Manway Liu

mTORC1 Inhibition Is Required for Sensitivity to PI3K p110α Inhibitors in PIK3CA-Mutant Breast Cancer

Persistent mTORC1 signaling correlates with resistance to PI3K p110α inhibition in breast cancer, which can be overcome by inhibiting mTORC1. Caveat mTOR In recent years, numerous new drugs have been developed to take advantage of specific molecular changes in cancer cells. Unfortunately, tumors are often a step ahead of the scientists, becoming resistant to these targeted drugs just when they seem to be working perfectly. Now, two groups of researchers have developed rational combination treatments that block resistance to targeted cancer drugs by inhibiting mTOR. Elkabets and coauthors were working on breast cancer, where the PIK3CA gene is frequently mutated. Inhibitors of PI3K (the protein product of PIK3CA) are currently in clinical trials, but some of the cancers are resistant to these drugs. The authors have discovered that breast cancers resistant to the PI3K inhibitor BYL719 had persistently active mTOR signaling, both in cultured cell lines and in patient tumors. Adding an mTOR inhibitor to the treatment regimen could reverse the resistance and kill the tumor cells. Corcoran et al. found a similar mTOR-dependent drug resistance mechanism to be active in melanoma as well. BRAF-mutant melanomas, the most common type, are frequently treated with RAF and MEK inhibitors, but only with mixed results, because melanomas quickly develop resistance to these drugs. Now, the authors have shown that drug-resistant melanomas also have persistent activation of mTOR, and adding an mTOR inhibitor to the treatment regimen can block drug resistance and kill the cancer cells. In both studies, the activation of mTOR in drug-resistant tumors has been confirmed in human patients, but the combination treatments have only been tested in cells and in mouse models thus far. Thus, the next critical step would be to confirm that adding mTOR inhibition to treatment regimens for these cancers is effective in the clinical setting as well. Some mTOR inhibitors are already available for use in patients, so hopefully soon mTOR activation will not be something to beware of, but something to monitor and target with specific drugs. Activating mutations of the PIK3CA gene occur frequently in breast cancer, and inhibitors that are specific for phosphatidylinositol 3-kinase (PI3K) p110α, such as BYL719, are being investigated in clinical trials. In a search for correlates of sensitivity to p110α inhibition among PIK3CA-mutant breast cancer cell lines, we observed that sensitivity to BYL719 (as assessed by cell proliferation) was associated with full inhibition of signaling through the TORC1 pathway. Conversely, cancer cells that were resistant to BYL719 had persistently active mTORC1 signaling, although Akt phosphorylation was inhibited. Similarly, in patients, pS6 (residues 240/4) expression (a marker of mTORC1 signaling) was associated with tumor response to BYL719, and mTORC1 was found to be reactivated in tumors from patients whose disease progressed after treatment. In PIK3CA-mutant cancer cell lines with persistent mTORC1 signaling despite PI3K p110α blockade (that is, resistance), the addition of the allosteric mTORC1 inhibitor RAD001 to the cells along with BYL719 resulted in reversal of resistance in vitro and in vivo. Finally, we found that growth factors such as insulin-like growth factor 1 and neuregulin 1 can activate mammalian target of rapamycin (mTOR) and mediate resistance to BYL719. Our findings suggest that simultaneous administration of mTORC1 inhibitors may enhance the clinical activity of p110α-targeted drugs and delay the appearance of resistance.

Characterization of the novel and specific PI3Kα inhibitor NVP-BYL719 and development of the patient stratification strategy for clinical trials.

Somatic PIK3CA mutations are frequently found in solid tumors, raising the hypothesis that selective inhibition of PI3Kα may have robust efficacy in PIK3CA-mutant cancers while sparing patients the side-effects associated with broader inhibition of the class I phosphoinositide 3-kinase (PI3K) family. Here, we report the biologic properties of the 2-aminothiazole derivative NVP-BYL719, a selective inhibitor of PI3Kα and its most common oncogenic mutant forms. The compound selectivity combined with excellent drug-like properties translates to dose- and time-dependent inhibition of PI3Kα signaling in vivo, resulting in robust therapeutic efficacy and tolerability in PIK3CA-dependent tumors. Novel targeted therapeutics such as NVP-BYL719, designed to modulate aberrant functions elicited by cancer-specific genetic alterations upon which the disease depends, require well-defined patient stratification strategies in order to maximize their therapeutic impact and benefit for the patients. Here, we also describe the application of the Cancer Cell Line Encyclopedia as a preclinical platform to refine the patient stratification strategy for NVP-BYL719 and found that PIK3CA mutation was the foremost positive predictor of sensitivity while revealing additional positive and negative associations such as PIK3CA amplification and PTEN mutation, respectively. These patient selection determinants are being assayed in the ongoing NVP-BYL719 clinical trials. Mol Cancer Ther; 13(5); 1117–29. ©2014 AACR.

