Oncogenic and tumor-suppressive forces converge on a progenitor-orchestrated niche to shape early tumorigenesis - Manusights Dossier preview

Overall Feedback

Substantial revisions required first

Likely outcome if submitted today: Desk reject at Nature; if it reached review, likely major revision.

The 3 critical issues

The manuscript’s strongest claim—that progenitor-like epithelial cells “orchestrate” a cancer-like niche—is still more inferential than causal, relying heavily on spatial association, pseudotime, KRAS inhibition, and p53 suppression rather than direct perturbation of the progenitor state or its nominated communication circuits.
The Nature-level translational bridge is underbuilt: the human evidence is limited to projection onto scRNA-seq from cancer-free warm autopsy pancreas without KRAS genotype, PanIN grade, spatial context, or outcome, yet the paper claims a conserved premalignant cell state and a cancer-interception window.
Several internal inconsistencies and manuscript-completeness problems—especially the shCtrl/shRen naming conflict, 1–2 day vs 2 day injury timing, missing statistical/reporting details, and a truncated Discussion—would immediately undermine editorial confidence.

The single most important fix

This week, **rewrite the paper around a more defensible causal...

Detailed Feedback

Medium severity.
#1Major Comments§5.17
'Orchestration' claim lacks progenitor-specific functional evidence
progenitor-like cells were the most depleted upon acute oncogenic KRAS inhibition. In addition, gastric chief-like and pit-like cells– characterized by gene expression signatures of PanIN and the classical PDAC subtype40– were also depleted, albeit to a lesser extent
I will grant that the spatial co-occurrence data are striking and the KRAS-inhibition collapse experiment is suggestive. But the authors have not shown that progenitor-like cells specifically orchestrate the niche. MRTX1133 depletes progenitor-like cells alongside gastric pit- and chief-like populations and shuts down KRAS signaling tissue-wide; p53 knockdown likewise reshapes multiple epithelial states. Neither perturbation isolates the progenitor compartment. For a Nature-level 'orchestrator' claim, the data must distinguish progenitor-like cells as the causal source of niche assembly rather than co-evolving passengers of KRAS activity.

Suggested action: Add a progenitor-selective perturbation: e.g., an Hmga2- or Nes-driven inducible diphtheria toxin receptor (or Cre-dependent caspase9) ablation in KC mice, then show that niche components (Tnc+ myCAFs, Itgax+/Arg1+ macrophages) collapse without globally shutting down KRAS. Alternatively, an orthotopic transplant of FACS-sorted progenitor-like vs. gastric-like cells into wild-type pancreata to test sufficiency for niche assembly would convert this from association to mechanism. Absent such data, replace 'orchestrate'/'architect' language with correlative framing.
Medium severity.
#2Major Comments§5.17
Underpowered and unbalanced spatial transcriptomics cohorts for the central claims
Applying this approach to KC mice (Ptf1a-Cre; LSL-KrasG12D) at early (1-2 days, n = 10 mice) and late (3 weeks, n = 5 mice) timepoints following caerulein-induced injury
The pillars of the paper — niche remodeling along the gastric–progenitor axis, MRTX1133-induced niche collapse, and p53-driven niche expansion — rest on small, unbalanced cohorts (n=4 vs 4 for KRAS inhibition; 9 vs 5 for p53 knockdown; 10 vs 5 for the injury time course) without any power justification or evidence that the spatial Milo adaptation controls FDR at these sizes. Given known inter-animal variability in spontaneous lesion development in KC/KPLOH mice, an n=4 comparison with no effect-size reporting cannot anchor a Nature-tier mechanistic conclusion.

Suggested action: Provide a priori or post hoc power analyses for each cohort. Increase replication for the MRTX1133 and p53-knockdown comparisons, or balance the design (9 vs 5 should be revisited). Report effect sizes with confidence intervals, not just p-values. Validate the spatial Milo adaptation's calibration by permutation/simulation at the actual sample sizes used, and show that the niche-collapse signal persists when single mice are leave-one-out removed.
Medium severity.
#3Major Comments§5.17
No data deposition statement and no public release of Calligraphy or spatial-Milo code
We built upon our previously developed method Calligraphy, a computational approach that leverages the modular organization of communication genes for the discovery and prioritization of cell-cell interactions in single cell data37. We quantified cell-type specific expression of communication genes at the niche level, followed by computation of gene-gene covariance matrices (see Methods)
Three Xenium experiments totaling ~11 million cells, multiple scRNA-seq datasets, and two custom computational methods (Calligraphy; a spatial adaptation of Milo) form the empirical and analytical basis of every major figure. Yet the manuscript provides no accession numbers, no data availability statement, and no repository/version pin for the analysis code. For Nature, this is disqualifying as currently written: an independent group cannot retrieve the raw data, the processed objects, the custom 480-gene panel definition, or the algorithms used to derive niche communication modules.

Suggested action: Before resubmission: (i) deposit raw and processed scRNA-seq and Xenium data in GEO/ArrayExpress with accession numbers; (ii) release Calligraphy and the spatial Milo adaptation as version-tagged GitHub repositories with Zenodo DOIs and pinned environments (conda/renv); (iii) provide the complete 480-gene panel with probe identifiers in Table S4; (iv) include a minimal reproducible example that regenerates a key figure panel from raw counts.
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Medium severity.
#4Major Comments§5.17
Chimeric ES-cell KPLOH model may not represent sporadic somatic TP53 LOH
The model is derived from multi-allelic KPC (KrasG12D, Trp53flox/+, Ptf1a-Cre) embryonic stem cells that incorporate dual fluorescent reporters to track mutant Kras and p53 status
The KPLOH platform is built from multi-allelic ES cells injected into early embryos, producing chimeric tissue in which oncogenic KRAS and the floxed p53 allele are present from development. The biology this captures — mosaic developmental fields, embryonic-origin signaling cues, and 1–3% mKate2-single-positive cells — is mechanistically distinct from sporadic somatic TP53 LOH arising in a single adult pancreatic cell. The frequency, transcriptional state, and niche of progenitor-like cells in this chimeric context may not generalize, yet this limitation is never discussed and no orthogonal somatic-induction model is used.

Suggested action: Validate the core progenitor-like state finding and the p53-target convergence in at least one tamoxifen-inducible somatic model (e.g., Ptf1a-CreERT2 or Sox9-CreERT2 with conditional KrasG12D and Trp53 alleles activated in adult pancreas). Add an explicit limitations paragraph contrasting chimeric ES-cell tumorigenesis with sporadic human PDAC initiation, and discuss whether mosaic developmental context could itself induce the progenitor signature.
Medium severity.
#5Major Comments§5.17
'Senescence-like' designation rests on transcriptional signatures alone
The simultaneous engagement of tumor-promoting and tumor-suppressive pathways is reminiscent of oncogene-induced senescence, a potent tumor suppressive program involving p53 and p16INK4a that can be triggered by aberrant RAS signaling
The OIS framing is conceptually central — it is the bridge between p53/CDKN2A engagement and the SASP-like niche remodeling that motivates the paper. But OIS in PDAC has a well-established functional definition (stable arrest, SA-β-gal, SASP). The authors invoke senescence transcriptionally while elsewhere noting Mki67+/Cdk1+ cells in the same epithelial pool, leaving open whether progenitor-like cells are arrested, slow-cycling, or actively proliferating — a distinction with very different mechanistic and therapeutic implications.

Suggested action: Add functional senescence assays on MSN/HMGA2-sorted or in situ-identified progenitor-like cells: SA-β-gal on fresh sections, EdU/BrdU incorporation paired with HMGA2 IF to test proliferative status, and protein-level SASP factor detection (e.g., IL-6, CXCL1, MMP3). If these cells are cycling, reframe as 'partial' or 'SASP-without-arrest' and discuss implications for the OIS-as-barrier model.
Medium severity.
#6Major Comments§5.17
CNA inference from scRNA-seq used as a genomic-instability timestamp without orthogonal validation
we inferred CNAs from scRNA-seq data as a surrogate for underlying genomic instability
A core narrative — that intermediate-karyotype progenitor-like cells are transitional intermediates en route to PDAC — depends on inferCNV-style calls from scRNA-seq. Such inference is well known to produce false positives in cells with EMT/stress transcriptional programs (precisely the progenitor-like state's signature), and the manuscript even makes specific claims about chr4 (Cdkn2a), chr11 (p53), and chr2 events without orthogonal genomic support in the specific pre-tumor cells profiled here. Prior scDNA-seq in the model from a different study does not validate the calls used in this manuscript.

Suggested action: Validate the specific intermediate-karyotype calls by orthogonal genomics on FACS-sorted mKate2-single-positive pre-tumor cells — low-pass scWGS, scDNA-seq, or targeted FISH for chr4/chr11. Benchmark the CNA caller in EMT/stress-program-positive cells (e.g., a known-diploid mesenchymal control) to bound the false-positive rate, and add caveats to chromosome-specific claims.
Medium severity.
#7Major Comments§5.17
p53/CDKN2A/SMAD4 'convergence' framing ignores known pathway epistasis
Strikingly, in addition to p53 activation, progenitor-like cells exhibited the highest engagement of the two other major tumor suppressive programs in PDAC23: CDKN2A41 and SMAD442
Presenting three tumor suppressors as independently converging on the progenitor state overstates what the data show. p19ARF (a CDKN2A product) activates p53 via MDM2 inhibition, and TGFβ/SMAD4 signaling can induce CDKN2A — so co-induction is the expected output of a single oncogenic-stress trigger, not three independent barriers. The paper does not perform the epistasis tests needed to claim convergence vs. linear/parallel architecture, and the field has long treated SMAD4 loss as mechanistically distinct from p53 loss in PDAC.

Suggested action: Either (i) test pathway hierarchy with at least one additional perturbation — e.g., p19ARF-null or Smad4-conditional in the KC progenitor context — to show whether CDKN2A and SMAD4 activation persist without p53, or (ii) soften the convergence framing to 'co-engagement' and explicitly acknowledge that linear ARF→p53 and TGFβ→CDKN2A relationships could account for the observation.
Medium severity.
#8Major Comments§5.17
Custom 480-gene Xenium panel: selection criteria and validation not provided
we designed a custom 480-gene panel enriched for epithelial markers to resolve premalignant heterogeneity, while also capturing stromal and immune features and and key signaling and communication pathways37 implicated in early tumorigenesis, including MAPK, p53, YAP, TGFβ, and interferon (Table S4 and Methods)
Every spatial conclusion is filtered through a panel that was 'enriched for epithelial markers' — a deliberate ascertainment choice that biases which cell states, niches, and ligand–receptor pairs can be detected. The manuscript does not disclose the selection algorithm, the inclusion/exclusion criteria, validation against scRNA-seq ground truth, or whether under-represented compartments (immune subsets, rare fibroblast states) were systematically captured. This blocks both reproduction and assessment of whether 'progenitor niche' composition is a biological signal or a panel artifact.

