Generating patient-derived ascites-dependent xenograft mouse models of peritoneal carcinomatosis.

Clinically relevant animal models are crucial for effective development of therapeutics for peritoneal carcinomatosis (PC). This protocol describes the generation of patient-derived ascites-dependent xenograft (PDADX) models from the cellular component of ascites. The use of routine intraperitoneal injection of the fluid component of ascites is analogous to the biological events occurring intra-abdominally in patients with PC. By serving as a proxy, PDADX models represent a valuable tool for preclinical testing of new therapeutics for PC. For complete details on the use and execution of this protocol, please refer to Hendrikson et al. (2022).

Ligand-mediated PAI-1 inhibition in a mouse model of peritoneal carcinomatosis.

Peritoneal carcinomatosis (PC) present a ubiquitous clinical conundrum in all intra-abdominal malignancies. Via functional and transcriptomic experiments of ascites-treated PC cells, we identify STAT3 as a key signaling pathway. Integrative analysis of publicly available databases and correlation with clinical cohorts (n = 7,359) reveal putative clinically significant activating ligands of STAT3 signaling. We further validate a 3-biomarker prognostic panel in ascites independent of clinical covariates in a prospective study (n = 149). Via single-cell sequencing experiments, we uncover that PAI-1, a key component of the prognostic biomarker panel, is largely secreted by fibroblasts and mesothelial cells. Molecular stratification of ascites using PAI-1 levels and STAT3 activation in ascites-treated cells highlight a therapeutic opportunity based on a phenomenon of paracrine addiction. These results are recapitulated in patient-derived ascites-dependent xenografts. Here, we demonstrate therapeutic proof of concept of direct ligand inhibition of a prognostic target within an enclosed biological space.

In Vitro Susceptibility of Typhoidal, Nontyphoidal, and Extended-Spectrum-ß-Lactamase-Producing Salmonella to Ceftolozane/Tazobactam.

Both typhoidal and nontyphoidal salmonellae are included in the top 15 drug-resistant threats described by the U.S. Centers for Disease Control and Prevention. There is an urgent need to look for alternative antibiotics for the treatment of Salmonella infections. We used the broth microdilution test to examine the in vitro susceptibilities of typhoidal and nontyphoidal salmonellae, including isolates positive for extended-spectrum ß-lactamase (ESBL), to ceftolozane/tazobactam and six other antibiotics. Of the 313 (52 typhoidal and 261 nontyphoidal) Salmonella isolates tested, 98.7% were susceptible to ceftolozane/tazobactam. Based on the overall MIC50/90 values, Salmonella isolates were more susceptible to ceftolozane/tazobactam (0.25/0.5 mg/L) than all the comparator agents: ampicillin (≥64/≥64 mg/L), levofloxacin (0.25/1 mg/L), azithromycin (4/16 mg/L), ceftriaxone (≤0.25/4 mg/L), chloramphenicol (8/≥64 mg/L), and trimethoprim/sulfamethoxazole (1/≥8 mg/L). Comparison of the activities of the antimicrobial agents against nontyphoidal Salmonella isolates according to their serogroups showed that ceftolozane/tazobactam had the highest activity (100%) against Salmonella serogroup D, G, I, and Q isolates, whereas the lowest activity (85.7%) was observed against serogroup E isolates. All 10 ESBL-producing Salmonella isolates (all nontyphoidal), of which 8 were CTX-M-55 producers and 2 were CTX-M-65 producers, were sensitive to ceftolozane/tazobactam, albeit with MIC50/90 values higher (1/2 mg/L) than those for non-ESBL producers (0.25/0.5 mg/L). In summary, our data indicate that ceftolozane/tazobactam is active against most strains of both typhoidal and nontyphoidal salmonellae and also against ESBL-producing salmonellae.

A machine learning approach to identify predictive molecular markers for cisplatin chemosensitivity following surgical resection in ovarian cancer.

Ovarian cancer is associated with poor prognosis. Platinum resistance contributes significantly to the high rate of tumour recurrence. We aimed to identify a set of molecular markers for predicting platinum sensitivity. A signature predicting cisplatin sensitivity was generated using the Genomics of Drug Sensitivity in Cancer and The Cancer Genome Atlas databases. Four potential biomarkers (CYTH3, GALNT3, S100A14, and ERI1) were identified and optimized for immunohistochemistry (IHC). Validation was performed on a cohort of patients (n = 50) treated with surgical resection followed by adjuvant carboplatin. Predictive models were established to predict chemosensitivity. The four biomarkers were also assessed for their ability to prognosticate overall survival in three ovarian cancer microarray expression datasets from The Gene Expression Omnibus. The extreme gradient boosting (XGBoost) algorithm was selected for the final model to validate the accuracy in an independent validation dataset (n = 10). CYTH3 and S100A14, followed by nodal stage, were the features with the greatest importance. The four gene signature had comparable prognostication as clinical information for two-year survival. Assessment of tumour biology by means of gene expression can serve as an adjunct for prediction of chemosensitivity and prognostication. Potentially, the assessment of molecular markers alongside clinical information offers a chance to further optimise therapeutic decision making.

