A reliable and quick method for screening alternative splicing variants for low-abundance genes.

Alternative splicing (AS) is a universal phenomenon in eukaryotes, and it is still challenging to identify AS events. Several methods have been developed to identify AS events, such as expressed sequence tags (EST), microarrays and RNA-seq. However, EST has limitations in identifying low-abundance genes, while microarray and RNA-seq are high-throughput technologies, and PCR-based technology is needed for validation. To overcome the limitations of EST and shortcomings of high-throughput technologies, we established a method to identify AS events, especially for low-abundance genes, by reverse transcription (RT) PCR with gene-specific primers (GSPs) followed by nested PCR. This process includes two major steps: 1) the use of GSPs to amplify as long as the specific gene segment and 2) multiple rounds of nested PCR to screen the AS and confirm the unknown splicing variants. With this method, we successfully identified three new splicing variants, namely, GenBank Accession No. HM623886 for the bdnf gene (GenBank GeneID: 12064), GenBank Accession No. JF417977 for the trkc gene (GenBank GeneID: 18213) and GenBank Accession No. HM623888 for the glb-18 gene (GenBank GeneID: 172485). In addition to its reliability and simplicity, the method is also cost-effective and labor-intensive. In conclusion, we developed an RT-nested PCR method using gene-specific primers to efficiently identify known and novel AS variants. This approach overcomes the limitations of existing methods for detecting rare transcripts. By enabling the discovery of new isoforms, especially for low-abundance genes, this technique can aid research into aberrant splicing in disease. Future studies can apply this method to uncover AS variants involved in cancer, neurodegeneration, and other splicing-related disorders.

Correction: The role of the Basic Public Health Service program in the control of hypertension in China: Results from a cross-sectional health service interview survey.

[This corrects the article DOI: 10.1371/journal.pone.0217185.].

A review of machine learning-based methods for predicting drug-target interactions.

The prediction of drug-target interactions (DTI) is a crucial preliminary stage in drug discovery and development, given the substantial risk of failure and the prolonged validation period associated with in vitro and in vivo experiments. In the contemporary landscape, various machine learning-based methods have emerged as indispensable tools for DTI prediction. This paper begins by placing emphasis on the data representation employed by these methods, delineating five representations for drugs and four for proteins. The methods are then categorized into traditional machine learning-based approaches and deep learning-based ones, with a discussion of representative approaches in each category and the introduction of a novel taxonomy for deep neural network models in DTI prediction. Additionally, we present a synthesis of commonly used datasets and evaluation metrics to facilitate practical implementation. In conclusion, we address current challenges and outline potential future directions in this research field.

Effectiveness of exercise-based cardiac rehabilitation for patients with left ventricular assist device: A systematic review and meta-analysis.

PURPOSE: Exercise-based cardiac rehabilitation (EBCR) improves functional capacity in heart failure (HF). However, data on the effect of EBCR in patients with advanced HF and left ventricular assist devices (LVADs) are limited. This meta-analysis aimed to evaluate the impact of EBCR on the functional ability of LVAD patients by comparing the corresponding outcome indicators between the EBCR and ST groups. METHODS: PubMed, Embase, Clinical Trials, and Cochrane Library databases were searched for studies assessing and comparing the effects of EBCR and standard therapy (ST) in patients following LVAD implantation. Using pre-defined criteria, appropriate studies were identified and selected. Data from selected studies were extracted in a standardized fashion, and a meta-analysis was performed using a fixed-effects model. The protocol was registered on INPLASY (202340073). RESULTS: In total, 12 trials involving 477 patients were identified. The mean age of the participants was 52.9 years, and 78.6% were male. The initiation of EBCR varied from LVAD implantation during the index hospitalization to 11 months post-LVAD implantation. The median rehabilitation period ranged from 2 weeks to 18 months. EBCR was associated with improved peak oxygen uptake (VO2) in all trials. Quantitative analysis was performed in six randomized studies involving 214 patients (EBCR: n = 130, ST: n = 84). EBCR was associated with a significantly high peak VO2 (weighted mean difference [WMD] = 1.64 mL/kg/min; 95% confidence interval [CI], 0.20-3.08; p = .03). Similarly, 6-min walk distance (6MWD) showed significantly greater improvement in the EBCR group than in the ST group (WMD = 34.54 m; 95% CI, 12.47-56.42; p = .002) in 266 patients (EBCR, n = 140; ST, n = 126). Heterogeneity was low among the included trials. None of the included studies reported serious adverse events related to EBCR, indicating the safety of EBCR after LVAD implantation. CONCLUSION: This study demonstrated that EBCR following LVAD implantation is associated with greater improvement in functional capacity compared with ST as reflected by the improved peak VO2 and 6MWD values. Considering the small number of patients in this analysis, further research on the clinical impact of EBCR in LVAD patients is warranted.

