Integrative Transcriptomic Analysis Reveals Distinctive Molecular Traits and Novel Subtypes of Collecting Duct Carcinoma

Chiara Gargiuli, Pierangela Sepe, Anna Tessari, Tyler Sheetz, Maurizio Colecchia, Filippo Guglielmo Maria de Braud, Giuseppe Procopio, Marialuisa Sensi, Elena Verzoni, Matteo Dugo, Chiara Gargiuli, Pierangela Sepe, Anna Tessari, Tyler Sheetz, Maurizio Colecchia, Filippo Guglielmo Maria de Braud, Giuseppe Procopio, Marialuisa Sensi, Elena Verzoni, Matteo Dugo

Abstract

Collecting duct carcinoma (CDC) is a rare and highly aggressive kidney cancer subtype with poor prognosis and no standard treatments. To date, only a few studies have examined the transcriptomic portrait of CDC. Through integration of multiple datasets, we compared CDC to normal tissue, upper-tract urothelial carcinomas, and other renal cancers, including clear cell, papillary, and chromophobe histologies. Association between CDC gene expression signatures and in vitro drug sensitivity data was evaluated using the Cancer Therapeutic Response Portal, Genomics of Drug Sensitivity in Cancer datasets, and connectivity map. We identified a CDC-specific gene signature that predicted in vitro sensitivity to different targeted agents and was associated to worse outcome in clear cell renal cell carcinoma. We showed that CDC are transcriptionally related to the principal cells of the collecting ducts providing evidence that this tumor originates from this normal kidney cell type. Finally, we proved that CDC is a molecularly heterogeneous disease composed of at least two subtypes distinguished by cell signaling, metabolic and immune-related alterations. Our findings elucidate the molecular features of CDC providing novel biological and clinical insights. The identification of distinct CDC subtypes and their transcriptomic traits provides the rationale for patient stratification and alternative therapeutic approaches.

Keywords: collecting duct carcinoma; gene expression; histological classification; kidney cancer; molecular subtypes; prognostic/predictive biomarkers; renal cell carcinomas; transcriptomic.

Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Differential gene expression between CDC and normal kidney. (A) Volcano plot showing differentially expressed genes between CDC and normal samples in INT dataset. The x-axis shows the log2 fold change. The y-axis shows the −log10 of the false discovery rate. An absolute log2 fold-change ≥ 1 and an FDR < 0.25, represented by the vertical and horizontal dashed lines, respectively, were used to select differentially expressed genes. Up- and downregulated genes in CDC are highlighted in red and blue, respectively. The top-10 up- and downregulated genes are reported. (B) Enrichment plots from GSEA conducted with the INT signature of up- and downregulated genes in CDC in three independent datasets of CDC and normal kidney samples. The red-to-blue color bar shows the ranking of the genes of each dataset from up- to downregulated in CDC. The vertical black bars indicate the position of the genes in the INT signatures along the ranked gene list. The green line shows the running enrichment score (ES) along the ranked gene list. NES: normalized enrichment score.
Figure 2
Figure 2
Validation and functional annotation of CDC deregulated genes. (A) Intersection of the genes significantly up- (upper panel) and downregulated (lower panel) in CDC versus normal kidney in INT cohort and three additional independent datasets. Genes found in at least three out of four datasets were highlighted in red; (B) network of pathways significantly over-represented (FDR < 0.05) in the list of validated differentially expressed genes. Red and blue nodes represent pathways significantly enriched in CDC and normal samples, respectively. Clusters of interconnected nodes identify pathways with genes in common above a Cohen’s kappa statistic of 0.35 and linked to the same biological process.
Figure 3
Figure 3
CDC-specific gene signature. (A) Boxplot of 31 genes specifically upregulated in CDC compared to normal and clear cell RCC samples in INT dataset; (B) distribution of the single-sample enrichment scores of the CDC-specific INT signature in transcriptomic datasets of different kidney cancer histologies. The black dot and line represent the mean and standard deviation of the score in each dataset.
