A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis.

Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we report the results and insights gained from the DREAM 9 Acute Myeloid Prediction Outcome Prediction Challenge (AML-OPC), a crowdsourcing effort designed to promote the development of quantitative methods for AML prognosis prediction. We identify the most accurate and robust models in predicting patient response to therapy, remission duration, and overall survival. We further investigate patient response to therapy, a clinically actionable prediction, and find that patients that are classified as resistant to therapy are harder to predict than responsive patients across the 31 models submitted to the challenge. The top two performing models, which held a high sensitivity to these patients, substantially utilized the proteomics data to make predictions. Using these models, we also identify which signaling proteins were useful in predicting patient therapeutic response

The role of patient outcome and proteomics data in determining prediction accuracy.

<p>A) The probability density of prediction accuracy evaluated separately for CR and Resistant patients. (B) Comparison of individual model accuracy for CR and Resistant patients (right) compared to the distribution over the population (left). The midline of the box plot indicated median accuracy while the lower and upper box edge indicated 25^th and 75^th percentile. (C) The distribution of scores obtained using scrambled RPPA data for the two top performing teams in SC1 (Rank #1 and Rank #2). For each metric, the score obtained using the original RPPA data (not scrambled) is indicated by a diamond. (D) Heat map showing the percent difference in score (average of BAC and AUROC) between predictions obtained using the original RPPA data (not scrambled) and predictions made using data where each protein was scrambled separately over 100 assessments. The y-axis indicates the result for each scrambled protein assessment, 1–100, while the x-axis indicates each protein.</p

Model performance.

<p>The performance of each model was tracked during each week of the challenge. Each sub-challenge was scored using two different metrics. BAC and AUROC were used for SC1, while CI and PC were chosen for SC2 and SC3. The score of the highest performing model was determined each week, either using each metric independently, or by averaging both metrics, and is shown for SC1 (A), SC2 (B), and SC3 (C). Note, if the highest score for any week did not exceed the previous weeks score, the previous score was maintained. The probability density of the final scores (normalized to a maximum of 1) was also determined and for each metric in SC1 (B), SC2 (D), and SC3 (F). The probability density of the null hypothesis, determined by scoring random predictions, is also indicated.</p

Stability of model performance.

<p>Model stability was evaluated for SC1 (A), SC2 (B, left) and SC3 (B, right) by scoring final predictions on 1000 different random subsets of the test set samples (each subset was 60 patients, ~80% of the week 13 test set). The resulting distribution of scores was plotted against each teams overall challenge rank. Note, the center horizontal line of each box indicates the median score. Challenge ranks are ordered from highest to lowest, where a rank of 1 indicates the highest rank.</p

Aggregate and individual model scores.

<p>Aggregate scores were determined by averaging the predictions of each model with the predictions from all the models that out-performed it. Model rank is plotted along the x-axis from highest to lowest, with a rank of 1 assigned to the top performing team. Therefore, any given point along the x-axis indicates the minimum rank of the model included in the aggregate score, e.g., a minimum challenge rank of 2 includes predictions from both the rank 2 team and the rank 1 team which out-performed it. The aggregate scores (red lines) were compared to individual team scores (blue lines) for SC1, SC2, and SC3. In each case, the scores reported are the average of the two metrics used for that sub-challenge.</p