1
|
Pakula H, Omar M, Carelli R, Pederzoli F, Fanelli GN, Pannellini T, Socciarelli F, Van Emmenis L, Rodrigues S, Fidalgo-Ribeiro C, Nuzzo PV, Brady NJ, Dinalankara W, Jere M, Valencia I, Saladino C, Stone J, Unkenholz C, Garner R, Alexanderani MK, Khani F, de Almeida FN, Abate-Shen C, Greenblatt MB, Rickman DS, Barbieri CE, Robinson BD, Marchionni L, Loda M. Distinct mesenchymal cell states mediate prostate cancer progression. Nat Commun 2024; 15:363. [PMID: 38191471 PMCID: PMC10774315 DOI: 10.1038/s41467-023-44210-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024] Open
Abstract
In the complex tumor microenvironment (TME), mesenchymal cells are key players, yet their specific roles in prostate cancer (PCa) progression remain to be fully deciphered. This study employs single-cell RNA sequencing to delineate molecular changes in tumor stroma that influence PCa progression and metastasis. Analyzing mesenchymal cells from four genetically engineered mouse models (GEMMs) and correlating these findings with human tumors, we identify eight stromal cell populations with distinct transcriptional identities consistent across both species. Notably, stromal signatures in advanced mouse disease reflect those in human bone metastases, highlighting periostin's role in invasion and differentiation. From these insights, we derive a gene signature that predicts metastatic progression in localized disease beyond traditional Gleason scores. Our results illuminate the critical influence of stromal dynamics on PCa progression, suggesting new prognostic tools and therapeutic targets.
Collapse
Affiliation(s)
- Hubert Pakula
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Mohamed Omar
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, Belfer Research Building, 413 East 69th Street, New York, NY, 10021, USA
| | - Ryan Carelli
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Filippo Pederzoli
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Giuseppe Nicolò Fanelli
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
- Department of Laboratory Medicine, Pisa University Hospital, Division of Pathology, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, 56126, Italy
| | - Tania Pannellini
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Fabio Socciarelli
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Lucie Van Emmenis
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Silvia Rodrigues
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Caroline Fidalgo-Ribeiro
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Pier Vitale Nuzzo
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Nicholas J Brady
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Wikum Dinalankara
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Madhavi Jere
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Itzel Valencia
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Christopher Saladino
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Jason Stone
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Caitlin Unkenholz
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Richard Garner
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Mohammad K Alexanderani
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Francesca Khani
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Francisca Nunes de Almeida
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Cory Abate-Shen
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, 10032, USA
- Department of Molecular Pharmacology and Therapeutics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, 10032, USA
- Department of Pathology and Cell Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, 10032, USA
- Department of Urology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, 10032, USA
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Matthew B Greenblatt
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - David S Rickman
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Christopher E Barbieri
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, Belfer Research Building, 413 East 69th Street, New York, NY, 10021, USA
- Department of Urology, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Brian D Robinson
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, Belfer Research Building, 413 East 69th Street, New York, NY, 10021, USA
- Department of Urology, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Luigi Marchionni
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Massimo Loda
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10021, USA.
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, Belfer Research Building, 413 East 69th Street, New York, NY, 10021, USA.
- Department of Oncologic Pathology, Dana-Farber Cancer Institute and Harvard Medical School, 450 Brookline Ave, Boston, MA, 02215, USA.
- University of Oxford, Nuffield Department of Surgical Sciences, Oxford, UK.
| |
Collapse
|
2
|
Pakula H, Omar M, Carelli R, Pederzoli F, Fanelli GN, Pannellini T, Van Emmenis L, Rodrigues S, Fidalgo-Ribeiro C, Nuzzo PV, Brady NJ, Jere M, Unkenholz C, Alexanderani MK, Khani F, de Almeida FN, Abate-Shen C, Greenblatt MB, Rickman DS, Barbieri CE, Robinson BD, Marchionni L, Loda M. Distinct mesenchymal cell states mediate prostate cancer progression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.29.534769. [PMID: 37034687 PMCID: PMC10081210 DOI: 10.1101/2023.03.29.534769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Alterations in tumor stroma influence prostate cancer progression and metastatic potential. However, the molecular underpinnings of this stromal-epithelial crosstalk are largely unknown. Here, we compare mesenchymal cells from four genetically engineered mouse models (GEMMs) of prostate cancer representing different stages of the disease to their wild-type (WT) counterparts by single-cell RNA sequencing (scRNA-seq) and, ultimately, to human tumors with comparable genotypes. We identified 8 transcriptionally and functionally distinct stromal populations responsible for common and GEMM-specific transcriptional programs. We show that stromal responses are conserved in mouse models and human prostate cancers with the same genomic alterations. We noted striking similarities between the transcriptional profiles of the stroma of murine models of advanced disease and those of of human prostate cancer bone metastases. These profiles were then used to build a robust gene signature that can predict metastatic progression in prostate cancer patients with localized disease and is also associated with progression-free survival independent of Gleason score. Taken together, this offers new evidence that stromal microenvironment mediates prostate cancer progression, further identifying tissue-based biomarkers and potential therapeutic targets of aggressive and metastatic disease.
