Zhen Lu

DIRAS3 Inhibits Ovarian Cancer Cell Growth by Blocking the Fibronectin-Mediated Integrin β1/FAK/AKT Signaling Pathway

Autophagy is a crucial cellular process responsible for sustaining homeostasis through the degradation and recycling of proteins and organelles, providing energy during amino acid starvation and hypoxia. In cancer, autophagy can either inhibit tumor growth or support cancer cell survival. Our previous studies have shown that re-expression of the tumor suppressor gene DIRAS3 inhibits growth of ovarian cancer cells, promotes autophagic cell death in vitro, and induces tumor dormancy in vivo. Growth factors and extracellular matrix (ECM) components can, however, inhibit DIRAS3-induced autophagic cell death. This study explores whether fibronectin (FN) can counteract the growth inhibition induced by DIRAS3 in ovarian cancer cells. FN was found to inhibit DIRAS3-induced autophagy and to partially rescue ovarian cancer cells from DIRAS3-induced cell death while reducing DIRAS3-induced inhibition of p-FAK and p-AKT. Inhibiting FAK with defactinib in ovarian cancer cells enhanced DIRAS3-induced autophagy and cell death. Re-expression of DIRAS3 and treatment with defactinib produced tumor regression in xenograft models. Our findings suggest that ECM components in the tumor microenvironment like FN enhance the activities of β1 integrin, FAK, and AKT to inhibit DIRAS3-induced autophagic cell death, thereby promoting ovarian cancer cell survival.

Crizotinib Enhances PARP Inhibitor Efficacy in Ovarian Cancer Cells and Xenograft Models by Inducing Autophagy

Abstract Poly (ADP-ribose) polymerase inhibitors (PARPi) can encounter resistance through various mechanisms, limiting their effectiveness. Our recent research showed that PARPi alone can induce drug resistance by promoting autophagy. Moreover, our studies have revealed that anaplastic lymphoma kinase (ALK) plays a role in regulating the survival of ovarian cancer cells undergoing autophagy. Here, we explored whether the ALK-inhibitor crizotinib could enhance the efficacy of PARPi by targeting drug-induced autophagic ovarian cancer cell and xenograft models. Our investigation demonstrates that crizotinib enhances the anti-tumor activity of PARPi across multiple ovarian cancer cells. Combination therapy with crizotinib and olaparib reduced cell viability and clonogenic growth in two-olaparib resistant cell lines. More importantly, this effect was consistently observed in patient-derived organoids. Furthermore, combined treatment with crizotinib and olaparib led to tumor regression in human ovarian xenograft models. Mechanistically, the combination resulted in increased levels of reactive oxygen species (ROS), induced DNA damage, and decreased the phosphorylation of AKT, mTOR, and ULK-1, contributing to increased olaparib-induced autophagy and apoptosis. Notably, pharmacologic, or genetic inhibition or autophagy reduced the sensitivity of ovarian cancer cell lines to olaparib and crizotinib treatment, underscoring the role of autophagy in cell death. Blocking ROS mitigated olaparib/crizotinib-induced autophagy and cell death while restoring levels of phosphorylated AKT, mTOR and ULK-1. These findings suggest that crizotinib can improve the therapeutic efficacy of olaparib by enhancing autophagy. Implications: The combination of crizotinib and PARPi presents a promising strategy, that could provide a novel approach to enhance outcomes for patients with ovarian cancer.

DIRAS3 induces autophagy and enhances sensitivity to anti-autophagic therapy in KRAS-driven pancreatic and ovarian carcinomas

Pancreatic ductal adenocarcinoma (PDAC) and low-grade ovarian cancer (LGSOC) are characterized by the prevalence of KRAS oncogene mutations. DIRAS3 is the first endogenous non-RAS protein that heterodimerizes with RAS, disrupts RAS clustering, blocks RAS signaling, and inhibits cancer cell growth. Here, we found that DIRAS3-mediated KRAS inhibition induces ROS-mediated apoptosis in PDAC and LGSOC cells with KRAS mutations, but not in cells with wild-type KRAS, by downregulating NFE2L2/Nrf2 transcription, reducing antioxidants, and inducing oxidative stress. DIRAS3 also induces cytoprotective macroautophagy/autophagy that may protect mutant KRAS cancer cells from oxidative stress, by inhibiting mutant KRAS, activating the STK11/LKB1-PRKAA/AMPK pathway, increasing lysosomal CDKN1B/p27 localization, and inducing autophagic gene expression. Treatment with chloroquine or the novel dimeric chloroquine analog DC661 significantly enhances DIRAS3-mediated inhibition of mutant KRAS tumor cell growth in vitro and in vivo. Taken together, our study demonstrates that DIRAS3 plays a critical role in regulating mutant KRAS-driven oncogenesis in PDAC and LGSOC.

