Biology subjects

Ruppin, E.

Publications and source records attributed to Ruppin, E..

65 records · Page 4Linked to original sources

bioRxiv2020

The Landscape of Precision Cancer Combination Therapy: A Single-Cell Perspective

The availability of single-cell transcriptomics data opens new opportunities for rational design of combination cancer treatments. Mining such data, we employed combinatorial optimization techniques to explore the landscape of optimal combination therapies in solid tumors including brain, head and neck, melanoma, lung, breast and colon cancers. We assume that each individual therapy can target any one of 1269 genes encoding cell surface receptors, which may be targets of CAR-T, conjugated antibodies or coated nanoparticle therapies. As a baseline case, we studied the killing of at least 80% of the tumor cells while sparing more than 90% of the non-tumor cells in each patient, as a putative regimen. We find that in most cancer types, personalized combinations composed of at most four targets are then sufficient. However, the number of distinct targets that one would need to assemble to treat all patients in a cohort accordingly would be around 10 in most cases. Further requiring that the target genes be also lowly expressed in healthy tissues uncovers qualitatively similar trends. However, as one asks for more stringent and selective killing beyond the baseline regimen we focused on, we find that the number of targets needed rises rapidly. Emerging individual promising receptor targets include PTPRZ1, which is frequently found in the optimal combinations for brain and head and neck cancers, and EGFR, a recurring target in multiple tumor types. In sum, this systematic single-cell based characterization of the landscape of combinatorial receptor-mediated cancer treatments establishes first of their kind estimates on the number of targets needed, identifying promising ones for future development.

bioinformatics↗

bioRxiv2020

Division of labor between YAP and TAZ in non-small cell lung cancer

Lung cancer is the leading cause of cancer-related deaths worldwide. The paralogous transcriptional cofactors Yes-associated protein (YAP) and transcriptional co-activator with PDZ-binding motif (TAZ, also called WWTR1), the main downstream effectors of the Hippo signal transduction pathway, are emerging as pivotal determinants of malignancy in lung cancer. Traditionally, studies have tended to consider YAP and TAZ as functionally redundant transcriptional cofactors, with similar biological impact. However, there is growing evidence that each of them also possesses distinct attributes. Here, we sought to systematically characterize the division of labor between YAP and TAZ in non-small cell lung cancer (NSCLC), the most common histological subtype of lung cancer. Employing representative NSCLC cell lines, as well as patient-derived data, we show that the two paralogs orchestrate non-overlapping transcription programs in this cancer type: whereas YAP preferentially regulates gene sets associated with cell division and cell cycle progression, TAZ preferentially regulates genes associated with extracellular matrix organization. Concordantly, depletion of YAP, but not TAZ, leads to growth arrest, while YAP overexpression promotes cell proliferation. Likewise, depletion of TAZ, but not YAP, compromises cell migration, whereas TAZ overexpression enhances migration. Importantly, the differential effects of YAP vs TAZ on key cellular processes are also associated with differential response to anti-cancer therapies. Uncovering the different activities and downstream effects of YAP and TAZ may thus facilitate better stratification of lung cancer patients for anti-cancer therapies.

cancer biology↗

bioRxiv2019

Contribution of synthetic lethality to cancer risk, onset age, and tumor suppressor specificity across human tissues

Various characteristics of cancers exhibit tissue-specificity, including lifetime cancer risk, onset age and cancer driver genes. Previously, the large variation in cancer risk across human tissues was found to strongly correlate with the number of stem cell divisions and abnormal DNA methylation levels occurring in them. Here we study the role of another potentially important factor, synthetic lethality, in cancer risk. Analyzing transcriptomics data in the GTEx compendium we quantify the extent of co-inactivation of cancer synthetic lethal (cSL) gene pairs in normal tissues and find that normal tissues with more down-regulated cSL gene pairs have lower and delayed cancer risk. We also show that the tissue-specificity of numerous tumor suppressor genes is strongly associated with the expression of their cSL partner genes in the corresponding normal tissues. Overall, our findings uncover the role of synthetic lethality as a novel important factor involved in tumorigenesis.

cancer biology↗

bioRxiv2019

Higher prevalence of homologous recombination-deficiency in lung squamous carcinoma from African Americans

