Overweight, obesity, and related chronic diseases are becoming serious public health concerns in rural areas of India. Compounded with the existing issue of underweight, such concerns expose the double burden of disease and may put stress on rural healthcare. The purpose of this article was to present the prevalence and factors associated with underweight, overweight, and obesity in an area of rural south India.During 2013 and 2014, a random sample of adults aged 20–80 years were selected for participation in a cross-sectional study that collected information on diet (using a food frequency questionnaire), physical activity , socioeconomic position , rurality , education, and a variety of descriptive factors. BMI was measured using standard techniques. Using a multivariate linear regression analysis and multivariate logistic regression analyses, we examined associations between BMI, overweight, obesity, and underweight, and all potential risk factors included in the survey.Age and sex-adjusted prevalence of overweight, obesity class I, and obesity class II were 14.9, 16.1, and 3.3 % respectively. Prevalence of underweight was 22.7 %. The following variables were associated with higher BMI and/or increased odds of overweight, obesity class I, and/or obesity class II: Low physical activity, high wealth index, no livestock, low animal fat consumption, high n-6 polyunsaturated fat consumption, television ownership, time spent watching television, low rurality index, and high caste. The following variables were associated with increased odds of underweight: low wealth index, high rurality index, and low intake of n-6 PUFAs.Underweight, overweight, and obesity are prevalent in rural regions of southern India, indicating a village-level dual burden. A variety of variables are associated with these conditions, including physical activity, socioeconomic position, rurality, television use, and diet. To address the both underweight and obesity, policymakers must simultaneously focus on encouraging positive behaviour through education and addressing society-level risk factors that inhibit individuals from achieving optimal health. Obesity and its associated health effects are quickly becoming serious health concerns in low- and middle-income countries . In 20102 could be found in Asian Indians at a much lower BMI. Such studies indicate that Asian Indians are more susceptible to the negative health consequences of overweight and obesity. Even more troubling is the co-occurrence of undernutrition and overnutrition in rural villages and households, termed the ‘double burden’ of disease, which serves to exacerbate poverty and limit economic growth in these areas [Overweight and obesity are associated with increased risk of non-communicable diseases such as metabolic syndrome, high cholesterol, type 2 diabetes mellitus, high blood pressure, and cardiovascular disease; conditions that are already serious public health concerns in rural and urban India alike , 7. Studse areas . While ase areas , 11 and se areas , 13, litse areas .The present study was conducted from November 2013 to March 2014 and examined a random sample of 753 people living in a rural region of northern Tamil Nadu. The objectives of the study were: (1) to determine the prevalence of underweight, overweight, and obesity among the study population; and (2) to determine the risk factors that affect BMI, underweight, overweight, obesity in rural regions of India using forwards stepwise linear and logistic regression models. The study was carried out in collaboration with the Development for Humane Action Network (DHAN) Foundation as part of a DFATD/IDRC-funded project entitled “Revalorizing small millets: Enhancing the food and nutritional security of women and children in rainfed regions of South Asia using underutilized species”.panchayat councils, local police officials, and hospital medical staff) and sought and obtained permission to carry out the study. Informed consent to participate in the study was obtained from all participants prior to their enrollment.We obtained clearance for the study from the University of Guelph Research Ethics Board (certificate reference number 12MY019). Permission for the study was granted by the High Commission of India in Ottawa, Canada. Upon arrival to the research site, and prior to the recruitment process, we approached local authorities , in the Krishnagiri District of Tamil Nadu. The region is comprised of several small villages surrounding the central market village of Anchetty.The sampling frame consisted of the entire adult population (>19 years old) of two rural Our target was to sample 800 participants following a sample size calculation for a sub-study published elsewhere . A randoStanding height was measured at end of expiration against a flat wall using a ruler pressed against the crown of the head and a measuring tape. Weight was measured in light clothes with bare feet using a household digital scale (NOVA™ BGS1207 model). Blood pressure measurements were recorded as the average of two readings using an Omron™ BP786-10 handheld electronic blood pressure monitor in the sitting position using the right upper arm and one of three sized cuffs after a period of 5 min sitting. Participants completed a structured questionnaire about age, sex, occupation, education, medical history, tobacco use, socioeconomic status, physical activity, and dietary intake.We created a wealth index using a modified subset of 13 of 29 questions taken from the Standard of Living Index used by the 2nd round of the National Health and Family Survey . We seleAll previous epidemiology studies in India known to the authors define households as either rural or urban based on a dichotomous typology, which fails to adequately capture the variability inherent in the urban/rural continuum . We creaWe conducted the Global Physical Activity Questionnaire (GPAQ) with each participant, designed by the World Health Organization (WHO) as part of the WHO STEPwise approach to chronic disease risk factor surveillance . GPAQ coDiet was assessed using a south Indian food frequency questionnaire (FFQ), validated for Tamil Nadu by the Madras Diabetes Research Foundation (MDRF) . MDRF prpaan at least once per day. High blood pressure was defined as mean systolic blood pressure ≥ 140 mm Hg and/or mean diastolic blood pressure ≥ 90 mm Hg and/or treatment with blood pressure medication [2).Literacy was defined as self-professed fluency in reading and writing. Tobacco consumers were defined as individuals who currently smoked at least one cigarette and/or chewed dication . For eac2, overweight was defined as BMI ≥ 23.0 kg/m2 but <25 kg/m2, obesity class I was defined as BMI ≥ 25 kg/m2 and < 30 kg/m2, and obesity class II was defined as BMI ≥ 30 kg/m2 [As per the definitions of the International Obesity Task Force (IOTF), underweight was defined as BMI < 18.5 kg/m30 kg/m2 . Low casWe identified outliers, cleaned the data set, and completed summary statistics in Microsoft Excel 10.0 . No valuX2 test were used to examine differences across outcome groups. Following this, three multivariate regression models were built in STATA 13.0 [Underweight, overweight, and obesity prevalence data were standardized using state-level age and sex data from the 2011 national census . In sepaATA 13.0 .p-value < 0.2) in univariate analyses were considered for an initial multivariate linear regression model. We then employed a forward stepwise model building process and eliminated non-significant variables  from the multivariate model, assuming a lack of confounding if coefficients of all remaining variables did not change by more than 20 % after addition or removal of the potential confounder [We employed a forward stepwise linear regression model-building process to determine associations between BMI as a continuous variable and putative risk factors. All factors were first analyzed in univariate regressions  with any of the outcome groups were included in the initial multivariate analysis. We then employed a forward stepwise process and eliminated non-significant variables  using the same methods as those used in model one to determine adjusted odds ratios (ORs) and 95 % confidence intervals (CIs).In a second step, we fit a multinomial logistic regression with overweight, class I obesity, and class II obesity as separate outcome groups. Underweight and normal weight classes were combined into a referent category to increase the statistical power of the analysis . We examp-value less than 0.05) in the final model.In a final step, we fit a multivariate logistic regression with underweight as an outcome group. All other weight categories were combined as the referent group . We examOf the 812 individuals recruited for the study, 753 (92.7 %) participated (341 men and 412 women), of whom 752 (92.6 %) completed an FFQ and 745 (91.7 %) consented to anthropometric measurements. Response rate was 87.4 % among men and 99.2 % among women. Disparity in the response rate was primarily due to migration among local men and thus unavailability at the time of sampling. The mean age was 47 ± 14.7 and the literacy rate was 35.1 %. Crude prevalence rates of underweight, overweight, obesity class I, and obesity class II were 22.7, 15.8, 17.7, and 3.5 %, respectively. Age- and sex-adjusted prevalence of underweight, overweight, obesity class I, and obesity class II were 22.7, 14.9, 16.1, and 3.3 % respectively.Baseline characteristics for the study population by BMI category are outlined in Table The multivariate linear regression model Table  had an RTable Table p < 0.05), a finding that is consistent with several other studies in India [Age and sex-adjusted prevalence of underweight, overweight, obesity class I, and obesity class II were 22.7, 14.9, 16.1, and 3.3 %, respectively. Prevalence rates of underweight, overweight, and obesity class I were not significantly different between women and men. Obesity class II was more common among women  . While wWhile most studies examining risk factors contributing to underweight, overweight, or obesity categorize or dichotomize BMI and analyze associations using logistic regressions e.g. , 13, 41), 4113, 42 decrease in BMI. After adjusting for other variables, each hour of moderate physical activity was associated with a 0.085 kg/m2 decrease in BMI. This is equivalent to -231 g for a typical Indian man  and -196 g for a typical Indian woman  . Althoual. 2011 . Due to al. 2011 , 47. Phyal. 