25 research outputs found

    Leveraging big data resources and data integration in biology: applying computational systems analyses and machine learning to gain insights into the biology of cancers

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    Recently, many "molecular profiling" projects have yielded vast amounts of genetic, epigenetic, transcription, protein expression, metabolic and drug response data for cancerous tumours, healthy tissues, and cell lines. We aim to facilitate a multi-scale understanding of these high-dimensional biological data and the complexity of the relationships between the different data types taken from human tumours. Further, we intend to identify molecular disease subtypes of various cancers, uncover the subtype-specific drug targets and identify sets of therapeutic molecules that could potentially be used to inhibit these targets. We collected data from over 20 publicly available resources. We then leverage integrative computational systems analyses, network analyses and machine learning, to gain insights into the pathophysiology of pancreatic cancer and 32 other human cancer types. Here, we uncover aberrations in multiple cell signalling and metabolic pathways that implicate regulatory kinases and the Warburg effect as the likely drivers of the distinct molecular signatures of three established pancreatic cancer subtypes. Then, we apply an integrative clustering method to four different types of molecular data to reveal that pancreatic tumours can be segregated into two distinct subtypes. We define sets of proteins, mRNAs, miRNAs and DNA methylation patterns that could serve as biomarkers to accurately differentiate between the two pancreatic cancer subtypes. Then we confirm the biological relevance of the identified biomarkers by showing that these can be used together with pattern-recognition algorithms to infer the drug sensitivity of pancreatic cancer cell lines accurately. Further, we evaluate the alterations of metabolic pathway genes across 32 human cancers. We find that while alterations of metabolic genes are pervasive across all human cancers, the extent of these gene alterations varies between them. Based on these gene alterations, we define two distinct cancer supertypes that tend to be associated with different clinical outcomes and show that these supertypes are likely to respond differently to anticancer drugs. Overall, we show that the time has already arrived where we can leverage available data resources to potentially elicit more precise and personalised cancer therapies that would yield better clinical outcomes at a much lower cost than is currently being achieved

    Mutational landscape of cancer-driver genes across human cancers

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    Abstract The genetic mutations that contribute to the transformation of healthy cells into cancerous cells have been the subject of extensive research. The molecular aberrations that lead to cancer development are often characterised by gain-of-function or loss-of-function mutations in a variety of oncogenes and tumour suppressor genes. In this study, we investigate the genomic sequences of 20,331 primary tumours representing 41 distinct human cancer types to identify and catalogue the driver mutations present in 727 known cancer genes. Our findings reveal significant variations in the frequency of cancer gene mutations across different cancer types and highlight the frequent involvement of tumour suppressor genes (94%), oncogenes (93%), transcription factors (72%), kinases (64%), cell surface receptors (63%), and phosphatases (22%), in cancer. Additionally, our analysis reveals that cancer gene mutations are predominantly co-occurring rather than exclusive in all types of cancer. Notably, we discover that patients with tumours displaying different combinations of gene mutation patterns tend to exhibit variable survival outcomes. These findings provide new insights into the genetic landscape of cancer and bring us closer to a comprehensive understanding of the underlying mechanisms driving the development of various forms of cancer

    Integrated molecular characterisation of the MAPK pathways in human cancers reveals pharmacologically vulnerable mutations and gene dependencies

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    Musalula Sinkala et al. report that collectively, specific mutations of genes in 5 MAPK pathways are associated with worse patient survival. They identify gene components that are pharmacologically vulnerable to pathway inhibitors in cancer cell models, suggesting potential clinical applications of these findings

    Kidney injury molecule-1 and microalbuminuria levels in Zambian population: biomarkers of kidney injury