Rebaudioside B Attenuates Lung Ischemia-reperfusion Injury-associated Apoptosis and Inflammation.

OBJECTIVE: At present, no proven effective treatment is available for Lung Ischemiareperfusion Injury (LIRI). Natural compounds offer promising prospects for developing new drugs to address various diseases. This study sought to explore the potential of Rebaudioside B (Reb B) as a treatment compound for LIRI, both in vivo and in vitro. METHODS: This study involved utilizing the human pulmonary alveolar cell line A549, consisting of epithelial type II cells, subjected to Oxygen-glucose Deprivation/recovery (OGD/R) for highthroughput in vitro cell viability screening. The aim was to identify the most promising candidate compounds. Additionally, an in vivo rat model of lung ischemia-reperfusion was employed to evaluate the potential protective effects of Reb B. RESULTS: Through high-throughput screening, Reb B emerged as the most promising natural compound among those tested. In the A549 OGD/R models, Reb B exhibited a capacity to enhance cell viability by mitigating apoptosis. In the in vivo LIRI model, pre-treatment with Reb B notably decreased apoptotic cells, perivascular edema, and neutrophil infiltration within lung tissues. Furthermore, Reb B demonstrated its ability to attenuate lung inflammation associated with LIRI primarily by elevating IL-10 levels while reducing levels of IL-6, IL-8, and TNF-α. CONCLUSION: The comprehensive outcomes strongly suggest Reb B's potential as a protective agent against LIRI. This effect is attributed to its inhibition of the mitochondrial apoptotic pathway and its ability to mitigate the inflammatory response.

A bimodal feature fusion convolutional neural network for detecting obstructive sleep apnea/hypopnea from nasal airflow and oximetry signals.

The most prevalent sleep-disordered breathing condition is Obstructive Sleep Apnea (OSA), which has been linked to various health consequences, including cardiovascular disease (CVD) and even sudden death. Therefore, early detection of OSA can effectively help patients prevent the diseases induced by it. However, many existing methods have low accuracy in detecting hypopnea events or even ignore them altogether. According to the guidelines provided by the American Academy of Sleep Medicine (AASM), two modal signals, namely nasal pressure airflow and pulse oxygen saturation (SpO2), offer significant advantages in detecting OSA, particularly hypopnea events. Inspired by this notion, we propose a bimodal feature fusion CNN model that primarily comprises of a dual-branch CNN module and a feature fusion module for the classification of 10-second-long segments of nasal pressure airflow and SpO2. Additionally, an Efficient Channel Attention mechanism (ECA) is incorporated into the second module to adaptively weight feature map of each channel for improving classification accuracy. Furthermore, we design an OSA Severity Assessment Framework (OSAF) to aid physicians in effectively diagnosing OSA severity. The performance of both the bimodal feature fusion CNN model and OSAF is demonstrated to be excellent through per-segment and per-patient experimental results, based on the evaluation of our method using two real-world datasets consisting of polysomnography (PSG) recordings from 450 subjects.

A Multitask Dynamic Graph Attention Autoencoder for Imbalanced Multilabel Time Series Classification.

Graph learning is widely applied to process various complex data structures (e.g., time series) in different domains. Due to multidimensional observations and the requirement for accurate data representation, time series are usually represented in the form of multilabels. Accurately classifying multilabel time series can provide support for personalized predictions and risk assessments. It requires effectively capturing complex label relevance and overcoming imbalanced label distributions of multilabel time series. However, the existing methods are unable to model label relevance for multilabel time series or fail to fully exploit it. In addition, the existing multilabel classification balancing strategies suffer from limitations, such as disregarding label relevance, information loss, and sampling bias. This article proposes a dynamic graph attention autoencoder-based multitask (DGAAE-MT) learning framework for multilabel time series classification. It can fully and accurately model label relevance for each instance by using a dynamic graph attention-based graph autoencoder to improve multilabel classification accuracy. DGAAE-MT employs a dual-sampling strategy and cooperative training approach to improve the classification accuracy of low-frequency classes while maintaining the classification accuracy of high-frequency and mid-frequency classes. It avoids information loss and sampling bias. DGAAE-MT achieves a mean average precision (mAP) of 0.955 and an F1 score of 0.978 on a mixed medical time series dataset. It outperforms state-of-the-art works in the past two years.

