Influence of microbiota-associated metabolic reprogramming on clinical outcome in patients with melanoma from the randomized adjuvant dendritic cell-based MIND-DC trial.

Tumor immunosurveillance plays a major role in melanoma, prompting the development of immunotherapy strategies. The gut microbiota composition, influencing peripheral and tumoral immune tonus, earned its credentials among predictors of survival in melanoma. The MIND-DC phase III trial (NCT02993315) randomized (2:1 ratio) 148 patients with stage IIIB/C melanoma to adjuvant treatment with autologous natural dendritic cell (nDC) or placebo (PL). Overall, 144 patients collected serum and stool samples before and after 2 bimonthly injections to perform metabolomics (MB) and metagenomics (MG) as prespecified exploratory analysis. Clinical outcomes are reported separately. Here we show that different microbes were associated with prognosis, with the health-related Faecalibacterium prausnitzii standing out as the main beneficial taxon for no recurrence at 2 years (p = 0.008 at baseline, nDC arm). Therapy coincided with major MB perturbations (acylcarnitines, carboxylic and fatty acids). Despite randomization, nDC arm exhibited MG and MB bias at baseline: relative under-representation of F. prausnitzii, and perturbations of primary biliary acids (BA). F. prausnitzii anticorrelated with BA, medium- and long-chain acylcarnitines. Combined, these MG and MB biomarkers markedly determined prognosis. Altogether, the host-microbial interaction may play a role in localized melanoma. We value systematic MG and MB profiling in randomized trials to avoid baseline differences attributed to host-microbe interactions.

Very Long-Term Complete Remission Can Be Achieved in Men With High-Risk Localized Prostate Cancer and a Very High PSA Value: An Analysis of the GETUG 12 Phase 3 Trial.

INTRODUCTION: Serum prostate specific antigen (PSA) is a well-known prognostic parameter in men with prostate cancer. The treatment of men with very high PSA values and apparently no detectable metastases is not fully established. PATIENTS AND METHODS: Ancillary analysis from the GETUG 12 phase 3 trial. Patients with non-metastatic high-risk prostate cancer by bone and computerized tomography (CT) scan were randomly assigned to receive androgen deprivation therapy (ADT) and docetaxel plus estramustine or ADT alone. Relapse-free survival (RFS), clinical RFS, metastases-free survival (MFS), overall survival (OS), and prostate cancer-specific survival (PCSS) were estimated using the Kaplan-Meier method for different levels of PSA (50 ng/mL, 75 ng/mL, and 100 ng/mL). The relationship between PSA and outcomes was studied using residual-based approaches and spline functions. RESULTS: The median follow-up was 12 years (range: 0-15.3). Baseline PSA (<50 ng/mL, n = 328; ≥50ng/mL, n = 85) was associated with improved RFS (P = .0005), cRFS (P = .0024), and MFS (P = .0068). The 12-year RFS rate was 46.33% (CI 40.59-51.86), 33.59% (CI 22.55-44.97), and 11.76% (1.96-31.20) in men with PSA values <50 ng/mL (n = 328), 50-100 ng/mL (n = 68), and ≥100 ng/mL (n = 17), respectively. Exploratory analyses revealed no deviation from the linear relationship assumption between PSA and the log hazard of events. CONCLUSIONS: Men with apparently localized prostate cancer and a high baseline PSA value have a reasonable chance of being long-term disease-free when treated with curative intent combining systemic and local therapy.

Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a rare cancer, characterized by high metastatic potential and poor prognosis, and has limited treatment options. The current standard of care in nonmetastatic settings is neoadjuvant chemotherapy (NACT), but treatment efficacy varies substantially across patients. This heterogeneity is still poorly understood, partly due to the paucity of curated TNBC data. Here we investigate the use of machine learning (ML) leveraging whole-slide images and clinical information to predict, at diagnosis, the histological response to NACT for early TNBC women patients. To overcome the biases of small-scale studies while respecting data privacy, we conducted a multicentric TNBC study using federated learning, in which patient data remain secured behind hospitals' firewalls. We show that local ML models relying on whole-slide images can predict response to NACT but that collaborative training of ML models further improves performance, on par with the best current approaches in which ML models are trained using time-consuming expert annotations. Our ML model is interpretable and is sensitive to specific histological patterns. This proof of concept study, in which federated learning is applied to real-world datasets, paves the way for future biomarker discovery using unprecedentedly large datasets.

