNA) biology, are enabling the detection of tumor-specific changes with extremely high resolution and new analysis concepts beyond genetic alterations, including methylomics, fragmentomics, and nucleosomics. The interrogation of a large number of markers and the high complexity of data render traditional correlation methods insufficient. In this regard, machine learning (ML) algorithms are increasingly being used to decipher disease- and tissue-specific signals from cfDNA. Here, we review recent insights into biological ctDNA features and how these are incorporated into sophisticated ML applications.

Self-Healing Electronics for Prognostic Monitoring of Methylated Circulating Tumor DNAs

Methylated circulating DNAs (ctDNAs) have recently been reported as a promising biomarker for early cancer diagnostics, but limited tools are currently available for continuous and dynamic profiling of ctDNAs and their methylation levels, especially when such assays need to be conducted in point-of-care (POC) scenarios. Here, a self-healing bioelectronic patch (iMethy) is developed that combines transdermal interstitial fluid (ISF) extraction and field effect transistor-based (FET-based) biosensing for dynamic monitoring of methylated ctDNAs as a prognostic approach for cancer risk management. The projection micro-stereolithography-based 3D patterning of an Eutectic Gallium-Indium (EGaIn) circuit with an unprecedented 10 µm resolution enables the construction of self-healing EGaIn microfluidic circuits that remain conductive under 100% strain and self-healing under severe destruction. In combination with continuous transdermal ISF sampling of methylated ctDNAs, iMethy can detect ctDNAs as low as 10^-16  m in cellular models and is capable of phenotypic analysis of tumor growth in rodent animals. As the first demonstration of a wearable device for real-time in vivo analysis of disease-indicative biomarkers, this proof-of-concept study well demonstrated the potential of the iMethy platform for cancer risk management based on dynamic transdermal surveillance of methylated ctDNAs via a painless and self-administrable procedure.

Circulating DNA fragmentomics and cancer screening

The high fragmentation of nuclear circulating DNA (cirDNA) relies on chromatin organization and protection or packaging within mononucleosomes, the smallest and the most stabilized structure in the bloodstream. The detection of differing size patterns, termed fragmentomics, exploits information about the nucleosomal packing of DNA. Fragmentomics not only implies size pattern characterization but also considers the positioning and occupancy of nucleosomes, which result in cirDNA fragments being protected and persisting in the circulation. Fragmentomics can determine tissue of origin and distinguish cancer-derived cirDNA. The screening power of fragmentomics has been considerably strengthened in the omics era, as shown in the ongoing development of sophisticated technologies assisted by machine learning. Fragmentomics can thus be regarded as a strategy for characterizing cancer within individuals and offers an alternative or a synergistic supplement to mutation searches, methylation, or nucleosome positioning. As such, it offers potential for improving diagnostics and cancer screening.

A hitchhiker's guide to cell-free DNA biology

Liquid biopsy provides a noninvasive window to the cancer genome and physiology. In particular, cell-free DNA (cfDNA) is a versatile analyte for guiding treatment, monitoring treatment response and resistance, tracking minimal residual disease, and detecting cancer earlier. Despite certain successes, brain cancer diagnosis is amongst those applications that has so far resisted clinical implementation. Recent approaches have highlighted the clinical gain achievable by exploiting cfDNA biological signatures to boost liquid biopsy or unlock new applications. However, the biology of cfDNA is complex, still partially understood, and affected by a range of intrinsic and extrinsic factors. This guide will provide the keys to read, decode, and harness cfDNA biology: the diverse sources of cfDNA in the bloodstream, the mechanism of cfDNA release from cells, the cfDNA structure, topology, and why accounting for cfDNA biology matters for clinical applications of liquid biopsy.

Circulating Cell-Free DNA in Renal Cell Carcinoma: The New Era of Precision Medicine

Simple Summary

Early diagnosis of renal cell carcinoma (RCC) is challenging and typically incidental. Currently, several therapeutic strategies are used for the treatment; however, no established predictive biomarker has been established yet, and the optimal treatment choice and sequence of use remain unclear. Moreover, the recurrence occurs in about one-third of patients after tumor resection. Although several prognostic classification systems have been proposed, most of them showed only limited potential in recurrence prediction. Therefore, identifying simple, reliable, and easily accessible biomarkers to anticipate the diagnosis, effectively evaluate the risk of relapse, and predict the response to the therapeutic regimens is an unmet clinical need. Circulating cell-free DNA (cfDNA), released from cancer cells into the bloodstream, was shown to be a non-invasive, viable, inexpensive method to diagnose and monitor several solid malignancies, designed as a potential blood RCC biomarker. This review aims to summarize the state of the art of the current genetic and epigenetic techniques of plasma and serum cfDNA detection and outline the potential application of liquid biopsy in RCC.

