Synergistic Anti-Tumor Effect of mTOR Inhibitors with Irinotecan on Colon Cancer Cells.

Advanced colorectal cancer has a poor prognosis because of metastasis formation and resistance to combined therapies. Downstream of PI3K/Akt and Ras/MAPK pathways, the mTOR kinase plays a decisive role in treatment failure. We previously established that irinotecan has antiangiogenic properties and it is known that new mammalian target of rapamycin (mTOR) catalytic AZD inhibitors, unlike rapamycin, target both mTORC1 and mTORC2. Thus, we hypothesized that the complete inhibition of the PI3K/AKT/mTOR/HIF-1α axis with mTOR catalytic inhibitors and low doses of irinotecan may have antitumor effects. We showed that the AZD8055 and AZD2014 inhibitors were much more potent than rapamycin to reduce cell viability of four colon cell lines. On the other hand, whereas AZD2014 alone inhibits migration by 40%, the drug combination led to 70% inhibition. Similarly, neither irinotecan nor AZD2014 significantly reduced cell invasion, whereas a combination of the two inhibits invasion by 70%. In vivo, irinotecan and AZD2014 combination drastically reduced ectopic patient-derived colon tumor growth and this combination was more potent than Folfox or Folfiri. Finally, the combination totally inhibited liver and lung metastases developed from orthotopic implantation of SW480 cells. Thus, the use of mTOR catalytic inhibitors, in association with other chemotherapeutic agents like irinotecan at low doses, is potentially a hope for colon cancer treatment.

Development of a memetic clustering algorithm for optimal spectral histology: application to FTIR images of normal human colon.

The coupling between Fourier-transform infrared (FTIR) imaging and unsupervised classification is effective in revealing the different structures of human tissues based on their specific biomolecular IR signatures; thus the spectral histology of the studied samples is achieved. However, the most widely applied clustering methods in spectral histology are local search algorithms, which converge to a local optimum, depending on initialization. Multiple runs of the techniques estimate multiple different solutions. Here, we propose a memetic algorithm, based on a genetic algorithm and a k-means clustering refinement, to perform optimal clustering. In addition, this approach was applied to the acquired FTIR images of normal human colon tissues originating from five patients. The results show the efficiency of the proposed memetic algorithm to achieve the optimal spectral histology of these samples, contrary to k-means.

Fully unsupervised inter-individual IR spectral histology of paraffinized tissue sections of normal colon.

In label-free Fourier-transform infrared histology, spectral images are individually recorded from tissue sections, pre-processed and clustered. Each single resulting color-coded image is annotated by a pathologist to obtain the best possible match with tissue structures revealed after Hematoxylin-Eosin staining. However, the main limitations of this approach are the empirical choice of the number of clusters in unsupervised classification, and the marked color heterogeneity between the clustered spectral images. Here, using normal murine and human colon tissues, we developed an automatic multi-image spectral histology to simultaneously analyze a set of spectral images (8 images mice samples and 72 images human ones). This procedure consisted of a joint Extended Multiplicative Signal Correction (EMSC) to numerically deparaffinize the tissue sections, followed by an automated joint K-Means (KM) clustering using the hierarchical double application of Pakhira-Bandyopadhyay-Maulik (PBM) validity index. Using this procedure, the main murine and human colon histological structures were correctly identified at both the intra- and the inter-individual levels, especially the crypts, secreted mucus, lamina propria and submucosa. Here, we show that batched multi-image spectral histology procedure is insensitive to the reference spectrum but highly sensitive to the paraffin model of joint EMSC. In conclusion, combining joint EMSC and joint KM clustering by double PBM application allows to achieve objective and automated batched multi-image spectral histology.

Development of a hierarchical double application of crisp cluster validity indices: a proof-of-concept study for automated FTIR spectral histology.

Fourier-transform infrared (FTIR) spectral imaging is currently used as a non-destructive and label-free method for analyzing biological specimens. However, to highlight the different tissue regions, unsupervised clustering methods are commonly used leading to a subjective choice of the number of clusters. Here, we develop a hierarchical double application of 9 selected crisp cluster validity indices (CCVIs) using K-Means clustering. This approach when tested first on an artificial dataset showed that the indices Pakhira-Bandyopadhyay-Maulik (PBM) and Sym-Index (SI) perfectly estimated the expected 9 sub-clusters. Then, the concept was applied to a real dataset consisting of FTIR spectral images of normal human colon tissue samples originating from 5 patients. PBM and SI were revealed to be the most efficient indices that correctly identified the different colon histological components including crypts, lamina propria, muscularis mucosae, submucosa, and lymphoid aggregates. In conclusion, these results strongly suggest that the hierarchical double CCVI application is a promising method for automated and informative spectral histology.