Biology subjects

Barshack, I.

Publications and source records attributed to Barshack, I..

5 recordsLinked to original sources

bioRxiv2023

Inactivation of p53 drives breast cancer brain metastasis by altering fatty acid metabolism

Brain metastasis (BM) is a dire prognosis across cancer types. It is largely unknown why some tumors metastasize to the brain whereas others do not. We analyzed genomic and transcriptional data from clinical samples of breast cancer BM (BCBM) and found that nearly all of them carried p53-inactivating genetic alterations through mutations, copy-number loss, or both. Importantly, p53 pathway activity was already perturbed in primary tumors giving rise to BCBM, often by loss of the entire 17p chromosome-arm. This association was recapitulated across other carcinomas. Experimentally, p53 knockout was sufficient to drastically increase BCBM formation and growth in vivo, providing a causal link between p53 inactivation and brain tropism. Mechanistically, p53-deficient BC cells exhibited altered lipid metabolism, particularly increased fatty acid (FA) synthesis and uptake, which are characteristic of brain-metastasizing cancer cells. FA metabolism was further enhanced by astrocytes in a p53-dependent manner, as astrocyte-conditioned medium increased FASN, SCD1, and CD36 expression and activity, and enhanced the survival, proliferation and migration of p53-deficient cancer cells. Consequently, these cells were more sensitive than p53-competent cells to FA synthesis inhibitors, in isogenic cell cultures, in BCBM-derived spheroids, and across dozens of BC cell lines. Lastly, a significant association was observed between p53 inactivation, astrocyte infiltration, and SCD1 expression in clinical human BCBM samples. In summary, our study identifies p53 inactivation as a driver of BCBM and potentially of BM in general; suggests a p53-dependent effect of astrocytes on BC cell behavior; and reveals FA metabolism as an underlying, therapeutically-targetable molecular mechanism.

cancer biology↗

bioRxiv2023

Amniotic Fluid Organoids As Personalized Tools For Real-Time Modeling Of The Developing Fetus

Despite biomedical advances, major knowledge gaps regarding human development remain, and many developmental disorders lack effective treatment, representing a huge clinical burden. This results from fetuses being largely inaccessible for analysis. Here, we employ fetal cells in human amniotic fluid (AF) to establish personalized fetal kidney and lung organoids (AFKO and AFLO, respectively), recapitulating fetal organs at single-cell resolution. AFKO harbor key fetal kidney cell populations, including nephrogenic, urothelial and stromal, endocytose albumin, and model PAX2-related anomalies. Strikingly, upon injection into the nephrogenic cortex of human fetal kidney explants, AFKO-derived progenitors integrate into the host progenitor niche and contribute to developing nephrons. AFLO comprise alveolar cells and most airway cell types in a typical pseudostratified structure, upregulate surfactant expression upon corticosteroid treatment, and show functional CFTR channels. Overall, this platform represents a new personalized tool that can be applied to virtually any fetus in real-time, affording unprecedented options in studying development, uncovering mechanisms of in utero pathologies (e.g., congenital anomalies, infections or chemical teratogens) deciphering the developmental origins of chronic diseases, and tailoring treatments for these pathologies, as well as for prematurity-related complications. Importantly, since AF contains cells from additional tissues (e.g., skin and gastrointestinal tract), and is derived in a procedure already performed in many patients, this platform may well become a broadly applicable tool in fetal medicine.

developmental biology↗

bioRxiv2023

Chemoresistome Mapping in Individual Breast Cancer Patients Unravels Diversity in Dynamic Transcriptional Adaptation

Emerging evidence reinforce the role of non-genetic adaptive resistance to chemotherapy, that involves rewiring of transcriptional programs in surviving tumors. We combined longitudinal transcriptomics with temporal pattern analysis to dissect patient-specific emergence of resistance in breast cancer. Matched triplets of tumor biopsies (pre-treatment, post-treatment and adjacent normal) were collected from breast cancer patients who received neo-adjuvant chemotherapy. Full transcriptome was analyzed by longitudinal pattern classification to follow patient-specific expression modulations. We found that dynamics of gene expression dictates resistance-related modulations. The results unraveled important principles in emergence of adaptive resistance: 1. Genes with resistance patterns are already dysregulated in the primary tumor, supporting a primed drug-tolerant state. 2. In each patient, multiple resistance-related genes are rewired but converge into few dysregulated modules. 3. Rewiring of diverse genes and pathway dysregulation vary among individuals who receive the same treatments. Patient-specific chemoresistome maps disclosed tumors acquired resistance and exposed their vulnerabilities. Mapping the complexity of dysregulated pathways in individual patients revealed important insights on adaptive resistance mechanisms. To survive the toxic drug effect, tumor cells either sustain a drug-tolerant state or intensify it, specifically bypassing the drugs interference. Depicting an individual road map to resistance can offer personalized therapeutic strategies.

