Biology subjects

Ruppin, E.

Publications and source records attributed to Ruppin, E..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

ecPath detects ecDNA in tumors from histopathology images

Circular extrachromosomal DNA (ecDNA) can drive tumor initiation, progression and resistance in some of the most aggressive cancers and is emerging as a promising anti-cancer target. However, detection currently requires costly whole-genome sequencing (WGS) or labor-intensive cytogenetic or FISH imaging, limiting its application in routine clinical diagnosis. To overcome this, we developed ecPath (ecDNA from histopathology), a computational method for predicting ecDNA status from routinely available hematoxylin and eosin (H&E) images. ecPath implements a deep-learning method we call transcriptomics-guided learning, which utilizes both transcriptomics and H&E images during the training phase to enable successful ecDNA prediction from H&E images alone, a task not achievable with models trained on H&E images only. It is trained on more than 6,000 tumor whole-slide images from the TCGA cohort with the best performance in predicting ecDNA status in brain and stomach tumors (average AUC=0.78). ecPath revealed that ecDNA-positive tumors are enriched with pleomorphic, larger and high-density nuclei. Testing in an independent cohort, ecPath predicted ecDNA status of 985 pediatric brain tumor patients with an AUC of 0.72. Finally, we applied ecPath to identify ecDNA-positive tumors in the TCGA cohort for which no WGS data were available. Like WGS-based ecDNA-positive labels, the predicted ecDNA-positive status also identify poor prognoses for low grade glioma patients. These results demonstrate that ecPath enables the detection of ecDNA from routinely available H&E imaging alone and help nominate aggressive tumors with ecDNA to study and target it.

pathology↗

bioRxiv2024

Path2Space: An AI Approach for Cancer Biomarker Discovery Via Histopathology Inferred Spatial Transcriptomics

Spatial transcriptomics (ST) is transforming our understanding of tumor heterogeneity by enabling high-resolution, location-specific mapping of gene expression across tumors and their microenvironment. However, the associated high cost of the assay has limited cohort size and hence large-scale biomarker discovery. Here we present Path2Space, a deep learning approach that predicts spatial gene expression directly from histopathology slides. Trained on substantial breast cancer ST data, it robustly predicts the spatial expression of over 4,300 genes in independent validations, markedly outperforming existing ST predictors. Path2Space additionally accurately infers cell-type abundances in the tumor microenvironment (TME) based on the inferred ST data. Applied to more than a thousand breast tumor histopathology slides from the TCGA, Path2Space characterizes their TME on an unprecedented scale and identifies three new spatially-grounded breast cancer subgroups with distinct survival rates. Path2Space-inferred TME landscapes enable more accurate predictions of patients response to chemotherapy and trastuzumab directly from H&E slides than those obtained by existing established sequencing-based biomarkers. Path2Space thus offers a transformative, fast and cost-effective approach to robustly delineate the TME directly from their histopathology slides, facilitating the development of spatially-grounded biomarkers to advance precision oncology.

cancer biology↗

bioRxiv2024

Identification of targetable vulnerabilities of PLK1-overexpressing cancers by synthetic dosage lethality

Tumor heterogeneity poses a significant challenge in combating treatment resistance. Despite Polo-like kinase 1 (PLK1) being universally overexpressed in cancers and contributing to chromosomal instability (CIN), direct PLK1 inhibition hasnt yielded clinical progress. To address this, we utilized the synthetic dosage lethality (SDL) approach, targeting PLK1s genetic interactions for selective killing of overexpressed tumor cells while mitigating heterogeneity-associated challenges. Employing computational methods, we conducted a genome-wide shRNA screen, identifying 105 SDL candidates. Further in vivo CRISPR screening in a breast cancer xenograft model and in vitro CRISPR analysis validated these candidates. Employing Perturb-seq revealed IGF2BP2/IMP2 as a key SDL hit eliminating PLK1-overexpressing cells. Suppression of IGF2BP2, genetically or pharmacologically, downregulated PLK1 and limited tumor growth. Our findings strongly propose targeting PLK1s genetic interactions as a promising therapeutic approach, holding broad implications across multiple cancers where PLK1 is overexpressed.

