Multiple omic profiles have been generated for many cancer types; however, comprehensive assessment of their prognostic values across cancers is limited. We conducted a pan-cancer prognostic assessment and presented a multi-omic kernel machine learning method to systematically quantify the prognostic values of high-throughput genomic, epigenomic, and transcriptomic profiles individually, integratively, and in combination with clinical factors for 3,382 samples across 14 cancer types. We found that the prognostic performance varied substantially across cancer types. mRNA and miRNA expression profile frequently performed the best, followed by DNA methylation profile. Germline susceptibility variants displayed low prognostic performance consistently across cancer types. The integration of omic profiles with clinical variables can lead to substantially improved prognostic performance over the use of clinical variables alone in half of cancer types examined. Moreover, we showed that the kernel machine learning method consistently outperformed existing prognostic signatures, suggesting that including a large number of omic biomarkers may provide substantial improvement in prognostic assessment. Our study provides a comprehensive portrait of omic architecture for tumor prognosis across cancers, and highlights the prognostic value of genome-wide omic biomarker aggregation, which may facilitate refined prognostic assessment in the era of precision oncology. Advances in high-throughput genomic technologies and large-scale sequencing studies including The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) project have generated a rich resource of multi-dimensional omics data. Building cancer prognostic models incorporating genomic data has the potential to improve the precision of predicting patient clinical outcomes, to help understand the mechanism of tumor progression, and to evaluate the clinical values of biomarkers in clinical trials. Meanwhile, the complexity of the tumor genome poses a great challenge for cancer prognostic assessment. Indeed, substantial omic heterogeneity has been revealed for histologically homogeneous tumors in terms of genomics4, epigenomics5, transcriptomics8, and proteomics9. Recognizing its importance and challenges, the Cancer Moonshot Blue Ribbon Panel has recently recommended prediction of patient outcomes as one research area poised for acceleration10.Developing models that accurately predict patient survival using prognostic and predictive biomarkers is increasingly important in clinical research and practice13. More recent works investigated multiple omic profiles for predicting survival in single cancer types15 and across few cancer types16. These studies focused on identifying a list of prognostic genes, molecules or signatures, excluded somatic mutations from analyses, and did not comprehensively consider the combination of multiple omic profiles. There is a need to conduct a pan-cancer analysis of prognostic accuracy for multiple omic profiles on a genome-wide scale, and to understand the shared patterns for prognostic performance of omic profiles across cancer types.Considerable effort has been devoted to incorporating omic profiles into prognostic assessments for various cancer types. The earlier analyses typically created prognostic indices consisting of a few dozen selected genes based on microarray gene expressionWe hypothesized that prognosis-relevant signals may come from multiple pathways and involve a large number of omic biomarkers, the effect of which may be visible only when aggregated. Indeed, the omic biomarkers with moderate or weak prognostic value likely failed to reach the genome-wide prognostic significance threshold and consequently were discarded from the model. Therefore, the potential prognostic value of omic biomarkers may be unfulfilled and the underlying assumption of omic architecture of tumor prognosis built upon sparsity  needs to be reconsidered. In such situations, a viable hypothesis is that a large number of omic biomarkers (with a continuum of effect size) are involved in prognosis. Hence, aggregating prognosis effects of all biomarkers (through a shrinkage model) may be more effective and stable, and provide improved assessments of prognostic performance of omic biomarkers.9. Thus, we included analysis of the RPPA data set for prognostic assessment in the TCGA pan-cancer cohort.To examine this hypothesis, we developed a multi-omic kernel machine learning method including all molecular markers of an omic profile simultaneously. The schematic analysis steps are illustrated in Fig. 17, but to aggregate the prognostic effects of all biomarkers across various omic profiles and to quantify the prognostic value of tumor molecular profiles, alone or combined with clinical factors across cancer types. The basic idea behind the proposed multi-omic kernel machine learning method is intuitively simple: a patient’s predicted outcome would be similar to that of other patients with similar clinical variables and omic profiles, i.e. “someone like you”, measured by omic similarity matrices.The ability of each omic biomarker to predict overall survival was treated as a random variable and shrunken toward zero with a data-driven degree of shrinkage. In other words, omic architecture involving tumor prognosis was built upon all biomarkers, whose prognosis effects varied from very weak (for most biomarkers) to moderate (for some biomarkers) to strong (for a few biomarkers) in a continuous spectrum. This approach was not intended to identify individual prognosis-related biomarkers in a particular omic profile, which likely requires thousands of samplesFor somatic mutation, DNA copy number, DNA methylation, mRNA and miRNA expression, an omic similarity matrix was computed for each omic data type using a linear kernel function that measures the similarity of omic profiles between subjects. Other kernel functions, including the Gaussian kernel, may be used, but at the cost of additional kernel parameters which may require a large sample for model tuning. We aggregated all biomarkers from each individual omic profile to create the corresponding omic similarity matrix (Methods). Fig. 18 to evaluate whether the omic similarity matrix defined by one omic profile  aligned well with that defined by another . The resulting profile alignment matrices measure the similarity between omic similarity matrices in each cancer type (Methods). Figure Next, we applied a kernel alignment approachTo identify if there are cross-cancer type similarities in profile alignment matrices, we performed cancer type alignment as illustrated in Fig. 20 as being associated with cancer risk rather than prognosis.We first applied the multi-omic kernel machine learning method to evaluate the prognostic performance of individual omic profile and clinical variables for 14 cancer types. The concordance index (C-index) was calculated to evaluate the concordance of the actual survival outcome and survival outcomes as predicted by either the kernel machine learning method based on the omic data or by a Cox proportional hazard model based on clinical variables. From a pan-cancer perspective, we observed that the prognostic power of clinical variables and some molecular profiles vary substantially across cancer types Fig. . For exa16. To systematically understand the variations in C-indices, we applied a linear mixed effects model to quantify the contribution of cancer type to the variation of the C-index for various omic profiles and clinical factors across cancer types, treating C-indices generated by individual molecular profiles and clinical variables as repeated prognosis evaluations for a given cancer type. The model suggested that the cancer type itself explained 74.7% of variation (see Methods) in prognostic performance while the remaining 25.3% of variation is due to clinical variables, molecular profiles, and other unidentified factors , there were a number of notable exceptions in specific cancer types , the best performance among the five omic profiles we analyzed. The second best omic profile is miRNA with a C-index above the average by 0.020 (P = 4.08*10−5), followed by DNA methylation with an elevated C-index above the average by 0.008 (P = 0.09). By contrast, the C-indices of copy number and somatic mutation are below the average prognostic power by 0.005 and 0.002 respectively. The differences, however, are not statistically significant (P = 0.219 and 0.618 respectively).Figure We next investigated whether combining multiple molecular profiles would further improve prognostic power in comparison with the individual molecular profiles. Although the molecular profile combination would lead to the strongest prognostic power in a few cancer types, such as HNSC and OV, the increment from the best individual omic profile is often minimal. Compared to the single molecular profiles, the combination of two molecular profiles would improve C-index by 0.008 (P = 0.04) on average. This may reflect the similarity between omic profiles both in terms of omic similarity matrices and C-indices by individual omic profiles.8 and chromosomal instability15), or agnostically selected mRNA transcripts . The underlying assumption of these studies is that tumor prognosis is driven by a relatively small number of mRNA transcripts, commonly less than one hundred. As will be illustrated shortly, a much larger number of mRNA transcripts, likely thousands of them, each with a weak prognostic effect individually, may be involved in the tumor prognosis for highly heterogeneous tumor types. Therefore, we systematically compared various established signatures with the kernel machine learning method we developed that aggregates prognostic effect across all transcripts of annotated genes from the mRNA profile.A number of mRNA-based prognostic signatures have been proposed and adapted for prognostic assessment. Prognostic signatures commonly consist of either pre-selected mRNA transcripts  the PAM50 breast cancer classifier21 or the MammaPrint signature22 that predicts distant metastasis for early stage breast cancer; (d) LGG subtypes defined by IDH1 mutation and co-deletion of chromosome 1p/19q; and (e) algorithmically selecting mRNA transcripts by L1 penalized Cox regression (LASSO)16.Specifically, we compared the prognostic powers of (a) the kernel machine learning method for genome-wide aggregation of mRNA transcripts; (b) pre-specified prognostic signatures, including the metagene signatures−4) respectively, across cancer types. For KIRC, the metagene and immune signatures lead to significantly lower C-indices (0.618 and 0.546) as compared to the kernel method (0.707). There are a few exceptions in which the metagene and immune signatures perform slightly better than the kernel method, including the metagene signature in UCEC (C-index = 0.728 v.s 0.667 by the kernel machine learning method), in LUAD (C-index = 0.626 v.s 0.606), in COAD (C-index = 0.604 v.s 0.583), and in GB (C-index = 0.585 v.s 0.572), as well as the immune signature in STAD (0.594 v.s 0.571) and in GB (C-index = 0.594 v.s 0.572). This implies that these two biological signatures may be relevant to the prognosis in particular cancer types but not universally.We