The BAD protein integrates survival signaling by EGFR/MAPK and PI3K/Akt kinase pathways in PTEN-deficient tumor cells

SummaryTumor cells with mutated PTEN proliferate in an EGFR-independent manner. Induction of PTEN sensitizes cells to EGFR inhibition, and the combination causes synergistic apoptosis. Synergy is due to inhibition of two parallel pathways that phosphorylate the proapoptotic protein BAD at distinct sites. Serine 112 phosphorylation is EGFR/MEK/MAPK dependent, whereas serine 136 phosphorylation is PI3K/Akt dependent. Either phosphorylation is sufficient to sequester BAD to 14-3-3. BAD is released and apoptosis is induced only if both serines are dephosphorylated in response to inhibition of both pathways. Reduction of BAD expression by RNA interference prevents apoptosis in response to pathway inhibition. Thus, BAD integrates the antiapoptotic effects of both pathways. Combined inhibition of EGFR and PI3K signaling may be a useful therapeutic strategy

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data

Tumour micro-environment elicits innate resistance to RAF inhibitors through HGF secretion.

Drug resistance presents a challenge to the treatment of cancer patients. Many studies have focused on cell-autonomous mechanisms of drug resistance. By contrast, we proposed that the tumour micro-environment confers innate resistance to therapy. Here we developed a co-culture system to systematically assay the ability of 23 stromal cell types to influence the innate resistance of 45 cancer cell lines to 35 anticancer drugs. We found that stroma-mediated resistance is common, particularly to targeted agents. We characterized further the stroma-mediated resistance of BRAF-mutant melanoma to RAF inhibitors because most patients with this type of cancer show some degree of innate resistance. Proteomic analysis showed that stromal cell secretion of hepatocyte growth factor (HGF) resulted in activation of the HGF receptor MET, reactivation of the mitogen-activated protein kinase (MAPK) and phosphatidylinositol-3-OH kinase (PI(3)K)-AKT signalling pathways, and immediate resistance to RAF inhibition. Immunohistochemistry experiments confirmed stromal cell expression of HGF in patients with BRAF-mutant melanoma and showed a significant correlation between HGF expression by stromal cells and innate resistance to RAF inhibitor treatment. Dual inhibition of RAF and either HGF or MET resulted in reversal of drug resistance, suggesting RAF plus HGF or MET inhibitory combination therapy as a potential therapeutic strategy for BRAF-mutant melanoma. A similar resistance mechanism was uncovered in a subset of BRAF-mutant colorectal and glioblastoma cell lines. More generally, this study indicates that the systematic dissection of interactions between tumours and their micro-environment can uncover important mechanisms underlying drug resistance

Mutation location on the RAS oncogene affects pathologic features and survival after resection of colorectal liver metastases

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136316/1/cncr30351_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136316/2/cncr30351.pd

Perturbation biology: inferring signaling networks in cellular systems.

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology

Tumor microenvironment induces innate RAF-inhibitor resistance through HGF secretion

Drug resistance remains a vexing problem in the treatment of cancer patients. While many studies have focused on cell autonomous mechanisms of drug resistance, we hypothesized that the tumor microenvironment may confer innate resistance to therapy. Here we developed a co-culture system to systematically assay the ability of 23 stromal cell types to influence the innate resistance of 45 cancer cell lines to 35 anti-cancer drugs. We found that stroma-mediated resistance is surprisingly common – particularly to targeted agents. We further characterized the stroma-mediated resistance of BRAF-mutant melanoma to RAF inhibition because most of these patients exhibit some degree of innate resistance1-4. Proteomic analysis showed that stromal secretion of the growth factor hepatocyte growth factor (HGF) resulted in activation of the HGF receptor MET, reactivation of the MAPK and PI3K/AKT pathways, and immediate resistance to RAF inhibition. Immunohistochemistry confirmed stromal HGF expression in patients with BRAF-mutant melanoma and a statistically significant correlation between stromal HGF expression and innate resistance to treatment. Dual inhibition of RAF and MET resulted in reversal of drug resistance, suggesting RAF/MET combination therapy as a potential therapeutic strategy for BRAF-mutant melanoma. A similar resistance mechanism was uncovered in a subset of BRAF-mutant colorectal and glioblastoma cell lines. More generally, these studies indicate that the systematic dissection of tumor-microenvironment interactions may reveal important mechanisms underlying drug resistance

Lobular Carcinomas In Situ Display Intralesion Genetic Heterogeneity and Clonal Evolution in the Progression to Invasive Lobular Carcinoma

