Citations

Abstract Therapeutic resistance in ovarian cancer is frequently driven by persistent replication stress, yet the molecular mechanisms that convert replication stress into a therapeutically exploitable vulnerability remain incompletely understood. Here, we identify drug-induced nuclear cathepsin L (nCTSL) as a previously unrecognized regulator of replication stress and DNA repair. Clofarabine (CLF) combined with the ATR inhibitor AZD6738, or the CHK1 inhibitor prexasertib promoted nuclear accumulation of CTSL, where it remodeled the replication machinery through degradation of CCNE1, MCM3, MCM6, and geminin, accompanied by loss of RAD51 and 53BP1 and increased γH2AX and phospho-RPA2. DNA fiber analysis demonstrated marked inhibition of replication fork progression following CLF-based combinations, whereas CTSL depletion accelerated fork progression and abolished therapy-induced replication stress. Reconstitution with the nuclear M1F CTSL isoform restored replication restraint, confirming a direct role for nuclear CTSL in regulating replication dynamics. GFP-based DNA repair reporter assays further revealed that CLF-based combinations suppress DNA repair competence in a CTSL-dependent manner, indicating that nuclear CTSL couples replication stress amplification with functional inhibition of repair pathways. Functionally, CLF-based combinations selectively targeted transformed fallopian tube secretory epithelial cells while sparing non-transformed counterparts, demonstrated broad activity in patient-derived ovarian cancer ascites spheroids, and significantly inhibited tumor growth and prolonged survival in vivo . Collectively, our findings identify nuclear CTSL as a mechanistic driver of replication stress that remodels the replication machinery, impairs DNA repair, and creates a therapeutically exploitable vulnerability in ovarian cancer. We propose that nuclear CTSL promotes a transition from replication competence to replication catastrophe, thereby establishing a conceptual framework for biomarker-guided therapeutic strategies targeting CTSL-dependent replication stress.

ABSTRACT Background Family history of pancreatic cancer (PC), pathogenic germline variants (PGVs), and increased intrapancreatic fat (IPF) are individually associated with increased PC risk. Whether IPF is elevated in high‐risk individuals (HRIs) with PC family history or PGVs is unknown. Although MRI is the preferred modality for IPF measurement, region‐of‐interest–based approaches are prone to sampling bias because pancreatic fat is heterogeneously distributed. We aimed to compare IPF among HRIs and matched non‐HRI controls using an Artificial intelligence (AI)‐based tool for whole pancreatic IPF measurement applied to magnetic resonance imaging (MRI). Methods We developed and validated a deep‐learning pipeline for IPF measurement that incorporates automated pancreas segmentation on T1‐weighted MRI images registered to the corresponding iterative decomposition of water and fat with echo asymmetry and least squares estimation images to compute IPF using proton density fat fraction (PDFF). This pipeline was used to quantify IPF and assess IPF distribution patterns in 85 HRIs and 170 non‐HRIs matched 1:2 for age, sex, BMI, and diabetes status. A surface erosion approach was developed to minimize the impact of peripancreatic fat on IPF measurement. Linear models adjusting for matching variables were used to assess associations. Results Model‐expert agreement for whole‐pancreas PDFF was excellent (intraclass correlation coefficient 0.99; 95% CI, 0.98–0.99 mean absolute error 1.4%). Mean PDFF did not differ between HRIs and controls (25.8% [11.5] vs. 26.9% [11.4]; p = 0.32). PDFF increased with age ( p = 0.015), male sex ( p < 0.001), presence of diabetes ( p = 0.009), BMI ( p < 0.001). IPF distribution patterns did not differ by risk group. Conclusion An AI‐based approach provides accurate, scalable, and objective assessment of IPF quantity and distribution. IPF quantity and spatial distribution do not appear to differ based on germline or familial predisposition to PC.

