For Baber Makayla Ashford 4 Week 3 Assignment Immune Respons

For Baber Makaylaashford 4 Week 3 Assignmentimmune Responsesabnor

For Baber Makaylaashford 4: - Week 3 - Assignment Immune Responses Abnormal immune responses can trigger a range of autoimmune diseases, in which an individual’s immune system is attacking normal tissues in the body. Well-known examples of autoimmune diseases are type 1 diabetes mellitus, lupus, and multiple sclerosis. Ideally, one would like to harness the immune system to attack abnormal substances or tissues like cancer, while sparing the normal (unaffected) tissue. Many tumor cells produce antigens (proteins) that theoretically ought to trigger an immune response: that is, one’s immune system ought to recognize cancer cells as somehow foreign or abnormal, and thereafter eliminate these cells from the body.

The field of cancer immunotherapy is actively pursuing this study. Tumor antigens may also be useful for diagnostic tests; high levels of tumor antigens could be taken as markers or indicators of cancer. In this assignment, you will be examining levels of tumor-associated antigens (TAAs) as determined from immunoassays (i.e., biochemical tests that measure the concentrations of the tumor-associated antigens in serum samples). Download the Excel file MHA610_assignment_3_data.xls , and open it. The spreadsheet contains data on 250 individuals: 90 normal individuals from San Diego (the controls), and 160 individuals from Korea and China, all of whom were diagnosed with hepatocellular carcinoma (HCC).

Serum samples were taken from the controls and from the cases at the time of HCC diagnosis. Levels of a panel of 12 tumor-associated antigens (TAAs) were assessed via immunoassays in all individuals; The levels are given in the columns with headings Ab14, HCC1, IMP1, KOC, MDM2, NPM1, P16, P53, P90, RaIA, and Survivin. (These are the designations of the 12 TAAs, all of which were thought to be potentially predictive of cancer.) The underlying question is whether we can effectively discriminate between the cases and controls based on the levels of these TAAs. This is sometimes called a classification problem in the statistics and biostatistics literature: we wish to classify individuals as normal or cancer patients based on their TAA levels.

We will examine these data in Statdisk. Use the MHA610_Week 3_Assignment_Data.CSV file to upload this information into Statdisk. If you choose the latter option, start Statdisk, then choose File > Open and select the .csv file. Ensure the box that specifies the data contains column titles or headers is checked, select Comma separated for data delimiters, click finish, and the dataset will be loaded into Statdisk. Note that Statdisk operates on columns of data, with both cases and controls contained in each column of TAA levels. You will need to separate the cases and controls for further analyses, either by copying within Statdisk or by editing the Excel file, then exporting as CSV and re-importing. Explain if you would characterize any or all of the TAA levels as approximately normally distributed for the controls and for the cases, providing plots and statistics to support your conclusions. Additionally, determine if any of the TAAs are useful for discriminating between cases and controls, supported by plots and statistical analysis.

Furthermore, the assignment involves assessing whether pooling all cases from different regions (China and Korea) is appropriate, or whether TAA levels differ significantly between these groups. Provide evidence and reasoning for your conclusion based on the data.

Paper For Above instruction

The investigation into immune responses, particularly abnormal or autoimmune responses, and their relation to cancer diagnosis via tumor-associated antigens (TAAs) is central to advancing immunotherapy and diagnostic strategies. This exploration involves understanding the distribution and discriminatory power of TAA levels between healthy controls and cancer patients, as well as considering regional variations among cases.

Autoimmune diseases, such as type 1 diabetes mellitus, systemic lupus erythematosus, and multiple sclerosis, exemplify cases where the immune system erroneously targets normal tissues. Understanding these responses provides a baseline for contrast with abnormal responses seen in cancer, where the immune system might recognize tumor antigens as foreign. The goal of harnessing the immune system in oncology—cancer immunotherapy—aims to boost immune recognition of tumor cells without harming normal tissues. Tumor antigens serve as molecular markers that are potentially exploitable for both immunotherapy and diagnostic purposes. Elevated levels of TAAs in serum samples can indicate the presence of cancer, making them valuable for early detection and monitoring.

The dataset provided for analysis includes serum levels of 12 TAAs across 250 individuals, partitioned into controls and hepatocellular carcinoma (HCC) cases from diverse geographic origins. Initial statistical analysis involves characterizing the distribution of these TAA levels within each group. Visual tools such as histograms, boxplots, and Q-Q plots, coupled with descriptive statistics like skewness, kurtosis, and normality tests (e.g., Shapiro-Wilk), help determine whether TAA levels approximate normal distributions. For example, if the histograms in the control group show symmetric bell-shaped curves and the Shapiro-Wilk test fails to reject normality, then parametric tests and models assuming normality could be appropriate.

In the case of deviations from normality, data transformation techniques might be necessary, such as logarithmic or square root transformations, to stabilize variances and improve the approximation to normality. These steps are critical because many analytical methods—like t-tests or linear discriminant analysis—rely on the assumption of normality of variables within groups.

Next, evaluating the discriminatory power of each TAA involves comparing their levels between cases and controls. Statistical tests such as independent samples t-tests (if assumptions hold) or non-parametric alternatives like Mann-Whitney U tests provide insights into whether differences are statistically significant. Effect sizes, confidence intervals, and measures like the area under the receiver operating characteristic (ROC) curve (AUC) further quantify each TAA’s usefulness as a classifier.

The ROC analysis, for instance, plots true positive rate against false positive rate at various thresholds, and an AUC closer to 1 indicates high discrimination. Conversely, an AUC near 0.5 suggests no better than chance performance. By examining these metrics across all 12 TAAs, we can identify which markers are most promising for diagnostic purposes.

Finally, regional differences among cases from China versus Korea should be examined to determine if pooling is justified. Statistical comparisons of TAA levels between these subgroups—using t-tests or non-parametric tests—allow for the detection of significant differences. If significant regional variation exists, it may necessitate stratified analyses or region-specific markers to optimize diagnostic accuracy.

Overall, this comprehensive assessment combines distributional characterization, diagnostic evaluation, and regional comparison, providing valuable insights into the utility of TAAs in cancer detection and the importance of considering demographic heterogeneity.

References

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• Fang, H., et al. (2021). Application of ROC curve analysis in biomarker evaluation. Biometrics, 77(2), 508-517.
• Huang, Y., & Li, X. (2020). Statistical methods for analyzing serum biomarker levels. Statistics in Medicine, 39(24), 3778-3790.
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• Li, R., et al. (2020). Regional differences in tumor marker expression levels: Implications for diagnosis. Cancer Epidemiology, 66, 101749.
• McLachlan, G. J. (2004). Discriminant Analysis and Statistical Pattern Recognition. John Wiley & Sons.
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