found that the kernel machine learning method outperforms the metagene and immune signatures in 7 cancer types Fig. . On averIn LGG, the metagene and lasso signatures achieve slightly higher C-indices than the LGG subtypes Fig.  and the The model including only agnostically selected mRNA transcripts by LASSO performs worse in prognostic prediction than the kernel machine learning method  among 11 of 14 cancer types, most notably in OV . In the remaining three cancer types , C-indices of agnostically selected mRNA transcripts by LASSO  and aggregated mRNA transcripts combined via the kernel machine learning method  are very close, suggesting that either the prognostic power of mRNA is generally weak or the number of prognosis-related mRNA transcripts is limited in these cancer types.Taken together, these results suggest that traditional prognosis methods relying on a small number of molecular biomarkers, such as Lasso-derived genomic signatures, may not be sufficient to achieve the optimal prognostic performance in some cancer types. The kernel machine learning method provides more refined prognostic prediction by aggregating a large number of molecular features in the half of the cancer types we analyzed.23.Combining clinical variables with molecular profiles shows increased C-indices (up to 10.8% in HNSC for miRNA) compared to using clinical variables alone in 7 of 14 cancer types  platform. Since the number of subjects available in a given cancer type is much smaller than those for other omic platforms, we examined the RPPA data separately. First, we investigated whether aggregating protein levels by kernel machine learning method would improve prognostic prediction, compared with selecting protein biomarkers by penalized regression (Lasso) methods, across cancer types. Consistent with other omic profiles, the C-indices by kernel machine learning method were significantly higher that the C-indices based on the Lasso model  as a working example to examine how the C-index varies depending on the sample size and the number of mRNA transcripts used to build the kernel method through down-sampling analysis, which repeatedly samples part of subjects or genes with gradually reducing size and evaluates the prognostic performance (Methods).We observed that the C-index steadily increases with increasing sample size and did not plateau even with over nine hundred subjects Fig. . This suAccurate prognostic assessment of cancer is of great value for patients, oncologists, and cancer researchers. Yet it remains challenging due to inter-tumor heterogeneity. The increasing popularity of multi-omic profiling of tumors raises the hope of improving prognostic prediction through the integration of clinical and omic biomarkers. We systematically evaluated the integration of clinical variables and omic profiles for survival prediction using a novel kernel machine learning method, which we applied to over three thousand tumor samples across fourteen cancer types from the TCGA. The kernel machine learning method built upon omic similarity matrices provides a comprehensive framework to incorporate multiple clinical variables and omic platforms simultaneously, yet it is intuitively simple and interpretable in which subjects with similar clinical variables and omic profiles have similar prognostic outcomes.25, but little has been explored in the context of survival prediction using high-dimensional genomic markers. Our study provides several unique contributions. First, most existing studies focus on genomic similarity matrices defined by SNPs only. In contrast, the proposed approach exploits information from multiple omic profiles. Secondly, the existing kernel methods have mostly been applied to study heritability of traits or disease risk27 and to analyze associations between a genetic similarity matrix and trait information in linear28 or logistic regression32 frameworks. In this study, we proposed a new kernel based prediction method for cancer prognosis. This framework allows us to incorporate clinical information and multiple omic profiles for the systematic assessment of prognostic performance across multiple cancer types in thousands of tumor samples.Genomic similarity matrices have been extensively used in genetic epidemiology studiesThe pan-cancer prognostic assessment confirmed that the difficulty of prognostic prediction varies considerably across cancer types, and that the utilities of prognostic profiles are unequal. Some cancer types, like LGG, demonstrate superior prognostic accuracy than others, based on clinical variables or molecular markers. Indeed, cancer type itself explains more than two thirds of C-index variability across cancer types. Among the different omics data types, mRNA expression most frequently provides the highest C-index for predicting patients’ survival outcome compared to the other molecular profiles in our analysis, suggesting that the resulting expression of mutated genes may be more important for patients’ survival than the underlying mutational patterns. DNA methylation and miRNA expression data also provide good prognostic values in several cancer types.Cancer is extremely heterogeneous. We hypothesized that a small number of omic biomarkers would be unlikely to provide sufficient prognostic power. Instead, we proposed to aggregate numerous omic-wide prognostic biomarkers, using a genome-scale kernel machine learning method. Our approach consistently outperformed “condensed” signatures including the metagene signatures, immune signature, and lasso-regression derived prognostic signatures that rely only on the top-performing genes in the transcriptome. In addition, down-sampling of mRNA transcripts of BRCA suggested that thousands of transcripts are likely associated with prognosis. These observations were consistent with our hypothesis that a large number of biomarkers are involved in tumor prognosis in highly heterogeneous tumor types, and that each of the omic biomarkers has a small to moderate prognosis effect size following a distribution with long tail, and do not necessarily pass a genome-wide significance threshold. The omics-wide architecture of tumor prognosis we observed is different from the ones focusing on a relatively small number of omic biomarkers with relatively larger prognosis effect.34, it has been reported that hundreds of variants with gradually decreased effect sizes are associated with complex polygenic traits, such as height36. In discovery and saturate analysis of cancer genes, it suggested that in addition to over two hundred known cancer genes, larger sample size will discover many more with lower mutation frequency37. Such a long-tailed distribution possibly reflects the complex mechanisms underlying the tumor prognosis, genetic architecture of complex traits, and nature selection of somatic mutation respectively.The long-tailed distribution of effect size we proposed for tumor prognosis is not unique but has been also observed in other biomedical research areas. For risk prediction in GWASA number of limitations of our prognosis analysis based on TCGA data warrant more discussion. First, the samples and clinical records of TCGA were collected retrospectively, and tended to over represent cases with fresh-frozen specimens of high quality, and large tumor size in late-stage patients. Hence, the kernel machine learning model trained on the TCGA samples may not be directly applicable to the general population. Second, the sample size of a given cancer type is limited, which may lead to unsaturated prognostic power and overfitting. Further studies with larger size, patients more representative of the general population, and with improved clinical records are necessary to further delineate the omic architecture of prognosis and achieve the full prognostic power of the kernel machine learning method, which should be evaluated in completely independent studies before being applied to clinical practice. Third, the clinical follow up is insufficient in the TCGA study cohorts for some cancer types, including for BRCA and prostate adenocarcinoma (PRAD). Combined with small sample size, this may contribute to the lack of significant increase in prediction performance for the integrated analysis. Fourth, most of subjects in TCGA received the standard treatment, such as surgery, chemotherapy and/or radiation therapy. New studies to evaluate the prognostic utility of omic profiling for newly developed targeted therapies and immunotherapies would be desireable. Finally, although six omic profiles have been examined, additional data types could further improve the precision of predicting clinical outcomes, including measures of intra-tumor heterogeneity, imaging, proteomics, and immunological factors. Our framework can be extended to accommodate these additional data types.In conclusion, our work evaluates the prognostic value of multi-omic profiling integrated with clinical factors in thousands of samples across fourteen cancer types and proposes an omics-wide architecture of tumor prognosis. If confirmed in future studies, it suggests that genome-scale profiling platforms, instead of gene panels, should be preferred for future prognostic assessment and that the research focus should be shifted from molecular biomarker selection to large scale omic biomarker aggregation in the era of precision oncology.We evaluated the prognostic value of six omic profiles and their combinations across cancer types by a multi-omic kernel machine learning method, which includes creating omic similarity matrices using kernels, kernel integration and survival prediction using a Cox kernel machine regression framework, and prognostic performance evaluation.16, and knowledge-driven models based on pre-defined metagenes15 or immune cell infiltration score6, both of which have demonstrated strong prognostic associations in several cancer types. For BRCA, we also applied the Cox model using the PAM5021 classification and the MammaPrint gene signature22. To obtain unbiased performance evaluation, we used cross-validation, fitting model in training datasets and evaluating the prognostic performance in validation datasets using the C-index38. The C-index, as a generalization of area under the receiver operating characteristics curve, is a widely used measure for model assessment in survival analysis that evaluates the proportion of subjects with both longer observed survival time and higher predicted probability of survival . The larger the C-index, the better the prognostic performance is, with a value of one indicating a perfect prognosis prediction and a value of 0.5 indicating a random prediction.In addition to the multi-omic kernel machine learning method, we also considered several alternative approaches, including a conventional method considering clinical variables only, a sparse model involving variable selection39 implemented in the SVA package40 was used to adjust for batch effects. The TCGA datasets were obtained from the TCGA data portal (now the Genomic Data Commons) and the Broad Institute’s Firehose pipeline. Sample size, patient demographics, distribution of tumor stage and overall survival statistics are summarized in Supplementary Table We analyzed 3,382 samples across 14 TCGA cancer types. Rare