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Mittempergher, Lorenza (2009) Identification of drug-resistance predictive genes in breast cancer neoadjuvant chemotherapy. [Tesi di dottorato]

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Abstract (inglese)

Breast cancer is a heterogeneous disease and markers for therapy response remain poorly defined. Since the effectiveness of treatment differs between individual patients, during the last years much effort has being invested in the identification of new markers, to estimate patients's outcome (prognostic markers) and to indicate which treatment is most effective for an individual patient (predictive markers).
The implementation of predictive factors in clinical setting is a big challenge of the cancer research and it will provide the opportunity to guide treatment decisions. Only patients that are likely to benefit from a specific treatment will receive this specific treatment. An individualized therapy will avoid the administration of ineffective chemotherapy that increases mortality and decreases quality of life in cancer patients.
For many years research has focused on the identification of single markers predicting tumour response to chemotherapy. However it is unlikely that the chemotherapy resistance/responsiveness in breast cancer is the result of one or limited number of genes, because of the complexity of pathways involved in tumour response to chemotherapy and the heterogeneity of the individual tumours. The microarray technology made possible to study gene expression profiling of breast cancer on a global scale. It was successfully applied on the identification of breast cancer subgroups and profiles predicting patient's prognosis. More recently microarrays have been also focused on identifying gene expression profiles predicting response to chemotherapy. With the introduction of preoperative chemotherapy (neoadjuvant chemotherapy) it has become possible to directly evaluate the sensitivity of breast cancer to chemotherapy by the clinical/pathological response of the patient to the treatment. The main goal of this thesis was to identify predictive genes of response to a specific neoadjuvant chemotherapy regimen based on paclitaxel and anthracyclines (doxorubicin and epirubicin) drugs in breast cancer patients.
From 41 pre-treatment breast tumours biopsies good quality RNA was obtained and gene expression profiling was performed. Gene expression patterns of 37 patients were analyzed using Operon v2.0 70mer oligos collection at CRIBI Biotech centre and 4 patients were profiled with Operon v3.0 70mer oligos collection at Netherlands Cancer Institute. Clinical responses of 34 (out of 41) patients were recorded after administration of the neoadjuvant chemotherapy. Complete Responses (CR) to the treatment were observed in 3 patients, Partial Responses in 18 (PR) patients, No Change of the tumour mass (NC) in 11 patients and Progressive Disease (PD) in 2 patients.
First of all, a correlation analysis between the ImmunoHistoChemical data of six prognostic markers (ER, PR, Erb-B2, Bcl-2, Ki-67, p53) and the gene expression data was carried out. The results showed a significant correlation for ER, PR and Bcl-2 markers. Moreover Bcl-2 status measured by ImmunoHistoChemistry (IHC) was significantly associated with the clinical response to neoadjuvant chemotherapy.
The molecular subtypes of 37 breast tumours analyzed with Operon v2.0 were identified using the "intrinsic gene signature" of Perou and colleagues. Most part of the patients were luminal-like subtype (28 of 37), 7 patients showed an erb-B2+ molecular subtype and 2 patients belonged to the basal-like group. Since it was reported that breast cancer molecular subtypes respond differently to neoadjuvant chemotherapy, I also checked how the clinical response to the treatment were associated to the molecular subtypes. From the analysis emerged that the luminal-like and erb-B2+ molecular subtypes were enriched of PR patients.
A hierarchical cluster analysis on the pre-treatment tumours (analyzed with Operon v2.0 and with clinical response available) was performed in order to evaluate how the patients would have been separated on the basis of their gene expression profile, using an unsupervised approach. As expected, no clear separation between Responders (PR + CR) and Non Responders (NC + PD) was found. The results did not change if we included in the responder group only the PR patients. We hypothesized that the predictive genes of resistance/sensitivity to the chemotherapy were a subtle set. The high number of differentially expressed genes would have masked the "real" predictive gene set, leading to a clustering of the patients based on biological parameters different from the clinical response. In addition the small size of the dataset was a limiting factor in the analysis.
In light of this result we opted for a supervised approach that consisted in dividing the tumours into Responders and Non Responders and searching for the genes (the drug-resistance predictive genes) that could correctly distinguish the two classes of response. I considered two datasets of patients, the dataset I including PR patients against not responders patients (NC + PD) and the dataset II with responders patients (PR and CR) against not responders patients (NC + PD).
The first approach, based on the software PAM (Prediction Analysis of Microarray), did not give a good prediction performance on both dataset of patients, misclassifying ca 36% of patients. Therefore, a more effective analysis in terms of classification accuracy was requested. A gene selection process based on the Support Vector Machines (SVMs) was considered a good choice in light of the characteristics of the study: low number of patients (examples) and high number of genes (or features). SVMs are a supervised learning algorithm that work well at high dimensionality, overcoming the risk of overfitting due to a number of features much larger than the numbers of examples. A specific recursively feature selection procedure based on SVMs (R-SVM) was used to select the set of genes with the lowest error of classification on the dataset of patients. Because of the small sample size, it was not possible to have a training set and a test set completely separated, so a Leave-One-Out Cross Validation (LOO-CV) procedure was used to assess the performance of the feature selection process. The analysis identified a set of 54 genes able to classify the 28 patients of the dataset I with an accuracy of 85% (4 patients misclassified on 28) and a set of 14 genes able to classify the 30 patients of the dataset II with an accuracy of 76% (7 patients misclassified on 30). The lower accuracy obtained on the dataset II was attributed to the introduction of the cCR patients in the group of Responders. The cCR patients were probably too much dissimilar in terms of clinical response in respect to the PR patients, thus rendering the group of Responders not enough homogeneous. For this reason I focused the following analysis only on the dataset I.
The accuracy of 85% obtained for the dataset I was an encouraging result although the small size of the dataset.
The biological function and cellular localization of the 54 genes was examined by using GoMiner, a web tool to find associations of Gene Ontology categories within a specific group of genes. As emerged from the analysis, there were several functional categories related to the tumourigenesis processes ("cell adhesion", "insulin receptor signaling pathway", "cell proliferation", "regulation of cell proliferation"). Some categories were more closely related to cellular processes and compartments target of the chemotherapy agents used in this study ("cell cycle", "cell cycle arrest", "nucleus") and to responsiveness to the treatment ("response to hypoxia").
A literature research focused on each gene of the predictive signature showed that some of these genes (MYC, NUF2, SPC25; KFL5, CDKN1b, ITGA6, POSTN) are 'biologically plausible', since they have some connections with the drug resitance phenomenon investigated in this study. Others of the 54 genes are related to breast cancer progression and metastasis (CXCL9, CEBPD, IRS2, TCF8, ADAMTS5, PPARGC1A), but their direct involvement in drug resistance to paclitaxel/anthracycline neoadjuvant chemotherapy did not emerged.
At this point of my analysis, I tried to find out how to use the 54 genes signature as a predictive tool of responsiveness to paclitaxel/anthracyclines based chemotherapy treatment. On the basis of the 54 genes was trained a SVM model that could be used to classify a new patient, not yet classified, as partial responder or not responder. However, the SVM output is a value not so easily usable in statistics prediction problems. Therefore using a sigmoid function, we translated the SVM outputs into probability values that offered a more direct evaluation of the response class of the patient. In practice we transformed the SVM scores obtained for each patient of the dataset in a measure of probability, from 0 to 1, of belonging to the positive class of response (PR patients). Using the trained SVM model on a new, not-yet classified patient, it will make possible to map his SVM score on the sigmoid function and to have a corresponding probability value to belong to the positive class of response.
The results reported in this thesis look promising but have to be considered as preliminary, since they were obtained from a study investigating only a small number of patients and need to be validated in a completely independent test set of patients. Thus a validated gene expression signature may improve our understanding of neoadjuvant chemotherapy response mechanisms and in the future may lead to more individual, patient-tailored therapy decisions.

