Effective drug combinations in breast, colon and pancreatic cancer cells.

Combinations of anti-cancer drugs can overcome resistance and provide new treatments1,2. The number of possible drug combinations vastly exceeds what could be tested clinically. Efforts to systematically identify active combinations and the tissues and molecular contexts in which they are most effective could accelerate the development of combination treatments. Here we evaluate the potency and efficacy of 2,025 clinically relevant two-drug combinations, generating a dataset encompassing 125 molecularly characterized breast, colorectal and pancreatic cancer cell lines. We show that synergy between drugs is rare and highly context-dependent, and that combinations of targeted agents are most likely to be synergistic. We incorporate multi-omic molecular features to identify combination biomarkers and specify synergistic drug combinations and their active contexts, including in basal-like breast cancer, and microsatellite-stable or KRAS-mutant colon cancer. Our results show that irinotecan and CHEK1 inhibition have synergistic effects in microsatellite-stable or KRAS-TP53 double-mutant colon cancer cells, leading to apoptosis and suppression of tumour xenograft growth. This study identifies clinically relevant effective drug combinations in distinct molecular subpopulations and is a resource to guide rational efforts to develop combinatorial drug treatments.

Project Score database: a resource for investigating cancer cell dependencies and prioritizing therapeutic targets.

CRISPR genetic screens in cancer cell models are a powerful tool to elucidate oncogenic mechanisms and to identify promising therapeutic targets. The Project Score database (https://score.depmap.sanger.ac.uk/) uses genome-wide CRISPR-Cas9 dropout screening data in hundreds of highly annotated cancer cell models to identify genes required for cell fitness and prioritize novel oncology targets. The Project Score database currently allows users to investigate the fitness effect of 18 009 genes tested across 323 cancer cell models. Through interactive interfaces, users can investigate data by selecting a specific gene, cancer cell model or tissue type, as well as browsing all gene fitness scores. Additionally, users can identify and rank candidate drug targets based on an established oncology target prioritization pipeline, incorporating genetic biomarkers and clinical datasets for each target, and including suitability for drug development based on pharmaceutical tractability. Data are freely available and downloadable. To enhance analyses, links to other key resources including Open Targets, COSMIC, the Cell Model Passports, UniProt and the Genomics of Drug Sensitivity in Cancer are provided. The Project Score database is a valuable new tool for investigating genetic dependencies in cancer cells and the identification of candidate oncology targets.

The Emerging Role of Non-traditional Ubiquitination in Oncogenic Pathways.

The addition of ubiquitin to a target protein has long been implicated in the process of degradation and is the primary mediator of protein turnover in the cell. Recently, however, many non-proteolytic functions of ubiquitination have emerged as key regulators of cellular homeostasis. In this review, we will describe the various non-traditional functions of ubiquitination, with particular focus on how they can be used as signaling entities in cancer formation and progression. Elaboration of this topic can lead to a better understanding of oncogenic mechanisms, as well as the discovery of novel druggable proteins within the ubiquitin pathway.

A Functional Genomic Screen Identifies the Deubiquitinase USP11 as a Novel Transcriptional Regulator of ERα in Breast Cancer.

Approximately 70% of breast cancers express estrogen receptor α (ERα) and depend on this key transcriptional regulator for proliferation and differentiation. While patients with this disease can be treated with targeted antiendocrine agents, drug resistance remains a significant issue, with almost half of patients ultimately relapsing. Elucidating the mechanisms that control ERα function may further our understanding of breast carcinogenesis and reveal new therapeutic opportunities. Here, we investigated the role of deubiquitinases (DUB) in regulating ERα in breast cancer. An RNAi loss-of-function screen in breast cancer cells targeting all DUBs identified USP11 as a regulator of ERα transcriptional activity, which was further validated by assessment of direct transcriptional targets of ERα. USP11 expression was induced by estradiol, an effect that was blocked by tamoxifen and not observed in ERα-negative cells. Mass spectrometry revealed a significant change to the proteome and ubiquitinome in USP11-knockdown (KD) cells in the presence of estradiol. RNA sequencing in LCC1 USP11-KD cells revealed significant suppression of cell-cycle-associated and ERα target genes, phenotypes that were not observed in LCC9 USP11-KD, antiendocrine-resistant cells. In a breast cancer patient cohort coupled with in silico analysis of publicly available cohorts, high expression of USP11 was significantly associated with poor survival in ERα-positive (ERα+) patients. Overall, this study highlights a novel role for USP11 in the regulation of ERα activity, where USP11 may represent a prognostic marker in ERα+ breast cancer. SIGNIFICANCE: A newly identified role for USP11 in ERα transcriptional activity represents a novel mechanism of ERα regulation and a pathway to be exploited for the management of ER-positive breast cancer.