A Fifteen-Gene Classifier to Predict Neoadjuvant Chemotherapy Responses in Patients with Stage IB to IIB Squamous Cervical Cancer

MAP4K4 and WT1 mediate SOX6-induced cellular senescence by synergistically activating the ATF2-TGFβ2-Smad2/3 signaling pathway in cervical cancer

SRY-box transcription factor 6 (SOX6) is a member of the SOX gene family and inhibits the proliferation of cervical cancer cells by inducing cell cycle arrest. However, the final cell fate and significance of these cell-cycle-arrested cervical cancer cells induced by SOX6 remains unclear. Here, we report that SOX6 inhibits the proliferation of cervical cancer cells by inducing cellular senescence, which is mainly mediated by promoting transforming growth factor beta 2 (TGFB2) gene expression and subsequently activating the TGFβ2-Smad2/3-p53-p21WAF1/CIP1-Rb pathway. SOX6 promotes TGFB2 gene expression through the MAP4K4-MAPK (JNK/ERK/p38)-ATF2 and WT1-ATF2 pathways, which is dependent on its high-mobility group (HMG) domain. In addition, the SOX6-induced senescent cervical cancer cells are resistant to cisplatin treatment. ABT-263 (navitoclax) and ABT-199 (venetoclax), two classic senolytics, can specifically eliminate the SOX6-induced senescent cervical cancer cells, and thus significantly improve the chemosensitivity of cisplatin-resistant cervical cancer cells. This study uncovers that the MAP4K4/WT1-ATF2-TGFβ2 axis mediates SOX6-induced cellular senescence, which is a promising therapeutic target in improving the chemosensitivity of cervical cancer.

An MRI-based machine learning radiomics can predict short-term response to neoadjuvant chemotherapy in patients with cervical squamous cell carcinoma: A multicenter study

BACKGROUND AND PURPOSE

Neoadjuvant chemotherapy (NACT) has become an essential component of the comprehensive treatment of cervical squamous cell carcinoma (CSCC). However, not all patients respond to chemotherapy due to individual differences in sensitivity and tolerance to chemotherapy drugs. Therefore, accurately predicting the sensitivity of CSCC patients to NACT was vital for individual chemotherapy. This study aims to construct a machine learning radiomics model based on magnetic resonance imaging (MRI) to assess its efficacy in predicting NACT susceptibility among CSCC patients.

METHODS

This study included 234 patients with CSCC from two hospitals, who were divided into a training set (n = 180), a testing set (n = 20), and an external validation set (n = 34). Manual radiomic features were extracted from transverse section MRI images, and feature selection was performed using the recursive feature elimination (RFE) method. A prediction model was then generated using three machine learning algorithms, namely logistic regression, random forest, and support vector machines (SVM), for predicting NACT susceptibility. The model's performance was assessed based on the area under the receiver operating characteristic curve (AUC), accuracy, and sensitivity.

RESULTS

The SVM approach achieves the highest scores on both the testing set and the external validation set. In the testing set and external validation set, the AUC of the model was 0.88 and 0.764, and the accuracy was 0.90 and 0.853, the sensitivity was 0.93 and 0.962, respectively.

CONCLUSIONS

Machine learning radiomics models based on MRI images have achieved satisfactory performance in predicting the sensitivity of NACT in CSCC patients with high accuracy and robustness, which has great significance for the treatment and personalized medicine of CSCC patients.

Downregulation of VPS13C promotes cisplatin resistance in cervical cancer by upregulating GSTP1

Cisplatin resistance remains a major obstacle limiting the effectiveness of chemotherapy in cervical cancer. However, the underlying mechanism of cisplatin resistance is still unclear. In this study, we demonstrate that vacuolar protein sorting 13 homolog C (VPS13C) deficiency promotes cisplatin resistance in cervical cancer. Moreover, through an RNA sequencing screen, VPS13C deficiency was identified as negatively correlated with the high expression of glutathione S-transferase pi gene (GSTP1). Mechanistically, loss of VPS13C contributes to cisplatin resistance by influencing the expression of GSTP1 and inhibiting the downstream c-Jun N-terminal kinase (JNK) pathway. In addition, targeting GSTP1 with the inhibitor NBDHEX effectively rescued the cisplatin resistance induced by VPS13C deficiency. Overall, our findings provide insights into the underlying mechanisms of VPS13C in cisplatin resistance and identify VPS13C as a promising candidate for the treatment of chemoresistance in cervical cancer.

SETD8, a frequently mutated gene in cervical cancer, enhances cisplatin sensitivity by impairing DNA repair

BACKGROUND

Cisplatin is commonly used to treat cervical cancer while drug resistance limits its effectiveness. There is an urgent need to identify strategies that increase cisplatin sensitivity and improve the outcomes of chemotherapy.

RESULTS

We performed whole exome sequencing (WES) of 156 cervical cancer tissues to assess genomic features related to platinum-based chemoresistance. By using WES, we identified a frequently mutated locus SETD8 (7%), which was associated with drug sensitivity. Cell functional assays, in vivo xenografts tumor growth experiments, and survival analysis were used to investigate the functional significance and mechanism of chemosensitization after SETD8 downregulation. Knockdown of SETD8 increased the responsiveness of cervical cancer cells to cisplatin treatment. The mechanism is exerted by reduced binding of 53BP1 to DNA breaks and inhibition of the non-homologous end joining (NHEJ) repair pathway. In addition, SETD8 expression was positively correlated with resistance to cisplatin and negatively associated with the prognosis of cervical cancer patients. Further, UNC0379 as a small molecule inhibitor of SETD8 was found to enhance cisplatin sensitivity both in vitro and in vivo.

CONCLUSIONS

SETD8 was a promising therapeutic target to ameliorate cisplatin resistance and improve the efficacy of chemotherapy.

Deciphering gene contributions and etiologies of somatic mutational signatures of cancer

Somatic mutational signatures (MSs) identified by genome sequencing play important roles in exploring the cause and development of cancer. Thus far, many such signatures have been identified, and some of them do imply causes of cancer. However, a major bottleneck is that we do not know the potential meanings (i.e. carcinogenesis or biological functions) and contributing genes for most of them. Here, we presented a computational framework, Gene Somatic Genome Pattern (GSGP), which can decipher the molecular mechanisms of the MSs. More importantly, it is the first time that the GSGP is able to process MSs from ribonucleic acid (RNA) sequencing, which greatly extended the applications of both MS analysis and RNA sequencing (RNAseq). As a result, GSGP analyses match consistently with previous reports and identify the etiologies for a number of novel signatures. Notably, we applied GSGP to RNAseq data and revealed an RNA-derived MS involved in deficient deoxyribonucleic acid mismatch repair and microsatellite instability in colorectal cancer. Researchers can perform customized GSGP analysis using the web tools or scripts we provide.