Biological and molecular heterogeneity of breast cancers correlates with their cancer stem cell content.

Pathways that govern stem cell (SC) function are often subverted in cancer. Here, we report the isolation to near purity of human normal mammary SCs (hNMSCs), from cultured mammospheres, on the basis of their ability to retain the lipophilic dye PKH26 as a consequence of their quiescent nature. PKH26-positive cells possess all the characteristics of hNMSCs. The transcriptional profile of PKH26-positive cells (hNMSC signature) was able to predict biological and molecular features of breast cancers. By using markers of the hNMSC signature, we prospectively isolated SCs from the normal gland and from breast tumors. Poorly differentiated (G3) cancers displayed higher content of prospectively isolated cancer SCs (CSCs) than did well-differentiated (G1) cancers. By comparing G3 and G1 tumors in xenotransplantation experiments, we directly demonstrated that G3s are enriched in CSCs. Our data support the notion that the heterogeneous phenotypical and molecular traits of human breast cancers are a function of their CSC content.

Machine learning-based reclassification of germline variants of unknown significance: The RENOVO algorithm.

The increasing scope of genetic testing allowed by next-generation sequencing (NGS) dramatically increased the number of genetic variants to be interpreted as pathogenic or benign for adequate patient management. Still, the interpretation process often fails to deliver a clear classification, resulting in either variants of unknown significance (VUSs) or variants with conflicting interpretation of pathogenicity (CIP); these represent a major clinical problem because they do not provide useful information for decision-making, causing a large fraction of genetically determined disease to remain undertreated. We developed a machine learning (random forest)-based tool, RENOVO, that classifies variants as pathogenic or benign on the basis of publicly available information and provides a pathogenicity likelihood score (PLS). Using the same feature classes recommended by guidelines, we trained RENOVO on established pathogenic/benign variants in ClinVar (training set accuracy = 99%) and tested its performance on variants whose interpretation has changed over time (test set accuracy = 95%). We further validated the algorithm on additional datasets including unreported variants validated either through expert consensus (ENIGMA) or laboratory-based functional techniques (on BRCA1/2 and SCN5A). On all datasets, RENOVO outperformed existing automated interpretation tools. On the basis of the above validation metrics, we assigned a defined PLS to all existing ClinVar VUSs, proposing a reclassification for 67% with >90% estimated precision. RENOVO provides a validated tool to reduce the fraction of uninterpreted or misinterpreted variants, tackling an area of unmet need in modern clinical genetics.

Comprehensive genomic profiling on metastatic Melanoma: results from a network screening from 7 Italian Cancer Centres.

BACKGROUND: The current therapeutic algorithm for Advanced Stage Melanoma comprises of alternating lines of Targeted and Immuno-therapy, mostly via Immune-Checkpoint blockade. While Comprehensive Genomic Profiling of solid tumours has been approved as a companion diagnostic, still no approved predictive biomarkers are available for Melanoma aside from BRAF mutations and the controversial Tumor Mutational Burden. This study presents the results of a Multi-Centre Observational Clinical Trial of Comprehensive Genomic Profiling on Target and Immuno-therapy treated advanced Melanoma. METHODS: 82 samples, collected from 7 Italian Cancer Centres of FFPE-archived Metastatic Melanoma and matched blood were sequenced via a custom-made 184-gene amplicon-based NGS panel. Sequencing and bioinformatics analysis was performed at a central hub. Primary analysis was carried out via the Ion Reporter framework. Secondary analysis and Machine Learning modelling comprising of uni and multivariate, COX/Lasso combination, and Random Forest, was implemented via custom R/Python scripting. RESULTS: The genomics landscape of the ACC-mela cohort is comparable at the somatic level for Single Nucleotide Variants and INDELs aside a few gene targets. All the clinically relevant targets such as BRAF and NRAS have a comparable distribution thus suggesting the value of larger scale sequencing in melanoma. No comparability is reached at the CNV level due to biotechnological biases and cohort numerosity. Tumour Mutational Burden is slightly higher in median for Complete Responders but fails to achieve statistical significance in Kaplan-Meier survival analysis via several thresholding strategies. Mutations on PDGFRB, NOTCH3 and RET were shown to have a positive effect on Immune-checkpoint treatment Overall and Disease-Free Survival, while variants in NOTCH4 were found to be detrimental for both endpoints. CONCLUSIONS: The results presented in this study show the value and the challenge of a genomics-driven network trial. The data can be also a valuable resource as a validation cohort for Immunotherapy and Target therapy genomic biomarker research.

