Zain Aryanpour
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View article: Lobular Breast Carcinoma Presenting as an Endometrial Polyp Metastasis: A Report of a Novel Asynchronous Presentation
Lobular Breast Carcinoma Presenting as an Endometrial Polyp Metastasis: A Report of a Novel Asynchronous Presentation Open
View article: Supplementary Data from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma
Supplementary Data from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma Open
Overview of supplementary data
View article: Figure S5 from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma
Figure S5 from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma Open
In vivo modeling with loss of CBX2 expression and Nanostring pathway analysis
View article: Table S3 from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma
Table S3 from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma Open
Nanostring
View article: Table S4 from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma
Table S4 from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma Open
TIMERv2
View article: Table S1 from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma
Table S1 from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma Open
Antibodies
View article: Figure S4 from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma
Figure S4 from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma Open
M1/M2 macrophages convey differential survival outcomes. M1/M2 gating strategy. CD68 gating of monocytes in culture system.
View article: Figure S1 from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma
Figure S1 from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma Open
CBX2 binds to promoter regions of cytokine genes and CBX2 is significantly associated with immune signatures.
View article: Table S2 from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma
Table S2 from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma Open
Primers
View article: Figure S3 from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma
Figure S3 from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma Open
Modulation of CBX2 enhances monocyte infiltration
View article: Figure S2 from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma
Figure S2 from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma Open
CBX2 expression associates with epithelial state 6. CBX2 protein expression does not correlate with T cell infiltration. CXCL1, 5, and CXCL8 expression correlation with Macrophage M0_CIBERSORT infiltration.
View article: Ovarian Cancer Think Tank: The Use of Integrated Machine Learning and Computational Biology in Ovarian Cancer Diagnosis and Treatment
Ovarian Cancer Think Tank: The Use of Integrated Machine Learning and Computational Biology in Ovarian Cancer Diagnosis and Treatment Open
View article: Figure S11 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma
Figure S11 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma Open
Fig S11. Example feature representation maps. Feature representation (FR) map images are shown here for an example FOV (FOV ID 9). FR maps are de-noised using a customized pipeline for analyzing MIBI data and were used for cell phenotyping.
View article: Table S13 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma
Table S13 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma Open
Table S13. Random forest predictive performance results for only primary tumor samples. Full results for all random forest models using 15 different feature sets, trained on only primary tumor samples (n = 69). Mean and standard dev…
View article: Table S3 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma
Table S3 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma Open
Table S3. Noise filtering parameters for image data. Configuration parameters for noise filtering of multiplexed images.
View article: Figure S20 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma
Figure S20 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma Open
Fig S20. Random forest feature importance results for only primary tumor samples. Main text Figure 7 with random forest models trained only on primary tumor samples for the same binary outcome variables of low vs. high OS and PFS (n…
View article: Data from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma
Data from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma Open
Ovarian cancer is the deadliest gynecologic malignancy, and therapeutic options and mortality rates over the last three decades have largely not changed. Recent studies indicate that the composition of the tumor immune microenvironment (TI…
View article: Figure S17 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma
Figure S17 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma Open
Fig S17. Outcome variable distributions. Histograms of the two outcome variables used in our analysis (A) OS, in months, and (B) PFS, in months. For predictive modeling, both outcome variables are split at their respective medians into hig…
View article: Figure S10 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma
Figure S10 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma Open
Fig S10. Example antibody staining images. Raw antibody staining images are shown here for an example FOV (FOV ID 9).
View article: Table S9 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma
Table S9 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma Open
Table S9. H&E imaging file guide. A spreadsheet mapping the positions in TMA1B and TMA2B to the unique TMA IDs.
View article: Table S12 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma
Table S12 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma Open
Table S12. Random forest predictive performance results. Full results for all random forest models using 15 different feature sets. Mean and standard deviation AUC results across 500 evaluations are reported for both OS and PFS.
View article: Figure S15 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma
Figure S15 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma Open
Fig S15. Cell type region size distributions. (A) The proportion of cells of each type found within a region of equal or larger size, displayed with a logarithmic x-axis. (B) The log-log complementary cumulative distribution functio…
View article: Figure S5 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma
Figure S5 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma Open
Fig S5. Random forest feature importance results with increased spatial network trimming threshold. Main text Figure 7 repeated if edges in spatial networks are trimmed with a higher threshold of 100 pixels (∼48.8 μm).
View article: Figure S7 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma
Figure S7 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma Open
Fig S7. Random forest predictive performance results with features derived from missing cell types all treated as NA. Main text Figure 6 repeated with missing cell types handled differently in data preprocessing.
View article: Table S16 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma
Table S16 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma Open
Table S16. Clustering analysis results.
View article: Table S6 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma
Table S6 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma Open
Table S6. Multivariate Cox regression result. Full multivariate Cox regression results for OS and PFS, using the top 5 features from univariate analyses.
View article: Figure S23 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma
Figure S23 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma Open
Fig S23. Survival analysis of unsupervised clustering of samples. Using all the features for the samples, unsupervised KMeans clustering was performed to identify tumors with similar feature patterns with the intent of comparing survival o…
View article: Figure S22 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma
Figure S22 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma Open
Fig S22. Random forest feature importance results split by feature type - PFS. Feature importance results for progression-free survival show in Figure 7B, split by feature type and split by subtype for spatial network features.
View article: Table S7 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma
Table S7 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma Open
Table S7. Results of 5-fold cross-validation with hyperparameter selection. Results across the 15 models evaluated for overall survival and progression-free survival across 5-folds with random forest hyperparameter selection.
View article: Table S14 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma
Table S14 from The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma Open
Table S14. Ranking of feature importance in the random forest model for only primary tumor samples. Ranking of median Gini importance values for all features across 500 evaluations predicting OS and PFS, with the model trained only on prim…