Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer.

BACKGROUND: Only a subset of patients with gastric cancer experience long-term benefits from immune checkpoint inhibitors (ICIs). Currently, there is a deficiency in precise predictive biomarkers for ICI efficacy. The aim of this study was to develop and validate a pathomics-driven ensemble model for predicting the response to ICIs in gastric cancer, using H&E-stained whole slide images (WSI). METHODS: This multicenter study retrospectively collected and analyzed H&E-stained WSIs and clinical data from 584 patients with gastric cancer. An ensemble model, integrating four classifiers: least absolute shrinkage and selection operator, k-nearest neighbors, decision trees, and random forests, was developed and validated using pathomics features, with the objective of predicting the therapeutic efficacy of immune checkpoint inhibition. Model performance was evaluated using metrics including the area under the curve (AUC), sensitivity, and specificity. Additionally, SHAP (SHapley Additive exPlanations) analysis was used to explain the model's predicted values as the sum of the attribution values for each input feature. Pathogenomics analysis was employed to explain the molecular mechanisms underlying the model's predictions. RESULTS: Our pathomics-driven ensemble model effectively stratified the response to ICIs in training cohort (AUC 0.985 (95% CI 0.971 to 0.999)), which was further validated in internal validation cohort (AUC 0.921 (95% CI 0.839 to 0.999)), as well as in external validation cohort 1 (AUC 0.914 (95% CI 0.837 to 0.990)), and external validation cohort 2 (0.927 (95% CI 0.802 to 0.999)). The univariate Cox regression analysis revealed that the prediction signature of pathomics-driven ensemble model was a prognostic factor for progression-free survival in patients with gastric cancer who underwent immunotherapy (p<0.001, HR 0.35 (95% CI 0.24 to 0.50)), and remained an independent predictor after multivariable Cox regression adjusted for clinicopathological variables, (including sex, age, carcinoembryonic antigen, carbohydrate antigen 19-9, therapy regime, line of therapy, differentiation, location and programmed death ligand 1 (PD-L1) expression in all patients (p<0.001, HR 0.34 (95% CI 0.24 to 0.50)). Pathogenomics analysis suggested that the ensemble model is driven by molecular-level immune, cancer, metabolism-related pathways, and was correlated with the immune-related characteristics, including immune score, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data score, and tumor purity. CONCLUSIONS: Our pathomics-driven ensemble model exhibited high accuracy and robustness in predicting the response to ICIs using WSIs. Therefore, it could serve as a novel and valuable tool to facilitate precision immunotherapy.

Comprehensive assessment of immune context and immunotherapy response via noninvasive imaging in gastric cancer.

BACKGROUNDThe tumor immune microenvironment can provide prognostic and therapeutic information. We aimed to develop noninvasive imaging biomarkers from computed tomography (CT) for comprehensive evaluation of immune context and investigate their associations with prognosis and immunotherapy response in gastric cancer (GC).METHODSThis study involved 2,600 patients with GC from 9 independent cohorts. We developed and validated 2 CT imaging biomarkers (lymphoid radiomics score [LRS] and myeloid radiomics score [MRS]) for evaluating the IHC-derived lymphoid and myeloid immune context respectively, and integrated them into a combined imaging biomarker [LRS/MRS: low(-) or high(+)] with 4 radiomics immune subtypes: 1 (-/-), 2 (+/-), 3 (-/+), and 4 (+/+). We further evaluated the imaging biomarkers' predictive values on prognosis and immunotherapy response.RESULTSThe developed imaging biomarkers (LRS and MRS) had a high accuracy in predicting lymphoid (AUC range: 0.765-0.773) and myeloid (AUC range: 0.736-0.750) immune context. Further, similar to the IHC-derived immune context, 2 imaging biomarkers (HR range: 0.240-0.761 for LRS; 1.301-4.012 for MRS) and the combined biomarker were independent predictors for disease-free and overall survival in the training and all validation cohorts (all P < 0.05). Additionally, patients with high LRS or low MRS may benefit more from immunotherapy (P < 0.001). Further, a highly heterogeneous outcome on objective response ​rate was observed in 4 imaging subtypes: 1 (-/-) with 27.3%, 2 (+/-) with 53.3%, 3 (-/+) with 10.2%, and 4 (+/+) with 30.0% (P < 0.0001).CONCLUSIONThe noninvasive imaging biomarkers could accurately evaluate the immune context and provide information regarding prognosis and immunotherapy for GC.

Modification of Pillared Intercalated Montmorillonite Clay as Heterogeneous Pd Catalyst Supports.