A non-parametric Bayesian joint model for latent individual molecular profiles and survival in oncology.

The development of prognostic molecular signatures considering the inter-patient heterogeneity is a key challenge for the precision medicine. We propose a joint model of this heterogeneity and the patient survival, assuming that tumor expression results from a mixture of a subset of independent signatures. We deconvolute the omics data using a non-parametric independent component analysis with a double sparseness structure for the source and the weight matrices, corresponding to the gene-component and individual-component associations, respectively. In a simulation study, our approach identified the correct number of components and reconstructed with high accuracy the weight ([Formula: see text]0.85) and the source ([Formula: see text]0.75) matrices sparseness. The selection rate of components with high-to-moderate prognostic impacts was close to 95%, while the weak impacts were selected with a frequency close to the observed false positive rate ([Formula: see text]25%). When applied to the expression of 1063 genes from 614 breast cancer patients, our model identified 15 components, including six associated to patient survival, and related to three known prognostic pathways in early breast cancer (i.e. immune system, proliferation, and stromal invasion). The proposed algorithm provides a new insight into the individual molecular heterogeneity that is associated with patient prognosis to better understand the complex tumor mechanisms.

Complex Disease Individual Molecular Characterization Using Infinite Sparse Graphical Independent Component Analysis.

Identifying individual mechanisms involved in complex diseases, such as cancer, is essential for precision medicine. Their characterization is particularly challenging due to the unknown relationships of high-dimensional omics data and their inter-patient heterogeneity. We propose to model individual gene expression as a combination of unobserved molecular mechanisms (molecular components) that may differ between the individuals. Considering a baseline molecular profile common to all individuals, these molecular components may represent molecular pathways differing from the population background. We defined an infinite sparse graphical independent component analysis (isgICA) to identify these molecular components. This model relies on double sparseness: the source matrix sparseness defines the subset of genes involved in each molecular component, whereas the weight matrix sparseness identifies the subset of molecular components associated with each patient. As the number of molecular components is unknown but likely high, we simultaneously inferred it and the weight matrix sparseness using the beta-Bernoulli process (BBP). We simulated data from a double sparse ICA with 10/30 components with specific sparseness structures for 100/500 individuals and 500/1000/5000 genes with different noise variance levels to evaluate the reconstruction of the latent structures by our model. For all simulations, the isgICA was able to reconstruct with higher accuracy than 2 state-of-the-art methods (ica and fastICA) the number of components, the weight and source matrix sparsenesses (correlation simulated/estimated >.8). Applying our model to the expression of 1063 genes of 614 breast cancer patients, the isgICA identified 22 components. According to the source matrix, 7 of these 22 components seemed to be specifically related to 3 known molecular pathways with a prognostic effect in early breast cancer (immune system, proliferation, and stroma invasion). This proposed algorithm provides an insight into individual molecular heterogeneity to better understand complex disease mechanisms.

The Polarity and Specificity of Antiviral T Lymphocyte Responses Determine Susceptibility to SARS-CoV-2 Infection in Patients with Cancer and Healthy Individuals.