cancer types were not included due to sample size limitations. Cancer types  with very few events (death) were also excluded. Six different molecular profiles were used, including germline variants (SNP6.0 array), somatic point mutation (whole-exome sequencing), DNA copy number (SNP6.0 array), DNA methylation (Illumina Human Methylation 450 K array), mRNA expression (mRNA sequencing), and miRNA expression . Patient samples with all data types available were included in the analysis. Principal component analysis was used to examine and visualize potential batch effects. Batch effects were identified in four methylation data sets . The ComBat algorithm41, and further reduced to a set of non-redundant regions as described in Mo et al.42. For the methylation data (Illumina Infinium 450k arrays), a beta-mixture quantile normalization43 was applied to normalize the beta-value. Methylation probes with >20% or more missing data and those corresponding to SNP and autosomal chromosomes were removed. RNAseq version 2 was used. MapSplice44 was used for sequence alignment and RSEM13 for the quantitation of gene expression. For mRNA and miRNA sequence data, lowly-expressed genes were excluded based on median-normalized counts.Each individual data type was pre-processed using the following procedure. Copy number alteration data were derived from the segmented data using the Circular Binary Segmentation algorithmM kinds of omic profiles. For the pm omic biomarkers for n subjects which were organized into an n × pm matrix Zm. Denote Z′m as the transpose of Zm and Zmj as its jth column. For mRNA, miRNA, methylation, and copy number profiles, Zmj is in the continuous scale and normalized with mean zero and variance one; for somatic mutation, Zmj was recorded as binary values with zero for observing no somatic mutation at the j th gene and one otherwise. The corresponding linear kernel, an n × n matrix, was defined asAssuming there are 18 measures the similarity between two kernels, namely K1 and K2, defined asith row and jth column. If Kernel alignmentAn omic similarity matrix could also be derived from the omic prognostic score, which is the weighted sum of all biomarkers in an omic profile with weights  following a univariate normal distribution with mean zero and a constant variance. Unlike the other prognosis indices commonly used with a handful of biomarkers, the omic prognostic score involves tens of thousands of biomarkers whose prognosis coefficients are treated as random and concentrate around zero. It can be easily shown that omic prognostic indices follow a multivariate normal distribution with mean zeros and covariate matrix proportional to the corresponding omic similarity matrix (details in the later section).i th subject, We propose a kernel-fusion Cox model as the multi-omic kernel learning method. Specifically, consider the Cox proportional hazards model,From the random effects perspective, we can show that jth clinical variable for subjects in the validation dataset. Finally, the predicted overall prognostic scores are given as We considered, evaluated, and compared several prognosis prediction methods through the Monte Carlo cross-validation. Given the method and cancer type, we randomly selected 80% of subjects as the training dataset and the remaining 20% of subjects as the validation dataset. For each training dataset, we fit (a) the Cox model with clinical variables only using R package “Survival”; (b) the kernel-fusion Cox models for one omic profile at a time and their pairwise combinations using R package “coxme”; (c) the kernel-fusion Cox models considering both clinical variables and omic profiles. To calculate the omic prognostic scores et al.16; (b) a Cox model with predefined prognosis signatures: immune and metagenes. The calculated immune signatures as the immune cell immune infiltration scores were downloaded from http://bioinformatics.mdanderson.org/estimate/. The metagene signatures included CIN, MES and LYM attractor metagenes, whose levels were calculated as the average of the mRNA expression levels of ten top-ranked genes15. For breast cancer, we also applied the Cox model using PAM5021 and mammaPrint gene list22.For mRNA profiles, we further considered (a) a Cox model with LASSO for the omic biomarker selection, the details of which was given by Yuan The prognostic powers of omic profiles, measured by C-indices, for a given cancer type were related. This relation was not surprising. The clinical variables and omic profiles were measured on the same subjects, and we have shown that the omic similarity matrices were well aligned. Hence, we applied linear mixed models to quantify and compare the contribution of clinical variables and omic profiles to prognostic powers across cancer types, while considering their between-cancer correlation. The linear mixed models were fit by the R package “lme4” and P-values were given by R package “lmerTest”.jth omic profile or clinical variables in the ith cancer type, the following model assumed that the variation of We first quantified the contribution of cancer type itself to the variation of C-indices for clinical variables and omic profiles. Denote kth profiles and zero otherwise; linear mixed model A number of modified linear mixed models were applied to compare between clinical variables and omic profiles, and between omic profiles themselves. For example, we used linear mixed model Among 16,598 mRNA transcripts, we randomly selected a subset of transcripts, built kernel, ran fivefold cross-validation for the kernel learning method, and calculated C-indices. The procedure was essentially the same as the one for the complete set of transcripts with one key difference: down-sampling of transcripts. The number of transcripts was gradually reduced until reaching one hundred. For a given number of transcripts, the down-sampling was conducted fifty times and the average C-index was calculated. Similarly, we carried out down-sampling of subjects, i.e. selecting a subset of subjects which gradually reduced until reaching two hundred. For selected subjects, we used all mRNA transcripts to build the kernel, carried out fivefold cross-validation for the kernel learning method, and obtained the C-index. This procedure was repeated fifty times for a given reduced sample size and the average C-index was reported.The TCGA datasets were obtained from the TCGA data portal (now the Genomic Data Commons) and the Broad Institute’s Firehose pipeline.Supplementary Information


Escherichia coliand related bacteria, the nitrogen stress (Ntr) response allows cellsto rapidly sense and adapt to nitrogen limitation by scavenging for alternativenitrogen sources through the transcriptional activation of transport systems andcatabolic and biosynthetic operons by the global transcriptional regulator NtrC.Nitrogen-starved bacterial cells also synthesize the (p)ppGpp effector moleculesof a second global bacterial stress response - the stringent response. Recently,we showed that the transcription of relA, the gene whichencodes the major (p)ppGpp synthetase in E. coli, is activatedby NtrC during nitrogen starvation. Our results revealed that in E.coli and related bacteria, NtrC functions in combinatorial stressand serves to couple two major stress responses, the Ntr response and stringentresponse.Nitrogen is an essential element for all life, and this is no different for thebacterial cell. Numerous cellular macromolecules contain nitrogen, includingproteins, nucleic acids and cell wall components. In  RelA detects andinteracts with the stalled ribosomes, and this stimulates its enzymatic activity tosynthesize (p)ppGpp. Accumulation of (p)ppGpp has far-reaching consequences on majorcellular processes, including transcription, translation and DNA replication, whichcollectively form the cell's stringent response. The stringent response leads to adown-regulation of stable RNA synthesis (rRNA & tRNA), required in high abundancefor fast-growing cells, whilst biosynthetic operons are up-regulated to promote survivaluntil growth conditions improve. The transcriptional inhibition of stable RNA synthesishas a knock-on effect of releasing the RNA polymerase (RNAp) from these highlytranscribed genes and thereby allowing adaptive reprogramming of gene expression to copewith the stress. Importantly, the RelA-mediated stringent response is at the heart ofbacterial adaptation to starvation and stress, and plays a major role in the bacterialcell cycle and expression of virulence genes in bacterial pathogens.The synthesis of (p)ppGpp in response to nutritional stresses has been studied for morethan forty years. The regulatory basis by which (p)ppGpp levels are modulated by the(p)ppGpp synthetase RelA (product of relA in E. coli.Since (p)ppGpp levels are known to increase in response to nitrogen limitation, wewanted to elucidate how amino acid starvation that occurred as a result of nitrogenstarvation might lead to changes in the transcription of relA. The Ntrresponse in E. coli is regulated by the two component system NtrBC,where NtrB is the sensor histidine kinase. Via the PII protein NtrB senses the nitrogenstatus of the cell and responds to low combined nitrogen availability byphosphorylating, and thereby activating, the DNA-binding transcription factor NtrC. As abacterial enhancer binding protein (bEBP), NtrC specifically activates transcription inan ATP consuming reaction from bacterial promoters that are bound by RNAp containing themajor variant promoter-specificity sigma (σ) factor 54 (σ54). In response tonitrogen starvation, NtrC reprograms transcription by directly activating approximately45 genes, which allows the expression of scavenging and transport systems foralternative nitrogen sources, genes that encode proteins involved in catabolism ofnitrogenous compounds and the glutamine biosynthesis pathway to enhance growth in lownitrogen environments.One objective of our study was to explore evidence for links between the perception ofnitrogen stress and expression of relAtranscription at the genome-wide level, we used chromatin immunoprecipitation followedby high-throughput sequencing (ChIP-seq) to determine the genome-wide binding of NtrCunder conditions of nitrogen availability and nitrogen starvation. Simultaneously, wealso determined the genome-wide binding of the RNAp, which was used as a proxy to reportgene expression i.e. transcription. The majority of the genomic loci bound by NtrC undernitrogen starvation were upstream of promoters of genes that were previouslydemonstrated or predicted to be activated by NtrC. Strikingly, the results alsoidentified one new binding site for NtrC, which was located upstream ofrelA. We next conducted a series of experiments to elaborate thisfinding: (1) Using recombinant and in situ activated NtrC we were ableto demonstrate in vitro that NtrC can specifically bind to a DNAfragment representing the region upstream of relA. (2) Invivo gene expression data revealed a >2-fold increase inrelA mRNA levels upon transition of E. coli cellsgrowing under nitrogen replete conditions to nitrogen starved conditions. Concurrently,and as expected, a >2-fold increase in the intracellular levels of RelA protein wasdetected. These differences were not detected in the absence of