294 research outputs found
Supplementary_Table_2 – Supplemental material for Imitating Pathologist Based Assessment With Interpretable and Context Based Neural Network Modeling of Histology Images
No full textSupplemental material, Supplementary_Table_2 for Imitating Pathologist Based Assessment With Interpretable and Context Based Neural Network Modeling of Histology Images by Arunima Srivastava, Chaitanya Kulkarni, Kun Huang, Anil Parwani, Parag Mallick and Raghu Machiraju in Biomedical Informatics Insights</p
Supplementary_Table_1 – Supplemental material for Imitating Pathologist Based Assessment With Interpretable and Context Based Neural Network Modeling of Histology Images
No full textSupplemental material, Supplementary_Table_1 for Imitating Pathologist Based Assessment With Interpretable and Context Based Neural Network Modeling of Histology Images by Arunima Srivastava, Chaitanya Kulkarni, Kun Huang, Anil Parwani, Parag Mallick and Raghu Machiraju in Biomedical Informatics Insights</p
Supplementary_Table_3 – Supplemental material for Imitating Pathologist Based Assessment With Interpretable and Context Based Neural Network Modeling of Histology Images
No full textSupplemental material, Supplementary_Table_3 for Imitating Pathologist Based Assessment With Interpretable and Context Based Neural Network Modeling of Histology Images by Arunima Srivastava, Chaitanya Kulkarni, Kun Huang, Anil Parwani, Parag Mallick and Raghu Machiraju in Biomedical Informatics Insights</p
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Multi-omic Analysis of B-Cell Lymphoma Reveals Novel Mechanisms of Chemotherapeutic Drug Resistance
No full textThe genetic origins of chemotherapy resistance are well established, however the role of the epigenome and post-transcriptional regulation in drug resistance is less well understood. To investigate mechanisms of drug resistance we performed a systematic genetic, epigenetic, transcriptomic and proteomic analysis of a mafosfamide sensitive and resistant murine lymphoma cell line, along with a series of resistant lines derived by drug dose escalation. Our data suggest that acquired resistance could not be explained by genetic alterations. By integrating our transcriptional profiles with transcription factor binding data we hypothesize that the resistance was associated with changes in the activity of the polycomb repressive complex (Prc2) as well as the transcription factor E2a. We verified that the resistant cells had distinct H3K27me3 and DNA methylation profiles, compared to the parental lines, and differentially expressed genes were enriched for targets of E2a. In addition, the resistant lines appear to de-differentiate to a less mature state along the B cell maturation axis. Overall, we propose that resistant lines are transformed by an E2a and Pcr2 driven cellular program that leads to a less mature B cell state in which the apoptotic cascade induced by mafosfamide treatment is attenuated. Furthermore, combined transcriptomic and proteomic data analysis elucidated mechanisms of resistance involving the ubiquitination activating enzyme Uba1 which were not revealed by analysis of either transcriptomic or proteomic data alone
The mutational ordering of cancer hallmarks
No full textCancer hallmarks represent fundamental properties that distinguish malignant cells
from normal tissues across all cancer types. While computational models have
assumed a specific order of hallmark acquisition, this temporal sequence has never
been explored from a genetic perspective. This thesis presents a comprehensive
investigation into the emergence of mutations associated with cancer hallmarks,
using variant allele frequency as a temporal indicator. My investigation begins with
an analysis of The Cancer Genome Atlas (TCGA) primary tumours, comparing findings
with normal tissue data from the Genotype-Tissue Expression (GTEx) project, and
validating results in an independent primary tumour cohort from the Pan-Cancer
Analysis of Whole Genomes (PCAWG). This first chapter examines hallmark
acquisition patterns at patient-specific, cancer-type, and pancancer levels, while also
evaluating whether patient clusters based on hallmark acquisition sequences
correlate with survival outcomes. To strengthen these findings, complementary
analyses employ cancer cell fraction and restrict variant allele frequency analyses to
diploid genomic regions, providing methodological validation of the initial results.
The thesis further explores how genetic and environmental factors, specifically
mutational signatures and immune system interactions, influence the sequence of
hallmark acquisition. The final chapter extends this investigation to metastatic
tumours using the Hartwig Medical Foundation dataset, revealing evolutionary
distinctions between primary and metastatic lesions. Together, these analyses
provide novel insights into cancer evolution, with potential implications for improved
prognostic strategies and therapeutic interventions
Optimized Enrichment of Phosphoproteomes by Fe-IMAC Column Chromatography
No full textPhosphorylation is among the most important post-translational modifications of proteins and has numerous regulatory functions across all domains of life. However, phosphorylation is often substoichiometric, requiring selective and sensitive methods to enrich phosphorylated peptides from complex cellular digests. Various methods have been devised for this purpose and we have recently described a Fe-IMAC HPLC column chromatography setup which is capable of comprehensive, reproducible, and selective enrichment of phosphopeptides out of complex peptide mixtures. In contrast to other formats such as StageTips or batch incubations using TiO2 or Ti-IMAC beads, Fe-IMAC HPLC columns do not suffer from issues regarding incomplete phosphopeptide binding or elution and enrichment efficiency scales linearly with the amount of starting material. Here, we provide a step-by-step protocol for the entire phosphopeptide enrichment procedure including sample preparation (lysis, digestion, desalting), Fe-IMAC column chromatography (column setup, operation, charging), measurement by LC-MS/MS (nHPLC gradient, MS parameters) and data analysis (MaxQuant). To increase throughput, we have optimized several key steps such as the gradient time of the Fe-IMAC separation (15 min per enrichment), the number of consecutive enrichments possible between two chargings (>20) and the column recharging itself (90 %) identification of more than 10,000 unique phosphopeptides from 1 mg of HeLa digest within 2 h of measurement time (Q Exactive Plus)
Neural networks for analysis of trabecular bone in osteoarthritis
No full textThis study investigated the correlation of age in male and female specimens with physico-mechanical properties of trabecular bone including compressive strength, bone volume fraction, structural model index, trabecular thickness factor, level of inter-connectivity and pore morphology. An artificial neural network was designed to analyse 35 available samples in order to account for complex inter-dependencies of the key parameters in multi-dimensional space. Trained by using Levenberg-Marquardt back propagation algorithm, the network achieved regression factor of 0·96 by optimisation and showed that age correlates strongly with the physical properties of the bone affected by severe osteoarthritis. In addition, the compressive strength was found to be the most important factor for predicting the bone aging. Within the limitations of the input data set, the model developed provides a reliable predictive tool to tissue engineering applications
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