<table><caption>**Table 1** Demographic and clinical characteristics of the studied population</caption><thead><tr><th></th><th>Control<br>(n=43)</th><th>Presymptomatic<br>(n=45)</th><th>Patient<br>(n=22)</th><th>χ<sup>2</sup><br>P value</th><th>Kruskal-Wallis P value</th><th>Comparison</th><th>Dunn's test P value</th></tr></thead><tbody><tr><td>Female gender</td><td>23 (53.5%)</td><td>28 (62.2%)</td><td>10 (45.4%)</td><td>0.408</td><td></td><td></td><td></td></tr><tr><td>Age at inclusion (years)</td><td>46.4±13.5</td><td>41.8±11.8</td><td>62.7±10.5</td><td>&lt;0.001</td><td></td><td>Control vs presymptomatic<br>Control vs patient<br>Presymptomatic vs patient</td><td>0.118<br>&lt;0.001<br>&lt;0.001</td></tr><tr><td>ALS-FRS</td><td>39.5±1.3</td><td>39.5±1.9</td><td>33.4±7.7</td><td>&lt;0.001</td><td></td><td>Control vs presymptomatic<br>Control vs patient<br>Presymptomatic vs patient</td><td>0.827<br>&lt;0.001<br>&lt;0.001</td></tr><tr><td>MMSE</td><td>29±1.2</td><td>28.5±1.4</td><td>17.8±8.4</td><td>&lt;0.001</td><td></td><td>Control vs presymptomatic<br>Control vs patient<br>Presymptomatic vs patient</td><td>0.183<br>&lt;0.001<br>&lt;0.001</td></tr><tr><td>MDRS</td><td>142.1±1.8</td><td>141.2±3.0</td><td>97.3±36.7</td><td>&lt;0.001</td><td></td><td>Control vs presymptomatic<br>Control vs patient<br>Presymptomatic vs patient</td><td>0.431<br>&lt;0.001<br>&lt;0.001</td></tr><tr><td>FAB</td><td>17±1.2</td><td>17.2±0.9</td><td>9.7±5.3</td><td>&lt;0.001</td><td></td><td>Control vs presymptomatic<br>Control vs patient<br>Presymptomatic vs patient</td><td>0.583<br>&lt;0.001<br>&lt;0.001</td></tr><tr><td>Ekman faces test</td><td>30.1±2.6</td><td>30.1±2.3</td><td>18±9.1</td><td>0.001</td><td></td><td>Control vs presymptomatic<br>Control vs patient<br>Presymptomatic vs patient</td><td>0.694<br>&lt;0.001<br>0.001</td></tr><tr><td>FBI</td><td>0.9±1.8</td><td>1.5±2.7</td><td>28.5±15.2</td><td>&lt;0.001</td><td></td><td>Control vs presymptomatic<br>Control vs patient<br>Presymptomatic vs patient</td><td>0.387<br>&lt;0.001<br>&lt;0.001</td></tr><tr><td>AES</td><td>4.8±3.9</td><td>6.5±3.6</td><td>23.5±13.1</td><td>&lt;0.001</td><td></td><td>Control vs presymptomatic<br>Control vs patient<br>Presymptomatic vs patient</td><td>0.095<br>&lt;0.001<br>0.004</td></tr></tbody></table>

Values are expressed as mean±SD, or as number (%). Demographic characteristics were compared between groups using the χ² test for gender and Kruskal-Wallis with Dunn's test for numerical variables.

Statistically significant p values are in bold.

AES, Apathy Evaluation Scale; ALS-FRS, ALS Functional Rating Scale; FAB, Frontal Assessment Battery; FBI, Frontal Behavioral Inventory; MDRS, Mattis Dementia Rating Scale; MMSE, mini-mental state examination.

polyA tails. The resulting sequences were aligned with Bowtie²⁴ and sorted by genomic location with Samtools sort.²⁵ PCR bias was corrected with UMI-tools, its efficacy was assessed per chromosome with Samtools idxstats. After controlling for the overlap/ambiguity between miRNAs enrichment and Gencode annotation with FeatureCounts,²⁶ miRNAs were counted with miRDeep2.²⁷

## Statistical analysis

Statistical analyses were performed using R V.3.6.1 (R Foundation for Statistical Computing, Vienna, Austria). The differential expression of miRNAs between clinical groups was assessed with the R package EdgeR.²⁸ The analysis began with a count matrix with 2576 rows (one per miRNA *i*) and 110 columns (one per individual *j*). Only miRNAs considered above noise level (minimum count of 50 reads for at least one sample and a minimum total count of 1000) were retained for statistical analyses, reducing the count matrix to 589 rows. We assumed that miRNA counts followed a negative binomial distribution with mean $\mu_{ij}$ and dispersion $\phi_i$ and used generalised linear models to fit a log-linear model

$$log_2\mu_{ij} = x_i^T\beta_i$$

for each miRNA, where $x_i$ is the vector of covariates that describes sample *j* and $\beta_i$ is the vector of coefficients to be fitted for miRNA *i*. To control for possible batch, centre, age and gender effects, we added these variables as covariates in the model, in addition of clinical status. Raw counts were normalised using a trimmed mean of M-values.²⁹ Once the models were fitted, quasi-likelihood F-test was employed to determine the subset of miRNAs differentially expressed between clinical conditions (miRNA signature). Statistical significance was set at level $\alpha$ = 0.05 and p values were adjusted for multiple testing using the Benjamini-Hochberg method.

## Machine learning for binary classification

After the differentially expressed miRNAs were identified, we implemented logistic regression classifiers with L2 regularisation in Python 3.8.0 using scikit-learn³⁰ V.0.22.1. We used the expression levels of the miRNA signature as features to train binary classification models for each pairwise comparison between clinical status: controls versus presymptomatic individuals, controls versus patients and presymptomatic individuals versus patients. A stratified nested cross-validation strategy (online supplemental figure A1) was chosen to find the optimal hyperparameter (L2 regularisation coefficient) and to assess model performance using
the area under the receiver operating characteristic curve (ROC AUC). We computed 90% CIs for the ROC AUC scores from 2000 bootstrap samples, by taking the 5th and 95th percentiles of the bootstrap distribution. Stratification with respect to clinical status was performed to preserve the proportion of healthy controls, presymptomatic subjects and patients in each fold.

## Generalisation analysis

Since the differentially expressed miRNAs were computed with the entire data set, the test folds of the cross-validation were also used in the feature selection for our classification models, which could inflate prediction performance. To estimate this possible bias, we then incorporated feature selection in the nested 5-fold cross-validation process: differentially expressed miRNAs were computed using only the outer cross-validation loop training data (four out of five folds) at each iteration. The nested cross-validation was repeated 100 times with different fold splits to assess the generalisation performance of our classifiers.

## Analysis of the transitional stage to clinical FTD/ALS disease

Since we hypothesised that the expression levels of differentially expressed miRNAs might provide information relevant to C9orf72 disease progression, we designed an experiment to evaluate prediction performance of clinical conversion to FTD/ALS in presymptomatic carriers. A logistic regression classifier was fitted with the expression levels of differentially expressed miRNAs from controls and patients. We used a regular 5-fold cross-validation to determine the optimal hyperparameter (L2 regularisation coefficient). Subsequently, this model was tested with the expression levels from the four known presymptomatic carriers who were in their transitional stage to the clinical disease. Scores from 0 to 1 were provided for each subject, indicating proximity with the expression levels of controls (scores near 0) or patients (scores closer to 1).

## Target prediction and pathway analysis

A target-gene based miRNA enrichment analysis was performed, to discover potential biological functions regulated by the differentially expressed miRNAs. We used the publicly available tool DIANA-miRPath V.3,³¹ which implements an in silico miRNA target prediction algorithm (DIANA-microT-CDS) as well as an experimentally validated miRNA:gene interaction dataset (DIANA-TarBase V.7.0). Both approaches were carried out to identify target genes and the associated Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, using the set of differentially expressed miRNAs as input. The enrichment analysis method consisted of Fisher's exact test (hypergeometric distribution) with Benjamini-Hochberg adjusted p value threshold of 0.05, giving as output a union set of associated KEGG pathways.

## RESULTS

### Differentially expressed miRNAs computed with the entire data set

Table 2 displays all miRNAs identified as differentially expressed, for each pairwise comparison between clinical status, after correction for multiple comparisons. Four miRNAs were computed as differentially expressed between healthy controls and patients: miR-34a-5p and miR-345-5p were overexpressed, while miR-200c-3p and miR-10a-3p were underexpressed in symptomatic mutation carriers. Interestingly, miR-34a-5p was identified as significantly overexpressed also in presymptomatic mutation carriers compared with healthy controls, suggesting that miR-34a-5p expression is associated with C9orf72 mutation

<table><caption>**Table 2** Differentially expressed miRNAs identified by EdgeR, after correction for multiple comparisons, for each pairwise comparison between clinical status: Control (n=43), presymptomatic (n=45) and patient (n=22)</caption><thead><tr><th>miRNA</th><th>Log-fold change</th><th>P value</th><th>Adjusted p value</th></tr></thead><tbody><tr><td colspan="4"><strong>Control vs presymptomatic</strong></td></tr><tr><td>miR-34a-5p</td><td>-1.433</td><td>5.251e-16</td><td>3.093e-13</td></tr><tr><td colspan="4"><strong>Control vs patient</strong></td></tr><tr><td>miR-34a-5p</td><td>-1.239</td><td>1.650e-8</td><td>9.720e-6</td></tr><tr><td>miR-345-5p</td><td>-0.540</td><td>1.131e-5</td><td>3.330e-3</td></tr><tr><td>miR-200c-3p</td><td>0.333</td><td>3.109e-5</td><td>6.104e-3</td></tr><tr><td>miR-10a-3p</td><td>0.697</td><td>7.141e-5</td><td>1.051e-2</td></tr><tr><td colspan="4"><strong>Presymptomatic vs patient</strong></td></tr><tr><td>miR-345-5p</td><td>-0.528</td><td>3.610e-5</td><td>2.126e-2</td></tr></tbody></table>

miRNA, microRNA.

status. Additionally, miR-345-5p was also significantly overexpressed in patients when compared with presymptomatic carriers. When removing the four prodromal subjects from the presymptomatic group, the same miRNAs were identified as differentially expressed, indicating that the differences between the presymptomatic and other groups were not mainly driven by the four prodromal subjects.

We considered these four miRNAs (miR-34a-5p, miR-345-5p, miR-200c-3p and miR-10a-3p) as our miRNA signature for further analyses. The complete output from EdgeR is available in online supplemental table A2.

Figure 1 displays boxplots with the expression levels, for each clinical group, of the four miRNAs identified as differentially expressed. There is a clear difference in miR-34a-5p expression levels between controls and C9orf72 expansion carriers (presymptomatic and symptomatic). Moreover, the other three identified miRNAs differentiate the mutation carriers at different stages of the pathology: miR-345-5p showed increased expression in patients, while miR-200c-3p and miR-10a-3p exhibited decreased expression. An expression heatmap of the miRNA signature is displayed in online supplemental figure A2.

### MiRNA signature to classify between clinical groups

To assess whether the identified miRNA signature could distinguish between clinical groups, we implemented logistic regression models using as features the expression levels of the four differentially expressed miRNAs (miR-34a-5p, miR-345-5p, miR-200c-3p and miR-10a-3p). The ROC AUC for the classification of healthy controls and presymptomatic mutation carriers was 0.90 (90% CI 0.83 to 0.95), for controls and patients was 0.90 (90% CI 0.82 to 0.97) and to distinguish presymptomatic carriers and patients was 0.80 (90% CI 0.67 to 0.90) (figure 2). The distributions of the bootstrapped ROC AUC scores are displayed in online supplemental figure A3.

### Generalisation analysis

Since we used the entire data set to identify the miRNA signature, including test data, classification performance could be inflated. In order to assess the generality of our classification scores, we then incorporated feature selection in the nested cross-validation scheme (online supplemental figure A1), by using only the training data from the outer cross-validation loop to compute differentially expressed miRNAs. Figure 3 shows the distribution of miRNAs identified as differentially expressed after performing nested 5-fold cross-validation with 100 different
<figure><img src="image_42.png" /><figcaption>**Figure 1** Boxplots depicting the normalised log2 expression levels of the four microRNAs identified as differentially expressed. Box boundaries represent the first and third quartiles and the median is indicated by the line dividing the IQR. The upper whiskers extend to the values that are within 1.5×IQR over the third quartiles. The lower whiskers extend to the values that are within 1.5×IQR under the first quartiles.</figcaption></figure>

fold splits. Notably, the most frequent miRNAs (highlighted in blue) correspond to the ones computed using the entire data set: miR-34a-5p (500 occurrences) when comparing healthy controls and presymptomatic mutation carriers; miR-34a-5p, miR-345-5p, miR-200c-3p and miR-10a-3p (respectively 497, 335, 259 and 196 occurrences) for controls and patients; miR-345-5p (157 occurrences) when analysing presymptomatic subjects and patients.

Regarding prediction performance, the average ROC AUC when classifying controls versus presymptomatic subjects was 0.88 (90% CI 0.83 to 0.91), for controls versus patients was 0.89 (90% CI 0.83 to 0.94) and for presymptomatic individuals versus patients was 0.67 (90% CI 0.52 to 0.77). The distributions of the ROC AUC scores computed with 100 different fold splits are displayed in online supplemental figure A4.

<figure><img src="image_43.png" /><figcaption>**Figure 2** ROC (receiver operating characteristic) curves for each pairwise classification (control vs presymptomatic, control vs patient and presymptomatic vs patient) obtained with logistic regression using as features the expression levels of the microRNAs signature (miR-34a-5p, miR-345-5p, miR-200c-3p and miR-10a-3p). Bootstrapped 90% CIs are reported in brackets. AUC, area under the ROC curve.</figcaption></figure>

## Analysis of the transitional stage to clinical FTD/ALS disease

We evaluated the performance to predict the transitional stage to FTD/ALS disease by training a logistic regression classifier with the expression levels from patients and controls and testing with the expression levels of presymptomatic individuals. The probability scores computed for the four subjects in their transitional stage were all above 0.50, indicating a stronger similarity with patients: 0.54, 0.75, 0.80 and 0.82. The distribution of probability scores for all presymptomatic subjects is displayed in online supplemental figure A5.

<figure><img src="image_44.png" /><figcaption>**Figure 3** Number of times each miRNA was found differentially expressed, when performing a repeated 5-fold nested cross-validation for 100 times with different fold splits. In each step of the outer cross-validation loop, four of the five folds were used to identify differentially expressed miRNAs. Since one outer loop consists of five steps, and we performed 100 repetitions, 500 sets of miRNAs were computed for each pairwise comparison between groups, respectively: control vs presymptomatic, control vs patient and presymptomatic vs patient. MiRNAs from the signature computed with the entire data set are highlighted. miRNA, microRNA.</figcaption></figure>
<table><caption>**Table 3** Results from pathway analysis using the four differentially expressed microRNAs as input</caption><thead><tr><th>Category</th><th>KEGG pathway</th><th>P value microT-CDS</th><th>P value TarBase</th></tr></thead><tbody><tr><td rowspan="5">Cancer</td><td>Proteoglycans in cancer</td><td>7.941e-4</td><td>4.259e-8</td></tr><tr><td>MicroRNAs in cancer</td><td>1.386e-3</td><td>3.356e-8</td></tr><tr><td>Glioma</td><td>6.554e-2</td><td>1.423e-2</td></tr><tr><td>Renal cell carcinoma</td><td>1.098e-2</td><td>9.254e-2</td></tr><tr><td>Small cell lung cancer</td><td>3.220e-1</td><td>3.341e-2</td></tr><tr><td rowspan="5">Cell signalling/apoptosis</td><td>Hippo signalling pathway</td><td>4.556e-2</td><td>5.622e-4</td></tr><tr><td>TGF-beta signalling pathway</td><td>5.008e-2</td><td>9.288e-4</td></tr><tr><td>Thyroid hormone signalling pathway</td><td>2.132e-3</td><td>1.502e-2</td></tr><tr><td>FoxO signalling pathway</td><td>2.368e-1</td><td>1.449e-2</td></tr><tr><td>Neurotrophin signalling pathway</td><td>9.801e-3</td><td>3.113e-1</td></tr><tr><td rowspan="2">Intermediary metabolism</td><td>Lysine degradation</td><td>1.606e-2</td><td>7.882e-4</td></tr><tr><td>Glycosphingolipid biosynthesis - lacto and neolacto series</td><td>3.885e-10</td><td>4.423e-2</td></tr><tr><td>Meiosis</td><td>Oocyte meiosis</td><td>2.487e-1</td><td>2.446e-3</td></tr></tbody></table>

Only significant pathways for at least one approach are shown. Statistically significant p values are in bold.

KEGG, Kyoto Encyclopedia of Genes and Genomes.

## Target prediction and pathway analysis

Using the four differentially expressed miRNAs (miR-34a-5p, miR-345-5p, miR-200c-3p and miR-10a-3p) as input, we performed target prediction and pathway analysis with two methods available in DIANA-miRPath V.3. The in silico miRNA target prediction algorithm (microT-CDS) identified 31 influenced pathways (14 significant after Benjamini-Hochberg correction), while the experimentally supported approach (TarBase) resulted in 54 associated pathways (38 significant after Benjamini-Hochberg correction). Complete outputs concerning the list of the putative target genes and their related pathways are given in online supplemental tables A3 and A4. Table 3 reports the 13 pathways that were identified by both methods and have significant adjusted p values in at least one of them.

Online supplemental figure A6 shows miRNA versus KEGG pathways heatmaps, which depict the level of enrichment in significant KEGG pathways for the four differentially expressed miRNAs as computed by the two approaches.

## DISCUSSION

The present study aimed to identify fluid biomarkers by analysing expression levels of plasma miRNAs without a priori knowledge in a large cohort of healthy controls, presymptomatic and symptomatic C9orf72 carriers. We identified four miRNAs differentially expressed between clinical conditions: miR-34a-5p, miR-345-5p, miR-200c-3p and miR-10a-3p. Significantly higher expression of miR-34a-5p was found in mutation carriers when compared with healthy controls, which suggests that miR-34a-5p expression is deregulated in cases with C9orf72 mutation. Additionally, we observed miR-345-5p expression to be significantly increased in patients when compared with presymptomatic carriers, which supports the correlation of miR-345-5p expression with the progression of C9orf72-associated disease. Finally, our results also suggest that miR-200c-3p and miR-10a-3p underexpression might be associated with full-blown disease as decreased expression levels were significant only between patients and healthy controls.

We used the expression levels of the miRNA signature to train logistic regression classifiers, which were able to differentiate individuals from different clinical groups with good predictive performance (figure 2). Notably, presymptomatic and symptomatic C9orf72 carriers were distinguished with ROC AUC of 0.80 (90% CI 0.67 to 0.90), which suggests the suitability of plasma

miRNAs for following preclinical progression and determining disease onset. We believe that this score was lower in our generalisation analysis (0.67, 90% CI 0.52 to 0.77) because the limited number of patients (22) led to a higher variability in the differentially expressed miRNAs in each step of the cross-validation loop (figure 3). Furthermore, we have obtained promising results regarding prediction performance of conversion from the presymptomatic to the clinical stage of FTD/ALS. The four presymptomatic subjects in transitional stage exhibited scores above 0.50, denoting a stronger similarity with the expression levels of patients. Although preliminary, these results suggest that the expression levels of our miRNA signature might be used as early predictors of the C9orf72 disease conversion.