Vaccination against coronavirus disease 2019 (COVID-19) relies on the in-depth understanding of protective immune responses to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). We characterized the polarity and specificity of memory T cells directed against SARS-CoV-2 viral lysates and peptides to determine correlates with spontaneous, virus-elicited, or vaccine-induced protection against COVID-19 in disease-free and cancer-bearing individuals. A disbalance between type 1 and 2 cytokine release was associated with high susceptibility to COVID-19. Individuals susceptible to infection exhibited a specific deficit in the T helper 1/T cytotoxic 1 (Th1/Tc1) peptide repertoire affecting the receptor binding domain of the spike protein (S1-RBD), a hotspot of viral mutations. Current vaccines triggered Th1/Tc1 responses in only a fraction of all subject categories, more effectively against the original sequence of S1-RBD than that from viral variants. We speculate that the next generation of vaccines should elicit Th1/Tc1 T-cell responses against the S1-RBD domain of emerging viral variants. SIGNIFICANCE: This study prospectively analyzed virus-specific T-cell correlates of protection against COVID-19 in healthy and cancer-bearing individuals. A disbalance between Th1/Th2 recall responses conferred susceptibility to COVID-19 in both populations, coinciding with selective defects in Th1 recognition of the receptor binding domain of spike. See related commentary by McGary and Vardhana, p. 892. This article is highlighted in the In This Issue feature, p. 873.

Intestinal Akkermansia muciniphila predicts clinical response to PD-1 blockade in patients with advanced non-small-cell lung cancer.

Aside from PD-L1 expression, biomarkers of response to immune checkpoint inhibitors (ICIs) in non-small-cell lung cancer (NSCLC) are needed. In a previous retrospective analysis, we documented that fecal Akkermansia muciniphila (Akk) was associated with clinical benefit of ICI in patients with NSCLC or kidney cancer. In the current study, we performed shotgun-metagenomics-based microbiome profiling in a large cohort of patients with advanced NSCLC (n = 338) treated with first- or second-line ICIs to prospectively validate the predictive value of fecal Akk. Baseline stool Akk was associated with increased objective response rates and overall survival in multivariate analyses, independent of PD-L1 expression, antibiotics, and performance status. Intestinal Akk was accompanied by a richer commensalism, including Eubacterium hallii and Bifidobacterium adolescentis, and a more inflamed tumor microenvironment in a subset of patients. However, antibiotic use (20% of cases) coincided with a relative dominance of Akk above 4.8% accompanied with the genus Clostridium, both associated with resistance to ICI. Our study shows significant differences in relative abundance of Akk that may represent potential biomarkers to refine patient stratification in future studies.

Immune Infiltrate and Tumor Microenvironment Transcriptional Programs Stratify Pediatric Osteosarcoma into Prognostic Groups at Diagnosis.

The outcomes of adolescents/young adults with osteosarcoma have not improved in decades. The chaotic karyotype of this rare tumor has precluded the identification of prognostic biomarkers and patient stratification. We reasoned that transcriptomic studies should overcome this genetic complexity. RNA sequencing (RNA-seq) of 79 osteosarcoma diagnostic biopsies identified stable independent components that recapitulate the tumor and microenvironment cell composition. Unsupervised classification of the independent components stratified this cohort into favorable (G1) and unfavorable (G2) prognostic tumors in terms of overall survival. Multivariate survival analysis ranked this stratification as the most influential variable. Functional characterization associated G1 tumors with innate immunity and G2 tumors with angiogenic, osteoclastic, and adipogenic activities as well as PPARγ pathway upregulation. A focused gene signature that predicted G1/G2 tumors from RNA-seq data was developed and validated within an independent cohort of 82 osteosarcomas. This signature was further validated with a custom NanoString panel in 96 additional osteosarcomas. This study thus proposes new biomarkers to detect high-risk patients and new therapeutic options for osteosarcoma. SIGNIFICANCE: These findings indicate that the osteosarcoma microenvironment composition is a major feature to identify hard-to-treat patient tumors at diagnosis and define the biological pathways and potential actionable targets associated with these tumors.

Tumor infiltrating lymphocyte stratification of prognostic staging of early-stage triple negative breast cancer.