Abstract

Tumor biopsy is still the gold standard for diagnosing and prognosis renal cell carcinoma (RCC). However, its invasiveness, costs, and inability to accurately picture tumor heterogeneity represent major limitations to this procedure. Analysis of circulating cell-free DNA (cfDNA) is a non-invasive cost-effective technique that has the potential to ease cancer detection and prognosis. In particular, a growing body of evidence suggests that cfDNA could be a complementary tool to identify and prognosticate RCC while providing contemporary mutational profiling of the tumor. Further, recent research highlighted the role of cfDNA methylation profiling as a novel method for cancer detection and tissue-origin identification. This review synthesizes current knowledge on the diagnostic, prognostic, and predictive applications of cfDNA in RCC, with a specific focus on the potential role of cell-free methylated DNA (cfMeDNA).

Genome-wide mutational signatures in low-coverage whole genome sequencing of cell-free DNA

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.

Circulating Tumor DNA as a Biomarker for Monitoring Patients with Solid Cancers: Comparison with Standard Protein Biomarkers

BACKGROUND

Protein-based biomarkers are widely used in monitoring patients with diagnosed cancer. These biomarkers however, lack specificity for cancer and have poor sensitivity in detecting early recurrences and monitoring therapy effectiveness. Emerging data suggest that the use of circulating tumor DNA (ctDNA) has several advantages over standard biomarkers.

CONTENT

Following curative-intent surgery for cancer, the presence of ctDNA is highly predictive of early disease recurrence, while in metastatic cancer an early decline in ctDNA following the initiation of treatment is predictive of good outcome. Compared with protein biomarkers, ctDNA provides greater cancer specificity and sensitivity for detecting early recurrent/metastatic disease. Thus, in patients with surgically resected colorectal cancer, multiple studies have shown that ctDNA is superior to carcinoembryonic antigen (CEA) in detecting residual disease and early recurrence. Similarly, in breast cancer, ctDNA was shown to be more accurate than carbohydrate antigen 15-3 (CA 15-3) in detecting early recurrences. Other advantages of ctDNA over protein biomarkers in monitoring cancer patients include a shorter half-life in plasma and an ability to predict likely response to specific therapies and identify mechanisms of therapy resistance. However, in contrast to proteins, ctDNA biomarkers are more expensive to measure, less widely available, and have longer turnaround times for reporting. Furthermore, ctDNA assays are less well standardized.

SUMMARY

Because of their advantages, it is likely that ctDNA measurements will enter clinical use in the future, where they will complement existing biomarkers and imaging in managing patients with cancer. Hopefully, these combined approaches will lead to a better outcome for patients.

Longitudinal monitoring of disease burden and response using ctDNA from dried blood spots in xenograft models

Whole-genome sequencing (WGS) of circulating tumour DNA (ctDNA) is now a clinically important biomarker for predicting therapy response, disease burden and disease progression. However, the translation of ctDNA monitoring into vital preclinical PDX models has not been possible owing to low circulating blood volumes in small rodents. Here, we describe the longitudinal detection and monitoring of ctDNA from minute volumes of blood in PDX mice. We developed a xenograft Tumour Fraction (xTF) metric using shallow WGS of dried blood spots (DBS), and demonstrate its application to quantify disease burden, monitor treatment response and predict disease outcome in a preclinical study of PDX mice. Further, we show how our DBS-based ctDNA assay can be used to detect gene-specific copy number changes and examine the copy number landscape over time. Use of sequential DBS ctDNA assays could transform future trial designs in both mice and patients by enabling increased sampling and molecular monitoring.

