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REVIEW 3 major objections 9 minor 31 references

Characterization of DLBCL cell of origin-phenotypes based on tumor microenvironment features

T0 review · 3 major / 9 minor · reviewed 2026-07-08 · glm-5.2

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Pith's one-line read ABC lymphoma tumors host distinct immune neighborhoods

desk verdict A well-built multiplexed imaging pipeline for DLBCL TME characterization, but the statistical framework cannot support the significance claims as written. read the letter →

arxiv 2607.06129 v1 pith:FDNJEHAL submitted 2026-07-07 q-bio.QM

Stefano Ugliano , Martim Dias Gomes , Noémie Moreau , Marta Pistone , Marcel Kirchner , Alexandra Just , Adrian Georg Simon , Christian Kukat
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Reinhardt Buettner Katarzyna Bozek
This is my paper · ORCID
classification q-bio.QM
keywords dlbcltumorcellanalysisclassifierfeatureshansimmune
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 applies a deep-learning pipeline to multiplexed fluorescence images of 106 diffuse large B-cell lymphoma (DLBCL) patient samples to show that the two main DLBCL subtypes — germinal center B-cell-like (GCB) and activated B-cell-like (ABC), as defined by the standard Hans classifier — have systematically different tumor microenvironments. ABC tumors are enriched in immune cells, including M2-macrophages and CD8+ T-cells, and show preferential spatial interaction between M2-macrophages and tumor cells. GCB tumors, by contrast, are relatively immune-poor, with more B-cells and a different macrophage balance. The pipeline segments cell nuclei, classifies cells by protein-marker positivity, constructs proximity graphs to quantify which cell types sit next to each other, and extracts morphological features — yielding a 470-feature vector per tumor sample. A statistical comparison identifies the morphology of M2-macrophages and CD8+ T-cells, along with specific cell-cell interaction patterns, as the most significant discriminators between the two subtypes.

What carries the argument

The pipeline combines Cellpose-based nuclear segmentation, Otsu-thresholded marker binarization for cell-type classification, centroid-distance proximity graphs for spatial interaction analysis, and distribution-summary morphological features (area, eccentricity, circularity, etc.) aggregated into per-sample feature vectors. The Hans classifier serves as the reference subtype assignment against which all TME features are compared.

What would settle it

If the Otsu thresholds do not correspond to biologically meaningful positivity boundaries, the cell-type labels — and therefore all composition, interaction, and morphology-by-cell-type results — could be artifacts of thresholding rather than reflections of genuine TME differences between GCB and ABC tumors.

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Extended reading notes

Core claim

The central finding is that GCB and ABC DLBCL subtypes, classified by the Hans algorithm based on tumor-cell protein markers, also differ sharply in the composition, spatial organization, and morphology of surrounding non-tumor cells. ABC tumors carry a richer immune infiltrate with preferential M2-macrophage–tumor-cell contact, while GCB tumors are comparatively immune-sparse. The morphological features of M2-macrophages and CD8+ T-cells are among the strongest statistical discriminators between the two subtypes, suggesting that single-cell shape characteristics carry subtype-distinguishing information beyond what cell counts alone provide.

Load-bearing premise

The pipeline uses Otsu's thresholding method to binarize each cell's protein-expression signal into positive or negative for each marker. Otsu assumes the signal distribution splits into two clear groups, but fluorescence intensities in multiplexed imaging are often continuous rather than bimodal. If the threshold mislabels cells — for instance, calling a cell CD20-positive when it is not — every downstream cell-type proportion, interaction count, and morphological comparison

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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 9 minor

Summary. The manuscript presents a deep learning-based pipeline for analyzing multiplexed immunofluorescence images of DLBCL tumor microarrays. The pipeline segments nuclei (Cellpose), classifies cells into 10 types using Otsu-thresholded marker expression, and extracts 470 features per tissue sample spanning morphology, cell-type proportions, and spatial interaction patterns. These features are compared between GCB and ABC subtypes (as defined by the Hans classifier) using Mann-Whitney U tests. The authors report that ABC tumors are immune-rich with preferential M2-macrophage interactions, while GCB tumors are immune-poor, and identify morphometric differences in M2-macrophages and CD8+ T-cells as the most discriminating features. The pipeline is modular and the dataset (106 patients, 559 tissue samples, 14 markers) is substantial.