Clinical Impact of Metformin Therapy on Gestational Diabetes Mellitus and Maternal-Infant Health Outcomes.

Objective: This study aimed to assess the impact of metformin treatment on clinical parameters (blood glucose, inflammation, hormone levels) and outcomes for both mothers and infants in cases of gestational diabetes mellitus (GDM). Methods: A comparative study with a retrospective cohort design was conducted. A total of 96 patients diagnosed with gestational diabetes mellitus over the past three years in our hospital were included. The participants were divided into two groups: a control group receiving insulin treatment and a study group receiving metformin treatment. We compared the clinical effects between the two groups. Results: After treatment, the levels of postprandial 2-hour blood glucose, fasting blood glucose, and glycosylated hemoglobin significantly improved in both groups compared to pre-treatment levels. Moreover, the study group exhibited superior outcomes compared to the control group (P < .05). The levels of interleukin-8 (IL-8), tumor necrosis factor-α (TNF-α), and interleukin-1ß (IL-1ß) demonstrated improvement in both groups, with the study group outperforming the control group (P < .05). Additionally, the levels of Cystatin C (CysC) and Homocysteine (Hcy) in both groups improved post-treatment, with the study group showing better results than the control group (P < .05). Notably, the study group exhibited a lower incidence of adverse outcomes than the control group (P < .05). Conclusions: Metformin therapy demonstrated a significant clinical impact on gestational diabetes mellitus. Compared to insulin therapy, metformin showed superior effects on blood glucose, inflammation, hormone levels, and maternal and infant outcomes, suggesting its adoption for patient consideration.

THEMIS is a substrate and allosteric activator of SHP1, playing dual roles during T cell development.

THEMIS plays an indispensable role in T cells, but its mechanism of action has remained highly controversial. Using the systematic proximity labeling methodology PEPSI, we identify THEMIS as an uncharacterized substrate for the phosphatase SHP1. Saturated mutagenesis assays and mass spectrometry analysis reveal that phosphorylation of THEMIS at the evolutionally conserved Tyr34 residue is oppositely regulated by SHP1 and the kinase LCK. Similar to THEMIS-/- mice, THEMISY34F/Y34F knock-in mice show a significant decrease in CD4 thymocytes and mature CD4 T cells, but display normal thymic development and peripheral homeostasis of CD8 T cells. Mechanistically, the Tyr34 motif in THEMIS, when phosphorylated upon T cell antigen receptor activation, appears to act as an allosteric regulator, binding and stabilizing SHP1 in its active conformation, thus ensuring appropriate negative regulation of T cell antigen receptor signaling. However, cytokine signaling in CD8 T cells fails to elicit THEMIS Tyr34 phosphorylation, indicating both Tyr34 phosphorylation-dependent and phosphorylation-independent roles of THEMIS in controlling T cell maturation and expansion.

Non-invasive decision support for clinical treatment of non-small cell lung cancer using a multiscale radiomics approach.

BACKGROUND: Selecting therapeutic strategies for cancer patients is typically based on key target-molecule biomarkers that play an important role in cancer onset, progression, and prognosis. Thus, there is a pressing need for novel biomarkers that can be utilized longitudinally to guide treatment selection. METHODS: Using data from 508 non-small cell lung cancer (NSCLC) patients across three institutions, we developed and validated a comprehensive predictive biomarker that distinguishes six genotypes and infiltrative immune phenotypes. These features were analyzed to establish the association between radiological phenotypes and tumor genotypes/immune phenotypes and to create a radiological interpretation of molecular features. In addition, we assessed the sensitivity of the models by evaluating their performance at five different voxel intervals, resulting in improved generalizability of the proposed approach. FINDINGS: The radiomics model we developed, which integrates clinical factors and multi-regional features, outperformed the conventional model that only uses clinical and intratumoral features. Our combined model showed significant performance for EGFR, KRAS, ALK, TP53, PIK3CA, and ROS1 mutation status with AUCs of 0.866, 0.874, 0.902, 0.850, 0.860, and 0.900, respectively. Additionally, the predictive performance for PD-1/PD-L1 was 0.852. Although the performance of all models decreased to different degrees at five different voxel space resolutions, the performance advantage of the combined model did not change. CONCLUSIONS: We validated multiscale radiomic signatures across tumor genotypes and immunophenotypes in a multi-institutional cohort. This imaging-based biomarker offers a non-invasive approach to select patients with NSCLC who are sensitive to targeted therapies or immunotherapy, which is promising for developing personalized treatment strategies during therapy.

