Biology subjects

Miele, L.

Publications and source records attributed to Miele, L..

13 recordsLinked to original sources

bioRxiv2026

Spatial context of cellular heterogeneity in triple-negative breast cancer reveals actionable subtypes

Triple-negative breast cancer (TNBC) is an aggressive, heterogeneous disease with limited targeted treatment options. Leveraging bulk and spatial transcriptomics and cellular mapping of breast tissue from a large patient cohort, we identified three reproducible TNBC ecosystem subtypes that encode distinct spatial tumor-microenvironment architectures and clinically relevant treatment-response phenotype. Our analyses define: (1) an immune-exhausted subtype associated with immune checkpoint therapy (ICT); (2) a highly fibrotic/hypoxic subtype; and (3) a metabolically reprogrammed subtype characterized by nuclear-receptor and fatty-acid signaling. Spatial profiling further revealed subtype-specific organization of epithelial, immune, and stromal programs across tumor centers and invasive edges. Multiplexed imaging independently supported the contrasting immune-infiltrated and stromal-excluded architectures of the immune-engaged and fibrotic subtypes. The unique markers associated with the subtypes also provide the basis for subtyping and candidate therapeutic vulnerabilities. Our study provides a robust framework for personalized medicine, highlighting that targeting specific spatio-cellular architectures is critical to improving clinical outcomes.

cancer biology↗

bioRxiv2026

STRmie-HD enables interruption-aware HTT repeat genotyping and somatic mosaicism profiling across sequencing platforms

Short tandem repeat expansions in exon 1 of the HTT gene drive Huntingtons disease (HD) pathogenesis, with disease onset and progression heavily influenced by somatic mosaicism and sequence interruptions. While sequencing technologies enable repeat sizing, many computational tools lack the resolution to capture subtle interruption motifs and allele-specific somatic variation. We present STRmie-HD, an alignment-free, de novo framework for interruption-aware genotyping and quantitative profiling of somatic mosaicism at single-read resolution. The tool parses individual reads to quantify uninterrupted CAG tract length, CCG repeat content, and critical interruption variants, including Loss of Interruption (LOI) and Duplication of Interruption (DOI). Validated across Illumina, PacBio SMRT, and Oxford Nanopore platforms, STRmie-HD demonstrates high concordance with reference genotypes and superior sensitivity in identifying rare interruption patterns that conventional tools often overlook. Furthermore, it implements somatic mosaicism metrics to characterize repeat dynamics, successfully distinguishing the higher somatic expansion burden in brain tissues compared to peripheral blood. STRmie-HD offers a comprehensive and extensible solution for high-resolution molecular characterization of HTT variation, providing a robust framework for patient stratification and genetic research in HD. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=72 SRC="FIGDIR/small/713334v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@17a54aforg.highwire.dtl.DTLVardef@4dcfc5org.highwire.dtl.DTLVardef@8398edorg.highwire.dtl.DTLVardef@1acefde_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract: STRmie-HD flowchart. STRmie-HD is a comprehensive analytical framework that processes sequencing reads to analyze CAG/CCG trinucleotide repeats, interruption variants, and somatic mosaicism in the HTT gene. The workflow begins with sequencing reads (FASTA/FASTQ) that can undergo optional custom processing eq]based on the sequencing design. These reads are then fed into a regular expression-based engine (STRmie-HD) to identify CAG and CCG motifs. The identified motifs lead to the estimation of CAG/CCG alleles, visualized as distinct peaks representing different allele sizes, interruption variant assessment, and somatic mosaicism quantification. STRmie-HD produces an HTML output that wraps this information into a report. C_FIG

bioinformatics↗

bioRxiv2026

Spatial Landscape of Pregnancy-Associated Triple Negative Breast Cancer and Mammary Gland Involution

