Enhancing survival prognostication in. integrating pathologic, clinical and genetic predictors of metastasis

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1 The Royal Liverpool and Broadgreen University Hospitals NHS Trust NHS Enhancing survival prognostication in patients with choroidal melanoma by integrating pathologic, clinical and genetic predictors of metastasis Azzam F.G. Taktak 1, Antonio Eleuteri 1, Sarah E. Coupland 2 and Bertil E. Damato 3 1 Dept. Medical Physics and Clinical Engineering, Royal Liverpool University Hospital 2 Dept Molecular and Translational Cancer Medicine, University of Liverpool 3 Liverpool Ocular Oncology Service, Royal Liverpool University Hospital

2 Background More than 90% of intraocular melanomas involve the choroid About 50% cause fatal metastatic disease Univariateanalysis analysis provides only approximate survival estimates, relevant to large groups of patients Our aim was to create a prognostic model that combined pathological, clinical and genetic data We used imputation techniques to compensate for missing information.

3 Subjects Patients were selected from the database of the Liverpool Ocular Oncology Centre for the time period Patients from mainland Britain were enrolled National Health Service (NHS) Cancer Registry, which automatically informed us ofdate andcause of death resulting in complete follow up on these patients. Inclusion criteria diagnosed with uveal melanoma, clinically or histopathologically primarily treated by the last author (BD) or an associate resident in mainland Britain. Exclusion criteria bilateral melanoma missing data regarding basal tumour dimension or anterior tumour extension iris or ciliary body tumour not involving choroid residence overseas, including Northern Ireland.

4 Ethical Issues This study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice Guidelines Consent for the use of tissues and data for research was obtained from all patients Institutional Review Board/Ethics committee Institutional Review Board/Ethics committee approval was obtained

5 Input Variables for the Model Variable name Age Sex LUD UH Ora EO Epi Loops Mitosis Chr3 Description Age at treatment in years 0: Female 1: Male Largest tumour diameter from ultrasound in mm Largest tumour height from ultrasound in mm Anterior margin: 0: Post ora 1: Pre ora Extra ocular extension 0: No 1: Yes Tumour cell type 0: Spindle 1: Epithelioid/mixed Presence of extra vascular closed loop matrices 0: No 1: Yes Mitotic count per 40 high power fields 0: 0 1 1: 2 3 2: 4 7 3: >7 Chromosome 3 loss 0: No 1: Yes

6 Cohort The cohort comprised d3653 patients t Histologic data were available for 1778 tumours, of which, all contained the variable Epi. Of these, 1502 (84.5%) also contained either Loops, Mitosis or both Chr3 variable was available in 738 subjects of which 712 (96.5%) alsocontained the variable Epi A total of 1235 (34.1%) patients had died from all causes (33.8%) The median follow up time is 5.6 years (range, ) A total t of 2013 (55.1%) patients t had hdsurvival ltime of more than 5 years and 1013 (27.7%) more than 10 years

7 Univariate Analysis Clinical

8 Univariate Analysis Laboratory

9 Missing Data Two versions of the model dlwere created: one with clinical factors only and the other including pathological l and genetic dt data If Epi was present but no other laboratory data was available, survival was predicted for both values of Chr3. Mitosis and Loops data were imputed using alternating conditional i expectation. The confidence intervals were adjusted to take account of any error introduced

10 The Model Evaluation of the Grambsch Therneau residuals showed dthat t the null hypothesis of the proportionality of hazards was rejected (p<0.001) We used an Accelerated dfil Failure Time model dlas follows: log( T) = f( X) + σε Where ε is a logistic random variable with scale Continuous variables (Age and LUD) were modeled by 5 knots restricted cubic splines to take into account interaction with time k+ 1 k + k = 2 gx ( ) = b+ bx+ b ( x t) The knots were chosen based on the quantiles of the variable 0.05, 0.275, 0.5, 0.725, 0.95

11 Modelling Survival Time

12 Model Extraction Bootstrap re sampling was used. The entire dataset was repeatedly and randomly split into training and test datasets 200 times. A Bayesianregularization method was appliedby applying quadratic penalty term corresponding to a Gaussian prior distribution on the model parameters Such simplification was achieved by forcing the parameters to have as small a value as possible.

13 Model Validation Discrimination is the ability of the model to rank the outcomes as a function of the prognostic factors Calibration describes the precision of the predictions compared with actuarial outcome for different risk groups Discrimination Calibration Model C index 95% C.I. KS P Value Clinical 0.75 ( ) Laboratory 0.79 ( )

14 Model Calibration Observed mo ortality Observed mo ortality Predicted mortality Predicted mortality Clinical Model Laboratory Model

15 Model Calibration 1 1 l probability Survival Survival probability Time [Years] Time [Years] Clinical Model Laboratory Model

16 Discussion The model can stratify patients into various risk groups based on a combination i of clinical, i l histological i l and genetic information i The likelihood of metastatic death was estimated by comparing the patient s survival curve with that of the matched general population p thus eliminating the need to rely on certified cause of death. Patients with a good prognosis are advised that systemic screening for metastases is unlikely to be helpful. Patients with a poor prognosis undergo six monthly screening with liver function tests. Several have undergone partial hepatectomy or been entered into trials of chemotherapy.

17 Future Work We hope to be able to enter our patients into randomised trials of systemic adjuvant therapy, using our models to exclude patients without sufficient risk of metastasis. Before the model can be applied to data from other centres, external validation needs to be carried out Once we have a sufficient numbers of events, we hope to retrain our models to include more genetic data such as gains in chromosome 6p and 8q which have been shown to have strong correlation with poor survival

18 Thank You!