Journal Club Cremona 24 Maggio 2008 Genomica e proteomica: significato e utilità attuali Alberto Ballestrero Clinica di Medicina Interna a indirizzo Oncologico DIMI - Università di Genova Senza avere ancora raggiunto una chiara definizione della sua applicabilità clinica la genomica è già diventato un argomento molto vaso e articolato. Per limiti, principalmente, di conoscenza e anche, in parte di tempo, non sono in grado di trattare questo argomento in modo esauriente. Cercherò di sviluppare insieme a voi alcune riflessioni su pochi punti, scusandomi anticipatamente per le moltissime omissioni di questa mia relazione.

Incorporation of genomic into breast cancer management Gene expression analysis Prognosis Prediction Who treat? Which therapy? Classification/Gene discovery Improve biological knowledge

Gene expression analysis assumptions Expression analysis allows identifying the tumour transcriptional features (transcriptoma) 2) Transcriptoma contains the information required to predict tumour evolution and response to treatments

Technologies used for high-throughput gene expression analysis A. Breast cancer tumors are sampled at the treatment location and shipped to the central laboratory doing the assay, where pathologic review is done to assess cancer cell contents, followed by RNA preparation and integrity evaluation. Suitable samples are used to quantify RNA levels, thus assessing gene expression. When a gene is expressed, the transcription complex copies its DNA sequence into complementary RNA transcripts that are translated into proteins. High-throughput gene expression analysis aims to quantify messenger RNA (mRNA) populations in a given tissue. B. DNA microarray is the molecular biology technique enabling gene expression analysis in MammaPrint. RNA is labeled with fluorescent dye and hybridized against thousands of different nucleotide sequences corresponding to different genes and arrayed on a solid surface (that is, a modified microscope glass slide). On hybridization, fluorescence emitted by single locations on the microarray is used to estimate gene expression levels. In MammaPrint, a 2-color design is used, and RNA expression is estimated as a relative ratio between the sample and a reference RNA. For each patient, triplicate measurements are obtained from 2 microarrays inverting the labeling scheme. C. Real-time reverse transcriptase polymerase chain reaction (PCR) is the enabling technology to assess gene expression in Oncotype DX and H/I. This technique is based on reverse transcription (RT) of a specific mRNA into the complementary DNA (cDNA) molecule, which is used as a template in PCR. The production of double-stranded DNA is accompanied by emission of light, which is recorded throughout the process and correlates to the amount of DNA that is produced. The higher the initial amount of RNA, the earlier light is emitted during RT-PCR, a measurable difference that allows gene expression to be quantitated. D. Gene expression levels are mathematically transformed into indexes predicting disease recurrence. Marchionni, L. et. al. Ann Intern Med 2008;148:358-369

Netherlands signature 70 significant prognosis genes in N- patients Good signature 78 tumors Ogni riga rappresenta un paziente (78 pts) e ogni colonna un gene differente inserito nella “signature” (70 geni): il rosso rappresenta la upregulation, il verde la downregulation e il nero il livello normale di espressione (il confronto viene fatto con il pool degli RNA di tutti i pazienti, quindi si tratta di un controllo interno). Nel pannello a dx il bianco indica le pazienti che hanno sviluppato la malattia metastatica mentre il nero indica le pazienti libere da malattia (a 5 anni). La linea tratteggiata indica il cut-off con la migliore sensibilità (quella solida la migliore accuratezza): al di sopra la prognosi è migliore e al di sotto è peggiore. 3 delle 34 pazienti definite a prognosi migliore sono in realtà ricadute (8.8%) mentre 12 (27%) delle 44 definite a cattiva prognosi non sono in realtà mai ricadute nel periodo di osservazione. Questo profilo genico risulta un fattore prognostico molto più potente dei tradizionali fattori prognostici: grading, T> 2 cm, angioinvasione, età ≤ 40 anni, ER negativo (da 2.3, vs grading, a 6.25 volte, vs ER negativo). Inoltre è un fattore prognostico indipendente. Dal punto di vista funzionale occorre osservare che i geni upregolati nel sottogruppo di pazienti a prognosi peggiore appartengono a quelli coinvolti in: ciclo cellulare, invasività e metastasi, angiogenesi, trasduzione del segnale. Poor signature Van’t Veer et al. Nature, 2002