Significance. The study provides a useful, modular computational framework for quantitative TME characterization from multiplexed imaging data, and the DLBCL cohort is reasonably sized. The descriptive observations on cell-type composition differences between GCB and ABC subtypes (Fig. 2C, Fig. 3) are broadly consistent with published literature on immune-cold GCB and immune-hot ABC phenotypes. The label-permutation approach for assessing interaction enrichment is a sensible design choice. However, the formal statistical framework underlying the headline 'significantly different' claims has two load-bearing gaps that must be addressed before the central quantitative claims can be considered reliable.

major comments (3)
  1. §Results, 'Statistical analysis of aggregated features'; Fig. 4: The Mann-Whitney U tests are performed across 470 features (10 cell types × 5 morphological features × 7 distribution metrics + 10 cell proportions + 55 interaction features) with a p<0.05 threshold and no mention of multiple-testing correction (Bonferroni, Benjamini-Hochberg, or permutation-based FDR). At this threshold, approximately 23 features would be expected significant by chance alone. Several headline findings—morphometric differences in M2-macrophages and CD8+ T-cells, and specific cell interaction patterns—are drawn from these uncorrected tests. The authors should apply an appropriate multiple-testing correction and report which features survive.
  2. §Methods, 'DLBCL tissue cohort assembly'; §Methods, 'Feature Aggregation': The cohort comprises 106 patients with 'up to 3 tissue cores per patient' (559 total tissue samples), but the feature aggregation and statistical analysis refer to 'samples' without clarifying whether the unit of analysis is the patient or the individual core. If multiple cores from the same patient are treated as independent observations, pseudoreplication would inflate all p-values. The authors should state the unit of analysis explicitly and, if cores are used, either aggregate to patient level or use a mixed-effects model with patient as a random effect.
  3. §Methods, 'Data Extraction'; Table 4: Cell classification relies on Otsu thresholding of single-cell protein expression values to binarize each of the 11 markers. Otsu's method assumes a bimodal intensity distribution, but fluorescence signal distributions in multiplexed imaging are frequently continuous. If thresholds misclassify cells (e.g., calling a cell CD20+ when it is not), all downstream proportions, interaction analyses, and morphological comparisons are affected. The paper does not validate the Otsu thresholds against expert annotation, pathologist review, or any ground truth. The authors should provide at least a limited validation against manual annotation or justify the bimodality assumption with representative intensity histograms.
minor comments (9)
  1. §Methods, 'Spatial Organization of the DLBCLs': The proximity threshold d* = 0 is chosen 'based on qualitative analysis of graphs at various cutoffs.' A sensitivity analysis over a range of d* values, or at least a brief description of the qualitative criteria, would strengthen this choice and improve reproducibility.
  2. §Discussion, paragraph 3: The tumor cell definition (CD20+ with nuclear area ≥ 2× mean of CD20+ population) is introduced only in the Discussion. This is a load-bearing definition for several results and should be described in the Methods, with the nuclear area threshold listed as a parameter.
  3. Fig. 2C: The y-axis label and tick marks are unclear. Error bars or interquartile ranges should be specified in the figure legend.
  4. Fig. 3A: The color scale legend for the interaction enrichment heatmaps should be clarified—specifically, whether the color represents a z-score, log-fold change, or raw fraction of samples.
  5. Fig. 4B: The y-axes are labeled 'Arbitrary units' without further specification. For a statistical comparison figure, the axes should indicate the actual metric being plotted (e.g., U statistic, effect size).
  6. §Methods, 'Feature Aggregation': The text states '10 cell types, 5 morphological features and 7 distribution metrics, resulting in a vector of length 350,' but Table 6 lists 10 cell types including 'Other' and 'Tumor.' The total feature count of 470 (350 + 10 + 110) should be reconciled with the cell-type count used.
  7. Table 3: CD138, PD-L1, and CD56 are excluded due to poor staining quality, but the criteria for exclusion (e.g., signal-to-noise ratio threshold) are qualitative. A brief quantitative criterion or representative images would be useful.
  8. §Discussion, paragraph 5: The authors note that CD31 is expressed in monocytes and dendritic cells to a lesser extent, which could confound the endothelial cell classification. This limitation should also be acknowledged in the Methods or Results where endothelial interactions are reported.
  9. The manuscript would benefit from a data and code availability statement. If the pipeline code or processed data are available in a repository, this should be stated; if not, the authors should indicate how the pipeline can be accessed (e.g., upon request, pathologist review).

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for a careful and constructive reading of our manuscript. The referee identifies three important methodological points: (1) the absence of multiple-testing correction across 470 features tested with Mann-Whitney U tests, (2) potential pseudoreplication arising from multiple tissue cores per patient, and (3) the lack of validation for Otsu-based cell classification thresholds. We agree with all three points and will address each in a revised manuscript. Specifically, we will apply Benjamini-Hochberg FDR correction and report surviving features, clarify the unit of analysis and re-run statistics at the patient level (aggregating cores), and provide validation of Otsu thresholds against expert pathologist annotation on a representative subset of images. We also provide below our honest assessment of what can and cannot be fully resolved within the scope of the current dataset.

read point-by-point responses

  1. Referee: Mann-Whitney U tests across 470 features with p<0.05 threshold and no multiple-testing correction; approximately 23 false positives expected. Headline findings drawn from uncorrected tests.

