Search

Additional file 2: Fig. S1. Timeline of sample acquisition, sequencing, in vitro culture and TMZ screening. Fig. S2. Gaussian Mixture Model used to identify genes with the same expression profile between patient-derived cells (PDCs) and tumor tissues. Fig. S3. Principal Component Analysis and differentially expressed gene analysis on 34 tissue RNA-seq samples. Fig. S4. Association of MGMT promoter methylation status to survival and in vitro TMZ screening in the main cohort. Fig. S5. Copy number estimation by GliomaSCAN. Fig. S6. Copy number estimation by RNA-seq. Fig. S7. Machine learning model feature importance. Fig. S8. Correlations between GBM subtypes and TMZ response. Fig. S9. Comparison of survival prediction in TCGA cohort. Fig. S10. Genomic landscape of multi-sector samples. Fig. S11. TMZ-resistant marker expression and CNV comparison in patient M13 and M14. Fig. S12. Progression free survival difference in patients with multi-sector samples.

Additional file 3. Supplementary methods.

Additional file 4: Table S2. Clinicopathological features associated with response to pembrolizumab (p value by Fisher���s exact test). Table S3. Safety profiles. Fig. S1. Survival analysis. (A-B) PFS and OS of all patients (n = 60). (C-D) PFS and OS according to PR vs. SD/PD. (E-F) PFS and OS according to PD vs. PR/SD. Fig. S2. Prevalence of tumor-infiltrating lymphocytes (A) and PD-L1 expression level (B) based on CTNNB1 somatic mutation. Fig. S3. Summary of nonsynonymous mutations in 47 HCC patients. Fig. S4. Correlation between the expression of neutrophil gene markers and NLR (A). Expression level of neutrophil gene markers according to NLR (B). Fig. S5. GSEA plots representing CHIANG_LIVER_CANCER_SUBCLASS_PROLIFERATION_UP pathway (A) and CHIANG_LIVER_CANCER_SUBCLASS_CTNNB1_DN pathway (B) were enriched in responders (PR). Patients with CTNNB1 mutation showed the higher expression levels of CTNNB1_UP geneset (C) and the lower expression levels of CTNNB1_DN geneset (D)

Additional file 2: The protocol revision history.

Additional file 1: The study protocol.

Additional file 2. Review History.

Additional file 1: Figure S1. A schematic illustration on identification of MA-TAM signature and master regulators. Figure S2. MA-TAM target gene pathway enrichment. Figure S3. Global regulatory network of MA-TAM. Figure S4. Multi-color immunohistochemical images of MA-TAM encoding molecules. Figure S5. Effects of MARCO and CCL7 on mesenchymal markers. Figure S6. Effects of TAM-derived CM on GSC stemness in response to irradiation. Figure S7. Effects of anti-MARCO therapeutic antibodies. Figure S8. In vivo effects of MARCOhigh TAMs in PDX models. Figure S9. Clinical correlation of MA-TAM master regulators. Figure S10. Single cell analysis of MA-TAM signature. Figure S11. Transcriptome analysis of scTHI at single-cell resolution. Figure S12. Cytokine array-based characterization of MARCOhigh TAMs. Figure S13. Anatomical expression of MA-TAM signature.