The tumor suppressor p53 regulates polarity of self-renewing divisions in mammary stem cells.

Stem-like cells may be integral to the development and maintenance of human cancers. Direct proof is still lacking, mainly because of our poor understanding of the biological differences between normal and cancer stem cells (SCs). Using the ErbB2 transgenic model of breast cancer, we found that self-renewing divisions of cancer SCs are more frequent than their normal counterparts, unlimited and symmetric, thus contributing to increasing numbers of SCs in tumoral tissues. SCs with targeted mutation of the tumor suppressor p53 possess the same self-renewal properties as cancer SCs, and their number increases progressively in the p53 null premalignant mammary gland. Pharmacological reactivation of p53 correlates with restoration of asymmetric divisions in cancer SCs and tumor growth reduction, without significant effects on additional cancer cells. These data demonstrate that p53 regulates polarity of cell division in mammary SCs and suggest that loss of p53 favors symmetric divisions of cancer SCs, contributing to tumor growth.

From Cell Populations to Molecular Complexes: Multiplexed Multimodal Microscopy to Explore p53-53BP1 Molecular Interaction.

Surpassing the diffraction barrier revolutionized modern fluorescence microscopy. However, intrinsic limitations in statistical sampling, the number of simultaneously analyzable channels, hardware requirements, and sample preparation procedures still represent an obstacle to its widespread diffusion in applicative biomedical research. Here, we present a novel pipeline based on automated multimodal microscopy and super-resolution techniques employing easily available materials and instruments and completed with open-source image-analysis software developed in our laboratory. The results show the potential impact of single-molecule localization microscopy (SMLM) on the study of biomolecules' interactions and the localization of macromolecular complexes. As a demonstrative application, we explored the basis of p53-53BP1 interactions, showing the formation of a putative macromolecular complex between the two proteins and the basal transcription machinery in situ, thus providing visual proof of the direct role of 53BP1 in sustaining p53 transactivation function. Moreover, high-content SMLM provided evidence of the presence of a 53BP1 complex on the cell cytoskeleton and in the mitochondrial space, thus suggesting the existence of novel alternative 53BP1 functions to support p53 activity.

TMBleR: a bioinformatic tool to optimize TMB estimation and predictive power.

MOTIVATION: Tumor mutational burden (TMB) has been proposed as a predictive biomarker for immunotherapy response in cancer patients, as it is thought to enrich for tumors with high neoantigen load. TMB assessed by whole-exome sequencing is considered the gold standard but remains confined to research settings. In the clinical setting, targeted gene panels sampling various genomic sizes along with diverse strategies to estimate TMB were proposed and no real standard has emerged yet. RESULTS: We provide the community with TMBleR, a tool to measure the clinical impact of various strategies of panel-based TMB measurement. AVAILABILITY AND IMPLEMENTATION: R package and docker container (GPL-3 Open Source license): https://acc-bioinfo.github.io/TMBleR/. Graphical-user interface website: https://bioserver.ieo.it/shiny/app/tmbler. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Long non-coding RNA TINCR suppresses metastatic melanoma dissemination by preventing ATF4 translation.

