Biology subjects

Cescon, D. W.

Publications and source records attributed to Cescon, D. W..

5 recordsLinked to original sources

bioRxiv2024

Identification of KIFC1 as a putative vulnerability in lung cancers with centrosome amplification

Centrosome amplification (CA), an abnormal increase in the number of centrosomes in the cell, is a recurrent phenomenon in lung and other malignancies. Although CA contributes to tumor development and progression by promoting genomic instability (GIN), it also induces mitotic stress that jeopardizes cellular integrity. The presence of extra centrosomes leads to the formation of multipolar mitotic spindles prone to causing lethal chromosome segregation errors during cell division. To sustain the benefits of CA, malignant cells are dependent on adaptive mechanisms to mitigate its detrimental consequences, and these mechanisms represent therapeutic vulnerabilities. We aimed to discover genetic dependencies associated with CA in lung cancer. Combining a CRISPR/Cas9 functional genomics screen with analyses of tumor genomic data, we identified the motor protein KIFC1 as a putative vulnerability specifically in lung cancers with CA. KIFC1 expression was positively correlated with CA in lung adenocarcinoma (LUAD) cell lines and with a gene expression signature predictive of CA in LUAD tumor tissues. High KIFC1 expression was associated with worse patient outcomes, smoking history, and indicators of GIN. KIFC1 loss-of-function sensitized LUAD cells to potentiation of CA and sensitization was associated with a diminished ability of KIFC1-depleted cells to cluster extra centrosomes into pseudo-bipolar mitotic spindles. Our work suggests that KIFC1 inhibition represents a novel approach for potentiating GIN to lethal levels in LC with CA by forcing cells to divide with multipolar spindles, rationalizing the clinical development of KIFC1 inhibitors and further studies to investigate its therapeutic potential.

cancer biology↗

bioRxiv2023

Simulating cell-free chromatin using preclinical models for cancer-specific biomarker discovery

Cell-free chromatin (cf-chromatin) is a rich source of biomarkers across various conditions, including cancer. Tumor-derived circulating cf-chromatin can be profiled for epigenetic features, including nucleosome positioning and histone modifications that govern cell type-specific chromatin conformations. However, the low fractional abundance of tumor-derived cf-chromatin in blood and constrained access to plasma samples pose challenges for epigenetic biomarker discovery. Conditioned media from preclinical tissue culture models could provide an unencumbered source of pure tumor-derived cf-chromatin, but large cf-chromatin complexes from such models do not resemble the nucleosomal structures found predominantly in plasma, thereby limiting the applicability of many analysis techniques. Here, we developed a robust and generalizable framework for simulating cf-chromatin with physiologic nucleosomal distributions using an optimized nuclease treatment. We profiled the resulting nucleosomes by whole genome sequencing and confirmed that inferred nucleosome positioning reflected gene expression and chromatin accessibility patterns specific to the cell type. Compared with plasma, simulated cf-chromatin displayed stronger nucleosome positioning patterns at genomic locations of accessible chromatin from patient tissue. We then utilized simulated cf-chromatin to develop methods for genome-wide profiling of histone post-translational modifications associated with heterochromatin states. Cell-free chromatin immunoprecipitation and sequencing (cf-ChIP-Seq) of H3K27me3 identified heterochromatin domains associated with repressed gene expression, and when combined with H3K4me3 cfChIP-Seq revealed bivalent domains consistent with an intermediate state of transcriptional activity. Combining cfChIP-Seq of both modifications provided more accurate predictions of transcriptional activity from the cell of origin. Altogether, our results demonstrate the broad applicability of preclinical simulated cf-chromatin for epigenetic liquid biopsy biomarker discovery.

