Antitumour effect of the mitochondrial complex III inhibitor Atovaquone in combination with anti-PD-L1 therapy in mouse cancer models.

Immune checkpoint blockade (ICB) provides effective and durable responses for several tumour types by unleashing an immune response directed against cancer cells. However, a substantial number of patients treated with ICB develop relapse or do not respond, which has been partly attributed to the immune-suppressive effect of tumour hypoxia. We have previously demonstrated that the mitochondrial complex III inhibitor atovaquone alleviates tumour hypoxia both in human xenografts and in cancer patients by decreasing oxygen consumption and consequently increasing oxygen availability in the tumour. Here, we show that atovaquone alleviates hypoxia and synergises with the ICB antibody anti-PD-L1, significantly improving the rates of tumour eradication in the syngeneic CT26 model of colorectal cancer. The synergistic effect between atovaquone and anti-PD-L1 relied on CD8+ T cells, resulted in the establishment of a tumour-specific memory immune response, and was not associated with any toxicity. We also tested atovaquone in combination with anti-PD-L1 in the LLC (lung) and MC38 (colorectal) cancer syngeneic models but, despite causing a considerable reduction in tumour hypoxia, atovaquone did not add any therapeutic benefit to ICB in these models. These results suggest that atovaquone has the potential to improve the outcomes of patients treated with ICB, but predictive biomarkers are required to identify individuals likely to benefit from this intervention.

Robustness and reproducibility for AI learning in biomedical sciences: RENOIR.

Artificial intelligence (AI) techniques are increasingly applied across various domains, favoured by the growing acquisition and public availability of large, complex datasets. Despite this trend, AI publications often suffer from lack of reproducibility and poor generalisation of findings, undermining scientific value and contributing to global research waste. To address these issues and focusing on the learning aspect of the AI field, we present RENOIR (REpeated random sampliNg fOr machIne leaRning), a modular open-source platform for robust and reproducible machine learning (ML) analysis. RENOIR adopts standardised pipelines for model training and testing, introducing elements of novelty, such as the dependence of the performance of the algorithm on the sample size. Additionally, RENOIR offers automated generation of transparent and usable reports, aiming to enhance the quality and reproducibility of AI studies. To demonstrate the versatility of our tool, we applied it to benchmark datasets from health, computer science, and STEM (Science, Technology, Engineering, and Mathematics) domains. Furthermore, we showcase RENOIR's successful application in recently published studies, where it identified classifiers for SET2D and TP53 mutation status in cancer. Finally, we present a use case where RENOIR was employed to address a significant pharmacological challenge-predicting drug efficacy. RENOIR is freely available at https://github.com/alebarberis/renoir .

A machine learning and directed network optimization approach to uncover TP53 regulatory patterns.

TP53, the Guardian of the Genome, is the most frequently mutated gene in human cancers and the functional characterization of its regulation is fundamental. To address this we employ two strategies: machine learning to predict the mutation status of TP53from transcriptomic data, and directed regulatory networks to reconstruct the effect of mutations on the transcipt levels of TP53 targets. Using data from established databases (Cancer Cell Line Encyclopedia, The Cancer Genome Atlas), machine learning could predict the mutation status, but not resolve different mutations. On the contrary, directed network optimization allowed to infer the TP53 regulatory profile across: (1) mutations, (2) irradiation in lung cancer, and (3) hypoxia in breast cancer, and we could observe differential regulatory profiles dictated by (1) mutation type, (2) deleterious consequences of the mutation, (3) known hotspots, (4) protein changes, (5) stress condition (irradiation/hypoxia). This is an important first step toward using regulatory networks for the characterization of the functional consequences of mutations, and could be extended to other perturbations, with implications for drug design and precision medicine.

A role for SETD2 loss in tumorigenesis through DNA methylation dysregulation.

SETD2-dependent H3 Lysine-36 trimethylation (H3K36me3) has been recently linked to the deposition of de-novo DNA methylation. SETD2 is frequently mutated in cancer, however, the functional impact of SETD2 loss and depletion on DNA methylation across cancer types and tumorigenesis is currently unknown. Here, we perform a pan-cancer analysis and show that both SETD2 mutation and reduced expression are associated with DNA methylation dysregulation across 21 out of the 24 cancer types tested. In renal cancer, these DNA methylation changes are associated with altered gene expression of oncogenes, tumour suppressors, and genes involved in neoplasm invasiveness, including TP53, FOXO1, and CDK4. This suggests a new role for SETD2 loss in tumorigenesis and cancer aggressiveness through DNA methylation dysregulation. Moreover, using a robust machine learning methodology, we develop and validate a 3-CpG methylation signature which is sufficient to predict SETD2 mutation status with high accuracy and correlates with patient prognosis.