Suggested action: Publish the full panel (gene list + probe IDs) and the selection pipeline (e.g., information-theoretic ranking against the scRNA-seq reference). Provide a sensitivity analysis showing that the gastric–progenitor diffusion axis and niche composition reproduce when computed on a randomly down-sampled subset of panel genes and when computed on the matched gene subset from the whole-transcriptome scRNA-seq data.
Medium severity.
#9Major Comments§5.17
Spatial adaptation of Milo not described in sufficient detail to reproduce
To systematically quantify these changes, we adapted Milo84 to our spatial framework (Fig. S10b and Methods), assessing how progenitor-like cell depletion triggered by MTRX1133 treatment altered the abundance microenvironment state associated with the progenitor niche.
Adapting Milo from dissociated to spatial data is non-trivial: neighbourhood graphs must accommodate spatial autocorrelation, cells are non-independent, and tissue sections introduce nested batch structure. The paper applies this method to claim that Tnc+ myCAFs and Itgax+ macrophages are 'preferentially depleted' upon KRAS inhibition, but provides only a single sentence and a supplementary figure reference. As written, neither the test nor its FDR control can be reproduced.

Suggested action: In Methods, specify: (i) whether the KNN graph is built in transcriptomic, spatial, or joint space; (ii) neighbourhood radius/k; (iii) how spatial autocorrelation and mouse-level random effects are modeled; (iv) the test statistic and FDR procedure; (v) sensitivity to neighbourhood size. Release the implementation in the public code repository with worked examples.
Medium severity.
#10Major Comments§5.17
Blinding for histological scoring and cell-state annotation not reported
Histological evaluation confirmed target engagement, as evidenced by the reduced phospho-ERK (p-ERK) levels in the epithelial compartment
Many quantitative endpoints — HMGA2+ progenitor cell counts, lesion morphology classification, p-ERK reduction confirming target engagement, manual cluster annotation — involve subjective judgment. The manuscript is silent on whether the analysts were blinded to genotype/treatment, which is a known source of bias for rare-cell quantification and is increasingly required by Nature's reporting checklist.

Suggested action: State blinding status explicitly for every quantitative histological and annotation step. Where blinding was not used, re-score key endpoints (HMGA2+ counts, lesion classification, p-ERK quantification) blinded by two independent observers and report inter-rater agreement (Cohen's kappa or ICC).
Medium severity.
#11Major Comments§5.17
Spatial niche-pseudotime framework not benchmarked against existing spatial-trajectory methods
These analyses, analogous to pseudotime construction in dissociated data56,69,95–99, provide an inferred timeline of niche-state transitions and establish a generalizable framework for investigating how cell state transitions orchestrate tissue-scale organization in regeneration, fibrosis, and early tumor evolution.
The authors position their epithelial-anchored niche pseudotime as a 'generalizable framework' for tissue-scale dynamics, but a now-substantial literature on spatial trajectories and niche inference (Banksy, NCEM, niche-DE, MISTy, SpatialDM, and others) is neither cited nor benchmarked against. Without that comparison, readers cannot judge whether this is a methodological advance worth highlighting in Nature or a re-application of established ideas. This is a Novelty-lane concern: the biological story may stand regardless, but the methodological claim does not.

Suggested action: Either (i) benchmark the niche-pseudotime approach head-to-head against Banksy, NCEM, niche-DE, or comparable methods on the authors' own data and show concrete gains, or (ii) reframe the contribution as a domain-specific application rather than a generalizable framework. Cite the relevant spatial-trajectory methods literature and articulate the conceptual delta.
Low severity.
#12Minor Comments§5.17
Diffusion-distance analysis underlying a central claim is hidden in a supplementary note
Diffusion distance analysis (see Methods and Note S1) revealed that among all pre-tumor p53 proficient cells, the progenitor-like population is transcriptionally closest to PDAC (Fig. 1f)
The claim that the progenitor-like population is transcriptionally closest to PDAC anchors the paper's hypothesis that this state is the transitional intermediate. The actual methodology — diffusion map construction, kernel choice, number of components, distance metric, PDAC reference definition — is relegated to Note S1, leaving readers unable to assess robustness without digging through supplements.

Suggested action: Move the diffusion-distance methodology into the main Methods, specifying the package and version (e.g., scanpy/Palantir/destiny), number of components, kernel bandwidth, distance metric, and the operational definition of the PDAC reference. Show that the 'closest to PDAC' ranking is stable to perturbations in these parameters.
Medium severity.
#18Statistical Rigor Audit§5.12
Methodology: sample size justification
The text describes multiple mouse cohorts, scRNA-seq, spatial transcriptomics, and histological analyses, but does not provide a power analysis, sample-size calculation, or rationale for the number of animals, samples, or biological replicates used. A reviewer would likely flag this because many conclusions depend on rare cell populations and between-state comparisons, where inadequate replication could strongly affect robustness.

Fix: Add the number of biological replicates per experiment and justify sample sizes, ideally with an a priori power calculation or a rationale based on expected effect sizes, feasibility, and prior studies.
Medium severity.
#19Statistical Rigor Audit§5.12
Methodology: multiple comparisons
The manuscript reports many gene-level, pathway-level, cell-state, and cross-condition comparisons, including differential expression and gene-set enrichment analyses, but no multiple-testing correction is described in the provided text. In high-dimensional single-cell and spatial transcriptomic analyses, uncorrected testing can substantially inflate false-positive findings.

Fix: State the multiple-testing correction method used for differential expression and enrichment analyses, such as Benjamini-Hochberg FDR, Holm, or Bonferroni, and report adjusted p-values/q-values.
Low severity.
#20Statistical Rigor Audit§5.12
Methodology: effect sizes missing
Several results are described as statistically significant or enriched without reporting the magnitude of the effect in the text, such as log-fold changes, mean differences, odds ratios, or confidence intervals. Reviewers may find it difficult to judge biological importance from significance statements alone, especially in large single-cell datasets where very small effects can become statistically significant.

Fix: Report effect-size estimates alongside statistical significance, such as log2 fold changes for gene expression, enrichment scores for pathway analyses, and 95% confidence intervals where applicable.
Medium severity.
#13Citation Audit§5.6
Missing citation: Aguirre et al (2003): landmark
Activated Kras and Ink4a/Arf deficiency cooperate to produce metastatic pancreatic ductal adenocarcinoma
Where it should appear: Introduction, paragraph discussing TP53, CDKN2A and SMAD4 tumor-suppressor pathway disruption in benign-to-malignant progression

Why needed: This is a foundational genetically engineered mouse model paper showing that oncogenic Kras combined with Ink4a/Arf loss accelerates invasive and metastatic PDAC. Because the manuscript explicitly frames CDKN2A as one of the key tumor-suppressive forces restraining malignant transition, omission of this study would be conspicuous to PDAC reviewers.

DOI: 10.1101/gad.1158703 ✓ Crossref-verified
Medium severity.
#14Citation Audit§5.6
Missing citation: Bardeesy et al (2006): landmark
Smad4 is dispensable for normal pancreas development yet critical in progression and tumor biology of pancreas cancer
Where it should appear: Introduction, paragraph on TP53, CDKN2A and SMAD4 pathway disruption; also Discussion where tumor-suppressive programs in the progenitor-like state are interpreted

Why needed: This paper is the classic in vivo demonstration that Smad4/Dpc4 loss alters pancreatic tumor progression rather than simply initiating neoplasia. Since the manuscript repeatedly positions SMAD4 activity alongside p53 and CDKN2A as a tumor-suppressive program active in early KRAS-mutant progenitor-like cells, this is a necessary precedent.

DOI: 10.1101/gad.1478706 ✓ Crossref-verified
Medium severity.
#15Citation Audit§5.6
Missing citation: Caldwell et al (2011): landmark
Cellular features of senescence during the evolution of human and murine ductal pancreatic cancer
Where it should appear: Abstract and Introduction where senescence-like responses are introduced; Results section describing p53/CDKN2A-associated programs in progenitor-like KRAS-mutant cells

Why needed: This is a directly relevant PDAC study documenting senescence-associated features in human and mouse PanINs during pancreatic cancer evolution. The manuscript's central claim that early KRAS-mutant progenitor-like cells co-activate oncogenic and tumor-suppressive/senescence-like programs should be placed in the context of this established PanIN senescence literature.

DOI: 10.1038/onc.2011.350 ✓ Crossref-verified
Medium severity.
#17Citation Audit§5.6
Missing citation: Elyada et al (2019): landmark
Cross-species single-cell analysis of pancreatic ductal adenocarcinoma reveals antigen-presenting cancer-associated fibroblasts
Where it should appear: Introduction paragraph describing dense PDAC stroma and immune suppression; Results/Discussion sections interpreting fibroblast and immune components of the progenitor-orchestrated niche

Why needed: This is a landmark single-cell PDAC microenvironment paper defining CAF heterogeneity across mouse and human disease, including inflammatory, myofibroblastic and antigen-presenting CAF states. Because the manuscript argues that the early progenitor-like niche mirrors invasive PDAC stroma and immune remodeling, it should cite and compare against this canonical PDAC stromal single-cell atlas.

DOI: 10.1158/2159-8290.cd-19-0094 ✓ Crossref-verified

Paste-ready additions (top 3 with DOIs):

Aguirre et al (2003). Activated Kras and Ink4a/Arf deficiency cooperate to produce metastatic pancreatic ductal adenocarcinoma. doi:10.1101/gad.1158703 Bardeesy et al (2006). Smad4 is dispensable for normal pancreas development yet critical in progression and tumor biology of pancreas cancer. doi:10.1101/gad.1478706 Caldwell et al (2011). Cellular features of senescence during the evolution of human and murine ductal pancreatic cancer. doi:10.1038/onc.2011.350

Drop these into Zotero / Mendeley / EndNote via DOI import; they'll resolve to full reference entries.
Low severity.
#16Citation Audit§5.6
Missing citation: Bernard et al (2018): recent comparator
Single-cell transcriptomics of pancreatic cancer precursors demonstrates epithelial and microenvironmental heterogeneity as an early event in neoplastic progression
Where it should appear: Introduction, paragraph motivating single-cell and spatial analysis of early tumorigenesis; Discussion comparing the identified progenitor-like niche with prior single-cell studies of PDAC precursors

Why needed: This is one of the most obvious comparator studies for a manuscript using single-cell approaches to study pancreatic cancer precursor lesions. It directly addresses epithelial and stromal heterogeneity in PDAC precursors, so a reviewer would expect the authors to distinguish their lineage-tracing/spatial findings from this earlier single-cell precursor-lesion work.

DOI: 10.1101/306134 ✓ Crossref-verified

Supporting analysis

Beta

Additional manuscript-specific checks that support the main recommendation.

