# Improved prediction of MAPKi response duration in melanoma patients using genomic data and machine learning

Sarah Dandou, Kriti Amin, Véronique D'hondt, Jérôme Solassol, Olivier Dereure, Peter Coopman, Ovidiu Radulescu, Holger Fröhlich, Romain Larive

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Sarah Dandou, Kriti Amin, Véronique D'hondt, Jérôme Solassol, Olivier Dereure, et al.. Improved prediction of MAPKi response duration in melanoma patients using genomic data and machine learning. npj Precision Oncology, 2025, 9, pp.231. 10.1038/s41698-025-00814-y. hal-05391985

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https://doi.org/10.1038/s41698-025-00814-y

# Improved prediction of MAPKi response duration in melanoma patients using genomic data and machine learning

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Sarah Dandou<sup>1,2,9</sup>, Kriti Amin<sup>3,4,9</sup>, Véronique D'Hondt<sup>1,5</sup>, Jérôme Solassol<sup>1,6</sup>, Olivier Dereure<sup>7</sup>, Peter J. Coopman<sup>1,8</sup>, Ovidiu Radulescu<sup>2,8,10</sup> ☒, Holger Fröhlich<sup>3,4,10</sup> ☒ & Romain M. Larive<sup>1,10</sup> ☒

Baseline genomic data have not demonstrated significant value for predicting the response duration to MAPK inhibitors (MAPKi) in patients with advanced BRAF<sup>V600</sup>-mutated melanoma. We used machine learning algorithms and pre-processed genomic data to test whether they could contain useful information to improve the progression-free survival (PFS) prediction. This exploratory analysis compared the predictive performance of a dataset that contained clinical features alone and supplemented with baseline genomic data. In the evaluation set (two cohorts, *n* = 111), the cross-validated model performance improved when pre-processed genomic data, such as mutation rates, were added to the clinical features. In the validation dataset (two cohorts, *n* = 73), the best model with genomic data outperformed the best model with clinical features alone. Finally, our best model outperformed with baseline genomic data, increasing the number of patients with a correctly predicted relapse by between +12% and +28%. In our models, baseline genomic data improved the prediction of response duration and could be incorporated into the development of predictive models of MAPKi treatment in melanoma.

MAPK inhibitors (MAPKi) and immune checkpoint inhibitors (ICI) have totally revolutionized the treatment of patients with advanced melanoma<sup>1-3</sup> However, their efficacy is hampered by primary or secondary tumor resistance. Patients with melanoma harboring BRAF<sup>V600</sup> mutations show a good overall response rate to MAPKi at treatment start, while ICI have a more durable response. Therefore, the choice between these therapeutic options requires a robust prediction of the response and its duration.

Baseline genomic data represent a reliable and easily accessible source of information to identify predictive biomarkers. However, for the prediction of MAPKi response and its duration, previous studies failed to show any predictive value of baseline genomic data<sup>4,5</sup> with the exception of *TERT* promoter mutations<sup>6,7</sup> and *PTEN* loss-of-function alterations<sup>8</sup>. Conversely, tumor mutation burden is an U.S. Food and Drug Administration-approved biomarker of the response duration to ICI<sup>9</sup>. Therefore, research on biomarkers of MAPKi treatment efficacy has progressively moved from

baseline tumor genomic data to melanoma cell identity<sup>10</sup>, tumor micro-environment immune components<sup>11</sup> and blood proteins<sup>12</sup>.

Machine learning models have already shown their value in prognosis and diagnosis<sup>13</sup>. Recently, machine learning approaches have been developed to identify predictive biomarkers of ICI treatment efficacy<sup>12,14-16</sup>, and also to predict early-stage melanoma recurrence<sup>17,18</sup> and sentinel lymph node status<sup>19</sup>.

In this study, we employed several machine learning models to assess the value of baseline genomic data to predict MAPKi response duration. To investigate a relevant number of patients, we collected in our database (MelanoDB) four independent cohorts of patients with melanoma treated with MAPKi and with pre-treatment whole or partial tumor exome sequencing data<sup>20</sup>. Our results showed a significant improvement in the accuracy of progression-free survival (PFS) prediction when pre-processed mutation data were added to the clinical characteristics of the two cohorts used for the training/evaluation step. We validated this observation using

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e-mail: ovidiu.radulescu@umontpellier.fr; holger.froehlich@scai.fraunhofer.de; romain.larive@umontpellier.fr

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THE HORMEL INSTITUTE

UNIVERSITY OF MINNESOTA

<footnote n="1">¹IRCM, Université de Montpellier, ICM, INSERM, Montpellier, France. ²LPHI, Université de Montpellier, CNRS, Montpellier, France. ³Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany. ⁴Bonn-Aachen International Center. for IT (b-it), University of Bonn, Friedrich-Hirzebruch-Allee 6, 53115 Bonn, Germany. ⁵Département d'Oncologie Médicale, Institut Régional du Cancer de Montpellier (ICM), Université de Montpellier, 34090 Montpellier, France. ⁶Laboratoire de Biologie des Tumeurs Solides, Département de Pathologie et Oncobiologie, CHU Montpellier, Université de Montpellier, 34295 Montpellier, France. ⁷Department of Dermatology, University of Montpellier, Montpellier, France. ⁸CNRS-Centre National de la Recherche Scientifique, 1919 Route de Mende, F-, 34293 Montpellier, France. ⁹These authors contributed equally: Sarah Dandou, Kriti Amin.¹⁰These authors jointly supervised this work: Ovidiu Radulescu, Holger Fröhlich, Romain M. Larive.</footnote>
the other two cohorts, and confirmed the prediction improvement in two clinical setting scenarios (duration of clinical benefit and disease progression before 12 months).