Figure 4
Figure 4
Predicted candidate drugs for CDC treatment. (A) Volcano plot showing the results of correlation analysis between the INT-CDC signature scores and drugs AUC values across cell lines of CTRP and GDSC datasets. Active compounds: negative correlation with FDR < 0.05; inactive compounds: positive correlation with FDR < 0.05. (B) Correlation between CTRP and GDSC Spearman’s correlation coefficients. Active compounds identified in both datasets are highlighted in green. Active compounds identified in GDSC only are highlighted in light blue. The blue line represents the regression line of the values. (C) Heatmap showing the modulation of target genes in the comparison between 17 CDC and 21 normal kidney samples. Up- or downregulation were defined according to an FDR < 0.05. (D) Heatmap showing the Connectivity Map scores of selected drugs in the nine cell lines profiled in the Touchstone dataset. The last column shows the mean score across the nine cell lines. Tissue of origin of human cancer cell lines: PC3: prostate; VCAP: prostate; A375: melanoma; A549: lung; HA1E: kidney; HCC515: lung; HT29: colon; MCF7: breast; HEPG2: liver.
Figure 5
Figure 5
Cell-of-origin of CDC tumors. (A) Boxplot showing the correlation between bulk gene expression profiles of different kidney tumor histologies and single-cell transcriptomics data of normal kidney cell types from dataset GSE131685; (B) distribution of the single-sample AUCell scores of the CDC-specific INT signature (left column) in each kidney cell type from dataset GSE131685. Null distribution of 1000 random gene sets of the same size of the CDC-specific INT signature is reported in the right column. p-values by Wicoxon rank-sum test between each cell type and collecting duct principal cells.
Figure 6
Figure 6
CDC-specific pathways modulation. (A) Number of differentially expressed Reactome gene sets (FDR < 0.05) between CDC and other RCC histologies; (B) heatmap showing Reactome gene sets significantly up- or downregulated in CDC compared to all other kidney cancer histologies and normal kidney. Functional categories defined by the Reactome hierarchy are reported on the right.
Figure 7
Figure 7
Functional analysis of CDC subtypes. (A) Treemap showing functional categories of Reactome gene sets significantly (FDR < 0.05) differentially enriched in CDC-S1 versus CDC-S2 subtypes. Gene sets were grouped according to the Reactome pathway hierarchy, and they are highlighted by different colors. (B) Bonferroni-adjusted p-values of the similarity between CDC and TCGA ccRCC (KIRC) or papillary (KIRP) subtypes. An adjusted p-value < 0.05 indicates significant similarity. (C) Kaplan–Meier curves referred to overall survival (OS, upper panel) and progression free-interval (PFI, lower panel) of TCGA ccRCC patients stratified according to the median expression of the top 150 genes upregulated in CDC-S2 subtype. HR: hazard ratio.

References

    1. Moch H., Cubilla A.L., Humphrey P.A., Reuter V.E., Ulbright T.M. The 2016 WHO Classification of Tumours of the Urinary System and Male Genital Organs—Part A: Renal, Penile, and Testicular Tumours. Eur. Urol. 2016;70:93–105. doi: 10.1016/j.eururo.2016.02.029.
    1. Ciszewski S., Jakimów A., Smolska-Ciszewska B. Collecting (Bellini) Duct Carcinoma: A Clinical Study of a Rare Tumour and Review of the Literature. Can. Urol. Assoc. J. 2015;9:E589–E593. doi: 10.5489/cuaj.2932.
    1. Gupta R., Billis A., Shah R.B., Moch H., Osunkoya A.O., Jochum W., Hes O., Bacchi C.E., de Castro M.G., Hansel D.E., et al. Carcinoma of the Collecting Ducts of Bellini and Renal Medullary Carcinoma: Clinicopathologic Analysis of 52 Cases of Rare Aggressive Subtypes of Renal Cell Carcinoma With a Focus on Their Interrelationship. Am. J. Surg. Pathol. 2012;36:1265–1278. doi: 10.1097/PAS.0b013e3182635954.
    1. Srigley J.R., Delahunt B. Uncommon and Recently Described Renal Carcinomas. Mod. Pathol. 2009;22:S2–S23. doi: 10.1038/modpathol.2009.70.
    1. Dimopoulos M.A., Logothetis C.J., Markowitz A., Sella A., Amato R., Ro J. Collecting Duct Carcinoma of the Kidney. Br. J. Urol. 1993;71:388–391. doi: 10.1111/j.1464-410X.1993.tb15978.x.
