MRDagent: iterative and adaptive parameter optimization for stable ctDNA-based MRD detection in heterogeneous samples.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40889285", "kind": "science-record", "revision": 2, "content_hash": "sha256:080b8371ffecd0d1324881b5b823849cb3253d5be2ad219dd72fb9412db7e305", "created_at": "2026-11-01T20:31:00Z", "updated_at": "2026-11-01T21:35:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "MRDagent: iterative and adaptive parameter optimization for stable ctDNA-based MRD detection in heterogeneous samples.", "doi": "10.1093/bioinformatics/btaf485", "pmid": "40889285", "authors": ["Tianci Wang", "Xin Lai", "Shenjie Wang", "Yuqian Liu", "Xiaoyan Zhu", "Jiayin Wang"], "journal": "Bioinformatics (Oxford, England)", "year": 2025, "abstract": "MOTIVATION: Minimal residual disease (MRD) as critical biomarker for cancer prognosis and management plays a crucial role in improving patient outcomes. However, detecting MRD via next-generation sequencing-based circulating tumor DNA variant calling remains unstable due to the extremely low variant allele frequency and significant inter- and intra-sample heterogeneity. Although parameter optimization can theoretically enhance the detection performance of variants, achieving stable MRD detection remains challenging due to three key factors: (i) the necessity for individualized parameter tuning across numerous heterogeneous genomic intervals within each sample, (ii) the tightly interdependent parameter requirements across different stages of variant detection workflows, and (iii) the limitations of current automated parameter optimization methods.\nRESULTS: In this study, we propose MRDagent, a novel variant detection tool designed specifically for MRD detection. MRDagent incorporates an iterative and self-adaptive optimization framework capable of handling unknown objectives, varying constraints, and highly coupled parameters across stages. A key innovation of MRDagent is the integration of a convolutional neural network-based meta-model, trained on historical data to enable rapid parameter prediction. This significantly enhances computational efficiency and generalization performance. Extensive evaluations on simulated and real-world datasets demonstrate MRDagent's superior and stable performance, providing an efficient, reliable solution for MRD detection in clinical and high-throughput research applications.\nAVAILABILITY AND IMPLEMENTATION: MRDagent is freely available at https://github.com/aAT0047/MRDagent.git. The corresponding dataset and software archive are available at Zenodo: https://doi.org/10.5281/zenodo.15458496.", "claims": [{"id": "claim-method", "statement": "Finally, we used a 10-fold cross-validation approach to rigorously evaluate the CNN-based meta-model, enabling rapid generalization from previous tasks across samples and genomic regions to new tasks.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40889285", "locator": "sections[11] \"2.4.3 Training process\" paragraph 2", "quote": "Finally, we used a 10-fold cross-validation approach to rigorously evaluate the CNN-based meta-model, enabling rapid generalization from previous tasks across samples and genomic regions to new tasks."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data underlying this article were accessed from the International Cancer Genome Consortium (ICGC, https://dcc.icgc.org/). Access to the original data requires compliance with the ICGC Data Access Policies and approval by the relevant Data Access Committee. The derived data generated in this study are available from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40889285", "locator": "sections[30] \"Data availability\" paragraph 2", "quote": "The data underlying this article were accessed from the International Cancer Genome Consortium (ICGC, https://dcc.icgc.org/). Access to the original data requires compliance with the ICGC Data Access Policies and approval by the relevant Data Access Committee. The derived data generated in this study are available from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "MRDagent attained a mean F1 score of 0.78 across the 473 subsamples, with a variance of merely 0.04, and notably lower RMSE and CV compared to other methods.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40889285", "locator": "sections[17] \"3.2.3 Stability analysis of intrasample samples\" paragraph 2", "quote": "MRDagent attained a mean F1 score of 0.78 across the 473 subsamples, with a variance of merely 0.04, and notably lower RMSE and CV compared to other methods."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "MRDagent attained a mean F1 score of 0.78 across the 473 subsamples, with a variance of merely 0.04, and notably lower RMSE and CV compared to other methods.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40889285"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40889285", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1093/bioinformatics/btaf485", "sha256": "sha256:4e2ac6d6453b19a8860ddf0547012cb8d406b634f3731778234e559137203aeb", "bytes": 44845, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1093/bioinformatics/btaf485", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12462387", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": null, "specific": null, "label": "10-fold cross-validation", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Finally, we used a 10-fold cross-validation approach to rigorously evaluate the CNN-based meta-model, enabling rapid generalization from previous tasks across samples and genomic regions to new tasks."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R4", "expressible": false, "required_operations": [], "missing_operations": [], "basis": "The reader quoted the method the paper names (10-fold cross-validation), but the corpus's method extractor could not map it onto a DSL operation, so the method is published but not expressible."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["The data underlying this article were accessed from the International Cancer Genome Consortium (ICGC, https://dcc.icgc.org/). Access to the original data requires compliance with the ICGC Data Access Policies and approval by the relevant Data Access Committee. The derived data generated in this study are available from the corresponding author upon reasonable request."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:40889285"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T20:00:53Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 12032, "completion_tokens": 1668, "cached_tokens": 128}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:40889285", "type": "paper", "label": "pmid:40889285"}, {"id": "source:pmid:40889285", "type": "manuscript", "label": "PMID 40889285 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:40889285", "type": "paper", "label": "pmid:40889285"}, {"id": "source:pmid:40889285", "type": "manuscript", "label": "PMID 40889285 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-40889285", "type": "extraction", "used": ["source:pmid:40889285"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:40889285", "parent_hash": "sha256:f8d13e47a1a7b4bef8e2fca3215f88b3f5b39989b93d4856cb1a6634086d7ae1", "fork_of": null, "generation": 1}, "gaps": ["execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"role": "data_generation", "definition_id": "amplicon_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["patient outcomes. However, detecting MRD via next-generation sequencing-based circulating tumor DNA variant calling", "on typically uses ctDNA as input, leveraging NGS to obtain high-resolution genomic data, foll", "workflow of ctDNA-based MRD detection using NGS. ctDNA is extracted from plasma samples and", "tracted from plasma samples and subjected to NGS to obtain high-resolution genomic data. Vari"], "order_basis": "paper_text_order:254", "id": "assay:1"}]}