Abstract Pancreatic cancer disproportionately affects Black individuals in the United States, but they have limited representation in genetic studies of pancreatic ductal adenocarcinoma (PDAC). To address this gap, we performed admixture mapping and genome-wide association analysis (GWAS) in genetically inferred African ancestry individuals (1,030 cases and 889 controls). Admixture mapping identified three regions with a significantly higher proportion of African ancestry in cases compared to controls (5q33.3, 10p1, 22q12.3). GWAS identified a genome-wide significant association at 5p15.33 ( CLPTM1L , rs383009:T>C, T Allele Frequency=0.51, OR:1.45, P value=1.24×10 -8 ), a locus previously associated with PDAC. Known loci at 5p15.33, 7q32.3, 8q24.21 and 7q25.1 also replicated (P value <0.01). Multi-ancestral fine-mapping identified two potential causal SNPs (rs3830069 and rs2735940) at 5p15.33. Collectively these findings identified novel PDAC risk loci and expanded our understanding of this deadly cancer in underrepresented populations, emphasizing the multifactorial nature of PDAC risk including inherited genetic and non-genetic factors. Statement of Significance To understand how genetic variation contributes to PDAC risk in Black people in North American, we studied individuals of genetically-inferred African ancestry. We identified novel risk loci and differences in the contribution of known loci. This demonstrates that ancestry-informed genetic analyses improve our understanding of PDAC risk and enhances discovery.

Abstract Despite long-standing interest in combining topoisomerase I (TOP1) poisons with PARP inhibitors (PARPis), the mechanistic basis for the variable and often unpredictable response to this strategy remains unclear. Early preclinical studies suggested that PARP inhibition amplifies TOP1 poison-induced DNA damage, implying broad therapeutic potential; however, emerging translational and clinical data—particularly in ovarian cancer—indicate that synergy is context-dependent and governed by tumor-intrinsic features that have not been fully defined.Here we assessed determinants of response in five HR proficient ovarian cancer (OC) patient-derived xenografts (PDXs) previously treated with the SN-38 prodrug etirinotecan pegol (NKTR-102) in combination with the PARPi rucaparib (C. Nitschmann, Clin. Cancer Res. 22(2) Suppl. B50, 2016). Although all models were refractory to single-agent rucaparib, 50% exhibited enhancement of NKTR-102-induced tumor regression with the addition of rucaparib. Neither PARP1 abundance nor extent of rucaparib-mediated poly(ADP-ribose) suppression correlated with therapeutic benefit. Instead, sensitization was restricted to PDXs intrinsically responsive to NKTR-102 and was strongly associated with high levels of NKTR-102-induced TOP1-DNA covalent complexes. In further analysis, 2 of 3 resistant PDXs exhibited high levels of the ABC transporter ABCG2 despite lack of previous therapy, revealing a previously underappreciated mechanism of de novo resistance that limits effective TOP1 inhibitor activity independent of homologous recombination status. Notably, NKTR-102 response did not track with platinum/taxane sensitivity, highlighting the distinct molecular circuitry governing TOP1 poison susceptibility.Collectively, these findings demonstrate that rucaparib can potentiate NKTR-102-induced regression in tumors capable of forming abundant TOP1-DNA covalent complexes. This work identifies TOP1-DNA complex trapping and lack of ABCG2 expression as mechanistic predictors of benefit, providing an updated framework for selecting patients most likely to respond to TOP1 poison/PARPi combinations and challenging the long-standing assumption that PARP inhibition can universally resensitize tumors to TOP1 poisons. Citation Format: Annapoorna Venkatachalam, Caroline C. Nitschmann, Karen S. Flatten, Rachel Hurley, Xiaonan Hou, Cristina Correia, Paula A. Schenider, Krista Goergen, Matthew Maurer, Ethan P. Heinzen, Ann L. Oberg, S John Weroha, Paul Haluska, Scott H. Kaufmann, Elizabeth M. Swisher. TOP1-DNA protein complex as a predictive biomarker for NKTR-102/Rucaparib combination therapy in PARP-inhibitor resistant ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr LB364.

Abstract Purpose: The Pancreatic Cancer Detection Consortium (PCDC) performed a blinded Early Detection Research Network–defined phase II biomarker bakeoff study of blood-based biomarker panels. The aims were to evaluate panel performance, to compare the panels’ performance with that of cancer antigen 19-9 (CA19-9) alone, and to evaluate the performance of new combinations of the individual biomarkers. Experimental Design: Ten biomarkers representing eight biomarker panels and CA19-9 were evaluated using plasma, serum, and germline DNA from 140 stage I to IV pancreatic ductal adenocarcinoma (PDAC) cases and 140 controls from three tertiary care institutions, with controls frequency matched to cases on age and sex. LASSO regression was employed to explore new biomarker combinations. The primary metric was area under the receiver operating characteristic curve (AUC). Results: The study population was 51% female, with median age 67.3 (minimum: 45, maximum: 90) years. Biomarker panel AUCs ranged from 89.9 to 96.3; the AUC for serum CA19-9 alone was 91.7 [95% confidence interval (CI), 87.8–95.6]. Two panels had significantly higher AUCs than serum CA19-9 alone, the CA19-9/FUT2/3 panel (AUC = 96.3, P = 0.002), and the tissue factor pathway inhibitor/tenascin C (TFPI/TNC-FNIII-C) panel (AUC = 95, P = 0.01). Exploratory models to recombine biomarkers retained all but two biomarkers [optimism-corrected AUC = 96.4 (94–98.9)]. Conclusions: The CA19-9/FUT2/3 panel was the best performing panel in this biomarker bakeoff. Its evaluation in larger studies is warranted. Biomarker bakeoffs are an effective strategy for comparing the performance of promising biomarkers for pancreatic cancer early detection, and the PCDC is well poised to conduct such studies. Recommendations for performing such studies are provided.

