stke.sciencemag.org/cgi/content/full/13/636/eaay1451/DC1 Supplementary Materials for High-throughput dynamic BH3 profiling may quickly and accurately predict effective therapies in solid tumors Patrick Bhola, Eman Ahmed, Jennifer L. Guerriero, Ewa Sicinska, Emily Su, Elizaveta Lavrova, Jing Ni, Otari Chipashvili, Timothy Hagan, Marissa S. Pioso, Kelley McQueeney, Kimmie Ng, Andrew J. Aguirre, James M. Cleary, David Cocozziello, Alaba Sotayo, Jeremy Ryan, Jean J. Zhao, Anthony Letai* *Corresponding author. Email: [email protected]Published 16 June 2020, Sci. Signal. 13, eaay1451 (2020) DOI: 10.1126/scisignal.aay1451 The PDF file includes: Text S1. Detailed HT-DBP protocol. Fig. S1. Schematic of DBP. Fig. S2. Selection of informative peptide screening concentrations. Fig. S3. Identification of a drug screening concentration. Fig. S4. HT-DBP enables identification of compounds that sensitize cancer cells for apoptosis. Fig. S5. Similarity between FACS and microscopy dynamic BH3 profiles. Fig. S6. Images of stained MMTV-PyMT tumor cells. Fig. S7. Example of cell masks. Fig. S8. Screening data for freshly isolated MMTV-PyMT tumors. Fig. S9. Counterscreens in healthy cells. Fig. S10. Supplemental data for in vivo validation experiments for HT-DBP. Fig. S11. Identification of compounds that sensitize the COCA9 colon cancer PDX for apoptosis. Fig. S12. Quantification of apoptotic chemical vulnerabilities in colon cancer PDX models. Fig. S13. Heat map of apoptotic chemical vulnerabilities in colon cancer PDX models. Fig. S14. Comparison of apoptotic chemical vulnerabilities in colon cancer PDX models. Fig. S15. Supplemental data for comparison of chemical vulnerabilities of freshly isolated and cultured cancer cells. Fig. S16. Supplemental data for comparison of chemical vulnerabilities of freshly isolated and cultured cancer cells. Legend for table S1 Legends for data files S1 to S16
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High-throughput dynamic BH3 profiling may quickly and accurately predict
effective therapies in solid tumors
Patrick Bhola, Eman Ahmed, Jennifer L. Guerriero, Ewa Sicinska, Emily Su, Elizaveta Lavrova, Jing Ni, Otari Chipashvili, Timothy Hagan, Marissa S. Pioso, Kelley McQueeney, Kimmie Ng, Andrew J. Aguirre, James M. Cleary,
David Cocozziello, Alaba Sotayo, Jeremy Ryan, Jean J. Zhao, Anthony Letai*
Published 16 June 2020, Sci. Signal. 13, eaay1451 (2020)
DOI: 10.1126/scisignal.aay1451
The PDF file includes:
Text S1. Detailed HT-DBP protocol. Fig. S1. Schematic of DBP. Fig. S2. Selection of informative peptide screening concentrations. Fig. S3. Identification of a drug screening concentration. Fig. S4. HT-DBP enables identification of compounds that sensitize cancer cells for apoptosis. Fig. S5. Similarity between FACS and microscopy dynamic BH3 profiles. Fig. S6. Images of stained MMTV-PyMT tumor cells. Fig. S7. Example of cell masks. Fig. S8. Screening data for freshly isolated MMTV-PyMT tumors. Fig. S9. Counterscreens in healthy cells. Fig. S10. Supplemental data for in vivo validation experiments for HT-DBP. Fig. S11. Identification of compounds that sensitize the COCA9 colon cancer PDX for apoptosis. Fig. S12. Quantification of apoptotic chemical vulnerabilities in colon cancer PDX models. Fig. S13. Heat map of apoptotic chemical vulnerabilities in colon cancer PDX models. Fig. S14. Comparison of apoptotic chemical vulnerabilities in colon cancer PDX models. Fig. S15. Supplemental data for comparison of chemical vulnerabilities of freshly isolated and cultured cancer cells. Fig. S16. Supplemental data for comparison of chemical vulnerabilities of freshly isolated and cultured cancer cells. Legend for table S1 Legends for data files S1 to S16
Other Supplementary Material for this manuscript includes the following: (available at stke.sciencemag.org/cgi/content/full/13/636/eaay1451/DC1)
