Journal: PLoS ONE

Article Title: Prospective evaluation of two screening methods for molecular testing of metastatic melanoma: Diagnostic performance of BRAF V600E immunohistochemistry and of a NRAS-BRAF fully automated real-time PCR-based assay

doi: 10.1371/journal.pone.0221123

Figure Lengend Snippet: Comparison of BRAF and NRAS results by allele specific amplification, Sanger sequencing, IHC and Idylla testing.

Article Snippet: Slides were stained with anti-BRAF V600E mutant-specific antibody (clone VE1, dilution 1:200, pH9, Eurobio) [ ] .

Techniques: Comparison, Amplification, Sequencing, Mutagenesis

Journal: PLoS ONE

Article Title: Prospective evaluation of two screening methods for molecular testing of metastatic melanoma: Diagnostic performance of BRAF V600E immunohistochemistry and of a NRAS-BRAF fully automated real-time PCR-based assay

doi: 10.1371/journal.pone.0221123

Figure Lengend Snippet: A. Control of samples found positive for BRAF mutation by ASA/Sequencing but negative by IHC. B. Control of samples found negative for BRAF mutation by ASA/sequencing and positive by IHC (wrong chromogen used). C. Control of samples found positive for NRAS mutation by sequencing but negative by Idylla. Yellow dots correspond to wild type DNA copies ( BRAF , panels A and B; NRAS , panel C). Green dots correspond to mutated DNA copies ( BRAF V600E, panels A and B; NRAS Q61R, panel C). Grey dots correspond to empty wells.

Article Snippet: Slides were stained with anti-BRAF V600E mutant-specific antibody (clone VE1, dilution 1:200, pH9, Eurobio) [ ] .

Techniques: Control, Mutagenesis, Sequencing

Journal: PLoS ONE

Article Title: Prospective evaluation of two screening methods for molecular testing of metastatic melanoma: Diagnostic performance of BRAF V600E immunohistochemistry and of a NRAS-BRAF fully automated real-time PCR-based assay

doi: 10.1371/journal.pone.0221123

Figure Lengend Snippet: Discordant genotyping results.

Article Snippet: Slides were stained with anti-BRAF V600E mutant-specific antibody (clone VE1, dilution 1:200, pH9, Eurobio) [ ] .

Techniques: Digital PCR

Journal: Scientific Reports

Article Title: Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution

doi: 10.1038/srep44206

Figure Lengend Snippet: ( A ) Computed tomography indicates the clinical course and timeline of disease in the patient with rapid progression on EGFR TKI therapy and shows the EGFR-mutant lung adenocarcinoma (red arrows) analyzed both prior to erlotinib treatment and upon resistance at 4 months. ( B ) Key somatic mutations identified by exon-capture and deep sequencing of the pre- and post-treatment tumor in ( A ) demonstrating concurrent alterations in EGFR and BRAF and the frequency of each mutation in pre- and post- treatment tumor samples. P-values indicated as determined by a two-tailed Fischer’s exact test. ( C ) DNA copy number alterations inferred from exon-capture and sequencing data indicate the focal amplification of the EGFRL858R-mutant allele was lost upon acquired resistance while the patient’s resistant tumor gained a focal amplification of MET, with no change in BRAF (relative positions indicated, chromosome 7).

Article Snippet: Sections were then incubated with EGFRL858R rabbit (43B2, Cell signaling) and BRAFV600E mouse (VE1, Spring Biosciences) primary antibodies at 1:50 in blocking buffer overnight at 4 °C in a humidified chamber.