Pregnancy-associated triple negative breast cancer (PA-TNBC) is one of the highest-risk breast cancers, marked by an aggressive phenotype that lacks targeted treatment options. Studies have shown that post-lactational mammary gland involution plays a role in this increased risk. To delineate the underlying mechanisms, our study characterized the transcriptional state of the epithelia and surrounding microenvironment in women with PA-TNBC, comparing those diagnosed pre-involution (PRE) and post-involution (POST, <3 years after delivery). Spatial transcriptomics using the GeoMx Digital Spatial Profiler was performed on treatment-naive PA-TNBC tissues from 33 women (10 PRE, 23 POST). Regions of interest were segmented with pan-cytokeratin staining. We found that the most prominent transcriptional differences between PRE and POST epithelia occurred in the adjacent non-invasive regions and during the transition into invasive TNBC. POST non-invasive epithelia uniquely showed inflammatory and developmental pathway activation, while the transition into TNBC involved increased chromatin remodeling and cell migration pathways. Further, the tumor microenvironment (TME) in POST showed the highest proportion of immune cells and the highest prevalence of tumor- and immune exhaustion-associated cell states. Finally, a pseudotime analysis of POST transcriptional dynamics found that women diagnosed 1-2 years after delivery exhibited the strongest evidence for inflammatory signaling across the tissue. Our results highlight biological mechanisms distinguishing PRE and POST PA-TNBC across tissue regions and cell types. We emphasize the importance of early detection of malignant molecular signatures in morphologically normal epithelium in post-involution women and suggest that targeting the TME may improve treatment efficacy in post-involution PA-TNBC.

cancer biology↗

bioRxiv2025

A novel subtyping method for TNBC with implications for prognosis and therapy

The biological heterogeneity of triple-negative breast cancer (TNBC) poses significant challenges for diagnosis, prognosis, and treatment. While prior TNBC subtype classifications exist, they are not widely used clinically. Here, we aimed to subtype TNBC based on transcriptomic profiles using cell type and state heterogeneity in tumor tissue from 250 pre-treatment women (127 African-American and 123 European-American). We identified three major subtypes and three distinct groups exhibiting unique cell-type composition and mechanisms: Subtype-1 immune signaling/T-cell response; Subtype-2 pro-fibrotic and immune desert; Subtype-3 fatty acid and nuclear receptor signaling. Subtype-1 showed potential responsiveness to immunotherapy, while Subtypes-2 and 3 suggested alternative therapeutic targets. In Subtype-3, which contained a patient group with high ESR1, (but not high ER protein expression) we identified putative mutations in the gene that are unique to these patients. This framework provides a path toward personalized TNBC treatment and is accessible through a user-friendly RShiny application for clinical use.

bioinformatics↗

bioRxiv2025

Spatially distinct cellular and molecular landscapes define prognosis in triple negative breast cancer

BackgroundTriple-negative breast cancer is a prevalent breast cancer subtype with the lowest 5-year survival. Several factors contribute to its treatment response, but the inherent molecular and cellular tumor heterogeneity are increasingly acknowledged as crucial determinants. MethodsSpatial transcriptomic profiling was performed on FFPE tissues from a retrospective, treatment-naive group of women with differential prognoses (17 with >15 years survival-good prognosis (GPx) and 15 with <3 years survival-poor prognosis (PPx)) using GeoMX(R) Digital Spatial Profiler. Regions of interest were segmented on pan-cytokeratin and analyzed for tumor and stromal components, probed using GeoMx human whole transcriptome atlas (WTA) panel. Data quality control, normalization, and differential analysis was performed in R using GeomxTools and linear mixed models. Additional analyses including cell-type deconvolution, spatial entropy, functional enrichment, TF-target / ligand-receptor analysis and convolution neural networks were employed to identify significant gene signatures contributing to differential prognosis. ResultsHere we report on the spatial and molecular heterogeneity underlying differential prognosis. We observe that the state of the epithelia and its microenvironment (TME) are transcriptionally distinct between the two groups. Invasive epithelia in GPx show a significant increase in immune transcripts with the TME exhibiting increased immune cell presence (via IF), while in PPx they are more metabolically and translationally active, with the TME being more mesenchymal/fibrotic. Specifically, pre-cancerous epithelia in PPx display a prescience of aggressiveness as evidenced by increased EMT-signaling. We identify distinct epithelial gene signatures for PPx and GPx, that can, with high accuracy, classify samples at the time of diagnosis and likely inform therapy. ConclusionsTo the best of our knowledge, this is the first study to leverage spatial transcriptomics for an in-depth delineation of the cellular and molecular underpinnings of differential prognosis in TNBC. Our study highlights the potential of spatial transcriptomics to not only uncover the molecular drivers of differential prognosis in TNBC but also to pave the way for precision diagnostics and tailored therapeutic strategies, transforming the clinical landscape for this aggressive breast cancer subtype.

cancer biology↗

bioRxiv2024

Artificial intelligence as a ploy to delve into the intricate relationship between genetics and mitochondria in MASLD patients