Montmorillonite clay was modified by pillaring with AlMn oxides in different Al/Mn ratios and intercalation of two kinds of N-containing polymers (i.e., chitosan (CS) and polyvinyl pyrrolidinone (PVP)) chains. The modified pillared montmorillonite clay (PM) showed a rich two-dimensional layered porous structure with tunable parameters, such as large interlayer spacing, high specific area, and large porous volume. They were then used as supports for Pd nanoparticles. As applied in coupling reactions of aryl halides with terminal alkynes, Pd@CS/AlMn-PM showed better comprehensive catalytic performance than Pd@PVP/AlMn-PM. This was mainly attributed to its higher specific area, stronger chelation to Pd species, and better solvent resistance.

Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics.

The tumor microenvironment (TME) plays a critical role in disease progression and is a key determinant of therapeutic response in cancer patients. Here, we propose a noninvasive approach to predict the TME status from radiological images by combining radiomics and deep learning analyses. Using multi-institution cohorts of 2,686 patients with gastric cancer, we show that the radiological model accurately predicted the TME status and is an independent prognostic factor beyond clinicopathologic variables. The model further predicts the benefit from adjuvant chemotherapy for patients with localized disease. In patients treated with checkpoint blockade immunotherapy, the model predicts clinical response and further improves predictive accuracy when combined with existing biomarkers. Our approach enables noninvasive assessment of the TME, which opens the door for longitudinal monitoring and tracking response to cancer therapy. Given the routine use of radiologic imaging in oncology, our approach can be extended to many other solid tumor types.

Cancer immunotherapy response prediction from multi-modal clinical and image data using semi-supervised deep learning.

BACKGROUND AND PURPOSE: Immunotherapy is a standard treatment for many tumor types. However, only a small proportion of patients derive clinical benefit and reliable predictive biomarkers of immunotherapy response are lacking. Although deep learning has made substantial progress in improving cancer detection and diagnosis, there is limited success on the prediction of treatment response. Here, we aim to predict immunotherapy response of gastric cancer patients using routinely available clinical and image data. MATERIALS AND METHODS: We present a multi-modal deep learning radiomics approach to predict immunotherapy response using both clinical data and computed tomography images. The model was trained using 168 advanced gastric cancer patients treated with immunotherapy. To overcome limitations of small training data, we leverage an additional dataset of 2,029 patients who did not receive immunotherapy in a semi-supervised framework to learn intrinsic imaging phenotypes of the disease. We evaluated model performance in two independent cohorts of 81 patients treated with immunotherapy. RESULTS: The deep learning model achieved area under receiver operating characteristics curve (AUC) of 0.791 (95% CI 0.633-0.950) and 0.812 (95% CI 0.669-0.956) for predicting immunotherapy response in the internal and external validation cohorts. When combined with PD-L1 expression, the integrative model further improved the AUC by 4-7% in absolute terms. CONCLUSION: The deep learning model achieved promising performance for predicting immunotherapy response from routine clinical and image data. The proposed multi-modal approach is general and can incorporate other relevant information to further improve prediction of immunotherapy response.

Noninvasive imaging evaluation of peritoneal recurrence and chemotherapy benefit in gastric cancer after gastrectomy: a multicenter study.

BACKGROUND: Peritoneal recurrence (PR) is the predominant pattern of relapse after curative-intent surgery in gastric cancer (GC) and indicates a dismal prognosis. Accurate prediction of PR is crucial for patient management and treatment. The authors aimed to develop a noninvasive imaging biomarker from computed tomography (CT) for PR evaluation, and investigate its associations with prognosis and chemotherapy benefit. METHODS: In this multicenter study including five independent cohorts of 2005 GC patients, the authors extracted 584 quantitative features from the intratumoral and peritumoral regions on contrast-enhanced CT images. The artificial intelligence algorithms were used to select significant PR-related features, and then integrated into a radiomic imaging signature. And improvements of diagnostic accuracy for PR by clinicians with the signature assistance were quantified. Using Shapley values, the authors determined the most relevant features and provided explanations to prediction. The authors further evaluated its predictive performance in prognosis and chemotherapy response. RESULTS: The developed radiomics signature had a consistently high accuracy in predicting PR in the training cohort (area under the curve: 0.732) and internal and Sun Yat-sen University Cancer Center validation cohorts (0.721 and 0.728). The radiomics signature was the most important feature in Shapley interpretation. The diagnostic accuracy of PR with the radiomics signature assistance was improved by 10.13-18.86% for clinicians ( P <0.001). Furthermore, it was also applicable in the survival prediction. In multivariable analysis, the radiomics signature remained an independent predictor for PR and prognosis ( P <0.001 for all). Importantly, patients with predicting high risk of PR from radiomics signature could gain survival benefit from adjuvant chemotherapy. By contrast, chemotherapy had no impact on survival for patients with a predicted low risk of PR. CONCLUSION: The noninvasive and explainable model developed from preoperative CT images could accurately predict PR and chemotherapy benefit in patients with GC, which will allow the optimization of individual decision-making.