Exploring non-invasive precision treatment in non-small cell lung cancer patients through deep learning radiomics across imaging features and molecular phenotypes.

BACKGROUND: Accurate prediction of tumor molecular alterations is vital for optimizing cancer treatment. Traditional tissue-based approaches encounter limitations due to invasiveness, heterogeneity, and molecular dynamic changes. We aim to develop and validate a deep learning radiomics framework to obtain imaging features that reflect various molecular changes, aiding first-line treatment decisions for cancer patients. METHODS: We conducted a retrospective study involving 508 NSCLC patients from three institutions, incorporating CT images and clinicopathologic data. Two radiomic scores and a deep network feature were constructed on three data sources in the 3D tumor region. Using these features, we developed and validated the 'Deep-RadScore,' a deep learning radiomics model to predict prognostic factors, gene mutations, and immune molecule expression levels. FINDINGS: The Deep-RadScore exhibits strong discrimination for tumor molecular features. In the independent test cohort, it achieved impressive AUCs: 0.889 for lymphovascular invasion, 0.903 for pleural invasion, 0.894 for T staging; 0.884 for EGFR and ALK, 0.896 for KRAS and PIK3CA, 0.889 for TP53, 0.895 for ROS1; and 0.893 for PD-1/PD-L1. Fusing features yielded optimal predictive power, surpassing any single imaging feature. Correlation and interpretability analyses confirmed the effectiveness of customized deep network features in capturing additional imaging phenotypes beyond known radiomic features. INTERPRETATION: This proof-of-concept framework demonstrates that new biomarkers across imaging features and molecular phenotypes can be provided by fusing radiomic features and deep network features from multiple data sources. This holds the potential to offer valuable insights for radiological phenotyping in characterizing diverse tumor molecular alterations, thereby advancing the pursuit of non-invasive personalized treatment for NSCLC patients.

Development of high-performance point-of-care aqueous VEGF detection system and proof-of-concept validation in RVO patients.

OBJECTIVES: To develop a sensitive point-of-care testing (POCT) aqueous vascular endothelial growth factor (VEGF) detection system, and assess its role for predicting the response to anti-VEGF treatment in macular edema secondary to retinal vein occlusion (RVO-ME) patients. METHODS: An automatic point-of-care aqueous humor Magnetic Particle Chemiluminescence Enzyme Immuno-Assay (MPCLEIA) VEGF detection system was developed. The predictive values of aqueous cytokine levels, in combination with imaging parameters, on anatomical treatment response (ATR, the relative central macular thickness change [ΔCMT/bl-CMT]) were analyzed. RESULTS: The automatic MPCLEIA system was able to provide results in 45 min with only 20 µL sample. Among the 57 eyes with available pre- and post-treatment evaluation, ATR significantly correlated with levels of interleukin (IL)-6, IL-8, monocyte chemoattractant protein-1 (MCP-1) and VEGF measured by Luminex xMAP platform, and VEGF measured by MPCLEIA. Optimal cut-off values for these biomarkers were 13.26 ng/L, 23.57 ng/L, 1,110.12 ng/L, 105.52 ng/L, and 85.39 ng/L, respectively. Univariate analysis showed significant associations between ATR category (good response if ATR≤-25 % or poor response otherwise) and IL-6, IL-8, MCP-1, VEGF-xMAP, and VEGF-MPCLEIA (p<0.05). Multivariate logistic regression revealed that ATR category was significantly associated with aqueous VEGF-MPCLEIA (p=0.006) and baseline(bl)-CMT (p=0.008). Receiver operating characteristics analysis yielded an AUC of 0.959 for the regression model combining VEGF-MPCLEIA and bl-CMT, for predicting ATR category. CONCLUSIONS: Our novel MPCLEIA-based automatic VEGF detection system enables accurate POCT of aqueous VEGF, which shows promise in predicting the treatment response of RVO-ME to anti-VEGF agents when combined with bl-CMT.

Precise Therapeutic Targeting of Distinct NRXN1+/- Mutations.