cancer biology↗

bioRxiv2024

The expression patterns of different cell types and their interactions in the tumor microenvironment are predictive of breast cancer patient response to neoadjuvant chemotherapy

The tumor microenvironment (TME) is a complex ecosystem of diverse cell types whose interactions govern tumor growth and clinical outcome. While multiple studies have extensively charted the TMEs impact on immunotherapy, its role in chemotherapy response remains less explored. To address this, we developed DECODEM (DEcoupling Cell-type-specific Outcomes using DEconvolution and Machine learning), a generic computational framework leveraging cellular deconvolution of bulk transcriptomics to associate gene expression of individual cell types in the TME with clinical response. Employing DECODEM to analyze gene expression of breast cancer patients treated with neoadjuvant chemotherapy across three bulk cohorts, we find that the expression of specific immune cells (myeloid, plasmablasts, B-cells) and stromal cells (endothelial, normal epithelial, CAFs) are highly predictive of chemotherapy response, achieving the same prediction levels as the expression of malignant cells. Notably, ensemble models integrating the estimated expression of different cell types perform best and outperform models built on the original tumor bulk expression. These findings and the models generalizability are further tested and validated in two single-cell (SC) cohorts of triple negative breast cancer. To investigate the possible role of immune cell-cell interactions (CCIs) in mediating chemotherapy response, we extended DECODEM to DECODEMi to identify such key functionally important CCIs, validated in SC data. Our findings highlight the importance of active pre-treatment immune infiltration for chemotherapy success. DECODEM and DECODEMi are made publicly available to facilitate studying the role of the TME in mediating response in a wide range of cancer indications and treatments.

bioinformatics↗

bioRxiv2024

Chromosome 7 to the rescue: overcoming chromosome 10 loss in gliomas

The co-occurrence of chromosome 10 loss and chromosome 7 gain in gliomas is the most frequent loss-gain co-aneuploidy pair in human cancers, a phenomenon that has been investigated without resolution since the late 1980s. Expanding beyond previous gene-centric studies, we investigate the co-occurrence in a genome-wide manner taking an evolutionary perspective. First, by mining large tumor aneuploidy data, we predict that the more likely order is 10 loss followed by 7 gain. Second, by analyzing extensive genomic and transcriptomic data from both patients and cell lines, we find that this co-occurrence can be explained by functional rescue interactions that are highly enriched on 7, which can possibly compensate for any detrimental consequences arising from the loss of 10. Finally, by analyzing transcriptomic data from normal, non-cancerous, human brain tissues, we provide a plausible reason why this co-occurrence happens preferentially in cancers originating in certain regions of the brain.

cancer biology↗

bioRxiv2023

Temporal genomic analysis of melanoma rejection identifies regulators of tumor immune evasion

Decreased intra-tumor heterogeneity (ITH) correlates with increased patient survival and immunotherapy response. However, even highly homogenous tumors may display variability in their aggressiveness, and how immunologic-factors impinge on their aggressiveness remains understudied. Here we studied the mechanisms responsible for the immune-escape of murine tumors with low ITH. We compared the temporal growth of homogeneous, genetically-similar single-cell clones that are rejected vs. those that are not- rejected after transplantation in-vivo using single-cell RNA sequencing and immunophenotyping. Non- rejected clones showed high infiltration of tumor-associated-macrophages (TAMs), lower T-cell infiltration, and increased T-cell exhaustion compared to rejected clones. Comparative analysis of rejection- associated gene expression programs, combined with in-vivo CRISPR knockout screens of candidate mediators, identified Mif (macrophage migration inhibitory factor) as a regulator of immune rejection. Mif knockout led to smaller tumors and reversed non-rejection-associated immune composition, particularly, leading to the reduction of immunosuppressive macrophage infiltration. Finally, we validated these results in melanoma patient data. Statement of significanceHere, we uncover the association of Mif expression with tumor growth and aggressiveness, specifically in low ITH tumors. These findings could facilitate the development of new strategies to treat patients with homogeneous, high-MIF expressing tumors that are unresponsive to immune checkpoint therapy.

