Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment

doi:10.1038/s41585-023-00805-3

Review

. 2024 Apr;21(4):214-242.

doi: 10.1038/s41585-023-00805-3. Epub 2023 Aug 21.

Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment

Ali Hashemi Gheinani^{1

2

3

4

5}, Jina Kim^{6

7

8}, Sungyong You^{6

7

8}, Rosalyn M Adam^{9

10

11}

Affiliations

¹ Department of Urology, Boston Children's Hospital, Boston, MA, USA.
² Department of Surgery, Harvard Medical School, Boston, MA, USA.
³ Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁴ Department of Urology, Inselspital, Bern, Switzerland.
⁵ Department for BioMedical Research, University of Bern, Bern, Switzerland.
⁶ Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
⁷ Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
⁸ Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
⁹ Department of Urology, Boston Children's Hospital, Boston, MA, USA. rosalyn.adam@childrens.harvard.edu.
¹⁰ Department of Surgery, Harvard Medical School, Boston, MA, USA. rosalyn.adam@childrens.harvard.edu.
¹¹ Broad Institute of MIT and Harvard, Cambridge, MA, USA. rosalyn.adam@childrens.harvard.edu.

PMID: 37604982
DOI: 10.1038/s41585-023-00805-3

Review

Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment

Ali Hashemi Gheinani et al. Nat Rev Urol. 2024 Apr.

. 2024 Apr;21(4):214-242.

doi: 10.1038/s41585-023-00805-3. Epub 2023 Aug 21.

Authors

Ali Hashemi Gheinani^{1

2

3

4

5}, Jina Kim^{6

7

8}, Sungyong You^{6

7

8}, Rosalyn M Adam^{9

10

11}

Affiliations

¹ Department of Urology, Boston Children's Hospital, Boston, MA, USA.
² Department of Surgery, Harvard Medical School, Boston, MA, USA.
³ Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁴ Department of Urology, Inselspital, Bern, Switzerland.
⁵ Department for BioMedical Research, University of Bern, Bern, Switzerland.
⁶ Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
⁷ Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
⁸ Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
⁹ Department of Urology, Boston Children's Hospital, Boston, MA, USA. rosalyn.adam@childrens.harvard.edu.
¹⁰ Department of Surgery, Harvard Medical School, Boston, MA, USA. rosalyn.adam@childrens.harvard.edu.
¹¹ Broad Institute of MIT and Harvard, Cambridge, MA, USA. rosalyn.adam@childrens.harvard.edu.

PMID: 37604982
DOI: 10.1038/s41585-023-00805-3

Abstract

The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.

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References

1. Goldenberg, S. L., Nir, G. & Salcudean, S. E. A new era: artificial intelligence and machine learning in prostate cancer. Nat. Rev. Urol. 16, 391–403 (2019). - PubMed - DOI
1. Bentellis, I., Guerin, S., Khene, Z. E., Khavari, R. & Peyronnet, B. Artificial intelligence in functional urology: how it may shape the future. Curr. Opin. Urol. 31, 385–390 (2021). - PubMed - DOI
1. Brodie, A. et al. Artificial intelligence in urological oncology: an update and future applications. Urol. Oncol. 39, 379–399 (2021). - PubMed - DOI
1. Bzdok, D., Altman, N. & Krzywinski, M. Statistics versus machine learning. Nat. Methods 15, 233–234 (2018). - PubMed - PMC - DOI
1. Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017). - PubMed - PMC - DOI

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[1] Goldenberg, S. L., Nir, G. & Salcudean, S. E. A new era: artificial intelligence and machine learning in prostate cancer. Nat. Rev. Urol. 16, 391–403 (2019). - PubMed - DOI

[2] Goldenberg, S. L., Nir, G. & Salcudean, S. E. A new era: artificial intelligence and machine learning in prostate cancer. Nat. Rev. Urol. 16, 391–403 (2019). - PubMed - DOI

[3] Bentellis, I., Guerin, S., Khene, Z. E., Khavari, R. & Peyronnet, B. Artificial intelligence in functional urology: how it may shape the future. Curr. Opin. Urol. 31, 385–390 (2021). - PubMed - DOI

[4] Bentellis, I., Guerin, S., Khene, Z. E., Khavari, R. & Peyronnet, B. Artificial intelligence in functional urology: how it may shape the future. Curr. Opin. Urol. 31, 385–390 (2021). - PubMed - DOI

[5] Brodie, A. et al. Artificial intelligence in urological oncology: an update and future applications. Urol. Oncol. 39, 379–399 (2021). - PubMed - DOI

[6] Brodie, A. et al. Artificial intelligence in urological oncology: an update and future applications. Urol. Oncol. 39, 379–399 (2021). - PubMed - DOI

[7] Bzdok, D., Altman, N. & Krzywinski, M. Statistics versus machine learning. Nat. Methods 15, 233–234 (2018). - PubMed - PMC - DOI

[8] Bzdok, D., Altman, N. & Krzywinski, M. Statistics versus machine learning. Nat. Methods 15, 233–234 (2018). - PubMed - PMC - DOI

[9] Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017). - PubMed - PMC - DOI

[10] Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017). - PubMed - PMC - DOI

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Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment

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