Jianqun Ma

Cisplatin resistance represents a major barrier to effective treatment of lung adenocarcinoma (LUAD), yet its metabolic underpinnings remain incompletely defined. Here, we demonstrate that the transcription factor KLF15 governs cisplatin sensitivity by orchestrating mitochondrial biogenesis and redox homeostasis. KLF15 is downregulated in cisplatin-resistant LUAD cells, which display reduced mitochondrial content and suppressed reactive oxygen species (ROS) accumulation. Restoring KLF15 expression resensitizes LUAD cells to cisplatin both in vitro and in vivo. Mechanistically, KLF15 directly transactivates PGC1α, and loss of PGC1α abrogates KLF15-mediated cisplatin sensitization, restoring drug resistance by attenuating apoptosis. Functionally, LUAD subpopulations with low mitochondrial mass or low KLF15 expression exhibit intrinsic resistance, whereas Mito-high xenografts show enhanced therapeutic response. Together, our findings identify a KLF15-PGC1α regulatory axis that dictates mitochondrial reprogramming and cisplatin responsiveness, highlighting a potential therapeutic axis to overcome chemoresistance in LUAD.

Patients with unresectable locally advanced esophageal squamous cell carcinoma (ESCC) face limited treatment options and poor outcomes. Preclinica

Background: Neoadjuvant chemoimmunotherapy (NACI) regimen (camrelizumab plus paclitaxel and nedaplatin) has shown promising potential in patients with esophageal squamous cell carcinoma (ESCC), but accurately predicting the therapeutic response remains a challenge. Objective: To develop and validate a CT-based machine learning model that incorporates both intratumoral and peritumoral heterogeneity for predicting the pathological response of ESCC patients after NACI. Methods: Patients with ESCC who underwent surgery following NACI between June 2020 and July 2024 were included retrospectively and prospectively. Univariate and multivariate logistic regression analyses were performed to identify clinical variables associated with pathological response. Traditional radiomics features and habitat radiomics features from the intratumoral and peritumoral regions were extracted from posttreatment CT images, and 6 predictive models were established using 14 machine learning algorithms. The combined model was developed by integrating intratumoral and peritumoral habitat radiomics features with clinical variables. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC). Results: A total of 157 patients (mean [SD] age, 59.6 [6.5] years) were enrolled in our study, of whom 60 (38.2%) achieved major pathological response (MPR) and 40 (25.5%) achieved pathological complete response (pCR). The combined model demonstrated excellent predictive ability for MPR after NACI, with an AUC of 0.915 (95% CI: 0.844–0.981), accuracy of 0.872, sensitivity of 0.733, and specificity of 0.938 in the test set. In sensitivity analysis focusing on pCR, the combined model exhibited robust performance, with an AUC of 0.895 (95% CI: 0.782 – 0.980) in the test set. Conclusion: The combined model integrating intratumoral and peritumoral habitat radiomics features with clinical variables can accurately predict MPR in ESCC patients after NACI and shows promising potential in predicting pCR.

Activated Cdc42-associated kinase 1 (ACK1) is an oncogenic non-receptor kinase that promotes tumor cell survival and impairs T-cell activation. Targeting ACK1 has great promise in cancer control. However, tumor adaptive responses that may limit the anticancer efficacy of ACK1 inhibition (ACK1i) remain unclear. We found that ACK1i treatment triggered the PINK1/PARKIN-mediated adaptive mitophagy by upregulating the mitophagy receptor BNIP3. Mass/Spectrometry and co-immunoprecipitation (Co-IP) results indicated that ACK1 interacted with transcription factor regulatory factor X 2 (RFX2) through its MHR domain, and competitively inhibits RFX2 ubiquitination via the E3 ubiquitin ligase MIB1. Conversely, ACK1i facilitates MIB1-mediated RFX2 ubiquitination and degradation. Moreover, we observed that RFX2 is a transcriptional suppressor of BNIP3 using luciferase reporter gene assays and chromatin immunoprecipitation (ChIP). Overall, ACK1i treatment causes RFX2 instability and thereby diminishes RFX2's suppressive effects on BNIP3 transcription, leading to BNIP3 accumulation and the activation of mitophagy pathways. This adaptive mitophagy allows NSCLC cells to survive under ACK1 inhibition, potentially reducing the efficacy of ACK1i. ACK1i combined with mitophagy-inhibiting agents may attain a more accomplished response in NSCLC. In conclusion, ACK1i induced mitophagy through the release of RFX2 inhibition on BNIP3 transcription, thereby driving adaptive resistance. Inhibiting mitophagy sensitizes NSCLC to ACK1i.