cancer biology↗

bioRxiv2023

Charting the transcriptomic landscape of primary and metastatic cancers in relation to their origin and target normal tissues

Metastasis is a leading cause of cancer-related deaths, yet understanding how metastatic tumors adapt from their origin to target tissues is challenging. To address this, we assessed whether primary and metastatic tumors resemble their tissue of origin or target tissue in terms of gene expression. We analyzed gene expression profiles from various cancer types, including single-cell and bulk RNA-seq data, in both paired and unpaired primary and metastatic patient cohorts. We quantified the transcriptomic distances between tumor samples and their normal tissues, revealing that primary tumors are more similar to their tissue of origin, while metastases shift towards the target tissue. Pathway-level analysis highlighted critical transcriptomic changes during metastasis. Notably, primary cancers exhibited higher activity in cancer hallmarks, including Activating Invasion and Metastasis, compared to metastatic cancers. This comprehensive landscape analysis provides insight into how cancer tumors adapt to their metastatic environments, providing a transcriptome-wide view of the processes involved.

cancer biology↗

bioRxiv2023

Deactivation of ligand-receptor interactions enhancing lymphocyte infiltration drives melanoma resistance to Immune Checkpoint Blockade

Immune checkpoint blockade (ICB) is a promising cancer therapy; however, resistance often develops. To learn more about ICB resistance mechanisms, we developed IRIS (Immunotherapy Resistance cell-cell Interaction Scanner), a machine learning model aimed at identifying candidate ligand-receptor interactions (LRI) that are likely to mediate ICB resistance in the tumor microenvironment (TME). We developed and applied IRIS to identify resistance-mediating cell-type-specific ligand-receptor interactions by analyzing deconvolved transcriptomics data of the five largest melanoma ICB therapy cohorts. This analysis identifies a set of specific ligand-receptor pairs that are deactivated as tumors develop resistance, which we refer to as resistance deactivated interactions (RDI). Quite strikingly, the activity of these RDIs in pre-treatment samples offers a markedly stronger predictive signal for ICB therapy response compared to those that are activated as tumors develop resistance. Their predictive accuracy surpasses the state-of-the-art published transcriptomics biomarker signatures across an array of melanoma ICB datasets. Many of these RDIs are involved in chemokine signaling. Indeed, we further validate on an independent large melanoma patient cohort that their activity is associated with CD8+ T cell infiltration and enriched in hot/brisk tumors. Taken together, this study presents a new strongly predictive ICB response biomarker signature, showing that following ICB treatment resistant tumors turn inhibit lymphocyte infiltration by deactivating specific key ligand-receptor interactions.

bioinformatics↗

bioRxiv2023

Robust prediction of patient outcomes with immune checkpoint blockade therapy for cancer using common clinical, pathologic, and genomic features

Despite the revolutionary impact of immune checkpoint blockade (ICB) in cancer treatment, accurately predicting patients responses remains elusive. We analyzed eight cohorts of 2881 ICB-treated patients across 18 solid tumor types, the largest dataset to date, examining diverse clinical, pathologic, and genomic features. We developed the LOgistic Regression-based Immunotherapy-response Score (LORIS) using a transparent, compact 6-feature logistic regression model. LORIS outperforms previous signatures in ICB response prediction and can identify responsive patients, even those with low tumor mutational burden or tumor PD-L1 expression. Importantly, LORIS consistently predicts both objective responses and short-term and long-term survival across most cancer types. Moreover, LORIS showcases a near-monotonic relationship with ICB response probability and patient survival, enabling more precise patient stratification across the board. As our method is accurate, interpretable, and only utilizes a few readily measurable features, we anticipate it will help improve clinical decision-making practices in precision medicine to maximize patient benefit.

cancer biology↗

bioRxiv2023

Predicting tumor immune microenvironment and checkpoint therapy response of head & neck cancer patients from blood immune single-cell transcriptomics

