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mQC: A Heuristic Quality-Control Metric for High-Throughput Drug Combination Screening

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ABSTRACT

Quality control (QC) metrics are critical in high throughput screening (HTS) platforms to ensure reliability and confidence in assay data and downstream analyses. Most reported HTS QC metrics are designed for plate level or single well level analysis. With the advent of high throughput combination screening there is a need for QC metrics that quantify the quality of combination response matrices. We introduce a predictive, interpretable, matrix-level QC metric, mQC, based on a mix of data-derived and heuristic features. mQC accurately reproduces the expert assessment of combination response quality and correctly identifies unreliable response matrices that can lead to erroneous or misleading characterization of synergy. When combined with the plate-level QC metric, Z’, mQC provides a more appropriate determination of the quality of a drug combination screen. Retrospective analysis on a number of completed combination screens further shows that mQC is able to identify problematic screens whereas plate-level QC was not able to. In conclusion, our data indicates that mQC is a reliable QC filter that can be used to identify problematic drug combinations matrices and prevent further analysis on erroneously active combinations as well as for troubleshooting failed screens. The R source code of mQC is available at http://matrix.ncats.nih.gov/mQC.

No MeSH data available.


Comparison of readout, size of matrix and cell with respect to 7 feature distributions.(A) Readout (Caspase-Glo(CG) vs. CellTiter-Glo(CTG)). (B) Size of matrix (6 × 6 vs. 10 × 10). (C) Cell. * = contaminated cell line. The arrows indicate the major difference between groups which significantly affects the mQC assessment.
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f6: Comparison of readout, size of matrix and cell with respect to 7 feature distributions.(A) Readout (Caspase-Glo(CG) vs. CellTiter-Glo(CTG)). (B) Size of matrix (6 × 6 vs. 10 × 10). (C) Cell. * = contaminated cell line. The arrows indicate the major difference between groups which significantly affects the mQC assessment.

Mentions: Very often Z’ is determined based on the effect of a positive control on the assay. However, in some cases, the positive control is not available or cannot produce the maximum change in signal that the assay can measure. For example, Promega Caspase-Glo 3/7 (CG) used in many of our combination screens measures the induction of apoptosis as an increase in luminescence signal. Bortezomib is a proteasome inhibitor which is a potent cytotoxic compound for most of the cells tested and it is used as a positive control in the cell proliferation assay. However, Bortezomib does not produce cytotoxic effects by induction of apoptosis in all cells, and therefore, for its use as a positive control for Caspase-Glo assay readout is not appropriate for some cell lines. For example, we are able to confirm several synergistic combinations against L1236 cell line in a CG screen (assay ID 3785 in Table 2)22, although Bortezomib failed to induce significant Caspase activity compare with DMSO (Supplementary Fig. S5A). Besides, mQC offers an alternative QC metric to compare different assay readouts independent of the availability of the positive control. When comparing Promega CellTiter-Glo (CTG) and CG, we observed that the quality of CTG is significantly better than CG from 3,084 paired comparisons of response matrices, in which mQC of CTG was found better in 949 cases, worse in 191 cases and equal in 1944 cases (p-value = 1.18 × 10−63). Compared with CTG, CG has a significantly higher occurrence of rugged activity pattern (smoothness.p > 10−4), random spatial autocorrelation (moran.p < 10−7) and non-monotonic dose response (mono.v < 0.7) (Fig. 6A). This result indicates that the assay readouts which measure conditional enzymatic activity (e.g., apoptosis via caspase activity) can be more challenging to optimize and less stable than simple readouts that measure the baseline metabolites (e.g., cell viability via ATP amount) for cHTS.


mQC: A Heuristic Quality-Control Metric for High-Throughput Drug Combination Screening
Comparison of readout, size of matrix and cell with respect to 7 feature distributions.(A) Readout (Caspase-Glo(CG) vs. CellTiter-Glo(CTG)). (B) Size of matrix (6 × 6 vs. 10 × 10). (C) Cell. * = contaminated cell line. The arrows indicate the major difference between groups which significantly affects the mQC assessment.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5121902&req=5

f6: Comparison of readout, size of matrix and cell with respect to 7 feature distributions.(A) Readout (Caspase-Glo(CG) vs. CellTiter-Glo(CTG)). (B) Size of matrix (6 × 6 vs. 10 × 10). (C) Cell. * = contaminated cell line. The arrows indicate the major difference between groups which significantly affects the mQC assessment.
Mentions: Very often Z’ is determined based on the effect of a positive control on the assay. However, in some cases, the positive control is not available or cannot produce the maximum change in signal that the assay can measure. For example, Promega Caspase-Glo 3/7 (CG) used in many of our combination screens measures the induction of apoptosis as an increase in luminescence signal. Bortezomib is a proteasome inhibitor which is a potent cytotoxic compound for most of the cells tested and it is used as a positive control in the cell proliferation assay. However, Bortezomib does not produce cytotoxic effects by induction of apoptosis in all cells, and therefore, for its use as a positive control for Caspase-Glo assay readout is not appropriate for some cell lines. For example, we are able to confirm several synergistic combinations against L1236 cell line in a CG screen (assay ID 3785 in Table 2)22, although Bortezomib failed to induce significant Caspase activity compare with DMSO (Supplementary Fig. S5A). Besides, mQC offers an alternative QC metric to compare different assay readouts independent of the availability of the positive control. When comparing Promega CellTiter-Glo (CTG) and CG, we observed that the quality of CTG is significantly better than CG from 3,084 paired comparisons of response matrices, in which mQC of CTG was found better in 949 cases, worse in 191 cases and equal in 1944 cases (p-value = 1.18 × 10−63). Compared with CTG, CG has a significantly higher occurrence of rugged activity pattern (smoothness.p > 10−4), random spatial autocorrelation (moran.p < 10−7) and non-monotonic dose response (mono.v < 0.7) (Fig. 6A). This result indicates that the assay readouts which measure conditional enzymatic activity (e.g., apoptosis via caspase activity) can be more challenging to optimize and less stable than simple readouts that measure the baseline metabolites (e.g., cell viability via ATP amount) for cHTS.

View Article: PubMed Central - PubMed

ABSTRACT

Quality control (QC) metrics are critical in high throughput screening (HTS) platforms to ensure reliability and confidence in assay data and downstream analyses. Most reported HTS QC metrics are designed for plate level or single well level analysis. With the advent of high throughput combination screening there is a need for QC metrics that quantify the quality of combination response matrices. We introduce a predictive, interpretable, matrix-level QC metric, mQC, based on a mix of data-derived and heuristic features. mQC accurately reproduces the expert assessment of combination response quality and correctly identifies unreliable response matrices that can lead to erroneous or misleading characterization of synergy. When combined with the plate-level QC metric, Z&rsquo;, mQC provides a more appropriate determination of the quality of a drug combination screen. Retrospective analysis on a number of completed combination screens further shows that mQC is able to identify problematic screens whereas plate-level QC was not able to. In conclusion, our data indicates that mQC is a reliable QC filter that can be used to identify problematic drug combinations matrices and prevent further analysis on erroneously active combinations as well as for troubleshooting failed screens. The R source code of mQC is available at http://matrix.ncats.nih.gov/mQC.

No MeSH data available.