TiO2-Modified Montmorillonite-Supported Porous Carbon-Immobilized Pd Species Nanocomposite as an Efficient Catalyst for Sonogashira Reactions.

In this study, a combination of the porous carbon (PCN), montmorillonite (MMT), and TiO2 was synthesized into a composite immobilized Pd metal catalyst (TiO2-MMT/PCN@Pd) with effective synergism improvements in catalytic performance. The successful TiO2-pillaring modification for MMT, derivation of carbon from the biopolymer of chitosan, and immobilization of Pd species for the prepared TiO2-MMT/PCN@Pd0 nanocomposites were confirmed using a combined characterization with X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), N2 adsorption-desorption isotherms, high-resolution transition electron microscopy (HRTEM), X-ray photoelectron spectroscopy (XPS), and Raman spectroscopy. It was shown that the combination of PCN, MMT, and TiO2 as a composite support for the stabilization of the Pd catalysts could synergistically improve the adsorption and catalytic properties. The resultant TiO2-MMT80/PCN20@Pd0 showed a high surface area of 108.9 m2/g. Furthermore, it exhibited moderate to excellent activity (59-99% yield) and high stability (recyclable 19 times) in the liquid-solid catalytic reactions, such as the Sonogashira reactions of aryl halides (I, Br) with terminal alkynes in organic solutions. The positron annihilation lifetime spectroscopy (PALS) characterization sensitively detected the development of sub-nanoscale microdefects in the catalyst after long-term recycling service. This study provided direct evidence for the formation of some larger-sized microdefects during sequential recycling, which would act as leaching channels for loaded molecules, including active Pd species.

Lineage-specific regulatory changes in hypertrophic cardiomyopathy unraveled by single-nucleus RNA-seq and spatial transcriptomics.

Hypertrophic cardiomyopathy (HCM) is the most common cardiac genetic disorder characterized by cardiomyocyte hypertrophy and cardiac fibrosis. Pathological cardiac remodeling in the myocardium of HCM patients may progress to heart failure. An in-depth elucidation of the lineage-specific changes in pathological cardiac remodeling of HCM is pivotal for the development of therapies to mitigate the progression. Here, we performed single-nucleus RNA-seq of the cardiac tissues from HCM patients or healthy donors and conducted spatial transcriptomic assays on tissue sections from patients. Unbiased clustering of 55,122 nuclei from HCM and healthy conditions revealed 9 cell lineages and 28 clusters. Lineage-specific changes in gene expression, subpopulation composition, and intercellular communication in HCM were discovered through comparative analyses. According to the results of pseudotime ordering, differential expression analysis, and differential regulatory network analysis, potential key genes during the transition towards a failing state of cardiomyocytes such as FGF12, IL31RA, and CREB5 were identified. Transcriptomic dynamics underlying cardiac fibroblast activation were also uncovered, and potential key genes involved in cardiac fibrosis were obtained such as AEBP1, RUNX1, MEOX1, LEF1, and NRXN3. Using the spatial transcriptomic data, spatial activity patterns of the candidate genes, pathways, and subpopulations were confirmed on patient tissue sections. Moreover, we showed experimental evidence that in vitro knockdown of AEBP1 could promote the activation of human cardiac fibroblasts, and overexpression of AEBP1 could attenuate the TGFß-induced activation. Our study provided a comprehensive analysis of the lineage-specific regulatory changes in HCM, which laid the foundation for targeted drug development in HCM.

Non-invasive CT imaging biomarker to predict immunotherapy response in gastric cancer: a multicenter study.