Table S1 (Microsoft Excel format). List of compounds used in the chemical screen. Data file S1 (Microsoft Excel format). Cell count and delta priming of MDA-MB-231 line. Data file S2 (Microsoft Excel format). Apoptosis and delta priming of MDA-MB-231 line. Data file S3 (Microsoft Excel format). Cell count and delta priming of MMTV-PyMT tumor. Data file S4 (Microsoft Excel format). Technical replicate of MMTV-PyMT delta priming. Data file S5 (Microsoft Excel format). Biological replicate of MMTV-PyMT delta priming. Data file S6 (Microsoft Excel format). Cell count and delta priming of primary mouse hepatocytes. Data file S7 (Microsoft Excel format). Cell count and delta priming of human foreskin fibroblasts. Data file S8 (Microsoft Excel format). Delta priming in adult mouse hepatocytes and MMTV-PyMT tumors. Data file S9 (Microsoft Excel format). Delta priming of freshly isolated MMTV-PyMT tumors by nominal drug target. Data file S10 (Microsoft Excel format). Normalized delta priming of colorectal PDX models. Data file S11 (Microsoft Excel format). Normalized delta priming of colorectal PDX models by nominal target. Data file S12 (Microsoft Excel format). Delta priming of MMTV-PyMT–derived cell line. Data file S13 (Microsoft Excel format). Correlation between MMTV-PyMT tumor and derived cell line. Data file S14 (Microsoft Excel format). Dose response by delta priming in MMTV-PyMT tumor and derived cell line. Data file S15 (Microsoft Excel format). Delta priming in colorectal PDX models. Data file S16 (Microsoft Excel format). Delta priming in primary human CRC cells ex vivo.
Text S1. Detailed HT-DBP protocol HT-DBP involves the steps below:
1. Cell plating and compound treatment. 2. Titration plate to identify the most informative BH3 peptide concentration. 3. Application of the ideal peptide concentration to compound treated plates. 4. Imaging the plates, and image analysis.
Cell plating and compound treatment: 30 µL of dissociated cells were plated in each well of a 384 well plate (Corning 3764 or 3542 plates) using the Thermo Combi multi-well dispenser (Thermo Fisher Scientific). Cell numbers per well varied from 1000-5000 cells per well depending on cell size and adhesive properties of cells. For mouse tumors or PDX models, cells were plated in collagen I coated 384 well plates. MMTV-PyMT cells were plated at a concentration of 4000 cells per well. DF-BM355 cells were plated at a concentration of 5000 cells per well. Colon PDX models were plated at a concentration of 4000-8000 cells per well. Plating for chemical screening was performed in technical duplicate. Cells were given time to settle to the bottom of plates for approximately 20-30 mins, and cells were subsequently drugged using pin transfer machines (ICCB at Harvard Medical School or Broad Institute). Cells and drugs (table S1) were incubated overnight at 37 °C in a 5% CO2 incubator. Titration plate: To identify the optimal peptide concentration for performing chemical screens, we performed a titration of the Bim BH3 peptide to identify the highest Bim peptide concentration where there was little or no loss of cytochrome c. We expect that this concentration will be the most informative at identifying compounds that sensitize cells for apoptosis. Bim BH3 titration plates were generated by making a master plate of 2X MEB solution with digitonin (to have 0.002% final for human cells, and 0.001% for mouse cells), and peptides. A linear, two-fold, 15-point titration was performed for all cells except for the BM355 tumors where there was limited number of cells, and where only an 8-point titration was performed. The titration was performed in at least technical triplicates. To perform the peptide titration, plated cells were washed three times in 1X PBS using the BioTek plate washer (using the “manifold wash” setting), leaving 15 µL residual PBS in the wells. A mixture from the 2X MEB master plate was added to each well (15 µL per well), and cells were incubated at 30 °C for 1 hour. Cells were fixed by adding 10 µL of 8% PFA for 15 mins. Plates were aspirated to a residual volume of 20 µL using the plate washer and 20 µL of the N2 neutralization buffer was added for 5 minutes. Plates were aspirated to a residual volume of 20 µL using the plate washer and 10 µL of a 3X staining solution was added. Cytochrome c was added at a concentration of 1:200. Hoechst was added to a concentration of 1:2000. Cells were incubated for 1 to 2 hours at room temperature then washed and imaged. Details on image analysis are