Abstract (italiano)

Il tumore al seno è una patologia clinicamente eterogenea e marker biologici in grado di predirne in modo affidabile evoluzione e soprattutto sensibilità ai trattamenti farmacologici rimangono poco definiti. Negli ultimi anni la ricerca ha cercato così di identificare nuovi marker predittivi di risposta, per consentire trattamenti più efficace per ogni singola paziente. Riuscire ad implementare i nuovi fattori predittivi nella pratica clinica rappresenta un importante obiettivo nella ricerca sul tumore al seno. Si potranno così evitare a priori trattamenti inefficaci, che inciderebbero solo negativamente sulla qualità di vita delle pazienti.
Per molti anni si è parlato di marker singoli di risposta, ma, alla luce della complessità dei pathway cellulari coinvolti nella risposta del tumore alla chemioterapia ed all'eterogeneità tra i singoli tumori, è improbabile che la risposta o la resistenza ad un trattamento sia determinata dall'azione di un numero limitato di geni.
La tecnologia dei microarray ha reso così possibile un'analisi su larga scala dei profili di espressione genica dei tumori al seno ed è stata uno strumento efficace per identificarne sottogruppi molecolari e profili di espressione con valore prognostico. Più recentemente i microarray sono stati anche applicati alla ricerca di geni predittivi di risposta alla chemioterapia.
Con l'introduzione della chemioterapia neoadiuvante, ossia somministrata prima dell'intervento chirurgico, è divenuto possibile valutare direttamente la sensibilità del tumore al trattamento chemioterapico attraverso la risposta clinica e patologica della paziente.
L'obiettivo principale di questa tesi è stato infatti quello di identificare un set di geni predittivo della risposta ad un particolare trattamento chemioterapico neoadiuvante basato su taxani (paclitaxel) e antracicline (adriamicina o epirubicina).
Sono stati analizzati mediante microarray di oligonucleotidi 41 biopsie di tumore al seno prima della somministrazione della chemioterapia neoadiuvante. Delle 41 biopsie raccolte, 37 sono state analizzate con la piattaforma di oligonucleotidi Operon v2.0 presso il CRIBI e 4 sono state analizzate presso il Netherlands Cancer Institute con la piattaforma Operon v3.0. Al termine del trattamento è stato rese noto per 37 pazienti (su 41) l'esito della chemioterapia: 3 pazienti hanno mostrato una risposta clinica completa (cCR), 18 una risposta parziale al trattamento (PR), 13 pazienti non hanno risposto al trattamento, in 11 casi non si è avuto nessun cambiamento nella grandezza della massa tumorale (NC) ed in 2 casi un aumento di quest'ultima (PD).
La prima analisi condotta è stata quella volta a verificare la correlazione tra i dati di immunoistochimica (IHC) ottenuti per i 6 marker prognostici ER, PR, Erb-B2, Bcl-2, Ki-67 e p53 ed i livelli di espressione dei rispettivi geni misurati con i microarray. Una significativa correlazione è stata trovata per ER, PR e Bcl-2. Il livello di Bcl-2 ottenuto dall'analisi IHC si è rivelato inoltre significativamente associato con la risposta alla chemioterapia neoadiuvante.
Successivamente sono stati identificati i sottotipi molecolari dei 37 tumori analizzati con la piattaforma Operon v2.0 utilizzando l'intrinsic gene set individuato da Perou e colleghi. La maggior parte dei pazienti apparteneva al sottotipo luminale (28 su 37), 7 a quello erb-B2+ e 2 a quello basale. Poiché è stato riportato in letteratura che i sottotipi molecolari di tumore al seno rispondono in modo differente alla chemioterapia neoadiuvante, ho valutato come fossero distribuiti quelli da me identificati rispetto alla risposta clinica al trattamento, se disponibile. Dall'analisi è emerso che i sottogruppi luminale e erb-B2+ erano arricchiti di pazienti PR.
E' stata quindi eseguita una cluster analysis gerarchica dei 30 profili di espressione genica (ottenuti con Operon v2.0) delle pazienti di cui era disponibile la risposta alla chemioterapia, per valutare come si sarebbero separate sulla base dell'intero profilo di espressione con un approccio unsupervised (senza cioè dare a priori l'informazione sul tipo di risposta clinica). Le pazienti non si sono separati in sensibili (cCR + PR) e resistenti (NC + PD) al trattamento. Questo risultato ha confermato l'ipotesi che il set di geni predittivi fosse ristretto e che probabilmente venisse mascherato dal grande numero di geni differenzialmente espressi dal tumore. Inoltre il numero limitato di paziente è stato un fattore limitante all'analisi.
Sono passata quindi ad un approccio di tipo supervised cercando quei geni in grado di distinguere tumori sensibili e tumori resistenti al trattamento, cioè i geni predittivi della farmacoresistenza. Ho considerato due dataset di pazienti, il dataset I che includeva pazienti PR vs pazienti resistenti (NC e PD) e il dataset II che considerava anche i pazienti cCR nel gruppo di tumori sensibili al trattamento.
Il programma PAM (Prediction Analysis of Microarray) ha individuato set di geni predittivi con una bassa performance di classificazione dei pazienti in entrambi i dataset (il 36% dei pazienti veniva classificato in modo sbagliato). Si è reso quindi necessario un nuovo metodo di analisi, più efficace in termini di accuracy di classificazione. Una selezione dei geni significativi basata sulle Support Vector Machines (SVM) è stata considerata una scelta appropriata alla luce delle caratteristiche dello studio: basso numero di pazienti (o esempi) e alto numero di geni (o features). Le SVM infatti sono degli algoritmi di apprendimento supervisionati che lavorano bene in questi casi abbassando il rischio di overfitting, dovuto al numero troppo elevato di features rispetto agli esempi da classificare. In particolare è stato utilizzato l'algoritmo di feature selection R-SVM (Recursive Support Vector Machine) per selezionare quel set di geni con il più basso errore di classificazione sul dataset di pazienti (I e II). Per validare la performance di classificazione dei set di geni selezionati è stata usata una Leave One Out Cross Validation non essendo possibile, a causa del numero ridotto di pazienti, suddividere i dataset in un training and in un test set indipendenti. L'analisi R-SVM ha identificato un set di 54 geni in grado di classificare i 28 pazienti del dataset con un'accuratezza pari all'85% (4 pazienti sbagliati su 28) e un set di 14 geni in grado di classificare le 30 pazienti del dataset II con un'accuratezza del 76% (7 pazienti sbagliati su 30). L'abbassamento del grado di accuracy nel dataset II è stato attribuito al fatto di aver incluso nel gruppo dei pazienti sensibili al trattamento anche i pazienti cCR; in realtà essi avrebbero costituito una classe troppo diversa dai pazienti PR tale da non poter essere inclusa nello stesso gruppo di questi ultimi. Alla luce di quanto detto ho considerato solo il dataset I nelle analisi successive.
L'analisi di Gene Ontology sui 54 geni identificati nel dataset I ha rivelato che alcuni di questi geni sono annotati a livello di processi biologici caratteristici della tumorigenesi in generale ("adesione cellulare", "vie di segnalazione dell'insulina", "proliferazione cellulare", "regolazione della proliferazione cellulare"). Alcune categorie funzionali sono invece più legate a processi e compartimenti cellulari target dei farmaci utilizzati in questo studio ("ciclo cellulare", "arresto del ciclo cellulare", "nucleo") ed alla risposta al trattamento ("risposta all'ipossia"). Da una ricerca in letteratura mirata a ciascuno dei 54 geni della lista è emerso che alcuni di essi (MYC, NUF2, SPC25; KFL5, CDKN1b, ITGA6, POSTN) sono implicati nel fenomeno di resistenza a paclitaxel ed antracicline. Altri (CXCL9, CEBPD, IRS2, TCF8, ADAMTS5, PPARGC1A) dimostrano di avere un ruolo in processi collegati a progressione tumorale ed a metastasi ma non hanno un coinvolgimento diretto con la farmacoresistenza oggetto dello studio.
A questo punto del lavoro è stato naturale chiedersi come utilizzare il modello SVM allenato usando i 54 geni per predire la risposta alla chemioterapia (con paclitaxel ed antracicline) di un nuovo paziente, non ancora classificato come sensibile o resistente al trattamento. Dal momento che l'output di una SVM è una misura di distanza dall'iperpiano che separa i pazienti positivi (sensibili al trattamento) da quelli negativi (resistenti al trattamento) a cui non è associato un significato statistico, si è pensato di trasformare questo valore in una misura di probabilità di appartenenza alla classe positiva di risposta. Per fare questo è stato utilizzato un modello parametrico definito da una sigmoide che ha consentito di trasformare gli output SVM dei 28 pazienti in corrispondenti valori di probabilità.
I risultati ottenuti in questa tesi si sono rivelati interessanti anche se vanno considerati preliminari alla luce del numero limitato di pazienti. Si renderà necessaria pertanto una validazione su un gruppo indipendente di pazienti e, in caso di conferma dei risultati, questo lavoro potrà contribuire alla scelta di trattamenti più efficaci per il tumore al seno.

