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The recombinant fusion protein aflibercept (ziv-aflibercept in the United States) binds VEGF-A, VEGF-B, and placental growth factor (PlGF). The monoclonal antibody bevacizumab binds VEGF-A. Recent studies hypothesized that dual targeting of VEGF/PlGF is more beneficial than targeting either ligand. We compared activity of aflibercept versus bevacizumab in 48 patient-derived xenograft (PDX) colorectal cancer models. Nude mice engrafted subcutaneously with PDX colorectal cancer tumors received biweekly aflibercept, bevacizumab, or vehicle injections. Differential activity between aflibercept and bevacizumab, determined by mouse (m), human (h), VEGF-A, and PlGF levels in untreated tumors, was measured. Aflibercept induced complete tumor stasis in 31 of 48 models and bevacizumab in 2 of 48. Based on statistical analysis, aflibercept was more active than bevacizumab in 39 of 48 models; in 9 of 39 of these models, bevacizumab was considered inactive. In 9 of 48 remaining models, aflibercept and bevacizumab had similar activity. Tumor levels of hVEGF-A (range 776–56,039 pg/mg total protein) were ∼16- to 1,777-fold greater than mVEGF-A (range 8–159 pg/mg total protein). Tumor levels of mPlGF (range 104–1,837 pg/mg total protein) were higher than hPlGF (range 0–543 pg/mg total protein) in 47 of 48 models. Tumor cells were the major source of VEGF; PlGF was primarily produced by tumor stroma. Because tumor levels of hVEGF-A were far greater than mVEGF-A, bevacizumab's inability to bind mVEGF-A is unlikely to explain higher and more consistent aflibercept activity. Neutralizing PlGF and VEGFR-1 activation may be a factor and should be investigated in future studies. In these colorectal cancer PDX models, aflibercept demonstrated greater antitumor activity than bevacizumab. Mol Cancer Ther; 13(6); 1636–44. ©2014 AACR.

PDF - 6591K, Tumor growth curves utilizing median ATVs of the 48 CRC PDXs: Tumor growth curves for all 48 patient-derived CRC tumors in mice engrafted SC, and which were treated with aflibercept SC injection (2x/week, 25 mg/kg), vehicle (SC injection, 2x/week), or bevacizumab (IV injection, 2x/week, 25 mg/kg) for a total of 3 weeks. Tumor measurements were recorded twice per week. Error bars represent MAD.

PDF - 44K, Distribution of the tumor origin across PDX models: Of the 48 PDX tumors, 8 were derived from primary patient tumors, 36 from metastatic sites, 1 from a site of recurring disease, and 1 of unknown anatomical origin. All of the primary tumors (8/8) were associated with phenotype A where aflibercept is more efficacious than bevacizumab. PDX tumors that originated from metastatic tumors were represented in both phenotypes A (30/39) and B (9/9).

PDF - 107K, Genomic characterization of PDX models: 48 PDX tumors comprising similar mutational patterns of KRAS, BRAF, PIK3CA, and PTEN have been reported in several other publications.

PDF - 6683K, Tumor growth curves utilizing mean ATVs of the 48 CRC PDXs: Tumor growth curves for all 48 patient-derived CRC tumors in mice engrafted SC, and which were treated with aflibercept SC injection (2x/week, 25 mg/kg), vehicle (SC injection, 2x/week), or bevacizumab (IV injection, 2x/week, 25 mg/kg) for a total of 3 weeks. Tumor measurements were recorded twice per week. Error bars represent standard error of the mean.

PDF - 115K, Legends to Supplementary Figures. Supplementary Table S1. Characteristics of 48 patient-derived colon cancer tumor xenografts. Supplementary Table S2. Evaluation of statistical differences between treatment(s) groups at the terminal point.

