Alexandre Harlé

Etienne Rouleau

Voreak Suybeng

Florence Koeppel
email: florence.koeppel@gustaveroussy.fr

Comparison of pathogenicity prediction tools on somatic variants

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

With the progress of sequencing technologies, the use of sequencing data for cancer patient management has been increasing. Next generation sequencing produces a huge amount of data.

Sequencing data are first analyzed and annotated using bioinformatics pipelines. An expert interpretation is then mandatory in order to produce the clinical report, which will be used by the oncologist for discussion during a molecular tumor board and make the therapeutic decision. The interpretation is sometimes challenging. The difficulty is due to the complexity of the somatic context, but also to the larger number of variants typically observed, compared to germline variants. The stake of a correct variant interpretation is double; first, preventing orientation of a patient towards a therapeutic option, when the tumor does not carry the corresponding pathogenic alteration; second, allowing the orientation of a patient towards a therapeutic option, when the tumor does carry the corresponding pathogenic alteration. Therefore, prediction tools can be very valuable to assist therapeutic decision.

Several computational tools are available to predict the pathogenicity of variants. These tools provide a pathogenicity prediction based on various criteria such as localization within the protein, conservation amongst species, biochemical properties of the mutant and wild-type residues, and the potential impact of the variation on mRNA. Some tools consist in a combination of prediction tools.

Their performances have been evaluated for germline variants, variants from the IARC TP53 and from the ICGC databases 1 but to our knowledge, such evaluation is still missing for a large dataset of cancer somatic variants with curated pathogenicity classification. Results from prediction tools are often taken into account at least to some extent in the interpretation process of somatic variants. In some cases, prediction results are even used as inclusion criteria for clinical trials. It is important to understand how reliable these tools are and establish what role they should be given in the process of expert variant interpretation in the context of cancer management and clinical research. Increasing the quality of data interpretation is particularly crucial for variants of unknown significance. The key point is to retain variants potentially of interest in order to enrich associated therapeutic trials and therefore have opportunities to increase our knowledge of these variants. The aim is also to exclude variants which have enough evidence of inactivity.

In the current work, we are addressing the following questions. What is the reliability of prediction tools and databases most commonly used for interpretation of somatic variants? Should prediction tools be combined in order to increase reliability of the results? Is there an optimal combination of the currently available tools? Do the prediction performances for somatic variants depend on whether the mutation is present on an oncogene or on a tumor suppressor gene?

Materials and Methods

Benchmark dataset of missense variants

Three independent data sources of missense variants were used to construct our benchmark dataset and compare the performance of 22 prediction tools (Table 1).

The first set of missense variants was sourced from the OncoKB database [START_REF] Chakravarty | OncoKB: A Precision Oncology Knowledge Base[END_REF] (https://www.oncokb.org, last accessed on 2020, September 10). We downloaded the bulk data release (on 2019, February 23rd) and retained only missense variants classified as oncogenic (N=773), likely oncogenic (N=1,546) or likely neutral (N=501). Overall, the OncoKB database provided N=2,820 missense variants (Supplemental Table 1, worksheet 1). The second set of missense variants was sourced from CIViC database [START_REF] Griffith | CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer[END_REF] (https://civicdb.org, last accessed on 2020, September 10). We downloaded the bulk data release (on 2019, March 1st) and retained only missense variants with somatic origin and evidence supporting a clinical significance (predictive of response to therapy, prognostic and/or diagnostic) with an A (validated association, N=4) or B (clinical evidence, N=47) evidence level. Overall, the CIViC database provided N=51 missense variants (Supplemental Table 1, worksheet 2). The third set of missense variants was sourced from DoCM database [START_REF] Griffith | CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer[END_REF] (http://www.docm.info/, last accessed on 2020, September 10). We used the DoCM web interface to download variants (on 2019, March 9th) whose mutation types were missense and whose tags were pathogenic and/or likely pathogenic. Overall, the DoCM database provided N=1,134 missense variants (Supplemental Table 1, worksheet 3).

Before merging the three sets of missense variants into a single dataset, variant annotation was harmonized. Variants within a given gene could be annotated on multiple transcripts even if they were sourced from the same database. For example, TP53 had variants annotated on five different Ensembl transcripts (https://www.ensembl.org/Homo_sapiens/Info/Index, last accessed on 2020, September 10) when it was sourced from DoCM (ENST00000269305, ENST00000359597, ENST00000413465, ENST00000455263, ENST00000604348). As a consequence, for each gene, we considered a unique Ensembl transcript which was assessed in the OncoKB or DoCM database (Supplemental Table 1, worksheet 4) and we excluded from the three sets all variants which were annotated on a different transcript. We also excluded all variants with conflicting pathogenicity (N=19, Supplemental Table 1, worksheet 5). Finally, we merged the three sets of missense variants into a single dataset of N=3,176 missense variants (Supplemental Table 1, worksheet 6) covering 230 genes from OncoKB, CIViC and DoCM (Figure 1). We divided the missense variants into two groups based on variant pathogenicity: (1) an oncogenic group of variants (N=2,686) which included retained OncoKB variants described as oncogenic or likely oncogenic, and all retained CIViC and DoCM variants; (2) a neutral group of variants (N=490) which included OncoKB variants described as likely neutral.

In this compiled dataset of 3,176 missense variants, only 490 variants covering 83 genes were labeled as neutral and all were retrieved from OncoKB. In order to avoid an unbalanced dataset, we expanded the number of neutral missense variants by adding polymorphisms as follows. Within the assessed transcripts of the 230 genes with oncogenic missense variants in our dataset, we used dbNSFP3.5 [START_REF] Liu | dbNSFP v3.0: A One-Stop Database of Functional Predictions and Annotations for Human Nonsynonymous and Splice-Site SNVs[END_REF] to identify missense variants whose respective SNVs had an allele frequency (AF) ≥ 0.01 in any subpopulation from gnomAD exomes [START_REF] Karczewski | The mutational constraint spectrum quantified from variation in 141,456 humans[END_REF] . Those missense variants were considered as polymorphisms and were included in the neutral group of variants if they were not already present. By adding variants with an AF ≥ 0.01 into our neutral group, additional conflicting variants arose again in our dataset and consequently, they were excluded as well (N=25, Supplemental Table 1, worksheet 7) from both oncogenic and neutral group. Thus, after mapping all our missense variants to genomic coordinates using dbNSFP3.5, our final dataset included 4,319 SNVs (Supplemental Table 2) divided into two levels of clinical significance, namely oncogenic variants (N=2,996) and neutral variants (N=1,323). Variants covered 230 genes (Supplemental Table 1, worksheet 4), which were classified as oncogene (N=98), tumor suppressor gene (TSG) (N=97), both (N=6) or unknown (N=29) based on their main mutation effect (gain of function or loss of function) or based on the Cancer Gene Census resources [START_REF] Sondka | The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers[END_REF] . Figure 2 shows that our dataset contains oncogenic variants which are mostly extremely rare (AF<0.1% in general population) and neutral variants which are extremely rare, rare (between 0.1% and 1% in general population) or common (AF>1% in general population).

Prediction tools and prediction scores

We used dbNSFP v3.5a to annotate the 4,319 SNVs with 20 prediction tools including SIFT, Polyphen-2 HDIV, Polyphen-2 HVAR, LRT, MutationAssessor, FATHMM, PROVEAN, VEST3, MetaSVM, MetaLR, M-CAP, REVEL, MutPred, CADD, DANN, FATHMM-MKL, Eigen, Eigen-PC, GenoCanyon and fitCons (Table 1). We also used authors' publicly available website to annotate the 4,319 SNVs with UMD-Predictor [START_REF] Karczewski | The mutational constraint spectrum quantified from variation in 141,456 humans[END_REF] . Cut-off values recommended by dbNSFP or by the tools' authors were used to generate binary predictions for these SNVs (Supplemental Table 3). Note that although MutationTaster2 was also available through dbNSFP, it was not used in our study because it automatically predicts variants as disease causing when they are marked as pathogenic in ClinVar.

We added another prediction algorithm based on the position of the SNV within a functional domain or not. We used dbNSFP to assign the SNV position to a domain annotated in the Interpro database. If the SNV was located in any known domain, then it was predicted as being oncogenic by the algorithm. On contrary, if the SNV was not located in a known domain, then it was predicted as being neutral. We named this qualitative prediction algorithm a functional domain-based tool.

Overall, we processed predictions from 22 different tools on 4,319 SNVs. Some prediction tools provided multiple scores or predictions for the same SNV due to multiple transcripts for the same gene.

Most published articles used the most damaging scores [START_REF] Ghosh | Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines[END_REF] in their analyses or the average scores across all transcripts [START_REF] Ioannidis | REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants[END_REF] , but we chose to use only the scores specific to the Ensembl transcript assessed in the OncoKB or DoCM database. If a score for a given SNV could not be provided in this assessed transcript, then it was considered as a missing value. Thus, all tools could not provide scores for all SNVs in the assessed transcripts and in these cases, the scores and the predictions were considered as missing values.

When evaluating prediction tools on a dataset which comprises "likely" oncogenic SNVs or polymorphisms, a bias could be introduced in the results. Indeed, "likely" oncogenic SNVs may have been assessed by the very same tools that are being evaluated in this work. And in clinical routine, polymorphisms are generally not assessed by prediction tools, but they are rather filtered out by bioinformatic pipelines based on AF. Therefore, we also processed predictions on a subset of 1,440 SNVs retaining only "clearly" oncogenic SNVs (N=877) and likely neutral SNVs (N=563) from OncoKB database (Supplemental Table 4, worksheet 2). Finally, we generated all possible pairwise combinations of prediction tools. For a given combination of two tools, a variant prediction was considered as neutral if both tools called it neutral and it was considered as oncogenic if both tools called it oncogenic. However, it was considered as a missing value if both tools within the combination did not provide the same prediction. Combinations were evaluated on the subset of 1,440 SNVs.

Performance measures

The performance of each tool was evaluated as a single tool and as a combination of tools two by two. Accuracy, Matthews correlation coefficient (MCC), sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predicted value (NPV), false positive rate (FPR) and false negative rate (FNR) were computed based on the number of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN). We also plotted the Receiver Operating Characteristic (ROC) curves and computed the Area Under the Curve (AUC) for different tools by using the R "ROCR" package.