2011 , 49. HowSeveral researchers suggest that modernization has contributed to declining physical activity in a number of ways. An influx of vehicles has reduced the need for active transportation . MechaniThe Census Bureau of India classifies households as either urban or rural and policymakers enforce these definitions in health, food, and economic policies . ResearcResearchers in the United States , Canada The asset-based wealth index, analyzed as a proxy for SES, was positively associated with BMI and greater odds of obesity class I and obesity class II. Low wealth index was also associated with increased odds of underweight. Considering the large variance of the wealth index , the magnitude of differences between predicted values of BMI is large. When the coefficient was applied to the values for the 10th and 90th percentile of wealth index values, the model predicted a difference of 4.57 kg for a typical man and 3.88 kg for a typical woman.It is likely that SES acts is a distal factor and the exact mechanisms through which it may affect odds of underweight, overweight, and obesity are varied . FamilieA unique finding of the present study was the inverse association between livestock ownership and BMI. Although livestock ownership was included in the wealth index, it was also independently associated with lower BMI when controlling for extemporaneous variables. Due to the lack of formal research on this topic, our knowledge of this association is limited to conjecture. In rural Tamil Nadu, most farmers graze their animals on forested public lands , 72, whiPrevalence of television ownership was very high at 88.1 %. Although comparative data from other studies in India are rare, we found higher ownership rates than those reported by the 2006 National Readership Study, which found televisions in 76.2 % of households in Tamil Nadu . Our finTelevision ownership was associated with higher odds of overweight and class I obesity. This association is likely two-fold. First, television ownership may be indicative of overall wealth and SES. Although it was included in the wealth index, due to high prevalence of television ownership Table  and its High caste was associated with higher BMI and increased odds of obesity class I. These results correspond with those of Gaiha et al. (2010) , who foupaan [In India, tobacco is consumed in various forms. Cigarettes are growing in popularity, especially among urban and high-income populations . Bidis  increased while the mean body weight of the control group (n = 389) decreased. Other studies have found that higher n-6 PUFA to n-3 PUFA intake ratios result in increased risk of several chronic diseases, including coronary heart disease, hypertension, type 2 diabetes, renal disease, rheumatoid arthritis, ulcerative colitis, Crohn’s disease, and chronic obstructive pulmonary disease, many of which have independent associations with adiposity [Evidence from the present study suggests that such reviews may disregard the qualitative composition of fat intake and the role of different types of fats. Intake of n-6  polyunsaturated fatty acids (PUFAs) was positively associated with BMI and negatively associated with odds of underweight. Ailhaud et al. (2006) claim that increased consumption of n-6 PUFAs, especially when combined with low intake of n-3 PUFAs, may promote development of excessive adipose tissue . One cliIn India, n-6 PUFA intake has increased in recent years, especially in landlocked areas with little access to seafood . While mWe found animal fat intake (saturated fats in milk products and meat) was negatively associated with BMI. This trend conflicts with animal experiments showing that diets high in animal fats leads to obesity and insulin resistance . Less reThis is the first cross-sectional epidemiological study, to our knowledge, to find significant linear associations between fatty acid intake and BMI while controlling for many extemporaneous risk factors. However, we found no significant association between n-6 PUFA and animal fat consumption and overweight, obesity class I, or obesity class II during the logistic regression analysis, while n-6 PUFA intake was associated with decreased odds of underweight. This may indicate that associations between fatty acid intake and BMI are driven by values within the underweight and normal range. In addition, average fat intake of the study population was well below values reported elsewhere in India  and alsoOur study has a number of limitations. Most importantly, the cross-sectional nature of the study precludes the ability to distinguish causes from effects. BMI was used as a measure of adiposity instead of waist-to-hip ratio or triceps or subscapular skinfold thickness, which may be more valid methods of determining excess body fat and categorizing high-risk overweight and obesity . HoweverThe epidemic of overweight and obesity is a serious public health issue in rural India and raises concerns about the capacity of the health care system to cope with associated outcomes. Alarming prevalence data, including those in the current study, provide a dire picture and a call to action. A population-level strategy to prevent obesity is necessary. Such a strategy requires sound data to reduce inefficiencies and target risk factors that have the greatest impact. While further research is needed, results from our study suggest several risk factors that are associated with BMI, overweight, and obesity, and may inform public health programming. Risk factors identified by our study may be divided into two categories. One category is individual-level risk factors that may be targeted by behaviour changing programming. Such factors included physical activity, television usage, and dietary factors such as finger millet, oil, and fat consumption. The second category comprises society-level risk factors such as caste and socioeconomic status. These risk factors must be addressed on a macro- level through social welfare policy, public infrastructure and development programs. As India continues to develop, modernize, and urbanize, there needs to be a strong education program to ensure that populations are aware of the dangers of overweight, obesity, and related noncommunicable diseases.


Finding efficient analytical techniques is overwhelmingly turning into a bottleneck for the effectiveness of large biological data. Machine learning offers a novel and powerful tool to advance classification and modeling solutions in molecular biology. However, these methods have been less frequently used with empirical population genetics data. In this study, we developed a new combined approach of data analysis using microsatellite marker data from our previous studies of olive populations using machine learning algorithms. Herein, 267 olive accessions of various origins including 21 reference cultivars, 132 local ecotypes, and 37 wild olive specimens from the Iranian plateau, together with 77 of the most represented Mediterranean varieties were investigated using a finely selected panel of 11 microsatellite markers. We organized data in two ‘4-targeted’ and ‘16-targeted’ experiments. A strategy of assaying different machine based analyses  was devised to identify the most informative loci and the most diagnostic alleles to represent the population and the geography of each olive accession. These analyses revealed microsatellite markers with the highest differentiating capacity and proved efficiency for our method of clustering olive accessions to reflect upon their regions of origin. A distinguished highlight of this study was the discovery of the best combination of markers for better differentiating of populations via machine learning models, which can be exploited to distinguish among other biological populations. Recent advances in life technologies have led to an exponential growth in size and complexity of biological data. The vast variety of molecular methods developed during the last decade has made it possible to screen diversity at large in organismal populations. As a result, a critical need for new analytical tools seems to have emerged to interpret information, understand processes, and to make all this data meaningful. Machine learning methods such as decision tree and Naive Bayesian learning among others provide revolutionary solutions to pattern recognition, classification, prediction and modeling of the biological information –3. TheseSupervised learning is a major category of machine learning methods, in which items of a collection are assigned to different classes based on a set of attributes and via a series of devised rules. This is unlike unsupervised learning, where no predefined classes are available and the items are investigated only for possible similarities ,2,5,9.Decision trees and the Naive Bayesian classifier are two simple but effective methods of classifications based on supervised learning. Decision trees are graphical models illustrating sequential decision making under uncertainty conditions with the aim of finding best possible decisions. They represent outcomes of different combinations of classification decisions and provide values for each outcome on a probabilistic basis. These algorithms are constructed through analyzing a set of existing data of known classes, called training examples, and can be then used for classifying previously unseen examples –14. The Olea europaea) is an important agricultural species being cultivated since ancient times in many regions of the old world [The olive  experiment, 267 olive accessions were included from four different olive populations as follows: a) 21 reference cultivars grown commercially in different parts of Iran, mostly sampled in a few research stations in the North ; b) 132 cuspidata specimens were considered for the analysis based on their regions of origin. These 169 accessions originated from 16 of Iranian provinces including Bushehr, Charmahal & Bakhtiari, Esfahan, Fars, Golestan, Hormozgan, Ilam, Kerman, Kermanshah, Kohgiluye & Boyerahmad, Lorestan, Qom, Sistan & Baluchestan, Khorasan e Jonubi, Khuzestan, Yazd, Zanjan  experiment, in order to assess differentiating power of the informative loci to distinguish among different olive compartments, only two groups of accessions namely local ecotypes and , Zanjan . The 16 GenAlEx 6.5 [For each of the 11 microsatellite loci and each of the experiments, total alleles and their frequencies were determined using AlEx 6.5 . AllelicRapidMiner 5.3 .The 4-t and 16-t datasets were separately subjected to analytical procedures adopted for data cleaning, feature selection, and machine learning prediction among populations (described below in detail), using cuspidata specimens populations), and therefore of less significance in characterizing one population versus another. These attributes were removed from the general dataset. Also, some attributes were detected to behave similarly due to being highly correlated. Attributes with strong correlation (Pearson correlation coefficient >0.95) were therefore removed as well to avoid error. The remaining attributes  created a new dataset, here called the Final Cleaned database (FCdb), which was used as the input source for further selection of alleles (feature selection) through attribute weighting procedure.A considerable number of attributes  were found to be represented privately in certain accessions  was evaluated for each algorithm using a 10-fold cross validation procedure. Each of the datasets were shuffled and divided into 10 equally sized sets. The classifying algorithm was trained on 90% of the data, and the remaining 10% was used as an unseen test set to assess efficiency of the classifier. This procedure was repeated 10 times and the average accuracy was calculated. Accuracy was defined by the number of correct predictions over the number of total prediction examples, in percent. Correct prediction meant an example (prediction) for which the