The importance of integrating biomarkers into the TNM staging has been emphasized in the 8th Edition of the American Joint Committee on Cancer (AJCC) Staging system. In a pooled analysis of 2148 TNBC-patients in the adjuvant setting, TILs are found to strongly up and downstage traditional pathological-staging in the Pathological and Clinical Prognostic Stage Groups from the AJJC 8th edition Cancer Staging System. This suggest that clinical and research studies on TNBC should take TILs into account in addition to stage, as for example patients with stage II TNBC and high TILs have a better outcome than patients with stage I and low TILs.

Multilayer spectrum of intratumoral heterogeneity in basal bladder cancer.

Basal/squamous (Ba/Sq) subtype represents an intrinsic and robust group in the consensus molecular classification of muscle-invasive bladder cancer (MIBC), with poor outcome and controversial chemosensitivity. We aimed to investigate the spectrum of intratumor heterogeneity (ITH) in the Ba/Sq subtype. First, we validated a 29-gene NanoString CodeSet to predict the Ba/Sq subtype for FFPE samples. We identified heterogeneous Ba/Sq tumors in a series of 331 MIBC FFPE samples using dual GATA3/KRT5/6 immunohistochemistry (IHC). Heterogeneous regions with distinct immunostaining patterns were studied separately for gene expression using the 29-gene CodeSet, for mutations by targeted next-generation sequencing, and for copy number alteration (CNA) by microarray hybridization. Among 83 Ba/Sq tumors identified by GATA3/KRT5/6 dual staining, 19 tumors showed heterogeneity at the IHC level. In one third of the 19 cases, regions from the same tumor were classified in different distinct molecular subtypes. The mutational and CNA profiles confirmed the same clonal origin for IHC heterogeneous regions with possible subclonal evolution. Overall, two patterns of intratumoral heterogeneity (ITH) were observed in Ba/Sq tumors: low ITH (regions with distinct immunostaining, but common molecular subtype and shared CNA) or high ITH (regions with distinct immunostaining, molecular subtype, and CNA). These results showed multilayer heterogeneity in Ba/Sq MIBC. In view of personalized medicine, this heterogeneity adds complexity and should be taken into account for sampling procedures used for diagnosis and treatment choice. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Prolonged SARS-CoV-2 RNA virus shedding and lymphopenia are hallmarks of COVID-19 in cancer patients with poor prognosis.

Patients with cancer are at higher risk of severe coronavirus infectious disease 2019 (COVID-19), but the mechanisms underlying virus-host interactions during cancer therapies remain elusive. When comparing nasopharyngeal swabs from cancer and noncancer patients for RT-qPCR cycle thresholds measuring acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in 1063 patients (58% with cancer), we found that malignant disease favors the magnitude and duration of viral RNA shedding concomitant with prolonged serum elevations of type 1 IFN that anticorrelated with anti-RBD IgG antibodies. Cancer patients with a prolonged SARS-CoV-2 RNA detection exhibited the typical immunopathology of severe COVID-19 at the early phase of infection including circulation of immature neutrophils, depletion of nonconventional monocytes, and a general lymphopenia that, however, was accompanied by a rise in plasmablasts, activated follicular T-helper cells, and non-naive Granzyme B+FasL+, EomeshighTCF-1high, PD-1+CD8+ Tc1 cells. Virus-induced lymphopenia worsened cancer-associated lymphocyte loss, and low lymphocyte counts correlated with chronic SARS-CoV-2 RNA shedding, COVID-19 severity, and a higher risk of cancer-related death in the first and second surge of the pandemic. Lymphocyte loss correlated with significant changes in metabolites from the polyamine and biliary salt pathways as well as increased blood DNA from Enterobacteriaceae and Micrococcaceae gut family members in long-term viral carriers. We surmise that cancer therapies may exacerbate the paradoxical association between lymphopenia and COVID-19-related immunopathology, and that the prevention of COVID-19-induced lymphocyte loss may reduce cancer-associated death.

Ketogenic diet and ketone bodies enhance the anticancer effects of PD-1 blockade.