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Tipo di EPrint:Tesi di dottorato
Relatore:Valle, Giorgio
Dottorato (corsi e scuole):Ciclo 21 > Scuole per il 21simo ciclo > BIOCHIMICA E BIOTECNOLOGIE > BIOTECNOLOGIE
Data di deposito della tesi:01 Febbraio 2009
Anno di Pubblicazione:01 Febbraio 2009
Parole chiave (italiano / inglese):breast cancer, microarray technology, predictive signature, neoadjuvant chemotherapy
Settori scientifico-disciplinari MIUR:Area 05 - Scienze biologiche > BIO/11 Biologia molecolare
Struttura di riferimento:Dipartimenti > Dipartimento di Biologia
Codice ID:1860
Depositato il:01 Feb 2009
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Richert MM, Schwertfeger KL, Ryder JW, Anderson SM. An atlas of mouse mammary gland development. J Mammary Gland Biol Neoplasia. 2000;5(2):227-41. Cerca con Google

Woodward WA, Chen MS, Behbod F, Rosen JM. On mammary stem cells. J Cell Sci. 2005; 118(Pt 16):3585-94. Cerca con Google

Guinebretière JM, Menet E, Tardivon A, Cherel P, Vanel D. Normal and pathological breast, the histological basis. Eur J Radiol. 2005; 54(1):6-14. Cerca con Google

Kakarala M, Wicha MS. Implications of the cancer stem-cell hypothesis for breast cancer prevention and therapy. J Clin Oncol. 2008; 26(17):2813-20. Cerca con Google

Reya T, Morrison SJ, Clarke MF, Weissman IL. Stem cells, cancer, and cancer stem cells. Nature; 414(6859):105-11. Cerca con Google

Kumle M. Declining breast cancer incidence and decreased HRT use. Lancet. 2008; 372(9639):608-10. Cerca con Google

Parkin DM, Fernández LM. Use of statistics to assess the global burden of breast cancer. Breast J. 2006; 12 Suppl 1:S70-80. Cerca con Google

Botha JL, Bray F, Sankila R, Parkin DM. Breast cancer incidence and mortality trends in 16 European countries. Eur J Cancer. 2003 (12):1718-29. Cerca con Google

Polyak K. On the birth of breast cancer. Biochim Biophys Acta. 2001; 1552(1):1-13. Cerca con Google

Hanby AM. Aspects of molecular phenotype and its correlations with breast cancer behaviour and taxonomy. Br J Cancer. 2005; 92(4):613-7. Cerca con Google

Balslev I, Axelsson CK, Zedeler K, Rasmussen BB, Carstensen B, Mouridsen HT. The Nottingham Prognostic Index applied to 9,149 patients from the studies of the Danish Breast Cancer Cooperative Group (DBCG). Breast Cancer Res Treat. 1994; 32(3):281-90. Cerca con Google

Tavassoli, FA; Devilee, P. World Health Organization Classification of Tumours. Pathology and Genetics. Tumours of the Breast and Female Genital Organs. Lyon: IARC Press; 2003. p. 98. Cerca con Google

Fabbri A, Carcangiu ML, Carbone A. Histological Classification of Breast cancer. Breast Cancer Nuclear Medicine in Diagnosis and Therapeutic Options. 2007; 3-14. Cerca con Google

Holland R, Peterse JL, Millis RR, Eusebi V, Faverly D, van de Vijver MJ, Zafrani B. Ductal carcinoma in situ: a proposal for a new classification. Semin Diagn Pathol. 1994 ;11(3):167-80. Cerca con Google

Elston CW, Ellis IO. Pathological prognostic factors in breast cancer. The value of histological grade in breast cancer: Experience from a large study with long-term follow-up. Histopathology 19: 403–410. Cerca con Google

Singletary SE, Allred C, Ashley P, Bassett LW, Berry D, Bland KI, Borgen PI, Clark GM, Edge SB, Hayes DF, Hughes LL, Hutter RV, Morrow M, Page DL, Recht A, Theriault RL, Thor A, Weaver DL, Wieand HS, Greene FL. Staging system for breast cancer: revisions for the 6th edition of the AJCC Cancer Staging Manual. Surg Clin North Am. 2003; 83(4):803-19. Cerca con Google

Singletary SE, Greene FL; Breast Task Force. Revision of breast cancer staging: the 6th edition of the TNM Classification. Semin Surg Oncol. 2003; 21(1):53-9. Cerca con Google

Greene, FL, Page, DL, Fleming. AJCC (American Joint Committee on Cancer) Cancer Staging Manual, 6th ed ID Springer-Verlag, New York, 2002. Pp. 223-40. Cerca con Google

Rouzier R, Perou CM, Symmans WF, Ibrahim N, Cristofanilli M, Anderson K, Hess KR, Stec J, Ayers M, Wagner P, Morandi P, Fan C, Rabiul I, Ross JS, Hortobagyi GN, Pusztai L. Breast cancer molecular subtypes respond differently to preoperative chemotherapy. Clin Cancer Res. 2005; 11(16):5678-85. Cerca con Google

Nuyten DS, van de Vijver MJ. Using microarray analysis as a prognostic and predictive tool in oncology: focus on breast cancer and normal tissue toxicity. Semin Radiat Oncol. 2008; 18(2):105-14. Cerca con Google

Pusztai L, Ayers M, Stec J, Clark E, Hess K, Stivers D, Damokosh A, Sneige N, Buchholz TA, Esteva FJ, Arun B, Cristofanilli M, Booser D, Rosales M, Valero V, Adams C, Hortobagyi GN, Symmans WF. Gene expression profiles obtained from fine-needle aspirations of breast cancer reliably identify routine prognostic markers and reveal largescale molecular differences between estrogen-negative and estrogen-positive tumors. Clin Cancer Res. 2003; 9(7):2406-15. Cerca con Google

Ma XJ, Salunga R, Tuggle JT, Gaudet J, Enright E, McQuary P, Payette T, Pistone M, Stecker K, Zhang BM, Zhou YX, Varnholt H, Smith B, Gadd M, Chatfield E, Kessler J, Baer TM, Erlander MG, Sgroi DC. Gene expression profiles of human breast cancer progression. Proc Natl Acad Sci U S A. 2003; 100(10):5974-9. Cerca con Google

Perou CM, Sørlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, Fluge O, Pergamenschikov A, Williams C, Zhu SX, Lønning PE, Børresen-Dale AL, Brown PO, Botstein D. Molecular portraits of human breast tumours. Nature. 2000; 406(6797):747-52. Cerca con Google

Sørlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, Thorsen T, Quist H, Matese JC, Brown PO, Botstein D, Eystein Lønning P, Børresen-Dale AL. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A. 2001; 98(19):10869-74. Cerca con Google

Sotiriou C, Neo SY, McShane LM, Korn EL, Long PM, Jazaeri A, Martiat P, Fox SB, Harris AL, Liu ET. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci U S A. 2003; 100(18):10393-8. Cerca con Google

Rouzier R, Mathieu MC, Sideris L, Youmsi E, Rajan R, Garbay JR, André F, Marsiglia H, Spielmann M, Delaloge S. Breast-conserving surgery after neoadjuvant anthracyclinebased chemotherapy for large breast tumors. Cancer. 2004; 101(5):918-25. Cerca con Google

Millikan RC, Newman B, Tse CK, Moorman PG, Conway K, Dressler LG, Smith LV, Labbok MH, Geradts J, Bensen JT, Jackson S, Nyante S, Livasy C, Carey L, Earp HS, Perou CM. Epidemiology of basal-like breast cancer. Breast Cancer Res Treat. 2008; 109(1):123- 39. Cerca con Google

Carey LA, Perou CM, Livasy CA, Dressler LG, Cowan D, Conway K, Karaca G, Troester MA, Tse CK, Edmiston S, Deming SL, Geradts J, Cheang MC, Nielsen TO, Moorman PG, Earp HS, Millikan RC. Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study. JAMA. 2008; 295(21):2492-502. Cerca con Google

Mathieu MC, Rouzier R, Llombart-Cussac A, Sideris L, Koscielny S, Travagli JP, Contesso G, Delaloge S, Spielmann M. The poor responsiveness of infiltrating lobular breast carcinomas to neoadjuvant chemotherapy can be explained by their biological profile. Eur J Cancer. 2004; 40(3):342-51. Cerca con Google