Accuracy = Se = Sp = PPV = NPV = FPR = FNR = MCC = × × ( ) × ( ) × ( ) × ( )

Criteria of judgment

MCC is recommended over accuracy as it is less sensitive to the proportion of oncogenic and neutral variants in the benchmark dataset. The ROC curve exhibits the sensitivity and the specificity for different cut-off values. The AUC value was not computed for LRT because its prediction is not solely determined by its score and for the functional domain-based tool because it is a qualitative prediction tool. When evaluating prediction tools individually, the percentage of missing values was considered acceptable when it was below 5%.

Results

Evaluating on a benchmark database of 4,319 SNVs

Among the SNVs included in this study, 3,559 were sourced from curated somatic cancer variant databases (OncoKB, DoCM, CIViC) to support their classification as either oncogenic (N=2,996) or neutral (N= 563). In addition, 760 supplemental SNVs were considered as neutral because they had an AF≥0.01 in any subpopulation from gnomAD exomes. Hence, the performance of the 22 prediction tools was assessed using these 4,319 SNVs as a set of « gold standard » list of variants based either on their presence in curated databases or on their AF. To our knowledge, none of these tools trained their model using variants sourced from the OncoKB, DoCM or CIViC databases. Missing values, accuracy, MCC, AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), false positive rate (FPR) and false negative rate (FNR) were calculated to evaluate the performance of each prediction tool (Table 2).

For the 4,319 tested SNVs, the tools MutPred, M-CAP, VEST3 and LRT had No prediction tools could achieve over 80% sensitivity and specificity simultaneously. FATHMM-MKL achieved highest sensitivity (97.33%) but at the cost of lowest specificity (30.91%). In general, for a given tool, sensitivity was much higher than specificity, suggesting the tendency for all tools to predict somatic SNVs to be oncogenic, as it was already reported for germline variants [START_REF] Li | Performance evaluation of pathogenicity-computation methods for missense variants[END_REF][START_REF] Ghosh | Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines[END_REF] .

When evaluating overall performance, the best prediction tools would exhibit simultaneously highest MCC and AUC (Figure 3, Supplemental Figure 1). Hence, UMD-Predictor, CADD, Eigen/Eigen-PC, PROVEAN, REVEL and Polyphen HVAR/HDIV could be considered as the top performing tools (MCC ≥ 0.44 and AUC ≥ 0.78 simultaneously, Table 3). UMD-Predictor outperformed all predictions tools in the 2 metrics simultaneously (MCC = 0.58 and AUC = 0.84).

A second group of predictions tools with moderate overall performance (MCC ≥ 0.36 and AUC ≥ 0.73 simultaneously) could be described and includes SIFT, FATHMM-MKL, MetaSVM, DANN and MetaLR.

A last group of prediction tools with poorest overall performance (MCC ≤ 0.31 and AUC ≤ 0.72 simultaneously) could be described and includes the functional domain-based tool, Mutation Assessor, GenoCanyon, FATHMM and fitCons.

We selected from the benchmark dataset of

Evaluating on a benchmark database of 1,440 SNVs

In the above analysis, we merged SNVs with different levels of pathogenicity assertion (oncogenic and likely oncogenic SNVs from OncoKB database; A and B evidence level SNVs from CIViC database; pathogenic and likely pathogenic SNVs from DoCM database) into a single group of oncogenic variants and we included additional SNVs with AF ≥ 0.01 in the group of neutral variants. To identify if this approach could confound the analysis, performance of the prediction tools was also evaluated on a subset of 1,440 SNVs retaining only "clearly" oncogenic SNVs (N=877) and likely neutral SNVs (N=563) from OncoKB database (Supplemental Table 4, worksheet 2).

On this subset of 1,440 SNVs, the best overall performance tools based on AUC and MCC were confirmed (UMD-Predictor, Eigen, Eigen-PC, CADD, PROVEAN, REVEL, Polyphen-2 HVAR, Polyphen-2 HDIV) (Table 4). But interestingly, the ranking order of the prediction tools performance was not identical. Notably, PROVEAN ranked first with best MCC/AUC and Polyphen-2 HVAR ranked second. In addition, on this subset of 1,440 SNVs, almost all tools exhibited a performance drop in terms of MCC or AUC.

We also assessed whether the combination of prediction tools could improve the performance on this subset of 1,440 SNVs (Supplemental Table 4). Results for combinations of the best overall performance tools with missing values below 300/1,440 are shown in Table 5. In terms of accuracy and MCC, the three best combinations of prediction tools included PROVEAN: PROVEAN and Polyphen-2 HVAR/Eigen/UMD-Predictor. They could outperform the best individual prediction tool PROVEAN alone (accuracy 75.40%, MCC 0.47) due to a gain of sensibility but a loss in specificity. However, the number of missing values, mainly due to prediction discrepancies between two tools, increased drastically (about 20% vs. 0.63%).

Evaluating true concordance

When considering only the best overall performance tools, we found that 372/1,323 neutral SNVs were simultaneously correctly predicted neutral (104/466 SNVs from pure oncogenes and 214/702

SNVs from pure TSG) and 1,802/2,996 oncogenic SNVs were simultaneously correctly predicted oncogenic (902/1,557 SNVs from pure oncogenes and 831/1,311 SNVs from pure TSG).

Evaluating false concordance

There were 180/1,323 neutral SNVs distributed among 70 genes (30 oncogenes, 28 TSG, 2 both, 7 unknown) for which the best overall performance tools simultaneously mispredicted them as oncogenic (Supplemental Table 8, worksheet 1). Among them, 159 SNVs were considered neutral because their respective missense variants were sourced from OncoKB as Likely neutral and 21 SNVs were considered neutral because their AF was greater than 0.01 in any subpopulation from gnomAD exomes. Three of them were also confirmed as Likely benign or Benign by a ClinVar recognized expert panel (three-star review status).

Discussion

Performance results

We have shown on a benchmark dataset of 4,319 SNVs that UMD-Predictor consistently achieved the best overall performance as compared to others prediction tools. It achieved best accuracy, best MCC and best AUC. Following UMD-Predictor, the performance was acceptable for Eigen/Eigen PC, CADD, PROVEAN, REVEL and Polyphen-2. A widely used prediction tool as SIFT ranked in the medium performance category.

Better performances were obtained by almost all predictors in TSGs compared to oncogenes.

This might be explained by the broad range of inactivating mutations leading to loss-of-function often encountered in TSGs, while oncogenes usually exhibit well-localized hotspots of mutations leading to gain-of-function [START_REF] Sondka | The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers[END_REF] .

Another criterion which is widely used for the interpretation of somatic alterations is the localization of the variant with regards to the domains of the protein. Interestingly, the use of a prediction tool based on functional domains yielded extremely poor results for oncogenes (MCC=0.09) and better results for TSGs (MCC=0.48). This result suggests that predictions in oncogenes should rather be based on critical hotspots and functional domains of the protein (e.g. Tyrosine kinase, protein kinase and SH2 domains) than on any InterPro domain.

We have confirmed on a subset of 1,440 SNVs, without "likely" oncogenic SNVs and additional polymorphisms, that the same prediction tools (CADD, Eigen/Eigen-PC, Polyphen-2, PROVEAN, UMD-Predictor and REVEL) still achieved best overall performance. However, almost all prediction tools exhibited a performance drop due to a loss in specificity without substantial gain in sensitivity. This could suggest a higher ability for prediction tools to assess polymorphisms. In particular, UMD-Predictor prediction relies also on a penalty score for SNVs which are described with a frequency above 0.001 in the general population. This could explain why this prediction tool does not achieve the best overall performance anymore when switching on a dataset without additional polymorphisms. In clinical routine, polymorphisms are generally automatically filtered out by bioinformatic pipelines and are not reported. Consequently, to avoid bias, evaluation of any prediction tools should be made on rare SNVs only. But such a benchmark dataset with a significant number of neutral SNVs is difficult to construct.

We have also highlighted on the subset of 1,440 SNVs, three combinations of prediction tools which all included PROVEAN: PROVEAN + Polyphen-2 HVAR/Eigen/UMD-Predictor. These combinations performed better than any individual prediction tools based on accuracy and MCC. However, this is achieved at the price of missing values (around 20%) and with hardly enough specificity for clinical routine.

Due to inconsistent choice of transcripts between knowledge databases to describe a somatic variant, we selected the Ensembl transcript which was assessed in OncoKB or DoCM database. However, all prediction tools could not always provide scores for this Ensembl transcript. For example, VEST3 could provide scores for ALK variants in RefSeq transcript (NM_004304) but could not provide scores for Ensembl transcript (ENST00000389048) and thus, it might have performed with fewer missing values if Refseq transcripts had been considered.

Finally, the risk of circularity needs to be considered. Our benchmark dataset has a limited number of SNVs (N=4,319) and amongst neutral variants (N=1,323), a large proportion (N=760) was included based on an AF ≥ 0.01 in any subpopulation from gnomAD exomes. This subgroup of frequent germline variants could have been included in the training of those prediction tools. To our knowledge, none of the 22 tools trained their model using variants sourced from the OncoKB, DoCM or CIViC databases although we did not examine the possibility of type 1 circularity [START_REF] Grimm | The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity[END_REF] . In addition, due to the low number of neutral variants sourced in somatic databases, our benchmark dataset is particularly unbalanced towards oncogenic variants (N=2,996) vs. polymorphisms (N=1,323). For this reason, we cannot exclude type 2 circularity 24 either.

Quality of the sourced databases of missense variants

We restricted our benchmark dataset of missense variants to the high-confidence variants sourced from three well-curated somatic databases and to polymorphisms, resulting in a "gold standard" set of 4,319 SNVs, with 2,996 SNVs labeled as oncogenic and 1,323 SNVs labeled as neutral. In the collecting process of missense variants, we raised the question of the quality of the somatic databases due to the relatively high number of missense variants with conflicting pathogenicity and the low number of variants reviewed by an expert panel.

