Circulating Tumor DNA-Based Copy-Number Profiles Enable Monitoring Treatment Effects During Therapy in High-Grade Serous Carcinoma

doi:10.21203/rs.3.rs-3127401/v1

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Research Article

Circulating Tumor DNA-Based Copy-Number Profiles Enable Monitoring Treatment Effects During Therapy in High-Grade Serous Carcinoma

https://doi.org/10.21203/rs.3.rs-3127401/v1

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published 30 Nov, 2023

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Background

Circulating tumor DNA (ctDNA) analysis has emerged as a promising tool for detecting and profiling longitudinal genomic changes in cancer. While copy-number alterations (CNAs) play a major role in cancers, monitoring treatment effects using copy-number profiles has received limited attention compared to mutations. This is primarily due to the challenge of CNA analysis for real-life tumor-fraction ctDNA samples.

Method

Our study aimed to investigate the longitudinal copy number profiles of ctDNA and assess the role of ctDNA samples, even with low tumor fractions, in monitoring cancer treatment. We conducted copy-number analysis on 152 plasma samples collected from 29 patients diagnosed with high-grade serous carcinoma (HGSC). Our sequencing panel targeted more than 500 genes, enabling a comprehensive evaluation of genomic changes. Among the patients, 21 individuals had matched tissue and plasma samples collected at different time points, allowing us to assess the concordance between tissue and plasma and to evaluate the sensitivity of our approach using Kendall correlation values. Statistical comparisons were performed using the Wilcoxon rank test to ensure the robustness of the findings.

Result

Our approach successfully detected concordant CNA profiles in most plasma samples, even with tumor content as low as 3%. Additionally, highly amplified regions were identified in samples with approximately 1% tumor content. Longitudinal analysis revealed changes in CNA profiles in seven out of 11 patients with high tumor-content plasma samples at relapse. These changes included focal acquired or lost copy numbers, whereas the majority of the genome remained stable. Two patients displayed significant changes in their copy-number profile during therapy. Our analysis uncovered ctDNA-detectable subclonal selection resulting from surgery and chemotherapy.

Conclusion

Overall, our study demonstrated acquired and diminished CNAs at relapse compared with pre-treatment samples, highlighting the dynamic nature of CNA profiles during treatment. These findings provide compelling evidence for the potential of ctDNA analysis in tracking treatment responses and detecting genomic changes associated with relapse. Monitoring CNAs can serve as a complementary tool to understand cancer cell evolution and treatment efficacy.

ctDNA

copy-number alterations

targeted sequencing

treatment monitoring

high-grade serous carcinoma

Copy-number alterations (CNAs) are commonly detected genomic aberrations in various types of cancer ^1–4. They can affect the activation or silencing of gene expression or pathway activities ^5,6 and play a role in tumor initiation ^7–10. Several studies have detected CNAs to identify genomic changes driving to treatment resistance ^11–13 and to gain insights into tumor evolution ^{10,12,14–16}. While the role of CNAs in cancer is indisputable, obtaining tissue biopsies for CNA analysis is not always feasible. Circulating tumor DNA (ctDNA) provides a minimally invasive biopsy technique for treatment monitoring and guidance ^17–23.

High-grade serous carcinoma (HGSC) is the most common and deadliest subtype of epithelial ovarian cancer ²⁴. HGSC is considered as a copy-number-driven cancer with almost 100% prevalence of pathogenic mutations in TP53^25,26. While the majority of HGSC patients respond to the primary treatment, cancer relapses in 95% of cases, eventually leading to chemotherapy resistant disease ²⁷. Previous ctDNA studies in HGSC have focused on longitudinal tracking of mutation profiles, with many methods for constructing subclonal populations based on mutations ^28–30 and especially on TP53 mutations ^26,31,32, whereas CNAs have received less attention ^26,31,32.

Detecting CNAs from cell-free DNA (cfDNA) poses challenges because of their low tumor fraction (TF). High TF requirements, such as the previously employed threshold of 25% ³³ or 15% in our earlier study ¹⁷, led to the discarding a significant number of samples that still can provide information. To address this issue, we conducted a study to detect CNAs in 152 plasma samples collected from 29 HGSC patients collected before and after treatments, including a substantial number of low TF cfDNA samples. Our study aimed to assess the feasibility of utilizing low-TF samples and investigate changes in CNAs during different treatment phases.