Circulating Tumor DNA-Based Genomic Profiling Assays in Adult Solid Tumors for Precision Oncology: Recent Advancements and Future Challenges

Genomic profiling using tumor biopsies remains the standard approach for the selection of approved molecular targeted therapies. However, this is often limited by its invasiveness, feasibility, and poor sample quality. Liquid biopsies provide a less invasive approach while capturing a contemporaneous and comprehensive tumor genomic profile. Recent advancements in the detection of circulating tumor DNA (ctDNA) from plasma samples at satisfactory sensitivity, specificity, and detection concordance to tumor tissues have facilitated the approval of ctDNA-based genomic profiling to be integrated into regular clinical practice. The recent approval of both single-gene and multigene assays to detect genetic biomarkers from plasma cell-free DNA (cfDNA) as companion diagnostic tools for molecular targeted therapies has transformed the therapeutic decision-making procedure for advanced solid tumors. Despite the increasing use of cfDNA-based molecular profiling, there is an ongoing debate about a 'plasma first' or 'tissue first' approach toward genomic testing for advanced solid malignancies. Both approaches present possible advantages and disadvantages, and these factors should be carefully considered to personalize and select the most appropriate genomic assay. This review focuses on the recent advancements of cfDNA-based genomic profiling assays in advanced solid tumors while highlighting the major challenges that should be tackled to formulate evidence-based guidelines in recommending the 'right assay for the right patient at the right time'.

The applications of plasma cell-free DNA in cancer detection: Implications in the management of breast cancer patients

Liquid biopsy probes DNA, RNA, and proteins in body fluids for cancer detection and is one of the most rapidly developing areas in oncology. Tumor-derived DNA (circulating tumor DNA, ctDNA) in the context of cell-free DNA (cfDNA) in blood has been the main target for its potential utilities in cancer detection. Liquid biopsy can report tumor burden in real-time without invasive interventions, and would be feasible for screening tumor types that lack standard-of-care screening approaches. Two major approaches to interrogating ctDNA are genetic mutation and DNA methylation profiling. Mutation profiling can identify tumor driver mutations and guide precision therapy. Targeted genomic profiling of DNA methylation has become the main approach for cancer screening in the general population. Here we review the recent technological development and ongoing efforts in clinical applications. For clinical applications, we focus on breast cancer, in which subtype-specific biology demarcates the applications of ctDNA.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

This approach provides a generic analytical strategy for non-invasive pan-cancer detection and cancer type prediction.

A Pilot, Prospective, Observational Study to Investigate the Value of NGS in Liquid Biopsies to Predict Tumor Response After Neoadjuvant Chemo-Radiotherapy in Patients With Locally Advanced Rectal Cancer: The LiBReCa Study

Introduction

Circulating tumor DNA (ctDNA) correlates with the response to therapy in different types of cancer. However, in patients with locally advanced rectal cancer (LARC), little is known about how ctDNA levels change with neoadjuvant chemoradiation (Na-ChRT) and how they correlate with treatment response. This work aimed to explore the value of serial liquid biopsies in monitoring response after Na-ChRT with the hypothesis that this could become a reliable biomarker to identify patients with a complete response, candidates for non-operative management.

Materials and Methods

Twenty-five consecutive LARC patients undergoing long-term Na-ChRT therapy were included. Applying next-generation sequencing (NGS), we characterized DNA extracted from formalin-fixed paraffin embedded diagnostic biopsy and resection tissue and plasma ctDNA collected at the following time points: the first and last days of radiotherapy (T0, Tend), at 4 (T4), 7 (T7) weeks after radiotherapy, on the day of surgery (Top), and 3–7 days after surgery (Tpost-op). On the day of surgery, a mesenteric vein sample was also collected (TIMV). The relationship between the ctDNA at those time-points and the tumor regression grade (TRG) of the surgical specimen was statistically explored.

Results

We found no association between the disappearance of ctDNA mutations in plasma samples and pathological complete response (TRG1) as ctDNA was undetectable in the majority of patients from Tend on. However, we observed that the poor (TRG 4) response to Na-ChRT was significantly associated with a positive liquid biopsy at the Top.

Conclusions

ctDNA evaluation by NGS technology may identify LARC patients with poor response to Na-ChRT. In contrast, this technique does not seem useful for identifying patients prone to developing a complete response.

