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Sequencing of cancer cell subpopulations identifies micrometastases in a bladder cancer patient
Pathologic staging of bladder cancer patients remains a challenge. Standard-of-care histology exhibits limited sensitivity in detection of micrometastases, which can increase risk of cancer progression and delay potential adjuvant therapies. Here, we sought to develop a proof of concept novel molecu...
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Published in: | Oncotarget 2017-07, Vol.8 (28), p.45619-45625 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Pathologic staging of bladder cancer patients remains a challenge. Standard-of-care histology exhibits limited sensitivity in detection of micrometastases, which can increase risk of cancer progression and delay potential adjuvant therapies. Here, we sought to develop a proof of concept novel molecular approach to improve detection of cancer micrometastasis.
We combined fluorescence activated cell sorting and next-generation sequencing and performed whole-exome sequencing of total cancer cells and cancer cell subpopulations in multiple tumor specimens and regional lymph nodes in a single patient with muscle-invasive urothelial carcinoma of the bladder following radical cystectomy.
Mean allele frequency analysis demonstrated a significant correlation between primary tumor cancer cells and cancer cells isolated from the lymph nodes, confirming lymph node disease despite negative pathologic staging. RNA-sequencing revealed intratumoral heterogeneity as well as enrichment for immune system and lipid metabolism gene sets in the micrometastatic cancer cell subpopulations.
Our analysis illustrates how next-generation sequencing of cancer cell subpopulations may be utilized to enrich for cancer cell markers and enhance detection of bladder cancer micrometastases to improve pathologic staging and provide insight into cancer cell biology. |
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ISSN: | 1949-2553 1949-2553 |
DOI: | 10.18632/oncotarget.17312 |