Elena Cichero
email: cichero@unige.it

Laura Buffa

Paola Fossa

mutant HIV-1 non-nucleoside reverse transcriptase inhibitors: Dockingbased CoMFA and CoMSIA analyses

Keywords:

HAL is

Introduction

Reverse transcriptase (RT) is a key enzyme in the HIV replication cycle and is one of the main targets in the development of drugs for treating HIV-infection and AIDS [START_REF] Jonckheere | The HIV-1 reverse transcription (RT) process as target for RT inhibitors[END_REF][START_REF] Clercq | New developments in anti-HIV chemotherapy[END_REF][START_REF] Clercq | Emerging anti-HIV drugs[END_REF][START_REF] Clercq | New approaches toward anti-HIV chemotherapy[END_REF][START_REF] Barbaro | Highly active antiretroviral therapy:current state of the art, new agents and their pharmacological interactions useful for improving therapeutic outcome[END_REF]. RT catalyzes the conversion of viral RNA into double stranded DNA that is then integrated in the host genome. Non-nucleoside RT inhibitors (NNRTIs) bind to an allosteric hydrophobic pocket (NNRTI binding site: NNIBS), located at about 10 Å far from the polymerase active site and created upon inhibitor interaction, and lock the enzyme into an inactive form by affecting the geometry of the polymerase active site aspartyl residues [START_REF] Balzarini | Current status of the non-nucleoside reverse transcriptase inhibitors of human immunodeficiency virus type 1[END_REF]. In the past fifteen years, more than fifty structurally diverse NNRTIs have been described [START_REF] Balzarini | Current status of the non-nucleoside reverse transcriptase inhibitors of human immunodeficiency virus type 1[END_REF][START_REF] Clercq | The role of non-nucleoside reverse transcriptase inhibitors (NNRTIs) in the therapy of HIV-1 infection[END_REF][START_REF] Pedersen | Non-nucleoside reverse transcriptase inhibitors, the NNRTI boom[END_REF][START_REF] Pedersen | The flourishing syntheses of non-nucleoside reverse transcriptase inhibitors[END_REF][START_REF] Campiani | Non-nucleoside HIV-1 reverse transcriptase (RT) inhibitors:past, present, and future perspectives[END_REF][START_REF] Clercq | Non-nucleoside reverse transcriptase inhibitors (NNRTIs):past, present, and future[END_REF][START_REF] Pauwels | New non-nucleoside reverse transcriptase inhibitors (NNRTIs) in development for the treatment of HIV infections[END_REF]. The fact that cross-resistance extends to the whole NNRTI class calls for development of new agents capable of inhibiting clinically relevant NNRTI-resistant mutants [START_REF] Brown | Transmission fitness of drug-resistant human immunodeficiency virus and the prevalence of resistance in the antiretroviral-treated population[END_REF][START_REF] Richman | The prevalence of antiretroviral drug resistance in the United States[END_REF].

Triazole derivatives, and in particular 3,4,5-trisubstituted-1,2,4-4H-triazoles (TTs), have been recently described as a novel class of potent NNRTIs [START_REF] De | Tri-substituted triazoles as potent non-nucleoside inhibitors of the HIV-1 reverse transcriptase[END_REF][START_REF] Kirschberg | Triazole derivatives as non-nucleoside inhibitors of HIV-1 reverse transcriptase--structure-activity relationships and crystallographic analysis[END_REF]. In 2006 Girardet and coworkers discussed a series of TTs bearing an amide function at the linker group, positioned between the sulfur atom (at the triazole position 3) and the R1 substituent (Fig. 1) [START_REF] De | Tri-substituted triazoles as potent non-nucleoside inhibitors of the HIV-1 reverse transcriptase[END_REF].

Recently, Kirschberg and co-workers described a series of triazoles (active against WT HIV-1 RT) bearing a methylene as the linker located between the sulfur atom (at the triazole position 3) and the R1 substituent (Fig. 1) [START_REF] Kirschberg | Triazole derivatives as non-nucleoside inhibitors of HIV-1 reverse transcriptase--structure-activity relationships and crystallographic analysis[END_REF].

The first series of compounds displayed submicromolar or nanomolar potency against WT and (some of them) also against Y188L mutant while the second one was characterized by a loss of potency (micromolar log order) toward the WT.

With the aim to elucidate the most important RT/TT interactions and to identify the features really impacting the TT antiretroviral activity, we performed a computational study on a dataset of 97 TTs, selected from the two series of triazoles described above. Our aim was also to elaborate a quantitative structure-activity relationship (QSAR) model for enabling the TT antiretroviral activity prediction prior to synthesis, and to obtain useful suggestions for the design of new TTs with improved potency also against Y188L mutant.

On the basis of the crystal structure of RT in complex with 4-benzyl-3-[(2chlorobenzyl)sulfanyl]-5-thien-2-yl-4H-1,2,4-triazole (TT 18) [START_REF] Kirschberg | Triazole derivatives as non-nucleoside inhibitors of HIV-1 reverse transcriptase--structure-activity relationships and crystallographic analysis[END_REF], a flexible docking simulation was performed on compounds 1-97. The most probable docking poses were selected and submitted to 3D-QSAR studies involving Comparative Molecular Fields Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). Two 3D-QSAR CoMFA and CoMSIA models were derived, using as dependent variable the TT pEC 50 values measured against the wild-type (WT) HIV-1 (Model A) and the Y188L mutant form (Model B), respectively. These results allowed us to obtain useful information for the design of new compounds with improved potency towards WT HIV-1 or potentially active against Y188L mutant.

Materials and methods

Data set

A dataset of 97 triazoles was selected from two sample data sets. TTs 1-31 (Table 1) [START_REF] Kirschberg | Triazole derivatives as non-nucleoside inhibitors of HIV-1 reverse transcriptase--structure-activity relationships and crystallographic analysis[END_REF] and TTs 32-97 (Table 2) [START_REF] De | Tri-substituted triazoles as potent non-nucleoside inhibitors of the HIV-1 reverse transcriptase[END_REF] were screened against WT HIV-1 on MT-2 cells and HeLa-JC53 cells, respectively. Furthermore, some compounds belonging to series 2 (39-47, 53-68 and 75-96) were also screend against Y188L mutant.

Since 1-97 pEC 50 values are comparable (Efavirenz has been used as biological assays reference compound on both the two cell lines, displaying results within the same order of magnitude), all the compounds were submitted to CoMFA and CoMSIA analyses (using as the dependent variable the TT pEC 50 values measured against the WT HIV-1, Model A).

In addition, CoMFA and CoMSIA models were also derived, using as the dependent variable the 39-47, 53-68 and 75-96 pEC 50 values, measured against the Y188L mutant form (Model B).

The molecular structures of compounds 1-97 (Tables 1,2) were built, parameterized (Gasteiger-Hückel method) and energy minimized within MOE using MMFF94 force field [17].

Docking-based ligand alignment

To locate the appropriate binding orientations and conformations of triazole derivatives within the NNIBS, a computational searching method was applied. Starting from a database including all the 97 compounds, a docking procedure was performed. Thus, on the basis of the three-dimensional structure coordinates of the RT/18 complex (PDB entry 2RKI) [START_REF] Kirschberg | Triazole derivatives as non-nucleoside inhibitors of HIV-1 reverse transcriptase--structure-activity relationships and crystallographic analysis[END_REF] (18 in the bioactive conformation), each inhibitor was docked into the NNIBS using the flexible docking module implemented in Surflex [START_REF] Jain | Scoring noncovalent protein-ligand interactions:A continuous differentiable function tuned to compute binding affinities[END_REF]. Surflex-Dock uses an empirically derived scoring function that is based on the binding affinities of protein-ligand complexes and on their X-ray structures.

Since for all compounds the best-docked geometries were in agreement with the crystallographic data of the RT/18 complex (and thus already aligned), they were directly submitted to CoMFA [START_REF] Cramer | Recent advances in comparative molecular field analysis (CoMFA)[END_REF] and CoMSIA [START_REF] Klebe | Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity[END_REF] studies by Sybyl1.0 software [21].

3D-QSAR analysis

Training set and test set

For both the models (A and B), the training and the test set compounds were divided manually according to a representative range of biological activities and structural variations.

For QSAR analysis, EC 50 values have been transformed into pEC 50 values and then used as response variables. Compound binding affinity covered 4 log orders of magnitude.

Triazole derivatives 1-97 (TTs as WT NNRTIs, Model A)

All the compounds were grouped into a training set (1, 3, 4, 6, 7, 10, 11, 13, 14, 16, 19, 22, 24-31, 33, 36, 39, 41, 45, 48-51, 53, 54, 57-60, 64-71, 77, 78, 80, 81, 84, 86-88, 90, 91, 94-96) for model generation and into a test set (2, 5, 8, 9, 12, 15, 17, 18, 20, 21, 23, 32, 34, 35, 37, 38, 40, 42-44, 46, 47, 52, 55, 56, 61-63, 72-76, 79, 82, 83, 85, 89, 92, 93, 97) for model validation, containing 56 and 41 compounds respectively.

CoMFA and CoMSIA analyses

CoMFA method is a widely used 3D-QSAR technique to relate the biological activity of a series of molecules with their steric and electrostatic fields, which are calculated placing the aligned molecules, one by one, in a 3D cubic lattice with a 2 Å grid spacing. The van der Waals potential and Coulombic terms, which represent steric and electrostatic fields, respectively, were calculated using the standard Tripos force field method. The columnfiltering threshold value was set to 2.0 kcal mol -1 to improve the signal-noise ratio. A methyl probe with +1 charge was used to calculate the CoMFA steric and electrostatic fields. A 30 kcal mol -1 energy cut-off was applied to avoid infinity of energy values inside the molecule.

CoMSIA method calculates five descriptors, namely the steric, electrostatic, and hydrophobic parameters and the H-bond donor and H-bond acceptor properties. The similarity index descriptors were calculated using the same lattice box employed for the CoMFA calculations and an sp 3 carbon as a probe atom with +1 charge, +1 hydrophobicity and +1 H-bond donor and +1 H-bond acceptor properties.

