Biology subjects

Razavi, P.

Publications and source records attributed to Razavi, P..

11 recordsLinked to original sources

bioRxiv2026

Evasion of Hepatic ILC1 Immunosurveillance Drives Breast Cancer Liver Metastasis

Metastasis, the dissemination of cancer cells to distant organs, is an inefficient process1, yet remains the leading cause of cancer-related mortality2. The cellular barriers and evasion mechanisms governing organ-specific susceptibility remain incompletely understood3-5, hindering the development of effective anti-metastatic therapies6. Here, we show that liver-resident type 1 innate lymphoid cells (ILC1s) constitute an essential immunosurveillance barrier against breast cancer liver metastasis that can be bypassed through either cancer cell intrinsic or host-dependent mechanisms. In a multi-organ metastatic breast cancer patient cohort, liver metastasis occurred later and was associated with poorer survival than lung metastasis, with elevated body mass index emerging as a clinical risk factor. This metabolic vulnerability was recapitulated in mice, where a high-fat diet induced hepatosteatosis and selectively enhanced breast cancer colonization of the liver. Mechanistically, liver and lung colonization were restrained by ILC1s and circulating natural killer (NK) cells, respectively. Following cancer cell challenge, NK cells expanded and activated, employing lytic granule pathways and the death receptor DR5 for clearance, whereas activated ILC1s selectively required high DR5 expression on cancer cells, supported by AP-2a (TFAP2A) mediated transcriptional activation, for effective immunosurveillance. Heterozygous copy number deletion at TNFRSF10B (encoding DR5) in human tumours reduced DR5 expression and predisposed to liver metastasis. Hepatosteatosis further compromised this barrier by reducing IL-15 expression in macrophages, thereby depleting hepatic ILC1s effects reversible with glucagon-like peptide-1 (GLP-1) receptor agonists. Thus, hepatic ILC1s form a critical organ-specific barrier to breast cancer metastasis, vulnerable to both cancer cell-intrinsic DR5 downregulation and host metabolic dysfunction.

immunology↗

bioRxiv2026

Evolution of oncogene amplification across 86,000 cancer cell genomes

High-level copy-number (CN) amplification (HLAMP) is a major mechanism of oncogene activation in human cancer. Despite progress in therapeutically targeting amplified oncogenes, the processes underlying amplicon evolution remain incompletely understood, leaving critical knowledge gaps in their etiology and mechanisms of therapeutic response. To address this, we analyzed the evolutionary trajectories of HLAMPs using single-cell whole-genome sequencing data from 86,239 cancer cells across 93 patients and 9 experimental systems. We found that cell-to-cell CN variability provides a quantifiable readout of HLAMP mechanism, clearly distinguishing extrachromosomal circular DNA (ecDNA) from intrachromosomal amplification (ICamp) through characteristic CN distributions that reflect distinct modes of segregation and correspond to clonal architecture. Notably, ICamp events frequently showed multiple amplitude peaks specific to subclones, indicating punctuated shifts in oncogene dosage through numeric or structural modulatory mechanisms with transcriptional impact. In contrast, ecDNAs exhibited broad, continuous CN distribution with extreme high-copy outliers, consistent with asymmetric segregation. The CN and structural diversity of ecDNA regions enabled systematic deconvolution of ecDNA subspecies and estimation of their per-cell abundance, revealing the history of ecDNA-mediated oncogenesis at single-nucleotide resolution. We observed ecDNA diversification through internal rearrangements across cases and, notably, convergent evolution in glioblastoma cases marked by multiple, recurrent acquisition of EGFR-targeting ecDNAs. Finally, single-cell genome-based identification of ecDNAs showed substantial discrepancy with bulk genome graph-based predictions and reliably distinguished actively maintained ecDNAs from historical genomic footprints after chromosomal re-integration. These findings reveal marked tissue-type specificity of ecDNAs, suggesting that ecDNA-mediated oncogenesis may depend on a permissive tissue context.

genomics↗

bioRxiv2025

Tracking ongoing chromosomal instability using single-cell whole-genome sequencing