Transition from proliferative-to-invasive phenotypes promotes metastasis and therapy resistance in melanoma. Reversion of the invasive phenotype, however, is challenged by the poor understanding of mechanisms underlying its maintenance. Here, we report that the lncRNA TINCR is down-regulated in metastatic melanoma and its silencing increases the expression levels of invasive markers, in vitro migration, in vivo tumor growth, and resistance to BRAF and MEK inhibitors. The critical mediator is ATF4, a central player of the integrated stress response (ISR), which is activated in TINCR-depleted cells in the absence of starvation and eIF2α phosphorylation. TINCR depletion increases global protein synthesis and induces translational reprogramming, leading to increased translation of mRNAs encoding ATF4 and other ISR proteins. Strikingly, re-expression of TINCR in metastatic melanoma suppresses the invasive phenotype, reduces numbers of tumor-initiating cells and metastasis formation, and increases drug sensitivity. Mechanistically, TINCR interacts with mRNAs associated with the invasive phenotype, including ATF4, preventing their binding to ribosomes. Thus, TINCR is a suppressor of the melanoma invasive phenotype, which functions in nutrient-rich conditions by repressing translation of selected ISR RNAs.

Alterations induced by the PML-RARα oncogene revealed by image cross correlation spectroscopy.

The molecular mechanisms that underlie oncogene-induced genomic damage are still poorly understood. To understand how oncogenes affect chromatin architecture, it is important to visualize fundamental processes such as DNA replication and transcription in intact nuclei and quantify the alterations of their spatiotemporal organization induced by oncogenes. Here, we apply superresolution microscopy in combination with image cross correlation spectroscopy to the U937-PR9 cell line, an in vitro model of acute promyelocytic leukemia that allows us to activate the expression of the PML-RARα oncogene and analyze its effects on the spatiotemporal organization of functional nuclear processes. More specifically, we perform Tau-stimulated emission depletion imaging, a superresolution technique based on the concept of separation of photons by lifetime tuning. Tau-stimulated emission depletion imaging is combined with a robust image analysis protocol that quickly produces a value of colocalization fraction on several hundreds of single cells and allows observation of cell-to-cell variability. Upon activation of the oncogene, we detect a significant increase in the fraction of transcription sites colocalized with PML/PML-RARα. This increase of colocalization can be ascribed to oncogene-induced disruption of physiological PML bodies and the abnormal occurrence of a relatively large number of PML-RARα microspeckles. We also detect a significant cell-to-cell variability of this increase of colocalization, which can be ascribed, at least in part, to a heterogeneous response of the cells to the activation of the oncogene. These results prove that our method efficiently reveals oncogene-induced alterations in the spatial organization of nuclear processes and suggest that the abnormal localization of PML-RARα could interfere with the transcription machinery, potentially leading to DNA damage and genomic instability.

Hematological disorders after salvage PARPi treatment for ovarian cancer: Cytogenetic and molecular defects and clinical outcomes.

Inhibitors of poly(ADP-ribose) polymerase (PARPi) are increasingly employed as salvage therapy in epithelial ovarian cancer (EOC), but cytotoxic drug exposure along with PARP inhibition may favor development of hematological disorders. In our study, of 182 women with EOC treated with PARPi, 16 (8.7%) developed therapy-related myeloid neoplasms (t-MNs), with 12 cases of myelodysplasia and 4 of acute myeloid leukemia. All experienced persistent cytopenia after PARPi discontinuation. Seven patients had del(5q)/-5 and/or del(7q)/-7, nine had a complex karyotype and TP53 mutations, recently reported as risk factor for t-MNs in EOC post-PARPi, were found in 12 out of 13 tested patients. Four patients had a rapid and fatal outcome, one had stable disease, eleven underwent induction therapy, followed by allogeneic hematopoietic cell transplantation in seven. Three of these 11 patients experienced refractory disease, and 8 had complete remission. During a 6.8 months (range 2.3-49) median observation time, 3 out of 16 patients were alive, with one surviving patient free of both solid and hematological tumors. Ten patients died because of leukemia, two because of transplant-related events, one from heart failure. Five more patients experienced persistent cell blood count abnormalities following PARPi discontinuation, without reaching MDS diagnostic criteria. A customized Myelo-panel showed clonal hematopoiesis in all five patients. These findings confirm the actual risk of t-MNs in EOC patients after chemotherapy and prolonged PARPi therapy. The management of these patients is complex and outcomes are extremely poor. Careful diagnostic procedures are strongly recommended whenever unusual cytopenias develop in patients receiving PARPi therapy.

