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TSC2 loss in HCC cell lines leads to mTOR activation. (A) Reverse Phase Protein Array (RPPA) analysis of nine HCC cell lines for TSC2 protein levels and phosphorylation levels of S6K1, 4E-BP1, S6 and AKT. (B) Immunoblot of TSC2 wild-type HuH-7 cells treated with inhibitors of PI3K, AKT or MEK. (C) p-S6K1(T389) immunoblot of TSC2-null HCC cell lines treated with varying concentrations of everolimus.

PDF - 60K, Linear correlation observed between tumor growth inhibition (% T/C) or tumor regression and the fraction of time over the in vivo S473P-Akt IC80 in different cancer cell line-derived tumor xenografts implanted in nude mice (represented as dots) and nude rats (represented as triangles) following NVP-BYL719 treatment (R2=0.77, p

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.

TSC2 immunohistochemistry assay validation and analysis of HCC xenograft and primary tumors. (A) TSC2 immunohistochemistry (IHC) assay with HCC cell lines. (B) TSC2 IHC assay with HCC xenograft tumors. (C) TSC2 expression by IHC assay in HCC xenografts and their corresponding primary tumors.

Race distribution of HCC patients in EVOLVE-1 trial. Percentage of Caucasian, Asian and other race in different groups of EVOLVE-1 patients (A) and TSC2 IHC H-score ranges (B).

Mechanisms of TSC2 loss in HCC cell lines and xenografts. (A) Taqman RT-PCR analysis of TSC1 and TSC2 mRNA levels in HCC cell lines. (B) Taqman RT-PCR analysis of MZB1, TSC1 and TSC2 mRNA levels in TSC2-null Li-7 cells treated with epigenetic inhibitors. (C) Taqman RT-PCR analysis and (D) immunoblot analysis of TSC2 G305V mutant in HEK293T cells.

Genetic characterization of 35 established HCC PTX models by targeted DNA sequencing (Excel file).

PDF - 69K, Supplemental Figure Legend

PDF - 129K, Legends for Supplementary Figures 1 through 4 and Supplementary Tables 1 through 7. Supplementary Table 1. Determination of NVP-BYL719 in vitro effects on human protein kinases. Supplementary Table 2. Activity profile of NVP-BYL719 in the Invitrogen Kinase panel. Supplementary Table 3. Activity profile of NVP-BYL719 in the Ambit Kinase panel. Supplementary Table 4. NVP-BYL719 Kd (nmol/L) determination in the Ambit Kinase panel for the hits found to be inhibited >90%. Supplementary Table 5. NVP-BYL719 anti-tumor effects in diverse cancer cell line-derived xenograft models. Supplementary Table 6. CCLE cell lines responsive to NVP-BYL719. Supplementary Table 7 Anti-tumor effect of NVP-BYL719 in patient-derived xenograft models carrying PIK3CA genetic alterations.

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.

PDF - 769K, Heat maps from microarray analyses of gene expression changes seen in at least 13 of 16 experiments after exposure to NVP-AUY922

Supplementary Table 1. Quantification of phospho-protein levels in Figure 1B. Supplementary Table 2. Everolimus IC50 and growth inhibition at 1uM in HCC cell lines. Supplementary Table 3. Quantification of phospho-protein levels in Figure 2A.

PDF - 71K, Rat1-myr-p110alpha (blue), beta (green) or delta (red) cells were treated with increasing concentrations of NVP-BYL719 for 30 minutes. Levels of S473P-Akt in cell extracts were quantified by Reverse Phase Protein Array as described in (18) and plotted as percentage of untreated control cells. The graph illustrates n=3 independent experiments. IC50s (plus/minus) SD and IC80s (plus/minus) SD of n=3 independent experiments are reported..

Model of mTOR activation and everolimus sensitivity caused by TSC2 loss.