Collapse
Affiliation(s)
- Hubert Pakula
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Mohamed Omar
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Ryan Carelli
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Filippo Pederzoli
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Giuseppe Nicolò Fanelli
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
- Department of Laboratory Medicine, Pisa University Hospital, Division of Pathology, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa 56126, Italy
| | - Tania Pannellini
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Lucie Van Emmenis
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Silvia Rodrigues
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Caroline Fidalgo-Ribeiro
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Pier V. Nuzzo
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Nicholas J. Brady
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Madhavi Jere
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Caitlin Unkenholz
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Mohammad K. Alexanderani
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Francesca Khani
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, Belfer Research Building, 413 East 69th Street, New York, NY 10021, USA
- Department of Urology, Weill Cornell Medicine, New York, NY 10021, USA
| | - Francisca Nunes de Almeida
- Departments of Molecular Pharmacology and Therapeutics, Urology, Medicine, Pathology & Cell Biology and Systems Biology, Herbert Irving Comprehensive Cancer Center, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Cory Abate-Shen
- Departments of Molecular Pharmacology and Therapeutics, Urology, Medicine, Pathology & Cell Biology and Systems Biology, Herbert Irving Comprehensive Cancer Center, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Matthew B Greenblatt
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - David S. Rickman
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Christopher E. Barbieri
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, Belfer Research Building, 413 East 69th Street, New York, NY 10021, USA
- Department of Urology, Weill Cornell Medicine, New York, NY 10021, USA
| | - Brian D. Robinson
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, Belfer Research Building, 413 East 69th Street, New York, NY 10021, USA
- Department of Urology, Weill Cornell Medicine, New York, NY 10021, USA
| | - Luigi Marchionni
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Massimo Loda
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, Belfer Research Building, 413 East 69th Street, New York, NY 10021, USA
- Department of Oncologic Pathology, Dana-Farber Cancer Institute and Harvard Medical School, 450 Brookline Ave, Boston, MA, 02215, USA
| |
Collapse
|
3
|
NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images. Med Image Anal 2022; 78:102384. [PMID: 35217454 PMCID: PMC9972483 DOI: 10.1016/j.media.2022.102384] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 10/26/2021] [Accepted: 02/01/2022] [Indexed: 12/11/2022]
Abstract
Understanding the spatial interactions between the elements of the tumor microenvironment -i.e. tumor cells. fibroblasts, immune cells- and how these interactions relate to the diagnosis or prognosis of a tumor is one of the goals of computational pathology. We present NaroNet, a deep learning framework that models the multi-scale tumor microenvironment from multiplex-stained cancer tissue images and provides patient-level interpretable predictions using a seamless end-to-end learning pipeline. Trained only with multiplex-stained tissue images and their corresponding patient-level clinical labels, NaroNet unsupervisedly learns which cell phenotypes, cell neighborhoods, and neighborhood interactions have the highest influence to predict the correct label. To this end, NaroNet incorporates several novel and state-of-the-art deep learning techniques, such as patch-level contrastive learning, multi-level graph embeddings, a novel max-sum pooling operation, or a metric that quantifies the relevance that each microenvironment element has in the individual predictions. We validate NaroNet using synthetic data simulating multiplex-immunostained images where a patient label is artificially associated to the -adjustable- probabilistic incidence of different microenvironment elements. We then apply our model to two sets of images of human cancer tissues: 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancer patients; and 382 35-plex mass cytometry images from 215 breast cancer patients. In both synthetic and real datasets, NaroNet provides outstanding predictions of relevant clinical information while associating those predictions to the presence of specific microenvironment elements.