Gene Expression Changes Associated with Dedifferentiation in Liposarcoma Predict Overall Survival.

Up to 10% of well-differentiated liposarcoma (WDLS) progress to dedifferentiated liposarcoma (DDLS). We aimed to identify gene expression changes associated with dedifferentiation and whether these were informative of tumour biology of DDLS. We analysed datasets from the Gene Expression Omnibus (GEO, ID = GSE30929) database to identify differentially expressed genes between WDLS (n = 52) and DDLS (n = 39). We validated the signature on whole and laser-capture microdissected samples from patients with tumours consisting of mixed WDLS and DDLS components. A subset of this signature was applied to an independent dataset from The Cancer Genome Atlas (TCGA, n = 58 DDLS) database to segregate samples based on gene expression and compared for recurrence and overall survival (OS). A 15-gene signature consisting of genes with increased expression in DDLS compared to WDLS was generated. This signature segregated WDLS and DDLS samples from patients with mixed component tumours and across multiple recurrences. A further subset of this signature, consisting of five genes (AQP7, ACACB, FZD4, GPD1, LEP), segregated DDLS in a TCGA cohort with a significant difference in OS (p = 0.019) and recurrence-free survival (RFS) (p = 0.061). The five-gene model stratified DDLS into prognostic groups and outperformed clinical factors in existing models in retroperitoneal DDLS.

Postoperative Inflammatory Marker Surveillance in Colorectal Peritoneal Carcinomatosis.

BACKGROUND: The prognostic significance of inflammatory markers in solid cancers is well-established, albeit with considerable heterogeneity. This study sought to investigate the postoperative inflammatory marker trend in peritoneal carcinomatosis (PC), with a focus on colorectal PC (CPC), and to propose optimal surveillance periods and cutoffs. METHODS: Data were collected from a prospectively maintained database of PC patients treated at the authors' institution from April 2001 to March 2019. The platelet-lymphocyte ratio (PLR), the neutrophil-lymphocyte ratio (NLR), and the lymphocyte-monocyte ratio (LMR) were collected preoperatively and on postoperative days 0, 1 to 3, 4 to 7, 8 to 21, 22 to 56, and 57 to 90 as averages. Optimal surveillance periods and cutoffs for each marker were determined by maximally selected rank statistics. The Kaplan-Meier method and Cox proportional hazard regression models were used to investigate the association of inflammatory markers with 1-year overall survival (OS) and recurrence-free survival (RFS) using clinicopathologic parameters. RESULTS: The postoperative inflammatory marker trend and levels did not differ between the patients with and those without hyperthermic intraperitoneal chemotherapy (HIPEC). Low postoperative LMR (days 4-7), high postoperative NLR (days 8-21), and high postoperative PLR (days 22-56) were optimal for prognosticating poor 1-year OS, whereas high postoperative PLR and NLR (days 57-90) and low postoperative LMR (days 8-21) were associated with poor 1-year RFS. A composite score of these three markers was prognostic for OS in CPC. CONCLUSIONS: The reported cutoffs should be validated in a larger population of CPC patients. Future studies should account for the inflammatory response profile when selecting appropriate surveillance periods.

Automatic 4D reconstruction of patient-specific cardiac mesh with 1-to-1 vertex correspondence from segmented contours lines.

We propose an automatic algorithm for the reconstruction of patient-specific cardiac mesh models with 1-to-1 vertex correspondence. In this framework, a series of 3D meshes depicting the endocardial surface of the heart at each time step is constructed, based on a set of border delineated magnetic resonance imaging (MRI) data of the whole cardiac cycle. The key contribution in this work involves a novel reconstruction technique to generate a 4D (i.e., spatial-temporal) model of the heart with 1-to-1 vertex mapping throughout the time frames. The reconstructed 3D model from the first time step is used as a base template model and then deformed to fit the segmented contours from the subsequent time steps. A method to determine a tree-based connectivity relationship is proposed to ensure robust mapping during mesh deformation. The novel feature is the ability to handle intra- and inter-frame 2D topology changes of the contours, which manifests as a series of merging and splitting of contours when the images are viewed either in a spatial or temporal sequence. Our algorithm has been tested on five acquisitions of cardiac MRI and can successfully reconstruct the full 4D heart model in around 30 minutes per subject. The generated 4D heart model conforms very well with the input segmented contours and the mesh element shape is of reasonably good quality. The work is important in the support of downstream computational simulation activities.

Hierarchical and binary spatial descriptors for lung nodule image retrieval.

With the increasing amount of image data available for cancer staging and diagnosis, it is clear that content-based image retrieval techniques are becoming more important to assist physicians in making diagnoses and tracking disease. Domain-specific feature descriptors have been previously shown to be effective in the retrieval of lung tumors. This work proposes a method to improve the rotation invariance of the hierarchical spatial descriptor, as well as presents a new binary descriptor for the retrieval of lung nodule images. The descriptors were evaluated on the ELCAP public access database, exhibiting good performance overall.