Growth rate-corrected (GR) dose-response metric signatures across a panel of 71 breast cancer cell lines treated with a library of small molecule and antibody perturbagens. Dataset 4 of 4: Median, upper quartile, and lower quartile GR metrics per perturbagen class. - Dataset (ID:20271)
|HMS Dataset ID:||20271|
|Dataset Title:||Growth rate-corrected (GR) dose-response metric signatures across a panel of 71 breast cancer cell lines treated with a library of small molecule and antibody perturbagens. Dataset 4 of 4: Median, upper quartile, and lower quartile GR metrics per perturbagen class.|
|Screening Lab Investigator:||Marc Hafner, Laura M. Heiser|
|Screening Principal Investigator:||Peter K. Sorger, Joe W. Gray|
|Assay Description:||Dose-response data were collected for a panel of 73 breast cancer cell lines grown under standard or modified culture conditions and treated with 139 small molecule and antibody perturbagens. A subset of these data was previously described in Heiser et al. (2012) (PMID: 22003129) and Daemen et al. (2013) (PMID: 24176112). Here, the complete dataset was analyzed using the growth rate inhibition (GR) metrics described in Hafner et al. (2016) (PMID: 27135972) to account for differences in growth rates across the cell lines and treatment conditions.|
1. As reported previously in Kuo et al. (2009) (PMID: 20003408) and Heiser et al. (2012) (PMID: 22003129), cells were plated at proper density (from 1,000 to 15,000 cells/100 μL/well, depending on the cell line) in 96-well plates so that they remained in logarithmic growth at the time of the assay. |
2. The cells were allowed to attach overnight before being treated in triplicate with a set of nine doses in 1:5 serial dilution per small molecule or antibody perturbagen.
3. Adenosine triphosphate content was measured as a proxy for relative cell count using the CellTiter-Glo® (CTG) Luminescent Cell Viability Assay (Promega, WI, USA) with slight modifications of the manufacturer's protocol. Briefly, the CTG reagent was diluted with phosphate-buffered saline (PBS; 1:1, volume:volume), and the culture media was removed from the 96-well plate prior to adding 50 μL per well of the diluted CTG reagent. Luminescence from the assay was recorded using a BIO-TEK FLx800. Background CTG signal was determined by measuring the luminescent signal in wells without cells. CTG-based cell counts were obtained at day 0 from untreated plates grown in parallel to the point of treatment and at day 3 after perturbagen exposure from treated plates (doi: 10.7303/syn8094063.1 and the associated reference file package).
4. Consistent with the methods reported in Hafner et al. (2016) (PMID: 27135972), normalized growth rate inhibition (GR) values for each technical replicate of each biological replicate/cell line/perturbagen/concentration combination were calculated according to the following formula: 2^[log2(x(c)/x0)/log2(xctrl/x0)]-1 where x(c) is the background-subtracted average CTG value measured after a given treatment, x0 is the median background-subtracted CTG value from the day 0 untreated plate, and xctrl is the robust mean of the background-subtracted CTG value of the DMSO-treated control wells on the same treated plate (defined as xctrl=mean(x(abs(log10(x)-log10(mean(x)))<1.5)), where x are all DMSO-treated control values). Background-subtracted CTG values below 1, which occurred only for 0.1% of the data, were set to 1.
5. GR values for each biological replicate/condition combination then were calculated as the robust average of the three technical replicates (defined as x(c)= mean(x(abs(log10(x)-log10(mean(x)))<1)), where x are the technical replicates for a given condition).
6. The nominal division rate of each cell line, CtrlNDiv, was defined as log2(xctrl/x0).
7. The dataset was filtered to remove cell lines for which the untreated controls grew less then 23% over the course of the assay (corresponding to CtrlNDiv > 0.3) and to remove treatments involving reagents (cell lines and perturbagens) for which metadata could not be adequately defined. Following filtering, the dataset consisted of relative cell counts, calculated GR values, and calculated division rates (CtrlNDiv) across 8882 cell line/perturbagen combinations including replicates and 4788 unique cell line/perturbagen combinations excluding replicates (HMS LINCS Dataset #20268).
8. For each biological replicate/cell line/perturbagen combination, mean GR values across all tested concentrations were fitted to a sigmoidal curve to extract GR50, GRmax, GRinf, Hill coefficient, GR_AOC, GEC50, and r2 values (HMS LINCS Dataset #20269).
9. To extract GR metrics signatures across the perturbagens tested, we selected only the conditions in which the untreated division time was below 80 hours (corresponding to CtrlNDiv > 0.9) and averaged each GR metric across biological replicates (calculated as the geometric mean for GR50 and GEC50 and as the arithmetic mean for all other metrics) for 4650 cell line/perturbagen pairs (a median of 88 perturbagens per cell line). To extract signatures across all perturbagens, we evaluated the median, lower quartile, and upper quartile of each GR metric for each perturbagen across all cell lines (HMS LINCS Dataset #20270). To extract signatures across perturbagen classes, perturbagens were grouped by class based on their target, and the median, lower quartile, and upper quartile of each GR metric was evaluated across all cell line/perturbagen pairs for a given perturbagen class (HMS LINCS Dataset #20271).
|Assay Protocol Reference:||
1. Kuo, W.L., Das, D., Ziyad, S., Bhattacharya, S., Gibb, W.J., Heiser, L.M., Sadanandam, A., Fontenay, G.V., Hu, Z., Wang, N.J., Bayani, N., Feiler, H.S., Neve, R.M., Wyrobek, A.J., Spellman, P.T., Marton, L.J., and Gray, J.W. (2009) A systems analysis of the chemosensitivity of breast cancer cells to the polyamine analogue PG-11047. BMC Med. 7:77. doi:10.1186/1741-7015-7-77 PMID: 20003408 PMCID: PMC2803786|
2. Heiser, L.M., Sadanandam, A., Kuo, W.L., Benz, S.C., Goldstein, T.C., Ng, S., Gibb, W.J., Wang, N.J., Ziyad, S., Tong, F., Bayani, N., Hu, Z., Billig, J.I., Dueregger, A., Lewis, S., Jakkula, L., Korkola, J.E., Durinck, S., Pepin, F., Guan, Y., Purdom, E., Neuvial, P., Bengtsson, H., Wood, K.W., Smith, P.G., Vassilev, L.T., Hennessy, B.T., Greshock, J., Bachman, K.E., Hardwicke, M.A., Park, J.W., Marton, L.J., Wolf, D.M., Collisson, E.A., Neve, R.M., Mills, G.B., Speed, T.P., Feiler, H.S., Wooster, R.F., Haussler, D., Stuart, J.M., Gray, J.W., and Spellman, P.T. (2012) Subtype and pathway specific responses to anticancer compounds in breast cancer. Proc Natl Acad Sci USA. 109(8):2724-9. doi:10.1073/pnas.1018854108 PMID: 22003129 PMCID: PMC3286973
3. Heiser, L.M., Wang, N.J., Korkola, J.E., and Gray, J.W. (2017) Synapse. doi:10.7303/syn8094063.1
4. Hafner, M., Heiser, L.M., Williams, E.H., Wang, N.J., Korkola, J.E., Gray, J.W., and Sorger, P. K. (2017) Reference file package
5. Hafner, M., Niepel, M., Chung, M., and Sorger, P.K. (2016) Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs. Nat Methods. 13(6):521-527. doi:10.1038/nmeth.3853 PMID: 27135972 PMCID: PMC4887336
|HMS Dataset Type:||Analysis|
|Date Publicly Available:||2016-12-22|
|Most Recent Update:||2017-02-01|