provided below. Screening plates: Once the ideal peptide titration was calculated, a 2X MEB buffer was made with the appropriate concentration of the Bim BH3 peptide and digitonin. Cells were washed 3 times with 1X PBS to a residual volume of 15 µL. The 2X MEB mixture was added to cells (15 µL), and the cells were incubated for 1 hour at 30 °C. Cells were fixed by adding 10 µL of 8% PFA and waiting for 15 mins at room temperature. Liquid in wells were aspirated to 20 µL and 20µL of the N2 solution was added. Plates were incubated at room temperature for 5 mins. Liquid in wells were aspirated to 20 µL and 10 µL of the stain solution was added to cells. Staining solution consisted of 1:2000 of cytochrome c and Hoechst
33342 stain. Stain solutions were incubated overnight at 4C. Cells were subsequently washed 3 times in 1X PBS, and imaged. Image collection: Images were acquired using an IXM or IXM4 (Molecular Devices) microscope. Briefly, we used a 10x widefield objective to capture 1 to 4 images per well, representing about 60-80% of the well bottom, depending on the plate type. Image analysis in metamorph: Images were acquired using the IXM XLS automated microscope. One to four images per field were acquired. Exposure times were adjusted per sample to have the greatest dynamic range of fluorescent signal. For each screen on a cell/model/patient sample, the exposure time was kept constant. Cytochrome c quantification was performed using the multiwavelength cell scoring module in metamorph. Briefly, this module segments cells based on an intensity above local background of cytochrome c. This results in an approximate single cell segmentation (for example, as shown in fig S7) and a quantification of the area of cytochrome c staining. Cells were scored as positive or negative based on whether the area exceeded a user-specified cutoff. Cutoffs were identified by using known positive- and negative-control drugs. Typically, several cutoffs were selected, and the best cutoff was identified from the screen. Quantification of delta priming: The percentage of cytochrome c positive cells in a well was translated into delta priming measurements as described in the text. Briefly, the percentage of cytochrome c positive cells were calculated for all DMSO wells, and the difference between positive cells in DMSO and drug treated wells was calculated. To calculate delta priming, we use the percent positive cytochrome c cells in DMSO wells from each plate (and not across the whole experiment). This can mitigate plate effects in screening.
For each well, we identified the delta priming value. These raw values were matched to the well map using excel. Hits were identified as compounds that increase apoptotic priming at least 3 standard deviation above the background seen in DMSO treated wells. Comparisons of delta priming between different tumors is complicated by two factors: (i) assay noise in present in the DMSO treated wells, and (ii) the maximum level of cytochrome c observed. DMSO served as the negative control in our screen. Using typical screening approaches, hits were identified as compounds that have a delta priming signal at least 3 times greater than the standard deviation observed in the negative control. In different colon PDX models, we observed different hit cutoffs (fig. S11A). Furthermore, we observe that there is a different maximum delta priming that we can measure for each tumor. This is a result of the amount of cytochrome c loss present in DMSO-treated wells which itself is dependent on the viability of cells after 24 hours, and the baseline cytochrome c release at the screening peptide concentration. To enable a comparison of delta priming of different PDX models, we performed a linear normalization of delta priming so that 0 corresponds to 3 times the standard deviation of DMSO treated wells and 100 corresponds to the maximum delta priming value measured. All other compounds that do not increase delta priming above the noise cutoff are set to 0. The formula for normalized delta priming is described below:
(green), and Alexa-647-conjugated cytochrome c antibody. A few cells are Epcam-negative, indicated by
white arrows. Scale bars, 100 µm.