Chromosomal instability (CIN) generates aneuploid genomes that are characteristic of most cancers. While bulk genome sequencing reveals historical CIN, it lacks the resolution to identify ongoing CIN that actively shapes genome evolution. Here, we present a computational framework that leverages single-cell whole-genome sequencing (scWGS) to identify and quantify ongoing CIN by detecting cell-unique copy number alterations and probabilistically mapping them to known CIN signatures. We validated this framework generating in vitro models with four types of induced CIN, correctly identifying the induced-CIN type in each case. When applied to cell lines and organoids with ongoing homologous recombination deficiency, our method showed improved identification of sensitivity to PARP inhibition and platinum-based chemotherapy. Analysing scWGS data from 8 triple-negative breast cancers, we linked ongoing impaired non-homologous end joining to subclonal diversification, a finding further supported in cohorts of 179 unmatched primary and metastatic TNBCs and 39 matched cases. Collectively, our results demonstrate that distinguishing ongoing from historical chromosomal instability uncovers a distinct dimension of tumour evolution, suggesting that effective precision oncology will require integrating measurements of both past genomic scars and active mutational processes.

genomics↗

bioRxiv2025

Integrated histopathologic modeling of detailed tumor subtypes and actionable biomarkers

Accurate cancer subtyping with accompanying molecular characterization is critical for precision oncology. While machine learning approaches have been applied to both digital pathology and cancer genomics, previous work has been limited in sample size and has typically aggregated granular cancer subtypes into coarse groupings, likely obfuscating informative molecular and prognostic associations and phenotypic variation of more detailed tumor subtypes. Accordingly, we collated 378,123 hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) with matched targeted DNA clinical sequencing results and OncoTree detailed cancer subtypes from a real-world cohort of 71,142 patients. Using this scaled, granular dataset and a cancer subtype knowledge graph, we developed Mosaic: a family of calibrated machine learning models using H&E WSI embeddings to classify tumors and identify molecular phenotypes across 163 detailed subtypes. The cancer subtyping module (Aeon) achieved an area under the receiver operating characteristic curve (AUROC) of 0.992 overall, with 161/163 subtypes reaching an AUROC [≥] 0.90 and improved performance over a state-of-the-art genomics-based classifier. The genomic inference module (Paladin) achieved an AUROC [≥] 0.80 for 167 pairs of detailed subtypes and genomic targets. We further used the learned histopathologic representations to i) identify key associations of the histopathologic embeddings with clinical biomarkers; ii) identify unsupervised sub-clusters of tumors with genomic determinants of tumor phenotype; iii) specify granular diagnoses for cancers of unknown primary, evaluated by genomic associations and expected clinical outcome distributions; iv) annotate functional significance for variants of uncertain significance (VUS); and v) identify cases that mimic the phenotypic effect of known DNA variants on H&E in the absence of detectable DNA alterations. Taken together, this work advances our understanding of phenotypic variation of granular tumor subtypes, their relevance to enhanced diagnostics, and their potential utility in risk stratification with multimodal machine learning in cancer.

cancer biology↗

bioRxiv2024

APOBEC3 mutagenesis drives therapy resistance in breast cancer

Acquired genetic alterations commonly drive resistance to endocrine and targeted therapies in metastatic breast cancer1-7, however the underlying processes engendering these diverse alterations are largely uncharacterized. To identify the mutational processes operant in breast cancer and their impact on clinical outcomes, we utilized a well-annotated cohort of 3,880 patient samples with paired tumor-normal sequencing data. The mutational signatures associated with apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like 3 (APOBEC3) enzymes were highly prevalent and enriched in post-treatment compared to treatment-naive hormone receptor-positive (HR+) cancers. APOBEC3 mutational signatures were independently associated with shorter progression-free survival on antiestrogen plus CDK4/6 inhibitor combination therapy in patients with HR+ metastatic breast cancer. Whole genome sequencing (WGS) of breast cancer models and selected paired primary-metastatic samples demonstrated that active APOBEC3 mutagenesis promoted resistance to both endocrine and targeted therapies through characteristic alterations such as RB1 loss-of-function mutations. Evidence of APOBEC3 activity in pre-treatment samples illustrated a pervasive role for this mutational process in breast cancer evolution. The study reveals APOBEC3 mutagenesis to be a frequent mediator of therapy resistance in breast cancer and highlights its potential as a biomarker and target for overcoming resistance.