Purpose:; Lobular carcinoma; in situ; (LCIS) is a preinvasive lesion of the breast. We sought to define its genomic landscape, whether intralesion genetic heterogeneity is present in LCIS, and the clonal relatedness between LCIS and invasive breast cancers.; Experimental Design:; We reanalyzed whole-exome sequencing (WES) data and performed a targeted amplicon sequencing validation of mutations identified in 43 LCIS and 27 synchronous more clinically advanced lesions from 24 patients [9 ductal carcinomas; in situ; (DCIS), 13 invasive lobular carcinomas (ILC), and 5 invasive ductal carcinomas (IDC)]. Somatic genetic alterations, mutational signatures, clonal composition, and phylogenetic trees were defined using validated computational methods.; Results:; WES of 43 LCIS lesions revealed a genomic profile similar to that previously reported for ILCs, with; CDH1; mutations present in 81% of the lesions. Forty-two percent (18/43) of LCIS were found to be clonally related to synchronous DCIS and/or ILCs, with clonal evolutionary patterns indicative of clonal selection and/or parallel/branched progression. Intralesion genetic heterogeneity was higher among LCIS clonally related to DCIS/ILC than in those nonclonally related to DCIS/ILC. A shift from aging to APOBEC-related mutational processes was observed in the progression from LCIS to DCIS and/or ILC in a subset of cases.; Conclusions:; Our findings support the contention that LCIS has a repertoire of somatic genetic alterations similar to that of ILCs, and likely constitutes a nonobligate precursor of breast cancer. Intralesion genetic heterogeneity is observed in LCIS and should be considered in studies aiming to develop biomarkers of progression from LCIS to more advanced lesions

Phase 2 study of buparlisib (BKM120), a pan-class I PI3K inhibitor, in patients with metastatic triple-negative breast cancer

Treatment options for triple-negative breast cancer remain limited. Activation of the PI3K pathway via loss of PTEN and/or INPP4B is common. Buparlisib is an orally bioavailable, pan-class I PI3K inhibitor. We evaluated the safety and efficacy of buparlisib in patients with metastatic triple-negative breast cancer. This was a single-arm phase 2 study enrolling patients with triple-negative metastatic breast cancer. Patients were treated with buparlisib at a starting dose of 100 mg daily. The primary endpoint was clinical benefit, defined as confirmed complete response (CR), partial response (PR), or stable disease (SD) for ≥ 4 months, per RECIST 1.1. Secondary endpoints included progression-free survival (PFS), overall survival (OS), and toxicity. A subset of patients underwent pre- and on-treatment tumor tissue biopsies for correlative studies. Fifty patients were enrolled. Median number of cycles was 2 (range 1-10). The clinical benefit rate was 12% (6 patients, all SD ≥ 4 months). Median PFS was 1.8 months (95% confidence interval [CI] 1.6-2.3). Median OS was 11.2 months (95% CI 6.2-25). The most frequent adverse events were fatigue (58% all grades, 8% grade 3), nausea (34% all grades, none grade 3), hyperglycemia (34% all grades, 4% grade 3), and anorexia (30% all grades, 2% grade 3). Eighteen percent of patients experienced depression (12% grade 1, 6% grade 2) and anxiety (10% grade 1, 8% grade 2). Alterations in PIK3CA / AKT1 / PTEN were present in 6/27 patients with available targeted DNA sequencing (MSK-IMPACT), 3 of whom achieved SD as best overall response though none with clinical benefit ≥ 4 months. Of five patients with paired baseline and on-treatment biopsies, reverse phase protein arrays (RPPA) analysis demonstrated reduction of S6 phosphorylation in 2 of 3 patients who achieved SD, and in none of the patients with progressive disease. Buparlisib was associated with prolonged SD in a very small subset of patients with triple-negative breast cancer; however, no confirmed objective responses were observed. Downmodulation of key nodes in the PI3K pathway was observed in patients who achieved SD. PI3K pathway inhibition alone may be insufficient as a therapeutic strategy for triple-negative breast cancer. Registered on 13 February 2013; . Registered on 27 June 2012

Mutant N-RAS Protects Colorectal Cancer Cells from Stress-Induced Apoptosis and Contributes to Cancer Development and Progression

N-Ras is one member of a family of oncoproteins that are commonly mutated in cancer. Activating mutations in N-Ras occur in a subset of colorectal cancers, but little in known about how the mutant protein contributes to onset and progression of the disease. Using genetically engineered mice, we find that mutant N-Ras strongly promotes tumorigenesis in the context of inflammation. The pro-tumorigenic nature of mutant N-Ras is related to its anti-apoptotic function, which is mediated by activation of a non-canonical MAPK pathway that signals through Stat3. As a result, inhibition of MEK selectively induces apoptosis in autochthonous colonic tumors expressing mutant N-Ras. The translational significance of this finding is highlighted by our observation that NRAS mutation correlates with a less favorable clinical outcome for colorectal cancer patients. These data demonstrate for the first time the important role that N-Ras plays in colorectal cancer.