Previous studies have shown the potential of miRNAs in serum, plasma or CSF as diagnostic biomarkers for FTD and ALS,⁹¹⁷⁻²⁰ focussing on comparing healthy controls and patients. However, our findings differ from preceding results: only two miRNAs from our signature (miR-345-5p and miR-200c-3p) were identified as differentially expressed in one of these studies,²⁰ none in the others.¹⁷⁻¹⁹ Results are conflicting probably due to restricted choices for the analysed miRNAs¹⁷¹⁸ and heterogeneous cohorts, either with sporadic forms¹⁸¹⁹ or a mixture of sporadic and familial forms with different mutations.²⁰ To the best of our knowledge, the present work is the first to compare the expression levels of plasma miRNAs between presymptomatic and symptomatic carriers focussing on C9orf72 mutation, in addition to providing a plasma miRNA signature that may contribute to the assessment of preclinical progression for C9orf72-associated FTD and ALS. Table 4 displays a comparison among studies evaluating miRNAs from blood samples (serum or plasma) of patients with FTD and/or ALS.

Overall, our work suggests that miR-34a-5p, miR-345-5p, miR-200c-3p and miR-10a-3p are likely involved in neuronal degeneration and C9orf72-associated pathogenesis. Among the KEGG pathways identified in this study, some involved in neurodevelopment (Hippo signalling and FoxO signalling), inflammation (TGF-beta signalling), intracellular transduction (neurotrophin signalling) and apoptosis (TGF-beta and FoxO signalling) were relevant as previously shown to be involved in C9orf72-disease.³²⁻³⁴ Accordingly, these four miRNAs have been previously linked with a range of neurodevelopmental processes, neuropsychiatric and neurodegenerative conditions.³⁵⁻³⁸ For instance, miR-200c and miR-34a family members are implicated
<table><caption>**Table 4** Comparison of studies investigating miRNAs from blood samples (serum or plasma) of patients with FTD and/or ALS</caption><thead><tr><th></th><th>Freischmidt et al 2014*<sup>11</sup></th><th>Sheinerman et al 2017<sup>17</sup></th><th>Piscopo et al 2018<sup>18</sup></th><th>Grasso et al 2019<sup>19</sup></th><th>Magen et al 2020<sup>20</sup></th><th>This study†</th></tr></thead><tbody><tr><td>Disease</td><td>ALS</td><td>FTD, ALS</td><td>FTD</td><td>FTD</td><td>FTD, ALS</td><td>FTD, ALS</td></tr><tr><td>Cohort</td><td>Separate sporadic/genetic‡</td><td>Not mentioned</td><td>Sporadic</td><td>Sporadic</td><td>Mixed sporadic/genetic§</td><td>C9orf72</td></tr><tr><td>Patients, n=</td><td>9/13 genetic</td><td>50 FTD</td><td>54</td><td>10/48</td><td>52/117 FTD</td><td>22</td></tr><tr><td>Discovery/replication</td><td>14 sporadic</td><td>50 ALS</td><td></td><td></td><td>115 ALS</td><td></td></tr><tr><td>Presymptomatic carriers, n=</td><td>18</td><td>—</td><td>—</td><td>—</td><td>—</td><td>45</td></tr><tr><td>Methods of analysis</td><td>Microarrays</td><td>37 selected miRNAs (qRT-PCR)</td><td>9 selected miRNAs (qRT-PCR)</td><td>752 selected miRNAs (qRT-PCR)</td><td>Large scale sequencing (RNA-seq)</td><td>Large scale sequencing (RNA-seq)</td></tr><tr><td>Major deregulated miRNAs</td><td>miR-4745-5p miR-3665<br>miR-1915-3p miR-4530<br>(validated from panel of 30 miRNAs)</td><td>miR-9/let-7e,<br>miR-7/miR-451,<br>miR335-5p/<br>let-5e (FTD)<br>miR-206/miR-338-3p,<br>miR-9/<br>miR-129-3p,<br>miR-335-5p/<br>miR-338-3p<br>(ALS)</td><td>miR-127-3p</td><td>miR-663a<br>miR-502-3p<br>miR-206</td><td>Panels of 20, 147, 121<br>miRNAs for each cohort</td><td>miR-34a-5p miR-345-5p<br>miR-200c-3p miR-10a-3p</td></tr></tbody></table>

in synaptic function, neuronal maturation, differentiation and survival.³⁹⁴⁰ Aberrant expression of miR-34a and miR-345 are also associated with neuronal apoptosis,⁴¹ whereas members of miR-10a family were found to be differentially expressed in the muscle tissue of patients with ALS.⁴²

How these four miRNAs are implicated in C9orf72-associated pathogenesis, and their relevance in brain pathology are important questions to go further. So far, only few studies addressing miRNA dysregulation in brain tissues of patients with FTD/ALS have been performed, and are summarised in online supplemental table A5. They specifically addressed GRN-associated,⁴³⁴⁴ sporadic FTD,⁴⁵⁴⁶ sporadic⁴⁷ or mixed genetic-sporadic ALS patients.⁴⁸ Notably, there was no miRNA dysregulation in common between the aforementioned studies, nor between any of those studies on the brain and ours on plasma. Those discrepancies may stem from the heterogeneity of the previous autoptic cohorts and the differences in the methods of miRNA expression analysis. Noteworthy, and differently from our investigation, none of the patient cohorts mentioned in online supplemental table A5 were exclusively made up of C9orf72 carriers. Additionally, the observed differences between brain tissue and plasma miRNA profiles may be due to the tissue-specific expression of miRNA on the one hand, and to the time-dependent variations of detectable miRNAs all along the disease course on the other. Due to the disease process itself and other potential confounding factors, significant changes in miRNA expression are likely to occur between a relatively early phase of the disease, in which plasma miRNAs may be used as biomarkers, and the ultimate disease stage, at the moment of brain sampling. At this point, further miRNA profiling studies on C9orf72 brain tissue are needed to better understand whether tissue miRNAs correlate with plasma expression profiles and their contribution to the disease pathogenesis.

Regardless, it is noteworthy that some studies pointed towards a direct relationship between these miRNAs and C9orf72 pathogenesis. C9orf72 stands as a putative target of miR-34a-5p, likely acting as a negative regulator of C9orf72 mRNA expression.⁴⁹

Additionally, miR-200c-3p and miR-345-5p are down-regulated and up-regulated, respectively, in the extracellular vesicles secreted by induced astrocytes obtained from C9orf72 patients.⁵⁰ Even if not completely explained so far, these important results parallel our study showing a comparable upregulation of miR-34a-5p and miR-345-5p and downregulation of miR-200c-3p in carriers, and provide converging evidence for a link between our set of miRNAs and C9orf72-pathogenesis, which will need further investigations.

Previous studies have provided the proof-of-concept that specific sets of miRNAs have the potential to serve as biomarkers of the preclinical/premanifest stages of other neurodegenerative diseases, such as ALS,¹¹ Huntington³⁹ and Prion diseases.⁵¹ Our study supports the usefulness of our four miRNAs as biomarkers of disease progression from the presymptomatic to the symptomatic phase of C9orf72 disease. Nevertheless, some of them may be dysregulated in a broader range of neurodegenerative conditions. For instance, miR-345 and miR-200c-3p were also dysregulated during the presymptomatic stage of Prion⁵¹ and Huntington's diseases,³⁹ respectively. This would not prevent, however, their use in longitudinal monitoring of specific genetic neurodegenerative disorders, possibly in combination with other biomarkers. Together, all these studies and ours suggest that dysregulation of such miRNAs is dynamically altered throughout neurodegenerative diseases progression, and can be detectable even long before clinical onset.

The current study has limitations. First, the significant age difference between patients and the other clinical groups may have introduced a confounding factor, which we considered by including age as a covariate. Second, the absence of validation in other tissues or of a replication cohort means that further studies in independent cohorts are required to confirm our results, even though our generalisation analysis confirmed the identified miRNA signature. Finally, the limited number of patients does not allow any conclusions about the correlation of plasma miRNAs and different disease phenotypes. Future work will explore longitudinal analyses of plasma miRNAs to assess their use as biomarkers of FTD and ALS progression.

<footnote>*in serum.</footnote>

<footnote>†in plasma.</footnote>

<footnote>‡SOD1, FUS, C9orf72, PFN1.</footnote>

<footnote>§C9orf72, MAPT, GRN, TBK1.</footnote>

<footnote>ALS, amyotrophic lateral sclerosis; FTD, frontotemporal dementia; miRNA, microRNA; qRT-PCR, quantitative real-time PCR; RNA-seq, RNA sequencing.</footnote>
In summary, the current work revealed significant differences in miRNA expression levels in plasma when comparing healthy controls, presymptomatic and symptomatic C9orf72 mutation carriers. Specifically, we highlighted the potential of miR-34a-5p, miR-345-5p, miR-200c-3p and miR-10a-3p expression levels in plasma as biomarkers of preclinical progression for C9orf72-associated FTD and ALS. Our results encourage the use of plasma miRNAs, possibly in combination with other markers, to improve the design of clinical trials for these neurodegenerative disorders.

**Author affiliations**

**Twitter** Emmanuelle Becker @EmmanuelleBeck4

**Acknowledgements** We thank Justine Guégan, from iCONICS (ICM bioinformatics facility) for microRNA raw reads to counts pipeline handling. We also thank Yannick Marie and Delphine Bouteiller, from iGenSeq (ICM sequencing facility), for library preparation and sequencing. We thank the DNA and cell bank of the ICM for the technical assistance, notably Philippe Martin-Hardy (DNA and cell bank, ICM). The study was conducted with the support of the Centre d'Investigation Clinique Neuroscience (CIC 1422), GH Pitié Salpêtrière, Paris, and the Centre pour l'Acquisition et le Traitement des Images platform.

**Collaborators** The PREV-DEMALs study group includes: Eve Benchetrit (Hôpital de la Salpêtrière, Paris), Anne Bertrand (Hôpital de la Salpêtrière, Paris), Anne Bissery (Hôpital de la Salpêtrière, Paris), Marie-Paule Boncoeur (CHU Dypuytren, Limoges), Stéphanie Bombois (CHU Roger Salengro, Lille), Agnès Camuzat (ICM, Paris), Mathieu Chastan (CHU Charles Nicolle, Rouen), Yaohua Chen (CHU Roger Salengro, Lille), Marie Chupin (ICM, Paris), Olivier Collier (ICM, Paris), Philippe Couratier (CHU Dypuytren, Limoges), Xavier Delbeuck (CHU Roger Salengro, Lille), Vincent Deramecourt (CHU Roger Salengro, Lille), Christine Delmaire (CHU Roger Salengro, Lille), Emmanuel Gerardin (CHU Charles Nicolle, Rouen), Claude Hossein-Foucher (CHU Roger Salengro, Lille), Bruno Dubois (Hôpital de la Salpêtrière, Paris), Marie-Odile Habert (Hôpital de la Salpêtrière, Paris), Didier Hannequin (CHU Charles Nicolle, Rouen), Géraldine Lautrete (CHU Dypuytren, Limoges), Thibaud Lebouvier (CHU Roger Salengro, Lille), Isabelle Le Ber (Hôpital de la Salpêtrière, Paris), Benjamin Le Toullec (ICM, Paris), Richard Levy (Hôpital de la Salpêtrière, Paris), Olivier Martinaud (CHU Charles Nicolle, Rouen), Kelly Martineau (ICM, Paris), Marie-Anne Mackowiak (CHU Roger Salengro, Lille), Jacques Monteil (CHU Dypuytren, Limoges), Florence Pasquier (CHU Roger Salengro, Lille), Gregory Petty (CHU Roger Salengro, Lille), Pierre-François Pradat (Hôpital de la Salpêtrière, Paris), Assi-Hervé Oya (Hôpital de la Salpêtrière, Paris), Armelle Rametti-Lacroux (Hôpital de la Salpêtrière, Paris), Daisy Rinaldi (Hôpital de la Salpêtrière, Paris), Adeline Rollin-Sillaire (CHU Roger Salengro, Lille), François Salachas (Hôpital de la Salpêtrière, Paris), Sabrina Sayah (Hôpital de la Salpêtrière, Paris), David Wallon (CHU Charles Nicolle, Rouen).

**Contributors** VK had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concepts and study design: VK, VA, DS, ILB, OC and EB. Acquisition, analysis or interpretation of data: All authors. Manuscript drafting or manuscript revision for important intellectual content: All authors. Approval of final version of submitted manuscript: All authors. Literature research: VK, VA, DS and ILB. Statistical analysis: VK. Obtained funding: ILB and OC. Administrative, technical or material support: ILB, OC and EB. Study supervision: ILB, OC and EB.

**Funding** The research leading to these results has received funding from the programme 'Investissements d'avenir' ANR-10IAIHU-06, from the French government under management of Agence Nationale de la Recherche as part of the 'Investissements d'avenir' programme, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute), from Agence Nationale de la Recherche/DGOS (project ANR-PRTS PREV-DEMALs, grant number ANR-14-CE15-0016-07, promotion Assistance Publique-

Hopitaux de Paris), from the Inria Project Lab Program (project Neuromarkers), from Fondation Vaincre Alzheimer FR-17035 and from the Institut Français de Bioinformatique (ANR-11-INSB-0013).

**Competing interests** OC reports having received consulting fees from AskBio (2020), having received fees for writing a lay audience short paper from Expression Santé (2019), having received speaker fees for a lay audience presentation from Palais de la découverte (2017) and that his laboratory has received grants from Qynapse (2017-present). Members from his laboratory have co-supervised a PhD thesis with myBrainTechnologies (2016-present). OC's spouse is an employee of myBrainTechnologies (2015-present). OC has submitted a patent to the International Bureau of the World Intellectual Property Organization (PCT/IB2016/0526993, Schiratti J-B, Allassonniere S, Colliot O, Durrleman S, A method for determining the temporal progression of a biological phenomenon and associated methods and devices (2016). ILB served as a member of advisory boards for Prevail Therapeutic and received research grants from ANR, DGOS, PHRC, Vaincre Alzheimer Association, ARSla Association, Fondation Plan Alzheimer outside of the present work. PG is co-founder and director of GenoSplice. NR is employee at GenoSplice.

**Patient consent for publication** Not required.

**Ethics approval** This study was approved by the Comité de Protection des Personnes CPP Ile-De-France VI (CPP 68-15 and ID RCB 2015-A00856-43).

**Provenance and peer review** Not commissioned; externally peer reviewed.

**Data availability statement** Data are available upon reasonable request ( isabelle.leber@upmc.fr).

**Supplemental material** This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

**Open access** This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc-4.0/.

**ORCID iD**

Virgilio Kmetzsch http://orcid.org/0000-0003-3691-0180

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RESEARCH ARTICLE

# MicroRNA signatures in genetic frontotemporal dementia and amyotrophic lateral sclerosis

Virgilio Kmetzsch<sup>1,2 ID</sup>, Morwena Latouche<sup>3</sup>, Dario Saracino<sup>1,4,5</sup>, Daisy Rinaldi<sup>3,4,5</sup>, Agnès Camuzat<sup>3,6</sup>, Thomas Gareau<sup>3</sup>, the French Research Network on FTD/ALS, Isabelle Le Ber<sup>3,4,5,7</sup>, Olivier Colliot<sup>1</sup> & Emmanuelle Becker<sup>2</sup>

**Correspondence**

Emmanuelle Becker, Université de Rennes 1 – Campus Beaulieu, 263 Avenue Général Leclerc, 35042 Rennes, France. Tel: 0033 2 9984 2567; Fax: +33 2 99 84 71 71; E-mail: emmanuelle.becker@univ-rennes1.fr

**Funding Information**

The research leading to these results has received funding from the program "Investissements d'avenir" ANR-10-IAIHU-06, from the French government under management of Agence Nationale de la Recherche as part of the "Investissements d'avenir" program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute), from Agence Nationale de la Recherche/ DGOS (project ANR-PRTS PREV-DEMALS, grant number ANR-14-CE15-0016-07, promotion Assistance Publique – Hôpitaux de Paris), from the Programme Hospitalier de Recherche Clinique (PHRC) Predict-PGRN (to ILB, promotion by Assistance Publique – Hôpitaux de Paris), from Fondation Plan Alzheimer (to ILB), from the Inria Project Lab Program (project Neuromarkers), from Fondation Vaincre Alzheimer FR-17035 and from the Institut Français de Bioinformatique (ANR-11-INSB-0013).

Received: 15 July 2022; Revised: 12 September 2022; Accepted: 20 September 2022

Annals of Clinical and Translational Neurology 2022; 9(11): 1778–1791

doi: 10.1002/acn3.51674

**Abstract**

**Objective:** MicroRNAs are promising biomarkers of frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS), but discrepant results between studies have so far hampered their use in clinical trials. We aim to assess all previously identified circulating microRNA signatures as potential biomarkers of genetic FTD and/or ALS, using homogeneous, independent validation cohorts of C9orf72 and GRN mutation carriers. **Methods:** 104 individuals carrying a C9orf72 or a GRN mutation, along with 31 controls, were recruited through the French research network on FTD/ALS. All subjects underwent blood sampling, from which circulating microRNAs were extracted. We measured differences in the expression levels of 65 microRNAs, selected from 15 published studies about FTD or ALS, between 31 controls, 17 C9orf72 presymptomatic subjects, and 29 C9orf72 patients. We also assessed differences in the expression levels of 30 microRNAs, selected from five studies about FTD, between 31 controls, 30 GRN presymptomatic subjects, and 28 GRN patients. **Results:** More than half (35/65) of the selected microRNAs were differentially expressed in the C9orf72 cohort, while only a small proportion (5/30) of microRNAs were differentially expressed in the GRN cohort. In multivariate analyses, only individuals in the C9orf72 cohort could be adequately classified (ROC AUC up to 0.98 for controls versus presymptomatic subjects, 0.94 for controls versus patients, and 0.77 for presymptomatic subjects versus patients) with some of the signatures. **Interpretation:** Our results suggest that previously identified microRNAs using sporadic or mixed cohorts of FTD and ALS patients could potentially serve as biomarkers of C9orf72-associated disease, but not GRN-associated disease.

