1. INTRODUCTION:

The management of advanced solid tumors demands accurate, real-time assessment of treatment response, early identification of resistance, and sensitive detection of minimal residual disease. Conventional monitoring strategies primarily computed tomography (CT) imaging and serum tumor markers suffer from well-documented limitations: imaging cannot reliably detect subcentimeter disease or peritoneal metastases, exhibits inter-reader variability, and provides only intermittent snapshots of tumor burden.^1,2 Circulating tumor DNA (ctDNA) has emerged as a transformative liquid biopsy biomarker, with mutation-based approaches now integrated into clinical practice for targeted therapy selection and MRD detection.^3,4 However, mutation-based ctDNA assays require either prior knowledge of tumor-specific mutations or expensive broad-panel sequencing, and may fail to detect disease in tumors with low mutational burden or extensive intratumor heterogeneity.⁵

Cell-free DNA fragmentomics the systematic analysis of cfDNA fragmentation patterns represents a fundamentally distinct approach that exploits the non-random nature of DNA fragmentation. When cells undergo apoptosis or necrosis, the resulting cfDNA fragments retain signatures of the chromatin architecture, nucleosome positioning, and epigenetic state of their cells of origin.^6–8 Cancer cells, with their globally altered epigenomes and aberrant chromatin organization, produce cfDNA fragments with distinctive profiles that differ from those of healthy hematopoietic cells.^9,10 These fragmentomic signatures encompass multiple dimensions: fragment size distributions, 5′-end sequence motifs, nucleosome occupancy footprints, preferred end positions, and regional fragmentation patterns across the genome.^11,12

The foundational work of Cristiano and colleagues established that genome-wide cfDNA fragmentation patterns, analyzed through the DELFI (DNA EvaLuation of Fragments for early Interception) approach, could detect seven cancer types with high sensitivity and predict tissue of origin.¹³ Subsequent studies from Lo and colleagues provided a comprehensive conceptual framework linking cfDNA fragmentation to underlying biology.⁸ These pioneering contributions catalyzed a rapidly expanding field, and recent reviews have comprehensively addressed the early detection landscape.^14,15 However, the equally important and arguably more clinically actionable application of fragmentomics to treatment monitoring, response prediction, and resistance detection has not been systematically examined.

This narrative review addresses this gap by providing the first comprehensive synthesis of cfDNA fragmentomics specifically for treatment-phase applications across solid tumors. We evaluate the evidence base for fragmentomics in treatment response monitoring, MRD detection, immunotherapy response prediction, and resistance identification, while examining the technical platforms and proposing a framework for clinical implementation.

2. Biological Rationale for Fragmentomics In Treatment Monitoring:

The utility of fragmentomics for treatment monitoring rests on a critical biological premise: anti-cancer therapies alter the quantity, composition, and fragmentation characteristics of cfDNA in ways that reflect therapeutic efficacy. Effective cytotoxic therapy induces tumor cell death, initially releasing increased quantities of tumor derived cfDNA followed by a decline as tumor burden decreases.^16,17 This dynamic behavior creates a quantifiable signal that fragmentomic analysis can capture without requiring prior knowledge of tumor mutations.

The fragmentation of cfDNA is governed by multiple deterministic processes rather than random degradation. Nucleosome positioning represents the primary determinant: cfDNA fragments preferentially correspond to nucleosome-protected segments of approximately 167 base pairs (bp), with the distribution of fragment lengths reflecting the chromatin accessibility landscape of the cell of origin.^8,18 Cancer cells exhibit globally altered nucleosome positioning due to widespread epigenetic dysregulation, resulting in cfDNA fragments with distinct size distributions notably shorter fragments and altered short to long fragment ratios compared with cfDNA derived from non-malignant cells.^9,13

Beyond fragment size, additional layers of information are encoded in cfDNA structure. The 5′-end sequence motifs of cfDNA fragments reflect the nuclease preferences of the cell of origin, with DNASE1L3 and DFFB generating distinct cleavage patterns that differ between apoptotic and necrotic cell death pathways differentially engaged by various therapeutic modalities.^11,19 Nucleosome footprinting at transcription factor binding sites and gene promoters provides a readout of the transcriptional activity of cfDNA source cells, enabling inference of gene expression states from fragmentation data alone.^20,21 These multidimensional fragmentomic features collectively create a rich, therapy-responsive signal that can be monitored longitudinally.