Partial least square analysis and models validation

Partial least-squares (PLS) method approach, an extension of the multiple regression analysis, was used to derive the 3D-QSAR models in which the CoMFA and CoMSIA descriptors were used as independent variables and pEC 50 values were used as dependent variables. Leave one out (LOO) cross-validation method was used to check the predictivity of the derived model and to identify the optimal number of components (ONC) leading to the highest crossvalidated r 2 (r 2 cv ). In the LOO methodology, one molecule is omitted from the dataset and a model is derived involving the rest of the compounds. Then, employing this model, the activity of the omitted molecule is predicted.

The ONC obtained from cross-validation methodology was used in the subsequent regression model. Final CoMFA and CoMSIA models were generated using non-cross-validated PLS analysis. To further assess the statistical confidence and robustness of the derived models, a 100-cycle bootstrap analysis was performed. This is a procedure in which n random selections out of the original set of n objects are performed several times (100-times were required to obtain a good statistical information). In each run, some objects may not be included in the PLS analysis, whereas some others might be included more then once. The mean correlation coefficient is represented as bootstrap r 2 (r 2 boot ). To validate the CoMFA and CoMSIA derived models, the predictive ability for the test set of compounds (expressed as r 2 pred ) was determined by using the following equation:

r 2 pred = (SD -PRESS)/SD
SD is the sum of the squared deviations between the biological activities of the test set molecules and the mean activity of the training set compounds and PRESS is the sum of the squared deviation between the observed and the predicted activities of the test set compounds.

All the calculations were carried out using a PC with the Windows XP operating system.

Results and discussion

Docking-based ligand alignment

In this work, for the molecular modeling analysis we used the X-ray coordinates of the crystal structure of TT 18/RT complex. According to our calculations, all inhibitors display at least an H-bond between the K103 -amino group and the N1 or N2 atoms of the triazole ring, while several van der Waals and interactions with two hydrophobic pockets (P 1 and P 2 ) are detected. As shown in Fig. 2, region P 1 (occupied by the TTs-R2 substituent) includes P95, Y181, Y188 and W229 residues, while P 2 (occupied by the TTs-R1 substituent) consists of K104, V106, F227, L234, H235, P236 and Y318 residues. All the compounds orient the R3 group towards the NNIBS residues E138, V179, I180.

In addition, compounds 75-96 (the most active of the series, pEC 50 = 8.40-10.00) display Hbond interactions between the R1 sulfonamide moiety and NNIBS residues K104 (the backbone carbonyl oxygen atom) and V106 (the backbone nitrogen portion), while the amide linker (located between the R1 group and the sulfur atom at the triazole position 3) is involved in a double H-bond with H235 and Y318. On the contrary, compounds 1-31 do not display additional H-bond interactions within the P2 pocket, showing a loss of potency (pEC 50 = 4.29-6.88) toward the WT.

In order to validate the docking procedure, we compared the most probable binding pose, selected for each compound, with the X-ray structure of 18 in complex with RT, obtaining ligand conformations perfectly superposed on the available crystallographic data. Thus, they

were aligned together inside the NNIBS for the following 3D-QSAR analysis.

CoMFA and CoMSIA analyses

Model A

CoMFA and CoMSIA studies were developed using, respectively, CoMFA steric and electrostatic fields and CoMSIA steric, electrostatic, hydrophobic, H-bond donor and H-bond acceptor properties, as independent variables, and the ligand pEC 50 as a dependent variable. As shown in Fig. 3a (for simplicity, only the structure of compound 15 and 83, displaying high WT pEC 50 values, are depicted as representative of the two series), the steric contour map predicts for compounds 1-31 (series 1) favorable interaction polyhedra (green areas) near one of the two ortho positions of the R1 phenyl ring while unfavorable areas (reported in yellow) are depicted around the two R1 meta positions. Yellow polyhedra also surround the flexible R2 benzyl moiety. Accordingly, compounds 1-31 (pEC 50 = 4.55 -6.88; bearing a benzyl moiety at the R2 substituent) show lower pEC 50 values in comparison with those of compounds 32-97 (pEC 50 = 5.64 -10.00¸ bearing no flexible R2 substituents).

As far as compounds 32-97 (series 2) are concerned, favorable interaction polyhedra (green) are located in proximity of the 1-naphthyl position 4 and 8 at the R2 substituent (or around the position 2 and 4 for those compounds bearing a phenyl ring instead of a 1-naphthyl one), and around the para and one of the two ortho positions of the R1 phenyl moiety. The reliability of the steric map calculations is verified by the higher pEC 50 values of those compounds bearing a 4-substituted-1-naphthyl moiety (or a 4-substituted phenyl ring) at the R2 substituent in comparison with those of TTs bearing the 1-naphthyl or phenyl moiety (unsubstituted) in R2.

These results are also supported by the high pEC 50 values of those triazoles bearing a sulfonyl moiety at the R1 phenyl ring para-position (instead of a methyl one).. On the contrary, unfavorable polyhedra (yellow) are depicted around the linker amide group oxygen atom and in proximity of the 1-naphthyl position 6 and 7 at the R2 substituent (underlining the beneficial introduction of the phenyl moiety, instead of the 1-naphthyl one).

The electrostatic field contour map of the CoMFA analysis plotted in Fig. 3b As shown in Fig. 6a (for simplicity, only the structure of compound 92 is depicted as representative), the steric contour map predicts favorable interaction polyhedra (green) around the the R2 substituent 8-quinoline (or 1-naphthyl) moiety positions 2 and 5 (or positions 4 and 7). The introduction of a small group at the positions 4 and 6 of the 8-quinoline moiety is also allowed (or around the positions 3 and 5 for those TTs bearing a 1-naphthyl ring in R2). The reliability of the steric map calculations is verified by the higher pEC 50 values of those compounds bearing a 4-substituted-1-naphthyl moiety (or a 4-substituted phenyl ring) at the R2 substituent in comparison with those of TTs bearing the 1-naphthyl or phenyl moiety (unsubstituted) in R2.

Unfavorable polyhedra (yellow) surround the oxygen atom of the linker carbonyl group, one of the ortho position and both the meta positions of the para substituted phenyl ring (R1 substituent). Yellow areas are also located around the trifluoromethyl substituent (R3 substituent) and around the 8-quinoline (or 1-naphthyl) moiety positions 1 and 3 (or positions 6 and 8).

On the basis of the electrostatic fields contour map of the CoMFA analysis plotted in Fig. 6b (for simplicity, only the structure of compound 92 is depicted as representative), less positive moieties would be favored (red polyhedra) in proximity of the amide and sulfonyl oxygen atoms (see 32-97 linker and R1 substituent), near the 8-quinolinyl (or 1-naphthyl) position 6 (or position 3) at the R2 substituent and around the trifluoromethyl group (R3 substituent). On the other hand, more electropositive substituents are predicted to be beneficial (blue area) around the 8-quinolinyl (or 1-naphthyl) position 1 (or position 8) at the R2 substituent.

These results are supported by the high pEC 50 values of those triazoles bearing a sulfonyl moiety at the R1 phenyl ring para-position (instead of a methyl one), a trifluoromethyl group at the R3 substituent (instead of a methyl one) or a 1-naphthyl ring (instead of a 8-quinoline moiety) at the R2 substituent.

The CoMSIA steric and electrostatic regions are in agreement with the CoMFA steric and electrostatic areas.

As shown in Fig. 7 (for simplicity, only the structure of compound 92 is depicted as representative), the introduction of small hydrophobic substituents (yellow polyhedra) in proximity of the 8-quinolinyl (or 1-naphthyl) moiety positions 1 and 5 (or position 4 and 8) at the R2 substituent, around the trifluoromethyl substituent (R3 substituent) and around one of the R1 phenyl ring ortho-position is allowed. The reliability of the hydrophobic map calculation is verified by the higher pEC 50 values of those TTs bearing a 4-alkyl-1-naphthyl moiety at the R2 substituent in comparison with those of TTs bearing a 4-methoxy-1-naphthyl moiety in R2. These results are also supported by the high pEC 50 values of those TTs bearing a 1-naphthyl group (instead of a 8-quinoline moiety) at the R2 substituent or a trifluoromethyl group (instead of a methyl one) at the R3 substituent.

The model B CoMSIA H-bond acceptor map (see Online Resource 5) shows that the introduction of groups bearing H-bond acceptor functions on the R1 phenyl ring para-position would be beneficial (magenta polyhedra). On the contrary, H-bond acceptor functions are predicted to be disfavored (green areas) in proximity of the 8-quinolinyl position 1 (or 1naphthyl position 8) at the R2 substituent. Accordingly, H-bond donors are predicted to be favorable (cyan region) and unfavorable (purple region) near the R1 sulfonylamide nitrogen atom and oxygen atoms, respectively (see Online Resource 5). In addition, purple polyhedra are also located around the R2 8-quinolinyl (or 1-naphthyl) position 7 (or position 2). These results are supported by the evidence of the high affinity levels of compounds bearing a sulfonyl moiety at the R1 phenyl ring para-position (instead of a methyl one).

Comparing these results with those obtained from model A, we have identified the following key features enhancing the TT activity towards the Y188L mutant (instead of against the WT): (i) the introduction of a small substituent at the 1-naphthyl position 3 is allowed (compare CoMFA steric contour map around R2, reported in Fig. 7a and in Fig. 3a), (ii) electronegative groups around the 1-naphthyl position 3 (compare CoMFA electrostatic contour map around R2, reported in Fig. 7b and in Fig. 3b) and (iii) hydrophobic group in proximity of R3 (compare CoMSIA hydrophobic contour map reported in Fig. 4 and in Fig. 7).

On the basis of these results, the introduction of a 3-quinolinyl moiety at substituent R2, and of an alkyl-(isopropyl-, cyclopropyl-or t-butyl) group at substituent R3 seem particularly favorable to increase the TT potency against the Y188L mutant. In addition, a 3,5-dimethylphenyl or a 2-naphthyl moiety could result to be effective as an R2 substituent.

Conclusions

The computational studies presented here analyze the main interactions responsible for TTs activity and give useful suggestions for the synthesis of new analogues with improved potency against the WT HIV-1 and also against the Y188L mutant.

The models elaborated will be exploited to design new TTs and predict their activity prior to synthesis.