The genomic landscape of 8-oxodG reveals enrichment at specific inherently fragile promoters.

8-Oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG) is the most common marker of oxidative stress and its accumulation within the genome has been associated with major human health issues such as cancer, aging, cardiovascular and neurodegenerative diseases. The characterization of the different genomic sites where 8-oxodG accumulates and the mechanisms underlying its formation are still poorly understood. Using OxiDIP-seq, we recently derived the genome-wide distribution of 8-oxodG in human non-tumorigenic epithelial breast cells (MCF10A). Here, we identify a subset of human promoters that accumulate 8-oxodG under steady-state condition. 8-oxodG nucleotides co-localize with double strand breaks (DSBs) at bidirectional and CG skewed promoters and their density correlate with RNA Polymerase II co-occupancy and transcription. Furthermore, by performing OxiDIP-seq in quiescent (G0) cells, we found a strong reduction of oxidatively-generated damage in the majority of 8-oxodG-positive promoters in the absence of DNA replication. Overall, our results suggest that the accumulation of 8-oxodG at gene promoters occurs through DNA replication-dependent or -independent mechanisms, with a possible contribution to the formation of cancer-associated translocation events.

Beyond Genetics: Metastasis as an Adaptive Response in Breast Cancer.

Metastatic disease represents the primary cause of breast cancer (BC) mortality, yet it is still one of the most enigmatic processes in the biology of this tumor. Metastatic progression includes distinct phases: invasion, intravasation, hematogenous dissemination, extravasation and seeding at distant sites, micro-metastasis formation and metastatic outgrowth. Whole-genome sequencing analyses of primary BC and metastases revealed that BC metastatization is a non-genetically selected trait, rather the result of transcriptional and metabolic adaptation to the unfavorable microenvironmental conditions which cancer cells are exposed to (e.g., hypoxia, low nutrients, endoplasmic reticulum stress and chemotherapy administration). In this regard, the latest multi-omics analyses unveiled intra-tumor phenotypic heterogeneity, which determines the polyclonal nature of breast tumors and constitutes a challenge for clinicians, correlating with patient poor prognosis. The present work reviews BC classification and epidemiology, focusing on the impact of metastatic disease on patient prognosis and survival, while describing general principles and current in vitro/in vivo models of the BC metastatic cascade. The authors address here both genetic and phenotypic intrinsic heterogeneity of breast tumors, reporting the latest studies that support the role of the latter in metastatic spreading. Finally, the review illustrates the mechanisms underlying adaptive stress responses during BC metastatic progression.

From Double-Strand Break Recognition to Cell-Cycle Checkpoint Activation: High Content and Resolution Image Cytometry Unmasks 53BP1 Multiple Roles in DNA Damage Response and p53 Action.

53BP1 protein has been isolated in-vitro as a putative p53 interactor. From the discovery of its engagement in the DNA-Damage Response (DDR), its role in sustaining the activity of the p53-regulated transcriptional program has been frequently under-evaluated, even in the case of a specific response to numerous DNA Double-Strand Breaks (DSBs), i.e., exposure to ionizing radiation. The localization of 53BP1 protein constitutes a key to decipher the network of activities exerted in response to stress. We present here an automated-microscopy for image cytometry protocol to analyze the evolution of the DDR, and to demonstrate how 53BP1 moved from damaged sites, where the well-known interaction with the DSB marker γH2A.X takes place, to nucleoplasm, interacting with p53, and enhancing the transcriptional regulation of the guardian of the genome protein. Molecular interactions have been quantitatively described and spatiotemporally localized at the highest spatial resolution by a simultaneous analysis of the impairment of the cell-cycle progression. Thanks to the high statistical sampling of the presented protocol, we provide a detailed quantitative description of the molecular events following the DSBs formation. Single-Molecule Localization Microscopy (SMLM) Analysis finally confirmed the p53-53BP1 interaction on the tens of nanometers scale during the distinct phases of the response.

Chromatin investigation in the nucleus using a phasor approach to structured illumination microscopy.

