Clinicopathological and immune characterization of mismatch repair deficient endocervical adenocarcinoma.

Endocervical adenocarcinoma (ECA) is reported increasingly often in young women, and this aggressive disease lacks effective methods of targeted therapy. Since mismatch repair deficiency (dMMR) is an important biomarker for predicting response to immune checkpoint inhibitors, it is important to investigate the clinicopathological features and immune microenvironment of dMMR ECAs. We assessed 617 ECAs from representative tissue microarray sections, gathered clinicopathologic information, reviewed histological characteristics, and performed immunohistochemical staining for MMR, programmed cell death 1 (PD-L1), and other immune markers. Of 617 ECA samples, 20 (3.2%) cases had dMMR. Among them, loss of MMR-related proteins expression was observed in 17/562 (3.0%) human papilloma virus-associated (HPVA) adenocarcinoma and 3/55 (5.5%) non-HPV-associated (NHPVA) adenocarcinoma. In NHPVA cohort, dMMR status was observed in 3 (3/14, 15.0%) patients with clear cells. dMMR ECAs had a higher tendency to have a family history of cancer, larger tumor size, p16 negative, HPV E6/E7 mRNA in situ hybridization (HPV E6/E7 RNAscope) negative, and lower ki-67 index. Among the morphological variables evaluated, poor differentiation, necrosis, stromal tumor-infiltrating lymphocytes, peritumoral lymphocytes, and lymphoid follicles were easily recognized in the dMMR ECAs. In addition, dMMR ECAs had higher CD3+, CD8+, CD38+, CD68+ and PD-1+ immune cells. A relatively high prevalence of PD-L1 expression was observed in dMMR ECAs. dMMR ECAs were significantly more likely to present with a tumor-infiltrating lymphocytes -high/PD-L1-positive status. In conclusion, dMMR ECAs have some specific morphological features and a critical impact on the immune microenvironment, which may provide insights into improving responses to immunotherapy-included comprehensive treatment for ECAs in the future.

Mining the interpretable prognostic features from pathological image of intrahepatic cholangiocarcinoma using multi-modal deep learning.

BACKGROUND: The advances in deep learning-based pathological image analysis have invoked tremendous insights into cancer prognostication. Still, lack of interpretability remains a significant barrier to clinical application. METHODS: We established an integrative prognostic neural network for intrahepatic cholangiocarcinoma (iCCA), towards a comprehensive evaluation of both architectural and fine-grained information from whole-slide images. Then, leveraging on multi-modal data, we conducted extensive interrogative approaches to the models, to extract and visualize the morphological features that most correlated with clinical outcome and underlying molecular alterations. RESULTS: The models were developed and optimized on 373 iCCA patients from our center and demonstrated consistent accuracy and robustness on both internal (n = 213) and external (n = 168) cohorts. The occlusion sensitivity map revealed that the distribution of tertiary lymphoid structures, the geometric traits of the invasive margin, the relative composition of tumor parenchyma and stroma, the extent of necrosis, the presence of the disseminated foci, and the tumor-adjacent micro-vessels were the determining architectural features that impacted on prognosis. Quantifiable morphological vector extracted by CellProfiler demonstrated that tumor nuclei from high-risk patients exhibited significant larger size, more distorted shape, with less prominent nuclear envelope and textural contrast. The multi-omics data (n = 187) further revealed key molecular alterations left morphological imprints that could be attended by the network, including glycolysis, hypoxia, apical junction, mTORC1 signaling, and immune infiltration. CONCLUSIONS: We proposed an interpretable deep-learning framework to gain insights into the biological behavior of iCCA. Most of the significant morphological prognosticators perceived by the network are comprehensible to human minds.

A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma.

Integrating genomics and histology for cancer prognosis demonstrates promise. Here, we develop a multi-classifier system integrating a lncRNA-based classifier, a deep learning whole-slide-image-based classifier, and a clinicopathological classifier to accurately predict post-surgery localized (stage I-III) papillary renal cell carcinoma (pRCC) recurrence. The multi-classifier system demonstrates significantly higher predictive accuracy for recurrence-free survival (RFS) compared to the three single classifiers alone in the training set and in both validation sets (C-index 0.831-0.858 vs. 0.642-0.777, p < 0.05). The RFS in our multi-classifier-defined high-risk stage I/II and grade 1/2 groups is significantly worse than in the low-risk stage III and grade 3/4 groups (p < 0.05). Our multi-classifier system is a practical and reliable predictor for recurrence of localized pRCC after surgery that can be used with the current staging system to more accurately predict disease course and inform strategies for individualized adjuvant therapy.