We found 19 missense variants with conflicting pathogenicity (Supplemental Table 1, worksheet 5) which were sourced as Likely neutral from OncoKB but Pathogenic and/or Likely pathogenic from DoCM. We also found 25 missense variants (Supplemental Table 1, worksheet 7) in our combined dataset which were described as oncogenic but whose population allele frequency was greater than 0.01 in any subpopulation from gnomAD exomes. Among them, 24 were sourced from one single database (either OncoKB or DoCM) but 1 was sourced from both OncoKB and DoCM (ENST00000367921:G505S). Two missense variants, one in ERCC4 and one in PMS2, were even sourced as Oncogenic from OncoKB although their AF was respectively greater than 0.02 in Finnish gnomAD exomes and 0.09 in East Asian gnomAD exomes.

Moreover, among the 4,319 SNVs in our combined dataset, only 139 variants were reviewed by an expert panel on ClinVar database (Supplemental Table 1, worksheet 8). These variants included 96 neutral variants sourced as Benign or Likely benign and 41 oncogenic variants sourced as Pathogenic, Likely pathogenic or Drug response. However, 1 neutral variant in BRCA1 (ENST00000357654:C1787S) was sourced as Likely neutral from OncoKB but was interpreted as Pathogenic by ENIGMA expert panel, and 1 neutral variant in TP53 (ENST00000269305:P72R), with an AF>0.73 in NFE gnomAD exomes, was interpreted as Drug response by PharmGKB expert panel.

Although all missense variants with conflicting pathogenicity were carefully filtered out to collect a "gold standard" set of SNVs, the quality of the benchmark dataset is also a limitation of all studies on pathogenicity prediction tools.

Overall, these findings demonstrate that despite huge efforts from well-curated databases, a lot of work remains to be done to catalog and curate somatic variants. To achieve this goal, a consensus guideline on somatic variant interpretation is necessary.

Conclusion

This work helps to better assess the performance of prediction tools for the somatic variant classification. Six prediction tools, namely CADD, Eigen/Eigen-PC, Polyphen-2, PROVEAN, UMD-Predictor and REVEL, have shown good performance in comparison to others. Performances on tumor suppressor gene variants were better than on oncogene variants. The combination of tools weakly improved performances at the expense of increased missing values. Those tools have to be combined with other classification evidence in order to decide the classification of a variant. The standardization of the classification of somatic variants is really important to achieve, as some discrepancies were identified in the different databases used in this study.

Figure 1 :

 1 Figure 1 : Venn diagram representing compiled dataset of missense variants from OncoKB, CIViC and

Figure 2 :

 2 Figure 2 : Distribution of Allele Frequency in general population for variants in our dataset (2,996

Figure 3 .

 3 Figure 3. ROC curves (including AUC) for six tools (two tools in each performance group).

  //www.ncbi.nlm.nih.gov/refseq/ , transcript NM_004304). Consequently, performance of these four prediction tools (namely MutPred, M-CAP, VEST3 and LRT) were not further evaluated as their high percentage of missing values (above 5%) could introduce a bias in the interpretation of their results.The analysis of the accuracy (Table2, Supplemental Table5) revealed that the proportion of

	(https:SNVs classified correctly by the different prediction tools varied substantially (median 77.14%; range
	from 60.18% to 83.19%). UMD-Predictor was the most accurate (83.19%), while FATHMM was the least
	accurate (60.18%).
						1,197 (27.71%), 772
	(17.87%), 627 (14.52%), 395 (9.15%) missing prediction scores respectively. For VEST3, this can be
	explained by the fact that this tool could not provide prediction scores for some SNVs in the assessed
	Ensembl transcripts. For example, VEST3 could not provide scores for any SNVs in the Ensembl transcript
	of ALK which was assessed in the OncoKB database (ENST00000389048) but could provide scores in
	other	Ensembl	isoforms	(ENST00000453137	and	ENST00000453137)	or	RefSeq

  4,319 SNVs, either SNVs in oncogenes (N=2,023) or SNVs in TSGs (N=2,013) to assess whether prediction tools would have different performances. When SNVs in oncogenes only were assessed (Supplemental Table6), UMD-Predictor achieved again best MCC (0.50) and best AUC (0.78). When SNVs in TSGs only were assessed (Supplemental Table7), UMD-Predictor achieved also best MCC (0.64) whereas REVEL achieved best AUC (0.89). Overall, the three groups of performance level remained unchanged beside REVEL which performed much better for SNVs in TSGs relatively to SNVs in oncogenes in terms of MCC (0.61 vs. 0.36) and AUC (0.89 vs. 0.76).

Interestingly, considering MCC and AUC, almost all predictors performed better in TSGs variants compared to oncogenes variants.

  There were 46/2,996 oncogenic SNVs distributed among 218 genes (96 oncogenes, 90 TSG, 6 both, 26 unknown) for which the best overall performance tools simultaneously mispredicted them as neutral (Supplemental Table8, worksheet 2). All of them were considered oncogenic because their respective missense variants were sourced from OncoKB, CiVIC or DoCM. However, none of them were confirmed Likely pathogenic or Pathogenic by a Clinvar recognized expert panel. It is interesting to note that CDH1 p.T340A was considered Oncogenic by OncoKB but has been recently reviewed and interpreted as benign by an expert panel based on a BA1 criteria of the ACMG/AMP classification. To date, this SNV is still sourced as Oncogenic from OncoKB (data version: v2.7).

Table 1 . List of prediction tools.

 1

	Tools	Ref Training data	Type	Criteria
	SIFT	2	None	Function prediction	Conservation data
	PolyphenHDIV	3	HumDiv	Function prediction	Conservation data, protein functional
					domain data and protein structural features
	PolyphenHsVAR	3	HumVar	Function prediction	Conservation data, protein functional
					domain data and protein structural features
	LRT	4		Function prediction
	MutationAssessor	5		Function prediction
	FATHMM	6	HGMD, SwissProt	Function prediction	Evolutionary conservation, for coding and
					non-coding variants
	PROVEAN	7		Function prediction
	VEST3	8		Combination
	MetaSVM	9		Combination	SIFT, PolyPhen, MutationAssessor…
	MetaLR	9		Combination	Very similar to MetaSVM, similar
					performance, more interpretable model
	M-CAP	10		Combination
	REVEL	11	HGMD, rare EPS	Combination
	MutPred			Function prediction
	CADD	12	Simulated, Swissvar,	Combination	Integrates SIFT, GERP++, PolyPhen, CPG
			HumVar		distance, GC content
	DANN	11		Combination
	fathmm-MKL	13		Combination
	Eigen	14		Combination
	Eigen-PC	14		Combination
	GenoCanyon	15		Combination
	fitCons	16	none	Function prediction	Functional genomics data mainly sourced

Table 2 . Performance measures of the prediction tools on a dataset of 4,319 SNVs.

 2

		Missing value Accuracy MCC	AUC	Se	Sp	PPV	NPV	FPR	FNR
	UMD-Predictor	23 (0.53%)	83.19%	0.58	0.84 93.40% 59.95% 84.15% 79.96% 40.05%	6.60%
	Eigen	65 (1.50%)	80.61%	0.52	0.80 91.92% 55.09% 82.19% 75.16% 44.91%	8.08%
	Eigen PC	65 (1.50%)	80.16%	0.50	0.80 93.11% 50.96% 81.06% 76.64% 49.04%	6.89%
	CADD	0 (0.00%)	79.56%	0.48	0.80 94.29% 46.18% 79.87% 78.13% 53.82%	5.71%
	PROVEAN	33 (0.76%)	77.60%	0.50	0.78 80.08% 71.93% 86.71% 61.23% 28.07% 19.92%
	REVEL	1 (0.02%)	77.30%	0.49	0.83 80.01% 71.18% 86.29% 61.10% 28.82% 19.99%
	Polyphen-2 HVAR	52 (1.20%)	77.41%	0.47	0.80 83.20% 64.30% 84.06% 62.86% 35.70% 16.80%
	Polyphen-2 HDIV	52 (1.20%)	77.43%	0.44	0.78 87.83% 53.90% 81.17% 66.20% 46.10% 12.17%
	SIFT	93 (2.15%)	76.08%	0.42	0.77 85.06% 55.85% 81.26% 62.42% 44.15% 14.94%
	FATHMM-MKL	0 (0.00%)	76.99%	0.41	0.76 97.33% 30.91% 76.14% 83.64% 69.09%	2.67%
	MetaSVM	1 (0.02%)	69.25%	0.38	0.78 66.92% 74.51% 85.61% 49.85% 25.49% 33.08%
	DANN	0 (0.00%)	74.18%	0.38	0.73 82.41% 55.56% 80.77% 58.24% 44.44% 17.59%
	MetaLR	1 (0.02%)	67.99%	0.36	0.79 65.85% 72.84% 84.61% 48.49% 27.16% 34.15%
	Mutation Assessor	82 (1.89%)	67.64%	0.31	0.72 69.04% 64.49% 81.39% 48.08% 35.51% 30.96%
	GenoCanyon	0 (0.00%)	68.84%	0.23	0.67 81.94% 39.15% 75.31% 48.91% 60.85% 18.06%
	Functional domain	0 (0.00%)	72.59%	0.29		89.39% 34.54% 75.56% 58.97% 65.46% 10.61%
	FATHMM	17 (0.39%)	60.18%	0.20	0.48 59.34% 62.10% 78.07% 40.18% 37.90% 40.66%
	fitCons	65 (1.50%)	61.99%	0.14	0.59 69.12% 45.91% 74.23% 39.74% 54.09% 30.88%
	VEST3	627 (14.52%)	79.52%	0.49	0.83 87.38% 60.65% 84.21% 66.67% 39.35% 12.62%
	LRT	395 (9.14%)	80.45%	0.51		91.42% 55.43% 82.39% 73.91% 44.57%	8.58%
	MutPred	1197 (27.71%)	76.11%	0.16	0.67 81.19% 39.31% 90.64% 22.41% 60.69% 18.81%
	M.CAP	772 (17.87%)	80.97%	0.10	0.62 94.56% 12.29% 84.49% 30.90% 87.71%	5.44%

Table 3 . Ranking of the evaluated prediction tools on the data set of 4,319 SNVs.

