A pharmacogenomic method for individualized prediction of drug sensitivity

Identifying the best drug for each cancer patient requires an efficient individualized strategy. We present MATCH (M erging genomic and pharmacologic A nalyses for T herapy CH oice), an approach using public genomic resources and drug testing of fresh tumor samples to link drugs to patients. Valproic acid (VPA) is highlighted as a proof‐of‐principle. In order to predict specific tumor types with high probability of drug sensitivity, we create drug response signatures using publically available gene expression data and assess sensitivity in a data set of >40 cancer types. Next, we evaluate drug sensitivity in matched tumor and normal tissue and exclude cancer types that are no more sensitive than normal tissue. From these analyses, breast tumors are predicted to be sensitive to VPA. A meta‐analysis across breast cancer data sets shows that aggressive subtypes are most likely to be sensitive to VPA, but all subtypes have sensitive tumors. MATCH predictions correlate significantly with growth inhibition in cancer cell lines and three‐dimensional cultures of fresh tumor samples. MATCH accurately predicts reduction in tumor growth rate following VPA treatment in patient tumor xenografts. MATCH uses genomic analysis with in vitro testing of patient tumors to select optimal drug regimens before clinical trial initiation.     

A framework for personalized medicine: prediction of drug sensitivity in cancer by proteomic profiling

Background

The goal of personalized medicine is to provide patients optimal drug screening and treatment based on individual genomic or proteomic profiles. Reverse-Phase Protein Array (RPPA) technology offers proteomic information of cancer patients which may be directly related to drug sensitivity. For cancer patients with different drug sensitivity, the proteomic profiling reveals important pathophysiologic information which can be used to predict chemotherapy responses.

Results

The goal of this paper is to present a framework for personalized medicine using both RPPA and drug sensitivity (drug resistance or intolerance). In the proposed personalized medicine system, the prediction of drug sensitivity is obtained by a proposed augmented naive Bayesian classifier (ANBC) whose edges between attributes are augmented in the network structure of naive Bayesian classifier. For discriminative structure learning of ANBC, local classification rate (LCR) is used to score augmented edges, and greedy search algorithm is used to find the discriminative structure that maximizes classification rate (CR). Once a classifier is trained by RPPA and drug sensitivity using cancer patient samples, the classifier is able to predict the drug sensitivity given RPPA information from a patient.

Conclusion

In this paper we proposed a framework for personalized medicine where a patient is profiled by RPPA and drug sensitivity is predicted by ANBC and LCR. Experimental results with lung cancer data demonstrate that RPPA can be used to profile patients for drug sensitivity prediction by Bayesian network classifier, and the proposed ANBC for personalized cancer medicine achieves better prediction accuracy than naive Bayes classifier in small sample size data on average and outperforms other the state-of-the-art classifier methods in terms of classification accuracy.

     

Colorectal cancer spheroid biobanks: multi-level approaches to drug sensitivity studies

Biobanking of molecularly characterized colorectal cancer stem cells (CSCs) generated from individual patients and growing as spheroids in defined serum-free media offer a fast, feasible, and multi-level approach for the screening of targeted therapies and drug resistance molecular studies. By combining in vitro and in vivo analyses of cetuximab efficacy with genetic data on an ongoing collection of stem cell-enriched spheroids, we describe the identification and preliminary characterization of microsatellite stable (MSS) CSCs that, despite the presence of the KRAS (G12D) mutation, display epidermal growth factor (EGF)-dependent growth and are strongly inhibited by anti-EGF-receptor (EGFR) treatment. In parallel, we detected an increased resistance to anti-EGFR therapy of microsatellite instable (MSI) CSC lines irrespective of KRAS mutational status. MSI CSC lines carried mutations in genes coding for proteins with a role in RAS and calcium signaling, highlighting the role of a genomically unstable context in determining anti-EGFR resistance. Altogether, these results argue for a multifactorial origin of anti-EGFR resistance that emerges as the effect of multiple events targeting direct and indirect regulators of the EGFR pathway. An improved understanding of key molecular determinants of sensitivity/resistance to EGFR inhibition will be instrumental to optimize the clinical efficacy of anti-EGFR agents, representing a further step towards personalized treatments.     

