News and opinions

It is time for me to advertise the second instance of the course “Scientific data analysis: from statistics to data science and back”. Starting in February 2024, it is part of the curriculum atThe Free University (Brīvā universitāte) – a largely on-line school created by academics who had to leave Russian universities due to late developments, i.e. the war of Russia against Ukraine.The course would address basic concepts of statistical and data analyses and cover details of what I am … Continue reading “Course “Scientific data analysis: from statistics to data science and back””

We analyzed exosomal RNAs from longitudinally sampled plasma of osimertinib-treated EGFR T790M NSCLC patients in order to identify biomarkers of acquired drug resistance. RNA was subjected to transcriptomics analysis with Clariom D. While differential expression of 128 transcripts was detected, w further applied network enrichment analysis (NEA) at the pathway level. This revealed alterations in pathways related to EGFR and PI3K as well as to syndecan and glypican pathways. NEA then detected 16 immune-related gene sets. This demonstrated a potential … Continue reading “Transcriptomics footprint of resistance to EGFR inhibitor developed by lung tumors, untangled with network analysis”

Most of the cancer driver mutations were discovered due to their frequent occurrence in sequenced genomes. Still in many tumors none of these drivers is present – except maybe p53. The remaining “dark matter” of driver space likely consists of individually rare events, which escape the frequency-based detection and – consequently – existing targeted therapies. Our new method, NEAdriver individualized the discovery by accounting for mutations’ co-occurrence and potential of interacting with cancer pathways. Its ability to spot previously unnoticed … Continue reading “NEAdriver”

Good luck, Konrad!

EviCor combines access to very large cancer datasets with advanced analysis options, such as pre-computed drug resistance correlates, building multivariate models, network analysis. Thanks Iurii Petrov for both architecture design and code development – he just defended his PhD thesis on this at Karolinska Institutet. Find details in the publication https://www.sciencedirect.com/science/article/pii/S0022283622001024

Pathway enrichment analyses are usually limited to a subset of genes (or proteins) assigned to pathways (<50%) AND covered by the data profiling (transcriptomics, variant calling, a protein panel – 1…5% of the known proteins). This limitation does not exist for our method of network enrichment analysis (NEA): indeed, practically every known gene possesses a number of network links sufficient for its functional characterization against pathways, GO terms, or even single network nodes of key importance.The web interface of NEA … Continue reading “Network enrichment analysis”