Simultaneous Inference of Cancer Pathways and Tumor Progression from Cross-Sectional Mutation Data

2014 ◽  
pp. 250-264 ◽  
Author(s):  
Benjamin J. Raphael ◽  
Fabio Vandin
Keyword(s):  
Tumor Progression ◽  
Simultaneous Inference ◽  
Cross Sectional ◽  
Cancer Pathways ◽  
Mutation Data

scholarly journals Inferring restrictions in the temporal order of mutations during tumor progression: effects of passengers, evolutionary models, and sampling

2014 ◽  
Author(s):  
Ramon Diaz-Uriarte
Keyword(s):  
Tumor Progression ◽  
Cancer Progression ◽  
Performance Metrics ◽  
Simulated Data ◽  
Evolutionary Model ◽  
Data Sets ◽  
Evolutionary Models ◽  
Cross Sectional ◽  
Order Restrictions ◽  
Sampling Schemes

Cancer progression is caused by the sequential accumulation of mutations, but not all orders of accumulation of mutations are equally likely. When the fixation of some mutations depends on the presence of previous ones, identifying restrictions in the order of accumulation of mutations can lead to the discovery of therapeutic targets and diagnostic markers. Using simulated data sets, I conducted a comprehensive comparison of the performance of all available methods to identify these restrictions from cross-sectional data. In contrast to previous work, I embedded restrictions within evolutionary models of tumor progression that included passengers (mutations not responsible for the development of cancer, known to be very common). This allowed me to asses the effects of having to filter out passengers, of sampling schemes, and of deviations from order restrictions. Poor choices of method, filtering, and sampling lead to large errors in all performance metrics. Having to filter passengers lead to decreased performance, especially because true restrictions were missed. Overall, the best method for identifying order restrictions were Oncogenetic Trees, a fast and easy to use method that, although unable to recover dependencies of mutations on more than one mutation, showed good performance in most scenarios, superior to Conjunctive Bayesian Networks and Progression Networks. Single cell sampling provided no advantage, but sampling in the final stages of the disease vs.\ sampling at different stages had severe effects. Evolutionary model and deviations from order restrictions had major, and sometimes counterintuitive, interactions with other factors that affected performance. This paper provides practical recommendations for using these methods with experimental data. Moreover, it shows that it is both possible and necessary to embed assumptions about order restrictions and the nature of driver status within evolutionary models of cancer progression to evaluate the performance of inferential approaches.

scholarly journals Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?

2021 ◽  
Vol 17 (12) ◽  
pp. e1009055
Author(s):  
Juan Diaz-Colunga ◽  
Ramon Diaz-Uriarte
Keyword(s):  
Tumor Progression ◽  
Cancer Progression ◽  
Fitness Landscape ◽  
Tumor Evolution ◽  
Data Sets ◽  
Short Term ◽  
Global Features ◽  
Cross Sectional ◽  
Cancer Data ◽  
Landscape Characteristics

Accurate prediction of tumor progression is key for adaptive therapy and precision medicine. Cancer progression models (CPMs) can be used to infer dependencies in mutation accumulation from cross-sectional data and provide predictions of tumor progression paths. However, their performance when predicting complete evolutionary trajectories is limited by violations of assumptions and the size of available data sets. Instead of predicting full tumor progression paths, here we focus on short-term predictions, more relevant for diagnostic and therapeutic purposes. We examine whether five distinct CPMs can be used to answer the question “Given that a genotype with n mutations has been observed, what genotype with n + 1 mutations is next in the path of tumor progression?” or, shortly, “What genotype comes next?”. Using simulated data we find that under specific combinations of genotype and fitness landscape characteristics CPMs can provide predictions of short-term evolution that closely match the true probabilities, and that some genotype characteristics can be much more relevant than global features. Application of these methods to 25 cancer data sets shows that their use is hampered by a lack of information needed to make principled decisions about method choice. Fruitful use of these methods for short-term predictions requires adapting method’s use to local genotype characteristics and obtaining reliable indicators of performance; it will also be necessary to clarify the interpretation of the method’s results when key assumptions do not hold.

scholarly journals Every which way? On predicting tumor evolution using cancer progression models

2018 ◽  
Author(s):  
Ramon Diaz-Uriarte ◽  
Claudia Vasallo
Keyword(s):  
Tumor Progression ◽  
Disease Progression ◽  
Cancer Progression ◽  
Genetic Alterations ◽  
Fitness Landscapes ◽  
Data Sets ◽  
Sample Sizes ◽  
Cross Sectional ◽  
Cancer Data ◽  
Therapeutic Decisions