2.1 Cohort and samples

Patients were selected from DECIDER cohort (NCT04846933) treated at Turku University Hospital (Turku, Finland). Patients were treated with either with primary debulking surgery (PDS) followed by platinum-taxane chemotherapy (n = 14), or neoadjuvant chemotherapy (NACT), where interval debulking surgery was performed after NACT, and followed by adjuvant chemotherapy (n = 15). Three patients in the NACT group did not undergo debulking surgery. All patients participating in the study provided informed consent and the study was approved by the Ethics Committee of the Hospital District of Southwest Finland (ETMK 145/1801/2015).

Samples included i) cfDNA from plasma, ii) genomic DNA from fresh frozen tumor tissue or ascites, and iii) genomic DNA from the blood of the same patient serving as a germline control. Sample preparation and DNA extraction protocols were performed as previously reported ¹⁷.

2.2 Deep sequencing of plasma samples and target regions

The samples were sequenced on an Illumina HiSeq platform using BGI Oseq™ ctDNA panel (Oseq panel) to target cancer-related genes. Plasma samples were sequenced with 1000x coverage, and genomic DNA from the tumor tissue and germline controls were sequenced with 200x coverage.

There were multiple batches of data, owing to the upgraded panels in the BGI. A panel of approximately 500 protein-coding cancer-related genes ¹⁷ (Supplementary Table S1) was used to successfully sequence 29 tissue samples, 100 plasma samples, and 15 germline control samples. Later, a larger panel of over 700 protein-coding cancer-related genes (Supplementary Table S2) was applied to 17 tissue, 52 plasma, and 16 germline control samples (Supplementary Table S3).

2.3 Tools and Algorithms

Sequencing data processing

The quality control and alignment followed same procedures than previously ¹⁷ described and were based on GATK ³⁴ best practice workflows in preprocessing sequencing data. After filtering for quality, 152 out of 166 originally sent plasma samples from 29 patients remained for analysis.

Identifying regions of interest (ROIs)

To identify regions of interest (ROIs) from the Oseq panel, we calculated coverage at every base of germline control samples (n = 30) and considered regions with a mean of coverage at least 100. Then, we excluded black-listed regions using ENCODE blacklist ³⁵ and regions shorter than 200bp. To curate the defined ROIs, they were checked with Integrated Genome Browser (IGB) ³⁶ alignment visualization (Supplementary Figure S1A) and the R package TarSeqQC ³⁷ (Supplementary Figure S1B). The final curated ROIs were then used for the CNA calling pipeline (Supplement Methods).

Estimation of ctDNA content in plasma samples

We used the truncal TP53 variant allele frequency (TP53 VAF) to estimate the ctDNA fraction for plasma samples. TP53 VAF values were obtained by jointly calling all samples from a patient with GATK ³⁴, version 3.7, with targeted option of gene region of TP53.

To gain further insight into the ctDNA fraction, we used Picard ³⁸ to calculate the median fragment size and number of short (≤ 150bp) and long (> 150bp) DNA fragments. Subsequently, we determined the short-long ratio by dividing the number of short fragments by the number of long fragments.

CNA pipeline

CNAs were called from the targeted sequencing data by using PureCN ³⁹. PureCN calculates coverage for each sample in the ROIs, corrects for GC content and mappability, and normalizes the coverages against Panel of Normals (PoN). Separate PoNs were used for plasma and tissue samples due to differences in background noise (Supplementary Methods). The workflow of the CNA calling pipeline for a single sample is shown in Supplementary Figure S2. The segmentation profiles were visualized interactively on a web browser with GenomeSpy 40and as static figures using the R package copynumber ⁴¹.

From the CNA profiles, we calculated the CNA burden represented by focal and broad CNA signal scores (Supplement Methods) by using formula described by Franch-Expósito et al. ⁴².