PURPOSE To compare mortality risk among long-term survivors of high-grade serous ovarian cancer (HGSOC) with that of the general US population and to examine how mortality and recurrence trends have evolved under changing treatment approaches. MATERIALS AND METHODS This is a retrospective cohort study including patients with histologically confirmed HGSOC treated at a single referral cancer center from 1991 to 2022. Mortality risk, expressed as standardized mortality ratios (SMRs, ratio of observed deaths in the study population to expected deaths in an age-matched US population), was assessed at diagnosis and yearly on patients without events (disease persistence, recurrence, or death from any cause). RESULTS A total of 2,074 consecutive patients were included. The median follow-up was 12.5 years (IQR, 11.7-13.8). Most patients were stage III (1,396, 69.2%), 1,299 (62.8%) underwent primary cytoreductive surgery, and 1,688 (87.4%) received platinum-taxane as first-line therapy. At diagnosis, the mortality rate was 7.40 times higher than that in the general population (95% CI, 7.04 to 7.77). By 7 event-free years, the 95% CI for the SMR included 1 for the first time, and at 10 years, the SMR was 1.05 (95% CI, 0.72 to 1.49), indicating no statistically significant excess mortality compared with the general population. In those event-free at 10 years, the 5-year cumulative incidence of ovarian cancer–related deaths (8.2% [95% CI, 4.2 to 16.0]) was comparable with that of non–ovarian cancer–related deaths (6.3% [95% CI, 2.9 to 13.8]). CONCLUSION In this cohort, mortality risk steadily declined for patients who remained event-free, approaching that of the general population by 7 years after diagnosis. These results emphasize the importance of individualized, long-term follow-up to address both recurrence risk and survivorship needs.

Abstract Recent work on double descent has challenged classical bias–variance tradeoffs, showing that test error can decrease, increase sharply near the interpolation threshold, and then decrease again as model capacity grows. This phenomenon has been documented in regression and classification, but its relevance to survival analysis remains unclear. Survival data are subject to censoring, which obscures true event times, and widely used models such as the Cox proportional hazards model are optimized via partial likelihoods that emphasize ranking rather than calibrated risk estimation. It is therefore unknown whether double descent occurs in this setting, how censoring influences its manifestation, or how it interacts with standard performance metrics.We investigate these questions using synthetic survival data generated from Weibull hazards with controlled censoring, allowing systematic variation of model capacity from under to over parameterized regimes. While we verified double descent occurs in survival models, calibration plateaus and decouples from discrimination, even under strong $\ell_2$ regularization. This decoupling arises because the Cox partial likelihood optimizes rankings rather than magnitudes, producing extreme risk scores that break the Breslow estimator used to estimate survival probabilities. These results highlight limitations of discrimination-based model selection in survival analysis and underscore the need for calibration-aware evaluation in high-capacity prognostic models.

