Figure 4.
Identification of potential upstream MRs that drive MM progression in the PrEDiCT model. (A) Prediction of MRs. A total of 28 MRs were predicted to be significant (FDR <5%), including 15 activated and 13 repressed ones. Examples of significant MRs were labeled. The full list of significant MRs is shown in supplemental Table 3. (B) Achilles CRISPR dependency scores of all significant MRs, including activated (orange) and repressed regulated (blue) MRs. (C) Example of known and novel MRs in MM. Out of activated (Act) MRs, MYC and CDKN2A rank at the top and are known to be involved in MM progression. HMGA1, PA2G4, and TRIM28 were selected for further experimental validation. (D) Validation of selected MRs by in vivo targeted CRISPR screen. Late-timepoint BM samples from 8 mice were compared with matched primary tumor samples using MAGeCK. The resulting log2 fold changes for each sgRNA were summarized by their targeting gene.

Identification of potential upstream MRs that drive MM progression in the PrEDiCT model. (A) Prediction of MRs. A total of 28 MRs were predicted to be significant (FDR <5%), including 15 activated and 13 repressed ones. Examples of significant MRs were labeled. The full list of significant MRs is shown in supplemental Table 3. (B) Achilles CRISPR dependency scores of all significant MRs, including activated (orange) and repressed regulated (blue) MRs. (C) Example of known and novel MRs in MM. Out of activated (Act) MRs, MYC and CDKN2A rank at the top and are known to be involved in MM progression. HMGA1, PA2G4, and TRIM28 were selected for further experimental validation. (D) Validation of selected MRs by in vivo targeted CRISPR screen. Late-timepoint BM samples from 8 mice were compared with matched primary tumor samples using MAGeCK. The resulting log2 fold changes for each sgRNA were summarized by their targeting gene.

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