    1. Levin H.S., Myles J.L. The Pathology of Renal Neoplasms. In: Bukowski R.M., Novick A.C., editors. Renal Cell Carcinoma: Molecular Biology, Immunology, and Clinical Management. Humana Press; Totowa, NJ, USA: 2000. pp. 15–38. Current Clinical Oncology.
    1. Oudard S., Banu E., Vieillefond A., Fournier L., Priou F., Medioni J., Banu A., Duclos B., Rolland F., Escudier B., et al. Prospective Multicenter Phase II Study of Gemcitabine Plus Platinum Salt for Metastatic Collecting Duct Carcinoma: Results of a GETUG (Groupe d’Etudes Des Tumeurs Uro-Génitales) Study. J. Urol. 2007;177:1698–1702. doi: 10.1016/j.juro.2007.01.063.
    1. Mizutani K., Horie K., Nagai S., Tsuchiya T., Saigo C., Kobayashi K., Miyazaki T., Deguchi T. Response to Nivolumab in Metastatic Collecting Duct Carcinoma Expressing PD-L1: A Case Report. Mol. Clin. Oncol. 2017;7:988–990. doi: 10.3892/mco.2017.1449.
    1. Yasuoka S., Hamasaki T., Kuribayashi E., Nagasawa M., Kawaguchi T., Nagashima Y., Kondo Y. Nivolumab Therapy for Metastatic Collecting Duct Carcinoma after Nephrectomy: A Case Report. Medicine. 2018;97:e13173. doi: 10.1097/MD.0000000000013173.
    1. Rimar K.J., Meeks J.J., Kuzel T.M. Anti-Programmed Death Receptor 1 Blockade Induces Clinical Response in a Patient with Metastatic Collecting Duct Carcinoma. Clin. Genitourin. Cancer. 2016;14:e431–e434. doi: 10.1016/j.clgc.2016.02.013.
    1. Pal S.K., Choueiri T.K., Wang K., Khaira D., Karam J.A., Van Allen E., Palma N.A., Stein M.N., Johnson A., Squillace R., et al. Characterization of Clinical Cases of Collecting Duct Carcinoma of the Kidney Assessed by Comprehensive Genomic Profiling. Eur. Urol. 2016;70:516–521. doi: 10.1016/j.eururo.2015.06.019.
    1. Wang J., Papanicolau-Sengos A., Chintala S., Wei L., Liu B., Hu Q., Miles K.M., Conroy J.M., Glenn S.T., Costantini M., et al. Collecting Duct Carcinoma of the Kidney Is Associated with CDKN2A Deletion and SLC Family Gene Up-Regulation. Oncotarget. 2016;7:29901–29915. doi: 10.18632/oncotarget.9093.
    1. Creighton C.J., Morgan M., Gunaratne P.H., Wheeler D.A., Gibbs R.A., Gordon Robertson A., Chu A., Beroukhim R., Cibulskis K., Signoretti S., et al. Comprehensive Molecular Characterization of Clear Cell Renal Cell Carcinoma. Nature. 2013;499:43–49. doi: 10.1038/nature12222.
    1. Wach S., Taubert H., Weigelt K., Hase N., Köhn M., Misiak D., Hüttelmaier S., Stöhr C.G., Kahlmeyer A., Haller F., et al. RNA Sequencing of Collecting Duct Renal Cell Carcinoma Suggests an Interaction between MiRNA and Target Genes and a Predominance of Deregulated Solute Carrier Genes. Cancers. 2020;12:64. doi: 10.3390/cancers12010064.
    1. Malouf G.G., Compérat E., Yao H., Mouawad R., Lindner V., Rioux-leclercq N., Verkarre V., Leroy X., Dainese L., Classe M., et al. Unique Transcriptomic Profile of Collecting Duct Carcinomas Relative to Upper Tract Urothelial Carcinomas and Other Kidney Carcinomas. Sci. Rep. 2016;6:30988. doi: 10.1038/srep30988.
    1. Srigley J.R., Delahunt B., Eble J.N., Egevad L., Epstein J.I., Grignon D., Hes O., Moch H., Montironi R., Tickoo S.K., et al. The International Society of Urological Pathology (ISUP) Vancouver Classification of Renal Neoplasia. Am. J. Surg. Pathol. 2013;37:1469–1489. doi: 10.1097/PAS.0b013e318299f2d1.