AXL Mediates Resistance to PI3Kα Inhibition by Activating the EGFR/PKC/mTOR Axis in Head and Neck and Esophageal Squamous Cell Carcinomas

Phosphoinositide-3-kinase (PI3K)-α inhibitors have shown clinical activity in squamous cell carcinomas (SCCs) of head and neck (H&N) bearing PIK3CA mutations or amplification. Studying models of therapeutic resistance, we have observed that SCC cells that become refractory to PI3Kα inhibition maintain PI3K-independent activation of the mammalian target of rapamycin (mTOR). This persistent mTOR activation is mediated by the tyrosine kinase receptor AXL. AXL is overexpressed in resistant tumors from both laboratory models and patients treated with the PI3Kα inhibitor BYL719. AXL dimerizes with and phosphorylates epidermal growth factor receptor (EGFR), resulting in activation of phospholipase Cγ (PLCγ)-protein kinase C (PKC), which, in turn, activates mTOR. Combined treatment with PI3Kα and either EGFR, AXL, or PKC inhibitors reverts this resistance.

Pharmacogenomic agreement between two cancer cell line data sets

Large cancer cell line collections broadly capture the genomic diversity of human cancers and provide valuable insight into anti-cancer drug response. Here we show substantial agreement and biological consilience between drug sensitivity measurements and their associated genomic predictors from two publicly available large-scale pharmacogenomics resources: The Cancer Cell Line Encyclopedia and the Genomics of Drug Sensitivity in Cancer databases.

Loss of Tuberous Sclerosis Complex 2 (TSC2) Is Frequent in Hepatocellular Carcinoma and Predicts Response to mTORC1 Inhibitor Everolimus

Hepatocellular carcinoma (HCC) is the third leading cause of cancer deaths worldwide and hyperactivation of mTOR signaling plays a pivotal role in HCC tumorigenesis. Tuberous sclerosis complex (TSC), a heterodimer of TSC1 and TSC2, functions as a negative regulator of mTOR signaling. In the current study, we discovered that TSC2 loss-of-function is common in HCC. TSC2 loss was found in 4 of 8 HCC cell lines and 8 of 28 (28.6%) patient-derived HCC xenografts. TSC2 mutations and deletions are likely to be the underlying cause of TSC2 loss in HCC cell lines, xenografts, and primary tumors for most cases. We further demonstrated that TSC2-null HCC cell lines and xenografts had elevated mTOR signaling and, more importantly, were significantly more sensitive to RAD001/everolimus, an mTORC1 inhibitor. These preclinical findings led to the analysis of TSC2 status in HCC samples collected in the EVOLVE-1 clinical trial of everolimus using an optimized immunohistochemistry assay and identified 15 of 139 (10.8%) samples with low to undetectable levels of TSC2. Although the sample size is too small for formal statistical analysis, TSC2-null/low tumor patients who received everolimus tended to have longer overall survival than those who received placebo. Finally, we performed an epidemiology survey of more than 239 Asian HCC tumors and found the frequency of TSC2 loss to be approximately 20% in Asian HBV+ HCC. Taken together, our data strongly argue that TSC2 loss is a predictive biomarker for the response to everolimus in HCC patients. Mol Cancer Ther; 14(5); 1224–35. ©2015 AACR.

Abstract 3749: Single agent activity of PIK3CA inhibitor BYL719 in a broad cancer cell line panel

NVP-BYL719 is a p110α isoform-specific small molecule inhibitor that is currently in a Phase I clinical trial. To identify cancer populations that most likely to respond to BYL719 treatment, we initiated a comprehensive in vitro pharmacologic profiling screen across a large panel of cancer cell lines that have been previously characterized at the molecular level as part of the Cancer Cell Line Encyclopedia (CCLE) effort. We found that BYL719 responsive cell lines are enriched in indications such as Her2 positive and luminal breast cancer, while lacking in other indications such as Glioblastoma and Melanoma. Further exploration of the underlying genetic and pathway aberrations revealed that BYL719 sensitivity is positively associated with PIK3CA mutation, ERBB2 amplification and PIK3CA amplification/copy number gain. PTEN and BRAF mutations on the other hand, are associated with BYL719 insensitivities. KRAS mutation alone is neither associated with enhanced sensitivity nor insensitivity, however, co-commitant PIK3CA and KRAS mutants are more likely to be insensitive to BYL719 treatment. These findings will help to guide our clinical development plan. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3749. doi:1538-7445.AM2012-3749

