Biology subjects

Malla, C. U. P.

Publications and source records attributed to Malla, C. U. P..

2 recordsLinked to original sources

bioRxiv2024

Aggressive KRAS mutations direct TGF-β response towards partial EMT in patient-derived colorectal cancer tumoroids

Transforming growth factor beta (TGF-{beta}) exhibits complex and context-dependent cellular responses. While it mostly induces tumor-suppressive effects in early stages of tumorigenesis, its tumor promoting properties are evident in advanced disease. This TGF-{beta} duality is still not fully understood, and whether TGF-{beta} supports invasion and metastasis by influencing cancer cells directly, or rather through the stromal tumor compartment remains a matter of debate. Here, we utilized a library of colorectal cancer (CRC) patient-derived tumoroids (PDTs), representing a spectrum of tumor stages, to study cancer cell-specific responses to TGF-{beta}. Using medium conditions allowing for the differentiation of PDTs, we observed TGF-{beta} induced tumor-suppressive effects in early-stage tumoroids. PDTs with TGF-{beta} pathway mutations or PDTs derived from metastatic tumors were insensitive to the treatment. Notably, one tumoroid line harboring an atypical KRASQ22K mutation underwent partial epithelial-to-mesenchymal transition (EMT), associated with morphological changes and increased invasiveness. On a molecular level, this was accompanied by elevated expression of mesenchymal genes, as well as deregulation of pathways associated with matrix remodeling and cell adhesion. Our results suggest that tumor cell intrinsic responses to TGF-{beta} are critical in determining its tumor-suppressive or -promoting effects.

cancer biology↗

bioRxiv2019

Evaluation of Enhanced Learning Techniques for Segmenting Ischaemic Stroke Lesions in Brain Magnetic Resonance Perfusion Images using a Convolutional Neural Network Scheme

Magnetic resonance (MR) perfusion imaging non-invasively measures cerebral perfusion, which describes the bloods passage through the brains vascular network. Therefore it is widely used to assess cerebral ischaemia. Convolutional Neural Networks (CNN) constitute the state-of-the-art method in automatic pattern recognition and hence, in segmentation tasks. But none of the CNN architectures developed to date have achieved high accuracy when segmenting ischaemic stroke lesions, being the main reasons their heterogeneity in location, shape, size, image intensity and texture, especially in this imaging modality. We use a freely available CNN framework, developed for MR imaging lesion segmentation, as core algorithm to evaluate the impact of enhanced machine learning techniques, namely data augmentation, transfer learning and post-processing, in the segmentation of stroke lesions using the ISLES 2017 dataset, which contains expert annotated diffusion-weighted perfusion and diffusion brain MRI of 43 stroke patients. Of all the techniques evaluated, data augmentation with binary closing achieved the best results, improving the mean Dice score in 17% over the baseline model. Consistent with previous works, better performance was obtained in the presence of large lesions.

neuroscience↗