# Results

## Patient characteristics and genomic data pre-processing

In this study we investigated data from four patient cohorts. The clinical features of the four patient cohorts did not show any significant imbalance (Table 1). Although the number of sequenced genes greatly varied among cohorts (from full exon sequencing to a list of 35 genes), the percentage of mutated genes in tumors before treatment was comparable among cohorts (Table 1). Only in the Catalanotti et al. cohort, the mean percentage of mutated genes per patient was higher, although the number of sequenced genes was intermediate compared with the other cohorts.

Melanoma is among the tumors with the highest mutational burden²¹, making it difficult to distinguish between disease-driver and neutral mutations. To extract features that might be informative for predicting PFS, we pre-processed the initial mutational data using three strategies and obtained eight different genomic datasets (Fig. 1 and Supplementary Table 1). The first strategy was to select genes with mutations that had a functional consequence (*Fathmm*, *Cscape* and *Cscape*<sup>High</sup> datasets). The second strategy was to select genes involved in melanoma signaling pathways (*KEGG-Mel* and *VCELLS-Mel* datasets). The third strategy was to consider only the mutation rate in generic (*MR*<sup>KEGG</sup> dataset) or melanoma (*MR*<sup>KEGG-Mel</sup> and *MR*<sup>VCELLS-Mel</sup> datasets) signaling pathways. Supplementary Tables 2 to 6 recapitulate the lists of genes that make up each of the datasets.

# Evaluation of the model performance

To evaluate the value of baseline genomic data for predicting PFS, we compared the results obtained using clinical features alone and supplemented with each of the eight genomic datasets (Supplementary Table 1). To train and test our models, we chose to work with the two patient cohorts with the highest number of sequenced genes (Catalanotti et al.; Van Allen et al.) (Supplementary Figure 1). As the prediction was constrained by few data and cases, we embedded the best overall response in the data used for the prediction (Supplementary Table 7). We then applied a panel of statistical and machine learning algorithms adapted to survival analysis to predict progression-free survival during treatment (Fig. 1).

All models using the *Clinical-only* showed a good C-index (0.72–0.74), and the RSF and SVM algorithms displayed the best score (0.74) (Fig. 2a). We observed a similar stability for the models using the *MR*<sup>KEGG-Mel</sup> and *MR*<sup>VCELLS-Mel</sup> datasets that contained only one additional variable compared with the *Clinical-only* dataset. Conversely, other models using clinical and genomic data and containing more variables had more fluctuating scores in function of the algorithm, for example between 0.63 and 0.74-0.75 for the *MR*<sup>KEGG</sup> and *Cscape* datasets. This suggested that the addition of variables made the models more sensitive to the type of algorithm. Nevertheless, when we averaged the scores obtained with the different datasets, we observed that all algorithms displayed a C-index between 0.71 and 0.74.

Among the models using clinical features supplemented with genomic data, those using the *MR*<sup>KEGG-Mel</sup> and *MR*<sup>VCELLS-Mel</sup> datasets had a significantly higher mean C-index than models using clinical features alone (Fig. 2a). Specifically, the best dataset/algorithm pairs were *MR*<sup>VCELLS-Mel</sup>/GBS and *MR*<sup>VCELLS-Mel</sup>/SVM. Indeed, they displayed significantly higher C-index values than the *Clinical-only* /RSF or SVM pair (Fig. 2b). Additionally, the