3. Treatment Response Monitoring:

3.1 Delfi-tf: A Paradigm for Tumor-Naive Monitoring:

The development of DELFI-TF (DELFI-Tumor Fraction) by van ’t Erve and colleagues represents a landmark advance in fragmentomics-based treatment monitoring.²² Using low-coverage whole-genome sequencing (approximately ~1–2× depth to ~6× depth), DELFI-TF applies machine learning to genome-wide cfDNA fragmentation patterns to estimate tumor fraction without requiring any prior genetic information from the tumor. In validation cohorts of patients with metastatic colorectal cancer (mCRC) and lung cancer (n = 158), DELFI-TF scores demonstrated strong correlation with circulating tumor DNA levels (r = 0.90, p < 0.0001, Pearson correlation), critically including cases where somatic mutations were undetectable by conventional assays.²²

The clinical implications of DELFI-TF are substantial. Baseline DELFI-TF scores prior to therapy initiation were independently associated with overall survival (HR = 9.84, 95% CI 1.72–56.10, p < 0.0001), and patients with lower DELFI-TF scores during treatment experienced significantly longer overall survival (62.8 versus 29.1 months, HR = 3.12, 95% CI 1.62–6.00, p < 0.001).²² Importantly, DELFI-TF predicted clinical outcomes more accurately than CT imaging, suggesting that fragmentomic assessment of tumor burden provides a more sensitive and dynamic measure of treatment efficacy than conventional radiographic evaluation.

The DOLPHIN (DNA fragmentOmics of Liquid bioPsies in Histologic and Imaging-based moNitoring) trial, a prospective, multi-center, observational study embedded within the Prospective Dutch ColoRectal Cancer cohort (PLCRC), represents the first dedicated prospective validation of fragmentomics for treatment monitoring.²³ This study evaluates whether DELFI-TF can serve as a sensitive, affordable, and broadly applicable test to monitor treatment response in mCRC patients, with results presented at the 2024 and 2025 AACR Annual Meetings confirming the feasibility and clinical utility of the approach.²⁴

3.2 Neoadjuvant Therapy Response Prediction:

Fragmentomics has demonstrated particular promise for predicting pathological complete response (pCR) after neoadjuvant therapy, a setting where accurate response prediction can guide organ-preserving strategies. Wang and colleagues evaluated cfDNA fragmentomics in 119 patients with locally advanced rectal cancer (LARC) receiving neoadjuvant chemoradiotherapy.²⁵ Using fragment size profiles and 5′-end motif patterns extracted from targeted panel sequencing, the investigators constructed predictive models that achieved an area under the curve (AUC) of 0.87 in an independent validation cohort for pCR prediction. Notably, fragmentomic features captured during treatment (at mid-treatment and pre-surgery timepoints) provided superior predictive performance compared with baseline-only assessment, supporting the value of serial fragmentomic monitoring.

Similar findings have been reported in cervical cancer, where Peng and colleagues demonstrated that cfDNA fragmentome profiling could predict neoadjuvant chemotherapy response prior to treatment initiation.²⁶ Across multiple tumor types, a consistent pattern emerges: fragmentomic features provide treatment-response information that is complementary to, and in some cases superior to, conventional mutation-based ctDNA analysis and imaging.

3.3 Cross-Tumor Monitoring Applications:

The pan-cancer applicability of fragmentomic monitoring has been substantiated through several independent studies. The DELFI-TF technology has been adopted by five top-20 pharmaceutical companies for guiding oncology drug development decisions, and is being applied across multiple solid tumor types including colorectal, lung, breast, pancreatic, ovarian, and head and neck cancers.²⁴ A retrotransposon-based fragmentomic assay using quantitative PCR has been developed as a low-cost monitoring approach in patients with stage IV disease across lung, breast, and colorectal cancers, demonstrating that fragmentomic monitoring need not rely on expensive whole-genome sequencing.²⁷ The convergence of evidence across tumor types and analytical platforms reinforces the fundamental biological robustness of fragmentomics as a monitoring paradigm.

4. Immunotherapy Response Prediction Via Fragmentomics”:

Predicting response to immune checkpoint inhibitors (ICIs) remains one of the most pressing challenges in precision oncology, as established biomarkers PD-L1 expression and tumor mutational burden (TMB) fail to reliably identify responders across tumor types.^28,29 Fragmentomics offers a uniquely attractive approach for ICI response prediction because it captures genome-wide epigenomic information reflective of both tumor biology and the immune microenvironment.