A multicenter proof-of-concept study on deep learning-based intraoperative discrimination of primary central nervous system lymphoma.

Accurate intraoperative differentiation of primary central nervous system lymphoma (PCNSL) remains pivotal in guiding neurosurgical decisions. However, distinguishing PCNSL from other lesions, notably glioma, through frozen sections challenges pathologists. Here we sought to develop and validate a deep learning model capable of precisely distinguishing PCNSL from non-PCNSL lesions, especially glioma, using hematoxylin and eosin (H&E)-stained frozen whole-slide images. Also, we compared its performance against pathologists of varying expertise. Additionally, a human-machine fusion approach integrated both model and pathologic diagnostics. In external cohorts, LGNet achieved AUROCs of 0.965 and 0.972 in distinguishing PCNSL from glioma and AUROCs of 0.981 and 0.993 in differentiating PCNSL from non-PCNSL lesions. Outperforming several pathologists, LGNet significantly improved diagnostic performance, further augmented to some extent by fusion approach. LGNet's proficiency in frozen section analysis and its synergy with pathologists indicate its valuable role in intraoperative diagnosis, particularly in discriminating PCNSL from glioma, alongside other lesions.

Prognostic Nutritional Index Predicts Efficacy and Immune-Related Adverse Events of First-Line Chemoimmunotherapy in Patients with Extensive-Stage Small-Cell Lung Cancer.

Background: Currently, there is a lack of well-established markers to predict the efficacy of chemoimmunotherapy in small-cell lung cancer (SCLC). Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), advanced lung cancer inflammation index (ALI) and prognostic nutritional index (PNI) are associated with prognosis in several tumors, whereas their predictive role in SCLC remains unclear. Methods: A retrospective study was conducted at Sun Yat-sen University Cancer Center, involving extensive-stage SCLC (ES-SCLC) patients who received first-line chemoimmunotherapy between January 2020 and December 2021. Peripheral blood biomarkers were extracted from medical records and their correlation with prognosis and immune-related adverse events (IRAEs) was analyzed. Results: A total of 114 patients were included. Patients with a low PLR, high ALI and high PNI had prolonged progression-free survival (PFS) compared to those with a high PLR, low ALI and low PNI. Patients with a low NLR, low PLR, high ALI and high PNI had prolonged overall survival (OS) compared to those with a high NLR, high PLR, low ALI and low PNI. Cox regression model showed that PNI was an independent risk factor for both PFS and OS. ROC curve showed that PNI outperforms NLR, PLR and ALI in predicting both PFS and OS. The PNI-based nomogram demonstrated strong predictive capability for both PFS and OS. In addition, there was a significant correlation between PNI and IRAEs. Conclusion: A high baseline PNI might be associated with improved prognosis and the occurrence of IRAEs in ES-SCLC patients treated with first-line chemoimmunotherapy.

An interpretable deep learning model for identifying the morphological characteristics of dMMR/MSI-H gastric cancer.

Accurate tumor diagnosis by pathologists relies on identifying specific morphological characteristics. However, summarizing these unique morphological features in tumor classifications can be challenging. Although deep learning models have been extensively studied for tumor classification, their indirect and subjective interpretation obstructs pathologists from comprehending the model and discerning the morphological features accountable for classifications. In this study, we introduce a new approach utilizing Style Generative Adversarial Networks, which enables a direct interpretation of deep learning models to detect significant morphological characteristics within datasets representing patients with deficient mismatch repair/microsatellite instability-high gastric cancer. Our approach effectively identifies distinct morphological features crucial for tumor classification, offering valuable insights for pathologists to enhance diagnostic accuracy and foster professional growth.

Dual specific phosphatase 4 suppresses ferroptosis and enhances sorafenib resistance in hepatocellular carcinoma.

