Targeted prognostic analysis permits to assess the prognostic impact of one to ten genes on a selected population.
You have 3 criteria to choose.
Criterion 1: gene(s) to be tested
Fill the textbox with actualised* gene symbol(s) or
Affymetrix probeset ID(s).
For more than two genes, identifiers must be separated by ";" (e.g.: SERPINE1;
No more than 10 genes are allowed.
Choose the characteristics (nodal and oestrogen receptor status)
of the cohorts to be explored. Mixed status includes patients with any status.
These datasets are retrieved from
Criterion 3: endpoint
Choose the kind of event used for survival analyses.
- "metastatic relapse": first pejorative event represented by distant relapse.
- "any event": first pejorative event represented by any relapse or death
Once the 3 criteria have been chosen, click on "Submit".
Validation of analysis plan.
After submission, a validation page shows detailed information about:
- tested gene(s)
- patients from original studies tested,
1 complete data before filtering;
2 results of filtering process,
- 3 number of genes found;
4 patients and 5 events finally analysed
(if no missing genomic data).
You can then choose to validate or cancel your submission according
to these intermediate descriptive data summarized at the bottom of the page.
"Start analysis" will launch
with criteria shown in the validation table and direct you to targeted analysis results page.
"Cancel" will redirect you back to previous screen, and offer you to choose new criteria on the form.
Results are presented in a table summarizing univariate Cox scores
(p-values, hazard ratios) for each cohort fulfilling the chosen criteria and all of
these cohorts pooled together, along with a forest plot and Kaplan-Meier survival curves.
Multivariate Cox scores (adjusted on NPI/AOL/Proliferation score) are also displayed.
Detailed results are provided for each tested gene
For each gene (i.e. AURKA), detailed results are shown according to each study in which the gene is found and for the pool.
For each cohort, gene's significance for survival is determined in subcohorts of
patients fulfilling the selected criteria. p-value is checked off in green when a
significant link exists (p < 0.05), in orange when there is a trend (p = 0.05-0.10)
or in red when there is no link (p > 0.10).
Moreover the column "Good prognosis' RNA level" shows how the expression level of the gene considered should be in case of good prognosis:
an arrow pointing up indicates that patients with good prognosis have a higher expression level than patients with a bad prognosis;
on the contrary, an arrow pointing down indicates that patients with good prognosis have a lower expression level than patients with bad prognosis.
First, look at p-value: significance is reached when
p-value is below 0.05 and the smallest it is, the best is the prognostic informativity of the gene.
If p-value < 0.05, you then look at the hazard ratio (HR) and its 95% confidence interval (CI) :
HR is calculated from raw gene's values.
So its value corresponds to the factor by which event-risk is multiplied
when gene raw value increases by 1; a small range of its 95% CI means a good precision.
if HR > 1, gene is pejorative and patients
with gene's high values have generally worse prognosis than patients
with gene's low values; the furthest from 1 is the lower bound of the
95% confidence interval, the strongest is the prognostic value of this pejorative gene.
if HR < 1, gene is protective and patients
with gene's high values have generally better prognosis than patients
with gene's low values; the furthest from 1 is the higher bound,
the strongest is the prognostic value of this protective gene.
Number of patients and especially number
of events used to calculate hazard ratios must also be carefully considered.
When the number of events is 10 or over, robustness of the result is considered strong.
In addition, supplementary results are displayed in two figures and two tables.
displays the same results than the table with a graphical approach.
curves permit to visualize results in a different way. Pool's patients are split into two groups
according to gene’s median, and both survival curves are traced. The green curve represents the 50%
patients whose gene's value is low for a pejorative gene or high for a protective gene. On the contrary,
the red dashed curve represents the 50% patients whose gene's value is high for a pejorative gene or
low for a protective gene.
Relative risk of event (HR 2/1) (patients with high values / patients with low values) is displayed
with its significance and confidence interval. At the bottom of the graph, number of subjects at risk
are displayed along time of follow-up. In order to minimize unreliability at the end of the curve,
the 15% of patients with the longest follow-up are not plotted.
Patients with AURKA > pool's median have a risk of metastatic relapse almost x 2 relative to patients
with AURKA < pool's median. This HR-value (based on "relative risk of event occurrence" between
the group of patients with AURKA over median value in the selected population and the group with
AURKA under median value in the selected population) is slightly different from the one displayed
in the table or the forest-plot (based on averaged evolution of the risk when AURKA increases by 1 unit).
Nota bene: the value of gene median taken as a cutoff to dichotomise gene expression values and
perform Kaplan-Meier curves on the pool is an arbitrary value and may not be - and in most case is not -
the best cutoff for the specific gene. Hence, a gene that is significant when considering continue values
might not remain significant after dichotomisation.
permit to determine if the studied gene bares independent prognostic information over classical
prognostic indexes such as NPI or Adjuvant OnLine and over proliferation score in the pool of cohorts with
NPI, AOL and proliferation score available data. Gene's adjusted HRs are calculated by multivariate Cox.
In this example, it appears that AURKA does not bring additional prognostic information to NPI or Adjuvant! but
brings slight additional prognostic information to proliferation score.
Significant results may be considered robust if individual hazard ratios are concordant for a majority of the cohorts,
and Cox tests results for pool are concordant with those results.
Significant results that remain significant in adjusted analyses
would give a supplementary evidence of the gene prognostic interest.