cancer biology↗

bioRxiv2022

A novel deep learning pipeline for cell typing and phenotypic marker quantification in multiplex imaging

BackgroundMultiplex immunofluorescence (mIF) can provide invaluable insights into spatial biology and the complexities of the immune tumor microenvironment (iTME). However, existing analysis approaches are both laborious and highly user-dependent. In order to overcome these limitations we developed a novel, end-to-end deep learning (DL) pipeline for rapid and accurate analysis of both tumor-microarray (TMA) and whole slide mIF images. MethodsOur pipeline consists of two DL models: a multi-classifier for classifying multi-channel cell images into 12 different cell types, and a binary classifier for determining the positivity of a given marker in single-channel images. The DL multi-classifier was trained on 7,000 tiles labeled with cell annotations from a publicly available CODEX dataset, consisting of 140 tissue cores from 35 colorectal cancer (CRC) patients. For the binary classifier training, the multi-channel tiles were further split into [~]100,000 single-channel tiles, for which the ground truth was inferred from the known expression of these markers in each cell-type. This DL binary classifier was then utilized to quantify the positivity of various cell state (phenotypic) markers. In addition, the binary classifier was exploited as a cell-typing tool, by predicting the positivity of individual lineage cell markers. The performance of our DL models was evaluated on 1,800 annotations from 14 test tissue cores. The models were further evaluated on a new 6-plex melanoma cohort, stained with PhenoImager(R), and were compared to the performance of clustering, manual thresholding or machine learning-based cell-typing methods applied on the same test sets. ResultsOur DL multi-classifier achieved highly accurate results, outperforming all of the tested cell-typing methods, including clustering, manual-thresholding and ML-based approaches, in both CODEX CRC and PhenoImager melanoma cohorts (accuracy of 91% and 87%, respectively), with F1-scores above 80% in the vast majority of cell types. Our DL binary classifier, which was trained solely on the lineage markers of the CRC dataset, also outperformed existing methods, demonstrating excellent F1-scores (>80%) for determining the positivity of unseen phenotypic and lineage markers across the two tumor types and imaging modalities. Notably, as little as 20 annotations were required in order to boost the performance on an unseen dataset to above 85% accuracy and 80% F1-scores. As a result, the DL binary classifier could successfully be used as a cell-typing model, in a manner that is transferable between experimental approaches. ConclusionsWe present a novel state-of-the-art DL-based framework for multiplex imaging analysis, that enables accurate cell typing and phenotypic marker quantification, which is robust across markers, tumor indications, and imaging modalities.

immunology↗

bioRxiv2021

Proteomic landscape of multi-layered breast cancer internal tumor heterogeneity

Despite extensive research, internal tumor heterogeneity presents enormous challenges to achieve complete therapeutic responses. Changes in protein expression are central determinants of cancer phenotypes that reflect potential therapeutic targets. However, previous proteomic studies did not address internal heterogeneity, therefore, masked the necessary spatial resolution to achieve a comprehensive understanding of cancer complexity. Here we present the first large-scale multi-focal breast cancer proteomic study of 330 tumor regions which associated cancer cell function, pathological parameters, and spatial localization of each tumor region. We found marked internal proteomic heterogeneity even within tumors presenting homogeneous receptor expression. Additionally, analysis of the internal heterogeneity, based on coexisting receptor expression or histological patterns in single tumors, showed significant functional differences between homogeneous and heterogeneous tumors related to cancer metabolism, immunogenicity, and proliferation. We anticipate that this study will serve as a starting point towards the development of improved cancer therapy and diagnostics.

cancer biology↗