BACKGROUND: Despite remarkable benefits have been provided by immune checkpoint inhibitors in gastric cancer (GC), predictions of treatment response and prognosis remain unsatisfactory, making identifying biomarkers desirable. The aim of this study was to develop and validate a CT imaging biomarker to predict the immunotherapy response in patients with GC and investigate the associated immune infiltration patterns. METHODS: This retrospective study included 294 GC patients who received anti-PD-1/PD-L1 immunotherapy from three independent medical centers between January 2017 and April 2022. A radiomics score (RS) was developed from the intratumoral and peritumoral features on pretreatment CT images to predict immunotherapy-related progression-free survival (irPFS). The performance of the RS was evaluated by the area under the time-dependent receiver operating characteristic curve (AUC). Multivariable Cox regression analysis was performed to construct predictive nomogram of irPFS. The C-index was used to determine the performance of the nomogram. Bulk RNA sequencing of tumors from 42 patients in The Cancer Genome Atlas was used to investigate the RS-associated immune infiltration patterns. RESULTS: Overall, 89 of 294 patients (median age, 57 years (IQR 48-66 years); 171 males) had an objective response to immunotherapy. The RS included 13 CT features that yielded AUCs of 12-month irPFS of 0.787, 0.810 and 0.785 in the training, internal validation, and external validation 1 cohorts, respectively, and an AUC of 24-month irPFS of 0.805 in the external validation 2 cohort. Patients with low RS had longer irPFS in each cohort (p<0.05). Multivariable Cox regression analyses showed RS is an independent prognostic factor of irPFS. The nomogram that integrated the RS and clinical characteristics showed improved performance in predicting irPFS, with C-index of 0.687-0.778 in the training and validation cohorts. The CT imaging biomarker was associated with M1 macrophage infiltration. CONCLUSION: The findings of this prognostic study suggest that the non-invasive CT imaging biomarker can effectively predict immunotherapy outcomes in patients with GC and is associated with innate immune signaling, which can serve as a potential tool for individual treatment decisions.

WFDC2 gene deletion in mouse led to severe dyspnea and type-I alveolar cell apoptosis.

HE4 (Human Epididymis Protein 4) encoded by the wfdc2 gene was first identified as a highly expressed factor in human epididymis. HE4 expression levels in malignant lesions are correlated with the clinical manifestations of gynecologic cancers. HE4 serum test has been widely used for the triage of patients suspected of gynecologic cancers, prognosis of cancer patients, and monitoring cancer recurrence. While it is reported that HE4 may actively participate in the regulation of cancer cell proliferation, migration and drug sensitivity, the physiological role(s) of HE4 in embryo development remains unknown. We applied the TALEN-based strategy to generate wfdc2 gene deletion mice for observation of HE4 function in organogenesis. While heterozygous mice were normal in terms of birth weight, reproductivity, and general behaviors, all the neonates with homozygous wfdc2 deletion suffered severe dyspnea and died in 10 h after birth. Biopsy detected pale-colored lungs, and mechanistic studies indicated increased apoptosis in type-I alveolar cells in lung tissues, which caused hypovascular lung tissue, then led to severe dyspnea in wfdc2-/- neonates. The HE4 knockout mouse has provided an in vivo model for studying the patho-physiological function and relevant molecular pathways of HE4 for the development of respiratory system.

Generation of H11-albumin-rtTA Transgenic Mice: A Tool for Inducible Gene Expression in the Liver.

The modification of the mouse genome by site-specific gene insertion of transgenes and other genetic elements allows the study of gene function in different developmental stages and in the pathogenesis of diseases. Here, we generated a "genomic safe harbor" Hipp11 (H11) locus-specific knock-in transgenic mouse line in which the albumin promoter is used to drive the expression of the reverse tetracycline transactivator (rtTA) in the liver. The newly generated H11-albumin-rtTA transgenic mice were bred with tetracycline-operator-Histone-2B-green fluorescent protein (TetO-H2BGFP) mice to assess inducibility and tissue-specificity. Expression of the H2BGFP fusion protein was observed exclusively upon doxycycline (Dox) induction in the liver of H11-albumin-rtTA/TetO-H2BGFP double transgenic mice. To further analyze the ability of the Dox-inducible H11-albumin-rtTA mice to implement conditional DNA recombination, H11-albumin-rtTA transgenic mice were crossed with TetO-Cre and Ai14 mice to generate H11-albumin-rtTA/TetO-Cre/Ai14 triple transgenic mice. We successfully confirmed that the Cre-mediated recombination efficiency was as strong in Dox-induced H11-albumin-rtTA /TetO-Cre/Ai14 mice as in the control albumin-Cre/A14 mice. Finally, to characterize the expression-inducing effects of Dox in H11-albumin-rtTA/TetO-H2BGFP mice in detail, we examined GFP expression in embryos at different developmental stages and found that newly conceived H11-albumin-rtTA/TetO-H2BGFP embryos of Dox-treated pregnant female mice were expressing reporter GFP by E16.5. Our study demonstrates that these new H11-albumin-rtTA transgenic mice are a powerful and efficient tool for the temporally and spatially conditional manipulation of gene expression in the liver, and illustrates how genetic crosses with these new mice enable the generation of complex multi-locus transgenic animals for mechanistic studies.