AbstractSuccessful prediction of the likely paths of tumor progression is valuable for diagnostic, prognostic, and treatment purposes. Cancer progression models (CPMs) use cross-sectional samples to identify restrictions in the order of accumulation of driver mutations and thus CPMs encode the paths of tumor progression. Here we analyze the performance of four CPMs to examine whether they can be used to predict the true distribution of paths of tumor progression and to estimate evolutionary unpredictability. Employing simulations we show that if fitness landscapes are single peaked (have a single fitness maximum) there is good agreement between true and predicted distributions of paths of tumor progression when sample sizes are large, but performance is poor with the currently common much smaller sample sizes. Under multi-peaked fitness landscapes (i.e., those with multiple fitness maxima), performance is poor and improves only slightly with sample size. In all cases, detection regime (when tumors are sampled) is a key determinant of performance. Estimates of evolutionary unpredictability from the best performing CPM, among the four examined, tend to overestimate the true un-predictability and the bias is affected by detection regime; CPMs could be useful for estimating upper bounds to the true evolutionary unpredictability. Analysis of twenty-two cancer data sets shows low evolutionary unpredictability for several of the data sets. But most of the predictions of paths of tumor progression are very unreliable, and unreliability increases with the number of features analyzed. Our results indicate that CPMs could be valuable tools for predicting cancer progression but that, currently, obtaining useful predictions of paths of tumor progression from CPMs is dubious, and emphasize the need for methodological work that can account for the probably multi-peaked fitness landscapes in cancer.Author SummaryKnowing the likely paths of tumor progression is instrumental for cancer precision medicine as it would allow us to identify genetic targets that block disease progression and to improve therapeutic decisions. Direct information about paths of tumor progression is scarce, but cancer progression models (CPMs), which use as input cross-sectional data on genetic alterations, can be used to predict these paths. CPMs, however, make assumptions about fitness landscapes (genotype-fitness maps) that might not be met in cancer. We examine if four CPMs can be used to predict successfully the distribution of tumor progression paths; we find that some CPMs work well when sample sizes are large and fitness landscapes have a single fitness maximum, but in fitness landscapes with multiple fitness maxima prediction is poor. However, the best performing CPM in our study could be used to estimate evolutionary unpredictability. When we apply the best performing CPM in our study to twenty-two cancer data sets we find that predictions are generally unreliable but that some cancer data sets show low unpredictability. Our results highlight that CPMs could be valuable tools for predicting disease progression, but emphasize the need for methodological work to account for multi-peaked fitness landscapes.

scholarly journals Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?

2020 ◽  
Author(s):  
Juan Diaz-Colunga ◽  
Ramon Diaz-Uriarte
Keyword(s):  
Tumor Progression ◽  
Cancer Progression ◽  
Fitness Landscape ◽  
Tumor Evolution ◽  
Data Sets ◽  
Short Term ◽  
Global Features ◽  
Cross Sectional ◽  
Cancer Data ◽  
Landscape Characteristics

AbstractAccurate prediction of tumor progression is key for adaptive therapy and precision medicine. Cancer progression models (CPMs) can be used to infer dependencies in mutation accumulation from cross-sectional data and provide predictions of tumor progression paths. But their performance when predicting the complete evolutionary paths is limited by violations of assumptions and the size of available data sets. Instead of predicting full tumor progression paths, we can focus on short-term predictions, more relevant for diagnostic and therapeutic purposes. Here we examine if five distinct CPMs can be used to answer the question “Given that a genotype with n mutations has been observed, what genotype with n + 1 mutations is next in the path of tumor progression” or, shortly, “What genotype comes next”. Using simulated data we find that under specific combinations of genotype and fitness landscape characteristics CPMs can provide predictions of short-term evolution that closely match the true probabilities, and that some genotype characteristics (fitness and probability of being a local fitness maximum) can be much more relevant than global features. Thus, CPMs can provide short-term predictions even when global, long-term predictions are not possible because fitness landscape- and evolutionary model-specific assumptions are violated. When good performance is possible, we observe significant variation in the quality of predictions of different methods. Genotype-specific and global fitness landscape characteristics are required to determine which method provides best results in each case. Application of these methods to 25 cancer data sets shows that their use is hampered by lack of the information needed to make principled decisions about method choice and what predictions to trust. Fruitful use of these methods for short-term predictions requires adapting method’s use to local genotype characteristics and obtaining reliable indicators of performance; it will also be necessary to clarify the interpretation of the method’s results when key assumptions do not hold.

