MRI-based radiomic models to predict surgical margin status and infer tumor immune microenvironment in breast cancer patients with breast-conserving surgery: a multicenter validation study.

OBJECTIVES: Accurate preoperative estimation of the risk of breast-conserving surgery (BCS) resection margin positivity would be beneficial to surgical planning. In this multicenter validation study, we developed an MRI-based radiomic model to predict the surgical margin status. METHODS: We retrospectively collected preoperative breast MRI of patients undergoing BCS from three hospitals (SYMH, n = 296; SYSUCC, n = 131; TSPH, n = 143). Radiomic-based model for risk prediction of the margin positivity was trained on the SYMH patients (7:3 ratio split for the training and testing cohorts), and externally validated in the SYSUCC and TSPH cohorts. The model was able to stratify patients into different subgroups with varied risk of margin positivity. Moreover, we used the immune-radiomic models and epithelial-mesenchymal transition (EMT) signature to infer the distribution patterns of immune cells and tumor cell EMT status under different marginal status. RESULTS: The AUCs of the radiomic-based model were 0.78 (0.66-0.90), 0.88 (0.79-0.96), and 0.76 (0.68-0.84) in the testing cohort and two external validation cohorts, respectively. The actual margin positivity rates ranged between 0-10% and 27.3-87.2% in low-risk and high-risk subgroups, respectively. Positive surgical margin was associated with higher levels of EMT and B cell infiltration in the tumor area, as well as the enrichment of B cells, immature dendritic cells, and neutrophil infiltration in the peritumoral area. CONCLUSIONS: This MRI-based predictive model can be used as a reliable tool to predict the risk of margin positivity of BCS. Tumor immune-microenvironment alteration was associated with surgical margin status. CLINICAL RELEVANCE STATEMENT: This study can assist the pre-operative planning of BCS. Further research on the tumor immune microenvironment of different resection margin states is expected to develop new margin evaluation indicators and decipher the internal mechanism. KEY POINTS: • The MRI-based radiomic prediction model (CSS model) incorporating features extracted from multiple sequences and segments could estimate the margin positivity risk of breast-conserving surgery. • The radiomic score of the CSS model allows risk stratification of patients undergoing breast-conserving surgery, which could assist in surgical planning. • With the help of MRI-based radiomics to estimate the components of the immune microenvironment, for the first time, it is found that the margin status of breast-conserving surgery is associated with the infiltration of immune cells in the microenvironment and the EMT status of breast tumor cells.

Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of LncRNAs in patients with breast cancer: a multicenter cohort study.

BACKGROUND: Several studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. Herein, we aim to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological mechanisms. METHODS: In this multicenter study, 1113 nonmetastatic invasive breast cancer patients were included, and were divided into the training cohort (n = 698), the validation cohort (n = 171), and the testing cohort (n = 244). The Radiomic DeepSurv Net (RDeepNet) model was constructed using the Cox proportional hazards deep neural network DeepSurv for predicting individual recurrence risk. RNA-sequencing was performed to explore the association between radiomics and tumor microenvironment. Correlation and variance analyses were conducted to examine changes of radiomics among patients with different therapeutic responses and after neoadjuvant chemotherapy. The association and quantitative relation of radiomics and epigenetic molecular characteristics were further analyzed to reveal the mechanisms of radiomics. RESULTS: The RDeepNet model showed a significant association with recurrence-free survival (RFS) (HR 0.03, 95% CI 0.02-0.06, P < 0.001) and achieved AUCs of 0.98, 0.94, and 0.92 for 1-, 2-, and 3-year RFS, respectively. In the validation and testing cohorts, the RDeepNet model could also clarify patients into high- and low-risk groups, and demonstrated AUCs of 0.91 and 0.94 for 3-year RFS, respectively. Radiomic features displayed differential expression between the two risk groups. Furthermore, the generalizability of RDeepNet model was confirmed across different molecular subtypes and patient populations with different therapy regimens (All P < 0.001). The study also identified variations in radiomic features among patients with diverse therapeutic responses and after neoadjuvant chemotherapy. Importantly, a significant correlation between radiomics and long non-coding RNAs (lncRNAs) was discovered. A key lncRNA was found to be noninvasively quantified by a deep learning-based radiomics prediction model with AUCs of 0.79 in the training cohort and 0.77 in the testing cohort. CONCLUSIONS: This study demonstrates that machine learning radiomics of MRI can effectively predict RFS after surgery in patients with breast cancer, and highlights the feasibility of non-invasive quantification of lncRNAs using radiomics, which indicates the potential of radiomics in guiding treatment decisions.

