A reference-free strategy for circulating tumor DNA detection from whole-genome sequencing data

PLOS Computational Biology

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PLOS Computational Biology

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PLOS Computational Biology

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note that one review is uploaded as an attachment.

Reviewer #1: In this manuscript, Oroperv and colleagues propose a reference free, “tumor informed” cfDNA approach based on k mer analysis across three matched samples per patient, tumor, germline, and plasma cfDNA; each sequenced at ~30× whole genome depth (on the order of hundreds of millions of fragments per sample). The model is then applied to a cohort of stage III colorectal cancer patients (n=90) with three year follow up.

From a computational/bioinformatics standpoint, the technique is novel and could have utility beyond the specific use case studied. However, as presented, the performance and clinical applicability are unclear and underwhelming. The strategy effectively requires ~90× WGS per patient (3×30×), yet the detection limit appears to lie between ~0.1% and 1% (Figure 2F), which substantially limits clinical utility and is not competitive in the MRD setting. By comparison, simple targeted capture methods focusing on recurrent SNVs can reach limits around ~0.01% at roughly an order of magnitude lower cost than ~90× WGS. Moreover, for standard ~30× WGS, alignment is performed in routine workflows; thus, the “reference free” paradigm does not realistically prevent mapping. That said, I would be interested to see whether k mer–based signals provide orthogonal information that synergizes with post alignment features (e.g., variants, copy number, indels, fragment length).

I encourage the authors to consider reframing the contribution toward contexts such as novel sequence identification or motif discovery, rather than MRD detection or circulating tumor fraction estimation. Hence, I recommend major revision.

Major Comments

1. The k mer analysis is highly computationally intensive, requiring analysis of billions of reads. Without alignment, it is also difficult to confirm the biological validity of findings—e.g., whether observed signals correspond to mutations relevant to the cancer type of interest.

2. Please comment on the impact of PCR induced duplications on the k mer counts and downstream results. How are duplicates detected/handled, and how sensitive are results to duplication rates?

3. The selected k values (31, 41, 51, 61) appear primarily empirical. While k=51 performed best in FRFR samples and k=31 in FFPE samples, the rationale for limiting the search to this range is not fully clear. Why were multi k strategies not explored? Since the optimal k seems to differ by sample type, please discuss whether broader sensitivity analyses or adaptive k mer selection might improve robustness across sequencing contexts.

4. On page 21, the manuscript suggests that smaller k mers yield better results, which contrasts with earlier choices and highlights the framework’s sensitivity to k. This raises robustness concerns that should be addressed explicitly.

5. Consider mapping reads carrying “germline” k mers back to the reference and testing for enrichment of known population SNPs (e.g., from gnomAD) within a statistical framework to substantiate the interpretation of these signals.

6. When reporting sensitivity and specificity, please also provide sensitivity at clinically relevant fixed specificity thresholds (e.g., 95% or 98%), which facilitates comparison to established assays.

7. The manuscript lacks an LOD analysis, which is standard for evaluating detection frameworks. This could be addressed via in silico spike in (mixture) experiments across a range of tumor fractions.

Reviewer #2: Uploaded as attachment

Reviewer #3: In this expansive manuscript, the authors propose a novel approach to analysing circulating tumour DNA and apply their approach to data collected from colorectal cancer patients follow surgical resection of the primary tumour. This manuscript is a nice use-case and illustrates the potential benefits of the proposed approach. I have a few comments on the current manuscript.

1) It was not clear to me how the authors decided on a value of k (other than requiring it to be an odd number). This seems to be an important aspect of the approach and it wasn't clear how this value was chosen, nor how the results depend on this parameter.

2) The authors state that they remove " k-mers with low or high counts"; what is the threshold for removal at the low and high end? How does this decision to remove low counts influence the ability of this approach to identify de novo mutations within the tumour (as might be the case in the development of adaptive resistance to targeted therapies; I appreciate the authors are considering surgically resected tumours here, but my impression is that ctDNA could be a useful biomarker to identify the selection of resistant mutants during small molecule treatment and I wonder if this approach would be applicable in the non-surgical case).