PDF - 92K, NVP-BYL719 does not inhibit mTOR and PIKKs involved in DNA damage-repair processes. A. TSC1 -/- MEFs cells were grown in a 96-well format and treated for 1 h with increased concentrations of RAD001 or NVP-BYL719 (from 0.5 nmol/L to 10 ?mol/L in 1 third dilution steps) and immediately fixed. S235/236P-RPS6 levels were measured and IC50 determined with the Excel module XLfit. Background (no primary Ab incubated); BL, Baseline. B: TSC1 -/- MEFs cells were treated with increasing concentrations of NVP-BYL719 as indicated or RAD001 at 500 nmol/L or an equivalent DMSO concentration for 30 minutes. Levels of S235/236P-RPS6 and total RPS6 in protein- normalized lysates were detected by Western blot analyzis using an activation-state specific antibody, followed by incubation with species- specific HRP-labeled secondary antibody and signal development by ECL. C: 24 h post seeding, A549 cells were treated at the same time with Actinomycin D (Act D) at a concentration of 5 ?mol/L (an agent used to induce DNA damage), and with increasing concentrations of NVP-BYL719 as indicated or with the vehicle control (DMSO) for 1 h. Levels of S15P-p53 and tubulin in protein-normalized lysates were detected by Western blot analysis using an activation-state specific antibody, followed by incubation with species- specific HRP-labeled secondary antibody and signal development by ECL. D: 24 h post seeding, U2OS cells were pre-treated for 1 h with increased concentrations of NVP-BYL719 or KU55933 a specific small molecular mass inhibitor of ATM (Supplementary reference 1) at a concentration of 10 ?mol/L or with the vehicle control (DMSO). The cells were then irradiated with 15 Gy and re-incubated at 37 degrees C for 1 h and then lysed. Levels of S1981P-ATM in protein- normalized lysates were detected by Western blot analysis using an activation-state specific antibody, followed by incubation with species- specific HRP-labeled secondary antibody and signal development by ECL.

PDF - 37K, NVP-BYL719 was tested at 10 different concentrations (from 0.5 nmol/L to 10 μmol/L in 1 third dilution steps) against CLK2 (A) and LRRK2 (B). SelectScreenTM Kinase Profiling Service uses XLfit from IDBS to determine the IC50 values (IC50 CLK2=621 nmol/L; IC50 LRRK2=3'220 nmol/L). The dose response curve is curve fit to model number 205 (sigmoidal dose-response model).

PDF - 47B, A tabular demonstration of the efficacy and tolerability of NVP-AUY922 in 32 mice carrying subcutaneous A549 or H1975 xenograft tumors randomized

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.

Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines.

