Publications by authors named "Vito Quaranta"

Unlabelled: Neoadjuvant chemotherapy (NAC) is a standard-of-care treatment for locally advanced triple negative breast cancer (TNBC) before surgery. The early assessment of TNBC response to NAC would enable an oncologist to adapt the therapeutic plan of a non-responding patient, thereby improving treatment outcomes while preventing unnecessary toxicities. To this end, a promising approach consists of obtaining in silico personalized forecasts of tumor response to NAC via computer simulation of mechanistic models constrained with patient-specific magnetic resonance imaging (MRI) data acquired early during NAC.

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Article Synopsis
  • - Small cell lung cancer (SCLC) is known for its resistance to therapy, making it essential to identify phenotypes that contribute to this resistance and immune evasion; previous studies have indicated that DNA damage response (DDR) mechanisms may play a role in these issues across various cancers.
  • - A new method was developed to analyze DDR genes in SCLC clinical samples, revealing three distinct DDR phenotypes characterized by differences in DNA repair gene expression, replication stress, and G2/M cell cycle arrest, which correlate with how SCLC tumors respond to chemotherapy.
  • - The study concludes that understanding these DDR clusters can improve our knowledge of SCLC biology and treatment responses, suggesting that targeting specific DDR phenotypes may enhance patient outcomes in the
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WD40 Repeat Domain 5 (WDR5) is a highly conserved nuclear protein that recruits MYC oncoprotein transcription factors to chromatin to stimulate ribosomal protein gene expression. WDR5 is tethered to chromatin via an arginine-binding cavity known as the "WIN" site. Multiple pharmacological inhibitors of the WDR5-interaction site of WDR5 (WINi) have been described, including those with picomolar affinity and oral bioavailability in mice.

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Drug tolerance is a major cause of relapse after cancer treatment. Despite intensive efforts, its molecular basis remains poorly understood, hampering actionable intervention. We report a previously unrecognized signaling mechanism supporting drug tolerance in BRAF-mutant melanoma treated with BRAF inhibitors that could be of general relevance to other cancers.

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Drug tolerance is a major cause of relapse after cancer treatment. In spite of intensive efforts, its molecular basis remains poorly understood, hampering actionable intervention. We report a previously unrecognized signaling mechanism supporting drug tolerance in BRAF-mutant melanoma treated with BRAF inhibitors that could be of general relevance to other cancers.

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Phenotypic plasticity of cancer cells can lead to complex cell state dynamics during tumor progression and acquired resistance. Highly plastic stem-like states may be inherently drug-resistant. Moreover, cell state dynamics in response to therapy allow a tumor to evade treatment.

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Mechanistic models of biological processes can explain observed phenomena and predict responses to a perturbation. A mathematical model is typically constructed using expert knowledge and informal reasoning to generate a mechanistic explanation for a given observation. Although this approach works well for simple systems with abundant data and well-established principles, quantitative biology is often faced with a dearth of both data and knowledge about a process, thus making it challenging to identify and validate all possible mechanistic hypothesis underlying a system behavior.

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Small cell lung cancer (SCLC) is an aggressive cancer recalcitrant to treatment, arising predominantly from epithelial pulmonary neuroendocrine (NE) cells. Intratumor heterogeneity plays critical roles in SCLC disease progression, metastasis, and treatment resistance. At least five transcriptional SCLC NE and non-NE cell subtypes were recently defined by gene expression signatures.

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Multiple intermediate epithelial-mesenchymal transition (EMT) states reflecting hybrid epithelial and mesenchymal phenotypes were observed in physiological and pathological conditions. Previous theoretical models explaining multiple EMT states rely on regulatory loops involving transcriptional feedback, which produce three or four attractors. This is incompatible with the observed continuum-like EMT spectrum.

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With the clinical approval of T-cell-dependent immune checkpoint inhibitors for many cancers, therapeutic cancer vaccines have re-emerged as a promising immunotherapy. Cancer vaccines require the addition of immunostimulatory adjuvants to increase vaccine immunogenicity, and increasingly multiple adjuvants are used in combination to bolster further and shape cellular immunity to tumor antigens. However, rigorous quantification of adjuvants' synergistic interactions is challenging due to partial redundancy in costimulatory molecules and cytokine production, leading to the common assumption that combining both adjuvants at the maximum tolerated dose results in optimal efficacy.

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Small cell lung cancer (SCLC) tumors comprise heterogeneous mixtures of cell states, categorized into neuroendocrine (NE) and non-neuroendocrine (non-NE) transcriptional subtypes. NE to non-NE state transitions, fueled by plasticity, likely underlie adaptability to treatment and dismal survival rates. Here, we apply an archetypal analysis to model plasticity by recasting SCLC phenotypic heterogeneity through multi-task evolutionary theory.

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Inhibitors of the mitotic kinesin Kif11 are anti-mitotics that, unlike vinca alkaloids or taxanes, do not disrupt microtubules and are not neurotoxic. However, development of resistance has limited their clinical utility. While resistance to Kif11 inhibitors in other cell types is due to mechanisms that prevent these drugs from disrupting mitosis, we find that in glioblastoma (GBM), resistance to the Kif11 inhibitor ispinesib works instead through suppression of apoptosis driven by activation of STAT3.