    1. Yusenko M.V., Kuiper R.P., Boethe T., Ljungberg B., van Kessel A.G., Kovacs G. High-Resolution DNA Copy Number and Gene Expression Analyses Distinguish Chromophobe Renal Cell Carcinomas and Renal Oncocytomas. BMC Cancer. 2009;9:152. doi: 10.1186/1471-2407-9-152.
    1. Msaouel P., Malouf G.G., Su X., Yao H., Tripathi D.N., Soeung M., Gao J., Rao P., Coarfa C., Creighton C.J., et al. Comprehensive Molecular Characterization Identifies Distinct Genomic and Immune Hallmarks of Renal Medullary Carcinoma. Cancer Cell. 2020;37:720–734.e13. doi: 10.1016/j.ccell.2020.04.002.
    1. Colaprico A., Silva T.C., Olsen C., Garofano L., Cava C., Garolini D., Sabedot T.S., Malta T.M., Pagnotta S.M., Castiglioni I., et al. TCGAbiolinks: An R/Bioconductor Package for Integrative Analysis of TCGA Data. Nucleic Acids Res. 2016;44:e71. doi: 10.1093/nar/gkv1507.
    1. Liu J., Lichtenberg T., Hoadley K.A., Poisson L.M., Lazar A.J., Cherniack A.D., Kovatich A.J., Benz C.C., Levine D.A., Lee A.V., et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell. 2018;173:400–416.e11. doi: 10.1016/j.cell.2018.02.052.
    1. Zhang J., Baran J., Cros A., Guberman J.M., Haider S., Hsu J., Liang Y., Rivkin E., Wang J., Whitty B., et al. International Cancer Genome Consortium Data Portal—A One-Stop Shop for Cancer Genomics Data. Database. 2011;2011 doi: 10.1093/database/bar026.
    1. Robinson B.D., Vlachostergios P.J., Bhinder B., Liu W., Li K., Moss T.J., Bareja R., Park K., Tavassoli P., Cyrta J., et al. Upper Tract Urothelial Carcinoma Has a Luminal-Papillary T-Cell Depleted Contexture and Activated FGFR3 Signaling. Nat. Commun. 2019;10:2977. doi: 10.1038/s41467-019-10873-y.
    1. Cerami E., Gao J., Dogrusoz U., Gross B.E., Sumer S.O., Aksoy B.A., Jacobsen A., Byrne C.J., Heuer M.L., Larsson E., et al. The CBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data. Cancer Disccv. 2012;2:401–404. doi: 10.1158/-12-0095.
    1. Barretina J., Caponigro G., Stransky N., Venkatesan K., Margolin A.A., Kim S., Wilson C.J., Lehár J., Kryukov G.V., Sonkin D., et al. The Cancer Cell Line Encyclopedia Enables Predictive Modelling of Anticancer Drug Sensitivity. Nature. 2012;483:603–607. doi: 10.1038/nature11003.
    1. Seashore-Ludlow B., Rees M.G., Cheah J.H., Cokol M., Price E.V., Coletti M.E., Jones V., Bodycombe N.E., Soule C.K., Gould J., et al. Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset. Cancer Discov. 2015;5:1210–1223. doi: 10.1158/-15-0235.
    1. Athar A., Füllgrabe A., George N., Iqbal H., Huerta L., Ali A., Snow C., Fonseca N.A., Petryszak R., Papatheodorou I., et al. ArrayExpress Update-from Bulk to Single-Cell Expression Data. Nucleic Acids Res. 2019;47:D711–D715. doi: 10.1093/nar/gky964.
    1. Iorio F., Knijnenburg T.A., Vis D.J., Bignell G.R., Menden M.P., Schubert M., Aben N., Gonçalves E., Barthorpe S., Lightfoot H., et al. A Landscape of Pharmacogenomic Interactions in Cancer. Cell. 2016;166:740–754. doi: 10.1016/j.cell.2016.06.017.