Hu Z, Fan C, Oh DS, Marron JS, He X, Qaqish BF, Livasy C, Carey LA, Reynolds E, Dressler L, Nobel A, Parker J, Ewend MG, Sawyer LR, Wu J, Liu Y, Nanda R, Tretiakova M, Ruiz Orrico A, Dreher D, Palazzo JP, Perreard L, Nelson E, Mone M, Hansen H, Mullins M, Quackenbush JF, Ellis MJ, Olopade OI, Bernard PS, Perou CM. The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics. 2006; 7:96. Cerca con Google

Sorlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, Deng S, Johnsen H, Pesich R, Geisler S, Demeter J, Perou CM, Lønning PE, Brown PO, Børresen-Dale AL, Botstein D. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci U S A. 2003; 100(14):8418-23. Cerca con Google

Reis-Filho JS, Tutt AN. Triple negative tumours: a critical review. Histopathology. 2008; 52(1):108-18. Cerca con Google

Reis-Filho JS, Milanezi F, Steele D, Savage K, Simpson PT, Nesland JM, Pereira EM, Lakhani SR, Schmitt FC. Metaplastic breast carcinomas are basal-like tumours. Histopathology. 2006; 49(1):10-21. Cerca con Google

Abd El-Rehim DM, Pinder SE, Paish CE, Bell J, Blamey RW, Robertson JF, Nicholson RI, Ellis IO. Expression of luminal and basal cytokeratins in human breast carcinoma. J Pathol. 2004; 203(2):661-71. Cerca con Google

Carey LA, Dees EC, Sawyer L, Gatti L, Moore DT, Collichio F, Ollila DW, Sartor CI, Graham ML, Perou CM. The triple negative paradox: primary tumor chemosensitivity of breast cancer subtypes. Clin Cancer Res. 2007; 13(8):2329-34. Cerca con Google

Foulkes WD, Brunet JS, Stefansson IM, Straume O, Chappuis PO, Bégin LR, Hamel N, Goffin JR, Wong N, Trudel M, Kapusta L, Porter P, Akslen LA. The prognostic implication of the basal-like (cyclin E high/p27 low/p53+/glomeruloid-microvascular-proliferation+) phenotype of BRCA1-related breast cancer. Cancer Res. 2004; 64(3):830-5. Cerca con Google

Turner NC, Reis-Filho JS, Russell AM, Springall RJ, Ryder K, Steele D, Savage K, Gillett CE, Schmitt FC, Ashworth A, Tutt AN. BRCA1 dysfunction in sporadic basal-like breast cancer. Oncogene. 2007; 26(14):2126-32. Cerca con Google

Abd El-Rehim DM, Ball G, Pinder SE, Rakha E, Paish C, Robertson JF, Macmillan D, Blamey RW, Ellis IO. High-throughput protein expression analysis using tissue microarray technology of a large well-characterised series identifies biologically distinct classes of breast cancer confirming recent cDNA expression analyses. Int J Cancer. 2005 Sep 1;116(3):340- 50. Cerca con Google

Rottenberg S, Nygren AO, Pajic M, van Leeuwen FW, van der Heijden I, van de Wetering K, Liu X, de Visser KE, Gilhuijs KG, van Tellingen O, Schouten JP, Jonkers J, Borst P. Selective induction of chemotherapy resistance of mammary tumors in a conditional mouse model for hereditary breast cancer. Proc Natl Acad Sci U S A. 2007; 104(29):12117- 22. Cerca con Google

Yap TA, Boss DS, Fong PC. First in human phase I pharmacokinetic (PK) and pharmacodynamic (PD) study of KU-0059436 (Ku), a small molecule inhibitor of poly ADPribose polymerase (PARP) in cancer patients (p), including BRCA1/2 mutation carriers. J Clin Oncol. 2007; 25: 3529 (Abstract). Cerca con Google

Sachelarie I, Grossbard ML, Chadha M, Feldman S, Ghesani M, Blum RH. Primary systemic therapy of breast cancer. Oncologist. 2006; 11(6):574-89. Cerca con Google

Gonzales-Angulo AM, Morales-Vasquez F and Hortobagyi GN. Overview of Resistance to systemic therapy in patients with breast cancer. Breast Cancer Chemosensitivity. 2007; chapter 1: 1-. Cerca con Google

43 Hannemann J. Gene expression profiling in breast cancer: a link between biology and clinical decision making. 2008. Academisch Proefscrift ( Chapter 1: 1-3. Vai! Cerca con Google

van 't Veer LJ and Bernards R. Enabling personalized cancer medicine through analysis of gene-expression patterns. Nature. 2008; 452: 564-569. Cerca con Google

DeRisi J, Penland L, Brown PO, Bittner ML, Meltzer PS, Ray M, Chen Y, Su YA, Trent JM. Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nat Genet. 1996 Dec;14(4):457-60. Cerca con Google

Schena M, Shalon D, Heller R, Chai A, Brown PO, Davis RW. Parallel human genome analysis: microarray-based expression monitoring of 1000 genes. Proc Natl Acad Sci U S A. 1996; 93(20):10614-9. Cerca con Google

Olivotto IA, Bajdik CD, Ravdin PM, Speers CH, Coldman AJ, Norris BD, Davis GJ, Chia SK, Gelmon KA. Population-based validation of the prognostic model ADJUVANT! for early breast cancer. J Clin Oncol. 2005; 23(12):2716-25. Cerca con Google

Galea MH, Blamey RW, Elston CE, Ellis IO. The Nottingham Prognostic Index in primary breast cancer. Breast Cancer Res Treat. 1992; 22(3):207-19. Cerca con Google

Goldhirsch A, Wood WC, Gelber RD, Coates AS, Thürlimann B, Senn HJ. Meeting highlights: updated international expert consensus on the primary therapy of early breast cancer. J Clin Oncol. 2003; 21(17):3357-65. Cerca con Google

Eifel P, Axelson JA, Costa J, Crowley J, Curran WJ Jr, Deshler A, Fulton S, Hendricks CB, Kemeny M, Kornblith AB, Louis TA, Markman M, Mayer R, Roter D. J Natl Cancer Inst. National Institutes of Health Consensus Development Conference Statement: adjuvant therapy for breast cancer, November 1-3, 2000. 2001; 93(13):979-89. Cerca con Google

Morris SR, Carey LA. Curr Opin Oncol. Gene expression profiling in breast cancer. 2007; 19(6):547-51. Cerca con Google

Weigelt B, Peterse JL, van 't Veer LJ. Breast cancer metastasis: markers and models. Nat Rev Cancer. 2005; 5(8):591-602. Cerca con Google

van 't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002; 415(6871):530-6. Cerca con Google

van de Vijver MJ, He YD, van't Veer LJ, Dai H, Hart AA, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ, Parrish M, Atsma D, Witteveen A, Glas A, Delahaye L, van der Velde T, Bartelink H, Rodenhuis S, Rutgers ET, Friend SH, Bernards R. A geneexpression signature as a predictor of survival in breast cancer.N Engl J Med. 2002; 347(25):1999-2009. Cerca con Google

Desmedt C, Ruíz-García E, André F. Gene expression predictors in breast cancer: current status, limitations and perspectives. Eur J Cancer. 2008; 44(18):2714-20. Cerca con Google

Sotiriou C, Wirapati P, Loi S, Harris A, Fox S, Smeds J, Nordgren H, Farmer P, Praz V, Haibe-Kains B, Desmedt C, Larsimont D, Cardoso F, Peterse H, Nuyten D, Buyse M, Van de Vijver MJ, Bergh J, Piccart M, Delorenzi M. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst. 2006; 98(4):262-72. Cerca con Google

Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, Baehner FL, Walker MG, Watson D, Park T, Hiller W, Fisher ER, Wickerham DL, Bryant J, Wolmark N. A multigene assay to References predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004; 351(27):2817-26. Cerca con Google

Ma XJ, Hilsenbeck SG, Wang W, Ding L, Sgroi DC, Bender RA, Osborne CK, Allred DC, Erlander MG. The HOXB13:IL17BR expression index is a prognostic factor in early-stage breast cancer. J Clin Oncol. 2006; 24(28):4611-9. Cerca con Google

