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Phospholipid ether analogs for the detection of colorectal tumors.

ABSTRACT: The treatment of localized colorectal cancer (CRC) depends on resection of the primary tumor with adequate margins and sufficient lymph node sampling. A novel imaging agent that accumulates in CRCs and the associated lymph nodes is needed. Cellectar Biosciences has developed a phospholipid ether analog platform that is both diagnostic and therapeutic. CLR1502 is a near-infrared fluorescent molecule, whereas 124/131I-CLR1404 is under clinical investigation as a PET tracer/therapeutic agent imaged by SPECT. We investigated the use of CLR1502 for the detection of intestinal cancers in a murine model and 131I-CLR1404 in a patient with metastatic CRC. Mice that develop multiple intestinal tumors ranging from adenomas to locally advanced adenocarcinomas were utilized. After 96 hours post CLR1502 injection, the intestinal tumors were analyzed using a Spectrum IVIS (Perkin Elmer) and a Fluobeam (Fluoptics). The intensity of the fluorescent signal was correlated with the histological characteristics for each tumor. Colon adenocarcinomas demonstrated increased accumulation of CLR1502 compared to non-invasive lesions (total radiant efficiency: 1.76×10(10) vs 3.27×10(9) respectively, p?=?0.006). Metastatic mesenteric tumors and uninvolved lymph nodes were detected with CLR1502. In addition, SPECT imaging with 131I-CLR1404 was performed as part of a clinical trial in patients with advanced solid tumors. 131I-CLR1404 was shown to accumulate in metastatic tumors in a patient with colorectal adenocarcinoma. Together, these compounds might enhance our ability to properly resect CRCs through better localization of the primary tumor and improved lymph node identification as well as detect distant disease.

SUBMITTER: Deming DA

PROVIDER: S-EPMC4186834 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Phospholipid ether analogs for the detection of colorectal tumors.

Deming Dustin A DA Maher Molly E ME Leystra Alyssa A AA Grudzinski Joseph P JP Clipson Linda L Albrecht Dawn M DM Washington Mary Kay MK Matkowskyj Kristina A KA Hall Lance T LT Lubner Sam J SJ Weichert Jamey P JP Halberg Richard B RB

PloS one 20141006 10

The treatment of localized colorectal cancer (CRC) depends on resection of the primary tumor with adequate margins and sufficient lymph node sampling. A novel imaging agent that accumulates in CRCs and the associated lymph nodes is needed. Cellectar Biosciences has developed a phospholipid ether analog platform that is both diagnostic and therapeutic. CLR1502 is a near-infrared fluorescent molecule, whereas 124/131I-CLR1404 is under clinical investigation as a PET tracer/therapeutic agent imaged ...[more]

PMID: 25286226

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Project description:Background & aimsMicrosatellite instability (MSI) and mismatch-repair deficiency (dMMR) in colorectal tumors are used to select treatment for patients. Deep learning can detect MSI and dMMR in tumor samples on routine histology slides faster and less expensively than molecular assays. However, clinical application of this technology requires high performance and multisite validation, which have not yet been performed.MethodsWe collected H&E-stained slides and findings from molecular analyses for MSI and dMMR from 8836 colorectal tumors (of all stages) included in the MSIDETECT consortium study, from Germany, the Netherlands, the United Kingdom, and the United States. Specimens with dMMR were identified by immunohistochemistry analyses of tissue microarrays for loss of MLH1, MSH2, MSH6, and/or PMS2. Specimens with MSI were identified by genetic analyses. We trained a deep-learning detector to identify samples with MSI from these slides; performance was assessed by cross-validation (N = 6406 specimens) and validated in an external cohort (n = 771 specimens). Prespecified endpoints were area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC).ResultsThe deep-learning detector identified specimens with dMMR or MSI with a mean AUROC curve of 0.92 (lower bound, 0.91; upper bound, 0.93) and an AUPRC of 0.63 (range, 0.59-0.65), or 67% specificity and 95% sensitivity, in the cross-validation development cohort. In the validation cohort, the classifier identified samples with dMMR with an AUROC of 0.95 (range, 0.92-0.96) without image preprocessing and an AUROC of 0.96 (range, 0.93-0.98) after color normalization.ConclusionsWe developed a deep-learning system that detects colorectal cancer specimens with dMMR or MSI using H&E-stained slides; it detected tissues with dMMR with an AUROC of 0.96 in a large, international validation cohort. This system might be used for high-throughput, low-cost evaluation of colorectal tissue specimens.

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Phospholipid ether analogs for the detection of colorectal tumors.

Publications

Phospholipid ether analogs for the detection of colorectal tumors.

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