The Value of Phenotypic Precision Medicine in Prostate Cancer

Hawkey, Nathan; Broderick, Amanda J.; George, Daniel J.; Armstrong, Andrew J.

doi:10.1093/oncolo/oyac198

Cited by 7 publications

(5 citation statements)

References 92 publications

(84 reference statements)

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“…Precision medicine uses molecular testing to identify specific genetic mutations and biomarkers in an individual patient’s cancer cells that can guide treatment decisions. Examples include alterations in androgen receptor signaling (associated with resistance to bicalutamide and enzalutamide); genomic alterations leading to activation of the PTEN-PI3K-AKT pathway (increases response to capivasertib); and disruption of the DNA repair pathway (enhances response to PARP inhibitors) [ 34 ].…”

Section: Resultsmentioning

confidence: 99%

Analysis of Clinical Trials and Review of Recent Advances in Therapy Decisions for Locally Advanced Prostate Cancer

Williams¹

2023

JPM

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Despite the implementation of screening and early detection in many countries, the prostate cancer mortality rate remains high, particularly when the cancer is locally advanced. Targeted therapies with high efficacy and minimal harms should be particularly beneficial in this group, and several new approaches show promise. This article briefly analyses relevant clinical studies listed on ClinicalTrials.gov, combined with a short literature review that considers new therapeutic approaches that can be investigated in future clinical trials. Therapies using gold nanoparticles are of special interest in low-resource settings as they can localize and enhance the cancer-cell killing potential of X-rays using equipment that is already widely available.

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Section: Resultsmentioning

confidence: 99%

Analysis of Clinical Trials and Review of Recent Advances in Therapy Decisions for Locally Advanced Prostate Cancer

Williams¹

2023

JPM

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“…More than 80% of men with PCa have metastatic disease in bones and it is a known challenge to obtain adequate bone tissue samples for genetic sequencing. Moreover, proteomics, organoid cultures, and patient-derived xenograft-model may not always reveal the pathophysiology of a disease [ 94 , 95 , 96 ]. Prior to the invention of the CR technique, it was challenging to develop efficient and straightforward procedures in a single model with a high success rate.…”

Section: Applications Of Cr Cells In Pca Initiation and Progressionmentioning

confidence: 99%

Patient-Derived Conditionally Reprogrammed Cells in Prostate Cancer Research

Elbialy,

Kappala,

Desai

et al. 2024

Cells

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Prostate cancer (PCa) remains a leading cause of mortality among American men, with metastatic and recurrent disease posing significant therapeutic challenges due to a limited comprehension of the underlying biological processes governing disease initiation, dormancy, and progression. The conventional use of PCa cell lines has proven inadequate in elucidating the intricate molecular mechanisms driving PCa carcinogenesis, hindering the development of effective treatments. To address this gap, patient-derived primary cell cultures have been developed and play a pivotal role in unraveling the pathophysiological intricacies unique to PCa in each individual, offering valuable insights for translational research. This review explores the applications of the conditional reprogramming (CR) cell culture approach, showcasing its capability to rapidly and effectively cultivate patient-derived normal and tumor cells. The CR strategy facilitates the acquisition of stem cell properties by primary cells, precisely recapitulating the human pathophysiology of PCa. This nuanced understanding enables the identification of novel therapeutics. Specifically, our discussion encompasses the utility of CR cells in elucidating PCa initiation and progression, unraveling the molecular pathogenesis of metastatic PCa, addressing health disparities, and advancing personalized medicine. Coupled with the tumor organoid approach and patient-derived xenografts (PDXs), CR cells present a promising avenue for comprehending cancer biology, exploring new treatment modalities, and advancing precision medicine in the context of PCa. These approaches have been used for two NCI initiatives (PDMR: patient-derived model repositories; HCMI: human cancer models initiatives).

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“…Precision medicine, a field rooted in analysing samples accompanied by clinical data, relies heavily on a vast amount of information to stratify patient treatment. This necessity is linked to the often weak connections between cancer phenotypes and clinical variables [ 24 ]. Consequently, in precision medicine research, “big data analytics” and “AI” have become indispensable.…”

Section: Next-generation Biobanking: Transitioning From Traditional T...mentioning

confidence: 99%

Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking

Frascarelli,

Bonizzi,

Musico

et al. 2023

JPM

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Background. Biobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and governed manner. Exploiting diverse datasets produced by the biobanks and the downstream research from various sources and integrating bioinformatics and “omics” data has proven instrumental in advancing research such as cancer research. Biobanks offer different types of biological samples matched with rich datasets comprising clinicopathologic information. As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research. Methods. In this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. We look at the different data that ranges from specimen-related data, including digital images, patient health records and downstream genetic/genomic data and resulting “Big Data” and the analytic approaches used for analysis. Results. These cutting-edge technologies can address the challenges faced by translational and clinical research, enhancing their capabilities in data management, analysis, and interpretation. By leveraging AI, biobanks can unlock valuable insights from their vast repositories, enabling the identification of novel biomarkers, prediction of treatment responses, and ultimately facilitating the development of personalized cancer therapies. Conclusions. The integration of biobanking with AI has the potential not only to expand the current understanding of cancer biology but also to pave the way for more precise, patient-centric healthcare strategies.

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The Value of Phenotypic Precision Medicine in Prostate Cancer

Cited by 7 publications

References 92 publications

Analysis of Clinical Trials and Review of Recent Advances in Therapy Decisions for Locally Advanced Prostate Cancer

Analysis of Clinical Trials and Review of Recent Advances in Therapy Decisions for Locally Advanced Prostate Cancer

Patient-Derived Conditionally Reprogrammed Cells in Prostate Cancer Research

Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking

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