Techniques: Computed Tomography, Mutagenesis, Sequencing, Two Tailed Test, Amplification

Journal: Scientific Reports

Article Title: Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution

doi: 10.1038/srep44206

Figure Lengend Snippet: ( A ) A simulation of the mathematical model of lung adenocarcinoma evolution (SI, Equation (S1)) in the presence of 1 μ M erlotinib, given the patient-derived pretreatment initial tumor cell subpopulations (94% EGFRL858R, 6% BRAF V600E, 0.01% MET amplification of EGFRL858R, BRAFV600E and EGFRT790M). Parameters used in the simulation were derived from growth and viability assays of parental 11–18 EGFRL858R-positive lung adenocarcinoma cells or those cells engineered to express mutations listed above and treated with 0 or 50 ng/ml HGF, in the presence of varying concentrations of erlotinib and fit according to Equations S8, S9 and S11. ( B ) Tumor cell populations present at day 0, 6 and 17 of the simulation in ( A ), including the total HGF+ cell population at day 17 (gray). The model qualitatively captures a possible evolutionary trajectory and results in a similar final tumor cell composition as that of the patient, ( B ) day 17 vs. .

Article Snippet: Sections were then incubated with EGFRL858R rabbit (43B2, Cell signaling) and BRAFV600E mouse (VE1, Spring Biosciences) primary antibodies at 1:50 in blocking buffer overnight at 4 °C in a humidified chamber.

Techniques: Derivative Assay, Amplification

Journal: Scientific Reports

Article Title: Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution

doi: 10.1038/srep44206

Figure Lengend Snippet: ( A ) Drug efficacy as measured by the effect of 1.5 μ M erlotinib or 0.5 μ M afatinib in combination with either 0.5 μ M MET inhibitor crizotinib, 0.5 μ M MEK inhibitor trametinib or 5 μ M BRAF inhibitor vemurafenib on cell growth (SI, Equation S1) of parental 11–18 EGFRL858R-positive lung adenocarcinoma cells or those cells engineered to express mutations listed above and treated with 0 or 50 ng/ml HGF. ( B ) Concentrations of EGFR TKIs afatinib and erlotinib in combination with either 0.5 μ M crizotinib, 0.5 μ M trametinib or 5 μ M vemurafenib that guarantee progression free tumor reduction for any HGF− or HGF+ initial tumor subpopulations according to the model, measured by the minimum concentration of erlotinib or afatinib that results in exponential stability of the evolutionary dynamics model (SI, Section 3.2). ( C ) Simulations of the lung adenocarcinoma model for combinations of 0.5M afatinib + 0.5 μ M trametinib and 1.5 μ M erlotinib + 0.5 μ M μ crizotinib for the HGF− and HGF+ tumors specified. ( D ) (Left) Simulations of the evolutionary dynamics of different HGF− lung adenocarcinoma initial tumor subpopulations with a constant treatment of 0.7 μ M, 0.5, 0.3 or 0.1 μ M afatinib in combination with 0.5 μ M of trametinib (red) and of different HGF+ lung adenocarcinoma initial tumor subpopulations with a constant treatment of 8.32 μ M, 3.2 μ M, 1.5 μ M or 0.75 μ M erlotinib in combination with 0.5 μ M crizotinib (blue). (Right) Maximum eigenvalue decompositions (SI, Section 3.2) classify which subpopulations can lead to progression at different concentrations of EGFR TKI for the afatinib + trametinib combination and the erlotinib + crizotinib combination.

Article Snippet: Sections were then incubated with EGFRL858R rabbit (43B2, Cell signaling) and BRAFV600E mouse (VE1, Spring Biosciences) primary antibodies at 1:50 in blocking buffer overnight at 4 °C in a humidified chamber.

