The following article features coverage from the American Association for Cancer Research (AACR) 2019 meeting. Click here to read more of Cancer Therapy Advisor‘s conference coverage.

Researchers from the SUC2-NSF-TVF Drug Combination Convergence Team reported on the development of a mechanistic model of cancer cell signaling for the prediction of drug resistance in estrogen receptor (ER)-positive, PIK3CA-mutant breast cancer, as well as results of experiments performed in breast cancer cell lines designed to test these predictions. The findings from this study were presented at the American Association for Cancer Research Annual Meeting 2019.

Limitations in the understanding of the molecular mechanisms of intrinsic and acquired resistance to specific therapies, as well as in the identification of drug combinations to prevent the development of (or to circumvent) these resistance mechanisms, have interfered with the attainment of durable disease control in the setting of many invasive solid tumors.

Related Articles

To address this problem, members of the SUC2-NSF-TVF Drug Combination Convergence Team developed a comprehensive mechanistic network model of signal transduction in ER-positive, PIK3CA-mutant breast cancer that incorporated known mechanisms of drug resistance; this model predicted that loss of RB1, FOXO3, P27, or PRAS40 may be involved in these processes, and that enhancement of the effectiveness of phosphoinositide 3-kinase (PI3K) inhibitor therapy would be attained through its combination with an MCL1 inhibitor.

Continue Reading

To experimentally test the model predictions, researchers utilized genome-wide clustered regularly interspaced short palindromic repeats (CRISPR) technology to develop screens of 2 breast cancer cell lines. 

More specifically, these types of screens often include library cell populations in which specific genes are knocked out; hence, the subsequent application of selective pressure through drug exposure followed by high-throughput gene sequencing can allow for the interrogation of the roles of specific genes involved in a particular phenotype. 

In this study, cells in the CRISPR screen were found to be enriched in loss-of-function alterations in the predicted genes RB1, FOXO3, P27, and PRAS40 following treatment with 2 different PI3K inhibitors (BYL719 and GDC0032), and some resistance genes were specific to certain cell lines. Furthermore, follow-up experiments, including cell viability assays and cell death assays, showed that the antiapoptotic MCL1 inhibitor, S63845, enhanced the effectiveness of a PI3K inhibitor, BYL719, and that this effect was also cell-line–specific. 

“Next iterations of the model will incorporate the identified discrepancies and newly identified resistance mechanisms to drugs of clinical interest,” the researchers noted. 

Disclosure: The presenters disclosed financial ties to various pharmaceutical companies. For a full list of disclosures, please see the original abstract.

Read more of Cancer Therapy Advisor‘s coverage of AACR 2019 meeting by visiting the conference page.


  1. Gomez Tejeda Zañudo J, Mao P, Montero J, et al. Network modeling of drug resistance mechanisms and drug combinations in breast cancer. Presented at: American Association for Cancer Research (AACR) Annual Meeting 2019; March 29–April 3, 2019; Atlanta, GA. Abstract 675/8.