<footnote n="1"><sup>1</sup>Sorbonne Université, Institut du Cerveau – Paris Brain Institute – ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013, Paris, France</footnote>

<footnote n="2"><sup>2</sup>Univ Rennes, Inria, CNRS, IRISA, F-35000, Rennes, France</footnote>

<footnote n="3"><sup>3</sup>Sorbonne Université, Institut du Cerveau – Paris Brain Institute – ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013, Paris, France</footnote>

<footnote n="4"><sup>4</sup>Centre de référence des démences rares ou précoces, IM2A, Département de Neurologie, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France</footnote>

<footnote n="5"><sup>5</sup>Département de Neurologie, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France</footnote>

<footnote n="6"><sup>6</sup>EPHE, PSL Research University, Paris, France</footnote>

<footnote n="7"><sup>7</sup>Paris Brain Institute – Institut du Cerveau – ICM, FrontLab, Paris, France</footnote>
**Introduction**

Frontotemporal dementia (FTD) is a neurodegenerative disease characterized by brain atrophy in the frontal and temporal lobes, causing severe changes in personality and social behavior.¹ The most prevalent genetic causes of FTD are GGGGCC repeat expansions in the *C9orf72* gene and mutations in the *GRN* gene.²,³ FTD shares disease pathways with amyotrophic lateral sclerosis (ALS), a debilitating motor neuron disease that causes progressive motor deficit and muscle wasting.⁴ The *C9orf72* hexanucleotide repeat expansion has been identified as the most common genetic cause of both familial FTD and ALS, as well as of their sporadic counterparts.²

There are currently no disease-modifying treatments that can stop the course of FTD or ALS. New therapeutic trials depend on robust progression biomarkers to assess treatment outcomes. The study of FTD/ALS genetic forms is particularly important, since asymptomatic mutation carriers may provide insights about the early disease stages, before any irreversible neuronal damage.⁵

Among the potential noninvasive biomarkers of neurodegenerative diseases, circulating microRNAs (miRNAs) constitute a promising approach.⁶ MiRNAs are short non-coding RNAs that negatively regulate gene expression.⁷ There is increasing evidence of a link between miRNA expression levels and the diagnosis of FTD⁸⁻¹² and ALS.¹¹⁻²² However, there are strong inconsistencies between the identified miRNA signatures in different studies. The examined cohorts are highly heterogeneous, most of them being sporadic or mixed cohorts of sporadic and genetic forms. Importantly, it is unclear which miRNAs are specific to a particular genetic mutation or might serve as biomarkers for several genetic forms. It is also uncertain whether miRNAs found in sporadic forms are differentially expressed in genetic forms. Furthermore, several of the published articles lacked an independent validation cohort, which also might have caused disparity between results. Finally, differences in lifestyle factors (e.g. diet, exercise, cognitive training) across the studied cohorts also influence the levels of blood-based biomarkers, and thus may contribute to non-reproducible results.²³,²⁴ This absence of convergence among different studies so far hinders the use of miRNAs in clinical trials.

The present work aims at testing circulating miRNA signatures identified in the literature, using two independent homogeneous cohorts of patients and presymptomatic carriers: one focused on *C9orf72* expansion carriers and another comprising *GRN* mutation carriers. For that purpose, we selected all published studies that identified specific miRNAs isolated from plasma or serum as potential biomarkers of FTD and/or ALS. With

a preregistered study design, we investigated whether (1) miRNAs revealed in cohorts of sporadic patients (or in mixed cohorts with sporadic and genetic forms) may be biomarkers in *C9orf72* and/or *GRN* genetic forms, (2) miRNAs identified in a *C9orf72* cohort are validated in an independent *C9orf72* cohort, and (3) miRNAs discovered in a *C9orf72* cohort may be relevant in a *GRN* cohort.

We hypothesize that if a miRNA is a potential biomarker in a particular genetic form, it will be differentially expressed (adjusted *p* value below 0.05) between controls and presymptomatic subjects, controls and patients, or presymptomatic subjects and patients in an independent cohort of subjects carrying that mutation. Moreover, we consider that a miRNA signature will constitute an acceptable biomarker if a logistic regression model (using these miRNAs as features) classifies subjects between clinical groups with an area under the ROC curve greater than 0.70.²⁵

**Materials and Methods**

This research was conducted according to the preregistration available in https://osf.io/4pw8f.

**Participants of the validation cohorts**

Between 2011 and 2021, 135 individuals were recruited through the French research network on FTD/ALS (Inserm RBM02-59) and investigated with the same protocol, as previously described in detail.²⁶ All participants signed written informed consents. This study was approved by the Comité de Protection des Personnes CPP Ile-De-France VI (CPP 36-09/ID RCB 2008-A01376-49 and CPP 68-15/ID RCB 2015-A00856-43).

Two cohorts were studied. One cohort was focused on *C9orf72* mutation carriers, including 29 patients (20 FTD, 6 FTD/ALS and 3 ALS) and 17 carriers in the presymptomatic phase. Another cohort was focused on *GRN* mutation carriers, comprising 28 FTD patients and 30 presymptomatic carriers. The control group, shared between the two cohorts, was made up of 31 neurologically healthy individuals that did not carry any of these mutations. Table 1 displays the demographic characteristics of the studied cohorts.

Standardized interviews with family members, full neurological examinations, quantitative motor testing, and extensive neuropsychological tests measuring all cognitive domains were used to assess each participant's cognitive and clinical conditions. All subjects underwent blood tests, and collected samples were stored in the Paris Brain Institute (ICM) DNA and cell bank.
<table><caption>**Table 1.** Demographic characteristics of the studied cohorts.</caption><thead><tr><th></th><th>C9orf72 patients</th><th>C9orf72 presymptomatic carriers</th><th>GRN patients</th><th>GRN presymptomatic carriers</th><th>Controls</th></tr></thead><tbody><tr><td>No.</td><td>29</td><td>17</td><td>28</td><td>30</td><td>31</td></tr><tr><td>Female, No. (%)</td><td>14 (48.3)</td><td>10 (58.8)</td><td>10 (35.7)</td><td>17 (56.7)</td><td>18 (58.1)</td></tr><tr><td>Age at inclusion, mean (SD)</td><td>66.2 (8.8)</td><td>51.7 (12.1)</td><td>62.9 (11.2)</td><td>42.5 (11)</td><td>47.1 (14.6)</td></tr><tr><td>Disease duration at inclusion, mean (SD)</td><td>4.9 (3.8)</td><td>—</td><td>3.2 (1.4)</td><td>—</td><td>—</td></tr><tr><td>GRN mutation (No.)</td><td>—</td><td>—</td><td>c.1157G > A (1), c.1231_1232dup (2), c.1252C > T (1), c.138 + 1G > A (1), c.1492G > T (1), c.1494_1498del (1), c.19 T > C (1), c.234_235del (1), c.25del (1), c.328C > T (2), c.380_381del (2), c.421_422del (2), c.559del (1), c.559delC (1) c.708 + 6_708 + 9del (1), c.745C > T (1), c.759_760del (1), c.768_769dup (1), c.87_90dup (1), c.900_901dup (1), c.907del (1), c.942C > A (1), Exon 1 del (2)</td><td>c.1201C > T (3), c.1231_1232dup (3), c.138 + 1G > A (1), c.1494_1498del (2), c.19 T > C (1), c.328C > T (1), c.361delG (1), c.380_381del (1), c.443_444del (1), c.675_676del (1), c.745C > T (3), c.768_769dup (2), c.813_816del (6), c.907del (2), c.988_989del (1), GRN del (1)</td><td>—</td><td>—</td></tr></tbody></table>

## Plasma preparation, miRNA sequencing and computation pipeline

Blood samples from all participants were collected on EDTA following standardized collection and handling procedures. The mean disease duration at sampling was 4.9 (SD 3.8) years in the C9orf72 patients' group and 3.2 (SD 1.4) years in the GRN patients' group. All participants were in fasted state. At the ICM DNA and cell bank, all samples were centralized and processed in conformity with the same procedure. Blood samples were centrifuged at 2500 rpm for 10 min before plasma was extracted at room temperature. At a temperature of -80°C, 1 ml aliquots were stored in polypropylene tubes.

Following the instructions provided by the manufacturer, miRNA extraction was carried out using a miRNeasy Serum/Plasma Kit (Qiagen). We used 200 µl of plasma that was progressively melted at 4°C and added directly to 1 ml of QIAzol solution. Using the QIAseq miRNA Library Kit (Qiagen) in accordance with the manufacturer's protocol, miRNAs were eluted in 14 µl of water; 5 µl were utilized to prepare the miRNA sequencing library. Targeting a minimum of 10 million mapped reads per sample, two independent batches of miRNA sequencing were performed on the Illumina NovaSeq 6000.

The quantification of miRNAs was carried out according to recommendations by Potla et al.²⁷ The quality of

reads was assessed with FastQC (Andrews S. 2010, http://www.bioinformatics.babraham.ac.uk/projects/fastqc).

Next, UMI-tools²⁸ and Cutadapt²⁹ were used to clean sequences and extract unique molecular identifiers (UMIs). Then, the resulting reads were aligned to the mature miRNA sequences from the miRBase (https://www.mirbase.org) database version 22.1, using Bowtie.³⁰ After that, the PCR duplicates were removed with UMI-tools. Finally, miRNA count tables were created with Samtools idxstats.³¹

## Selected studies

We aimed to find all papers that identified specific miRNAs extracted from human plasma or serum as potential biomarkers of FTD and/or ALS, excluding reviews and meta-analyses. We thus conducted the following search in PubMed (https://pubmed.ncbi.nlm.nih.gov/), on March 10, 2022:

(microRNA[Title] OR microRNAs[Title] OR miR[Title] OR miRNA[Title]) AND (serum[Title] OR circulating[Title] OR plasma[Title]) AND (ALS[Title] OR FTD[Title] OR amyotrophic[Title] OR frontotemporal[Title] OR (neurodegenerative[Title] AND (frontotemporal[Title/Abstract] OR amyotrophic[Title/Abstract])) NOT mice[Title/Abstract] NOT mouse[Title/Abstract] NOT extracellular vesicles[Title] NOT review[PT] NOT meta-analysis[PT] NOT (comment[PT])
<table><caption>**Table 2.** Selected studies investigating circulating microRNA expression (from serum or plasma) of patients with FTD or ALS. Columns indicate each reference, studied disease (FTD, ALS, or both), type of the analyzed cohort (sporadic, genetic, or mixed), number of patients in the discovery and replication (if available) cohorts, number of presymptomatic carriers included in the study, method of analysis (qRT-PCR, microarrays, RNA-sequencing, or a combination), and the identified miRNA signature.</caption><thead><tr><th>Article</th><th>Disease</th><th>Cohort</th><th>Patients, No. (discovery/replication)</th><th>Presymto-matic carriers, No.</th><th>Method of analysis</th><th>MiRNA signature</th></tr></thead><tbody><tr><td>Grasso et al., 2019<sup>8</sup></td><td>FTD</td><td>Sporadic</td><td>10/48 split of same cohort</td><td>–</td><td>qRT-PCR of 752 miRNAs</td><td>miR-663a, miR-502-3p, miR-206</td></tr><tr><td>Piscopo et al., 2018<sup>9</sup></td><td>FTD</td><td>Sporadic</td><td>54</td><td>–</td><td>qRT-PCR of 9 miRNAs linked with apoptosis</td><td>miR-127-3p</td></tr><tr><td>Denk et al., 2018<sup>10</sup></td><td>FTD</td><td>Sporadic</td><td>48</td><td>–</td><td>qRT-PCR of 96 miRNAs identified in preliminary study</td><td>let-7b-5p, let-7 g-5p, miR-106a-5p, miR-106b-5p, miR-18b-5p, miR-223-3p, miR-26a-5p, miR-26b-5p, miR-301a-3p, miR-30b-5p, miR-146a-5p, miR-15a-5p, miR-22-3p, miR-320a, miR-320b, miR-92a-3p, miR-1246</td></tr><tr><td>Kmetzsch et al., 2021<sup>11</sup></td><td>FTD, ALS</td><td>Genetic (C9orf72)</td><td>22</td><td>45</td><td>RNA-sequencing of 2576 miRNAs</td><td>miR-34a-5p, miR-345-5p, miR-200c-3p, miR-10a-3p</td></tr><tr><td>Sheinerman et al., 2017<sup>12</sup></td><td>FTD, ALS</td><td>Unspecified</td><td>For each disease, 25/25 split of same cohort</td><td>–</td><td>qRT-PCR of 37 brain-enriched miRNAs</td><td>miR-9/let-7e, miR-7/miR-451, miR-335-5p/let-7e (FTD) and miR-206/miR-338-3p, miR-9/miR-129-3p, miR-335-5p/miR-338-3p (ALS)</td></tr><tr><td>Magen et al., 2021<sup>13</sup></td><td>ALS</td><td>Mixed sporadic and genetic (C9orf72)</td><td>126/122 split of same cohort</td><td>–</td><td>RNA-sequencing of 125 miRNAs identified in longitudinal study</td><td>miR-181a-5p, miR-181b-5p</td></tr><tr><td>Soliman et al., 2021<sup>14</sup></td><td>ALS</td><td>Mixed sporadic and genetic (unspecified mutation)</td><td>30</td><td>–</td><td>qRT-PCR of 7 miRNAs involved in ALS</td><td>miR-206, miR-143-3p, miR-142-3p</td></tr><tr><td>Dobrowolny et al., 2021<sup>15</sup></td><td>ALS</td><td>Mixed sporadic and genetic (unspecified mutation)</td><td>13/23</td><td>–</td><td>RNA-sequencing followed by qRT-PCR</td><td>miR-151a-5p, miR-199a-5p, miR-423-3p</td></tr><tr><td>Raheja et al., 2018<sup>16</sup></td><td>ALS</td><td>Mixed sporadic and genetic (C9orf72, SOD1)</td><td>23</td><td>–</td><td>qRT-PCR of 191 miRNAs identified on prior study</td><td>miR-29b-3p, miR-320c, miR-34a-5p, miR-29c-3p, miR-320a, miR-22-3p, miR-1, miR-133a-3p, miR-191-5p, miR-144-5p, miR-320b, miR-423-3p, miR-192-5p, miR-133b, miR-194-5p, miR-7-1-3p, miR-19a-3p, miR-425-5p, miR-145-5p, miR-144-3p</td></tr><tr><td>Waller et al., 2017<sup>17</sup></td><td>ALS</td><td>Sporadic</td><td>27/23</td><td>–</td><td>qRT-PCR of 750 miRNAs</td><td>miR-206, miR-143-3p, miR-374b-5p</td></tr><tr><td>Tasca et al., 2016<sup>18</sup></td><td>ALS</td><td>Sporadic</td><td>14</td><td>–</td><td>qRT-PCR of 9 muscle-specific, miR-133a, miR-133b, miR-27a</td><td>miR-206, miR-133a, miR-133b, miR-27a</td></tr></tbody></table>

(Continued)
<table><caption>**Table 2** Continued.</caption><thead><tr><th>Article</th><th>Disease</th><th>Cohort</th><th>Patients, No. (discovery/replication)</th><th>Presymto-matic carriers, No.</th><th>Method of analysis</th><th>MiRNA signature</th></tr></thead><tbody><tr><td>Takahashi et al., 2015<sup>19</sup></td><td>ALS</td><td>Sporadic</td><td>16/48 split of same cohort</td><td>—</td><td>inflammatory, or angiogenic miRNAs<br>Microarrays, followed by qRT-PCR of 9 miRNAs</td><td>miR-4649-5p, miR-4299</td></tr><tr><td>Freischmidt et al., 2015<sup>20</sup></td><td>ALS</td><td>Sporadic</td><td>18/20</td><td>—</td><td>Microarrays of 1733 miRNAs, followed by qRT-PCR of 2 miRNAs</td><td>miR-1234-3p, miR-1825</td></tr><tr><td>Freischmidt et al., 2014<sup>21</sup></td><td>ALS</td><td>Separate sporadic and genetic (SOD1, FUS, C9orf72)</td><td>9/13 (genetic), 14 (sporadic)</td><td>18</td><td>Microarrays of 1733 miRNAs and qRT-PCR of 4 miRNAs</td><td>miR-4745-5p, miR-3665, miR-1915-3p, miR-4530</td></tr><tr><td>De Felice et al., 2014<sup>22</sup></td><td>ALS</td><td>Sporadic</td><td>10</td><td>—</td><td>qRT-PCR of 1 miRNA</td><td>miR-338-3p</td></tr></tbody></table>

This search resulted in 19 journal articles. Two papers<sup>6,32</sup> were excluded because they were review studies, one<sup>33</sup> was discarded because it was focused on protein levels, and one<sup>34</sup> was excluded because it was focused on one microRNA from serum exosomes.

Our final selection therefore contained 15 articles. These selected papers, along with the studied diseases (FTD, ALS, or both), cohort types (sporadic, genetic, or mixed), cohort sizes, methods of analyses (qRT-PCR, microarrays, RNA-sequencing, or a combination), and the identified miRNA signatures are displayed in Table 2. We note that three of these studies<sup>8–10</sup> identified three different miRNA signatures associated with FTD, 10 articles<sup>13–22</sup> pointed out 10 distinct miRNA signatures related to ALS, one investigation<sup>11</sup> revealed another miRNA signature for both FTD and ALS, and one study<sup>12</sup> found two separate signatures for FTD and ALS. In all, considering both FTD and ALS, we thus analyzed 16 miRNA signatures previously identified in the literature. Since C9orf72 expansions can cause both diseases, all miRNA signatures were tested with our C9orf72 cohort. However, since GRN mutations cause exclusively FTD, only five miRNA signatures (the ones associated with FTD) were tested with our GRN cohort.

Regarding the sizes of the identified miRNA signatures, most of the studies<sup>8,9,11,13–15,17–22</sup> pointed out four or less miRNAs, one article identified six miRNAs per disease,<sup>12</sup> and two investigations<sup>10,16</sup> proposed larger signatures containing respectively 17 or 20 miRNAs. When analyzing the intersections between the different signatures, we note that some miRNAs were identified by multiple studies, for instance miR-206,<sup>8,12,14,17,18</sup> but most miRNAs were found by a single study. When considering the union of the miRNA signatures from the selected articles, the set of

miRNAs associated with either FTD or ALS is composed of 65 miRNAs, and the set of miRNAs associated only with FTD is composed of 30 miRNAs.

As previously mentioned in the description of our computation pipeline, we used the miRNA nomenclature from the miRBase database version 22.1. However, most of the selected articles were based on previous versions of the miRBase. We thus performed the following conversions for compatibility:

* miR-320a: miR-320a-5p plus miR-320a-3p

* miR-9: miR-9-5p plus miR-9-3p

* let-7e: let-7e-5p plus let-7e-3p

* miR-1: miR-1-5p plus miR-1-3p

* miR-133-a: miR-133-a-5p plus miR-133a-3p

* miR-27a: miR-27a-5p plus miR-27a-3p

* miR-7: miR-7-5p

* miR-451: miR-451a

* miR-129-3p: miR-129-1-3p

## Differential expression

Differential expression analyses were performed using the R package EdgeR.<sup>35</sup> After microRNA extraction, sequencing and quality control steps, our dataset contained the expression levels of 2656 miRNAs (denoted by *i*, corresponding to all miRNA sequences mapped in miRBase version 22.1) for each of the 135 subjects (represented by *j*). First, we created two count matrices: one containing the miRNA counts from the C9orf72 patients, presymptomatic subjects and controls, and another containing the miRNA counts from the GRN patients, presymptomatic individuals, and controls. Second, for each count matrix, we used generalized linear models (GLM) to fit a log-
linear model to each miRNA, following a negative binomial distribution with mean $\mu_{ij}$ and dispersion $\phi_i$:

$$\log_2\mu_{ij} = x_j^T\beta_i$$

where $x_j$ denotes the covariates describing sample j and $\beta_i$ denotes the coefficients to be fitted for miRNA i. To control for possible age, sex, center and batch effects, we included them as covariates in the models, in addition to the clinical group (control, presymptomatic or patient). A trimmed mean of M-values³⁶ was used to normalize raw counts. Finally, after the log-linear models were fitted, quasi-likelihood (QL) F-tests were used to identify the differentially expressed miRNAs.