Chromatin in the nucleus is organized in functional sites at variable level of compaction. Structured illumination microscopy (SIM) can be used to generate three-dimensional super-resolution (SR) imaging of chromatin by changing in phase and in orientation a periodic line illumination pattern. The spatial frequency domain is the natural choice to process SIM raw data and to reconstruct an SR image. Using an alternative approach, we demonstrate that the additional spatial information encoded in the knowledge of the position of the illumination pattern can be efficiently decoded using a generalized version of separation of photon by lifetime tuning (SPLIT) that does not require lifetime measurements. In the resulting SPLIT-SIM, the SR image is obtained by isolating a fraction of the intensity corresponding to the center of the diffraction-limited point spread function. This extends the use of the SPLIT approach from stimulated emission depletion microscopy to SIM. The SPLIT-SIM algorithm is based only on phasor analysis and does not require deconvolution. We show that SPLIT-SIM can be used to generate SR images of chromatin organizational motifs with tunable resolution and can be a valuable tool for the imaging of functional sites in the nucleus.

Cerebro: interactive visualization of scRNA-seq data.

Despite the growing availability of sophisticated bioinformatic methods for the analysis of single-cell RNA-seq data, few tools exist that allow biologists without extensive bioinformatic expertise to directly visualize and interact with their own data and results. Here, we present Cerebro (cell report browser), a Shiny- and Electron-based standalone desktop application for macOS and Windows which allows investigation and inspection of pre-processed single-cell transcriptomics data without requiring bioinformatic experience of the user. Through an interactive and intuitive graphical interface, users can (i) explore similarities and heterogeneity between samples and cell clusters in two-dimensional or three-dimensional projections such as t-SNE or UMAP, (ii) display the expression level of single genes or gene sets of interest, (iii) browse tables of most expressed genes and marker genes for each sample and cluster and (iv) display trajectories calculated with Monocle 2. We provide three examples prepared from publicly available datasets to show how Cerebro can be used and which are its capabilities. Through a focus on flexibility and direct access to data and results, we think Cerebro offers a collaborative framework for bioinformaticians and experimental biologists that facilitates effective interaction to shorten the gap between analysis and interpretation of the data. AVAILABILITY AND IMPLEMENTATION: The Cerebro application, additional documentation, and example datasets are available at https://github.com/romanhaa/Cerebro. Similarly, the cerebroApp R package is available at https://github.com/romanhaa/cerebroApp. All components are released under the MIT License. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-wide mapping of 8-oxo-7,8-dihydro-2'-deoxyguanosine reveals accumulation of oxidatively-generated damage at DNA replication origins within transcribed long genes of mammalian cells.

8-Oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG) is one of the major DNA modifications and a potent pre-mutagenic lesion prone to mispair with 2'-deoxyadenosine (dA). Several thousand residues of 8-oxodG are constitutively generated in the genome of mammalian cells, but their genomic distribution has not yet been fully characterized. Here, by using OxiDIP-Seq, a highly sensitive methodology that uses immuno-precipitation with efficient anti-8-oxodG antibodies combined with high-throughput sequencing, we report the genome-wide distribution of 8-oxodG in human non-tumorigenic epithelial breast cells (MCF10A), and mouse embryonic fibroblasts (MEFs). OxiDIP-Seq revealed sites of 8-oxodG accumulation overlapping with γH2AX ChIP-Seq signals within the gene body of transcribed long genes, particularly at the DNA replication origins contained therein. We propose that the presence of persistent single-stranded DNA, as a consequence of transcription-replication clashes at these sites, determines local vulnerability to DNA oxidation and/or its slow repair. This oxidatively-generated damage, likely in combination with other kinds of lesion, might contribute to the formation of DNA double strand breaks and activation of DNA damage response.

Measuring Nanoscale Distances by Structured Illumination Microscopy and Image Cross-Correlation Spectroscopy (SIM-ICCS).

