Construction and optimization of gene expression signatures for prediction of survival in two-arm clinical trials.

BACKGROUND: Gene expression signatures for the prediction of differential survival of patients undergoing anti-cancer therapies are of great interest because they can be used to prospectively stratify patients entering new clinical trials, or to determine optimal treatment for patients in more routine clinical settings. Unlike prognostic signatures however, predictive signatures require training set data from clinical studies with at least two treatment arms. As two-arm studies with gene expression profiling have been rarer than similar one-arm studies, the methodology for constructing and optimizing predictive signatures has been less prominently explored than for prognostic signatures. RESULTS: Focusing on two "use cases" of two-arm clinical trials, one for metastatic colorectal cancer (CRC) patients treated with the anti-angiogenic molecule aflibercept, and the other for triple negative breast cancer (TNBC) patients treated with the small molecule iniparib, we present derivation steps and quantitative and graphical tools for the construction and optimization of signatures for the prediction of progression-free survival based on cross-validated multivariate Cox models. This general methodology is organized around two more specific approaches which we have called subtype correlation (subC) and mechanism-of-action (MOA) modeling, each of which leverage a priori knowledge of molecular subtypes of tumors or drug MOA for a given indication. The tools and concepts presented here include the so-called differential log-hazard ratio, the survival scatter plot, the hazard ratio receiver operating characteristic, the area between curves and the patient selection matrix. In the CRC use case for instance, the resulting signature stratifies the patient population into "sensitive" and "relatively-resistant" groups achieving a more than two-fold difference in the aflibercept-to-control hazard ratios across signature-defined patient groups. Through cross-validation and resampling the probability of generalization of the signature to similar CRC data sets is predicted to be high. CONCLUSIONS: The tools presented here should be of general use for building and using predictive multivariate signatures in oncology and in other therapeutic areas.

An integrative approach of digital image analysis and transcriptome profiling to explore potential predictive biomarkers for TGFß blockade therapy.

Increasing evidence suggests that the presence and spatial localization and distribution pattern of tumor infiltrating lymphocytes (TILs) is associate with response to immunotherapies. Recent studies have identified TGFß activity and signaling as a determinant of T cell exclusion in the tumor microenvironment and poor response to PD-1/PD-L1 blockade. Here we coupled the artificial intelligence (AI)-powered digital image analysis and gene expression profiling as an integrative approach to quantify distribution of TILs and characterize the associated TGFß pathway activity. Analysis of T cell spatial distribution in the solid tumor biopsies revealed substantial differences in the distribution patterns. The digital image analysis approach achieves 74% concordance with the pathologist assessment for tumor-immune phenotypes. The transcriptomic profiling suggests that the TIL score was negatively correlated with TGFß pathway activation, together with elevated TGFß signaling activity observed in excluded and desert tumor phenotypes. The present results demonstrate that the automated digital pathology algorithm for quantitative analysis of CD8 immunohistochemistry image can successfully assign the tumor into one of three infiltration phenotypes: immune desert, immune excluded or immune inflamed. The association between "cold" tumor-immune phenotypes and TGFß signature further demonstrates their potential as predictive biomarkers to identify appropriate patients that may benefit from TGFß blockade.

Pan-TGFß inhibition by SAR439459 relieves immunosuppression and improves antitumor efficacy of PD-1 blockade.

TGFß is a pleiotropic cytokine that may have both tumor inhibiting and tumor promoting properties, depending on tissue and cellular context. Emerging data support a role for TGFß in suppression of antitumor immunity. Here we show that SAR439459, a pan-TGFß neutralizing antibody, inhibits all active isoforms of human and murine TGFß, blocks TGFß-mediated pSMAD signaling, and TGFß-mediated suppression of T cells and NK cells. In vitro, SAR439459 synergized with anti-PD1 to enhance T cell responsiveness. In syngeneic tumor models, SAR439459 treatment impaired tumor growth, while the combination of SAR439459 with anti-PD-1 resulted in complete tumor regression and a prolonged antitumor immunity. Mechanistically, we found that TGFß inhibition with PD-1 blockade augmented intratumoral CD8+ T cell proliferation, reduced exhaustion, evoked proinflammatory cytokines, and promoted tumor-specific CD8+ T cell responses. Together, these data support the hypothesis that TGFß neutralization using SAR439459 synergizes with PD-1 blockade to promote antitumor immunity and formed the basis for the ongoing clinical investigation of SAR439459 in patients with cancer (NCT03192345).

Isatuximab Acts Through Fc-Dependent, Independent, and Direct Pathways to Kill Multiple Myeloma Cells.