 3

		UMD-Predictor
		Eigen/Eigen PC
		CADD
	Best overall performance tools
		PROVEAN
		REVEL
		Polyphen-2 HVAR/HDIV
		SIFT
		FATHMM-MKL
	Medium overall performance tools	MetaSVM
		DANN
		MetaLR
		Mutation Assessor
		GenoCanyon
	Low overall performance tools	Functional domain-based tool
		FATHMM
		fitCons
		VEST3
	Excluded tools	LRT
	(missing values> 5%)	MutPred
		M.CAP

Table 4 . Performance measures of the prediction tools on a dataset of 1,440 SNVs.

 4

		Missing value Accuracy	MCC	AUC	Se	Sp	PPV	NPV	FPR	FNR
	UMD-Predictor	1 (0.07%)	70.88%	0.38	0.71 95.43% 32.68% 68.81% 82.14% 67.32%	4.57%
	Eigen	27 (1.88%)	71.69%	0.40	0.70 94.17% 37.05% 69.75% 80.47% 62.95%	5.83%
	Eigen PC	27 (1.88%)	71.83%	0.40	0.70 95.10% 35.97% 69.60% 82.64% 64.03%	4.90%
	CADD	0 (0.00%)	69.03%	0.34	0.68 96.35% 26.47% 67.12% 82.32% 73.53%	3.65%
	PROVEAN	9 (0.63%)	75.40%	0.47	0.74 86.40% 58.09% 76.44% 73.08% 41.91% 13.60%
	REVEL	0 (0.00%)	66.94%	0.28	0.71 82.21% 43.16% 69.26% 60.90% 56.84% 17.79%
	Polyphen-2 HVAR	22 (1.53%)	71.79%	0.39	0.72 86.64% 48.83% 72.36% 70.28% 51.17% 13.36%
	Polyphen-2 HDIV	22 (1.53%)	69.75%	0.34	0.70 90.71% 37.34% 69.12% 72.22% 62.66%	9.29%
	SIFT	18 (1.25%)	66.95%	0.27	0.65 86.21% 36.59% 68.18% 62.73% 63.41% 13.79%
	FATHMM-MKL	0 (0.00%)	68.06%	0.32	0.68 98.18% 21.14% 65.98% 88.15% 78.86%	1.82%
	MetaSVM	0 (0.00%)	61.32%	0.19	0.64 67.62% 51.51% 68.48% 50.52% 48.49% 32.38%
	DANN	0 (0.00%)	66.94%	0.27	0.64 83.92% 40.50% 68.72% 61.79% 59.50% 16.08%
	MetaLR	0 (0.00%)	58.82%	0.13	0.62 66.70% 46.54% 66.03% 47.29% 53.46% 33.30%
	Mutation Assessor	47 (3.26%)	57.93%	0.13	0.60 62.90% 50.36% 65.88% 47.12% 49.64% 37.10%
	GenoCanyon	0 (0.00%)	69.31%	0.33	0.62 89.51% 37.83% 69.16% 69.84% 62.17% 10.49%
	Functional domain	0 (0.00%)	64.86%	0.21		92.70% 21.49% 64.78% 65.41% 78.51%	7.30%
	FATHMM	1 (0.07%)	54.69%	0.06	0.55 60.73% 45.29% 63.33% 42.57% 54.71% 39.27%
	fitCons	27 (1.88%)	52.02%	-0.01	0.51 60.68% 38.67% 60.39% 38.95% 61.33% 39.32%
	VEST3	246 (17.08%)	64.24%	0.23	0.71 88.09% 30.78% 64.09% 64.83% 69.22% 11.91%
	LRT	193 (13.40%)	73.22%	0.43		94.06% 41.02% 71.13% 81.71% 58.98%	5.94%
	MutPred	254 (17.64%)	69.39%	0.23	0.66 83.52% 37.12% 75.22% 49.63% 62.88% 16.48%
	M.CAP	35 (2.43%)	64.48%	0.17	0.62 97.14% 10.90% 64.15% 69.88% 89.10%	2.86%

Table 5 . Performance measures of the combinations of the best overall performance tools with missing values < 300/1,440.

 5

	Tools combination	Missing value Accuracy	MCC	Se	Sp	PPV	NPV	FPR	FNR
	SIFT + Polyphen-2 HDIV	288 (20.0%)	72.31%	0.38 95.05% 33.25% 70.97% 79.66% 66.75%	4.95%
	SIFT + REVEL	277 (19.2%)	70.85%	0.33 90.38% 36.94% 71.34% 68.86% 63.06%	9.62%
	SIFT + CADD	249 (17.3%)	71.70%	0.36 97.11% 26.57% 70.14% 83.82% 73.43%	2.89%
	SIFT + Eigen	286 (19.9%)	73.48%	0.40 96.34% 33.09% 71.79% 83.64% 66.91%	3.66%
	Polyphen-2 HDIV + Polyphen-2 HVAR	131 (9.1%)	72.50%	0.39 90.81% 42.19% 72.22% 73.50% 57.81%	9.19%
	Polyphen-2 HDIV + CADD	203 (14.1%)	72.03%	0.36 96.99% 26.82% 70.59% 83.10% 73.18%	3.01%
	Polyphen-2 HDIV + FATHMM-MKL	263 (18.3%)	72.64%	0.37 99.35% 21.14% 70.84% 94.44% 78.86%	0.65%
	Polyphen-2 HDIV + Eigen	219 (15.2%)	73.30%	0.41 95.99% 34.15% 71.55% 83.15% 65.85%	4.01%
	Polyphen-2 HDIV + Eigen PC	251 (17.4%)	74.01%	0.43 97.62% 32.95% 71.69% 88.82% 67.05%	2.38%
	Polyphen-2 HDIV + UMD	235 (16.3%)	73.78%	0.41 97.56% 30.28% 71.90% 87.16% 69.72%	2.44%
	Polyphen-2 HVAR + PROVEAN	278 (19.3%)	78.66%	0.52 92.07% 54.78% 78.38% 79.51% 45.22%	7.93%
	Polyphen-2 HVAR + CADD	266 (18.5%)	74.45%	0.41 96.73% 32.93% 72.88% 84.38% 67.07%	3.27%
	Polyphen-2 HVAR + Eigen	260 (18.1%)	75.34%	0.46 95.43% 41.06% 73.42% 84.04% 58.94%	4.57%
	REVEL + CADD	292 (20.3%)	72.56%	0.36 96.24% 28.78% 71.41% 80.56% 71.22%	3.76%
	REVEL + Eigen	293 (20.3%)	73.58%	0.40 94.41% 36.56% 72.57% 78.65% 63.44%	5.59%
	PROVEAN + Eigen	292 (20.3%)	78.75%	0.51 95.50% 46.56% 77.44% 84.33% 53.44%	4.50%
	PROVEAN + UMD	291 (20.2%)	79.03%	0.51 96.86% 43.64% 77.32% 87.50% 56.36%	3.14%
	CADD + FATHMM-MKL	130 (9.0%)	70.38%	0.34 99.52% 18.82% 68.45% 95.70% 81.18%	0.48%
	CADD + Eigen	143 (9.9%)	72.01%	0.39 97.43% 28.75% 69.95% 86.79% 71.25%	2.57%
	CADD + Eigen PC	157 (10.9%)	72.33%	0.40 98.39% 27.85% 69.95% 91.03% 72.15%	1.61%
	CADD + UMD	172 (11.9%)	72.63%	0.37 98.67% 23.98% 70.81% 90.60% 76.02%	1.33%
	FATHMM-MKL + Eigen	179 (12.4%)	72.09%	0.36 98.53% 23.60% 70.28% 89.74% 76.40%	1.47%
	FATHMM-MKL + Eigen PC	155 (10.8%)	71.75%	0.36 98.43% 23.75% 69.91% 89.34% 76.25%	1.57%
	FATHMM-MKL + UMD	147 (10.2%)	71.69%	0.36 99.16% 21.62% 69.76% 93.40% 78.38%	0.84%
	Eigen + Eigen PC	75 (5.2%)	72.53%	0.42 96.14% 36.01% 69.91% 85.78% 63.99%	3.86%
	Eigen + UMD	200 (13.9%)	74.03%	0.43 98.36% 30.87% 71.63% 91.39% 69.13%	1.64%

This research benefited from a funding by the French National Cancer Institute (INCa), grant number 2017-189.

Disclosures: None declared.


Anthony Geneyton

L O Filippov
email: lev.filippov@univ-lorraine.fr

T Heinig

N Buaron

Nour- Eddine Menad

N.-E Menad

fatty acids collectors using a lanthanum salt as a selective phosphate activator

Keywords: backscattered electron, EDX: energy disperse X-ray, HREE: heavy rare-earth elements, ICP-AES: inductively coupled plasma atomic emission spectroscopy, ICP-MS: inductively coupled plasma mass spectrometry, LREE: light rare-earth elements, MIBC: methyl isobutyl carbinol, MLA: mineral liberation analysis, REE: rare-earth elements, REM: rare-earth mineral, SEM: scanning electron microscope Rare earths, Monazite, Flotation, Phosphate mineral, Lanthanum chloride, Carboxylate collector

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The transition to renewable energy sources and the development of electric vehicles prompted a significant growth in the demand for light rare-earth elements (LREEs). Currently, their production largely relies on the exploitation of a few massive carbonatite-related deposits in which bastnaesite [(Ce,La,Nd)CO3F], the main rare-earth mineral (REM), and monazite [(Ce,La,Nd)PO4] are associated with other semi-soluble salts such as calcite (CaCO3), fluorite (CaF2), and barite (BaSO4). In recent years, the LREE production from monazite-dominated primary deposits significantly increased with the rise in the production capacity of the Mount Weld carbonatite laterite deposit operations in Australia. As the current LREE production is dominated by the exploitation of these two types of giant deposits which are not evenly distributed around the globe, the supply chain risk for importing countries [START_REF] Keilhacker | Supply chain risk management for critical commodities: A system dynamics model for the case of the rare earth elements[END_REF] and the environmental impact of the required mining and extractive metallurgy operations (Tian et al., 2018b;[START_REF] Wang | Source apportionment of soil-contamination in Baotou City (North China) based on a combined magnetic and geochemical approach[END_REF][START_REF] Wang | Atmospheric thorium pollution and inhalation exposure in the largest rare earth mining and smelting area in China[END_REF] are frequently debated.