Background There is interest in using convolutional neural networks (CNNs) to analyze medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested that image classification CNNs may not generalize to new data as well as previously believed. We assessed how well CNNs generalized across three hospital systems for a simulated pneumonia screening task. Methods and findings A cross-sectional design with multiple model training cohorts was used to evaluate model generalizability to external sites using split-sample validation. A total of 158,323 chest radiographs were drawn from three institutions: National Institutes of Health Clinical Center (NIH; 112,120 from 30,805 patients), Mount Sinai Hospital (MSH; 42,396 from 12,904 patients), and Indiana University Network for Patient Care (IU; 3,807 from 3,683 patients). These patient populations had an age mean (SD) of 46.9 years (16.6), 63.2 years (16.5), and 49.6 years (17) with a female percentage of 43.5%, 44.8%, and 57.3%, respectively. We assessed individual models using the area under the receiver operating characteristic curve (AUC) for radiographic findings consistent with pneumonia and compared performance on different test sets with DeLong’s test. The prevalence of pneumonia was high enough at MSH (34.2%) relative to NIH and IU (1.2% and 1.0%) that merely sorting by hospital system achieved an AUC of 0.861 (95% CI 0.855–0.866) on the joint MSH–NIH dataset. Models trained on data from either NIH or MSH had equivalent performance on IU (P values 0.580 and 0.273, respectively) and inferior performance on data from each other relative to an internal test set (i.e., new data from within the hospital system used for training data; P values both, Eric Oermann and colleagues ask whether a DL-based model for pneumonia detection performs well in external validation and consider the effects of hospital system–specific biases., Author summary Why was this study done? Early results in using convolutional neural networks (CNNs) on X-rays to diagnose disease have been promising, but it has not yet been shown that models trained on X-rays from one hospital or one group of hospitals will work equally well at different hospitals. Before these tools are used for computer-aided diagnosis in real-world clinical settings, we must verify their ability to generalize across a variety of hospital systems. What did the researchers do and find? A cross-sectional design was used to train and evaluate pneumonia screening CNNs on 158,323 chest X-rays from the National Institutes of Health Clinical Center (NIH; n = 112,120 from 30,805 patients), Mount Sinai Hospital (42,396 from 12,904 patients), and Indiana University Network for Patient Care (n = 3,807 from 3,683 patients). In 3 out of 5 natural comparisons, performance on chest X-rays from outside hospitals was significantly lower than on held-out X-rays from the original hospital system. CNNs were able to detect where a radiograph was acquired (hospital system, hospital department) with extremely high accuracy and calibrate predictions accordingly. What do these findings mean? The performance of CNNs in diagnosing diseases on X-rays may reflect not only their ability to identify disease-specific imaging findings on X-rays but also their ability to exploit confounding information. Estimates of CNN performance based on test data from hospital systems used for model training may overstate their likely real-world performance.

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.

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.

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.

The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens.

OBJECTIVE To characterize the hormonal milieu and adipose gene expression in response to catch-up growth (CUG), a growth pattern associated with obesity and diabetes risk, in a mouse model of low birth weight (LBW). RESEARCH DESIGN AND METHODS ICR mice were food restricted by 50% from gestational days 12.5–18.5, reducing offspring birth weight by 25%. During the suckling period, dams were either fed ad libitum, permitting CUG in offspring, or food restricted, preventing CUG. Offspring were killed at age 3 weeks, and gonadal fat was removed for RNA extraction, array analysis, RT-PCR, and evaluation of cell size and number. Serum insulin, thyroxine (T4), corticosterone, and adipokines were measured. RESULTS At age 3 weeks, LBW mice with CUG (designated U-C) had body weight comparable with controls (designated C-C); weight was reduced by 49% in LBW mice without CUG (designated U-U). Adiposity was altered by postnatal nutrition, with gonadal fat increased by 50% in U-C and decreased by 58% in U-U mice (P < 0.05 vs. C-C mice). Adipose expression of the lipogenic genes Fasn, AccI, Lpin1, and Srebf1 was significantly increased in U-C compared with both C-C and U-U mice (P < 0.05). Mitochondrial DNA copy number was reduced by >50% in U-C versus U-U mice (P = 0.014). Although cell numbers did not differ, mean adipocyte diameter was increased in U-C and reduced in U-U mice (P < 0.01). CONCLUSIONS CUG results in increased adipose tissue lipogenic gene expression and adipocyte diameter but not increased cellularity, suggesting that catch-up fat is primarily associated with lipogenesis rather than adipogenesis in this murine model.

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.

Carboxyl-ester lipase (CEL) maturity-onset diabetes of the young (MODY) is a monogenic form of diabetes and pancreatic exocrine dysfunction due to mutations in the CEL gene encoding CEL. The pathogenic mechanism for diabetes development is unknown. Since CEL is expressed mainly in pancreatic acinar cells, we asked whether we could find structural pancreatic changes in CEL-MODY subjects during the course of diabetes development. Furthermore, we hypothesized that the diseased pancreas releases proteins that are detectable in pancreatic fluid and potentially reflect activation or inactivation of disease-specific pathways. We therefore investigated nondiabetic and diabetic CEL-mutation carriers by pancreatic imaging studies and secretin-stimulated duodenal juice sampling. The secretin-stimulated duodenal juice was studied using cytokine assays, mass spectrometry (MS) proteomics, and multiplexed MS-based measurement of kinase activities. We identified multiple pancreatic cysts in all eight diabetic mutation carriers but not in any of the four nondiabetic mutation carriers or the six healthy controls. Furthermore, we identified upregulated mitogen-activated protein kinase (MAPK) target proteins and MAPK-driven cytokines and increased MAPK activity in the secretin-stimulated duodenal juice. These findings show that subjects with CEL-MODY develop multiple pancreatic cysts by the time they develop diabetes and that upregulated MAPK signaling in the pancreatic secretome may reflect the pathophysiological development of pancreatic cysts and diabetes.