Isatuximab is a monoclonal antibody targeting the transmembrane receptor and ectoenzyme CD38, a protein highly expressed on hematological malignant cells, including those in multiple myeloma (MM). Upon binding to CD38-expressing MM cells, isatuximab is thought to induce tumor cell killing via fragment crystallizable (Fc)-dependent mechanisms, including antibody-dependent cellular cytotoxicity (ADCC), antibody-dependent cellular phagocytosis (ADCP), and complement-dependent cytotoxicity (CDC), as well as via direct Fc-independent mechanisms. Here, these mechanisms of action were investigated in MM and diffuse large B-cell lymphoma (DLBCL) cell lines, as well as in peripheral blood mononuclear cells derived from healthy donors, and in MM patient-derived samples. Our findings show that isatuximab-mediated cytotoxicity occurred primarily via ADCC and ADCP in MM cell lines and via ADCC and apoptosis in DLBCL cell lines expressing high levels of CD38. We identified the programmed cell death-1/programmed cell death-ligand 1 (PD-1/PD-L1) pathway and MM cell-secreted transforming growth factor-beta (TGF-ß) as tumor cell-related features that could suppress CD38-mediated ADCC. Furthermore, we established that isatuximab can directly activate natural killer (NK) cells and promote NK cell-mediated cytotoxicity via crosslinking of CD38 and CD16. Finally, isatuximab-induced CDC was observed in cell lines with high CD38 receptor density (>250,000 molecules/cell) and limited expression of inhibitory complement regulatory proteins (CD46, CD55, and CD59; <50,000 molecules/cell). Taken together, our findings highlight mechanistic insights for isatuximab and provide support for a range of combination therapy approaches that could be tested for isatuximab in the future.

Utility of the RIG-I Agonist Triphosphate RNA for Melanoma Therapy.

The pattern recognition receptor RIG-I plays an important role in the recognition of nonself RNA and antiviral immunity. RIG-I's natural ligand, triphosphate RNA (ppp-RNA), is proposed to be a valuable addition to the growing arsenal of cancer immunotherapy treatment options. In this study, we present comprehensive data validating the concept and utility of treatment with synthetic RIG-I agonist ppp-RNA for the therapy of human cancer, with melanoma as potential entry indication amenable to intratumoral treatment. Using mRNA expression data of human tumors, we demonstrate that RIG-I expression is closely correlated to cellular and cytokine immune activation in a wide variety of tumor types. Furthermore, we confirm susceptibility of cancer cells to ppp-RNA treatment in different cellular models of human melanoma, revealing unexpected heterogeneity between cell lines in their susceptibility to RNA agonist features, including sequence, secondary structures, and presence of triphosphate. Cellular responses to RNA treatment (induction of type I IFN, FasR, MHC-I, and cytotoxicity) were demonstrated to be RIG-I dependent using KO cells. Following ppp-RNA treatment of a mouse melanoma model, we observed significant local and systemic antitumor effects and survival benefits. These were associated with type I IFN response, tumor cell apoptosis, and innate and adaptive immune cell activation. For the first time, we demonstrate systemic presence of tumor antigen-specific CTLs following treatment with RIG-I agonists. Despite potential challenges in the generation and formulation of potent RIG-I agonists, ppp-RNA or analogues thereof have the potential to play an important role for cancer treatment in the next wave of immunotherapy.

Identification of a molecular signature of sarcopenia.

Investigating the molecular mechanisms underlying sarcopenia in humans with the use of microarrays has been complicated by low sample size and the variability inherent in human gene expression profiles. We have conducted a study using Affymetrix GeneChips to identify a molecular signature of aged skeletal muscle. The molecular signature was defined as the set of expressed genes that best distinguished the vastus lateralis muscle of young (n = 10) and older (n = 12) male subjects, when a k-nearest neighbor supervised classification method was used in conjunction with a signal-to-noise ratio gene selection method and a holdout cross-validation procedure. The age-specific expression signature was comprised of 45 genes; 27 were upregulated and 18 were downregulated. This signature also correctly classified 75% of the muscle samples from young and older subjects published by an independent laboratory, based on their expression profiles. The signature revealed increased expression of several genes involved in mediating cellular responses to inflammation and apoptosis, including complement component C1QA, Galectin-1, C/EBP-beta, and FOXO3A, among others. The increased expressions of genes that regulate pre-mRNA splicing, localization, and modification of RNA comprise markers of the aging signature. Downregulated genes in the signature were the glutamine transporter SLC38A1, a TRAF-6 inhibitory zinc finger protein, and membrane-bound transcription factor protease S2P, among others. The sarcopenia signature developed here will be useful as a molecular model to judge the effectiveness of exercise and other therapeutic treatments aimed at ameliorating the effects of muscle loss associated with aging.