Stutheit-Zhao and colleagues provided seminal evidence in the INSPIRE trial, a pan-cancer phase II study of pembrolizumab.³⁰ Analyzing 204 plasma samples from 87 patients using cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq), they derived both a cancer-specific methylation score (CSM) and a fragment-length score (FLS). Early kinetics of CSM predicted overall survival and progression-free survival independently of tumor type, PD-L1 expression, and TMB. Critically, early kinetics of FLS a purely fragmentomic measure—were independently associated with overall survival even after adjusting for the methylation-based score. This finding demonstrates that fragment length information captures biological information about treatment response that is not fully captured by methylation analysis alone.

Complementary evidence comes from DELFI-TF monitoring of ICI-treated patients. Alipanahi and colleagues presented data showing that DELFI-TF dynamics correlate with RECIST response assessments and predict progression-free survival across solid tumor types treated with ICIs.³¹ The EPIC-seq (Epigenomic Profiling to Infer Clinical Sequencing) approach developed by Esfahani and colleagues further expands the fragmentomic toolkit for immunotherapy monitoring by inferring gene expression programs from cfDNA fragmentation patterns at transcription start sites, enabling non-invasive assessment of tumor-intrinsic immune evasion programs.³²

5. Minimal Residual Disease Detection:

The detection of MRD after curative-intent surgery is a critical determinant of adjuvant therapy decisions and long-term outcomes. Mutation-based ctDNA assays have demonstrated clinical validity for MRD detection in colorectal, lung, and breast cancers, with landmark trials such as DYNAMIC demonstrating the feasibility of ctDNA-guided adjuvant therapy.^33,34 However, mutation-based approaches have inherent sensitivity limitations, particularly in tumors with low mutational burden, and tumor-informed approaches require costly tissue sequencing.

Fragmentomics substantially enhances MRD detection sensitivity when integrated with mutational analysis. Wang, Xia and colleagues demonstrated this principle in resectable non-small cell lung cancer (NSCLC), where a multimodal approach combining fragmentomic profiles, somatic mutations, and copy number alterations achieved MRD sensitivity of 78.3% compared with 43.5% for mutations alone.³⁵ Patients with positive fragmentomics-enhanced MRD at 7 days and 6 months post-surgery faced significantly elevated recurrence risk (HR 4.6–8.3), enabling earlier identification of patients who would benefit from adjuvant or escalated therapy.

Helzer and colleagues introduced an innovative approach demonstrating that fragmentomic analysis could be extracted from existing targeted ctDNA panels without additional sequencing, effectively providing MRD sensitivity enhancement at no marginal cost.³⁶ This approach analyzed fragment length distributions and end-motif patterns from standard ctDNA panels across multiple tumor types, improving detection of residual disease in cases where mutations were at or below the limit of detection.

The tumor-naïve nature of fragmentomic MRD detection represents a fundamental advantage. Nguyen and colleagues validated a multimodal profiling approach integrating mutations, copy number alterations, and fragmentomics in Vietnamese breast cancer and colorectal cancer cohorts without requiring prior tissue sequencing, establishing the cross-ethnic generalizability of the approach.³⁷ The Guardant Reveal assay, which integrates epigenomic features including methylation and fragmentomic signals for MRD detection in colorectal cancer, has demonstrated improved sensitivity over mutation-only approaches in clinical validation studies.³⁸

6. Resistance Detection And Emerging Frontiers:

The early identification of treatment resistance represents perhaps the most clinically impactful yet least explored application of fragmentomics. Preliminary evidence suggests that rising DELFI-TF scores precede radiographic evidence of progressive disease, potentially enabling preemptive therapeutic adaptation.^22,24 DELFI Diagnostics has positioned the technology to deliver insights at every stage of the metastatic cancer journey, specifically including resistance detection as a core application alongside baseline assessment and response monitoring.²⁴

The biological basis for fragmentomics-based resistance detection is compelling. Treatment-resistant clones may harbor distinct epigenomic profiles including altered chromatin accessibility, differentiation states, and transcriptional programs that would generate distinguishable cfDNA fragmentation signatures even before clonal expansion produces detectable mutations.^39,40 EPIC-seq enables inference of gene expression from cfDNA fragmentation patterns, offering the possibility of non-invasively detecting the activation of resistance programs such as epithelial mesenchymal transition, drug efflux pathways, or lineage plasticity.³²This domain remains an emerging frontier requiring dedicated investigation. Future studies combining serial fragmentomic monitoring with resistance mechanism characterization particularly in the context of targeted therapy and immunotherapy will be essential to establish the clinical utility of fragmentomics for adaptive treatment strategies.

7. Technical Platforms and Analytical Advances:

The evolution of fragmentomic analysis platforms has paralleled the expansion of clinical applications. Low-pass whole-genome sequencing (lpWGS) at approximately 1–2× depth remains the most widely used platform for comprehensive fragmentomic profiling, offering genome-wide coverage at a cost of approximately $100–150 per sample.^13,22 This cost-effectiveness compares favorably with tumor-informed ctDNA assays requiring prior tissue sequencing and with broad mutation panels, making serial monitoring economically feasible.

Cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) provides simultaneous access to methylomic and fragmentomic information, as demonstrated in the INSPIRE trial.³⁰ The integration of methylation and fragmentation data from a single assay maximizes information extraction per sample, a critical consideration for monitoring applications requiring frequent sampling. Targeted panel-based fragmentomic analysis, as demonstrated by Helzer and colleagues, enables extraction of fragmentomic features from standard clinical ctDNA panels, providing an immediate pathway for clinical adoption.³⁶

Machine learning has emerged as an indispensable component of fragmentomic analysis. The DELFI and DELFI-TF approaches employ gradient boosted machine learning models trained on genome wide fragmentation features.^13,22 More recently, the ELSM (Early-Late fusion with Sample-Modality evaluation) framework demonstrated that neural network-based integration of 13 fragmentomic feature spaces with sample-wise modality weighting achieved an AUC of 0.972 for pan-cancer diagnosis across 1,994 samples from 10 cancer types.⁴¹ Multidimensional fragmentomic approaches integrating fragment size ratios, fragment size distributions, end motifs, copy number variation, and nucleosome footprinting have been validated by Bao and colleagues and Cao and colleagues for cancer detection, with methodologies directly transferable to monitoring contexts.^42,43

Long-read sequencing technologies, including those from Oxford Nanopore Technologies and Pacific Biosciences, represent an emerging frontier. These platforms provide native access to both fragmentation patterns and epigenetic modifications (particularly 5-methylcytosine) from a single molecule, offering the potential for unprecedented multi-layered cfDNA characterization for treatment monitoring.⁴⁴

8. Toward Clinical Implementation:

The translation of fragmentomics from research tool to clinical monitoring assay requires attention to several critical implementation considerations. First, pre-analytical standardization is essential: cfDNA fragmentation patterns are sensitive to blood collection, storage, and processing conditions, necessitating rigorous protocols for plasma separation, cfDNA extraction, and library preparation.^45,46 Second, analytical validation must establish precision, reproducibility, and limit of detection across clinical laboratories, building on the Research Use Only framework under which DELFI-TF currently operates.²⁴

The integration of fragmentomic monitoring into clinical decision-making frameworks presents opportunities and challenges. For treatment response monitoring, the key question is whether fragmentomic data can identify response or progression sufficiently earlier than imaging to enable clinically meaningful treatment modifications. The DOLPHIN trial is specifically designed to address this question in mCRC, and additional prospective interventional trials will be needed across tumor types.²³ For MRD detection, the integration of fragmentomics with existing ctDNA MRD platforms rather than replacement represents the most pragmatic path to clinical adoption, as demonstrated by the multimodal aproaches of Wang/Xia and Guardant Reveal.^35,38

The health-economic profile of fragmentomics-based monitoring is favorable. Low-pass WGS at $100–150 per timepoint, without the requirement for prior tissue sequencing, compares favorably with tumor-informed ctDNA assays costing $500–2,000 per timepoint and CT imaging at $500–1,500 per scan.⁴⁷ A monitoring strategy incorporating fragmentomic assessment every 3–4 weeks during active treatment, with imaging reserved for clinical decision points, could reduce healthcare costs while improving outcome prediction.

9. Future Directions and Challenges:

Several critical gaps must be addressed to fully realize the clinical potential of fragmentomics for treatment monitoring. Prospective, randomized interventional trials comparing fragmentomics guided treatment decisions against standard of care imaging-guided management are the highest priority. Such trials should incorporate adaptive designs enabling treatment escalation, de-escalation, or switching based on fragmentomic signals.⁴⁸

The biological understanding of how specific therapeutic modalities modulate cfDNA fragmentation requires systematic investigation. Different mechanisms of cell death apoptosis induced by chemotherapy, necroptosis triggered by immunotherapy, ferroptosis from targeted agents may generate distinct fragmentomic signatures that could provide insight into therapeutic mechanism and resistance.^17,49 The confounding effect of clonal hematopoiesis of indeterminate potential (CHIP) on fragmentomic signals in treatment-exposed patients also requires careful characterization.⁵⁰

The development of multi-cancer monitoring panels that integrate fragmentomic, methylomic, and mutational features into a single low-cost assay would represent a transformative advance. Early evidence from cfMeDIP-seq and multidimensional fragmentomic approaches suggests this integration is technically feasible.^30,42 As machine learning methods continue to mature, the identification of treatment specific and resistance-associated fragmentomic signatures across tumor types may enable precision monitoring that guides not only whether treatment is working, but why it may be failing.

