High serum levels of inflammatory markers are associated with early recurrence in patients with high-grade serous ovarian cancer after platinum therapy.

OBJECTIVE: To determine if inflammatory biomarkers can predict the long-term outcome of platinum therapy in patients with high-grade serous ovarian cancer. METHODS: Women diagnosed with high-grade serous epithelial ovarian cancer (n = 70) at a single institution were enrolled in a prospective serum collection study between 2005 and 2020. Seventeen markers of inflammation and oxidative stress were measured in serum samples on a chemistry analyzer. Association was tested for serum levels with progression-free survival (PFS), time to recurrence (TTR), overall survival (OS), and time to death (TTD) using Cox proportional hazards and Kaplan-Meier curves. Patient survival was censored at 10 years. RESULTS: Higher serum levels of LDH were associated with worse PFS (HR 2.57, p = 0.028). High serum levels of BAP (HR 0.38, p = 0.025), GSP (HR 0.40, p = 0.040), HDL-c (HR 0.27, p = 0.002), and MG (HR 0.36, p = 0.017) were associated with improved PFS. Higher expression of LDH was associated with worse OS (HR 2.16, p = 0.023). Higher levels of CK.nac (HR 0.39, p = 0.033) and HDL-c (HR 0.35, p = 0.029) were associated with improved OS. Similar outcomes were found with TTR and TTD analyses. CONCLUSION: General inflammatory biomarkers may serve as a guide for prognosis and treatment benefit. Future studies needed to further define their role in predicting prognosis or how these markers may affect response to therapy.

Some Mushrooms are Hard to Digest: Gastrostomy Tube Exchange.

BACKGROUND: Percutaneous endoscopic gastrostomy (PEG) is an effective and safe mode of enteral nutrition for patients needing chronic enteric nutritional support. Exchanging PEG tubes may result in complications due to inexperience as well as due to lack of protocol. CASE REPORT: We encountered a 73 year-old female with unnoticed, accidently detached portion of the internal bumper of a PEG tube in the gastric lumen after a challenging gastrostomy tube exchange. CONCLUSIONS: This case report discusses the complications associated with gastrostomy tube exchange and proposes a planned protocol for successful gastrostomy tube exchange.

Ovarian recurrence risk assessment using machine learning, clinical information, and serum protein levels to predict survival in high grade ovarian cancer.

In ovarian cancer, there is no current method to accurately predict recurrence after a complete response to chemotherapy. Here, we develop a machine learning risk score using serum proteomics for the prediction of early recurrence of ovarian cancer after initial treatment. The developed risk score was validated in an independent cohort with serum collected prospectively during the remission period. In the discovery cohort, patients scored as low-risk had a median time to recurrence (TTR) that was not reached at 10 years compared to 10.5 months (HR 4.66, p < 0.001) in high-risk patients. In the validation cohort, low-risk patients had a median TTR which was not reached compared to 4.7 months in high-risk patients (HR 4.67, p = 0.009). In advanced-stage patients with a CA125 < 10, low-risk patients had a median TTR of 68 months compared to 6 months in high-risk patients (HR 2.91, p = 0.02). The developed risk score was capable of distinguishing the duration of remission in ovarian cancer patients. This score may help guide maintenance therapy and develop innovative treatments in patients at risk at high-risk of recurrence.

Development of a Single Molecule Counting Assay to Differentiate Chromophobe Renal Cancer and Oncocytoma in Clinics.

Malignant chromophobe renal cancer (chRCC) and benign oncocytoma (RO) are two renal tumor types difficult to differentiate using histology and immunohistochemistry-based methods because of their similarity in appearance. We previously developed a transcriptomics-based classification pipeline with "Chromophobe-Oncocytoma Gene Signature" (COGS) on a single-molecule counting platform. Renal cancer patients (n = 32, chRCC = 17, RO = 15) were recruited from Augusta University Medical Center (AUMC). Formalin-fixed paraffin-embedded (FFPE) blocks from their excised tumors were collected. We created a custom single-molecule counting code set for COGS to assay RNA from FFPE blocks. Utilizing hematoxylin-eosin stain, pathologists were able to correctly classify these tumor types (91.8%). Our unsupervised learning with UMAP (Uniform manifold approximation and projection, accuracy = 0.97) and hierarchical clustering (accuracy = 1.0) identified two clusters congruent with their histology. We next developed and compared four supervised models (random forest, support vector machine, generalized linear model with L2 regularization, and supervised UMAP). Supervised UMAP has shown to classify all the cases correctly (sensitivity = 1, specificity = 1, accuracy = 1) followed by random forest models (sensitivity = 0.84, specificity = 1, accuracy = 1). This pipeline can be used as a clinical tool by pathologists to differentiate chRCC from RO.