To improve our understanding of the longstanding disparities in incidence and mortality across multiple cancer types among minority populations, we performed a systematic comparative analysis of molecular features in tumors from African American (AA) and European American (EA) ancestry. Our pan-cancer analysis on the cancer genome atlas (TCGA) and a more focused analysis of genome-wide somatic copy number profiles integrated with tumor-normal RNA sequencing in a racially balanced cohort of 222 non-small cell lung cancers (NSCLC) reveals more aggressive genomic characteristics of AA tumors. In general, we find AA tumors exhibit higher genomic instability (GI), homologous recombination-deficiency (HRD) levels, and more aggressive molecular features such as chromothripsis across many cancer types, including lung squamous carcinoma (LUSC). GI and HRD levels are strongly correlated across AA tumors, indicating that HRD plays an important role in GI in these patients. The prevalence of germline HRD is higher in AA tumors, suggesting that the somatic differences observed have genetic ancestry origins. Finally, we identify AA-specific copy number-based arm, focal and gene level recurrent features in lung cancer, including a higher frequency of PTEN deletion and KRAS amplification and a lower frequency of CDKN2A deletion. These results highlight the importance of including minority and under-represented populations in genomics research and may have therapeutic implications.

cancer biology↗

bioRxiv2019

Functional Genomic Complexity Defines Intratumor Heterogeneity and Tumor Aggressiveness in Liver Cancer

BackgroundChronic inflammation and chromosome aneuploidy are major traits of primary liver cancer (PLC), which represent the second most common cause of cancer related death worldwide. Increased cancer fitness and aggressiveness of PLC may be achieved by enhancing tumoral genomic complexity that alters tumor biology.\n\nMethodHere, we developed a scoring method, namely functional genomic complexity (FGC), to determine the degree of molecular heterogeneity among 580 liver tumors with diverse ethnicities and etiologies by assessing integrated genomic and transcriptomic data.\n\nResultsWe found that tumors with higher FGC scores are associated with chromosome instability and TP53 mutations, and a worse prognosis, while tumors with lower FGC scores have elevated infiltrating lymphocytes and a better prognosis. These results indicate that FGC scores may serve as a surrogate to define genomic heterogeneity of PLC linked to chromosomal instability and evasion of immune surveillance.\n\nConclusionOur findings demonstrate an ability to define genomic heterogeneity and corresponding tumor biology of liver cancer based only on bulk genomic and transcriptomic data. Our data also provide a rationale for applying this approach to survey liver tumor immunity and to stratify patients for immune-based therapy.\n\nSTATEMENT OF SIGNIFICANCEGenomic heterogeneity contributes to therapeutic failure and poor outcome in patients with liver cancer and poses a challenge in defining targeted therapy. Our findings demonstrate an ability to define genomic heterogeneity and corresponding tumor biology of liver cancer based only on bulk genomic and transcriptomic data. Our data also provide a rationale for applying this approach to survey liver tumor immunity and to stratify patients for immune-based therapy. \\body

genomics↗

bioRxiv2019

Cancer-type specific aneuploidies hard-wire chromosome-wide gene expression patterns of their tissue of origin

Most carcinomas have characteristic chromosomal aneuploidies specific to the tissue of tumor origin. The reason for this specificity is unknown. As aneuploidies directly affect gene expression, we hypothesized that cancer-type specific aneuploidies, which emerge at early stages of tumor evolution, confer adaptive advantages to the physiological requirements of the tissue of origin. To test this hypothesis, we compared chromosomal aneuploidies reported in the TCGA database to chromosome arm-wide gene expression levels of normal tissues from the GTEx database. We find that cancer-type specific chromosomal aneuploidies mirror differential gene expression levels specific to the respective normal tissues which cannot be explained by copy number alterations of resident cancer driver genes. We propose that cancer-type specific aneuploidies "hard-wire" chromosome arm-wide gene expression levels present in normal tissues, favoring clonal expansion and tumorigenesis. One sentence summaryThe clonal evolution of cancer is initiated by tissue-specific transcriptional requirements

cancer biology↗

bioRxiv2018

A systematic genome-wide mapping of the oncogenic risks associated with CRISPR-Cas9 editing

Recent studies have reported that CRISPR-Cas9 gene editing induces a p53-dependent DNA damage response in primary cells, which may select for cells with oncogenic p53 mutations11,12. It is unclear whether these CRISPR-induced changes are applicable to different cell types, and whether CRISPR gene editing may select for other oncogenic mutations. Addressing these questions, we analyzed genome-wide CRISPR and RNAi screens to systematically chart the mutation selection potential of CRISPR knockouts across the whole exome. Our analysis suggests that CRISPR gene editing can select for mutants of KRAS and VHL, at a level comparable to that reported for p53. These predictions were further validated in a genome-wide manner by analyzing independent CRISPR screens and patients tumor data. Finally, we performed a new set of pooled and arrayed CRISPR screens to evaluate the competition between CRISPR-edited isogenic p53 WT and mutant cell lines, which further validated our predictions. In summary, our study systematically charts and points to the potential selection of specific cancer driver mutations during CRISPR-Cas9 gene editing.

