Renal cancer: new models and approach for personalizing therapy

S. di Martino, G. De Luca, L. Grassi, G. Federici, R. Alfonsi, M. Signore, A. Addario, L. De Salvo, F. Francescangeli, M. Sanchez, V. Tirelli, G. Muto, I. Sperduti, S. Sentinelli, M. Costantini, L. Pasquini, M. Milella, M. Haoui, G. Simone, M. GallucciR. De Maria, D. Bonci

Research output: Contribution to journalArticle

Abstract

BACKGROUND: Clear cell RCC (ccRCC) accounts for approximately 75% of the renal cancer cases. Surgery treatment seems to be the best efficacious approach for the majority of patients. However, a consistent fraction (30%) of cases progress after surgery with curative intent. It is currently largely debated the use of adjuvant therapy for high-risk patients and the clinical and molecular parameters for stratifying beneficiary categories. In addition, the treatment of advanced forms lacks reliable driver biomarkers for the appropriated therapeutic choice. Thus, renal cancer patient management urges predictive molecular indicators and models for therapy-decision making. METHODS: Here, we developed and optimized new models and tools for ameliorating renal cancer patient management. We isolated from fresh tumor specimens heterogeneous multi-clonal populations showing epithelial and mesenchymal characteristics coupled to stem cell phenotype. These cells retained long lasting-tumor-propagating capacity provided a therapy monitoring approach in vitro and in vivo while being able to form parental tumors when orthotopically injected and serially transplanted in immunocompromised murine hosts. RESULTS: In line with recent evidence of multiclonal cancer composition, we optimized in vitro cultures enriched of multiple tumor-propagating populations. Orthotopic xenograft masses recapitulated morphology, grading and malignancy of parental cancers. High-grade but not the low-grade neoplasias, resulted in efficient serial transplantation in mice. Engraftment capacity paralleled grading and recurrence frequency advocating for a prognostic value of our developed model system. Therefore, in search of novel molecular indicators for therapy decision-making, we used Reverse-Phase Protein Arrays (RPPA) to analyze a panel of total and phosphorylated proteins in the isolated populations. Tumor-propagating cells showed several deregulated kinase cascades associated with grading, including angiogenesis and m-TOR pathways. CONCLUSIONS: In the era of personalized therapy, the analysis of tumor propagating cells may help improve prediction of disease progression and therapy assignment. The possibility to test pharmacological response of ccRCC stem-like cells in vitro and in orthotopic models may help define a pharmacological profiling for future development of more effective therapies. Likewise, RPPA screening on patient-derived populations offers innovative approach for possible prediction of therapy response.
Original languageEnglish
Pages (from-to)4
Number of pages1
JournalJournal of experimental & clinical cancer research : CR
Volume37
Issue number1
DOIs
Publication statusPublished - Sep 5 2018

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Kidney Neoplasms
Neoplasms
Therapeutics
Protein Array Analysis
Population
Decision Making
Stem Cells
Pharmacology
Molecular Models
Immunocompromised Host
Heterografts
Disease Progression
Phosphotransferases
Transplantation
Biomarkers
Phenotype
Recurrence

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Martino, S. D., Luca, G. D., Grassi, L., Federici, G., Alfonsi, R., Signore, M., ... Bonci, D. (2018). Renal cancer: new models and approach for personalizing therapy. Journal of experimental & clinical cancer research : CR, 37(1), 4. https://doi.org/10.1186/s13046-018-0874-4 [doi]

Renal cancer: new models and approach for personalizing therapy. / Martino, S. di; Luca, G. De; Grassi, L.; Federici, G.; Alfonsi, R.; Signore, M.; Addario, A.; Salvo, L. De; Francescangeli, F.; Sanchez, M.; Tirelli, V.; Muto, G.; Sperduti, I.; Sentinelli, S.; Costantini, M.; Pasquini, L.; Milella, M.; Haoui, M.; Simone, G.; Gallucci, M.; Maria, R. De; Bonci, D.

In: Journal of experimental & clinical cancer research : CR, Vol. 37, No. 1, 05.09.2018, p. 4.

Research output: Contribution to journalArticle

Martino, SD, Luca, GD, Grassi, L, Federici, G, Alfonsi, R, Signore, M, Addario, A, Salvo, LD, Francescangeli, F, Sanchez, M, Tirelli, V, Muto, G, Sperduti, I, Sentinelli, S, Costantini, M, Pasquini, L, Milella, M, Haoui, M, Simone, G, Gallucci, M, Maria, RD & Bonci, D 2018, 'Renal cancer: new models and approach for personalizing therapy', Journal of experimental & clinical cancer research : CR, vol. 37, no. 1, pp. 4. https://doi.org/10.1186/s13046-018-0874-4 [doi]
Martino, S. di ; Luca, G. De ; Grassi, L. ; Federici, G. ; Alfonsi, R. ; Signore, M. ; Addario, A. ; Salvo, L. De ; Francescangeli, F. ; Sanchez, M. ; Tirelli, V. ; Muto, G. ; Sperduti, I. ; Sentinelli, S. ; Costantini, M. ; Pasquini, L. ; Milella, M. ; Haoui, M. ; Simone, G. ; Gallucci, M. ; Maria, R. De ; Bonci, D. / Renal cancer: new models and approach for personalizing therapy. In: Journal of experimental & clinical cancer research : CR. 2018 ; Vol. 37, No. 1. pp. 4.
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T1 - Renal cancer: new models and approach for personalizing therapy

AU - Martino, S. di

AU - Luca, G. De

AU - Grassi, L.