Ma XJ, Wang Z, Ryan PD, Isakoff SJ, Barmettler A, Fuller A, Muir B, Mohapatra G, Salunga R, Tuggle JT, Tran Y, Tran D, Tassin A, Amon P, Wang W, Wang W, Enright E, Stecker K, Estepa-Sabal E, Smith B, Younger J, Balis U, Michaelson J, Bhan A, Habin K, Baer TM, Brugge J, Haber DA, Erlander MG, Sgroi DC. A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell. 2004; 5(6):607-16. Cerca con Google

Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, Talantov D, Timmermans M, Meijer-van Gelder ME, Yu J, Jatkoe T, Berns EM, Atkins D, Foekens JA. Cerca con Google

Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 2005; 365(9460):671-9. Cerca con Google

Huang E, Cheng SH, Dressman H, Pittman J, Tsou MH, Horng CF, Bild A, Iversen ES, Liao M, Chen CM, West M, Nevins JR, Huang AT. Gene expression predictors of breast cancer outcomes. Lancet. 2003; 361(9369):1590-6. Cerca con Google

Chang HY, Sneddon JB, Alizadeh AA, Sood R, West RB, Montgomery K, Chi JT, van de Rijn M, Botstein D, Brown PO. Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds. PLoS Biol. 2004; 2(2):E7. Cerca con Google

Dai H, van't Veer L, Lamb J, He YD, Mao M, Fine BM, Bernards R, van de Vijver M, Deutsch P, Sachs A, Stoughton R, Friend S. A cell proliferation signature is a marker of extremely poor outcome in a subpopulation of breast cancer patients. Cancer Res. 2005; 65(10):4059-66. Cerca con Google

Chi JT, Wang Z, Nuyten DS, Rodriguez EH, Schaner ME, Salim A, Wang Y, Kristensen GB, Helland A, Børresen-Dale AL, Giaccia A, Longaker MT, Hastie T, Yang GP, van de Vijver MJ, Brown PO. Gene expression programs in response to hypoxia: cell type specificity and prognostic significance in human cancers. PLoS Med. 2006; 3(3):e47. Cerca con Google

Liu R, Wang X, Chen GY, Dalerba P, Gurney A, Hoey T, Sherlock G, Lewicki J,Shedden K, Clarke MF. The prognostic role of a gene signature from tumorigenic breastcancer cells. N Engl J Med. 2007; 356(3):217-26. Cerca con Google

Fan C, Oh DS, Wessels L, Weigelt B, Nuyten DS, Nobel AB, van't Veer LJ, Perou CM. Concordance among gene-expression-based predictors for breast cancer. N Engl J Med. 2006; 355(6):560-9. Cerca con Google

Foekens JA, Atkins D, Zhang Y, Sweep FC, Harbeck N, Paradiso A, Cufer T, Sieuwerts AM, Talantov D, Span PN, Tjan-Heijnen VC, Zito AF, Specht K, Hoefler H, Golouh R, Schittulli F, Schmitt M, Beex LV, Klijn JG, Wang Y. Multicenter validation of a gene expression-based prognostic signature in lymph node-negative primary breast cancer. J Clin Oncol. 2006; 24(11):1665-71. Cerca con Google

Ein-Dor L, Kela I, Getz G, Givol D, Domany E. Outcome signature genes in breast cancer: is there a unique set? Bioinformatics. 2005; 21(2):171-8. Cerca con Google

de Azambuja E, Cardoso F, de Castro G Jr, Colozza M, Mano MS, Durbecq V, Sotiriou C, Larsimont D, Piccart-Gebhart MJ, Paesmans M. Ki-67 as prognostic marker in early breast cancer: a meta-analysis of published studies involving 12155 patients. Br J Cancer. 2007; 96(10):1504-13. Cerca con Google

Molecular classification of breast cancer: implications for selection of adjuvant chemotherapy. Andre F, Pusztai L. Nat Clin Pract Oncol. 2006; 3(11):621-32. Cerca con Google

Desmedt C, Haibe-Kains B, Wirapati P, Buyse M, Larsimont D, Bontempi G, Delorenzi M, Piccart M, Sotiriou C. Biological processes associated with breast cancer clinical outcome depend on the molecular subtype. Clin Cancer Res. 2008; 14(16): 5158-65. Cerca con Google

Paik S, Tang G, Shak S, Kim C, Baker J, Kim W, Cronin M, Baehner FL, Watson D, Bryant J, Costantino JP, Geyer CE Jr, Wickerham DL, Wolmark N. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol. 2006; 24(23): 3207-14. Cerca con Google

Buyse M, Loi S, van't Veer L, Viale G, Delorenzi M, Glas AM, d'Assignies MS, Bergh J, Lidereau R, Ellis P, Harris A, Bogaerts J, Therasse P, Floore A, Amakrane M, Piette F, Rutgers E, Sotiriou C, Cardoso F, Piccart MJ; TRANSBIG Consortium. Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J Natl Cancer Inst. 2006; 98(17):1183-92. Cerca con Google

Desmedt C, Piette F, Loi S, Wang Y, Lallemand F, Haibe-Kains B, Viale G, Delorenzi M, Zhang Y, d'Assignies MS, Bergh J, Lidereau R, Ellis P, Harris AL, Klijn JG, Foekens JA, Cardoso F, Piccart MJ, Buyse M, Sotiriou C; TRANSBIG Consortium. Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series. Clin Cancer Res. 2007; 13(11):3207- 14. Cerca con Google

Tewari M, Krishnamurthy A, Shukla HS. Predictive markers of response to neoadjuvant chemotherapy in breast cancer. Surg Oncol. 2008; 17(4):301-11. Cerca con Google

Greenberg PAC, Hortobagyi GN. The importance of chemotherapy in locally advanced breast cancer. In: Wise L, Johnson Jr H, editors. Breast cancer: controversies in management. Armonk, NY: Futura publishing company Inc.; 1994. p. 439-58. Cerca con Google

Portera CC, Swain SM. Neoadjuvant chemotherapy: a step closer to individualized therapy. In: Govindan R, editor. ASCO educational book. Alexandria, VA: ASCO; 2007. p. 51- 5. Cerca con Google

Ross AA, Cooper BW, Lazarus HM, Mackay W, Moss TJ, Ciobanu N, Tallman MS, Kennedy MJ, Davidson NE, Sweet D. Detection and viability of tumor cells in peripheral blood stem cell collections from breast cancer patients using immunohistochemical and clonogenic assay techniques. Blood. 1993; 82(9):2605-10. Cerca con Google

Retsky M, Bonadonna G, Demicheli R, Folkman J, Hrushesky W, Valagussa P. Hypothesis: Induced angiogenesis after surgery in premenopausal node positive breast cancer patients is a major underlying reason why adjuvant chemotherapy works particularly well for those patients. Breast Cancer Research. 2004; 6(4):372-4. Cerca con Google

Goldie JH, Coldman AJ. A mathematical model for relating thedrug sensitivity of tumors to their spontaneous mutation rate. Cancer Treatment Reports. 1979; 63(11- 12):1727-33. Cerca con Google

Norton L, Simon R. Tumor size, sensitivity to therapy and design of treatment schedules. Cancer Treatment Reports. 1977; 61(7):1307-17. Cerca con Google

Therasse P, Arbuck SG, Eisenhauer EA, Wanders J, Kaplan RS, Rubinstein L, Verweij J, Van Glabbeke M, van Oosterom AT, Christian MC, Gwyther SG. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst. 2000; 92(3):205-16. Cerca con Google

Hayward JL, Carbone PP, Heuson JC, Kumaoka S, Segaloff A, Rubens RD. Assessment of response to therapy in advanced breast cancer: a project of the Programme on Clinical Oncology of the International Union Against Cancer, Geneva, Switzerland. Cancer. 1977; 39(3):1289-94. Cerca con Google

Jones RL, Smith IE. Neoadjuvant treatment for early-stage breast cancer: opportunities to assess tumour response. Lancet Oncology. 2006; 7(10):869-74. Cerca con Google

Bear HD, Anderson S, Smith RE, Geyer CE Jr, Mamounas EP, Fisher B, Brown AM, Robidoux A, Margolese R, Kahlenberg MS, Paik S, Soran A, Wickerham DL, Wolmark N. Sequential preoperative or postoperative docetaxel added to preoperative doxorubicin plus cyclophosphamide for operable breast cancer:National Surgical Adjuvant Breast and Bowel Project Protocol B-27. J Clin Oncol. 2006; 24(13):2019-27. Cerca con Google