Statistical analyses

Statistical analyses were performed using the R software (version 4.3.0). A threshold of 0.05 p-value was applied to define the significant statistical comparisons.

Comparison between samples

To compare the detected CNAs in tissue and plasma samples, we calculated concordances between samples with TP53 VAF > 10% in tissue and > 1% in plasma, which resulted in 24 pairs of plasma and tissue samples from the same timepoints (Supplementary Table S4). Comparisons were performed using the Kendall correlation (R package 'stats') using the log₂(R) ratio (logR) values of CNA segments between the matched tissue and plasma samples.

Associations between ctDNA and clinical factors

We applied linear regression and the Wilcoxon rank sum test from R package 'rstatix' to explore associations between ctDNA features and clinical factors.

Gene-level CNA status with GISTIC2 analysis

We run GISTIC2 analysis ⁴³ for 29 pretreatment tissue samples to determine the prevalence of gene-level status. Gene-level scores resulting from GISTIC2 were used to infer the gene-level CNA status with thresholds of ± 0.5. The parameters for running GISTIC2 are specified in the Supplementary Methods.

3.1 ctDNA detection and tumor fraction evaluation

We sequenced 152 plasma samples from 29 HGSC patients belonging to the DECIDER cohort (Table 1), expanding the previously published set of 12 patients ¹⁷. Additionally, 46 tumor tissues and ascites samples were used for comparison and TP53 mutation detection.

Most plasma samples had detectable ctDNA levels, as measured by TP53 VAF ^26,31,32 (70%, 106 samples). However, the ctDNA fraction (tumor fraction, TF) was generally low, most commonly less than 1% (90 samples), and only 22 samples (from 12 patients) had TP53 VAF > 10%. As expected, TF was the highest in the pretreatment (median TP53 VAF 0.04) and relapse samples (median TP53 VAF 0.029), of which 85% had detectable ctDNA (Fig. 1).

Table 1

Summary of clinical factors in the cohort of 152 plasma samples from 29 patients. Plasma samples were collected at initial surgery (laparoscopy or PDS, called pretreatment here), during primary treatment, at follow-up, and at relapse.
Number of patients	29
Median age	68 (57–81)
Stage
IIB	1
IIIC	19
IVA	5
IVB	4
Treatment strategy
PDS	14
NACT	15
Average samples collected per patient	7 (3–24)
Plasma samples per treatment phase
Pretreatment	23
Primary treatment	58
Follow-up	13
Relapse	58

We further examined tumor fraction (TF) estimates, especially the use of DNA fragment sizes for plasma samples. Although cell-free DNA (cfDNA) typically exhibits a median read length of 167bp ⁴⁴, ctDNA fragment sizes are generally shorter, ranging from 134-144bp ⁴⁵. Thus, DNA fragment size proposes an independent measure for ctDNA proportion that is not influenced by TP53 mutation status or its copy-number values. Consistent with TP53 VAF, we observed longer median fragment sizes in follow-up and during primary treatment, indicative of lower tumor fractions (Fig. 2B). The correlation between TP53 VAF and the short-long ratio (SLR) of ctDNA fragments in pretreatment samples was 0.84 (Supplementary Figure S3A) while the correlations with SLR of relapse samples were not statistically significant (Supplementary Figure S3B). Moreover, in a few samples during primary chemotherapy with low TP53 VAF, we detected unexpectedly short median fragment sizes (Fig. 2B), possibly indicating an effect of chemotherapy on fragment length. Thus, TP53 remained the primary TF estimate for further analyses, whereas DNA fragment size provided a complementary inference, specifically limited to pretreatment samples.

Furthermore, we explored the relationships between TP53 VAF values in pretreatment and relapse ctDNA and various clinical factors, such as age at diagnosis, CA125 levels, cancer stage, and progression-free interval (PFI), categorized as either shorter or longer than 6 months. However, our analysis revealed that most clinical factors were not significantly associated with TP53 VAF values (Supplementary Figure S3). In conclusion, the studied clinical factors did not explain the detected variability between patients in ctDNA proportions in the plasma, even after pretreatment.