Tables

No. compounds 56

Optimal number of components (ONC) 5

Leave one out r 2 (r

  The final CoMFA model was generated employing non-cross-validated PLS analysis with the optimum number of components (ONC = 5) to give a non-cross validated r 2 (r 2 ncv ) = 0.984, test set r 2 (r 2 pred ) = 0.80, Standard Error of Estimate (SEE) = 0.229, steric contribution = 0.700 and electrostatic contribution = 0.300. The reliability of the model thus generated was supported by bootstrapping results. A CoMSIA model consisting of steric, electrostatic, hydrophobic, H-bond donor and H-bond acceptor fields with a r 2 ncv = 0.970, r 2 pred = 0.82, SEE = 0.314, steric contribution = 0.178, electrostatic contribution = 0.144, hydrophobic contribution = 0.257, H-bond acceptor contribution = 0.230 and H-bond donor contribution = 0.191 was derived. All statistical parameters supporting CoMFA and CoMSIA models are reported in Online Resource 1. Experimental and predicted binding affinities values for the training set and test set are reported in Online Resource 2.

  (for simplicity, only the structure of compound 15 and 83, displaying high WT pEC 50 values, are depicted as representative of the two series), predicts for compounds 1-31 (series 1) favorable electropositive substituents (blu polyhedra) around the R1 (hetero)aryl moiety para position and in proximity of compound 15 3-thienyl ring sulfur atom (R3 substituent). These results are in agreement with the slightly higher pEC 50 values of 14 (R1 = 4-methoxy-phenyl; pEC 50 = 5.52) in comparison with that of 12 (R1 = 4-cyanophenyl; pEC 50 = 4.63) and are also verified by the difference in pEC 50 among those compounds bearing a 2-thienyl moiety at the triazole R3 substituent (instead of a 3-thienyl one).Regarding TTs 32-97 (series 2), less positive moieties are predicted to be favored (red areas) in proximity of the R1 phenyl ring para-position (83 sulfonyl oxygen atoms), near the triazole N1 and N2 nitrogen atoms and around the linker amide group. Accordingly, compounds 32-97 (pEC 50 = 5.64 -10.00; bearing an amide function as a linker between the sulfur atom at the triazole position 3 and the R1 phenyl moiety) show higher pEC 50 values in comparison with compounds 1-31 [pEC 50 = 4.55 -6.88; bearing a methylene linker between the sulfur atom at the triazole position 3 and the R1 (hetero)aryl moiety].In addition, more electropositive substituents are predicted to be beneficial (blue area) around the linker methylene, on the R1 phenyl ring plane, and in proximity of the 1-naphthyl ring position 2 (R2 substituent). The presence of electropositive groups is also allowed near the 1naphthyl position 8. These results are supported by the high pEC 50 values of those triazoles bearing a sulfonyl moiety at the R1 phenyl ring para-position (instead of a methyl one)], or a 1-naphthyl group (instead of a 8-quinoline moiety) at the triazole R2 substituentThe CoMSIA steric and electrostatic regions are in agreement with the CoMFA steric and electrostatic areas.The calculated CoMSIA hydrophobic contours (Fig.4, for simplicity, only the structure of compound 15 and 83, displaying high WT pEC 50 values, are depicted as representative of the two series) display favorable hydrophobic substituents (yellow polyhedra) in proximity of the methylene of the R2 benzyl group (compounds 1-31, series 1) and of the carbon atom of the R2 1-naphthyl group (compounds 32-97, series 2). Yellow polyhedra also surround the meta positions of the R1 (hetero)aryl moiety (compounds 1-31). On the contrary, hydrophobic moieties are predicted to be disfavored (white areas) in proximity of compound 15 3-thienyl ring sulfur atom (R3 substituent, compounds 1-31) and near compounds 32-97 (i) R1 phenyl ring para position, (ii) R3 substituent and (iii) around the linker amide group.CoMSIA H-bond acceptor map (see Online Resource 3) illustrates that H-bond acceptor groups are predicted to be beneficial (magenta areas) near the triazole N1 and N2 nitrogen atoms (for both the two series) and in proximity of compounds 32-97 R1 phenyl ring para position and of the linker amide group oxygen atoms. The presence of H-bond acceptor seems to be disfavored near the meta positions of the R1 (hetero)aryl moiety (compounds 1-31) and around the R1 sulfonylamide moiety nitrogen atom (compounds 75-96). the higher pEC 50 values of those triazoles bearing a sulfonyl moiety at the R1 phenyl ring para-position (instead of a methyl one) and with the difference in pEC 50 values between compounds 32-97 (pEC 50 = 5.64 -10.00¸ bearing an amide group as a linker between the sulfur atom at the triazole position 3 and the R1 phenyl moiety) and 1-31 [pEC 50 = 4.55 -6.88; bearing a methylene linker between the sulfur atom at the triazole position 3 and the R1 (hetero)aryl moiety].Accordingly, H-bond donor functions (see Online Resource 3) would result to be beneficial (cyan areas) and detrimental (purple polyhedra) around 75-96 R1 sulfonylamide moiety nitrogen and oxygen atoms, respectively. Purple polyhedra are also located in the proximity of the triazole N1 and N2 nitrogen atoms (for both the two series) and near the linker amide function (for compounds 32-97).In order to verify the reliability of the 3D-QSAR analyses, Model A CoMFA and CoMSIA maps have been compared with the results of the docking analysis. Briefly, the CoMFA steric model proves to match with the NNIBS 3D topology, suggesting bulky substitution in proximity of the P 1 pocket occupied by the R2 substituent (involved in stacking interactions with Y181, Y188 and W229). The CoMFA electrostatic map points out the beneficial presence of small electropositive substituents at the R3 substituent, establishing hydrophobic contacts with V179 and I180.The CoMSIA H-bond acceptor and H-bond donor maps confirm the importance of the formation of hydrogen bond interactions to stabilize the RT/TT complex. Accordingly, compounds 75-96 (the most active TTs of the two series, pEC 50 range = 8.40-10.00) bear the following three H-bond acceptor centers: (i) at the para position of the R1 phenyl ring (Hbond with H235 and Y318), (ii) at the linker portion (H-bond with K104 and V106), (iii) and at the triazole ring (K103). On the contrary, compounds 1-31 show lower pEC 50 values (pEC 50 range = 4.29-6.88) because of the absence of H-bond acceptor functions at the TT R1 and linker groups. Model B CoMFA and CoMSIA studies were developed using, respectively, CoMFA steric and electrostatic fields and CoMSIA steric, electrostatic, hydrophobic, H-bond donor and H-bond acceptor properties, as independent variables, and the ligand pEC 50 as a dependent variable. The final CoMFA model was generated employing non-cross-validated PLS analysis with the optimum number of components (ONC = 6) to give a non-cross validated r 2 (r 2 ncv ) = 0.967, test set r 2 (r 2 pred ) = 0.64, Standard Error of Estimate (SEE) = 0.149, steric contribution = 0.684 and electrostatic contribution = 0.316. The reliability of the model thus generated was supported by bootstrapping results. A CoMSIA model consisting of steric, electrostatic, hydrophobic, H-bond donor and H-bond acceptor fields with a r 2 ncv = 0.913, r 2 pred = 0.73, SEE = 0.236, steric contribution = 0.172, electrostatic contribution = 0.153, hydrophobic contribution = 0.231, H-bond acceptor contribution = 0.164 and H-bond donor contribution = 0.281 was derived. All statistical parameters supporting CoMFA and CoMSIA models are reported in Online Resource 4. Experimental and predicted binding affinities values for the training set and test set are reported in Online Resource 5. Distribution of experimental and predicted pEC 50 values for training set and for test set compounds according to model B CoMFAand CoMSIA analyses is reported in Fig. 5

Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7

 234567 Figure captions

3, 4 , 5 -Trisubstituted- 1 , 2 , 4 - 3 , 4 , 5 -Trisubstituted- 1 , 2 , 4 -

 45124345124 4H-triazoles as WT and Y188L mutant HIV-1 non-nucleoside reverse transcriptase inhibitors: Docking-based CoMFA and CoMSIA analyses Journal of Molecular Modeling Elena Cichero*, Laura Buffa and Paola Fossa Dipartimento di Scienze Farmaceutiche. Università degli Studi di Genova. Viale Benedetto XV n.3. 16132 -Genova. Italy *Corresponding author: phone: +39-010-3538361; fax: +39-010-3538358; email:cichero@unige.it Model B CoMSIA hydrogen bond acceptor polihedra (a) and hydrogen bond donor polihedra (b) are shown around compounds 92, depicted in stick mode and coloured by atom type. H-bond acceptor groups: magenta, favoured; green, disfavoured. H-bond donor groups: cyan, favoured; purple, disfavoured 4H-triazoles as WT and Y188L mutant HIV-1 non-nucleoside reverse transcriptase inhibitors: Docking-based CoMFA and CoMSIA analyses Journal of Molecular Modeling Elena Cichero*, Laura Buffa and Paola Fossa Dipartimento di Scienze Farmaceutiche. Università degli Studi di Genova. Viale Benedetto XV n.3. 16132 -Genova. Italy *Corresponding author: phone: +39-010-3538361; fax: +39-010-3538358; email:cichero@unige.it

Triazole derivatives 39-47, 53-68 and 75-96

  (TTs as Y188L mutant NNRTIs, Model B)

	Compounds 39-

47, 53-68 and 75-96 were

  divided into a training set (39-44, 47

, 53-59, 61-64, 67, 68, 75-80, 82-92, 95) for

  model generation and into a test set (45, 46

, 60, 65, 66, 81, 93, 94, 96) for

  model validation, containing 38 and 9 compounds respectively.