genomics↗

bioRxiv2023

The clinical, genomic, and transcriptomic landscape of BRAF mutant cancers

BackgroundBRAF mutations are classified into 4 molecularly distinct groups, and Class 1 (V600) mutant tumors are treated with targeted therapies. Effective treatment has not been established for Class 2/3 or BRAF Fusions. We investigated whether BRAF mutation class differed according to clinical, genomic, and transcriptomic variables in cancer patients. MethodsUsing the AACR GENIE (v.12) cancer database, the distribution of BRAF mutation class in adult cancer patients was analyzed according to sex, age, primary race, and tumor type. Genomic alteration data and transcriptomic analysis was performed using The Cancer Genome Atlas. ResultsBRAF mutations were identified in 9515 (6.2%) samples among 153,834, with melanoma (31%), CRC (20.7%), and NSCLC (13.9%) being the most frequent cancer types. Class 1 harbored co-mutations outside of the MAPK pathway (TERT, RFN43) vs Class 2/3 mutations (RAS, NF1). Across all tumour types, Class 2/3 were enriched for alterations in genes involved in UV response and WNT/{beta}-catenin. Pathway analysis revealed enrichment of WNT/{beta}-catenin and Hedgehog signaling in non-V600 mutated CRC. Males had a higher proportion of Class 3 mutations vs. females (17.4% vs 12.3% q = 0.003). Non-V600 mutations were generally more common in older patients (aged 60+) vs younger (38% vs 15% p<0.0001), except in CRC (15% vs 30% q = 0.0001). Black race was associated with non-V600 BRAF alterations (OR: 1.58; p<0.0001). ConclusionsClass 2/3 BRAF are more present in Black, male patients with co-mutations outside of the MAPK pathway, likely requiring additional oncogenic input for tumorigenesis. Improving access to NGS and trial enrollment will help development of targeted therapies for non-V600 BRAF mutations. Statement of Translational RelevanceBRAF mutations are classified in 4 categories based on molecular characteristics, but only Class 1 BRAF V600 have effective targeted treatment strategies. With increasing access to next-generation sequencing, oncologists are more frequently uncovering non-V600 BRAF mutations, where there remains a scarcity of effective therapies. Responsiveness to MAPK pathway inhibitors differs according to BRAF mutation class and primary tumor type. For this reason, we sought to determine whether key demographic, genomic, and transcriptomic differences existed between classes. This cross-sectional study analyzes the largest dataset of BRAF-mutated cancers to date. Our findings propose insights to optimize clinical trial design and patient selection in the pursuit of developing effective treatment strategies for patients whose tumors harbor non-V600 BRAF mutations. This study also offers insights into the potential of targeting alternative pathways in addition to the MAPK pathway as part of combinatorial treatment strategies.

cancer biology↗

bioRxiv2020

Computational pharmacogenomics screen identifies synergistic statin-compound combinations as anti-breast cancer therapies

Statins are a family of FDA-approved cholesterol-lowering drugs that inhibit the rate-limiting enzyme of the metabolic mevalonate pathway, which have been shown to have anti-cancer activity. As therapeutic efficacy is increased when drugs are used in combination, we sought to identify agents, like dipyridamole, that potentiate statin-induced tumor cell death. As an antiplatelet agent dipyridamole will not be suitable for all cancer patients. Thus, we developed an integrative pharmacogenomics pipeline to identify agents that were similar to dipyridamole at the level of drug structure, in vitro sensitivity and molecular perturbation. To enrich for compounds expected to target the mevalonate pathway, we took a pathway-centric approach towards computational selection, which we called mevalonate drug network fusion (MVA-DNF). We validated two of the top ranked compounds, nelfinavir and honokiol and demonstrated that, like dipyridamole, they synergize with fluvastatin to potentiate tumor cell death by blocking the restorative feedback loop. This is achieved by inhibiting activation of the key transcription factor that induces mevalonate pathway gene transcription, sterol regulatory element-binding protein 2 (SREBP2). Mechanistically, the synergistic response of fluvastatin+nelfinavir and fluvastatin+honokiol was associated with similar transcriptomic and proteomic pathways, indicating a similar mechanism of action between nelfinavir and honokiol when combined with fluvastatin. Further analysis identified the canonical epithelial-mesenchymal transition (EMT) gene, E-cadherin as a biomarker of these synergistic responses across a large panel of breast cancer cell lines. Thus, our computational pharmacogenomic approach can identify novel compounds that phenocopy a compound of interest in a pathway-specific manner. Significance StatementWe provide a rapid and cost-effective strategy to expand a class of drugs with a similar phenotype. Our parent compound, dipyridamole, potentiated statin-induced tumor cell death by blocking the statin-triggered restorative feedback response that dampens statins pro-apoptotic activity. To identify compounds with this activity we performed a pharmacogenomic analysis to distinguish agents similar to dipyridamole in terms of structure, cell sensitivity and molecular perturbations. As dipyridamole has many reported activities, we focused our molecular perturbation analysis on the pathway inhibited by statins, the metabolic mevalonate pathway. Our strategy was successful as we validated nelfinavir and honokiol as dipyridamole-like drugs at both the phenotypic and molecular levels. Our pathway-specific pharmacogenomics approach will have broad applicability.

cancer biology↗

bioRxiv2020

KuLGaP: A Selective Measure for Assessing Therapy Response in Patient-Derived Xenografts

Quantifying response to drug treatment in mouse models of human cancer is important for treatment development and assignment, and yet remains a challenging task. A preferred measure to quantify this response should take into account as much of the experimental data as possible, i.e. both tumor size over time and the variation among replicates. We propose a theoretically grounded measure, KuLGaP, to compute the difference between the treatment and control arms. KuLGaP is more selective than currently existing measures, reduces the risk of false positive calls and improves translation of the lab results to clinical practice.

bioinformatics↗