    Authors: The referee is correct. We performed 470 Mann-Whitney U tests without multiple-testing correction, and at a nominal p<0.05 threshold the expected number of false positives is indeed approximately 23. This is a genuine gap in our statistical framework. We will apply Benjamini-Hochberg FDR correction (q<0.05) to all 470 tests and report which features survive correction. We will update Figure 4 and the associated text to reflect corrected p-values, and we will revise all headline claims to reference only features that survive FDR correction. If certain features that we currently highlight do not survive correction, we will state this transparently and adjust our conclusions accordingly. We note that several of our findings—particularly the compositional differences shown in Figure 2C (cell-type proportions between GCB and ABC)—are broadly consistent with the published literature on immune-cold GCB and immune-hot ABC phenotypes, which provides external corroboration independent of our statistical testing. However, we agree that the formal statistical claims must rest on corrected tests. revision: yes

  2. Referee: Unit of analysis unclear: 106 patients with up to 3 cores each (559 samples), but statistics refer to 'samples' without clarifying whether cores are treated as independent. Pseudoreplication would inflate p-values.

    Authors: The referee raises a valid and important concern. In our current analysis, the unit of analysis is the individual tissue core (sample), not the patient. Multiple cores from the same patient were treated as independent observations. We agree that this constitutes pseudoreplication and could inflate our p-values. In the revised manuscript, we will aggregate features to the patient level by computing the median across cores from the same patient, and re-run all Mann-Whitney U tests (with FDR correction as addressed above) at the patient level (n=106). We will state the unit of analysis explicitly in the Methods. We will also report the number of patients with 1, 2, and 3 cores, respectively, so that the degree of within-patient sampling is transparent. We note that aggregating to patient level will reduce our effective sample size, which may reduce statistical power for some features. We will report which findings persist and which do not, and we will adjust our conclusions accordingly. revision: yes

  3. Referee: Otsu thresholding assumes bimodal intensity distributions; fluorescence signals are frequently continuous. No validation against expert annotation or ground truth provided. Misclassification affects all downstream analyses.

    Authors: The referee is correct that Otsu's method assumes a bimodal intensity distribution and that this assumption may not hold for all markers in multiplexed fluorescence imaging. We did not validate the Otsu thresholds against expert annotation, and this is a genuine limitation. In the revised manuscript, we will address this in two ways. First, we will provide representative intensity histograms for each marker so that readers can assess the bimodality assumption directly. Second, we will perform a limited validation: a board-certified pathologist (co-author R.B.) will manually annotate cell types on a representative subset of tissue regions (at least 5 cores covering both GCB and ABC subtypes), and we will compute agreement metrics (Cohen's kappa or similar) between manual annotation and our Otsu-based classification. We will report these validation results and discuss markers for which classification agreement is poor. We acknowledge that for markers with genuinely continuous distributions, Otsu thresholding may introduce systematic misclassification, and we will discuss this as a limitation. We note that our cell classification scheme uses combinations of markers (not single markers alone) for most cell types, which provides some robustness to threshold errors on individual markers, but this does not eliminate the concern. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: observational comparative study with externally defined group labels and independently extracted image features.

full rationale

This paper is an observational, comparative study—not a derivational one. The Hans classifier (an external pathology assessment) defines the GCB and ABC groups. The pipeline independently extracts TME features (cell proportions, morphology, spatial interactions) from multiplexed immunofluorescence images via segmentation (Cellpose), Otsu thresholding, and proximity graph construction. The statistical comparison (Mann-Whitney U tests) then tests whether these independently extracted features differ between the pre-defined Hans groups. No feature is defined in terms of the group labels, no parameter is fitted to one subset and then 'predicted' on closely related data, and no self-citation chain substitutes for a derivation. The concerns raised by the skeptic (multiple-testing correction, pseudoreplication, Otsu threshold validity) are correctness and methodology concerns, not circularity. The derivation chain is self-contained against external benchmarks.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The pipeline introduces three hand-set parameters (d*=0, 25-pixel expansion, 2x nuclear area threshold) without quantitative justification or sensitivity analysis. The Otsu thresholding and Hans classifier accuracy are domain assumptions that are standard but unvalidated in this study. No new biological entities, particles, or forces are postulated.