The impact of survivorship bias in glioblastoma research.

Despite advances in the therapy of Central Nervous System (CNS) malignancies, treatment of glioblastoma (GB) poses significant challenges due to GB resistance and high recurrence rates following post-operative radio-chemotherapy. The majority of prognostic and predictive GB biomarkers are currently developed using tumour samples obtained through surgical interventions. However, the selection criteria adopted by different neurosurgeons to determine which cases are suitable for surgery make operated patients not representative of all GB cases. Particularly, geriatric and frail individuals are excluded from surgical consideration in some cancer centers. Such selection generates a survival (or selection) bias that introduces limitations, rendering the patients or data chosen for downstream analyses not representative of the entire community. In this review, we discuss the implication of survivorship bias on current and novel biomarkers for patient selection, stratification, therapy, and outcome analyses.

Correction to: Disruption of hypoxia-inducible fatty acid binding protein 7 induces beige fat-like differentiation and thermogenesis in breast cancer cells.

[This corrects the article DOI: 10.1186/s40170-020-00219-4.].

Disruption of hypoxia-inducible fatty acid binding protein 7 induces beige fat-like differentiation and thermogenesis in breast cancer cells.

BACKGROUND: Humans produce heat through non-shivering thermogenesis, a metabolic process that occurs in inducible beige adipocytes expressing uncoupling protein 1 (UCP1). UCP1 dissipates the proton gradient of the mitochondrial inner membrane and converts that energy into heat. It is unclear whether cancer cells can exhibit autonomous thermogenesis. Previously, we found that the knockdown of hypoxia-inducible fatty acid binding protein 7 (FABP7) increased reactive oxygen species (ROS) in breast cancer cells. ROS are known to induce beige adipocyte differentiation. METHODS: We investigated the association of tumor hypoxia, FABP7, and UCP1 across breast cancer patients using METABRIC and TCGA data sets. Furthermore, using a breast cancer cell line, HCC1806, we tested the effect of FABP7 knockdown on cellular physiology including thermogenesis. RESULTS: We found a strong mutual exclusivity of FABP7 and UCP1 expression both in METABRIC and in TCGA, indicating major metabolic phenotypic differences. FABP7 was preferentially distributed in poorly differentiated-, estrogen receptor (ER) negative tumors. In contrast, UCP1 was highly expressed in normal ducts and well-differentiated-, ER positive-, less hypoxic tumors. In the cell line-based experiments, UCP1 and its transcriptional regulators were upregulated upon FABP7 knockdown. UCP1 was induced in about 20% of cancer cells, and the effect was increased further in hypoxia. UCP1 depolarized mitochondrial membranes at the site of expression. UCP1 induction was associated with the increase in proton leak, glycolysis, and maximal respiration, mimicking the typical energy profile of beige adipocytes. Most importantly, UCP1 induction elevated cancer cell temperature associated with increased vulnerability to hypoxia and γ-irradiation. CONCLUSIONS: We demonstrated that breast cancer cells can undergo thermogenesis through UCP1 induction. Disrupting FABP7-mediated fatty acid metabolism can unlock UCP1-mediated thermogenesis, potentially making it possible to develop therapies to target thermogenesis. Further study would be warranted to investigate the effect of rise in temperature of cancer cells on patients' outcomes and the relationship to other metabolic pathways.

Guidelines for using sigQC for systematic evaluation of gene signatures.

With the increased use of next-generation sequencing generating large amounts of genomic data, gene expression signatures are becoming critically important tools for the interpretation of these data, and are poised to have a substantial effect on diagnosis, management, and prognosis for a number of diseases. It is becoming crucial to establish whether the expression patterns and statistical properties of sets of genes, or gene signatures, are conserved across independent datasets. Conversely, it is necessary to compare established signatures on the same dataset to better understand how they capture different clinical or biological characteristics. Here we describe how to use sigQC, a tool that enables a streamlined, systematic approach for the evaluation of previously obtained gene signatures across multiple gene expression datasets. We implemented sigQC in an R package, making it accessible to users who have knowledge of file input/output and matrix manipulation in R and a moderate grasp of core statistical principles. SigQC has been adopted in basic biology and translational studies, including, but not limited to, the evaluation of multiple gene signatures for potential clinical use as cancer biomarkers. This protocol uses a previously obtained signature for breast cancer metastasis as an example to illustrate the critical quality control steps involved in evaluating its expression, variability, and structure in breast tumor RNA-sequencing data, a different dataset from that in which the signature was originally derived. We demonstrate how the outputs created from sigQC can be used for the evaluation of gene signatures on large-scale gene expression datasets.

