02331nas a2200265 4500008004100000022001400041245012500055210006900180260000800249300001400257490000700271520136700278653004601645653001201691653003401703653003001737653005201767653002101819100001901840700001801859700001801877700001901895700002101914856013001935 2020 eng d a1747-027700aPrediction of survival rate and effect of drugs on cancer patients with somatic mutations of genes: an AI-based approach0 aPrediction of survival rate and effect of drugs on cancer patien cSEP a1005-10190 v963 a

The causal role of somatic mutation and its interrelationship with gene expression profile during tumor development has already been observed, which plays a major role to decide the cancer grades and overall survival. Accurate and robust prediction of tumor grades and patients' overall survival are important for prognosis, risk factors identification and betterment of the treatment strategy, especially for highly lethal tumors, like gliomas. Here, with the help of more accurate and widely used machine learning-based approaches, we propose an integrative computational pipeline that incorporates somatic mutations and gene expression profile for survival and grade prediction of glioma patients and simultaneously relates it to the drugs to be administered. This study gives us a clear understanding that the same drug is not effective for the treatment of same grade of cancer if the gene mutations are different. The alteration in a specific gene plays a very important role in tumor progression and should also be considered for the selection of appropriate drugs. This proposed framework includes all the necessary factors required for enhancement of therapeutic designs and could be useful for clinicians in determining an accurate and personalized treatment strategy for individual patients suffering from different life threatening diseases.

10agene expression and copy number variation10agliomas10agrade and survival prediction10amachine learning strategy10asignificant gene prediction and effect of drugs10asomatic mutation1 aSaurabh, Rochi1 aNandi, Sutanu1 aSinha, Noopur1 aShukla, Mudita1 aSarkar, Ram, Rup uhttps://library.ncl.res.in/content/prediction-survival-rate-and-effect-drugs-cancer-patients-somatic-mutations-genes-ai-based02765nas a2200229 4500008004100000022001400041245012900055210006900184260000800253300000800261490000800269520187300277653005302150653002102203653003202224653003602256653003402292653003802326100001902364700002102383856013102404 2024 eng d a1617-461500aDifferential cellular communication in tumor immune microenvironment during early and advanced stages of lung adenocarcinoma0 aDifferential cellular communication in tumor immune microenviron cDEC a1000 v2993 a

Heterogeneous behavior of each cell type and their cross-talks in tumor immune microenvironment (TIME) refers to tumor immunological heterogeneity that emerges during tumor progression and represents formidable challenges for effective anti-tumor immune response and promotes drug resistance. To comprehensively elucidate the heterogeneous behavior of individual cell types and their interactions across different stages of tumor development at system level, a computational framework was devised that integrates cell specific data from single-cell RNASeq into networks illustrating interactions among signaling and metabolic response genes within and between cells in TIME. This study identified stage specific novel markers which remodel the cross-talks, thereby facilitating immune stimulation. Particularly, multicellular knockout of metabolic gene APOE (Apolipoprotein E in mast cell, myeloid cell and fibroblast) combined with signaling gene CAV1 (Caveolin1 in endothelial and epithelial cells) resulted in the activation of T-cell mediated signaling pathways. Additionally, this knockout also initiated intervention of cytotoxic gene regulations during tumor immune cell interactions at the early stage of Lung Adenocarcinoma (LUAD). Furthermore, a unique interaction motif from multiple cells emerged significant in regulating the overall immune response at the advanced stage of LUAD. Most significantly, FCER1G (Fc Fragment of IgE Receptor Ig) was identified as the common regulator in activating the anti-tumor immune response at both stages. Predicted markers exhibited significant association with patient overall survival in patient specific dataset. This study uncovers the significance of signaling and metabolic interplay within TIME and discovers important targets to enhance anti-tumor immune response at each stage of tumor development.

10aEarly and advanced stages of Lung Adenocarcinoma10aImmunomodulation10aMulticellular communication10aSignaling-metabolic cross-talks10aTumor immune microenvironment10aTumor immunological heterogeneity1 aShukla, Mudita1 aSarkar, Ram, Rup uhttps://library.ncl.res.in/content/differential-cellular-communication-tumor-immune-microenvironment-during-early-and-advanced02364nas a2200217 4500008004100000022001400041245011200055210006900167260000900236490000700245520157000252653002801822653003701850653002101887653003001908100001901938700001901957700002001976700002101996856012902017 2024 eng d a2054-570300aMetabolic reprogramming and signalling cross-talks in tumour-immune interaction: a system-level exploration0 aMetabolic reprogramming and signalling crosstalks in tumourimmun cMAR 0 v113 a

Tumour-immune microenvironment (TIME) is pivotal in tumour progression and immunoediting. Within TIME, immune cells undergo metabolic adjustments impacting nutrient supply and the anti-tumour immune response. Metabolic reprogramming emerges as a promising approach to revert the immune response towards a pro-inflammatory state and conquer tumour dominance. This study proposes immunomodulatory mechanisms based on metabolic reprogramming and employs the regulatory flux balance analysis modelling approach, which integrates signalling, metabolism and regulatory processes. For the first time, a comprehensive system-level model is constructed to capture signalling and metabolic cross-talks during tumour-immune interaction and regulatory constraints are incorporated by considering the time lag between them. The model analysis identifies novel features to enhance the immune response while suppressing tumour activity. Particularly, altering the exchange of succinate and oxaloacetate between glioma and macrophage enhances the pro-inflammatory response of immune cells. Inhibition of glutamate uptake in T-cells disrupts the antioxidant mechanism of glioma and reprograms metabolism. Metabolic reprogramming through adenosine monophosphate (AMP)-activated protein kinase (AMPK), coupled with glutamate uptake inhibition, was identified as the most impactful combination to restore T-cell function. A comprehensive understanding of metabolism and gene regulation represents a favourable approach to promote immune cell recovery from tumour dominance.

10ametabolic reprogramming10asignalling-metabolic cross-talks10asystem modelling10atumour-immune interaction1 aShukla, Mudita1 aBhowmick, Rupa1 aGanguli, Piyali1 aSarkar, Ram, Rup uhttps://library.ncl.res.in/content/metabolic-reprogramming-and-signalling-cross-talks-tumour-immune-interaction-system-level