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The SChISM study: Cell-free DNA size profiles as predictors of progression in advanced carcinoma treated with immune-checkpoint inhibitors

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read Pre-treatment blood DNA size profiles predict early progression and survival on immunotherapy in a prospective multi-cancer study.

desk verdict A clinically promising cfDNA biomarker for ICI response, but the headline predictor R>1650 is measured in a size range the device itself says is unreliable, so the central claim needs stronger support before it can be trusted. read the letter →

arxiv 2509.04939 v1 pith:F4P6LVJW submitted 2025-09-05 q-bio.QM

Linh Nguyen Phuong (COMPO) , Frederic Fina , Laurent Greillier (COMPO , APHM) , Pascale Tomasini (APHM , COMPO) , Jean-Laurent Deville (TIMONE) , Romain Zakrasjek (COMPO)
show 7 more authors
Lucie Della-Negra (COMPO) Audrey Boutonnet Frédéric Ginot Jean-Charles Garcia Sébastien Benzekry (COMPO) Sébastien Salas (COMPO TIMONE)
This is my paper · ORCID
classification q-bio.QM
keywords cell-freeDNAfragmentomicsliquidbiopsyimmunotherapyearlyprogressionprogression-freesurvivalbiomarkerBIABooster
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that the size distribution of cell-free DNA in blood drawn before starting immune-checkpoint inhibitor therapy predicts whether advanced cancer patients will progress early. In 126 patients across four carcinoma types, the relative amount of very long fragments (>1650 bp) showed the strongest association: patients with more of these fragments were less likely to progress at first imaging and had longer progression-free survival. These associations held after adjustment for age, sex, performance status, tumor type, and neutrophil-to-lymphocyte ratio, and in two homogeneous subgroups, suggesting a multi-cancer, treatment-independent signal. If true, a simple blood test could complement or outperform PD-L1 and NLR for treatment stratification.

What carries the argument

The central object is the cfDNA fragment size profile, obtained by capillary electrophoresis and summarized as the relative concentration of fragments in size bins aligned to nucleosome multiples. The key predictive variable, R>1650, is the relative quantity of fragments longer than 1650 base pairs; it carries the argument because patients with more of these very long fragments show markedly lower early-progression risk and longer progression-free survival, and the signal remains after multivariable adjustment.

What would settle it

Measure the >1650 bp fraction in the same baseline plasma samples with an orthogonal high-resolution method that can truly resolve long fragments, such as nanopore sequencing or pulsed-field electrophoresis; if the orthogonal long-fragment fraction does not correlate with R>1650 or does not reproduce the progression-free survival association, the central claim is an artifact of the uncalibrated BIABooster range.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that baseline cfDNA size profiles, measured with the BIABooster device, contain a predictive signal for immune-checkpoint inhibitor response. The proportion of fragments longer than 1650 bp (R>1650) was the single best discriminator: it was associated with lower odds of early progression (OR = 0.39) and longer progression-free survival (HR = 0.54), with an AUC of 0.73 and a C-index of 0.69. Longer dinucleosomal fragments and a wider gap between the first two nucleosomal peaks also predicted better outcomes, while high total cfDNA concentration and an abundance of short mononucleosomal fragments predicted worse outcomes. The paper interprets

Load-bearing premise

The key predictor is a relative signal from fragments longer than 1650 bp, a range where the device's own methods say size determination is unreliable; if that signal just tracks total cfDNA concentration or an instrument artifact, the association is not about long fragments at all.

