WeSME: uncovering mutual exclusivity of cancer drivers and beyond.

Motivation: Mutual exclusivity is a widely recognized property of many cancer drivers. Knowledge about these relationships can provide important insights into cancer drivers, cancer-driving pathways and cancer subtypes. It can also be used to predict new functional interactions between cancer driving genes and uncover novel cancer drivers. Currently, most of mutual exclusivity analyses are preformed focusing on a limited set of genes in part due to the computational cost required to rigorously compute P -values. Results: To reduce the computing cost and perform less restricted mutual exclusivity analysis, we developed an efficient method to estimate P -values while controlling the mutation rates of individual patients and genes similar to the permutation test. A comprehensive mutual exclusivity analysis allowed us to uncover mutually exclusive pairs, some of which may have relatively low mutation rates. These pairs often included likely cancer drivers that have been missed in previous analyses. More importantly, our results demonstrated that mutual exclusivity can also provide information that goes beyond the interactions between cancer drivers and can, for example, elucidate different mutagenic processes in different cancer groups. In particular, including frequently mutated, long genes such as TTN in our analysis allowed us to observe interesting patterns of APOBEC activity in breast cancer and identify a set of related driver genes that are highly predictive of patient survival. In addition, we utilized our mutual exclusivity analysis in support of a previously proposed model where APOBEC activity is the underlying process that causes TP53 mutations in a subset of breast cancer cases. Availability and Implementation: http://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/index.cgi#wesme. Contact: przytyck@ncbi.nlm.nih.gov. Supplementary information: Supplementary data are available at Bioinformatics online.

BeWith: A Between-Within method to discover relationships between cancer modules via integrated analysis of mutual exclusivity, co-occurrence and functional interactions.

The analysis of the mutational landscape of cancer, including mutual exclusivity and co-occurrence of mutations, has been instrumental in studying the disease. We hypothesized that exploring the interplay between co-occurrence, mutual exclusivity, and functional interactions between genes will further improve our understanding of the disease and help to uncover new relations between cancer driving genes and pathways. To this end, we designed a general framework, BeWith, for identifying modules with different combinations of mutation and interaction patterns. We focused on three different settings of the BeWith schema: (i) BeME-WithFun, in which the relations between modules are enriched with mutual exclusivity, while genes within each module are functionally related; (ii) BeME-WithCo, which combines mutual exclusivity between modules with co-occurrence within modules; and (iii) BeCo-WithMEFun, which ensures co-occurrence between modules, while the within module relations combine mutual exclusivity and functional interactions. We formulated the BeWith framework using Integer Linear Programming (ILP), enabling us to find optimally scoring sets of modules. Our results demonstrate the utility of BeWith in providing novel information about mutational patterns, driver genes, and pathways. In particular, BeME-WithFun helped identify functionally coherent modules that might be relevant for cancer progression. In addition to finding previously well-known drivers, the identified modules pointed to other novel findings such as the interaction between NCOR2 and NCOA3 in breast cancer. Additionally, an application of the BeME-WithCo setting revealed that gene groups differ with respect to their vulnerability to different mutagenic processes, and helped us to uncover pairs of genes with potentially synergistic effects, including a potential synergy between mutations in TP53 and the metastasis related DCC gene. Overall, BeWith not only helped us uncover relations between potential driver genes and pathways, but also provided additional insights on patterns of the mutational landscape, going beyond cancer driving mutations. Implementation is available at https://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/software/bewith.html.

Pan-Cancer Analysis of Patient Tumor Single-Cell Transcriptomes Identifies Promising Selective and Safe Chimeric Antigen Receptor Targets in Head and Neck Cancer.

Chimeric antigen receptor (CAR) T cell therapies have yielded transformative clinical successes for patients with blood tumors, but their full potential remains to be unleashed against solid tumors. One challenge is finding selective targets, which we define intuitively to be cell surface proteins that are expressed widely by cancer cells but minimally by healthy cells in the tumor microenvironment and other normal tissues. Analyzing patient tumor single-cell transcriptomics data, we first defined and quantified selectivity and safety scores of existing CAR targets for indications in which they are in clinical trials or approved. We then sought new candidate cell surface CAR targets that have better selectivity and safety scores than those currently being tested. Remarkably, in almost all cancer types, we could not find such better targets, testifying to the near optimality of the current target space. However, in human papillomavirus (HPV)-negative head and neck squamous cell carcinoma (HNSC), for which there is currently a dearth of existing CAR targets, we identified a total of twenty candidate novel CAR targets, five of which have both superior selectivity and safety scores. These newly identified cell surface targets lay a basis for future investigations that may lead to better CAR treatments in HNSC.

