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Lung cancer is the leading cause of cancer-related deaths in the United States, with estimates of 236,740 new cases and 118,830 deaths in 2022 secondary to the disease. Blood-based liquid biopsies hold promise to reduce morbidity and mortality from lung cancer by enabling early detection to downstage disease at diagnosis, theragnostic identification of patients most likely to be helped or harmed by therapy, monitoring of therapeutic efficacy, and detection of residual disease. PrognomiQ’s multi-omics platform comprehensively profiles proteins, metabolites, lipids, mRNA, and cfDNA in blood samples which can be used for the development of liquid biopsy tests with high sensitivity and specificity for lung cancer. We conducted a case-control study comprising 1031 subjects: 361 subjects with untreated non-small-cell lung cancer (NSCLC) and 670 matched controls which included 340 subjects with salient pulmonary and gastrointestinal co-morbidities. Blood samples from each subject were processed to provide 7 different `omics readouts. LCMS was used to detect and quantify proteins, metabolites, and lipids. In addition, cfDNA and mRNA were assayed using next-generation sequencing. cfDNA reads were analyzed to estimate fragment-lengths, copy-number variation, and CpG site methylation. All molecular data were normalized using standard methods specific to each assay. Univariate analyses of cases vs controls were performed to identify differentially abundant features on all available samples per assay. We detected 9,868 proteins, 605 lipids, 329 metabolites, and 109,070 mRNA transcripts. Of these, 3,098 proteins, 210 lipids, 57 metabolites, and 30,236 mRNA transcripts were significantly different (FWER < 0.05) in cases versus controls. Gene set enrichment analysis on statistically significant transcripts and proteins identified multiple gene-ontology terms associated with cancer including the Wnt signaling process and IgA immunoglobulin complex, respectively. From cfDNA data, we identified 234 non-contiguous genomic regions associated with the fragment-length disorder, 4,790 with copy-number variation, and 74 differentially methylated genomic regions spanning 184 CpG sites (FWER < 0.05). With the premise that deviations from copy number neutrality are more likely to indicate a tumor contribution, we then focused our examination on those differentially expressed proteins that overlap with differentially expressed mRNA transcripts as well as CNV genomic regions. We identified 52 protein coding genes including E-cadherin (associated with EMT) and related binding proteins such as RAB11B, CAPZB, EPS15, FLNB, MYH9, STK24 and YWHAE. Ongoing machine-learning-based classifier training to distinguish between cancer and non-cancer can serve as the basis for the development of high-sensitivity liquid-biopsy tests for lung cancer. Citation Format: Jinlyung Choi, Ajinkya Kokate, Ehdieh Khaledian, Manway Liu, Preethi Prasad, John Blume, Jessica Chan, Rea Cuaresma, Kevin Dai, Manoj Khadka, Thidar Khin, Yuya Kodama, Joon-Yong Lee, Hoda Malekpour, Megan Mora, Nithya Mudaliar, Sara Nouri Golmaei, Madhuvanthi Ramaiah, Saividya Ramaswamy, Peter Spiro, Dijana Vitko, Kavya Swaminathan, James Yee, Brian Young, Chinmay Belthangady, Bruce Wilcox, Brian Koh, Philip Ma. Biomarker discovery in non-small-cell lung cancer enabled by deep multi-omics profiling of proteins, metabolites, transcripts, and genes in blood. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6606.