Abstract S4-04: Overcoming resistance to PI3K inhibitors in PIK3CA mutant breast cancer using CDK4/6 inhibition: Results from a combinatorial drug screen

Various components of the phosphatidylinositol 3-kinase (PI3K) pathway are deregulated across a spectrum of human cancers. Notably, cancers with PIK3CA mutations, seen in roughly 30% of breast cancers, are amongst the most sensitive to PI3K inhibitors (PI3Ki) as single agents. Therefore, there have been great efforts to develop PI3K inhibitors specifically for these types of cancers, and many agents have already entered the clinic. Although initial responses and prolonged stable disease have been observed, resistance frequently emerges. Moreover, there is a subset of PIK3CA mutated cancers that unexpectedly do not exhibit an initial response or disease stabilization upon exposure to PI3K inhibitors, despite presence of the mutation. These cancers are said to have de novo resistance to PI3K inhibition. To determine methods of overcoming resistance to PI3K inhibitors, we generated two models with acquired resistance to the p110a isoform specific inhibitor BYL-719 (BYL) using MDA-MB-453 (453) and T47D. We also established one model of resistance to the pan-PI3K inhibitor GDC-0941 using MCF7 cells. These lines were chosen because of their PIK3CA mutated status and sensitivity to PI3K inhibition. Each cell line was grown in increasing concentrations of PI3K inhibitor until the cells proliferated readily at a dose of drug that effectively reduced cell viability and inhibited pAKT in the sensitive parental cell lines. Interestingly, both BYL resistant cells (453R and T47DR) were cross resistant GDC and the MCF7R line was refractory to BYL. To elucidate mechanisms to overcome resistance to PI3K inhibitors, we undertook a combinatorial drug screen, in which PI3K inhibitor resistant cells were treated with escalating doses of a panel of 45 targeted agents, both in the presence and absence of a fixed dose of PI3Ki, to determine which agents synergized effectively with PI3K inhibition in these resistant cells. We observed in each of the three PI3Ki resistant models a synergy between the CDK4/6 inhibitor LEE-011 and PI3K inhibition. We furthermore tested this combination of agents in a PIK3CA mutated breast cancer model with de novo resistance to PI3K inhibitors, CAL51, and again noted efficacy with the combination of GDC and LEE-011 while either agent on its own displayed minimal activity. To determine whether addition of CDK 4/6 inhibition might be an effective addition to PI3Ki in the upfront setting in vivo, we injected each of the PIK3CA mutated lines MCF7, 453, and T47D into female nude mice and treated with vehicle, BYL, LEE-011, or the combination. We noted in each of the three models that the combination of agents, led to tumor regression that was more substantial than single agent treatment, and furthermore delayed the acquisition of resistance relative to single agent therapy. We furthermore tested GDC with LEE-011 singly and in combination in both MCF7 and CAL51 xenografts and again noted that the combination of agents led to tumor regression, whereas in these instances, single agent treatment did not. We conclude that the combination of PI3K and CDK 4/6 inhibition may be an effective strategy for treating PIK3CA mutated breast cancer and deserves further study in the clinical setting. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr S4-04.

CANDI: an R package and Shiny app for annotating radiographs and evaluating computer-aided diagnosis

Abstract Motivation Radiologists have used algorithms for Computer-Aided Diagnosis (CAD) for decades. These algorithms use machine learning with engineered features, and there have been mixed findings on whether they improve radiologists’ interpretations. Deep learning offers superior performance but requires more training data and has not been evaluated in joint algorithm-radiologist decision systems. Results We developed the Computer-Aided Note and Diagnosis Interface (CANDI) for collaboratively annotating radiographs and evaluating how algorithms alter human interpretation. The annotation app collects classification, segmentation, and image captioning training data, and the evaluation app randomizes the availability of CAD tools to facilitate clinical trials on radiologist enhancement. Availability and implementation Demonstrations and source code are hosted at (https://candi.nextgenhealthcare.org), and (https://github.com/mbadge/candi), respectively, under GPL-3 license. Supplementary information Supplementary material is available at Bioinformatics online.