Characteristics of Human Papillomavirus Prevalence and Infection Patterns Among Women Aged 35–65 in Fujian Province, China: A Nine‐Year Retrospective Observational Study

ABSTRACTThe assessment of human papillomavirus (HPV) genotype distribution could inform targeted cervical cancer prevention strategies. The epidemiology of HPV genotypes in terms of age and cervical lesions in Fujian Province, China has not been well described. This 9‐year retrospective study aimed to delineate the prevalence pattern and trend of HPV genotypes among a large‐scale community‐based population. Deidentified data were retrieved from the national cervical cancer screening program in China. We included eligible women aged 35–65 years who underwent cervical cancer screening between 2014 and 2022 in Fujian Province. The HPV prevalence within distinct subpopulations was calculated, and trends in HPV prevalence over the years and across age groups were examined using the Cochran‐Armitage trend test. A total of 551 604 women (median age 49 years [42, 54]; 0.10% with cervical cancer) were included in this study. The overall HPV prevalence was 11.72% (95% CI: 11.63%–11.80%), with HR‐HPV (high‐risk HPV) and HPV 16/18 prevalence at 10.02% (9.94%–10.10%) and 1.74% (1.71%–1.78%), respectively. HPV‐52, 58, 16, 39, 51, and 68 were the most predominant genotypes in the general population. Nearly all genotypes, except for HPV‐39 and 66, showed a decreasing trend in prevalence over the years, while a relatively high prevalence of HR‐HPV was observed across all age groups. As lesion severity increased, HR‐HPV and 9v‐HPV prevalence also increased. Our study underscores the importance of ongoing surveillance of HPV prevalence in China. While the overall decline in HPV infections over the years is encouraging, the relatively high prevalence of HR‐HPV warrants continued attention. Strengthening public health strategies—including prioritizing and promoting the current 9‐valent vaccination, extending HPV testing and cervical cancer screening to older women where feasible, and developing future vaccines targeting more HR‐HPV genotypes—will be crucial in eliminating cervical cancer and HPV‐related disease in China and beyond.

A Blood-Based Metabolite Panel for Distinguishing Ovarian Cancer from Benign Pelvic Masses

Abstract Purpose: To assess the contributions of circulating metabolites for improving upon the performance of the risk of ovarian malignancy algorithm (ROMA) for risk prediction of ovarian cancer among women with ovarian cysts. Experimental Design: Metabolomic profiling was performed on an initial set of sera from 101 serous and nonserous ovarian cancer cases and 134 individuals with benign pelvic masses (BPM). Using a deep learning model, a panel consisting of seven cancer-related metabolites [diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminate, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid] was developed for distinguishing early-stage ovarian cancer from BPM. The performance of the metabolite panel was evaluated in an independent set of sera from 118 ovarian cancer cases and 56 subjects with BPM. The contributions of the panel for improving upon the performance of ROMA were further assessed. Results: A 7-marker metabolite panel (7MetP) developed in the training set yielded an AUC of 0.86 [95% confidence interval (CI): 0.76–0.95] for early-stage ovarian cancer in the independent test set. The 7MetP+ROMA model had an AUC of 0.93 (95% CI: 0.84–0.98) for early-stage ovarian cancer in the test set, which was improved compared with ROMA alone [0.91 (95% CI: 0.84–0.98); likelihood ratio test P: 0.03]. In the entire specimen set, the combined 7MetP+ROMA model yielded a higher positive predictive value (0.68 vs. 0.52; one-sided P < 0.001) with improved specificity (0.89 vs. 0.78; one-sided P < 0.001) for early-stage ovarian cancer compared with ROMA alone. Conclusions: A blood-based metabolite panel was developed that demonstrates independent predictive ability and complements ROMA for distinguishing early-stage ovarian cancer from benign disease to better inform clinical decision making.

Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study

Abstract Background Cervical cancer remains a major global health issue. Personalized, data-driven cervical cancer prevention (CCP) strategies tailored to phenotypic profiles may improve prevention and reduce disease burden. Objective This study aimed to identify subgroups with differential cervical precancer or cancer risks using machine learning, validate subgroup predictions across datasets, and propose a computational phenomapping strategy to enhance global CCP efforts. Methods We explored the data-driven CCP subgroups by applying unsupervised machine learning to a deeply phenotyped, population-based discovery cohort. We extracted CCP-specific risks of cervical intraepithelial neoplasia (CIN) and cervical cancer through weighted logistic regression analyses providing odds ratio (OR) estimates and 95% CIs. We trained a supervised machine learning model and developed pathways to classify individuals before evaluating its diagnostic validity and usability on an external cohort. Results This study included 551,934 women (median age, 49 years) in the discovery cohort and 47,130 women (median age, 37 years) in the external cohort. Phenotyping identified 5 CCP subgroups, with CCP4 showing the highest carcinoma prevalence. CCP2–4 had significantly higher risks of CIN2+ (CCP2: OR 2.07 [95% CI: 2.03‐2.12], CCP3: 3.88 [3.78‐3.97], and CCP4: 4.47 [4.33‐4.63]) and CIN3+ (CCP2: 2.10 [2.05‐2.14], CCP3: 3.92 [3.82‐4.02], and CCP4: 4.45 [4.31‐4.61]) compared to CCP1 (P<.001), consistent with the direction of results observed in the external cohort. The proposed triple strategy was validated as clinically relevant, prioritizing high-risk subgroups (CCP3-4) for colposcopies and scaling human papillomavirus screening for CCP1-2. Conclusions This study underscores the potential of leveraging machine learning algorithms and large-scale routine electronic health records to enhance CCP strategies. By identifying key determinants of CIN2+/CIN3+ risk and classifying 5 distinct subgroups, our study provides a robust, data-driven foundation for the proposed triple strategy. This approach prioritizes tailored prevention efforts for subgroups with varying risks, offering a novel and scalable tool to complement existing cervical cancer screening guidelines. Future work should focus on independent external and prospective validation to maximize the global impact of this strategy.