<table><caption>**Table 1 | Summary of clinical features, treatment outcomes and genomic data collected for the study**</caption><thead><tr><th>Feature</th><th>Values</th><th>Total (%)</th><th>Van Allen et al.</th><th>Blateau et al.</th><th>Catalanotti et al.</th><th>Louveau et al.</th></tr></thead><tbody><tr><td>Total number of patients</td><td>-</td><td>184</td><td>45</td><td>53</td><td>66</td><td>20</td></tr><tr><td rowspan="2">Sex</td><td>male</td><td>101 (55%)</td><td>22 (48%)</td><td>28 (53%)</td><td>41 (62%)</td><td>10 (50%)</td></tr><tr><td>female</td><td>83 (45%)</td><td>23 (52%)</td><td>25 (47%)</td><td>25 (38%)</td><td>10 (50%)</td></tr><tr><td>Age</td><td>Median (range), years</td><td>54 (18-94)</td><td>51 (25-76)</td><td>57 (21-90)</td><td>54.5 (21-83)</td><td>51.5 (18-94)</td></tr><tr><td rowspan="5">Lactate dehydrogenase</td><td>elevated</td><td>53 (29%)</td><td>6 (13%)</td><td>23 (43.3%)</td><td>24 (36%)</td><td>-</td></tr><tr><td>normal</td><td>63 (34%)</td><td>-</td><td>25 (47%)</td><td>38 (57.5%)</td><td>-</td></tr><tr><td>missing</td><td>68 (37%)</td><td>39 (87%)</td><td>5 (9.43%)</td><td>4 (6%)</td><td>20</td></tr><tr><td>jemurafenib</td><td>83 (45%)</td><td>31 (69%)</td><td>4 (7.5%)</td><td>48 (72%)</td><td>-</td></tr><tr><td>dabrafenib + trametinib</td><td>67 (36%)</td><td>-</td><td>42 (79%)</td><td>8 (12%)</td><td>17 (85%)</td></tr><tr><td rowspan="6">Type of drug</td><td>dabrafenib</td><td>22 (12%)</td><td>14 (31%)</td><td>2 (3.7%)</td><td>6 (9%)</td><td>-</td></tr><tr><td>jemurafenib + cobimetinib</td><td>11 (6%)</td><td>-</td><td>4 (7.5%)</td><td>4 (7%)</td><td>3 (15%)</td></tr><tr><td>N.A</td><td>1 (0.5%)</td><td>-</td><td>1 (2%)</td><td>-</td><td>-</td></tr><tr><td>progressive disease</td><td>59 (32%)</td><td>7 (15%)</td><td>40 (78%)</td><td>10 (15%)</td><td>2 (10%)</td></tr><tr><td>stable disease</td><td>30 (16%)</td><td>12 (27%)</td><td>3 (6%)</td><td>13 (20%)</td><td>2 (10%)</td></tr><tr><td rowspan="3">Best Overall Response</td><td>partial response</td><td>70 (38%)</td><td>25 (56%)</td><td>5 (9%)</td><td>31 (47%)</td><td>9 (45%)</td></tr><tr><td>complete response</td><td>19 (10%)</td><td>1 (2%)</td><td>3 (6%)</td><td>8 (12%)</td><td>7 (35%)</td></tr><tr><td>N.E = not evaluated</td><td>4 (2%)</td><td>-</td><td>2 (1%)</td><td>4 (6%)</td><td>-</td></tr><tr><td>Progression-free survival—months</td><td>0.7-68 (median = 6.0)</td><td>median</td><td>median = 5.2</td><td>median = 6.1</td><td>median = 5.75</td><td>median = 9.5</td></tr><tr><td>Overall survival—months</td><td>0.7-69 (median = 13.5)</td><td>median</td><td>-</td><td>median = 10.5</td><td>median = 14.5</td><td>median = 28</td></tr><tr><td rowspan="2">Vital status</td><td>1 = dead</td><td>90 (65%)</td><td>-</td><td>37 (71%)</td><td>45 (68%)</td><td>8 (40%)</td></tr><tr><td>0 = alive</td><td>48 (34%)</td><td>-</td><td>15 (29%)</td><td>21 (31%)</td><td>12 (60%)</td></tr><tr><td>Sequencing method</td><td>Whole exome or targeted sequencing</td><td>-</td><td>WES</td><td>Targeted (35 genes)</td><td>Targeted (300 genes)</td><td>Targeted (74 genes)</td></tr><tr><td>Mutated genes (%)</td><td></td><td>5.32%</td><td></td><td>5%</td><td>18.8%</td><td>2%</td></tr></tbody></table>

Each outcome is detailed by cohort. Categorical outcomes are described with numbers and percentages, and continuous outcomes using the median of the distribution for each cohort. The number of sequenced genes and the percentage of mutated genes in each cohort are indicated.
<figure><img src="image_7.png" /><figcaption>**Fig. 1 | Overall scheme of information extraction, training and testing procedures.** Background knowledge (e.g. melanoma network and signaling pathway data) were extracted from the KEGG and VCELLS databases. FATHMM and Cscape were used to retrieve putative cancer-promoting/driver mutation. These background knowledge was then used to define features based one each patient's genomic data. Five survival analysis models were trained using the generated genomic datasets after data pre-processing and tested through a 5-fold nested-cross validation process. For model evaluation, survival analysis metrics (i.e. Uno's C-index, dynamic AUC and integrated Brier score) were used to assess the model performance. Finally, the Wilcoxon signed-rank test was used to identify the best performing algorithm/data set pair.</figcaption></figure>

models using the MR<sup>VCELLS-Mel</sup> dataset had a significantly lower integrated Brier score (i.e. better calibration) than models using clinical features alone (Supplementary Figure 2). They also performed better than models using clinical features alone at all time points, as indicated by the time-dependent AUC values (Supplementary Figure 3). This means that the addition of the mutation rate of genes implicated in melanoma signaling pathways improved the prediction obtained with clinical features alone. This effect was less important when the lists of mutated genes were added.

## Validation of the predictive value of baseline genomic data

To validate the predictive value of our models, we chose to test them using data from the two patient cohorts not used for training and testing (Blateau et al.; Louveau et al.). Genomic data from these two cohorts were generated by targeted sequencing of a limited number of cancer-related genes, carried out routinely for diagnostic purposes at these two hospital centers. To take into account the bias due to the lower number of genes sequenced in these two cohorts, we only used the genomic datasets with the mutation rate. These were also the ones that produced the best scores in the testing step.

The C-index score of the MR<sup>KEGG</sup>/GBS pair was higher than that of the *Clinical-only* /SVM pair (0.78 vs 0.76) (Fig. 3). This result validated the improved prediction of PFS by adding baseline genomic data.

## Assessment of the improved prediction in clinical context

To assess the improved prediction of our models in a clinical context, we built two scenarios and tested them by censoring data in line with each scenario. In the first “duration of clinical benefit” scenario, patients would start MAPKi treatment, and those with disease progression would stop treatment in the first few months. In this scenario, the question was to predict the duration of clinical benefit for the remaining patients. For this scenario, we censored patients with progressive disease as best overall response. In the second scenario “progression before 12 months”, the question was to predict before treatment initiation, which patients would progress during treatment before 12 months. For this scenario, we censored progression-free survival data at 12 months.