    1. Genomics of Drug Sensitivity in Cancer. [(accessed on 14 October 2020)]; Available online:
    1. McCall M.N., Bolstad B.M., Irizarry R.A. Frozen Robust Multiarray Analysis (FRMA) Biostatistics. 2010;11:242–253. doi: 10.1093/biostatistics/kxp059.
    1. Huber W., Carey V.J., Gentleman R., Anders S., Carlson M., Carvalho B.S., Bravo H.C., Davis S., Gatto L., Girke T., et al. Orchestrating High-Throughput Genomic Analysis with Bioconductor. Nat. Methods. 2015;12:115–121. doi: 10.1038/nmeth.3252.
    1. Irizarry R.A., Hobbs B., Collin F., Beazer-Barclay Y.D., Antonellis K.J., Scherf U., Speed T.P. Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data. Biostatistics. 2003;4:249–264. doi: 10.1093/biostatistics/4.2.249.
    1. Carvalho B.S., Irizarry R.A. A Framework for Oligonucleotide Microarray Preprocessing. Bioinformatics. 2010;26:2363–2367. doi: 10.1093/bioinformatics/btq431.
    1. Du P., Kibbe W.A., Lin S.M. Lumi: A Pipeline for Processing Illumina Microarray. Bioinformatics. 2008;24:1547–1548. doi: 10.1093/bioinformatics/btn224.
    1. Miller J.A., Cai C., Langfelder P., Geschwind D.H., Kurian S.M., Salomon D.R., Horvath S. Strategies for Aggregating Gene Expression Data: The CollapseRows R Function. BMC Bioinform. 2011;12:322. doi: 10.1186/1471-2105-12-322.
    1. Dobin A., Davis C.A., Schlesinger F., Drenkow J., Zaleski C., Jha S., Batut P., Chaisson M., Gingeras T.R. STAR: Ultrafast Universal RNA-Seq Aligner. Bioinformatics. 2013;29:15–21. doi: 10.1093/bioinformatics/bts635.
    1. Robinson M.D., Oshlack A. A Scaling Normalization Method for Differential Expression Analysis of RNA-Seq Data. Genome Biol. 2010;11:R25. doi: 10.1186/gb-2010-11-3-r25.
    1. Robinson M.D., McCarthy D.J., Smyth G.K. EdgeR: A Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data. Bioinformatics. 2010;26:139–140. doi: 10.1093/bioinformatics/btp616.
    1. Stuart T., Butler A., Hoffman P., Hafemeister C., Papalexi E., Mauck W.M., Hao Y., Stoeckius M., Smibert P., Satija R. Comprehensive Integration of Single-Cell Data. Cell. 2019;177:1888–1902.e21. doi: 10.1016/j.cell.2019.05.031.
    1. Korsunsky I., Millard N., Fan J., Slowikowski K., Zhang F., Wei K., Baglaenko Y., Brenner M., Loh P., Raychaudhuri S. Fast, Sensitive and Accurate Integration of Single-Cell Data with Harmony. Nat. Methods. 2019;16:1289–1296. doi: 10.1038/s41592-019-0619-0.
    1. Liao J., Yu Z., Chen Y., Bao M., Zou C., Zhang H., Liu D., Li T., Zhang Q., Li J., et al. Single-Cell RNA Sequencing of Human Kidney. Sci. Data. 2020;7:4. doi: 10.1038/s41597-019-0351-8.
    1. Ritchie M.E., Phipson B., Wu D., Hu Y., Law C.W., Shi W., Smyth G.K. Limma Powers Differential Expression Analyses for RNA-Sequencing and Microarray Studies. Nucleic Acids Res. 2015;43:e47. doi: 10.1093/nar/gkv007.
    1. Law C.W., Chen Y., Shi W., Smyth G.K. Voom: Precision Weights Unlock Linear Model Analysis Tools for RNA-Seq Read Counts. Genome Biol. 2014;15:R29. doi: 10.1186/gb-2014-15-2-r29.
    1. Benjamini Y., Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Society Ser. B Methodol. 1995;57:289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x.
    1. Raudvere U., Kolberg L., Kuzmin I., Arak T., Adler P., Peterson H., Vilo J. g:Profiler: A Web Server for Functional Enrichment Analysis and Conversions of Gene Lists (2019 Update) Nucleic Acids Res. 2019;47:W191–W198. doi: 10.1093/nar/gkz369.