Evaluation of chemotherapy and radiotherapy in the adjuvant management of uterine carcinosarcoma: a population-based analysis.

PURPOSE: To evaluate the effects of adjuvant chemotherapy (CT) and radiotherapy (RT) on the survival of uterine carcinosarcoma (UCS) patients. METHODS: We analyzed 3207 patients with uterine carcinosarcoma without distant metastasis after surgery from 2004 to 2015 by utilizing data from the Surveillance, Epidemiology, and End Results database. Generally, cancer-specific survival (CSS) and overall survival (OS) outcomes were analyzed by Kaplan-Meier and Cox proportional hazards regression models. Further subgroup survival analysis was performed for those receiving RT and chemoradiotherapy (CRT). RESULTS: In general, both univariate and multivariate analyses showed that age, race, marital status, stage, lymph node metastasis, lymphadenectomy (LND), RT, and chemotherapy (CT) were associated with improved CSS and OS (P < 0.05). Further subgroup analysis showed that CRT exhibited a survival advantage over RT or CT alone in different groups. Various RT modalities, including brachytherapy (BT), external radiotherapy (EBRT), and EBRT + BT, were correlated with improved survival for patients aged 60-69 years with stage III-IV disease and lymph node metastasis. Patients with stage I-II disease aged > 70 years seemed to gain survival benefits from brachytherapy (BT) alone. BT with or without external radiotherapy was associated with improved survival for those who did not undergo lymphadenectomy. CONCLUSION: For UCS without distant metastasis after surgery, CRT should be considered. Regarding RT, BT alone is efficient in improving survival, especially for patients with stage I-II disease aged > 70 years old. EBRT alone does not show results in survival improvement for patients who did not undergo LND and those with lymph node metastasis. However, considering the limitation of SEER database, further studies with more large sample size and strict study design are needed to confirm it.

A Noninvasive Tool Based on Magnetic Resonance Imaging Radiomics for the Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.

PURPOSE: This study aimed to identify patients with pathological complete response (pCR) and make better clinical decisions by constructing a preoperative predictive model based on tumoral and peritumoral volumes of multiparametric magnetic resonance imaging (MRI) obtained before neoadjuvant chemotherapy (NAC). METHODS: This study investigated MRI before NAC in 448 patients with nonmetastatic invasive ductal breast cancer (Sun Yat-sen Memorial Hospital, Sun Yat-sen University, n = 362, training cohort; and Sun Yat-sen University Cancer Center, n = 86, validation cohort). The tumoral and peritumoral volumes of interest (VOIs) were segmented and MRI features were extracted. The radiomic features were filtered via a random forest algorithm, and a supporting vector machine was used for modeling. The receiver operator characteristic curve and area under the curve (AUC) were calculated to assess the performance of the radiomics-based classifiers. RESULTS: For each MRI sequence, a total of 863 radiomic features were extracted and the top 30 features were selected for model construction. The radiomic classifiers of tumoral VOI and peritumoral VOI were both promising for predicting pCR, with AUCs of 0.96 and 0.97 in the training cohort and 0.89 and 0.78 in the validation cohort, respectively. The tumoral + peritumoral VOI radiomic model could further improve the predictive accuracy, with AUCs of 0.98 and 0.92 in the training and validation cohorts. CONCLUSIONS: The tumoral and peritumoral multiparametric MRI radiomics model can promisingly predict pCR in breast cancer using MRI images before surgery. Our results highlighted the potential value of the tumoral and peritumoral radiomic model in cancer management.

Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study.