Table 1

 1 Molecular structure of triazoles derivatives 1-31

			N	N
		R1	S	N	R3
				R2
	Comp.	R1	R2		R3	WT pEC 50
					CH 3	4.63
						5.34
					O
						4.98
						5.43
					S
			Cl
					H	5.55
		N				4.38
					S
		N
						4.64
					S
		N				5.32
					S
		N
						5.08
					S
		N
						5.08
					S
						4.55
					S
						4.63
					S
		N
		O				5.24
					S
		CH
		3
		O				5.52
					S
		CH
		3
		Cl
						6.88
					S
		Cl
						5.80
					S

-Trisubstituted-1,2,4-4H-triazoles as WT and Y188L mutant HIV-1 non-nucleoside reverse transcriptase inhibitors: Docking-based CoMFA and CoMSIA analyses

	3,4,5Journal of Molecular Modeling Experimental and predicted pEC 50 values of compounds 1, 3, 4, 6, 7
	Elena Cichero*, Laura Buffa and Paola Fossa
			2	loo )	0.886
	Cross-validated r 2 (r 2 Dipartimento di Scienze Farmaceutiche. Università degli Studi di Genova. Viale Benedetto cv ) 0.892
	Std. error of estimate (SEE) XV n.3. 16132 -Genova. Italy	0.314
	Non cross-validated r 2 (r 2 *Corresponding author: phone: +39-010-3538361; fax: +39-010-3538358; e-ncv ) 0.970
	F value mail:cichero@unige.it	314.920
	Steric contribution		0.178
	Electrostatic contribution	0.144
	Hydrophobic contribution	0.257
	H-bond acceptor contribution	0.230
	H-bond donor contribution	0.191
	Bootstrap r 2 (r 2	boot )		0.974
	Standard error of estimate r 2	boot (SEE r 2	boot )	0.298
	Test set r 2 (r 2	pred )		0.82

, 10, 11, 13, 14, 16, 19, 22, 24-31, 33, 36, 39, 41, 45, 48-51, 53, 54, 57-60, 64-71, 77, 78, 80, 81, 84, 86-88, 90, 91

	continued
	32, 34,						,
	94-96 (model A training set)	CoMFA Model	CoMSIA Model
	Compound	Exp. pEC 50 Pred. pEC 50	Residual	Pred. pEC 50	Residual
		7.00	CoMFA Model 7.50 -0.50	CoMSIA Model 7.86	-0.86
	Compound	Exp. pEC 50 Pred. pEC 50 8.30 8.00	Residual 0.30	Pred. pEC 50 7.84	Residual 0.46
	1	4.63 8.70	5.34 8.71	-0.71 -0.01	4.94 8.60		-0.31 0.10
	3	4.98 8.15	5.11 7.86	-0.13 0.29	4.88 7.82		0.10 0.34
	4	5.43 9.30	5.17 7.26	0.26 2.04	5.08 7.29		0.36 2.01
	6	4.38 9.00	4.48 9.24	-0.10 -0.24	4.52 9.14		-0.14 -0.14
	7	4.64 9.52	4.76 9.46	-0.12 0.06	4.54 9.19		0.10 0.33
		5.08 9.52	5.17 9.60	-0.09 -0.08	4.81 9.23		0.27 0.29
		4.55 9.10	4.63 8.82	-0.08 0.28	4.88 8.73		-0.33 0.37
		5.24 9.10	5.20 8.95	0.04 0.15	5.07 9.30		0.18 -0.20
		5.52 8.52	5.46 8.57	0.06 -0.05	5.17 8.97		0.35 -0.45
		5.80 9.00	5.51 8.98	0.29 0.02	5.66 9.39		0.14 -0.39
		5.77 9.40	5.53 9.41	0.24 -0.01	5.69 9.26		0.08 0.14
		5.77 9.40	6.04 9.40	-0.27 0.00	5.92 9.28		-0.15 0.12
		5.05 7.15	5.36 6.73	-0.31 0.42	5.57 6.89		-0.52 0.26
		5.48 7.00	5.53 6.88	-0.05 0.12	5.76 7.01		-0.28 -0.01
		5.89 8.40	5.67 8.51	0.22 -0.11	5.82 8.41		0.07 -0.01
		5.62 8.70	5.95 8.71	-0.33 -0.01	6.03 8.81		-0.41 -0.11
		5.46 9.40	5.37 9.33	0.09 0.08	5.54 9.38		-0.08 0.02
		6.00 9.00	5.63 8.93	0.37 0.07	5.71 8.60		0.29 0.40
		5.96 8.52	5.75 8.53	0.21 -0.01	5.89 8.77		0.07 -0.25
		6.14 9.15	5.89 9.23	0.25 -0.08	5.96 9.28		0.18 -0.13
		6.40 9.40	6.57 9.15	-0.17 0.25	6.94 9.18		-0.54 0.22
		6.96 9.10	6.93 9.31	0.04 -0.21	7.33 8.99		-0.37 0.11
		8.40 8.40	8.56 8.45	-0.16 -0.05	8.45 8.33		-0.04 0.07
		9.00 9.15	8.86 8.95	0.15 0.20	8.62 9.02		0.38 0.13
		9.00 8.70	9.08 9.10	-0.08 -0.40	8.78 9.43		0.22 -0.73
		8.52 8.70	8.51 8.79	0.01 -0.09	8.18 8.66		0.34 0.04
		8.15 9.10	8.07 9.19	0.08 -0.09	8.20 9.16		-0.04 -0.06

35, 37, 38, 40, 42-44, 46, 47, 52, 55, 56, 61-63, 72-76, 79, 82, 83, 85, 89, 92, 93, 97

  (model A test set).

				11
	76 continued	9.30	9.13	0.17	9.19		0.11
			CoMFA Model	CoMSIA Model
	Compound	Exp. pEC 50 Pred. pEC 50	Residual	Pred. pEC 50	Residual
		9.70	CoMFA Model 8.62 1.08	CoMSIA Model 8.32	1.38
	Compound	Exp. pEC 50 Pred. pEC 50 9.22 9.64	Residual -0.42	Pred. pEC 50 9.53	Residual -0.31
	2	5.34 10.00	4.83 8.95	0.51 1.05	5.03 9.08		0.31 0.92
	5	5.55 9.22	5.54 8.89	0.01 0.33	4.99 8.80		0.56 0.42
	8	5.32 9.00	4.76 8.80	0.56 0.20	4.64 8.87		0.69 0.13
	9	5.08 9.00	5.11 8.70	-0.03 0.30	4.57 8.88		0.51 0.12
		4.63 9.30	5.73 9.26	-1.10 0.04	5.80 9.47		-1.17 -0.17
		6.88 9.30	5.32 8.01	1.56 1.29	5.54 8.26		1.34 1.04
		4.29	4.97	-0.68	5.20		-0.91
		6.36	5.53	0.83	5.69		0.67
		5.70	5.35	0.35	5.31		0.39
		6.24	5.82	0.42	5.77		0.48
		5.33	5.73	-0.40	5.89		-0.56
		7.22	7.16	0.06	7.08		0.15
		9.15	7.48	1.67	7.58		1.57
		9.22	8.18	1.04	8.41		0.82
		9.70	8.21	1.49	8.58		1.12
		10.00	8.36	1.65	8.73		1.27
		8.10	8.17	-0.07	7.96		0.14
		9.00	8.32	0.68	8.35		0.65
		9.00	8.40	0.60	8.57		0.43
		9.00	8.40	0.61	8.20		0.80
		9.00	9.76	-0.76	9.22		-0.22
		9.00	8.59	0.41	8.55		0.45
		9.40	8.00	1.40	8.20		1.20
		9.00	8.89	0.11	8.92		0.08
		9.22	8.27	0.95	8.13		1.10
		9.30	9.48	-0.18	9.02		0.28
		9.30	8.13	1.17	8.29		1.01
		9.15	9.54	-0.39	9.20		-0.05
		10.00	8.88	1.12	9.00		1.00
		9.30	8.77	0.53	7.98		1.32
		9.40	8.76	0.64	8.43		0.97
		9.30	8.57	0.73	8.43		0.87

Table 9 . continued

 9

	80, 82-92, 95 (model B training set)
			CoMFA Model	CoMSIA Model
	Compound	Exp. pEC 50 Pred. pEC 50	Residual	Pred. pEC 50	Residual
		6.23	6.17	0.06	6.23	0.00
		5.24	5.21	0.03	5.44	-0.20
		6.70	6.58	0.12	6.62	0.08
		7.00	6.97	0.03	6.75	0.25
		6.80	7.01	-0.21	6.84	-0.04
		6.96	6.99	-0.03	7.11	-0.15
		6.49	6.52	-0.03	6.47	0.02
		6.14	6.21	-0.07	6.30	-0.16
		5.38	5.43	-0.04	5.67	-0.29
		6.68	6.60	0.08	6.41	0.27
		7.16	7.04	0.12	6.98	0.18
		6.82	6.83	-0.01	6.87	-0.05
		6.32	6.69	-0.37	6.59	-0.27
		6.48	6.48	0.00	6.69	-0.21
		7.01	7.03	-0.02	6.98	0.03
		7.24	7.12	0.12	6.99	0.26
		6.34	6.22	0.13	6.13	0.21
		6.70	6.64	0.07	6.52	0.18
		6.96	7.08	-0.12	6.97	-0.01
		6.62	6.71	-0.09	6.63	-0.01
		6.72	6.76	-0.04	6.86	-0.14
		7.31	7.24	0.07	7.11	0.20
		5.62	5.59	0.03	5.49	0.13
		6.74	6.73	0.01	6.94	-0.20
		7.70	7.51	0.19	7.73	-0.03
		7.60	7.88	-0.28	7.54	0.06
		8.22	8.03	0.19	7.93	0.30
		8.40	8.36	0.04	8.45	-0.05
		7.59	7.84	-0.25	8.16	-0.57
		7.82	7.86	-0.04	7.67	0.15
		6.96	7.17	-0.21	7.47	-0.51
		7.85	7.60	0.25	7.46	0.39
		6.43	6.24	0.19	6.26	0.17

Model A CoMSIA hydrogen bond acceptor polihedra (a) and hydrogen bond donor polihedra (b) are shown around compounds 15 (as representative of series 1 TTs, C: pink) and 83 (as representative of series 2 TTs, C: white), depicted in stick mode and coloured by atom type. H-bond acceptor groups: magenta, favoured; green, disfavoured. H-bond donor groups: cyan favoured; purple, disfavoured

Acknowledgments

This work was financially supported by the University of Genova. Dr. B. Domenichini is gratefully acknowledged. E.C. was financially supported by a post-doc fellowship, Area Chimica, University of Genova.


M.D M Martin
email: mmartin@geicam.org

M.D A Romero

Ph.D Mcu Cheang

M.D J A López García- Asenjo

M.D J A García-Saenz

Ph.D B Oliva

M.D J M Román

Ph.D X He

M.D A Casado

J De La Torre

V Furio

M.D. Ph.D J Puente

M.D T Caldés

M.D J A Vidart

Sara Lopez-Tarruella

M.D E Diaz-Rubio

Ph.D C M Perou

Genomic predictors of response to doxorubicin versus docetaxel in primary breast cancer

Purpose

Taxanes and anthracyclines improve the outcome of early breast cancer, although the benefit is limited to a small proportion of patients and are toxic. We prospectively looked for predictors of response to these drugs.