Concretely, we tested each of the 65 miRNAs associated with either FTD or ALS in the literature, to identify which were differentially expressed between (a) controls versus C9orf72 presymptomatic subjects, (b) controls versus C9orf72 patients, and (c) C9orf72 presymptomatic subjects versus C9orf72 patients. Additionally, we tested the 30 miRNAs associated with only FTD in the literature, to highlight which were differentially expressed between (d) controls versus GRN presymptomatic subjects, (e) controls versus GRN patients, and (f) GRN presymptomatic subjects versus GRN patients.

All $p$ values were 2-tailed, and the level of statistical significance was set at 0.05. The Benjamini-Hochberg³⁷ procedure was used to adjust $p$ values for multiple testing. Additionally, we considered as suggestive, but not statistically significant, miRNAs with adjusted $p$ values between 0.05 and 0.1. Our differential expression analyses resulted in two meta-signatures for each cohort: one meta-signature including miRNAs with adjusted $p$ values below 0.05 (thus including only statistically significant miRNAs), and one containing miRNAs with adjusted $p$ values below 0.1 (thus including both statistically significant and suggestive miRNAs).

## Binary classification

To test if the miRNA signatures described in the literature could discriminate between clinical groups, we trained L2-regularized logistic regression classifiers, using Python 3.8.5 with scikit-learn³⁸ 0.23.2. We first organized the miRNA expression data into six datasets, one for each relevant pairwise comparison: (a) controls versus C9orf72 presymptomatic subjects, (b) controls versus C9orf72 patients, (c) C9orf72 presymptomatic subjects versus C9orf72 patients, (d) controls versus GRN presymptomatic subjects, (e) controls versus GRN patients, and (f) GRN presymptomatic subjects versus GRN patients. A total of 18 classifiers were trained for each of the comparisons (a), (b) and (c): 16 classifiers used as features each

of the miRNA signatures identified in the literature, and two were trained with meta-signatures containing the differentially expressed miRNAs identified in the univariate analyses (a), (b) and (c), respectively with adjusted $p$ values lower than 0.05 and 0.1. In addition, seven classifiers were built for each of the comparisons (d), (e) and (f): five of them used as features each of the miRNA signatures associated with FTD in the literature, and two were trained with meta-signatures containing the differentially expressed miRNAs identified in the univariate analyses (d), (e) and (f), respectively with adjusted $p$ values lower than 0.05 and 0.1.

Each logistic regression model was trained with a stratified nested 5-fold cross-validation strategy followed by bootstrapping to compute confidence intervals (CIs), as previously detailed.¹¹ The inner cross-validation loop was used for hyperparameter (L2 regularization coefficient) search. The outer cross-validation loop was used to assess each classifier's performance by computing the area under the ROC curve. Since a k-fold cross-validation does not provide an unbiased estimator of the variance,³⁹ it cannot be used to compute CIs. Therefore, we used 2000 bootstrap samples to compute empirical 90% CIs for the ROC AUC scores, by considering the 5th and 95th percentiles of the bootstrap distribution. A miRNA signature was considered an acceptable biomarker for a given comparison if the corresponding ROC AUC was above 0.70.

## Results

### Differential expression in the C9orf72 cohort

The first analysis consisted of testing which of the 65 miRNAs identified in the literature as potential biomarkers of FTD and/or ALS were differentially expressed in our C9orf72 cohort. For this analysis, we considered the miRNA counts obtained from sequencing plasma samples from the C9orf72 patients, C9orf72 presymptomatic subjects, and controls. After negative binomial generalized linear models were fitted for each miRNA, we thus performed 65 quasi-likelihood F-tests per pairwise comparison (controls versus C9orf72 presymptomatic subjects, controls versus C9orf72 patients, and C9orf72 presymptomatic subjects versus C9orf72 patients), adjusting for multiple comparisons using the Benjamini-Hochberg³⁷ method.

All differentially expressed miRNAs identified in this analysis are displayed in Table 3 (second to fourth columns). We can see that a considerable amount of miRNAs (35 of the 65 miRNAs identified in the literature) were significantly differentially expressed (adjusted $p$
<table><caption>**Table 3.** Differentially expressed miRNAs for at least one pairwise comparison between clinical groups, considering both cohorts. The \* indicates in which comparisons each miRNA was significantly differentially expressed (adjusted *p* values below 0.05), while the (+) denotes adjusted *p* values between 0.05 and 0.1.</caption><thead><tr><th>miRNA</th><th>Controls vs. C9orf72 presymptomatic subjects</th><th>Controls vs. C9orf72 patients</th><th>C9orf72 presymptomatic subjects vs. C9orf72 patients</th><th>Controls vs. GRN presymptomatic subjects</th><th>Controls vs. GRN patients</th><th>GRN presymptomatic subjects vs. GRN patients</th></tr></thead><tbody><tr><td>miR-34a-5p</td><td>*</td><td>*</td><td></td><td></td><td></td><td></td></tr><tr><td>miR-338-3p</td><td>*</td><td></td><td>*</td><td></td><td></td><td></td></tr><tr><td>miR-142-3p</td><td>*</td><td></td><td>*</td><td></td><td></td><td></td></tr><tr><td>miR-320a</td><td>*</td><td></td><td>*</td><td></td><td></td><td></td></tr><tr><td>miR-145-5p</td><td>*</td><td></td><td>*</td><td></td><td></td><td></td></tr><tr><td>miR-92a-3p</td><td>*</td><td></td><td></td><td></td><td>(+)</td><td></td></tr><tr><td>let-7g-5p</td><td>*</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>miR-199a-5p</td><td>*</td><td></td><td>*</td><td></td><td></td><td></td></tr><tr><td>miR-206</td><td>*</td><td>*</td><td>*</td><td></td><td></td><td></td></tr><tr><td>miR-30b-5p</td><td>*</td><td></td><td>*</td><td></td><td></td><td></td></tr><tr><td>miR-191-5p</td><td>*</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>miR-27a</td><td>*</td><td></td><td>*</td><td></td><td></td><td></td></tr><tr><td>miR-320b</td><td>*</td><td></td><td>*</td><td></td><td></td><td></td></tr><tr><td>miR-143-3p</td><td>*</td><td></td><td>*</td><td></td><td></td><td></td></tr><tr><td>miR-1246</td><td>*</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>miR-223-3p</td><td>*</td><td></td><td>*</td><td></td><td></td><td></td></tr><tr><td>miR-144-3p</td><td>*</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>miR-451</td><td></td><td>*</td><td>(+)</td><td></td><td>*</td><td>(+)</td></tr><tr><td>miR-194-5p</td><td></td><td>*</td><td>*</td><td></td><td></td><td></td></tr><tr><td>miR-144-5p</td><td></td><td>*</td><td>(+)</td><td></td><td></td><td></td></tr><tr><td>miR-29b-3p</td><td></td><td>*</td><td>*</td><td></td><td></td><td></td></tr><tr><td>miR-29c-3p</td><td></td><td>*</td><td>*</td><td></td><td></td><td></td></tr><tr><td>miR-192-5p</td><td></td><td>*</td><td></td><td></td><td></td><td></td></tr><tr><td>miR-19a-3p</td><td></td><td>*</td><td>*</td><td></td><td></td><td></td></tr><tr><td>miR-502-3p</td><td></td><td>*</td><td></td><td></td><td>*</td><td></td></tr><tr><td>miR-15a-5p</td><td></td><td>*</td><td></td><td></td><td>*</td><td></td></tr><tr><td>miR-374b-5p</td><td>(+)</td><td></td><td>*</td><td></td><td></td><td></td></tr><tr><td>miR-7-1-3p</td><td></td><td></td><td>*</td><td></td><td></td><td></td></tr><tr><td>miR-320c</td><td>(+)</td><td></td><td>*</td><td></td><td></td><td></td></tr><tr><td>miR-106b-5p</td><td>(+)</td><td></td><td>*</td><td></td><td>(+)</td><td></td></tr><tr><td>miR-146a-5p</td><td>(+)</td><td></td><td>*</td><td></td><td></td><td></td></tr><tr><td>miR-133b</td><td></td><td>(+)</td><td>*</td><td></td><td></td><td></td></tr><tr><td>let-7b-5p</td><td>(+)</td><td></td><td>*</td><td></td><td>(+)</td><td></td></tr><tr><td>miR-345-5p</td><td></td><td></td><td>*</td><td></td><td></td><td></td></tr><tr><td>miR-22-3p</td><td></td><td></td><td>*</td><td></td><td></td><td></td></tr><tr><td>miR-7</td><td></td><td></td><td></td><td></td><td>*</td><td>(+)</td></tr><tr><td>miR-18b-5p</td><td></td><td>(+)</td><td></td><td></td><td>*</td><td></td></tr><tr><td>miR-151a-5p</td><td>(+)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>miR-1234-3p</td><td>(+)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>miR-26a-5p</td><td>(+)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>miR-301a-3p</td><td>(+)</td><td></td><td>(+)</td><td></td><td></td><td></td></tr><tr><td>let-7e</td><td></td><td>(+)</td><td></td><td></td><td></td><td></td></tr><tr><td>miR-106a-5p</td><td></td><td>(+)</td><td></td><td></td><td>(+)</td><td></td></tr><tr><td>miR-1915-3p</td><td></td><td>(+)</td><td></td><td></td><td></td><td></td></tr><tr><td>miR-9</td><td></td><td></td><td>(+)</td><td></td><td></td><td></td></tr></tbody></table>

values smaller than 0.05) in at least one comparison: miR-34a-5p, miR-338-3p, miR-142-3p, miR-320a, miR-145-5p, miR-92a-3p, let-7 g-5p, miR-199a-5p, miR-206,

miR-30b-5p, miR-191-5p, miR-27a, miR-320b, miR-143-3p, miR-1246, miR-223-3p, miR-144-3p, miR-451, miR-194-5p, miR-144-5p, miR-29b-3p, miR-29c-3p, miR-192-
5p, miR-19a-3p, miR-502-3p, miR-15a-5p, miR-374b-5p, miR-7-1-3p, miR-320c, miR-106b-5p, miR-146a-5p, miR-133b, let-7b-5p, miR-345-5p, and miR-22-3p. Moreover, the following 9 miRNAs had a *p* value between 0.05 and 0.1, close to significance value: miR-151a-5p, miR-1234-3p, miR-26a-5p, miR-301a-3p, let-7e, miR-18b-5p, miR-106a-5p, miR-1915-3p, and miR-9.

The complete output from the differential expression analyses in the *C9orf72* cohort, including log-fold changes indicating the intensity of underexpression or overexpression, as well as computed *p* values, are displayed in Table S1. Expression heatmaps of the differentially expressed miRNAs are shown in Figure S1.

## Differential expression in the GRN cohort

The second analysis focused on identifying which of the 30 miRNAs linked with FTD in the literature were differentially expressed in our GRN cohort. For this experiment, we used the miRNA counts acquired by sequencing plasma samples from the GRN patients, GRN presymptomatic individuals, and controls. Once negative binomial generalized linear models were fitted for each miRNA, we conducted 30 quasi-likelihood F-tests per pairwise comparison (controls versus GRN presymptomatic subjects, controls versus GRN patients, and GRN presymptomatic subjects versus GRN patients), adjusting for multiple comparisons using the Benjamini-Hochberg³⁷ method.

Table 3 (fifth to seventh columns) shows all differentially expressed miRNAs identified in this experiment. Contrary to what was observed with the *C9orf72* cohort, we note that only a small proportion of miRNAs (5 of the 30 miRNAs identified in the literature) were significantly differentially expressed (adjusted *p* values lower than 0.05), all of them when comparing controls and GRN patients: miR-451, miR-15a-5p, miR-502-3p, miR-7, and miR-18b-5p. Additionally, 4 miRNAs had a *p* value close to significance value, between 0.05 and 0.1: miR-106a-5p, miR-92a-3p, miR-106b-5p, and let-7b-5p.

Table S2 summarizes the complete results of the differential expression experiments in the GRN cohort, including log-fold changes reflecting the degree of underexpression or overexpression of each miRNA in each pairwise comparison, and the calculated *p* values. Figure S1 displays expression heatmaps of the differentially expressed miRNAs.

Finally, Table 3 also allows comparing the results obtained with the *C9orf72* and the GRN cohorts. Remarkably, the three comparisons involving the *C9orf72* cohort revealed significantly differentially expressed miRNAs, but that was the case for only one comparison involving the GRN cohort (controls versus GRN patients). We also note

that only a small minority of miRNAs (3) was significantly differentially expressed in both cohorts.

Taken together, these results offer evidence for the potential contribution of miRNAs identified in previous studies as biomarkers of *C9orf72*-associated disease, but not GRN-associated disease.

## Binary classification in the C9orf72 cohort

The first set of binary classification experiments focused on the *C9orf72* cohort. We trained 18 logistic regression classifiers for each pairwise comparison between clinical groups (controls versus *C9orf72* presymptomatic subjects, controls versus *C9orf72* patients, and *C9orf72* presymptomatic subjects versus *C9orf72* patients). For each of the 18 classifiers, we used as features the expression levels of distinct sets of miRNAs: the 16 miRNA signatures identified in the previously published studies about FTD and ALS, and two meta-signatures obtained from our differential expression analyses with the *C9orf72* cohort (one containing miRNAs with adjusted *p* value <0.05, and another including miRNAs with adjusted *p* value <0.1).

Figure 1 displays the areas under the ROC curves obtained by each of the 18 logistic regression classifiers in the three pairwise comparisons, as well as the empirical 90% confidence intervals. We observe that more than half of the classifiers (10 of the 18) achieved a ROC AUC greater than 0.70 in at least one comparison: those using the miRNA signatures from Kmetzsch et al., 2021,¹¹ Sheinerman et al., 2017¹² (FTD), Sheinerman et al., 2017¹² (ALS), Soliman et al., 2021,¹⁴ Dobrowolny et al., 2021,¹⁵ Raheja et al., 2018,¹⁶ Waller et al., 2017,¹⁷ Tasca et al., 2016,¹⁸ and the two meta-signatures from our differential expression analyses. The miRNA signatures with the largest ROC AUC were from Kmetzsch et al., 2021¹¹ (0.98 for controls versus presymptomatic subjects), Raheja et al., 2018¹⁶ (0.94 for controls versus patients), and the meta-signature with *p* < 0.1 (0.77 for presymptomatic subjects versus patients).

As well as in the differential expression analyses, these findings provide support for the potential use of several miRNAs identified in previous studies as biomarkers of *C9orf72*-associated FTD and ALS.

## Binary classification in the GRN cohort

The second set of binary classification experiments consisted of training seven logistic regression classifiers for each pairwise comparison in the GRN cohort (controls versus GRN presymptomatic subjects, controls versus GRN patients, and GRN presymptomatic subjects versus GRN patients). Each of the seven classifiers was trained with a different set of features: the expression levels of the
<figure><img src="image_45.png" /><figcaption>**Figure 1.** Area under the ROC curve results when classifying groups from the C9orf72 cohort. The solid circles indicate the areas under the ROC curves obtained for each pairwise comparison using 18 different miRNA signatures. The whiskers denote empirical 90% confidence intervals obtained with 2000 bootstrap samples.</figcaption></figure>
<figure><img src="image_46.png" /><figcaption>**Figure 2.** Area under the ROC curve results when classifying groups from the *GRN* cohort. The solid circles indicate the areas under the ROC curves obtained for each pairwise comparison using seven different miRNA signatures. The whiskers denote empirical 90% confidence intervals obtained with 2000 bootstrap samples.</figcaption></figure>

five miRNA signatures linked with FTD in previously published studies, and two meta-signatures obtained in our differential expression analyses with the *GRN* cohort

(one consisting of miRNAs with an adjusted *p* value smaller than 0.05, and another comprising miRNAs with an adjusted *p* value smaller than 0.1).
The areas under the ROC curves and empirical 90% confidence intervals for each of the seven logistic regression classifiers, in the three pairwise comparison involving the GRN cohort, are shown in Figure 2. Strikingly, only one ROC AUC was (slightly) greater than 0.70: when classifying GRN presymptomatic subjects and GRN patients, using the meta-signature from our differential expression analysis comprising miRNAs with *p* value lower than 0.1. The miRNA signatures with the largest ROC AUC were from Grasso et al., 2019⁸ (0.53 for controls versus presymptomatic subjects), and the meta-signature with *p* < 0.1 (0.63 for controls versus patients, and 0.72 for presymptomatic subjects versus patients).

It is noteworthy that these results are consistent with our differential expression analyses with the GRN cohort, offering evidence that most miRNAs identified in previous studies about FTD are not useful biomarkers of GRN-associated disease.

## Discussion

The goal of this study was to assess all circulating miRNA signatures previously published in the literature as possible biomarkers of FTD and/or ALS, by testing them in two separate homogenous cohorts of C9orf72 and GRN mutation carriers, comprising patients and presymptomatic subjects. The results of this work demonstrate that (1) several miRNAs identified in sporadic or mixed FTD/ALS cohorts could potentially be used as biomarkers of C9orf72 disease; (2) some miRNAs revealed in a C9orf72 cohort are validated in an independent C9orf72 cohort; and (3) most miRNAs associated with FTD in sporadic or mixed cohorts, or in a cohort of C9orf72 mutation carriers, are not relevant biomarkers of GRN disease.

First, differential expression results (Table 3) showed that more than half (35/65) of the miRNAs linked with FTD and/or ALS in the literature were significantly differentially expressed in the C9orf72 cohort. Remarkably, only four of the 15 selected studies included C9orf72 mutation carriers,<sup>11,13,16,21</sup> three of which focused exclusively on ALS.<sup>13,16,21</sup> Therefore, these outcomes reveal similarities in miRNA dysregulation between individuals with sporadic forms of FTD/ALS and C9orf72-associated disease. Classification results with the C9orf72 cohort (Fig. 1) also corroborate these findings, since half of the examined miRNA signatures (8/16) yielded at least one pairwise comparison with acceptable performance, and all comparisons employing the meta-signatures had acceptable performance. We considered as an acceptable biomarker any signature with a ROC AUC above 0.70, in accordance with the recommendation by Mandrekar.<sup>25</sup> This means that such a signature has some

discriminatory power and is worthy of further exploration. It does not mean that the signature is suitable for clinical use.

