Myotonic dystrophy type 1 (DM1) is a clinically challenging multisystem neuromuscular hereditary disorder, with generational increase in severity and earlier age at onset. It is caused by an unstable cytosine-thymine-guanine repeat expansion at the DMPK locus, accompanied by associated genetic and epigenetic modifications. While somatic mosaicism and meiotic instability are well established, to the best of our knowledge, no study has performed a genome-wide interrogation for global inherited instability. Performing whole-genome optical mapping, with sequence base-resolved structural variant verification, we examine global inherited genomic instability in an atypical paternally transmitted DM1 family presenting with a range of neurological manifestations, including early-onset Parkinson's disease (PD). While the juvenile-onset DM1 proband presented with a 10-fold repeat expansion with associated hypermethylation, her partially hypermethylated asymptomatic protomutation father transmitted a 1.8-fold contracted allele in the younger premutation sibling. Adult-onset symptomatic DM1 and PD phenotypic paternal aunts showed significant genome-wide copy number alteration, including PD-associated chr19 aneuploidy loss, with additional losses on chr16, 17, and 22. In the absence of potentially pathogenic de novo or maternally inherited structural variants, the proband presented with large paternally inherited aberrations impacting gene candidates CASC15, CBFA2T3, GPHN, H3F3A, SDK1, and SPAG16, with advanced global hypomethylation. Here we suggest that inherited genomic instability may contribute to phenotypic variability, including multi-neurological presentations and single-generation repeat expansion or contraction. By providing a landscape of inherited large structural variants, this single-family study expands knowledge of this broad and growing class of diseases. © 2026 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

ABSTRACT Background Pancreatic ductal adenocarcinoma (PDAC) is a particularly lethal malignancy with few treatment options available. Extensive remodelling of extracellular matrix (ECM) generates a highly fibrotic tumour landscape, which impairs therapeutic response. Objective We investigated whether stromal priming via the highly specific Focal Adhesion Kinase (FAK) inhibitor narmafotinib (AMP945) in combination with the two major standard-of-care chemotherapies in PDAC, gemcitabine/Abraxane and FOLFIRINOX, reduces fibrosis and enhances treatment efficacy. Design 3D organotypic matrices, intravital imaging, and in vivo subcutaneous and orthotopic PDAC models were used to provide a rationale for a first-line priming regimen of narmafotinib prior to chemotherapy. Results Neoadjuvant chemotherapy induces fibrosis in PDAC indicating a need for upfront first-line priming of the ECM to normalise the stroma for optimal treatment response. Narmafotinib is a new potent small molecule FAK inhibitor. Phase I safety data shows excellent safety, tolerability, and pharmacokinetics following oral administration in humans. We reveal that narmafotinib treatment during early ECM remodelling (‘priming’) reduces fibrosis, while limiting subsequent PDAC invasion. Moreover, intravital imaging demonstrates real-time FAK inactivation and cell cycle stalling, leading to improved chemotherapeutic efficacy upon narmafotinib priming in vivo . Long-term assessment in patient-derived models shows that narmafotinib priming prior to gemcitabine/Abraxane or FOLFIRINOX reduces PDAC progression and extends survival in both chemotherapy settings. Conclusions Our results using these Phase II-ready drug combinations strongly support the clinical assessment of narmafotinib in PDAC. Narmafotinib is currently in Phase Ib/IIa trials, assessing a pulsed dosing regimen prior to gemcitabine/Abraxane, and warrants further clinical assessment in combination with FOLFIRINOX. SIGNIFICANCE OF THIS STUDY What is already known on this topic Pancreatic cancer (PC) is one of the most lethal malignancies and is characterised by a dense, fibrotic stroma, which impairs chemotherapy efficacy. The non-receptor tyrosine kinase FAK is known to promote cancer fibrosis and therefore represents a therapeutic target to normalise the PC stroma and to improve chemotherapy performance. What this study adds Neoadjuvant chemotherapy induces early fibrosis indicating a need for upfront first-line priming of the ECM to blunt or normalise stromal fibrosis for optimal response to therapy. The small molecule inhibitor narmafotinib (which is currently under Phase Ib/IIa clinical trial assessment) shows high specificity towards FAK as well as desirable pharmacokinetics and pharmacodynamics in healthy human volunteers. Early short-term narmafotinib priming reduces fibrosis and improves the efficacy of subsequent standard-of-care gemcitabine/Abraxane chemotherapy. FOLFIRINOX (oxaliplatin, irinotecan, leucovorin and 5-fluorouracil) is a multi-agent chemotherapy preferentially used in PDAC patients with good performance status. Our results demonstrate that narmafotinib priming also improves FOLFIRINOX efficacy, leading to extended survival in patient-derived PDAC models. How this study might affect research, practice, or policy This study supports the clinical development of narmafotinib in combination with both gemcitabine/Abraxane (ACCENT trial) and further FOLFIRINOX standard-of-care chemotherapies for PDAC patient treatment. The first-line priming strategy and early ECM normalisation used in this study may also be applicable to other combination therapy settings and warrants further investigation in ongoing clinical studies.

Supplementary Figure 2 shows figures related to the Consensus clustering analyses. (A) A delta area plot displays the relative change in the cumulative distribution function (CDF) curve comparing k and k-1 clusters from our cohort. (B) The silhouette plot displays the silhouette width for the four clusters determined by consensus clustering within this cohort. (C) Kaplan-Meier plot of the recurrence-free survival rates of the combined clusters (Clusters 1 with 3 and Clusters 2 with 4).

Supplementary Figure 6 shows a forest plot detailing the hazard ratio of the proteomic risk score and clinically relevant variables for PDA within our study cohort using multivariable Cox regression modeling.