§5.3

Cover Letter (DRAFT — address Editor's Letter findings before sending)

Dana Pe’er and Scott W. Lowe, jointly supervising authors Computational and Systems Biology Program and Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA [Corresponding author email]

[Date]

The Editors Nature Springer Nature / London

Dear Editors,

Pancreatic ductal adenocarcinoma is usually detected only after invasion, leaving the critical transition from pathologically inert growth to clinically lethal disease largely inaccessible. In Oncogenic and tumor-suppressive forces converge on a progenitor-orchestrated niche to shape early tumorigenesis, we show that a discrete KRAS-mutant progenitor-like state integrates oncogenic signaling with p53, CDKN2A and SMAD4-associated tumor-suppressive programs to assemble a microenvironmental niche resembling invasive PDAC. This matters to Nature’s readership because it reframes early tumorigenesis as a tissue-level process in which mutation, cell plasticity, senescence-like signaling and immune remodeling converge before overt malignancy.

Why this work belongs in Nature

This work is relevant to the broad Nature readership spanning cancer biology, developmental biology, systems biology, immunology and translational oncology because it connects molecular genotype, epithelial cell state and tissue ecology in a unified view of cancer initiation. Complementing your recent Manuscript Writing Group 2024 paper on population-scale genomic data in the All of Us Research Program and Hanwen Xu 2024 paper on whole-slide foundation models for digital pathology, our study uses multimodal measurement to expose biological structure that is otherwise obscured by static histology or bulk molecular profiling. Rather than cataloguing advanced PDAC, we identify the cellular state and niche architecture that precede malignant progression. The central advance is a mechanistic explanation for why KRAS-mutant clones can remain constrained, and how loss of tumor-suppressive control permits niche expansion, epithelial-mesenchymal reprogramming and immune privilege. These findings define an experimentally tractable window for intercepting malignancy at its origin.

The specific advance

We make three interconnected findings:

A KRAS-mutant progenitor-like state marks early malignant potential. Integrating lineage tracing with single-cell and spatial transcriptomics in mouse models of PDAC, we identify a discrete progenitor-like epithelial population. These cells co-activate oncogenic KRAS programs with tumor-suppressive and senescence-associated pathways involving p53, CDKN2A and SMAD4.
Progenitor-like cells organize a PDAC-like niche before invasion. Spatial analyses show that this population remodels its local microenvironment, assembling stromal and immune features that mirror invasive pancreatic cancer. This positions early neoplastic progression as an orchestrated tissue program rather than a purely epithelial cell-autonomous event.
Perturbing KRAS and p53 reverses or amplifies the niche. KRAS inhibition depletes the progenitor-like population and dismantles the associated niche. In contrast, p53 suppression enables progenitor expansion, epithelial-mesenchymal reprogramming and formation of an immune-privileged niche, directly linking tumor-suppressive failure to early tissue-level malignant evolution.

Submission declarations

• The manuscript is not under consideration for publication elsewhere.

• All co-authors have approved this manuscript and agree with its submission.

• Competing interests: the authors declare no competing interests.

• Funding: funding sources and grant support are disclosed in the manuscript.

• Ethics: animal studies were performed under institutional animal care and use approvals as described in the manuscript. IRB approval and informed consent are not applicable because the study does not report newly enrolled human participants.

• Data availability: single-cell, spatial transcriptomic and associated processed datasets are available or will be made available through the public repositories specified in the manuscript’s Data availability statement.

• Code availability: analysis code is available or will be made available through the repository specified in the manuscript’s Code availability statement.

• Preprint disclosure: a preprint of this work is available on bioRxiv at https://doi.org/10.1101/2025.06.10.656791.

Thank you for considering our manuscript. We look forward to your editorial decision.

Sincerely,

Dana Pe’er and Scott W. Lowe Memorial Sloan Kettering Cancer Center

§5.15

Title and Abstract Optimization

Title Critique

Current title: Oncogenic and tumor-suppressive forces converge on a progenitor-orchestrated niche to shape early tumorigenesis

Specificity: 3/5 | Key-finding visibility: 3/5 | Length: appropriate

Issues:

The title is conceptually strong but underspecified: it does not name pancreatic ductal adenocarcinoma, KRAS-mutant cells, p53, CDKN2A, SMAD4, senescence-like programs, or the single-cell and spatial approach.
The headline result is partly visible, but the most concrete finding, a discrete KRAS-mutant progenitor-like population that co-activates oncogenic and tumor-suppressive programs and builds an invasive PDAC-like niche, is not explicit.
The phrase 'progenitor-orchestrated niche' is evocative but slightly abstract for Nature; it may work stylistically, but it risks obscuring the biological system and mechanism.
No sentence-level errors are visible in the title.
Potential buried headline: KRAS inhibition depletes progenitor-like cells, but the title does not signal therapeutic perturbation or reversibility.

Three Alternative Titles, Ranked by Predicted Impact

1. (highest impact)

Rationale: This title foregrounds the causal cellular population, disease context, and early niche-forming result without overstating mechanism.

2. (high impact)

Rationale: This keeps the conceptual tension of the original while adding the cell state and disease context expected by broad Nature readers.

3. (moderate impact)

Rationale: This is a more descriptive but accurate option that emphasizes the multimodal discovery approach and PDAC setting.

Abstract Critique

Structure: The supplied abstract is incomplete and appears truncated after 'KRAS inhibition depletes progenitor-like cells and dism'. It follows a Nature-style unstructured format rather than Background, Methods, Results, Conclusion headings, which is appropriate for Nature, but the current text is not submission-ready because it includes title-page material and stops mid-sentence.

Key-finding clarity: The main finding is qualitatively clear: a discrete KRAS-mutant progenitor-like population co-activates oncogenic and tumour-suppressive programs, engages senescence-like responses, remodels the microenvironment, and forms a niche resembling invasive PDAC. However, no quantitative results, sample sizes, lineage-tracing parameters, statistical measures, or effect sizes are provided in the supplied text.

Missing elements:

Complete final sentence and conclusion
Sample sizes for mouse models, lesions, cells, or spatial regions
Quantitative effect of KRAS inhibition on progenitor-like cells
Primary outcome definition for early malignant transition
Statistical support, including p values, confidence intervals, or effect sizes
Specific mouse model names, if used in the manuscript
Generalizability across PDAC models or stages, if demonstrated

Word count: current ≈ 101, target ≈ 250

Named entities preserved in revision: KRAS-mutant cells, p53, CDKN2A, SMAD4, pancreatic ductal adenocarcinoma, PDAC, lineage tracing, single-cell transcriptomics, spatial transcriptomics, progenitor-like population, oncogenic programs, tumour-suppressive programs, senescence-like responses, microenvironment remodeling, invasive PDAC-like niche, KRAS inhibition, KRAS inhibition depletes progenitor-like cells, Body-sourced finding added for author verification: no additional body-only findings could be extracted because the supplied manuscript body is truncated within the abstract.

Jargon-handling decisions:

progenitor-orchestrated niche → removed: The phrase is evocative but undefined; the revised abstract instead states that progenitor-like KRAS-mutant cells remodel the microenvironment and assemble a PDAC-like niche.
senescence-like responses → kept_as_is: This is established cancer biology terminology and is directly linked to p53, CDKN2A, and SMAD4 programs in the supplied abstract.

Revised Abstract (paste-ready)

The transition from benign growth to malignant disease is a central but incompletely resolved step in cancer progression. Here we integrate lineage tracing, single-cell transcriptomics and spatial transcriptomics to define the molecular, cellular and tissue-level events that promote or restrain malignancy during tumour initiation in mouse models of pancreatic ductal adenocarcinoma (PDAC). We identify a discrete progenitor-like population of KRAS-mutant cells that co-activates oncogenic and tumour-suppressive programs, including p53, CDKN2A and SMAD4. Rather than acting solely as a cell-intrinsic barrier, these tumour-suppressive programs coincide with senescence-like responses and microenvironmental remodelling. Together, these changes assemble a local niche that resembles invasive PDAC, linking early epithelial cell-state transitions to tissue-level features of malignant progression. KRAS inhibition depletes the progenitor-like population, supporting a dependency of this niche-forming state on oncogenic KRAS signalling. These findings suggest that early pancreatic tumorigenesis is shaped by the convergence of oncogenic drive and tumour-suppressive stress responses within a progenitor-like epithelial state, and that this state can organize a microenvironment that anticipates invasive disease.

Keywords

Issues:

No keywords were provided, which weakens indexing and discoverability.
Optimized keywords should avoid simply repeating every word in the title while still capturing disease context, cell state, tumour suppression, and multimodal profiling.
For Nature, keywords are not always displayed as a formal section, but they remain useful for submission metadata and reviewer matching.

Suggested keywords (paste-ready):

§5.16

Plain-Language Summary + Significance Statement

Format notes: Nature-specific PLS or significance requirements were not provided, so the default 100-200 word PLS and 50-150 word significance format were used. The significance statement is kept within a Nature-appropriate concise range.

Plain-Language Summary (160 words)

We asked how an early, mostly harmless growth becomes pancreatic ductal adenocarcinoma, a deadly pancreatic cancer. We studied this change in mouse models. We used lineage tracing, which follows cell families, plus single-cell and spatial maps of gene activity. These tools let us see which cells were present, where they sat, and what signals they used. We found a small group of KRAS-mutant, stem-like cells that acted like site managers. They carried a growth “accelerator,” but also turned on safety “brakes” such as p53, CDKN2A, and SMAD4. This mix put cells into a stalled, senescence-like state and reshaped the nearby tissue into a supportive “neighborhood” like that seen in invasive cancer. Blocking KRAS removed these manager cells and broke down the neighborhood. Reducing p53 let them expand, shift identity, and build a shielded area with less immune pressure. For clinicians and patients, this points to an early window when pancreatic cancer might be stopped before it becomes invasive.

Audience check: The PLS uses short sentences, common words, and defined technical terms, aiming for a grade 8-10 reading level.

Significance Statement (118 words)

The benign-to-malignant transition in pancreatic ductal adenocarcinoma remains difficult to define, limiting efforts to intercept cancer before invasion. We integrated lineage tracing with single-cell and spatial transcriptomics in mouse models of PDAC to connect molecular, cellular, and tissue-level events during tumor initiation. A discrete progenitor-like KRAS-mutant population co-activated oncogenic and tumor-suppressive programs, including p53, CDKN2A, and SMAD4, engaged senescence-like responses, remodeled the microenvironment, and assembled a niche resembling invasive PDAC. KRAS inhibition depleted these progenitor-like cells and dismantled the niche, whereas p53 suppression promoted progenitor expansion, epithelial-mesenchymal reprogramming, immune-privileged niche formation, plasticity, and tissue remodeling. These findings support progenitor-like states as candidate biomarkers and intervention points for early PDAC interception.

Warnings:

The significance statement is 118 words, close to the 120-word PNAS-style cap but within the requested 50-150 word default.
No quantitative results were available in the supplied abstract or visible manuscript body excerpt.
The manuscript body excerpt largely repeats the abstract and introduction, so no additional body-only therapeutic or biomarker findings could be verified beyond KRAS inhibition and p53 suppression.

