Biology subjects

Siavelis, I.

Publications and source records attributed to Siavelis, I..

5 recordsLinked to original sources

bioRxiv2026

RNA splicing factor mutations drive myeloid neoplasm oncogenesis through protein complex poisoning

RNA splicing factor mutations (SFmut) are founding oncogenic events which cause RNA splicing errors with unpredictable gene expression dynamics. Despite extensive transcriptomic studies, SFmut cancer-initiating mechanisms remain elusive. Among SFmut cancers, myeloid neoplasms (MN) alone offer a setting where true cancer-driving SFmut stem cells can be identified, namely the hematopoietic stem cell (HSC). Within a cohort of 62 MN patients and 20 healthy donors, we conducted long/short-read single-cell transcriptomics (10X-ONT, n = 21) and immunophenotype-resolved low-cell proteomics (pauciproteomics, n = 78) to resolve gene expression, RNA splicing and protein expression dynamics across healthy and SFmut MN hematopoiesis. During hematopoietic differentiation, SFmut RNA mis-splicing decouples proteotranscriptomic dynamics in a mutation-specific manner. Pauciproteomics defines the functional effects of SFmut RNA mis-splicing on the protein layer, identifying early-stage protein effects which poison entire functional protein complexes and progressively disrupt the proteome-wide network during cellular maturation. Importantly, this information could neither be resolved nor predicted by transcriptomic analyses. Finally, we functionally validate the proteome dynamics of HSC through induced pluripotent stem cell culture models of early hematopoietic differentiation, identifying candidate mechanisms for SFmut oncogenesis. Overall, these data elucidate SFmut MN disease biology with unprecedented granularity, defining the molecular consequences of SFmut RNA mis-splicing and offering a generalizable framework for cancer stem cell studies.

cancer biology↗

bioRxiv2026

Proteomic profiling reveals pleiotropic antimetabolite activity of triciribine in acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) exhibits marked genetic and metabolic heterogeneity that limits the efficacy of targeted therapies. Antimetabolite strategies remain central to ALL treatment, yet the mechanisms underlying differential drug sensitivity are incompletely defined. Here, we investigated the activity of the purine analog triciribine (TCN) across diverse ALL cellular models. We find that TCN exerts potent cytotoxic effects in multiple ALL cell lines, exceeding those observed with canonical Akt inhibitors in our datasets. Phosphoproteomic analyses indicate that, at early time points, TCN does not primarily suppress Akt signaling but instead induces transient pathway activation accompanied by inhibition of cyclin-dependent kinases in both sensitive and resistant cells. The monophosphorylated metabolite TCN-P represents the predominant intracellular species and requires adenosine kinase (ADK) for activity, with ADK protein levels positively correlating with TCN sensitivity in both cell lines and primary patient samples. Using Proteome Integral Solubility Alteration profiling, we identify candidate protein interactions of TCN-P distributed across multiple cellular pathways, including nucleotide metabolism, DNA replication, and protein synthesis. These interactions are accompanied by impaired purine biosynthesis, DNA damage, translational stress, and cell-cycle arrest, as supported by time-course quantitative proteomics and immunoblot analyses. Together, these findings characterize triciribine as an antileukemic agent with pleiotropic antimetabolite activity in ALL and highlight ADK-dependent metabolism as a key determinant of therapeutic sensitivity, suggesting that ADK levels may serve as a predictive biomarker to stratify patients for triciribine-based precision treatment strategies.

cancer biology↗

bioRxiv2024

Pathway activation model for personalized prediction of drug synergy

Targeted monotherapies for cancer often fail due to inherent or acquired drug resistance. By aiming at multiple targets simultaneously, drug combinations can produce synergistic interactions that increase drug effectiveness and reduce resistance. Computational models based on the integration of omics data have been used to identify synergistic combinations, but predicting drug synergy remains a challenge. Here, we introduce DIPx, an algorithm for personalized prediction of drug synergy based on biologically motivated tumor- and drug-specific pathway activation scores (PASs). We trained and validated DIPx in the AstraZeneca-Sanger (AZS) DREAM Challenge dataset using two separate test sets: Test Set 1 comprised the combinations already present in the training set, while Test Set 2 contained combinations absent from the training set, thus indicating the models ability to handle novel combinations. The Spearman correlation coefficients between predicted and observed drug synergy were 0.50 (95% CI: 0.47-0.53) in Test Set 1 and 0.26 (95% CI: 0.22-0.30) in Test Set 2, compared to 0.38 (95% CI: 0.34-0.42) and 0.18 (95% CI: 0.16-0.20), respectively, for the best performing method in the Challenge. We show evidence that higher synergy is associated with higher functional interaction between the drug targets, and this functional interaction information is captured by PAS. We illustrate the use of PAS to provide a potential biological explanation in terms of activated pathways that mediate the synergistic effects of combined drugs. In summary, DIPx can be a useful tool for personalized prediction of drug synergy and exploration of activated pathways related to the effects of combined drugs.

bioinformatics↗

bioRxiv2022

Integrative multi-omics analysis reveals molecular subtypes and tumor evolution of synovial sarcoma

Synovial sarcomas (SS) are malignant mesenchymal tumors characterized by the SS18-SSX fusion gene, which drives tumorigenesis by altering the composition of the BAF complex. Secondary genomic alterations that determine variations in tumor phenotype or clinical presentation are largely unknown. Herein, we present transcriptome, targeted DNA-sequencing, and proteomics analysis of 91 synovial sarcomas from 55 patients. We identified three SS clusters (SSCs) characterized by distinct histology, tumor microenvironments, genomic complexities, therapeutic effects, and clinical outcomes. Eight BAF complex components are differentially expressed among SSCs, and their role in mesenchymal-epithelial-transition is supported by single cell sequencing. The epithelial cells of biphasic tumors are more susceptible to developing copy number alterations, including amplification of PDCD1 and TMPRSS2. Our findings explain broad concepts in SS biology and imply that the BAF composition at the start of the tumorigenesis (i.e. the cellular linage) may determine the SS subtype, providing a rationale for individualized treatment strategies.

cancer biology↗

bioRxiv2022

Proteogenomics refines the molecular classification of chronic lymphocytic leukemia

Cancer heterogeneity at the proteome level may explain differences in therapy response and prognosis beyond the currently established genomic and transcriptomic based diagnostics. The relevance of proteomics for disease classifications remains to be established in clinically heterogeneous cancer entities such as chronic lymphocytic leukemia (CLL). Here, we characterized the proteome and transcriptome in-depth alongside genetic and ex-vivo drug response profiling in a clinically well annotated CLL discovery cohort (n= 68). Unsupervised clustering of the proteome data revealed six subgroups. Five of these proteomic groups were associated with genetic features, while one group was only detectable at the proteome level. This new group was characterized by accelerated disease progression, high spliceosomal protein abundances associated with aberrant splicing, and low B cell receptor signaling protein abundances (ASB-CLL). We developed classifiers to identify ASB-CLL based on its characteristic proteome or splicing signature in two independent cohorts (n= 165, n= 169) and confirmed that ASB-CLL comprises about 20 % of CLL patients. The inferior overall survival observed in ASB-CLL was independent of both TP53- and IGHV mutation status. Our multi-omics analysis refines the classification of CLL and highlights the potential of proteomics to improve cancer patient stratification beyond genetic and transcriptomic profiling. Single sentence summaryWe performed the largest proteogenomic analysis of CLL, linked proteomic profiles to clinical outcomes, and discovered a new poor outcome subgroup (ASB-CLL).

cancer biology↗