The immune state of tumor microenvironment is crucial for determining immunotherapy response but is not readily accessible. Here we investigate if we can infer the tumor immune state from the blood and further predict immunotherapy response. First, we analyze a dataset of head and neck squamous cell carcinoma (HNSCC) patients with matched scRNA-Seq of peripheral blood mononuclear cells (PBMCs) and tumor tissues. We find that the tumor immune cell fractions of different immune cell types and many of the genes they express can be inferred from the matched PBMC scRNA-Seq. Second, analyzing another HNSCC dataset with PBMC scRNA-Seq and immunotherapy response, we find that the inferred ratio between tumor memory B and regulatory T cell fractions is predictive of immunotherapy response and is superior to the well-established cytolytic and exhausted T-cell signatures. Overall, these results showcase the potential of scRNA-Seq liquid biopsies in cancer immunotherapy, calling for their larger-scale testing. SignificanceThis head and neck cancer study demonstrates the potential of using blood single-cell transcriptomics to (1) infer the tumor immune status and (2) predict immunotherapy response from the tumor immune status inferred from blood. These results showcase the potential of single-cell transcriptomics liquid biopsies for further advancing personalized cancer immunotherapy.

bioinformatics↗

bioRxiv2023

Decoupling the correlation between cytotoxic and exhausted T lymphocyte transcriptomic signatures enhances melanoma immunotherapy response prediction

BackgroundCytotoxic T lymphocytes (CTL) play a crucial role in anti-cancer immunity. Progression of CTL to terminal exhausted T lymphocytes (ETL) that overexpress inhibitory receptors can substantially decrease effector cytokines production and diminish cytolytic activity and terminal exhausted T cell cannot be reprogrammed by ICIs in tumor microenvironment (TME). However, while the activity levels of CTL and ETL are considered important determinants of immune checkpoint inhibitors (ICIs) response, it has been repeatedly observed that their predictive power of the latter is quite limited. Studying this conundrum on a large scale across the TCGA cohort, we find that ETL and CTL activity (estimated based on conventional gene signatures in the bulk tumor expression) is strongly positively correlated in most cancer types. We hypothesized that the limited predictive power of CTL activity might result from the high concordance of CTL and ETL activities, which mutually cancels out their individual antagonistic effects on ICI response. MethodsConsequently, we have set out to identify a set of genes whose expression identifies a subset of patients where the CTL and ETL correlation is diminished, such that the association between these CD8+ T cell states and ICIs response is enhanced. Resultsnalyzing TCGA melanoma bulk gene expression, we identified a set of genes whose over-expression markedly diminishes the CTL and ETL correlation, termed a decoupling signature (DS). Reassuringly, we first find that the correlation between ETL and CTL activities is indeed markedly lower across high scoring DS patients than that observed across low scoring DS patients in numerous independent melanoma ICIs cohorts. Second, indeed, this successful decoupling increases the power of CTL activity in predicting ICIs response in high DS scoring patients. We show that the resulting prediction accuracy is superior to other state-of-art ICI predictive transcriptomic signatures. ConclusionThe new decoupling score boosts the power of CTL activity in predicting ICIs response in melanoma from the tumor bulk expression. Its use enables a two-step stratification approach, where the response of high scoring DS patient can be predicted more accurately that with extant transcriptomic signatures. What is already known on this topicThe predictive power of CTL activity based on bulk tumor transcriptomics, despite being a widely studied important determinant of ICI treatment, is very limited. What this study addsThe efficacy of CTL activity in predicting ICI therapy response is significantly higher among patients with decoupled CTL and ETL activities. How this study might affect research, practice or policyWe identified a set of genes as the decoupling signature, whose upregulation markedly diminishes the correlation between CTL and ETL activities. Our decoupling signature enhances the power of CTL in predicting ICI treatment response, outperforming other extant expression-based signatures.

bioinformatics↗

bioRxiv2022

Fraction of copy-number alterations significantly predicts survival following immunotherapy in a few cancers