Techniques: Concentration Assay

Journal: Scientific Reports

Article Title: Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution

doi: 10.1038/srep44206

Figure Lengend Snippet: ( A ) Switching strategies are more beneficial to tumor cell populations with more initial heterogeneity. (Left) Fold change in final lung adenocarcinoma tumor cell populations at day 30 versus day 0 over the course of the optimal 30, 15, 10, 5, 3, and 1 day treatment strategies solved by algorithm 1 (SI, Section 2.2) and normalized by fold change in final tumor cell population for the constant 30 day treatment strategy for an initial tumor cell population comprised of (90% EGFRL858R, 10% H1975 EGFRL858R, T790M) and another comprised of (89% EGFRL858R, 10% BRAFV600E, 1% EGFRL858R, T790M) subclones. (Right) Sum of fold change for the final lung adenocarcinoma populations (SI, Equation S5) for select initial tumor cell distributions ( , ) and their corresponding optimal 30, 15, 10, 5, 3, and 1 day treatment strategies, categorized by the number of subclones in the initial tumor cell population. Smaller fold change sums indicate that more switching is beneficial to reduce final populations, whereas larger fold changes indicate that more switching does not necessarily help in reducing the final tumor populations. ( B ) EGFR TKI dose perturbations. (Left) Fold change in number of lung adenocarcinoma cells between day 30 and day 0, as a function of percent EGFR TKI dose reduction for the optimal 30, 15, 10, 5 and 1 day strategies solved by algorithm 1 (SI, Section 2.2) for tumor cell populations indicated above. The shaded areas indicate the regions of the perturbation space where the treatment strategy reduces the initial tumor cell population by more than 30% (response, light blue), increases the size of the original tumor population size by more than 20% (progression, red), or maintains the original tumor population size between the two (stability, white). (Right) Bar graphs indicate the maximum reduction in EGFR TKI dose supported by the optimal strategy such that there is still reduction in tumor size at day 30 with respect to day 0 for the V600E and the pretreatment MET tumor. ( C ) The average maximum percent EGFR TKI dose reduction supported before progression for lung adenocarcinoma tumors with different number of initial tumor cell subpopulations and for predicted optimal 30, 15, 10, 5, and 1 day switching strategies.

Article Snippet: Sections were then incubated with EGFRL858R rabbit (43B2, Cell signaling) and BRAFV600E mouse (VE1, Spring Biosciences) primary antibodies at 1:50 in blocking buffer overnight at 4 °C in a humidified chamber.

Techniques:

Journal: Scientific Reports

Article Title: Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution

doi: 10.1038/srep44206

Figure Lengend Snippet: ( A ) Simulations of the optimal treatment strategy predicted by algorithm 1 (SI, Section 2.2) consisting of 1.5 μ M erlotinib + 0.5 μ M crizotinib for days (0–5) followed by 0.5 μ M afatinib + 0.5 μ M trametinib for days (5–30); the same strategy but with the switch occurring at day 10 and, constant strategies of 0.5 μ M afatinib + 0.5 μ M trametinib or 1.5 μ M erlotinib + 0.5 μ M crizotinib for 30 days, for an initial tumor cell population of 89% EGFRL858R, 10% EGFRL858RBRAFV600E, 1% EGFRL858R, T790M, HGF treated. ( B ) Evolution experiment shows that the predicted strategy for an initial tumor cell population of 89% EGFRL858R, 10% EGFRL858RBRAFV600E, 1% EGFRL858R, T790M, treated with 50 ng/ml HGF, is optimal. Overlaid numbers indicate the relative cell density of each well at day 30 compared to the erlotinib + crizotinib well (magenta). Computational simulations in ( A ) show that the predicted optimal strategy has the greatest reduction in tumor cells in vitro ( B , red) compared to the same strategy with a 10 day switch (yellow). A simulation of the model predicts that a constant treatment of afatinib + trametinib produces little change in number of tumor cells ( B , blue) and that a constant treatment of erlotinib + crizotinib predicts the exponential outgrowth of the initial EGFRL858R, T790M MET amplified subpopulation, experimentally validated in ( B , magenta).

Article Snippet: Sections were then incubated with EGFRL858R rabbit (43B2, Cell signaling) and BRAFV600E mouse (VE1, Spring Biosciences) primary antibodies at 1:50 in blocking buffer overnight at 4 °C in a humidified chamber.

Techniques: In Vitro, Amplification