As genetic studies continue to identify risk loci that are significantly associated with risk for neuropsychiatric disease, a critical unanswered question is the extent to which diverse mutations--sometimes impacting the same gene-- will require tailored therapeutic strategies. Here we consider this in the context of rare neuropsychiatric disorder-associated copy number variants (2p16.3) resulting in heterozygous deletions in NRXN1, a pre-synaptic cell adhesion protein that serves as a critical synaptic organizer in the brain. Complex patterns of NRXN1 alternative splicing are fundamental to establishing diverse neurocircuitry, vary between the cell types of the brain, and are differentially impacted by unique (non-recurrent) deletions. We contrast the cell-type-specific impact of patient-specific mutations in NRXN1 using human induced pluripotent stem cells, finding that perturbations in NRXN1 splicing result in divergent cell-type-specific synaptic outcomes. Via distinct loss-of-function (LOF) and gain-of-function (GOF) mechanisms, NRXN1+/- deletions cause decreased synaptic activity in glutamatergic neurons, yet increased synaptic activity in GABAergic neurons. Stratification of patients by LOF and GOF mechanisms will facilitate individualized restoration of NRXN1 isoform repertoires; towards this, antisense oligonucleotides knockdown mutant isoform expression and alters synaptic transcriptional signatures, while treatment with ß-estradiol rescues synaptic function in glutamatergic neurons. Given the increasing number of mutations predicted to engender both LOF and GOF mechanisms in brain disease, our findings add nuance to future considerations of precision medicine.

Resveratrol alleviates amyloid ß-induced neuronal apoptosis, inflammation, and oxidative and endoplasmic reticulum stress by circ_0050263/miR-361-3p/PDE4A axis during Alzheimer's disease.

Resveratrol (Res) has been identified to reduce neurodegeneration. Circular RNAs (circRNAs) are stable noncoding RNAs that are considered to be ideal biomarkers for molecular targeting treatment. Here, this study focused on investigating the function and relationship of circ_0050263 and Res in Alzheimer's Disease (AD). Human neuroblastoma cell line SK-N-SH was exposed to amyloid-ß (Aß) to induce AD cell model in vitro. Cell viability, apoptosis, and inflammatory reaction were evaluated by CCK-8 assay, flow cytometery, and ELISA analysis. The oxidative stress and endoplasmic reticulum stress (ERS) were determined by detecting related markers. Levels of genes and proteins were detected by qRT-PCR and Western blot. Dual-luciferase reporter assay was adopted to verify the binding between miR-361-3p and circ_0050263 or PDE4A (Phosphodiesterase 4A). Subsequently, we found that Res treatment alleviated Aß-induced apoptosis, inflammatory response, oxidative stress, and ERS in SK-N-SH cells. Circ_0050263 is a stable circRNA, which was increased by Aß, but decreased by Res in SK-N-SH cells. Circ_0050263 overexpression reversed Res-induced neuroprotective effects. Mechanistically, circ_0050263 acted as a sponge for miR-361-3p, which targeted PDE4A. Circ_0050263 silencing abated Aß-induced neuronal injury, which were counteracted by following PDE4A overexpression. Moreover, PDE4A upregulation could attenuate Res-mediated neuroprotective effects. In all, Res alleviated Aß-induced neuronal apoptosis, inflammation, oxidative stress, and ERS via circ_0050263/miR-361-3p/PDE4A axis, providing new insights for AD therapy.

Improving adversarial robustness of medical imaging systems via adding global attention noise.

Recent studies have found that medical images are vulnerable to adversarial attacks. However, it is difficult to protect medical imaging systems from adversarial examples in that the lesion features of medical images are more complex with high resolution. Therefore, a simple and effective method is needed to address these issues to improve medical imaging systems' robustness. We find that the attackers generate adversarial perturbations corresponding to the lesion characteristics of different medical image datasets, which can shift the model's attention to other places. In this paper, we propose global attention noise (GATN) injection, including global noise in the example layer and attention noise in the feature layers. Global noise enhances the lesion features of the medical images, thus keeping the examples away from the sharp areas where the model is vulnerable. The attention noise further locally smooths the model from small perturbations. According to the characteristic of medical image datasets, we introduce Global attention lesion-unrelated noise (GATN-UR) for datasets with unclear lesion boundaries and Global attention lesion-related noise (GATN-R) for datasets with clear lesion boundaries. Extensive experiments on ChestX-ray, Dermatology, and Fundoscopy datasets show that GATN improves the robustness of medical diagnosis models against a variety of powerful attacks and significantly outperforms the existing adversarial defense methods. To be specific, the robust accuracy is 86.66% on ChestX-ray, 72.49% on Dermatology, and 90.17% on Fundoscopy under PGD attack. Under the AA attack, it achieves robust accuracy of 87.70% on ChestX-ray, 66.85% on Dermatology, and 87.83% on Fundoscopy.