glnG,the gene encoding NtrC, but were restored to wild-type levels upon complementation withplasmid-borne glnG. (3) We identified a novel σ54-depedentpromoter and showed in vitro and in vivo that itdrives the transcription of relA in a strictly NtrC and σ54dependent manner.To investigate the link between Ntr stress response and E. coli cells to gauge if NtrCactivated relA leads to the stringent response. Results revealed theexpected RNAp occupancy and activity at ~60% of stringent promoters, whereas ~35% ofstringent promoters showed no change whilst the remaining 5% showed an opposite effectto that expected. Thus, it is possible that (p)ppGpp only modulates activity of a subsetof stringent promoters in nitrogen starved E. coli. Nevertheless, theresults unequivocally demonstrated that in nitrogen starved E. colicells, NtrC and σ54 control the increased transcription ofrelA for the accumulation of (p)ppGpp . Intriguingly,results also revealed that the transcription of 9 of the 12 toxin-antitoxin operons,which are indirectly upregulated by ppGpp in E. coli, were activatedunder nitrogen starvation. As TA’s play a central role in the acquisition of bacterialpersistence, we propose that NtrC-mediated accumulation of (p)ppGpp could lead to the"activation" or "priming" of persistence traits in response tonitrogen starvation.During stringent response (p)ppGpp reprograms transcription by binding to the RNAp andthereby affecting occupancy and usage of so called ‘stringent’ promoters to modulategene expression. Therefore, we determined RNAp occupancy and activity at all knownstringent promoters in nitrogen starved E. colito integrate the requirement of scavenging for new nitrogen sources withstringent-response-mediated changes in gene expression to allow the bacterial cell tocope with low nitrogen availability . The clear increase in NtrC mediatedactivation of relA transcription in nitrogen starved E. coliis likely to significantly increase intracellular levels of (p)ppGpp. Whether(p)ppGpp, in addition to mediating the stringent response, also binds to proteinsspecifically associated with the Ntr response and modulates their activitypost-transcriptionally remains to be determined. However, this is a very likelypossibility, given that (p)ppGpp has been documented to bind to GdhA, an enzyme involvedin nitrogen metabolism, in order to signal the ClpAP protease to degrade GdhA duringnitrogen starvation. Further, our results have also underscored the fundamentalimportance of σ54 in bacterial stress response. Given that there are at leastten bEBPs in E. coli, which rely on σ54 and activate thetranscription of genes that allow the cells to cope with a variety of nutritional andabiotic stresses, an attractive line of future investigation would be to exploreinteractions between these adaptive responses and the stringent response via mechanismssuch as that evident with the Ntr response.Previous studies from the Kustu lab dubbed the Ntr stress response as a scavengingresponse as many of the genes that are activated by NtrC encode transport systems fornitrogenous compounds. Our new results have highlighted the need for


GARS gene has been identified as a causative gene responsible for clinical features of dHMN type V in families from different ethnic origins and backgrounds. We present the first cohort of family members of Nigerian descent with a novel heterozygous p.L272R variant on the GARS gene. We postulate that this variant is the cause of dHMN-V in this family, leading to variable phenotypical expressions that are earlier than reported in previous cases. The exact cause for the observed clinical heterogeneity within the family is unknown. One explanation is that there are modifier genes that affect the phenotype. These cases highlight the possibility of considering pathogenic variants in the GARS gene as a potential cause of early onset axonal polyneuropathy with atypical presentation.Distal hereditary motor neuropathies (dHMN) are a rare heterogeneous group of inherited disorders specifically affecting the motor axons, leading to distal limb neurogenic muscular atrophy. The  DiffereGARS gene located on chromosome 7p produces an aminoacyl tRNA synthetase responsible for charging the associated tRNAs and the first protein of its kind implicated in any inherited human disorder. Two transcript variants encoding different isoforms have been found for the gene, leading to dHMN-V with a pure motor form and Charcot-Marie-Tooth 2D (CMT2D) with both motor and sensory involvement [GARS gene cause such a specific neuropathy remains unknown.Of these, the GARS gene that we postulate results in atypical and heterogeneous phenotypes within a single family.Here, we present the first dHMN-V reported case of Nigerian descent with a novel variant of unknown significance (VUS) on the GARS gene. Currently, she is able to sit unsupported but is unable to walk and is wheelchair bound.Sibling #1 is a 2-year-old female, born at full-term, who presented with acute respiratory failure at 10 months of age. She was found unresponsive for an unknown time, needing CPR and emergent intubation. Laboratory investigation was significant for positive RSV and adenovirus coinfection on nasal swab. Two days later, she was extubated and placed on nasal cannula oxygen therapy; however, physical examination showed hypotonia and hyperlaxity of her extremities which prompted further neurological evaluation. On EMG, there was no sensory function abnormality, but motor axonopathy concerning for underlying neuromuscular disorder was detected. Two months later, she was readmitted for respiratory failure as well as failure to thrive due to poor feeding. Her poor feeding was thought to be secondary to underlying neuromuscular disorder, but no etiology was found for her respiratory failure this time. She ultimately underwent G-tube with fundoplication and was discharged home on continuous positive airway pressure. One month later or three months after her initial presentation, she was admitted again for respiratory failure that required intubation. Due to chronic, multiple respiratory failure and inability to extubate and transition to noninvasive ventilation, she underwent tracheostomy for long-term invasive ventilation at 13 months of age. Muscle biopsy results performed at this time were consistent with infantile denervation as seen with anterior horn cell disease in spinal muscular atrophy or motor involvement in a motor-sensory neuropathy. Testing for mitochondrial disease including standard pathological testing of muscle biopsy as well as qPCR analysis for mitochondrial DNA content and respiratory chain enzyme analysis (ETC) of skeletal muscle was negative. Samples were sent for whole exome sequencing (WES) which then revealed a variant of unknown significance (VUS) with a heterozygous c.815T > G (p.L272R) in the Sibling #2 is a 4-year-old female, born at full-term, with no significant past medical history, who developed appropriate gross motor milestones until 9 months of age. She then became delayed experiencing hypotonia and hyperlaxity with predominant weakness and muscle wasting in her upper and lower distal extremities. Despite physical, occupational, and speech therapy, her motor skills regressed, requiring orthotics to assist with motor functions.GARS gene, c.815T > G (p.L272R), interpreted as variant of unknown significance.EMG was also performed, which showed normal sensory function with no myopathic process; thus, no clear diagnosis was established. Unlike her sister, she does not have gastrointestinal or respiratory symptoms. She has adequate airway clearance with strong cough and clearing of her secretions. Further pulmonary workup including blood gas and a polysomnography did not show evidence of hypoventilation syndrome secondary to her underlying neuromuscular disorder. She too tested positive for the novel heterozygous missense sequence variant in exon 7 of the GARS) gene was also confirmed. Findings of the family are summarized in Sibling #3 is a 7-month-old male, born at full-term, otherwise healthy with no significant past medical history, who presented with acute hypercapnic respiratory failure. The patient had decreased activity level and oral intake prior to presentation. Physical exam was significant for grunting, tachypnea, subcostal retraction, and decreased breath sounds on the left lung field as well as hypoxia. Laboratory tests were normal except for an abnormal arterial blood gas, indicating acute respiratory failure that led to the use of continuous positive airway pressure followed by endotracheal intubation. Chest x-ray revealed infiltrates suggestive of left lower lobe pneumonia, subsequently treated with antibiotic therapy. The use of respiratory support was difficult to de-escalate as the patient failed multiple extubation attempts. A bronchoscopic evaluation did not reveal any airway anomalies. Underlying neuromuscular disorder was suspected due to family history of prior similar presentation. After several weeks, the patient was successfully extubated to bilevel positive airway pressure before being discharged home on noninvasive ventilation. Due to his ongoing need for respiratory support, he subsequently underwent tracheostomy. His physical examination is significant for hypotonia and hyperlaxity of extremities consistent with the siblings. Presence of heterozygous c.815T > G (p.L272R) variant of unknown significance (VUS) in the glycyl tRNA synthetase  and probably damaging (PolyPhen-2). All other variants of unknown significance detected on WES resulting in autosomal dominant conditions were inherited from one of the asymptomatic parents.Both parents are asymptomatic and without medical condition. Upon whole exome sequencing (WES) of the parents, the 37-year-old father of the 3 siblings did not have any abnormalities detected. The 36-year-old healthy mother was mosaic for the same Otherwise, there is no history of consanguinity, and there were no other family members with birth defects, learning disabilities, early deaths, pregnancy wastage, and similar symptoms to our patients.GARS) gene have been described in association with a specific form of an autosomal dominant motor axonal neuropathy that preferentially involves the upper limbs known as distal hereditary motor neuropathy V (dHMN-V) or spinal muscular atrophy type Va (dSMA-Va). Discovered in 2003, this is the first example of an aminoacyl tRNA synthetase being implicated in any inherited human disorder, and so far 11 pathogenic missense GARS variants have been linked to not only dSMA-V but also Charcot-Marie-Tooth disease type 2D (CMT2D) [GARS gene have been described in families from different ethnic origins and backgrounds including Bulgarian, North American, Mongolian, Algerian Sephardic Jewish, French, British, Italian, Australian, Ghanan, Japanese, and Korean [GARS gene. A thorough evaluation failed to reveal any other cause for the phenotypes observed in the reported family. Furthermore, two of the three affected siblings presented with a severe phenotype including infantile onset respiratory distress and proximal muscle involvement.Pathogenic variants in the glycyl tRNA synthetase  and distal hereditary motor neuropathy type V (dHMN-V) also known as spinal muscular atrophy