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.

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.

OBJECTIVE Type 2 diabetes and obesity are increasingly affecting human populations around the world. Our goal was to identify early molecular signatures predicting genetic risk to these metabolic diseases using two strains of mice that differ greatly in disease susceptibility. RESEARCH DESIGN AND METHODS We integrated metabolic characterization, gene expression, protein-protein interaction networks, RT-PCR, and flow cytometry analyses of adipose, skeletal muscle, and liver tissue of diabetes-prone C57BL/6NTac (B6) mice and diabetes-resistant 129S6/SvEvTac (129) mice at 6 weeks and 6 months of age. RESULTS At 6 weeks of age, B6 mice were metabolically indistinguishable from 129 mice, however, adipose tissue showed a consistent gene expression signature that differentiated between the strains. In particular, immune system gene networks and inflammatory biomarkers were upregulated in adipose tissue of B6 mice, despite a low normal fat mass. This was accompanied by increased T-cell and macrophage infiltration. The expression of the same networks and biomarkers, particularly those related to T-cells, further increased in adipose tissue of B6 mice, but only minimally in 129 mice, in response to weight gain promoted by age or high-fat diet, further exacerbating the differences between strains. CONCLUSIONS Insulin resistance in mice with differential susceptibility to diabetes and metabolic syndrome is preceded by differences in the inflammatory response of adipose tissue. This phenomenon may serve as an early indicator of disease and contribute to disease susceptibility and progression.

Type 2 diabetes mellitus is a complex disorder associated with multiple genetic, epigenetic, developmental, and environmental factors. Animal models of type 2 diabetes differ based on diet, drug treatment, and gene knockouts, and yet all display the clinical hallmarks of hyperglycemia and insulin resistance in peripheral tissue. The recent advances in gene-expression microarray technologies present an unprecedented opportunity to study type 2 diabetes mellitus at a genome-wide scale and across different models. To date, a key challenge has been to identify the biological processes or signaling pathways that play significant roles in the disorder. Here, using a network-based analysis methodology, we identified two sets of genes, associated with insulin signaling and a network of nuclear receptors, which are recurrent in a statistically significant number of diabetes and insulin resistance models and transcriptionally altered across diverse tissue types. We additionally identified a network of protein–protein interactions between members from the two gene sets that may facilitate signaling between them. Taken together, the results illustrate the benefits of integrating high-throughput microarray studies, together with protein–protein interaction networks, in elucidating the underlying biological processes associated with a complex disorder., Author Summary Type 2 diabetes mellitus currently affects millions of people. It is clinically characterized by insulin resistance in addition to an impaired glucose response and associated with numerous complications including heart disease, stroke, neuropathy, and kidney failure, among others. Accurate identification of the underlying molecular mechanisms of the disease or its complications is an important research problem that could lead to novel diagnostics and therapy. The main challenge stems from the fact that insulin resistance is a complex disorder and affects a multitude of biological processes, metabolic networks, and signaling pathways. In this report, the authors develop a network-based methodology that appears to be more sensitive than previous approaches in detecting deregulated molecular processes in a disease state. The methodology revealed that both insulin signaling and nuclear receptor networks are consistently and differentially expressed in many models of insulin resistance. The positive results suggest such network-based diagnostic technologies hold promise as potentially useful clinical and research tools in the future.

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.