A novel, integrated in vitro carcinogenicity test to identify genotoxic and non-genotoxic carcinogens using human lymphoblastoid cells

Human exposure to carcinogens occurs via a plethora of environmental sources, with 70–90% of cancers caused by extrinsic factors. Aberrant phenotypes induced by such carcinogenic agents may provide universal biomarkers for cancer causation. Both current in vitro genotoxicity tests and the animal-testing paradigm in human cancer risk assessment fail to accurately represent and predict whether a chemical causes human carcinogenesis. The study aimed to establish whether the integrated analysis of multiple cellular endpoints related to the Hallmarks of Cancer could advance in vitro carcinogenicity assessment. Human lymphoblastoid cells (TK6, MCL-5) were treated for either 4 or 23 h with 8 known in vivo carcinogens, with doses up to 50% Relative Population Doubling (maximum 66.6 mM). The adverse effects of carcinogens on wide-ranging aspects of cellular health were quantified using several approaches; these included chromosome damage, cell signalling, cell morphology, cell-cycle dynamics and bioenergetic perturbations. Cell morphology and gene expression alterations proved particularly sensitive for environmental carcinogen identification. Composite scores for the carcinogens’ adverse effects revealed that this approach could identify both DNA-reactive and non-DNA reactive carcinogens in vitro. The richer datasets generated proved that the holistic evaluation of integrated phenotypic alterations is valuable for effective in vitro risk assessment, while also supporting animal test replacement. Crucially, the study offers valuable insights into the mechanisms of human carcinogenesis resulting from exposure to chemicals that humans are likely to encounter in their environment. Such an understanding of cancer induction via environmental agents is essential for cancer prevention

Spartalizumab or placebo in combination with dabrafenib and trametinib in patients with BRAF\textit{BRAF}V600-mutant melanoma: exploratory biomarker analyses from a randomized phase 3 trial (COMBI-i)

BackgroundThe randomized phase 3 COMBI-i trial did not meet its primary endpoint of improved progression-free survival (PFS) with spartalizumab plus dabrafenib and trametinib (sparta-DabTram) vs placebo plus dabrafenib and trametinib (placebo-DabTram) in the overall population of patients with unresectable/metastatic $\textit{BRAF}$V600-mutant melanoma. This prespecified exploratory biomarker analysis was performed to identify subgroups that may derive greater treatment benefit from sparta-DabTram.MethodsIn COMBI-i (ClinicalTrials.gov, NCT02967692), 532 patients received spartalizumab 400 mg intravenously every 4 weeks plus dabrafenib 150 mg orally two times daily and trametinib 2 mg orally one time daily or placebo-DabTram. Baseline/on-treatment pharmacodynamic markers were assessed via flow cytometry-based immunophenotyping and plasma cytokine profiling. Baseline programmed death ligand 1 (PD-L1) status and T-cell phenotype were assessed via immunohistochemistry; $\textit{BRAF}$V600 mutation type, tumor mutational burden (TMB), and circulating tumor DNA (ctDNA) via DNA sequencing; gene expression signatures via RNA sequencing; and CD4$^{+}$/CD8$^{+}$ T-cell ratio via immunophenotyping.ResultsExtensive biomarker analyses were possible in approximately 64% to 90% of the intention-to-treat population, depending on sample availability and assay. Subgroups based on PD-L1 status/TMB or T-cell inflammation did not show significant differences in PFS benefit with sparta-DabTram vs placebo-DabTram, although T-cell inflammation was prognostic across treatment arms. Subgroups defined by $\textit{BRAF}$V600K mutation (HR 0.45 (95% CI 0.21 to 0.99)), detectable ctDNA shedding (HR 0.75 (95% CI 0.58 to 0.96)), or CD4$^{+}$/CD8$^{+}$ ratio above median (HR 0.58 (95% CI 0.40 to 0.84)) derived greater PFS benefit with sparta-DabTram vs placebo-DabTram. In a multivariate analysis, ctDNA emerged as strongly prognostic (p=0.007), while its predictive trend did not reach significance; in contrast, CD4$^{+}$/CD8$^{+}$ ratio was strongly predictive (interaction p=0.0131).ConclusionsThese results support the feasibility of large-scale comprehensive biomarker analyses in the context of a global phase 3 study. T-cell inflammation was prognostic but not predictive of sparta-DabTram benefit, as patients with high T-cell inflammation already benefit from targeted therapy alone. Baseline ctDNA shedding also emerged as a strong independent prognostic variable, with predictive trends consistent with established measures of disease burden such as lactate dehydrogenase levels. CD4$^{+}$/CD8$^{+}$ T-cell ratio was significantly predictive of PFS benefit with sparta-DabTram but requires further validation as a biomarker in melanoma. Taken together with previous observations, further study of checkpoint inhibitor plus targeted therapy combination in patients with higher disease burden may be warranted

