The dynamics of gene expression changes in a mouse model of oral tumorigenesis may help refine prevention and treatment strategies in patients with oral cancer.

A better understanding of the dynamics of molecular changes occurring during the early stages of oral tumorigenesis may help refine prevention and treatment strategies. We generated genome-wide expression profiles of microdissected normal mucosa, hyperplasia, dysplasia and tumors derived from the 4-NQO mouse model of oral tumorigenesis. Genes differentially expressed between tumor and normal mucosa defined the "tumor gene set" (TGS), including 4 non-overlapping gene subsets that characterize the dynamics of gene expression changes through different stages of disease progression. The majority of gene expression changes occurred early or progressively. The relevance of these mouse gene sets to human disease was tested in multiple datasets including the TCGA and the Genomics of Drug Sensitivity in Cancer project. The TGS was able to discriminate oral squamous cell carcinoma (OSCC) from normal oral mucosa in 3 independent datasets. The OSCC samples enriched in the mouse TGS displayed high frequency of CASP8 mutations, 11q13.3 amplifications and low frequency of PIK3CA mutations. Early changes observed in the 4-NQO model were associated with a trend toward a shorter oral cancer-free survival in patients with oral preneoplasia that was not seen in multivariate analysis. Progressive changes observed in the 4-NQO model were associated with an increased sensitivity to 4 different MEK inhibitors in a panel of 51 squamous cell carcinoma cell lines of the areodigestive tract. In conclusion, the dynamics of molecular changes in the 4-NQO model reveal that MEK inhibition may be relevant to prevention and treatment of a specific molecularly-defined subgroup of OSCC

A WIN Consortium phase I study exploring avelumab, palbociclib, and axitinib in advanced non-small cell lung cancer

Genomics; Transcriptomics; Lung cancerGenómica; Transcriptómica; Cáncer de pulmónGenòmica; Transcriptòmica; Càncer de pulmóBackground The Worldwide Innovative Network (WIN) Consortium has developed the Simplified Interventional Mapping System (SIMS) to better define the cancer molecular milieu based on genomics/transcriptomics from tumor and analogous normal tissue biopsies. SPRING is the first trial to assess a SIMS-based tri-therapy regimen in advanced non-small cell lung cancer (NSCLC). Methods Patients with advanced NSCLC (no EGFR, ALK, or ROS1 alterations; PD-L1 unrestricted; ≤2 prior therapy lines) received avelumab, axitinib, and palbociclib (3 + 3 dose escalation design). Results Fifteen patients were treated (five centers, four countries): six at each of dose levels 1 (DL1) and DL2; three at DL3. The most common ≥Grade 3 adverse events were neutropenia, hypertension, and fatigue. The recommended Phase II dose (RP2D) was DL1: avelumab 10 mg/kg IV q2weeks, axitinib 3 mg po bid, and palbociclib 75 mg po daily (7 days off/21 days on). Four patients (27%) achieved a partial response (PR) (progression-free survival [PFS]: 14, 24, 25 and 144+ weeks), including two after progression on pembrolizumab. Four patients attained stable disease (SD) that lasted ≥24 weeks: 24, 27, 29, and 64 weeks. At DL1 (RP2D), four of six patients (66%) achieved stable disease (SD) ≥6 months/PR (2 each). Responders included patients with no detectable PD-L1 expression and low tumor mutational burden. Conclusions Overall, eight of 15 patients (53%) achieved clinical benefit (SD ≥ 24 weeks/PR) on the avelumab, axitinib, and palbociclib combination. This triplet showed antitumor activity in NSCLC, including in tumors post-pembrolizumab progression, and was active at the RP2D, which was well tolerated.This work was supported by the ARC Foundation for Cancer Research, Villejuif, France and Worldwide Innovative Network (WIN) Association––WIN Consortium, Villejuif, France, sponsor of the study. WIN was responsible for the study design, collection, analysis, and interpretation of data as well as writing of the report. The study drugs were provided by Pfizer, as part of an alliance between Merck KGaA, Darmstadt, Germany and Pfizer. Funded in part by National Cancer Institute grant P30 CA023100 and the Joan and Irwin Jacobs Fund philanthropic fund (RK)

Endothelial cell Bcl‐2 and lymph node metastasis in patients with oral squamous cell carcinoma

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90040/1/j.1600-0714.2011.01081.x.pd