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    Introduction: kidney injury affects renal excretion of plasma analytes and metabolic waste products with grave pathologic consequences. Early detection, thus of kidney injury is essential for injury specific intervention that may avert permanent renal damage and delay progression of kidney injury. We aimed to evaluate Kidney Injury Molecule-1 (KIM-1) and Microalbuminuria (MAU), as biomarkers of kidney injury, in comparison with creatinine. Methods: we compared the levels of urine MAU, urine KIM-1 and other plasma biochemical tests in specimens from 80 individuals with and without kidney disease. Results: we found no difference in KIM-1 levels between the kidney disease group (2.82± 1.36ng/mL) and controls (3.29 ± 1.14ng/mL), p = 0.122. MAU was higher in participants with kidney disease (130.809± 84.744 µg/mL) than the controls (15.983± 20.442µg/mL), p ?0.001. KIM-1 showed a weak negative correlation with creatinine (r = -0.279, p = 0.09), whereas MAU was positively correlated with creatinine in participants with kidney disease with statistical significance (r = 0.556, p = 0.001). Conclusion: the study demonstrated that in Zambian setting MAU and creatinine are sensitive biomarkers in the diagnosis of kidney damage. We moreover propose further evaluation of KIM-1 as a biomarker of kidney injury.The Pan African Medical Journal 2016;2

    A genome-wide association study identifies distinct variants associated with pulmonary function among European and African ancestries from the UK Biobank

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    A genome-wide association study using summary statistics from the UK Biobank identifies ancestry-specific variants associated with pulmonary function among European and African ancestry cohorts

    Leucocytosis and Asymptomatic Urinary Tract Infections in Sickle Cell Patients at a Tertiary Hospital in Zambia

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    Sickle cell anaemia (SCA) is an inherited disease resulting from mutations in the β-globin chain of adult haemoglobin that results in the formation of homozygous sickle haemoglobin. It is associated with several complications including an altered blood picture and damage in multiple organs, including the kidneys. Kidney disease is seen in most patients with SCA and may affect glomerular and/or tubular function, thereby putting these patients at risk of urinary tract infections. However, there is a paucity of data on the prevalence of urinary tract infections (UTIs) among SCA patients in Zambia. This study aimed to determine the prevalence of UTIs and haematological and kidney function profiles among SCA patients at the University Teaching Hospitals, Lusaka, Zambia. This was a cross-sectional study conducted between April and July 2019 involving 78 SCA patients who presented at the UTH. Blood and midstream urine samples were collected from each participant using the standard specimen collection procedures. Full blood counts and kidney function tests were determined using Sysmex XT-4000i haematology analyser and the Pentra C200 by Horiba, respectively. Bacterial profiles of the urine samples were determined using conventional microbiological methods. We found that all the measured patients’ haemoglobin (Hb) levels fell below the WHO-recommended reference range with a minimum of 5 g/dl, a maximum of 10.5 g/dl, and a mean of 8 ± 1 g/dl. Fifty percent of the participants had moderate anaemia, while the other 50% had severe anaemia. The minimum WBC count of the participants was 0.02 × 109/L with a maximum of 23.36 × 109/L and a mean of 13.48 ± 3.87 × 109/L. Using the one-way analysis of variance test, we found no significant difference in mean WBC count and Hb concentration across various age-group categories that we defined. Bacteriuria was found in 25% of participants. The most common bacterial isolates were Staphylococcus aureus (32%) and coagulase-negative Staphylococci (32%). Klebsiella pneumoniae was 16%. We found no significant association between bacterial isolates and white blood cell count, age groups, sex, and anaemia severity p=0.41. None of the participants were diagnosed with kidney disease. There was a high prevalence of asymptomatic UTIs among SCA patients at UTH, which, when coupled with the marked leukocytosis and anaemia, may negatively impact the clinical outcome of the patients. Therefore, we recommend close monitoring of sickle cell patients in Zambia for such conditions to improve patients’ outcomes

    The value of procalcitonin and C-reactive protein as early markers of bacteraemia among patients with haematological malignancies receiving chemotherapy: a cross-sectional study