GeneSigDB: a manually curated database and resource for analysis of gene expression signatures

GeneSigDB (http://www.genesigdb.org or http://compbio.dfci.harvard.edu/genesigdb/) is a database of gene signatures that have been extracted and manually curated from the published literature. It provides a standardized resource of published prognostic, diagnostic and other gene signatures of cancer and related disease to the community so they can compare the predictive power of gene signatures or use these in gene set enrichment analysis. Since GeneSigDB release 1.0, we have expanded from 575 to 3515 gene signatures, which were collected and transcribed from 1604 published articles largely focused on gene expression in cancer, stem cells, immune cells, development and lung disease. We have made substantial upgrades to the GeneSigDB website to improve accessibility and usability, including adding a tag cloud browse function, facetted navigation and a ‘basket’ feature to store genes or gene signatures of interest. Users can analyze GeneSigDB gene signatures, or upload their own gene list, to identify gene signatures with significant gene overlap and results can be viewed on a dynamic editable heatmap that can be downloaded as a publication quality image. All data in GeneSigDB can be downloaded in numerous formats including .gmt file format for gene set enrichment analysis or as a R/Bioconductor data file. GeneSigDB is available from http://www.genesigdb.org

A Phase 2, Multicenter, Open-Label Study of Anti-Lag-3 Ieramilimab in Combination With Anti-Pd-1 Spartalizumab in Patients With Advanced Solid Malignancies

Ieramilimab, a humanized anti-LAG-3 monoclonal antibody, was well tolerated in combination with the anti-PD-1 antibody spartalizumab in a phase 1 study. This phase 2 study aimed to further investigate the efficacy and safety of combination treatment in patients with selected advanced (locally advanced or metastatic) solid malignancies. Eligible patients with non-small cell lung cancer (NSCLC), melanoma, renal cell carcinoma (RCC), mesothelioma, and triple-negative breast cancer (TNBC) were grouped depending on prior anti-PD-1/L1 therapy (anti-PD-1/L1 naive or anti-PD-1/L1 pretreated). Patients received ieramilimab (400 mg) followed by spartalizumab (300 mg) every 3 weeks. The primary endpoint was objective response rate (ORR), along with safety, pharmacokinetics, and biomarker assessments. Of 235 patients, 142 were naive to anti-PD-1/L1 and 93 were pretreated with anti-PD-1/L1 antibodies. Durable responses (\u3e24 months) were seen across all indications for patients naive to anti-PD-1/L1 and in melanoma and RCC patients pretreated with anti-PD1/L1. The most frequent study drug-related AEs were pruritus (15.5%), fatigue (10.6%), and rash (10.6%) in patients naive to anti-PD-1/L1 and fatigue (18.3%), rash (14.0%), and nausea (10.8%) in anti-PD-1/L1 pretreated patients. Biomarker assessment indicated higher expression of T-cell-inflamed gene signature at baseline among responding patients. Response to treatment was durable (\u3e24 months) in some patients across all enrolled indications, and safety findings were in accordance with previous and current studies exploring LAG-3/PD-1 blockade

Improving Specificity for Ovarian Cancer Screening Using a Novel Extracellular Vesicle–Based Blood Test: Performance in a Training and Verification Cohort

The low incidence of ovarian cancer (OC) dictates that any screening strategy needs to be both highly sensitive and highly specific. This study explored the utility of detecting multiple colocalized proteins or glycosylation epitopes on single tumor-associated extracellular vesicles from blood. The novel Mercy Halo Ovarian Cancer Test (OC Test) uses immunoaffinity capture of tumor-associated extracellular vesicles, followed by proximity-ligation real-time quantitative PCR to detect combinations of up to three biomarkers to maximize specificity and measures multiple combinations to maximize sensitivity. A high-grade serous carcinoma (HGSC) case-control training set of EDTA plasma samples from 397 women was used to lock down the test design, the data interpretation algorithm, and the cutoff between cancer and noncancer. Performance was verified and compared with cancer antigen 125 in an independent blinded case-control set of serum samples from 390 women (132 controls, 66 HGSC, 83 non-HGSC OC, and 109 benign). In the verification study, the OC Test showed a specificity of 97.0% (128/132; 95% CI, 92.4%–99.6%), a HGSC sensitivity of 97.0% (64/66; 95% CI, 87.8%–99.2%), and an area under the curve of 0.97 (95% CI, 0.93–0.99) and detected 73.5% (61/83; 95% CI, 62.7%–82.6%) of the non-HGSC OC cases. This test exhibited fewer false positives in subjects with benign ovarian tumors, nonovarian cancers, and inflammatory conditions when compared with cancer antigen 125. The combined sensitivity and specificity of this new test suggests it may have potential in OC screening