Guarneri V, Broglio K, Kau SW, Cristofanilli M, Buzdar AU, Valero V, Buchholz T, Meric F, Middleton L, Hortobagyi GN, Gonzalez-Angulo AM.. Prognostic value of pathologic complete response after primary chemotherapy in relation to hormone receptor status and other factors. Journal of Clinical Oncology. 2006; 24(7):1037-44. Cerca con Google

Hannemann J. Gene expression profiling in breast cancer: a link between biology and clinical decision making. Academisch Proefscrift ( 2008; Chapter 2: 5-23. Vai! Cerca con Google

Sparano JA. Taxanes for breast cancer: an evidence-based review of randomized phase II and phase III trials. Clin Breast Cancer. 2000; 1(1): 32-40. Cerca con Google

Cristofanilli M, Gonzalez-Angulo A, Sneige N, Kau SW, Broglio K, Theriault RL, Valero V, Buzdar AU, Kuerer H, Buccholz TA, Hortobagyi GN.. Invasive lobular carcinoma classic type: response to primary chemotherapy and survival outcomes. Journal of Clinical Oncology. 2005; 23(1):41-8. Cerca con Google

Yarden Y, Sliwkowski MX. Untangling the the ErbB signalling network. Nat Rev Mol Cell Biol. 2001; 2(2):127-137. Cerca con Google

Sullivan DM, Latham MD, Ross WE. Proliferation dependent topoisomerase II content as a determinant of antineoplastic drug action in human, mouse, and Chinese hamster ovary cells. Cancer Research. 1987; 47(15):3973-9. Cerca con Google

Kariya S, Ogawa Y, Nishioka A, Moriki T, Ohnishi T, Ito S, Murata Y, Yoshida S. Relationship between hormonal receptors, HER-2, p53 protein, Bcl-2, and MIB-1 status and the antitumor effects of neoadjuvant anthracycline-based chemotherapy in invasive breast cancer patients. Radiation Medicine. 2005; 23(3):189-94. Cerca con Google

Staunton JE, Slonim DK, Coller HA, Tamayo P, Angelo MJ, Park J, Scherf U, Lee JK, Reinhold WO, Weinstein JN, Mesirov JP, Lander ES, Golub TR. Chemosensitivity prediction by transcriptional profiling. Proc Natl Acad Sci U S A. 2001; 98(19):10787–92. Cerca con Google

Ayers M, Symmans WF, Stec J, Damokosh AI, Clark E, Hess K, Lecocke M, Metivier J, Booser D, Ibrahim N, Valero V, Royce M, Arun B, Whitman G, Ross J, Sneige N, Hortobagyi GN, Pusztai L. Gene expression profiles predict complete pathologic response to neoadjuvant paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide chemotherapy in breast cancer. J Clin Oncol. 2004; 22(12):2284–93. Cerca con Google

Chang JC, Wooten EC, Tsimelzon A, Hilsenbeck SG, Gutierrez MC, Elledge R, Mohsin S, Osborne CK, Chamness GC, Allred DC, O'Connell P. Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer. Lancet. 2003; 362(9381):362–9. Cerca con Google

Chang JC, Wooten EC, Tsimelzon A, Hilsenbeck SG, Gutierrez MC, Tham YL, Kalidas M, Elledge R, Mohsin S, Osborne CK, Chamness GC, Allred DC, Lewis MT, Wong H, O'Connell P. Patterns of resistance and incomplete response to docetaxel by gene expression profiling in breast cancer patients. J Clin Oncol. 2005; 23(6):1169–77. Cerca con Google

Hannemann J, Oosterkamp HM, Bosch CA, Velds A, Wessels LF, Loo C, Rutgers EJ, Rodenhuis S, van de Vijver MJ. Changes in gene expression associated with response to neoadjuvant chemotherapy in breast cancer. J Clin Oncol. 2005; 23(15):3331–42. Cerca con Google

Gianni L, Zambetti M, Clark K, Baker J, Cronin M, Wu J, Mariani G, Rodriguez J, Carcangiu M, Watson D, Valagussa P, Rouzier R, Symmans WF, Ross JS, Hortobagyi GN, Pusztai L, Shak S. Gene expression profiles in paraffin-embedded core biopsy tissue predict response to chemotherapy in women with locally advanced breast cancer. Journal of Clinical Oncology. 2005; 23(29):7265-77. Cerca con Google

Thuerigen O, Schneeweiss A, Toedt G, Warnat P, Hahn M, Kramer H, Brors B, Rudlowski C, Benner A, Schuetz F, Tews B, Eils R, Sinn HP, Sohn C, Lichter P. Gene expression signature predicting pathologic complete response with gemcitabine, epirubicin, and docetaxel in primary breast cancer. Journal of Clinical Oncology. 2006; 24(12):1839-45. Cerca con Google

Dressman HK, Hans C, Bild A, Olson JA, Rosen E, Marcom PK, Liotcheva VB, Jones EL, Vujaskovic Z, Marks J, Dewhirst MW, West M, Nevins JR, Blackwell K. Gene expression profiles of multiple breast cancer phenotypes and response to neoadjuvant chemotherapy. Clin Cancer Res. 2006; 2(3 Pt 1):819-26. Cerca con Google

Hess KR, Anderson K, Symmans WF, Valero V, Ibrahim N, Mejia JA, Booser D, Theriault RL, Buzdar AU, Dempsey PJ, Rouzier R, Sneige N, Ross JS, Vidaurre T, Gómez HL, Hortobagyi GN, Pusztai L. Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer. Journal of Clinical Oncology. 2006; 24(26):4236-44. Cerca con Google

Györffy B, Serra V, Jürchott K, Abdul-Ghani R, Garber M, Stein U, Petersen I, Lage H, Dietel M, Schäfer R. Prediction of doxorubicin sensitivity in breast tumors based on gene expression profiles of drug-resistant cell lines correlates with patient survival. Oncogene. 2005; 24(51):7542-51. Cerca con Google

Bonnefoi H, Potti A, Delorenzi M, Mauriac L, Campone M, Tubiana-Hulin M, Petit T, Rouanet P, Jassem J, Blot E, Becette V, Farmer P, André S, Acharya CR, Mukherjee S, Cameron D, Bergh J, Nevins JR, Iggo RD. Validation of gene signatures that predict the response of breast cancer to neoadjuvant chemotherapy: a substudy of the EORTC 10994/BIG 00-01 clinical trial. Lancet Oncology. 2007; 8(12):1071-8. Cerca con Google

Nagasaki K, Miki Y. Molecular prediction of therapeutic response to neoadjuvant chemotherapy in breast cancer. Breast Cancer. 2008; 15(2):117-120. Cerca con Google

Jansen MP, Foekens JA, van Staveren IL, Dirkzwager-Kiel MM, Ritstier K, Look MP, Meijer-van Gelder ME, Sieuwerts AM, Portengen H, Dorssers LC, Klijn JG, Berns EM. Molecular classification of tamoxifenresistant breast carcinomas by gene expression profiling. J Clin Oncol. 2005; 23(4):732–40. Cerca con Google

Schardt JA, Meyer M, Hartmann CH, Schubert F, Schmidt-Kittler O, Fuhrmann C, Polzer B, Petronio M, Eils R, Klein CA. Genomic analysis of single cytokeratin-positive cells from bone marrow reveals early mutational events in breast cancer. Cancer Cel. 2005; 8(3):227–39. Cerca con Google

Nagrath S, Sequist LV, Maheswaran S, Bell DW, Irimia D, Ulkus L, Smith MR, Kwak EL, Digumarthy S, Muzikansky A, Ryan P, Balis UJ, Tompkins RG, Haber DA, Toner M. Isolation of rare circulating tumour cells in cancer patients by microchip technology. Nature. 2007; 450(7173):1235–9. Cerca con Google

Pando MP, Kotraiah V, McGowan K, Bracco L, Einstein R. Alternative isoform discrimination by the next generation of expression profiling microarrays. Expert Opin Ther Targets 2006; 10(4):613–25. Cerca con Google

Simon R. Roadmap for developing and validating therapeutically relevant genomic classifiers. Journal of Clinical Oncology. 2005; 23(29): 7332-41. Cerca con Google

Gonzalez-Angulo AM, Morales-Vasquez F, Hortobagyi GN. Overview of resistance to systemic therapy in patients with breast cancer. Adv Exp Med Biol. 2007; 608:1-22. Cerca con Google

Gottesman MM, Fojo T, Bates SE. Multidrug resistance in cancer: role of ATPdependent transporters. Nature Rev. 2002; 2(1):48-58. Cerca con Google

