Showing papers by "Alejandro A. Schäffer published in 2023"
TL;DR: Madan et al. as discussed by the authors performed a genome wide search across many different solid tumor types to identify new and candidate CAR-T targets with better selectivity and safety scores than extant ones.
Abstract:
Chimeric antigen receptor T (CAR-T) cell therapies have revolutionized cancer treatment. While CAR-T has yielded tremendous clinical success for patients with liquid tumors, its potential remains to be unleashed against solid tumors. One key challenge is identifying optimal targets for these therapies: cell surface proteins that are expressed highly and uniformly by a tumor’s constituent malignant cells, and minimally so by healthy tissues. Employing a systematic, data-driven analysis, we first charted the landscape of existing CAR-T targets in the clinic, identifying the leading targets in each indication based on tumor selectivity and safety metrics. Next, from patient tumor single cell transcriptomics data, we performed a genome wide search across many different solid tumor types to identify new and candidate CAR-T targets with better selectivity and safety scores than extant ones. Remarkably, in almost all indications, we could not find such better targets, testifying to the near optimality of the current target space, at least in accordance with our measures. However, one striking exception is HPV-negative head and neck squamous cell carcinoma (HNSC), for which there is currently a dearth of existing CAR-T targets in clinics. Specifically, our investigation has discovered 20 novel CAR-T targets for treating HNSC and one for treating glioblastoma more precisely and safely.
Citation Format: Sanna Madan, Sanju Sinha, Alejandro A. Schäffer, Eytan Ruppin. Identifying novel targets for CAR-T therapies from single cell RNA-sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB062.
TL;DR: In this paper , the authors measured genome-wide DNA methylation in 22 normal oral tissues, 22 leukoplakia, and 74 GBC-OSCC tissue samples, and identified potential biomarkers from integrative analysis in gingivobuccal complex cancers and validated them in an independent cohort.
Abstract: Gingivobuccal complex oral squamous cell carcinoma (GBC-OSCC) is an aggressive malignancy with high mortality often preceded by premalignant lesions, including leukoplakia. Previous studies have reported genomic drivers in OSCC, but much remains to be elucidated about DNA methylation patterns across different stages of oral carcinogenesis.There is a serious lack of biomarkers and clinical application of biomarkers for early detection and prognosis of gingivobuccal complex cancers. Hence, in search of novel biomarkers, we measured genome-wide DNA methylation in 22 normal oral tissues, 22 leukoplakia, and 74 GBC-OSCC tissue samples. Both leukoplakia and GBC-OSCC had distinct methylation profiles as compared to normal oral tissue samples. Aberrant DNA methylation increases during the different stages of oral carcinogenesis, from premalignant lesions to carcinoma. We identified 846 and 5111 differentially methylated promoters in leukoplakia and GBC-OSCC, respectively, with a sizable fraction shared between the two sets. Further, we identified potential biomarkers from integrative analysis in gingivobuccal complex cancers and validated them in an independent cohort. Integration of genome, epigenome, and transcriptome data revealed candidate genes with gene expression synergistically regulated by copy number and DNA methylation changes. Regularised Cox regression identified 32 genes associated with patient survival. In an independent set of samples, we validated eight genes (FAT1, GLDC, HOXB13, CST7, CYB5A, MLLT11, GHR, LY75) from the integrative analysis and 30 genes from previously published reports. Bisulfite pyrosequencing validated GLDC (P = 0.036), HOXB13 (P < 0.0001) promoter hypermethylation, and FAT1 (P < 0.0001) hypomethylation in GBC-OSCC compared to normal controls.Our findings identified methylation signatures associated with leukoplakia and gingivobuccal complex cancers. The integrative analysis in GBC-OSCC identified putative biomarkers that enhance existing knowledge of oral carcinogenesis and may potentially help in risk stratification and prognosis of GBC-OSCC.
TL;DR: Dinstag et al. as discussed by the authors presented ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient's response to a variety of therapies in multiple cancer types, importantly, without training on previous treatment response data.
Abstract:
Background: Precision 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.
Methods: We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient’s response to a variety of therapies in multiple cancer types, importantly, without training on previous treatment response data. Consequently, in addition to its ability to predict patients' response to approved and well-studied therapies, ENLIGHT can predict the response to new treatments in early development, even before clinical data has accumulated. Accordingly, we study ENLIGHT in two translationally relevant scenarios: Personalized Oncology (PO), aimed at prioritizing approved treatments to a given patient, and Clinical Trial Design (CTD), selecting the subset of most likely responders in a patient cohort.
Results: Evaluating ENLIGHT’s performance on 21 blinded clinical trial datasets spanning 11 indications and 15 different drugs in the PO setting, we show that it can effectively predict a patient’s treatment response across multiple therapies and cancer types, with an overall odds ratio of 2.59 (p=3.41e-8), and a 36% increase in response rate over the baseline (p=3.30e-13). Its prediction accuracy is better than other state-of-the-art transcriptomics-based signatures. Unlike most signatures that are prognostic or provide insights for only very few, specific treatments, ENLIGHT provides matching scores to a broad range of treatments. Quite strikingly, its performance is comparable to that of supervised predictors developed for specific indications and drugs. In combination with the IFN-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, our results show that by excluding non-responders ENLIGHT can enhance clinical trial success for immunotherapies and other monoclonal antibodies, achieving > 90% of the response rate attainable under an optimal exclusion strategy.
Conclusion: ENLIGHT is a powerful transcriptomics-based precision oncology pipeline developed by Pangea Biomed that broadly predicts response to both extant and novel targeted and immune therapies, going beyond context-specific biomarkers.
