Biology subjects

Livanova, A.

Publications and source records attributed to Livanova, A..

3 recordsLinked to original sources

bioRxiv2026

Epigenetic evolution of colorectal cancer and its microenvironment reveals new vulnerabilities

Epigenetics is central to tumorigenesis, but the co-evolution of the cancer epigenome and its microenvironment is severely understudied. Here, we measure chromatin accessibility and transcriptome, at single cell resolution, of a set of normal colon, primary colorectal cancers and metastases, and identify recurrent epigenetic alterations in tumour cells. We also found that the normal epithelium adjacent to the cancer had recurrent epigenetic alterations associated with inflammatory programs that were partially shared with tumour cells. Distinct tumour-intrinsic transcription factor binding programs were associated with differential abundance of malignant stroma cell identities. We then leveraged matched patient-derived organoids, to assess the functional impact of the most recurrent epigenetic alterations on cancer cell viability, using CRISPR interference. We found a set of epigenetic-driven cancer dependencies, related to developmental reprogramming and cellular stress resilience, representing new potential therapeutic targets.

cancer biology↗

bioRxiv2026

CRISPR-enhanced assessment of variants of unknown significance nominates oncology therapeutic targets and drug repositioning opportunities

Interpreting infrequent somatic variants remains a challenge in cancer genomics. We developed CRISPR-VUS, a framework that uses public Cancer Dependency Map data to identify Dependency-Associated Mutations (DAMs) - variants linked to increased host-gene dependency - with resolution extending to singleton events. Analysis of 977 cell lines across 36 cancer types identified 2,376 DAMs in 1,383 genes, including 1,260 not established as cancer drivers. DAM-bearing genes converge on oncogenic networks, while recurrence in histology-matched tumours, functional-impact predictions, tractability and pharmacological associations enable systematic prioritisation. Prime editing showed that the prioritised NSCLC-specific RTN4IP1-A80T DAM conferred a significant competitive growth advantage in a lung epithelial model, nominating a candidate driver allele. Exploratory pharmacological testing showed a greater maximal response to istaroxime in ATP1B3-I189M-bearing than in ATP1B3-wild-type cells. CRISPR-VUS combines dependency-based rare-variant discovery with evidence-guided prioritisation to nominate candidate drivers, therapeutic targets and drug-repositioning hypotheses. Interactive results are available at https://vus-portal.fht.org/.

bioinformatics↗

bioRxiv2025

A simple circuit to sustain intact tumor microenvironments for complex drug interrogations

Deep learning and large language models can integrate complex datasets to uncover biological insights that are often undetectable through conventional analyses. With application to translational cancer research, these computational tools have positioned 3D patient-derived tumor avatars front and center as crucial data input sources. However, a major challenge remains: the lack of standardization in media composition in 3D patient-derived tumor models unpredictably affects cell behavior and limit the utility beyond predicting treatment responses. To address this unmet need, we developed a simple, reproducible perfusion circuit system to approximate in vivo physiology using autologous patient plasma. With peritoneal metastases and core needle biopsies across multiple tumor histologies, we demonstrate preservation of the tumor microenvironment for up to 48 hours using multi-modal interrogation techniques. With proof-of-concept experiments, we display the systems ability to unveil complex drug-dependent biology within this time window. Standardizable, physiologically relevant platforms for 3D patient-derived tumor avatars will yield unprecedented insights through the integration of data from broad groups of patients and the use of an expanding armamentarium of artificial intelligence capabilities.

cancer biology↗