value of the predicted attribute was equal to the value of the target (label) attribute. Comparisons of performance among algorithms could also reveal alleles with a key role in assigning populations, besides highlighting the most efficient algorithms and datasets for prediction of unknown accessions for future works.Random Forest, Decision Tree, Decision Tree Parallel, and Decision Stump [Gain Ratio, Information Gain, Gini Index, and Accuracy [Tree induction is an efficient and popular method to construct classification models. These graphic models are easy to interpret and show which attributes are used to classify groups ,13. Decion Stump ,12 to thAccuracy ,41, usinNaive Bayes  and Naive Bayes Kernel (returns classification model using estimated Kernel densities) were trained with 10-fold cross validation [The Naive Bayes classifier is a simple and effective inductive model of machine learning ,15. In olidation  on all eA summary of the above procedure and the applied methods is presented in For both 4-t and 16-t experiments, alleles across 11 microsatellite loci were screened. In the 4-t experiment with 267 accessions, DCA16 with 39 and EMO90 with 12 alleles were the most and the least variable loci, respectively . In total, 258 microsatellite alleles represented in 132 different tandem repeat lengths were observed. Allelic number  ranged from 7.54 for reference cultivars to 17.54 for local ecotypes.cuspidata specimens to 17.54 for local ecotypes.In the 16-t experiment with 169 accessions, DCA16 with 37 and EMO90 with 12 alleles were found to be the most and the least variable loci, respectively . TotallyAmong the 11 investigated loci, six loci with above 50% effective alleles were selected through the 4-t experiment as the most informative loci reserved for further analyses. These included DCA03, DCA09, DCA14, DCA16, UDO43 and EMO90. Throughout the data cleaning procedure, 157 alleles for the 4-t experiment and 149 alleles for the 16-t experiment were listed as effective alleles in FCdb .The seven attribute weighting algorithms abovementioned were applied to the 4-t and 16-t datasets and produced results as follows:-190, and DCA3-239 weighed high values by at least four of the seven models  with high efficiency. In the 4-t experiment, the highest accuracy (84.30%) was obtained when Decision Tree and Decision Tree parallel algorithms ran with gain ratio criterion on Chi Squared dataset. Prediction rates for these algorithms are presented in In the 16-t experiment, the highest accuracy (61.7%) was obtained when Performances of all algorithms are presented in cuspidata specimens.For the 4-t experiment, most of the graphic models showed a high level of complexity. This was due to high similarity between labels, so that more branches had to be generated to distinguish among olive populations based on many attributes. The induced tree selected allele EMO90-200 as the main attribute, and used it together with allele DCA14-149 to categorize Decision Tree model based on FCdb. DCA14-149 was selected as the root of the tree, by which any accession would be categorized in the cuspidata specimen population. Meanwhile, presence of any of the three alleles DCA14-149, -147, and -145 would help to separate cuspidata specimens from other populations. Accessions that do not show any of these alleles but represent any of the alleles DCA16-154, DCA3-239, EMO90-190, or UDO43-208 would be categorized as Mediterranean varieties. The two other populations were too complicated for assigning attributes to them.Decision Tree model with Information Gain criterion on FCdb for the 16-t experiment. Allele DCA16-178 was used as a diagnostic attribute to build root for the constructed decision trees. In combination with allele EMO90-192, the tree was able to identify cultivars from Bushehr province, while absence of allele DCA16-178 combined with presence of alleles DCA14-193, EMO90-202 and DCA9-188 identified accessions from Esfahan province.Naive Bayes models on the eight datasets of the 4-t experiment was 90.98%, which was achieved when Naive Bayes and Naive Bayes Kernel ran on FCdb  from a starting set of eleven loci were selected based on their efficiency in characterizing the four populations in this study. The informative features of UDO43 and DCA16 have been reported in previous studies ,33,42–44ong them . Baldoniultivars ,35,46,47When the number of target groups was raised from the first 4-t) to the second (16-t) experiment, an increase in the number of informative loci was observed. The diagnostic alleles reported previously ,33,42–44-t to theet al., analyses showed that microsatellite alleles are shared among cuspidata specimens and local ecotypes and/or reference cultivars [cuspidata specimens. This study revealed that 50.27% of the alleles are shared between Iranian reference cultivars/local ecotypes and Mediterranean varieties, while only 24.73% of alleles are shared in direct comparison of Mediterranean varieties and cuspidata specimens and 7.19% in a comparison of reference cultivars/local ecotypes with cuspidata specimens. Herein, we introduce a method of dissection by which accessions at the presence of any of the three alleles DCA14-149, -147 and -145 are classified as cuspidata specimens, representing separate populations. This offers a highly useful diagnostic tool for further studies of cuspidata populations.According to Hosseini-Mazinani ultivars . They alNaive Bayes and Naive Bayes Kernel retrieved an accuracy of 90.98%  markers in this study.(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(XLS)Click here for additional data file.S5 Table(XLS)Click here for additional data file.S6 Table(PDF)Click here for additional data file.


TP53 that is mutated in virtually 100% of these tumors, there is no other gene mutated at high frequency. There is extensive copy number variation, as well as changes in methylation patterns that will influence gene expression. To date, the role of histones and their post-translational modifications in ovarian cancer is a relatively understudied field. Post-translational histone modifications play major roles in gene expression as they direct the configuration of chromatin and so access by transcription factors. Histone modifications include methylation, acetylation, and monoubiquitination, with involvement of enzymes including histone methyltransferases, histone acetyltransferases/deacetylases, and ubiquitin ligases/deubiquitinases, respectively. Complexes such as the Polycomb repressive complex also play roles in the control of histone modifications and more recently roles for long non-coding RNA and microRNAs are emerging. Epigenomic-based therapies targeting histone modifications are being developed and offer new approaches for the treatment of ovarian cancer. Here, we discuss histone modifications and their aberrant regulation in malignancy and specifically in ovarian cancer. We review current and upcoming histone-based therapies that have the potential to inform and improve treatment strategies for women with ovarian cancer.Epithelial ovarian cancer has the highest mortality of the gynecological malignancies. High grade serous epithelial ovarian cancer (SEOC) is the most common subtype, with the majority of women presenting with advanced disease where 5-year survival is around 25%. Platinum-based chemotherapy in combination with paclitaxel remains the most effective treatment despite platinum therapies being introduced almost 40 years ago. Advances in molecular medicine are underpinning new strategies for the treatment of cancer. Major advances have been made by international initiatives to sequence cancer genomes. For SEOC, with the exception of  Epithelial ovarian cancer has the highest mortality of all of the gynecological malignancies, with high grade serous epithelial ovarian cancer (SEOC) the most common subtype. Due to general or non-descript symptoms of early stage disease, the majority of women initially present with advanced malignancy (Stage III or IV) where 5-year survival can be as low as 25% , 2. StanAdditional factors complicating the success of current treatment strategies for SEOC is lack of a clear understanding of the true site and cells of origin of this malignancy, with evidence mounting that SEOC may in fact arise in the secretory fimbrial cells of the fallopian tube , 11. MolTP53 that is mutated in almost 100% of these cancers, there is a relatively low frequency of mutations (approximately 2–6%) in genes including BRCA1, BRCA2, CSMD3, NF1, CDK12, FAT3, GABRA6, and RB1, that might otherwise have been more directive for therapeutic targeting  project has been revealing. With the exception of argeting . Determiargeting . Furtherargeting , 15.® , Rucaparib® , and Veliparib®   polymerase, PARP] inhibitors, including drugs such as Olaparib, Abbot) , 17. PAR, Abbot) . In cellound 17% , 20. Whiound 17% –23 and tound 17% . It is iBRCA1 and the PTEN tumor suppressors by promoter hypermethylation  expression have also been identified in SEOC , 26. DNAhylation , 28. Of hylation . Other cd HOXA10 , 31. Epiromoters . Treatmehylation . While uhylation . A numbehylation , 35, 36.tipping point” for epigenetic based therapies for the treatment of cancer  and histone deacetyltransferases (HDACs) . Acetyla+) – dependent lysine deacetylase and a class III HDAC. SIRT1 expression was reported to be higher in malignant EOC compared to benign, and expression was seen more commonly in SEOC relative to mucinous tumors , that as well as functioning as HDACs can act as deacetylases for non-histone proteins such as p53 . SIRT1 is tumors . To dates tumors . For allThere is emerging evidence to support the theory that sub-populations of cells exist in SEOC that are of a stem cell-like nature, demonstrate resistance to chemotherapy and are responsible for the development of chemoresistance in women with ovarian cancer –58. ThisStudies of histone PTMs in primary tumors support discoveries in cancer cell line models of complex histone modifications and furthermore, have been shown to correlate with tumor stage and prognosis. Loss of global H3K27me3 has been shown in ovarian, as well as breast and pancreatic cancers, correlating with shorter overall survival . Loss ofHistone cross-talk is defined as the influence that one or more post-translationally modified histones have on the writing, erasing, and reading of other histone PTMs. The language of histones is both complex and wide spread, influencing processes involved in development, stem cell differentiation, transcription, replication, and DNA repair with a major role in the regulation of gene expression , 69. ExaThe study of histone splice variants in ovarian cancer is still in its infancy, with few studies published to date, although evidence suggests roles in tumor progression . Slight Another histone variant identified to be down-regulated in ovarian cancer is histone variant H2A.Z, loss of which resulted in tumor progression . H2A.Z iThe surprising finding that at least 80% of the human genome is transcribed has boosted an interest in understanding the role of non-coding RNAs (lncRNAs) in biological processes and diseases. The number of protein coding genes has remained relatively