Molecular analysis for ovarian cancer detection in patient-friendly samples

BACKGROUND

High ovarian cancer mortality rates motivate the development of effective and patient-friendly diagnostics. Here, we explored the potential of molecular testing in patient-friendly samples for ovarian cancer detection.

METHODS

Home-collected urine, cervicovaginal self-samples, and clinician-taken cervical scrapes were prospectively collected from 54 patients diagnosed with a highly suspicious ovarian mass (benign n = 25, malignant n = 29). All samples were tested for nine methylation markers, using quantitative methylation-specific PCRs that were verified on ovarian tissue samples, and compared to non-paired patient-friendly samples of 110 age-matched healthy controls. Copy number analysis was performed on a subset of urine samples of ovarian cancer patients by shallow whole-genome sequencing.

RESULTS

Three methylation markers are significantly elevated in full void urine of ovarian cancer patients as compared to healthy controls (C2CD4D, P = 0.008; CDO1, P = 0.022; MAL, P = 0.008), of which two are also discriminatory in cervical scrapes (C2CD4D, P = 0.001; CDO1, P = 0.004). When comparing benign and malignant ovarian masses, GHSR shows significantly elevated methylation levels in the urine sediment of ovarian cancer patients (P = 0.024). Other methylation markers demonstrate comparably high methylation levels in benign and malignant ovarian masses. Cervicovaginal self-samples show no elevated methylation levels in patients with ovarian masses as compared to healthy controls. Copy number changes are identified in 4 out of 23 urine samples of ovarian cancer patients.

CONCLUSIONS

Our study reveals increased methylation levels of ovarian cancer-associated genes and copy number aberrations in the urine of ovarian cancer patients. Our findings support continued research into urine biomarkers for ovarian cancer detection and highlight the importance of including benign ovarian masses in future studies to develop a clinically useful test.

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.

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.

Genome-wide and panel-based cell-free DNA characterization of patients with resectable esophageal adenocarcinoma

Circulating tumor DNA (ctDNA) holds promise in resectable esophageal adenocarcinoma (EAC) to predict patient outcome but is not yet sensitive enough to be clinically applicable. Our aim was to combine ctDNA mutation data with shallow whole-genome sequencing (sWGS)-derived copy number tumor fraction estimates (ichorCNA) to improve pathological response and survival prediction in EAC. In total, 111 stage II/III EAC patients with baseline (n = 111), post-neoadjuvant chemoradiotherapy (nCRT) (n = 68), and pre-surgery (n = 92) plasma samples were used for ctDNA characterization. sWGS (<5× coverage) was performed on all time-point samples, and copy number aberrations were estimated using ichorCNA. Baseline and pre-surgery samples were sequenced using a custom amplicon panel for mutation detection. Detection of baseline ctDNA was successful in 44.3% of patients by amplicon sequencing and 10.5% by ichorCNA. Combining both, ctDNA could be detected in 50.5% of patients. Baseline ctDNA positivity was related to higher T stage (cT3, 4) (p = 0.017). There was no relationship between pathological response and baseline ctDNA positivity. However, baseline ctDNA metrics (variant allele frequency > 1% or ichorCNA > 3%) were associated with a high risk of disease progression [HR = 2.23 (95% CI 1.22-4.07), p = 0.007]. The non-clearance of a baseline variant or ichorCNA > 3% in pre-surgery samples was related to early progression [HR = 4.58 (95% CI 2.22-9.46), p < 0.001]. Multi-signal analysis improves detection of ctDNA and can be used for prognostication of resectable EAC patients. Future studies should explore the potential of multi-modality sequencing for risk stratification and treatment adaptation based on ctDNA results. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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.

Outcome prediction in resectable esophageal adenocarcinoma based on clinical variables, radiomics, and ctDNA