Background and AimsMitochondrial (mt-) dysfunction is a hallmark of progressive MASLD. MtDNA copy number (mtDNA-CN) and cell-free circulating mtDNA (ccf-mtDNA), which reflect mt-mass and mt-dysfunction, respectively, are gaining attention as non-invasive disease biomarkers. We previously demonstrated that PNPLA3/MBOAT7/TM6SF2 deficiency in HepG2 cells increased mt-mass, mtDNA-CN and ccf-mtDNA. This study furtherly explored mt-biogenesis, function and mt-biomarkers in biopsied MASLD patients from a Discovery (n=28) and a Validation (n=824) cohort, stratified by the number of risk variants (NRV=3). We took advantage of artificial intelligence (AI) to develop new risk scores, predicting MASLD evolution by integrating anthropometric and genetic data (Age, BMI, NRV) with mt-biomarkers. MethodsHepatic mt-morphology and dynamics were assessed by TEM, IHC and gene expression. mtDNA-CN and ccf-mtDNA were measured in PBMCs and serum samples. GPT-4 was employed as AI tool to support the construction of novel risk scores for MASLD progressive forms (MASH, fibrosis and HCC). ResultsIn the Discovery cohort, NRV=3 patients showed the highest mt-mass and significant mt-morphological changes (i.e. membranes rupture). An elevated PGC-1, OPA1, DRP1 and PINK1, markers of mt-biogenesis, fusion and fission were found in these patients, supporting an enhanced mt-dynamics. However, PRKN protein levels were reduced, suggesting a premature block of mitophagy. In the Validation cohort, PGC-1 mRNA levels and mtDNA-CN were significantly higher in NRV=3 compared to patients with 1,2 or no variants. Circulating mtDNA-CN and ccf-mtDNA were augmented in NRV=3 patients and correlated with genetics and MASLD severity at multivariate analysis, supporting that both may independently modulate mt-dynamics and activity. By exploiting rsGPT-4 we then optimized the combination of non-invasive variables to get prediction models named Mitochondrial, Anthropometric, and Genetic Integration with Computational intelligence ("MAGIC-") for assessing MASH, fibrosis, and HCC, respectively. The MAGIC-MASH and MAGIC-Fib models showed AUCs of 73% and 76% in detecting MASH and fibrosis >1. Of note, MAGIC-HCC achieved an AUC of 86% (95% CI: 0.823-0.885), with 78.6% sensitivity and 81.5% specificity thus resulting the best score for the desired outcome. ConclusionsmtDNA-CN and ccf-mtDNA may have pathological and prognostic significance in MASLD patients, especially in those genetically-predisposed.

genetics↗

bioRxiv2024

Cell expansion-division under resource sharing: a novel framework for modeling fruit growth dynamics

Fruit growth is determined by cell expansion and division, processes that are closely linked to the availability and allocation of resources. By influencing environmental and growing conditions, agricultural practices affect these processes and ultimately many key indicators of fruit quality and yield. Given the multifaceted complexity underlying fruit growth dynamics, a process-based framework that can link genetic and environmental inputs to observables of economic and nutritional interest has become essential for predicting and improving agricultural outcomes. Here we present a mathematical framework that integrates cellular dynamics with resource allocation mechanisms to model the interplay between division, expansion and resource limitation in fruit cells. The model captures the temporal evolution of total fruit mass, cell number and cell mass distribution, reflecting data across genotypes and environmental conditions from tomato growth experiments. This sheds light on the possible relationships between genetic traits, growth conditions and fruit traits, and provides a predictive tool for optimizing fruit yield and quality under agricultural practices. Our minimal but mechanistic approach has broader implications for understanding resource-limited growth in multicellular systems.

ecology↗

bioRxiv2023

The efficacy of CB-103, a first-in-class transcriptional Notch inhibitor, in preclinical models of breast cancer

