Dataset Information

Algorithmic methods to infer the evolutionary trajectories in cancer progression.

ABSTRACT: The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next-generation sequencing data and machine learning for the inference of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional -omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly stemming from the dramatic heterogeneity of the disease. In this paper, we build on our recent work on the "selective advantage" relation among driver mutations in cancer progression and investigate its applicability to the modeling problem at the population level. Here, we introduce PiCnIc (Pipeline for Cancer Inference), a versatile, modular, and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline has many translational implications because it combines state-of-the-art techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations, and progression model inference. We demonstrate PiCnIc's ability to reproduce much of the current knowledge on colorectal cancer progression as well as to suggest novel experimentally verifiable hypotheses.

SUBMITTER: Caravagna G

PROVIDER: S-EPMC4948322 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Algorithmic methods to infer the evolutionary trajectories in cancer progression.

Caravagna Giulio G Graudenzi Alex A Ramazzotti Daniele D Sanz-Pamplona Rebeca R De Sano Luca L Mauri Giancarlo G Moreno Victor V Antoniotti Marco M Mishra Bud B

Proceedings of the National Academy of Sciences of the United States of America 20160628 28

The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next-generation sequencing data and machine learning for the inference of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional -omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly stemming from the dramatic heterogeneity of the disease. In ...[more]

PMID: 27357673

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Project description:The clonal relationship between ovarian high-grade serous carcinoma (HGSC) and its presumed precursor lesion, serous tubal intraepithelial carcinoma (STIC), has been reported. However, when analyzing patients with concurrent ovarian carcinoma and tubal lesion, the extensive carcinoma tissues present at diagnosis may have effaced the natural habitat of precursor clone(s), obscuring tumor clonal evolutionary history, or may have disseminated to anatomically adjacent fimbriae ends, masquerading as precursor lesions. To circumvent these limitations, we analyzed the genomic landscape of incidental tubal precursor lesions including p53 signature, dormant STIC or serous tubal intraepithelial lesion (STIL) and proliferative STIC in women without ovarian carcinoma or any cancer diagnosis using whole-exome sequencing and amplicon sequencing. In three of the four cancer-free women with multiple discrete tubal lesions we observed non-identical TP53 mutations between precursor lesions from the same individual. In one of the four women with co-existing ovarian HGSC and tubal precursor lesion we found non-identical TP53 mutations and a lack of common mutations shared between her precursor lesion and carcinoma. Analyzing the evolutionary history of multiple tubal lesions in the same four patients with concurrent ovarian carcinoma indicated distinct evolution trajectories. Collectively, the results support diverse clonal origins of tubal precursor lesions at the very early stages of tumorigenesis. Mathematical modeling based on lesion-specific proliferation rates indicated that p53 signature and dormant STIC may take a prolonged time (two decades or more) to develop into STIC, whereas STIC may progress to carcinoma in a much shorter time (6 years). The above findings may have implications for future research aimed at prevention and early detection of ovarian cancer. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Project description:ImportancePredictive models using machine learning techniques have potential to improve early detection and management of Alzheimer disease (AD). However, these models potentially have biases and may perpetuate or exacerbate existing disparities.ObjectiveTo characterize the algorithmic fairness of longitudinal prediction models for AD progression.Design, setting, and participantsThis prognostic study investigated the algorithmic fairness of logistic regression, support vector machines, and recurrent neural networks for predicting progression to mild cognitive impairment (MCI) and AD using data from participants in the Alzheimer Disease Neuroimaging Initiative evaluated at 57 sites in the US and Canada. Participants aged 54 to 91 years who contributed data on at least 2 visits between September 2005 and May 2017 were included. Data were analyzed in October 2022.ExposuresFairness was quantified across sex, ethnicity, and race groups. Neuropsychological test scores, anatomical features from T1 magnetic resonance imaging, measures extracted from positron emission tomography, and cerebrospinal fluid biomarkers were included as predictors.Main outcomes and measuresOutcome measures quantified fairness of prediction models (logistic regression [LR], support vector machine [SVM], and recurrent neural network [RNN] models), including equal opportunity, equalized odds, and demographic parity. Specifically, if the model exhibited equal sensitivity for all groups, it aligned with the principle of equal opportunity, indicating fairness in predictive performance.ResultsA total of 1730 participants in the cohort (mean [SD] age, 73.81 [6.92] years; 776 females [44.9%]; 69 Hispanic [4.0%] and 1661 non-Hispanic [96.0%]; 29 Asian [1.7%], 77 Black [4.5%], 1599 White [92.4%], and 25 other race [1.4%]) were included. Sensitivity for predicting progression to MCI and AD was lower for Hispanic participants compared with non-Hispanic participants; the difference (SD) in true positive rate ranged from 20.9% (5.5%) for the RNN model to 27.8% (9.8%) for the SVM model in MCI and 24.1% (5.4%) for the RNN model to 48.2% (17.3%) for the LR model in AD. Sensitivity was similarly lower for Black and Asian participants compared with non-Hispanic White participants; for example, the difference (SD) in AD true positive rate was 14.5% (51.6%) in the LR model, 12.3% (35.1%) in the SVM model, and 28.4% (16.8%) in the RNN model for Black vs White participants, and the difference (SD) in MCI true positive rate was 25.6% (13.1%) in the LR model, 24.3% (13.1%) in the SVM model, and 6.8% (18.7%) in the RNN model for Asian vs White participants. Models generally satisfied metrics of fairness with respect to sex, with no significant differences by group, except for cognitively normal (CN)-MCI and MCI-AD transitions (eg, an absolute increase [SD] in the true positive rate of CN-MCI transitions of 10.3% [27.8%] for the LR model).Conclusions and relevanceIn this study, models were accurate in aggregate but failed to satisfy fairness metrics. These findings suggest that fairness should be considered in the development and use of machine learning models for AD progression.

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Algorithmic methods to infer the evolutionary trajectories in cancer progression.

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Algorithmic methods to infer the evolutionary trajectories in cancer progression.

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