Collapse
|
4
|
Chilamakuri R, Agarwal S. Dual Targeting of PI3K and HDAC by CUDC-907 Inhibits Pediatric Neuroblastoma Growth. Cancers (Basel) 2022; 14:cancers14041067. [PMID: 35205815 PMCID: PMC8870466 DOI: 10.3390/cancers14041067] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 02/17/2022] [Accepted: 02/17/2022] [Indexed: 02/07/2023] Open
Abstract
Simple Summary High-risk neuroblastoma (NB) is an aggressive cancer of very young children and accounts for almost 15% of all pediatric cancer deaths. Current therapies include high-dose chemotherapy and radiation, which have long-term toxic side effects. Despite these intensive therapies, the overall 5-year survival rate of NB is less than 50%. Therefore, developing novel therapeutic approaches targeting the molecular mechanisms that drive NB progression is very important. In the present study, we repurpose CUDC-907, a dual inhibitor of PI3K and histone deacetylases. These regulators are known to regulate MYCN expression, a key prognostic marker of NB. CUDC-907 potently inhibits NB growth and 3D spheroid tumor growth by inhibiting PI3K, HDAC, and MYCN. Overall, our pre-clinical data demonstrate that repurposing CUDC-907 as a single drug is a novel and effective therapeutic approach for NB. Abstract The dysregulation of PI3K, HDACs, and MYCN are well known for promoting multiple cancer types, including neuroblastoma (NB). Targeting the upstream regulators of MYCN, including HDACs and PI3K, was shown to suppress cancer growth. In the present study, we analyze different NB patient datasets to reveal that high PI3K and HDAC expression is correlated with overall poor NB patient survival. High PI3K level is also found to be associated with high MYCN level and NB stage progression. We repurpose a dual inhibitor CUDC-907 as a single agent to directly target both PI3K and HDAC in NB. We use in vitro methodologies to determine the efficacy and selectivity of CUDC-907 using six NB and three control fibroblast cell lines. Our results show that CUDC-907 significantly inhibits NB proliferation and colony growth, induces apoptosis, blocks cell cycle progression, inhibits MYCN, and enhances H3K9Ac levels by inhibiting the PI3K/AKT signaling pathway and HDAC function. Furthermore, CUDC-907 significantly inhibits NB tumor growth in a 3D spheroid tumor model that recapitulates the in vivo tumor growth. Overall, our findings highlight that the dual inhibition of PI3K and HDAC by CUDC-907 is an effective therapeutic strategy for NB and other MYC-dependent cancers.
Collapse
|
5
|
Flores-Téllez TDNJ, Baena E. Experimental challenges to modeling prostate cancer heterogeneity. Cancer Lett 2022; 524:194-205. [PMID: 34688843 DOI: 10.1016/j.canlet.2021.10.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 09/23/2021] [Accepted: 10/09/2021] [Indexed: 12/24/2022]
Abstract
Tumor heterogeneity plays a key role in prostate cancer prognosis, therapy selection, relapse, and acquisition of treatment resistance. Prostate cancer presents a heterogeneous diversity at inter- and intra-tumor and inter-patient levels which are influenced by multiple intrinsic and/or extrinsic factors. Recent studies have started to characterize the complexity of prostate tumors and these different tiers of heterogeneity. In this review, we discuss the most common factors that contribute to tumoral diversity. Moreover, we focus on the description of the in vitro and in vivo approaches, as well as high-throughput technologies, that help to model intra-tumoral diversity. Further understanding tumor heterogeneities and the challenges they present will guide enhanced patient risk stratification, aid the design of more precise therapies, and ultimately help beat this chameleon-like disease.
Collapse
Affiliation(s)
- Teresita Del N J Flores-Téllez
- Prostate Oncobiology Group, Cancer Research UK Manchester Institute, The University of Manchester, Alderley Park, Alderley Edge, Macclesfield, SK10 4TG, UK
| | - Esther Baena
- Prostate Oncobiology Group, Cancer Research UK Manchester Institute, The University of Manchester, Alderley Park, Alderley Edge, Macclesfield, SK10 4TG, UK; Belfast-Manchester Movember Centre of Excellence, Cancer Research UK Manchester Institute, The University of Manchester, Alderley Park, SK10 4TG, UK.