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Small Cell Lung Cancer (SCLC) is a highly aggressive, neuroendocrine tumor. Traditional reductionist approaches have proven ineffective to ameliorate the uniformly dismal outcomes for SCLC - survival at 5 years remains less than 5%. A major obstacle to improving treatment is that SCLC tumor cells disseminate early, with a strong propensity for metastasizing to the brain.

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The drug-induced proliferation (DIP) rate is a metric of drug response that avoids inherent biases in commonly used metrics such as 72 h viability. However, DIP rate measurements rely on direct cell counting over time, a laborious task that is subject to numerous challenges, including the need to fluorescently label cells and automatically segment nuclei. Moreover, it is incredibly difficult to directly count cells and accurately measure DIP rates for cell populations in suspension.

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Drug combination discovery depends on reliable synergy metrics but no consensus exists on the correct synergy criterion to characterize combined interactions. The fragmented state of the field confounds analysis, impedes reproducibility, and delays clinical translation of potential combination treatments. Here we present a mass-action based formalism to quantify synergy.

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Advances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, named Automated Cell Detection and Counting (ACDC), designed for activity detection of fluorescent labeled cell nuclei in time-lapse microscopy.

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Tumor heterogeneity is a primary cause of treatment failure and acquired resistance in cancer patients. Even in cancers driven by a single mutated oncogene, variability in response to targeted therapies is well known. The existence of additional genomic alterations among tumor cells can only partially explain this variability.

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High-throughput cell proliferation assays to quantify drug-response are becoming increasingly common and powerful with the emergence of improved automation and multi-time point analysis methods. However, pipelines for analysis of these datasets that provide reproducible, efficient, and interactive visualization and interpretation are sorely lacking. To address this need, we introduce Thunor, an open-source software platform to manage, analyze, and visualize large, dose-dependent cell proliferation datasets.

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ASCL1 is a neuroendocrine lineage-specific oncogenic driver of small cell lung cancer (SCLC), highly expressed in a significant fraction of tumors. However, ∼25% of human SCLC are ASCL1-low and associated with low neuroendocrine fate and high MYC expression. Using genetically engineered mouse models (GEMMs), we show that alterations in in the mouse lung induce an ASCL1 state of SCLC in multiple cells of origin.

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Introduction: The programmed death-ligand 1 (PD-L1) immune checkpoint inhibitors, atezolizumab and durvalumab, have received regulatory approval for the first-line treatment of patients with extensive-stage SCLC. Nevertheless, when used in combination with platinum-based chemotherapy, these PD-L1 inhibitors only improve overall survival by 2 to 3 months. This may be due to the observation that less than 20% of SCLC tumors express PD-L1 at greater than 1%.

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Despite molecular and clinical heterogeneity, small cell lung cancer (SCLC) is treated as a single entity with predictably poor results. Using tumor expression data and non-negative matrix factorization, we identify four SCLC subtypes defined largely by differential expression of transcription factors ASCL1, NEUROD1, and POU2F3 or low expression of all three transcription factor signatures accompanied by an Inflamed gene signature (SCLC-A, N, P, and I, respectively). SCLC-I experiences the greatest benefit from the addition of immunotherapy to chemotherapy, while the other subtypes each have distinct vulnerabilities, including to inhibitors of PARP, Aurora kinases, or BCL-2.

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Melanomas harboring mutations can be treated with inhibitors (i), but responses are varied and tumor recurrence is inevitable. Here we used an integrative approach of experimentation and mathematical flux balance analyses in -mutated melanoma cells to discover that elevated antioxidant capacity is linked to i sensitivity in melanoma cells. High levels of antioxidant metabolites in cells with reduced i sensitivity confirmed this conclusion.

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Cancer cells adjust their metabolic profiles to evade treatment. Metabolic adaptation is complex and hence better understood by an integrated theoretical-experimental approach. Using a minimal kinetic model, we predicted a previously undescribed Low/Low (L/L) phenotype, characterized by low oxidative phosphorylation (OXPHOS) and low glycolysis.

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Even as the clinical impact of drug combinations continues to accelerate, no consensus on how to quantify drug synergy has emerged. Rather, surveying the landscape of drug synergy reveals the persistence of historical fissures regarding the appropriate domains of conflicting synergy models - fissures impacting all aspects of combination therapy discovery and deployment. Herein we chronicle the impact of these divisions on: (i) the design, interpretation, and reproducibility of high-throughput combination screens; (ii) the performance of algorithms to predict synergistic mixtures; and (iii) the search for higher-order synergistic interactions.

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The outcomes of patients with SCLC have not yet been substantially impacted by the revolution in precision oncology, primarily owing to a paucity of genetic alterations in actionable driver oncogenes. Nevertheless, systemic therapies that include immunotherapy are beginning to show promise in the clinic. Although, these results are encouraging, many patients do not respond to, or rapidly recur after, current regimens, necessitating alternative or complementary therapeutic strategies.

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