Biology subjects

Naxerova, K.

Publications and source records attributed to Naxerova, K..

7 recordsLinked to original sources

bioRxiv2025

Age distinguishes selection from causation in cancer genomes

Cancer genome sequencing efforts have revealed hundreds of genes under positive selection, many of which are now being developed as therapeutic targets. However, positively selected mutations also populate our aging tissues in the absence of cancer. For most mutations, it is currently unknown whether they are recurrently found in cancer genomes because they cause cancer or because they expand during normal tissue evolution and are passively inherited. Here, we develop a mathematical and statistical framework that distinguishes these two factors. We discover - across thousands of cancer and normal tissue genomes - that mutations that most strongly increase cancer risk are enriched in younger patients cancers, whereas mutations that are positively selected in normal tissue without causing cancer are enriched in older patients. Focusing on a particularly data-rich cancer type, acute myeloid leukemia, we show that genetic differences between young- and adult-onset cancers can largely be explained by the cumulative effects of normal tissue evolution, contradicting the long-standing notion that childhood cancers require a distinct set of causal mutations. Our framework establishes patient age as a powerful resource for clarifying whether positively selected mutations in cancer genomes are truly disease-promoting.

cancer biology↗

bioRxiv2024

A unique interplay of access and selection shapes peritoneal metastasis evolution in colorectal cancer

Whether metastasis in humans can be accomplished by most primary tumor cells or requires the evolution of a specialized trait remains an open question. To evaluate whether metastases are founded by non-random subsets of primary tumor lineages requires extensive, difficult-to-implement sampling. We have realized an unusually dense multi-region sampling scheme in a cohort of 26 colorectal cancer patients with peritoneal metastases, reconstructing the evolutionary history of on average 28.8 tissue samples per patient with a microsatellite-based fingerprinting assay. To assess metastatic randomness, we evaluate inter- and intra-metastatic heterogeneity relative to the primary tumor and find that peritoneal metastases are more heterogeneous than liver metastases but less diverse than locoregional metastases. Metachronous peritoneal metastases exposed to systemic chemotherapy show significantly higher inter-lesion diversity than synchronous, untreated metastases. Projection of peritoneal metastasis origins onto a spatial map of the primary tumor reveals that they often originate at the deep-invading edge, in contrast to liver and lymph node metastases which exhibit no such preference. Furthermore, peritoneal metastases typically do not share a common subclonal origin with distant metastases in more remote organs. Synthesizing these insights into an evolutionary portrait of peritoneal metastases, we conclude that the peritoneal-metastatic process imposes milder selective pressures onto disseminating cancer cells than the liver-metastatic process. Peritoneal metastases unique evolutionary features have potential implications for staging and treatment.

genetics↗

bioRxiv2024

Therapeutic Spp1 silencing in TREM2+ cardiac macrophages suppresses atrial fibrillation

Atrial fibrillation (AFib) and the risk of its lethal complications are propelled by fibrosis, which induces electrical heterogeneity and gives rise to reentry circuits. Atrial TREM2+ macrophages secrete osteopontin (encoded by Spp1), a matricellular signaling protein that engenders fibrosis and AFib. Here we show that silencing Spp1 in TREM2+ cardiac macrophages with an antibody-siRNA conjugate reduces atrial fibrosis and suppresses AFib in mice, thus offering a new immunotherapy for the most common arrhythmia.

immunology↗

bioRxiv2024

A deep profile of gene expression across 18 human cancers

Clinically and biologically valuable information may reside untapped in large cancer gene expression data sets. Deep unsupervised learning has the potential to extract this information with unprecedented efficacy but has thus far been hampered by a lack of biological interpretability and robustness. Here, we present DeepProfile, a comprehensive framework that addresses current challenges in applying unsupervised deep learning to gene expression profiles. We use DeepProfile to learn low-dimensional latent spaces for 18 human cancers from 50,211 transcriptomes. DeepProfile outperforms existing dimensionality reduction methods with respect to biological interpretability. Using DeepProfile interpretability methods, we show that genes that are universally important in defining the latent spaces across all cancer types control immune cell activation, while cancer type-specific genes and pathways define molecular disease subtypes. By linking DeepProfile latent variables to secondary tumor characteristics, we discover that tumor mutation burden is closely associated with the expression of cell cycle-related genes. DNA mismatch repair and MHC class II antigen presentation pathway expression, on the other hand, are consistently associated with patient survival. We validate these results through Kaplan-Meier analyses and nominate tumor-associated macrophages as an important source of survival-correlated MHC class II transcripts. Our results illustrate the power of unsupervised deep learning for discovery of cancer biology from existing gene expression data.

bioinformatics↗

bioRxiv2022

Losartan controls immune checkpoint blocker-induced edema and improves survival in glioblastoma

Immune checkpoint blockers (ICBs) have failed in all Phase III glioblastoma trials. Here, we found that ICBs induce cerebral edema in some patients and mice with glioblastoma. Through single-cell RNA sequencing, intravital imaging, and T cell blocking studies in mice, we demonstrated that this edema results from an inflammatory response following anti-PD1 antibody treatment that disrupts the blood-tumor-barrier. Used in lieu of immunosuppressive corticosteroids, the angiotensin receptor blocker losartan prevented this ICB-induced edema and reprogrammed the tumor microenvironment, curing 20% of mice which increased to 40% in combination with standard of care treatment. Using a bihemispheric tumor model, we identified a "hot" tumor immune signature prior to losartan+anti-PD1 therapy that predicted long-term survival. Our findings provide the rationale and associated biomarkers to test losartan with ICBs in glioblastoma patients. One-Sentence SummaryLosartan prevents immunotherapy-associated edema and enhances the outcome of immunotherapy in glioblastoma.

cancer biology↗

bioRxiv2021

Uncovering expression signatures of synergistic drug response using an ensemble of explainable AI models

Complex machine learning models are poised to revolutionize the treatment of diseases like acute myeloid leukemia (AML) by helping physicians choose optimal combinations of anti-cancer drugs based on molecular features. While accurate predictions are important, it is equally important to be able to learn about the underlying molecular basis of anti-cancer drug synergy. Explainable AI (XAI) offers a promising new route for data-driven cancer pharmacology, combining highly accurate models with interpretable insights into model decisions. Due to the highly correlated, high-dimensional nature of cancer transcriptomic data, however, we find that existing XAI approaches are suboptimal when applied naively to large transcriptomic datasets. We show how a novel approach based on model ensembling helps to increase the quality of explanations. We then use our method to demonstrate that a hematopoietic differentiation signature underlies synergy for a variety of anti-AML drug combinations.

cancer biology↗

bioRxiv2019

Lymphatic metastases have more diverse roots than distant metastases

Both lymphatic and distant metastases arise through cancer cell migration and colonization of ectopic sites. Nonetheless, the two metastasis types are associated with significantly different clinical outcomes, suggesting that distinct biological mechanisms may drive their formation. Here we show fundamental differences in the seeding patterns of lymphatic and distant metastases. Analyzing the reconstructed phylogenies of human colorectal cancers, we find that distant metastases typically are monophyletic, originating from one common ancestor. Lymphatic metastases, in contrast, are almost exclusively polyphyletic and can be seeded from many primary tumor regions. We develop a rigorous mathematical framework for quantifying the phylogenetic diversity of metastases while accounting for differential lesion sampling among patients. Our results indicate that a smaller fraction of primary tumor cells gives rise to distant metastases than lymphatic metastases. Thus, the two metastasis types exhibit profoundly distinct phylogenetic traits, indicating that different evolutionary mechanisms may drive their formation and influence their clinical behavior.

cancer biology↗