Since the introduction of super-resolution microscopy, there has been growing interest in quantifying the nanoscale spatial distributions of fluorescent probes to better understand cellular processes and their interactions. One way to check if distributions are correlated or not is to perform colocalization analysis of multi-color acquisitions. Among all the possible methods available to study and quantify the colocalization between multicolor images, there is image cross-correlation spectroscopy (ICCS). The main advantage of ICCS, in comparison with other co-localization techniques, is that it does not require pre-segmentation of the sample into single objects. Here we show that the combination of structured illumination microscopy (SIM) with ICCS (SIM-ICCS) is a simple approach to quantify colocalization and measure nanoscale distances from multi-color SIM images. We validate the SIM-ICCS analysis on SIM images of optical nanorulers, DNA-origami-based model samples containing fluorophores of different colors at a distance of 80 nm. The SIM-ICCS analysis is compared with an object-based analysis performed on the same samples. Finally, we show that SIM-ICCS can be used to quantify the nanoscale spatial distribution of functional nuclear sites in fixed cells.

Modeling cell proliferation in human acute myeloid leukemia xenografts.

MOTIVATION: Acute myeloid leukemia (AML) is one of the most common hematological malignancies, characterized by high relapse and mortality rates. The inherent intra-tumor heterogeneity in AML is thought to play an important role in disease recurrence and resistance to chemotherapy. Although experimental protocols for cell proliferation studies are well established and widespread, they are not easily applicable to in vivo contexts, and the analysis of related time-series data is often complex to achieve. To overcome these limitations, model-driven approaches can be exploited to investigate different aspects of cell population dynamics. RESULTS: In this work, we present ProCell, a novel modeling and simulation framework to investigate cell proliferation dynamics that, differently from other approaches, takes into account the inherent stochasticity of cell division events. We apply ProCell to compare different models of cell proliferation in AML, notably leveraging experimental data derived from human xenografts in mice. ProCell is coupled with Fuzzy Self-Tuning Particle Swarm Optimization, a swarm-intelligence settings-free algorithm used to automatically infer the models parameterizations. Our results provide new insights on the intricate organization of AML cells with highly heterogeneous proliferative potential, highlighting the important role played by quiescent cells and proliferating cells characterized by different rates of division in the progression and evolution of the disease, thus hinting at the necessity to further characterize tumor cell subpopulations. AVAILABILITY AND IMPLEMENTATION: The source code of ProCell and the experimental data used in this work are available under the GPL 2.0 license on GITHUB at the following URL: https://github.com/aresio/ProCell. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The endless frontier? The recent increase of R&D productivity in pharmaceuticals.

BACKGROUND: Studies on the early 2000s documented increasing attrition rates and duration of clinical trials, leading to a representation of a "productivity crisis" in pharmaceutical research and development (R&D). In this paper, we produce a new set of analyses for the last decade and report a recent increase of R&D productivity within the industry. METHODS: We use an extensive data set on the development history of more than 50,000 projects between 1990 and 2017, which we integrate with data on sales, patents, and anagraphical information on each institution involved. We devise an indicator to quantify the novelty of each project, based on its set of mechanisms of action. RESULTS: First, we investigate how R&D projects are allocated across therapeutic areas and find a polarization towards high uncertainty/high potential reward indications, with a strong focus on oncology. Second, we find that attrition rates have been decreasing at all stages of clinical research in recent years. In parallel, for each phase, we observe a significant reduction of time required to identify projects to be discontinued. Moreover, our analysis shows that more recent successful R&D projects are increasingly based on novel mechanisms of action and target novel indications, which are characterized by relatively small patient populations. Third, we find that the number of R&D projects on advanced therapies is also growing. Finally, we investigate the relative contribution to productivity variations of different types of institutions along the drug development process, with a specific focus on the distinction between the roles of Originators and Developers of R&D projects. We document that in the last decade Originator-Developer collaborations in which biotech companies act as Developers have been growing in importance. Moreover, we show that biotechnology companies have reached levels of productivity in project development that are equivalent to those of large pharmaceutical companies. CONCLUSIONS: Our study reports on the state of R&D productivity in the bio-pharmaceutical industry, finding several signals of an improving performance, with R&D projects becoming more targeted and novel in terms of indications and mechanisms of action.