Are results reproducible? September 2006 Median coefficient of variation for: within-laboratory replicates = 5-15% between-laboratory replicates = 10-20% Questi coefficienti di variazione sono simili, se non migliori, di quelli riportati per l’analisi immunoistochimica dello stato ormonale nel carcinoma della mammella.

Building a genomic classifier Patients of interest Testing set Internal validation Training set Classifier Esternal validation: Retrospective Prospective Classifier gene selection: discriminant analysis linear or not linear Commenti: La dimensione del training set è importante, se è piccolo comporta una perdita di informazione nel processo di costruzione del classificatore. La dimensione del testing set è importante, se è piccolo la stima della performance del classificatore è più incerta. Independent patients

70-gene prognostic signature (“Netherlands signature”) 1st Validation Study van de Vijver et al. N Engl J Med, 2002 Van’t Veer et al. Nature, 2002 Mammaprint Agendia

Netherlands signature: 151 N- patients Van de Vijver MJ et al. N Engl J Med 347:1999-2009, 2002

Netherlands signature: 144 N+ patients Van de Vijver MJ et al. N Engl J Med 347:1999-2009, 2002

Reclassify St. Gallen and NIH subgroups according to 70-gene signature: 151 N- patients The high-risk group defined according to the NIH criteria included many patients who had a good-prognosis signature and a good outcome. Conversely, the low-risk group identified by the NIH criteria included patients with a poor-prognosis signature and poor outcome. Similar subgroups were identified within the high-risk and low-risk groups identified according to the St. Gallen criteria. Van de Vijver MJ et al. N Engl J Med 347:1999-2009, 2002 The gene-expression profile is a more powerful predictor of the outcome of disease in young patients with breast cancer than standard systems based on clinical and histologic criteria.

2nd Validation Study Buyse et al. J Natl Cancer Inst, 2006 Risk assessments for metastases and death: 70-gene signature vs Adjuvant 2nd Validation Study Buyse et al. J Natl Cancer Inst, 2006 N° of patients in risk groups Mts within 5 yrs Deaths within 10 yrs Risk classification High Low Sensitivity Specificity 70-gene signature 194 113 0.90 (0.78-0.95) 0.42 (0.36-0.48) 0.84 (0.73-0.92) Adjuvant! 222 80 0.87 (0.75-0.94) 0.29 (0.24-0.35) 0.82 (0.71-0.90) (0.23-0.35) Sensibilità simile: sono ugualmente capaci di identificare chi recidiva (pochi falsi negativi) ma la specificità di Adjuvant è bassa  dà molti falsi positivi: troppi positivi che in realtà non ricadranno mai. Both test correctly identify the high risk patients. Gene signature is superior in correctly identifying the low risk patients.

30% discordant cases between 70-gene signature and Adjuvant 90% (CI 85%-96%) DMF 71% (CI 65%-78%) 30% discordant cases between 70-gene signature and Adjuvant The Kaplan-Meier estimates of time to distant metastases, overall survival, and disease-free survival for the four groups of patients ( Fig. 1 ) suggest that, for groups with discordant risk assessments (i.e., high risk accordino to one risk classifier and low risk according to the other), the gene signature provided stronger prognostic information than the clinicopathologic criteria. Buyse et al. J Natl Cancer Inst 2006

MINDACT trial a testing hypotesis for a key question Key question for use of 70-gene to decide on chemotherapy. Evaluate the risk of undertreating patients who would otherwise get chemotherapy per clinical-pathological criteria. Testing hypotesis. The patients who have a low risk gene prognosis signature and high risk clinical-pathologic criteria, and who were randomized to receive no chemotherapy has a 5-year DMFS = 92% (null hypothesis). Se utilizzo il classificatore genomico per escludere le pazienti dalla chemioterapia non rischio di sottotrattarne almeno una parte? We hypothesize that the lowrisk group (as defi ned by the gene signature) will have an excellent distant metastases – free survival at 5 years without adjuvant chemotherapy, thus sparing a substantial proportion of women who previously would have received adjuvant chemotherapy (because they were deemed at high risk for relapse by Adjuvant! software) unnecessary toxicity. The trial will provide level-1 evidence about the clinical relevance of the 70-gene signature.