Next, we observed that a miRNA signature previously identified in a homogeneous C9orf72 cohort<sup>11</sup> and another one revealed in a mixed cohort of sporadic and familial ALS<sup>16</sup> displayed an outstanding result (ROC AUC above 0.90) when classifying controls versus C9orf72 presymptomatic subjects and controls versus C9orf72 patients (Fig. 1). These two signatures have in common the presence of miR-34a-5p, which has the smallest adjusted *p* value in the differential expression analyses regarding these comparisons (Table S1, respectively *p* = 2.42E-08 and *p* = 5.06E-06). In contrast, the performance of both of these signatures classifying C9orf72 presymptomatic individuals from patients was unsatisfactory. Indeed, neither of them contained miR-206, which is the most differentially expressed miRNA in this comparison (Table S1, *p* = 9.04E-05). The overexpression of miR-206 in ALS patients had already been evidenced,<sup>40</sup> and the results of the present work extend this association also to C9orf72-disease. Nevertheless, even using the expression levels of miR-206, the classification of C9orf72 presymptomatic subjects versus patients led to lower performances than comparisons involving the control group: the highest ROC AUC was 0.77, using the meta-signature with *p* < 0.1.

Finally, our results with the GRN cohort suggest that previously identified miRNAs have a weaker correlation with disease diagnosis and progression in this genetic form. Only a small proportion (5/30) of the miRNAs associated with FTD in previous studies was significantly differentially expressed in the GRN cohort (Table 3), and not a single miRNA was differentially expressed between controls and presymptomatic GRN carriers. Regarding the classification experiments, none of the studied miRNA signatures in the GRN cohort exhibited an acceptable performance (Fig. 2), and the only ROC AUC slightly above 0.70 was obtained when classifying GRN presymptomatic carriers and patients using the largest meta-signature (miRNAs with *p* value <0.1). One should note that none of the previous studies included GRN participants. Thus, our results demonstrate that miRNAs associated with sporadic FTD or genetic FTD due to C9orf72 are not relevant for GRN-associated disease. Further studies are needed to determine if other miRNAs, not analyzed in the present paper, are useful in GRN-associated disease.

Validation studies using independent datasets, such as this one, are crucial to assess the utility of biomarker candidates, fostering research rigor and reproducibility. Notably, we carefully defined our research questions and analysis plan before data analysis, and preregistered our
study. Preregistration has the strong benefit of leaving no flexibility for changes in analytical decisions after observing the data, which has been highlighted as a major source of false discoveries and replication failure.⁴¹

The main limitation of this work is the size of the studied cohorts, particularly the small group of *C9orf72* presymptomatic carriers (17) in comparison with the other groups, due to the rarity of genetic FTD. Additionally, due to the low number of *C9orf72* patients with different phenotypes (20 FTD, 6 FTD/ALS and 3 ALS), no conclusions can be drawn concerning the relationship between miRNAs and different disease manifestations. Moreover, this study did not investigate the influence of lifestyle factors in microRNA levels. Future work will explore the combination of circulating microRNAs with other biomarkers, such as gray matter volume,⁵ white matter integrity,⁴² and neurofilament light chain level.⁴³ Multimodality will be crucial to accurately assess progression in *GRN*-associated FTD, and will likely improve the understanding of *C9orf72*-associated disease.

In summary, the present work revealed that most miRNAs previously identified in sporadic or mixed FTD/ALS cohorts are potential biomarkers of *C9orf72*-associated FTD/ALS, but not of *GRN*-associated FTD. Longitudinal studies are needed to confirm our findings and to determine miRNAs expression levels changes throughout disease progression, before circulating miRNAs can be used to assess treatment outcomes in *C9orf72*-associated disease clinical trials.

## Acknowledgements

We thank Justine Guégan for microRNA raw reads to counts pipeline handling. The study was conducted with the support of the Centre d'Investigation Clinique Neuroscience (CIC 1422), GH Pitié Salpêtrère, Paris, and the Centre pour l'Acquisition et le Traitement des Images platform. The research leading to these results has received funding from the program "Investissements d'avenir" ANR-10-IAIHU-06, from the French government under management of Agence Nationale de la Recherche as part of the "Investissements d'avenir" program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute), from Agence Nationale de la Recherche/DGOS (project ANR-PRTS PREV-DEMALS, grant number ANR-14-CE15-0016-07, promotion Assistance Publique – Hôpitaux de Paris), from the Programme Hospitalier de Recherche Clinique (PHRC) Predict-PGRN (to ILB, promotion by Assistance Publique – Hôpitaux de Paris), from Fondation Plan Alzheimer (to ILB), from the Inria Project Lab Program (project Neuromarkers), from Fondation Vaincre Alzheimer FR-17035 and from the Institut Français de Bioinformatique (ANR-11-INSB-0013).

## Conflict of Interest

The authors declare no conflict of interest.

## Author Contributions

Virgilio Kmetzsch had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Conception and design of the study: all authors. Data acquisition, analysis and interpretation: all authors. Statistical analysis: VK. Drafting of the manuscript: VK. Revising the manuscript: all authors. The French research network on FTD/ALS contributed to data acquisition: Sophie Auriacombe (CHU Pellegrin, Bordeaux), Serge Belliard (CHU Rennes), Frédéric Blanc (Hôpitaux Civils, Strasbourg), Claire Boutoleau-Bretonnière (CHU Laennec, Nantes), Alexis Brice (Hôpital Pitié-Salpêtrière, Paris), Agnès Camuzat (ICM, Paris), Mathieu Ceccaldi (CHU La Timone, Marseille), Philippe Couratier (CHU Limoges), Vincent Deramecourt (CHU Roger Salengro, Lille), Mira Didic (CHU La Timone, Marseille), Charles Duyckaerts (Hôpital Pitié-Salpêtrière, Paris), Frédérique Etcharry-Bouyx (CHU Angers), Maïté Formaglio (CHU Lyon), Véronique Golfier (CHU Rennes), Didier Hannequin (CHU Charles Nicolle, Rouen), Lucette Lacomblez (Hôpital Pitié-Salpêtrière, Paris), Isabelle Le Ber (Hôpital Pitié-Salpêtrière, Paris), Bernard-François Michel (CH Sainte-Marguerite, Marseille), Jérémie Pariente (CHU Rangueil, Toulouse), Florence Pasquier (CHU Lille), Daisy Rinaldi (CHU Pitié-Salpêtrière, Paris), Adeline Rollin-Sillaire (CHU Roger Salengro, Lille), Mathilde Sauvée (CHU Grenoble Alpes), François Sellal (CH Colmar), Christel Thauvin-Robinet (CHU Dijon), Catherine Thomas-Anterion (CH Plein-Ciel, Lyon), Martine Vercelletto (CHU Laennec, Nantes) and David Wallon (CHU Charles Nicolle Rouen).

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**Supporting Information**

Additional supporting information may be found online in the Supporting Information section at the end of the article.

**Figure S1** Expression heatmaps of differentially expressed microRNAs. Rows represent microRNAs and columns represent individuals ordered by clinical status (control, presymptomatic, and patient). The log2 expression values of microRNAs are standardized (mean of 0 and standard deviation of 1), and z-scores are indicated by colors (blue indicates underexpression and red indicates overexpression). (A) All 35 differentially expressed microRNAs identified in the C9orf72 cohort. (B) Zoom over five of the most differentially expressed microRNAs identified in the C9orf72 cohort. (C) All five differentially expressed microRNAs identified in the GRN cohort.

**Table S1** Complete output from differential expression analyses in the C9orf72 cohort, for each pairwise comparison between the clinical groups. The columns show the 30 studied miRNAs, the log-fold change when comparing the clinical groups, the unadjusted *p* values, and finally the adjusted *p* values after Benjamini-Hochberg. For each pairwise comparison, a positive log-fold change means that the miRNA is overexpressed in the first group. Controls (*n* = 31), C9orf72 presymptomatic subjects (*n* = 17), and C9orf72 patients (*n* = 29). Adjusted *p* values lower than 0.05 are shown in bold.

**Table S2** Complete output from differential expression analyses in the GRN cohort, for each pairwise comparison between clinical groups. The columns show the 30 studied miRNAs, the log-fold change when comparing the clinical groups, the unadjusted *p* values, and finally the adjusted *p* values after Benjamini-Hochberg. For each pairwise comparison, a positive log-fold change means that the miRNA is overexpressed in the first group. Controls (*n* = 31), GRN presymptomatic subjects (*n* = 30), GRN patients (*n* = 28). Adjusted *p* values lower than 0.05 are shown in bold.
C.2 Contributions jointes en support au chapitre 3

[31] Kmetzsch V, **Becker E**, Saracino D, Anquetil V, Rinaldi D, Camuzat A, Gareau T, Le Ber I, Colliot O and the PREV-DEMALS study group.

*A multimodal variational autoencoder for estimating progression scores from imaging and microRNA data in neurodegenerative diseases.*

SPIE Medical Imaging 2022, 2022

[34] Kmetzsch V, **Becker E**, Saracino D, Rinaldi D, Camuzat A, Le Ber I, and Colliot O.

*Disease progression score estimation from multimodal imaging and mi-*

*croRNA data using supervised variational autoencoders.*

accepted in IEEE J Biomed Health Inform
# A multimodal variational autoencoder for estimating progression scores from imaging and microRNA data in rare neurodegenerative diseases

Virgilio Kmetzsch, Emmanuelle Becker, Dario Saracino, Vincent Anquetil, Daisy Rinaldi, Agnès Camuzat, Thomas Gareau, Isabelle Le Ber, Olivier Colliot

<figure><img src="image_47.png" /></figure>

## To cite this version:

Virgilio Kmetzsch, Emmanuelle Becker, Dario Saracino, Vincent Anquetil, Daisy Rinaldi, et al. A multimodal variational autoencoder for estimating progression scores from imaging and microRNA data in rare neurodegenerative diseases. SPIE Medical Imaging 2022: Image Processing, Feb 2022, San Diego, California, United States. pp.376-382, 10.1117/12.2607250. hal-03576117

HAL Id: hal-03576117

https://hal.archives-ouvertes.fr/hal-03576117

Submitted on 16 Feb 2022

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# A multimodal variational autoencoder for estimating progression scores from imaging and microRNA data in rare neurodegenerative diseases

Virgilio Kmetzsch<sup>a, b</sup>, Emmanuelle Becker<sup>c</sup>, Dario Saracino<sup>a, b, d, e</sup>, Vincent Anquetil<sup>b</sup>, Daisy Rinaldi<sup>b, d, e</sup>, Agnès Camuzat<sup>b, f</sup>, Thomas Gareau<sup>b</sup>, Isabelle Le Ber<sup>b, d, e, g</sup>, Olivier Colliot<sup>b, a</sup>, and The PREV-DEMALS study group

<sup>a</sup>Inria, Aramis project-team, F-75013, Paris, France

<sup>b</sup>Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France

<sup>c</sup>Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France

<sup>d</sup>Centre de référence des démences rares ou précoces, IM2A, AP-HP, Paris, France

<sup>e</sup>DMU Neurosciences, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France

<sup>f</sup>EPHE, PSL Research University, Paris, France

<sup>g</sup>Paris Brain Institute – Institut du Cerveau – ICM, FrontLab, Paris, France

## ABSTRACT

Frontotemporal dementia (FTD) is a rare neurodegenerative disease, often of genetic origin, with no effective treatment. There is a substantial pathophysiological overlap with amyotrophic lateral sclerosis (ALS), mutations in the *C9orf72* gene being their most common genetic cause. In these disorders, no single biomarker can accurately measure progression, thus it is crucial to combine complementary information from multiple modalities to evaluate new therapeutic interventions. In particular, neuroimaging and transcriptomic (microRNA) data have been shown to have value to track FTD and ALS progression. As these conditions are rare, large samples are not available, hence the need for methods to fuse multimodal data from small samples. In this paper, we propose a method for computing a disease progression score (DPS) from cross-sectional multimodal data, based on variational autoencoders (VAE). We show that unsupervised training leads to the estimation of meaningful latent spaces, where subjects with similar disease states are clustered together and from which a DPS may be inferred. Models were evaluated on 14 patients, 40 presymptomatic mutation carriers and 37 healthy controls from the PREV-DEMALS study. Since there is no ground truth for the DPS, we used the inferred scores to perform pairwise classification as a proxy metric. Presymptomatic subjects and patients were classified with an average area under the ROC curve of 0.83 and 0.94, respectively without and with feature selection. The proposed approach has the potential to leverage cross-sectional multimodal datasets with small sample sizes in order to objectively measure disease progression.

**Keywords:** Multimodal, neuroimaging, transcriptomics, microRNA, variational autoencoder, deep learning, disease progression score, neurodegenerative disease

## 1. INTRODUCTION

Frontotemporal dementia (FTD) is a rare heterogeneous neurodegenerative disease characterized by progressive behavioral changes, executive dysfunction and language impairments.<sup>1</sup> A large proportion of FTD cases are due to genetic mutations, the most frequent being an expansion in the *C9orf72* gene.<sup>2,3</sup> *C9orf72* expansions are also an important genetic cause of amyotrophic lateral sclerosis (ALS), a motor neuron disease leading to muscle atrophy, progressive weakness and eventual paralysis.<sup>4</sup> These fatal disorders, which may occasionally co-occur in *C9orf72*-mutated individuals, have no effective treatment to date.
Presymptomatic carriers of the *C9orf72* mutation, with no current clinical symptoms, are an ideal population for the evaluation of new disease-modifying treatments, before any irreversible brain damage has occurred. Previous work demonstrated the importance of neuroimaging⁵ and transcriptomics (microRNA)⁶ biomarkers to better understand the *C9orf72* disease progression. However, when analysed independently, neuroimaging and microRNA data provide incomplete views of FTD and ALS. Therefore, in order to monitor the effect of experimental therapies, it is critical to leverage the complementary information provided by these modalities.

Since different biomarkers characterize a disease in different stages, several biomarkers could be combined to represent the entire progression with a single disease progression score (DPS). Many approaches have been developed for data-driven disease progression modeling, including event-based models (EBM).⁷,⁸ a vertex-wise model of brain pathology fitted with expectation-maximisation,⁹ non-linear mixed-effects models,¹⁰,¹¹ alternating least squares to fit sigmoid functions,¹² Gaussian processes,¹³ Recurrent Neural Networks¹⁴ and M-estimation.¹⁵ Most of these approaches require a large amount of longitudinal data, which is not available for FTD/ALS. The only published methods that infer a disease progression score from cross-sectional data are event-based models,⁷,⁸ but these approaches do not scale well for hundreds of biomarkers, such as microRNA data.

In the present work, we proposed a method for inferring a disease progression score (a latent trait) based on a multimodal variational autoencoder (VAE).¹⁶ VAEs are powerful generative models that project data in a regularized latent low dimensional space and have been shown to be effective in high dimensional low sample size settings.¹⁷ These models have already been used with multimodal data,¹⁸ although not with the goal of inferring a DPS. We hypothesized that the inferred score, based on cross-sectional neuroimaging and microRNA data, could represent the distance traveled along the underlying FTD/ALS pathophysiological pathway, and thus be used to monitor disease progression and evaluate novel treatments.

## 2. MATERIALS AND METHODS

### 2.1 Studied population

Participants were recruited through the PREV-DEMALS study (https://clinicaltrials.gov, ID NCT02590276), a cohort focused on *C9orf72* expansion carriers, comprising neuroimaging and microRNA sequencing data. MicroRNAs (miRNAs) are a class of noncoding RNAs that negatively regulate gene expression,¹⁹ being detected in blood plasma and correlating with the progression of many neurodegenerative diseases,²⁰ including FTD and ALS.

Our study comprised 110 individuals, divided into three groups: 22 symptomatic carriers of a pathogenic expansion (patient group), 45 asymptomatic carriers (presymptomatic group) and 43 asymptomatic non-carriers (control group). Written informed consents were obtained from all participants and the study was approved by the ethics committee (Comité de Protection des Personnes CPP Ile-De-France VI, CPP 68-15 and ID RCB 2015-A00856-43).

### 2.2 Data acquisition and preprocessing

All individuals had transcriptomic data available, consisting of the expression levels of 589 miRNAs. However, only 91 (14 patients, 40 presymptomatic carriers and 37 controls) had also neuroimaging data available, consisting in grey matter volumes extracted from anatomical MRI (T1) including 68 cortical regions of interest (ROIs) (Desikan atlas) and 18 subcortical ROIs (Aseg atlas) as well as the estimated total intracranial volume, thus resulting in 87 imaging features. Details regarding features and population can be found in Ref. 6 and Ref. 5. Subjects were divided into two datasets: 19 subjects with only microRNA data, used as a discovery set for feature selection, and 91 subjects with multimodal neuroimaging and microRNA data, used as input to our models. Features were rescaled from 0 to 1 and ordered via principal component analysis in the transposed data matrix: we projected features into the first principal component and used the coordinate values to sort them.

We also conducted experiments with two simulated datasets, based on the real one. To build the simulated data matrices, we simply increased (or decreased) each feature value by 5% or 15% for all patients and healthy controls, to accentuate their means' difference. The presymptomatic participants remained unchanged.
## 2.3 Multimodal variational autoencoder

In order to build disease progression scores, we propose a multimodal variational autoencoder for estimating a latent space representation. Let $x \in \mathcal{X}$ represent a set of multimodal data, where each point is a vector with concatenated neuroimaging and microRNA data. A variational autoencoder (VAE)¹⁶ is a generative model which aims to learn the training data distribution using a latent representation model:

$$p(x) = \int p(x|z)p(z)dz, \qquad (1)$$

where $z \in \mathcal{Z}$ is a lower dimensional latent variable and $p(z)$ is its prior distribution (commonly a multivariate unit Gaussian). VAEs learn two mappings in the form of neural networks: an encoder $q_{\phi}(z|x)$ which maps data $x$ to its latent representation $z$, and a decoder $p_{\theta}(x|z)$ which maps from the latent representation $z$ back to the input space. Since the marginal log-likelihood of the data is intractable, VAEs are trained to maximize the variational lower bound of the marginal log-likelihood, known as ELBO (Evidence Lower Bound):

$$\log p(x) \geq \mathbb{E}_{z \sim q_{\phi}}[\log p_{\theta}(x|z)] - D_{KL}[q_{\phi}(z|x)||p(z)], \qquad (2)$$

where $D_{KL}[q_{\phi}(z|x)||p(z)]$ is the Kullback-Leibler divergence between the approximated posterior $q_{\phi}(z|x)$ and the prior distribution $p(z)$ and acts as a regularization term.

Our encoder consisted of a 1-dimensional convolution layer, followed by two fully-connected layers, while the decoder was implemented with two fully-connected layers followed by a 1-dimensional transposed convolutional layer. After each layer, batch normalization²¹ was applied for its regularization properties and to avoid vanishing or exploding gradients. The nonlinear activation function was the rectified linear unit (ReLU) $f(x) = max(0, x)$ in all layers except the decoder's last one, which used a sigmoid function $f(x) = \frac{1}{1+e^{-x}}$ in order to have the output normalized between 0 and 1. The loss function was optimized using Adam.²²

Two slightly different networks were used as our final models. For the experiments with no feature selection (589 miRNAs + 87 neuroimaging features), we identified that 64 channels and a kernel of dimension 80 with a stride of 10 was a good parametrization, along with a hidden layer of 400 units and a latent space of dimension 5. For the experiments with the discovery set and feature selection (68 miRNAs + 87 neuroimaging features), we chose 32 channels, a kernel of dimension 20 with a stride of 5, along with a hidden layer of 50 units and 2-dimensional latent space. The VAEs were implemented in Python 3.8.5 using PyTorch 1.8.1, and trained with batches of 32 subjects for 250 epochs using a learning rate of $10^{-3}$.