Limited experimental evidence bridges nutrition and cancer immunosurveillance. Here, we show that ketogenic diet (KD) - or its principal ketone body, 3-hydroxybutyrate (3HB), most specifically in intermittent scheduling - induced T cell-dependent tumor growth retardation of aggressive tumor models. In conditions in which anti-PD-1 alone or in combination with anti-CTLA-4 failed to reduce tumor growth in mice receiving a standard diet, KD, or oral supplementation of 3HB reestablished therapeutic responses. Supplementation of KD with sucrose (which breaks ketogenesis, abolishing 3HB production) or with a pharmacological antagonist of the 3HB receptor GPR109A abolished the antitumor effects. Mechanistically, 3HB prevented the immune checkpoint blockade-linked upregulation of PD-L1 on myeloid cells, while favoring the expansion of CXCR3+ T cells. KD induced compositional changes of the gut microbiota, with distinct species such as Eisenbergiella massiliensis commonly emerging in mice and humans subjected to carbohydrate-low diet interventions and highly correlating with serum concentrations of 3HB. Altogether, these results demonstrate that KD induces a 3HB-mediated antineoplastic effect that relies on T cell-mediated cancer immunosurveillance.

Immunodynamics of explanted human tumors for immuno-oncology.

Decision making in immuno-oncology is pivotal to adapt therapy to the tumor microenvironment (TME) of the patient among the numerous options of monoclonal antibodies or small molecules. Predicting the best combinatorial regimen remains an unmet medical need. Here, we report a multiplex functional and dynamic immuno-assay based on the capacity of the TME to respond to ex vivo stimulation with twelve immunomodulators including immune checkpoint inhibitors (ICI) in 43 human primary tumors. This "in sitro" (in situ/in vitro) assay has the potential to predict unresponsiveness to anti-PD-1 mAbs, and to detect the most appropriate and personalized combinatorial regimen. Prospective clinical trials are awaited to validate this in sitro assay.

Identifying clusters of health risk behaviors and their predictors in adult survivors of childhood cancer: A report from the French Childhood Cancer Survivor Study.

OBJECTIVE: Health risk behaviors (HRB) of childhood cancer survivors (CCS) are generally studied separately, despite the evidence suggesting that HRB are not independent. To our knowledge, few studies have examined HRB profiles in the former pediatric cancer patients. In this study, we identified HRB profiles and examined predictors engaging in unhealthy behaviors in CCS. METHODS: We used data from a French cohort of CCS that includes five-year survivors diagnosed between 1945 and 2000 and treated before reaching age 18, in five centers in France. A total of 2961 adult CCS answered a self-reported questionnaire pertaining to HRB. Latent class analysis was used to identify HRB profiles combining physical activity, smoking, cannabis use, and alcohol drinking. Multinomial logistic analyses examined predictors for engaging in unhealthy behaviors. RESULTS: Three HRB patterns emerged: "Low-risk" (n = 1846, 62.3%) included CCS who exhibited the highest frequency for usual physical activity and the lowest probabilities for current smoking or cannabis use, but most drank at least moderately; "Moderate-risk behaviors" (n = 291, 9.8%), and "High-risk behaviors" (n = 824, 27.8%) for CCS who exhibited the highest frequencies for current smoking, cannabis use, and heavy drinking. The multivariable regression revealed that male CCS, less educated or not married were significantly more likely to be in the high-risk behaviors group than the low-risk group. CONCLUSIONS: As CCS remain a vulnerable population, screening for HRB should be routinized in long-term follow-up care and interventions targeting multiple HRB simultaneously among survivors should be developed.

Tumor-Infiltrating Lymphocytes and Prognosis: A Pooled Individual Patient Analysis of Early-Stage Triple-Negative Breast Cancers.