RANDOMIZE decision-making Use clinical risk Use genomic risk EORTC-BIG MINDACT TRIAL DESIGN 6,000 Node negative women Assess clinical risk and genomic risk DISCORDANT Clinical and Genomic Risks Clinical and Genomic BOTH HIGH RISK Clinical and Genomic BOTH LOW RISK RANDOMIZE decision-making Patients are evaluated as low clinical-pathological risk, if their 10-year disease specific survival (without chemotherapy or endocrine therapy) is estimated by Adjuvant! Online (Baum M and Ravdin PM) as greater than 88% for ER-positive patients, and greater than 92% for ER-negative patients. In the set of patients who have a low risk gene prognosis signature using the 70-gene signature and high risk clinical-pathological criteria, and who were randomized (R-T) to use the 70-gene segnature prognosis and thus receive no chemotherapy, a null hypothesis of a 5-year distant metastasis free survival (DMFS) of 92% will be tested. With 6,000 patients accrued overall, this set has an expected size of 672 patients. With an accrual of 3 years, and a total duration of 6 years (so 3 to 6 years follow up for each patient), a one-sided test at 97.5% confidence level has 80% power to reject this hypothesis if the true 5-year DMFS is 95%. Baum, M. and Ravdin, P.M. Decision-making in early breast cancer: guidelines and decision tools. Eur J Cancer 38, 745-9 (2002). Use clinical risk Use genomic risk High risk Low risk High risk Low risk Chemotherapy No chemotherapy

Oncotype DX® 21-Gene Recurrence Score (RS) Assay 16 Cancer and 5 Reference Genes From 3 Studies PROLIFERATION Ki-67 STK15 Survivin Cyclin B1 MYBL2 ESTROGEN ER PR Bcl2 SCUBE2 RS = + 0.47 x HER2 Group Score - 0.34 x ER Group Score + 1.04 x Proliferation Group Score + 0.10 x Invasion Group Score + 0.05 x CD68 - 0.08 x GSTM1 - 0.07 x BAG1 GSTM1 BAG1 INVASION Stromelysin 3 Cathepsin L2 The final gene set used for the Oncotype DX™ assay includes the 16 cancer genes identified in the clinical trials: 5 genes are in the proliferation group, 2 in the HER2 group, 4 in the estrogen receptor group, 2 in the invasion group, and 3 are unaligned. Some of the genes are well known in the breast cancer literature; others are relatively new. The 5 reference genes are used for normalizing the expression of the cancer-related genes. As was previously stated, it is important to note that there are other genes linked to breast cancer (eg, the 250 candidate genes from which the 16 genes were selected). The 16 genes presented in this slide were selected for the Oncotype DX™ assay based on the three clinical trials, which demonstrated a consistent statistical link between these genes and distant breast cancer recurrence and the most robust predictive power across the three studies. The Recurrence Score is calculated from the expression results for each of the 16 cancer-related genes by the equation shown in this slide. The Recurrence Score (RS) ranges from 0 to 100. Although the coefficients for each gene or gene group influence the RS result, the quantitative expression for each gene can have a dominant effect. For example, there is a 200-fold range of expression of ER in the quantitative RT-PCR assay. For individual tumors, the expression of any one gene can affect the Recurrence Score to a large degree. Cut-off points for Recurrence Score risk groups were defined prior to the initiation of the validation study: A low-risk group with an RS of <18 An intermediate-risk group with an RS between 18 and 30 A high-risk group with an RS of 31 If the right number of genes to address these questions had been 6 or 60, we would have designed the assay accordingly. As it turned out, the assay was designed to include expression of 16 genes because the development studies indicated these genes provided the most robust predictive score. CD68 Category RS (0-100) Low risk RS <18 Int risk RS ≥18 and <31 High risk RS ≥31 REFERENCE Beta-actin GAPDH RPLPO GUS TFRC HER2 GRB7 Paik et al. N Engl J Med. 2004;351:2817-2826.