Experimental design

Four cycles of doxorubicin (75 mg/m 2 ) or docetaxel (100 mg/m 2 ) were compared as presurgical chemotherapy for breast cancer. Biomarkers were determined by immunohistochemistry and fluorescent in-situ hybridization using prechemotherapy core biopsies. Tumors were also classified into one of the molecular intrinsic subtypes using an immunohistochemical panel of 5 biomarkers and genomic profiles. Single genes and intrinsic subtypes were correlated with response to doxorubicin versus docetaxel .

Results

Among the 204 evaluable patients, significant predictors of sensitivity in multivariate analysis were low topo2a expression and ER-negative status for doxorubicin and small tumor size and ER-negative status for docetaxel. Predictors of resistance in multivariate analysis were triple-negative status (ER/PgR/HER2 negative by IHC/FISH) for doxorubicin, and high TNM stage for docetaxel. Triple-negative tumors were associated with topo2a overexpression more than the other subtypes. In 94 patients with gene expression profiles, docetaxel was superior to doxorubicin in the basal-like subtype (good pathological response rate -PCR+class I-of 56% vs. 0%; p=0.034); no significant differences were observed in the other subtypes when comparing these two drugs.

Conclusions

Low topo2a expression and ER-negative status were predictors of response to doxorubicin, while small tumor size and ER-negative status predicted response to docetaxel. Docetaxel was superior to doxorubicin in triple-negative/basal-like tumors, while no significant differences were seen in the remaining intrinsic subtypes.

INTRODUCTION

The initial trials of adjuvant chemotherapy (CMF-like regimens) showed that these regimens could improve the outcomes of operable breast cancer patients [START_REF]Polychemotherapy for early breast cancer: an overview of the randomised trials. Early Breast Cancer Trialists' Collaborative Group[END_REF]. Later, anthracycline combinations were found to be slightly but significantly more effective than the CMF regimen [START_REF]Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials[END_REF]. More recently, several adjuvant phase III trials have shown that taxane regimens further reduce the likelihood of recurrence and death, compared to anthracycline-containing combinations [START_REF] Henderson | Improved outcomes from adding sequential paclitaxel but not from escalating doxorubicin dose in an adjuvant chemotherapy regimen for patients with node-positive primary breast cancer[END_REF][START_REF] Mamounas | Paclitaxel after doxorubicin plus cyclophosphamide as adjuvant chemotherapy for nodepositive breast cancer: results from NSABP-B 28[END_REF][START_REF] Martin | Adjuvant docetaxel for node positive breast cancer[END_REF][START_REF] Roche | Sequential adjuvant epirubicin-based and docetaxel chemotherapy for node-positive breast cancer patients: The FNCLCC PASC 01 Trial[END_REF][START_REF] Jones | Docetaxel with cyclophosphamide is associated with an overall survival benefit compared with doxorubicin and cyclophosphamide: 7-year follow-up of US Oncology Research Trial 9735[END_REF][START_REF] Martin | Randomized phase 3 trial of fluorouracil, epirubicin, and cyclophosphamide alone or followed by paclitaxel for early breast cancer[END_REF][START_REF] Francis | Adjuvant chemotherapy with sequential or concurrent anthracycline and docetaxel: Breast International Group 02-98 randomized trial[END_REF][START_REF] Gianni | Phase III trial valuating the addition of paclitaxel to doxorubicin followed by cyclophosphamide, methotrexate, and fluorouracil as adjuvant or primary systemic therapy: European Cooperative Trial in Operable Breast Cancer[END_REF], but the absolute benefit was again small, and some trials were unable to show any advantage for the taxane arm [START_REF] Goldstein | Concurrent doxorubicin plus docetaxel is not more effective than concurrent doxorubicin plus cyclophosphamide in operable breast cancer with 0 to 3 positive axillary nodes: North American Breast Cancer Intergroup Trial E 2197[END_REF][START_REF] Ellis | Sequential docetaxel as adjuvant chemotherapy for early breast cancer (TACT): an open-label, phase III, randomised controlled trial[END_REF]. Taxanes and anthracyclines can induce some acute and chronic toxicities of considerable clinical concern. Hence there is a pressing need to identify predictors for anthracycline and taxane response in breast cancer treatment.

The aforementioned adjuvant trials were conducted using unselected populations of breast cancer patients. Genomic studies performed in the early 2000s have revealed that breast cancer is a molecularly heterogeneous disease consisting of at least 4-5 different tumor subtypes [START_REF] Perou | Molecular portraits of human breast tumours[END_REF][START_REF] Sorlie | Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications[END_REF][START_REF] Sorlie | Repeated observation of breast tumor subtypes in independent gene expression data sets[END_REF][START_REF] Cheang | Ki67 index, HERT2 status, and prognosis of patients with luminal B breast cancer[END_REF]. In neoadjuvant trials using combinations of anthracycline and taxane, the sensitivity of the different molecular subtypes to chemotherapy varies; the basal-like and Her2-enriched subtypes showing the best response [START_REF] Carey | The triple negative paradox: primary tumor chemosensitivity of breast cancer subtypes[END_REF][START_REF] Rouzier | Breast cancer molecular subtypes respond differently to preoperative chemotherapy[END_REF]. However, the relative contributions of each of these two classes of agents to response is still unknown.

To identify predictors of response, we performed this comparative, randomized neoadjuvant study comparing single agent docetaxel versus single agent doxorubicin in patients with locally advanced breast cancer.

PATIENTS AND METHODS

Women eligible were aged between 18 and 79 years with clinical stage IIB, IIIA or IIIB breast cancer not amenable to breast preserving surgery. The study was approved by the institutional review board of the institutions (identifier code: NCT 00123929). All patients signed an informed consent form before being enrolled in the trial. A complete staging workup, including bilateral mammography and MRI, sonography of the affected breast, body CT scan, and bone scan, was carried-out prior to recruitment into the study.

Study Objectives

The primary study goal was to identify differential molecular/genomic predictors of response and resistance to single agents docetaxel vs doxorubicin.

Study procedures

Core biopsies of the tumor were obtained following the patient's informed consent for participation in the trial. Eligible patients were then randomly assigned to receive 4 cycles of either doxorubicin (75 mg/m 2 body-surface area) or docetaxel (100 mg/m 2 body-surface area) every 3 weeks followed by surgery. After surgery, patient treatment assignment was crossed-over to receive 4 cycles of the opposite drug, plus radiation therapy. Patients whose tumors were positive for hormone receptors received tamoxifen, or aromatase inhibitors, or a sequence of both for at least 5 years. From May 2005 onwards, patients with her2amplified tumors received trastuzumab after surgery.

Evaluation of response

Clinical response was evaluated according to RECIST criteria comparing pre and postchemotherapy MRI breast assessments.

Pathological response was evaluated in the surgery specimen (either lumpectomy or mastectomy; both with additional axillary clearance) according to the residual cancer burden (RCB) classification of Symmans et al [START_REF] Symmans | Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy[END_REF]. Patients with PCR and class I were considered as having a good pathological response (good PathResp) since both have an equivalent good prognosis, while those patients with class III were considered as resistant to therapy.

This classification also provides a continuous variable (residual cancer burden -RCB-) which measures the amount of residual tumor burden, which could provide additional information.

Molecular and genomic studies

IHC and FISH techniques

Paraffin-embedded tumor samples from core biopsies were evaluated by IHC analysis for p27 protein (mouse monoclonal antibody NCL-p27, Clone 1B4, 1:30 Leica Microsystems); Tau protein positivity was defined as ≥20% of stained cells, since the staining in our normal control breast tissue was always below that figure. The same cut-off point was used for Ki67.

Since there is no standard cut-off for topoisomerase II alpha positivity, both means (20% of stained cells, pre-specified cut-off point in the protocol) and median values (10% of stained cells) were used for repeated analysis. Positivity for p27, BCL-2, EGFR and CK 5/6 was defined as any degree of positive staining. The cut-off value for ER and PR positivity was established at ≥10% of stained cells in the original protocol but, again, the univariate and multivariate analysis were also repeated using a cut-off point of 1%.

HER2 and TOP2A gene amplification were evaluated by fluorescence in situ hybridization (FISH) (HER2 FISH 30-161060 Path Vysion HER-2 DNA and the TOPII α FISH 32-190095 Vysis LSI TOPII α respectively) and to centromere 17 according to manufacturer's instructions. Tumors were considered positive for HER2 if amplification ratio more than 2.2 and for TOP2A if amplification ratio more than 2 . The cut off for the deletion positivity was established as a TOP2A genes to chromosome 17 ratio of less than 0.5. All the cut-off points were predefined before the correlations with response were performed. In all the determinations, the pathologist was blinded from patient's outcome and treatments.

Tumors were classified into molecular intrinsic subtypes based either on IHC/FISH parameters or using gene expression profiles [START_REF] Parker | Supervised risk predictor of breast cancer based on intrinsic subtypes[END_REF][START_REF] Prat | Phenotyphic and molecular characterization of the claudin-low intrinsic subtype of breast cancer[END_REF][START_REF] Hugh | Breast cancer subtypes and response to docetaxel in node-positive breast cancer: Use of an immunohistochemical definition in the BCIRG 001 Trial[END_REF] .The first method was based on an immunopanel of 4 biomarkers previously described by Hugh et al [START_REF] Hugh | Breast cancer subtypes and response to docetaxel in node-positive breast cancer: Use of an immunohistochemical definition in the BCIRG 001 Trial[END_REF] that includes 4 subtypes (luminal A and B, HER2, and triple negative):

-luminal A: ER + and/or PR + , HER2 -(FISH), KI67 ≤13% -luminal B: ER + and/or PR + , and either HER2 + (FISH) or KI67 14% -HER2: ER -and PR -, HER2 + (FISH) -Triple-negative: ER -, PR -and HER2 -(FISH)

The second approach was based on the "gold-standard" of gene expression profiles as assayed by DNA microarrays. The background subtracted Lowess normalized log2 ratio of Cy3 and Cy5 intensity values were retrieved and used for all subsequent analyses. The primary microarray data presented in this study is available in the GEO under accession number GSE21997. Tumors were classified into an intrinsic subtype (Luminal A, Luminal B, Her2-enriched, Basal-like and Normal-like) using the PAM50 50-gene assays a as described in Parker et al [START_REF] Symmans | Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy[END_REF]. In addition, a recent identified subtype, namely Claudin-low, was also scored for using a centroid based predictor for this subtype [START_REF] Prat | Phenotyphic and molecular characterization of the claudin-low intrinsic subtype of breast cancer[END_REF]; in total, these two predictors (i.e. PAM50 and Claudin-low versus not, result in a 6 subtype classification system that is Luminal A, Luminal B, Basal-like, HER2-enriched, Claudin-low, and Normal-like).

