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.

Transcriptomic convergence despite genomic divergence drive field cancerization in synchronous squamous tumors.

Introduction: Field cancerization is suggested to arise from imbalanced differentiation in individual basal progenitor cells leading to clonal expansion of mutant cells that eventually replace the epithelium, although without evidence. Methods: We performed deep sequencing analyses to characterize the genomic and transcriptomic landscapes of field change in two patients with synchronous aerodigestive tract tumors. Results: Our data support the emergence of numerous genetic alterations in cancer-associated genes but refutes the hypothesis that founder mutation(s) underpin this phenomenon. Mutational signature analysis identified defective homologous recombination as a common underlying mutational process unique to synchronous tumors. Discussion: Our analyses suggest a common etiologic factor defined by mutational signatures and/or transcriptomic convergence, which could provide a therapeutic opportunity.

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.

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.

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.

Axillary Lymph Node Dissection in Angiosarcomas of the Breast: An Asian Institutional Perspective.

Angiosarcomas of the breast (ASB) are rare, making up to less than 8% of all angiosarcomas. The surgical management for this disease continues to vary throughout centres worldwide due to the current limited evidence. We aim to examine the necessity of axillary lymph node dissection in this pathology through a retrospective study of axillary metastasis and recurrence patterns in patients treated at our institution. A retrospective review of a prospectively-maintained database was performed. All adult patients with a histologically confirmed diagnosis of ASB seen at the National Cancer Centre Singapore between 2006 and 2019 were identified. Axillary lymph node status, treatment, survival, and recurrence data were collated. Thirteen patients were identified with a confirmed diagnosis of ASB, of which there were 11 primary and 2 secondary angiosarcoma cases. Eight patients had some form of axillary lymph node dissection and 5 did not. No positive nodes were found in any examined axillary nodes despite high median number of nodes harvested (13, range 8-24). 5/13 patients had disease progression, of whom none had locoregional recurrence to the axilla. ASB continues to be rare and recurrent and presents as a challenge to treat. Axillary lymph node involvement is most likely not present in a majority of patients. Prophylactic removal is unwarranted in patients presenting without lymph node involvement due to the lack of axillary metastasis.

Pairing a prognostic target with potential therapeutic strategy for head and neck cancer.

OBJECTIVES: We have previously identified and validated a panel of molecular prognostic markers (ATP13A3, SSR3, and ANO1) for Head and Neck Squamous Cell Carcinoma (HNSCC). The aim of this study was to investigate the consequence of ATP13A3 dysregulation on signaling pathways, to aid in formulating a therapeutic strategy targeting ATP13A3-overexpressing HNSCC. MATERIALS AND METHODS: Gene Set Enrichment Analysis (GSEA) was performed on HNSCC microarray expression data (Internal local dataset [n = 92], TCGA [n = 232], EMBL [n = 81]) to identify pathways associated with high expression of ATP13A3. Validation was performed using immunohistochemistry (IHC) on tissue microarrays (TMAs) of head and neck cancers (n = 333), staining for ATP13A3 and phosphorylated Aurora kinase A (phospho-T288). Short interfering RNA was used to knockdown ATP13A3 expression in patient derived HNSCC cell lines. Protein expression of ATP13A3 and Aurora kinase A was then assessed by immunoblotting. RESULTS: GSEA identified Aurora kinase pathway to be associated with high expression of ATP13A3 (p = 0.026). The Aurora kinase pathway was also associated with a trend towards poor prognosis and tumor aggressiveness (p = 0.086, 0.094, respectively). Furthermore, the immunohistochemical staining results revealed a significant association between Aurora kinase activity and high ATP13A3 expression (p < 0.001). Knockdown of ATP13A3 in human head and neck cell lines showed decrease in Aurora kinase A levels. CONCLUSION: Tumors with high ATP13A3 are associated with high Aurora kinase activity. This suggests a potential therapeutic role of Aurora kinase inhibitors in a subset of poor prognosis HNSCC patients with overexpression of ATP13A3.

An Optimised Protocol Harnessing Laser Capture Microdissection for Transcriptomic Analysis on Matched Primary and Metastatic Colorectal Tumours.

Generation of large amounts of genomic data is now feasible and cost-effective with improvements in next generation sequencing (NGS) technology. Ribonucleic acid sequencing (RNA-Seq) is becoming the preferred method for comprehensively characterising global transcriptome activity. Unique to cytoreductive surgery (CRS), multiple spatially discrete tumour specimens could be systematically harvested for genomic analysis. To facilitate such downstream analyses, laser capture microdissection (LCM) could be utilized to obtain pure cell populations. The aim of this protocol study was to develop a methodology to obtain high-quality expression data from matched primary tumours and metastases by utilizing LCM to isolate pure cellular populations. We demonstrate an optimized LCM protocol which reproducibly delivered intact RNA used for RNA sequencing and quantitative polymerase chain reaction (qPCR). After pathologic annotation of normal epithelial, tumour and stromal components, LCM coupled with cDNA library generation provided for successful RNA sequencing. To illustrate our framework's potential to identify targets that would otherwise be missed with conventional bulk tumour sequencing, we performed qPCR and immunohistochemical technical validation to show that the genes identified were truly expressed only in certain sub-components. This study suggests that the combination of matched tissue specimens with tissue microdissection and NGS provides a viable platform to unmask hidden biomarkers and provides insight into tumour biology at a higher resolution.

A set of molecular markers predicts chemosensitivity to Mitomycin-C following cytoreductive surgery and hyperthermic intraperitoneal chemotherapy for colorectal peritoneal metastasis.

Cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) is associated with significant perioperative morbidity and mortality. We aim to generate and validate a biomarker set predicting sensitivity to Mitomycin-C to refine selection of patients with colorectal peritoneal metastasis (CPM) for this treatment. A signature predicting Mitomycin-C sensitivity was generated using data from Genomics of Drug Sensitivity in Cancer and The Cancer Genome Atlas. Validation was performed on CPM patients who underwent CRS-HIPEC (n = 62) using immunohistochemistry (IHC). We determined predictive significance of our set using overall survival as a surrogate endpoint via a logistic regression model. Three potential biomarkers were identified and optimized for IHC. Patients exhibiting lower expression of PAXIP1 and SSBP2 had poorer survival than those with higher expression (p = 0.045 and 0.140, respectively). No difference was observed in patients with differing DTYMK expression (p = 0.715). Combining PAXIP1 and SSBP2 in a set, patients with two dysregulated protein markers had significantly poorer survival than one or no dysregulated marker (p = 0.016). This set independently predicted survival in a Cox regression model (HR 5.097; 95% CI 1.731-15.007; p = 0.003). We generated and validated an IHC prognostic set which could potentially identify patients who are likely to benefit from HIPEC using Mitomycin-C.