PURPOSE: The aim of the current study was to conduct a pooled analysis of studies that have investigated the prognostic value of tumor-infiltrating lymphocytes (TILs) in early-stage triple negative breast cancer (TNBC). METHODS: Participating studies had evaluated the percentage infiltration of stromally located TILs (sTILs) that were quantified in the same manner in patient diagnostic samples of early-stage TNBC treated with anthracycline-based chemotherapy with or without taxanes. Cox proportional hazards regression models stratified by trial were used for invasive disease-free survival (iDFS; primary end point), distant disease-free survival (D-DFS), and overall survival (OS), fitting sTILs as a continuous variable adjusted for clinicopathologic factors. RESULTS: We collected individual data from 2,148 patients from nine studies. Average age was 50 years (range, 22 to 85 years), and 33% of patients were node negative. The average value of sTILs was 23% (standard deviation, 20%), and 77% of patients had 1% or more sTILs. sTILs were significantly lower with older age ( P = .001), larger tumor size ( P = .01), more nodal involvement ( P = .02), and lower histologic grade ( P = .001). A total of 736 iDFS and 548 D-DFS events and 533 deaths were observed. In the multivariable model, sTILs added significant independent prognostic information for all end points (likelihood ratio χ2, 48.9 iDFS; P < .001; χ2, 55.8 D-DFS; P < .001; χ2, 48.5 OS; P < .001). Each 10% increment in sTILs corresponded to an iDFS hazard ratio of 0.87 (95% CI, 0.83 to 0.91) for iDFS, 0.83 (95% CI, 0.79 to 0.88) for D-DFS, and 0.84 (95% CI, 0.79 to 0.89) for OS. In node-negative patients with sTILs ≥ 30%, 3-year iDFS was 92% (95% CI, 89% to 98%), D-DFS was 97% (95% CI, 95% to 99%), and OS was 99% (95% CI, 97% to 100%). CONCLUSION: This pooled data analysis confirms the strong prognostic role of sTILs in early-stage TNBC and excellent survival of patients with high sTILs after adjuvant chemotherapy and supports the integration of sTILs in a clinicopathologic prognostic model for patients with TNBC. This model can be found at www.tilsinbreastcancer.org .

Safety, tolerability and antitumour activity of LY2780301 (p70S6K/AKT inhibitor) in combination with gemcitabine in molecularly selected patients with advanced or metastatic cancer: a phase IB dose escalation study.

BACKGROUND: LY2780301, a dual inhibitor of protein kinase B (AKT) and the downstream effector p70 ribosomal protein S6 kinase (p70S6K), may inhibit progression in tumours relying on phosphatidylinositol 3-kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR) signalling pathway activation. This phase IB trial investigated the maximum tolerated dose (MTD), dose-limiting toxicities (DLTs), safety, pharmacokinetics (PK) and antitumour activity of LY2780301 plus gemcitabine in patients with advanced/metastatic solid tumours. METHODS: This was a non-randomised, open-label, dose escalation and dose expansion trial. Patients harbouring molecular alterations of the PI3K/AKT/mTOR pathway received once daily (QD) oral LY2780301 (400 or 500 mg) in combination with intravenous gemcitabine (750 or 1000 mg/m2) on days 1, 8 and 15 of a 28-d cycle. Dose escalation followed a 3 + 3 design. Assessments included adverse events (AEs), PK and preliminary antitumour activity. RESULTS: Fifty patients (median age, 53 years; 74% female) predominantly with mutations/amplifications of PI3K (60%) and phosphatase and tensin homologue (PTEN) gene/protein inactivation (42%) were treated for up to 14 cycles. The MTD was LY2780301 500 mg QD with gemcitabine 750 mg/m2. DLTs during cycle 1 were grade IV thrombocytopenia, grade III skin rash and grade III increase in alkaline phosphatase, gamma glutamyltransferase and alanine aminotransferase, occurring in one patient each. Most common AEs were anaemia (84%), fatigue (84%), transaminase increase (74%), thrombocytopenia (74%), nausea/vomiting (70%), neutropenia (68%) and lymphopenia (56%). Among the efficacy-evaluable population, two patients (5%) had a partial response; the disease control rate was 74% at cycle 2. CONCLUSIONS: Addition of LY2780301 to gemcitabine showed manageable toxicity and encouraging antitumour activity in patients with molecular alterations of the PI3K/AKT/mTOR pathway. CLINICAL TRIAL REGISTRATION NUMBER: NCT02018874.