A new approach to high sensitivity differential hybridization

We describe a new approach to differential hybridization, designed to identify cDNA clones representing rare mRNA species. Duplicate filters carrying a library of cDNA from phorbolmyristate acetate (PMA)-induced EL-4 cells in λgt11 were hybridized with high concentrations of unlabeled, cloned, single-stranded cDNA from induced and control EL-4 cells, respectively. Plaques binding single-stranded cDNA were revealed by a second round of hybridization with ³⁵S-labeled DNA complementary to the vector moiety of the single-stranded cDNA. Plaques corresponding to PMA-induced mRNAs occurring at a level of about 1 part in 15000 were isolated. We believe the method is at least ten times more sensitive than conventional differential hybridization.     

A hierarchical approach to all-atom protein loop prediction

The application of all-atom force fields (and explicit or implicit solvent models) to protein homology-modeling tasks such as side-chain and loop prediction remains challenging both because of the expense of the individual energy calculations and because of the difficulty of sampling the rugged all-atom energy surface. Here we address this challenge for the problem of loop prediction through the development of numerous new algorithms, with an emphasis on multiscale and hierarchical techniques. As a first step in evaluating the performance of our loop prediction algorithm, we have applied it to the problem of reconstructing loops in native structures; we also explicitly include crystal packing to provide a fair comparison with crystal structures. In brief, large numbers of loops are generated by using a dihedral angle-based buildup procedure followed by iterative cycles of clustering, side-chain optimization, and complete energy minimization of selected loop structures. We evaluate this method by using the largest test set yet used for validation of a loop prediction method, with a total of 833 loops ranging from 4 to 12 residues in length. Average/median backbone root-mean-square deviations (RMSDs) to the native structures (superimposing the body of the protein, not the loop itself) are 0.42/0.24 A for 5 residue loops, 1.00/0.44 A for 8 residue loops, and 2.47/1.83 A for 11 residue loops. Median RMSDs are substantially lower than the averages because of a small number of outliers; the causes of these failures are examined in some detail, and many can be attributed to errors in assignment of protonation states of titratable residues, omission of ligands from the simulation, and, in a few cases, probable errors in the experimentally determined structures. When these obvious problems in the data sets are filtered out, average RMSDs to the native structures improve to 0.43 A for 5 residue loops, 0.84 A for 8 residue loops, and 1.63 A for 11 residue loops. In the vast majority of cases, the method locates energy minima that are lower than or equal to that of the minimized native loop, thus indicating that sampling rarely limits prediction accuracy. The overall results are, to our knowledge, the best reported to date, and we attribute this success to the combination of an accurate all-atom energy function, efficient methods for loop buildup and side-chain optimization, and, especially for the longer loops, the hierarchical refinement protocol.     

A segment-based approach to protein secondary structure prediction.

Amino acid sequence patterns have been used to identify the location of turns in globular proteins [Cohen et al. (1986) Biochemistry 25, 266-275]. We have developed sequence patterns that facilitate the prediction of helices in all helical proteins. Regular expression patterns recognize the component parts of a helix: the amino terminus (N-cap), the core of the helix (core), and the carboxy terminus (C-cap). These patterns recognize the core features of helices with a 95% success rate and the N- and C-capping features with success rates of 56% and 48%, respectively. A metapattern language, ALPPS, coordinates the recognition of turns and helical components in a scheme that predicts the location and extent of alpha-helices. On the basis of raw residue scoring, a 71% success rate is observed. By focusing on the recognition of core helical features, we achieve a 78% success rate. Amended scoring procedures are presented and discussed, and comparisons are made to other predictive schemes.     

A network-based approach to integrate nutrient microenvironment in the prediction of synthetic lethality in cancer metabolism

Synthetic Lethality (SL) is currently defined as a type of genetic interaction in which the loss of function of either of two genes individually has limited effect in cell viability but inactivation of both genes simultaneously leads to cell death. Given the profound genomic aberrations acquired by tumor cells, which can be systematically identified with -omics data, SL is a promising concept in cancer research. In particular, SL has received much attention in the area of cancer metabolism, due to the fact that relevant functional alterations concentrate on key metabolic pathways that promote cellular proliferation. With the extensive prior knowledge about human metabolic networks, a number of computational methods have been developed to predict SL in cancer metabolism, including the genetic Minimal Cut Sets (gMCSs) approach. A major challenge in the application of SL approaches to cancer metabolism is to systematically integrate tumor microenvironment, given that genetic interactions and nutritional availability are interconnected to support proliferation. Here, we propose a more general definition of SL for cancer metabolism that combines genetic and environmental interactions, namely loss of gene functions and absence of nutrients in the environment. We extend our gMCSs approach to determine this new family of metabolic synthetic lethal interactions. A computational and experimental proof-of-concept is presented for predicting the lethality of dihydrofolate reductase (DHFR) inhibition in different environments. Finally, our approach is applied to identify extracellular nutrient dependences of tumor cells, elucidating cholesterol and myo-inositol depletion as potential vulnerabilities in different malignancies.     