Over the last years, numerous alternative REE sources such as urban mining [START_REF] Binnemans | Recycling of rare earths: A critical review[END_REF][START_REF] Jowitt | Recycling of the rare earth elements[END_REF][START_REF] Omodara | Recycling and substitution of light rare earth elements, cerium, lanthanum, neodymium, and praseodymium from end-of-life applications -A review[END_REF][START_REF] Tunsu | Reclaiming rare earth elements from end-of-life products: A review of the perspectives for urban mining using hydrometallurgical unit operations[END_REF], phosphate fertiliser production residues [START_REF] Binnemans | Towards zerowaste valorisation of rare-earth-containing industrial process residues: a critical review[END_REF][START_REF] Cánovas | Leaching of rare earth elements (REEs) and impurities from phosphogypsum: A preliminary insight for further recovery of critical raw materials[END_REF][START_REF] Rychkov | Recovery of rare earth elements from phosphogypsum[END_REF] and coal-related materials [START_REF] Zhang | A Comprehensive Review of Rare Earth Elements Recovery from Coal-Related Materials[END_REF] have been explored in order to minimise both the risk of LREE supply shortage and the environmental footprint of their production. Recent studies also examined the extraction of REM from mine wastes and extractive metallurgy tailings (Abaka-Wood et al., 2018b;[START_REF] Dehaine | Rare-metal granites as a potential source of critical metals: A geometallurgical case study[END_REF][START_REF] Jyothi | Review of rare earth elements recovery from secondary resources for clean energy technologies: Grand opportunities to create wealth from waste[END_REF][START_REF] Peelman | Hydrometallurgical extraction of rare-earth elements from low grade mine tailings[END_REF][START_REF] Tunsu | Recovery of critical materials from mine tailings: A comparative study of the solvent extraction of rare earths using acidic, solvating and mixed extractant systems[END_REF]. Many of these studies reported the presence of monazite as main REM but, apart from certain tailings of heavy mineral sand exploitations that contains from a few percent to a few dozen percent of monazite [START_REF] Szamałek | New potential source of rare earth elements[END_REF][START_REF] Tranvik | Towards cleaner production -Using flotation to recover monazite from a heavy mineral sands zircon waste stream[END_REF], its concentration is generally only between a few hundred ppm and several thousand ppm, this mineral being frequently associated with silicate-rich gangues [START_REF] Filippov | Rare earths (La, Ce, Nd) and rare metals (Sn, Nb, W) as by-products of kaolin production -Part 3: Processing of fines using gravity and flotation[END_REF]Purwadi et al., 2020;[START_REF] Tunsu | Recovery of critical materials from mine tailings: A comparative study of the solvent extraction of rare earths using acidic, solvating and mixed extractant systems[END_REF].

In the case of these low monazite-grade, silicate-rich tailings, there is an economic interest in concentrating monazite before REEs are extracted in a hydrometallurgical plant. However, poor monazite flotation performance is reported, due probably to both the low monazite content in these specific tailings and the limited selectivity of collectors towards REM. The flotation separation of semi-soluble REE salt minerals such as bastnaesite, monazite, and xenotime (YPO4) is generally achieved with carboxylates or hydroxamates, which are the deprotonated forms of carboxylic acids (e.g., fatty acids) and hydroxamic acids [START_REF] Chelgani | A review of rare earth minerals flotation: Monazite and xenotime[END_REF][START_REF] Houot | Recovery of rare-earth minerals, with emphasis on flotation process[END_REF][START_REF] Jordens | A review of the beneficiation of rare earth element bearing minerals[END_REF][START_REF] Marion | A review of reagents applied to rare-earth mineral flotation[END_REF]. In past decades, REM flotation techniques have been developed and optimised for high-grade carbonatite deposits. In contrast to certain other minerals, such as calcium bearing minerals [START_REF] Filippov | New reagent formulations for selective flotation of scheelite from a skarn ore with complex calcium minerals gangue[END_REF][START_REF] Foucaud | Investigation of the depressants involved in the selective flotation of scheelite from apatite, fluorite, and calcium silicates: Focus on the sodium silicate/sodium carbonate system[END_REF]Wang et al., 2020), the development of new reagents to improve the REM selectivity of carboxylate and hydroxamate collectors has been rarely addressed. As mine wastes and extractive metallurgy tailings have generally lower REE content and feature more exotic mineral associations than primary REE ores, their exploitation by the current flotation methods seems economically non-sustainable. Therefore, it is crucial to develop new practices for monazite flotation, particularly from silicate-rich gangues, to transform these residues into commodities.

Based on bench scale flotation tests with samples from Mountain pass, a major rare-earth deposit in a carbonatitic context, [START_REF] Fuerstenau | An alternate reagent scheme for the flotation of Mountain Pass rare-earth ore[END_REF] and [START_REF] Pradip | Targeting rare earth element bearing mine tailings on Bangka Island, Indonesia, with Sentinel-2 MSI[END_REF] concluded that fatty acids are poorly selective towards rare earth minerals compared with alkyl hydroxamate collectors. The findings of these case studies have largely been taken-up by several authors and a significant part of the emerging fundamental and applied REM flotation studies involve hydroxamate collectors (Abaka-Wood et al., 2018a;[START_REF] Cui | Alternative flowsheet for rare earth beneficiation of Bear Lodge ore[END_REF][START_REF] Cui | Spectroscopic investigation of the interaction of hydroxamate with bastnaesite (cerium) and rare earth oxides[END_REF][START_REF] Duan | Flotation separation of bastnaesite from calcite using novel octylmalon dihydroxamic acid as collector[END_REF][START_REF] Espiritu | The effect of dissolved mineral species on bastnäsite, monazite and dolomite flotation using benzohydroxamate collector[END_REF][START_REF] Lee | Structural and electrochemical characterization of a cerium(IV) hydroxamate complex: Implications for the beneficiation of light rare-earth ores[END_REF][START_REF] Li | The influence of temperature on rare earth flotation with naphthyl hydroxamic acid[END_REF][START_REF] Qi | β-Amino-hydroxamate surfactants: Preparation, and adsorption mechanism in bastnaesite flotation[END_REF][START_REF] Ren | Selective flotation of bastnaesite from monazite in rare earth concentrates using potassium alum as depressant[END_REF][START_REF] Sarvaramini | Hydroxamic acid interactions with solvated cerium hydroxides in the flotation of monazite and bastnäsite -Experiments and DFT study[END_REF][START_REF] Zhang | Flotation of monazite in the presence of calcite part II: Enhanced separation performance using sodium silicate and EDTA[END_REF][START_REF] Zhang | Surface chemistry aspects of bastnaesite flotation with octyl hydroxamate[END_REF]. Whereas fatty acids are affordable, non-toxic, bio-sourced compounds, notably obtained by distillation of crude tall oil, hydroxamic acids, which are conventionally synthesised by amidation of fatty acids or esters with hydroxylamines [START_REF] Massaro | Microwave-assisted transformation of esters into hydroxamic acids[END_REF], have variable production costs and toxicity.

Among this class, certain mutagen compounds, such as benzohydroxamic acid (Ching Yung [START_REF] Yung | Mutagenicity of hydroxamic acids for Salmonella typhimurium[END_REF][START_REF] Skipper | Mutagenicity of hydroxamic acids and the probable involvement of carbamoylation[END_REF] were used as the collector of choice in most of the recent studies. Hence, the environmental footprint of some hydroxamate collectors can be significantly larger than that of carboxylate collectors, especially when the former are used along with high quantities of heavy metal salts, such as lead nitrate, to improve the REM flotation performance [START_REF] Jordens | Beneficiation of the Nechalacho rare earth deposit: Flotation response using benzohydroxamic acid[END_REF][START_REF] Lin | Beneficial effects and mechanism of lead ions for bastnaesite flotation with octyl hydroxamic acid collector[END_REF][START_REF] Marion | A review of reagents applied to rare-earth mineral flotation[END_REF]Xia et al., 2015b). Therefore, the development of high-performance REM flotation techniques with carboxylates would provide a more efficient, cheaper, and greener alternative to the joint use of toxic hydroxamate collectors and heavy metal salts.

Although the REM flotation performance with carboxylate collectors increases at higher temperatures [START_REF] Bulatovic | Flotation of REO minerals[END_REF][START_REF] Morrice | Flotation of rare-earths from bastnäsite ore[END_REF][START_REF] Pradip | Targeting rare earth element bearing mine tailings on Bangka Island, Indonesia, with Sentinel-2 MSI[END_REF], the financial and environmental costs of this approach are certainly prohibitive at industrial scale. In contrast, the addition of soluble metal salts is a common method to tune the collector adsorption on a specific mineral surface. Although recent studies examined the use of lead nitrate/hydroxamate mixtures, the addition of metal salts to carboxylate or hydroxamate collectors for REM flotation remains relatively unexplored, as previous studies mainly addressed the release of alkaline earth metals, aluminium, and iron in the pulp by gangue minerals (Cao et al., 2018;Xia et al., 2015a).

In this preliminary case study, a novel approach to selectively increase the monazite flotation performance was developed by using LaCl3 to activate the adsorption of carboxylates on the surface of phosphate minerals.

Material and methods

Material origin

A processing residue of the ore extracted from the Kankberg mine, a gold and tellurium underground mine located in the Boliden area (Skellefte mining district, Sweden) and operated by Boliden Mineral AB since 2012, was used in this study. The Kankberg mine ore underwent different mineral processing and hydrometallurgy steps at the Boliden concentrator to recover contained gold and tellurium. A simplified bloc-diagram of these operations is depicted in Figure 1. Tailings were collected as a pulp with process water after cyanide destruction using the INCO air/SO2 process. After water removal by filtration, the material was dried at 40 °C, homogenised, riffled, and subsequently stored in the dark at -18°C to avoid material transformation. Importantly, this residue has been previously floated in the Boliden concentrator using potassium amyl xanthate as collector and polyglycol methyl ether as frother. In addition, it has been subjected to cyanide leaching operations using lime as a pH modifier with a subsequent cyanide-destruction process. Therefore, upon water removal and drying this material still contained non-negligible amounts of chemical reagents that may have affected the results of the performed flotation tests.