Various studies have shown that high tumor mutation burden (TMB) may predict response to immune checkpoint therapy, at least in some cancer types 1,2. However, identifying patients with low TMB that are still likely to respond to cancer immunotherapy is an important open challenge. Recently, Spurr et al. 3 reported that the tumor aneuploidy score (AS), defined as the fraction of chromosome arms with arm-level copy number alterations in a sample, is predictive of survival following immunotherapy in low-TMB patients across multiple cancers. By re-analyzing the same data set by performing survival analysis in individual cancer types separately, we find that AS only significantly predicts survival in one single cancer indication. We further find that another metric conceptually related to the AS, the fraction of genome encompassed by copy number alterations (FGA), if called with a conventional copy number calling cutoff, has stronger predictive power than the AS proposed in 3, and that this observation holds even if the FGA and AS thresholds for presence/absence of copy number events are set comparably. However, with the current available data, even FGA can predict survival following immunotherapy in only a few cancer indications.

cancer biology↗

bioRxiv2022

High Density Domain-Focused CRISPR Screens Reveal Epigenetic Regulators of HOX/MEIS Gene Expression in Acute Myeloid Leukemia

Aberrant expression of stem-cell-associated genes is a common feature in acute myeloid leukemia (AML) and is linked to leukemic self-renewal and therapy resistance. Using AF10-rearranged leukemia as a prototypical example displaying a recurrent "stemness" network activated in AML, we screened for chromatin regulators that sustain aberrant activation of these networks. We deployed a CRISPR-Cas9 screen with a bespoke domain-focused library and identified several novel chromatin-modifying complexes as regulators of the TALE domain transcription factor MEIS1, a key leukemia stem cell (LSC)-associated gene. CRISPR droplet sequencing revealed that many of these MEIS1 regulators coordinately controlled the transcription of several AML oncogenes. In particular, we identified a novel role for the Tudor-domain containing chromatin reader protein SGF29 in the transcription of key AML oncogenes. Furthermore, SGF29 deletion impaired leukemogenesis in models representative of multiple AML subtypes. Our studies reveal a novel role for SGF29 as a non-oncogenic dependency in AML and identify the SGF29 Tudor domain as an attractive target for drug discovery.

cancer biology↗

bioRxiv2022

Deep characterization of cancer drugs mechanism of action by integrating large-scale genetic and drug screens

Knowing a drugs mechanism of action (MOA) is essential for its clinical success by selecting the best indications, likely responders, and combinations. Yet knowledge of many drugs MOA remains lacking. Here we present DeepTarget, a computational tool for deep characterization of cancer drugs MOA by integrating existing large-scale genetic and drug screens. Spanning [~]1500 drugs across [~]18K possible target genes, DeepTarget predicts: (1) a drugs primary target(s), (2) whether it specifically targets the wild-type or mutated target forms, and (3) the secondary target(s) that mediate its response when the primary target is not expressed. We first tested and successfully validated DeepTarget in a total of eleven unseen gold-standard datasets, with an average AUC of 0.82, 0.77, and 0.92 for the above three prediction abilities, respectively. We then proceed to use it in a wide range of applications: First, we find that DeepTargets predicted specificity of a drug to its target is strongly associated with its success in clinical trials and is higher in its FDA-approved indications. Second, DeepTarget predicts candidate drugs for targeting currently undruggable cancer oncogenes and their mutant forms. Finally, DeepTarget predicts new targets for drugs with unknown MOA and new secondary targets of approved drugs. Taken together, DeepTarget is a new computational framework for accelerating drug prioritization and its target discovery by leveraging large-scale genetic and drug screens.

cancer biology↗

bioRxiv2022

Control of alveolar macrophage differentiation by Siah1a/2 ubiquitin ligases limits carcinogen-induced lung adenocarcinoma