scholarly journals Demand for integrative medicine among women with breast and gynecological cancer: a multicenter cross-sectional study in Southern and Northern Germany

2020 ◽  
Author(s):  
Donata Grimm ◽  
Sofia Mathes ◽  
Linn Woelber ◽  
Caroline Van Aken ◽  
Barbara Schmalfeldt ◽  
...  
Keyword(s):  
Side Effects ◽  
Tumor Progression ◽  
Gynecological Cancer ◽  
Cross Sectional Study ◽  
Vitamin Supplementation ◽  
Sectional Study ◽  
Short Term ◽  
Cross Sectional ◽  
Primary Tumor Site

Abstract Purpose The aim of this multicenter cross-sectional study was to analyze a cohort of breast (BC) and gynecological cancer (GC) patients regarding their interest in, perception of and demand for integrative therapeutic health approaches. Methods BC and GC patients were surveyed at their first integrative clinic visit using validated standardized questionnaires. Treatment goals and potential differences between the two groups were evaluated. Results 340 patients (272 BC, 68 GC) participated in the study. The overall interest in IM was 95.3% and correlated with older age, recent chemotherapy, and higher education. A total of 89.4% were using integrative methods at the time of enrolment, primarily exercise therapy (57.5%), and vitamin supplementation (51.4%). The major short-term goal of the BC patients was a side-effects reduction of conventional therapy (70.4%); the major long-term goal was the delay of a potential tumor progression (69.3%). In the GC group, major short-term and long-term goals were slowing tumor progression (73.1% and 79.1%) and prolonging survival (70.1% and 80.6%). GC patients were significantly more impaired by the side-effects of conventional treatment than BC patients [pain (p = 0.006), obstipation (< 0.005)]. Conclusion Our data demonstrate a high overall interest in and use of IM in BC and GC patients. This supports the need for specialized IM counseling and the implementation of integrative treatments into conventional oncological treatment regimes in both patient groups. Primary tumor site, cancer diagnosis, treatment phase, and side effects had a relevant impact on the demand for IM in our study population.

scholarly journals The Prognostic and Predictive Role of Chromogranin A in Gastroenteropancreatic Neuroendocrine Tumors – A Single-Center Experience

2021 ◽  
Vol 11 ◽  
Author(s):  
Hui-Jen Tsai ◽  
Chin-Fu Hsiao ◽  
Jeffrey S. Chang ◽  
Li-Tzong Chen ◽  
Ying-Jui Chao ◽  
...  
Keyword(s):  
Tumor Progression ◽  
Chromogranin A ◽  
Predictive Marker ◽  
Response Prediction ◽  
Tumor Burden ◽  
University Hospital ◽  
Gastroenteropancreatic Neuroendocrine Tumors ◽  
Cross Sectional ◽  
Study Results

Chromogranin A (CgA) is a non-specific biomarker excreted by neuroendocrine tumor (NET) cells. Elevation of circulating CgA level can be detected in gastroenteropancreatic (GEP)-NET patients and has been shown to correlate with tumor burden. The prognostic and predictive roles of CgA level and the change of CgA level are controversial. In this study, we retrospectively analyzed 102 grade 1/2 GEP-NET patients with available baseline or serial follow-up CgA levels from the National Cheng Kung University Hospital to evaluate the association between circulating CgA level and the tumor extent, overall survival (OS), and tumor response prediction. The baseline characteristics, baseline CgA level, and change of CgA level during follow-up and their association was analyzed. Sixty cases had baseline CgA levels available prior to any treatment and ninety-four cases had serial follow-up CgA levels available during treatment or surveillance. Baseline CgA levels were associated with stage and sex. Higher baseline CgA levels were associated with worse OS after adjusting for sex, stage, grade, primary site, and functionality (hazard ratio=13.52, 95% confidence interval (CI), 1.06-172.47, P=0.045). The cross-sectional analysis for the change of CgA level during follow-up showed that a ≥ 40% increase of CgA meant a higher probability of developing tumor progression or recurrence than those with a &lt; 40% increase of CgA level (odds ratio=5.04, 95% CI, 1.31-19.4, P=0.019) after adjusting for sex, age, grade, stage, and functionality. Our study results suggest that CgA may be a predictive marker for tumor burden, OS, and tumor progression in GEP-NET patients.

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