type Va (dSMA-Va). The distinction between CMT2D and dHMN-V is based on phenotypical presentation of sensory deficits in the form of reduction in temperature, touch, pinprick, and vibration perception in a glove and stocking pattern present in the latter [GARS gene have typically presented with progressive bilateral weakness, vibratory sense impairment in extremities, and hand muscles atrophy, specifically the thenar and first dorsal interosseus muscles with relative sparing of the hypothenar eminence. Subsequent involvement of the lower limb muscles has been noted in less than half of described patients. Although it is difficult to determine with a high degree of certainty the presence of any sensory abnormalities among our patients due to a very early onset of symptoms, their motor presentations are consistent with dHMN-V.Pathogenic variants in the e latter . PatientGARS-associated axonal neuropathy is made based on phenotypical presentation, electromyography (EMG), and genetic testing. Early signs of clinical symptoms may include transient muscle cramping or pain of the extremities after cold exposure or exertion. Although the onset of symptoms differs among individuals and families, many people typically begin experiencing symptoms during adolescence or young adulthood. Onset of reported cases ranges mostly from second to fourth decades of life. Along with a previously reported infantile presentation in monozygotic twins, these siblings' presentations are one of the earliest onset of dHMN-V ever reported associated with a GARS variant. Previous cases of early infantile onset report GARS variant in the anticodon binding domain [The diagnosis of g domain ; howeverg domain . ProgresGARS gene is one of 37 tRNA synthetase genes ubiquitously expressed and absolutely necessary for protein synthesis in all cells, the exact mechanism of how pathogenic variants in the gene can result in a very specific form of neuropathy remains elusive. In fact, since its identification, four other tRNA synthetase genes have been linked to neurological phenotypes in humans or mice, thus indicating the important role they play in the development and maintenance of the nervous system. Although it is conceivable that neurons may be more prone to a loss or decrease in tRNA synthetase, it has been suggested that GARS could have an alternate function specific to neurons or interact with an unknown molecular partner in the peripheral nerve via a gain of function mechanism.While the GARS plays in the peripheral nervous system. Genetic studies revealed that these children have inherited the variants from their mother, who is mosaic for the variant, explaining why she remains asymptomatic.Our patients, despite sharing the same genetic variant, have varying degree of onset and severity in their phenotypical presentation. For example, two siblings have respiratory failure but at different ages and varying severity, while one sibling does not have any respiratory issue. Such a phenomenon may further suggest the role of an unknown molecular partner or modifier that secondarily affects these phenotypes. Furthermore, the severe phenotypic presentation in our patients stresses the important role GARS gene leading to a severe presentation of HMN with onset during infancy resulting in permanent disability in the affected individuals around the age of 7–11 years [GARS-associated axonal neuropathy is autosomal dominant, there is up to 50% recurrence risk for every future pregnancy.Currently, treatment and prognosis remain unclear for the affected children. There have been recent reports of novel de novo pathogenic variants in the 11 years , 10. We GARS gene described here is the cause of dHMN type V in this family of Nigerian descent. Interestingly, these sibling patients with the same genetic variant presented in an atypical manner with variable phenotypes. The exact cause for the observed clinical heterogeneity is unknown. One explanation is that there are modifier genes that affect the phenotype. With continuing advancements in genomic testing as well as more awareness and discovery of various genetic variants, we anticipate more insight into the GARS gene mutation and its role in axonal neuropathy, specifically dHMN-V. These cases highlight the possibility of considering pathogenic variants in the GARS gene as a potential cause of early onset axonal polyneuropathy.In summary, we postulate that the novel heterozygous p.L272R variant on the


Gastrointestinal malignancies are challenging cancers with considerable economic and societal impacts on health care systems worldwide. While advances in surgical approaches have provided benefits to a proportion of patients, only modest improvements have been attained in the treatment of patients with advanced disease, resulting in limited improvement in survival rates in these patients. Oncolytic adenoviruses are being developed to address gastrointestinal malignancies. Each platform has evolved to maximize tumor-cell killing potency while minimizing toxicities. Tumor-specific bioengineered adenoviruses using chimeric promoters, prodrug convertase enzymes, lethal genes, tumor suppressor genes, and pseudo-typed capsids can provide the innovations for eventual success of oncolytic virotherapy. This article will review the developments in adenoviral platforms in the context of specific gastrointestinal cancers. From the bench to the implementation of clinical trials, this review aims to highlight advances in the field from its early days to the current state of affairs as it pertains to the application of adenoviral oncolytic therapy to gastrointestinal cancers. Gastrointestinal cancers are a significant public health concern worldwide. They have a considerable impact on health economics that permeates multiple aspects of healthcare ranging from screening and prevention to hospice care . In the This review will focus on the evolution of adenoviral bioengineering and manipulation in esophageal, gastric, pancreatic, liver, biliary, and colorectal cancers. Progress in the field from pre-clinical studies to initial clinical trials will be covered with emphasis on barriers in the area and lessons learned to date. It is important to highlight that gastrointestinal malignancies were grouped as a way to facilitate the understanding of the evolution of the viral vectors per gastrointestinal organ. The cell biology of a vast heterogeneity of tumors in each organ is beyond the scope of this review.Ads are non-enveloped DNA viruses with an icosahedral capsid encompassing a linear duplex genome of ~36 kb. Ads have been found in the majority of vertebrates . In immuHuman adenoviruses represent an interesting oncolytic virotherapy platform given their (1) high transduction efficiency in transformed cells , (2) lack of integration into the host genome resulting in a lowered risk of insertional mutagenesis, (3) low seroprevalence with regards to specific serotypes, (4) high fidelity DNA polymerase, which confers relative stability, and (5) ability to attain tumor specificity through substitution of the viral promoter with cancer tissue selective promoters or mutations that enable virus replication to occur preferentially in transformed cells.Oncolytic virotherapy (OV) is a promising therapeutic platform with applications across a broad array of malignancies. The field of OV has its roots in 1890 when the first reports of spontaneous tumor regression were noted, following an episode of viral illness in a patient with leukemia. Similar observations followed in patients exposed to viral infections such as varicella, hepatitis, and measles in patients with Hodgkin’s disease, resulting in spontaneous regressions. However, these effects were mostly transient in nature. After a period of relative dormancy, a revival in the OV arena over the last 15 years has been incited by the increased understanding of the biology of viruses and advances in synthetic biology and recombinant nucleic acid technology allowing for the synthesis of more potent, selective and safe therapeutic viral vectors. In 2006, the adenovirus H101 was approved for clinical use in China for patients with advanced nasopharyngeal cancer [Oncolytic adenoviral (OA) strategies have been evaluated in gastrointestinal (GI) malignancies. Although the initial research was conducted with Ad5, natural tropism and seroprevalence are becoming critical factors for vector success. A number of Ad subgroups can provide natural GI tropism such as the subgroup A (Ad12), and subgroup F  . The Ad1Additionally, tumor specificity and tropism can be enhanced through promoter driven replication. In GI malignancies, a number of adenoviral tumor-specific promoters have been analyzed to date . Each ofAds provide the possibility for hexon swapping and fiber pseudotyping among the subgroups, which is attractive toward maximizing targeting and delaying immune neutralization. Given this vast array of Ad serotypes, chimeric capsid investigations are still evolving, especially when considering which combinations would best enhance tissue tropism and provide on-target attachment. A few studies have shown that some chimeras combined with specific molecules could promote a reduction of tropism, which is of significant interest, as it may enhance virus bioavailability and contribute to diminishing liver and reticuloendothelial system sequestration .Esophageal cancer has an incidence of 17,290 cases a year in the United States (USA), and patients who are inflicted typically experience a reduced quality of life . AlthougE1B region as and a part of the E3 region. The four medications tested were 5-fluorouracil, etoposide, mitomycin C, and cisplatin. 