OBJECTIVES: To develop and validate a preoperative CT-based nomogram combined with radiomic and clinical-radiological signatures to distinguish preinvasive lesions from pulmonary invasive lesions. METHODS: This was a retrospective, diagnostic study conducted from August 1, 2018, to May 1, 2020, at three centers. Patients with a solitary pulmonary nodule were enrolled in the GDPH center and were divided into two groups (7:3) randomly: development (n = 149) and internal validation (n = 54). The SYSMH center and the ZSLC Center formed an external validation cohort of 170 patients. The least absolute shrinkage and selection operator (LASSO) algorithm and logistic regression analysis were used to feature signatures and transform them into models. RESULTS: The study comprised 373 individuals from three independent centers (female: 225/373, 60.3%; median [IQR] age, 57.0 [48.0-65.0] years). The AUCs for the combined radiomic signature selected from the nodular area and the perinodular area were 0.93, 0.91, and 0.90 in the three cohorts. The nomogram combining the clinical and combined radiomic signatures could accurately predict interstitial invasion in patients with a solitary pulmonary nodule (AUC, 0.94, 0.90, 0.92) in the three cohorts, respectively. The radiomic nomogram outperformed any clinical or radiomic signature in terms of clinical predictive abilities, according to a decision curve analysis and the Akaike information criteria. CONCLUSIONS: This study demonstrated that a nomogram constructed by identified clinical-radiological signatures and combined radiomic signatures has the potential to precisely predict pathology invasiveness. KEY POINTS: • The radiomic signature from the perinodular area has the potential to predict pathology invasiveness of the solitary pulmonary nodule. • The new radiomic nomogram was useful in clinical decision-making associated with personalized surgical intervention and therapeutic regimen selection in patients with early-stage non-small-cell lung cancer.

Clinical Evidence for Locoregional Surgery of the Primary Tumor in Patients with De Novo Stage IV Breast Cancer.

BACKGROUND: Whether primary tumor surgery is better than no surgery in patients with de novo stage IV breast cancer remains controversial. METHODS: This study combined prospective clinical trials and a multicenter cohort to evaluate the impact of locoregional surgery in de novo stage IV breast cancer. The GRADE approach was used to assess the quality of evidence in meta-analysis, and propensity score matching analysis was used in the cohort study. This study was registered with PROSPERO CRD42016043766 and ClinicalTrials.gov NCT04456855. RESULTS: A total of 1110 patients from six trials and 353 patients from the cohort study were included. The meta-analysis showed that compared with no surgery, locoregional surgery did not prolong overall survival (hazard ratio [HR] = 0.90, P = 0.40; moderate-quality) but had a significantly longer locoregional progression-free survival (HR = 0.23, P < 0.001; moderate-quality). The subgroup analysis of solitary bone-only metastasis (HR = 0.47, P = 0.04; high-quality) resulted in prolonged overall survival. In the cohort study, locoregional surgery showed a survival benefit (HR = 0.63, P = 0.041) before matching, but not (HR = 0.84, P = 0.579) after matching. Patients with bone-only metastasis showed a survival advantage in surgery compared with no surgery before matching (HR = 0.36, P = 0.034) as well as after matching (HR = 0.18, P = 0.017). CONCLUSIONS: This study indicated that locoregional surgery had a significantly longer locoregional progression-free survival than no surgery in de novo stage IV breast cancer, and patients with bone-only metastasis tended to show an overall survival benefit from surgery.

Biochemical changes of salivary gland adenoid cystic carcinoma cells induced by SGI-1776.

Provirus integration site for Moloney murine leukemia virus 1 (Pim-1) has proved to be an oncogene and it is known that to depress Pim-1 activity may be a novel oncological treatment strategy. SGI-1776, a small molecule, is the first clinically tested inhibitor of the Pim kinase family. Here, we aimed to explore the effect of SGI-1776 on salivary adenoid cystic carcinoma (SACC). Expression of Pim-1 was confirmed in SACC and control tissues by qRT-PCR. After SGI-1776 treatment, the Pim-1 expressions and Pim-1 kinase activity in both SACC-83 and SACC-LM cell lines were measured. Cell proliferation, cell invasion, cell cycle, apoptosis and mitochondrial membrane potential were analyzed. Also, the expression of FOXO3a, p-FOXO3a, RUNX3, Bcl-2, BAD, p-BAD, Bim and p-Bim were detected by Western blot. The results showed that Pim-1 was significantly overexpressed in SACC tissues. SGI-1776 down-regulated the Pim-1 expression, inhibited Pim-1 kinase activity, reduced cell proliferation, decreased invasive ability, increased caspase-3 activity and induced apoptosis, cell cycle arrest and mitochondrial depolarization. Reduced expression was also seen in p-FOXO3a, RUNX3, Bcl-2, p-BAD and p-Bim, whereas no significant changes were observed from FOXO3a, BAD and Bim. These results confirm the pivotal role of Pim-1 in SACC and suggest that targeting Pim-1 kinase signal pathway by SGI-1776 might be a promising therapeutic modality for SACC.