cancer biology↗

bioRxiv2024

Multimodal histopathologic models stratify hormone receptor-positive early breast cancer

For patients with hormone receptor-positive, early breast cancer without HER2 amplification, multigene expression assays including Oncotype DX (R) recurrence score (RS) have been clinically validated to identify patients who stand to derive added benefit from adjuvant cytotoxic chemotherapy. However, cost and turnaround time have limited its global adoption despite recommendation by practice guidelines. We investigated if routinely available hematoxylin and eosin (H&E)-stained pathology slides could act as a surrogate triaging data substrate by predicting RS using machine learning methods. We trained and validated a multimodal transformer model, Orpheus, using 6,203 patients across three independent cohorts, taking both H&E images and their corresponding synoptic text reports as input. We showed accurate inference of recurrence score from whole-slide images (r = 0.63 (95% C.I. 0.58 - 0.68); n = 1,029), the raw text of their corresponding reports (r = 0.58 (95% C.I. 0.51 - 0.64); n = 972), and their combination (r = 0.68 (95% C.I. 0.64 - 0.73); n = 964) as measured by Pearsons correlation. To predict high-risk disease (RS>25), our model achieved an area under the receiver operating characteristic curve (AUROC) of 0.89 (95% C.I. 0.83 - 0.94), and area under the precision recall curve (AUPRC) of 0.64 (95% C.I. 0.60 - 0.82), compared to 0.49 (95% C.I. 0.36 - 0.64) for an existing nomogram based on clinical and pathologic features. Moreover, our model generalizes well to external international cohorts, effectively identifying recurrence risk (r = 0.61, p < 10-4, n = 452; r = 0.60, p < 10-4, n = 575) and high-risk status (AUROC = 0.80, p < 10-4, AUPRC = 0.68, p < 10-4, n = 452; AUROC = 0.83, p < 10-4, AUPRC = 0.73, p < 10-4, n = 575) from whole-slide images. Probing the biologic underpinnings of the model decisions uncovered tumor cell size heterogeneity, immune cell infiltration, a proliferative transcription program, and stromal fraction as correlates of higher-risk predictions. We conclude that at an operating point of 94.4% precision and 33.3% recall, this model could help increase global adoption and shorten lag between resection and adjuvant therapy.

pathology↗

bioRxiv2024

Homologous recombination deficiency and tumor suppressor heterozygosity mediate resistance to front-line therapy in breast cancer

The co-occurrence of germline and somatic oncogenic alterations is frequently observed in breast cancer, but their combined biologic and clinical significance has not been evaluated. To assess the role of germline-somatic interactions on outcomes in routine practice, we developed an integrated clinicogenomic pipeline to analyze the genomes of over 4,500 patients with breast cancer. We find that germline (g)BRCA2-associated tumors are enriched for RB1 loss-of-function mutations and manifest poor outcomes on standard-of-care, front-line CDK4/6 inhibitor (CDK4/6i) combinations. Amongst these tumors, gBRCA2-related homologous recombination deficiency (HRD) as well as baseline RB1 LOH status promote acquisition of RB1 loss-of- function mutations under the selective pressure of CDK4/6i, causing therapy resistance. These findings suggest an alternative therapeutic strategy using sequential targeting of HRD in gBRCA- associated breast cancers through PARP inhibitors prior to CDK4/6i therapy to intercept deleterious RB1-loss trajectories and thus suppress the emergence of CDK4/6 inhibitor resistance. More broadly, our findings demonstrate how germline-somatic driven genomic configurations shape response to systemic therapy and can be exploited therapeutically as part of biomarker-directed clinical strategies.

genomics↗

bioRxiv2023

Long-term breast cancer response to CDK4/6 inhibition defined by TP53-mediated geroconversion