The second method was based on Agilent Human oligonucleotide microarrays. 94 fresh frozen core biopsies were assayed on customized 44,000 feature Agilent Human oligonucleotide microarrays. Total RNA purification and microarrays hybridization were done as previously described in Parker et al [START_REF] Parker | Supervised risk predictor of breast cancer based on intrinsic subtypes[END_REF]. The primary microarray data presented in this study is available in the GEO under accession number GSE21997. Tumors were classified into an intrinsic subtype using the PAM50 50-gene assays [START_REF] Symmans | Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy[END_REF]. In addition, a recent identified subtype, namely Claudin-low, was also scored for using a centroid based predictor for this subtype [START_REF] Prat | Phenotyphic and molecular characterization of the claudin-low intrinsic subtype of breast cancer[END_REF]; in total, these two predictors result in a 6 subtype classification system that is Luminal A, Luminal B, Basal-like, HER2-enriched, Claudin-low, and Normal-like.

Statistical Analysis

An empirical sample size of 100 evaluable patients in each treatment arm was established based on an assumption that if powerful predictors to response for these two drugs did exist, this sample size would be sufficient to detect a difference, and large enough to rule out the weak association of less important molecular markers.

The significant asassociation between categorical variables was tested by either chi-squared or Fisher exact test when appropriate. The association between the RBC index scores and other clinicopathological variables was assessed by U Mann-Whitney/t-student test.

Variables with clear or borderline statistical significance response to treatment in univariate analysis were included in a multivariable step-wise logistic regression model. Likelihood ratio test were used if the variables add significance to the predictive model.

Finally, a logistic regression analysis was performed for PathResp involving the overall population (204 patients). The covariates used in the analysis were those previously described as well as the presence/absence of interaction between the molecular predictors of response to either drug (top2A and ER expression) and the chemotherapy treatment.

Adjustments were made for multiple comparisons. The statistical analysis was performed using SPSS 15.0/strata 10 package. All tests were two-tailed and p-values < 0.05 were considered significant.

Results

226 patients were initially registered; 211 were eligible and randomized to docetaxel (n=104) or doxorubicin (n=107); 204 patients (100 in the docetaxel arm and 104 in the doxorubicin arm) were fully evaluable for the statistical analyses of biomarker predictors and pathological response (see Apendix 1 Consort diagram). The clinical and demographic characteristics of the patients are shown in Table 1. Genomic profiling was successfully performed in 94 patients (46% of total), whose characteristics were not significantly different from the overall sample (data not shown).

Anti-tumor activity

Clinical objective response rates (RECIST criteria) were 67% for doxorubicin and 77% for docetaxel (p=0.12). The rates of good PathResp were 19% in the doxorubicin arm and 20% in the docetaxel arm (p=0.89).

A. Prediction of anti-tumor activity: single biomarker model

A.1. Prediction of chemo-sensitivity (good PathResp; Symmans class 0+I)

Table 2 summarized the results of univariate analysis by treatment arm. The multivariable logistic regression analysis (Table 3) showed that topo2a and ER expression were the two independent significant molecular markers to response to doxorubicin. ER status provided significant additional information over topo2a (Wald likelihood-ratio test; p=0.0105). Tumor size (Tsize) and ER status were the two independent predictors to response to docetaxel.

Again, ER status provided additional information over Tsize (Wald likelihood-ratio test; p=0.0004).

The results were remarkably similar when a cut point of 10% (topo2a) and 1% (hormone receptors status) were used to define positivity (data not shown).

To investigate if there was an interaction between the significant variables and treatments, an additional multivariable logistic model was built by combining the doxorubicin-treated and docetaxel-treated patients. The interaction terms, topo2a expression/treatment and ER status/treatment interactions were included as covariates. In this multivariable model, there was a significant interaction between topo2a expression and treatments (p=0.048), but there was no significant interaction between ER status and treatments (on line only, Table 3e).

Figure 1 shows the correlation between topo2a protein expression and good PathRes in both arms.

A.2. Prediction of chemo-resistance (poor PathResp, RCB class III)

Among patients treated with doxorubicin, no significant variables predicting chemoresistance (as defined as being RCB class III) were found (Table 43, on line only). Tumor size and tumor stage were associated with a significantly higher Symmans class III in univariate analysis on patients treated with docetaxel. In multivariable logistic regression analysis, tumor stage was the only independent predictor of chemo-resistance to docetaxel. The

B. Predicting anti-tumor activity by intrinsic breast cancer subtypes

B.1. IHC-FISH-based classification

The differential response of intrinsic tumor subtypes to doxorubicin versus docetaxel is shown in Table 4. No selective differences in activity between doxorubicin and docetaxel were seen in the luminal and her2 subtypes. However, triple negative patients treated with doxorubicin had a significantly higher mean RCB (3.255 vs. 2.238; p=0.025) and a significantly higher proportion of poor PathResp (70% vs. 32%, p=0.010) than those treated with docetaxel. The good PathResp rate was also numerically superior with docetaxel (10% vs. 29%), although this difference did not reach statistical significance (p=0.160).

In view of these findings, we included a binary variable defined as triple-negative vs. not to the multivariable models predicting good and bad PathRes respectively. In the model predicting good PathResp, triple negative phenotype was not an independent predictor of response to either docetaxel or doxorubicin. On the other hand, in the model predicting bad PathResp, triple-negative phenotype was the only independent predictor of poor response to doxorubicin. Triple negative patients treated with doxorubicin had an odds ratio for bad PathResp of 4.42 (95% CI 1.53-12.73, p=0.006) with respect to non-triple negative patients treated.

B.2. Gene expression profiles-based classification of molecular intrinsic subtypes

A subset of evaluable patients (94/204) were also expression profiled on 44,000 feature full genome Agilent microarrays. Basal-like breast cancer was the only subtype that showed selective benefit for docetaxel over doxorubicin in terms of good PathRes (56% vs 0%, p = 0.029), and mean RCB (1.626 vs 3.245, p=0.039) (Table 5).

C. Correlation between variables

Figure 2 shows the relationship between topo2a protein overexpression and intrinsic subtype based on IHC/FISH. The rate of topo2a protein overexpression was different among subtypes (p=0.006), with the triple negative subset showing the higher rate (50%).

DISCUSSION

In our trial, doxorubicin and docetaxel had similar antitumor activity in primary breast cancer.

The few neoadjuvant studies conducted to-date with single agent doxorubicin or docetaxel reported a very similar activity [START_REF] Arriola | Predictive value of HER-2 and topoisomerase IIα in response to primary doxorubicin in breast cancer[END_REF][START_REF] Tham | Clinical response to neoadjuvant docetaxel predicts improved outcome in patients with large locally advanced breast cancer[END_REF][START_REF] Gradishar | Neoadjuvant docetaxel followed by adjuvant doxorubicin and cyclophosphamide in patients with stage III breast cancer[END_REF]. In our trial, ER negative status was a strong but unspecific predictor of sensitivy to either docetaxel and doxorubicine. Low topo2a protein expression was an independent predictor of response to doxorubicin, while small tumor size predicts the response to docetaxel. On the other hand, tumors with triple negative status (by IHC/FISH) and basal-like subtype tumors defined by gene expression profiles were sensitive to docetaxel but appears to be resistant to doxorubicin. There was no significant differences among the other genomic subtypes.

With respect to doxorubicin, several studies have tried to identify single gene/protein biomarkers to predict anti-tumor activity. Both HER2 and TOPA2 gene status/protein expression have been reported as predictors of response in the adjuvant setting [START_REF] Slamon | Alterations in the TOP2A and HER2 genes: association with adjuvant anthracycline sensitivity in human breast cancers[END_REF][START_REF] Pritchard | HER2 and responsiveness of breast cancer to adjuvant chemotherapy[END_REF]. A pooled analysis of trials published in the literature [START_REF] Gennari | HER2 status and efficacy of adjuvant anthracyclines in early breast cancer: a pooled analysis of randomized trials[END_REF] has suggested that HER2 overexpression/amplification predicts response to adjuvant anthracycline combinations. The final conclusions remains debatable as there might be a selection bias in these publications.

In patients with advanced tumors, the results of the trials evaluating relationships between HER2 and response to doxorubicin are contradictory [START_REF] Zhang | Correlation between HER-2 expression and response to neoadjuvant chemotherapy with 5-fluororuacil, doxorubicin, and cyclophosphamide in patients with breast carcinoma[END_REF][START_REF] Penault-Llorca | Induction chemotherapy for breast carcinoma: predictive markers and relation with outcome[END_REF][START_REF] Petit | Comparative value of tumour grade, hormonal receptors, Ki-67, HER-2 and topoisomerase II alpha status as predictive markers in breast cancer patients treated with neoadjuvant anthracycline-based chemotherapy[END_REF][START_REF] Geisler | Influence of TP53 gene alterations and c-erbB2 expression on the response to treatment with doxorubicin in locally advanced breast cancer[END_REF][START_REF] Leo | HE2/neu as a predictive marker in a population of advanced breast cancer patients randomly treated either with single-agent doxorubicin or single-agent docetaxel[END_REF][START_REF] Järvinen | Predictive value of topoisomerase IIα and other prognostic factors for epirubicin chemotherapy in advanced breast cancer[END_REF], but most studies had been retrospective and too underpowered to show clinically relevant correlations. Our study did not observe a significant relationship between HER2 gene amplification (as measured by FISH) and response to doxorubicin although, numerically, the absolute response rate for HER2 amplified tumors was higher than for HER2-normal tumors (28% vs. 16%).