Unraveling the unfolded protein response signature: implications for tumor immune microenvironment heterogeneity and clinical prognosis in stomach cancer.

BACKGROUND: Stomach cancer is a leading cause of cancer-related deaths globally due to its high grade and poor response to treatment. Understanding the molecular network driving the rapid progression of stomach cancer is crucial for improving patient outcomes. METHODS: This study aimed to investigate the role of unfolded protein response (UPR) related genes in stomach cancer and their potential as prognostic biomarkers. RNA expression data and clinical follow-up information were obtained from the TCGA and GEO databases. An unsupervised clustering algorithm was used to identify UPR genomic subtypes in stomach cancer. Functional enrichment analysis, immune landscape analysis, and chemotherapy benefit prediction were conducted for each subtype. A prognostic model based on UPR-related genes was developed and validated using LASSO-Cox regression, and a multivariate nomogram was created. Key gene expression analyses in pan-cancer and in vitro experiments were performed to further investigate the role of the identified genes in cancer progression. RESULTS: A total of 375 stomach cancer patients were included in this study. Analysis of 113 UPR-related genes revealed their close functional correlation and significant enrichment in protein modification, transport, and RNA degradation pathways. Unsupervised clustering identified two molecular subtypes with significant differences in prognosis and gene expression profiles. Immune landscape analysis showed that UPR may influence the composition of the tumor immune microenvironment. Chemotherapy sensitivity analysis indicated that patients in the C2 molecular subtype were more responsive to chemotherapy compared to those in the C1 molecular subtype. A prognostic signature consisting of seven UPR-related genes was constructed and validated, and an independent prognostic nomogram was developed. The gene IGFBP1, which had the highest weight coefficient in the prognostic signature, was found to promote the malignant phenotype of stomach cancer cells, suggesting its potential as a therapeutic target. CONCLUSIONS: The study developed a UPR-related gene classifier and risk signature for predicting survival in stomach cancer, identifying IGFBP1 as a key factor promoting the disease's malignancy and a potential therapeutic target. IGFBP1's role in enhancing cancer cell adaptation to endoplasmic reticulum stress suggests its importance in stomach cancer prognosis and treatment.

Deep learning-based multi-modal data integration enhancing breast cancer disease-free survival prediction.

Background: The prognosis of breast cancer is often unfavorable, emphasizing the need for early metastasis risk detection and accurate treatment predictions. This study aimed to develop a novel multi-modal deep learning model using preoperative data to predict disease-free survival (DFS). Methods: We retrospectively collected pathology imaging, molecular and clinical data from The Cancer Genome Atlas and one independent institution in China. We developed a novel Deep Learning Clinical Medicine Based Pathological Gene Multi-modal (DeepClinMed-PGM) model for DFS prediction, integrating clinicopathological data with molecular insights. The patients included the training cohort (n = 741), internal validation cohort (n = 184), and external testing cohort (n = 95). Result: Integrating multi-modal data into the DeepClinMed-PGM model significantly improved area under the receiver operating characteristic curve (AUC) values. In the training cohort, AUC values for 1-, 3-, and 5-year DFS predictions increased to 0.979, 0.957, and 0.871, while in the external testing cohort, the values reached 0.851, 0.878, and 0.938 for 1-, 2-, and 3-year DFS predictions, respectively. The DeepClinMed-PGM's robust discriminative capabilities were consistently evident across various cohorts, including the training cohort [hazard ratio (HR) 0.027, 95% confidence interval (CI) 0.0016-0.046, P < 0.0001], the internal validation cohort (HR 0.117, 95% CI 0.041-0.334, P < 0.0001), and the external cohort (HR 0.061, 95% CI 0.017-0.218, P < 0.0001). Additionally, the DeepClinMed-PGM model demonstrated C-index values of 0.925, 0.823, and 0.864 within the three cohorts, respectively. Conclusion: This study introduces an approach to breast cancer prognosis, integrating imaging and molecular and clinical data for enhanced predictive accuracy, offering promise for personalized treatment strategies.