To test our models, we chose to work with the two patient cohorts that had the highest number of sequenced genes (Catalanotti et al. and Van Allen
<figure><img src="image_8.png" /><figcaption>**Fig. 2 | Prediction of the model performance using the concordance index (C-index).**
a Heatmap showing the performance of each dataset/algorithm pair evaluated using the Uno's C-index. The median C-index was calculated during the testing procedure and is represented as a color gradient according to the distribution of the obtained values (0.63–0.76). The median performance values for each dataset with all algorithms and for each algorithm using all datasets are shown in the last column and last row, respectively. The Wilcoxon signed-rank test was used for pair</figcaption></figure>

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comparison; \*p < 0.05 (versus the *Clinical-only* dataset). b Boxplot showing the C-index values for the best dataset/algorithm pairs: ($MR^{VCELLS-Mel}$/GBS and SVM) compared with the best performing *Clinical-only* dataset/algorithm (RSF and SVM). Boxplots are built from C-index scores obtained during the testing procedure. The bar inside the box represents the median values of the obtained C-index, the extremities of the box correspond to the 1st and 3rd quartiles of the values and bars represent the min and max values of the datasets.

<figure><img src="image_10.png" /><figcaption>**Fig. 3 | Prediction performance in the two validation cohorts using the C-index.**
a Heatmap representation showing the performance of each dataset/algorithm pair using the Uno's C-index. Algorithms were trained using the training and testing cohorts (Catalanotti et al.; Van Allen et al.) and tested using data from the other two cohorts (Blateau et al.; Louveau et al.). The median C-index values are represented as a color gradient according to their distribution (0.65–0.78). The median performance values for each dataset using all algorithms and for each algorithm using all</figcaption></figure>

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datasets are shown in the last column and last row, respectively. b Boxplot showing the C-index values for the best dataset/algorithm pairs: *Clinical-only* /SVM and *MR<sup>KEGG</sup>/GBS*. \*p-value < 0.05. Boxplots are built from C-index scores obtained during the validation procedure (5-fold cross validation repeated over 10 times). The bar inside the box represents the median values of obtained C-index, the extremities of the box the 1st and 3rd quartiles of values and bars represents the min and max values of the datasets.

et al.) and with the genomic datasets that included the mutation rate. Overall, the C-index values were lower than those obtained during the evaluation and validation steps, certainly due to data censoring (Fig. 4). Nevertheless, we still obtained a trend of improved prediction when we added the baseline genomic data. For the “duration of clinical benefit” scenario, the best score was obtained by the $MR^{KEGG}$/SVM pair (C-index = 0.7) compared with the *Clinical-only* /SVM pair (C-index = 0.69) (Fig. 4a). For the “progression before 12 months” scenario, the median C-index values of the $MR^{VCELLS-Mel}$/RSF pair and *Clinical-only* /RSF pair were 0.64 and 0.61, respectively (Fig. 4b).

Finally, we assessed the percentage of additional patients with adequate categorization as MAPKi resistant using baseline genomic data. To determine which dataset/algorithm pair outperformed in this context, we translated the risk score (the output of our models) into the probability of disease progression. We then compiled a plot showing on the

x-axis the relapse probability threshold and on the y-axis the obtained prediction accuracy (Fig. 4c). We compared the results obtained with the *Clinical-only* dataset and the $MR^{VCELLS-Mel}$ dataset (the best genomic dataset for the training/testing cohorts) using all the algorithms except SVM which does not allow to evaluate survival predictions. The $MR^{VCELLS-Mel}$/GBS pair clearly outperformed the use of clinical data only. We then compared the number of patients correctly categorized with or without the genomic data using the GBS algorithm (Fig. 4d). With a threshold of 0.7 the increase in the number of correctly categorized patients with genomic data is +28% at 5 months and +12% at 6 months. With a threshold of 0.8 the increase in the number of correctly categorized patients with genomic data is +16% at 5 months and +18% at 6 months. These results reveal the value of baseline genomic data for predicting the response duration to MAPKi in clinical setting scenarios and justify the use of this approach for clinical decision making.
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<figure><img src="image_13.png" /><figcaption>(b)</figcaption></figure>

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Patients with correctly predicted relapse without or with genomic data (indexes *without* - *with* [% increase]))

<table><caption>(d)</caption><thead><tr><th rowspan="2">Relapse probability</th><th>5 months</th><th>6 months</th></tr></thead><tbody><tr><td>0.7</td><td>0.6-0.77 [+28 %]</td><td>0.65-0.73 [+12 %]</td></tr><tr><td>0.8</td><td>0.57-0.66 [+16 %]</td><td>0.6-0.71 [+18 %]</td></tr></tbody></table>

<caption>**Fig. 4 | Prediction performance in clinical scenarios.** Heatmap showing the performance of each dataset/algorithm pair evaluated using the Uno's C-index.</caption>

a "Duration of clinical benefit" scenario and (b) "Progression before 12 month" scenario. The median C-index was calculated during the testing procedure and is represented as a color gradient according to the distribution of the obtained values. The median values for each dataset using all algorithms and for each algorithm using all datasets are shown in the last column and last row, respectively. c Boxplot showing the evolution of prediction accuracy at 5 and 6 month (the median PFS) vs.

relapse probability threshold with the *Clinical-only* and the *MR<sup>VCELLS-Mel</sup>* datasets. The bar inside the box represents the median, the extremities of the box the 1st and 3rd quartiles of values and bars represents the min and max values. The Wilcoxon signed-rank test was used to evaluate the outperformance between one designed pair vs. the others; \*p < 0.05. d Comparison of the number of patients correctly categorized (relapse or not) at 5 and 6 months using the GBS algorithm with the *Clinical-only* and the *MR<sup>VCELLS-Mel</sup>* datasets.