Modal mineral composition and mineral association analyses

Samples were prepared with graphite and epoxy resin to prepare polished grain mounts [START_REF] Heinig | Monitoring gravitational and particle shape settling effects on MLA sample preparation[END_REF] for scanning electron microscopy (SEM; FEI Quanta 650F) at the Helmholtz Institute Freiberg for Resource Technology. Furthermore, the FEI MLA software package for automation and data processing was used to acquire backscattered electron (BSE) images and energy-dispersive X-ray (EDX) spectra with semi-automated grain mount mapping (MLA; GXMAP) for modal composition and mineral association [START_REF] Bachmann | Advanced identification and quantification of in-bearing minerals by scanning electron microscope-based image analysis[END_REF]. The analyses were conducted at 25 kV acceleration voltage, 10 nA probe current, and 1 µm/pixel resolution for BSE with EDX-spectra acquired every 6 pixels.

Chemical analyses

Samples were riffled, pulverised in a planetary ball mill using tungsten grinding media, and subsequently assayed at the Service d'Analyse des Roches et des Minéraux (SARM-CNRS, Nancy, France). The concentrations of major and trace elements were determined by Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES) and Mass Spectrometry (ICP-MS), respectively, following the analytical procedure reported by [START_REF] Carignan | Routine analyses of trace elements in geological samples using flow injection and low pressure on-line liquid chromatography coupled to ICP-MS: A study of geochemical reference materials BR, DR-N, UB-N, AN-G and GH[END_REF].

Bench-scale flotation

Two series of bench-scale flotation kinetics tests were performed with collector dosages of 50 g t -1 and 100 g t -1 (grams per tonne of material) to assess the effect of lanthanum ions on the flotation of monazite and other phosphate minerals and determine the LaCl3 dosage that results in the highest monazite flotation performance. Low collector dosages were preferred to ensure, in presence of a selective phosphate minerals activator, a proper monazite recovery with limited flotation of gangue minerals.

The flotation tests were conducted in a 1.5 L Agitair LA-500 flotation cell. The pulp treatment was performed at a rotor speed of 1000 rpm and solid content of 60 wt%, while flotation was performed at a rotor speed of 900 rpm, solid content of 30 wt%, and airflow of 4.5 L min -1 . After attrition of the mineral pulp for 5 min, a Na2O:SiO2 solution (0.3:1 ratio, 35.1 wt% Na2O+SiO2) was added at a dosage of 2850 g t -1 (dosage equivalent to 1000 g of Na2O+SiO2 per tonne of ore) to disperse fine particles and depress silicate gangue minerals.

The preliminary flotation tests indicated the presence of fast-floating particles, mainly of sphalerite and iron sulfides, which had been probably hydrophobised during the gold-tellurium processing route or cyanide destruction. To avoid the presence of these minerals in the floated product, after 8 min of treatment with the depressant, water is added to reach a solid content of 30 wt%, methyl isobutyl carbinol frother (MIBC) was added at a dosage of 5 g t -1 and these few highly hydrophobic particles were eliminated during a 2 min pre-flotation stage. The LaCl3 solution at a concentration of 0.5 %, 0.05 %, or 0.005 % (g LaCl3 per g of solution), produced by the dilution of a LaCl3 precipitate provided by Alfa Aesar (99.9 % purity) and the sodium oleate solution at a concentration of 10 -2 mol L -1 , produced by the dilution of a sodium oleate precipitate provided by Carl Roth (90 % purity), both at room temperature and with deionized water, were then added to the flotation cell either sequentially (2 min between the two additions) or as a mixture. After 2 min, air is injected and floated products were collected after 30 s, 1 min, 2 min, 4 min, 8 min, and 15 min of flotation. Importantly, the initial pulp temperature was stabilised at 25 °C and the pH was maintained at 8.2-8.4 during pulp treatment and flotation using USP grade hydrochloric acid and sodium hydroxide.

Theory

Despite the high affinity of lanthanum ions for carboxylate and phosphate species [START_REF] Hannah | Phosphate removal from wastewaters using lanthanum precipitation (No. 17010EFX 04/70[END_REF][START_REF] Janicki | Carboxylates of rare earth elements[END_REF][START_REF] Lu | Lanthanides: Carboxylates[END_REF][START_REF] Mills | Removal of Phosphate from Water[END_REF], the influence of lanthanide salts on the phosphate flotation with carboxylate collectors has been seldom studied. Using a mixture of tributyl phosphate and saponified waste oil as collector for the flotation of phosphate from a low-grade tailing, Al-Thyabat and [START_REF] Al-Thyabat | The use of lanthanum (La +3 ) and cerium (Ce +3 ) ions as phosphate activators in low grade phosphate flotation[END_REF] observed that the addition of a mixture of cerium compounds, lanthanum compounds, and soda ash was beneficial. They showed that an appropriate amount of this mixture may significantly improve phosphate recovery and the selectivity of the collector towards phosphate minerals. However, they highlighted that the activator dosage must be strictly controlled to avoid detrimental effects on the flotation performance. Furthermore, [START_REF] Sørensen | Selective flotation of steenstrupine and monazite from Kvanefjeld lujavrite[END_REF] studied the recovery of monazite and streenstrupine from an agpaitic rock and reported an increase in the flotation rate without any tangible loss of selectivity when lanthanum nitrates were added before the sodium oleate collector. Recently, LaCl3 was reported to promote the adsorption of carboxylate collectors on the monazite surface [START_REF] Geneyton | Synergistic adsorption of lanthanum ions and fatty acids for efficient rare-earth phosphate recovery: Surface analysis and ab initio molecular dynamics studies[END_REF]. Using thermodynamics calculation, X-ray photoelectron spectroscopy and density functional theory calculations, we found in this previous study that lanthanum ions adsorb in the form of LaClCO3 on the monazite surface and act as highly attractive adsorption sites for carboxylate collectors. The significant increase in surface hydrophobicity resulting from the treatment of monazite with sodium oleate and LaCl3, instead of sodium oleate alone, suggest that this reagent combination may improve the monazite flotation performance.

The use of soluble metal salts to specifically promote or limit the adsorption of collectors on the surface of certain minerals is a common practice in flotation research and numerous studies were conducted to understand the underlying mechanisms. Although the two proposed mechanisms differ in certain aspects, they both involve a configuration in which a cation plays the role of a chemical bridge between the mineral surface and the anionic collector. In the first mechanism, adsorption of a metal cation on the mineral surface either promotes or limits collector adsorption [START_REF] Ren | Selective flotation of bastnaesite from monazite in rare earth concentrates using potassium alum as depressant[END_REF][START_REF] Ren | Behavior of lead ions in cassiterite flotation using octanohydroxamic acid[END_REF]Tian et al., 2018a;Xia et al., 2015b;[START_REF] Xiao | Adsorption behavior and mechanism of Bi(III) ions on rutile-water interface in the presence of nonyl hydroxamic acid[END_REF]. In contrast, the second mechanism postulates that reaction between a metal and the collector in the bulk forms a complex that adsorbs more or less efficiently on the mineral surface [START_REF] Meng | Study on the activation mechanism of lead ions in the flotation of ilmenite using benzyl hydroxamic acid as collector[END_REF]Tian et al., 2018cTian et al., , 2018a)). As it is likely that both mechanisms coexist, pre-mixing the metal salt with the collector or adding first the metal salt in the pulp may favour one mechanism over the other. It is important to consider that stabilisation of the collector by the added metal species in the bulk may disfavour its adsorption on the mineral surface or lead to precipitation of a metal/collector salt. Hence, the effect of a metal salt on the monazite flotation performance depends on whether it is added to the pulp prior to introduction of the collector or pre-mixed with the collector. Given the strong affinity of lanthanum ions for carboxylate ligands and the low solubility of certain lanthanum carboxylates [START_REF] Lu | Lanthanides: Carboxylates[END_REF], this study examines the influence of the mode of addition of LaCl3 on the monazite flotation performance, providing useful insight for further work.

Results

Material characterisation Modal mineralogy and overall chemical composition

The concentrations of major and trace elements in the flotation feed material were determined by ICP-MS and ICP-AES and are summarised in Table 1. According to the modal mineralogy data presented in Table 2, quartz, muscovite, topaz, andalusite, and corundum are the main gangue minerals amongst the 38 identified minerals, thus explaining the particularly high Al2O3 content of the material. This mineral association is consistent with the fact that gold and tellurium mineralisation zones in the Skellefte district (Sweden) mainly occur within or along a strong Si-Al alteration zone dominated by silica-rich rocks with abundant topaz, andalusite, and corundum [START_REF] Wasström | Åkulla Östra Mine Investigation[END_REF].

In this work, REE refers only to lanthanides, excluding scandium and yttrium. Every lanthanide content except promethium content was quantified by the ICP-AES analyses. The directly measured REE and P2O5 contents of the flotation feed material (Table 1) were similar to the values estimated by recombining the flotation products analyses. The flotation feed material assay indicated that LREE are more than 10 times more abundant than heavy REE (HREE).

According to the MLA analyses, the main REM, monazite, accounted for 98.5-100 % of the total REE amount in the feed material (i.e., Ce/Gd/La/Nd/Pr/Sm). Furthermore, apatite was the main phosphate-bearing mineral, accounting for 88.6 % of the total phosphorus content in the feed material, whereas monazite accounted for 11.1 % of the total amount of phosphorus.

Mineral liberation analysis

Size-by-size chemical assays, presented in Table 3, revealed that approximately 50 % of the total REE amount was naturally concentrated in the <20 µm size class, which renders monazite concentration by conventional flotation technologies challenging due to the entrainment of gangue particles. The association diagrams for phosphate minerals and main gangue minerals are presented in

Flotation tests Influence of lanthanum ions on the monazite recovery

The yield and REE recovery (based on the quantification of each lanthanide except promethium)

during the flotation tests are presented in Figure 3. For a collector dosage of 50 g t -1 , the addition of LaCl3 before sodium oleate increased monazite recovery (Figure 3-A1). The enhancement of the monazite flotation performance is greater at a LaCl3 dosage of 1 g t -1 than 0.1 g t -1 (Figure 4).