## 2.4 Computing disease progression scores in the latent space

We used a stratified 5-fold cross-validation strategy, training the VAE with four folds and testing with the remaining fold in each iteration. Training was unsupervised: no clinical labels were used. Our hypothesis was that the VAE would identify a meaningful latent space, placing subjects with the same clinical status (and similar disease stage among presymptomatic individuals and patients) closer together.

Once each model was trained, we projected the training data in the latent space and used the clinical labels (patient, presymptomatic subject or control) to compute the centroid of each group. We then defined the trajectory to traverse the latent space as the line passing through the centroids of the presymptomatic and the patient groups. Finally, we encoded the test fold in the latent space and computed the DPS for each subject as the coordinate of their projection in this line.

Since there is no ground truth for the DPS, we applied a proxy metric to assess model performance: the inferred scores were used to classify subjects according to their clinical status. Therefore, labels were used during test time to compute the area under the receiver operating characteristic curve (ROC AUC) averaged over the five folds.
<figure><img src="image_48.png" /><figcaption>Figure 1. Example of (A) a 2-dimensional latent space encoding a test fold comprising 2 patients, 8 presymptomatic individuals and 8 controls and the trajectory to traverse this latent space (blue arrow); (B) the corresponding disease progression scores.</figcaption></figure>

## 3. RESULTS

Figure 1 depicts an example of a two-dimensional latent space obtained after training the VAE with four folds and using the trained model to encode the remaining test fold. In this particular fold, we observe a perfect separation between patients and the other two groups, and a clear (although not perfect) distinction between presymptomatic individuals and controls.

Table 1 displays the mean and standard deviation of the area under the ROC curve obtained after a 5-fold cross-validation, for each pairwise comparison between clinical groups. The models were initially trained without any feature selection, with the expression levels of 589 miRNAs and the grey matter volumes of 87 ROIs. Then, we used 19 subjects as a discovery set to identify the most differentially expressed miRNAs between clinical groups, reducing the dimension of the microRNA data to 68. Classification performance improved when feature selection was applied. Table 1 also shows the results with the two simulated datasets. As expected, performance increases when a dataset with more discriminating features is used as input.

<table><caption>Table 1. Area under the ROC curve (mean ± SD) for each pairwise classification, obtained using the inferred disease progression scores respectively without feature selection, with feature selection and with two simulated datasets.</caption><thead><tr><th>Comparison | Features</th><th>589 miRNAs,<br>87 T1 ROIs</th><th>68 miRNAs,<br>87 T1 ROIs</th><th>Simulated<br>dataset (5%)</th><th>Simulated<br>dataset (15%)</th></tr></thead><tbody><tr><td>Control vs Presymptomatic</td><td>0.57 ± 0.15</td><td>0.62 ± 0.20</td><td>0.76 ± 0.19</td><td>0.89 ± 0.12</td></tr><tr><td>Control vs Patient</td><td>0.88 ± 0.11</td><td>0.95 ± 0.07</td><td>0.99 ± 0.02</td><td>1.00 ± 0.00</td></tr><tr><td>Presymptomatic vs Patient</td><td>0.83 ± 0.19</td><td>0.94 ± 0.12</td><td>0.95 ± 0.10</td><td>0.98 ± 0.03</td></tr></tbody></table>

Finally, Fig. 2 presents the visualization of the inferred scores for all 91 subjects after a 5-fold cross validation. The scores were computed for each subject when included in a test fold, without or with feature selection in the microRNA data. There is a superior performance (better separation between groups) when miRNAs are selected using the discovery set.

## 4. DISCUSSION

We proposed a multimodal variational autoencoder for combining imaging and transcriptomic (microRNA) data. It allowed inferring a single score to represent disease progression, using only cross-sectional neuroimaging and microRNA data from less than a hundred subjects. We showed that variational autoencoders built with shallow 1-dimensional convolutional neural networks were able to infer meaningful latent spaces, putting closer together subjects from the same clinical groups (patients, presymptomatic individuals and controls) without using any
<figure><img src="image_49.png" /><figcaption>Figure 2. Disease progression score densities conditioned on clinical status, obtained from 5-fold cross-validation with 91 individuals, when using (A) all 589 miRNAs and all 87 T1 MRI ROIs and (B) selected 68 miRNAs and all 87 T1 MRI ROIs.</figcaption></figure>

labels during training. We were able to encode individuals from the test sets into the latent spaces and compute their corresponding DPS. Then using only the computed scores, presymptomatic subjects and patients were distinguished with an average ROC AUC of 0.83 and 0.94, respectively without and with feature selection.

Our experiments with the simulated datasets showed that more informative features will lead to better results. In addition, the presented approach is generic enough to be used with datasets from other neurodegenerative diseases, even though our experiments focused only on C9orf72-associated FTD and ALS. So our results motivate further experiments with other neurodegenerative diseases with well established biomarkers.

The current study has a limitation: the absence of ground truth for the progression scores, which led us to use classification performance as a proxy metric. Long-term longitudinal data would be needed to confirm the accuracy of the inferred DPS. For instance, we hypothesize that, for presymptomatic subjects, a higher DPS implies an earlier disease onset, and we would need long-term follow-up data to confirm this hypothesis. Future work could explore different network architectures (e.g. 2-dimensional inputs, different number of layers, feature maps and kernel sizes), investigate the integration of different prior information to order the input data and analyze different methods to traverse the latent space.

In summary, our results encourage the use of the proposed approach as a tool to measure disease progression in rare neurodegenerative diseases and evaluate potential treatments.

## ACKNOWLEDGMENTS

The PREV-DEMALS study group includes: Eve Benchetrit (Hôpital de la Salpêtrière, Paris), Anne Bertrand (Hôpital de la Salpêtrière, Paris), Anne Bissery (Hôpital de la Salpêtrière, Paris), Marie-Paule Boncoeur (CHU Dypuytren, Limoges), Stéphanie Bombois (CHU Roger Salengro, Lille), Agnès Camuzat (ICM, Paris), Mathieu Chastan (CHU Charles Nicolle, Rouen), Yaohua Chen (CHU Roger Salengro, Lille), Marie Chupin (ICM, Paris), Olivier Colliot (ICM, Paris), Philippe Couratier (CHU Dypuytren, Limoges), Xavier Delbeuck (CHU Roger Salengro, Lille), Vincent Deramecourt (CHU Roger Salengro, Lille), Christine Delmaire (CHU Roger Salengro, Lille), Emmanuel Gerardin (CHU Charles Nicolle, Rouen), Claude Hossein-Foucher (CHU Roger Salengro, Lille), Bruno Dubois (Hôpital de la Salpêtrière, Paris), Marie-Odile Habert (Hôpital de la Salpêtrière, Paris), Didier Hannequin (CHU Charles Nicolle, Rouen), Géraldine Lautrete (CHU Dypuytren, Limoges), Thibaud Lebouvier (CHU Roger Salengro, Lille), Isabelle Le Ber (Hôpital de la Salpêtrière, Paris), Benjamin Le Toullec (ICM, Paris), Richard Levy (Hôpital de la Salpêtrière, Paris), Olivier Martinaud (CHU Charles Nicolle, Rouen), Kelly Martineau (ICM, Paris), Marie-Anne Mackowiak (CHU Roger Salengro, Lille), Jacques Monteil (CHU Dypuytren, Limoges), Florence Pasquier (CHU Roger Salengro, Lille), Gregory Petyt (CHU Roger Salengro, Lille), Pierre-François Pradat (Hôpital de la Salpêtrière, Paris), Assi-Hervé Oya (Hôpital de la Salpêtrière, Paris), Armelle Rametti-Lacroux (Hôpital de la Salpêtrière, Paris), Daisy Rinaldi (Hôpital de la Salpêtrière, Paris), Adeline
Rollin-Sillaire (CHU Roger Salengro, Lille), François Salachas (Hôpital de la Salpêtrière, Paris), Sabrina Sayah (Hôpital de la Salpêtrière, Paris), David Wallon (CHU Charles Nicolle, Rouen).

The research leading to these results has received funding from the French government under management of Agence Nationale de la Recherche as part of the "Investissements d'avenir" program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute) and reference ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6), from Agence Nationale de la Recherche (project PREV-DEMALS, grant number ANR-14-CE15-0016-07), and from the Inria Project Lab Program (project Neuromarkers).

We thank Justine Guégan, from iCONICS (ICM bioinformatics facility) for microRNA raw reads to counts pipeline handling. We also thank Yannick Marie and Delphine Bouteiller, from iGenSeq (ICM sequencing facility), for library preparation and sequencing. Part of this work was carried out on the DNA and cell bank of ICM. We gratefully acknowledge Philippe Martin-Hardy and Ludmila Jornea for technical assistance. The study was conducted with the support of the Centre d'Investigation Clinique Neuroscience (CIC 1422), GH Pitié Salpêtrière, Paris, and the Centre pour l'Acquisition et le Traitement des Images platform.

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# Disease progression score estimation from multimodal imaging and microRNA data using supervised variational autoencoders

Virgilio Kmetzsch, Emmanuelle Becker, Dario Saracino, Daisy Rinaldi, Agnès Camuzat, Isabelle Le Ber, and Olivier Colliot, Member, IEEE, for the PREV-DEMALS study group

**Abstract**— Frontotemporal dementia and amyotrophic lateral sclerosis are rare neurodegenerative diseases with no effective treatment. The development of biomarkers allowing an accurate assessment of disease progression is crucial for evaluating new therapies. Concretely, neuroimaging and transcriptomic (microRNA) data have been shown useful in tracking their progression. However, no single biomarker can accurately measure progression in these complex diseases. Additionally, large samples are not available for such rare disorders. It is thus essential to develop methods that can model disease progression by combining multiple biomarkers from small samples. In this paper, we propose a new framework for computing a disease progression score (DPS) from cross-sectional multimodal data. Specifically, we introduce a supervised multimodal variational autoencoder that can infer a meaningful latent space, where latent representations are placed along a disease trajectory. A score is computed by orthogonal projections onto this path. We evaluate our framework with multiple synthetic datasets and with a real dataset containing 14 patients, 40 presymptomatic genetic mutation carriers and 37 controls from the PREV-DEMALS study. There is no ground truth for the DPS in real-world scenarios, therefore we use the area under the ROC curve (AUC) as a proxy metric. Results with the synthetic datasets support this choice, since the higher the AUC, the more accurate the predicted simulated DPS. Experiments with the real dataset demonstrate better performance in comparison

The research leading to these results has received funding from the French government under management of Agence Nationale de la Recherche as part of the "Investissements d'avenir" program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute) and reference ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6), from Agence Nationale de la Recherche (project PREV-DEMALS, grant number ANR-14-CE15-0016-07), and from the Inria Project Lab Program (project Neuromarkers).

V. Kmetzsch, D. Saracino and O. Colliot are with Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013, Paris, France (e-mails: virgilio.kmetzsch@inria.fr, dario.saracino@icm-institute.org, olivier.colliot@cnrs.fr).

E. Becker is with Univ Rennes, Inria, CNRS, IRISA, F-35000, Rennes, France (e-mail: emanuelle.becker@univ-rennes1.fr).

D. Rinaldi and A. Camuzat are with Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013, Paris, France (e-mails: daisy.rinaldi@icm-institute.org, agnes.camuzat@icm-institute.org).

I. Le Ber is with Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Institut of Memory and Alzheimer's Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN), Department of Neurology, DMU Neurosciences, F-75013, Paris, France (e-mail: isabelle.leber@icm-institute.org).

with a state-of-the-art approach. The proposed framework thus leverages cross-sectional multimodal datasets with small sample sizes to objectively measure disease progression, with potential application in clinical trials.

**Index Terms**— Disease progression score, Deep learning, MicroRNA, Multimodal data, Neurodegenerative disease, Neuroimaging, Variational autoencoder

## I. INTRODUCTION

FRONTEMPORAL dementia (FTD) and amyotrophic lateral sclerosis (ALS) are rare neurodegenerative disorders that have devastating personal and social consequences. Progressive cognitive and behavioural changes, emotional instability, and language impairment are the main symptoms of FTD [1]. ALS is a motor neuron disease characterized by gradual muscle wasting, ultimately leading to disability [2]. FTD and ALS may be sporadic (no previous family history) or genetically inherited. The most common genetic cause of FTD and ALS is a hexanucleotide repeat expansion in the C9orf72 gene [3], [4]. These fatal conditions can sometimes coexist in C9orf72-mutated individuals, and have no cure or standard treatment to date.

Carriers of the C9orf72 mutation that do not present clinical symptoms are considered presymptomatic, since they have a very high probability of manifesting FTD and/or ALS later in life. Clinical trials for potential therapies are likely to be most effective at this presymptomatic stage, before any irreversible brain damage has occurred. However, the evaluation of new treatments depends on an accurate measure of disease progression, which is not evident without observable symptoms. Therefore, it is crucial to identify biomarkers to assess disease progression in presymptomatic subjects. Indeed, previous work has shown the relevance of neuroimaging [5], [6] and transcriptomic (microRNA) [7] biomarkers for a better understanding of C9orf72-disease in presymptomatic carriers. Nevertheless, when these modalities are analysed separately, they provide only an incomplete picture of these complex neurodegenerative diseases. It is thus essential to develop methods that leverage the complementary information available from different modalities to accurately measure disease progression. As different biomarkers characterise distinct disease stages, various biomarkers can be combined to represent the entire
disease course with a single measure, commonly referred in
the literature as the *disease progression score* (DPS).

The idea of computing disease progression scores falls
within the larger topic of modeling disease progression. In
the past years, many approaches have been developed for
data-driven modeling of disease progression, such as event-
based models (EBM) [8], [9], different algorithms fitting
logistic functions to biomarker trajectories [10], [11], non-
linear mixed-effects models [12], [13], a vertex-wise model
of brain diseases fitted with expectation-maximisation [14],
Gaussian processes [15], topological profiles reflecting brain
connectivity [16], Bayesian multi-task learning [17], and re-
current neural networks [18].

Most of these approaches require longitudinal data. For
instance, the authors of [10] assume that the longitudinal
dynamic of each biomarker can be represented as a sigmoidal
function of the DPS. They propose a joint optimization al-
gorithm to compute the DPS, fit one sigmoid function per
biomarker using alternating least squares, and apply their
work to hundreds of patients with Alzheimer's disease (AD).
Similarly, a more recent method [11], also applied to AD,
uses M-estimation to map each subject's age to a DPS,
jointly fitting generalized logistic functions to the longitudinal
dynamics of biomarkers as functions of the DPS. Schiratti
et al [12] proposed a general non-linear mixed-effects model
for longitudinal data based on concepts from Riemannian
geometry. The application of this framework to AD, called
AD Course Map [13], allowed to map each subject to their
corresponding disease stage. The authors of [15] proposed a
probabilistic approach based on Gaussian process regression
from time-series of biomarker measurements. Yet another
framework, named Data-driven Inference of Vertexwise Evo-
lution (DIVE) [14] consists in identifying clusters of vertex-
wise biomarker measurements in the brain, and estimating
representative trajectories for these clusters. Finally, [18] uses
recurrent neural networks to predict biomarker values without
parametric assumptions about trajectories, with application to
AD. To the best of our knowledge, the only disease modeling
approaches that infer a DPS from cross-sectional data are
EBM [8], [9]. These models explore the temporal sequence in
which biomarkers become abnormal in the course of a disease.
They have been successfully applied to a variety of diseases
including AD [8], [9], [19]–[22], multiple sclerosis [23], [24],
Parkinson’s disease [25], Huntington’s disease [26] as well as
FTD [27], [28] and ALS [29]. However, in these works, EBMs
were applied to a relatively small number of features (typically
10-50) and it is unkwown if they would perform well in higher
dimensions.

Despite the recognized importance of estimating neurode-
generative diseases progression, research has tended to focus
mostly on higher prevalence conditions. Existing solutions are
thus inadequate to model rare diseases with high-dimensional
cross-sectional data, for three main reasons. First, we observe
that longitudinal data is needed for the vast majority of
approaches. However, C9orf72-associated FTD and ALS are
slowly progressive conditions in the presymptomatic phase,
which hinders the collection of meaningful longitudinal data.
Second, most published methods benefit from large samples,

which are not available for very low prevalence disorders such
as genetic FTD and ALS. Finally, it is unclear if event-based
models, the only methods suitable for cross-sectional data, can
be robustly applied to high-dimensional microRNA expression
data, which comprise hundreds of biomarkers.

In this paper, we thus present a novel framework to estimate
disease progression scores for rare neurodegenerative disorders
using only cross-sectional data. To that purpose, we introduce
a new supervised multimodal variational autoencoder (VAE)
trained with neuroimaging and microRNA data. Our working
hypothesis is that disease progression scores may be modelled
as underlying latent traits. Concretely, we aim to learn a
meaningful latent space, where the relative positions of latent
representations indicate the distance travelled along the disease
pathophysiological pathway.

VAEs are powerful generative models that project data into a
low-dimensional regularized latent space [30]. These models
have been previously used with multimodal data [31]–[33],
but not for the purpose of inferring a DPS. Usually VAEs are
trained in an unsupervised manner. However, extensions have
been proposed for semi-supervised [34]–[36] or supervised
[37] tasks. These studies demonstrate that providing super-
vision to the model imposes specific semantics on the latent
space, resulting in more meaningful and robust representa-
tions. In our context, explicit labels (control, presymptomatic,
patient) are already available for all subjects. We thus add
supervision during training, leveraging this information to
improve the separation of the groups in the latent space. Ad-
ditionally, we propose to split high-dimensional (neuroimag-
ing and microRNA data) and low-dimensional (demographic
information) modalities. Our model thus couples two neural
networks with different inputs: (1) an encoder/decoder that
learns a latent space from the high-dimensional features, and
(2) a classifier having as input the latent variables concatenated
with the low dimensional features, useful for the classification
task. As no ground truth is available for the DPS in real-
world scenarios, we evaluate our models with a proxy metric:
the area under the ROC curve (AUC) for each pairwise
classification between clinical groups, computed using only
the inferred DPS.

A preliminary version of this work has been published at the
SPIE Medical Imaging 2022 conference [38]. Compared to the
conference version, the present paper introduces the following
novelties: (1) a supervised instead of a standard unsupervised
VAE approach, (2) data split between low-dimensional and
high-dimensional modalities, (3) disease trajectory compu-
tation in the latent space using principal curves instead of
straight lines, (4) additional experiments with multiple syn-
thetic datasets, (5) a comparison with event-based models, and
(6) an ablation study.