| |
Collapse
|
6
|
Stopsack KH, Tyekucheva S, Wang M, Gerke TA, Vaselkiv JB, Penney KL, Kantoff PW, Finn SP, Fiorentino M, Loda M, Lotan TL, Parmigiani G, Mucci LA. Extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays. eLife 2021; 10:71265. [PMID: 34939926 PMCID: PMC8849344 DOI: 10.7554/elife.71265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 12/22/2021] [Indexed: 12/05/2022] Open
Abstract
Tissue microarrays (TMAs) have been used in thousands of cancer biomarker studies. To what extent batch effects, measurement error in biomarker levels between slides, affects TMA-based studies has not been assessed systematically. We evaluated 20 protein biomarkers on 14 TMAs with prospectively collected tumor tissue from 1448 primary prostate cancers. In half of the biomarkers, more than 10% of biomarker variance was attributable to between-TMA differences (range, 1–48%). We implemented different methods to mitigate batch effects (R package batchtma), tested in plasmode simulation. Biomarker levels were more similar between mitigation approaches compared to uncorrected values. For some biomarkers, associations with clinical features changed substantially after addressing batch effects. Batch effects and resulting bias are not an error of an individual study but an inherent feature of TMA-based protein biomarker studies. They always need to be considered during study design and addressed analytically in studies using more than one TMA. To understand cancer, researchers need to know which molecules tumor cells use. These so-called ‘biomarkers’ tag cancer cells as being different from healthy cells, and can be used to predict how aggressive a tumor may be, or how well it might respond to treatment. A popular technique for assessing biomarkers across multiple tumors is to use tissue microarrays. This involves taking samples from different tumors and embedding them in a block of wax, which is then cut into micro-thin slices and stained with reagents that can detect specific biomarkers, such as proteins. Each block contains hundreds of samples, which all experience the same conditions. So, any patterns detected in the staining are likely to represent real variations in the biomarkers present. Many cancer studies, however, often compare samples from multiple tissue microarrays, which may increase the risk of technical artifacts: for example, staining may look stronger in one batch of tissue samples than another, even though the amount of biomarker present in these different arrays is roughly the same. These ‘batch effects’ could potentially bias the results of the experiment and lead to the identification of misleading patterns. To evaluate how batch effects impact tissue microarray studies, Stopsack et al. examined 14 wax blocks which contained tumor samples from 1,448 men with prostate cancer. This revealed that for some biomarkers, but not others, there were noticeable differences between tissue microarrays that were clearly the result of batch effects. Stopsack et al. then tested six different ways of fixing these discrepancies using statistical methods. All six approaches were successful, even if the arrays included tumors with different characteristics, such as tumors that had been diagnosed more or less recently. This work highlights the importance of considering batch effects when using tissue microarrays to study cancer. Stopsack et al. have used their statistical approaches to develop freely available software which can reduce the biases that sometimes arise from these technical artifacts. This could help researchers avoid misleading patterns in their data and make it easier to detect real variations in the biomarkers present between tumor samples.
Collapse
Affiliation(s)
- Konrad H Stopsack
- Department of Epidemiology, Harvard T.H. Chan School of Public Health
| | | | - Molin Wang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health
| | - Travis A Gerke
- Department of Cancer Epidemiology, Moffitt Cancer Center
| | - J Bailey Vaselkiv
- Department of Epidemiology, Harvard T.H. Chan School of Public Health
| | | | | | | | | | - Massimo Loda
- Department of Pathology, Weill Cornell Medical Center
| | | | | | - Lorelei A Mucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health
| |
Collapse
|
7
|
Iacobas S, Iacobas DA. A Personalized Genomics Approach of the Prostate Cancer. Cells 2021; 10:cells10071644. [PMID: 34209090 PMCID: PMC8305988 DOI: 10.3390/cells10071644] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 06/24/2021] [Accepted: 06/28/2021] [Indexed: 12/19/2022] Open
Abstract
Decades of research identified genomic similarities among prostate cancer patients and proposed general solutions for diagnostic and treatments. However, each human is a dynamic unique with never repeatable transcriptomic topology and no gene therapy is good for everybody. Therefore, we propose the Genomic Fabric Paradigm (GFP) as a personalized alternative to the biomarkers approach. Here, GFP is applied to three (one primary—“A”, and two secondary—“B” & “C”) cancer nodules and the surrounding normal tissue (“N”) from a surgically removed prostate tumor. GFP proved for the first time that, in addition to the expression levels, cancer alters also the cellular control of the gene expression fluctuations and remodels their networking. Substantial differences among the profiled regions were found in the pathways of P53-signaling, apoptosis, prostate cancer, block of differentiation, evading apoptosis, immortality, insensitivity to anti-growth signals, proliferation, resistance to chemotherapy, and sustained angiogenesis. ENTPD2, AP5M1 BAIAP2L1, and TOR1A were identified as the master regulators of the “A”, “B”, “C”, and “N” regions, and potential consequences of ENTPD2 manipulation were analyzed. The study shows that GFP can fully characterize the transcriptomic complexity of a heterogeneous prostate tumor and identify the most influential genes in each cancer nodule.
Collapse
Affiliation(s)
- Sanda Iacobas
- Department of Pathology, New York Medical College, Valhalla, NY 10595, USA;
| | - Dumitru A. Iacobas
- Personalized Genomics Laboratory, Center for Computational Systems Biology, Roy G Perry College of Engineering, Prairie View A&M University, Prairie View, TX 77446, USA
- Correspondence: ; Tel.: +1-936-261-9926
| |
Collapse
|