Should BRAFV600E be Incorporated into Treatment Recommendations for Thyroid Cancer?

Around 90% of all well-differentiated thyroid cancers are papillary thyroid carcinomas (PTC). PTCs have a recurrence rate of around 20% and a low mortality rate of around 5%. Within PTCs, around 60% of them have the BRAFV600E mutation. Currently, there is a debate on whether BRAFV600E is an independent predictor of tumor aggressiveness and recurrence. This study looks at whether BRAFV600E is an independent predictor of recurrence and outcomes in PTC. Tissue microarrays (TMA) were made from well-differentiated thyroid tumors and stained for the BRAFV600E mutation. BRAFV600E expression was calculated using an H-score: the staining intensity (0-3) multiplied by the amount of tumor that stained positive. A univariate analysis showed that BRAFV600E was significantly associated with age (p=0.0259), gender (p=0.019), extrathyroidal extension (p=0.049), positive margins (p=0.033), lymph node ratio (p=0.0106), N stage (p=0.015), AJCC 8 stage (p=0.0042), ATA risk category (p=0.018), and time to recurrence (p=0.0487). A multivariable analysis found that only extrathyroidal extension was an independent predictor of recurrence. Overall, BRAFV600E was not an independent predictor of recurrence in this cohort. Current treatment plans based on risk of recurrence appear to be appropriate, and it is not recommended that BRAFV600E be included as an independent variable.https://digitalcommons.unmc.edu/surp2021/1058/thumbnail.jp

Investigating Immune Profiles in Differentiated Thyroid Cancer by Multiplex Immunofluorescence

BACKGROUND: As the most common endocrine malignancy in the United States (U.S.), differentiated thyroid cancer (DTC) accounts for 3.8% of all cancers in the U.S., with roughly 10% of cases progressing to distant metastatic DTC, which is associated with a poor five year survival outcome despite conventional management, including surgery and radioactive iodine ablation. Recently, novel immunotherapies have garnered attention as a viable therapeutic resource for patients with advanced DTC. However, the response to therapy has been variable and unpredictable, which may be associated with an immune suppressive circulating phenotype. Nonetheless, the intra-tumoral immune infiltrate remains to be elucidated, demonstrating a critical need to address the gap in understanding in order to better prognosticate the disease. OBJECTIVE: To identify and compare tumor-infiltrating immune markers with those present in the adjacent normal thyroid tissue, and collate these immune infiltrates with tumor characteristics. METHODS: Twenty-nine adult tissue samples containing tumor and stromal regions were collected from patients with DTC. The samples were analyzed using multiplex immunofluorescence (MxIF) with antibodies against cell-surface molecules CD56, PD-1, PD-L1, FOXP3, CD3, CD8, CD4, CD45, CD68, CD163, INOS, HLA-DR, CD33, and CD19. 17 of the specimens were analyzed using HALO and a positive threshold was assigned based on review by a trained researcher. RESULTS: In evaluating the immune profiles, important differences in the immune infiltrates between different stages of the cancer were observed. Generally, PD-1 and PD-L1 were highly expressed within the tumor, despite variability in lymphocyte infiltration, indicating the importance of PD-1 and PD-L1 as potential predictive biomarkers for the aggressiveness of thyroid cancer. Tumor from patients with distant metastases demonstrated higher T cell infiltration, T regulatory cells, macrophages and PD-L1 positive cells as compared to localized tumor. CONCLUSION: Immune profiling demonstrated significant differences between tumor and adjacent healthy regions, particularly in terms of PD-1 and PD-L1 expression and lymphocyte infiltration, indicating that higher intratumor infiltration of T regulatory cells, macrophages and PD-1/PD-L1 positive cells may be associated with advanced thyroid cancer. Therefore, the data demonstrates the efficacy of MxIF in gathering valuable information regarding the tumor microenvironment, which will have major implications in guiding the selection of patients for immunotherapy.https://digitalcommons.unmc.edu/surp2021/1042/thumbnail.jp

Cerebral Blood Volume Changes During Radiotherapy May Predict Pseudoprogression versus Disease Progression for Patients with High Grade Glioma

https://openworks.mdanderson.org/sumexp21/1040/thumbnail.jp

Machine Learning Analyses of Highly-Multiplexed Immunofluorescence Identifies Distinct Tumor and Stromal Cell Populations in Primary Pancreatic Tumors

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a formidable challenge for patients and clinicians. OBJECTIVE: To analyze the distribution of 31 different markers in tumor and stromal portions of the tumor microenvironment (TME) and identify immune cell populations to better understand how neoplastic, non-malignant structural, and immune cells, diversify the TME and influence PDAC progression. METHODS: Whole slide imaging (WSI) and cyclic multiplexed-immunofluorescence (MxIF) was used to collect 31 different markers over the course of nine distinctive imaging series of human PDAC samples. Image registration and machine learning algorithms were developed to largely automate an imaging analysis pipeline identifying distinct cell types in the TME. RESULTS: A random forest algorithm accurately predicted tumor and stromal-rich areas with 87% accuracy using 31 markers and 77% accuracy using only five markers. Top tumor-predictive markers guided downstream analyses to identify immune populations effectively invading into the tumor, including dendritic cells, CD4+ T cells, and multiple immunoregulatory subtypes. CONCLUSIONS: Immunoprofiling of PDAC to identify differential distribution of immune cells in the TME is critical for understanding disease progression, response and/or resistance to treatment, and the development of new treatment strategies