3.2 Detection of copy-number events depends on the ctDNA fraction of the plasma samples

To evaluate the sensitivity of CNA detection, we tested whether copy-number values (log₂R) correlated between matched plasma and tissue samples. As expected, correlations were highest in the high-TF samples (Fig. 3A, Supplementary Table S4). Most plasma samples with > 1% TP53 VAF showed concordant CNAs with matched tissue samples (interactive visualization: https://csbi.ltdk.helsinki.fi/pub/home/nguyenma/GenomeSpy/publication/mnguyen_et_al_2023/?spec=spec.json#bookmark:Concordance-for-ctDNA-samples-with-TF-from-1-to-5%). Most CNA profiles of plasma samples that had no detectable TP53 VAF were flat and showed no CNAs (Supplementary Figure S4), suggesting that the false positive signal rate was low. Overall, more CNAs were detected in higher TF samples, as shown in Fig. 3C/D (interactive visualization: https://csbi.ltdk.helsinki.fi/pub/home/nguyenma/GenomeSpy/publication/mnguyen_et_al_2023/?spec=spec.json#bookmark:CNA-Signals-By-TP53-VAF).

CNA detection is most sensitive to highly amplified regions, which can also be detected also in low-TF samples. As an extreme example, highest amplifications at KRAS and CCNE1 in patient EOC740 were detected in plasma samples with TP53 VAF as low as 0.4% (Supplementary Figure S5, interactive visualization: https://csbi.ltdk.helsinki.fi/pub/home/nguyenma/GenomeSpy/publication/mnguyen_et_al_2023/?spec=spec.json#bookmark:High-Copy-Number-in-ctDNA-sample-with-0.4%-TF). Copy-number losses were more commonly missed in the lower TF samples.

3.3 Longitudinal copy number profiles showed genomic changes along the course of treatment

Because high copy-count amplifications can be detected most sensitively, we focused on studying the gene-level effects of MECOM (chr3q21), MYC (chr8q26), KRAS (chr12p21), and CCNE1 (chr19q12), which are known oncogenes ^24,46 and commonly amplified in these patients (Fig. 3A). Of note, MECOM region at chromosome 3 is sometimes referred as the PIK3CA region. Here, it is referred as the MECOM region since MECOM is the most frequently amplified gene in the region ¹⁵.

Examining the amplification statuses between the pretreatment tissue and plasma samples, we observed a predominantly concordant pattern (Fig. 3B). Most patients showed consistency in amplification status across both tissue and plasma samples. However, there have been a few discordant cases. For EOC1030, amplification was detected only in the ctDNA samples for KRAS, and for EOC587, amplification of CCNE1 was also specific to the ctDNA samples and was not observed in the tissue samples. Interestingly, in the case of EOC204, two amplifications were detected on both sides of KRAS, but closer examination suggests that these might be spurious detections in the lower TF pretreatment plasma sample.

Moving beyond pretreatment analysis, we explored the changes in amplification profiles between pretreatment and relapse samples. Both acquired and lost CNAs were detected in these genes, indicating dynamic changes during treatment (Fig. 3B). Despite the overall concordance in profiles between the pretreatment and relapse samples, some patients exhibited notable differences. For example, EOC736 showed loss of MECOM and MYC amplifications and acquired CCNE1 amplification. EOC587 showed loss of MYC and acquired MECOM amplifications. Heterogeneity in MYC and CCNE1 amplification status longitudinally and between tissue biopsies has been detected in a recent study ⁴⁷.

As an example, patient EOC587 showed focal changes during treatment. The CNA profile remained the same for the most part, focal changes are visible after laparoscopy and after chemotherapy (Fig. 4B). This patient had stage IV disease (Fig. 4A). She underwent diagnostic laparoscopy with removal of the ovaries and underwent NACT but progressed during adjuvant chemotherapy. Her CNA profile in ctDNA changed during laparoscopy and again at relapse. An amplification at PALB2 at chromosome 16 was acquired during laparoscopy, and it was recurrently detected at IDS from liver metastatic sample (Fig. 4B). In relapse plasma, this amplification was no longer detected and other acquired CNA events were detected instead. For example, acquired losses were detected on chromosome 14 (including XRCC3 and HSP90AA1) and the entire p arm of chromosome 7.