A comprehensive analysis of adverse events in the first 30 days of phase 1 pediatric CAR T-cell trials.

The tremendous success of chimeric antigen receptor (CAR) T cells in children and young adults (CAYAs) with relapsed/refractory B-cell acute lymphoblastic leukemia is tempered by toxicities such as cytokine release syndrome (CRS). Despite expansive information about CRS, profiling of specific end-organ toxicities secondary to CAR T-cell therapy in CAYAs is limited. This retrospective, single-center study sought to characterize end-organ specific adverse events (AEs) experienced by CAYAs during the first 30 days after CAR T-cell infusion. AEs graded using Common Terminology Criteria for Adverse Events were retrospectively analyzed for 134 patients enrolled in 1 of 3 phase 1 CAR T-cell trials (NCT01593696, NCT02315612, and NCT03448393), targeting CD19 and/or CD22. A total of 133 patients (99.3%) experienced at least 1 grade ≥3 (≥Gr3) AE across 17 organ systems, of which 75 (4.4%) were considered dose- or treatment-limiting toxicities. Excluding cytopenias, 109 patients (81.3%) experienced a median of 3 ≥Gr3 noncytopenia (NC) AEs. The incidence of ≥Gr3 NC AEs was associated with the development and severity of CRS as well as preinfusion disease burden (≥ 25% marrow blasts). Although those with complete remission trended toward experiencing more ≥Gr3 NC AEs than nonresponders (median, 4 vs 3), nonresponders experiencing CRS (n = 17; 37.8%) had the highest degree of NC AEs across all patients (median, 7 vs 4 in responders experiencing CRS). Greater understanding of these toxicities and the ability to predict which patients may experience more toxicities is critical as the array of CAR T-cell therapies expand. This retrospective study was registered at www.clinicaltrials.gov as NCT03827343.

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.

Correction: CAPN1 is a novel binding partner and regulator of the tumor suppressor NF1 in melanoma.

[This corrects the article DOI: 10.18632/oncotarget.25805.].

CAPN1 is a novel binding partner and regulator of the tumor suppressor NF1 in melanoma.

Neurofibromin 1 (NF1), a tumor suppressor that negatively regulates RAS through its GTPase activity, is highly mutated in various types of sporadic human cancers, including melanoma. However, the binding partners of NF1 and the pathways in which it is involved in melanoma have not been characterized in an in depth manner. Utilizing a mass spectrometry analysis of NF1 binding partners, we revealed Calpain1 (CAPN1), a calcium-dependent neutral cysteine protease, as a novel NF1 binding partner that regulates NF1 degradation in melanoma cells. ShRNA-mediated knockdown of CAPN1 or treatment with a CAPN1 inhibitor stabilizes NF1 protein levels, downregulates AKT signaling and melanoma cell growth. Combination treatment of Calpain inhibitor I with MEKi Trametinib in different melanoma cells is more effective in reducing melanoma cell growth compared to treatment with Trametinib alone, suggesting that this combination may have a therapeutic potential in melanoma. This novel mechanism for regulating NF1 in melanoma provides a molecular basis for targeting CAPN1 in order to stabilize NF1 levels and, in doing so, suppressing Ras activation; this mechanism can be exploited therapeutically in melanoma and other cancers.

Publisher Correction: Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma.

In the version of this article originally published, there was an error in the URL linked to by an accession code in the data availability section of the methods. The erroneous URL was: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE100351 . The correct URL is: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115821 . The error has been corrected in the HTML and PDF versions of this article.

Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma.

Immune checkpoint blockade (ICB) therapy provides remarkable clinical gains and has been very successful in treatment of melanoma. However, only a subset of patients with advanced tumors currently benefit from ICB therapies, which at times incur considerable side effects and costs. Constructing predictors of patient response has remained a serious challenge because of the complexity of the immune response and the shortage of large cohorts of ICB-treated patients that include both 'omics' and response data. Here we build immuno-predictive score (IMPRES), a predictor of ICB response in melanoma which encompasses 15 pairwise transcriptomics relations between immune checkpoint genes. It is based on two key conjectures: (i) immune mechanisms underlying spontaneous regression in neuroblastoma can predict melanoma response to ICB, and (ii) key immune interactions can be captured via specific pairwise relations of the expression of immune checkpoint genes. IMPRES is validated on nine published datasets1-6 and on a newly generated dataset with 31 patients treated with anti-PD-1 and 10 with anti-CTLA-4, spanning 297 samples in total. It achieves an overall accuracy of AUC = 0.83, outperforming existing predictors and capturing almost all true responders while misclassifying less than half of the nonresponders. Future studies are warranted to determine the value of the approach presented here in other cancer types.