Abstract B51: Image-based classification of cancer drug combination response surfaces

Recent advances in targeted chemotherapy have dramatically improved the survival odds of patients suffering from specific molecular subtypes of cancer. Nonetheless, while a fortunate few enjoy a durable and lasting response to treatment, the majority will eventually relapse after some grace period in remission due to the emergence of drug-resistant cancer cells. Acquired resistance is a complex phenomenon that likely involves intra-tumoral heterogeneity, tumor-stromal interactions, and natural selection due to the pressure of chemotherapy. One promising approach to overcome resistance involves utilization of two or more drug compounds in combination, where each compound targets a different member of a signaling pathway important in the disease. By targeting more proteins, the hope is that the probability of any particular cancer cell failing to respond will decrease as the barrier to survival increases. While drug combinations are an exciting and promising direction for chemotherapeutic research, such studies present additional challenges and opportunities. Interpretation of synergistic and efficacious drug combinations is not always straightforward and pairs of drugs may interact in surprising ways that deviate from expectation based on the molecular pathway relationships between their targets. Moreover, the question of which drugs to prioritize for testing in combination is an important one to which there is currently no standard solution. The brute force approach is not cost-effective due to the factorial growth in the number of possible higher-order drug combinations. In this work, we present and describe an algorithmic approach to (1) predict synergistic and efficacious drug combinations based on pathway relationships between their targets and (2) classify drug combination response surfaces into canonical archetypes of greatest pharmaceutical interest. Our approach borrows from classical work in image analysis and recognition. We trained and tested our algorithm on two publicly available datasets of drug combinations. We further trained on a private dataset of cancer drug combination data and made predictions that were later experimentally validated in vitro. To our knowledge, our approach is the first proof-of-concept algorithm that utilizes the actual shapes of response surfaces to categorize and prioritize drug combinations for further testing. Citation Format: Manway Liu, Thomas Horn, Matthew Greene, Joseph Lehar. Image-based classification of cancer drug combination response surfaces. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Drug Sensitivity and Resistance: Improving Cancer Therapy; Jun 18-21, 2014; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(4 Suppl): Abstract nr B51.

Abstract A140: Evaluation of prediction of in vivo activity from in vitro combinations: Examples using a MEK1/2 inhibitor combined with docetaxel in NSCLC models.

In an attempt to combat the resistance of tumors to chemotherapy, we have already described at this year9s AACR, the systematic evaluation of over 11,000 different compound combinations using the human cancer cell line encyclopedia. Correlations of synergy with genetic features were identified and some novel synergies discovered. Here, we describe the efficacy, tolerability and PK-PD of 23 different in vivo combinations selected from the screens to determine the predictability of in vitro screening, including two examples from the combination of a MEK1/2-inhibitor (MEK162) with the taxane, docetaxel. In vitro screens were conducted as previously described using 3-day viability assays, and inhibition of proliferation determined relative to untreated samples, and the degree of synergy scored using different types of analyses: Gaddum, Bliss, Loewe. Only those combinations showing synergy over a wide range of concentrations were chosen for in vivo study. For in vivo studies, cells were injected s.c. in the flank of athymic nude mice, and once tumors reached a mean size of at least 100 mm 3 were treated for 2-4 weeks with the appropriate dose and schedule of the compounds either as monotherapy, or in combination. Efficacy and tolerability were determined at the endpoint using the T/C TVol and T/C BW respectively to derive a combination-index as previously described by Clarke (1997), where a negative-value (-CCI) indicated synergy. In most cases, PK-PD was also measured in plasma and tumour either at steady-state and/or the endpoint to study the mechanism of the interaction and to check for drug-drug interactions and their eventual impact on PD and efficacy. Thus far, we have studied in vivo 13 different molecular targets across 6 different histotypes to give 23 different combinations. No antagonism was seen in vivo (+CCI), and 19/23 were deemed synergistic (CCI ≤-0.1), of which 8 showed regression which was not seen with the individual monotherapies. Of the 4 combinations showing no interaction, 2 were predicted by the in vitro score and the other 2 showed negative drug-drug interactions. There were no significant correlations between the CCI and the different types of in vitro score (p>0.35), but perhaps more importantly, cut-offs could be identified suggesting synergy could be predicted (p≤0.02) although not the extent of the interaction. Several novel combinations were identified for clinical investigation, including MEK162 combined with docetaxel in KRAS-mutant NSCLC, which in two different models in vivo had a CCI≤ -0.1, with PD-analyses showing that cytotoxic doses of the taxane activated the MAPK-pathway which was blocked by the combination. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):A140. Citation Format: Paul Martin J. Mcsheehy, Alex Cao, Giordi Caponigro, David Duhl, Brant Firestone, Tom Gesner, Daniel Guthy, Jocelyn Holash, Fred King, Joseph Lehar, Christopher Leroy, Manway Liu, Lilli Petruzzelli, Dale Porter, Daniel Menezes, Anupama Reddy, Johannes Roesel, Christian Schnell, Timothy Smith, Mark Stump, Markus Wartmann, Marion Wiesmann. Evaluation of prediction of in vivo activity from in vitro combinations: Examples using a MEK1/2 inhibitor combined with docetaxel in NSCLC models. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr A140.