AIMS: This investigation aims to elucidate the mechanism underlying sorafenib-induced ferroptosis in hepatocellular carcinoma (HCC). METHODS: The role of dual specificity phosphatase 4 (DUSP4) in sorafenib-treated HCC was investigated using comprehensive assessments both in vitro and in vivo, including Western blotting, qRT-PCR, cell viability assay, lipid reactive oxygen species (ROS) assay, immunohistochemistry, and xenograft tumor mouse model. Additionally, label-free quantitative proteomics was employed to identify potential proteins associated with DUSP4. RESULTS: Our study revealed that suppression of DUSP4 expression heightens the susceptibility of HCC cells to ferroptosis inducers, specifically sorafenib and erastin, in both in vitro and in vivo settings. Furthermore, we identified DUSP4-mediated regulation of key ferroptosis-related markers, such as ferritin light chain (FTL) and ferritin heavy chain 1 (FTH1). Notably, label-free quantitative proteomics unveiled the phosphorylation of threonine residue T148 on YTH Domain Containing 1 (YTHDC1) by DUSP4. Further investigations unraveled that YTHDC1, functioning as an mRNA nuclear export regulator, is a direct target of DUSP4, orchestrating the subcellular localization of FTL and FTH1 mRNAs. Significantly, our study highlights a strong correlation between elevated DUSP4 expression and sorafenib resistance in HCC. CONCLUSIONS: Our findings introduce DUSP4 as a negative regulator of sorafenib-induced ferroptosis. This discovery opens new avenues for the development of ferroptosis-based therapeutic strategies tailored for HCC treatment.

Perioperative toripalimab and chemotherapy in locally advanced gastric or gastro-esophageal junction cancer: a randomized phase 2 trial.

Perioperative chemotherapy is the standard treatment for locally advanced gastric or gastro-esophageal junction cancer, and the addition of programmed cell death 1 (PD-1) inhibitor is under investigation. In this randomized, open-label, phase 2 study (NEOSUMMIT-01), patients with resectable gastric or gastro-esophageal junction cancer clinically staged as cT3-4aN + M0 were randomized (1:1) to receive either three preoperative and five postoperative 3-week cycles of SOX/XELOX (chemotherapy group, n = 54) or PD-1 inhibitor toripalimab plus SOX/XELOX, followed by toripalimab monotherapy for up to 6 months (toripalimab plus chemotherapy group, n = 54). The primary endpoint was pathological complete response or near-complete response rate (tumor regression grade (TRG) 0/1). The results showed that patients in the toripalimab plus chemotherapy group achieved a higher proportion of TRG 0/1 than those in the chemotherapy group (44.4% (24 of 54, 95% confidence interval (CI): 30.9%-58.6%) versus 20.4% (11 of 54, 95% CI: 10.6%-33.5%)), and the risk difference of TRG 0/1 between toripalimab plus chemotherapy group and chemotherapy group was 22.7% (95% CI: 5.8%-39.6%; P = 0.009), meeting a prespecified endpoint. In addition, a higher pathological complete response rate (ypT0N0) was observed in the toripalimab plus chemotherapy group (22.2% (12 of 54, 95% CI: 12.0%-35.6%) versus 7.4% (4 of 54, 95% CI: 2.1%-17.9%); P = 0.030), and surgical morbidity (11.8% in the toripalimab plus chemotherapy group versus 13.5% in the chemotherapy group) and mortality (1.9% versus 0%), and treatment-related grade 3-4 adverse events (35.2% versus 29.6%) were comparable between the treatment groups. In conclusion, the addition of toripalimab to chemotherapy significantly increased the proportion of patients achieving TRG 0/1 compared to chemotherapy alone and showed a manageable safety profile. ClinicalTrials.gov registration: NCT04250948 .

The Chinese Society of Clinical Oncology (CSCO): Clinical guidelines for the diagnosis and treatment of gastric cancer, 2023.

The 2023 update of the Chinese Society of Clinical Oncology (CSCO) Clinical Guidelines for Gastric Cancer focuses on standardizing cancer diagnosis and treatment in China, reflecting the latest advancements in evidence-based medicine, healthcare resource availability, and precision medicine. These updates address the differences in epidemiological characteristics, clinicopathological features, tumor biology, treatment patterns, and drug selections between Eastern and Western gastric cancer patients. Key revisions include a structured template for imaging diagnosis reports, updated standards for molecular marker testing in pathological diagnosis, and an elevated recommendation for neoadjuvant chemotherapy in stage III gastric cancer. For advanced metastatic gastric cancer, the guidelines introduce new recommendations for immunotherapy, anti-angiogenic therapy and targeted drugs, along with updated management strategies for human epidermal growth factor receptor 2 (HER2)-positive and deficient DNA mismatch repair (dMMR)/microsatellite instability-high (MSI-H) patients. Additionally, the guidelines offer detailed screening recommendations for hereditary gastric cancer and an appendix listing drug treatment regimens for various stages of gastric cancer. The 2023 CSCO Clinical Guidelines for Gastric Cancer updates are based on both Chinese and international clinical research and expert consensus to enhance their applicability and relevance in clinical practice, particularly in the heterogeneous healthcare landscape of China, while maintaining a commitment to scientific rigor, impartiality, and timely revisions.