We conducted a comprehensive analysis of CNA profiles in samples with TP53 VAF exceeding 3% which showed focal CNAs, to assess heterogeneity and treatment effects (n = 18, Supplementary Figures S6-S23). Among these, 11 patients (Supplementary Figures S6-S16) had available tissue or plasma samples before treatment and after chemotherapy, enabling us to evaluate changes occurring during treatment. CNA profiles changed in the majority of patients; longitudinal changes in CNA profiles were detected in seven patients (Supplementary Figures S6-S12), indicating treatment-induced alterations. In contrast, only two patients showed no detectable changes in their copy number profiles (Supplementary Figures S13-S14). In two patients, the differences in the CNA profiles were not clearly discernible (Supplementary Figures S15-S16). Notably, most high TF relapse plasma samples were collected close to death (less than 2 months before death), but these included both changing and stable CNA profiles.

It is important to highlight that even in patients with a plasma sample having a TF of only 3%, we were able to detect focal amplifications, as demonstrated by patient EOC1120 (Supplementary Figure S11). This finding underscores the remarkable sensitivity of our approach for detecting genomic alterations, even at low TFs.

Notably, two patients, EOC482 (Supplementary Figure S8) and EOC736 (Supplementary Figure S10), displayed significant changes in their CNA profiles, which affecting multiple chromosomes. These patients initially achieved a complete response after receiving neoadjuvant chemotherapy (NACT) but experienced relapse within six months after primary treatment. Interestingly, in the case of EOC482, the altered CNA profile was detected at the second relapse, despite receiving no targeted treatments other than weekly paclitaxel and doxorubicin during the first and second relapses, respectively. Similarly, in the case of EOC736, the CNA profile changed between the diagnosis and the fourth relapse, following ctDNA-guided trastuzumab treatment during earlier relapses¹⁷. In particular, the ERBB2 (HER2) amplification detected in the pre-treatment sample was vanished after receiving targeted therapy and was not detected in later samples.

These findings provide compelling evidence for the dynamic nature of CNA profiles during treatment and highlight the potential of ctDNA analysis for tracking treatment response and detecting genomic changes associated with relapse. This emphasizes the importance of monitoring CNAs as a complementary tool to understand cancer cell evolution and treatment efficacy.

In this study, we aimed to assess the utility of ctDNA for longitudinal CNA profiling in patients with HGSC. Our findings revealed that although the overall CNA profiles remained relatively stable, seven of the 11 patients exhibited focal genomic changes, primarily consisting of acquired and lost amplifications, during and after primary therapy. Notably, even in a plasma sample with TP53 VAF as low as 3%, we were able to detect CNA events. Moreover, two patients displayed extensive changes in their CNA profiles, affecting multiple chromosomes, between the pretreatment and relapse samples. This observation contrasts with a previous result that observed no changes in CNAs ⁴⁸ with lower-resolution sequencing data.

Low ctDNA fractions may limit the usefulness of ctDNA, particularly for CNA calling. Here, we were able to detect high copy-count amplifications in a plasma sample with a TF of less than 1% (Supplementary Figure S5). Generally, copy-number profiles are recognizable in samples with more than 5% of tumors. Earlier studies have used the threshold of at least 20% tumor fraction for the analysis of ctDNA-based CNA ^33,49, the higher sensitivity here allowed us to define signals for ctDNA-based CNA profiles for majority of patients (18 out of 29, Supplementary Figure S6-S22).

Notably, the ctDNA fragmentation profile may be suitable for TF estimation of cfDNA samples ^44,45,50. However, our observations show poor correlation after chemotherapy between cfDNA fragment sizes in sequencing data and TP53 VAF. Additionally, samples with unexpectedly low DNA fragment sizes without detected TP53 VAFs did not have detectable CNAs. These results suggest that chemotherapy can affect cfDNA release from other cell types and alter cfDNA fragment sizes in plasma. Future studies focusing on fragmentomics-based approaches should evaluate these findings further.