Fig. S7. Example of cell masks. This is an example of the identification of nuclei and cytochrome c
staining of MMTv-PyMT tumor cells using the multi-wavelength cell scoring module in Metamorph. In the
merged image, blue represents Hoechst 33342 (nuclei), green represents EpCam, and red represents
cytochrome c. Cell boundaries were identified by cytochrome c signal.
Fig. S8. Screening data for freshly isolated MMTV-PyMT tumors. (A) Pre-screen Z’ scores of MMTV-PyMT tumors indicate negligible positional plate bias. (B) Z’ score from different plates for a screen of MMTV-PyMT tumors. DMSO is used as the negative control, 1 µM of AZD8055 is used as the positive control. Data is representative of 2 independent experiments. (C) Technical replicates of chemical
screens (R2 = 0.81; p < 0.001, by Pearson analysis from a single screen). (D) Biological replicates of
chemical screens (R2 = 0.81; p < 0.0001, by Pearson analysis).
Fig. S9. Counterscreens in healthy cells. (A) Bid peptide dose response curves in freshly isolated
mouse hepatocytes. Scale bars, 200 µm. Images are representative of n=2 screens. (B) Quantification of
the dose response curve in (A). Data is representative of n=2 screens. (C) HT-DBP on adult mouse
hepatocytes to identify drugs that increase apoptotic priming in hepatocytes. Data is mean of n=2
screens. (D) Bim peptide dose response curves in human foreskin fibroblasts. Scale bar, 100 µm. Images
are representative of n=2 screens. (E) Quantification of the dose response curve in (D). Data is
representative of n=2 screens. (F) HT-DBP on human foreskin fibroblasts shows several drugs that
increase apoptotic priming in fibroblasts. Data is mean of n=2 screens.
Fig. S10. Supplemental data for in vivo validation experiments for HT-DBP. (A) Images of Bim
peptide dose response curve in the BM-355 breast cancer PDX model. Scale bars, 200 µm. (B)
Quantification of the Bim peptide dose response curve in BM-355. For this dynamic BH3 profile, a
concentration of 0.205 µM Bim (indicated by the arrow) was chosen to perform the dynamic BH3 profile.
Images and quantification are representative of 2 independent experiments.
Fig. S11. Identification of compounds that sensitize the COCA9 colon cancer PDX for apoptosis.
(A) Example of staining by Hoechst 33342, Alexa488-conjugated antibody to EpCam, and Alexa647-
conjugated antibody to cytochrome c in COCA9 cells. Scale bars, 200 µm. (B) Images of cytochrome c
loss in COCA9 cells with increasing concentrations of the synthetic Bim BH3 peptide. Scale bars, 200 µm.
(C) Quantification of peptide-induced cytochrome c loss in COCA9 cells described in (B). Data represent
means ±SD. Arrow indicates the peptide screening concentration. (D) HT-DBP screen in COCA9 cells
using 1650 compounds from the Selleck Bioactive Library (table S1).
Fig. S12. Quantification of apoptotic chemical vulnerabilities in colon cancer PDX models. (A)
Demonstration that different HT-DBP screens in colon cancer PDX models have variable standard
deviation of delta priming in DMSO treated wells. (B) Comparison of priming across cell lines at 3 times
the standard deviation of DMSO-treated wells, as is often used as a cutoff for hits in chemical screens.
Variability of the maximum delta priming, and the cutoff priming value for a chemical hit makes tumor to