## Discussion

In this study, we tested whether baseline genomic data could improve the prediction of the response duration to MAPKi in patients with advanced BRAF<sup>V600E</sup>-mutated melanoma. To the best of our knowledge, machine learning approaches had never been used for this purpose. We used several algorithms in combination with different strategies for pre-processing tumor mutation data. Our results show that baseline (before treatment) tumor mutation data contain information that can be used to better predict progression-free survival.

This observation was not obvious because progression-free survival is influenced by primary and also acquired resistance. Although primary resistance is mainly linked to the initial tumor cell status and their micro-environment, to date there is still no biomarker to identify patients with BRAF<sup>V600E</sup>-mutated melanoma who will not respond to MAPKi. Acquired resistance involves the adaptation of tumor cells through genetic or non-genetic mechanisms<sup>22,23</sup>. A single-cell study showed that tumor heterogeneity and melanoma cell plasticity influence tumor behavior and evolution under MAPKi pressure<sup>24</sup>. Therefore, it would seem counter-intuitive to detect information relevant for predicting the response duration using pre-treatment bulk genomic sequencing data. The mechanistic relationships between the genetic context before treatment and the tumor response to treatment, including plasticity phenomena, are subjects for future research.

We acknowledge the constraints and limitations inherent in our study. First of all, our sample size was small, and we used data from different cohorts from different centers. Indeed, we grouped together patients treated with a BRAF inhibitor and patients treated with a combination of BRAF and MEK inhibitors. Despite this diversity, we found that baseline genomic data improved prediction of PFS. If the existing data were more easily accessible, we could work with more homogeneous patient cohorts. This would allow us to determine whether this observation is specific to the molecule administered, and maybe have allowed to construct a superior model. Second, the majority of sequencing data were only partial, and therefore, we probably missed gene alterations relevant to prediction of PFS. However, our results demonstrated that partial sequencing, which is routinely used in clinical practice, already provides potentially valuable information for predicting PFS. In many cases, primary and acquired resistance are mediated by additional mutations in the MAPK and related pathways<sup>25,26</sup>. Thus it was not surprising that reducing the analysis to the melanoma pathways KEGG-Mel and VCELLS-Mel improved prediction of PFS. A future analysis using whole exome sequencing data could allow determining the minimal and optimal gene set for prediction.

Our results demonstrate the predictive potential of baseline genomic data, and the value of integrating them in the development of predictive models for the response duration to MAPKi in the context of advanced
melanoma. Predictive models should also integrate clinical parameters associated with response duration, such as the number of metastatic sites, brain metastases or serum LDH and *TERT* promoter mutations⁷. Better exploiting the sequencing data already available at cancer centers will open new avenues for therapeutic decision-making. Currently, biomarkers are lacking to robustly predict the efficacy of therapy targeting the MAPK pathway in advanced melanoma. Therefore, in the clinic, a trial-and-error approach is often used. Baseline genomic mutation profiles represent a comparably stable biological readout that is easily accessible and measurable in clinical routine. Therefore, they might represent candidate predictive biomarker signatures. However, previous studies could not show a clear predictive signal for the MAPKi treatment efficacy in patients with BRAF<sup>V600</sup>- mutated melanoma. Here, our exploratory machine learning-based analysis highlighted an improved prediction of progression-free survival when clinical and genomic data were combined, even when using only partial exome sequencing data. This suggests that baseline genomic data could be incorporated in the development of predictive models of the MAPKi treatment efficacy in advanced melanoma by leveraging the results of current routine partial exome sequencing.

# Methods

## Patient data and tumor specimens

We collected the data of four cohorts of patients with BRAF<sup>V600E/K</sup>-mutated melanoma treated with MAPKi: (1) Blateau et al. (vemurafenib, vemurafenib and cobimetinib, dabrafenib, dabrafenib and trametinib; n = 53)⁵; (2) Catalanotti et al. (vemurafenib, vemurafenib and cobimetinib, dabrafenib, dabrafenib and trametinib; n = 66)⁵; (3) Louveau et al. (vemurafenib and cobimetinib, dabrafenib and trametinib; n = 20)⁵; (4) Van Allen et al. (vemurafenib, dabrafenib; n = 45)²⁷. Pretreatment tumors were from patients with stage IV or unresectable stage III BRAFV600E/K melanoma. 86% of patients received MAPKi as first-line therapy (Supplementary Table 8). Clinical responses to therapy were determined using RECIST criteria²⁸. The use of data from these patient cohorts has been approved by the ethics committee of the University of Montpellier, France (favorable advice UM 2022-009bis). As we did not generate new data for this study, no additional ethics approval was required. For each patient, tumor tissues were collected, stored as formalin-fixed, paraffin- embedded (FFPE), or frozen samples, and processed. Somatic single-nucleotide variants and small insertions or deletions were considered as mutations. For the Van Allen et al. and Catalanotti et al. cohorts, sequencing was performed on all exons of the sequenced genes. For the Blateau et al. and Louveau et al. cohorts, sequencing was performed on some exons of the sequenced genes, with partial overlap between both cohorts⁶,²⁹. In the training/test set (Van Allen et al. and Catalanotti et al. cohorts) on the one hand, and the validation set (Blateau et al. and Louveau et al.) on the other hand, only common genes were taken into account to maximize overlap and avoid missing values. Table 1 summarizes the patient demographic/clinical features, outcomes, and DNA sequencing methods/results. Data have been made available at the following link: https://www.ircm.fr/crcm_en/melanodb.html.