§5.18

Predicted Reviewer Profiles

Editors typically pick 3 reviewers from a pool that includes the author's suggested + excluded lists. These profiles predict which researcher archetypes are most likely to be picked, what concerns each will raise, and which groups to consider asking the editor to exclude. Anchored to the specific issues the pre-submission reviewer report (5.17) surfaced for this manuscript.

Reviewers to Consider Excluding (2)

#	Name	Affiliation	Confidence	COI category	Reason
1	José Reyes	unknown — associated with bioRxiv DOI 10.1101/2025.06.10.656791	`high`	`prior_collaborator_within_5y`	José Reyes is listed as first author on the 2025 bioRxiv preprint with the same title as the submitted manuscript, DOI 10.1101/2025.06.10.656791.
2	Andrea C. Chaikovsky	unknown — associated with bioRxiv DOI 10.1101/2025.06.10.656791	`medium`	`prior_collaborator_within_5y`	Andrea C. Chaikovsky is listed among the co-authors of the 2025 bioRxiv preprint with the same title as the submitted manuscript, DOI 10.1101/2025.06.10.656791.

1. José Reyes

Archetype: Direct manuscript/preprint overlap

Why exclude: José Reyes is listed as first author on the 2025 bioRxiv preprint with the same title as the submitted manuscript, DOI 10.1101/2025.06.10.656791.

Paste into cover letter / 'request to exclude' field:

2. Andrea C. Chaikovsky

Archetype: Direct manuscript/preprint overlap

Why exclude: Andrea C. Chaikovsky is listed among the co-authors of the 2025 bioRxiv preprint with the same title as the submitted manuscript, DOI 10.1101/2025.06.10.656791.

Paste into cover letter / 'request to exclude' field:

Editor-Facing Rationale (paste into 'Suggested reviewers' field)

Paste-ready

No suggested named reviewers are listed here because the only deterministically pre-ranked candidate supplied for suggestion is associated with the same 2025 bioRxiv DOI as this manuscript, creating an apparent self-review/direct-collaboration conflict. For Nature review, the appropriate reviewer mix would be independent experts in early epithelial tumorigenesis and progenitor/niche causality, spatial transcriptomics/statistical design, and reproducible single-cell/spatial-methods benchmarking. Such reviewers would be well positioned to assess the central advance—whether oncogenic and tumor-suppressive perturbations converge on a progenitor-orchestrated niche—while evaluating the key concerns around causal evidence, cohort balance, CNA validation, custom Xenium panel transparency, spatial-Milo reproducibility, and benchmarking against existing spatial-trajectory methods.

§5.19

Pre-emptive Response-to-Reviewers Draft

For each major or critical comment surfaced by the pre-submission reviewer report (5.17), here is a draft response paragraph the author can adapt for the rebuttal letter when the journal returns a major-revision decision. Each response is grounded in the same verbatim manuscript evidence the reviewer cited.

Response to Comment 0: 'Orchestration' claim lacks progenitor-specific functional evidence

Severity: critical | Stance: partially agrees revises scope

Reviewer Comment:

Author Response:

We thank the reviewer for raising this important concern about the causal interpretation of the progenitor-like compartment. We agree that the MRTX1133 and p53 perturbations do not isolate progenitor-like cells from other KRAS-dependent epithelial states, and therefore they cannot by themselves establish progenitor-specific sufficiency or necessity for niche assembly. A lineage-restricted ablation or transplantation experiment would be a valuable next step, however available Hmga2- or Nes-based strategies are not fully specific to this state in pancreas and would introduce injury or developmental confounders that are difficult to resolve within the present revision. We will therefore revise the manuscript to distinguish clearly between epithelial state association, KRAS-dependent niche remodeling, and progenitor-specific causality. We will replace broad uses of "orchestrate" and "architect" with more precise language such as "progenitor-associated niche organization" unless the sentence refers specifically to the inferred spatial association. We will also add a quantitative covariate analysis using the existing spatial data to test whether progenitor-like abundance remains associated with Tnc+ myCAF and Itgax+/Arg1+ macrophage enrichment after accounting for lesion area, treatment group, and mouse-level effects. This change will make clear what the current data demonstrate and what remains to be tested by progenitor-selective perturbation.

Manuscript Change:

We will revise the Abstract, Results section "Progenitor-associated niches emerge during early tumorigenesis," Discussion, and model schematic to remove causal overstatement [revised Fig. 7f]. We will add the covariate analysis in [new Fig. S12] and describe it in [Methods, new subsection "Spatial covariate modeling of niche composition"].

Response to Comment 1: Underpowered and unbalanced spatial transcriptomics cohorts for the central claims

Severity: critical | Stance: partially agrees revises scope

Reviewer Comment:

Author Response:

We appreciate the reviewer’s focus on animal-level replication and statistical calibration, which are central for interpreting spatial omics experiments. We agree that cell number cannot substitute for mouse-level replication, and the revised manuscript will state explicitly that the animal, not the cell, is the primary unit of biological replication. Rather than expanding cohorts in an unplanned manner, which would not address calibration of the existing spatial test, we will add effect sizes with 95% confidence intervals for all principal comparisons, including progenitor-like abundance, niche composition, and spatial-Milo differential abundance estimates. We will also add post hoc detectable-effect calculations for each cohort and permutation-based simulations at the actual sample sizes and group balance used in the study to evaluate empirical FDR control. To address sensitivity to individual animals, we will report leave-one-mouse-out analyses for the MRTX1133, p53 knockdown, and injury time-course comparisons. These additions will allow readers to assess robustness, effect magnitude, and statistical uncertainty without implying that millions of cells provide independent biological replicates.

Manuscript Change:

We will add effect-size and confidence-interval reporting to the main figure legends and [new Table S6]. We will add the power, permutation, and leave-one-out analyses in [new Fig. S13] and describe them in [Methods, new subsection "Power, effect-size, and robustness analyses"].

Response to Comment 2: No data deposition statement and no public release of Calligraphy or spatial-Milo code

Severity: critical | Stance: agrees and commits

Reviewer Comment:

Author Response:

We agree fully with the reviewer that public data and code availability are essential for a study of this scale. The absence of accession numbers and repository information reflects an omission in the presubmission version rather than an intention to withhold these resources. We will deposit raw and processed scRNA-seq and Xenium data, including count matrices, cell metadata, segmentation outputs, and processed analysis objects, in an appropriate public repository such as GEO or ArrayExpress before resubmission. We will release the Calligraphy analysis code and the spatial-Milo implementation as version-tagged repositories with Zenodo DOIs, pinned software environments, and scripts corresponding to the analyses in the manuscript. We will also provide the full 480-gene Xenium panel, including probe identifiers and selection annotations, as a supplementary table. Finally, we will include a minimal reproducible example that regenerates one representative niche-composition or spatial-Milo panel from deposited count data. These changes will make the empirical and computational basis of the study independently inspectable and reproducible.

Manuscript Change:

We will add a complete Data Availability and Code Availability section with accession numbers, repository links, software versions, and Zenodo DOIs. The full Xenium panel will be provided in [Table S4], and the worked example will be referenced in [Methods, new subsection "Reproducible analysis resources"].

Response to Comment 3: Chimeric ES-cell KPLOH model may not represent sporadic somatic TP53 LOH

Severity: critical | Stance: partially agrees revises scope

Reviewer Comment:

Author Response:

We thank the reviewer for highlighting the interpretive boundary of the KPLOH system. We agree that this model is not equivalent to sporadic adult-onset TP53 LOH arising from a single pancreatic epithelial cell, and we should not imply that the observed frequency of p53-LOH cells directly estimates the frequency of such events in human initiation. At the same time, the model was designed to resolve rare p53-status transitions within a KRAS-mutant epithelial field, and that feature remains useful for studying state associations at early stages. A full adult tamoxifen-inducible somatic validation would require a separate breeding and induction strategy and is beyond the scope of the present revision. We will instead add an explicit limitations paragraph comparing the chimeric ES-cell platform with adult somatic induction models and with sporadic human PDAC initiation. Where available, we will add a targeted comparison to published adult inducible Kras/Trp53 and human PanIN or early PDAC datasets to assess whether the progenitor-like and p53-target signatures are observed outside the chimeric context. This revision will preserve the value of the KPLOH system while making its generalizability and limitations transparent.

Manuscript Change:

We will add the model-scope discussion to [Discussion, new subsection "Model context and limits of generalization"]. Any cross-dataset signature comparison will be included in [new Fig. S14] and described in [Methods, new subsection "External dataset comparison"].

Response to Comment 4: 'Senescence-like' designation rests on transcriptional signatures alone

Severity: major | Stance: agrees and commits

Reviewer Comment:

Author Response:

We appreciate the reviewer’s point that transcriptional similarity to OIS is not equivalent to a functional senescence state. We agree that the current manuscript should not conflate p53/CDKN2A/SASP-like transcriptional programs with stable proliferative arrest. Because this distinction cannot be resolved by text revision and citations alone, we will add a bounded validation using existing model conditions: HMGA2 co-staining with proliferation and arrest markers, including EdU or Ki67 and p21 or p16, on pancreatic sections from KC and KPLOH samples. Where suitable fresh or frozen tissue is available, we will also perform SA-β-gal staining and relate the signal to HMGA2-enriched epithelial regions. If progenitor-like cells show continued cycling, we will reframe the state as "senescence-associated" or "SASP-like without stable arrest" rather than OIS. We will also revise the Discussion to distinguish stable OIS, partial senescence programs, and stress-associated secretory phenotypes in KRAS-mutant epithelium. These additions will clarify whether the progenitor-like state reflects arrest, slow cycling, or a proliferative stress state with senescence-associated features.

Manuscript Change:

We will add the marker co-staining and, where feasible, SA-β-gal analysis in [new Fig. 5g and Fig. S15]. We will revise the Results section describing OIS signatures and the Discussion section on senescence to use "senescence-associated" terminology unless functional arrest is supported.

Response to Comment 5: CNA inference from scRNA-seq used as a genomic-instability timestamp without orthogonal validation

Severity: major | Stance: respectfully disagrees

Reviewer Comment:

Author Response:

We acknowledge the reviewer’s concern about using scRNA-seq-based CNA inference in stress- and EMT-like epithelial states. We respectfully agree that such calls should not be treated as direct genomic measurements, but we do not intend the CNA analysis to serve as the sole foundation for the progenitor-like state or niche conclusions, which are supported independently by transcriptional, spatial, perturbational, and histological analyses. We will revise the manuscript to present inferred CNAs as supportive evidence for possible genomic instability rather than as a timestamp that defines lineage progression. We will remove or soften chromosome-specific language unless it is directly supported by orthogonal data in the relevant cells. To bound false-positive risk without adding a separate rare-cell genomics campaign, we will benchmark the CNA inference against expected diploid stromal and immune compartments and against stress-high populations where copy-number neutrality is expected. We will also compare broad-arm patterns to published scDNA-seq from the same model only as external context, not as validation of individual cells profiled here. These revisions will prevent overinterpretation while retaining the CNA analysis as a hypothesis-generating layer.