Deciphering the role of apoptosis signature on the immune dynamics and therapeutic prognosis in breast cancer: Implication for immunotherapy.

Background: In breast cancer oncogenesis, the precise role of cell apoptosis holds untapped potential for prognostic and therapeutic insights. Thus, it is important to develop a model predicated for breast cancer patients' prognosis and immunotherapy response based on apoptosis-related signature. Methods: Our approach involved leveraging a training dataset from The Cancer Genome Atlas (TCGA) to construct an apoptosis-related gene prognostic model. The model's validity was then tested across several cohorts, including METABRIC, Sun Yat-sen Memorial Hospital Sun Yat-sen University (SYSMH), and IMvigor210, to ensure its applicability and robustness across different patient demographics and treatment scenarios. Furthermore, we utilized Quantitative Polymerase Chain Reaction (qPCR) analysis to explore the expression patterns of these model genes in breast cancer cell lines compared to immortalized mammary epithelial cell lines, aiming to confirm their differential expression and underline their significance in the context of breast cancer. Results: Through the development and validation of our prognostic model based on seven apoptosis-related genes, we have demonstrated its substantial predictive power for the survival outcomes of breast cancer patients. The model effectively stratified patients into high and low-risk categories, with high-risk patients showing significantly poorer overall survival in the training cohort and across all validation cohorts. Importantly, qPCR analysis confirmed that the genes constituting our model indeed exhibit differential expression in breast cancer cell lines when contrasted with immortalized mammary epithelial cell lines. Conclusion: Our study establishes a groundbreaking prognostic model using apoptosis-related genes to enhance the precision of breast cancer prognosis and treatment, particularly in predicting immunotherapy response.

Nanotechnology-enabled sonodynamic therapy against malignant tumors.

Sonodynamic therapy (SDT) is an emerging approach for malignant tumor treatment, offering high precision, deep tissue penetration, and minimal side effects. The rapid advancements in nanotechnology, particularly in cancer treatment, have enhanced the efficacy and targeting specificity of SDT. Combining sonodynamic therapy with nanotechnology offers a promising direction for future cancer treatments. In this review, we first systematically discussed the anti-tumor mechanism of SDT and then summarized the common nanotechnology-related sonosensitizers and their recent applications. Subsequently, nanotechnology-related therapies derived using the SDT mechanism were elaborated. Finally, the role of nanomaterials in SDT combined therapy was also introduced.

Synergistic role of activated CD4+ memory T cells and CXCL13 in augmenting cancer immunotherapy efficacy.

The development of immune checkpoint inhibitors (ICIs) has significantly advanced cancer treatment. However, their efficacy is not consistent across all patients, underscoring the need for personalized approaches. In this study, we examined the relationship between activated CD4+ memory T cell expression and ICI responsiveness. A notable correlation was observed between increased activated CD4+ memory T cell expression and better patient survival in various cohorts. Additionally, the chemokine CXCL13 was identified as a potential prognostic biomarker, with higher expression levels associated with improved outcomes. Further analysis highlighted CXCL13's role in influencing the Tumor Microenvironment, emphasizing its relevance in tumor immunity. Using these findings, we developed a deep learning model by the Multi-Layer Aggregation Graph Neural Network method. This model exhibited promise in predicting ICI treatment efficacy, suggesting its potential application in clinical practice.

AI-Based multimodal Multi-tasks analysis reveals tumor molecular heterogeneity, predicts preoperative lymph node metastasis and prognosis in papillary thyroid carcinoma: A retrospective study.