SCF (Fbxl17) ubiquitylation of Sufu regulates Hedgehog signaling and medulloblastoma development.

Skp1-Cul1-F-box protein (SCF) ubiquitin ligases direct cell survival decisions by controlling protein ubiquitylation and degradation. Sufu (Suppressor of fused) is a central regulator of Hh (Hedgehog) signaling and acts as a tumor suppressor by maintaining the Gli (Glioma-associated oncogene homolog) transcription factors inactive. Although Sufu has a pivotal role in Hh signaling, the players involved in controlling Sufu levels and their role in tumor growth are unknown. Here, we show that Fbxl17 (F-box and leucine-rich repeat protein 17) targets Sufu for proteolysis in the nucleus. The ubiquitylation of Sufu, mediated by Fbxl17, allows the release of Gli1 from Sufu for proper Hh signal transduction. Depletion of Fbxl17 leads to defective Hh signaling associated with an impaired cancer cell proliferation and medulloblastoma tumor growth. Furthermore, we identify a mutation in Sufu, occurring in medulloblastoma of patients with Gorlin syndrome, which increases Sufu turnover through Fbxl17-mediated polyubiquitylation and leads to a sustained Hh signaling activation. In summary, our findings reveal Fbxl17 as a novel regulator of Hh pathway and highlight the perturbation of the Fbxl17-Sufu axis in the pathogenesis of medulloblastoma.

Paracrine effect of GTP cyclohydrolase and angiopoietin-1 interaction in stromal fibroblasts on tumor Tie2 activation and breast cancer growth.

Cancer-associated fibroblasts (CAFs) play a key role in promoting tumor growth, acting through complex paracrine regulation. GTP cyclohydrolase (GTPCH) expression for tetrahydrobiopterin synthesis in tumor stroma is implicated in angiogenesis and tumor development. However, the clinical significance of GTPCH expression in breast cancer is still elusive and how GTPCH regulates stromal fibroblast and tumor cell communication remains unknown. We found that GTPCH was upregulated in breast CAFs and epithelia, and high GTPCH RNA was significantly correlated with larger high grade tumors and worse prognosis. In cocultures, GTPCH expressing fibroblasts stimulated breast cancer cell proliferation and motility, cancer cell Tie2 phosphorylation and consequent downstream pathway activation. GTPCH interacted with Ang-1 in stromal fibroblasts and enhanced Ang-1 expression and function, which in turn phosphorylated tumor Tie2 and induced cell proliferation. In coimplantation xenografts, GTPCH in fibroblasts enhanced tumor growth, upregulating Ang-1 and alpha-smooth muscle actin mainly in fibroblast-like cells. GTPCH inhibition resulted in the attenuation of tumor growth and angiogenesis. GTPCH/Ang-1 interaction in stromal fibroblasts and activation of Tie2 on breast tumor cells could play an important role in supporting breast cancer growth. GTPCH may be an important mechanism of paracrine tumor growth and hence a target for therapy in breast cancer.

Experimental stimulation of bone healing with teriparatide: histomorphometric and microhardness analysis in a mouse model of closed fracture.

Fracture consolidation is a crucial goal to achieve as early as possible, but pharmacological stimulation has been neglected so far. Teriparatide has been considered for this purpose for its anabolic properties. We set up a murine model of closed tibial fracture on which different doses of teriparatide were tested. Closed fracture treatment avoids any bias introduced by surgical manipulations. Teriparatide's effect on callus formation was monitored during the first 4 weeks from fracture. Callus evolution was determined by histomorphometric and microhardness assessment. Daily administration of 40 µg/kg of teriparatide accelerated callus mineralization from day 9 onward without significant increase of sizes, and at day 15 the microhardness properties of treated callus were similar to those of bone tissue. Teriparatide considerably improved callus consolidation in the very early phases of bone healing.

Preferential killing of cancer cells using silicon carbide quantum dots.

Silicon carbide quantum dots are highly luminescent biocompatible nanoparticles whose properties might be of particular interest for biomedical applications. In this study we investigated Silicon Carbide Quantum Dots (3C-SiC QDs) cellular localisation and influence on viability and proliferation on oral squamous carcinoma (AT-84 and HSC) and immortalized cell lines (S-G). They clearly localize into the nuclei, but the presence of 3C-SiC QDs in culture medium provoke morphological changes in cultured cells. We demonstrate that 3C-SiC QDs display dose- and time-dependent selective cytotoxicity on cancer versus immortalized cells in vitro. Since one of the limitations of classical antineoplastic drugs is their lack of selectivity, these results open a new way in the search for antiproliferative drugs.