Abstract Purpose: Pancreatic ductal adenocarcinoma (PDAC) is typically detected too late for useful therapeutic interventions; hence, we sought blood biomarkers to detect early-stage disease. Experimental Design: Using mass spectrometry and ELISA on plasma pools from the University of Pennsylvania (Penn) and the Mayo Clinic (Mayo), we identified aminopeptidase N (ANPEP) and polymeric immunoglobulin receptor (PIGR) as increased in early-stage (stage I/II) PDAC plasma compared with controls. We tested ANPEP and PIGR, along with prior data for thrombospondin 2 (THBS2) and carbohydrate antigen 19-9 (CA19-9), in retrospective phase II studies using separate cohorts of PDAC plasmas at different stages versus healthy or nonmalignant disease controls (DC) from Penn (n = 135) and Mayo (n = 537). Results: Comparing healthy controls with stage I/II PDAC, we obtained area under the receiver-operating characteristic curves (AUC) of 0.78 [95% confidence interval (CI), 0.68–0.86]/0.80 (95% CI, 0.74–0.85; ANPEP) and 0.81 (95% CI, 0.70–0.88)/0.86 (95% CI, 0.82–0.90; PIGR) for the Penn/Mayo phase II studies, respectively. In multivariable models, CA19-9/THBS2/ANPEP, CA19-9/THBS2/PIGR, and CA19-9/THBS2/ANPEP/PIGR elicited AUC of 0.94 to 0.96 for Penn and 0.97 for Mayo. Notably, the four-marker panel elicited AUC of 0.87 for the Mayo stage I/II versus DC and 0.91 for stages I to IV versus DC. At a specificity of 95%, a plasma biomarker panel composed of CA19-9 (≥35 U/mL), THBS2 (≥42 ng/mL), ANPEP (≥2,995 ng/mL), and PIGR (≥1,800 ng/mL) yielded a sensitivity of 91.9% for PDAC stages I to IV and 87.5% for PDAC stage I/II. Conclusions: Adding ANPEP and PIGR to a plasma biomarker panel of CA19-9 and THBS2 enhances the detection of early-stage PDAC when comparing cancer versus healthy or nonmalignant DC. Given the concordance of our data in two retrospective phase II studies, assessments in prediagnostic cases are warranted.

Poly(ADP-ribose) polymerase (PARP) inhibitors (PARPis) are an important therapy for high-grade serous ovarian cancer (HGSOC). However, PARPi resistance frequently emerges, necessitating previously unrecognized approaches to improve HGSOC responses. Here, we showed that the anaplastic lymphoma kinase (ALK) inhibitor brigatinib enhances PARPi activity in HGSOC cells by disrupting an adaptive survival mechanism orchestrated by Fos-related antigen 1 (FRA1) in response to PARPi. This effect of brigatinib occurred through an ALK-independent pathway, wherein brigatinib induced a dual blockade of focal adhesion kinase (FAK) and EPH receptor A2 (EPHA2) tyrosine kinases, leading to the suppression of protein kinase B (Akt) and extracellular-regulated kinase (ERK) signaling accompanied by disruption of a phosphorylation event crucial for FRA1 protein stability. Moreover, in HGSOC patient-derived xenograft (PDX) models, brigatinib and PARPi combination therapy induced tumor regression and improved overall survival compared with PARPi alone, particularly in models with high FAK and EPHA2. These findings support dual targeting of FAK and EPHA2 as a strategy to achieve effective and durable PARPi responses and identify a promising biomarker-based combinatorial approach using brigatinib and PARPi for HGSOC, particularly the subset characterized by high FAK and EPHA2.

Abstract Background The associations between different types of diabetes, characterized by distinct pathophysiology and genetic architecture, and pancreatic ductal adenocarcinoma (PDAC) risk are not understood. Methods We investigated associations of genetic susceptibility to type 2 diabetes (T2D), 8 T2D mechanistic clusters, type 1 diabetes (T1D), and maturity-onset diabetes of the young (MODY) with PDAC risk. We used genome-wide association study (GWAS) summary-level statistics for T2D (242 283 cases, 1 569 734 controls), T1D (18 942 cases, 501 638 controls), and PDAC (10 244 cases and 360 535 controls) in individuals of European ancestry. Results Two-sample Mendelian randomization (MR) using the Robust Adjusted Profile Score (MR-RAPS) method indicated that genetically predicted T2D was associated with PDAC risk (OR = 1.10; 95% CI = 1.05 to 1.15), particularly the T2D obesity (OR = 1.28; 95% CI = 1.15 to 1.42) and lipodystrophy (OR = 1.25; 95% CI = 1.03 to 1.51) clusters. No association was observed for T1D with PDAC risk (OR = 1.01; 95% CI = 0.99 to 1.02). Pathway/gene-set analysis using the summary-based Adaptive Rank Truncated Product (sARTP) method revealed a significant association between the MODY gene-sets and PDAC risk (P = 1.5 × 10−8), which remained after excluding 20 known PDAC GWAS loci (P = 7.6 × 10−4). HNF1A, FOXA3, and HNF4A were the top contributing genes after excluding the previously identified GWAS loci regions. Conclusions Our results from this genetic association study support that T2D, particularly the obesity and lipodystrophy mechanistic clusters, and MODY genomic susceptibility regions play a role in the etiology of PDAC.