Citation Format: Gal Dinstag, Eldad D. Shulman, Efrat Elis, Doreen S. Ben-Zvi, Omer Tirosh, Eden Maimon, Isaac Meilijson, Emmanuel Elalouf, Boris Temkin, Philipp Vitkovsky, Eyal Schiff, Danh-Tai Hoang, Sanju Sinha, Nishanth Ulhas Nair, Joo Sang-Lee, Alejandro A. Schäffer, Ze'ev Ronai, Dejan Juric, Andrea B. Apolo, William L. Dahut, Stanley Lipkowitz, Raanan Berger, Razelle Kurzrock, Antonios Papanicolau-Sengos, Fatima Karzai, Mark R. Gilbert, Kenneth Aldape, Padma S. Rajagopal, Tuvik Beker, Eytan Ruppin, Ranit Aharonov. Prediction of patient response to targeted and immunotherapies from the tumor transcriptome in a wide set of indications and clinical trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 957.
TL;DR: In this paper , the authors comprehensively studied the ability of two common copy-number alteration (CNA) scores-the tumor aneuploidy score (AS) and the fraction of genome single nucleotide polymorphism encompassed by copynumber alterations (FGA)-to predict survival following immunotherapy in both pan-cancer and individual cancer types.
Abstract: Identifying patients that are likely to respond to cancer immunotherapy is an important, yet highly challenging clinical need. Using 3139 patients across 17 different cancer types, we comprehensively studied the ability of two common copy-number alteration (CNA) scores-the tumor aneuploidy score (AS) and the fraction of genome single nucleotide polymorphism encompassed by copy-number alterations (FGA)-to predict survival following immunotherapy in both pan-cancer and individual cancer types. First, we show that choice of cutoff during CNA calling significantly influences the predictive power of AS and FGA for patient survival following immunotherapy. Remarkably, by using proper cutoff during CNA calling, AS and FGA can predict pan-cancer survival following immunotherapy for both high-TMB and low-TMB patients. However, at the individual cancer level, our data suggest that the use of AS and FGA for predicting immunotherapy response is currently limited to only a few cancer types. Therefore, larger sample sizes are needed to evaluate the clinical utility of these measures for patient stratification in other cancer types. Finally, we propose a simple, non-parameterized, elbow-point-based method to help determine the cutoff used for calling CNAs.
TL;DR: The viRNAtrap pipeline as discussed by the authors uses a deep learning model trained to discriminate viral RNAseq reads to explore viral expression in cancers and apply it to 14 cancer types from The Cancer Genome Atlas (TCGA).
Abstract: About 15% of human cancer cases are attributed to viral infections. To date, virus expression in tumor tissues has been mostly studied by aligning tumor RNA sequencing reads to databases of known viruses. To allow identification of divergent viruses and rapid characterization of the tumor virome, we develop viRNAtrap, an alignment-free pipeline to identify viral reads and assemble viral contigs. We utilize viRNAtrap, which is based on a deep learning model trained to discriminate viral RNAseq reads, to explore viral expression in cancers and apply it to 14 cancer types from The Cancer Genome Atlas (TCGA). Using viRNAtrap, we uncover expression of unexpected and divergent viruses that have not previously been implicated in cancer and disclose human endogenous viruses whose expression is associated with poor overall survival. The viRNAtrap pipeline provides a way forward to study viral infections associated with different clinical conditions.
TL;DR: In this paper , the long-term health risks associated with Glucose-6-phosphate dehydrogenase (G6PD) deficiency had not been studied in a large population.
Abstract: BACKGROUND Glucose-6-phosphate dehydrogenase (G6PD) deficiency is an X-linked recessive enzymatic disorder, particularly prevalent in Africa, Asia and the Middle East. In the US, about 14% of black men are affected. Individuals with G6PD deficiency are often asymptomatic but may develop hemolysis following an infection or upon consumption of specific medications. Despite some evidence that G6PD deficiency affects the immune system, the long-term health risks associated with G6PD deficiency had not been studied in a large population. METHODS In this retrospective cohort study, health records from G6PD deficient individuals were compared to matched controls in a national healthcare provider in Israel (Leumit Health Services). Rates of infectious diseases, allergic conditions and autoimmune disorders were compared between groups. RESULTS The cohort included 7,473 G6PD deficient subjects (68.7% men) matched with 29,892 control subjects (4:1 ratio) of the same age, gender, socioeconomic status and ethnic group, followed during 14.3 years (standard deviation 6.2). Significantly increased rates for autoimmune disorders, infectious diseases and allergic conditions were observed throughout this period. Notable increases were observed for rheumatoid arthritis (OR 2.41, p<0.001), systemic lupus erythematosus (OR 4.56, p<0.001), scleroderma (OR 6.87, p<0.001), pernicious anemia (OR=18.70, P<0.001), fibromyalgia (OR 1.98, p<0.001), Graves' disease (OR 1.46, P=0.001), and Hashimoto's thyroiditis (OR 1.26, P=0.001). These findings were corroborated with elevated rates of positive autoimmune serology and higher rates of treatment with medications commonly used to treat autoimmune conditions in the G6PD deficient group. CONCLUSION G6PD deficient individuals suffer from higher rates of autoimmune disorders, infectious diseases, and allergic conditions.
TL;DR: In this article , the authors re-analyzed these data in a uniform, coherent manner aiming to resolve and extend past findings and shed light on the observation that females have higher AD incidence than males.