## Genomic data pre-processing

We used FATHMM³⁰, a software that predicts the functional consequences of coding and non-coding variants detected by genome sequencing. We used a binary classification to categorize genes based on the FATHMM results. If a somatic mutation detected in a gene was predicted to be a cancer-promoting/driver mutation by FATHMM, the reported value was 1, otherwise 0. We then selected the genes mutated in at least 5% of the patients (*Fathmm* dataset). Alternatively, we used *Cscape*³¹, that predicts the oncogenic status (i.e. disease-driver or neutral) of somatic point mutations in the coding and non-coding regions of the cancer genome. As done with FATHMM, we employed the obtained information to define a feature set con- sisting of binary indicators and selected the genes mutated in at least 5% of the patients (*Cscape* dataset). We also defined variants for which only high confidence predictions by *Cscape* were considered (*Cscape*High dataset).

To select genes involved in melanoma signaling pathways, we used the curated melanoma pathway from the KEGG database³²⁻³⁴ and the curated melanoma pathway model from the Virtual Melanoma Cell (VCELLS) project³⁵. We only considered the 70 proteins of the KEGG melanoma pathway (KEGG-Mel dataset), and the 119 proteins of the VCELLS melanoma pathway (VCELLS-Mel dataset), both represented by HGNC gene symbols.

As an alternative to the previously described binary indicator approach, we constructed features by statistical overrepresentation analysis (hyper-geometric test) of mutated genes in all KEGG pathways (MR<sup>KEGG</sup> dataset) and in the KEGG melanoma (MR<sup>KEGG-Mel</sup> dataset) and VCELLS melanoma (MR<sup>VCELLS-Mel</sup> dataset) pathways³⁶. We performed this analysis for each patient separately, and obtained one *p*-value per patient and per pathway. Because *p*-values span over several decades, we used the negative logarithm of this *p*-value as a feature value.

## Survival analysis algorithms

Survival analysis algorithms make predictions of the survival time until an event of interest occurs, i.e. melanoma cancer progression, and estimate the survival probability at the estimated survival time. Ensemble models are combination of simple individuals models that together create a powerful new models.

Random survival forest (RSF) is an ensemble method for survival analysis that uses survival trees as its base model. It is an aggregation of multiple decision trees, each decision tree uses subsets of a dataset. At each branch of the tree, the algorithm randomly selects a subset of features to make a prediction. The trees keep splitting the data based on these random features selection, asking “yes” or “no” questions, and are train to find the best splits in the data until it reaches good predictions. Once the forest is built, a prediction combines the output of multiple decision trees. RSF is based on the bagging method in which random samples are repeatedly drawn from the input data to reduce the base model variance and avoid overfitting. However, an extra level of randomness is added to RSF while growing the trees. This random subset of features is used for splitting a node instead of using all features. This reduces the correlation among trees in the forest and improves the prediction performance. Unlike the decision trees used in popular Random Forest classifiers (described by Breiman), RSF employs survival trees and averages the ensemble cumulative hazard function of each tree for prediction³⁷. Each node of the tree is split using the random subset of features to maximize the dissimilarity between child-nodes. The non-parametric Nelson-Aalen estimator is used to estimate the ensemble cumulative hazard function by taking the average of all cumulative hazard functions of each survival tree³⁸⁻⁴⁰.

Extra survival trees (EST), also known as extremely random survival forest, is a meta-estimator developed as an extension of the RSF method. This method fits several randomized survival trees, called extra-trees, and like RSF, uses averaging to improve the predictive accuracy and control overfitting. The extra level of randomness in extra survival trees comes from how splits are computed, which is different from the method used in RSF. Instead of taking the most discriminating thresholds at each node, thresholds for each feature are drawn randomly, and the best of these randomly generated thresholds is used for splitting the node. This approach tends to improve the model generalization by reducing the overall model bias and variance. The log-rank statistic is used as a splitting criterion for extra survival trees, like in RSF. In this study, we used the scikit-survival package that includes this method⁴¹.

Gradient boosting survival model (GBS) is an ensemble learning method that combines the predictions of multiple weak base learners to create a powerful overall model. The algorithm first creates a simple model (decision tree for example) to compute predictions from a given dataset. From predictions, we can observe the residuals between initial prediction and actual target values. Those differences help to quantify how far the initial prediction was from each target points. The next model is then specifically trained to improve predictions from upon the errors from the first model. Unlike random forest, GBS combines the base learners into a weighted ensemble model, giving extra weight to weak learners for correct predictions
(refs. 39,42). This process repeats, and each new model adds a “boost” to improve the overall accuracy. In survival analysis, the loss function is derived from the Cox partial likelihood function and regression trees are used as base learners³⁸. Specifically, the linear model in the partial likelihood function of the Cox model is replaced by an additive model, and the objective of the loss function is to maximize the log partial likelihood. Gradient boosting survival models are constructed sequentially in a greedy stagewise fashion.