The manuscript is organized as follows. Section II explains
our proposed framework, section III describes the analyzed
datasets, section IV details our experiments and corresponding
outcomes, and finally section V examines the meaning of our
results and highlights the broader implications of our study.
<figure><img src="image_50.png" /><figcaption>Fig. 1: Illustration of the proposed framework for disease progression scores (DPS) computation. 1) High-dimensional (neuroimaging and microRNAs expression data) and low-dimensional (demographic information) features are extracted; the former are fed to the encoder, the latter are concatenated with latent codes and fed to the classifier. 2) Once the model is trained, all training examples are encoded in the latent space and a principal curve is calculated to define the disease trajectory. 3) Test examples are encoded in the latent space and the latent representations are orthogonally projected onto the previously computed curve; the DPS correspond to their coordinates along the curve.</figcaption></figure>

## II. METHODOLOGY

We consider a dataset $(\mathcal{X}, \mathcal{Y}) = \{(x_1, y_1), \dots, (x_n, y_n)\}$. The $i$-th subject is characterized by a feature vector $x_i \in \mathbb{R}^m$ and a label $y_i \in \{0, 1, 2\}$ denoting the clinical group (control, presymptomatic, patient). Our aim is to estimate a DPS, denoted as $v_i \in [1, 100]$ (the interval for the scores is arbitrary), where a greater score corresponds to a higher disease severity. To that purpose, we assume that the observations have corresponding latent variables $z_i \in \mathbb{R}^l$. We will thus aim to estimate a latent representation and the DPS will be computed from a trajectory in the latent space.

Our framework is composed of three main steps, as illustrated in Fig. 1. First, we propose a supervised multimodal variational autoencoder to estimate the latent space. We leverage the fact that participants belong to different groups to introduce some supervision in order to improve the VAE training. The model aims at simultaneously reconstructing the data and classifying the participants. We propose to split low-dimensional sociodemographic data (denoted $\mathcal{X}_{ld}$, used only for the classification) from high-dimensional multimodal neuroimaging and transcriptomic data (denoted $\mathcal{X}_{hd}$, used both for reconstruction and classification). Second, we build a curve representing disease trajectory in the latent space. Finally, data from new subjects, not included in the training set, are encoded in the latent space and projected onto this trajectory, in order to obtain their DPS.

In this section, we first explain the three main steps of our framework, then we describe implementation details.

### A. Supervised multimodal VAE

A variational autoencoder (VAE) [30] is a generative model that learns the training data distribution $p(x)$ using a latent representation model:

$$p(x) = \int p(x|z)p(z)dz,$$

where $z$ is a continuous latent variable living in a lower dimensional space and $p(z)$ is its prior distribution, commonly a Gaussian with zero mean and identity covariance matrix. The solution of the inference problem to describe the latent space is given by deriving the posterior $p(z|x)$. However, there is no closed-form solution for complex real-world datasets. Therefore, VAEs introduce the idea of learning a variational approximation $q_\phi(z|x)$ of the true posterior, in the form of a neural network referred to as the encoder. The encoder maps data $x$ to a mean vector $z_{mean}$ and a log-variance vector $z_{logvar}$, that parametrize a Gaussian distribution from which we obtain the latent representation $z$. VAEs are also equipped with a generative function $p_\theta(x|z)$, parametrized by a neural network referred to as the decoder. The decoder transforms the latent representation $z$ back to the original input space.

During training, the vanilla VAE aims at maximizing the variational lower bound of the marginal log-likelihood, known as the evidence lower bound (ELBO). This is equivalent to minimizing a loss function with two terms:

$$\mathcal{L} = \mathcal{L}_r(x, \hat{x}) + \mathcal{L}_{KL}(q_\phi(z|x), p(z)).$$

The first term is the reconstruction error between the input data $x$ and the reconstructed data $\hat{x}$, typically a mean squared error (MSE). The second term is the Kullback-Leibler divergence
between the approximated posterior $q_{\phi}(z|x)$ and the prior distribution $p(z)$, acting as a regularization term.

We propose to insert a supervised branch in the vanilla VAE architecture in order to exploit the fact that our samples have different diagnostic labels, even though their DPS is unknown. Denoting $y$ as the true class label and $\hat{y}$ as the predicted class label, we define our training objective as:

$$ \mathcal{L} = \alpha_1 \cdot \mathcal{L}_r(x, \hat{x}) + \alpha_2 \cdot \mathcal{L}_{KL}(q_{\phi}(z|x), p(z)) + \alpha_3 \cdot \mathcal{L}_c(y, \hat{y}), $$

where $\mathcal{L}_r$ and $\mathcal{L}_{KL}$ correspond to the ELBO in vanilla VAEs and $\mathcal{L}_c$ is a cross-entropy term that penalizes the classification error. The hyperparameters $\alpha_k$ control the relative weights between the different loss terms ($\sum_{k=1}^3 \alpha_k = 1$).

Before training, we split the high-dimensional modalities (miRNA expression and neuroimaging) from the low-dimensional (demographic information). As it will be mentioned later in the datasets description, we consider one low-dimensional feature and $m-1$ high-dimensional features, although the same concepts can be applied to more low-dimensional features. So we use $m-1$ features to feed the encoder and one feature concatenated to the latent code to feed the classifier. Features are rescaled from 0 to 1. Our encoder consists of fully-connected layers of sizes $(m-1) \to 50 \to 2$, meaning our latent space is 2-dimensional. The decoder is implemented with fully-connected layers of sizes $2 \to 50 \to (m-1)$. The nonlinear activation function is the leaky rectified linear unit (ReLU) in all layers except the decoder's last layer which uses a sigmoid function to constrain the output between 0 and 1. The classifier network has one fully connected layer of $3 \to 3$ units, with a softmax function to normalize the output to probabilities over the predicted classes. We use the mean squared error as the reconstruction loss $\mathcal{L}_r$ and the cross-entropy as the classification loss $\mathcal{L}_c$.

## B. Trajectory definition

Once the model is trained, the next step is to encode the training data in the latent space. We then compute the straight line passing through the centroids of the control and patient clusters. This straight line could be used in downstream analyses as a rudimentary disease trajectory in the latent space. Instead, we obtain an improved nonlinear trajectory by using this line as initialization for the principal curve algorithm [39]. A principal curve is a smooth one-dimensional curve passing through the *middle* of given data points. The algorithm detailed in [39] finds a nonparametric curve by iteratively minimizing the orthogonal distances to the points until convergence.

## C. DPS computation

Once the disease trajectory curve is computed in the latent space, we can encode the test data. The next step is to orthogonally project the latent codes onto the computed curve. The DPS $v_i \in [1, 100]$ for each subject is the coordinate of their projection along this curve, 1 corresponding to the beginning and 100 to the end of the curve. The pseudo-code from model training to DPS computation is shown in Algorithm 1.

### Algorithm 1 DPS computation from latent representation

**Input:** features $\mathcal{X} = \{x_i\}_{i=1}^n \in \mathbb{R}^m$, labels $\mathcal{Y} = \{y_i\}_{i=1}^n \in \{0, 1, 2\}$, training set indices $I_{tr}$ and test set indices $I_{te}$ for one data split into training and test set.

**Output:** DPS $\{v_i\}_{i=I_{te}}$ of the subjects in the test set.

/* first step: supervised VAE training */

**for** epoch in [1,250] **do**

Sample batches $(\mathcal{X}_j, \mathcal{Y}_j)$ from $(\mathcal{X}_{I_{tr}}, \mathcal{Y}_{I_{tr}})$

**for** each batch $(\mathcal{X}_j, \mathcal{Y}_j)$ **do**

$\mathcal{X}_{hd}, \mathcal{X}_{ld} \leftarrow \text{split\_high\_low\_dimension}(\mathcal{X}_j)$
$\mathcal{Z}_{mean}, \mathcal{Z}_{logvar} \leftarrow \text{encoder}(\mathcal{X}_{hd})$
Draw latent codes $\mathcal{Z} \sim \mathcal{N}(\mathcal{Z}_{mean}, e^{\mathcal{Z}_{logvar}})$
$\mathcal{Y}_y \leftarrow \text{classifier}(\text{concatenate}(\mathcal{X}_{ld}, \mathcal{Z}_{mean}))$
$\mathcal{X}_{hd} \leftarrow \text{decoder}(\mathcal{Z})$
$\mathcal{L}_r \leftarrow \text{mean\_squared\_error}(\mathcal{X}_{hd}, \mathcal{X}_{hd})$
$\mathcal{L}_{KL} \leftarrow \text{kl\_divergence}(\mathcal{N}(\mathcal{Z}_{mean}, e^{\mathcal{Z}_{logvar}}), \mathcal{N}(0, I))$
$\mathcal{L}_c \leftarrow \text{cross\_entropy}(\mathcal{Y}_y, \mathcal{Y}_y)$
$\mathcal{L} \leftarrow \alpha_1 \cdot \mathcal{L}_r + \alpha_2 \cdot \mathcal{L}_{KL} + \alpha_3 \cdot \mathcal{L}_c$
Compute gradients, update network to minimize $\mathcal{L}$

**end for**

**end for**

/* second step: trajectory definition */

$\mathcal{Z}, \_ \leftarrow \text{encoder}(\mathcal{X}_{I_{te}})$
$c_{control} \leftarrow \text{mean}(\{\mathcal{Z}_j : y_j == 0\})$
$c_{patient} \leftarrow \text{mean}(\{\mathcal{Z}_j : y_j == 2\})$
$pc \leftarrow \text{principal\_curve}(c_{control}, c_{patient}, \text{degree} = 2)$

/* third step: DPS computation */

**for** $i$ in $I_{te}$ **do**

$z_{pc} \leftarrow \text{projection of } z_i \text{ into } pc$

$v_i \leftarrow \text{coordinate of } z_{pc} \in [0, 100]$

**end for**

return $\{v_i\}_{i=I_{te}}$

## D. Implementation details

The hyperparameters of the training objective were set as $\alpha_1 = 0.2$, $\alpha_2 = 0.2$, and $\alpha_3 = 0.6$. The loss function was optimized using Adam [40], with a learning rate of $10^{-3}$, batches of 32 observations and 250 epochs.

We carried out the experiments on a computer equipped with a 2.4 GHz Intel Quad-Core Core i5 processor and 16 GB of RAM. Models were implemented in Python 3.8.5 using PyTorch 1.8.1 and Scikit-learn 0.23.2 [41]. For the principal curves computation, we used the implementation provided in the Python package pcurvepy 0.0.10 (https://pypi.org/project/pcurvepy/), specifying 2 as the degree of the smoothing spline.

# III. DATASETS

## A. Synthetic datasets

Since ground truth disease progression scores are not available in real-world scenarios, we created synthetic datasets to better evaluate the proposed framework. Multiple datasets were generated, with different noise levels and distinct proportions of features correlating with the DPS.

Let $Y \in \{0, 1, 2\}$ indicate the class labels (respectively control, presymptomatic and patient). We created $n = 111$
<figure><img src="image_51.png" /><figcaption>Fig. 2: Synthetic ground truth disease progression scores {$v_i$}$_{i=1}^n \in [0, 100)$ for $n = 111$ subjects (37 subjects per group).</figcaption></figure>

synthetic participants (a number close to that of our real dataset) with class labels denoted by $y_i$ ($i = 1, ..., 111$),

$$ 
\begin{aligned}
y_{i=1,...,37} &= 0 \\
y_{i=38,...,74} &= 1 \\
y_{i=75,...,111} &= 2.
\end{aligned}
 $$

Next, we modeled the disease progression scores as continuous random variables following uniform distributions. Let $V \in [1, 100)$ represent the DPS values. We defined the conditional distribution of the DPS given the class labels as follows:

$$ 
\begin{aligned}
V|Y &= 0 \sim U[1, 34) \\
V|Y &= 1 \sim U[34, 67) \\
V|Y &= 2 \sim U[67, 100)
\end{aligned}
 $$

We then sampled the corresponding DPS $v_i$ from the conditional distributions defined above. The obtained disease progression scores are displayed in Fig. 2.

Once the synthetic ground truth DPS were created, we generated multiple datasets $D \in \mathbb{R}^{n \times m}$ containing $n = 111$ participants and $m = 160$ features. In order to simulate two modalities, features were initially sampled from two distributions: half from a negative binomial distribution (typical of miRNA expression data) and half from a normal distribution (representative of various real-world datasets). We denote the columns of $D$ by $C_1, ..., C_m$. The format of the synthetic datasets is illustrated in Fig. 3.

Each created dataset had a distinct proportion of features correlating with the DPS and different noise levels. The number of features from each modality to positively and negatively correlate with the DPS is denoted as $f$, and the standard deviation of the added zero-mean Gaussian noise as $s$. We used $f = \{0, 2, 5, 10, 15, 20, 25, 30, 35, 40\}$ and $s = \{0.001, 0.2, 0.5, 0.8, 1, 5\}$ and thus obtained a total of 60 synthetic datasets. The algorithm describing the set of operations performed for their generation is in Appendix I Algorithm A.1.

<figure><img src="image_52.png" /><figcaption>Fig. 3: Format of the synthetic datasets $D \in \mathbb{R}^{n \times m}$ containing $m$ features from $n$ individuals. Half of the features are initially sampled from a negative binomial distribution and half from a normal distribution.</figcaption></figure>

## B. Real dataset

Participants were recruited through the PREV-DEMALS study (https://clinicaltrials.gov, ID NCT02590276), a French multicentric prospective cohort focused on C9orf72 expansion carriers. Written informed consents were obtained from all participants. The study was approved by the ethics committee (Comité de Protection des Personnes CPP Ile-De-France VI, CPP 68-15 and ID RCB 2015-A00856-43). A detailed description of this cohort and its demographic profile can be found in [7].

We included 110 individuals in our analyses, divided into three groups, according to their clinical status:

* Patient group: 22 symptomatic (15 FTD, 4 FTD/ALS and 3 ALS) carriers of a pathogenic C9orf72 expansion;

* Presymptomatic group: 45 asymptomatic carriers;

* Control group: 43 asymptomatic non-carriers.

The dataset comprised multimodal data including miRNA (miRNA) sequencing data and neuroimaging data. These two modalities are described below.

1) MicroRNA data: MicroRNAs are a class of small non-coding RNAs that negatively regulate gene expression [42]. MicroRNAs expression in blood plasma has been shown to correlate with the diagnosis and progression of many neurodegenerative diseases [43], including FTD and ALS. All individuals included in this cohort underwent plasma sampling, from which miRNA sequencing was performed. Plasma collection and preparation, miRNA extraction and sequencing, quality control and the computational pipeline to obtain the miRNA counts are detailed in [7]. The initial miRNA dataset contained expression levels for all miRNAs mapped in the human genome (2576 miRNAs). We retained the 589 miRNAs with expression profiles above noise level (minimum total count of 1000 reads and at least 50 reads for one sample). A trimmed mean of M-values [44] implemented in the R package EdgeR [45] was used to normalize the raw counts.

2) Neuroimaging data: Neuroimaging data consisted of grey matter volumes extracted from T1-weighted anatomical magnetic resonance imaging (MRI), including the estimated total
intracranial volume (TIV), 68 cortical regions of interest (ROIs) using the Desikan atlas and 18 subcortical ROIs using the Aseg nomenclature, thus resulting in 87 neuroimaging features. The TIV was used to normalize the volume of each ROI,

$$NV_{ROI} = \frac{TIV_{m} \times V_{ROI}}{TIV},$$

where $V_{ROI}$ is the original volume of the ROI, $NV_{ROI}$ is the corresponding normalized volume and $TIV_{m}$ is the average TIV computed across all subjects. The MRI acquisition parameters, quality check and processing pipeline are thoroughly described in [6].

Only 91 subjects (14 patients, 40 presymptomatic carriers and 37 controls) had MRI scans collected. Hence, we divided our dataset into two subsets: 19 subjects that only had miRNA data available, and 91 subjects with multimodal neuroimaging and miRNA data. The former subset was used as a discovery set for miRNA feature selection: we used these 19 individuals to perform differential expression analysis (as described in [7]). The 68 miRNAs with the lowest p-values were selected for all downstream analyses.

Lastly, we also included age as demographic information for all subjects. So the total dimension of each feature vector was $m = 87 + 68 + 1 = 156$.

# IV. EXPERIMENTS AND RESULTS

## A. Synthetic datasets

We applied our framework to 60 synthetic datasets (described in Section III-A) with different noise levels and distinct number of features correlating with the ground truth DPS. Each synthetic dataset was divided into a training set of 90 subjects (30 per clinical group) and a test set of 21 individuals (7 per group). We trained one model per dataset, using the same hyperparameters as the experiments with the real dataset. After training each model, we computed the DPS for the subjects from the test set. We then calculated the Spearman correlations between the simulated ground-truth scores and the predicted scores. Finally, we evaluated the ROC AUC for each pairwise comparison between the three simulated clinical groups.

Fig. 4 presents the computed trajectories and the DPS obtained when 50% of the features are correlated (25% positively and 25% negatively correlated) with the disease progression, for different noise levels. The correlation matrices illustrate the strength of the relationships between the simulated features, for all investigated noise levels.

The results of the Spearman correlation between the estimated DPS and the ground truth data, as well as the average ROC AUC scores for the three pairwise comparison between groups, are showed in Fig. 5. As expected, we can observe that the DPS is very well estimated for lower noise levels and higher proportion of relevant features, while the performances decrease when the noise level becomes very high and when only few features are correlated with the DPS. Importantly, we observe that the Spearman correlation of the DPS and the ROC AUC have similar behaviors, indicating that the ROC AUC of

pairwise comparisons is a reasonable proxy to evaluate the DPS, as will be done with the real dataset.

## B. Real dataset

Experiments with the real dataset (described in Section III-B) were carried out with a cross-validation of 100 stratified randomized folds. For each fold split, we trained a model using 73 training subjects, and then computed the DPS for the 18 individuals in the test set. Fig. 6 displays an example of the latent space trajectory computed with one representative training data split, the corresponding test set projected in the latent space, and the obtained disease progression scores.

Unlike for the synthetic dataset, there is no ground truth for the DPS in the real dataset. We thus applied a proxy metric to assess model performance: using only the inferred DPS, we did pairwise comparisons between the clinical groups and computed the corresponding areas under the ROC curves. Specifically, we present the following experiments: (1) evaluation of the proposed method, (2) comparison with a state-of-the-art method for modeling disease progression, the discriminative event-based model, (3) ablation study, and (4) variation of hyperparameters.

1) **Evaluation of the proposed method:** First, we used the DPS computed in each fold to build ROC curves for the three pairwise comparisons between clinical groups. The average ROC curves are shown in Fig. 7. The ROC AUC for the classification of controls and presymptomatic subjects was 0.74 ± 0.13, for controls and patients was 0.98 ± 0.05 and to distinguish presymptomatic carriers and patients was 0.96 ± 0.07. These results reveal that it is harder to differentiate controls from presymptomatic individuals than it is to distinguish between patients and the other two groups. The histogram displayed in Fig. 8 illustrates the disease progression scores computed over all 100 test folds (18 subjects per test fold, corresponding to 1800 DPS). The distribution shapes highlight a clear separation between the patient group and the other groups. The distribution of the DPS for the presymptomatic group is more spread, which was expected as this group is the most heterogeneous. Some presymptomatic subjects are very far from onset and the neurodegenerative process has barely begun, they are thus closer to controls. Other presymptomatic subjects are closer to disease onset and thus their DPS is closer to that of patients.