free parameters (3)
  • Proximity threshold d* = 0
    Chosen by qualitative analysis of graphs at various cutoffs (Methods, Spatial Organization section). No quantitative optimization or sensitivity analysis provided.
  • Cytoplasm mask expansion = 25 pixels
    Nuclear masks expanded by 25 pixels to approximate cytoplasm (Methods, Image pre-processing). No justification for this specific value or sensitivity analysis.
  • Tumor cell nuclear area threshold = 2x mean CD20+ nuclear area
    Tumor cells defined as CD20+ with nuclear area at least 2x the mean of all CD20+ cells (Discussion). This is a heuristic threshold.
assumptions (4)
  • domain assumption Otsu's method produces biologically valid binary classifications of cell marker expression from fluorescence intensity distributions
    Methods, Data Extraction: single-cell expression values are thresholded with Otsu's algorithm to determine marker positivity. Assumes bimodal distributions, which may not hold for all markers.
  • domain assumption The Hans classifier correctly assigns GCB/ABC labels to the patient samples
    The entire comparison depends on the Hans classifier labels being accurate. The paper does not re-verify these labels or discuss misclassification rates.
  • domain assumption Nuclear morphology features are informative proxies for cell-level properties
    Methods, Morphological Analysis: nuclear mask shapes are used to derive morphological features. Assumes nuclear shape reflects cell state, which is reasonable but unvalidated in this context.
  • domain assumption Proximity graph edges at d*=0 represent biologically meaningful cell-cell interactions
    Methods, Spatial Organization: adjacency defined by D_ij < d* = 0. Assumes physical proximity implies biological interaction, which is a standard but imperfect assumption.

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Pith. "Pith review of Characterization of DLBCL cell of origin-phenotypes based on tumor microenvironment features." pith.science (2026). https://pith.science/paper/FDNJEHAL

@misc{pith2026260706129,
  author       = {Pith},
  title        = {Pith review of: Characterization of DLBCL cell of origin-phenotypes based on tumor microenvironment features},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FDNJEHAL}},
  note         = {Machine review of arXiv:2607.06129}
}
read the original abstract

Diffuse large B-cell lymphoma (DLBCL) is an aggressive form of non-Hodgkin lymphoma with a high recurrence rate. The molecular profiling of DLBCL tumors culminated in several immunohistochemistry algorithms for prognostic stratification. Among those, the Hans classifier is widely used for classifying DLBCL into germinal center B-cell-like (GCB) and non-germinal center/activated B-cell-like (non-GCB/ABC) subtypes. The Hans classifier primarily evaluates protein expression of tumor-associated markers, however the tumor microenvironment (TME) of DLBCL includes a myriad of immune and stromal cells, cytokines, and extracellular matrix components that contribute to tumor growth, immune evasion, and recurrence rate. Although the Hans classifier provides a practical method for subtype identification, incorporation of TME information may improve risk stratification and further refine patient groups. Here, we present an unbiased deep learning-based approach to extract meaningful features from TME of DLBCL tumors for the automated processing and analysis of multiplexed images of a DLBCL patient cohort. Our pipeline quantifies a range of features that describe tumor sample cell composition, morphology, and its spatial organization. We point to alterations in the proportions of several cell populations between GCB and ABC tumors including increased immune cell proportions of the ABC and its preferential interaction with the M2-macrophages. Our analysis offers an in-depth characterization of the DLBCL subtypes and is exemplary of how our pipeline can be used for detailed quantitative analysis of a tumor and its subtypes.

Figures

Figures reproduced from arXiv: 2607.06129 by the authors.

Figure 1
Figure 1. A) Analysis pipeline. Our pipeline consists of three stages: tissue preparation and imaging, image processing, and data extraction. Finally, we aggregate and analyze quantitative features of the ABC and GCB tumors in a statistical comparison. B) Example tissue region and representative markers. Left: Overlapping of DAPI and four markers in different colors. Right: the four markers separately. This region was cropped… view at source ↗
Figure 2
Figure 2. A) Fraction of cells (x axis) that are positive to a number of markers (y axis). Dark blue denotes cells that are negative to all markers. B) Top 10 most common marker combinations (y axis, bottom to top) and the cell class they denote. Their fraction among all cells positive to any marker (x axis) is shown in blue and the cumulative fraction in orange. C) Cell composition of Hans groups. Proportion of cell classes … view at source ↗
Figure 3
Figure 3. A) Cell type interaction enrichment in the tumor microenvironment of DLBCL per [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: A) Feature significance. The significance of all features (morphological, cell type frequency and proximity-likelihood) in distinguishing ABC vs GCB samples, according to Whitney U test, visualized in a volcano plot. The x axis denotes the U value while y is a decreasi…

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Reviewed July 8, 2026 · model on record in the stance chip above.