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    Background: The immune system of patients with haematological malignancies is suppressed during chemotherapy. This renders them vulnerable to frequent infections especially of the bacterial type. Timely diagnosis of these infections is difficult, because a severe infection may be asymptomatic or manifest only in the form of fever or malaise. There is need for laboratory markers that can detect an infectious process at an early stage. This study was aimed at determining the value of using Procalcitonin (PCT) and C reactive protein (CRP), for early diagnosis of infection in patients with haematological malignancies receiving chemotherapy.Methods: This was a cross sectional study consisting of sixty eight (68) patients with haematological malignancies. Data from each participant including sex, age, clinical and laboratory data were collected after obtaining informed consent. Blood specimens were then collected for measurement of PCT, CRP and bacteriological analysis. Patients were divided into two groups; those with a culture positive and negative result. PCT and CRP concentrations were compared between groups using t-test and nonparametric statistical tests respectively. The area under ROC curve, sensitivity, specificity, likelihood ratio, and Spearman's correlation coefficient were also calculated.Results: Atotal of 14 (20.6%) microorganisms were isolated, of which 10 were gram-positive bacteria and 4 were gram-negative bacilli. The mean values of PCT which were 6.1ng/mL in the bacteraemia group and 5.1ng/mL in the non-bacteraemia group, p=0.023 and median CRP values were 24.2 (6.43- 48.15) in the bacteraemia and 23.5 (6.03-75.44) in the non-bacteraemia group, p=0.832. The area under curves was 0.52 (95% CI=0.57-0.84) for CRP and 0.70 (95% CI=0.35-0.69) for PCT. PCT value of greater than 4.7 ng/mL is diagnostic for infections (sensitivity 86%, specificity 54%) while that of CRP was 21mg/mL with the sensitivity and specificity of 64% and 44% respectively. Elevated levels of PCT as well as fever were significantly associated with bacteraemia.Conclusion: PCT was a more reliable and sensitive marker of bacteraemia among patients with  haematological malignancies receiving chemotherapy than CRP.Keywords: Procalcitonin (PCT), C reactive protein (CRP), Haematological Malignancies, Bacteraemia Marke

    Model predictions vs. actual responses in drug adverse event profiling.

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    (A) A scatter plot showing the predicted response (y-axis) of the machine learning model plotted against the actual, correct response (x-axis). (B) An example of plots of adverse event prediction for the drug dasatinib. The predicted (blue markers) and actual proportions (orange markers) of individuals that experience adverse events related to a particular organ or body tissue (represented on the x-axis). The line connecting the marker represents the observed error between the predicted proportion of individuals that would experience adverse events against the actual proportion reported in clinical trials. Each prediction is obtained using a model that was trained without using the corresponding (held out) observations reported in breast cancer clinical trials that treated patients with dasatinib. (C) Box plot displaying the typical values of the reported adverse event affecting a particular tissue for the drug dasatinib and the predicted response, and any possible outliers. The central mark indicates the median, and the bottom and top edges of the box are the 25th and 75th percentiles, respectively. The whiskers extend from the boxes to the most extreme data points that are not considered outliers, whereas outliers are shown individually using the "+" symbol.</p

    Differentially expressed CSRs between PAM50 subtypes of breast cancer.

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    The sheets are named according to the comparison for which the differential expression CSRs results are represented. E.g., BRCA Basal-Vs-BRCA HER2 shows the results of Basal-like breast cancer subtype and HER2-positive breast cancer. (XLSX)</p

    Drug-response differences.

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    The spreadsheet contains the following results according to the sheet name. Dose-Response of cell lines; collated data of breast cancer cell lines profiled by the GDSC and the PAM50 subtype of each breast cancer cell line. Dose Response Anova Results; comparison of dose-response between each PAM50 subtype of breast cancer. Anova Statistics; ANOVA statistic for the comparison in the "Dose Response Anova Results" spreadsheet. CSR_based Response Comparison; Dose-response comparison between the GDSC breast cancer cell line that we segregated into two groups, those that expressed higher amounts of a particular drug target and those that expressed lower amounts of a particular drug target. (XLSX)</p
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