    1. Huang D.W., Sherman B.T., Tan Q., Collins J.R., Alvord W.G., Roayaei J., Stephens R., Baseler M.W., Lane H.C., Lempicki R.A. The DAVID Gene Functional Classification Tool: A Novel Biological Module-Centric Algorithm to Functionally Analyze Large Gene Lists. Genome Biol. 2007;8:R183. doi: 10.1186/gb-2007-8-9-r183.
    1. Shannon P., Markiel A., Ozier O., Baliga N.S., Wang J.T., Ramage D., Amin N., Schwikowski B., Ideker T. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res. 2003;13:2498–2504. doi: 10.1101/gr.1239303.
    1. Su G., Kuchinsky A., Morris J.H., States D.J., Meng F. GLay: Community Structure Analysis of Biological Networks. Bioinformatics. 2010;26:3135–3137. doi: 10.1093/bioinformatics/btq596.
    1. HUGO Gene Nomenclature Committee. [(accessed on 4 November 2020)]; Available online:
    1. Subramanian A., Tamayo P., Mootha V.K., Mukherjee S., Ebert B.L., Gillette M.A., Paulovich A., Pomeroy S.L., Golub T.R., Lander E.S., et al. Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles. Proc. Natl. Acad. Sci. USA. 2005;102:15545–15550. doi: 10.1073/pnas.0506580102.
    1. Liberzon A., Subramanian A., Pinchback R., Thorvaldsdóttir H., Tamayo P., Mesirov J.P. Molecular Signatures Database (MSigDB) 3.0. Bioinformatics. 2011;27:1739–1740. doi: 10.1093/bioinformatics/btr260.
    1. Yoshihara K., Shahmoradgoli M., Martínez E., Vegesna R., Kim H., Torres-Garcia W., Treviño V., Shen H., Laird P.W., Levine D.A., et al. Inferring Tumour Purity and Stromal and Immune Cell Admixture from Expression Data. Nat. Commun. 2013;4:2612. doi: 10.1038/ncomms3612.
    1. Foroutan M., Bhuva D.D., Lyu R., Horan K., Cursons J., Davis M.J. Single Sample Scoring of Molecular Phenotypes. BMC Bioinform. 2018;19:404. doi: 10.1186/s12859-018-2435-4.
    1. Aibar S., González-Blas C.B., Moerman T., Huynh-Thu V.A., Imrichova H., Hulselmans G., Rambow F., Marine J.-C., Geurts P., Aerts J., et al. SCENIC: Single-Cell Regulatory Network Inference and Clustering. Nat. Methods. 2017;14:1083–1086. doi: 10.1038/nmeth.4463.
    1. Hoshida Y., Brunet J.-P., Tamayo P., Golub T.R., Mesirov J.P. Subclass Mapping: Identifying Common Subtypes in Independent Disease Data Sets. PLoS ONE. 2007;2:e1195. doi: 10.1371/journal.pone.0001195.
    1. Reich M., Liefeld T., Gould J., Lerner J., Tamayo P., Mesirov J.P. GenePattern 2.0. Nat. Genet. 2006;38:500–501. doi: 10.1038/ng0506-500.
    1. Cancer Genome Atlas Research Network Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma. [(accessed on 12 April 2021)]; Available online: .
    1. Subramanian A., Narayan R., Corsello S.M., Peck D.D., Natoli T.E., Lu X., Gould J., Davis J.F., Tubelli A.A., Asiedu J.K., et al. A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell. 2017;171:1437–1452.e17. doi: 10.1016/j.cell.2017.10.049.
    1. Ayers M., Lunceford J., Nebozhyn M., Murphy E., Loboda A., Kaufman D.R., Albright A., Cheng J.D., Kang S.P., Shankaran V., et al. IFN-γ–Related MRNA Profile Predicts Clinical Response to PD-1 Blockade. J. Clin. Investig. 2017;127:2930–2940. doi: 10.1172/JCI91190.
    1. Danaher P., Warren S., Lu R., Samayoa J., Sullivan A., Pekker I., Wallden B., Marincola F.M., Cesano A. Pan-Cancer Adaptive Immune Resistance as Defined by the Tumor Inflammation Signature (TIS): Results from the Cancer Genome Atlas (TCGA) J. ImmunoTherapy Cancer. 2018;6:63. doi: 10.1186/s40425-018-0367-1.