Combining variant detection and fragment length analysis improves detection of minimal residual disease in post‐surgery circulating tumour DNA of stage II‐IIIA NSCLC patients

Stage II–IIIA nonsmall cell lung cancer (NSCLC) patients receive adjuvant chemotherapy after surgery as standard‐of‐care treatment, even though only approximately 5.8% of patients will benefit. Identifying patients with minimal residual disease (MRD) after surgery using tissue‐informed testing of postoperative plasma circulating cell‐free tumour DNA (ctDNA) may allow adjuvant therapy to be withheld from patients without MRD. However, the detection of MRD in the postoperative setting is challenging, and more sensitive methods are urgently needed. We developed a method that combines variant calling and a novel ctDNA fragment length analysis using hybrid capture sequencing data. Among 36 stage II–IIIA NSCLC patients, this method distinguished patients with and without recurrence of disease in a 20 times repeated 10‐fold cross validation with 75% accuracy (P = 0.0029). In contrast, using only variant calling or only fragment length analysis, no signification distinction between patients was shown (P = 0.24 and P = 0.074 respectively). In addition, a variant‐level fragmentation score was developed that was able to classify variants detected in plasma cfDNA into tumour‐derived or white‐blood‐cell‐derived variants with 84% accuracy. The findings in this study may help drive the integration of various types of information from the same data, eventually leading to cheaper and more sensitive techniques to be used in this challenging clinical setting.

Future perspectives of uveal melanoma blood based biomarkers

Uveal melanoma (UM) is the most common primary intraocular malignancy affecting adults. Despite successful local treatment of the primary tumour, metastatic disease develops in up to 50% of patients. Metastatic UM carries a particularly poor prognosis, with no effective therapeutic option available to date. Genetic studies of UM have demonstrated that cytogenetic features, including gene expression, somatic copy number alterations and specific gene mutations can allow more accurate assessment of metastatic risk. Pre-emptive therapies to avert metastasis are being tested in clinical trials in patients with high-risk UM. However, current prognostic methods require an intraocular tumour biopsy, which is a highly invasive procedure carrying a risk of vision-threatening complications and is limited by sampling variability. Recently, a new diagnostic concept known as "liquid biopsy" has emerged, heralding a substantial potential for minimally invasive genetic characterisation of tumours. Here, we examine the current evidence supporting the potential of blood circulating tumour cells (CTCs), circulating tumour DNA (ctDNA), microRNA (miRNA) and exosomes as biomarkers for UM. In particular, we discuss the potential of these biomarkers to aid clinical decision making throughout the management of UM patients.

Challenges and the Evolving Landscape of Assessing Blood-Based PD-L1 Expression as a Biomarker for Anti-PD-(L)1 Immunotherapy

While promising, PD-L1 expression on tumor tissues as assessed by immunohistochemistry has been shown to be an imperfect biomarker that only applies to a limited number of cancers, whereas many patients with PD-L1-negative tumors still respond to anti-PD-(L)1 immunotherapy. Recent studies using patient blood samples to assess immunotherapeutic responsiveness suggests a promising approach to the identification of novel and/or improved biomarkers for anti-PD-(L)1 immunotherapy. In this review, we discuss the advances in our evolving understanding of the regulation and function of PD-L1 expression, which is the foundation for developing blood-based PD-L1 as a biomarker for anti-PD-(L)1 immunotherapy. We further discuss current knowledge and clinical study results for biomarker identification using PD-L1 expression on tumor and immune cells, exosomes, and soluble forms of PD-L1 in the peripheral blood. Finally, we discuss key challenges for the successful development of the potential use of blood-based PD-L1 as a biomarker for anti-PD-(L)1 immunotherapy.

Treatment Sequencing in Resectable Lung Cancer: The Good and the Bad of Adjuvant Versus Neoadjuvant Therapy

The treatment scenario for patients with resectable non-small cell lung cancer has changed dramatically with the incorporation of immunotherapy. The introduction of immunotherapy into treatment algorithms has yielded improved clinical outcomes in several phase II and III trials in both adjuvant (Impower010 and PEARLS) and neoadjuvant settings (JHU/MSK, LCMC3, NEOSTAR, Columbia/MGH, NADIM, and CheckMate-816), leading to new U.S. Food and Drug Administration approvals in this sense. Different treatment options are now available for patients, making the optimal treatment scenario a matter of intense debate. In this review, we summarize the main results concerning treatment sequencing in resectable non-small cell lung cancer from the past 30 years in the preimmunotherapy era, focusing on recent advances after incorporation of immunotherapy. Finally, the utility of several parameters (PD-L1, tumor mutational burden, radiomics, circulating tumor DNA, T-cell receptor, and immune populations) as predictive biomarkers for therapy personalization is discussed.