Hahn WC, Weinberg RA. Modelling the molecular circuitry of cancer. Nat Rev Cancer. 2002; 2(5):331–41 Cerca con Google

Yague E, Raguz S. Drug resistance in cancer. British Journal of Cancer. 2005; 93(9): 973-76. Cerca con Google

Ferguson LR, De Flora S. Multiple drug resistance, antimutagenesis and anticarcinogenesis. Mutat Res. 2005; 591(1-2):24-33. Cerca con Google

Scheffer GL, Schroeijers AB., Izquierdo MA, Wiemer EA, Scheper RJ. Lung resistance-related protein/major vault protein and vaults in multidrug-resistant cancer. Curr. Opin. Oncol. 2000; 12(6):550–6. Cerca con Google

Schuetz EG, Beck WT, Schuetz JD. Modulators and substrates of P-glycoprotein and cytochrome P4503A coordinately up-regulate these proteins in human colon carcinoma cells. Mol. Pharmacol. 1996; 49(2):311–8. Cerca con Google

McCubrey JA, Steelman LS, Abrams SL, Lee JT, Chang F, Bertrand FE, Navolanic PM, Terrian DM, Franklin RA, D'Assoro AB, Salisbury JL, Mazzarino MC, Stivala F, Libra M. Roles of the RAF/MEK/ERK and PI3K/PTEN/AKT pathways in malignant transformation and drug resistance. Adv Enzyme Regul. 2006; 46:249-79. Cerca con Google

Simpson D, Plosker GL. Paclitaxel as adjuvant or neoadjuvant therapy in early breast cancer. Drugs. 2004; 64(16): 1839-1847. Cerca con Google

Rowinski EK, Donehower RC. Antimicrotubule agents. Pharmacology of Cancer Chemotherapy. Cancer: Principle and Practice of Oncology. 1997 Chapter 19.8: 467-82. Cerca con Google

Rowinsky EK, Donehower RC. Drug Therapy-Paclitaxel. New Engl Journal of Med. 1995; 332 (15): 1004-14. Cerca con Google

Jordan MA, Wilson L. Microtubules as a target for anticancer drugs. Nat Rev Cancer. 2004; 4(4):253-65. Cerca con Google

Martello L, Verdier-Pinard P, Shen HJ, He L, Torres K, Orr GA, Horwitz SB. Elevated levels of microtubule destabilizing factors in a taxol-resistant/dependent A549 cell line with an alpha-tubulin mutation. Cancer Res. 2003; 63:1207-1213. Cerca con Google

Rouzier R, Rajan R, Wagner P, Hess KR, Gold DL, Stec J, Ayers M, Ross JS, Zhang P, Buchholz TA, Kuerer H, Green M, Arun B, Hortobagyi GN, Symmans WF, Pusztai L. Microtubule-associated protein tau: a marker of paclitaxel sensitivity in breast cancer.Proc Natl Acad Sci U S A. 2005; 102(23):8315-20. Cerca con Google

Stewart CF, Ratain MJ. Topoisomerase Interactive Agents. Pharmacology of Cancer Chemotherapy. Cancer: Principle and Practice of Oncology. 1997. Chapter 19.7: 452-66. Cerca con Google

Binaschi M, Bigioni M, Cipollone A, Rossi C, Goso C, Maggi CA, Capranico G, Animati F. Anthracyclines: selected new developments. Curr Med Chem Anticancer Agents. 2001; 1(2):113-30. Cerca con Google

Peng H, Wang MM, Jiang LY, Liu HT, Sun JZ. Paclitaxel-doxorubicin sequence is more effective in breast cancer cells with heat shock protein 27 overexpression. Chinese Med Journal. 2008; 121(20):1975-9. Cerca con Google

Kubo A, Yoshikawa A, Hirashima T, Masuda N, Takada M, Takahara J, Fukuoka M, Nakagawa K. Point mutations of the topoisomerase IIalpha gene in patients with small cell lung cancer treated with etoposide. Cancer Res. 1996; 56(6):1232-6. Cerca con Google

Rayson D, Richel D, Chia S, Jackisch C, van der Vegt S, Suter T. Athracyclinetrastuzumab regimens for HER2/neu-overexpressing breast cancer: current experience and future strategies. Annals of Oncology. 2008; 19(9):1530-9. Cerca con Google

Park K, Kim J, Lim S, Han S. Topoisomerase II-alpha (topoII) and HER2 amplification in breast cancers and response to preoperative doxorubicin chemotherapy. Eur J Cancer. 2003; 39(5):631-4. Cerca con Google

Razis ED, Fountzilas G. Paclitaxel: Epirubicin in metastatic breast cancer- a review. 2001; 12(5):593-8. Cerca con Google

Chomczynski P, Mackey K, Drews R, Wilfinger W. DNAzol: a reagent for the rapid isolation of genomic DNA. Biotechniques. 1997; 22(3):550-3. Cerca con Google

Yue H, Eastman PS, Wang BB, Minor J, Doctolero MH, Nuttall RL, Stack R, Becker JW, Montgomery JR, Vainer M, Johnston R. An evaluation of the performance of cDNA microarrays for detecting changes in global mRNA expression. Nucleic Acids Res. 2001; 29(8):E41-1. Cerca con Google

Van Gelder RN, von Zastrow ME, Yool A, Dement WC, Barchas JD, Eberwine JH. Amplified RNA synthesized from limited quantities of heterogeneous cDNA. Proc Natl Acad Sci U S A. 1990; 87(5):1663-7. Cerca con Google

Feldman AL, Costouros NG, Wang E, Qian M, Marincola FM, Alexander HR, Libutti SK. Advantages of mRNA amplification for microarray analysis. Biotechniques. 2002; 33(4):906-12, 914. Cerca con Google

Polacek DC, Passerini AG, Shi C, Francesco NM, Manduchi E, Grant GR, Powell S, Bischof H, Winkler H, Stoeckert CJ Jr, Davies PF. Fidelity and enhanced sensitivity of differential transcription profiles following linear amplification of nanogram amounts of endothelial mRNA. Physiol Genomics. 2003; 13(2):147-56. Cerca con Google

't Hoen PA, de Kort F, van Ommen GJ, den Dunnen JT. Fluorescent labelling of cRNA for microarray applications. Nucleic Acids Res. 2003; 31(5):e20. Cerca con Google

Rosati P and Colombo R. Microscopia confocale. Tecniche per lo studio dei campioni biologici. La Cellula -Seconda edizione- 1999; 2: 50-1. Cerca con Google

Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 2002; 30(4):e15. Cerca con Google

Yang IV, Chen E, Hasseman JP, Liang W, Frank BC, Wang S, Sharov V, Saeed AI, White J, Li J, Lee NH, Yeatman TJ, Quackenbush J. Within the fold: assessing differential expression measures and reproducibility in microarray assays. Genome Biol. 2002; 3(11):research0062. Cerca con Google

Quackenbush J. Microarray data normalization and transformation. Nat Genet. 2002; 32 Suppl:496-501. Cerca con Google

Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman RB. Missing value estimation methods for DNA microarrays. Bioinformatics. 2001; 17(6):520-5. Cerca con Google

Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, Powell JI, Yang L, Marti GE, Moore T, Hudson J Jr, Lu L, Lewis DB, Tibshirani R, Sherlock G, Chan WC, Greiner TC, Weisenburger DD, Armitage JO, Warnke R, Levy R, Wilson W, Grever MR, Byrd JC, Botstein D, Brown PO, Staudt LM. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000; 403(6769):503-11. Cerca con Google

Tibshirani R, Hastie T, Narasimhan B, Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci U S A; 99(10):6567-72. Cerca con Google

Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A. 1998; 95(25):14863-8. Cerca con Google

Tibshirani R, Hastie T, Narasimhan B, Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci U S A. 2002; 99(10):6567-72. Cerca con Google

Mukherjee S. Classifyng microarray data using support vector machines. A Practical Approach to Microarray Data Analysis. 2003; Chapter 9:166-185. Cerca con Google

Vapnik V. Statistical Learning Theory. Wiley, 1998. Cerca con Google

Guyon I, Weston J, Barnhill S. Gene Selection for Cancer Classification using Support Vector Machines. Machine Learning. 2002, 46: 389-422. Cerca con Google

Ambroise C, McLachlan GJ. Selection bias in gene extraction on the basis of microarray gene-expression data. Proc Natl Acad Sci U S A. 2002;99(10):6562-6. Cerca con Google