BackgroundNotch signaling has been shown to mediate treatment resistance and support cancer stem cells (CSCs) in endocrine-resistant estrogen receptor positive (ER+) and triple negative breast cancers (TNBCs). The clinical development of GSIs, first generation Notch inhibitors, has been hindered by lack of Notch specificity and dose-limiting toxicity. Here we describe the safety and efficacy of a first-in-class, clinical stage, orally available small molecule pan-Notch inhibitor, CB-103. Due to its unique mode of action, CB-103 doesnt induce GI toxicities noted with GSIs. There is a critical need for effective, safe, targeted therapies for patients with endocrine-refractory metastatic breast cancer. Recently approved targeted therapies for TNBC are only effective for a subset of patients. Moreover, GSI-resistant, constitutively activating Notch1 or Notch2 mutations are observed in [~]10% of TNBC. Our study elucidating the synergy of CB-103 with fulvestrant and paclitaxel in preclinical models of both hormone-refractory ER+ BC and TNBC respectively provides a novel and unique opportunity to address major unmet therapeutic needs. MethodsCB-103 was screened in combination with a panel of anti-neoplastic drugs. We evaluated the anti-tumor activity of CB-103 with fulvestrant in ESR1-mutant (Y537S), endocrine-resistant BC xenografts. In the same model, we examined anti-CSC activity in mammosphere formation assays for CB-103 alone or in combination with fulvestrant or palbociclib. We also evaluated the effect of CB-103 plus paclitaxel on primary tumors and CSC in GSI-resistant TNBC model HCC1187. Comparisons between groups were performed with two-sided unpaired Students t-test. One-way or two-way ANOVA followed by Tukeys post analysis was performed to analyze in vivo efficacy study results. ResultsCB-103 showed synergism with fulvestrant in ER+ cells and with paclitaxel in TNBC cells. CB-103 combined with fulvestrant or paclitaxel potently inhibited mammosphere formation in both models. Combination of CB-103 and fulvestrant significantly reduced tumor volume in an ESR1-mutant, endocrine-resistant BC model. In a GSI-resistant TNBC model, CB-103 plus paclitaxel significantly delayed tumor growth compared to paclitaxel alone. ConclusionsOur data indicate that CB-103 is an attractive candidate for clinical investigation in endocrine resistant, recurrent breast cancers with biomarker-confirmed Notch activity in combination with SERDs and/or CDKis and in TNBCs with biomarker-confirmed Notch activity in combination with taxane-containing chemotherapy regimens.

cancer biology↗

bioRxiv2022

Targeting Cblb-Notch1 axis as a novel immunotherapeutic strategy to boost CD8+ T-cells responses

A critical feature of cancer is the ability to induce immunosuppression and evade immune responses. Tumor-induced immunosuppression diminishes the efficacy of endogenous immune responses and decreases the efficacy of cancer immunotherapy. In this study, we describe a new immunosuppressive pathway in which adenosine promotes Cbl-b-mediated Notch1 degradation, causing suppression of CD8+ T-cells effector functions. Genetic KO and pharmacological inhibition of Cbl-b prevents Notch1 degradation in response to adenosine and reactivates its signaling. Reactivation of Notch1 results in enhanced CD8+ T-cell effector functions, anti-cancer response and resistance to immunosuppression. Our work demonstrates that targeting Cbl-b-Notch1 axis is a novel promising strategy for cancer immunotherapy.

cancer biology↗

bioRxiv2021

Control strategies for heterogeneous plant pathogens: evolutionary and agricultural consequences

Disease control can induce both demographic and evolutionary responses in host-parasite systems. Foreseeing the outcome of control therefore requires knowledge of the eco-evolutionary feedback between control and system. Previous work has assumed that control strategies have a homogeneous effect on the parasite population. However, this is not true when control targets those traits that confer to the parasite heterogeneous levels of resistance, which can additionally be related to other key parasite traits, through evolutionary trade-offs. In this work, we develop a minimal model coupling epidemiological and evolutionary dynamics to explore possible trait-dependent effects of control strategies. Particularly, we consider a parasite expressing continuous levels of a trait determining resource exploitation, and a control treatment that can be either positively or negatively correlated with that trait. We demonstrate the potential of trait-dependent control by considering that the decision maker may want to minimise both the damage caused by the disease and the use of treatment, due to possible environmental or economic costs. We identify efficient strategies showing that the optimal type of treatment depends on the amount applied. Our results pave the way for the study of control strategies based on evolutionary constraints, such as collateral sensitivity and resistance costs, which are receiving increasing attention for both public health and agricultural purposes.

ecology↗

bioRxiv2021

Redundancy-selection trade-off in phenotype-structured populations

Realistic fitness landscapes generally display a redundancy-fitness trade-off: highly fit trait configurations are inevitably rare, while less fit trait configurations are expected to be more redundant. The resulting sub-optimal patterns in the fitness distribution are typically described by means of effective formulations. However, the extent to which effective formulations are compatible with explicitly redundant landscapes is yet to be understood, as well as the consequences of a potential miss-match. Here we investigate the effects of such trade-off on the evolution of phenotype-structured populations, characterised by continuous quantitative traits. We consider a typical replication-mutation dynamics, and we model redundancy by means of two dimensional landscapes displaying both selective and neutral traits. We show that asymmetries of the landscapes will generate neutral contributions to the marginalised fitness-level description, that cannot be described by effective formulations, nor disentangled by the full trait distribution. Rather, they appear as effective sources, whose magnitude depends on the geometry of the landscape. Our results highlight new important aspects on the nature of sub-optimality. We discuss practical implications for rapidly mutant populations such as pathogens and cancer cells, where the qualitative knowledge of their trait and fitness distributions can drive disease management and intervention policies.