Inhibition of CDK4/6 kinases has led to improved outcomes in breast cancer. Nevertheless, only a minority of patients experience long-term disease control. Using a clinically-annotated cohort of patients with metastatic HR+ breast cancer, we identified TP53 loss (28.8%) and MDM2 amplification (6.7%) to be associated with lack of long-term disease control. Human breast cancer models revealed that p53 loss did not affect CDK4/6 activity or G1-blockade, but instead promoted drug-insensitive p130 phosphorylation by CDK2. Persistence of phospho-p130 prevented DREAM complex assembly, enabling cell cycle reentry and tumor progression. Inhibitors of CDK2 could overcome p53 loss, leading to geroconversion and manifestation of senescence phenotypes. Complete inhibition of both CDK4/6 and CDK2 kinases appears to be necessary to facilitate long-term response across genomically-diverse HR+ breast cancers.

cancer biology↗

bioRxiv2023

Projection neurons are necessary for the maintenance, but not for the assembly, of the mouse olfactory circuit

The assembly and maintenance of neural circuits is crucial for proper brain function. Although the assembly of brain circuits has been extensively studied, much less is understood about the mechanisms controlling their maintenance as animals mature. In the olfactory system, the axons of olfactory sensory neurons (OSNs) expressing the same odor receptor converge into discrete synaptic structures of the olfactory bulb (OB) called glomeruli, forming a stereotypic odor map. The OB projection neurons, called mitral and tufted cells (M/Ts), have a single dendrite that branches into a single glomerulus, where they make synapses with OSNs. We used a genetic method to progressively eliminate the vast majority of M/T cells in early postnatal mice, and observed that the assembly of the OB bulb circuits proceeded normally. However, as the animals became adults the apical dendrite of remaining M/Ts grew multiple branches that innervated several glomeruli, and OSNs expressing single odor receptors projected their axons into multiple glomeruli, disrupting the olfactory sensory map. Moreover, ablating the M/Ts in adult animals also resulted in similar structural changes in the projections of remaining M/Ts and axons from OSNs. Interestingly, the ability of these mice to detect odors was relatively preserved despite only having 1-5% of projection neurons transmitting odorant information to the brain, and having highly disrupted circuits in the OB. These results indicate that a reduced number of projection neurons does not affect the normal assembly of the olfactory circuit, but induces structural instability of the olfactory circuitry of adult animals.

neuroscience↗

bioRxiv2021

Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients

Progression to metastatic disease remains the main cause of cancer death. Yet, the underlying genomic mechanisms driving metastasis remain largely unknown. Here, we present MSK-MET, an integrated pan-cancer cohort of tumor genomic and clinical outcome data from more than 25,000 patients. We analyzed this dataset to identify associations between tumor genomic alterations and patterns of metastatic dissemination across 50 tumor types. We found that chromosomal instability is strongly correlated with metastatic burden in some tumor types, including prostate adenocarcinoma, lung adenocarcinoma and HR-positive breast ductal carcinoma, but not in others, such as colorectal adenocarcinoma, pancreatic adenocarcinoma and high-grade serous ovarian cancer. We also identified specific somatic alterations associated with increased metastatic burden and specific routes of metastatic spread. Our data offer a unique resource for the investigation of the biological basis for metastatic spread and highlight the crucial role of chromosomal instability in cancer progression.

cancer biology↗

bioRxiv2019

Personalized Cancer Therapy Prioritization Based on Driver Alteration Co-occurrence Patterns

Identification of actionable genomic vulnerabilities is the cornerstone of precision oncology. Based on a large-scale drug screening in patient derived-xenografts, we uncover connections between driver gene alterations, derive Driver Co-Occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug response predictors attained an average balanced accuracy of 58% in a cross-validation setting, which rose to a 66% for the subset of high-confidence predictions. Morevover, we experimentally validated 12 out of 14 de novo predictions in mice. Finally, we adapted our strategy to obtain drug-response models from patients progression free survival data. By revealing unexpected links between oncogenic alterations, our strategy can increase the clinical impact of genomic profiling.

cancer biology↗