stable at approximately 21,000 during the last decade; however, the number of lncRNAs has grown to 9000 small lncRNAs (<200 nt) and 10,000–32,000 long lncRNAs (>200 nt) .The role of miRNAs, a subset of small lncRNAs, in regulation of post-transcriptional gene silencing has been well established; however, our understanding of their effects on biological networks is still far from complete . Global HOTAIR (HOX transcript antisense intergenic RNA), an approximately 2.1 kb lincRNA transcribed from the HOXC locus, is known to alter chromatin configuration and promote cancer. In healthy cells, HOTAIR functions to epigenetically silence approximately 40 kb spanning HOXD8–HOXD11 of the HOXD region on chromosome 2  to silence targets by catalyzing H3K27me3 and demethylating H3K4me2, depicted in Figure Non-coding RNAs are versatile, with roles ranging from chromatin structure modification, X chromosome inactivation, scaffold function, miRNA decoys, nuclear import and export, RNA splicing, and the regulation of gene expression –96. Recemosome 2 , 99. By HOTAIR promotes metastasis of breast, pancreatic, endometrial, colorectal, and other cancers  and epithelial-to-mesenchymal pathways  compared to non-stem cell-like cells (CD133−/CD44−), and its down-regulation reduced the number and size of colonies assessed by anchorage-independent growth  following TGF-β1 treatment in colon and breast cancer cell lines  proteins are involved in maintaining the repression of genes in specific cells and subsequent cells originating from them. These proteins are essential in lineage commitment where they and their antagonists, Trithorax proteins, selectively express and repress a subset of n cancer . Most Pcn cancer . Both PRn cancer .in vivo  or Kyprolis® (carfilzomib). For these reasons, extensive efforts are currently being focused on DUBs as drug targets  was the first indirect inhibitor of EZH2, leading to decreased global levels of H3K27me3 and restoration of expression of genes involved in growth inhibition or apoptosis . It was Results of preliminary studies of EZH2-inhibitors in combination with other drugs have been encouraging. DZNep was shown to enhance the anti-proliferative effects of Gemcitabine in pancreatic cancer cells . The comTargeting of histone modifications and the enzymes regulating them in the ovarian cancer epigenome represents, to date essentially an unmet opportunity. With further focus on this field, it is probable that within the next decade new drugs targeting HDACs, HTMases, and DUBs will emerge as the next generation of cancer therapeutics. Whether these drugs will be most efficacious as single agents, or in combinatorial approaches with more traditional DNA damage-based chemotherapeutics, or with other perhaps yet to be developed molecular based targeted drugs remains to be determined. It is clear that complex networks of histone cross-talk will need to be understood and markers of histone dysregulation will need to be identified to ensure that patients receive maximal benefit from these therapies . In summDeborah J. Marsh., Jaynish S. Shah, and Alexander J. Cole conceived, wrote, and critically revised this manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Determining the liver changes during the early stages of diabetes is critical to understand the nature of the disease and development of novel treatments for it. Advances in the use of animal models and next-generation sequencing technologies offer a powerful tool in connection between liver changes and the diabetes. Here, we created a tree shrew diabetes model akin to type 1 diabetes by using streptozotocin to induce hyperglycemia and hyperlipidemia. Using RNA-seq, we compiled liver transcriptome profiles to determine the differentially expressed genes and to explore the role of hyperglycemia in liver changes. Our results, respectively, identified 14,060 and 14,335 genes in healthy tree shrews and those with diabetes, with 70 genes differentially expressed between the two groups. Gene orthology and KEGG annotation revealed that several of the main biological processes of these genes were related to translational processes, steroid metabolic processes, oxidative stress, inflammation, and hypertension, all of which are highly associated with diabetes and its complications. These results collectively suggest that STZ induces hyperglycemia in tree shrew and that hyperglycemia induced oxidative stress led to high expression of aldose reductase, inflammation, and even cell death in liver tissues during the early stage of diabetes.  Decades of research into diabetes mellitus, a disease characterized by chronic hyperglycemia in the blood resulting from insulin resistance or insulin deficiency, paint a grim picture for the next 20 years. In 2010, 258 million adults had diabetes, but, despite ongoing searches for treatments paired with prevention efforts, that number is still expected to rise to nearly 439 million people by 2030, about 7.7% of the global adult population [Given the connections between diabetes, chronic hyperglycemia, and their comorbidities, researchers have increasingly focused on exploring the liver, the primary organ involved in glucose metabolism and regulation, and the major target organ of insulin action. Previous reports found a heightened prevalence of liver diseases among diabetic patients to the extent that liver disease ranks as a key cause of death among patients with type 2 diabetes –5. IndeeDuring the progression of diabetic liver diseases, increasing oxidative stress induced by the hyperglycemia plays several key roles in promoting liver changes or injury , 4 and i de novo assembly that does not require genomic sequences to produce a genome-scale transcription map [Tupaia belangeri chinensis) has been successfully used as an animal model of several human diseases, including cancer [Recent advances in both animal models and genomic research may be able to overcome the shortcomings of early studies and allow researchers to draw meaningful, translational results. In terms of technique, RNA sequencing (RNA-seq) has become a powerful tool for conducting transcriptome characterization and gene expression profiling in a high-throughput manner , 12, alltion map . With detion map  that cantion map , noncodition map , and postion map . Similartion map , 18, theg cancer –21, hepag cancer , and metg cancer .β-cell dysfunction and death. However, none of these studies has been able to fully explicate the gene expression changes in the liver cells on the early stage of diabetes. The characterization of the transcriptome landscape of the liver may be partially helpful in solving this problem, but without an effective animal model from which results can be accurately extrapolated, such findings would be limited at best. One of our previous studies suggested that streptozotocin (STZ), a glucosamine derivative of nitrosourea and preferentially toxic to pancreatic beta cells, can be used to induce tree shrew diabetes [To date, the number of RNA-Seq analyses on diabetes is scarce, and studies of diabetes in the tree shrew model are nearly nonexistent. The existing RNA-Seq analyses have primarily focused on the pancreatic islet from both type 1 diabetes and type 2 diabetes subjects , 25 and diabetes . We foundiabetes . AccordiIn this study, we first used STZ to induce tree shrews diabetes akin to type 1 diabetes and then took RNA-seq techniques to determine the transcriptome characterization and gene expression profiling of the diabetic and wild type tree shrews. The results of these analyses identified 14,060 and 14,335 genes, respectively, in healthy tree shrews and those with diabetes, with 70 genes differentially expressed between the two groups. Further GO and KEGG annotation revealed that several of the main biological processes of these genes were related to translational processes, steroid metabolic processes, oxidative stress, inflammation, and hypertension, all of which are highly associated with diabetes and its complications.Sixteen male tree shrews captured from the wild near the Animal Centre of Kunming Institute of Zoology and housed in captivity for more than 1 year were collected and then separated into two groups, a control group and a group treated with streptozotocin (STZ) used to induce diabetes which was akin to type 1 diabetes. After overnight fasting (14 hr), body weight and fasting blood glucose were measured prior to injection. The animals in the STZ-induced diabetic group received two intraperitoneal injections of a freshly prepared solution of STZ (Sigma-Aldrich) in 10 mM sodium citrate buffer (PH = 4.2–4.5) at 80 mg/kg of body weight given one week apart while the animals in control group were intraperitoneally injected 10 mM sodium citrate buffer (PH = 4.2–4.5) in comparable amounts. Following injection of either the sodium citrate buffer or STZ, body weight and the fasting blood glucose concentration were measured weekly. All animal care and experimental protocols were approved by the Animal Ethics Committee of Kunming Institute of Zoology, the Chinese Academy of Sciences .At 4 and 8 weeks after injection, 0.5 mL of femoral vein blood was collected from animals in both groups. The blood samples were treated with heparin lithium salt to anticoagulation, centrifuged at 3000 g for 3 min. The plasma triglycerides (TG), total cholesterol (TC), low-density lipoproteins (LDL), high-density lipoproteins (HDL), and glycated hemoglobin A1c (HbA1c) were assayed by an automatic blood biochemistry analyzer  at the First People's Hospital of Yunnan Province, China. The diabetic animals were verified by the concentration of fasting blood glucose and HbA1c. Finally, 5 of the original 8 animals in the STZ-induced diabetic group were regarded as the diabetic animals. At 8 weeks, a total of 6 trees shrews in the control group and 5 diabetic tree shrews were anesthetized, and tissue samples were harvested for further analyses. Each tissue sample was divided into several pieces and snap frozen using liquid nitrogen.OGTT was followed as previously described . BrieflyTotal RNA was extracted and purified from the frozen liver tissues of each animal using TRIzol (TRansgene) according to the manufacture's protocols. Total RNA was then quantified using the nucleic acid-protein quantitative instrument (Bio-RAD), before the samples from each group were mixed into one sample at the equivalent concentrations. Afterward, the mixed total RNA samples from both the control and diabetic group were packed in dry ice and sent on to Macrogen Millennium Genomics for further library preparation and sequencing. Sequencing was performed using Illumina HiSeq2000 instrument.http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Transcripts were assembled and quantified by Cufflinks 2.0.2 [All reads were mapped to the tree shrew genome (NCBI Ref. database: GCA_000334495.1)  using Toks 2.0.2 . IsoformE-value hit filter 1.00E − 5, at least 80% sequence identity, and at least 70% query sequence coverage. Database for Annotation, Visualization, and Integrated Discovery  [The Coding Potential Calculator (CPC)  was usedrf.gov/)  was usedμL containing 2 μg of total RNA, following the manufacturer protocol. The cDNA