scholarly journals Fluorescence Molecular Targeting of Colon Cancer to Visualize the Invisible

Cells ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 249
Author(s):  
Thinzar M. Lwin ◽  
Michael A. Turner ◽  
Siamak Amirfakhri ◽  
Hiroto Nishino ◽  
Robert M. Hoffman ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Normal Tissue ◽  
Curative Resection ◽  
Molecular Targeting ◽  
Common Cause ◽  
Guided Surgery ◽  
Specific Targeting ◽  
Fluorescence Guided Surgery ◽  
Tissue Interface

Colorectal cancer (CRC) is a common cause of cancer and cancer-related death. Surgery is the only curative modality. Fluorescence-enhanced visualization of CRC with targeted fluorescent probes that can delineate boundaries and target tumor-specific biomarkers can increase rates of curative resection. Approaches to enhancing visualization of the tumor-to-normal tissue interface are active areas of investigation. Nonspecific dyes are the most-used approach, but tumor-specific targeting agents are progressing in clinical trials. The present narrative review describes the principles of fluorescence targeting of CRC for diagnosis and fluorescence-guided surgery with molecular biomarkers for preclinical or clinical evaluation.

scholarly journals Fluorescence-guided surgery in colorectal cancer; A review on clinical results and future perspectives

2021 ◽  
Author(s):  
Hidde A. Galema ◽  
RubenP.J. Meijer ◽  
Lorraine J. Lauwerends ◽  
Cornelis Verhoef ◽  
Jacobus Burggraaf ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Clinical Results ◽  
Future Perspectives ◽  
Guided Surgery ◽  
Fluorescence Guided Surgery

Multi-omics characterization of left-right colorectal cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 3542-3542
Author(s):  
John Marshall ◽  
Takayuki Yoshino ◽  
Sun Young Rha ◽  
David N. Church ◽  
Anelisa Kruschewsky Coutinho ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Cancer Biology ◽  
Normal Tissue ◽  
Molecular Mechanisms ◽  
Clinical Information ◽  
Molecular Testing ◽  
Primary Tumors ◽  
Ischemia Time ◽  
Normal Tissues

3542 Background: Right (R) vs left (L) sided colorectal cancers are clinically distinguishable based on prognosis and response to certain therapies, but as of yet, limited data have emerged to explain these differences. The science of molecular testing has evolved rapidly. Enabled by improved technologies and computing power, it is now feasible to obtain to systematic multi-omic datasets covering DNA, RNA, proteins, phospho-proteins and metabolomics on large numbers of patients. Multi-omic analysis can further define disease specific subgroups but pre-analytic quality of the tissues (ischemia time) and comparison to normal tissue controls is paramount to optimize results. Methods: Following informed consent, 450 colorectal cancer primary tumors and paired normal tissues were collected following an SOP to minimize ischemia time, and were analyzed using comprehensive genomics, transcriptomics, proteomics, phosphoproteomics, morphology and annual clinical information. Right (C18.0,2,3) and left (C18.6,7) CRC tumors, normal tissue were compared using machine learning tools to unravel the molecular mechanisms that underpin these clinically distinguishable phenotypes as well as correlating with known genomic metrics such MSI and KRAS mutation status. Results: Through leveraging the tumor and paired normal patient samples, systematic differences between left and right tumor samples were observed including specific molecular events associated with these anatomical differences. The detailed results will be presented at the meeting. Conclusions: Progress in precision medicine requires the inclusion of multi-omics which in turn requires changes to our current SOPs of tissue collection. The ability to define molecular distinctions such as between R and L colon cancer will permit the rapid discovery of clinically useful prognostic and predictive markers, dramatically adding to our fundamental understanding to colon cancer biology. Future work will focus on the discovery of novel targets and signatures, creating innovative tools that depict multi-omic results for clinicians.

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