Penalized models introduce regularization by adding penalty terms to the model equation. The Elastic Net penalty regularization method is particularly useful in scenarios with more features than observations. It combines the strengths of lasso ($l_1$) and ridge ($l_2$) penalties, by performing feature selection like lasso and by shrinking correlated features together as done in ridge regression⁴³. The Cox proportional hazard (Cox PH) model estimates the effect of various features on the hazard function that describe the instantaneous risk of an event at a specific time t. In this case, Cox's PH model and Elastic net penalty are combined to analyse survival data. The Elastic Net (EN-COX) acts as a regularizer by adding a penalties term to the Cox PH model's objective function. The penalties prevent coefficients from becoming too large, essentially by shrinking them towards zero. In the Cox regression model, regularization is added to the log partial likelihood, resulting in the following equation:

$$ \beta = \underset{\beta}{\operatorname{argmax}} [l(\beta) - \lambda P_a(\beta)] \quad (1) $$

where $l(\beta)$ is the log partial likelihood, $\lambda \ge 0$ the regularization parameter and $P_a(\beta)$ a penalty form. In the elastic net penalized Cox regression model, the penalty term is expressed as:

$$ \lambda P_a(\beta) = \lambda \left( \alpha \sum_{i=1}^{p} |\beta_i| + \frac{1}{2} (1 - \alpha) \sum_{i=1}^{p} \beta_i^2 \right) \quad (2) $$

where $p$ is the number of variables and $\alpha \in (0, 1]$ is the additional parameter that controls the amount of shrinkage that comes from the $l_1$ and $l_2$ penalties. Setting $\alpha = 1$ corresponds to the lasso penalty.

Survival support vector machines (SVM) are supervised learning models initially used to maximize the margin between classes through features values and to find the separating hyperplane that minimizes misclassification between classes. In survival analysis, there is no separation between classes. Instead, the objective is to separate individuals based on their expected survival times. In our case SVMs are employed to learn a ranking based on all possible pairs of patients rather than a binary classification. More specifically, the SVM is trained to separate patients with shorter survival a lower rank from those with longer survival⁴⁴. The observations on the margin of the decision hyperplane are known as support vectors. SVM can manage non-linear relationships between features and survival data using the kernel trick. Non-linear kernel functions map the input features into high-dimensional feature spaces where a linear ranking function functioned can be estimated. Survival analysis in with SVM can be tackled in two different ways: (a) by ranking samples according to their survival times, and (b) by using a regression approach in order to find a function that estimates the observed survival times as continuous outcomes.

## Data pre-processing

To train the survival models, the input feature sets underwent various data pre-processing steps that included handling missing values, scaling numerical variables, and encoding categorical variables using one-hot encoding. The only missing values are for the clinical features LDH (40%), Best Overall Response (2%) and Type of drug (1%). For missing value imputation, we used Iterative Imputer with the Random Forest classifier as the imputation model. This strategy models each feature with missing values as a function of other features and uses the function to estimate values for imputation. As the features with missing values were categorical in nature, we opted for a Random Forest classifier. We fitted the imputer to the

training dataset only and then applied it to transform the test dataset. Imputation was carried out in the outer loop of the nested cross-validation procedure, and a random seed was used to reproduce the same imputed values for all feature sets across repetitive trials. This helped to ensure the feature set comparability. The imputation was only performed on the training data and not on the test set. Hence, imputation performance did not impact the reported prediction performance.

As the clinical features included predominantly categorical variables, we used one-hot encoding to transform them into numerical variables. We scaled continuous variables to a range of [0, 1] using max-min normalization to ensure that the same scales were used for all input variables.

We employed a nested, stratified 5-fold cross-validation with an inner 3-fold cross-validation loop to tune hyperparameters, train and evaluate over the outer cross-validation loop the time-to-event models. The dataset was split into five outer folds, while balancing samples from different sources. In each outer fold, the pre-processed training set was further split into three inner folds for training and tuning hyper-parameters. We selected the model and hyperparameters with the highest concordance index across the inner folds to evaluate the outer test dataset. We repeated this procedure with ten random seeds for all survival models with all feature sets for reproducibility.

## Evaluation of the model prediction performance

The concordance index was used as a metric to evaluate our models' performance. The Concordance Index (C-index) proposed by Uno et al.⁴⁵ is a widely used performance metric to compare survival models applied to right-censored data. The C-index value is defined by the proportion of concordant predictions and outcomes of all comparable pairs. It defines the number of correctly ordered pairs by models. The algorithm provided by scikit-survival doesn't directly output a survival time as a prediction but rather a risk score. This risk score is a numerical value that indicates the likelihood of an event occurring sooner rather than later. The calculation of the risk score is specific to each used model.

For ensemble models such as the Random Survival Forest, the Gradient Boosting or the Extra survival trees, models build multiple decision trees and combine their predictions. The risk score is typically an average of the predictions from the individual trees or a weighted sum of the predictions of each decision tree in the case of Gradient Boosting.

For the Elastic-Net Regularized Cox model, the risk score is derived from the Cox model's hazard ratio. It estimates the hazard ratio, which is the relative risk of an event occurring at a specific time point compared to a reference individual.

For the survival Support Vector Machine, the model learns hyperplanes that separate the observations based on their survival times. For predictions of new observations, the model calculates their distance from the decision boundary and this distance is used as a risk score.

Samples with a higher estimated risk score have a shorter actual survival time. Two samples are considered a comparable pair if they both experienced an event at two different times or if the one with a shorter observed survival time experienced an event, in which case the event-free subject “outlived” the other. Two samples are not comparable if they experienced events at the same time.

