Synergistic activity of ABT-263 and ONC201/TIC10 against solid tumor cell lines is associated with suppression of anti-apoptotic Mcl-1, BAG3, pAkt, and upregulation of pro-apoptotic Noxa and Bax cleavage during apoptosis.

A major underlying cause of the resistance of solid tumor cells to cancer therapy is the evasion of cell death following anti-cancer drug treatment. We explored the combination of TRAIL-inducing compound ONC201/TIC10 and Bcl-xL/Bcl-2 inhibitor ABT-263 to target the extrinsic and intrinsic apoptotic pathways, respectively, in solid tumor cell lines (N = 13) derived from different tissues (colon, prostate, lung, breast, ovary, bladder). We found an IC50 range of 0.83-20.10 µM for ONC201 and 0.06-14.75 µM for ABT-263 among the 13 cancer cell lines. We show that combination of ONC201 and ABT-263 produces a strong synergistic effect leading to tumor cell death, and that the combination is not toxic to human fibroblast cells. In OVCAR-3 ovarian cancer cells, 2.5 µM ONC201 and 1.25 µM ABT-263 yielded 37% and 27% inhibition of viability, respectively, while the combination of the two agents yielded 92% inhibition of viability, resulting in a high synergy score of 52; conversely, the same combination in the HFF-1 human fibroblast cells yielded 2.45% inhibition of viability and a synergy score of 6.92 (synergy scores were calculated using SynergyFinder; scores greater than 10 are considered synergistic). We also found that the combination of these two agents resulted in synergistic caspase activation and PARP cleavage consistent with induction of apoptosis. Combination therapy-induced cell death correlated with decreased levels of Mcl-1, BAG3, pAkt, and upregulation of Noxa along with Bax cleavage during apoptosis at 48 hours, and ATF4, TRAIL, and DR5 induction at 24 hours. There was some heterogeneity in the cell lines with regard to these responses. Our data provide evidence for synergy from the combination of ONC201 and ABT-263 against human solid tumor cell lines associated with alterations in cell death and pro-survival mediators. The combination of ONC201 and ABT-263 merits further exploration in vivo and in clinical trials against a variety of solid malignancies.

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning.

A principal challenge in the analysis of tissue imaging data is cell segmentation-the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets. We used TissueNet to train Mesmer, a deep-learning-enabled segmentation algorithm. We demonstrated that Mesmer is more accurate than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We then adapted Mesmer to harness cell lineage information in highly multiplexed datasets and used this enhanced version to quantify cell morphology changes during human gestation. All code, data and models are released as a community resource.