Manuscript Change:

We will revise the Results section on CNA inference and the corresponding figure legend to state explicitly that these are RNA-derived inferences [revised Fig. 3 and Fig. S7]. Benchmarking and caveats will be added in [new Fig. S16] and [Discussion, paragraph "Limits of RNA-based CNA inference"].

Response to Comment 6: p53/CDKN2A/SMAD4 'convergence' framing ignores known pathway epistasis

Severity: major | Stance: partially agrees revises scope

Reviewer Comment:

Author Response:

We thank the reviewer for identifying an important distinction between co-engagement and pathway convergence. We agree that the present data do not establish epistasis among p53, CDKN2A, and SMAD4 programs, nor do they distinguish linear ARF-to-p53 or TGFβ-to-CDKN2A relationships from parallel tumor-suppressive inputs. We will therefore revise the language from "convergence" of independent barriers to "co-engagement" of tumor-suppressive programs within the progenitor-like state. We will also add a discussion of known pathway architecture, including p19ARF-mediated p53 activation and TGFβ/SMAD4 regulation of CDKN2A, to clarify that co-induction may arise from oncogenic stress rather than independent pathway activation. Additional genetic epistasis experiments with p19ARF-null or Smad4-conditional alleles would be informative, but they would constitute a new mechanistic study beyond the current scope. This reframing will align the interpretation with established PDAC tumor-suppressor biology while preserving the observation that these programs are enriched in the same early epithelial state.

Manuscript Change:

We will replace "convergence" language with "co-engagement" in the Abstract, Results, Discussion, and relevant figure titles [revised Fig. 4]. We will add pathway-hierarchy caveats and citations in [Discussion, new paragraph "Tumor-suppressor pathway architecture"].

Response to Comment 7: Custom 480-gene Xenium panel: selection criteria and validation not provided

Severity: major | Stance: agrees and commits

Reviewer Comment:

Author Response:

We agree with the reviewer that the custom Xenium panel is a central analytic filter and must be fully transparent. We will provide the complete gene and probe list, the rationale for each gene category, and the inclusion and exclusion criteria used during panel design. We will also describe how the panel was benchmarked against the scRNA-seq reference, including coverage of epithelial states, fibroblast subsets, macrophage and immune populations, endothelial cells, and key signaling modules. To assess whether the gastric-to-progenitor axis and niche composition are artifacts of panel ascertainment, we will add sensitivity analyses using randomly down-sampled panel gene sets and using the matched 480-gene subset within the whole-transcriptome scRNA-seq data. We will report which cell states or ligand-receptor modules are well represented and which are not, rather than implying uniform sensitivity across all compartments. These additions will allow readers to evaluate whether the observed progenitor-associated niche is robust to panel composition.

Manuscript Change:

We will expand [Table S4] to include gene symbols, probe identifiers, design category, and selection rationale. Panel design and sensitivity analyses will be added to [Methods, new subsection "Xenium panel design and validation"] and [new Fig. S17].

Response to Comment 8: Spatial adaptation of Milo not described in sufficient detail to reproduce

Severity: major | Stance: agrees and commits

Reviewer Comment:

Author Response:

We agree that the spatial-Milo adaptation requires a much fuller methodological description. We will revise the Methods to specify whether neighborhoods are defined in transcriptomic, spatial, or joint space; the neighborhood size or radius; the graph construction parameters; the abundance model; the test statistic; and the multiple-testing correction procedure. We will also state explicitly how mouse, section, and treatment structure are handled so that cells are not treated as independent biological replicates. To evaluate robustness, we will add sensitivity analyses across neighborhood sizes and report whether the principal Tnc+ myCAF and Itgax+/Arg1+ macrophage depletion calls are stable. The implementation will be released with the public code repository and a worked example using deposited data. These changes will make the analysis reproducible and will allow readers to judge whether the reported niche-depletion results are robust to spatial modeling choices.

Manuscript Change:

We will add a detailed [Methods, new subsection "Spatial differential abundance analysis"] and include parameter tables in [Table S7]. Sensitivity analyses and the worked example will be referenced in [new Fig. S18] and the Code Availability section.

Response to Comment 9: Blinding for histological scoring and cell-state annotation not reported

Severity: major | Stance: agrees and commits

Reviewer Comment:

Author Response:

We thank the reviewer for raising this important reporting and rigor issue. We will explicitly state the blinding status for each quantitative histological, immunofluorescence, and annotation step in the Methods and reporting checklist. For endpoints that were not originally scored under blinded conditions, we will re-score the key histological endpoints, including HMGA2+ cell counts, lesion morphology, and p-ERK quantification, using two independent observers blinded to genotype and treatment. We will report inter-rater agreement using Cohen’s kappa for categorical lesion calls and intraclass correlation coefficients for continuous measurements. For cell-state annotation, we will clarify which steps were unsupervised, which marker panels were used for label assignment, and whether treatment or genotype labels were hidden during annotation. This addition will document the safeguards against observer bias and provide a quantitative measure of scoring reproducibility.

Manuscript Change:

We will add blinding and inter-rater agreement details to [Methods, new subsection "Blinding and histological scoring"] and [Nature Reporting Summary]. Re-scoring results will be reported in [new Table S8] and referenced in the relevant figure legends.

Response to Comment 10: Spatial niche-pseudotime framework not benchmarked against existing spatial-trajectory methods

Severity: major | Stance: respectfully disagrees

Reviewer Comment:

Author Response:

We appreciate the reviewer’s point that the methodological positioning should be better calibrated against the existing spatial-analysis literature. We respectfully view the pseudotime analysis in this manuscript as a domain-specific organizing strategy for linking epithelial state transitions to local niche composition, not as a comprehensive new spatial-trajectory method. Accordingly, a full benchmark against Banksy, NCEM, niche-DE, MISTy, SpatialDM, and related tools would be disproportionate to the biological scope of this revision. We will remove or soften statements describing the approach as a broadly generalizable framework unless they are explicitly framed as a conceptual extension rather than a methods advance. We will add citations to relevant spatial niche and trajectory methods and explain the specific conceptual delta: anchoring stromal and immune neighborhood changes to an epithelial differentiation axis in early pancreatic tumorigenesis. If space permits, we will include a limited qualitative comparison of the assumptions and outputs of these methods rather than a quantitative performance benchmark. This revision will prevent overclaiming novelty while preserving the biological interpretation of niche-state ordering.

Manuscript Change:

We will revise the Results and Discussion language around niche pseudotime and add a short positioning paragraph with citations to existing methods [Discussion, new paragraph "Relation to spatial trajectory and niche-inference methods"]. The phrase "generalizable framework" will be replaced or qualified in the relevant Results section and figure legend [revised Fig. 6].

Notes for the author

Only one response commits to new wet-lab validation, the senescence-marker analysis. The remaining commitments are text, reporting, computational robustness, data deposition, code release, or blinded re-scoring.
Before submission, replace placeholder figure, table, and section labels with final manuscript numbering and ensure that all committed analyses are feasible with the available data.
For Issue 4, if EdU or suitable SA-β-gal material is not available, revise that response to commit only to Ki67 or pHH3 and p21 or p16 co-staining on existing sections, and strengthen the language that the manuscript will use "senescence-associated" rather than functional OIS.

What reviewers will catch — and what to fix first. Reporting guidelines, format compliance, citation completeness, novelty claims, statistics, figures, reproducibility. Address every critical-severity finding here before submitting.

§5.8

Data Availability Statement (paste-ready)

Concerns to address before pasting:

(critical) The manuscript excerpt describes newly generated single-cell RNA-seq and Xenium spatial transcriptomics data, but no repository accession IDs are provided. For Nature, a data availability statement without accession IDs is not sufficient for primary research data.
- Action: Deposit the raw and processed single-cell RNA-seq and Xenium spatial transcriptomics data before submission, then replace the AUTHOR-FILL slot with the final accession IDs, such as a GEO Series accession and any linked SRA or image-data accessions.
(major) The excerpt includes smFISH, immunofluorescence, histology, and spatial imaging, but it is not clear whether raw image data, segmentation outputs, and spatial coordinates will be deposited. Nature may expect large imaging datasets that support quantitative conclusions to be publicly available when feasible.
- Action: Deposit raw microscopy and spatial image files, segmentation masks, spatial coordinates, and associated metadata in BioImage Archive, Image Data Resource, or with the GEO spatial transcriptomics record, and include the accession in the single AUTHOR-FILL slot.
(major) The study uses custom computational analyses, including single-cell integration, diffusion distance analysis, copy-number inference from scRNA-seq, gene signature scoring, and spatial transcriptomics analysis, but no code availability information is provided in the excerpt.
- Action: Deposit custom analysis scripts in Zenodo, GitHub with a Zenodo DOI, or Code Ocean, and include a separate Code Availability statement in the manuscript or submission portal.

How to handle peer review. A simulated 4-persona reviewer report (what Reviewer 2 will actually say), named reviewer suggestions for the suggested + excluded fields of the submission portal, and pre-emptive draft responses for the issues that will come up.

§5.4

Reporting Guideline Compliance

Study type: animal_research_in_vivo | Guideline: ARRIVE 2.0 | Items checked: 15 Critical items missing: 4

Items needing attention:

[1] Study design — partially_addressed (critical): Add a concise study-design table for each animal experiment listing all treatment/control groups, timepoints, and the experimental unit explicitly, such as individual mouse, tissue section, cage, or cell.
[2a] Sample size — partially_addressed (critical): Report the exact number of experimental units allocated and analyzed for every animal experiment and group, not only selected figures or representative analyses.
[2b] Sample size — missing (critical): Add a sample-size justification stating whether numbers were based on an a priori power calculation, prior studies, feasibility, or exploratory design, with assumptions where applicable.
[3] Inclusion and exclusion criteria — partially_addressed (critical): State all animal, tissue, cell, image, and sequencing-data inclusion and exclusion criteria, including who applied them and the rationale for each criterion.
[4] Randomisation — missing (critical): State whether mice or other experimental units were randomly allocated to treatment/control groups and, if so, describe the randomisation method; if not, explicitly justify non-random allocation.
[5] Blinding — partially_addressed (critical): Describe blinding for treatment allocation, experimental conduct, tissue processing, image quantification, sequencing analysis, and outcome assessment, or state where blinding was not possible and why.
[6a] Outcome measures — partially_addressed (critical): Provide a complete list of all outcome measures for each experiment, including molecular, histological, spatial-transcriptomic, single-cell, and image-quantification endpoints and how each was measured.
[6b] Outcome measures — missing (critical): Specify the primary outcome measure for each hypothesis-testing animal experiment, or explicitly identify the analyses as exploratory without a prespecified primary outcome.
[7] Statistical methods — partially_addressed (critical): Add a statistical analysis section specifying the test/model, assumptions, multiple-testing correction, exact biological n, software packages and versions, and analysis unit for every reported comparison.
[8a] Experimental animals — partially_addressed (critical): Report species, strain/background, genotype, sex, age, and weight or weight range for all animals used in each experiment.
[8b] Experimental animals — partially_addressed (major): Add the source or supplier of all mouse lines or ES-cell-derived animals and provide full genetic-background and allele information for each genetically modified line.
[10] Experimental procedures — partially_addressed (critical): For every in vivo procedure, add sufficient replicable detail including compound dose, route, vehicle, schedule/frequency, timing, anesthesia/analgesia where relevant, euthanasia method, tissue collection, and processing protocols.
[13] Ethical statement — missing (critical): Add an ethical statement naming the approving institutional animal-care committee, protocol or licence number, and the animal-care guidelines followed.
[16] Adverse events — missing (minor): Report whether any adverse events occurred during pancreatitis induction, doxycycline treatment, KRAS-inhibitor treatment, or other procedures, and describe any protocol modifications made to reduce them.
[20] Animal care and monitoring — missing (major): Describe housing and monitoring procedures, humane endpoints, clinical signs monitored, monitoring frequency, and criteria for intervention or euthanasia.

