High-Dimensional Analysis of Single-Cell Flow Cytometry Data Predicts Relapse in Childhood Acute Lymphoblastic Leukaemia

B-cell Acute Lymphoblastic Leukaemia is one of the most common cancers in childhood, with 20% of patients eventually relapsing. Flow cytometry is routinely used for diagnosis and follow-up, but it currently does not provide prognostic value at diagnosis. The volume and the high-dimensional character of this data makes it ideal for its exploitation by means of Artificial Intelligence methods. We collected flow cytometry data from 56 patients from two hospitals. We analysed differences in intensity of marker expression in order to predict relapse at the moment of diagnosis. We finally correlated this data with biomolecular information, constructing a classifier based on CD38 expression. Artificial intelligence methods may help in unveiling information that is hidden in high-dimensional oncological data. Flow cytometry studies of haematological malignancies provide quantitative data with the potential to be used for the construction of response biomarkers. Many computational methods from the bioinformatics toolbox can be applied to these data, but they have not been exploited in their full potential in leukaemias, specifically for the case of childhood B-cell Acute Lymphoblastic Leukaemia. In this paper, we analysed flow cytometry data that were obtained at diagnosis from 56 paediatric B-cell Acute Lymphoblastic Leukaemia patients from two local institutions. Our aim was to assess the prognostic potential of immunophenotypical marker expression intensity. We constructed classifiers that are based on the Fisher's Ratio to quantify differences between patients with relapsing and non-relapsing disease. We also correlated this with genetic information. The main result that arises from the data was the association between subexpression of marker CD38 and the probability of relapse

Machine Learning for Flow Cytometry Data Analysis.

This thesis concerns the problem of automatic flow cytometry data analysis. Flow cytometry is a technique for rapid cell analysis and widely used in many biomedical and clinical laboratories. Quantitative measurements from a flow cytometer provide rich information about various physical and chemical characteristics of a large number of cells. In clinical applications, flow cytometry data is visualized on a sequence of two-dimensional scatter plots and analyzed through a manual process called “gating”. This conventional analysis process requires a large amount of time and labor and is highly subjective and inefficient. In this thesis, we present novel machine learning methods for flow cytometry data analysis to address these issues. We first begin by a method for generating a high dimensional flow cytometry dataset from multiple low dimensional datasets. We present an imputation algorithm based on clustering and show that it improves upon a simple nearest neighbor based approach that often induces spurious clusters in the imputed data. This technique enables the analysis of multi-dimensional flow cytometry data beyond the fundamental measurement limits of instruments. We then present two machine learning methods for automatic gating problems. Gating is a process of identifying interesting subsets of cell populations. Pathologists make clinical decisions by inspecting the results from gating. Unfortunately, this process is performed manually in most clinical settings and poses many challenges in high-throughput analysis. The first approach is an unsupervised learning technique based on multivariate mixture models. Since measurements from a flow cytometer are often censored and truncated, standard model-fitting algorithms can cause biases and lead to poor gating results. We propose novel algorithms for fitting multivariate Gaussian mixture models to data that is truncated, censored, or truncated and censored. Our second approach is a transfer learning technique combined with the low-density separation principle. Unlike conventional unsupervised learning approaches, this method can leverage existing datasets previously gated by domain experts to automatically gate a new flow cytometry data. Moreover, the proposed algorithm can adaptively account for biological variations in multiple datasets. We demonstrate these techniques on clinical flow cytometry data and evaluate their effectiveness.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/89818/1/gyemin_1.pd

Wedang Uwuh Berbasis Daun Janggelan (Mesona palustris BL) Sebagai Imunomodulator Terhadap Tikus (Rattus norvegicus) Diabetes

Janggelan (Mesona palustris BL) mempunyai potensi untuk diformulasikan dengan rempah kering lainnya menjadi minuman tradisional, wedang uwuh. Pencampuran rempah-rempah dalam formulasi minuman dapat dilakukan untuk memperoleh suatu kombinasi antioksidan dengan khasiat yang beragam. Pangan fungsional yang mengandung flavonoid dapat berpotensi sebagai imunomodulator karena kemampuan antioksidan sebagai anti-inflamasi. Penelitian ini bertujuan untuk mendapatkan suatu formulasi optimum wedang uwuh dengan campuran janggelan, jahe merah, kayu secang, cengkeh dan daun sirsak, dan menguji wedang uwuh optimasi sebagai imunomodulator pada tikus diabetes. Penelitian ini terdiri atas 2 tahap. Tahap pertama yakni optimasi wedang uwuh berbasis janggelan melalui optimasi formulasi menggunakan metode dengan D-optimal Mixture Design. Rancangan ini menggunakan variabel bebas janggelan, jahe merah, kayu secang, cengkeh dan daun sirsak dengan respon IC50 minimum, total fenol dan total flavonoid maksimum. Tahap berikutnya pengujian pengaruh produk minuman fungsional wedang uwuh berbasis janggelan sebagai imunomodulator secara in-vivo, yaitu tikus diabetes yang diinduksi aloksan. Perlakuan tikus terdiri dari kontrol tikus sehat, kontrol tikus diabetes, dan tiga kelompok tikus diabetes yang diberi wedang uwuh. Wedang uwuh diberikan tiga hari setelah tikus diinduksi aloksan, pemberian wedang uwuh dilanjutkan selama 28 hari. Selanjutnya diambil limpa untuk pengujian imunomodulator dengan mengamati sel CD4+ , sitokin pro-inflamasi (TNF-α dan IFN-Ɣ) dan anti-inflamasi (IL-10 dan TGF-β). Hasil penelitian menunjukkan formulasi optimum wedang uwuh berbasis janggelan adalah janggelan sebanyak 2,832 g, jahe merah sebanyak 0,1 g, kayu secang sebanyak 0,38 g, cengkeh sebanyak 0,114 g, dan daun sirsak sebanyak 0,074 g dengan volume air mendidih sebanyak 350 ml. Formulasi terpilih ini menunjukkan nilai IC50, total fenol dan total flavonoid masing-masing sebesar 404,99±3,71 ppm; 16,17±0,04 mg GAE/g; 6,69±0,31 mg QE/g. Secara kualtitaif senyawa bioaktif yang teridentifikasi pada wedang uwuh berbasis janggelan adalah asam kafeat, eugenol, kuersetin, 10-shogaol, 8-shogaol, 6-shogaol, 6-gingerol, 8-gingerol 10-gingerol. Minuman fungsional wedang uwuh berbasis janggelan secara signifikan menghambat ekspresi sitokin pro-inflamasi (IFN-Ɣ dan TNF-α) dan tercapainya keseimbangan antara sitokin pro-inflamasi dan anti-inflamasi yang tidak berbeda nyata dengan kontrol tikus sehat