evolutionary biology↗

bioRxiv2020

TM6SF2/PNPLA3/MBOAT7 loss-of-function genetic variants impact on NAFLD development and progression both in patients and in in vitro models

Background and aimsThe I148M PNPLA3, the rs641738 in MBOAT7/TMC4 locus and the E167K TM6SF2 polymorphisms represent the main predisposing factors to non-alcoholic fatty liver disease (NAFLD) development and progression. We previously generated a full knockout of MBOAT7 in HepG2 cells (MBOAT7-/-), homozygous for the I148M PNPLA3. Therefore, we aimed to:1) investigate the synergic impact of the 3 at-risk variants on liver injury and hepatocellular carcinoma (HCC) in a large cohort of NAFLD patients;2) create in vitro models of genetic NAFLD by silencing TM6SF2 in both HepG2 and MBOAT7-/- cells. MethodsNAFLD patients (n=1380) of whom 121 had HCC were stratified with a semi-quantitative score ranging from 0 to 3 according to the number of PNPLA3, TM6SF2 and MBOAT7 at-risk variants. TM6SF2 was silenced in HepG2 (TM6SF2-/-) and MBOAT7-/- (MBOAT7-/-TM6SF2-/-) through CRISPR/Cas9. ResultsIn NAFLD patients, the additive weight of these mutations was associated with liver disease severity and increased risk to develop HCC. In HepG2 cells, TM6SF2 silencing altered lipid composition and induced the accumulation of micro-vesicular LDs, whereas the MBOAT7-/-TM6SF2-/- cells showed a mixed micro/macro pattern of LDs. TM6SF2 deletion strongly affected endoplasmic reticulum (ER) and mitochondria ultrastructures thus increasing ER/oxidative stress. Mitochondrial number raised in both TM6SF2-/- and MBOAT7-/-TM6SF2-/- models, suggesting an unbalancing in mitochondrial dynamics and the silencing of both MBOAT7 and TM6SF2 impaired mitochondrial activity with a shift towards anaerobic glycolysis. MBOAT7-/-TM6SF2-/- cells also showed the highest proliferation rate. ConclusionsThe co-presence of the 3 at-risk variants impacts on NAFLD course, in both patients and experimental models affecting LDs accumulation, mitochondrial functionality and metabolic reprogramming towards HCC.

genetics↗

bioRxiv2019

Evaluation of deacetylase inhibition in metaplastic breast carcinoma using multiple derivations of preclinical models of a new patient-derived tumor

Metaplastic breast carcinoma (MBC) is a clinically aggressive and rare subtype of breast cancer, with similar features to basal-like breast cancers. Due rapid growth rates and characteristic heterogeneity, MBC is often unresponsive to standard chemotherapies; and novel targeted therapeutic discovery is urgently needed. Histone deacetylase inhibitors (DACi) suppress tumor growth and metastasis through regulation of the epithelial-to-mesenchymal transition axis in various cancers, including basal-like breast cancers. We utilized a new MBC patient-derived xenograft (PDX) to examine the effect of DACi therapy on MBC. Cell morphology, cell cycle-associated gene expressions, transwell migration, and metastasis were evaluated in patient-derived cells and tumors after treatment with romidepsin and panobinostat. Derivations of our PDX model, including cells, spheres, organoids, explants, and in vivo implanted tumors were treated. Finally, we tested the effects of combining DACi with approved chemotherapeutics on relative cell biomass. DACi significantly suppressed the total number of lung metastasis in vivo using our PDX model, suggesting a role for DACi in preventing circulating tumor cells from seeding distal tissue sites. These data were supported by our findings that DACi reduced cell migration, populations, and expression of mesenchymal-associated genes. While DACi treatment did affect cell cycle-regulating genes in vitro, tumor growth was not affected compared to controls. Importantly, gene expression results varied depending on the cellular or tumor system used, emphasizing the importance of using multiple derivations of cancer models in preclinical therapeutic discovery research. Furthermore, DACi sensitized and produced a synergistic effect with approved oncology therapeutics on inherently resistant MBC. This study introduced a role for DACi in suppressing the migratory and mesenchymal phenotype of MBC cells through regulation of the epithelial-mesenchymal transition axis and suppression of the CTC population. Preliminary evidence that DACi treatment in combination with MEK1/2 inhibitors exerts a synergistic effect on MBC cells was also demonstrated.

cancer biology↗