Last updated: 2020-09-25

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Knit directory: BH3profiling/analysis/

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Rmd 90ada8f Junyan Lu 2020-08-26 Start workflowr project.

Source materials for figures in the manuscript

Analysis using individual concentration

1. Explore the landscape of baseline BH3 profile of CLL samples

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  • Description: In this analysis, I explored the associations between baseline BH3 profile and genomics/transcriptomic/proteomic and other phenotypic feature of CLL samples in our cohort. The purpose is to understand the factors that determinate the heterogeneity of CLL baseline BH3 profile.

  • Key findings:

  1. Overall, the key genomic determinants are IGHV and trisomy12 as PC1 and PC2 are associated with trisomy12 and IGHV, respectively, which is not unexpected. Interestingly, PC3 shows specific associations with IP-CLL group, which is not fully understood.

  2. Beside IGHV and trisomy12, del17p and NOTCH1 also co-determines some BH3 peptide responses.

  3. On transcriptomic levels, some genes are identified as significantly correlated BH3 profile. I found one gene, PMAIP1, is quite interesting, as it's associated with mitochondrial membrane changes. There are also some other genes, I didn't check them one by one, but there should be similar cases. Based on enrichment analysis, down-regulated oxidative phosphorylation pathway is associated with higher cytC release while up-regulated TNF/NFKB pathway is correlated with higher cytC release.

  4. There are also quite a lot proteins associated with BH3 profile. There quite a few redox related proteins showed negative correlations with cytC release, which indicate the ROS pathway maybe involved. But the pathway enrichment results look a little different.

  5. The BH3 profile strongly associates with the spontaneous apoptosis of CLL cells during culture. The high cytC release after peptide treatments significantly correlate cell survival measure by both microscope and ATP assay.

2. Predict drug responses using baseline BH3 profile

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  • Description: In this analysis, I tried to find whether BH3 profile can predict ex-vivo drug responses measured by either ATP assay or Annexin assay and whether BH3 profiling can add information for predicting drugs response compared to genomics and other omics.

  • Key findings:

  1. The baseline BH3 profile already shows quite substantial correlations with ex-vivo drug responses measured by ATP assays. I used two in-house drug screening assays. The results show some similarity and also discrepancy between those two datasets. We need to discuss further about how to interpret the results and what dataset to use in the final manuscript.

  2. In the annexin dataset, the baseline BH3 profile does not strongly associate with drug treatment along. But it shows pretty strong associations with drug+venetoclax effect as well as synergistic effect. I think this is because drug treatment alone does not really induce strong apoptosis in Annexin assay. Interestingly, the drug only effect in the annexin data does not reproduce the ATP assay results for the same drugs, but the reproducibility is improved with drug + venetoclax, even though we only used single drug in the ATP assay. This may mean drug treatment alone may reduce viability, but not sufficient to induce apoptosis.

  3. The baseline BH3 profile can indeed add information to explain drug response when compared to genomic data. But transcriptomic data is still much better than BH3 profile in explaining drug responses, although there are a few exceptions.

  4. The BH3 profile also predict in vivo treatment responses. However, the statistical power is not enough as there are only very few samples with clinical drug response data. But it looks anyway very interesting.

3. Predict clinical outcomes using baseline BH3 profile

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  • Description: In this analysis, I tried to find whether BH3 profile can predict clinical outcomes like time to treatment (TTT) and overall survival (OS), but it seems the BH3 profile is not very informative in predicting outcomes, especially when compared to established risk factors.

4. Explore dynamic BH3 profile

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  • Description: This is a preliminary analysis of the dynamic BH3 profiling data. I focused on the synergistic effect of drug + peptide and whether this synergistic effect is associated with genomics and synergistic in drug responses.

  • Key findings:

  1. I observed some synergistic effect in inducing cytC release with drug + peptide. The synergistic effect indicates peptide + drug performs better in inducing cytC release than expected combined effect. Note that this analysis is not suitable for Idelalisib, as it was screen on a different plate without DMSO/DMSO controls.

  2. Some mutations seem to increase or decrease the synergistic effect, such as NOTCH1, trisomy12, TP53 and ATM mutations, which also make sense.

  3. The synergistic effect in BH3 profile also correlated with the synergistic effect in Annexin assay.

  4. The overall cytC release with drug + peptide treatment also associate with ex-vivo drug responses from ATP assay. Maybe even better than baseline BH3 profile. But I need to do more tests.

Analysis using area under curve (AUC) of the cytC release

1. Explore the landscape of baseline BH3 profile of CLL samples]

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2. Predict drug responses using baseline BH3 profile

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3. Predict clinical outcomes using baseline BH3 profile

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4. Explore dynamic BH3 profile

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