BACKGROUND: Papillary thyroid carcinoma (PTC) is the predominant form of thyroid cancer globally, especially when lymph node metastasis (LNM) occurs. Molecular heterogeneity, driven by genetic alterations and tumor microenvironment components, contributes to the complexity of PTC. Understanding these complexities is essential for precise risk stratification and therapeutic decisions. METHODS: This study involved a comprehensive analysis of 521 patients with PTC from our hospital and 499 patients from The Cancer Genome Atlas (TCGA). The real-world cohort 1 comprised 256 patients with stage I-III PTC. Tissues from 252 patients were analyzed by DNA-based next-generation sequencing, and tissues from four patients were analyzed by single-cell RNA sequencing (scRNA-seq). Additionally, 586 PTC pathological sections were collected from TCGA, and 275 PTC pathological sections were collected from the real-world cohort 2. A deep learning multimodal model was developed using matched histopathology images, genomic, transcriptomic, and immune cell data to predict LNM and disease-free survival (DFS). RESULTS: This study included a total of 1,011 PTC patients, comprising 256 patients from cohort 1, 275 patients from cohort 2, and 499 patients from TCGA. In cohort 1, we categorized PTC into four molecular subtypes based on BRAF, RAS, RET, and other mutations. BRAF mutations were significantly associated with LNM and impacted DFS. ScRNA-seq identified distinct T cell subtypes and reduced B cell diversity in BRAF-mutated PTC with LNM. The study also explored cancer-associated fibroblasts and macrophages, highlighting their associations with LNM. The deep learning model was trained using 405 pathology slides and RNA sequences from 328 PTC patients and validated with 181 slides and RNA sequences from 140 PTC patients in the TCGA cohort. It achieved high accuracy, with an AUC of 0.86 in the training cohort, 0.84 in the validation cohort, and 0.83 in the real-world cohort 2. High-risk patients in the training cohort had significantly lower DFS rates (P<0.001). Model AUCs were 0.91 at 1 year, 0.93 at 3 years, and 0.87 at 5 years. In the validation cohort, high-risk patients also had lower DFS (P<0.001); the AUCs were 0.89, 0.87, and 0.80 at 1, 3, and 5 years. We utilized the GradCAM algorithm to generate heatmaps from pathology-based deep learning models, which visually highlighted high-risk tumor areas in PTC patients. This enhanced clinicians' understanding of the model's predictions and improved diagnostic accuracy, especially in cases with lymph node metastasis. CONCLUSION: The AI-based analysis uncovered vital insights into PTC molecular heterogeneity, emphasizing BRAF mutations' impact. The integrated deep learning model shows promise in predicting metastasis, offering valuable contributions to improved diagnostic and therapeutic strategies.

Deep learning combining mammography and ultrasound images to predict the malignancy of BI-RADS US 4A lesions in women with dense breasts: a diagnostic study.

OBJECTIVES: The authors aimed to assess the performance of a deep learning (DL) model, based on a combination of ultrasound (US) and mammography (MG) images, for predicting malignancy in breast lesions categorized as Breast Imaging Reporting and Data System (BI-RADS) US 4A in diagnostic patients with dense breasts. METHODS: A total of 992 patients were randomly allocated into the training cohort and the test cohort at a proportion of 4:1. Another, 218 patients were enrolled to form a prospective validation cohort. The DL model was developed by incorporating both US and MG images. The predictive performance of the combined DL model for malignancy was evaluated by sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The combined DL model was then compared to a clinical nomogram model and to the DL model trained using US image only and to that trained MG image only. RESULTS: The combined DL model showed satisfactory diagnostic performance for predicting malignancy in breast lesions, with an AUC of 0.940 (95% CI: 0.874-1.000) in the test cohort, and an AUC of 0.906 (95% CI: 0.817-0.995) in the validation cohort, which was significantly higher than the clinical nomogram model, and the DL model for US or MG alone ( P <0.05). CONCLUSIONS: The study developed an objective DL model combining both US and MG imaging features, which was proven to be more accurate for predicting malignancy in the BI-RADS US 4A breast lesions of patients with dense breasts. This model may then be used to more accurately guide clinicians' choices about whether performing biopsies in breast cancer diagnosis.

Unraveling the role of M1 macrophage and CXCL9 in predicting immune checkpoint inhibitor efficacy through multicohort analysis and single-cell RNA sequencing.

