Variability in Minimal Clinically Important Difference Calculation and Reporting in the Otolaryngology Literature.

OBJECTIVE: Best practices for calculation of the minimal clinically important difference (MCID) of outcome measures include the use of complementary methodologies (broadly classified as anchor-based and distribution-based) and reporting of the MCID's predictive ability. We sought to determine MCID calculation and reporting patterns within the otolaryngology literature. METHODS: A systematic search strategy of Embase, PubMed, and Web of Science databases was developed and implemented to identify studies reporting the determination of an MCID for an outcome measure. Studies specifically within the otolaryngology literature (defined as journals classified as "otorhinolaryngology" in the Journal Citation Reports database) were included. All those journals were additionally searched for relevant articles. RESULTS: There were 35 articles that met the inclusion criteria. Of these studies, 88.6% reported MCID of a patient-reported outcome measure and the remainder were for objective outcome measurements. Anchor-based methods were used by 82.9% of studies and distribution-based methods were used by 68.6% of studies. Of all studies, 31.4% utilized anchor-based methods alone, 17.1% utilized distribution-based methods alone, and 51.4% used both methods. Only 25.7% of studies reported the sensitivity (median: 60.8%, range: 40.5%-86.7%) and specificity (median: 80.4%, range: 63.5%-88.0%) of the MCID to detect patients experiencing clinically important change. CONCLUSION: Deviation from best practices in MCID calculation and reporting exists within the otolaryngology literature, with almost half of all studies only using one method of MCID calculation and almost three-quarters not reporting the predictive ability (sensitivity/specificity) of the calculated MCID. When predictive ability is reported, however, MCIDs appear to be more specific than sensitive. LEVEL OF EVIDENCE: NA Laryngoscope, 134:2059-2069, 2024.

Quantification of spatial pharmacogene expression heterogeneity in breast tumors.

BACKGROUND: Chemotherapeutic drug concentrations vary across different regions of tumors and this is thought to be involved in development of chemotherapy resistance. Insufficient drug delivery to some regions of the tumor may be due to spatial differences in expression of genes involved in the disposition, transport, and detoxification of drugs (pharmacogenes). Therefore, in this study, we analyzed the spatial expression of 286 pharmacogenes in six breast cancer tissues using the recently developed Visium spatial transcriptomics platform to (1) determine if these pharmacogenes are expressed heterogeneously across tumor tissue and (2) to determine which pharmacogenes have the most spatial expression heterogeneity. METHODS AND RESULTS: The spatial transcriptomics technology sequences the transcriptome of 55 um diameter barcoded sections (spots) across a tissue sample. We analyzed spatial gene expression profiles of four biobank-sourced breast tumor samples in addition to two breast tumor sample datasets from 10× Genomics. We define heterogeneity as the interquartile range of read counts. Collectively, we identified 8887 spots in tumor regions, 3814 in stroma, 44 in lymphocytes, and 116 in normal regions based on pathologist annotation of the tissues. We showed statistically significant differences in expression of pharmacogenes in tumor regions compared to surrounding non-tumor regions. We also observed that the most heterogeneously expressed genes within tumor regions were involved in reactive oxygen species (ROS) handling and detoxification mechanisms. GPX4, GSTP1, MGST3, SOD1, CYP4Z1, CYB5R3, GSTK1, and NAT1 showed the most heterogeneous expression within tumor regions. CONCLUSIONS: The heterogeneous expression of these pharmacogenes may have important implications for cancer therapy due to their ability to impact drug distribution and efficacy throughout the tumor. Our results suggest that chemoresistance caused by expression of GPX4, GSTP1, MGST3, and SOD1 may be intrinsic, not acquired, since the heterogeneity is not specific to chemotherapy-treated samples or cell type. Additionally, we identified candidate chemoresistance pharmacogenes that can be further tested through focused follow-up studies.