Prediction of recurrence in N0 ER positive patients (TAM treated) Paik et al. N Engl J Med. 2004;351:2817-2826

Prediction of chemotherapy benefit in Node-negative, ER-positive breast cancer: NSABP B-20 (Paik S et al. JCO 2006) TAM vs TAM + CT - 651 evaluable patients Patient reclassification Low risk 4.4 absolute benefit from TAM+CT RS Intermediate risk High risk

RS predicts chemotherapy benefit

Node-Neg, ER-Pos Breast Cancer Chemotherapy + Hormone Schema: TAILORx Node-Neg, ER-Pos Breast Cancer Oncotype DX® Assay RS 11-25 Randomize Hormone vs Chemotherapy + Hormone RS <10 Hormone Therapy RS >25 Chemotherapy + Hormone This is the schema of the TAILORx trial. The eligible patients for this trial are N–, ER+ and are candidates for chemotherapy (ie, patients who do not have comorbid conditions that would preclude them from receiving chemotherapy and who are willing to take it if recommended). The fact that the Breast Cancer Intergroup is stratifying patients for the TAILORx trial by the Oncotype DX™ assay demonstrates that this assay is widely accepted and validated in the study population. Treatment will be based on the results of the assay. Patients will be stratified as follows: Patients with a Recurrence Score below 11 will receive hormonal therapy alone. Patients with a Recurrence Score between 11 and 25 will be randomized to either hormonal therapy alone or hormonal therapy + chemotherapy. This is the primary study group. This corresponds approximately to a risk of recurrence at 10 years of 10%-20%. "(at upper bound of 95% CI) Patients with a Recurrence Score greater than 25 will receive chemotherapy + hormonal therapy. Since this trial has a dealer’s choice–type design, individual investigators can select the type of hormonal therapy and chemotherapy from a list included in the protocol. The groups in this trial do not correspond to the low-, intermediate-, and high-risk cutoffs found on the Oncotype DX™ report. The treatment groups for the TAILORx trial were selected for different purposes from those involved with the selection of cutoffs for the validation trial and the Oncotype DX report.  The treatment groups for the TAILORx study were selected to correspond with specific levels of risk of recurrence and likelihood of chemotherapy benefit.  The TAILORx investigators felt it would not be ethical to withhold chemotherapy from women who have a 20% risk of recurrence Primary study group To determine whether adjuvant hormonal therapy is not inferior to adjuvant chemohormonal for patients in the “primary study group”

Oncologist treatment recommendations after RS: N- ER+ patients

Others Genetic Element of Interest Chin L, Gray JW. Nature 2008

Why proteomic? Cellular signaling events are driven by protein-protein interactions, post-translational protein modifications and enzymatic activities that cannot be predicted accurately or described by transcriptional profiling methods alone.

Proteomic analysis of human breast cancer J Proteome Res, 2008 Unsupervised hierarchical clustering of breast cancer surgical specimens. Patient samples are oriented on the vertical axis and the signaling end points tested are oriented on the horizontal axis. Clustering of patients into subgroups broadly based on EGFRfamily signaling, AKT/mTOR pathway activation, c-kit/abl growth factor signaling, and ERK pathway activation is apparent. EGFR family signaling, AKT/mTOR pathway activation, c-kit/abl growth factor signaling and ERK pathway

Tumour as a system = organoid Tumour stem cells Tumour cell subpopulations Microenvironment Immune cells