2) **Comparison with DEBM:** Next, we compared our results to a discriminative event-based model (DEBM) [9], a method that also infers a DPS from cross-sectional data. For that experiment, the same cross-validation strategy of 100 stratified folds was applied. We built the DEBM models and computed the DPS using the Python package pyebm 2.0.3 (https://pypi.org/project/pyebm/). Table I displays the corresponding ROC AUC results for each pairwise comparison. We can observe that our model achieves a substantially better classification performance for all pairwise comparisons. Additionally, our approach used less computing time: our framework took 2 seconds per fold for training and DPS computation, while the DEBM algorithm took on average 180 seconds per fold.
<figure><img src="image_53.png" /><figcaption>**Fig. 4:** Results on synthetic data when 50% of the features are correlated with the disease progression score. The rows indicate different noise levels (zero-mean Gaussian noise with different standard deviations). Each column displays, respectively: (1) correlation matrices showing the strength of the relationships between the simulated features, (2) inferred trajectories and test sets projected in the latent space, and (3) estimated DPS vs. ground truth DPS.</figcaption></figure>
<figure><img src="image_54.png" /><figcaption>Fig. 5: Results on synthetic data. Macro-average ROC AUC and Spearman correlation between ground truth and estimated DPS, for different noise levels (zero-mean Gaussian with 0.001, 0.2, 0.5, 0.8, 1, and 5 as standard deviation) and several proportions (0% to 100%) of features correlating with the disease progression score. Random chance is denoted by the dashed lines (ROC AUC = 0.5 and Spearman Rho = 0).</figcaption></figure>

<figure><img src="image_55.png" /><figcaption>Fig. 6: Results on real data. (a) Training data projected in the latent space and the corresponding computed trajectory for one of the 100 fold splits. (b) Test data projected in the latent space, along with the previously computed trajectory. (c) Scores computed after the projection of the latent representation of the test data onto the trajectory.</figcaption></figure>

<table><caption>TABLE I: Results on real data: comparison between our approach and a discriminative event-based model (DEBM) [9]. ROC AUC (mean ± standard deviation) over 100 stratified splits.</caption><thead><tr><th>Comparison</th><th>Our model</th><th>DEBM</th></tr></thead><tbody><tr><td>Control vs. Pre</td><td><strong>0.74 ± 0.13</strong></td><td>0.67 ± 0.14</td></tr><tr><td>Control vs. Patient</td><td><strong>0.98 ± 0.05</strong></td><td>0.76 ± 0.17</td></tr><tr><td>Pre vs. Patient</td><td><strong>0.96 ± 0.07</strong></td><td>0.65 ± 0.17</td></tr></tbody></table>

3) Ablation study: Afterwards, to investigate the impact of certain components of our framework, we conducted an ablation study. We changed some elements of the proposed approach to obtain three alternative models:

* Linear instead of curved trajectory: rather than computing the trajectory in the latent space using principal curves, we simply used a straight line.

* No supervised branch: we removed the classification component of the loss function, thus performing unsupervised training.

* Joint low-dimensional modality: we concatenated the low-dimensional modality (demographic information) with the high-dimensional modalities (neuroimaging and miRNA expression) in the encoder input, and used only the latent codes as input for the classifier.

For each alternative model, we conducted the same cross-validation strategy of 100 stratified folds, computing the DPS for the test sets and the corresponding areas under the ROC curves. The results, displayed in Table II, show that the proposed model has a better and more stable performance in all comparisons, with the highest average ROC AUC and lowest standard deviation among the splits.
<figure><img src="image_56.png" /><figcaption>**Fig. 7:** Results on real data. Average ROC (receiver operating characteristic) curves for each pairwise comparison between clinical groups, over 100 stratified splits. The shaded areas correspond to one standard deviation. The areas under the ROC curves (ROC AUC) are shown as mean ± standard deviation. Random chance is indicated by the dashed line.</figcaption></figure>

**4) Variation of hyperparameters:** Finally, we checked whether our results were robust to reasonable changes in the hyperparameters. Notably, we tested different numbers of hidden units in the fully-connected layers, and different combinations of the relative weights between the loss terms. These results are summarized in Appendix II Table B.1 and Table B.2. The slightly different but overall similar results demonstrate that our hyperparameter choice is not overfitting the data.

<figure><img src="image_57.png" /><figcaption>**Fig. 8:** Results on real data. Histogram of the disease progression scores (DPS) inferred for 18 test subjects over 100 stratified splits. The distribution shapes are approximated with kernel density estimates.</figcaption></figure>

<table><caption>**TABLE II:** Results on real data: ablation study. ROC AUC results (mean ± standard deviation) for the proposed model and three alternative models from the ablation study, respectively using a linear instead of a curved trajectory, removing the classification branch, and concatenating the low-dimensional modality with the high-dimensional ones.</caption><thead><tr><th>Comparison</th><th>Proposed model</th><th>Linear trajectory</th><th>No supervision</th><th>Joint low-dim.</th></tr></thead><tbody><tr><td>Control vs. Pre</td><td><b>0.74 ± 0.13</b></td><td>0.62 ± 0.15</td><td>0.67 ± 0.15</td><td>0.72 ± 0.15</td></tr><tr><td>Control vs. Patient</td><td><b>0.98 ± 0.05</b></td><td>0.93 ± 0.12</td><td>0.96 ± 0.06</td><td>0.95 ± 0.17</td></tr><tr><td>Pre vs. Patient</td><td><b>0.96 ± 0.07</b></td><td>0.93 ± 0.11</td><td>0.94 ± 0.10</td><td>0.91 ± 0.18</td></tr></tbody></table>

## V. DISCUSSION

In this paper, we proposed a new approach for estimating disease progression scores from cross-sectional neuroimaging and transcriptomic data that is applicable in small samples, which are typically found in rare diseases. The approach was designed and evaluated on data from C9orf72-associated FTD and ALS, but is potentially applicable to other diseases. Results on synthetic data demonstrated the ability of the method to accurately estimate the DPS, and experiments on real data, in the absence of ground truth DPS, showed the separation of different diagnostic classes. The findings of this study validated the usefulness of supervised variational autoencoders to infer disease trajectories from cross-sectional multimodal data, indicating that a single disease progression score may be used to represent progression of neurodegenerative diseases. Remarkably, our results revealed that the DPS may be inferred using only cross-sectional data from a small sample of subjects.

Experiments with a cohort of C9orf72-mutation carriers demonstrate that subjects from the same clinical groups (patients, presymptomatic individuals and controls) are clustered together in the latent space (Fig. 6), allowing the inference of a disease trajectory. After training the model, data from new individuals is encoded in the latent space and orthogonally projected onto this trajectory to compute the DPS. Notably, using only the computed DPS, we are able to classify presymptomatic subjects and patients with an average ROC AUC of 0.96 over 100 stratified fold splits (Fig. 7). Of the
three possible pairwise comparisons between clinical groups,
this is the most relevant. It illustrates how much the DPS
reflects the degree of disease progression in mutation carriers.
Unsurprisingly, it is harder to differentiate between controls
and presymptomatic individuals, as indicated by the average
ROC AUC of 0.74 and displayed in Fig. 8. This stems from the
fact that, during earlier disease stages, most biomarker levels
are closer to normal ranges, so the presymptomatic class is
more heterogeneous.

To the best our of knowledge, event-based models are
the only published methods to compute disease progression
scores from cross-sectional data, other approaches requiring
longitudinal data. The comparison summarized in Table I
reveals that our approach resulted in considerably higher ROC
AUC than DEBM for all pairwise classifications. This suggests
that the proposed approach is more suitable than event-based
models for DPS computation with high-dimensional features,
such as microRNA data. Indeed, published studies using
event-based models explored a substantially lower number of
features. For instance, in Alzheimer's disease, EBM exper-
iments were carried our with 13 to 50 [8], [9], [19]–[22]
biomarkers. Studies focusing on FTD analyzed 21 [27] or
7 [28] biomarkers, while multiple sclerosis was investigated
with 25 [23] or 24 [24] biomarkers. Other conditions such as
Parkinson's disease [25], ALS [29] and Huntington's disease
[26] were modeled with respectively 42, 19 and 8 biomark-
ers. Nevertheless, the EBM model presents useful additional
features, beyond the computation of DPS. In particular, it can
provide a temporal ordering of when the different biomarkers
become abnormal, which is useful for understanding disease
progression. Moreover, a balance has to be found between
the number of features and the number of subjects in each
dataset. Indeed, we also had to perform feature selection to
decrease the number of microRNAs in our study. It should
be noted that this feature selection was unbiased, since it was
performed using a completely separate set of participants that
was not used in the rest of the study. The proposed framework
was able to achieve a good performance with 156 features and
less than a hundred subjects, thus demonstrating its potential
for dealing with higher dimensional datasets.

An ablation study evaluated the impact of different com-
ponents of our approach (Table II). We observed that each
component positively impacted the framework’s performance.
First, it can be seen that a curved trajectory better fits the dis-
ease pathway in the latent space when compared to a straight
line. The use of principal curves has been inspired from
their application in a similar task: pseudotime inference for
single-cell transcriptomics, as shown in [46]. In that context,
pseudotime represents an underlying temporal variable driving
a smooth transition between cellular states, and principal
curves are used to infer a trajectory in a low-dimensional
space. Second, it is clear that the addition of supervision with
a classifier branch improves the separation between clinical
groups in the latent space. Rather than discrete clusters, our
experiments demonstrate that latent representations are placed
along a continuous path. Specifically, supervision adds mean-
ing to the relative positions between points in the latent space.
Finally, results show the contribution of splitting high and

low-dimensional features. When using the low-dimensional
features concatenated with the latent codes as inputs to the
classifier, the model's performance is enhanced. The same
pattern is observed in [37], although in a totally different
context (failure detection in robotics). Concretely, a low-
dimensional feature can directly contribute to the classifier,
without the need for encoding.

Regarding the experiments with simulated datasets, it is
crucial to highlight the relationship of the average ROC
AUC with the Spearman correlation between ground truth
and estimated DPS (Fig. 5). The simulation supports that the
higher the ROC AUC, the more accurate the predicted DPS.
Therefore, for real-world scenarios without ground truth DPS,
our choice of the ROC AUC as proxy metric is corroborated.
Furthermore, evidence was found that the models do not overfit
the data, since it is clear that larger noise levels lead to poorer
results, eventually equivalent to random chance. The effect
of noise is further illustrated in Fig. 4. We observe that lower
noise levels induce more evident clusters and more meaningful
trajectories in the latent space. Consequently, the estimated
DPS are closer to the ground truth. These simulations also
confirm one intuition behind our model: the more features
correlate with disease progression, the closer the estimated
DPS are to the ground truth.

Our study has the following limitations. First, there is
no ground truth for the progression scores in real datasets.
Although the experiments with synthetic data showed that the
ROC AUC is an adequate proxy metric, long-term follow-up
of patients will be necessary to assess the accuracy of the
computed DPS. For instance, we need follow-up data to con-
firm the hypothesis that a higher DPS implies an earlier disease
onset for a presymptomatic subject. Another limitation was the
lack of a replication cohort. This will be necessary to further
support the clinical relevance of our findings. Future work will
concentrate on the integration of more data sources, such as
positron emission tomography (PET) scans and neurofilament
light chain (NfL) levels in blood.

In conclusion, we proposed a new approach to measure
disease progression from multimodal imaging and microRNA
data in rare neurodegenerative disorders using only cross-
sectional data. Even though we focused on C9orf72-associated
FTD and ALS, our framework is generic. It has the potential
to be useful for a variety of other diseases, enabling the
evaluation of novel treatments even when only cross-sectional
data from small cohorts are available.

ACKNOWLEDGMENT

The PREV-DEMALS study group includes: Eve Benchetrit
(Hôpital de la Salpêtrière, Paris), Anne Bertrand (Hôpital de
la Salpêtrière, Paris), Anne Bissery (Hôpital de la Salpêtrière,
Paris), Marie-Paule Boncoeur (CHU Dypuytren, Limoges),
Stéphanie Bombois (CHU Roger Salengro, Lille), Agnès Ca-
muzat (ICM, Paris), Mathieu Chastan (CHU Charles Nicolle,
Rouen), Yaohua Chen (CHU Roger Salengro, Lille), Marie
Chupin (ICM, Paris), Olivier Colliot (ICM, Paris), Philippe
Couratier (CHU Dypuytren, Limoges), Xavier Delbeuck (CHU
Roger Salengro, Lille), Vincent Deramecourt (CHU Roger
Salengro, Lille), Christine Delmaire (CHU Roger Salengro, Lille), Emmanuel Gerardin (CHU Charles Nicolle, Rouen), Claude Hossein-Foucher (CHU Roger Salengro, Lille), Bruno Dubois (Hôpital de la Salpêtrière, Paris), Marie-Odile Habert (Hôpital de la Salpêtrière, Paris), Didier Hannequin (CHU Charles Nicolle, Rouen), Géraldine Lautrete (CHU Dypuytren, Limoges), Thibaud Lebouvier (CHU Roger Salengro, Lille), Isabelle Le Ber (Hôpital de la Salpêtrière, Paris), Benjamin Le Toullec (ICM, Paris), Richard Levy (Hôpital de la Salpêtrière, Paris), Olivier Martinaud (CHU Charles Nicolle, Rouen), Kelly Martineau (ICM, Paris), Marie-Anne Mackowiak (CHU Roger Salengro, Lille), Jacques Monteil (CHU Dypuytren, Limoges), Florence Pasquier (CHU Roger Salengro, Lille), Gregory Petyt (CHU Roger Salengro, Lille), Pierre-François Pradat (Hôpital de la Salpêtrière, Paris), Assi-Hervé Oya (Hôpital de la Salpêtrière, Paris), Armelle Rametti-Lacroux (Hôpital de la Salpêtrière, Paris), Daisy Rinaldi (Hôpital de la Salpêtrière, Paris), Adeline Rollin-Sillaire (CHU Roger Salengro, Lille), François Salachas (Hôpital de la Salpêtrière, Paris), Sabrina Sayah (Hôpital de la Salpêtrière, Paris), David Wallon (CHU Charles Nicolle, Rouen).

We thank Vincent Anquetil for plasma collection and preparation, Justine Guégan and Thomas Gareau for microRNA raw reads to counts pipeline handling, Yannick Marie and Delphine Bouteiller for library preparation and sequencing, and Philippe Martin-Hardy and Ludmila Jornea for technical assistance.

# APPENDIX I
## ALGORITHM FOR SYNTHETIC DATASETS GENERATION

### Algorithm A.1 Synthetic datasets generation

**Input:** number of subjects $n$, number of features $m$, disease progression scores values $v_i$ ($i=1,...,n$).

**Output:** set $L$ containing the datasets $\mathcal{D} \in \mathbb{R}^{n \times m}$
$L = \{\}$

**for** $f$ **in** $\{0, 2, 5, 10, 15, 20, 25, 30, 35, 40\}$ **do**

**for** $s$ **in** $\{0.001, 0.2, 0.5, 0.8, 1, 5\}$ **do**

$C_{1,..., \frac{m}{2}} \leftarrow \text{NB}(r = 3000, p = 0.75, size = (n, \frac{m}{2}))$

$C_{\frac{m}{2}+1,...,m} \leftarrow \mathcal{N}(\mu=1000, \sigma=200, size=(n, \frac{m}{2}))$

/* f features from each modality positively correlate with disease progression */

**for** $j$ **in** $\{1,...,f\} \cup \{\frac{m}{2}+1,...,\frac{m}{2}+1+f\}$ **do**

$C_j \leftarrow v \odot C_j$

**end for**

/* the next f features are negatively correlated */

**for** $j$ **in** $\{f,...,2f\} \cup \{\frac{m}{2}+1+f,...,\frac{m}{2}+1+2f\}$ **do**

$C_j \leftarrow \frac{1}{v} \odot C_j$

**end for**

/* normalize and add zero-mean Gaussian noise */

**for** $j$ **in** $\{1,...,m\}$ **do**

$C_j \leftarrow \frac{C_j - \text{min}(C_j)}{\text{max}(C_j) - \text{min}(C_j)}$

**end for**

$\mathcal{D} \leftarrow \mathcal{D} + \mathcal{N}(\mu=0, \sigma=s, size=(n, m))$

$L = L \cup \{D\}$

**end for**

**end for**

return $L$

# APPENDIX II
## RESULTS VARYING HYPERPARAMETERS

<table><caption>TABLE B.1: Results on real data. ROC AUC results (mean ± standard deviation) over 100 stratified splits when changing the number of units of the hidden layers. Original results, with 50 units, are shown in bold.</caption><thead><tr><th>Hidden units</th><th>50</th><th>100</th><th>80</th><th>25</th></tr></thead><tbody><tr><td>Control vs. Pre</td><td><b>0.74 ± 0.13</b></td><td>0.73 ± 0.13</td><td>0.71 ± 0.12</td><td>0.71 ± 0.13</td></tr><tr><td>Control vs. Patient</td><td><b>0.98 ± 0.05</b></td><td>0.98 ± 0.04</td><td>0.97 ± 0.05</td><td>0.98 ± 0.05</td></tr><tr><td>Pre vs. Patient</td><td><b>0.96 ± 0.07</b></td><td>0.96 ± 0.06</td><td>0.96 ± 0.06</td><td>0.96 ± 0.06</td></tr></tbody></table>

<table><caption>TABLE B.2: Results on real data. ROC AUC results (mean ± standard deviation) over 100 stratified splits when changing the weights of the loss function terms. Original results, with $\alpha_1=0.2, \alpha_2=0.2, \alpha_3=0.6$, are shown in bold.</caption><thead><tr><th>Weights α<sub>k</sub></th><th>0.2, 0.2, 0.6</th><th>0.1, 0.1, 0.8</th><th>0.1, 0.2, 0.7</th><th>0.3, 0.2, 0.5</th></tr></thead><tbody><tr><td>Control vs. Pre</td><td><b>0.74 ± 0.13</b></td><td>0.72 ± 0.12</td><td>0.73 ± 0.12</td><td>0.72 ± 0.14</td></tr><tr><td>Control vs. Patient</td><td><b>0.98 ± 0.05</b></td><td>0.97 ± 0.08</td><td>0.97 ± 0.06</td><td>0.98 ± 0.05</td></tr><tr><td>Pre vs. Patient</td><td><b>0.96 ± 0.07</b></td><td>0.94 ± 0.10</td><td>0.95 ± 0.09</td><td>0.96 ± 0.07</td></tr></tbody></table>

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C.3 Contributions jointes en support au chapitre 4

[29] Moreews F, Simon H, Siegel A, Gondret F, **Becker E.**
*PAX2GRAPHML : a python library for large-scale regulation network analysis using BIOPAX.*
Bioinformatics, 2021.

[30] Melkonian M, Juigné C, Dameron O, Rabut G* and **Becker E***. *Towards a reproducible interactome: semantic-based detection of redundancies to unify protein-protein interaction databases.*
Bioinformatics, 2022.

[33] Juigné C, Dameron O, Moreews F, Gondret F and **Becker E.**
*Detection and correction of non-conformities and redundancies in complexes of molecules in BioPAX.*
Actes JOBIM 2022, Rennes, 5-8 juillet 2022