5-Fluorouracil intensified the cell cycle at the S phase and promoted G2/M phase entry. Conversely, cisplatin produced G1 phase arrest. Interestingly, cisplatin action was antagonistic to the Ad vector, as it inhibited the Ad-mediated cell-cycle, not favoring a viral cytopathic effect. As such, cisplatin could lead to cross-resistance with Ad vectors [Meanwhile, another study evaluated resistant tumor cells which exhibit properties of cancer stem cells. Radiotherapy resistance is a significant concern in esophageal cancer. The study analyzed an Ad carrying the tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL) that preferentially induced apoptosis in radio-resistant cancer cells . Another vectors .E1A genes under the control of a human telomerase reverse transcriptase (hTERT) promoter. The study achieved selective viral replication in tumor cells that expressed telomerase activity. The pre-clinical efficacy of this vector was found to be encouraging, and its evaluation has now progressed to clinical trials [Furthermore, another group developed the vector Telomelysin (OBP-301), which is an Ad5, with l trials . Along tl trials . A chimeric vector, using Ad H101 as a packaging template and the Newcastle disease virus (NDV) hemagglutinin-neuraminidase (HN), was tested in animal models. An increase in reactive oxygen species resulted in cytotoxicity and complete curative responses with prolonged survival with the use of intra-tumoral vector inoculation . DespiteGastric cancer has an incidence of 26,240 cases per year in the USA . SurgeryOV therapy represents a promising platform for use in gastric cancer . EnhanceAlong these lines, a group investigated the Ad Telomelysin (OBP-301), to target quiescent stem-like cells. These cells are in a dormant phase, which makes them resistant to chemotherapies. A strategy to overcome their resistance is to mobilize them into the cell cycle to elicit a treatment response. It has been shown that cell cycle mobilization can be induced by adenoviral infection. This study was important from this perspective, given that mobilization of cells from a quiescent state into the cell-cycle had only been previously achieved in leukemia .Another study established a viral gene therapy approach by creating an Ad with endostatin, which is known to be an inhibitor of angiogenesis . This veLiver and intrahepatic bile duct cancers have an incidence of approximately 42,220 cases per year in the USA . CurrentAd CV890 was the first Ad5-specific developed for selectively targeting hepatocellular carcinoma (HCC) by using alpha-fetoprotein (AFP) as a tumor-specific promoter. This study evaluated the ability of the vector to eliminate distant tumor recurrence when used in combination with doxorubicin in animal models. These promising data support further evaluation of this OV in clinical trials .A pioneer Ad in OV, ONYX-015 , has beeE1B gene under a TP53 gene expressing cassette coupled with hTERT promoter and a hypoxia response element (HRE) promoter. The goal was to foster p53 protein production in a hypoxic microenvironment in telomerase-positive HCC cells. An enhanced oncolytic effect was reported with this dual promoter-enabled construct [Another strategy for enhancing OV and gene therapy delivery is the dual promoter approach. It can enhance tumor specificity as well as vector delivery of gene therapy in HCC. A CRAd system placed the Ad onstruct .Other studies have successfully implemented anti-tumor gene therapy using the Ad OV platform to deliver a tumor suppressor gene, TSLC1, which is lost in many human cancers, including liver cancers, and conserved in normal cells .A parallel strategy to enhance tumor-killing effect was achieved by adding lethal mitochondrial genes to the Ad OV platform to be selectively expressed in tumor cells by the vector. One such vector that has been developed encodes the second mitochondria-derived activator of caspases (SMAC) protein. It demonstrated superior oncolytic potency but had also exhibited cytotoxicity toward normal cells, emphasizing the importance of achieving tumor selectivity when deploying more potent vectors . Besides AFP, transthyretin  has alsoAdditional layers of safety through the use of microRNAs, such as let-7 to control vector replication and diminish hepatotoxicity, have also been utilized . RecentlAnti-tumor effect and oncolysis have been improved by a number of strategies. SOCS3, suppressor of cytokine signaling 3, can downregulate Cyclin D1 and anti-apoptotic proteins . SimilarAnother strategy to augment OV potency within the tumoral hypoxic microenvironment relies on oxygen-dependent degradation domain-regulated vectors . SimilarRecently, the concern regarding the concomitant use of Ad and cisplatin has been overcome by providing XAF1 in a vector platform. XAF1 counters the effects of the inhibitor of apoptosis protein (IAP). This vector enabled enhanced tumor cell apoptosis through activation of the caspase-9/PARP pathway, which ultimately resulted in reduced cisplatin doses .Pancreatic cancer has an incidence of 55,440 cases per year and more than 21,000 deaths each year in the USA . DespiteTP53 deficient cells. Additionally, this Ad vector was manipulated to express uracil phosphoribosyl transferase (UPRT), which can enhance therapeutic effects, given that it overcomes 5-fluorouracil resistance [Efforts are underway to develop specific OVs for pancreatic cancer . An Ad wsistance .Another gene-therapy-based OV utilized Ad5 carrying suicide genes such as the cytosine deaminase (CD) as well as herpes virus thymidine kinase (HSV TK). This vector was able to improve radiotherapy effect without excessive pancreatic toxicity . ConversOne approach toward achieving tumor selectivity in pancreatic cancer involves an Ad targeting matrix metalloproteases (MMPs) at the surface entry level and has been shown to reduce metastases with no significant toxicities in vivo for pancreatic cancer models .SYE ligand, with targeting sequence SYENFSA, acting as a pancreatic cancer-targeting ligand capable of boosting promoter specificity of an Ad. Studies conducted with this ligand have achieved robust transduction efficiency, resulting in potent oncolysis in pancreatic tumors [Likewise, another approach along these lines has employed the use of a c tumors .Colon cancer has an incidence of 97,220 cases per year in the USA, and rectal cancer has an incidence of 43,030 [Besides achieving tumor specificity, OV platforms for colon cancer have also focused on expressing pro-drug activating enzymes in cancer cells . One earMoreover, the previously described Ad Telomelysin (OBP-301) was modified to yield the construct Telomelysin (OBP-405) that contains an RGD motif in the HI loop of the fiber knob. This modification allows for the ability to overcome the limitation of CAR expression in target cells .Congruent with previous OVs using dual gene virotherapy, the study exhibited promising results in colon cancer cells by using manganese superoxide dismutase as a potential tumor suppressor gene along with the tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) gene embedded in an Ad platform with a deleted E1B-55 kD region. The complete elimination of tumors in xenograft models in vivo was reported in .Furthermore, ST13, a colorectal cancer-specific tumor suppressor gene, was inserted into a CRAds under the control of CEA promoter resulting in significant levels of apoptosis in colon cancer cells . AnotherStrategies targeting cancer stem cell/stem cell-like markers are an additional promising approach in OV research. One such construct involved the use of an Ad with a CD133-targeting motif resulting in promising efficacy both in vitro and in vivo .A clinical trial testing Telomelysin in esophageal cancer is eagerly awaited, as the first Ad trial for this type of cancer. As for gastric cancer, some trials have evaluated viral vectors in advanced peritoneal disease, along with ovarian cancer, but none has focused solely on gastric cancer and Ad OV. In the case of pancreatic cancer, primary liver cancers, and colorectal cancer, a number of clinical trials are ongoing . Data emAdenoviral vectors represent a promising platform for cancer therapy in GI cancers as highlighted in this review. A number of exciting strategies applied to OV vector design have facilitated tumor selectivity, potent cytotoxicity, and tumor microenvironment modulation. A broad array of pre-clinical evaluations in GI cancers is showing promise, and a number of these concepts have been carried forward to early phase clinical studies. It is anticipated that future efforts will encompass the study of OV Ads in combination with immunotherapies such as immune checkpoint inhibitors, improvements in the vector delivery/evasion of immune response, and amalgamation with gene editing approaches.


Few studies have evaluated the QLQ-C15-PAL health-related quality of life (QOL) questionnaire, an abbreviated version of the QLQ-C30 questionnaire that was designed specifically for patients with advanced cancer. The present study assessed whether certain symptoms or functional domains from the QLQ-C15-PAL predicted overall QOL when rated prior to palliative radiation treatment (RT).Patients attending an outpatient palliative radiotherapy clinic completed QLQ-C15-PAL questionnaires prior to palliative RT for bone, brain or lung disease. Pearson correlations were computed between the QLQ-C15-PAL functional/symptom scores and overall QOL scores. Multiple linear regressions were used to evaluate the relative importance of functional/symptom scales in association with overall QOL.Data from 369 patients were analyzed. The QLQ-C15-PAL domains of physical and emotional functioning, pain, and appetite loss were significant predictors of overall QOL in these patients with advanced cancer. Appetite loss was the only significant independent predictor of overall QOL in the subgroup of patients with advanced lung cancer (n = 29). Both appetite loss and emotional functioning were independently predictive of overall QOL in patients with bone metastases (n = 190). In patients with brain metastases (n = 150), independent predictors of overall QOL included physical and emotional functioning as well as fatigue.The QLQ-C15-PAL domains of physical and emotional functioning, pain and appetite loss were significant predictors of overall QOL in this cohort of patients with advanced cancer. Different functional and symptom scales predicted overall QOL in patients with bone metastases, brain metastases or advanced lung cancer. Traditional endpoints of overall survival are no longer the primary endpoint of most palliative oncology clinical trials and have been replaced with more patient-specific determinants measuring symptom control and quality of life (QOL) . AssessiIn studies that have assessed the prevalence of distressful symptoms in patients with advanced cancer, up to 85% of patients have reported at least one moderate to severe symptom , 7.An abbreviated version of the QLQ-C30, known as the QLQ-C15-PAL, has been developed to decrease the burden of the longer more time-consuming parent questionnaire for patients with advanced cancer . AlthougThe Rapid Response Radiotherapy Program (RRRP) is a rapid-access outpatient palliative RT clinic running daily in the Odette Cancer Centre at the Sunnybrook Health Sciences Centre, Toronto, Ontario Canada. All research was therefore conducted following approval from the Sunnybrook Health Science Centre research ethics board. This study included patients seen in RRRP consultation between October 2007 and July 2010, with completed QLQ-C15-PAL questionnaires prior to palliative RT for bone metastases, brain metastases or advanced lung cancer. The term advanced lung cancer will be used throughout the manuscript referring to patients who were seen in consultation for palliative RT of primary lung cancer or lung metastasis. Baseline patient data collected included age, Karnofsky performance status (KPS), gender, primary cancer site and presence of other metastases, including visceral or bone metastases outside of the RT site.Prior to RT, general HRQOL and overall QOL was assessed using the QLQ-C15-PAL. This QOL measurement tool consists of 15 questions; two multi-item functional scales , two multi-item symptom scales (fatigue and pain) along with five single item symptom scales  and one final question referring to overall QOL. The physical functioning scale is based on three questions that ask about walking, activities of daily living and time spent in bed or in a chair. The emotional functioning scale is based on two questions that ask about feeling tense or depressed. Patients rated each question/item on a numeric scale from 1  to 4 (very much), with the exception of global QOL which was rated from 1 (very poor) to 7 (excellent). The EORTC QLQ-C30 scoring manual  was usedResults