Cell Line-Specific Network Models of ER+ Breast Cancer Identify Potential PI3Kα Inhibitor Resistance Mechanisms and Drug Combinations

Boolean modeling of breast cancer signaling pathways uncovers mechanisms of drug synergy

Breast cancer is one of the most common types of cancer in females. While drug combinations have shown potential in breast cancer treatments, identifying new effective drug pairs is challenging due to the vast number of possible combinations among available compounds. Efforts have been made to accelerate the process with in silico predictions. Here, we developed a Boolean model of signaling pathways in breast cancer. The model was tailored to represent five breast cancer cell lines by integrating information about cell-line specific mutations, gene expression, and drug treatments. The models reproduced cell-line specific protein activities and drug-response behaviors in agreement with experimental data. Next, we proposed a calculation of protein synergy scores (PSSs), determining the effect of drug combinations on individual proteins' activities. The PSSs of selected proteins were used to investigate the synergistic effects of 150 drug combinations across five cancer cell lines. The comparison of the highest single agent (HSA) synergy scores between experiments and model predictions from the MDA-MB-231 cell line achieved the highest Pearson's correlation coefficient of 0.58 with a great balance among the classification metrics (AUC = 0.74, sensitivity = 0.63, and specificity = 0.64). Finally, we clustered drug pairs into groups based on the selected PSSs to gain further insights into the mechanisms underlying the observed synergistic effects of drug pairs. Clustering analysis allowed us to identify distinct patterns in the protein activities that correspond to five different modes of synergy: 1) synergistic activation of FADD and BID (extrinsic apoptosis pathway), 2) synergistic inhibition of BCL2 (intrinsic apoptosis pathway), 3) synergistic inhibition of MTORC1, 4) synergistic inhibition of ESR1, and 5) synergistic inhibition of CYCLIN D. Our approach offers a mechanistic understanding of the efficacy of drug combinations and provides direction for selecting potential drug pairs worthy of further laboratory investigation.

FOXO3a-interacting proteins' involvement in cancer: a review

Due to its role in apoptosis, differentiation, cell cycle arrest, and DNA damage repair in stress responses (oxidative stress, hypoxia, chemotherapeutic drugs, and UV irradiation or radiotherapy), FOXO3a is considered a key tumor suppressor that determines radiotherapeutic and chemotherapeutic responses in cancer cells. Mutations in the FOXO3a gene are rare, even in cancer cells. Post-translational regulations are the main mechanisms for inactivating FOXO3a. The subcellular localization, stability, transcriptional activity, and DNA binding affinity for FOXO3a can be modulated via various post-translational modifications, including phosphorylation, acetylation, and interactions with other transcriptional factors or regulators. This review summarizes how proteins that interact with FOXO3a engage in cancer progression.

Phosphoinositide 3-Kinase (PI3K) Inhibitors and Breast Cancer: An Overview of Current Achievements

The phosphatidylinositol 3-kinase (PI3K) pathway is one of the most altered pathways in human cancers, and it plays a central role in cellular growth, survival, metabolism, and cellular mobility, making it a particularly interesting therapeutic target. Recently, pan-inhibitors and then selective p110α subunit inhibitors of PI3K were developed. Breast cancer is the most frequent cancer in women and, despite therapeutic progress in recent years, advanced breast cancers remain incurable and early breast cancers are at risk of relapse. Breast cancer is divided in three molecular subtypes, each with its own molecular biology. However, PI3K mutations are found in all breast cancer subtypes in three main "hotspots". In this review, we report the results of the most recent and main ongoing studies evaluating pan-PI3K inhibitors and selective PI3K inhibitors in each breast cancer subtype. In addition, we discuss the future of their development, the various potential mechanisms of resistance to these inhibitors and the ways to circumvent them.

Bcl-xL inhibition radiosensitizes PIK3CA/PTEN wild-type triple negative breast cancers with low Mcl-1 expression