Gene Expression Clustering and Selected Head and Neck Cancer Gene Signatures Highlight Risk Probability Differences in Oral Premalignant Lesions

Background: Oral premalignant lesions (OPLs) represent the most common oral precancerous conditions. One of the major challenges in this field is the identification of OPLs at higher risk for oral squamous cell cancer (OSCC) development, by discovering molecular pathways deregulated in the early steps of malignant transformation. Analysis of deregulated levels of single genes and pathways has been successfully applied to head and neck squamous cell cancers (HNSCC) and OSCC with prognostic/predictive implications. Exploiting the availability of gene expression profile and clinical follow-up information of a well-characterized cohort of OPL patients, we aim to dissect tissue OPL gene expression to identify molecular clusters/signatures associated with oral cancer free survival (OCFS). Materials and methods: The gene expression data of 86 OPL patients were challenged with: an HNSCC specific 6 molecular subtypes model (Immune related: HPV related, Defense Response and Immunoreactive; Mesenchymal, Hypoxia and Classical); one OSCC-specific signature (13 genes); two metabolism-related signatures (3 genes and signatures raised from 6 metabolic pathways associated with prognosis in HNSCC and OSCC, respectively); a hypoxia gene signature. The molecular stratification and high versus low expression of the signatures were correlated with OCFS by Kaplan\u2013Meier analyses. The association of gene expression profiles among the tested biological models and clinical covariates was tested through variance partition analysis. Results: Patients with Mesenchymal, Hypoxia and Classical clusters showed an higher risk of malignant transformation in comparison with immune-related ones (log-rank test, p = 0.0052) and they expressed four enriched hallmarks: \u201cTGF beta signaling\u201d \u201cangiogenesis\u201d, \u201cunfolded protein response\u201d, \u201capical junction\u201d. Overall, 54 cases entered in the immune related clusters, while the remaining 32 cases belonged to the other clusters. No other signatures showed association with OCFS. Our variance partition analysis proved that clinical and molecular features are able to explain only 21% of gene expression data variability, while the remaining 79% refers to residuals independent of known parameters. Conclusions: Applying the existing signatures derived from HNSCC to OPL, we identified only a protective effect for immune-related signatures. Other gene expression profiles derived from overt cancers were not able to identify the risk of malignant transformation, possibly because they are linked to later stages of cancer progression. The availability of a new well-characterized set of OPL patients and further research is needed to improve the identification of adequate prognosticators in OPLs

Apport et défis des Big Data en cancérologie

International audienceDepuis le premier séquençage du génome humain en 2001, le développement de nouvelles technologies à haut débit, ainsi que la diminution considérable du coût du séquençage ont permis des avancées importantes en oncologie. La caractérisation moléculaire des cancers a notamment permis d’identifier des anomalies oncogéniques clés au cours du processus tumoral, permettant le développement de stratégies thérapeutiques personnalisées. Cependant, la quantité d’information considérable ainsi générée a créé de nouveaux défis à relever comme le stockage, le traitement ou encore l’exploitation de l’information. Dans cet article, nous décrivons l’apport et les défis représentés par les Big Data en cancérologie

Impaired DNA replication within progenitor cell pools promotes leukemogenesis.

Impaired cell cycle progression can be paradoxically associated with increased rates of malignancies. Using retroviral transduction of bone marrow progenitors followed by transplantation into mice, we demonstrate that inhibition of hematopoietic progenitor cell proliferation impairs competition, promoting the expansion of progenitors that acquire oncogenic mutations which restore cell cycle progression. Conditions that impair DNA replication dramatically enhance the proliferative advantage provided by the expression of Bcr-Abl or mutant p53, which provide no apparent competitive advantage under conditions of healthy replication. Furthermore, for the Bcr-Abl oncogene the competitive advantage in contexts of impaired DNA replication dramatically increases leukemogenesis. Impaired replication within hematopoietic progenitor cell pools can select for oncogenic events and thereby promote leukemia, demonstrating the importance of replicative competence in the prevention of tumorigenesis. The demonstration that replication-impaired, poorly competitive progenitor cell pools can promote tumorigenesis provides a new rationale for links between tumorigenesis and common human conditions of impaired DNA replication such as dietary folate deficiency, chemotherapeutics targeting dNTP synthesis, and polymorphisms in genes important for DNA metabolism

Bystander effectors of chondrosarcoma cells irradiated at different LET impair proliferation of chondrocytes