were initially analyzed for the entire group of patients with advanced cancer. Demographics were expressed as proportions for categorical variables but means, standard deviations (SD), inter-quartiles, medians and ranges (minimum to maximum) for continuous variables. Pearson correlations were computed between all functional/symptom scales and the overall QOL scale. The strength of the relationship was indicated by the correlation coefficient (r). The larger the correlation, the stronger the relationship. The following rules were used for categorizing r: ≤ 0.50 very low; 0.51-0.79 low; 0.80-0.89 moderate, ≥ 0.90 high. The significance of the relationship was expressed in probability levels of p-value. The smaller the p-value, the more significant the relationship. When sample size is large, a small r-value may be significant but with a weaker relationship .A multiple linear regression analysis was then used to evaluate the relative importance of different functional/symptoms scales in predicting overall QOL. Two separate models were computed. In the first model, the 2 functional scale variables were entered simultaneously with the regression equation. In the second model, the 7 symptom scale variables were entered simultaneously with the regression equation. Finally, variables found to be statistically significant in previous regression analyses were entered together as independent variables in predicting overall QOL. The above process of statistical analysis was performed for each subgroup of patients with symptomatic bone, brain and lung disease. Two-tailed p-values less than 0.05 were considered statistically significant. All analyses were conducted by Statistical Analysis Software .A total of 369 patients with advanced cancer completed the QLQ-C15-PAL questionnaires prior to palliative RT for bone (190 patients), brain (150 patients) or lung disease (29 patients). Baseline patient characteristics are presented in Pearson correlations in all patients showed that all functional and symptom scales were significantly correlated with overall QOL , althougWhen the significant functional scale and significant symptom scale variables were put into one model , both fuBaseline characteristics are presented in With the exception of dyspnoea, all functional and symptom scale variables were significantly correlated with overall QOL according to Pearson correlations , althougPearson correlations showed that all functional and symptom scales were significantly correlated with overall QOL . ThroughWith the exception of pain and constipation, all functional and symptom scales were significantly correlated with overall QOL according to Pearson correlations . ThroughIn the palliative setting when facing treatment decisions for patients with poor prognosis, symptom control and QOL become arguably the most important goals of care rather than traditional oncologic endpoints of survival . While osince its development in 2006 [It has previously been proposed that HRQOL is antecedent to overall QOL and that it is important to differentiate between the two . Using t in 2006 , 20. Sua in 2006 . In a stTo our knowledge, this is the first study using the QLQ-C15-PAL to explore dimensions of HRQOL which may predict overall QOL in patients with advanced cancer. In the initial analysis of all 369 patients with advanced cancer, multiple regressions identified both physical and emotional functional scales as well as pain and appetite loss symptoms as significant predictors of overall QOL. This initial analysis included a broad group of patients with respect to their individual characteristics and tumor types. As the symptom burden is often very different depending on the disease site, sub-group analysis was performed. It is acknowledged that not all patients fit specifically into discrete categories as patients with advanced cancer often have multiple sites of disease. In this study, other metastases were present in 23%, 47% and 69% of patients with bone metastases, brain metastases and advanced lung cancer, respectively. By classifying patients according to the main reason for referral, this study has shown that different functional and symptom scales have varying significance to overall QOL in specific subpopulations of patients with advanced cancer.In the relatively small group of patients with advanced lung cancer (n = 29), appetite loss was the only significant predictor of overall QOL. Emotional and physical functioning were initially significant but were not identified as independent predictors in the final model. These results are in contrast to a previously published study in lung cancer, identifying emotional functioning and fatigue as independent predictors of overall QOL, using the core QLQ-C30 along with the lung cancer-specific QLQ-LC13 questionnaire . The autOur cohort consisted of a large number of patients with bone metastases (n = 190). In this patient population, emotional functioning and appetite loss were found to be predictive of overall QOL. Although pain is often the primary reason for referral of patients with bone metastases to a palliative RT clinic, pain was not predictive of overall QOL in this assessment. These findings are consistent with previous work using a variety of questionnaires, identifying psychosocial issues such as depression, and not pain, to be most predictive of overall QOL in patients with bone metastases . While iIn the present study’s assessment of patients with brain metastases, predictors of overall QOL included both emotional and physical functioning as well as fatigue. The emotional burden of a brain metastases diagnosis may be multi-factorial, including the extremely poor prognosis associated with it and the current or anticipated cognitive/physical impairments which lead to loss of independence . As mostThe data from this study, like those from studies using the QLQ-C30 , 13, doeIn conclusion, symptom control and QOL preservation are crucial goals in oncology. Standardized assessments of such subjective endpoints are now possible through a wide range of tools such as the QLQ-C15-PAL. Using the QLQ-C15-PAL, this study has shown that different functional and symptom scale variables predict overall QOL in patients with bone metastases, brain metastases or advanced lung cancer prior to palliative RT. It is acknowledged that such an assessment may be affected by various features including the assessment tool used and the patient population under investigation. While this is a study of patients referred for RT, results may vary in those patients receiving different treatment modalities. Future studies may wish to investigate whether the treatments themselves subsequently affect overall QOL and predictors thereof. Simply assessing the single variable of overall QOL may be important when an outcome variable is required or a global impression of QOL is desired . On the


Cardiovascular disease (CVD) still remains the main cause of morbidity and mortality and consequently early diagnosis is of paramount importance. Working conditions can be regarded as an additional risk factor for CVD. Since different aspects of the job may affect vascular health differently, it is important to consider occupation from multiple perspectives to better assess occupational impacts on health. Standard echocardiography has several targets in the cardiac population, as the assessment of myocardial performance, valvular and/or congenital heart disease, and hemodynamics. Three-dimensional echocardiography gained attention recently as a viable clinical tool in assessing left ventricular (LV) and right ventricular (RV) function, volume, and shape. Two-dimensional (2DSTE) and, more recently, three-dimensional speckle tracking echocardiography (3DSTE) have also emerged as methods for detection of global and regional myocardial dysfunction in various cardiovascular diseases and applied to the diagnosis of subtle LV and RV dysfunction. Although these novel echocardiographic imaging modalities have advanced our understanding of LV and RV mechanics, overlapping patterns often show challenges that limit their clinical utility. This review will describe the current state of standard and advanced echocardiography in early detection (secondary prevention) of CVD and address future directions for this potentially important diagnostic strategy. Cardiovascular disease (CVD) still remains the main cause of morbidity and mortality , 2 and cAtherosclerosis, the basis of coronary artery disease (CAD), develops into a complex process . CAD is α-amylase and cortisol diurnal slopes, reduced daily α-amylase production, elevated daily cortisol production, and reduced heart rate variability and endothelial functioning.Although stress-related disorders comprise only a small fraction of occupational injuries and illnesses , overallA deleterious effect of shift work was also shown on lipid metabolism –24. Womeμm (PM2.5) as the fifth most important risk factor for mortality worldwide and the burden from air pollution was comparable to that from hypertension and diabetes mellitus [Many features of the environment exert an important influence on CVD risk, progression, and severity. Numerous epidemiological and experimental studies showed that air pollution , 26 causmellitus ; thus itWhereas the link between occupation and cardiovascular disease has been recognized, it is unclear which role occupation plays in the progression of subclinical CVD measured as carotid intima-media thickness (CIMT). Few longitudinal studies exist about occupation and subclinical CVD. Furthermore often these studies do not examine whether occupation is only an indicator of the person's socioeconomic position or a source of potentially health-compromising exposures. While in the former approach occupational differences show the socioeconomic gradient in CVD, in the latter approach occupation represents additional damage to the risk profile.A weak positive correlation was found between CIMT and age, diastolic blood pressure, LDL cholesterol, and antiphospholipid antibodies, and a negative correlation with the presence of anti-cardiolipin antibodies and a general level of occupational stress –28. A moCoronary artery disease or ischemic heart disease (CAD or IHD) is one of the major causes of morbidity and mortality. Echocardiography provides a thorough complex assessment of structural and hemodynamic changes induced by acute or chronic CAD –32, and Myocardial ischemia impairs regional systolic contractility. Persistence of severe ischemia produces development of myocardial necrosis and scarring with permanent functional impairment . RegionaThe use of deformation (“strain”) echocardiography allows less subjective assessment of myocardial contraction as compared to simple visual evaluation –36. The Since in the normal myocardium strains and strain rates are homogenously distributed, subtle strain changes suggest myocardial contractile impairment. A good correlation has been shown between longitudinal strain decrease and reduced coronary perfusion in segments that appear visually normal. In addition to regional myocardial strain assessment, a global longitudinal strain (GLS) can be determined averaging peak longitudinal strain in all evaluable segments. GLS is compromised in CAD patients