Phase 1 Dose-Escalation Study of Pegylated Arginine Deiminase, Cisplatin, and Pemetrexed in Patients With Argininosuccinate Synthetase 1-Deficient Thoracic Cancers

$\textbf{Purpose}$ Pegylated arginine deiminase (ADI-PEG 20) depletes essential amino acid levels in argininosuccinate synthetase 1 (ASS1) –negative tumors by converting arginine to citrulline and ammonia. The main aim of this study was to determine the recommended dose, safety, and tolerability of ADI-PEG 20, cisplatin, and pemetrexed in patients with ASS1-deficient malignant pleural mesothelioma (MPM) or non–small-cell lung cancer (NSCLC). $\textbf{Patients and Methods}$ Using a 3 + 3 + 3 dose-escalation study, nine chemotherapy-naïve patients (five MPM, four NSCLC) received weekly ADI-PEG 20 doses of 18 mg/m$^{2}$, 27 mg/m$^{2}$, or 36 mg/m$^{2}$, together with pemetrexed 500 mg/m$^{2}$ and cisplatin 75 mg/m$^{2}$ which were given every three weeks (maximum of six cycles). Patients achieving stable disease or better could continue ADI-PEG 20 monotherapy until disease progression or withdrawal. Adverse events were assessed by Common Terminology Criteria for Adverse Events version 4.03, and pharmacodynamics and immunogenicity were also evaluated. Tumor response was assessed by Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 for NSCLC and by modified RECIST criteria for MPM. $\textbf{Results}$ No dose-limiting toxicities were reported; nine of 38 reported adverse events (all grade 1 or 2) were related to ADI-PEG 20. Circulating arginine concentrations declined rapidly, and citrulline levels increased; both changes persisted at 18 weeks. Partial responses were observed in seven of nine patients (78%), including three with either sarcomatoid or biphasic MPM. $\textbf{Conclusion}$ Target engagement with depletion of arginine was maintained throughout treatment with no dose-limiting toxicities. In this biomarker-selected group of patients with ASS1-deficient cancers, clinical activity was observed in patients with poor-prognosis tumors. Therefore, we recommend a dose for future studies of weekly ADI-PEG 20 36 mg/m$^{2}$ plus three-weekly cisplatin 75 mg/m$^{2}$ and pemetrexed 500 mg/m$^{2}$.We thank the Department of Health, England, for financial support via the National Institute for Health Research (NIHR) Biomedical Research Centre, and Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London and the King’s College Hospital NHS Foundation Trust (and NIHR Clinical Research Facility). Barts, Cambridge, and King’s College London are Experimental Cancer Medicine Centers supported by Cancer Research UK and the Department of Health, England. Patients were treated using the facilities provided by the Welcome Trust, Addenbrookes Centre for Clinical Investigations

The Network of Cancer Genes (NCG):a comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens

Abstract The Network of Cancer Genes (NCG) is a manually curated repository of 2372 genes whose somatic modifications have known or predicted cancer driver roles. These genes were collected from 275 publications, including two sources of known cancer genes and 273 cancer sequencing screens of more than 100 cancer types from 34,905 cancer donors and multiple primary sites. This represents a more than 1.5-fold content increase compared to the previous version. NCG also annotates properties of cancer genes, such as duplicability, evolutionary origin, RNA and protein expression, miRNA and protein interactions, and protein function and essentiality. NCG is accessible at http://ncg.kcl.ac.uk/

USP7 inactivation suppresses APC-mutant intestinal hyperproliferation and tumor development

Adenomatous polyposis coli (APC) mutation is the hallmark of colorectal cancer (CRC), resulting in constitutive WNT activation. Despite decades of research, targeting WNT signaling in cancer remains challenging due to its on-target toxicity. We have previously shown that the deubiquitinating enzyme USP7 is a tumor-specific WNT activator in APC-truncated cells by deubiquitinating and stabilizing β-catenin, but its role in gut tumorigenesis is unknown. Here, we show in vivo that deletion of Usp7 in Apc-truncated mice inhibits crypt hyperproliferation and intestinal tumor development. Loss of Usp7 prolongs the survival of the sporadic intestinal tumor model. Genetic deletion, but not pharmacological inhibition, of Usp7 in Apc+/- intestine induces colitis and enteritis. USP7 inhibitor treatment suppresses growth of patient-derived cancer organoids carrying APC truncations in vitro and in xenografts. Our findings provide direct evidence that USP7 inhibition may offer a safe and efficacious tumor-specific therapy for both sporadic and germline APC-mutated CRC

Graph convolutional networks improve the prediction of cancer driver genes.

Despite the vast increase of high-throughput molecular data, the prediction of important disease genes and the underlying molecular mechanisms of multi-factorial diseases remains a challenging task. In this work we use a powerful deep learning classifier, based on Graph Convolutional Networks (GCNs) to tackle the task of cancer gene prediction across different cancer types. Compared to previous cancer gene prediction methods, our GCN-based model is able to combine several heterogeneous omics data types with a graph representation of the data into a single predictive model and learn abstract features from both data types. The graph formalizes relations between genes which work together in regulatory cellular pathways. GCNs outperform other state-of-the-art methods, such as network propagation algorithms and graph attention networks in the prediction of cancer genes. Furthermore, they demonstrate that including the interaction network topology greatly helps to characterize novel cancer genes, as well as entire disease modules. In this work, we go one step forward and enable the interpretation of our deep learning model to answer the following question: what is the molecular cause underlying the prediction of a disease genes and are there differences across samples?