Diagnostic potential of extracellular vesicles in meningioma patients

BACKGROUND

Extracellular vesicles (EVs) play an important role in cell-cell communication, and tumor-derived EVs circulating in patient blood can serve as biomarkers. Here, we investigated the potential role of plasma EVs in meningioma patients for tumor detection and determined whether EVs secreted by meningioma cells reflect epigenetic, genomic, and proteomic alterations of original tumors.

METHODS

EV concentrations were quantified in patient plasma (n = 46). Short-term meningioma cultures were established (n = 26) and secreted EVs were isolated. Methylation and copy number profiling was performed using 850k arrays, and mutations were identified by targeted gene panel sequencing. Differential quantitative mass spectrometry was employed for proteomic analysis.

RESULTS

Levels of circulating EVs were elevated in meningioma patients compared to healthy individuals, and the plasma EV concentration correlated with malignancy grade and extent of peritumoral edema. Postoperatively, EV counts dropped to normal levels, and the magnitude of the postoperative decrease was associated with extent of tumor resection. Methylation profiling of EV-DNA allowed correct tumor classification as meningioma in all investigated cases, and accurate methylation subclass assignment in almost all cases. Copy number variations present in tumors, as well as tumor-specific mutations were faithfully reflected in meningioma EV-DNA. Proteomic EV profiling did not permit original tumor identification but revealed tumor-associated proteins that could potentially be utilized to enrich meningioma EVs from biofluids.

CONCLUSIONS

Elevated EV levels in meningioma patient plasma could aid in tumor diagnosis and assessment of treatment response. Meningioma EV-DNA mirrors genetic and epigenetic tumor alterations and facilitates molecular tumor classification.

cfTrack: A Method of Exome-Wide Mutation Analysis of Cell-free DNA to Simultaneously Monitor the Full Spectrum of Cancer Treatment Outcomes Including MRD, Recurrence, and Evolution

PURPOSE

Cell-free DNA (cfDNA) offers a noninvasive approach to monitor cancer. Here we develop a method using whole-exome sequencing (WES) of cfDNA for simultaneously monitoring the full spectrum of cancer treatment outcomes, including minimal residual disease (MRD), recurrence, evolution, and second primary cancers.

EXPERIMENTAL DESIGN

Three simulation datasets were generated from 26 patients with cancer to benchmark the detection performance of MRD/recurrence and second primary cancers. For further validation, cfDNA samples (n = 76) from patients with cancer (n = 35) with six different cancer types were used for performance validation during various treatments.

RESULTS

We present a cfDNA-based cancer monitoring method, named cfTrack. Taking advantage of the broad genome coverage of WES data, cfTrack can sensitively detect MRD and cancer recurrence by integrating signals across known clonal tumor mutations of a patient. In addition, cfTrack detects tumor evolution and second primary cancers by de novo identifying emerging tumor mutations. A series of machine learning and statistical denoising techniques are applied to enhance the detection power. On the simulation data, cfTrack achieved an average AUC of 99% on the validation dataset and 100% on the independent dataset in detecting recurrence in samples with tumor fractions ≥0.05%. In addition, cfTrack yielded an average AUC of 88% in detecting second primary cancers in samples with tumor fractions ≥0.2%. On real data, cfTrack accurately monitors tumor evolution during treatment, which cannot be accomplished by previous methods.

CONCLUSIONS

Our results demonstrated that cfTrack can sensitively and specifically monitor the full spectrum of cancer treatment outcomes using exome-wide mutation analysis of cfDNA.

Residual ctDNA after treatment predicts early relapse in patients with early-stage non-small cell lung cancer

BACKGROUND

Identification of residual disease in patients with localized non-small cell lung cancer (NSCLC) following treatment with curative intent holds promise to identify patients at risk of relapse. New methods can detect circulating tumour DNA (ctDNA) in plasma to fractional concentrations as low as a few parts per million, and clinical evidence is required to inform their use.

PATIENTS AND METHODS

We analyzed 363 serial plasma samples from 88 patients with early-stage NSCLC (48.9%/28.4%/22.7% at stage I/II/III), predominantly adenocarcinomas (62.5%), treated with curative intent by surgery (n = 61), surgery and adjuvant chemotherapy/radiotherapy (n = 8), or chemoradiotherapy (n = 19). Tumour exome sequencing identified somatic mutations and plasma was analyzed using patient-specific RaDaR™ assays with up to 48 amplicons targeting tumour-specific variants unique to each patient.