The association between HER2 status and sensitivity to doxorubicin could be related to the close topographical location of the HER2 and TOP2A genes [START_REF] Slamon | Alterations in the TOP2A and HER2 genes: association with adjuvant anthracycline sensitivity in human breast cancers[END_REF]; TOP2A amplification almost always occurs within the context of simultaneous HER2 amplification.

The correlation between TOP2A gene status/topo2a protein expression and response to anthracyclines has been addressed by a legion of studies [START_REF] Arriola | Predictive value of HER-2 and topoisomerase IIα in response to primary doxorubicin in breast cancer[END_REF][START_REF] Slamon | Alterations in the TOP2A and HER2 genes: association with adjuvant anthracycline sensitivity in human breast cancers[END_REF][START_REF] Penault-Llorca | Induction chemotherapy for breast carcinoma: predictive markers and relation with outcome[END_REF][START_REF] Esteva | Topoisomerase IIα amplification and anthracycline-based chemotherapy: The jury is still out[END_REF][START_REF] Pritchard | Are HER2 and TOP2A useful as prognostic or predictive biomarkers for anthracycline-based adjuvant chemotherapy for breast cancer?[END_REF][START_REF] Gianni | Anthracyclines and early breast cancer: The end of an era?[END_REF][START_REF] Buzdar | Topoisomerase IIα gene amplification and response to anthracyclinecontaining adjuvant chemotherapy in breast cancer[END_REF][START_REF] Coon | Amplification and overexpression of topoisomerase IIα predict response to anthracycline-based therapy in locally advanced breast cancer[END_REF][START_REF] Cardoso | Correlation between complete response to anthracycline-based chemotherapy and topoisomerase IIα gene amplification and protein expression in locally advanced breast cancer[END_REF][START_REF] Knoop | Retrospective analysis of topoisomerase IIa amplifications and deletions as predictive marker in primary breast cancer patients randomly assigned to cyclophosphamide, methotrexate, and fluorouracil or cyclophosphamide, epirubicin and fluorouracil: Danish Breast Cancer Cooperative Group[END_REF][START_REF] Tanner | Topoisomerase II-alpha gene amplification predicts favourable treatment response to tailored and dose-escalated anthracycline-based adjuvant chemotherapy in HER-2/neu-amplified breast cancer: Scandinavian Breast Group Trial 9401[END_REF][START_REF] Leo | HER-2 amplification and topoisomerase IIα gene aberrations as predictive markers in node-positive breast cancer patients randomly treated either with an anthracycline-based therapy or with cyclophosphamide, methotrexate, and 5-fluorouracil[END_REF][START_REF] Park | Topoisomerase II-α (topoII) and HER2 amplification in breast cancers and response to preoperative doxorubicin chemotherapy[END_REF][START_REF] Tubbs | Outcome of patients with early-stage breast cancer treated with doxorubicin-based adjuvant chemotherapy as a function of HER2 and TOP2A status[END_REF][START_REF] O´malley | Topoisomerase II alpha and responsiveness of breast cancer to adjuvant chemotherapy[END_REF][START_REF] Harris | Topoisomerase IIα amplification does not predict benefit from dose-intense cyclophosphamide, doxorubicin, and fluorouracil therapy in HER-2amplified early breast cancer: Results of CALGB 8541/150013[END_REF] and are currently a matter of considerable debate. Most studies were essentially retrospective, and included breast cancer patients treated with anthracycline combinations rather than single agent anthracycline and, therefore, the activity of the other drugs of the combination could obscure any existing relationship. Finally, many of the studies were too underpowered to show clinically relevant relationships. A prospective, single arm neoadjuvant study, the TOP trial [START_REF] Desmedt | Predicting the efficacy of anthracyclines in breast cancer (BC) patients: Results of the neoadjuvant TOP trial[END_REF] has addressed the factors predictive of anti-tumor efficacy of epirubicin (100 mg/m 2 every 2 or 3 weeks) in 149 breast cancer patients carrying exclusively ER -tumors. TOP2A copy number alterations were highly predictive of pCR (p=0.0002). However, the target of doxorubicin is the topo2a protein rather than the gene, and there is a lack of correlation between gene status and protein expression [START_REF] Coon | Amplification and overexpression of topoisomerase IIα predict response to anthracycline-based therapy in locally advanced breast cancer[END_REF][START_REF] Durbecq | Correlation between topoisomerase IIα (Topo-II) gene amplification and protein expression in her-2 amplified breast cancer patients[END_REF][START_REF] Mueller | Amplification of the TOP2A gene does not predict high levels of topoisomerase II alpha protein in human breast tumor samples[END_REF], probably because of a posttranscriptional regulation of the topo2a protein. As with the TOP2A gene status, the results of the trials that evaluated the correlation of topo2a protein expression and the response to anthracyclines are contradictory, but these studies presented the same methodological caveats previously mentioned [START_REF] Durbecq | Topoisomerase-IIα expression as a predictive marker in a population of advanced breast cancer patients randomly treated either with single-agent doxorubicin or single-agent docetaxel[END_REF][START_REF] Järvinen | Predictive value of topoisomerase IIα and other prognostic factors for epirubicin chemotherapy in advanced breast cancer[END_REF][START_REF] Leo | HER-2 and topo-isomerase IIα as predictive markers in a population of node-positive breast cancer patients randomly treated with adjuvant CMF or epirubicin plus cyclophosphamide[END_REF]. In our study, we found that the overexpression of topo2a protein was the stronger predictor of resistance to doxorubicin in a multivariate analyses. Further, this relationship was specific for doxorubicin, as shown by the significant topo2a protein expression-treatment interaction found in the multivariate analysis in which the whole study sample was included. The value of our findings is strengthened by the prospective and planned nature of the study, as well as the study design (comparative, single drug arms). In our study, an excess of the target enzyme/protein is detrimental in the anti-tumor activity of doxorubicin. As Esteva and Hortobagyi [START_REF] Esteva | Topoisomerase IIα amplification and anthracycline-based chemotherapy: The jury is still out[END_REF] have highlighted in an Editorial discussing the relationship of topo2a and anthracycline responsiveness, increased concentration of the target enzyme does predict reduced activity of other anti-tumor drugs (methotrexate, for example); this explanation fits well with our model. With respect to docetaxel, a number of single genes or gene products have been proposed as predictors of response (ER, tau protein, class I, II or III β-tubulin, bcl-2, ki-67, p53, among others) [START_REF] Noguchi | Predictive factors for response to docetaxel in human breast cancer[END_REF][START_REF] Pustzai | Markers predicting clinical benefit in breast cancer from microtubuletargeting agents[END_REF]. As in the case of doxorubicin, most published studies contain significant weaknesses (retrospective and unplanned nature, and small sample size) and therefore, the real predictive values of HER2 [START_REF] Gonzalez-Angulo | Lack of association between amplification of her-2 and response to preoperative taxanes in patients with breast carcinoma[END_REF][START_REF] Learn | HER2/neu expression as predictor of response to neoadjuvant docetaxel in patients with operable breast carcinoma[END_REF], microtubule associated parameters [START_REF] Galmarini | Class III β-tubulin isotype predicts response in adjuvant breast cancer patients randomly treated either with single-agent doxorubicin or docetaxel[END_REF][START_REF] Hasegawa | Prediction of response to docetaxel by quantitative analysis of class I and III β-tubulin isotype mRNA expression in human breast cancers[END_REF][START_REF] Bernard-Marty | Microtubule-associated parameters as predictive markers of docetaxel activity in advanced breast cancer patients: results of a pilot study[END_REF], and ki-activity, while the remaining markers (PR, HER2, TOP2A gene alterations or topo2a protein expression, tau protein, p27, Ki67) were not.

The ability of single genes or single gene products to predict response to cytotoxic drugs is likely a limited approach. This is not surprising since these agents do not have a single target. Anthracyclines, for instance, have many mechanisms of action other than topo2A inhibition (such as intercalation into DNA leading to inhibited synthesis of macromolecules, generation of free radicals, DNA binding and alkylation, DNA cross-linking, etc) [START_REF] Minotti | Anthracyclines: Molecular advances and pharmacologic developments in antitumor activity and cardiotoxicity[END_REF]. Multiple gene models might predict response to these agents more accurately than single gene predictors.

The intrinsic subtype classification, an approach that integrates multiple individual biomarkers together to identify biologically based groups, have previously shown to exhibit prognostic value [START_REF] Sorlie | Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications[END_REF][START_REF] Sorlie | Repeated observation of breast tumor subtypes in independent gene expression data sets[END_REF][START_REF] Symmans | Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy[END_REF] ; besides, it could be useful in predicting response to chemotherapy. A retrospective subtype analysis of patients from the Canadian MA5 study suggested that CEF is better than CMF in patients with HER-2 overexpressing tumors, but worse than CMF in the basal-like subtype; CEF and CMF were similar in ER-positive, HER-2 negative luminal subtypes [START_REF] Cheang | Anthracyclines in basal breast cancer: The NCIC-CTG trial MA5 comparing adjuvant CMF to CEF[END_REF]. In the BCIRG 001 adjuvant study, an improved 3-year DFS with TAC versus FAC treatment schemes was shown in the luminal B group (p=0.025), with a marginal trend in the triple negatives (p=0.051) and HER2 (p=0.068) subtypes. No DFS advantage was seen in the luminal A population [START_REF] Hugh | Breast cancer subtypes and response to docetaxel in node-positive breast cancer: Use of an immunohistochemical definition in the BCIRG 001 Trial[END_REF]. Similarly, the GEICAM 9906 trial comparing FEC to FEC followed by weekly paclitaxel, found that the paclitaxel benefit was mainly concentrated in the triple negative/basal-like patient population [START_REF] Martín | Molecular predictors of efficacy of adjuvant weekly paclitaxel in early breast cancer[END_REF]. However, all these studies have the disadvantage of having compared multi-drug regimens and, as such, attributing benefit to any of drug individually becomes very difficult. Our study comparing head-to-head single agent doxorubicin with single agent docetaxel showed a significant difference between doxorubicin and docetaxel: in the triple negative/basal-like tumors, doxorubicin was associated with a lower response rate and higher RCB score relative to docetaxel. Indeed, the triple negative status, as defined by IHC/FISH, was the only independent predictor of resistance (poor PathResp) to doxorubicin in a logistic regression analysis. The claudin-low subtype showed some sensitivity to chemotherapy. Both doxorubicin and docetaxel induced good pathological responses in 20-27% of patients. In our study, 40% of tumors classified as claudin-low expressed the triple negative phenotype while 44% were ER-positive, her-2-negative. The claudin-low subtype seems to be related to the mammary epithelial stem cell [START_REF] Prat | Phenotyphic and molecular characterization of the claudin-low intrinsic subtype of breast cancer[END_REF] and was initially considered to be resistant to chemotherapy [START_REF] Fillmore | Human breast cancer cell lines contain stem-like cells that self-renew, give rise to phenotypically diverse progeny and survive chemotherapy[END_REF]. A recent study, however, has challenged this concept [START_REF] Aulmann | Reduction of CD44+/CD24-breast cancer cells by conventional cytotoxic chemotherapy[END_REF].