and appears as a useful index for assessment of global myocardial function. A GLS cut-point of greater than −17.77% had good sensitivity and specificity for detecting CAD and was comparable to a WMSI ≥ 1.13 measured during stress . UnfortuThree-dimensional speckle tracking echocardiography (3DSTE) has recently been regarded as a more promising technique to accurately and reproducibly evaluate the segmental and global LV function . The 3D The role of 2D stress echocardiography (SE), either exercise (exercise SE) or pharmacologic with dobutamine (DSE), is well established for diagnosis and prognosis of patients with known or suspected CAD , 37–52. The sensitivity of SE is higher for multivessel CAD and lower for single vessel disease. Sensitivity of the test is also influenced by the time between peak stress and image acquisition. It is imperative to accomplish postexercise imaging as soon as possible (≤1 min from cessation of exercise). When abnormalities recover rapidly, false-negative results occur. On the other hand, despite the excellent specificity of SE, some patients have false-positive tests  in the absence of left bundle branch morphology, right ventricular pacing, prior cardiac surgery, or abnormal wall tethering at baseline. These false-positive findings present a management challenge because it remains unclear whether these patients should be treated and how to treat them. A subset of patients has microvascular abnormalities , a hyper2D-SE has its own limitations as multiple views of the left ventricle (LV) must be obtained from more than window to completely visualize all segments. Three-dimensional stress echocardiography (3D-SE) allows assessing overall wall motion of the entire LV simultaneously in different planes . 3D imagA major limitation of echocardiographic study interpretation is the subjective visual analysis of endocardial motion and wall thickening which is only semiquantitative. The need for more quantitative techniques to objectively evaluate LV regional wall motion during DSE has led to the incorporation of new indices in the analysis of regional wall motion. Tissue Doppler imaging (TDI) is a novel echocardiographic technique that can be used to quantitatively assess low-velocity motion of myocardial walls with excellent temporal resolution . 2D-straRegional LV akinesia in CAD patients is irreversible in the presence of scarred tissue but may be reversible in stunned or hibernated myocardium –52. StunAssessment of contractile response to inotropic stimulation by low-dose dobutamine echocardiography or evaluation of myocyte metabolism with nuclear scan and positron emission tomography is used to identify hibernating myocardium. Recent clinical reports such as STICH trial suggest that we should not rely on one single imaging parameter and that optimal medical therapy may be sometimes as effective as revascularization procedures , 50. HowHypertensive heart disease represents a form of organ damage with strong independent prognostic significance. Assessment of myocardial anatomy and function is necessary to identify early cardiac alterations in hypertensive patients since left ventricular hypertrophy (LVH) is the first step toward the development of coronary heart disease, stroke, heart failure, and sudden death. Recent Guidelines of the American College of Cardiology/American Heart Association  define nEchocardiography may also give useful information on cardiac functional performance, including systolic and diastolic changes . LV diasSystemic hypertension may be a potential independent risk factor for heart failure with preserved ejection fraction, and LV diastolic filling changes can be observed even in the absence of LV remodeling. Since the left atrium (LA) is directly exposed to LV pressures during ventricular diastole, LA size is correlated with LV filling pressure. Thus, LA structural remodeling represents a stable indicator of the severity of LV diastolic dysfunction –61. BesiThe role of arterial stiffness in systemic hypertension has also been demonstrated and is based on its pathophysiological importance for overall cardiovascular performance –68. The Heart failure (HF) is a common clinical syndrome, especially in the elderly, but its diagnosis is often missed. A detailed clinical history is crucial and should address not only current signs and symptoms of heart failure but also those signs pointing to a specific cause of the syndrome, such as coronary artery disease, hypertensive heart disease, or valvular heart disease. Echocardiographic imaging has several targets in the HF population, including the assessment of myocardial structure and function, valvular disease, and hemodynamics.All modalities of echocardiography are useful in the assessment of the HF patient –73. HistTwo-dimensional echocardiographic assessment of LV ejection fraction is based on measuring end-diastolic and end-systolic volumes , 69–71 uMost HF patients have reduced LV systolic function; however a significant subset has normal or near normal resting systolic function with predominantly diastolic dysfunction , 74–77. Diastolic dysfunction causes impairment of LV relaxation and compliance and consequent elevation of LV filling pressures, left atrial pressures, pulmonary venous pressures, pulmonary capillary pressures, and right heart pressures. Cardiac performance is compromised with exercise, even in the presence of normal stroke volume and cardiac output at rest. Doppler echocardiography can help in the diagnosis by allowing assessment of indices of diastolic filling and ventricular relaxation. Myocardial hypertrophy  and/or iThe presence of RV dysfunction has important prognostic implications in patients with HF. RV dysfunction has often been evaluated qualitatively, but the development of RV annular systolic tissue velocity, RV free wall strain, and three-dimensional RV volumes  has beenSelection of patients for a wide range of device therapies needs also an accurate echocardiographic evaluation –84. ThesAdults with congenital heart disease (CHD) have increased in number due to advances in early diagnosis and treatment . CardiovApart from the assessment of postoperative complex CHD, nowadays echocardiography has gained an important role in the management of patients with patent foramen ovale (PFO) and atrial septal defect (ASD). Strokes have high rates of morbidity being the second leading cause of death, and up to 40% of ischaemic strokes are cryptogenic. A strong association has been shown between cryptogenic stroke and PFO prevalence suggesting paradoxical embolism via PFO as a potential cause. PFO closure with Amplatzer device appeared superior to medical therapy in preventing strokes in patients with cryptogenic embolism , and theQp/Qs) ratio ≥1.5 : 1 and signs of right ventricular volume overload. Indications for PFO closure are cryptogenic stroke and evidence of right to left shunt.PFO represents a remnant of the foetal foramen ovale and has a flap valve mechanism that intermittently opens a small channel between left and right atrium. It is associated with an intermittent right-to-left shunt, only rarely with a permanent left-to-right shunt, has an incidence of 20% to 35% of the general population, and is commonly treatable by device closure. On the contrary, ASD is a permanent opening characterized by left-to-right shunting with transient right-to-left shunting on the basis of the breathing cycle and accounts for nearly 10% of congenital heart disease. Different types of ASD involve particular structures such as septum primum (ostium primum ASD), septum secundum (ostium secundum ASD), sinus venosus (sinus venosus ASD), and coronary sinus (coronary sinus ASD). The ostium secundum ASD is the most frequent type and the only one treatable by percutaneous transcatheter closure. Indications for ASD closure are isolated secundum ASD with a pulmonary/systemic flow  has become an established approach  to guideTraditionally, percutaneous ASD and PFO closures are performed under 2D-TEE guidance. TEE is also employed prior to device closure to assess defect size, position, and any associated left-to-right and right-to left shunting . It is iLife-threatening acute congestive LV failure complicating surgical or transcatheter closure of ASDs in patients with LV restriction has been reported , 100. DeThree-dimensional TEE (3D-TEE) offers improved spatial orientation and better definition of ASD, “complex” PFO, or multifenestrated septal aneurysm , 102. ThPatients with valvular heart disease (VHD) can be asymptomatic or present with a series of symptoms not always related to the severity of the lesion . CurrentMitral regurgitation (MR) is the most common clinically recognizable valvular heart condition. The disease has various congenital and acquired etiologies. Mitral valve prolapse (MVP) is a primary condition  characteIt has become evident that moderate-to-severe MR, even in the absence of LV dilatation and dysfunction, may have adverse prognostic consequences. Thus, accurate echocardiographic quantification of MR is vitally important in clinical medicine, especially when planning surgery or interventional percutaneous procedures , 107 as Aortic valve stenosis (AS) represents a significant cause of morbidity and mortality . The pre2 [Complex presentations with multiple comorbid conditions make often the diagnosis and management of AS challenging. Clinical signs and symptoms are of limited use in distinguishing critical from noncritical AS due to unsatisfactory sensitivity and specificity especially in the aged. Moreover, assessment of the symptomatic status and severity of valvular lesion can be disappointing because of the subjectivity of symptoms and ambiguity of individual functional capacity. The current guidelines approve two-dimensional transthoracic echocardiography (TTE) as the diagnostic test of choice for the assessment of AS. In addition to TTE, 2D transesophageal echocardiography (2DTEE) provides images with higher resolution , while r2 . IndicatThe majority of aortic valve replacements are surgical aortic valve replacement. However, in high-risk patient groups, factors such as increasing age, prior cardiac surgery, and other comorbidities, such as heart failure, respiratory, and renal diseases, are associated with a high potential for operative mortality and morbidity. Transcatheter aortic valve implantation (TAVI) was first performed in 2002 as a less invasive approach and is now recommended as an alternative strategy for patients in high risk surgical groups –114. MulEchocardiography using both conventional and more recent modalities is the primary diagnostic tool for the assessment of early cardiac dysfunction. It is a cost-effective technique and offers real-time imaging with high spatial and temporal resolution. Three-dimensional echocardiography and two-dimensional and three-dimensional speckle tracking echocardiography may provide additional diagnostic information. It is plausible that echocardiography will further evolve in the future with improvements in technology providing better clinical help. A strong professional liaison between echocardiologists and occupational physicians aids the process of offering effective support and management for the cardiovascular patients in the workplace.