Additional file 1: Table S1. of Assessing biological and technological variability in protein levels measured in pre-diagnostic plasma samples of women with breast cancer

Subject characteristics of blood plasma samples from the Northern California site of the Breast Cancer Family Registry. Table S2 Targeted proteins in the antibody-based proteomics platforms. (DOCX 25 kb

Additional file 2: of Assessing biological and technological variability in protein levels measured in pre-diagnostic plasma samples of women with breast cancer

Measured Protein Levels Across LC-MS/MS, Olink, and Myriad-RBM platforms. (XLSX 98 kb

Evolutionary Modeling of Combination Treatment Strategies To Overcome Resistance to Tyrosine Kinase Inhibitors in Non-Small Cell Lung Cancer

Many initially successful anticancer therapies lose effectiveness over time, and eventually, cancer cells acquire resistance to the therapy. Acquired resistance remains a major obstacle to improving remission rates and achieving prolonged disease-free survival. Consequently, novel approaches to overcome or prevent resistance are of significant clinical importance. There has been considerable interest in treating non-small cell lung cancer (NSCLC) with combinations of EGFR-targeted therapeutics (e.g., erlotinib) and cytotoxic therapeutics (e.g., paclitaxel); however, acquired resistance to erlotinib, driven by a variety of mechanisms, remains an obstacle to treatment success. In about 50% of cases, resistance is due to a T790M point mutation in EGFR, and T790M-containing cells ultimately dominate the tumor composition and lead to tumor regrowth. We employed a combined experimental and mathematical modeling-based approach to identify treatment strategies that impede the outgrowth of primary T790M-mediated resistance in NSCLC populations. Our mathematical model predicts the population dynamics of mixtures of sensitive and resistant cells, thereby describing how the tumor composition, initial fraction of resistant cells, and degree of selective pressure influence the time until progression of disease. Model development relied upon quantitative experimental measurements of cell proliferation and death using a novel microscopy approach. Using this approach, we systematically explored the space of combination treatment strategies and demonstrated that optimally timed sequential strategies yielded large improvements in survival outcome relative to monotherapies at the same concentrations. Our investigations revealed regions of the treatment space in which low-dose sequential combination strategies, after preclinical validation, may lead to a tumor reduction and improved survival outcome for patients with T790M-mediated resistance

Plots: cumulative number of MS/MS spectra versus cumulative number of distinct peptides (all spectra and peptides correspond to peptide identifications with probabilities ≥0

<p><b>Copyright information:</b></p><p>Taken from "The PeptideAtlas project"</p><p>Nucleic Acids Research 2005;34(Database issue):D655-D658.</p><p>Published online 28 Dec 2005</p><p>PMCID:PMC1347403.</p><p>© The Author 2006. Published by Oxford University Press. All rights reserved</p>9). The figure will show saturation when new spectra do not yield new peptide identifications. Tables: statistics for atlas builds (number of experiments, number of spectra that yielded a peptide identification with probability ≥ 0.9, number of distinct peptides identified, number of proteins identified and percentage of all genes to which the proteins map)

Longitudinal Monitoring of Antibody Responses against Tumor Cells Using Magneto-nanosensors with a Nanoliter of Blood

Each immunoglobulin isotype has unique immune effector functions. The contribution of these functions in the elimination of pathogens and tumors can be determined by monitoring quantitative temporal changes in isotype levels. Here, we developed a novel technique using magneto-nanosensors based on the effect of giant magnetoresistance (GMR) for longitudinal monitoring of total and antigen-specific isotype levels with high precision, using as little as 1 nL of serum. Combining <i>in vitro</i> serologic measurements with <i>in vivo</i> imaging techniques, we investigated the role of the antibody response in the regression of firefly luciferase (FL)-labeled lymphoma cells in spleen, kidney, and lymph nodes in a syngeneic Burkitt’s lymphoma mouse model. Regression status was determined by whole body bioluminescent imaging (BLI). The magneto-nanosensors revealed that anti-FL IgG2a and total IgG2a were elevated and sustained in regression mice compared to non-regression mice (<i>p</i> < 0.05). This platform shows promise for monitoring immunotherapy, vaccination, and autoimmunity

Necrotic cell fraction in murine lymphoma tumors after treatment with Dox.

<p>Data are shown for tumor slices S1 through S5. Most of the necrosis is a result of the drug treatment since necrosis measured in untreated tumors was negligible (<b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129433#pone.0129433.t002" target="_blank">Table 2</a></b>). Note that the drug-sensitive tumors shrank in size after treatment and thus had one less histological slice than the drug-resistant tumors (to account for this, two slices of the drug-resistant tumor are included in the central region S3, i.e., five total slices for <i>Eμ-myc/Arf-/-</i> and six for <i>Eμ-myc/p53-/-</i>). All error bars represent standard deviation from at least n = 3 measurements in each section. Asterisks show level of statistical significance determined by student’s <i>t</i>-test with α = 0.05 (asterisk, <i>P</i> < 0.05).</p

Whole-tumor measurement of lymphoma characteristics.

<p>Measurements from the IHC data after treatment with Dox shows cell fractions for: (<b>A</b>) apoptosis, (<b>B</b>) endothelium, (<b>C</b>) hypoxia, (<b>D</b>) proliferation. Note that the drug-sensitive tumors shrank in size after treatment and thus had one less histological slice than the drug-resistant tumors in the middle Set (S3). Error bars represent standard deviation (n = 3 regions of interest per slice).</p

Drug response experiments <i>in vitro</i>.

<p>(<i>Left</i>) Measurement of <i>in vitro</i> cell kill in cell culture for <i>Eμ-myc/Arf-/-</i> and <i>Eμ-myc/p53-/-</i> cells after 48 hours at 50 nM Dox concentration (N.S.: not statistically significant). (Right) Results from a flow cytometry study were used to measure apoptotic cells. <i>Eμ-myc/p53-/-</i> cells are displayed along the top row with <i>Eμ-myc/Arf-/-</i> cells along the bottom; controls (no drug) are in the left column, and drug-treated cells (Dox) are in the right column. For each block, lower left quadrant represents live (proliferating) cells; lower right quadrant shows apoptotic cells; upper right quadrant shows dead cells.</p

Strategy for model calibration and validation.

<p>Values for mathematical model input parameters are initially calibrated from experimental data obtained from untreated subjects and cell culture, yielding blood volume fraction, diffusion penetration distance, radius of blood sources, and fraction of cells killed in culture. Based on these parameter values, the model then calculates the fraction of tumor volume that would be killed <i>in vivo</i>, which can be compared to experimental data obtained from treated subjects.</p

Average of tumor measurements from IHC used for model calibration.

<p>Average of tumor measurements from IHC used for model calibration.</p