Pancreatic cancer (PC) is a highly metastatic malignancy. More than 80% of patients with PC present with advanced-stage disease, preventing potentially curative surgery. The neuropeptide Y (NPY) system, best known for its role in controlling energy homeostasis, has also been shown to promote tumorigenesis in a range of cancer types, but its role in PC has yet to be explored. We show that expression of NPY and NPY1R are up-regulated in mouse PC models and human patients with PC. Moreover, using the genetically engineered, autochthonous KPR172HC mouse model of PC, we demonstrate that pancreas-specific and whole-body knockout of Npy1r significantly decreases metastasis to the liver. We identify that treatment with the NPY1R antagonist BIBO3304 significantly reduces KPR172HC migratory capacity on cell-derived matrices. Pharmacological NPY1R inhibition in an intrasplenic model of PC metastasis recapitulated the results of our genetic studies, with BIBO3304 significantly decreasing liver metastasis. Together, our results reveal that NPY/NPY1R signaling is a previously unidentified antimetastatic target in PC.

Supplementary Figure 9: Kaplan-Meier plot for patients dichotomized by a proteomic risk score that uses only the ten proteins detected in blood by mass spectrometry (PURB, GALM, SERPINA3, OAS3, KRT2, NUDT2, SERPINA4, CUTA, POSTN, CLEC11A).

Supplementary Figure 7 shows the proteomic signature performance based on the Receiver operator characteristic curve analysis. (A) Receiver operator characteristic curve (ROC) at 1 year of follow-up for the proteomic signature (purple) and other clinically relevant variables for PDA within our cohort. (B) An Area Under the Curve (AUC) plot displays the AUC as a function of time for the proteomic risk score (purple) and other clinically relevant variables for PDA within our cohort.

Supplementary Figure 16 shows a Forest plot presentation of the hazard ratios for each KRT protein detected in the tumor samples from the study cohort, derived from a univariate Cox regression analysis with overall survival as the outcome of interest. Note that this figure excludes KRT75, which was detected in only two samples.

A schematic representing (A) data collection from 30-μm sections of 115 PDA and 61 adjacent normal fresh-frozen tissues that were prepared (19) for (B) DIA-MS. C, Protein quantification from DIA-MS data files using DIA-NN and MaxLFQ software for data normalization, QC, and peptide-to-protein inference and (D) the downstream analyses of the protein data. FC, fold change.

The distribution of clinical variables across the four clusters.

Supplementary Figure 12 shows a volcano plot displaying the differentially abundant proteins between tumors harboring a KRAS-G12D mutation versus those with any other KRAS-G12 mutation

Supplementary Table 9 shows the differential abundance of the KRT protein family across the different clinical variables of interest, where “Up” means upregulated and “Down” means downregulated in the group of interest versus others. *Note that KRT9, KRT10, KRT20, KRT23, KRT77, KRT74, KRT79 and KRT80 were not differentially abundant among any subgroup

Proteomic risk score for mortality. A, Forest plot detailing the multivariable HRs for the overall survival of each of the proteins used to build the proteomic risk score. B, Kaplan–Meier curve displaying the overall survival probability of the low- and high-risk groups within our cohort. C, Kaplan–Meier curve displaying the survival probability of the low- and high-risk groups within the CPTAC validation dataset.

Supplementary Figure 10 shows the differential abundance and pathway enrichment analyses based on KRAS mutations. (A) Volcano plot displaying the differentially abundant proteins between KRAS mutant PDA and KRAS wild-type PDA. (B) Pathways enriched in Gene Ontology molecular function database from upregulated proteins in tumors that harbored KRAS mutations.

Proteomic subtypes of PDA. A, A clustered heatmap displaying the highly variable protein intensities across the four clusters detected within this cohort. B, Kaplan–Meier plots of the survival rates of the four clusters. C, Kaplan–Meier plot of the survival rates of the clusters combined according to prognosis (cluster 1 with 3 and cluster 2 with 4).

Supplementary Table 4 shows previous evidence regarding each protein included in the risk score in terms of their association with different types of cancer diagnosis and prognosis. *Based on data extracted from the Human Protein Atlas (https://www.proteinatlas.org/)

List of the different differentially abundant proteins for the different groups of interest

Supplementary Figure 3 shows differential abundance and pathway enrichment analyses of clusters. (A) Volcano plot displaying the differentially expressed proteins between PDA samples classified between groups 1 and 3 against groups 2 and 4. (B) Pathways enriched in KEGG, Reactome, and Wikipathways for proteins upregulated in samples from clusters 2 and 4 compared to clusters 1 and 3.

Supplementary Table 2 shows the distribution of the clinical variables across the four proteomic-based clusters. Gln61Arg: Glutamine to Arginine at position 61, Gln61His: Glutamine to Histidine at position 61, Gly12Ala: Glycine to Alanine at position 12, Gly12Arg: Glycine to Arginine at position 12, Gly12Asp: Glycine to Aspartic Acid at position 12, Gly12Cys: Glycine to Cysteine at position 12, Gly12Leu: Glycine to Leucine at position 12, Gly12Val: Glycine to Valine at position 12. Note that all patients showed positivity for COSMIC signature 1, while no patients showed positivity for COSMIC signatures 6, 20, 25, and 26. For COSMIC signatures 13, 18, 28, and 30, only one patient was in the positive group.

DAPs and dysregulated pathways in PDA tumors compared with adjacent normal tissue. A, DAPs (n = 395; P < 0.05) between PDA and adjacent normal tissue. B, Top enriched pathway proteins upregulated in PDA tissue. KEGG, Kyoto Encyclopedia of Genes and Genomes. C, Gene linkage network of enriched pathways from Gene Ontology biological processes database for upregulated proteins in PDA tumors. FC, fold change.