Pancreatic ductal adenocarcinoma (PDAC) is currently the 3rd leading cause of cancer-related deaths in the US. Although the all-stage 5-year survival rate is ~10%, early-stage 5-year survival is markedly superior and in excess of 40%. Hence, early detection of PDAC via blood-based liquid biopsies holds promise to reduce morbidity and mortality. PrognomiQ’s multi-omics platform performs deep and unbiased molecular profiling of blood samples to detect proteins, metabolites, lipids, mRNA, miRNA, cfDNA fragmentation and copy-number, and CpG methylation. Here we report results from training and validation of a classifier on a subset of that multi-omic data with the potential to enable the development of high sensitivity and specificity tests for early detection of PDAC.We conducted a case-control study comprising 146 subjects across 16 clinical sites, including 63 pathology-confirmed, untreated PDAC cases (12 stage I, 8 stage II, 4 stage III, 36 stage IV, and 3 stage unknown) and 83 age- and gender- matched controls without any known cancer. For each subject, venous blood samples including plasma were collected. Unbiased LCMS was used to detect and quantify proteins, and targeted, multiplexed MRM-LCMS assays were used for both metabolites and lipids. After data processing, we detected 54,114 proteomic features, 898 lipids, and 373 metabolites. 445 proteomic features, 170 lipids, and 37 metabolites were found to be significantly different as determined by Bonferroni-corrected Wilcoxon tests with FWER < 0.05. For classification, the dataset was split into training (37 cases and 37 controls) and validation (26 cases and 46 controls) sets, with control for collection site and date, age, and gender. XGBoost models were constructed for each analyte class using ten repeats of 10-fold cross-validation. To improve specificity to PDAC, all proteomic features which mapped to GOBP terms associated with acute-phase response, inflammation, and immune response were excluded prior to training. The best-performing hyperparameters were used for a final model built on the full training set and then used for inference on the validation set. At 99% specificity, the proteomic classifier had sensitivities of 77%, 57%, and 88% for Stages 1-4, Stages 1-2, and Stages 3-4, respectively, estimated by bootstrap re-sampling of the validation results. Metabolomics had sensitivities of 81%, 71%, and 88%. Lipidomics had sensitivities of 65%, 71%, and 65%. A joint, multi-omic model was constructed by averaging the scaled probabilities of all models. This joint model improved performance at 99% specificity with sensitivities of 92%, 86%, and 94%, highlighting the synergy of multi-omics data, particularly phenotypically related omics such as those described here. Multi-omic classifiers such as these can serve as the foundation for blood-based liquid biopsies for the early detection of PDAC. Citation Format: John Blume, Ghristine Bundalian, Jessica Chan, Connie Chao-Shern, Jinlyung Choi, Rea Cuaresma, Kevin Dai, Sara N. Golmaei, Jun Heok Jang, Manoj Khadka, Ehdieh Khaledian, Thidar Khin, Yuya Kodama, Ajinkya Kokate, Joon-Yong Lee, Manway Liu, Hoda Malekpour, Megan Mora, Nithya Mudaliar, Preethi Prasad, Madhuvanthi Ramaiah, Saividya Ramaswamy, Peter Spiro, Kavya Swaminathan, Dijana Vitko, James Yee, Brian Young, Susan Zhang, Chinmay Belthangady, Bruce Wilcox, Brian Koh, Philip Ma. A multi-omics classifier achieves high sensitivity and specificity for pancreatic ductal adenocarcinoma in a case-control study of 146 subjects [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6597.

Pancreatic cancer is the third leading cause of cancer-related deaths in the United States. Disease biomarkers quantified from blood-based assays may help reduce mortality by enabling early detection, treatment selection, or response and resistance assessment. PrognomiQ has developed a multi-omics assay and analysis platform that comprehensively profiles blood samples to detect proteins, metabolites, lipids, mRNA, miRNA, cfDNA fragments, and methylation at CpG sites. This platform can provide deep insights into the biology of pancreatic cancer and could enable the development of high sensitivity and specificity tests for the early detection of pancreatic cancer. We conducted a case-control study comprising 196 subjects: 92 with untreated pancreatic cancer and 104 matched controls without pancreatic cancer. For each subject, blood was collected in assay-specific tubes and processed to provide 7 different `omics readouts. cfDNA and mRNA were isolated from samples and assayed following standard NGS protocols. cfDNA fragments were processed to estimate fragment-length disorder and copy-number variation along with CpG site methylation. In addition, targeted and untargeted LCMS were used to detect and quantify proteins, metabolites, and lipids. After normalization, non-parametric univariate analyses of cases versus controls were performed to identify differentially abundant features on all available samples for each assay. Unsupervised learning was used to investigate the separation of subjects into groups based on disease status for the subset of 157 subjects for which complete data on all 7 readouts were available. We detected 2,812 proteins, 811 lipids, 373 metabolites, and 110,864 mRNA transcripts in all samples where data for each assay was available. Of these, 275 proteins, 232 lipids, 49 metabolites, and 3385 mRNA transcripts were significantly different (FWER < 0.05) in cases versus controls. From cfDNA data, we identified 35 non-contiguous genomic regions associated with fragment-length disorder, 557 with copy-number variation, and 5 with multiple, differentially methylated CpGs (FWER < 0.05) that aggregately span 307 protein-coding genes; of these, the overlap with the differentially expressed proteins included E-cadherin (tumor suppressor) and N-cadherin (involved in epithelial-to-mesenchymal transition). Statistically significant genes and proteins were found to be associated with processes including Wnt signaling, regulation of focal adhesion assembly, and actin cytoskeleton organization. Multi-omics, unsupervised learning showed separation of early- and late-stage cases and controls. High-dimensional bioinformatics analyses systematically decomposed each `omics data type into joint and orthogonal components associated with pancreatic cancer. Ongoing multivariate analyses, including supervised machine learning, will further elucidate the biology of pancreatic cancer development, and serve as the foundation for high-sensitivity blood tests for the early detection and monitoring of pancreatic cancer. Citation Format: Ehdieh Khaledian, Preethi Prasad, John Blume, Ghristine Bundalian, Connie Chao-Shern, Jinlyung Choi, Rea Cuaresma, Jared Deyarmin, Jun Heok Jang, Manoj Khadka, Thidar Khin, Yuya Kodama, Ajinkya Kokate, Joon-Yong Lee, Manway Liu, Nithya Mudaliar, Madhuvanthi Ramaiah, Saividya Ramaswamy, Peter Spiro, Kavya Swaminathan, Preston Williams, Mi Yang, James Yee, Brian Young, Robert Zawada, Susan Zhang, Chinmay Belthangady, Bruce Wilcox, Philip Ma. High-dimensional, multi-omics analyses of proteins, metabolites, transcripts, and genes enable biomarker discovery in early- and late-stage pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A038.