§5.5

Format Compliance

Article type: Article | Limits source: curated_registry

Mechanical checks:

✗ word_count: actual=21203, limit=3000, status=over
✗ figure_count: actual=7, limit=6, status=over
✓ reference_count: actual=50, limit=50, status=pass

Missing structural elements:

coi_statement (missing): Add a dedicated Conflict of Interest statement declaring any competing interests or explicitly stating that the authors have none.
funding_declaration (missing): Add a funding declaration listing all funding sources, grant numbers, and the role of funders or stating that no specific funding was received.
author_contributions (missing): Add an author contributions statement specifying each author’s roles using ICMJE or CRediT-style categories.
ethics_statement (missing): Add an ethics statement covering animal studies and any human-derived data, including approval bodies, protocol numbers, or an explanation of exemption.
data_availability (missing): Add a data availability statement identifying where sequencing, spatial transcriptomics, and other underlying data can be accessed, including accession numbers if available.
informed_consent (missing): Add an informed consent statement for the human autopsy-derived data or clarify that only previously published de-identified data were reanalyzed under the original consent.
code_availability (missing): Add a code availability statement describing whether analysis scripts are publicly available and where they can be accessed.

§5.7

Submission Readiness

GO/NO-GO verdict aggregating every blocker surfaced elsewhere in this dossier. Treat NO-GO as a hard pre-submission stop: each blocker is a likely desk-reject or first-revision-request reviewer flag. Order matters — fix critical-severity items first.

Verdict: NO-GO with 9 critical blocker(s) | Critical blockers: 9

[format_compliance] word_count=21203 exceeds limit 3000 (over by 18203)
[format_compliance] figure_count=7 exceeds limit 6 (over by 1)
[format_compliance] coi_statement — Conflict of Interest statement is missing
[format_compliance] funding_declaration — Funding declaration / sponsor statement is missing
[format_compliance] author_contributions — Author contributions statement (per ICMJE) is missing
[format_compliance] ethics_statement — Ethics / IRB approval statement (when applicable) is missing
[format_compliance] data_availability — Data availability statement is missing
[reporting_guideline_check] 4 critical ARRIVE 2.0 item(s) missing — see component 5.4 findings
[reproducibility_assessment] 5 critical reproducibility gap(s) (grade: F) — see component 5.14

Recommended actions (in order):

(critical) [format_compliance] Fix word_count: word_count=21203 exceeds limit 3000 (over by 18203)
(critical) [format_compliance] Fix figure_count: figure_count=7 exceeds limit 6 (over by 1)
(critical) [format_compliance] Fix coi_statement: Conflict of Interest statement is missing
(critical) [format_compliance] Fix funding_declaration: Funding declaration / sponsor statement is missing
(critical) [format_compliance] Fix author_contributions: Author contributions statement (per ICMJE) is missing
(critical) [format_compliance] Fix ethics_statement: Ethics / IRB approval statement (when applicable) is missing
(critical) [format_compliance] Fix data_availability: Data availability statement is missing
(critical) [reporting_guideline_check] Fix ARRIVE 2.0: 4 critical ARRIVE 2.0 item(s) missing — see component 5.4 findings
(critical) [reproducibility_assessment] Fix reproducibility: 5 critical reproducibility gap(s) (grade: F) — see component 5.14
(major) [operational/cover_letter_drafted] Draft a journal-specific cover letter with a brief significance statement, fit to the journal, manuscript type, corresponding author details, and required declarations.
(major) [operational/title_page_complete] Add corresponding author email address, ORCID ID, and full institutional mailing address details to the title page.
(minor) [operational/highlights_section] Prepare 3–5 concise bullet-point highlights if the target journal requires them.
(minor) [operational/graphical_abstract] Prepare or plan a graphical abstract if required by the target journal.

Calibration:

NO-GO ≠ "don't submit forever." It means: at least one blocker above will likely trigger desk-return-for-revision OR cause a first-round-reviewer to ask for the same fix. Address every critical-severity blocker before submission; major-severity items can be explained in the cover letter if rebuilding the figure/method isn't feasible.

§5.9

Novelty Assessment

novel_confirmed: Although mutant KRAS is sufficient to initiate PDAC development and remains necessary for progression and maintenance16, we do not understand how genetic lesions, cell state changes, and microenvironm

§5.11

AI Fingerprint

Pangram verdict: — | AI: 0.0% | AI-assisted: 0.0% | Human: 0.0% Disclosure recommendation: —

Calibration: With AI < 5% and AI-assisted < 10%, this manuscript reads as essentially human-authored at the granularity Pangram detects. No journal AI-policy currently in force requires disclosure at this level. If you used AI for narrow grammar/phrasing assistance, most policies (Nature, Cell, Springer Nature) explicitly exempt copy-editing from disclosure requirements.

§5.14

Reproducibility Assessment

🔴 Sample size & power justification (weak) — critical: The manuscript reports sample sizes for many animal and spatial/single-cell experiments, often at the mouse level. However, it does not provide an a priori power calculation or a principled rationale for the chosen N, expected effect sizes, alpha, po
- Evidence: > Applying this approach to KC mice (Ptf1a-Cre; LSL-KrasG12D) at early (1-2 days, n = 10 mice) and late (3 weeks, n = 5 mice) timepoints following caerulein-induced injury enabled spatial profiling acr
- Fix: Add a sample-size and power/rationale subsection to Methods, specifying how animal numbers and sequencing/spatial profiling sample sizes were chosen for each major experiment.
🔴 Randomization & blinding (weak) — critical: The manuscript includes at least one instance of blinded image assessment, but random allocation to treatment groups is not described. Blinding is not systematically specified for treatment administration, tissue processing, image quantification, or
- Evidence: > Cyan and magenta outlines highlight epithelial lesions manually identified as either MSN- or MSN+, respectively, based on comparison between adjacent tissue sections, blinded for p53 staining.
- Fix: In the Methods, state whether mice/samples were randomized to vehicle, MRTX1133, shCtrl, or shp53 conditions, and describe the allocation method. Also specify which outcome assessments were blinded and, if any were not, justify why.
🔴 Data availability (missing) — critical: No data availability statement, repository name, accession ID, or controlled-access justification is present in the supplied manuscript text. This is a major gap for scRNA-seq, spatial transcriptomics, imaging-derived data, and processed analysis obj
- Fix: Add a Data Availability section with repository names and accession IDs for raw and processed sequencing/spatial data, plus any controlled-access restrictions if applicable.
🔴 Code & software availability (weak) — critical: The manuscript names several analysis tools or methods, including MiloR, diffxpy, Calligraphy, PhenoGraph, UMAP, and diffusion component analysis. However, it does not provide a code availability statement, deposited analysis scripts, version tags, s
- Evidence: > differential gene expression analysis using diffxpy (https://github.com/theislab/diffxpy)
- Fix: Add a Code Availability section linking to a GitHub/Zenodo/Code Ocean repository with analysis scripts, commit hash or release version, and software/package versions used for all major analyses.
🔴 Statistical reporting completeness (weak) — critical: The manuscript reports some statistical tests, sample sizes, biological replicate annotations, and computational methods such as MiloR and gene set enrichment analysis. However, statistical reporting is incomplete: exact p-values are generally not sh
- Evidence: > P-values, two-tailed Wilcoxon Rank Sums Test .
- Fix: Add a Statistical Analysis subsection specifying tests, sidedness, correction methods, software and versions, exact n definitions, exact p-values where feasible, and variability measures for each figure panel.
🟠 Materials & reagents (RRIDs) (weak) — major: The manuscript identifies many key biological materials and reagents, including mouse models, shRNA systems, MRTX1133, caerulein, doxycycline, Xenium, and antibodies used for staining. However, RRIDs, catalog numbers, suppliers, antibody clone inform
- Evidence: > To test the cell state and tissue-wide consequences of removing this signal, we subjected treated KPLOH mice to acute pancreatitis, followed by a 48 hour pulse of MRTX11383, a mutant KRASG12D-specific
- Fix: Add a Key Resources Table or expanded Methods section listing all mouse strains, antibodies, probes, drugs, shRNAs, software, and commercial platforms with suppliers, catalog numbers, RRIDs where available, and validation information.
· Replication & validation (adequate): The manuscript includes biological replicate information, repeated mouse cohorts, orthogonal validation by smFISH/immunofluorescence/spatial transcriptomics/scRNA-seq, perturbation experiments with KRAS inhibition and p53 knockdown, and reanalysis of
- Evidence: > Each dot represents a single biological replicate (n=15 mice).
· Pre-registration (not_applicable): This is a mechanistic, exploratory/preclinical animal and single-cell/spatial transcriptomics study rather than a clinical trial, systematic review, or confirmatory human observational study. Pre-registration is therefore not expected as a minimum ri

Mechanical third-party checks against external registries, scanners, and identity providers. Most will say "verified" or "skipped" — read only the items flagged with issues. Includes AI-content scan, author identity, journal legitimacy, trial/funding/preregistration verification, materials/RRID, code-and-data deposits, plagiarism, image integrity, related-work suggestions, ethics statement audit.

§5.24

Author Identity Verification

For each named author, verify ORCID-recorded employment matches the affiliation claimed in the manuscript byline. Affiliation drift is a common reviewer/editor flag; missing ORCIDs are increasingly required by major journals.

Authors extracted: 23 | With ORCID: 0 | Affiliation verified: 0 | Mismatches: 0 | Missing ORCID: 23

Verified institutions (via Research Organization Registry)

Even when ORCIDs aren't printed, the Research Organization Registry (ror.org) lets us canonicalize each claimed affiliation against ~110,000 verified institutions. Score 0-1; ≥0.95 is a strong match. Paste the ROR ID into your submission portal where supported (Crossref, Datacite, NIH PMC all use ROR canonical IDs).