was diluted 20-fold and 2 μL was used as template in subsequent qRT-PCR reactions. Each sample was analyzed in triplicate with the following reaction conditions: 30 seconds at 95°C, followed by 40 cycles of 5 seconds at 95°C, 30 seconds at 60°C, and 20 seconds at 72°C. A dissociation curve was drawn for each primer pair. Relative expression levels of interested genes were determined using 2−ΔΔCt method, and the gene expression levels were normalized to β-actin measured in parallel.Quantitative RT-PCR analysis of selected genes was performed using SYBR green method  on an ABI PRISM 7900HT . The isolated RNA of 3 individuals either from the control group or from the diabetic animals was reverse-transcribed into cDNA using the RT reagent Kit with gDNA Eraser  in a total volume of 20 The raw dataset have been submitted to NCBI Sequence Read Archive (SRA) under Accession SRX1009946 and SRX1017387, Bioproject: PRJNA282350.P < 0.0001) over the 4 wk period (The average body weights of tree shrews in the control group and the diabetic group induced by STZ did not change significantly over the 8 wk testing period . Similark period . These rk period . At 8 wkk period . These rP < 0.05)  and total cholesterol (TC) in diabetic animals were both significantly higher than those in the control group animals over the total 8-week period , intron retention events , intergenic transcripts , potentially novel isoforms of genes , pre-mRNA molecules , and polymerase run-on fragments  .In sum, the total amount of expressed transcripts was 55,976, which generated a total of 24,694 genes in the liver tissues of the control and diabetic groups. A total amount of 7,036 noncoding genes and 2,834 potential coding genes was detected using Coding Potential Calculator (CPC). BLASTP analysis of the 2,834 potential coding genes finally yielded 943 annotated genes.With a total of 14,060 and 14,335 genes, respectively, expressed in the control and diabetic groups, we separately set the threshold value of FPKM to 0.1, 0.2, and 0.3 to balance the numbers of false positives and false negatives and obtained, respectively, 12,418, 11,811, and 11,411 in the control group and 12,578, 11,912, and 11,485 in the diabetic group . Approxiadrb2, akr1b1, cox5b, crp, prlr, mcp1, prdx1, and romo1) involved in different biological processes were selected for quantitative Real Time PCR (qRT-PCR)  . The expTo explore differences in the biological processes between the healthy and diabetic groups, DAVID  was usedacaa2)  and the enoyl coenzyme A hydratase, short chain, 1, mitochondrial (echs1) , involved in mitochondrial fatty acid beta-oxidation, showed a markedly lower expression in the diabetic group, implying that the fatty acid breakdown ability was decreased in the diabetic tree shrews. The enzymes echs1 and acaa2, which separately catalyze the second and last steps of the mitochondrial fatty acid beta-oxidation spiral and produce acetyl coenzyme A, were previously reported to candidate genes in type 2 diabetes [ echs1 and acaa2 inhibits the production of acetyl coenzyme A, leading to the production of ketone bodies and oxidative phosphorylation [ cyp7a1, cyp11a1, cyp11b2, and cyp17a1) was upregulated in the diabetic group but not in the control group. These genes appear to be involved in the steroid metabolic process; for example, cyp7a1 encodes the enzyme cholesterol 7α-hydroxylase, which catalyzes the initial step in cholesterol catabolism and bile acid synthesis, while cyp11a1, cyp11b2, and cyp17a1 are involved in the process of steroid hormone synthesis. Collectively, this upregulation of genes within the cytochrome P450 family may suggest an increased biosynthesis from cholesterol to the bile acid and steroid hormone.Our results showed that two genes involved in lipid metabolism were significantly downregulated, potentially due the diabetic status of the tree shrews in the STZ-induced group. The enzymes, acetyl coenzyme A acyltransferase 2  were all downregulated in the diabetic group as compared to the control group. Of these genes, cox5a, cox5b, and cox6c are involved in complex IV of the mitochondrial electron transport chain, and downregulation may block the electron transfer through the mitochondrial electron transport chain to generate superoxide. Additionally, within the diabetic group of tree shrews, genes promoting cell apoptosis  were found to be upregulated, while antiapoptotic genes  were downregulated. Likewise, genes associated with the inflammatory process were detected in the RNA-seq data for the diabetic group, including cxcl13 and crp. The cxcl13 gene, a biomarker of B-cell, displayed a decrease mRNA expression  in the diabetic group. crp, the inflammatory biomarker, showed a significantly higher expression  in the diabetic group. Finally, we also determined that the genes putatively involved in feeding behavior  were expressed in the diabetic group, but the expression was markedly low among the healthy control group.We also found that several genes associated with the process of oxidative phosphorylation , the rate-limiting enzyme of the polyol pathway, which suggests that hyperglycemia may upregulate the expression of aldose reductase (AR) and increase the flux through the polyol pathway in liver tissue of diabetic tree shrews. Accumulation of intracellular sorbitol as a consequence of increased aldose reductase activity was previously implicated in the development of various secondary complications of diabetes. Several studies using experimental animals reported that inhibition of akr1b1 may be effective in the prevention of some diabetic complications, including kidney, retina, lens, and peripheral neuron tissues [ akr1b1 was only observed in liver tissues of STZ-induced type 1 diabetic mice on 4-week period [In the present study we detected a significantly increased expression of the aldose reductase  manifested a significantly lower expression as compared with the control group. This result suggests that the block of electron transfer through the mitochondrial electron transport chain immediately led to the overproduction of superoxide [Impaired mitochondrial oxidative phosphorylation is the primary source of oxidation products , which plays pivotal roles in the genesis of diabetic chronic liver diseases , 54. In peroxide . Howeverperoxide . This dicyp11a1), reactive oxygen species modulator 1 (romo1), and peroxiredoxin 1 (prdx1). Mitochondrial cytochrome P450 systems have been suggested as a source of mitochondrial ROS production that can play a role in the induction of mitochondrial apoptosis [ cyp11a1 was purported to potentially indicate an increased ROS in mitochondria of diabetic animals [ romo1 increased the level of cellular ROS that originate from the mitochondria [ prdx1, ROS scavenging enzymes, was upregulated in diabetic animals, indicating the increasing ability to clean out the free radical, which would alleviate the accumulation of free radical in diabetic liver. Subsequent identification of these genes indicates that oxidative stress in liver tissues occurred during the early stage of diabetes, a finding that may be critical in elucidating the mechanisms that generate the liver changes seen in diabetic patients.Diabetes and hyperglycemia are both known to increase oxidative stress , 58. Conpoptosis . Moreove animals ; and thechondria . Additio cxcl13 and crp. The gene cxcl13, a biomarker of B-cell, displayed a marked decrease of mRNA expression in the diabetic group, while, crp, the inflammatory biomarker, showed significantly higher expression in the same group. Together, these results suggest that inflammatory reactions also occurred in the liver tissues during the early stage of diabetes and that the observed inflammation was mainly induced by ROS. Following this line of evidence, attenuating the oxidative stress and inflammation may prove a useful target for therapeutics aimed at preventing liver injury among diabetes patients.Increased oxidative stress and the simultaneous decline of antioxidant defense mechanisms can lead to inflammation, damage of cellular organelle, and cell death –68. The mtco1, mtco3, mtnd4, and mtatp6) downregulated among the diabetic tree shrews  and a concurrent downregulation of antiapoptotic genes  among the STZ-induced tree shrews, as compared to the control group. This discrepancy implies that mitochondria may be more vulnerable than other organelles in the hepatic cells during the early stage of diabetes. Furthermore, the dysfunction of mitochondria may further promote inflammation and cell death in the diabetic liver cells. Numerous studies have demonstrated that mitochondrial factors are critical in the development of diabetes [Mitochondria are thought to be the primary target of oxidative damage, as ROS are generated mainly as by-products of mitochondrial respiration . Severale shrews . Since tdiabetes . Microardiabetes , 10, 56. de novo cholesterol biosynthesis in the body. Here, we observed a downregulation of sc4mol, a gene involved in the cholesterol biosynthetic process, among diabetic group  were upregulated in the diabetic group. These genes were previously reported to be involved in the steroid metabolic process, which were in the synthetic processes from cholesterol to the bile acid and steroid hormone. Pullinger et al. [ cyp7a1 led to the hypercholesterolemic phenotype among humans, but conversely Pandak et al. [ cyp7a1 in human liver cells might help to lower the concentration of serum cholesterol. By extension, the upregulation of these genes may be helpful in lowering the concentration of serum cholesterol in tree shrew. These results may also hint at a protective mechanism capable of decreasing the concentration of total cholesterol in the liver tissue of the diabetic group, because the high circulation concentration of total cholesterol is the main risk factor for development of cardiovascular diseases. We also observed an increase in the circulation HDL cholesterol particles among the diabetic tree shrews, further hinting at some sort of a protection that prevents the development of atherosclerosis. She et al. [β-cell mass, and hepatic glucose overproduction in the liver [The liver plays a critical role in regulation of lipid metabolism, accounting for the generation and degradation of fatty acids, steroids, triacylglycerols, cholesterols, bile salts, and so forth, and it is also the central synthetic site of theic group . This rer et al.  demonstrk et al.  found the et al.  previouse et al. , 10, 56.e et al. . Their rhe liver . Therefohe liver , 56. HowIn summary, the present study found that STZ can adequately induce diabetes in the tree shrew model, accompanied by higher serum TAG, CHOL, HDL, and LDL. The number of expressed genes and the dominant categories of biological processes in liver transcriptional profile of tree shrew were quite similar to other mammary animals, including humans, pigs, and cows. The differentially expressed genes between the two tested groups of tree shrews also indicated that liver changes accompanied the early stage of diabetes in tree shrew, and hyperglycemia-induced oxidative stress can further lead to higher expression of AR, inflammation, and even cell death in liver tissues.