    1. Damotte D., Warren S., Arrondeau J., Boudou-Rouquette P., Mansuet-Lupo A., Biton J., Ouakrim H., Alifano M., Gervais C., Bellesoeur A., et al. The Tumor Inflammation Signature (TIS) Is Associated with Anti-PD-1 Treatment Benefit in the CERTIM Pan-Cancer Cohort. J. Transl. Med. 2019;17:357. doi: 10.1186/s12967-019-2100-3.
    1. Ohe C., Smith S.C., Sirohi D., Divatia M., de Peralta-Venturina M., Paner G.P., Agaimy A., Amin M.B., Argani P., Chen Y.-B., et al. Reappraisal of Morphologic Differences between Renal Medullary Carcinoma, Collecting Duct Carcinoma, and Fumarate Hydratase–Deficient Renal Cell Carcinoma. Am. J. Surg. Pathol. 2018;42:279–292. doi: 10.1097/PAS.0000000000001000.
    1. Pagani F., Colecchia M., Sepe P., Apollonio G., Claps M., Verzoni E., de Braud F., Procopio G. Collecting Ducts Carcinoma: An Orphan Disease. Literature Overview and Future Perspectives. Cancer Treat. Rev. 2019;79:101891. doi: 10.1016/j.ctrv.2019.101891.
    1. Ji X., Qian J., Rahman S.M.J., Siska P.J., Zou Y., Harris B.K., Hoeksema M.D., Trenary I.A., Heidi C., Eisenberg R., et al. XCT (SLC7A11)-Mediated Metabolic Reprogramming Promotes Non-Small Cell Lung Cancer Progression. Oncogene. 2018;37:5007–5019. doi: 10.1038/s41388-018-0307-z.
    1. Drayton R.M., Dudziec E., Peter S., Bertz S., Hartmann A., Bryant H.E., Catto J.W. Reduced Expression of MiRNA-27a Modulates Cisplatin Resistance in Bladder Cancer by Targeting the Cystine/Glutamate Exchanger SLC7A11. Clin. Cancer Res. 2014;20:1990–2000. doi: 10.1158/1078-0432.CCR-13-2805.
    1. Liu C., Gong X., Zhang S., Shi W., Xie F., Wang A., Zhao Z., Tan M., Zhang P., Du P., et al. PT321-Comprehensive Molecular Characterization of Clear Cell Renal Cell Carcinoma with Caval Tumour Thrombus. Eur. Urol. Suppl. 2019;18:e2100. doi: 10.1016/S1569-9056(19)31522-2.
    1. Schaller L., Lauschke V.M. The Genetic Landscape of the Human Solute Carrier (SLC) Transporter Superfamily. Hum. Genet. 2019;138:1359–1377. doi: 10.1007/s00439-019-02081-x.
    1. Higuchi K., Sakamoto S., Ando K., Maimaiti M., Takeshita N., Okunushi K., Reien Y., Imamura Y., Sazuka T., Nakamura K., et al. Characterization of the Expression of LAT1 as a Prognostic Indicator and a Therapeutic Target in Renal Cell Carcinoma. Sci. Rep. 2019;9:16776. doi: 10.1038/s41598-019-53397-7.
    1. He L., Vasiliou K., Nebert D.W. Analysis and Update of the Human Solute Carrier (SLC) Gene Superfamily. Hum. Genom. 2009;3:195. doi: 10.1186/1479-7364-3-2-195.
    1. Bhutia Y.D., Babu E., Ramachandran S., Yang S., Thangaraju M., Ganapathy V. SLC Transporters as a Novel Class of Tumour Suppressors: Identity, Function and Molecular Mechanisms. Biochem. J. 2016;473:1113–1124. doi: 10.1042/BJ20150751.
    1. Dizman N., Philip E.J., Pal S.K. Genomic Profiling in Renal Cell Carcinoma. Nat. Rev. Nephrol. 2020;16:435–451. doi: 10.1038/s41581-020-0301-x.