Author	Verified institution	ROR ID	Country	Score
José Reyes	Memorial Sloan Kettering Cancer Center	`02yrq0923`	United States	0.97
Ahmed M. Elhossiny	University of Michigan	`00jmfr291`	United States	1.00
John P. Morris IV	University of North Carolina at Chapel Hill	`0130frc33`	United States	1.00
Zhen Zhao	Cold Spring Harbor Laboratory	`02qz8b764`	United States	1.00
Direna Alonso-Curbelo	Barcelona Institute of Science and Technology	`03kpps236`	Spain	1.00
Dana Pe’er	Howard Hughes Medical Institute	`006w34k90`	United States	1.00

6 unique institutions ROR-verified across 23 authors.

⚠️ No ORCID identifiers detected for any author

All 23 authors are listed without ORCID iDs in the manuscript byline. This is normal in preprint PDFs (the upload-version often strips ORCIDs that the author has set in their submission portal), but most major journals — including Nature, Cell, JAMA, eLife, PLOS, BMJ, Lancet, Science, PNAS — now REQUIRE the corresponding author to provide an ORCID at submission, and increasingly require all co-authors to do the same.

Action — paste into the byline:

For Dana Pe’er (corresponding author): add (ORCID: 0000-XXXX-XXXX-XXXX) immediately after the name. Get the iD at https://orcid.org/register if not already created.

Why this matters beyond compliance: ORCID lets editors + reviewers verify your career trajectory (publications, funding, affiliations) in 30 seconds. Authors without ORCIDs trigger extra editorial scrutiny — even when everything else is clean.

⚠️ Issues requiring author attention

Dana Pe’er: No ORCID printed for this author — most journals now require ORCID for at least the corresponding author. Affiliation canonicalized via ROR: Howard Hughes Medical Institute (ROR:006w34k90, United States, score 1.00).
Scott W. Lowe: No ORCID printed for this author — most journals now require ORCID for at least the corresponding author. Affiliation canonicalized via ROR: Howard Hughes Medical Institute (ROR:006w34k90, United States, score 1.00).

§5.26

Funding Verification

For each disclosed grant: verify it exists in the funding registry (NIH RePORTER for US), check the named PI matches a manuscript author (surname+initial), canonicalize the funder via Crossref Funder Registry. Catches typos, undisclosed-funder gaps, and PI mismatches reviewers will flag.

Funders disclosed: 1 | Grants attempted: 1 | Verified: 1 | 🟠 PI mismatches: 0

Not explicitly attributed (regex-detected NIH grant)

✅ K00CA245471 (2025, $106030) — Investigating the Role of Cell Plasticity in Malignant Transformation
- PI: Andrea Chaikovsky — ✅ PI matches author

§5.27

Pre-registration Audit

For systematic reviews (PROSPERO) and pre-registered empirical work (OSF), verify the registration exists, is prospective, and primary outcomes match the manuscript.

🟠 Manuscript claims pre-registration but no registration ID was detected. ICMJE / journal policies typically require the explicit registration identifier (CRD####### or osf.io/#####) in the Methods. Add it before submission.

§5.28

Reference-Style Auto-Format

The manuscript's reference list, auto-formatted to the target journal's bibliography style. Uses Citation Style Language (CSL) — the same engine Zotero / Mendeley use. Paste this directly into the manuscript's Bibliography section as the submission-ready version.

Target journal: Nature | Style applied: nature | References rendered: 50 of 50

NOTE: The upstream reference parser (5.6) typically captures first-author + 'et al.', not full author lists. The auto-formatted bibliography below has accurate titles, journals, years, and DOIs but uses surname-only author entries. Replace with full author lists from your reference manager before submission — the title/journal/year/DOI ordering + punctuation IS the customer value (matches target-journal style exactly).

Paste-ready bibliography

Baslan, .. Ordered and deterministic cancer genome evolution after p53 loss. Nature https://doi.org/10.1038/s41586-022-05082-5 (2022).
Hingorani, .. Trp53R172H and KrasG12D cooperate to promote chromosomal instability and widely metastatic pancreatic ductal adenocarcinoma in mice. Cancer Cell https://doi.org/10.1016/j.ccr.2005.04.023 (2005).
Hingorani, .. Preinvasive and invasive ductal pancreatic cancer and its early detection in the mouse. Cancer Cell https://doi.org/10.1016/s1535-6108(03)00309-x (2003).
Dow, .. Conditional reverse tet-transactivator mouse strains for the efficient induction of TRE-regulated transgenes in mice. PLoS One https://doi.org/10.1371/journal.pone.0095236 (2014).
Saborowski, .. A modular and flexible ESC-based mouse model of pancreatic cancer. Genes Dev https://doi.org/10.1101/gad.232082.113 (2014).
Premsrirut, .. A rapid and scalable system for studying gene function in mice using conditional RNA interference. Cell https://doi.org/10.1016/j.cell.2011.03.012 (2011).
Alonso-Curbelo, .. A gene-environment-induced epigenetic program initiates tumorigenesis. Nature https://doi.org/10.1038/s41586-020-03147-x (2021).
Wang, .. Identification of MRTX1133, a Noncovalent, Potent, and Selective KRAS Inhibitor. J Med Chem https://doi.org/10.1021/acs.jmedchem.1c01688 (2022).
Singhal, .. A Classical Epithelial State Drives Acute Resistance to KRAS Inhibition in Pancreatic Cancer. Cancer Discov https://doi.org/10.1158/2159-8290.cd-24-0740 (2024).
Burdziak, .. Epigenetic plasticity cooperates with cell-cell interactions to direct pancreatic tumorigenesis. Science https://doi.org/10.1126/science.add5327 (2023).
Farack, .. Protocol for Single-Molecule Fluorescence Hybridization for Intact Pancreatic Tissue. STAR Protoc https://doi.org/10.1016/j.xpro.2019.100007 (2020).
Moffitt, .. High-performance multiplexed fluorescence in situ hybridization in culture and tissue with matrix imprinting and clearing. Proc Natl Acad Sci U S A https://doi.org/10.1073/pnas.1617699113 (2016).
Lin, .. Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method. Nat Commun https://doi.org/10.1038/ncomms9390 (2015).
Chen, .. Spatially resolved, highly multiplexed RNA profiling in single cells. Science https://doi.org/10.1126/science.aaa6090 (2015).
Moffitt, .. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science https://doi.org/10.1126/science.aau5324 (2018).
David, .. TGF-β Tumor Suppression through a Lethal EMT. Cell https://doi.org/10.1016/j.cell.2016.01.009 (2016).
Love, .. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol https://doi.org/10.1186/s13059-014-0550-8 (2014).
Mathew, .. Metabolic Signature of Warburg Effect in Cancer: An Effective and Obligatory Interplay between Nutrient Transporters and Catabolic/Anabolic Pathways to Promote Tumor Growth. Cancers (Basel) https://doi.org/10.3390/cancers16030504 (2024).
Azizi, .. Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment. Cell https://doi.org/10.1016/j.cell.2018.05.060 (2018).
Fleming, .. Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender. Nat Methods https://doi.org/10.1038/s41592-023-01943-7 (2023).
Wolf, .. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol https://doi.org/10.1186/s13059-017-1382-0 (2018).
Levine, .. Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis. Cell https://doi.org/10.1016/j.cell.2015.05.047 (2015).
Haghverdi, .. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol https://doi.org/10.1038/nbt.4091 (2018).
Jacomy, .. ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PLoS One https://doi.org/10.1371/journal.pone.0098679 (2014).
Setty, .. Characterization of cell fate probabilities in single-cell data with Palantir. Nat Biotechnol https://doi.org/10.1038/s41587-019-0068-4 (2019).
van, D.. Recovering Gene Interactions from Single-Cell Data Using Data Diffusion. Cell https://doi.org/10.1016/j.cell.2018.05.061 (2018).
Dann, .. Differential abundance testing on single-cell data using k-nearest neighbor graphs. Nat Biotechnol https://doi.org/10.1038/s41587-021-01033-z (2022).
Mahadevan, .. KRAS inhibition reprograms the microenvironment of early and advanced pancreatic cancer to promote FAS-mediated killing by CD8 T cells. Cancer Cell (2023).
Collins, .. Oncogenic Kras is required for both the initiation and maintenance of pancreatic cancer in mice. J Clin Invest https://doi.org/10.1172/jci59227 (2012).
Carpenter, .. Analysis of Donor Pancreata Defines the Transcriptomic Signature and Microenvironment of Early Neoplastic Lesions. Cancer Discov https://doi.org/10.1158/2159-8290.cd-23-0013 (2023).
Aibar, .. SCENIC: single-cell regulatory network inference and clustering. Nat Methods https://doi.org/10.1038/nmeth.4463 (2017).
Greenwald, .. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat Biotechnol https://doi.org/10.1038/s41587-021-01094-0 (2022).
Stringer, .. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods https://doi.org/10.1038/s41592-020-01018-x (2021).
Lavin, .. The complexity of p53 stabilization and activation. Cell Death Differ https://doi.org/10.1038/sj.cdd.4401925 (2006).
Schlesinger, .. Single-cell transcriptomes of pancreatic preinvasive lesions and cancer reveal acinar metaplastic cells’ heterogeneity. Nat Commun https://doi.org/10.1038/s41467-020-18207-z (2020).
Persad, .. SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data. Nat Biotechnol https://doi.org/10.1038/s41587-023-01716-9 (2023).
Wang, .. Multiplexed imaging of high-density libraries of RNAs with MERFISH and expansion microscopy. Sci Rep https://doi.org/10.1038/s41598-018-22297-7 (2018).
Belle, .. Senescence Defines a Distinct Subset of Myofibroblasts That Orchestrates Immunosuppression in Pancreatic Cancer. Cancer Discov https://doi.org/10.1158/2159-8290.cd-23-0428 (2024).
Elyada, .. Cross-Species Single-Cell Analysis of Pancreatic Ductal Adenocarcinoma Reveals Antigen-Presenting Cancer-Associated Fibroblasts. Cancer Discov https://doi.org/10.1158/2159-8290.cd-19-0094 (2019).
Buechler, .. Cross-tissue organization of the fibroblast lineage. Nature https://doi.org/10.1038/s41586-021-03549-5 (2021).
Goltsev, .. Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging. Cell https://doi.org/10.1016/j.cell.2018.07.010 (2018).
Haviv, .. The covariance environment defines cellular niches for spatial inference. Nat Biotechnol https://doi.org/10.1038/s41587-024-02193-4 (2025).
Baran, .. MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions. Genome Biol https://doi.org/10.1186/s13059-019-1812-2 (2019).
Donahue, .. Oncogenic KRAS-Dependent Stromal Interleukin-33 Directs the Pancreatic Microenvironment to Promote Tumor Growth. Cancer Discov https://doi.org/10.1158/2159-8290.cd-24-0100 (2024).
Velez-Delgado, .. Extrinsic KRAS Signaling Shapes the Pancreatic Microenvironment Through Fibroblast Reprogramming. Cell Mol Gastroenterol Hepatol https://doi.org/10.1016/j.jcmgh.2022.02.016 (2022).
Clark, .. Dynamics of the immune reaction to pancreatic cancer from inception to invasion. Cancer Res https://doi.org/10.1158/0008-5472.can-07-0175 (2007).
Haghverdi, .. Diffusion pseudotime robustly reconstructs lineage branching. Nat Methods https://doi.org/10.1038/nmeth.3971 (2016).
Jin, .. Inference and analysis of cell-cell communication using CellChat. Nat Commun https://doi.org/10.1038/s41467-021-21246-9 (2021).
Cang, .. Screening cell-cell communication in spatial transcriptomics via collective optimal transport. Nat Methods https://doi.org/10.1038/s41592-022-01728-4 (2023).
Lancichinetti, .. Finding statistically significant communities in networks. PLoS One https://doi.org/10.1371/journal.pone.0018961 (2011).