Pancreatic cancer is the seventh leading cause of cancer-related death worldwide and the third leading cause of cancer-related death in the USA. The low survival rate of pancreatic cancer is due to the challenges in early detection of disease, highlighting the need for early diagnostic test development. While cancer signatures are less challenging to identify at the localized pancreatic tumor via biopsy, cancer signals found in the bloodstream due to cellular leakage, metastasis, signaling, innate immune response, remain of key interest due to reduced invasive sampling. The key challenges encountered in liquid biopsy cancer biomarker discovery studies are analyte degradation and dilution in a complex biological matrix, which limits high specificity and sensitivity measurements. To overcome these challenges, PrognomiQ has developed a comprehensive multi-omics platform that facilitates uncovering previously untapped information to gain a more holistic biological perspective at unprecedented depths and integrate molecular signatures across complex levels of biology. Implementation of this approach has led to the discovery of new pancreatic cancer-specific biomarkers and a deeper understanding of the integrated pathways of pancreatic cancer. In this case-control study, the plasma proteome and metabolome data were collected from 193 human plasma samples comprising 92 pancreatic cancer and 104 healthy subjects utilizing liquid chromatography-mass spectrometry. Subject samples were collected post-diagnosis, but pre-treatment for cancer subjects versus non-cancer controls. Sample collection and handling were the same for all samples. In our initial analysis, we detected 3,381 proteins in all samples (minimum of 3 samples per class), and utilizing a Bonferroni correction (FDR = 0.05) we showed 124 proteins to be statistically significant. We also determined ~200 lipids out of 678 total lipids and 49 of 299 metabolites present in all samples (minimum of 3 samples per class) to be statistically significant with a Bonferroni correction (FDR = 0.05). The detected analytes (proteins, lipids, and metabolites) are both known, and unknown, to have an association with pancreatic cancer. Analysis of the data collected from the described cohort will continue to determine Analysis is ongoing to integrate the multi-omics datasets and determine multivariate statistical performance to detect pancreatic cancer. Conclusion: The intent of this cohort and study was to detect biological signal for pancreatic cancer, and this preliminary analysis suggests there are significant differences between classes in the samples as collected. It remains to be seen if combining features within and across analyte classes improve detection. This is a case-control study, not an intent to test study but shows promise for the detection of pancreatic cancer across a multitude of analyte classes. Citation Format: Bruce Wilcox, John Blume, Kavya Swaminathan, Preston Williams, Manoj Khadka, Jared Deyarmin, Saividya Ramaswamy, Yuya Kodama, Brian Young, Chinmay Belthangady, Manway Liu, Mi Yang, Philip Ma. Deep, unbiased multi-omics approach for identification of pancreatic cancer biomarkers from blood [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3924.