Patients with radioresistant breast cancers, including a large percentage of women with triple negative breast cancer (TNBC), demonstrate limited response to radiation (RT) and increased locoregional recurrence; thus, strategies to increase the efficacy of RT in TNBC are critically needed. We demonstrate that pan Bcl-2 family inhibition (ABT-263, rER: 1.52-1.56) or Bcl-xL specific inhibition (WEHI-539, A-1331852; rER: 1.31-2.00) radiosensitized wild-type PIK3CA/PTEN TNBC (MDA-MB-231, CAL-120) but failed to radiosensitize mutant PIK3CA/PTEN TNBC (rER: 0.90 - 1.07; MDA-MB-468, CAL-51, SUM-159). Specific inhibition of Bcl-2 or Mcl-1 did not induce radiosensitization, regardless of PIK3CA/PTEN status (rER: 0.95 - 1.07). In wild-type PIK3CA/PTEN TNBC, pan Bcl-2 family inhibition or Bcl-xL specific inhibition with RT led to increased levels of apoptosis (p < 0.001) and an increase in cleaved PARP and cleaved caspase 3. CRISPR-mediated PTEN knockout in wild-type PIK3CA/PTEN MDA-MB-231 and CAL-120 cells induced expression of pAKT/Akt and Mcl-1 and abolished Bcl-xL inhibitor-mediated radiosensitization (rER: 0.94 - 1.07). Similarly, Mcl-1 overexpression abolished radiosensitization in MDA-MB-231 and CAL-120 cells (rER: 1.02 - 1.04) but transient MCL1 knockdown in CAL-51 cells promoted Bcl-xL-inhibitor mediated radiosensitization (rER 2.35 ± 0.05). In vivo, ABT-263 or A-1331852 in combination with RT decreased tumor growth and increased tumor tripling time (p < 0.0001) in PIK3CA/PTEN wild-type TNBC cell line and patient-derived xenografts. Collectively, this study provides the preclinical rationale for early phase clinical trials testing the safety, tolerability, and efficacy of Bcl-xL inhibition and RT in women with wild-type PIK3CA/PTEN wild-type TNBC at high risk for recurrence.

RB loss determines selective resistance and novel vulnerabilities in ER-positive breast cancer models

The management of metastatic estrogen receptor (ER) positive HER2 negative breast cancer (ER+) has improved; however, therapeutic resistance and disease progression emerges in majority of cases. Using unbiased approaches, as expected PI3K and MTOR inhibitors emerge as potent inhibitors to delay proliferation of ER+ models harboring PIK3CA mutations. However, the cytostatic efficacy of these drugs is hindered due to marginal impact on the expression of cyclin D1. Different combination approaches involving the inhibition of ER pathway or cell cycle result in durable growth arrest via RB activation and subsequent inhibition of CDK2 activity. However, cell cycle alterations due to RB loss or ectopic CDK4/cyclin D1 activation yields resistance to these cytostatic combination treatments. To define means to counter resistance to targeted therapies imparted with RB loss; complementary drug screens were performed with RB-deleted isogenic cell lines. In this setting, RB loss renders ER+ breast cancer models more vulnerable to drugs that target DNA replication and mitosis. Pairwise combinations using these classes of drugs defines greater selectivity for RB deficiency. The combination of AURK and WEE1 inhibitors, yields synergistic cell death selectively in RB-deleted ER+ breast cancer cells via apoptosis and yields profound disease control in vivo. Through unbiased efforts the XIAP/CIAP inhibitor birinapant was identified as a novel RB-selective agent. Birinapant further enhances the cytotoxic effect of chemotherapies and targeted therapies used in the treatment of ER+ breast cancer models selectively in the RB-deficient setting. Using organoid culture and xenograft models, we demonstrate the highly selective use of birinapant based combinations for the treatment of RB-deficient tumors. Together, these data illustrate the critical role of RB-pathway in response to many agents used to treat ER+ breast cancer, whilst informing new therapeutic approaches that could be deployed against resistant disease.

Adapted to Survive: Targeting Cancer Cells with BH3 Mimetics

A hallmark of cancer is cell death evasion, underlying suboptimal responses to chemotherapy, targeted agents, and immunotherapies. The approval of the antiapoptotic BCL2 antagonist venetoclax has finally validated the potential of targeting apoptotic pathways in patients with cancer. Nevertheless, pharmacologic modulators of cell death have shown markedly varied responses in preclinical and clinical studies. Here, we review emerging concepts in the use of this class of therapies. Building on these observations, we propose that treatment-induced changes in apoptotic dependency, rather than pretreatment dependencies, will need to be recognized and targeted to realize the precise deployment of these new pharmacologic agents.

SIGNIFICANCE

Targeting antiapoptotic family members has proven efficacious and tolerable in some cancers, but responses are infrequent, particularly for patients with solid tumors. Biomarkers to aid patient selection have been lacking. Precision functional approaches that overcome adaptive resistance to these compounds could drive durable responses to chemotherapy, targeted therapy, and immunotherapies.

Parity and time reversal elucidate both decision-making in empirical models and attractor scaling in critical Boolean networks

We present new applications of parity inversion and time reversal to the emergence of complex behavior from simple dynamical rules in stochastic discrete models. Our parity-based encoding of causal relationships and time-reversal construction efficiently reveal discrete analogs of stable and unstable manifolds. We demonstrate their predictive power by studying decision-making in systems biology and statistical physics models. These applications underpin a novel attractor identification algorithm implemented for Boolean networks under stochastic dynamics. Its speed enables resolving a long-standing open question of how attractor count in critical random Boolean networks scales with network size and whether the scaling matches biological observations. Via 80-fold improvement in probed network size (N = 16,384), we find the unexpectedly low scaling exponent of 0.12 ± 0.05, approximately one-tenth the analytical upper bound. We demonstrate a general principle: A system's relationship to its time reversal and state-space inversion constrains its repertoire of emergent behaviors.