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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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