Tumor microenvironment components, including T and myeloid cells, play important roles in lung adenocarcinoma (LADC) progression and response to therapy. Here, we identify a role for Siah1a/2 ubiquitin ligases in controlling alveolar macrophage (AM) differentiation and urethane-induced LADC. Genetic ablation of Siah1a/2 in AMs enriched their immature state, coinciding with increased pro-tumorigenic and inflammatory gene signatures and abundance of Siah1a/2 substrates NRF2 and {beta}-catenin. Urethane administration to mice enriched the population of monocytic and immature-like AMs, which were more prevalent in the lungs of mice carrying Siah1a/2-ablated macrophages. While resembling transitional profibrotic macrophages often seen in lung fibrosis, enrichment of immature AMs coincided with the development of more frequent and larger lung tumors in urethane-treated mice harboring Siah1a/2 ablated macrophages compared with controls. Gene expression signature of Siah1a/2 ablated immature-like AMs is associated with increased infiltration of CD14+ immune cells and worse survival of LADC patients. Our findings identify Siah1a/2 as gatekeepers of cancer development by controlling AM differentiation and profibrotic phenotypes contributing to carcinogen-induced lung cancer.

cancer biology↗

bioRxiv2022

Synthetic lethality-based prediction of cancer treatment response from histopathology images

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an approach for predicting response to multiple targeted and immunotherapies from H&E-slides. In difference from existing approaches that aim to predict treatment response directly from the slides, ENLIGHT-DeepPT is an indirect two-step approach consisting of (1) DeepPT, a new deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response based on the DeepPT inferred expression values. DeepPT successfully predicts transcriptomics in all 16 TCGA cohorts tested and generalizes well to two independent datasets. Importantly, ENLIGHT-DeepPT successfully predicts true responders in five independent patients cohorts involving four different treatments spanning six cancer types with an overall odds ratio of 2.44, increasing the baseline response rate by 43.47% among predicted responders, without the need for any treatment data for training. Furthermore, its prediction accuracy on these datasets is comparable to a supervised approach predicting the response directly from the images, trained and tested on the same cohort in cross validation. Its future application could provide clinicians with rapid treatment recommendations to an array of different therapies and importantly, may contribute to advancing precision oncology in developing countries. Statement of SignificanceENLIGHT-DeepPT is the first approach shown to successfully predict response to multiple targeted and immune cancer therapies from H&E slides. In distinction from all previous H&E slides prediction approaches, it does not require supervised training on a specific cohort for each drug/indication treatment but is trained to predict expression on the TCGA cohort and then can predict response to an array of treatments without any further training. ENLIGHT-DeepPT can provide rapid treatment recommendations to oncologists and help advance precision oncology in underserved regions and low-income countries.

cancer biology↗

bioRxiv2022

Transcriptomics based prediction of survival and response to therapy in malignant mesothelioma

Malignant mesothelioma is an aggressive cancer with limited treatment options and poor prognosis. Better understanding of mesothelioma genomics and transcriptomics could advance novel therapies. We performed whole-exome and RNA-sequencing of germline and tumors of 122 patients with pleural, peritoneal, and tunica-vaginalis mesothelioma. We identify a 48 gene prognostic signature that is highly predictive of mesothelioma patient survival including CCNB1, whose expression is highly predictive of patient survival on its own. Using a synthetic-lethality (SL) based pipeline for analyzing the patients transcriptomic data, we identified SL-based signatures predictive of response to an anti-PD1 immune checkpoint inhibitor and combination therapies with pemetrexed. These SL-profiles successfully predict the overall patient-response observed across targeted, immuno- and chemotherapies in 11 independent mesothelioma clinical trials spanning 7 different treatments. These findings lay a basis for future studies aimed specifically at testing the ability of these SL profiles to serve as treatment biomarkers in mesothelioma.

cancer biology↗

bioRxiv2022

Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome

BackgroundPrecision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. MethodsWe present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patients response to a variety of therapies in multiple cancer types, without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: Personalized Oncology (PO), aimed at prioritizing treatments for a single patient, and Clinical Trial Design (CTD), selecting the most likely responders in a patient cohort. FindingsEvaluating ENLIGHTs performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patients treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable to that of supervised predictors developed for specific indications and drugs. In combination with the IFN-{gamma}signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders, while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy. ConclusionENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome. FundingThis research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.

bioinformatics↗