10. DISCLOSURE:

The authors declare no conflicts of interest.

11. Conclusion:

Cell-free DNA fragmentomics is poised to transform cancer treatment monitoring. The evidence synthesized in this review demonstrates that fragmentomic analysis provides tumor-naïve, mutation-agnostic, cost effective, and clinically actionable assessment of treatment response, MRD, and emerging resistance across solid tumor types. The convergence of robust biological rationale, validated analytical platforms, and compelling clinical data from studies such as the DELFI-TF validation, the INSPIRE trial, and the DOLPHIN study establishes fragmentomics as a mature technology ready for prospective clinical evaluation. As the field transitions from early detection where fragmentomics has already demonstrated transformative potential to treatment navigation, the systematic integration of fragmentomic monitoring into precision oncology practice has the potential to improve outcomes through earlier, more sensitive, and more dynamic assessment of therapeutic efficacy.

Table 1. Summary of Key Studies Evaluating Cell-Free DNA Fragmentomics for Cancer Treatment Monitoring and Minimal Residual Disease Detection.

Study	Year	Cancer Type	N	Fragmentomic Features	Key Findings
Multidimensional fragmentomics integration study⁴²	2025	Multi-cancer	>3000	Multidimensional fragmentomics (FSR, FSD, end motifs, CNV, NF)	5-feature integration; methodology directly transferable to monitoring contexts
Machine-learning fragmentomics fusion framework⁴¹	2025	10 cancer types	1994	13 fragmentomic feature spaces with sample-wise ML fusion	AUC 0.972 pan-cancer; adaptive modality weighting; scalable to monitoring
DELFI-TF treatment monitoring study²²	2024	CRC, Lung	208	DELFI-TF: genome-wide fragment profiles via lpWGS	r=0.90 correlation with MAF; HR=9.84 for baseline; OS 62.8 vs 29.1 months; superior to imaging
DOLPHIN prospective monitoring trial²³	2024	mCRC	Ongoing	DELFI-TF: prospective multi-center (DOLPHIN trial)	Prospective validation of DELFI-TF for treatment response vs CT imaging in mCRC
INSPIRE immunotherapy monitoring trial³⁰	2024	Pan-cancer	87 pts, 204 samples	Fragment-length score + methylation via cfMeDIP-seq	FLS and CSM predict PFS/OS with pembrolizumab; independent of PD-L1 and TMB
Cervical cancer neoadjuvant response study²⁶	2024	Cervical	84	cfDNA fragmentome profiling	Predicts neoadjuvant chemotherapy response; identifies responders pre-treatment
Targeted-panel fragmentomics MRD study³⁶	2023	Multi-tumor	518	Fragmentomic analysis from targeted ctDNA panels	Enhanced MRD sensitivity without extra sequencing; from existing targeted panels
Neoadjuvant CRT response prediction study²⁵	2023	Rectal	119	Fragment profiles + 5'-end motifs from panel sequencing	Predicts pCR after neoadjuvant CRT; AUC 0.87 in validation cohort
Multimodal fragmentomics-based MRD study³⁵	2023	NSCLC	87	Fragment profiles + mutations + CNA multimodal	MRD sensitivity 78.3% vs 43.5% mutations alone; HR 4.6-8.3 for recurrence
Foundational DELFI fragmentation study¹³	2019	7 cancer types	236 pts	DELFI: genome-wide cfDNA fragmentation in 5-Mb bins	Foundational DELFI method; AUC 0.94; tissue-of-origin; platform for monitoring

Abbreviations: CRC, colorectal cancer; mCRC, metastatic CRC; NSCLC, non-small cell lung cancer; lpWGS, low-pass whole-genome sequencing; cfMeDIP-seq, cell-free methylated DNA immunoprecipitation sequencing; FSR, fragment size ratio; FSD, fragment size distribution; NF, nucleosome footprint; pCR, pathological complete response; CRT, chemoradiotherapy; MAF, mutant allele frequency; MRD, minimal residual disease; CNA, copy number alteration; HR, hazard ratio; AUC, area under the curve; OS, overall survival; PFS, progression-free survival.

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Received on 04.02.2026 Revised on 03.03.2026

Accepted on 27.03.2026 Published on 10.07.2026

Available online from July 14, 2026

Res.J. Pharmacology and Pharmacodynamics.2026;18(3):247-253.

DOI: 10.52711/2321-5836.2026.00033

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.