At low lanthanum salt dosages (0.1-1 g t -1 ), selectivity is enhanced, as shown by the improvement in REE recovery at a given mass yield (Figure 3-A1) and the improvement in REE grade at a given REE recovery (Figure 4). Moreover, although higher LaCl3 dosages (10 g t -1 ) did not further improve the monazite recovery or its flotation kinetics, the significant increase in the yield suggests activation of gangue minerals (Figure 3-A1). Therefore, the highest improvement of the monazite flotation performance was achieved at a LaCl3 dosage of approximately 1 g t -1

(Figure 4). Although the influence of LaCl3 on the monazite flotation kinetics is noticeable at a sodium oleate dosage of 100 g t -1 and a LaCl3 dosage of 0.1-10 g t -1 (Figure 3-B2), its influence on the monazite recovery was less pronounced (Figure 3-B1). Indeed, LaCl3 slightly improved monazite recovery only at a significant amount (10 g t -1 ). Nonetheless, this high LaCl3 dosage likely lead to a strong gangue mineral activation, as suggested by the yield increase (Figure 3-B1) and the concentrate REE grade decrease (Figure 4).

Previously, we reported that the adsorption of lanthanum ions on the monazite surface facilitates the adsorption of the carboxylate collectors [START_REF] Geneyton | Synergistic adsorption of lanthanum ions and fatty acids for efficient rare-earth phosphate recovery: Surface analysis and ab initio molecular dynamics studies[END_REF]. This phenomenon explains the considerable increase in the monazite flotation performance at a collector dosage of 50 g t -1 (Figure 4). In contrast, at a collector dosage of 100 g t -1 , the collector alone ensures an efficient monazite recovery (Figure 3-B1), low LaCl3 dosages (0.1-1 g t -1 ) slightly improve the monazite flotation kinetics, and only a high LaCl3 dosage (10 g t -1 ) improved monazite recovery as well as the yield (Figure 3-B1), resulting in a decrease of the REE grade (Figure 4). Hence, we postulated that, for the two collector dosages tested, at low dosages (0.1-1 g t -1 ), lanthanum ions mostly adsorb on the monazite surface, limiting the collector adsorption on gangue mineral surfaces. In contrast, at higher dosages (such as 10 g t -1 ), the lanthanum ions also adsorb on the surfaces of gangue minerals, thus activating the collector adsorption on these mineral surfaces.

Influence of the lanthanum ions on the floatability of certain gangue minerals

In order to compare the flotation performance of monazite and apatite, the P2O5 recovery values were expressed as a function of the REE recovery at each step of flotation kinetics tests. The flotation tests also suggest that activation of gangue minerals occurs when LaCl3 is employed at a dosage of 10 g t -1 . To evaluate whether certain major gangue minerals are selectively activated under these conditions, the cumulative SiO2, Al2O3, K2O, and TiO2 enrichment in the floated products was calculated (Figure 6). The enrichment is obtained by dividing the grade of the floated product on the grade of the feed material. The results revealed that a sodium oleate dosage of 100 g t -1 and a LaCl3 dosage of 10 g t -1 lead to a floated product that is remarkably more enriched in Al2O3 and in TiO2 but also less enriched in K2O (Figure 6-B2, Figure 6-B4, and Figure 6-B3). This high LaCl3 dosage also resulted in higher TiO2 grades at a sodium oleate dosage of 50 g t -1 (Figure 6-A4). Therefore, the floatability of rutile, which accounts for 98.8 % of titanium in the feed material, is influenced by high LaCl3 concentrations. To identify the other main gangue minerals whose flotation behaviour may be affected by LaCl3, MLA measurements were performed on the flotation products obtained at a collector dosage of 100 g t -1 and various LaCl3 dosages. The back-calculated recoveries of main gangue minerals, presented in Figure 7, indicate that the flotation of rutile, topaz, and corundum, and that of quartz and andalusite to a lesser extent, were activated by LaCl3. The enhanced recovery of topaz, andalusite, and corundum, which account for 60.6 % of the total aluminium in the feed material, explain the higher Al2O3 enrichment visible in Figure 6-B2. The relatively unaffected recovery of muscovite, which accounts for 95.4 % of the total potassium in the feed material, suggest that the K2O grade decrease visible in Figure 6-B3 results from the dilution of muscovite by aluminium-rich minerals.

Effect of the pre-mixing of LaCl3 and sodium oleate

The influence of the mode of addition of LaCl3 was assessed by pre-mixing LaCl3 with a sodium oleate solution for 5 min before addition to the flotation cell. During the pre-mixing, lanthanum oleate precipitation occurred (Figure 8). The similar precipitation of a mixture of LaCl3 and potassium oleate salt in a water-alcohol medium had been previously reported [65] [START_REF] Mehrotra | Micellization and conductometric investigation on some lanthanide metal oleates[END_REF]. The precipitation of lanthanum oleate may reduce the collector availability and reduce the interaction between lanthanum ions and mineral surfaces. According to the stoichiometry, a sodium oleate dosage of 50 g t -1 and a LaCl3 dosage of 10 g t -1 imply that nearly 75 % of the carboxylate anions may react to form lanthanum trioleate. Given the extremely low solubility of lanthanide oleate in water [START_REF] Liu | Lanthanide oleates: Chelation, self-assembly, and exemplification of ordered nanostructured colloidal contrast agents for medical imaging[END_REF], the activity constant of lanthanum oleate has solely been measured in specific solvent media such as a benzene/methanol mixture [START_REF] Mehrotra | Micellization and conductometric investigation on some lanthanide metal oleates[END_REF]. Thus, an estimation of the partitioning of oleate into sodium oleate, aqueous lanthanum oleate, and solid lanthanum oleate in aqueous medium is impossible.

In the conducted series of flotation tests using a mixture of sodium oleate and LaCl3, a beneficial effect of the lanthanum ions on the monazite flotation performance may still be observed. Indeed, the monazite recovery and the concentrate REE grade increased when a LaCl3 dosage of 0.1-1 g t -1 and a collector dosage of 50 g t -1 were used (Figure 9-A and Figure 10), suggesting that under these operational conditions not all the lanthanum ions was converted to non-reactive lanthanum oleate, the remaining available lanthanum ions having a beneficial effect on the monazite flotation selectivity. Conversely, a higher LaCl3 dosage increased the yield and decreased the REE recovery (Figure 9-A), resulting in a significant decrease of the monazite flotation performance (Figure 10). The slightly improved TiO2 and Al2O3 enrichment, observed with this specific LaCl3 dosage (Figure 11-A1/A2), indicates that the surface of rutile and aluminium-rich minerals was activated by the lanthanum ions. Hence, we suggest that, with a LaCl3 dosage of 10 g t -1 , the collector adsorption on certain activated gangue minerals surfaces and the formation of non-reactive lanthanum oleate limit the collector adsorption on monazite surface resulting in a limited monazite recovery. At a collector dosage of 100 g t -1 , the addition of LaCl3 has no remarkable effect on the monazite recovery and monazite flotation kinetics (Figure 9-B). Hence, we hypothesise that the collector availability in the pulp was sufficient to ensure an appropriate monazite flotation performance without LaCl3. High LaCl3 dosages (1 and 10 g t -1 ) significantly increase the TiO2 content of the floated products (Figure 11-B2), suggesting that, despite the formation of nonreactive lanthanum oleate, the availability of the collector and lanthanum ions was sufficient to render the rutile surface more hydrophobic. Overall, the addition of LaCl3 has limited effect on the monazite flotation performance (Figure 10). It only decreased the REE content in the floated product during the first minutes of the flotation test when a LaCl3 dosage of 1 g t -1 was used. It probably stems from a temporarily slightly higher recovery of aluminium-rich minerals (Figure 11-B1) suggesting that LaCl3 affects the aluminium-rich minerals flotation kinetics without significantly affecting the final recovery of these minerals. Therefore, although the availability of the collector and lanthanum ions was sufficient to enhance the rutile flotation performance, it was likely insufficient to activate enough the flotation of aluminium-rich minerals and cause the significant yield increase and loss of selectivity we observed with a LaCl3 dosage of 10 g t -1 and a collector dosage of 100 g t-1 when these reagents were not pre-mixed (Figure 3-B1 and Figure 4).

Importantly, similar conclusions are valid with regard to the overall phosphate mineral flotation performance, confirming that LaCl3 affects the floatability of monazite and apatite likewise.

Discussion

In a previous study, we highlighted that lanthanum ions strongly adsorb on the monazite surface due to the under-coordinated phosphate groups, thus acting as highly attractive adsorption sites for carboxylate collectors [START_REF] Geneyton | Synergistic adsorption of lanthanum ions and fatty acids for efficient rare-earth phosphate recovery: Surface analysis and ab initio molecular dynamics studies[END_REF]. The observed higher hydrophobicity of the monazite surface upon treatment with LaCl3 and sodium oleate, rather than sodium oleate alone, likely arises from this increase in the affinity of the carboxylate collectors towards the monazite surface. The aim of the present study was to confirm that these synergistic effects effectively improve the monazite flotation performance when monazite is associated with gangue minerals.

The flotation tests featuring the sequential addition of LaCl3 and sodium oleate showed that very low LaCl3 dosages (0.1-1 g t -1 ) selectively enhance the flotation performance of monazite and apatite. At a collector dosage of 50 g t -1 , the addition of small amounts of LaCl3 significantly increases the recovery of these minerals (Figure 3-A2), whereas it only slightly increases the flotation kinetics at a collector dosage of 100 g t -1 (Figure 3-B2). In the latter case, it is likely that the collector availability in the pulp was sufficient to ensure an appropriate extent of collector adsorption on the phosphate mineral surface and consequent recovery in the absence of LaCl3.