Editorial extensions

If this is right

  • Baseline cfDNA size profiling could identify patients unlikely to progress early on ICI therapy, supporting earlier treatment adaptation or avoiding ineffective cycles.
  • R>1650 may serve as a multi-cancer predictive biomarker that outperforms PD-L1 and NLR, using a standardized noninvasive assay.
  • Combining cfDNA size features with NLR could catch progressors missed by either marker alone, since the two signals are weakly correlated.
  • If long cfDNA fragments indeed activate cGAS-STING, R>1650 could also guide patient selection for combination therapies involving STING agonists.
  • The consistency of the optimal R>1650 threshold across the overall cohort and two subgroups suggests the signal may be robust across tumor types and treatment lines.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because R>1650 is measured in a range where the device cannot reliably determine fragment size, the result may partly reflect total high-molecular-weight DNA or an electrophoretic artifact; orthogonal sizing could show whether the effect is truly length-specific.
  • The paper's proposed cGAS-STING mechanism is a hypothesis, not a demonstrated causal chain; if length-dependent STING activation holds, R>1650 could become a pharmacodynamic biomarker for STING-directed therapies.
  • The arbitrary size-bin boundaries leave information on the table; a data-driven continuous model of the fragment size curve might improve prediction beyond the single R>1650 variable.
  • An external prospective validation in an independent cohort, ideally with treatment-specific stratification, would be needed before this becomes a clinical decision tool.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The SChISM study reports a prospective, multi-center, real-world cohort of 126 patients with advanced NSCLC, HNSCC, UC, and ccRCC treated with immune-checkpoint inhibitors, for whom baseline plasma cfDNA size profiles were quantified using the BIABooster capillary electrophoresis device. The authors derive twelve cfDNA features (concentrations by size bins, peak positions, peak width, and total concentration) and test their association with early progression (EP) and progression-free survival (PFS). They report that higher total cfDNA and short fragments are associated with worse outcome, while long fragments, especially the relative quantity of fragments >1650 bp (R>1650), are associated with lower odds of EP and longer PFS, with AUC=0.73 and C-index=0.69. Associations persist after adjustment for age, sex, ECOG, tumor type, and NLR in the multi-cancer cohort, and in NSCLC and HNSCC subgroups. Bootstrap resampling provides out-of-bag estimates of accuracy and predictive values. The authors conclude that cfDNA size profiles, particularly R>1650, outperform PD-L1 and NLR and may reflect cGAS-STING-mediated immune activation.

Significance. If the central finding is valid, it would constitute a non-invasive, multi-cancer predictive biomarker for ICI response that is independent of tumor genotype and could be measured without DNA extraction. The study has several strengths: a prospective design with trial registration, inclusion of multiple tumor types, adjustment for established confounders, and an internal bootstrap validation with out-of-bag performance metrics. The subpopulation analyses, although small, provide some consistency checks. However, the headline predictor R>1650 is measured in a size range that the Methods explicitly state is outside the device's calibrated/validated range, and the statistical analysis involves in-sample variable selection and threshold optimization without multiplicity correction. These issues are load-bearing for the central claim and require substantive revision.

major comments (4)
  1. [Methods, 'BIABooster DNA analysis' and 'cfDNA variables'] The primary predictor R>1650 is derived from fluorescence signal above 1650 bp, a region where the Methods state 'all fragments migrate at similar speeds, making size determination unreliable' and where values 'don't represent true sizes or concentrations.' The manuscript also gives inconsistent calibrated ranges: the instrument description says 100-1500 bp, while the later paragraph claims reliable measurements between 75 and 1650 bp. Because R>1650 is the strongest predictor and the central claim of the paper, the entire conclusion rests on an uncalibrated signal. If this signal reflects total cfDNA concentration, injection overloading, or electrophoretic artifacts rather than true long-fragment abundance, the reported AUC=0.73 and C-index=0.69 are not interpretable as a biological association with fragment size. Please provide calibration/validation data for the >1650 bp region or rea
  2. [Methods, 'Statistical analysis'; Results, Table 2A] Twelve cfDNA variables were derived, and additional ratios were explored; the variable with the highest in-sample AUC (R>1650) was then reported as the best predictor. No correction for multiple testing is applied to the reported ORs, HRs, or p-values. Similarly, the Kaplan-Meier analyses and log-rank tests use thresholds optimized on the same data via surv_cutpoint, so the associated p-values are optimistic. The bootstrap analysis resamples performance after the variable is chosen, but it does not account for the selection step. This inflates the strength of evidence. Please report the number of tests performed, apply or justify a multiple-testing correction for the main analyses, and describe the threshold selection procedure transparently in the interpretation of p-values.
  3. [Abstract and Discussion] The conclusion that cfDNA size profiling 'outperforms PD-L1' is not directly supported by the analyses. PD-L1 (TPS/CPS) is not compared in the multi-cancer cohort in Table 2A; it is only available as a confounder in a subset of NSCLC patients, and CPS is available for only 10 HNSCC patients. The comparison in the Discussion uses pooled literature AUCs for TMB and PD-L1, which is not a head-to-head comparison in the same patient cohort. Please either provide a direct comparison with PD-L1 in the relevant subpopulations or soften the claim to 'compares favorably with literature benchmarks.'
  4. [Results, 'In NSCLC first-line subgroup' and Table 2B] In the NSCLC first-line subgroup, the univariable associations of P2, P2-P1, R>1650, and C_TOT with EP do not remain significant in multivariable analysis, despite AUCs above 0.83. The PFS associations remain significant, but the EP association is a central component of the paper's claim. The authors should discuss the instability of the EP association after adjustment, and avoid presenting the NSCLC subgroup as confirming the EP result.
minor comments (5)
  1. [Methods, 'Instrument and capillary assembly'] The size range is stated as 100-1500 bp in the instrument description and 75-1650 bp in the DNA analysis paragraph. Please harmonize these values and clarify which range is used for calibration.
  2. [Figure 4A legend] The legend uses 'R>1660' where it should be 'R>1650'.
  3. [Figure 2D] The label 'positive predive value' contains a typo: 'predictive.'
  4. [Discussion] The phrase 'avoid the unnecessary continuation of ICI therapy in sensitive patients' appears to say the opposite of what is intended; likely 'insensitive' or 'non-responsive' patients.
  5. [Results, 'Unsupervised hierarchical clustering'] The text says 'aross' (typo for 'across') in the sentence 'All tumor types were represented aross the three clusters.'