Other estimators have also been used to assess temporal performance. When predicting survival, the patients disease status is not fixed and can change over time. Consequently, performance measures, such as specificity and sensitivity, become time-dependent measures. We considered an estimator of the cumulative/dynamic area under the ROC curve (AUC) at specific time points to evaluate our models³²,⁴⁶. The function considers comparable pairs of instances [i.e. one instance experienced an event before time t ($t_i \le t$) and the other at time $t_i > t$, and calculates the AUC at discrete time points]. The time-dependent AUC for any specific survival time t can be calculated as follows:

$$ AUC(t) = P(\hat{t}_i < \hat{t}_j | t_i < t, t_j > t) = \frac{1}{\text{num}(t)} \sum_{i:t_i < t} \sum_{j:t_j > t} I(\hat{t}_i < \hat{t}_j) \quad (3) $$
where $t \in T$ is the set of all possible survival times, $t_i$ and $\dot{t}_i$ represent the observed time and the predicted value respectively, $num(t)$ is the number of comparable pairs at time $t$ and $I(.)$ is the indicator function$^{39}$. Then, we can then determine how well a model can distinguish subjects who fail (i.e. disease progression) before given time ($t_i \le t$) from subjects who fail after this time $t_i > t$.

In survival analysis, the time-dependent Brier score (BS) is also a crucial metric for assessing the model calibration over time because it takes into account dynamic changes in survival probabilities. It quantifies the concordance between predicted and observed outcomes at various time points, offering a comprehensive evaluation of model's calibration$^{47}$. We consider that for each patient, at $t=0$, we have information on a vector $\mathbf{X}$ of patient specific covariates. This is used to predict a time-to-event variable $T$. Let us suppose that the complete data ($T_i, \mathbf{X}_i, i=1, \dots, n$ is available for $n$ patients. We also consider that $T$ is censored and we observe $T_i = \min(T_i, C_i)$, $\delta_i = I(T_i \le C_i)$, where $C_i$ are the (hypothetical) times under observation. Under the common assumption of random censorship ($T$ and $\mathbf{X}$ are independent on $C$) the empirical Brier score is defined

as:

$$BS(t) = \frac{1}{n} \sum_{i=1}^{n} \left\{ I(\tilde{T}_i \le t, \delta_i = 1) \frac{(0 - \tilde{\pi}(t|\mathbf{X}_i))^2}{\hat{G}(\tilde{T}_i)} + I(\tilde{T}_i > t) \frac{(1 - \tilde{\pi}(t|\mathbf{X}_i))^2}{\hat{G}(t)} \right\} \quad (4)$$

where $\pi^*(t|\mathbf{X})$ is the predicted probability of remaining event-free up to time point $t$ given the feature vector $\mathbf{X}$ and $G(t)$ is the probability of censoring weight, estimated by the Kaplan-Meier estimator.

Additionally, the integrated Brier score provides a concise measure of calibration across the entire time period:

$$IBS = \frac{1}{t_{\max}} \int_{0}^{t_{\max}} BS(t)dt \quad (5)$$

We used the Wilcoxon signed-rank test to compare the performance of the survival models. This non-parametric test compares paired differences in the C-index, derived from the same sample, using a significance level of 0.05. We ensured reproducibility by using a consistent random state, resulting in the same training and test datasets in identical fold conditions for each model. Consequently, we could pair the C-indexes of two models. We then compared the performance of the top-performing models and feature sets. We also used a one-sided test, at 5% significance, to evaluate whether the median of the better-performing model was higher, effectively testing whether the highest-ranked model performance was significantly better. Finally, this test was also used to compare paired computed accuracies at 5 and 6 month with Clinical-only and MR$^{VCELLS-Mel}$ datasets. The test was performed on all combined relapse probability thresholds between 0.5 and 0.9, using a significance level of 0.05.

## Data availability

Data supporting the conclusions of this study are available as follows. The training/test data set is available on the website cBioPortal for Cancer Genomics$^{48-50}$. The validation dataset is available on request from the corresponding author R.M.L (romain.larive@umontpellier.fr). All datasets were formatted in the MelanoDB database, a collection of advanced melanoma patients treated with MAPK inhibitors$^{20}$. We provide a web application (https://www.ircmf.crcm_en/melanodb.html) to explore the integrated data and its distribution among the collected studies, and we share this dataset with the scientific community according to FAIR principles.

## Code availability

The open source code has been made available for the computational biology community: MelanomaMLProject.

Received: 15 January 2024; Accepted: 17 January 2025;

Published online: 09 July 2025

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## Acknowledgements

This work has been supported by the INCa-Cancéropôle GSO (Programme Emergence Intelligence Artificielle N°2022-IA20 and Programme Emergence N°2018-E1), the Fondation ARC pour la recherche sur le cancer (Projets ARC 2022 PJA2 N°ARCPJA2022060005107), and the Ligue nationale contre le cancer (Comité du Gard JPB/GA/MV/39-2019). SD is supported by the Agence nationale pour la recherche (Intelligence Artificielle en Santé et Environnement – AXIAUM N°ANR-20-THIA-0005-01) and the University of Montpellier/Ecole Doctorale CBS2.

## Author contributions

Concept and Design, Formal analysis, Methodology and Supervision: O.R., H.F. and R.M.L. Validation: V.H., J.S., O.D., P.J.C., O.R., H.F. and R.M.L. Data curation: S.D. and R.M.L. Funding acquisition: OR. and R.M.L.

Investigation: S.D., K.A. and R.M.L. Project administration: R.M.L. Software: S.D., K.A. and H.F. Writing – original draft: S.D., K.A. and R.M.L. Writing – review and editing: S.D., K.A., V.H., J.S., O.D., P.J.C., O.R., H.F. and R.M.L. Lead contact for this article is Romain M. Larive.

## Funding

Open Access funding enabled and organized by Projekt DEAL.

## Competing interests

The authors declare no competing interests.

## Additional information

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41698-025-00814-y.

**Correspondence** and requests for materials should be addressed to Ovidiu Radulescu, Holger Fröhlich or Romain M. Larive.

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