In summary, this prospective study has shown that, although doxorubicin and docetaxel are similarly effective overall as neoadjuvant therapy of breast cancer, docetaxel does seem to be more effective than doxorubicin in the triple negative/basal-like subtype of patients. Triple negative breast tumors currently are one of the biggest challenges in the breast cancer clinic. A new class of drugs, the inhibitors of poly(ADP-ribose) polymerase-1 (PARP-1), an enzyme involved in DNA repair, could open a window of hope. PARP-1 expression is often increased in triple negative tumors. Several PARP-1 inhibitors are currently under development for use in combination with chemotherapy in triple negative breast cancer; the optimum chemotherapy regimen for the combination remains to be established. Preclinical data suggests that DNA damaging agents (i.e. platinum salts) are the best partners for PARP-1 inhibitors, however, our results may also suggest that adding a taxane to PARP-1 inhibitors and platinum salts could be beneficial to basal-like/triple negative patients.

  topoisomerase IIα-topo2amouse monoclonal antibody NCL-Clone 3F6, 1:40 Leica Microsystems); BCL-2 (mouse monoclonal anti-human BCL-2 Oncoprotein, Clone 124, 1:100. Dako Cytomation), tau protein (Polyclonal Rabbit Anti-Human Tau dilution 1:200), estrogen receptor-ER (Dako Cytomation Clone 1D5, 1:35), progesterone receptor-PR (Dako Cytomation, Clone PgR 636, 1:50), epidermal growth factor receptor (EGFR, clone EGFRr.25, 1:50. Leica Microsystems), cytokeratins 5/6 (CK 5/6, clone D5/16B4, prediluted, Master Diagnostica) and Ki67 (Dako Cytomation, Clone MIB-1, 1:75). After incubation with the primary antibodies, the Bond Polymer Refine Detection with the Vision Biosystems Bondmax for immunostaining was applied.

  adjusted odds ratios for poor PathResp to docetaxel for Stage IIIA and IIIB tumors relative to Stage II tumors were 4.77 (95%CI: 1.33-17.11; p=0.016) and 8.76 (95%CI: 2.53-30.353; p=0.001), respectively.

Figure 1 .Figure 2 :

 12 Figure 1. Correlation between topo2a expression and response to doxorubicin and docetaxel.The differences among categories are statistically significant in the case of doxorubicin (p=0.017) but not in the case of docetaxel (p=0.778; Pearson chi-squared test)

Table 2 .

 2 Prediction of good PathResp (PCR+class I): Univariate analysis by treatment arm

		DOXORUBICIN		DOCETAXEL
		Number of patients with	P	Number of patients with	P
	VARIABLE	good pathological response/		good pathological response/
		Number of determinations (%)		Number of determinations (%)
	ER negative	9/33 (27%)	0.156	16/48(33%)	0.002
	ER positive	11/71(15%)		4/52(8%)
	PR negative	11/44(25%)	0.201	14/53(26%)	0.089
	PR positive	9/60(15%)		6/47(13%)
	HER2 positive	8/29(28%)	0.179	7/26(27%)	0.305
	HER2 negative	12/75 (16%)		13/74 (18%)
	Topo IIα IHC high	2/33(6%)	0.016	4/26(15%)	0.769
	Topo IIα IHC normal	17/64(27%)		13/64(20%)
	Topo IIα FISH normal	16/64(25%)	0.541	16/69(23%)	0.444
	Topo IIα FISH abnormal	3/20(15%)		1/12(8%)
	Topo IIα FISH amplified	3/20 (15%)	0.749	0/8 (0%)	0.193
	Topo IIα FISH normal	16/64 (25%)		16/69 (23%)
	BCL-2 negative	8/34(23%)	0.194	7/43(16%)	0.611
	BCL-2 positive	8/61(13%)		10/49(20%)
	TAU negative	10/47(21%)	0.721	9/49(18%)	0.977
	TAU positive	9/49(18%)		8/43(19%)
	P27 negative	7/33(21%)	0.643	4/26(15%)	0.772

Table 3 .

 3 Multivariate analysis (logistic regression) of factors predictive of good PathResp

	Treatment arm	Variables	Adjusted OR	95%CI	P
	Doxorubicin
		Topo2a expression (IHC)	10.38	1.95-55.15	0.006
		ER status	4.64	1.40-15.31	0.012
	Docetaxel
		Tumor Size	13.38	3.42-56.29	<0.001
		ER status	8.32	2.25-30.73	0.001

Table 3e (on line only)

 3e

	Multivariate analysis: factors predictive of good PathRes (PCR+class I) in the whole
	sample
	Variable	Odds	95% CI	P value
		Ratio
	Topisomerase IIα expression	12.46	2.36-65.62	0.003
	ER	5.03	2.06-12.28	<0.0001
	T size	3.11	1.38-7.0	0.006
	Topisomerase IIα expression/treatment interaction 0.12	0.01-0.97	0.048

The factors predictive of good Path Res in the overall population are low topo IIa expression, ER-negativity, smaller T size. A significant interaction between Topisomerase IIα expression and treatment was also found.

Table 4e .

 4e Prediction of poor PathResp (class III): Univariate analysis by treatment arm

	P27 positive	40/69 (58%)		51/70 (73%)
	KI67 low	16/27 (60%)	0.941	19/31 (61%)	0.335
	KI67 high	45/77 (58%)		49/69 (71%)
	(on line only) EGFR positive	47/73 (64%)	0.108	44/63 (70%)	0.600
	EGFR negative	DOXORUBICIN 11/24 (46%)		DOCETAXEL 18/28 (64%)
	Cytokeratin 5/6 positive (OK)	Number of patients with 42/67 (63%)	P 0.460	Number of patients with 47/67 (70%)	P 0.958
	VARIABLE Cytokeratin 5/6 negative	poor pathological response/ 17/31 (55%)		poor pathological response/ 16/23 (70%)
	Tumor size >5 cm	Number of determinations (%) 27/44 (61%)	0.631	Number of determinations (%) 36/45 (80%)	0.020
	ER negative Tumor size≤5 cm	17/33 (51%) 34/60 (57%)	0.314	34/48 (71%) 32/55 (58%)	0.560
	ER positive Stage II	44/71 (62%) 26/37 (70%)	0.200	34/52 (65%) 33/37 (89%)	0.001
	PR negative Stage IIIA	22/44 (50%) 18/35 (51%)	0.125	37/53 (70%) 19/30 (63%)	0.680
	PR positive Stage IIIB	39/60 (65%) 17/32 (53%)		31/47 (66%) 16/33 (48%)
	HER2 positive Grade 3	43/75 (57%) 40/65 (61%)	0.660 0.441	48/74 (65%) 43/62 (70%)	0.257 0.607
	HER2 negative Grade 1+2	18/29 (62%) 21/39 (54%)		20/26 (77%) 25/38 (66%)	0.826
	Topo IIα IHC high Ductal	40/64 (62%) 44/83 (53%)	0.856	44/64 (69%) 53/78 (68%)	0.757 0.534
	Topo IIα IHC normal Lobular	20/33 (61%) 14/17 (82%)	0.065	17/26 (65%) 12/16 (75%)
	Topo IIα FISH normal Others	12/20 (60%) 3 /4 (75%)	0.647	7/12 (58%) 3/6 (50%)	0.442
	Topo IIα FISH abnormal	42/64 (66%)		48/69 (70%)
	BCL-2 negative	21/34 (62%)	0.677	28/43 (65%)	0.663
	BCL-2 positive	35/61 (57%)		34/49 (69%)
	Tau negative	28/47 (60%)	0.869	29/49 (60%)	0.123
	Tau positive	30/49 (61%)		32/43 (74%)
	P27 negative	19/33 (58%)	0.970	14/26 (54%)	0.077

Table 4 .

 4 Anti-tumor activity of doxorubicin and docetaxel in intrinsic subtypes based on IHC/FISH

		Good PathRes (pCR+class I)	Poor PathRes (class III)	Residual Cancer Burden
								(mean±SEM)
		doxorubicin docetaxel	P	doxorubicin docetaxel	P	doxorubicin	docetaxel	P
				value			value			value
	Luminal	3/18 (17%)	2/18	1.000	7/18 (39%)	7/18	1.000 2.622±0.3667 2.572±0.2915 0.680
	A		(11%)			(39%)
	Luminal	9/55 (16%)	6/42	0.779	20/55 (36%)	13/42	0.709 2.593±0.1776 2.621±0.1689 0.878
	B		(14%)			(31%)
	Her2	6/11 (55%)	4/12	0.414	2/11 (18%)	3/12	1.000	1.809±0.477	2.075±0.3568 0.557
			(33%)			(27%)
	Triple-	2/20 (10%)	8/28	0.160	14/20 (70%)	9/28	0,010 3.255±0.3291 2.232±0.2913 0.030

Table 5 :

 5 Anti-tumor activity of doxorubicin and docetaxel in intrinsic subtypes based on

	Agilent cDNA microarrays

This work was supported by grants from Fondo de Investigaciones Sanitarias (FIS PI07/0316), Red Temática de Investigación Cooperativa en Cáncer (RD06/0020/0021), Instituto de Salud Carlos III, Spanish Ministry of Science and Innovation; SEOM (Spanish Society for Medical Oncology); the NCI Breast SPORE program to UNC-CH (P50-CA58223-09A1); the Breast Cancer Research Foundation; and the V Foundation for Cancer Research. MCU Cheang is supported by Terry Fox Foundation Postdoctoral Fellowship.