Abstract 5582: A targeted molecular classifier of MYC activity and BCL-2 expression in aggressive B-cell lymphomas, designed for clinical practice

Abstract Background: High MYC and BCL2 co-expression as detected by immunohistochemical staining (IHC) of fixed biopsy samples identifies a sub-group of diffuse large B-cell lymphomas (DLBCLs) with inferior outcome among patients treated with standard chemotherapy, and the differential expression of MYC and BCL2 among DLBCL subtypes provides a biological basis for the prognostic value of the ‘Cell of Origin’ classification system. Thus quantifying MYC activity and BCL2 expression in formalin-fixed paraffin embedded (FFPE) biopsy specimens could help to identify patients who might benefit from more aggressive chemotherapy. Unfortunately, IHC is not a reproducibly quantitative test due to a number of pre- and post- analytical factors. In contrast, gene expression profiling (GEP) allows for the possibility of better standardization and quantitation of biomarkers in biopsy samples, but traditional GEP has required RNA isolated from frozen tissue. Design: We sought to develop a molecular classifier of MYC activity and BCL2 expression that is applicable to FFPE biopsy samples using the ‘NanoString nCounter’ platform in a 2-stage approach: 1. Discriminate between Burkitt Lymphoma (BL) and DLBCL using a selection of genes specific for each diagnostic category. 2. Quantify MYC and BCL2 expression using statistically justified ‘MYC target’ genes as well as other genes selected with an unbiased approach, those with significant differential expression between MYC IHC High and IHC Low cases. 3. Normalize data to selected housekeeping genes and assess the tissue microenvironment. The initial gene set was developed in silico based on the whole genome gene expression of 56 carefully selected de novo DLBCL that had companion MYC immunostaining. Results: An initial gene set comprising 200 genes was tested on a discovery cohort of FFPE biopsy samples of 42 aggressive B-cell lymphomas (12 Burkitt Lymphoma [BL] and 30 DLBCL). Differential analysis and prediction models were used to construct a classifier comprising 87 genes that resulted in the successful classification of these tumors. We next validated the approach using an independent cohort of FFPE tissue biopsies (12 BL, 7 genetic “double hit” lymphomas, and 38 DLBCL lacking MYC and BCL2 rearrangements). Targeted profiling and molecular classification correctly diagnosed 100% of tumors as either BL or DLBCL. For DLBCLs, the molecular classifier correctly predicted the MYC expression in 87% of cases when compared to a well-validated IHC assay. We conclude that a targeted gene expression profile, using the Nanostring nCounter platform, coupled with a validated molecular classifier can effectively distinguish DLBCL from BL and quantify MYC activity and BCL2 expression in DLBCL. This protocol will be useful for the routine diagnostic and prognostic stratification of aggressive B-cell lymphomas in clinical practice. Citation Format: Christopher D. Carey, Daniel Gusenleitner, Bjoern Chapuy, Heather Sun, Azra Ligon, Alexandra E. Kovach, Long P. Le, Aliyah R. Sohani, Margaret Shipp, Stefano Monti, Scott J. Rodig. A targeted molecular classifier of MYC activity and BCL-2 expression in aggressive B-cell lymphomas, designed for clinical practice. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5582. doi:10.1158/1538-7445.AM2014-5582</jats:p

multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles

Clustering is carried out to identify patterns in transcriptomics profiles to determine clinically relevant subgroups of patients. Feature (gene) selection is a critical and an integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing an appropriate methodology is difficult. In addition, extensive feature selection methods have not been supported by the available packages. Hence, we developed an integrative R-package called multiClust that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. Using multiClust, we identified the best performing clustering methodology in the context of clinical outcome. Our observations demonstrate that simple methods such as variance-based ranking perform well on the majority of data sets, provided that the appropriate number of genes is selected. However, different gene ranking and selection methods remain relevant as no methodology works for all studies