The exact function of M1 macrophages and CXCL9 in forecasting the effectiveness of immune checkpoint inhibitors (ICIs) is still not thoroughly investigated. We investigated the potential of M1 macrophage and C-X-C Motif Chemokine Ligand 9 (CXCL9) as predictive markers for ICI efficacy, employing a comprehensive approach integrating multicohort analysis and single-cell RNA sequencing. A significant correlation between high M1 macrophage and improved overall survival (OS) and objective response rate (ORR) was found. M1 macrophage expression was most pronounced in the immune-inflamed phenotype, aligning with increased expression of immune checkpoints. Furthermore, CXCL9 was identified as a key marker gene that positively correlated with M1 macrophage and response to ICIs, while also exhibiting associations with immune-related pathways and immune cell infiltration. Additionally, through exploring RNA epigenetic modifications, we identified Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3G (APOBEC3G) as linked to ICI response, with high expression correlating with improved OS and immune-related pathways. Moreover, a novel model based on M1 macrophage, CXCL9, and APOBEC3G-related genes was developed using multi-level attention graph neural network, which showed promising predictive ability for ORR. This study illuminates the pivotal contributions of M1 macrophages and CXCL9 in shaping an immune-active microenvironment, correlating with enhanced ICI efficacy. The combination of M1 macrophage, CXCL9, and APOBEC3G provides a novel model for predicting clinical outcomes of ICI therapy, facilitating personalized immunotherapy.

Artificial intelligence enables precision diagnosis of cervical cytology grades and cervical cancer.

Cervical cancer is a significant global health issue, its prevalence and prognosis highlighting the importance of early screening for effective prevention. This research aimed to create and validate an artificial intelligence cervical cancer screening (AICCS) system for grading cervical cytology. The AICCS system was trained and validated using various datasets, including retrospective, prospective, and randomized observational trial data, involving a total of 16,056 participants. It utilized two artificial intelligence (AI) models: one for detecting cells at the patch-level and another for classifying whole-slide image (WSIs). The AICCS consistently showed high accuracy in predicting cytology grades across different datasets. In the prospective assessment, it achieved an area under curve (AUC) of 0.947, a sensitivity of 0.946, a specificity of 0.890, and an accuracy of 0.892. Remarkably, the randomized observational trial revealed that the AICCS-assisted cytopathologists had a significantly higher AUC, specificity, and accuracy than cytopathologists alone, with a notable 13.3% enhancement in sensitivity. Thus, AICCS holds promise as an additional tool for accurate and efficient cervical cancer screening.

Tumor microenvironment RNA test to predict immunotherapy outcomes in advanced gastric cancer: The TIMES001 trial.

BACKGROUND: Clinical trials support the efficacy of immune checkpoint blockades (ICBs) plus chemotherapy in a subset of patients with metastatic gastric cancer (mGC). To identify the determinants of response, we developed a TMEscore model to assess tumor microenvironment (TME), which was previously proven to be a biomarker for ICBs. METHODS: A reference database of TMEscore assays was established using PCR assay kits containing 30 TME genes. This multi-center prospective clinical trial (NCT#04850716) included patients with mGC who were administered ICB combined with chemotherapy as a first-line regimen. Eighty-six tumor samples extracted from five medical centers before treatment were used to estimate the TMEscore, PD-L1 (CPS), and mismatch repair deficiency. FINDINGS: The objective response rate (ORR) and median PFS of the cohort were 31.4% and six months. Enhanced ORR was observed in TMEscore-high mGC patients (ORR = 59%). The survival analysis demonstrated that high TMEscore was significantly associated with a more favorable PFS and OS. Moreover, TMEscore was found to be a predictive biomarker that surpassed MSI and CPS (AUC = 0.873, 0.511, and 0.524, respectively). By integrating the TMEscore and clinical variables, the fused model further enhances the predictive efficiency and translational application in a clinical setting. CONCLUSIONS: This prospective clinical study indicates that the TMEscore assay is a robust biomarker for screening patients with mGC who may derive survival benefits from ICB plus chemotherapy. FUNDING: Guangdong Basic and Applied Basic Research Foundation (2023A1515011214), Science and Technology Program of Guangzhou (202206080011), and Guangzhou Science and Technology Project (2023A03J0722 and 2023A04J2357).

Association of PIK3CA mutation with outcomes in HER2-positive breast cancer treated with anti-HER2 therapy: A meta-analysis and bioinformatic analysis of TCGA­BRCA data.