RESULTS

ctDNA was detected before treatment in 24%, 77% and 87% of patients with stage I, II and III disease, respectively, and in 26% of all longitudinal samples. The median tumour fraction detected was 0.042%, with 63% of samples <0.1% and 36% of samples <0.01%. ctDNA detection had clinical specificity >98.5% and preceded clinical detection of recurrence of the primary tumour by a median of 212.5 days. ctDNA was detected after treatment in 18/28 (64.3%) of patients who had clinical recurrence of their primary tumour. Detection within the landmark timepoint 2 weeks to 4 months after treatment end occurred in 17% of patients, and was associated with shorter recurrence-free survival [hazard ratio (HR): 14.8, P <0.00001] and overall survival (HR: 5.48, P <0.0003). ctDNA was detected 1-3 days after surgery in 25% of patients yet was not associated with disease recurrence. Detection before treatment was associated with shorter overall survival and recurrence-free survival (HR: 2.97 and 3.14, P values 0.01 and 0.003, respectively).

CONCLUSIONS

ctDNA detection after initial treatment of patients with early-stage NSCLC using sensitive patient-specific assays has potential to identify patients who may benefit from further therapeutic intervention.

Current and Developing Liquid Biopsy Techniques for Breast Cancer

Breast cancer is the most commonly diagnosed cancer and leading cause of cancer mortality among woman worldwide. The techniques of diagnosis, prognosis, and therapy monitoring of breast cancer are critical. Current diagnostic techniques are mammography and tissue biopsy; however, they have limitations. With the development of novel techniques, such as personalized medicine and genetic profiling, liquid biopsy is emerging as the less invasive tool for diagnosing and monitoring breast cancer. Liquid biopsy is performed by sampling biofluids and extracting tumor components, such as circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), cell-free mRNA (cfRNA) and microRNA (miRNA), proteins, and extracellular vehicles (EVs). In this review, we summarize and focus on the recent discoveries of tumor components and biomarkers applied in liquid biopsy and novel development of detection techniques, such as surface-enhanced Raman spectroscopy (SERS) and microfluidic devices.

Longitudinal profiling of circulating tumour DNA for tracking tumour dynamics in pancreatic cancer

BACKGROUND

The utility of circulating tumour DNA (ctDNA) for longitudinal tumour monitoring in pancreatic ductal adenocarcinoma (PDAC) has not been explored beyond mutations in the KRAS proto-oncogene. Here, we aimed to characterise and track patient-specific somatic ctDNA variants, to assess longitudinal changes in disease burden and explore the landscape of actionable alterations.

METHODS

We followed 3 patients with resectable disease and 4 patients with unresectable disease, including 4 patients with ≥ 3 serial follow-up samples, of whom 2 were rare long survivors (> 5 years). We performed whole exome sequencing of tumour gDNA and plasma ctDNA (n = 20) collected over a ~ 2-year period from diagnosis through treatment to death or final follow-up. Plasma from 3 chronic pancreatitis cases was used as a comparison for analysis of ctDNA mutations.

RESULTS

We detected > 55% concordance between somatic mutations in tumour tissues and matched serial plasma. Mutations in ctDNA were detected within known PDAC driver genes (KRAS, TP53, SMAD4, CDKN2A), in addition to patient-specific variants within alternative cancer drivers (NRAS, HRAS, MTOR, ERBB2, EGFR, PBRM1), with a trend towards higher overall mutation loads in advanced disease. ctDNA alterations with potential for therapeutic actionability were identified in all 7 patients, including DNA damage response (DDR) variants co-occurring with hypermutation signatures predictive of response to platinum chemotherapy. Longitudinal tracking in 4 patients with follow-up > 2 years demonstrated that ctDNA mutant allele fractions and clonal trends were consistent with CA19-9 measurements and/or clinically reported disease burden. The estimated prevalence of 'stem clones' was highest in an unresectable patient where changes in ctDNA dynamics preceded CA19-9 levels. Longitudinal evolutionary trajectories revealed ongoing subclonal evolution following chemotherapy.

CONCLUSION

These results provide proof-of-concept for the use of exome sequencing of serial plasma to characterise patient-specific ctDNA profiles, and demonstrate the sensitivity of ctDNA in monitoring disease burden in PDAC even in unresectable cases without matched tumour genotyping. They reveal the value of tracking clonal evolution in serial ctDNA to monitor treatment response, establishing the potential of applied precision medicine to guide stratified care by identifying and evaluating actionable opportunities for intervention aimed at optimising patient outcomes for an otherwise intractable disease.