However, although it has no significant impact on the monazite flotation performance, the slight increase in the monazite and apatite flotation kinetics suggests that the addition of lanthanum ions can further increase the amount of collector adsorbed on the phosphate mineral surfaces.

The highest enhancement of the monazite and apatite flotation performance occurred with a LaCl3 dosage of 1 g t -1 and a collector dosage of 50 g t -1 . In the case of this particular tailing, higher LaCl3 dosages significantly increase the yield, especially with a collector dosage of 100 g t -1 (Figure 3-A1 and Figure 3-B1). MLA measurements performed on flotation products obtained at sodium oleate and LaCl3 dosages of 100 g t -1 and 10 g t -1 , respectively, reveal that the increase in the yield is associated with a higher recovery of rutile, topaz, andalusite, and corundum, suggesting that the lanthanum ions also activate the adsorption of carboxylate on the surfaces of these gangue minerals. Lanthanum ions are known to adsorb on rutile [START_REF] Fendorf | Sorption mechanisms of lanthanum on oxide minerals[END_REF] and γ-Al2O3 [START_REF] Wang | Dopants adsorbed as single atoms prevent degradation of catalysts[END_REF], indicating their strong affinity for the titanate and aluminate groups present on the surface of these minerals. Therefore, we assume that although lanthanum ions mainly adsorb on the phosphate groups of the monazite and apatite surfaces, the adsorption of lanthanum ions on the surface titanate and aluminate groups above a critical LaCl3 dosage becomes sufficient to activate the adsorption of the collector on the surface of rutile and aluminium-rich minerals, namely topaz, andalusite, and corundum. In the present case study, the use of LaCl3 dosages higher than a few grams per tonne of material was found to be detrimental for the monazite flotation performance. Nonetheless, we cannot exclude that such LaCl3 dosages further improve the monazite flotation performance in the case of silicate-rich tailings that contain lower amounts of these lanthanum-sensitive gangue minerals.

The comparison of the grade/recovery curves indicates that, with a collector dosage of 50 g t -1 , the sequential addition of LaCl3 and sodium oleate gave better improvement of the monazite flotation performance than the pre-mixing of these reagents. We hypothesise that this pre-mixing reduces the collector availability and limits the interactions between the lanthanum ions and mineral surfaces due to the formation of non-reactive lanthanum oleate. In contrast, in the case of sequential LaCl3 and sodium oleate addition, initial adsorption of the lanthanum ions on the mineral surfaces promotes the selective collector adsorption. The limited interactions between the mineral surfaces and both lanthanum ions and the collector, due to the formation of non-reactive lanthanum oleate, explained the limited activation of the flotation of rutile and the absence of significant activation of the flotation of aluminium-rich minerals with a collector dosage of 100 g t -1 .

The best monazite flotation performance was not sufficient to produce a marketable monazite concentrate (Figure 4 and Figure 10). However, the purpose of this study was not to develop an optimised flotation procedure for the Kankberg residues and, with an initial REE content of 349 ppm (Table 1), we did not expect to produce in a single flotation step a valuable concentrate. The present report, which highlights the effect of LaCl3 on the monazite flotation performance and the importance of its dosage and mode of addition, is a promising step towards the efficient beneficiation of monazite of low monazite-grade, silicate-rich tailings. The technical relevance of this practice must be further studied, notably with tailings that do not contain high amounts of lanthanum-sensitive gangue minerals such as rutile and aluminium-rich minerals. The economic relevance and environmental sustainability of this practice must also be evaluated. Such assessments require consolidated data that may be obtained by identifying the optimal reagent dosage for the roughing and cleaning flotation of different types of silicate-rich tailings. The main assessment criteria are the LaCl3 consumption to selectively promote monazite flotation, which is limited in the present case study, and the presence of lanthanum ions in the process water and non-floated product, which was not evaluated in this study.

Conclusion

Flotation tests performed on low monazite-grade, silicate-rich tailings indicated that the addition of LaCl3 before that of the carboxylate collectors affects the flotation performance of the phosphate minerals monazite and apatite. At a low sodium oleate dosage (50 g t -1 ), the addition of 0.1-1 g t -1 of LaCl3 selectively enhanced the monazite and apatite recovery, suggesting that the collector adsorption on these minerals was improved. At a sodium oleate dosage that enables sufficient phosphate recovery (100 g t -1 ), the addition of 0.1-1 g t -1 of LaCl3 only slightly enhanced the phosphate mineral flotation kinetics. In the present case study, the use of higher LaCl3 dosages was detrimental, as it led to recovery of gangue minerals. We highlighted that the sequential addition of LaCl3 and sodium oleate give better improvement of the monazite flotation performance than the pre-mixing of these reagents.

The flotation results presented herein confirm the previously reported synergistic effect of LaCl3 and carboxylate collectors on the floatability of phosphate minerals. Moreover, the adsorption of lanthanum ions on the surface titanate and aluminate groups occurs at high LaCl3 dosage, promoting collector adsorption on the surface of the rutile and aluminium-rich minerals topaz, andalusite, and corundum.

In this case study, we developed a new approach for the selective flotation of monazite using carboxylate collectors as inexpensive and environmentally friendly surfactants along with very small amounts of lanthanum salts to promote the selective collector adsorption on the monazite surface. Hence, this study may be regarded as a first step towards the promising development of new flotation practices for the exploitation of low monazite-grade, silicate-rich tailings as alternative REE sources.

Acknowledgements

This work was supported by the Carnot Institutes ICEEL and BRGM through the The authors would like to thank Jens Gutzmer (Helmholtz-Zentrum Dresden-Rossendorf) for his generous co-operation on the MLA study and Inna Filippova (GeoRessources, UMR 7359 CNRS-Université de Lorraine) for sharing her expertise on the flotation of non-sulphide ores with carboxylate collectors. The technical team of the STEVAL mineral processing platform (GeoRessources) was involved in this study and is acknowledged. In particular, we thank Boliden Minerals AB and our contact person Nils-Johan Bolin for providing Kankberg ore processing residues. Lastly, the authors wish to acknowledge Boliden Minerals AB for permission to publish this work.

Figure 1 :

 1 Figure 1: Gold and tellurium extractive metallurgy operations.

Figure 2

 2 Figure2and indicate that monazite and apatite are rarely associated with each other or with other gangue minerals. Therefore, it was possible to study their behaviour during flotation by assessing the progress of REE and phosphorus recovery. In general, rutile, topaz, andalusite, corundum, quartz, and muscovite, whose flotation behaviours are discussed herein, also exhibit a high proportion of free surface.

Figure 2 :

 2 Figure 2: Association diagrams for monazite, apatite, rutile, topaz, andalusite, corundum, quartz, and muscovite in the residue used as flotation feed material, measured with an MLA instrument.

Figure 3 :

 3 Figure 3: Influence of the sequential addition of LaCl3 and sodium oleate on the progress of REE recovery and yield during flotation.

Figure 4 :

 4 Figure 4: Influence of the sequential addition of LaCl3 and sodium oleate (NaOl) on the progress of REE recovery and concentrate REE grade during flotation.

Figure 5

 5 Figure 5 displays a correlation between the monazite recovery and the total phosphate minerals recovery. Given the strong relationship between the two, monazite and apatite, which account for 11.1 % and 88.6 %, respectively, of the phosphorus amount detected in the feed material, have similar flotation behaviour regardless of the collector or LaCl3 dosages. These results suggest that lanthanum ions affect the surface of monazite and apatite similarly.

Figure 5 :

 5 Figure 5: Correlation between REE and P2O5 recovery during the flotation.

Figure 6 :

 6 Figure 6: Influence of the sequential addition of LaCl3 and sodium oleate on the cumulative TiO2, SiO2, Al2O3, and K2O enrichment in the floated products. The (cumulative) enrichment is obtained by dividing the (cumulative) grade of the floated product on the grade of the feed material.

Figure 7 :

 7 Figure 7: Cumulative recovery of main gangue minerals after 30 s and 15 min of flotation using a 100 g t -1 collector dosage. These results are based on the MLA measurements of the flotation feed material, the flotation products collected after 30s and the non-floated products collected after 15 min of flotation.

Figure 8 :

 8 Figure 8: Mixed sodium oleate / LaCl3 solutions before addition to the flotation cell. "conc" are the concentrations of the individual solutions before mixing and "cons" are the equivalent reagents consumptions for 500 g of material treated.

Figure 9 :

 9 Figure 9: Influence of the addition of pre-mixed LaCl3 and sodium oleate on the progress of REE recovery and yield during flotation.

Figure 10 :

 10 Figure 10: Influence of the addition of pre-mixed LaCl3 and sodium oleate (NaOl) on the progress of REE recovery and concentrate REE grade during flotation.

Figure 11 :

 11 Figure 11: Influence of the addition of pre-mixed LaCl3 and sodium oleate on the cumulative TiO2 and Al2O3 enrichment of the floated product (enrichment over feed material).

  TERRAFINE Inter-Carnot Action (Carnot-ICEEL grant number 118-IC and IC-BRGM grant number 2013-01) and the LabEx RESSOURCES21 (contract Investissements d'Avenir, grant number ANR-10-LABX-0021).

Table 1 :

 1 Major and trace elements concentrations in the residue used as flotation feed material, determined by ICP-MS and ICP-AES analyses.

	SiO2 Al2O3 Fe2O3 TiO2 CaO MgO K2O Na2O P2O5
	Content (wt%) 63.1 27.7	0.71 1.11 0.54 0.52 2.75 0.38 0.18
	Zn LREE HREE	Y	Sc	Th	U	Nb
	Content (ppm) 411	322	27	19	15	12	7	14

Table 2 :

 2 Modal mineralogy of the residue used as flotation feed material, determined by MLA measurements.

	Mineral	Formula	Content (wt%)

Table 3 :

 3 Distribution of mass, P2O5, and REE by size fractions in the residue used as flotation feed material.

	Size fraction Mass (%) P2O5 (%) REE (%)
	>100 µm	6.4	3.4	2.0
	80-100 µm	4.3	3.8	1.9
	63-80 µm	14.4	14.4	8.0
	40-63 µm	17.4	17.4	13.4
	20-40 µm	26.2	26.2	25.4
	<20 µm	31.4	34.9	49.2