BACKGROUND: This study aimed to comprehensively explore the clinical significance of PIK3CA mutation in human epidermal growth factor receptor 2 (HER2)-positive breast cancer treated with anti-HER2 therapy. METHODS: We systematically searched PubMed, Embase, and the Cochrane databases for eligible studies assessing the association between PIK3CA mutation and outcomes in patients with HER2-positive breast cancer receiving anti-HER2 therapy. The main outcomes included: (1) pathological complete response (pCR) or disease-free survival (DFS) for the neoadjuvant setting; (2) DFS or invasive DFS for the adjuvant setting; (3) objective response rate (ORR), progression-free survival (PFS), time-to-progression (TTP), or overall survival (OS) for the metastatic setting. The mutational landscape of HER2-positive breast cancer according to PIK3CA mutation status was examined based on TCGA breast cancer dataset. RESULTS: Totally, 43 eligible studies, covering 11,099 patients with available data on PIK3CA mutation status, were identified. In the neoadjuvant setting, PIK3CA mutation was significantly associated with a lower pCR rate (OR=0.23, 95% CI 0.19-0.27, p<0.001). This association remained significant irrespective of the type of anti-HER2 therapy (single-agent or dual-agent) and hormone receptor status. There were no significant differences in DFS between PIK3CA mutated and wild-type patients in either the neoadjuvant or adjuvant settings. In the metastatic setting, PIK3CA mutation predicted worse ORR (OR=0.26, 95%CI 0.17-0.40, p<0.001), PFS (HR=1.28, 95%CI 1.03-1.59, p = 0.024) and TTP (HR=2.27, 95%CI 1.54-3.34, p<0.001). However, no significant association was observed between PIK3CA mutation status and OS. Distinct mutational landscapes were observed in HER2-positive breast cancer between individuals with PIK3CA mutations and those with wild-type PIK3CA. CONCLUSIONS: PIK3CA mutation was significantly associated with a lower pCR rate in HER2-positive breast cancer treated with neoadjuvant anti-HER2 therapy. In the metastatic setting, PIK3CA mutation was predictive of worse ORR, PFS and TTP. These results suggest the potential for developing PI3K inhibitors as a therapeutic option for these patients.

Impact of body mass index and its change on survival outcomes in patients with early breast cancer: A pooled analysis of individual-level data from BCIRG-001 and BCIRG-005 trials.

INTRODUCTION: The relationships between body mass index (BMI) and survival rates are complex, and have not been thoroughly investigated in breast cancer patients who received adjuvant chemotherapy. METHODS: We collected data on 2394 patients from two randomized, phase III clinical trials that investigated adjuvant chemotherapy in breast cancer identified in Project Data Sphere. The objective was to examine the effect of baseline BMI, BMI after adjuvant chemotherapy, and BMI change from baseline to post-adjuvant chemotherapy on disease-free survival (DFS) and overall survival (OS). Restricted cubic splines were used to examine potential non-linear associations between continuous BMI value and survival. Stratified analyses involved chemotherapy regimens. RESULTS: Severe obesity (BMI≥40.0 kg/m2) at baseline was independently associated with worse DFS (hazard ration [HR] = 1.48, 95% confidence interval [CI] 1.02-2.16, P = 0.04) and OS (HR = 1.79, 95%CI 1.17-2.74, P = 0.007) compared with underweight/normal weight (BMI≤24.9 kg/m2). A BMI loss >10% was also an independent prognostic factor for adverse OS (HR = 2.14, 95%CI 1.17-3.93, P = 0.014). Stratified analyses revealed that severe obesity adversely affected DFS (HR = 2.38, 95%CI 1.26-4.34, P = 0.007) and OS (HR = 2.90, 95%CI 1.46-5.76, P = 0.002) in the docetaxel-based group, but not in the non-docetaxel-based group. Restricted cubic splines revealed a "J-shaped" association of baseline BMI with risk of recurrence or all-cause death, and this relationship was more pronounced in the docetaxel-based group. CONCLUSIONS: In early breast cancer patients treated with adjuvant chemotherapy, baseline severe obesity was significantly linked to worse DFS and OS, and a BMI loss over 10% from baseline to post-adjuvant chemotherapy also negatively affected OS. Moreover, the prognostic role of BMI might differ between docetaxel-based and non-docetaxel-based groups.