This study aimed to develop and compare fusion models combining sub-regional radiomics with multichannel 2D and 3D DL to predict pCR in patients with LA-ESCC undergoing NACI. A total of 271 patients from three hospitals were divided into training, internal validation, and external validation cohorts. Tumor sub-regions were identified using K-means clustering based on radiomic features, and predictive features were extracted using PyRadiomics. Among all models, the DLRad1 model (radiomics + 2D DL) demonstrated the highest performance, with an AUC ranging from 0.793 to 0.910 across cohorts. Sub-region 1 features alone achieved an AUC of 0.823, while DLRad2 (radiomics + 3D DL) and other single-modality models showed lower AUCs (0.701–0.906). Spearman correlation analysis confirmed low redundancy among selected features. These findings support DLRad1 as a promising non-invasive tool to identify LA-ESCC patients most likely to benefit from NACI, potentially aiding personalized treatment decisions.

Background: Lung cancer (LC) is the most prevalent type of cancer, yet early prediction of hospital mortality and risk stratification remains inadequate. This study aims to develop a predictive model for in-hospital mortality among patients with LC. Methods: This study utilized data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database [2008–2019]. Patients were randomly assigned to training (70%) and validation (30%) cohorts. Baseline characteristics and their associations with LC outcomes were analyzed. Feature selection was performed using Cox regression in the training set. The selected variables were incorporated into a nomogram model, of which its predictive performance and clinical utility were evaluated using calibration plots, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) in both cohorts. Results: A total of 645 LC patients were included, with 451 in the training set and 194 in the validation set. Twenty-two baseline characteristics were significantly associated with LC mortality. Six key variables—alkaline phosphatase (ALP) minimum (min), bilirubin total min, length of stay (LOS), race, respiratory rate min, and Simplified Acute Physiology Score II (SAPS II)—were identified for nomogram development. The final model demonstrated strong predictive accuracy and clinical utility, achieving an area under the curve (AUC) exceeding 0.7 in both cohorts. Conclusions: This study identified key risk factors for in-hospital mortality among critically ill patients with LC and developed a robust predictive model using MIMIC-IV data. The findings provide valuable insights into LC prognosis and may aid in clinical decision-making.

Programmed cell deaths (PCDs) are crucial for tumor progression. By analyzing 18 PCDs, we generated a robust multigene signature, Combined Cell Death Index (CCDI), comprising necroptosis and autophagy genes for non-small cell lung cancer (NSCLC). The CCDI accurately stratified patients by survival prognosis and predicted immunotherapy responses. We validated CCDI and prioritized CCDI genes using five single-cell RNA sequencing and two spatial transcriptomics datasets. CCDI positively correlates with tumor malignancy, invasiveness, and immunotherapy resistance. Four necroptosis genes (PTGES3, MYO6, CCT6A, and CTSH) may affect cancer cell evolution. In vitro, CTSH overexpression or PTGES3 knockdown inhibited NSCLC cell proliferation and migration while inducing necroptosis with necrosome formation. Moreover, we observed diminished CTSH, heightened PTGES3, and low necroptosis activity in 12 pairs of NSCLC tumors and normal tissues. CTSH overexpression or PTGES3 knockdown induced necroptosis and improved anti-PD1 therapy efficiency in syngeneic cancer mouse models. These findings indicate necroptosis genes as potential therapeutic targets in cancer treatments.

Background: Thymomas, rare neoplasms of the mediastinum, present substantial surgical challenges when invading the superior vena cava (SVC). This report details a successful surgical resection of a thymoma with SVC involvement. Case Description: A 50-year-old male, asymptomatic at presentation, discovered a mass during self-examination. Computed tomography (CT) imaging revealed an anterior mediastinal mass consistent with a thymoma, with subsequent imaging confirming SVC invasion. The patient had no relevant medical history. Following preoperative neoadjuvant radiotherapy, the tumor demonstrated partial control, though partial remission (PR) was not achieved. After comprehensive multidisciplinary team consultation, surgical intervention was planned. The patient was positioned supine, and a median sternotomy was performed. The tumor, lacking clear demarcation, involved the bilateral pleura, pericardium, left and right brachiocephalic veins, SVC, and right upper lung. An en-bloc resection of the tumor, invaded pericardium, pleura, and right upper lung lobe was conducted. Bilateral brachiocephalic veins and the SVC were ligated, with invaded sections excised from the vessel walls. Adequate blood return was preserved to prevent postoperative reflux obstruction while ensuring negative tumor margins. The operation lasted 175 minutes, with an intraoperative blood loss of 600 mL. Postoperatively, the patient was admitted to the intensive care unit (ICU) for 48 hours and then transferred to the general ward. No significant postoperative complications were observed. The chest drainage tube was removed on postoperative day 3, and the patient ambulated independently on day 4. Postoperative pathological examination revealed a 6 cm tumor, consistent with a type B3 thymoma. Conclusions: This case illustrates that, despite the inherent challenges, surgical resection of a thymoma invading the SVC can be successfully achieved through meticulous preoperative planning and precise intraoperative management. It underscores the significance of a multidisciplinary approach and the advances in surgical techniques in managing complex thymomas.