GECKO: a complete large-scale gene expression analysis platform.

BACKGROUND: Gecko (Gene Expression: Computation and Knowledge Organization) is a complete, high-capacity centralized gene expression analysis system, developed in response to the needs of a distributed user community. RESULTS: Based on a client-server architecture, with a centralized repository of typically many tens of thousands of Affymetrix scans, Gecko includes automatic processing pipelines for uploading data from remote sites, a data base, a computational engine implementing approximately 50 different analysis tools, and a client application. Among available analysis tools are clustering methods, principal component analysis, supervised classification including feature selection and cross-validation, multi-factorial ANOVA, statistical contrast calculations, and various post-processing tools for extracting data at given error rates or significance levels. On account of its open architecture, Gecko also allows for the integration of new algorithms. The Gecko framework is very general: non-Affymetrix and non-gene expression data can be analyzed as well. A unique feature of the Gecko architecture is the concept of the Analysis Tree (actually, a directed acyclic graph), in which all successive results in ongoing analyses are saved. This approach has proven invaluable in allowing a large (approximately 100 users) and distributed community to share results, and to repeatedly return over a span of years to older and potentially very complex analyses of gene expression data. CONCLUSIONS: The Gecko system is being made publicly available as free software http://sourceforge.net/projects/geckoe. In totality or in parts, the Gecko framework should prove useful to users and system developers with a broad range of analysis needs.

Gene expression profiling of a hypoxic seizure model of epilepsy suggests a role for mTOR and Wnt signaling in epileptogenesis.

Microarray profiling was used to investigate gene expression in the hypoxic seizure model of acquired epilepsy in the rat, with the aim of characterizing functional pathways which are persistently activated or repressed during epileptogenesis. Hippocampal and cortical tissues were transcriptionally profiled over a one week period following an initial series of seizures induced by mild hypoxia at post-natal day 10 (P10), and the gene expression data was then analyzed with a focus on gene set enrichment analysis, an approach which emphasizes regulation of entire pathways rather than of individual genes. Animals were subjected to one of three conditions: a control with no hypoxia, hypoxic seizures, and hypoxic seizures followed by treatment with the AMPAR antagonist NBQX, a compound currently proposed to be a modulator of epileptogenesis. While temporal gene expression in the control samples was found to be consistent with known processes of neuronal maturation in the rat for the given time window, the hypoxic seizure response was found to be enriched for components of the PI3K/mTOR and Wnt signaling pathways, alongside gene sets representative of glutamatergic, synaptic and axonal processes, perhaps regulated as a downstream consequence of activation of these pathways. Wnt signaling components were also found enriched in the more specifically epileptogenic NBQX-responsive gene set. While activation of the mTOR pathway is consistent with its known role in epileptogenesis and strengthens the case for mTOR or PI3K pathway inhibitors as potential anti-epileptogenic drugs, investigation of the role of Wnt signaling and the effect of appropriate inhibitors might offer a parallel avenue of research toward anti-epileptogenic treatment of epilepsy.

Finding genes in the C2C12 osteogenic pathway by k-nearest-neighbor classification of expression data.

A supervised classification scheme for analyzing microarray expression data, based on the k-nearest-neighbor method coupled to noise-reduction filters, has been used to find genes involved in the osteogenic pathway of the mouse C2C12 cell line studied here as a model for in vivo osteogenesis. The scheme uses as input a training set embodying expert biological knowledge, and provides internal estimates of its own misclassification errors, which furthermore enables systematic optimization of the classifier parameters. On the basis of the C2C12-generated expression data set with 34,130 expression profiles across 2 time courses, each comprised of 6 points, and a training set containing known members of the osteogenic, myoblastic, and adipocytic pathways, 176 new genes in addition to 28 originally in the training set are selected as relevant to osteogenesis. For this selection, the estimated sensitivity is 42% and the posterior false-positive rate (fraction of candidates that are spurious) is 12%. The corresponding sensitivity and false-positive rate for detection of myoblastic genes are 9% and 31%, respectively, and only 4% and approximately 100%, respectively, for adipocytic genes, in accordance with an experimental design that predominantly stimulated the osteogenic pathway. Validation of this selection is provided by examining expression of the genes in an independent biological assay involving mouse calvaria (skull bone) primary cell cultures, in which a large fraction of the 176 genes are seen to be strongly regulated, as well as by case-by-case analysis of the genes on the basis of expert domain knowledge. The methodology should be generalizable to any situation in which enough a priori biological knowledge exists to define a training set.