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Background: Despite the advent of precision medicine, prediction of survival outcome of esophageal cancer patients remains a challenge. Here we aim to investigate the value of prediction models integrating multi-signal data including radiomics and circulating tumor DNA (ctDNA) data in addition to clinical data for the prediction of resectable esophageal adenocarcinoma (rEAC) related outcomes. Methods: In total n=111 rEAC patients treated with neoadjuvant chemoradiotherapy (nCRT; n=71) +/- anti-PD-L1 (n=40) were included. Baseline clinical variables (n=9) were based on the SOURCE survival prediction model (van den Boorn et al. JNCCN. 2021). The baseline ctDNA data from plasma was derived from fragmentomic and copy number aberrations (ichorCNA) from shallow whole genome sequencing (<5-fold coverage) and a custom next-generation sequencing panel (n=23 genes). Baseline radiomic original features were extracted by the pyradiomics package from CT-image delineated tumor volumes. An initial redundancy filtering was performed to remove correlating variables (r>0.6). We evaluated the predictive performance of baseline ctDNA and radiomics features on overall survival (OS), progression free survival (PFS), and time to progression (TTP), through fitting Cox-regression models. Four ctDNA features were included in the models: P20-150, ichorCNA, fragment end score and mutation detection. For the radiomics features we performed an additional back- and forward variable selection based on Akaike’s Information Criterion. Using the likelihood ratio test we tested if the model fit changed after adding ctDNA and radiomics features to a model with clinical variables. Results: The addition of radiomics to clinical variables improved model fit for OS (p=0.017). Baseline prediction of OS resulted in a C-index of 0.65 using clinical variables only, 0.65 with ctDNA, 0.68 with radiomics and 0.68 with ctDNA and radiomics combined. For PFS model fit improved after adding radiomics (p=0.020) and ctDNA and radiomics combined (p=0.017). Baseline prediction of PFS resulted in a C-index of 0.64 using clinical variables, 0.65 with ctDNA, 0.67 with radiomics, and 0.68 with ctDNA and radiomics combined. For TTP model fit improved after adding radiomics (p=0.008) and radiomics and ctDNA combined (p=0.002). Baseline prediction of TTP resulted in a C-index of 0.64 with clinical variables, 0.65 with ctDNA, 0.71 with radiomics, and 0.72 with ctDNA and radiomics combined. Based on the cox-regression models using clinical variables and radiomics, risk stratification by splitting the cohort in a high and low risk group was possible for OS, PFS and TTP (p<0.001). Conclusions: Combining clinical variables from SOURCE with radiomics data improved predictions of OS, PFS, and TTP among patients with rEAC. Multi-signal integration of clinical and radiomics variables could potentially be used to identify risk groups.

Fragmentation patterns and personalized sequencing of cell-free DNA in urine and plasma of glioma patients

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.

Circulating tumor DNA (ctDNA) analysis by low-coverage whole genome sequencing (lcWGS) of resectable esophageal adenocarcinoma (rEAC) patients

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Background: ctDNA is becoming an established marker to assess tumor burden, relapse after surgery, and to identify responders in immunotherapy studies. In the phase II PERFECT trial rEAC patients were treated with neoadjuvant chemoradiotherapy (nCRT) and a PD-L1 inhibitor (van den Ende et al. CCR. 2021). Here we evaluated the potential of cell-free DNA (cfDNA) to predict pathological complete response (pCR) and recurrence. Methods: The cohort consisted of 40 patients and 145 plasma samples. EDTA blood samples were drawn at baseline (B, N = 40), in week 5 of nCRT (W5, N = 40), before surgery (OR, N = 33) and 3 months after surgery (FU, N = 32). cfDNA was isolated by affinity columns (CNAkit, QIAgen) quantified by spectrofluorometer (BioAnalyzer, Agilent), sequencing libraries were prepared for lcWGS ( < 5-fold coverage, Tag-seq, Takara) and sequenced on a NovaSeq (S4, PE150). Sequencing data were processed with an in-house pipeline. Copy number aberrations (CNA) and the tumor fraction were estimated using the ichorCNA tool. Insert sizes were recovered and we determined a Tumor Enriched Fragment Fraction (TEFF), calculated by doing the ratio of fragments between 90-150 bp and 250-320 bp (enriched in tumor signal) and fragments between 150-250 bp and 320-360 bp (poor in tumor signal). ichorCNA and TEFF were used to quantify the ctDNA fraction in plasma samples. pCR was defined as ypT0N0. Residual tumor, progression or death before surgery were considered non-pCR. Relapse-free survival (RFS) was defined as the time after surgery until recurrence. Results: The pCR rate was 25% (10/40). The median fold change TEFF between B and W5 was -0.15 (range -0.67 to 0.44) in the pCR group and 0.16 (range -1.40 to 0.76) in the non-pCR group (Mann–Whitney U; p = 0.047). Of the 17 patients in whom ctDNA was detected (TEFF≥0.3 and/or ichorCNA≥0.03) in the FU sample, 13 (76%) showed a recurrence. Of the 15 patients with no ctDNA detected 5 (33%) showed a recurrence. Patients with ctDNA detected at FU had worse RFS, HR = 2.72 (95%CI 0.96-7.71; p = 0.050). Recurrences were detected earlier by FU ctDNA than by imaging due to physical complaints with a median of 312 days (163-798 days). Conclusions: lcWGS appears to be a useful tool to predict pCR and recurrence in resectable esophageal cancer. These lcWGS results will be further combined with fragmentomics analysis and targeted mutational data (Ion Torrent next-generation sequencing) in order to assess response to immunotherapy. Clinical trial information: NCT03087864.

Toward the Early Detection of Cancer by Decoding the Epigenetic and Environmental Fingerprints of Cell-Free DNA

Widespread adaptation of liquid biopsy for the early detection of cancer has yet to reach clinical utility. Circulating tumor DNA is commonly detected though the presence of genetic alterations, but only a minor fraction of tumor-derived cell-free DNA (cfDNA) fragments exhibit mutations. The cellular processes occurring in cancer development mark the chromatin. These epigenetic marks are reflected by modifications in the cfDNA methylation, fragment size, and structure. In this review, we describe how going beyond DNA sequence information alone, by analyzing cfDNA epigenetic and immune signatures, boosts the potential of liquid biopsy for the early detection of cancer.