Zhang X, Lu X, Shi Q, Xu XQ, Leung HC, Harris LN, Iglehart JD, Miron A, Liu JS, Wong WH. Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data. BMC Bioinformatics. 2006;7:197. Cerca con Google

Li F, Yang Y. Analysis of recursive gene selection approaches from microarray data. Bioinformatics. 2005;21(19):3741-7. Cerca con Google

Ivan Kojadinovic, Thomas Wottka. Comparison between a filter and a wrapper approach to variable subset selection in regression problems. European Symposium on Intelligent Techniques (ESIT) 2000. Cerca con Google

Platt J. Probabilistic Outputs for Support Vector Machines and Comparisons to regularized likelihood methods. Advances in Large Margin Classifiers. 1999. Cerca con Google

Armitage P, Berry G, Matthews JNS Analysing non-normal data (Rank-correlation). Statistical methods in medical research (Blackwell Science) fourth edition; cap. 10 par. 5: 288-292. Cerca con Google

Armitage P, Berry G, Matthews JNS General contingency tables (Comparison of several groups). Statistical methods in medical research (Blackwell Science) fourth edition; cap. 8 par. 6: 231-232. Cerca con Google

Clark JI, Brooksbank C, Lomax J. It's all GO for plant scientists. Plant Physiol. 2005; 138(3):1268-79. Cerca con Google

Zeeberg BR, Feng W, Wang G, Wang MD, Fojo AT, Sunshine M, Narasimhan S, Kane DW, Reinhold WC, Lababidi S, Bussey KJ, Riss J, Barrett JC, Weinstein JN. GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol. 2003;4(4):R28. Cerca con Google

Perou CM, Jeffrey SS, van de Rijn M, Rees CA, Eisen MB, Ross DT, Pergamenschikov Cerca con Google

A, Williams CF, Zhu SX, Lee JC, Lashkari D, Shalon D, Brown PO, Botstein D. Distinctive gene Cerca con Google

expression patterns in human mammary epithelial cells and breast cancers. Proc Natl Acad Sci U S A. 1999; 96(16):9212-7. Cerca con Google

Bertucci F, Nasser V, Granjeaud S, Eisinger F, Adelaïde J, Tagett R, Loriod B, Giaconia A, Benziane A, Devilard E, Jacquemier J, Viens P, Nguyen C, Birnbaum D, Houlgatte R. Gene expression profiles of poor-prognosis primary breast cancer correlate with survival. Hum Mol Genet. 2002; 11(8):863-72. Cerca con Google

Ginestier C, Charafe-Jauffret E, Bertucci F, Eisinger F, Geneix J, Bechlian D, Conte N, Adélaïde J, Toiron Y, Nguyen C, Viens P, Mozziconacci MJ, Houlgatte R, Birnbaum D, Jacquemier J. Distinct and complementary information provided by use of tissue and DNA microarrays in the study of breast tumor markers. Am J Pathol. 2002; 161(4):1223-33. Cerca con Google

Urruticoechea A, Smith IE, Dowsett M. Proliferation marker Ki-67 in early breast cancer. J Clin Oncol. 2005; 23(28):7212-20. Cerca con Google

Assersohn L, Salter J, Powles TJ, A'hern R, Makris A, Gregory RK, Chang J, Dowsett M. Studies of the potential utility of Ki67 as a predictive molecular marker of clinical response in primary breast cancer. Breast Cancer Res Treat. 2003; 82(2):113-23. Cerca con Google

Pusztai L, Krishnamurti S, Perez Cardona J, Sneige N, Esteva FJ, Volchenok M, Breitenfelder P, Kau SW, Takayama S, Krajewski S, Reed JC, Bast RC Jr, Hortobagyi GN. Cerca con Google

Expression of BAG-1 and BcL-2 proteins before and after neoadjuvant chemotherapy of locally advanced breast cancer. Cancer Invest. 2004; 22(2):248-56. Cerca con Google

Sullivan R, Paré GC, Frederiksen LJ, Semenza GL, Graham CH. Hypoxia-induced resistance to anticancer drugs is associated with decreased senescence and requires hypoxia-inducible factor-1 activity. Mol Cancer Ther. 2008; 7(7):1961-73. Cerca con Google

Liao DJ, Thakur A, Wu J, Biliran H, Sarkar FH. Perspectives on c-Myc, Cyclin D1, and their interaction in cancer formation, progression, and response to chemotherapy. Crit Rev Oncog. 2007; 13(2):93-158. Cerca con Google

Salter KH, Acharya CR, Walters KS, Redman R, Anguiano A, Garman KS, Anders CK, Mukherjee S, Dressman HK, Barry WT, Marcom KP, Olson J, Nevins JR, Potti A. An integrated approach to the prediction of chemotherapeutic response in patients with breast cancer. PLoS ONE. 2008; 3(4):e1908. Cerca con Google

DeLuca JG, Dong Y, Hergert P, Strauss J, Hickey JM, Salmon ED, McEwen BF. Hec1 and nuf2 are core components of the kinetochore outer plate essential for organizing microtubule attachment sites. Mol Biol Cell. 2005; 16(2):519-31. Cerca con Google

Ciferri C, De Luca J, Monzani S, Ferrari KJ, Ristic D, Wyman C, Stark H, Kilmartin J, Salmon ED, Musacchio A. Architecture of the human ndc80-hec1 complex, a critical constituent of the outer kinetochore. J Biol Chem. 2005; 280(32):29088-95. Cerca con Google

Zhu N, Gu L, Findley HW, Chen C, Dong JT, Yang L, Zhou M.J. KLF5 Interacts with p53 in regulating survivin expression in acute lymphoblastic leukemia. Biol Chem. 2006; 281(21):14711-8. Cerca con Google

Weng D, Song X, Xing H, Ma X, Xia X, Weng Y, Zhou J, Xu G, Meng L, Zhu T, Wang S, Ma D. Implication of the Akt2/survivin pathway as a critical target in paclitaxel treatment in human ovarian cancer cells. Cancer Lett. 2009; 273(2):257-65. Cerca con Google

Tong D, Czerwenka K, Heinze G, Ryffel M, Schuster E, Witt A, Leodolter S, Zeillinger R. Expression of KLF5 is a prognostic factor for disease-free survival and overall survival in patients with breast cancer. Clin Cancer Res. 2006; 12(8):2442-8. Cerca con Google

Yang Q, Sakurai T, Yoshimura G, Takashi Y, Suzuma T, Tamaki T, Umemura T, Nakamura Y, Nakamura M, Utsunomiya H, Mori I, Kakudo K. Overexpression of p27 protein in human breast cancer correlates with in vitro resistance to doxorubicin and mitomycin C. Anticancer Res. 2000; 20(6B):4319-22. Cerca con Google

Bagui TK, Cui D, Roy S, Mohapatra S, Shor AC, Ma L, Pledger WJ. Inhibition of p27(Kip1) gene transcription by mitogens. Cell Cycle. 2009 Jan;8(1). Cerca con Google

Liang Y, Meleady P, Cleary I, McDonnell S, Connolly L, Clynes M. Selection with melphalan or paclitaxel (Taxol) yields variants with different patterns of multidrug resistance, integrin expression and in vitro invasiveness. Eur J Cancer. 2001; 37(8):1041-52. Cerca con Google

Narita T, Kimura N, Sato M, Matsuura N, Kannagi R. Altered expression of integrins in doxorubicin-resistant human breast cancer cells. Anticancer Res. 1998; 18(1A):257-62. Cerca con Google

Quaresima B, Romeo F, Faniello MC, Di Sanzo M, Liu CG, Lavecchia A, Taccioli C, Gaudio E, Baudi F, Trapasso F, Croce CM, Cuda G, Costanzo F. BRCA1 5083del19 mutant allele selectively up-regulates periostin expression in vitro and in vivo. Clin Cancer Res. 2008 Nov; 14(21):6797-803. Cerca con Google

Byrski T, Gronwald J, Huzarski T, Grzybowska E, Budryk M, Stawicka M, Mierzwa T, Szwiec M, Wi|niowski R, Siolek M, Narod SA, Lubinski J; Polish Hereditary Breast Cancer Consortium. Response to neo-adjuvant chemotherapy in women with BRCA1-positive breast cancers. Breast Cancer Res Treat. 2008; 108(2):289-96. Cerca con Google

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