    1. Cheval L., Pierrat F., Rajerison R., Piquemal D., Doucet A. Of Mice and Men: Divergence of Gene Expression Patterns in Kidney. PLoS ONE. 2012;7:e46876. doi: 10.1371/journal.pone.0046876.
    1. Lin Y., Zheng Y. Approaches of Targeting Rho GTPases in Cancer Drug Discovery. Expert Opin. Drug Discov. 2015;10:991–1010. doi: 10.1517/17460441.2015.1058775.
    1. Bai L., Yang J.C., Ok J., Mack P.C., Kung H.-J., Evans C.P. Simultaneous Targeting of Src Kinase and Receptor Tyrosine Kinase Results in Synergistic Inhibition of Renal Cell Carcinoma Proliferation and Migration. Int. J. Cancer. 2012;130:2693–2702. doi: 10.1002/ijc.26303.
    1. Suwaki N., Vanhecke E., Atkins K.M., Graf M., Swabey K., Huang P., Schraml P., Moch H., Cassidy A.M., Brewer D., et al. A HIF-Regulated VHL-PTP1B-Src Signaling Axis Identifies a Therapeutic Target in Renal Cell Carcinoma. Sci. Transl. Med. 2011;3:85ra47. doi: 10.1126/scitranslmed.3002004.
    1. Miyake H., Haraguchi T., Takenaka A., Fujisawa M. Metastatic Collecting Duct Carcinoma of the Kidney Responded to Sunitinib. Int. J. Clin. Oncol. 2011;16:153–155. doi: 10.1007/s10147-010-0116-z.
    1. Mennitto A., Verzoni E., Peverelli G., Alessi A., Procopio G. Management of Metastatic Collecting Duct Carcinoma: An Encouraging Result in a Patient Treated with Cabozantinib. Clin. Genitourin. Cancer. 2018;16:e521–e523. doi: 10.1016/j.clgc.2018.03.010.
    1. Ansari J., Fatima A., Chaudhri S., Bhatt R.I., Wallace M., James N.D. Sorafenib Induces Therapeutic Response in a Patient with Metastatic Collecting Duct Carcinoma of Kidney. Oncol. Res. Treat. 2009;32:44–46. doi: 10.1159/000183736.
    1. Bronchud M.H., Castillo S., de Romaní S.E., Mourelo S., Fernández A., Baena C., Murillo J., Julia J.C., Esquius J., Romero R., et al. HER2 Blockade in Metastatic Collecting Duct Carcinoma (CDC) of the Kidney: A Case Report. Oncol. Res. Treat. 2012;35:776–779. doi: 10.1159/000345041.
    1. Procopio G., Testa I., Iacovelli R., Grassi P., Verzoni E., Garanzini E., Colecchia M., Torelli T., Braud F.D. Treatment of Collecting Duct Carcinoma: Current Status and Future Perspectives. Anticancer Res. 2014;34:1027–1030.
    1. Sheng X., Cao D., Yuan J., Zhou F., Wei Q., Xie X., Cui C., Chi Z., Si L., Li S., et al. Sorafenib in Combination with Gemcitabine plus Cisplatin Chemotherapy in Metastatic Renal Collecting Duct Carcinoma: A Prospective, Multicentre, Single-Arm, Phase 2 Study. Eur. J. Cancer. 2018;100:1–7. doi: 10.1016/j.ejca.2018.04.007.
    1. Tannir N.M., Plimack E., Ng C., Tamboli P., Bekele N.B., Xiao L., Smith L., Lim Z., Pagliaro L., Araujo J., et al. A Phase 2 Trial of Sunitinib in Patients with Advanced Non–Clear Cell Renal Cell Carcinoma. Eur. Urol. 2012;62:1013–1019. doi: 10.1016/j.eururo.2012.06.043.
    1. Litchfield K., Reading J.L., Puttick C., Thakkar K., Abbosh C., Bentham R., Watkins T.B.K., Rosenthal R., Biswas D., Rowan A., et al. Meta-Analysis of Tumor- and T Cell-Intrinsic Mechanisms of Sensitization to Checkpoint Inhibition. Cell. 2021;184:596–614.e14. doi: 10.1016/j.cell.2021.01.002.

Source: PubMed

Sponsors et collaborateurs

Les conditions médicales

Interventions en matière de drogue

3
S'abonner