A rational approach to cancer therapy

A report on the 20th Annual Lorne Cancer Conference, Lorne, Australia, 14-16 February 2008.     

利用胃癌MMP相关生物标志物预测MMP生物调节剂的活性

目的:根据疗效和安全性,MMP生物调节剂的临床试验结果成为急需探讨的问题.因为,目前为止MMP生物调节剂的临床试验是按照传统化疗方案进行,不是选择适合人群为基础.因此可预测性生物标志物的选择将对-抗MMP治疗是非常重要的.方法:在6个胃癌细胞株中MMP-2、MMP-9、MT1-MMP以及TIMP-2水平和用TIMP-2治疗之后在MT1-MMP以及MMP-2变化可做为生物标志物而被测定.做为MMP生物调节剂,Suramin和Fumagillin的药物敏感性是通过计算IC50值来测定.利用回归方程,用这些生物标志物推断对药物敏感性.最后其预测精度由ex vivo实验证实.结果:Suramin做为pro-MMP-2的特异性抑制剂.在对pro-MMP-2基础活性高的细胞群,其IC50值＜30ug/ml.相反,Fumagillin对胶原蛋白酶活性具有广谱的抑制作用.(IC50＜10-5M).通过回归分析,解释对Suramin敏感性的回归方程是40.7(pro-MMP-2),-10.0(MT1-MMP),-40.7,对Fumagillin 敏感性的回归方程是-11.5,(pro-MMP-2)-10.6(TIMP-2),+11.5.在exvivo,Suramin和Fumagillin的回归方程预测精度均为60%.结论:用生物标志物预测MMP抑制剂的敏感性已成为可能,利用生物标志物,不仅快速、安全筛选MMP抑制剂,而且通过个体化可改善治疗效果.     

Apoptosis induction with electric pulses — A new approach to cancer therapy with drug free

Electrical pulses have been widely used in biomedical fields, whose applications depend on the parameters such as durations and electric intensity. Conventional electroporation (0.1-1 kV/cm, 100 μs) has been used in cell fusion, transfection and electrochemotherapy. Recent studies with high-intensity (MV/cm) electric field applications with durations of several tens of nanoseconds can affect intracellular signal transduction and intracellular structures with plasma intact, resulting in an application of intracellular manipulation. The most recent development is the finding that parameters between those two ranges could be used to induce apoptosis of cancer cells. Proposal of apoptosis induction and tumor inhibition has advantages to pursue the treatment of cancer free of cytotoxic drugs.     

A neurochemical approach to valuation sensitivity over gains and losses

Prospect theory proposes the hypothesis that people have diminishing sensitivity in valuing increases in the size of monetary outcomes, for both gains and losses. For decision-making under risk, this implies a tendency to be risk-tolerant over losses while being generally risk averse over gains. We offer a neurochemistry-based model of the diminishing valuation sensitivity hypothesis. Specifically, we propose that dopamine tone modulates the sensitivity towards valuation of gains while serotonin tone modulates the sensitivity towards valuation of losses. Consequently, higher dopamine tone would yield a more concave valuation function over gains while higher serotonin tone would yield a more convex valuation function over losses. Using a neurogenetics strategy to test our neurochemical model, we find that subjects with the 9-repeat allele of DAT1 (lower DA tone) are more risk-tolerant over gains than subjects with the 10-repeat allele, and that subjects with the 10-repeat allele of STin2 (higher 5HT tone) are more risk-tolerant over losses than subjects with the 12-repeat allele. Overall, our results support the implications of our model and provide the first neurogenetics evidence that risk attitudes are partially hard-wired in differentiating between gain- and loss-oriented risks.     

A simplified approach to disulfide connectivity prediction from protein sequences

Background  

Prediction of disulfide bridges from protein sequences is useful for characterizing structural and functional properties of proteins. Several methods based on different machine learning algorithms have been applied to solve this problem and public domain prediction services exist. These methods are however still potentially subject to significant improvements both in terms of prediction accuracy and overall architectural complexity.