Dose-Finding Methods: Moving Away from the 3 + 3 to Include Richer Outcomes.

The most commonly used method for dose finding, the 3 + 3, has poor performance. New adaptive designs are more efficient. Nevertheless, they have reached a maximum performance level, and further improvement requires either larger sample sizes or outcomes measures richer than the simplistic severe toxicity measured at cycle 1. Clin Cancer Res; 23(15); 3977-9. ©2017 AACRSee related article by Yan et al., p. 3994.

A Dual Model for Prioritizing Cancer Mutations in the Non-coding Genome Based on Germline and Somatic Events.

We address here the issue of prioritizing non-coding mutations in the tumoral genome. To this aim, we created two independent computational models. The first (germline) model estimates purifying selection based on population SNP data. The second (somatic) model estimates tumor mutation density based on whole genome tumor sequencing. We show that each model reflects a different set of constraints acting either on the normal or tumor genome, and we identify the specific genome features that most contribute to these constraints. Importantly, we show that the somatic mutation model carries independent functional information that can be used to narrow down the non-coding regions that may be relevant to cancer progression. On this basis, we identify positions in non-coding RNAs and the non-coding parts of mRNAs that are both under purifying selection in the germline and protected from mutation in tumors, thus introducing a new strategy for future detection of cancer driver elements in the expressed non-coding genome.

Mining the coding and non-coding genome for cancer drivers.

Progress in next-generation sequencing provides unprecedented opportunities to fully characterize the spectrum of somatic mutations of cancer genomes. Given the large number of somatic mutations identified by such technologies, the prioritization of cancer-driving events is a consistent bottleneck. Most bioinformatics tools concentrate on driver mutations in the coding fraction of the genome, those causing changes in protein products. As more non-coding pathogenic variants are identified and characterized, the development of computational approaches to effectively prioritize cancer-driving variants within the non-coding fraction of human genome is becoming critical. After a short summary of methods for coding variant prioritization, we here review the highly diverse non-coding elements that may act as cancer drivers and describe recent methods that attempt to evaluate the deleteriousness of sequence variation in these elements. With such tools, the prioritization and identification of cancer-implicated regulatory elements and non-coding RNAs is becoming a reality.

Coronary stenosis risk analysis following Hodgkin lymphoma radiotherapy: A study based on patient specific artery segments dose calculation.

BACKGROUND AND PURPOSE: The dose effect-effect relationship for cardiac diseases following radiotherapy suffers from uncertainties. Three dimensional coronary artery (CA) dose calculation after mediastinal Hodgkin lymphoma radiotherapy was performed, using the patient's coronary CT angiography (CCTA), and the relationship between the coronary arteries' radiation doses and the risk of stenosis was estimated. MATERIALS AND METHODS: Radiotherapy simulation CT scans and CCTAs of patients treated for a mediastinal Hodgkin lymphoma were used to merge thoracic and detailed cardiovascular anatomies. Radiation treatment parameters were used to estimate CA radiation doses. Twenty-one patients without coronary stenosis (controls) were matched with twelve patients with stenosis (cases). CA segments were considered as sub-volumes of interest. Radiation doses to stenotic segments were compared with those received by normal segments (from cases and controls) using a logistic regression. RESULTS: In eleven cases out of twelve, the highest of the coronary dose distribution was on a damaged segment. Logistic regression with CA segments yielded an odds ratio associated with the risk of coronary stenosis of 1.049 per additional gray with the CA segment median dose (95% confidence interval, 1.004-1.095; p-value <0.05). CONCLUSION: The CA segment dose significantly increased the risk of stenosis on the segment. Such personalized CA dose calculations on larger cohorts are expected to improve the understanding of the cardiovascular radiation dose-effect relationship.