Radiomics under 2D regions, 3D regions, and peritumoral regions reveal tumor heterogeneity in non-small cell lung cancer: a multicenter study.

PURPOSE: Lung cancer has significant genetic and phenotypic heterogeneity, leading to poor prognosis. Radiomic features have emerged as promising predictors of the tumor phenotype. However, the role of underlying information surrounding the cancer remains unclear. MATERIALS AND METHODS: We conducted a retrospective study of 508 patients with NSCLC from three institutions. Radiomics models were built using features from six tumor regions and seven classifiers to predict three prognostically significant tumor phenotypes. The models were evaluated and interpreted by the mean area under the receiver operating characteristic curve (AUC) under nested cross-validation and Shapley values. The best-performing predictive models corresponding to six tumor regions and three tumor phenotypes were identified for further comparative analysis. In addition, we designed five experiments with different voxel spacing to assess the sensitivity of the experimental results to the spatial resolution of the voxels. RESULTS: Our results demonstrated that models based on 2D, 3D, and peritumoral region features yielded mean AUCs and 95% confidence intervals of 0.759 and [0.747-0.771] for lymphovascular invasion, 0.889 and [0.882-0.896] for pleural invasion, and 0.839 and [0.829-0.849] for T-staging in the testing cohort, which was significantly higher than all other models. Similar results were obtained for the model combining the three regional features at five voxel spacings. CONCLUSION: Our study revealed the predictive role of the developed methods with multi-regional features for the preoperative assessment of prognostic factors in NSCLC. The analysis of different voxel spacing and model interpretability strengthens the experimental findings and contributes to understanding the biological significance of the radiological phenotype.

Postoperative prognostic nomogram for adult grade II/III astrocytoma in the Chinese Han population.

Background: Prognostic models of glioma have been the focus of many studies. However, most of them are based on Western populations. Additionally, because of the complexity of healthcare data in China, it is important to select a suitable model based on existing clinical data. This study aimed to develop and independently validate a nomogram for predicting the overall survival (OS) with newly diagnosed grade II/III astrocytoma after surgery. Methods: Data of 472 patients with astrocytoma (grades II-III) were collected from Qilu Hospital as training cohort while data of 250 participants from Linyi People's Hospital were collected as validation cohort. Cox proportional hazards model was used to construct the nomogram and individually predicted 1-, 3-, and 5-year survival probabilities. Calibration ability, and discrimination ability were analyzed in both training and validation cohort. Results: Overall survival was negatively associated with histopathology, age, subtotal resection, multiple tumors, lower KPS and midline tumors. Internal validation and external validation showed good discrimination (The C-index for 1-, 3-, and 5-year survival were 0.791, 0.748, 0.733 in internal validation and 0.754, 0.735, 0.730 in external validation, respectively). The calibration curves showed good agreement between the predicted and actual 1-, 3-, and 5-year OS rates. Conclusion: This is the first nomogram study that integrates common clinicopathological factors to provide an individual probabilistic prognosis prediction for Chinese Han patients with astrocytoma (grades II-III). This model can serve as an easy-to-use tool to advise patients and establish optimized surveillance approaches after surgery. Supplementary Information: The online version contains supplementary material available at 10.1007/s13755-023-00223-0.

Development of the nonreceptor tyrosine kinase FER-targeting PROTACs as a potential strategy for antagonizing ovarian cancer cell motility and invasiveness.

Aberrant overexpression of nonreceptor tyrosine kinase FER (Fps/Fes Related) has been reported in various ovarian carcinoma-derived tumor cells and is a poor prognosis factor for patient survival. It plays an essential role in tumor cell migration and invasion, acting concurrently in both kinase-dependent and -independent manners, which is not easily suppressed by conventional enzymatic inhibitors. Nevertheless, the PROteolysis-TArgeting Chimera (PROTAC) technology offers superior efficacy over traditional activity-based inhibitors by simultaneously targeting enzymatic and scaffold functions. Hence in this study, we report the development of two PROTAC compounds that promote robust FER degradation in a cereblon-dependent manner. Both PROTAC degraders outperform a Food and Drug Administration-approved drug, brigatinib, in ovarian cancer cell motility suppression. Importantly, these PROTAC compounds also degrade multiple oncogenic FER fusion proteins identified in human tumor samples. These results lay an experimental foundation to apply the PROTAC strategy to antagonize cell motility and invasiveness in ovarian and other types of cancers with aberrant expression of FER kinase and highlight PROTACs as a superior strategy for targeting proteins with multiple tumor-promoting functions.