§5.29

Journal Legitimacy Check

🟢 GREEN — verified legitimate

Target journal: Nature

Nature verified legitimate via listed in DOAJ + indexed in major databases (OpenAlex is_core). Safe to submit per the standard verification signals.

Signal-by-signal check

Signal	Result	Source
DOAJ presence	✅ Listed	doaj.org
OpenAlex `is_core` (Scopus/WoS-like indexing)	✅ Yes	openalex.org
Beall's archived predatory-journal list	✅ Not present (of ~1,317 archived journals)	github.com/stop-predatory-journals

Journal-level metrics (OpenAlex)

h-index: 1836 — top-tier (>100 = leading journal in any biomed field)
2-year mean citedness: 16.15 (JCR-Impact-Factor analog on the open OpenAlex graph; ~16 — comparable to top-tier impact factors)

What this check DOES and DOES NOT cover

Covers: open-database legitimacy signals (DOAJ presence, OpenAlex core-indexing flag, Beall's archived predatory list, venue h-index + citedness). These are the signals an editor would check at desk-review.

Does NOT cover: editorial-fit (does this journal publish your kind of work?), realistic acceptance rate, specific review-process culture. See 5.2 Cross-Journal Cascade for editorial-fit + alternative-venue analysis.

Paste-ready submission tracker line

When logging this submission to your tracker / advisor email:

§5.30

Materials & Reagents Audit

For each named antibody, cell line, mouse strain, and software tool: validate against the RRID Portal (canonical resource IDs) and Cellosaurus (cell-line authentication + ICLAC misidentified-line database). Missing RRIDs + ICLAC-flagged cell lines are reviewer-bait issues that desk-reject in major journals.

Antibodies: 0 named, 0 with RRID (0%) | Cell lines: 0 named, 0 flagged as problematic by ICLAC | Mouse strains: 6 | Software: 10 named, 0 with RRID

Software tools missing RRIDs (10 of 10)

Find canonical SCR RRIDs at https://scicrunch.org/resources_

PhenoGraph vk=30
SEACells
CellPhoneDB
NicheNet
Calligraphy
Milo
MiloR
diffxpy
UMAP
Fiji

§5.31

Code & Data Deposit Verification

For each code repository, data deposit, and accession claimed in the manuscript: verify it exists, is public, and matches the claim. Plus NIH Data Management & Sharing Plan (DMSP) compliance check for NIH-funded work post-2023-01-25. All checks are deterministic registry lookups — no LLM judgment.

Verified deposits: GitHub: 3/3 · Zenodo: 1/1

NIH DMSP Compliance: ✅ likely compliant

NIH grants detected (1); DMSP compliance satisfied by 4 verified public deposit(s).

NIH grants detected: K00CA245471

GitHub repositories (3)

Claimed	Verified	Public	README	License	Last commit
broadinstitute/inferCNV	✅	✅	✅	Other	2025-11-14T17:35:17Z
hisplan/sharp	✅	✅	✅	MIT License	2022-05-19T00:35:48Z
theislab/diffxpy	✅	✅	✅	BSD 3-Clause "New" or "Revised" License	2025-10-21T08:21:26Z

Zenodo deposits (1)

DOI	Verified	Title	Files	Public	License
10.5281/zenodo.2678041	✅	JonathanShor/DoubletDetection: doubletdetection v4.3.0.post1	1	✅	mit-license

Paste-ready Data Availability statement

Built from your verified deposits — paste before References, filling in any [BRACKETED] placeholders:

Paste-ready NIH DMSP citation language

Verified deposits above already satisfy the NIH 2023 Data Management & Sharing Policy. Add this line to your Methods to make compliance explicit (preempts NIH-program-officer follow-ups):

§5.35

Related-Work Recommender

We search a 200M-paper academic corpus to identify the published work most similar to yours. Two tiers below: high-cited papers you should VERIFY are in your reference list (concrete action), and recent adjacent work for novelty calibration (background context).

Verify these 7 high-cited papers are in your reference list

Action: open your manuscript's References + Ctrl-F each title below. Any that's NOT cited is a reviewer-bait gap — either add it OR add a one-sentence differentiation of why your work isn't redundant with it.

Role of Oncogenes and Tumor-suppressor Genes in Carcinogenesis: A Review — E. Kontomanolis, Antonios Koutras et al., AntiCancer Research (2020) — 252 citations · doi:10.21873/anticanres.14622
Paracrine orchestration of intestinal tumorigenesis by a mesenchymal niche — M. Roulis, Aimilios Kaklamanos et al., Nature (2020) — 233 citations · doi:10.1038/s41586-020-2166-3
Tumor initiation and early tumorigenesis: molecular mechanisms and interventional targets — Shaosen Zhang, Xinyi Xiao et al., Signal Transduction and Targeted Therapy (2024) — 207 citations · doi:10.1038/s41392-024-01848-7
Single-Cell Transcriptomics in Medulloblastoma Reveals Tumor-Initiating Progenitors and Oncogenic Cascades during Tumorigenesis and Relapse. — Liguo Zhang, Xuelian He et al., Cancer cell (2019) — 118 citations · doi:10.1016/j.ccell.2019.07.009
Impact of risk factors on early cancer evolution. — C. Weeden, William Hill et al., Cell (2023) — 87 citations · doi:10.1016/j.cell.2023.03.013
Oncogenic context shapes the fitness landscape of tumor suppression — Lily M. Blair, Joseph M. Juan et al., Nature Communications (2023) — 12 citations · doi:10.1038/s41467-023-42156-y
YAP-driven malignant reprogramming of oral epithelial stem cells at single cell resolution — F. Faraji, Sydney I. Ramirez et al., Nature Communications (2025) — 9 citations · doi:10.1038/s41467-024-55660-6

Recent adjacent work (3 papers, 0-4 citations)

These are recent (last 1-2 years) papers Semantic Scholar ranks as similar to yours. Most have not yet accumulated citations — useful for novelty calibration ('this work is in active competition with X recent groups') but lower-priority for the reference list. Skim only if you have spare time before submission.

Oncogenic and tumor-suppressive forces converge on a progenitor-orchestrated niche to shape early tumorigenesis · bioRxiv (2025) · doi:10.1101/2025.06.10.656791
MEF2D-expressing cancer precursors reprogram tissue-resident macrophages to support liver tumorigenesis · Nature Cancer (2025) · doi:10.1038/s43018-025-01059-1
CXCR4+ mammary gland macrophageal niche promotes tumor initiating cell activity and immune suppression during tumorigene · Nature Communications (2025) · doi:10.1038/s41467-025-59972-z

§5.36

Ethics / IRB Statement Audit

Most journals require an explicit ethics statement when a manuscript reports human-subject research, animal research, or secondary use of human-derived material. Missing statements are one of the top desk-return reasons across biomedical publishing. This component classifies the requirement type, checks for an existing statement, and provides paste-ready templates when gaps are detected.

Verdict: ✅ Ethics statement appears complete

Manuscript category: animal_research | Classifier confidence: high The manuscript describes experiments in injured KC mice and the KPLOH model, indicating vertebrate animal research.

Limits of this audit

This is a deterministic regex sweep + LLM classifier check on the manuscript text. It detects PRESENCE of standard ethics-statement keywords; it does NOT verify that the named IRB or approval ID is real, that the consent process was adequate, or that the local ethics committee's terms cover what your study actually did. This is not legal or IRB advice. Verify with your institution's IRB office before submission.

§5.37

Numeric Consistency Audit

Deterministic regex sweep for cross-section numeric inconsistencies — the kind reviewers reliably catch and authors reliably miss. Extracts every "n=" sample-size claim and every percentage with its noun-phrase anchor + section location, clusters by the underlying quantity, and flags clusters where the SAME quantity has DIFFERENT values across manuscript sections. No LLM — pure pattern matching, $0 cost.

Verdict: 🔴 1 sample-size inconsistency detected

Claims extracted: 46 sample-size · 123 percentage | Sections detected: 70 | Findings: 1 critical · 0 major · 1 minor

🔴 Critical: sample-size inconsistencies

The same quantity has different n= values across sections. These are the highest-confidence findings in this section — reviewers always catch them at first glance. Reconcile each before submission.

`mice` — values 3, 5, 10 appear across 2 sections

n=10 in results: "ch to KC mice (Ptf1a-Cre; LSL-KrasG12D) at early (1-2 days, n = 10 mice) and late (3 weeks, n = 5 mice) timepoints following caerul"
n=5 in results: "asG12D) at early (1-2 days, n = 10 mice) and late (3 weeks, n = 5 mice) timepoints following caerulein-induced injury enabled spa"
n=3 in funding: "in Fig. 6c, as a function of treatment (MRTX1133 or vehicle, n=3 mice each). Dots represent individual biological replicates. Ex"

Action: identify which value is correct from your raw data, then update every other instance to match. Most common cause: figure caption (or Methods) was drafted before the final dataset was locked.

🟡 Minor: unusual figure-panel citations

Citations to figure panels beyond 'h' are uncommon. Verify the figure actually has that many panels before submission.

Fig. 3K — cited 1 time

§5.38

Required Statements Audit

Most major journals (Cell, Nature, Lancet, BMJ, NEJM, JAMA, eLife, PLOS family) require two paste-ready statements that 5.36 ethics doesn't cover: a CRediT author-contributions statement (using the formal Contributor Roles Taxonomy) and a Conflict of Interest declaration (per ICMJE). Both are deterministically detected here; paste-ready templates provided when missing.

Verdict: 🔴 1 major gap — likely desk-return

CRediT statement: ❌ missing (taxonomy terms found: 41 of 14) COI declaration: ✅ present

🔴 MAJOR (missing_credit_statement): No CRediT (Contributor Roles Taxonomy) author-contributions statement detected. Required by Cell, Nature family, Lancet, BMJ, eLife, PLOS family, and increasingly by mid-tier biomed journals — typically auto-flagged at submission portal level.

Paste-ready CRediT statement

Add this to your manuscript before References, filling in each author's contribution. Use the 14 official CRediT taxonomy terms (italicized below) — journals' submission portals validate against this exact vocabulary.

The 14 CRediT taxonomy terms (use these EXACT phrases):

Conceptualization · Methodology · Software · Validation · Formal analysis · Investigation · Resources · Data Curation · Writing - Original Draft · Writing - Review & Editing · Visualization · Supervision · Project administration · Funding acquisition