Little is known about the subcellular localization and function of programmed cell death 4 (PDCD4) in melanoma. Our past studies suggest PDCD4 interacts with Pleckstrin Homology Domain Containing A5 (PLEKHA5) to influence melanoma brain metastasis outcomes, as high intracranial PDCD4 expression leads to improved survival. We aimed to define the subcellular distribution of PDCD4 in melanoma and in the tumor microenvironment during neoplastic progression and its impact on clinical outcomes. We analyzed multiple tissue microarrays with well-annotated clinicopathological variables using quantitative immunofluorescence and evaluated single-cell RNA-sequencing on a brain metastasis sample to characterize PDCD4+ immune cell subsets. We demonstrate differences in PDCD4 expression during neoplastic progression, with high tumor and stromal PDCD4 levels associated with improved survival in primary melanomas and in intracranial metastases, but not in extracranial metastatic disease. While the expression of PDCD4 is well-documented on CD8+ T cells and natural killer cells, we show that it is also found on B cells and mast cells. PDCD4 expression in the tumor microenvironment is associated with increased immune cell infiltration. Further studies are needed to define the interaction of PDCD4 and PLEKHA5 and to evaluate the utility of this pathway as a therapeutic target in melanoma brain metastasis.

Background: Mortality from ovarian cancer remains high due to the lack of methods for early detection. The difficulty lies in the low prevalence of the disease necessitating a significantly high specificity and positive-predictive value (PPV) to avoid unneeded and invasive intervention. Currently, cancer antigen- 125 (CA-125) is the most commonly used biomarker for the early detection of ovarian cancer. In this study we determine the value of combining macrophage migration inhibitory factor (MIF), osteopontin (OPN), and prolactin (PROL) with CA-125 in the detection of ovarian cancer serum samples from healthy controls. Materials and Methods: A total of 432 serum samples were included in this study. 153 samples were from ovarian cancer patients and 279 samples were from age-matched healthy controls. The four proteins were quantified using a fully automated, multi-analyte immunoassay. The serum samples were divided into training and testing datasets and analyzed using four classification models to calculate accuracy, sensitivity, specificity, PPV, negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). Results: The four-protein biomarker panel yielded an average accuracy of 91% compared to 85% using CA-125 alone across four classification models (p = 3.224 × 10⁻⁹). Further, in our cohort, the four-protein biomarker panel demonstrated a higher sensitivity (median of 76%), specificity (median of 98%), PPV (median of 91.5%), and NPV (median of 92%), compared to CA-125 alone. The performance of the four-protein biomarker remained better than CA-125 alone even in experiments comparing early stage (Stage I and Stage II) ovarian cancer to healthy controls. Conclusions: Combining MIF, OPN, PROL, and CA-125 can better differentiate ovarian cancer from healthy controls compared to CA-125 alone.