Background: The Cancer Genome Atlas (TCGA) Network recently comprehensively catalogued the molecular aberrations in 487 high-grade serous ovarian cancers, with much remaining to be elucidated regarding the microRNAs (miRNAs). Here, using TCGA ovarian data, we surveyed the miRNAs, in the context of their predicted gene targets. Methods and Results: Integration of miRNA and gene patterns yielded evidence that proximal pairs of miRNAs are processed from polycistronic primary transcripts, and that intronic miRNAs and their host gene mRNAs derive from common transcripts. Patterns of miRNA expression revealed multiple tumor subtypes and a set of 34 miRNAs predictive of overall patient survival. In a global analysis, miRNA:mRNA pairs anti-correlated in expression across tumors showed a higher frequency of in silico predicted target sites in the mRNA 39-untranslated region (with less frequency observed for coding sequence and 59-untranslated regions). The miR-29 family and predicted target genes were among the most strongly anticorrelated miRNA:mRNA pairs; over-expression of miR-29a in vitro repressed several anti-correlated genes (including DNMT3A and DNMT3B) and substantially decreased ovarian cancer cell viability. Conclusions: This study establishes miRNAs as having a widespread impact on gene expression programs in ovarian cancer, further strengthening our understanding of miRNA biology as it applies to human cancer. As with gene transcripts, miRNAs exhibit high diversity reflecting the genomic heterogeneity within a clinically homogeneous disease population. Putative miRNA:mRNA interactions, as identified using integrative analysis, can be validated. TCGA data are a valuable resource for the identification of novel tumor suppressive miRNAs in ovarian as well as other cancers.

Background Serous ovarian cancer (SeOvCa) is an aggressive disease with differential and often inadequate therapeutic outcome after standard treatment. The Cancer Genome Atlas (TCGA) has provided rich molecular and genetic profiles from hundreds of primary surgical samples. These profiles confirm mutations of TP53 in ∼100% of patients and an extraordinarily complex profile of DNA copy number changes with considerable patient-to-patient diversity. This raises the joint challenge of exploiting all new available datasets and reducing their confounding complexity for the purpose of predicting clinical outcomes and identifying disease relevant pathway alterations. We therefore set out to use multi-data type genomic profiles (mRNA, DNA methylation, DNA copy-number alteration and microRNA) available from TCGA to identify prognostic signatures for the prediction of progression-free survival (PFS) and overall survival (OS). Methodology/Principal Findings We implemented a multivariate Cox Lasso model and median time-to-event prediction algorithm and applied it to two datasets integrated from the four genomic data types. We (1) selected features through cross-validation; (2) generated a prognostic index for patient risk stratification; and (3) directly predicted continuous clinical outcome measures, that is, the time to recurrence and survival time. We used Kaplan-Meier p-values, hazard ratios (HR), and concordance probability estimates (CPE) to assess prediction performance, comparing separate and integrated datasets. Data integration resulted in the best PFS signature (withheld data: p-value = 0.008; HR = 2.83; CPE = 0.72). Conclusions/Significance We provide a prediction tool that inputs genomic profiles of primary surgical samples and generates patient-specific predictions for the time to recurrence and survival, along with outcome risk predictions. Using integrated genomic profiles resulted in information gain for prediction of outcomes. Pathway analysis provided potential insights into functional changes affecting disease progression. The prognostic signatures, if prospectively validated, may be useful for interpreting therapeutic outcomes for clinical trials that aim to improve the therapy for SeOvCa patients.

It is shown that classical electrostatics quantitatively describes both the binding of the diatomic ligands XO (X = C, N, O) to the heme group in myoglobin and the dependence of their vibrational frequencies upon an external field, the vibrational Stark effect. The key is a proper treatment of induced dipoles. The results suggest that ligand binding occurs via an “electrostatic bond”, a generalization of the standard ionic bond to include induction, and, more generally, that classical electrostatics can replace quantum mechanics for a considerable simplification of some complex problems.

Far-infrared intermolecular and midinfrared vibrational spectra of CO liquid have been calculated by Fourier transforming the quantum-corrected classical dipole correlation. The time dependence of the coordinates is determined from a standard nonpolarizable force field, and the dipole is determined from the coordinates with a "spectroscopic model" proposed herein. The model includes intramolecular induction and atomic charges, polarizabilities, and permanent dipoles. A good agreement with available experimental spectra is achieved. Our results demonstrate that the use of an anharmonic potential is necessary to reproduce the experimentally observed shift upon going from gas to liquid. The behavior of the simulated dipole time correlation functions suggests that CO liquid at 80 K exhibits aspects of both free rotation and solidlike caging. The proposition of some free rotation present in CO liquid supports Ewing's experimental hypothesis.

A polarizable, flexible and transferable potential for water, POLIR, is presented. In addition to providing a good description of the usual structural and kinetic properties, POLIR correctly describes the vibrational frequencies, absolute infrared intensities, and HOH angle in clusters, liquid water, and ice, offering the possibility of a comprehensive classical theory of vibrational spectroscopy. The high degree of transferability suggests applications to solvation and to water that is confined, interfacial, and under the extreme conditions encountered in the geological and planetary sciences.