AU - Federici, G.

AU - Alfonsi, R.

AU - Signore, M.

AU - Addario, A.

AU - Salvo, L. De

AU - Francescangeli, F.

AU - Sanchez, M.

AU - Tirelli, V.

AU - Muto, G.

AU - Sperduti, I.

AU - Sentinelli, S.

AU - Costantini, M.

AU - Pasquini, L.

AU - Milella, M.

AU - Haoui, M.

AU - Simone, G.

AU - Gallucci, M.

AU - Maria, R. De

AU - Bonci, D.

N1 - LR: 20181114; JID: 8308647; 0 (Biomarkers, Tumor); OTO: NOTNLM; 2018/03/21 00:00 [received]; 2018/08/13 00:00 [accepted]; 2018/09/07 06:00 [entrez]; 2018/09/07 06:00 [pubmed]; 2018/10/20 06:00 [medline]; epublish

PY - 2018/9/5

Y1 - 2018/9/5

N2 - BACKGROUND: Clear cell RCC (ccRCC) accounts for approximately 75% of the renal cancer cases. Surgery treatment seems to be the best efficacious approach for the majority of patients. However, a consistent fraction (30%) of cases progress after surgery with curative intent. It is currently largely debated the use of adjuvant therapy for high-risk patients and the clinical and molecular parameters for stratifying beneficiary categories. In addition, the treatment of advanced forms lacks reliable driver biomarkers for the appropriated therapeutic choice. Thus, renal cancer patient management urges predictive molecular indicators and models for therapy-decision making. METHODS: Here, we developed and optimized new models and tools for ameliorating renal cancer patient management. We isolated from fresh tumor specimens heterogeneous multi-clonal populations showing epithelial and mesenchymal characteristics coupled to stem cell phenotype. These cells retained long lasting-tumor-propagating capacity provided a therapy monitoring approach in vitro and in vivo while being able to form parental tumors when orthotopically injected and serially transplanted in immunocompromised murine hosts. RESULTS: In line with recent evidence of multiclonal cancer composition, we optimized in vitro cultures enriched of multiple tumor-propagating populations. Orthotopic xenograft masses recapitulated morphology, grading and malignancy of parental cancers. High-grade but not the low-grade neoplasias, resulted in efficient serial transplantation in mice. Engraftment capacity paralleled grading and recurrence frequency advocating for a prognostic value of our developed model system. Therefore, in search of novel molecular indicators for therapy decision-making, we used Reverse-Phase Protein Arrays (RPPA) to analyze a panel of total and phosphorylated proteins in the isolated populations. Tumor-propagating cells showed several deregulated kinase cascades associated with grading, including angiogenesis and m-TOR pathways. CONCLUSIONS: In the era of personalized therapy, the analysis of tumor propagating cells may help improve prediction of disease progression and therapy assignment. The possibility to test pharmacological response of ccRCC stem-like cells in vitro and in orthotopic models may help define a pharmacological profiling for future development of more effective therapies. Likewise, RPPA screening on patient-derived populations offers innovative approach for possible prediction of therapy response.

AB - BACKGROUND: Clear cell RCC (ccRCC) accounts for approximately 75% of the renal cancer cases. Surgery treatment seems to be the best efficacious approach for the majority of patients. However, a consistent fraction (30%) of cases progress after surgery with curative intent. It is currently largely debated the use of adjuvant therapy for high-risk patients and the clinical and molecular parameters for stratifying beneficiary categories. In addition, the treatment of advanced forms lacks reliable driver biomarkers for the appropriated therapeutic choice. Thus, renal cancer patient management urges predictive molecular indicators and models for therapy-decision making. METHODS: Here, we developed and optimized new models and tools for ameliorating renal cancer patient management. We isolated from fresh tumor specimens heterogeneous multi-clonal populations showing epithelial and mesenchymal characteristics coupled to stem cell phenotype. These cells retained long lasting-tumor-propagating capacity provided a therapy monitoring approach in vitro and in vivo while being able to form parental tumors when orthotopically injected and serially transplanted in immunocompromised murine hosts. RESULTS: In line with recent evidence of multiclonal cancer composition, we optimized in vitro cultures enriched of multiple tumor-propagating populations. Orthotopic xenograft masses recapitulated morphology, grading and malignancy of parental cancers. High-grade but not the low-grade neoplasias, resulted in efficient serial transplantation in mice. Engraftment capacity paralleled grading and recurrence frequency advocating for a prognostic value of our developed model system. Therefore, in search of novel molecular indicators for therapy decision-making, we used Reverse-Phase Protein Arrays (RPPA) to analyze a panel of total and phosphorylated proteins in the isolated populations. Tumor-propagating cells showed several deregulated kinase cascades associated with grading, including angiogenesis and m-TOR pathways. CONCLUSIONS: In the era of personalized therapy, the analysis of tumor propagating cells may help improve prediction of disease progression and therapy assignment. The possibility to test pharmacological response of ccRCC stem-like cells in vitro and in orthotopic models may help define a pharmacological profiling for future development of more effective therapies. Likewise, RPPA screening on patient-derived populations offers innovative approach for possible prediction of therapy response.

U2 - 10.1186/s13046-018-0874-4 [doi]

DO - 10.1186/s13046-018-0874-4 [doi]

M3 - Article

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SP - 4

JO - Journal of Experimental and Clinical Cancer Research

JF - Journal of Experimental and Clinical Cancer Research

SN - 0392-9078

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