Building upon the findings from the field of automated recognition of respiratory sound patterns, we propose a wearable wireless sensor implementing on-board respiratory sound acquisition and classification, to enable continuous monitoring of symptoms, such as asthmatic wheezing. Low-power consumption of such a sensor is required in order to achieve long autonomy. Considering that the power consumption of its radio is kept minimal if transmitting only upon (rare) occurrences of wheezing, we focus on optimizing the power consumption of the digital signal processor (DSP). Based on a comprehensive review of asthmatic wheeze detection algorithms, we analyze the computational complexity of common features drawn from short-time Fourier transform (STFT) and decision tree classification. Four algorithms were implemented on a low-power TMS320C5505 DSP. Their classification accuracies were evaluated on a dataset of prerecorded respiratory sounds in two operating scenarios of different detection fidelities. The execution times of all algorithms were measured. The best classification accuracy of over 92%, while occupying only 2.6% of the DSP's processing time, is obtained for the algorithm featuring the time-frequency tracking of shapes of crests originating from wheezing, with spectral features modeled using energy. Long-term disease management is required in order to maintain the life quality of asthmatic patients and to prevent the progression of the disease. Management mainly consists of adherence to a prescribed medication plan and avoidance of asthmatic attack triggers. The occurrence of symptoms, such as “asthmatic wheezing” in respiratory sound, indicates a low level of control over the chronic disease .Recently, medical devices for the quantification of wheezing appeared on the market . The devThe concept of such a sensing system for the detection of asthmatic wheezing is shown in The low power consumption of the wearable, size-constrained wheeze detection sensor node is required in order to achieve long autonomy. In , the powetc.)  defined in k-point discrete Fourier transform of discrete-time windows w, sliding by step m over the signal, x. The non-rectangular window function is used to prevent spectral leakage due to finite window length N. Commonly, cosine window functions, such as Hann's or Hamming windows, are used. Furthermore, the overlap between successive windows may be used to show transients of a short duration in the signal   spectrum, spectral denoising and enhancement of the frequency resolution.Equalization of the spectrum is performed for the compensation of individual patient and measurement site variations. The equalization step is implemented by detrending the spectrum of normal respiratory sound, thus leaving only high-magnitude spectral peaks standing out. Depending on later processing steps, it may be accompanied by the normalization of the spectrum in order to make it independent of respiratory flow. Early wheeze detection algorithms implemented equalization by subtracting the mean value from the power spectrum and, afterwards, normalization by dividing the spectrum by the standard deviation . The equSpectral denoising is used in order to reduce the number of isolated transient peaks , but preserving spectral crests originating from wheezing. Wavelet denoising was proposed for this task by . Some auEnhancement of the frequency resolution of the STFT improves the frequency-localization of the spectral peaks originating from wheezing. Zero padding is the most straight-forward approach to increasing the frequency resolution . Spectro3.3.etc.) from the current signal segment, followed by; (b) tracking the temporal features  using information from prior segments. In order to reduce the number of temporal features processed in Step (b), several approaches are proposed in Step (a) for the discrimination of the spectral shapes originating from wheezing, from the isolated peaks of the noisy respiratory spectrum.Wheezing is discriminated from normal respiratory sound using spectral and temporal features extracted from STFT. The most commonly used are the features describing the shapes of the wheezing peaks in the time-frequency plane. Most algorithms using such features operate segment-wise, iterating two steps: (a) an extraction of spectral features  spectrum, appearing as flattened “spectral crests”, rather than isolated discrete spectral peaks. A common approach of modeling the shape of such spectral crests is by low order statistical moments: the mean and variance (or standard deviation). This approach was first introduced in  by posinAn alternative model of spectral crests was proposed in  with theThe extraction of the spectral and temporal features describing wheezing crest shapes can also be performed simultaneously in time and frequency (on 2D spectrograms) by using image processing techniques. In , the detf50/f90 ratio as spectral features. These features were later compared in , defined in The energy of current signal segment minE and maxE, given in EM, is chosen to cover the time-interval of at least one respiratory cycle.Minimal and maximal energies 4.2.2.m − 1 and m − 2). Based on this, the current signal segment's amplitude, Â, and phase, ϕ̂, spectra estimates are calculated, as shown in Spectrum tonality is a feature describing the existence of the harmonic content within each signal segment. It is calculated as proposed in . FirstlyW, defined in W is proportional to the estimation error of each frequency component, k, in the current signal segment, m.The amplitude and phase spectrum estimates are used for the calculation of weight coefficients W is then used to calculate the weighted segment's energy, wE[m], shown in T[m], related to the current signal segment, is defined in tonalδm, describing the duration of tonal sections, is extracted.Finally, by comparing the weighted and unweighted segment's energy, tonal index 4.2.3.m, the potential locations of frequency components originating from wheezing are first identified by searching the segment's power spectrum, P, for indices at which local maxima (peaks) occur. The peaks' magnitudes, peakP, their total number, pN[m] peakk Within each signal segment, 4.2.4.Due to its property of expressing signal complexity, we evaluate Shannon's entropy as a detector of grouping in the spectrum, thus indicating the occurrence of wheezing. We calculate it similarly as proposed in .peakP are rescaled according to normP,peak:First, extracted power spectrum peaks En[m], is expressed as in Then, the signal segment's entropy, ratioEn[m], defined in The most noticeable changes in entropy are expected upon the transition between signal segments of the normal respiratory sound and segments containing wheezing. Thus, a temporal feature, 4.2.5.bandP, forming a band around the central frequency, peakk, of the each of the pN power spectrum peaks  describing the distribution of the magnitudes of the subset of power spectrum components, eaks see . Bandwidrest see  is chosep bands bandP are calculated. Those peaks, peakP, the magnitudes of which exceed the condition defined in crestP, potentially originating from wheezing. The constants, mC, sC, are obtained during the training phase. Crest-peak frequencies crestk and the number of crests, cN, are also extracted, as shown in The mean value and the standard deviation of all power spectrum components within each of 4.2.6.An alternative approach to spectral crest modeling is to measure the distribution of energy localized around each identified spectral peak. This model is a modified version of the work presented in , with thpeakP, three bands are defined, concentrically spanning around the peak frequency, peakk: crestB < narrowB < wideB  and the time-frequency resolution of STFT. narrowB and wideB define the surroundings of each spectral peak and are empirically set to 80 or 120 Hz, respectively. Those spectral peaks for which crestP. Band energies narrowE and wideE are calculated analogously to crestE. Constants narrowC and wideC are obtained during training.For each of the identified peaks, wide see . Bcrest 4.2.7.Two temporal features of spectral crests are derived in order to discriminate longer spectral crests originating from wheezing from the short, isolated transients in the time-frequency plane.crestk, along the temporal axis, as shown in crestδk are extracted pairwise between the current signal segment, m, and each of its contM preceding neighbor segments, as shown in crestδm [c], of each continuous spectral crest.The first feature is the continuity of the spectral crests in the time-frequency plane. Continuity is described by extracting the deviations of each crest's peak frequency, The computational complexity of each feature extraction program block is listed in 4.3.A total of four wheeze detection algorithms are developed, by organizing subsets of features from Section 4.2 into decision trees, shown in 4.3.1.cN satisfies 1< cN < crestsC. Next, the temporal features of crests are evaluated.First, the existence of spectral crests is determined by modeling the surroundings of the power spectrum peaks, either using statistical moments as in Algorithm 1 see , or as icrestδk…crestδk are checked against contM thresholds contC[contC[contM] according to contM > 1, individual deviation thresholds may be chosen. Constants contC[contC[contM] are acquired during training.First, the continuity describing features δkcrest, c…δkcreδkcrest, c…δkcrecrestδm[c], of each spectral crest is evaluated to lie between the minimal and maximal durations, durM,min, and durM,max, respectively. durM,min is adjusted to the duration defining continuity, durM,min = contM. durM,max is chosen to reflect the maximal expected uninterrupted duration of wheezing, typically being a duration of the respiratory cycle.Finally, the duration, 4.3.2.T[m] > TC are considered tonal. Constant TC is acquired through training. In the final decision tree branch, the duration of the successive signal segments marked as tonal, tonalδm, is compared against constants durM,min and durM,max.The tonality tracking algorithm calculates the tonality of each signal segment according to 4.3.3.ratioEn[m]  was used for the signal transport from the ADC to the DSP. The direct memory access (DMA) units' interrupts were used for the synchronization of the main processing tasks: (a) the signal acquisition task; and (b) the classification task; shown in The algorithms described in Section 4.3 were first implemented in MATLAB and afterwards ported to DSP. A development board EZDSP-C5505-USB (Texas Instruments)  was usedN = 512 samples, corresponding to 64 ms. The task resulted in declaring each segment to either be the “wheezing” or “normal” class. The result was output by UART. To compensate for the signal attenuation around the cosine window edges, segments were overlapped by 50%, resulting in a total of 32 ms available for the processing of each signal segment. With the DSP core operating at a 100 MHz clock, this sufficed for maximally cyclN,tot = 3.2 × 106 single-cycle instructions for the processing of each segment, and this yields a power consumption of approximately 22 mW. For the remainder of the cycle, the DSP is kept in standby state, while the DMA periphery performs the acquisition task, while consuming only 0.4 mW. The DSP is woken up upon the DMA's interrupt.The classification task operated on fixed-sized signal segments of etc., in 16-bit fixed-point arithmetic. This ensures the reproducibility of the results and optimizes the execution performance by exploiting C5505′s architectural features, such as two multiply-and-accumulate (MAC) units and the FFT coprocessor.Texas Instruments “DSPlib” library functions  were use5.2.The wheeze detection algorithms were