Circularity Check

1 steps flagged · score 3.0 of 10

One fitted-parameter circularity in the optimal-cutpoint Kaplan-Meier analyses; the central continuous associations are not circular but are weakened by in-sample variable selection and an uncalibrated >1650 bp measurement.

  1. fitted input called prediction [Methods/Statistical analysis and Results (Figure 2A, HNSCC subsection)]
    "Continuous features were optimally dichotomized using survminer::surv_cutpoint 0.5.0, maximizing the log-rank statistic while ensuring a minimum of 20% of patients per group. ... Kaplan-Meier curves display progression-free survival stratified by cfDNA levels, display the log-rank test p-value and the optimal threshold computed to stratify patients between long and short progression-free survival (see Methods)."

    The threshold used to split patients into 'high' and 'low' R>1650 is chosen on the same cohort by maximizing the log-rank statistic, and the Kaplan-Meier p-value and hazard ratio for that split are then computed on the same cohort. This makes the dichotomized survival comparison equivalent to reporting the fit criterion itself: the threshold is a parameter fitted to the endpoint, so the subsequent log-rank test is not an independent confirmation. The continuous Cox regression and bootstrap OOB AUC analyses remain independent of this step, but the optimal-cutpoint survival claims in Figures 2A and 4D reduce to the in-sample optimization.

full rationale

The core derivation—baseline cfDNA size variables associated with EP and PFS—is empirical and not circular: R>1650 is defined as a ratio of measured signals, and the continuous logistic/Cox regressions and the bootstrap out-of-bag performance evaluation are self-contained statistical analyses. However, one load-bearing presentation is circular: the Kaplan-Meier curves with log-rank p-values use thresholds chosen by surv_cutpoint to maximize the log-rank statistic in the very same cohort, so those particular survival comparisons are fitted to the endpoint and do not provide independent evidence. The paper also selected R>1650 as the best of twelve variables on the full dataset before bootstrap validation, so the reported AUC/C-index are in-sample and the bootstrap does not fully account for selection across all twelve features; this is an overfitting/validity concern rather than a constructional circularity. The uncalibrated nature of the >1650 bp signal—the Methods state 'above 1650 bp, all fragments migrate at similar speeds, making size determination unreliable'—is a serious measurement-validity threat to the headline predictor, but it is not a circular derivation and therefore does not raise the circularity score. No load-bearing self-citation or imported uniqueness theorem was found. Overall, the central association has independent content, but the optimal-cutpoint survival claims are partially circular, giving a score of 3.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The analysis relies primarily on device-specific calibration assumptions and data-driven threshold selection. No new physical or biological entities are postulated. The key free parameters are the size bins and the optimal cut-points, both chosen from the data without external anchoring.