While the dose-response relationship of radiation-induced bystander effect (RIBE) is controversial at low and high linear energy transfer (LET), mechanisms and effectors of cell-to-cell communication stay unclear and highly dependent of cell type. In the present study, we investigated the capacity of chondrocytes in responding to bystander factors released by chondrosarcoma cells irradiated at different doses (0.05 to 8 Gy) with X-rays and C-ions. Following a medium transfer protocol, cell survival, proliferation and DNA damages were quantified in bystander chondrocytes. The bystander factors secreted by chondrosarcoma cells were characterized. A significant and major RIBE response was observed in chondrocyte cells (T/C-28a2) receiving conditioned medium from chondrosarcoma cells (SW1353) irradiated with 0.1 Gy of X-rays and 0.05 Gy of C-ions, resulting in cell survivals of 36% and 62%, respectively. Micronuclei induction in bystander cells was observed from the same low doses. The cell survival results obtained by clonogenic assays were confirmed using impedancemetry. The bystander activity was vanished after a heat treatment or a dilution of the conditioned media. The cytokines which are well known as bystander factors, TNF-alpha and IL-6, were increased as a function of doses and LET according to an ELISA multiplex analysis. Together, the results demonstrate that irradiated chondrosarcoma cells can communicate stress factors to non-irradiated chondrocytes, inducing a wide and specific bystander response related to both doses and LET

Radiomics combined with transcriptomics to predict response to immunotherapy from patients treated with PD-1/PD-L1 inhibitors for advanced NSCLC

IntroductionIn this study, we aim to build radiomics and multiomics models based on transcriptomics and radiomics to predict the response from patients treated with the PD-L1 inhibitor.Materials and methodsOne hundred and ninety-five patients treated with PD-1/PD-L1 inhibitors were included. For all patients, 342 radiomic features were extracted from pretreatment computed tomography scans. The training set was built with 110 patients treated at the Léon Bérard Cancer Center. An independent validation cohort was built with the 85 patients treated in Dijon. The two sets were dichotomized into two classes, patients with disease control and those considered non-responders, in order to predict the disease control at 3 months. Various models were trained with different feature selection methods, and different classifiers were evaluated to build the models. In a second exploratory step, we used transcriptomics to enrich the database and develop a multiomic signature of response to immunotherapy in a 54-patient subgroup. Finally, we considered the HOT/COLD status. We first trained a radiomic model to predict the HOT/COLD status and then prototyped a hybrid model integrating radiomics and the HOT/COLD status to predict the response to immunotherapy.ResultsRadiomic signature for 3 months’ progression-free survival (PFS) classification: The most predictive model had an area under the receiver operating characteristic curve (AUROC) of 0.94 on the training set and 0.65 on the external validation set. This model was obtained with the t-test selection method and with a support vector machine (SVM) classifier. Multiomic signature for PFS classification: The most predictive model had an AUROC of 0.95 on the training set and 0.99 on the validation set. Radiomic model to predict the HOT/COLD status: the most predictive model had an AUROC of 0.93 on the training set and 0.86 on the validation set. HOT/COLD radiomic hybrid model for PFS classification: the most predictive model had an AUROC of 0.93 on the training set and 0.90 on the validation set.ConclusionIn conclusion, radiomics could be used to predict the response to immunotherapy in non-small-cell lung cancer patients. The use of transcriptomics or the HOT/COLD status, together with radiomics, may improve the working of the prediction models

Proteomic analysis of the response to cell cycle arrests in human myeloid leukemia cells

Previously, we analyzed protein abundance changes across a ‘minimally perturbed’ cell cycle by using centrifugal elutriation to differentially enrich distinct cell cycle phases in human NB4 cells (Ly et al., 2014). In this study, we compare data from elutriated cells with NB4 cells arrested at comparable phases using serum starvation, hydroxyurea, or RO-3306. While elutriated and arrested cells have similar patterns of DNA content and cyclin expression, a large fraction of the proteome changes detected in arrested cells are found to reflect arrest-specific responses (i.e., starvation, DNA damage, CDK1 inhibition), rather than physiological cell cycle regulation. For example, we show most cells arrested in G2 by CDK1 inhibition express abnormally high levels of replication and origin licensing factors and are likely poised for genome re-replication. The protein data are available in the Encyclopedia of Proteome Dynamics (http://www.peptracker.com/epd/), an online, searchable resource. DOI: http://dx.doi.org/10.7554/eLife.04534.00