SummaryActivation of the phosphoinositide 3-kinase (PI3K) pathway occurs frequently in breast cancer. However, clinical results of single-agent PI3K inhibitors have been modest to date. A combinatorial drug screen on multiple PIK3CA mutant cancers with decreased sensitivity to PI3K inhibitors revealed that combined CDK 4/6-PI3K inhibition synergistically reduces cell viability. Laboratory studies revealed that sensitive cancers suppress RB phosphorylation upon treatment with single-agent PI3K inhibitors but cancers with reduced sensitivity fail to do so. Similarly, patients’ tumors that responded to the PI3K inhibitor BYL719 demonstrated suppression of pRB, while nonresponding tumors showed sustained or increased levels of pRB. Importantly, the combination of PI3K and CDK 4/6 inhibitors overcomes intrinsic and adaptive resistance leading to tumor regressions in PIK3CA mutant xenografts.

SummaryPhosphoinositide-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.

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.

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.

Heat shock protein 90 (HSP90) is involved in protein folding and functions as a chaperone for numerous client proteins, many of which are important in non–small cell lung cancer (NSCLC) pathogenesis. We sought to define preclinical effects of the HSP90 inhibitor NVP-AUY922 and identify predictors of response. We assessed in vitro effects of NVP-AUY922 on proliferation and protein expression in NSCLC cell lines. We evaluated gene expression changes induced by NVP-AUY922 exposure. Xenograft models were evaluated for tumor control and biological effects. NVP-AUY922 potently inhibited in vitro growth in all 41 NSCLC cell lines evaluated with IC50 < 100 nmol/L. IC100 (complete inhibition of proliferation) < 40 nmol/L was seen in 36 of 41 lines. Consistent gene expression changes after NVP-AUY922 exposure involved a wide range of cellular functions, including consistently decreased dihydrofolate reductase after exposure. NVP-AUY922 slowed growth of A549 (KRAS-mutant) xenografts and achieved tumor stability and decreased EGF receptor (EGFR) protein expression in H1975 xenografts, a model harboring a sensitizing and a resistance mutation for EGFR-tyrosine kinase inhibitors in the EGFR gene. These data will help inform the evaluation of correlative data from a recently completed phase II NSCLC trial and a planned phase IB trial of NVP-AUY922 in combination with pemetrexed in NSCLCs. Mol Cancer Ther; 12(6); 890–900. ©2013 AACR.

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.

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.

In an attempt to combat the resistance of tumors to chemotherapy, we have already described at this year's 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 mm3 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/CTVol and T/CBW 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.

The phosphatidylinositol 3-kinase (PI3K) pathway is deregulated across a broad spectrum of human cancers. Therefore, there have been strong efforts to inhibit this pathway, with many novel targeted agents, including isoform-specific PI3K inhibitors, pan-PI3K inhibitors, and dual PI3K/mTOR inhibitors, entering the clinic. Although initial responses have been observed in PIK3CA and HER2 amplified cancers, resistance frequently emerges, thereby limiting the benefit from these drugs. We developed several PI3K inhibitor resistant cell lines by culturing sensitive PIK3CA mutant cells in gradually escalating doses of PI3K inhibitor until resistance emerges, typically over a period of several months. We then used a drug screen utilizing ~40 targeted agents to identify drugs that synergize with PI3K inhibitors in the resistant cells. Subsequently, to elucidate mechanisms of resistance to PI3K inhibitors, we used a pooled shRNA screen to identify hairpins that sensitize the resistant cells to PI3K inhibition. These two complementary screens have identified mTORC as a likely mediator of resistance to PI3K inhibition: mTORC inhibitors synergize with PI3K inhibitors in the resistant cells, and furthermore, hairpins directed against mTORC (FRAP1) sensitize resistant cells to PI3K inhibition. Other promising therapeutic targets identified by these screens that may contribute to resistance to PI3K inhibitors include IGF1R and AKT. This abstract is also presented as Poster B069. Citation Format: Sadhna Vora, Moshe Elkabets, Nayoon Kim, Carlotta Costa, Manway Liu, Li Xiaoyan, Alex Cao, Robert Schlegel, Maurizio Scaltriti, Jose Baselga, Alan Huang, Jeffrey A. Engelman. Overcoming resistance to PI3K inhibitors in breast cancer: Results of a pooled shRNA screen and combinatorial drug screen. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research: Genetics, Biology, and Clinical Applications; Oct 3-6, 2013; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2013;11(10 Suppl):Abstract nr PR06.

