Chronic kidney disease (CKD) is a worldwide public health issue affecting 14% of the general population. However, research focusing on CKD mechanisms/treatment is limited because of a lack of animal models recapitulating the disease physiopathology, including its complications. We analyzed the effects of a three-week diet rich in sodium oxalate (OXA diet) on rats and showed that, compared to controls, rats developed a stable CKD with a 60% reduction in glomerular filtration rate, elevated blood urea levels and proteinuria. Histological analyses revealed massive cortical disorganization, tubular atrophy and fibrosis. Males and females were sensitive to the OXA diet, but decreasing the diet period to one week led to GFR significance but not stable diminution. Rats treated with the OXA diet also displayed classical CKD complications such as elevated blood pressure and reduced hematocrit. Functional cardiac analyses revealed that the OXA diet triggered significant cardiac dysfunction. Altogether, our results showed the feasibility of using a convenient and non-invasive strategy to induce CKD and its classical systemic complications in rats. This model, which avoids kidney mass loss or acute toxicity, has strong potential for research into CKD mechanisms and novel therapies, which could protect and postpone the use of dialysis or transplantation.

Supplementary Table 1 Overview of patients’ and disease’s characteristics of AURORA gene fusion project Supplementary Table 2 AURORA gene fusion catalog Supplementary Table 3 Gene fusions involving ESR1 as 5 or 3 prime partner Supplementary Table 4 Clinical characteristics of patients with ESR1 fusions Supplementary Table 5 Frequency of fusions by cytoband Supplementary Table 6 Results of the Spearman's correlation test between the number of fusions and Gene Expression signatures levels

Supplementary Figure 1. Clustered heatmap of binary distance across callers. Supplementary Figure 2. Flow diagram of gene fusion calling, validation and filtering process of number of fusions and number of patients. Barplots refers to within-sample validation rate Supplementary Figure 3. Upset plot showing molecular data available. Supplementary Figure 4. Tumor purity in samples with detected or undetected gene fusions. Supplementary Figure 5. Number of gene fusions in primary and metastatic samples across the different sites of metastases. Supplementary Figure 6. Frequency of totally or partially retained or lost kinase domain. Supplementary Figure 7. Number of fusions in primary and metastatic samples, for the different sites of metastasis and across the different IHC subtypes Supplementary Figure 8. Tumor purity in metastatic versus primary samples, and the number of lost and acquired fusions. Supplementary Figure 9. Number of fusions in primary and metastatic samples, and the number of acquired and lost gene fusions versus type of adjuvant treatment for ER+/HER2-. Supplementary Figure 10. Enrichment of hallmark mitotic spindle for genes involved in shared, lost and acquired fusions. Supplementary Figure 11. Recurrent acquired gene fusions. Supplementary Figure 12. Protein domains of recurrent 5’ and 3’ genes involved in fusions acquired in metastasis of patients with paired samples. Supplementary Figure 13. Protein domains retention in fusions involving ESR1 detected in metastatic samples and ESR1 gene expression in corresponding samples. Supplementary Figure 14. Correlation between the cytoband wise gene fusion and CN gain frequency for the different subtypes. Supplementary Figure 15. Frequency plot (cytoband-wise) of gene fusions for shared and acquired gene fusions detected in paired samples. Supplementary figure 16. Fraction of CN loss, neutral and gain events for fused genes. Supplementary Figure 17. Fusions detected in primary and metastatic samples for TP53 and PIK3CA mutated and wild-type across the different IHC subtypes Supplementary figure 18. Shared, lost and acquired fusions detected in primary adn metastatic samples for TP53 and PIK3CA mutated and wild-type across the different IHC subtypes Supplementary figure 19. Bar plots showing the frequency of samples with fusions involving ESR1 (orange) and not (light grey) from primary, metastatic, and paired primary/metastatic samples from recurrent ER+ patients Supplementary figure 20. Number of fusions in HRD score low and high primary samples Supplementary figure 21. Number of fusions in HRD score low and high primary samples across subtypes Supplementary Figure 22. Delta mutational load in paired metastatic vs primary samples versus the number of acquired gene fusions. Supplementary Figure 23. Delta mutational load in paired metastatic vs primary samples versus the number of acquired gene fusions across subtypes Supplementary Figure 24. Number of fusions versus the values of the CX signatures for primary samples from the AURORA dataset. Supplementary Figure 25. Number of fusions versus the values of the CX signatures for metastatic samples Supplementary Figure 26. Number of fusions versus the values of the CX signatures for samples from TCGA-BRCA dataset. Supplementary Figure 27. Frequency of intra-chromosomal fusions involving genes mapping to the same topologically associating domains (TAD) versus not across breast cancer subtypes for primary and metastatic samples from the AURORA dataset and for samples from the TCGA-BRCA dataset Supplementary Figure 28. Frequency of intra-chromosomal fusions involving genes mapping to the same topologically associating domains (TAD) versus not in metastatic samples across breast cancer subtypes for recurrent and de novo patients

Purpose: Gene fusions are molecular rearrangements with oncogenic potential. However, their role in disease progression and therapy resistance remains largely unexplored, particularly in breast cancer. Experimental Design: In this study, we utilized multiomics data from the AURORA program to characterize gene fusions in metastatic breast cancer. We analyzed RNA sequencing data from 325 primary tumors and 350 metastatic lesions across 476 patients to develop a high-confidence catalog of gene fusions. Results: In a subset of 398 matched samples from 199 patients, we observed a higher burden of gene fusions in metastatic tumors compared with their corresponding primary tumors. Metastatic-specific (acquired) gene fusions were characterized by few recurrent rearrangements and were generally absent in patients with de novo metastatic disease. We observed a colocalization of gene fusions with subtype-specific copy-number gains. Various scores indicative of genomic instability were found to be associated with the gene fusion burden. Fusions involving genes located within the same topologically associating domain (TAD) were common in HER2-positive tumors and frequently acquired in metastatic triple-negative breast cancer. The presence of gene fusions, particularly those that were acquired or involved in genes within the same TAD, was associated with poor prognosis in estrogen receptor–positive/HER2-negative tumors. Patients with acquired ESR1 fusions exhibited a more aggressive disease course, characterized by shorter treatment response and poorer clinical outcomes. Conclusions: The AURORA gene fusion catalog may serve as a valuable resource for identifying targetable genetic alterations, thereby supporting the development of more effective therapeutic strategies for breast cancer.