Estrogen responding genes per state.

<p>Among the entire gene set considered in the MCF-7 cell experiment, 1270 also responded in ZR-75.1 cells. These are referred to as common ‘estrogen-regulated genes’ (E2R genes) in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088485#pone.0088485-Cicatiello1" target="_blank">[4]</a>. ‘Primary genes’ are their subgroup having a ER transcription factor binding site within 10 kb around the TSS. The figures show how E2R and primary genes are responding across the single-cell states of a six-state model. (<b>A</b>) Fraction of up-regulated and down-regulated E2R genes. (<b>B</b>) Fraction of first-responding E2R genes, i.e., of genes that respond for the first time in a given state. (<b>C</b>) and (<b>D</b>) show the analogous pattern of primary genes.</p

The single-cell transition rates in the ZR-75.1 system.

<p>Results of the six-state model for time course data in hormone-starved ZR-75.1 cells responding to estrogen stimulation are shown for comparison with the MCF-7 system of <b>Fig. 3</b>. (<b>A</b>) Cell population dynamics. (<b>B</b>) Rates and mean times of transitions. In ZR-75.1 the response to estrogen is initially one order of magnitude faster than in MCF-7.</p

Marker genes in the MCF-7 system.

<p>In each state of a six-state model, genes are ranked by their state-expression fold change with respect to the first state. Here, only the top 50 are shown along with their ranking in the other states. For the top genes of state 2 also the rank assigned considering a maximum fold change criterion over the time course is shown for comparison (separated column). The state-based ranking criterion highlights marker genes which would otherwise pass unnoticed.</p

The number of single-cell states in the MCF-7 response to estrogen.

<p>(<b>A</b>) The mean squared error of the model fit to the microarray data decreases as function of the number of states: as expected, when the number of parameters increases, the quality of the fit improves. (<b>B</b>) The condition number is a measure of the similarity of the transcriptional profiles of the states. It increases as function of the number of states, , highlighting that over-fitting also increases with . A good balance between fit quality and over-fitting must be found. (<b>C</b>) The model posterior probability, derived by a Bayesian approach, has a peak at , which shows that a model with six states strikes a good balance between fit-to-data and model parsimony.</p

Fits to gene expression time-course data.

<p>The fit to some key genes, comprising the 11 primary transcription factors identified by Cicatiello et<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088485#pone.0088485-Cicatiello1" target="_blank">[4]</a> and other important estrogen-responsive genes <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088485#pone.0088485-Zhu1" target="_blank">[1]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088485#pone.0088485-Weisz1" target="_blank">[2]</a>, are shown: black circles represent time-course (standardized) data while green lines represents the gene expression predicted by the six-state model.</p

Decoupling of the PI3K Pathway via Mutation Necessitates Combinatorial Treatment in HER2+ Breast Cancer

<div><p>We report here on experimental and theoretical efforts to determine how best to combine drugs that inhibit HER2 and AKT in HER2⁺ breast cancers. We accomplished this by measuring cellular and molecular responses to lapatinib and the AKT inhibitors (AKT_i) GSK690693 and GSK2141795 in a panel of 22 HER2⁺ breast cancer cell lines carrying wild type or mutant PIK3CA. We observed that combinations of lapatinib plus AKT_i were synergistic in HER2⁺/PIK3CA^mut cell lines but not in HER2⁺/PIK3CA^wtcell lines. We measured changes in phospho-protein levels in 15 cell lines after treatment with lapatinib, AKT_i or lapatinib + AKT_i to shed light on the underlying signaling dynamics. This revealed that p-S6RP levels were less well attenuated by lapatinib in HER2⁺/PIK3CA^mutcells compared to HER2⁺/PIK3CA^wt cells and that lapatinib + AKT_i reduced p-S6RP levels to those achieved in HER2⁺/PIK3CA^wt cells with lapatinib alone. We also found that that compensatory up-regulation of p-HER3 and p-HER2 is blunted in PIK3CA^mut cells following lapatinib + AKT_itreatment. Responses of HER2⁺ SKBR3 cells transfected with lentiviruses carrying control or PIK3CA^mut sequences were similar to those observed in HER2⁺/PIK3CA^mut cell lines but not in HER2⁺/PIK3CA^wtcell lines. We used a nonlinear ordinary differential equation model to support the idea that PIK3CA mutations act as downstream activators of AKT that blunt lapatinib inhibition of downstream AKT signaling and that the effects of PIK3CA mutations can be countered by combining lapatinib with an AKT_i. This combination does not confer substantial benefit beyond lapatinib in HER2⁺/PIK3CA^wt cells.</p></div

Examples of A. synergistic (in SUM190PT) and B. antagonistic (in AU565) interactions between lapatinib (red curve) and GSK690693 (green curve) in breast cell lines.

<p>Combination treatment is shown in the blue curve.</p

RPPA analysis of the PI3K-AKT pathway at baseline in PI3K mutant (red) and wild-type (black) HER2⁺ cell lines (<i>PTEN</i> mutant and K111N <i>PIK3CA</i> mutant lines shown in blue).

<p>MDAMB231, MCF7, and HCC70 are HER2^- cell lines. Heat map represents average expression for DMSO treated cells (baseline), with red being relatively high levels of expression, green being relatively low levels of expression. Different phospho-proteins shown include p-AKT (S473), p-AKT (T308), p-4EBP1 (S65), mTOR (S2448), p-p70S6K (S371), p-RPS6 (S235), all of which in general show higher levels of expression in the mutant PI3K, HER2 positive cell lines.</p

Decoupling of the PI3K Pathway via Mutation Necessitates Combinatorial Treatment in HER2+ Breast Cancer - Fig 5

<p><b>A</b>. Western blots of SKBR3 cells transduced with retroviruses encoding either PIK3CA^{<i>H1047R</i>} or ^<i>E545K</i> mutant alleles or control vector (mCherry). Mutant transduced lines show increased levels of p-AKT and p-S6RP at baseline, and diminished response to lapatinib alone as measured by p-S6RP levels. Note that mutation has little effect on p-4EBP1 levels. <b>B</b>. Growth curves for control, E545K mutant, or H1047R mutant transduced SKBR3 cells treated with lapatinib (red), GSK690693 (green), or a combination of the two (blue). Introduction of mutations results in increased resistance to lapatinib and synergistic interactions between lapatinib and GSK690693 (doses with significant synergy are marked with a red asterisk), indicating a functional role for PIK3CA mutations in determining synergistic response to this combination. <b>C</b>. Analysis of RPPA time course data shows blunted recovery of p-HER3 levels in SKBR3 cells harboring PIK3CA mutations, while the control cell line shows the expected hyper-phosphorylation following lapatinib treatment. <b>D</b>. Western blotting confirms the lack of recovery in non-engineered cells lines with <i>PIK3CA</i> mutations compared to <i>PIK3CA</i> wild type lines.</p

Combination index (CI) values were calculated for each of the nine dose combinations tested for each cell line and then clustered to generate synergy heatmaps for HER2 positive breast cancer cell lines treated with AKT inhibitors: A. GSK690693; B. GSK2141795.

<p>Blue indicates a significant synergistic CI value (CI<0.8, upper 95% confidence interval is less than 1), white indicates additivity, and red indicates significant antagonism (CI>1.2, lower 95% confidence interval is greater than 1). Gray represents instances where no CI could be calculated. A significant association between synergy and PI3K pathway mutation status was indicated by clustering, which was confirmed by Fisher’s exact test for <b>A</b> and <b>B</b>. Cell lines in green harbor hotspot PIK3CA mutations.</p