tested on a database of prerecorded respiratory sounds. Our database consisted of a total of 26 recordings. Thirteen of them were of normal breathing (N01…N13), and each of the other 13 audio recordings, labeled W01…W13, contained more than one uninterrupted interval of wheezing. The number of recordings used in our study corresponds to the dataset sizes used throughout the literature .To simulate a realistic signal chain, training and testing was conducted by outputting test signals through the PC sound-card line-out to the C5505-EZDSP development board's ADC input. The results of the segment-wise two-class classification  were returned from the DSP to the PC through UART. Comparing each classification result of each (64 ms) signal segment to the referent annotation, each segment was designated into one of four categories, true positive (TP), true negative (TN), false positive (FP) or false negative (FN), enabling the calculation of the number of classification results belonging to each category , each segment corresponding to 64 ms of sound. For details, please refer to column “Seg.” in TPN, FPN , TNN  and FNN  were calculated based on the classification results obtained for each respiratory phase. Due to the DSP still operating segment-wise, the following mapping is introduced: the respiratory phase containing wheezing (annotated positive) was considered TP if containing at least one positively classified signal segment. Furthermore, the respiratory phase was categorized as FP in the case of the existence of positively detected signal segments in the respiratory phase lacking the occurrence of pathology. This is analogously so for TN and FN.Detection of the occurrence of wheezing in a respiratory phase. For this scenario, the annotations of the test-signals were readjusted for the classification results evaluated respiratory phase-wise. Whole respiratory phases containing more than one interval of wheezing were annotated as referent positives, and the phases without the occurrence of wheezing as referent negatives. Thus, the dataset consisted of a total of 65 positives (found throughout W01…W13) and 148 negatives (of those 66 in W01…W13 and the 82 in N01…N13), as seen from column “Resp. phases” in Classification accuracy was tested in two operating scenarios:TPN, TNN , FPN  and FNN , sensitivity SE, specificity SP and accuracy AC were calculated as defined in From N = 26 signals from the database, using the classification thresholds obtained through training on the remaining 25 signals. The training of the algorithm thresholds was performed by a grid-search hyper-parameter optimization procedure in which the goal function, shown in maxAUC, of the receiver operating characteristic (ROC), comparing the true positive rate (TPR = SE) against the false positive rate (FPR = 1 − SP).For both operating scenarios, the leave-one-out method was used for training and testing, due to the limited size of the test signal database. The method tested each of SE, SP and AC were calculated separately, both for test signals containing intervals of wheezing (W01…W13), for normal signals (N01…N13) and for the whole database, for each of the four algorithms. Training and testing were analogously repeated for both wheeze duration tracking and the wheeze occurrence detection operating scenario, resulting in After completing the leave-one-out procedure on all test signals, 5.4.2.Verification of the execution duration was performed using code profiling tools of the Code Composer Studio development environment (Texas Instruments). Algorithms were running on the DSP in debug mode. The time intervals of interest were measured using manually set breakpoints in the number ticks of the DSP core clock running at 100 MHz. A common, representative segment chosen from an interval of wheezing contained in test signal W08 was used throughout all execution duration measurements, yielding the worst case execution time for all algorithms. All measurements were repeated 10 times and averaged.cyclesN̅,total, was measured.Using such a setup, the durations of the execution of each program block from 5.4.3.SEμ, SPμ and ACμ, comparing, respectively, overall classification sensitivity execD. Processing duty-cycle execD is defined as the ratio between the average number of DSP instructions required for the execution of classification task over a single signal segment, cyclN,exec, and the total number of clock cycles between two successive signal segments . execD is directly related to the portion of time the DSP has to spend in the active state. The efficiency is measured for each of two operating scenarios: wheeze duration tracking  and wheeze occurrence detection .In order to evaluate the suitability of the implemented algorithms for long-term wheeze monitoring using a low-power wearable sensor, we assessed their execution efficiency. Therefore, we propose metrics, defined in 6.6.1.N = 26 iterations of leave-one-out training of each of four algorithms are compared in maxAUC, and the associated set of trained classification parameters by which they are obtained, are shown on each graph.The receiver operating curves averaged through all green, and the worst are colored red.Overall 6.2.N = 512, bN = 57, pN = 20, cB = 6, cN = 7, contM = 4, narrowB = 8, wideB = 12 and itNRN = 3, itTAN = 3, itTAN = 3, itTCN = 3, itNN = 5, itTLN = 5), are shown in Execution duration estimates, obtained by calculating expressions from clk,execN̅ and the associated processing duty-cycle based on 32 ms between the processing of successive segments is shown in 7.7.1.maxAUC). Additionally, by featuring a clear inflection point, they enable the unambiguous setting of the classification parameter thresholds, which yield the combination of the highest true positive rate (highest sensitivity) at the lowest false positive rate (highest specificity). Good wheeze duration tracking capability can be observed by the examples of the test results in The receiver operating curves of both crest tracking algorithms (Algorithms 1 and 2) exhibit the highest maximal area under the curve  also features a receiver operating curve with a clear inflection point, its Algorithm 3  features the most shallow receiver operating curve without a clear inflection point. Thus, the algorithm can be adjusted either for high sensitivity at the cost of low specificity , or on the other hand, it may be set for high specificity, at the cost of a high count of false negatives during the occurrence of wheezing  features the shortest overall execution duration, with the power spectrum peak detection program block being its bottleneck. Algorithms 1 and 2 are slower in execution than Algorithm 4, for 21% and 28%, respectively. They differ only in crest modeling blocks (labeled as “Crest freq.” in execD̅) for a clock set to 100 MHz. Thus, the remaining 96.91%–98.13% of time may be spent in a low power state, minimizing the DSP core consumption. According to According to AC,durμ in A,eventμ is only 5% lower in the event detection scenario. On the other hand, the absolute accuracies of Algorithms 1 and 2 are significantly higher than those of Algorithm 4  turns out to be the most efficient in both operating scenarios, thanks to its very short execution time see . In comp8.In this article, we evaluated the computational complexity, the execution time and the accuracy of the wheeze detection algorithms for optimizing the active time of the DSP of the wearable sensor for real-time asthmatic wheeze detection. Efficiency metrics were introduced comparing the experimentally obtained accuracies and execution durations of four representative algorithms in wheeze occurrence detection and duration tracking scenarios.The higher classification accuracies of crest tracking algorithms, obtained in both operating scenarios, have shown the advantage over the tonality or entropy-based ones. Though being the least accurate in the wheeze duration tracking scenario, tonality tracking proved more accurate than the entropy-based algorithm and comparable to the tracking of spectral crests modeled using statistical moments, in the event detection scenario.The implementation of each algorithm required the DSPs activity to be less than 3% of the time, for real-time operation. The highest execution speed was obtained for the entropy-based algorithm and the lowest for tonality tracking (65% lower).While a general purpose DSP proved valuable for the comparison of different algorithms, it does not define the absolute boundaries of the energy consumption cost of wheeze detection. Nevertheless, such an analysis provides the information necessary for the optimization of the architectural requirements of the DSP unit in future work.


Lung cancer is a heterogeneous disease responsible for the most cases of cancer-related deaths. The majority of patients are clinically diagnosed at advanced stages, with a poor survival rate. For this reason, the identification of oncodrivers and novel biomarkers is decisive for the future clinical management of this pathology. The rise of high throughput technologies popularly referred to as “omics” has accelerated the discovery of new biomarkers and drivers for this pathology. Within them, tyrosine kinase receptors (TKRs) have proven to be of importance as diagnostic, prognostic, and predictive tools and, due to their molecular nature, as therapeutic targets. Along this review, the role of TKRs in the different lung cancer histologies, research on improvement of anti-TKR therapy, and the current approaches to manage anti-TKR resistance will be discussed.  Lung cancer is responsible for most cases of cancer-related deaths , 2. ThisThe development of technology has made the analysis of high amounts of samples feasible through the so-called high throughput techniques. Regarding cancer, these techniques have allowed the identification of key biomarkers with translational relevance in lung cancer. Genomics, transcriptomics, miRNAomics, epigenomics, proteomics, metabolomics, lipidomics, glycomics, and many other “omics” techniques have been used to decipher the molecular pathogenesis of this disease. A proposed workflow for this aim through the use of the “omics” is shown in γ1 [The application of high throughput techniques in lung cancer has thus identified many gene alterations with a potential oncogenic role in this pathology. Many of these alterations take place in tyrosine kinase proteins, which integrate the so-called “kinome”. Among them, the tyrosine kinase receptors (TKRs)  are espeγ1 . The actγ1 , 15. Theγ1  or mechaγ1 . Howeverγ1 . Along tAlterations in TKRs have been detected in every histological type of lung cancer , with a There are well characterized lung cancer driver oncogenes, especially in ADC. In this lung cancer histology, mutations in KRAS and EGFR and ALK translocations account for the 15–25%, 10–35%, and 2–5% of cases, respectively, the two latter being TKRs . EpidermThe second most important altered TKR in lung adenocarcinoma, ALK (Anaplastic Lymphoma Kinase), is a transmembrane TKR integrated in the insulin receptor superfamily. This gene is susceptible to suffer a rearrangement resulting in a fusion protein together with the echinoderm microtubule-like protein 4 gene (EML4), which is involved in the correct microtubule formation. This fusion protein consists of the intracellular tyrosine kinase domain of ALK and different truncations of EML4, resulting in different fusion protein variants , 54. The in vitro to TKIs like TAE684 [Apart from these three oncodrivers, accounting for an important percentage of lung cancer cases, new molecular alterations in TKRs associated with oncogenicity have been recently described. One example of altered TKR is the ROS1 gene, which has been proved to be involved in lung