cancer biology↗

bioRxiv2018

Migration rather than proliferation transcriptomic signatures are strongly associated with breast cancer patient survival

The efficacy of prospective cancer treatments is routinely estimated by in vitro cell-line proliferation screens. However, it is unclear whether tumor aggressiveness and patient survival are influenced more by the proliferative or the migratory properties of cancer cells. To address this question, we experimentally measured proliferation and migration phenotypes across more than 40 breast cancer cell-lines. Based on the latter, we built and validated individual predictors of breast cancer proliferation and migration levels from the cells transcriptomics. We then apply these predictors to estimate the proliferation and migration levels of more than 1000 TCGA breast cancer tumors. Reassuringly, both estimates increase with tumors aggressiveness, as qualified by its stage, grade, and subtype. However, predicted tumor migration levels are significantly more strongly associated with patient survival than the proliferation levels. We confirm these finding by conducting siRNA knock-down experiments on the highly migratory MDA-MB-231 cell lines and deriving gene knock-down based proliferation and migration signatures. We show that cytoskeletal drugs might be more beneficial in patients with high predicted migration levels. Taken together, these results testify to the importance of migration levels in determining patient survival.

bioinformatics↗

bioRxiv2018

Criticality in Tumor Evolution and Clinical Outcome

How mutation and selection determine the fitness landscape of tumors and hence clinical outcome is an open fundamental question in cancer biology, crucial for the assessment of therapeutic strategies and resistance to treatment. Here we explore the mutation-selection phase-diagram of 6721 primary tumors representing 23 cancer types, by quantifying the overall somatic point mutation load (ML) and selection (dN/dS) in the entire proteome of each tumor. We show that ML strongly correlates with patient survival, revealing two opposing regimes around a critical point. In low ML cancers, high number of mutations indicates poor prognosis, whereas high ML cancers show the opposite trend, due to mutational meltdown. Although the majority of cancers evolve near neutrality, deviations are observed at extreme MLs. Cancers with the highest ML evolve under purifying selection, whereas those with the lowest ML show signatures of positive selection, demonstrating how selection affects cancer fitness. Moreover, different cancers occupy specific positions on the ML-dN/dS plane, revealing a diversity of evolutionary trajectories. These results support and expand the theory of tumor evolution and its non-linear effects on survival.\n\nSignificance StatementIt remains an open fundamental question how mutation and selection co-determine the course of cancer evolution. We construct a selection-mutation phase diagram, using tumor mutation load and selection strength as key variables, and assess their association with clinical outcome. We demonstrate the existence of a biphasic evolutionary regime, whereby beyond a critical ML, the fitness of tumors decreases with the number of mutations, while the proteome evolves near neutrality. Deviations from neutrality in extreme ML elucidate how positive and purifying selections maintain tumor fitness. These results empirically corroborate the existence of a critical state in cancer evolution predicted by theory, and have fundamental and likely clinical implications.

cancer biology↗

bioRxiv2018

Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy

Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Many of these events involve synthetic rescue (SR) interactions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (the rescuer). Here we perform a genome-wide prediction of SR rescuer genes by analyzing tumor transcriptomics and survival data of 10,000 TCGA cancer patients. Predicted SR interactions are validated in new experimental screens. We show that SR interactions can successfully predict cancer patients response and emerging resistance. Inhibiting predicted rescuer genes sensitizes resistant cancer cells to therapies synergistically, providing initial leads for developing combinatorial approaches to overcome resistance proactively. Finally, we show that the SR analysis of melanoma patients successfully identifies known mediators of resistance to immunotherapy and predicts novel rescuers.

cancer biology↗

bioRxiv2018

Beyond synthetic lethality: multiple gene interaction types play a key functional role in cancer

The phenotypic effect of perturbing a genes activity depends on the activity level of other genes, reflecting the notion that phenotypes are emergent properties of a network of functionally interacting genes. In the context of cancer, contemporary investigations have primarily focused on just one type of functional genetic interaction (GI) - synthetic lethality (SL). However, there may be additional types of GIs whose systematic identification would enrich the molecular and functional characterization of cancer. Here, we describe a novel data-driven approach called EnGIne, that applied to TCGA data identifies 71,946 GIs spanning 12 distinct types, only a small minority of which are SLs. The detected GIs explain cancer driver genes tissue-specificity and differences in patients response to drugs, and stratify breast cancer tumors into refined subtypes. These results expand the scope of cancer GIs and lay a conceptual and computational basis for future studies of additional types of GIs and their translational applications. The GI network is accessible online via a web portal [https://amagen.shinyapps.io/cancerapp/].

cancer biology↗