free parameters (3)
  • Optimal R>1650 threshold = 0.036-0.039 r.a.u.
    Dichotomization threshold computed with surv_cutpoint maximizing log-rank statistic on the same data; used for Kaplan-Meier stratification and associated p-values.
  • Optimal C_TOT threshold = ~14 pg/µL in multi-cancer and HNSCC; ~28 pg/µL in NSCLC
    Threshold optimized to maximize PFS separation in the respective cohorts; sensitive to outliers in NSCLC.
  • cfDNA size range boundaries = [75,111], [111,240], [240,370], [370,580], [580,1650], <75, >1650 bp
    Arbitrary bins set according to nucleosomal length multiples and device limits, as acknowledged in the Discussion: 'size ranges were arbitrary set according to technology limits and as nucleosomal-length bounds.'
assumptions (3)
  • ad hoc to paper BIABooster signal above 1650 bp, although not size-resolved, still reflects relative fragment amounts and is comparable across samples.
    Methods: 'Above 1650 bp, all fragments migrate at similar speeds, making size determination unreliable. However, the values do still reflect the relative amounts of fragments at these ranges.' This is load-bearing because R>1650 is the primary predictor.
  • domain assumption Baseline cfDNA size profile reflects systemic immune status relevant to ICI response.
    This is the study premise, stated in the Introduction and Discussion. It is a biological assumption not directly tested by the data; the mechanistic cGAS-STING link is speculative.
  • standard math Standard statistical model assumptions: linearity in logistic regression, proportional hazards in Cox regression, independent censoring.
    These are routine assumptions for the regression methods used; the paper does not provide diagnostics such as Schoenfeld residual tests.

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Cite this review

Pith. "Pith review of The SChISM study: Cell-free DNA size profiles as predictors of progression in advanced carcinoma treated with immune-checkpoint inhibitors." pith.science (2026). https://pith.science/paper/F4P6LVJW

@misc{pith2026250904939,
  author       = {Pith},
  title        = {Pith review of: The SChISM study: Cell-free DNA size profiles as predictors of progression in advanced carcinoma treated with immune-checkpoint inhibitors},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/F4P6LVJW}},
  note         = {Machine review of arXiv:2509.04939}
}
abstract

Background: Many advanced cancer patients experience progression under immune-checkpoint inhibitors (ICIs). Circulating cell-free DNA (cfDNA) size profiles offer a promising noninvasive multi-cancer approach to monitor and predict immunotherapy response. Methods: In the SChISM (Size CfDNA Immunotherapy Signature Monitoring) study (NCT05083494), pre-treatment plasmatic cfDNA size profiles from 126 ICI-treated advanced carcinomas were quantified using the BIABooster device. Fragmentomederived variables (concentration, peaks' position, and fragment size ranges) at baseline were analyzed for associations with early progression (EP, progression at first imaging) and progression-free survival (PFS), using logistic and Cox regression models. Bootstrap analysis validated robustness. Additional analyses were performed in homogeneous subpopulations: first-line lung cancer patients (n = 60) and head-andneck patients treated with Nivolumab (n = 25). Results: Higher cfDNA concentration and high quantities of short fragments (111-240 base pairs (bp)) were associated with poor response, unlike long fragments (> 300 bp). The proportion of fragments longer than 1650 bp demonstrated highest discriminatory power (AUC = 0.73, C-index = 0.69). It was significantly associated with non-EP (odds ratio = 0.39 [95% CI: 0.25-0.62]) and longer PFS (hazard ratio: 0.54 [95% CI: 0.42-0.68]). These associations remained significant when adjusted for confounders (age, sex, Eastern Cooperative Oncology Group performance status, tumor type, and neutrophil-to-lymphocyte ratio) and across both subpopulations. Bootstrap analysis confirmed robustness with mean accuracy of 70.1 $\pm$ 4.17% and positive predictive value of 55.6 $\pm$ 7.37%, in test sets. Conclusion: cfDNA size profiles significantly predicted ICI response and anticipate relapse, outperforming the routinely used marker programmed death-ligand 1 immunohistochemistry and reflecting enhanced immune system activation. Trial registration: (NCT05083494), date of registration: 2021-10-19.

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Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.