cancer. This receptor belongs to the subfamily of tyrosine kinase insulin receptor genes. ROS1 fusions were detected as a potential oncodriver in a NSCLC cancer patient (with the CD74-ROS1 rearrangement) . The ROSe TAE684 . The anae TAE684 .Another case of oncodriver TKR is RET, which is a tyrosine kinase receptor for the GNDF-family ligands (GFLs). A RET translocation (KIF5B-RET) was first identified by whole genome and transcriptome sequencing of tumor tissue from an adenocarcinoma patient in an advanced stage . After t In Situ Hybridization (FISH) techniques, an oncogenic fusion involving another TKR, NTRK1, was identified in 3 ADC patients with no known oncogenic alterations in a work involving 91 ADC patients [ in vitro, as drugs like lestaurtinib, ARRY-470, and crizotinib have proved efficacy in Ba/F3 cells expressing NTRK1 fusion proteins [Thanks to Next Generation Sequencing (NGS) and Fluorescencepatients . Furtherproteins . In vitro experiments show that cell lines harbouring exon 20 insertions in this gene are sensitive to trastuzumab and to the EGFR/ERBB2 dual inhibitors afatinib and neratinib [Another TKR which has proved to be of relevance in lung ADC is ERBB2. ERBB2 (HER2) is part of the ERBB family of receptor tyrosine kinases, as EGFR. Constitutive activation of this TKR through amplification and mutation has been reported in NSCLC , where eeratinib , 71, 72.Furthermore, many works have been carried out to identify novel oncodrivers in adenocarcinoma with the help of high throughput technologies. In a collaborative work 188 human lung adenocarcinomas DNA samples were sequenced for 623 genes with a potential role in cancer. This analysis revealed more than 1000 somatic mutations which occurred preferably in 26 genes, 30% of which were TKRs. Two of those were ERBB3 and ERBB4, from the same receptor family of ERBB1 (EGFR) and ERBB2. In ERRB4, a total of 9 mutations were detected. From those, two were located on the protein kinase domain and five around the receptor ligand binding domain. In ERBB3, 3 mutations were found in the ligand binding domain. In another tyrosine kinase receptor from the ephrin family, EPHA3, 11 mutations were found in the extracellular and kinase domains. One of those mutations found in EPHA3 kinase domain, K761N, is located at a highly conserved position analogous to the mutation K641 in FGFR2. A significant number of mutations were also identified in VEGFR and FGFR family member, especially in KDR and FGFR4, where four and three tyrosine kinase domain mutations were found, respectively . in vitro, where FGFR1-amplified cell lines have shown dependency on FGFR1 [As discussed in the latter paragraphs, during the last years, the most actionable TKR oncogenic mutations have been described in ADC. However, research on tumor biology of SCC has not resulted in as good results as in ADC at a therapeutical level so far. For that reason, efforts are currently being carried out to identify new oncogenes involved in the development of lung tumors of this histology. One of the most interesting TKRs in lung SCC is FGFR1. FGFR1 is part of the type 4 family of TKRs and has the ability to regulate proliferation via the MAPK and PI3K pathways, similarly to EGFR. A screen of SCC samples detected focal amplifications of the FGFR1 gene . This alon FGFR1 –75. Howeon FGFR1 , 76. FGFon FGFR1 , 75. Anoon FGFR1  and ongo in vitro study has found out that reduced proliferation after DDR2 silencing or dasatinib treatment is produced in DDR2-mutant cell lines [DDR2 is another receptor tyrosine kinase which has proven to be of relevance in SCC. This receptor binds to collagen in the extracellular matrix and regulates proliferation and migration. Mutations in DDR2 have been identified in this histologic subtype , suggestll lines . Severalll lines –81. Two ll lines , 78. Howll lines , 41. Howll lines , 83. Furll lines . All of  EphA2, was shown to be a relevance biomarker in SCC, where it promotes invasion, cell motility, and angiogenesis through the activation of Src [ EphA2 mutations are rare in NSCLC but are mainly present in SCC [In the last years, many other TKRs are gaining attention in the study of the oncogenesis of SCC. One example is the insulin-like growth factor receptor 1 (IGF1R), which is involved in proliferation and inhibition of apoptosis , 46. Then of Src , 88. Epht in SCC . in vivo evidence of the oncogenic role of EGFR vIII in a NSCLC murine model and of the efficacy of an EGFR inhibitor, HKI-272, in this model [EGFR vIII, a mutated form of EGFR found in SCC, harbours deletion in exons 2–7. This EGFR variant is not present in normal tissues and causes uncontrolled cell growth in tumors . Furtheris model . Severalis model , 92. in vitro and in vivo evidence that anti-FGFR therapy is effective in FGFR1 amplified SCLC [The molecular pathology of small-cell lung cancer has not yet been addressed as much as in non-small-cell lung cancer. However, some molecular alterations with the potential to be oncodrivers in this lung cancer histology have been identified. In one recent study, DNA from 98 SCLC tumors was sequenced and analysed for genomic alterations. Mutations in EGFR (5% of cases) and KIT (6%) and amplification of FGFR1 (4%), EPHA3 (3%), PDGFRA (2%), and MET (2%) were detected, suggesting that these TKRs could have a role in lung SCLC oncogenesis , 93. Butied SCLC , 97. Othied SCLC . VEGFR h in vitro, clinical trials results have been disappointing [Some TKR alterations are not specific of one concrete lung cancer histology. The TKR MET has proved to be of relevance in NSCLC after the large scale molecular profiling work by The Cancer Genome Atlas (TCGA) in lung ADC . MET altpointing , 105. Nopointing . Apart fpointing . In anotpointing .As commented before, acquired resistance to targeted therapy is a relevant problem in clinics. Besides, there are tumors potentially sensitive to a targeted therapy but which show innate resistance. For all these reasons, current efforts are focused on the managing and avoidance of these resistances, as well as on the improvement of eligibility criteria for TKI-therapy.In the case of EGFR, 20–50% of patients with clinical or biological predictors of anti EGFR-therapy sensitivity do not respond . This pr in vitro [However, the most frequent cause of acquired EGFR-TKIs resistance, accounting for 50% of resistant cases, is a mutation in exon 20 of the EGFR gene, T790M . Nonethein vitro  and in tin vitro , 122. Unin vitro .Apart from second- and third-generation EGFR-TKIs, other treatment strategies are being developed to overcome acquired resistance. The switching to chemotherapy after resistance has appeared to be the most accepted approach, although there are several retrospective studies with inconsistent results to this respect , 125. AnFurthermore, other more novel approaches aiming at EGFR-therapy resensitization have been recently proposed. A bispecific EGFR/MET antibody, called JNJ-61186372, has recently showed a potent inhibition of EGFR downstream effectors, resulting in tumor regression in NSCLC xenografts . In anotα and high soluble EGFR serum levels as negative and positive response predictive biomarkers to erlotinib, respectively [On the other hand, the expression of several lncRNAs has been associated with EGFR-targeted therapy resistance, suggesting a potential role for them as predictive and therapeutic biomarkers . In addiectively .Furthermore, the evaluation of circulating free DNA from liquid biopsies as a noninvasive method for resistance monitoring is currently under development, and promising results have been obtained for the detection of the T790M, c-MET amplification, and the C797S mutation , 141.For ALK gene rearrangement targeted treatment, as in the case of EGFR-TKI treatment, acquired resistance arises in less than a year after the beginning of the treatment . The besOn the other hand, noninvasive detection of ALK rearrangements has proven to be feasible. In one work, the EML4-ALK translocation has been detected in circulating blood platelets. This is because platelets are able to sequester RNA released into the blood by tumor cells, and this is why the ALK translocation could be found in platelet RNA transcripts .Currently, different novel therapeutic agents with improved characteristics are under evaluation  and, as The recent discovery of genetic alterations on TKRs in patient samples has opened the door to research works aiming to find an appropriate and targeted therapy for subsets of patients with characterized oncogenic alterations. in vivo models have been generated to test the efficacy of ROS1-targeted agents [ per se, and that crizotinib and ASP3026 (an ALK/ROS1 inhibitor) are potentially efficacious therapies to target them [For ROS1-fusion genes,d agents , 158. Twget them . Furtherget them . Some TKget them , 160. in vitro and in vivo antitumor efficacy in a work involving a cell line harbouring the CCDC6-RET fusion variant and its xenografts. In this work, a mechanism of resistance to dovitinib through the activation of Src was also described, and the use of a Src inhibitor, saracatinib, was proposed to overcome this resistance [RET rearrangements have also been object of interest to study potential targeted therapies in lung adenocarcinoma. As in the case of ROS1, genetically engineered mouse models have been established. In one of these models, the KIF5B-RET fusion was exogenously expressed specifically in lung alveolar epithelial cells, generating multiple tumors in the lungs. In this model, vandetanib, a RET inhibitor approved to be used in thyroid carcinoma, showed antitumor efficacy . This drsistance . in vitro and in vivo xenograft models have showed a correlation between efficacy of anti-FGFR therapy and FGFR1 amplification [ in vitro, that is, the DDR2 T654I mutation and NF1 loss [In lung SCC, probably the TKR which has attracted the most attention is FGFR1. Regarding anti-FGFR therapy, there has been some controversy about predicting treatment effectiveness. There are some works in whichfication , 163. Hofication . Anotherfication . Anotherfication . In addiNF1 loss . CurrentRegarding SCLC, several studies have investigated the role of FGFR1 in preclinical models , 97. TheCertainly, the targeted therapy has shown promising results, but so far the appearance of resistances seems unavoidable. For this reason, many current efforts are focused on therapeutic approaches that delay the appearance of resistances. Ongoing work is assessing the effectiveness of many other options of combination, including a TKI against the same target, but with a different resistance profile , 165, anThanks to the omics techniques and their high throughput capacity of analysis, many alterations with potential involvement in lung cancer have been identified and validated in the last years, leading to improvements in clinical practice. Many of these aberrations occur in TKRs, inducing a deregulated downstream signalling that leads to tumorigenesis. Due to the functional nature of TKRs, their action can be pharmacologically inhibited, making the TKRs very appealing for research in cancer. Indeed, addressing the TKRs has made very interesting achievements in lung cancer treatment, resulting in the development of targeted therapies that have provided a substantial benefit for patients eligible for those therapies. However, the benefit derived from any targeted therapy is unfortunately transient, due to the development of resistance to these therapies. Current and future research efforts will be focused on understanding the molecular nature of these resistances, aiming to find novel predictive biomarkers of therapy response and new therapeutic approaches that prevent, or at least delay, the appearance of resistances and tumor regression.