1. 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.

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

3. 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

4. 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:

5. 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

6. 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.

7. 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

8. 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

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

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

11. 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: , 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.

13. 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
( )
0.42
( )
0.84
( )
Adjuvant!
222 80
0.87
( )
0.29
( )
0.82
( )
( )
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.

14. 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

15. 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.

16. 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, (2002).
Use clinical risk
Use genomic risk
High risk
Low risk
High risk
Low risk
Chemotherapy
No chemotherapy

17. 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 =
x HER2 Group Score
x ER Group Score
x Proliferation Group Score
x Invasion Group Score
x CD68
x GSTM1
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:

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

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

20. RS predicts chemotherapy benefit

21. 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”

22. Oncologist treatment recommendations after RS: N- ER+ patients

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

24. 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.

25. 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

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