3) On page 10, the authors give the p value from the Mann-Whitney test, which may be skewed by the large number of samples. I would appreciate if the authors reported not just the mean, but also a measure of the heterogeneity of the TF fraction here. Further, I'm not sure how to interpret the threshold of 6x10^{-4} for recurrence- do they authors expect this threshold to extent to other settings? I would appreciate more details on how this may be extended outside of the current retrospective study.

4) I wonder if the 4th section of the results could be moved to the supplemental information, as this seems to be a validation/comparison of the proposed approach against existing methods.

5) I found the filtering section of the methods to be difficult to follow. I think this is a crucial part of the manuscript and approach, and wonder if it would be easier to follow with a schematic/figure demonstrating how the samples are filtered to identify the UT k-mers.

6) The authors match the maximal variance of the probabilistic model to the what was observed in the data. As the data may not fully account fo the potential variance in cfDNA and ctDNA (due to sampling difficulties etc.), I was wondering how crucial this assumption is and, more importantly, if this assumption may be imposing additional homogeneity on the data. Further, I would appreciate seeing the weights of the mixture distribution (or even a few representative examples) to see how distinct the ctDNA/Germline/noise distributions are.

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reproducibility:

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Attachments

Attachment

Submitted filename: ctDNAmer review plos_compbio 09.21.2025.pdf

An alignment-free strategy for circulating tumor DNA detection and tumor fraction estimation from whole-genome sequencing data

PLOS Computational Biology

Dear Dr. Besenbacher,

Thank you for submitting your manuscript to PLOS Computational Biology. After careful consideration, we feel that it has merit but does not fully meet PLOS Computational Biology's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by May 30 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at ploscompbiol@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pcompbiol/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

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If you would like to make changes to your financial disclosure, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter

We look forward to receiving your revised manuscript.

Kind regards,

Philipp Martin Altrock, Ph.D.

Academic Editor

PLOS Computational Biology

Shaun Mahony

Section Editor

PLOS Computational Biology

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #2: I find that the authors have addressed some of my concerns, and that the manuscript is improved with the additional analyses. However, I have a few concerns remaining.

Major remaining concerns:

I could not find where the authors mention temporal tumor heterogeneity as a limitation. Given that the sample-level sensitivity is 54%, subclonal evolution is a plausible contributor that should be acknowledged in the Discussion (again, apologies if it’s there and I could not find it).

The authors did not add a sensitivity analysis to show what happens when the window around the mean tumor count changes.

The biological validation with Mutect2/Delly does not apply for the FFPE cohort where k=21 is optimal. The authors should explicitly state this as a limitation.

The subsequent discussion of unique advantages present in WGS-based ctDNA detection could still be clearer. The authors could add a more direct discussion of how alignment-free detection might complement existing alignment-based approaches.

If computational efficiency will be mentioned in the main paper, the authors should at least highlight one quantitative comparison in the main paper. I do see that the authors added a reference to the supplementary table in the discussion, but it is easier on the reader to add example numbers as a frame of reference. Indeed, the authors include Supplementary Table 2 without any units, so that the mean running time sum of ~20 is essentially meaningless. The point that a k-mer based approach is faster is intuitive but is also not demonstrated in this table.

Minor points:

I would still like to see the authors add a few sentences to Methods (apologies if I could not find it) clearly stating all the inputs necessary to run the method, and what outputs are produced.

The original comment about the simulation was to understand what happens when the germline noise vs. technical noise contributions vary. Supplementary Figure 13 does not describe noise composition – if out of scope, perhaps it would be better to add to limitations.

Reviewer #3: The authors have satisfactorily addressed all my concerns. I appreciate the authors adding additional information and analyses to explore the role of the method's "hyper parameters" on the results. I think it might be worthwhile to explicitly list the assumptions underlying these hyperparameters, potentially in the SI, along with a clear explanation of which assumptions can potentially be relaxed, as in the response to my 2nd comment on the initial submission.

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The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #3: No

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Reproducibility:

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Dear Dr. Besenbacher,

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