In recent years great advances have been made in developing targeted cancer therapeutics that produce dramatic responses in a subset of rationally selected patients. The initial breakthrough of the targeted design concept was established by the treatment of chronic myelogenous leukemia with Abl inhibitors and has been expanded to other cancer indications. As a consequence of this early success in CML, the identification and targeting of genetic lesions that confer cancer dependence has become an established strategy for drug discovery efforts. However, this approach has met with mixed degrees of success as confounding factors, such as tumor heterogeneity, have often resulted in partial responses and/or the emergence of resistance when targeted therapies were employed as single agents. To improve the therapeutic benefit in cancer, rationally-devised novel combinations of two or more agents are being explored clinically. To discover combinations that may be more effective therapies, an unbiased, systematic approach was used to identify drug combinations in vitro, using a panel of genetically diverse, and well characterized cell lines from the cancer cell line encyclopedia (CCLE: Barretina et al. Nature 2012). For three cancer indications, all pairwise combinations of 18 selected drugs (both novel inhibitors and standards of care) were tested as dose matrices in a proliferation assay. Synergistic interactions were scored using isobologram/Loewe's excess inhibition and synergistic concentration ranges for each agent were identified. However, the clinical translation of positive combinations from in vitro matrix-based screens into clinically-relevant doses and schedules are challenging, due to host biology, tumor-stroma interactions, and the pharmacokinetic and pharmacodynamics of drug delivery. To explore this higher complexity, we evaluated the in vitro to in vivo translation of drug synergies in immune-compromised mouse tumor xenograft models. To recapitulate the pharmacological combination effects in vivo, mouse pharmacokinetic data and simulation was used to determine single agent doses that would result in the desired compound plasma concentration range and duration of action. Pharmakokinetics, pharmacodynamics, antitumor activity and tolerability of the combinations were then tested in tumor-bearing mice. Observed combination effects in vivo could in some cases be explained by either the expected biological pathway interactions or partially by physiological effects relating to drug-drug interactions. Citation Format: Marion Wiesmann, Mark Stump, Giordano Caponigro, David Duhl, Brant Firestone, Tom Gesner, Bjoern Gruenenfelder, Daniel Alexander Guthy, Jocelyn Holash, Fred King, Joseph Lehar, Christophe Leroy, Manway Liu, Lilli Petruzelli, Dale Porter, Paul McSheehy, Daniel Menezes, Anupama Reddy, Johannes Roesel, Christian Schnell, Timothy R. Smith, Markus Wartmann. Systematic evaluation of drug combinations in vitro and in vivo. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2058. doi:10.1158/1538-7445.AM2013-2058

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

We describe an integrative analysis approach leveraging the publicly available Cancer Cell Line Encyclopedia to uncover regulatory pathways in cancer. Six different lines of evidence are considered in identifying candidate factors associated with disease: 1) gene mutation status, 2) copy number variations, 3) RNAi growth inhibitions, 4) pharmacological growth inhibitions, 5) baseline gene expression data, and 6) physical protein-protein interactions. As each of these evidence sources measure a different aspect of cellular regulation, they may be considered in combination to uncover regulatory mechanisms associated with tumorigenesis. For example, gene mutation status or copy number variation data may be combined with baseline gene expressions to identify genes that are differentially expressed in cell lines with aberrant or deactivated versions of specific tumor suppressors and oncogenes. To assess our approach, we applied it to identification of genes involved in the well-known RAS-RAF regulatory signaling pathway in both tissuespecific and nonspecific contexts. The RAS-RAF signaling pathway plays a key role in cellular differentiation and proliferation and is frequently associated with tumorigenesis. Specifically, we identified differentially expressed genes in BRAF mutant vs. wild-type cell lines, genes associated with high and low RAF copy number alteration cell lines, genes correlating in expression to growth inhibition by RAF-targeting compounds, and genes that either rescue or sensitize cell lines to BRAF knockdown by RNA interference. We further considered these genes within the overall context of physical interactions between their protein products. Highlighting the significant, research potential of our approach, our RAF-focused analysis identified both well-known and novel genes, supported by different lines of evidence, that mediate RAS-RAF signaling across multiple tissues. Positive control genes include members of the classical pathway, such as MAPK1, MAP2K1, and PI3K. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the Second AACR International Conference on Frontiers in Basic Cancer Research; 2011 Sep 14-18; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2011;71(18 Suppl):Abstract nr C70.