Although our study demonstrates the potential of longitudinal plasma ctDNA sampling for CNA profiling, we acknowledge several limitations. First, the copy number calling approach used in this study, based on targeted sequencing, may not fully capture the entire genome's copy number profile, potentially leading to underestimation of genomic variation between samples. Therefore, it would be valuable to explore genome-wide approaches such as shallow whole-genome sequencing for further validation. Additionally, the small sample size of our study emphasizes the need for future validation using a larger cohort to confirm our findings.

We demonstrated that ctDNA can capture CNA information across different tumor sites and detect changes caused by surgeries and chemotherapies. ctDNA may reflect surgical removal of site-specific subclones, such as in the case of EOC587, chemotherapy-caused evolution between diagnosis and relapse, such as selection of resistant subclones due to treatment, such as loss of ERBB2 amplification in the case of EOC736, or novel genomic events. These findings are in line with recent tissue-based studies by us ¹⁵ and others ⁴⁷ showing subclonal heterogeneity at pretreatment and between pretreatment and relapse. Only two patients had major changes in the CNA profile, and similar major subclonal changes due to chemotherapy were rare in these studies, but heterogenic disease at relapse was commonly discovered.

Plasma samples offer a significant advantage in terms of availability, enabling the detection of genomic changes during treatment. They provide additional information during relapse, allowing for the estimation of heterogeneity and a more detailed assessment of potential drug target abundance in the entire tumor. Despite the limitations posed by low ctDNA levels, our analysis demonstrated the potential of ctDNA sampling for monitoring changes over time during relapse. Overall, our study highlights the potential of ctDNA sampling in the development of tailored treatment plans that can improve patient outcomes. Personalized medicine for cancer patients relies on their ability to monitor genomic changes and changes in abundance. CtDNA, particularly during relapse, provides a unique opportunity to detect genomic targets and biomarkers in a timely manner. However, it is important to better understand patients with low TF and their tumors as well as to develop more sensitive methods to address this challenge.

HGSC

High-Grade Serous Carcinoma

cfDNA

cell-free DNA; ctDNA:circulating tumor DNA

CNA

Copy Number Alteration

Tumor Fraction; VAF:Variant Allele Frequency

PDS

Primary Debulking Surgery; NACT:Neoadjuvant Chemotherapy

ROI

Region of Interest

PoN

Panel of Normals

Ethics approval and consent to participate

All patients participating in the study gave their informed consent, and the study was approved by the Ethics Committee of the Hospital District of Southwest Finland (ETMK 145/1801/2015).

Consent for publication

Not applicable

Availability of data and materials

The data used in article is available upon request to the corresponding author.

The scripts for the CNA calling pipeline and the main figures presented in this manuscript are available at: https://github.com/maitnnguyen/Oseq_CNV_Paper/tree/main

Competing interests

The authors declare that they have no competing interests.

Funding information

Authors’ contributions

J.H recruited patients and selected samples. J.O. and S.H. designed the study. M.T.N.N implemented the bioinformatics pipeline, analyzed, and interpreted data, and prepared the manuscript and the supplements. All authors reviewed the manuscript.

Acknowledgements

This project has been supported by Academy of Finland, Sigrid Jusélius Foundation, Sakari Alhopuron Säätiö, and the European Union’s Horizon 2020 research and innovation programme under grant agreement No 667403 for HERCULES and No 965193 for DECIDER. We thank MSc Kari Lavikka for help in the GenomeSpy visualizations. Computing resources from CSC — IT Center for Science are gratefully acknowledged.

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No competing interests reported.

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published 30 Nov, 2023

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Circulating Tumor DNA-Based Copy-Number Profiles Enable Monitoring Treatment Effects During Therapy in High-Grade Serous Carcinoma

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Abstract

Background

Method

Result

Conclusion

Figures

1. Introduction

2. Materials and Methods

2.1 Cohort and samples

2.2 Deep sequencing of plasma samples and target regions

2.3 Tools and Algorithms

3. Results

3.1 ctDNA detection and tumor fraction evaluation

3.2 Detection of copy-number events depends on the ctDNA fraction of the plasma samples

3.3 Longitudinal copy number profiles showed genomic changes along the course of treatment

4. Discussion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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