Improvement in prediction of prostate cancer prognosis with somatic mutational signatures

Zhang, Shengping; Xu, Yafei; Hui, Xinjie; Yang, Fei; Hu, Yueming; Shao, Jianlin; Liang, Hui; Wang, Yejun

doi:10.7150/jca.21261

Cited by 23 publications

(11 citation statements)

References 24 publications

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“…Our study supports the notion that model performance improves with greater granularity, and machine learning-trained models should have an advantage when incorporating new variables. 13,[18][19][20][21]28 As an example, Donovan and colleagues 20 combined standard variables with five molecular bio markers and automated histopathological image analysis to derive a prediction tool for biochemical recurrence after treatment. Their Precise Post-op model had a c-index of 0•77 for recurrence-free survival.…”

Section: Discussionmentioning

confidence: 99%

“…Their Precise Post-op model had a c-index of 0•77 for recurrence-free survival. Zhang and colleagues 21 combined somatic mutation signatures in a 43 gene panel with NICE risk criteria and improved the area under the curve for prediction of postsurgical biochemical recurrence from 0•62 to 0•75.…”

Section: Discussionmentioning

confidence: 99%

“…For prostate cancer prognostication, machine learning has so far been restricted to small, proof-of-concept studies without comparison with reference standards. [18][19][20][21] We aimed to investigate a novel machine learning approach, Survival Quilts, using a large national observational dataset to develop an improved prognostic model for predicting 10-year prostate cancer-specific mortality in men with nonmetastatic disease, 22 and compare its performance with existing validated models available in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

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Application of a novel machine learning framework for predicting non-metastatic prostate cancer-specific mortality in men using the Surveillance, Epidemiology, and End Results (SEER) database

Lee

Light

Alaa

et al. 2021

The Lancet Digital Health

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Background Accurate prognostication is crucial in treatment decisions made for men diagnosed with non-metastatic prostate cancer. Current models rely on prespecified variables, which limits their performance. We aimed to investigate a novel machine learning approach to develop an improved prognostic model for predicting 10-year prostate cancer-specific mortality and compare its performance with existing validated models. Methods We derived and tested a machine learning-based model using Survival Quilts, an algorithm that automatically selects and tunes ensembles of survival models using clinicopathological variables. Our study involved a US population-based cohort of 171 942 men diagnosed with non-metastatic prostate cancer between Jan 1, 2000, and Dec 31, 2016, from the prospectively maintained Surveillance, Epidemiology, and End Results (SEER) Program. The primary outcome was prediction of 10-year prostate cancer-specific mortality. Model discrimination was assessed using the concordance index (c-index), and calibration was assessed using Brier scores. The Survival Quilts model was compared with nine other prognostic models in clinical use, and decision curve analysis was done. Findings 647 151 men with prostate cancer were enrolled into the SEER database, of whom 171 942 were included in this study. Discrimination improved with greater granularity, and multivariable models outperformed tier-based models. The Survival Quilts model showed good discrimination (c-index 0•829, 95% CI 0•820-0•838) for 10-year prostate cancer-specific mortality, which was similar to the top-ranked multivariable models: PREDICT Prostate (0•820, 0•811-0•829) and Memorial Sloan Kettering Cancer Center (MSKCC) nomogram (0•787, 0•776-0•798). All three multivariable models showed good calibration with low Brier scores (Survival Quilts 0•036, 95% CI 0•035-0•037; PREDICT Prostate 0•036, 0•035-0•037; MSKCC 0•037, 0•035-0•039). Of the tier-based systems, the Cancer of the Prostate Risk Assessment model (c-index 0•782, 95% CI 0•771-0•793) and Cambridge Prognostic Groups model (0•779, 0•767-0•791) showed higher discrimination for predicting 10-year prostate cancer-specific mortality. c-indices for models from the National Comprehensive Cancer Care Network, Genitourinary Radiation Oncologists of Canada, American Urological Association, European Association of Urology, and National Institute for Health and Care Excellence ranged from 0•711 (0•701-0•721) to 0•761 (0•750-0•772). Discrimination for the Survival Quilts model was maintained when stratified by age and ethnicity. Decision curve analysis showed an incremental net benefit from the Survival Quilts model compared with the MSKCC and PREDICT Prostate models currently used in practice. Interpretation A novel machine learning-based approach produced a prognostic model, Survival Quilts, with discrimination for 10-year prostate cancer-specific mortality similar to the top-ranked prognostic models, using only standard clinicopathological variables. Future integration of additional data will likely ...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of a novel machine learning framework for predicting non-metastatic prostate cancer-specific mortality in men using the Surveillance, Epidemiology, and End Results (SEER) database

Lee

Light

Alaa

et al. 2021

The Lancet Digital Health

View full text Add to dashboard Cite

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“…For a variety of tumors, prognosis has been reported to be associated with somatic gene mutations ( Loi et al, 2013 ; Lee et al, 2017 ; Zhang et al, 2017 ; Cho et al, 2018 ; Yu et al, 2019 ). Despite the large heterogeneity of STADs, common genetic factors (e.g., BRCA2 and MUC16) were still identified and reported to be associated with the prognosis ( Chen et al, 2015 ; Li et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features

et al. 2020

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Background: Stomach adenocarcinoma (STAD) is one of the most common malignancies worldwide with poor prognosis. It remains unclear whether the prognosis is associated with somatic gene mutations. Methods: In this research, we collected two independent STAD cohorts with both genetic profiling and clinical follow-up data, systematically investigated the association between the prognosis and somatic mutations, and analyzed the influence of heterogeneity on the prognosis-genetics association. Results: Typical association was identified between somatic mutations and overall prognosis for individual cohorts. In The Cancer Genome Atlas (TCGA) cohort, a list of 24 genes was also identified that tended to mutate within cases of the poorest prognosis. The association showed apparent heterogeneity between different cohorts, although common signatures could be identified. A machine-learning model was trained with 20 common genes that showed a similar mutation rate difference between prognostic groups in the two cohorts, and it classified the cases in each cohort into two groups with significantly different prognosis. The model outperformed both single-gene models and TNM-based staging system significantly. Conclusion: The study made a systematic analysis on the association between STAD prognosis and somatic mutations, identified signature genes that showed mutation preference in different prognostic groups, and developed an effective multi-gene model that can effectively predict the overall prognosis of STAD in different cohorts.

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“…These methods can be used to identify breast cancer patients by genetic mutations as a result of applying gene learning methods to breast cancer samples [ 7 ]. One prostate cancer study has combined machine learning methods with National Institute for Health and Care Excellence features to observe the association between the prognosis of prostate cancer and genetic mutation profile [ 8 ]. In addition, previous studies have applied machine learning using healthy eating index scores to predict the interaction between colorectal cancer and overweight status [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Association of specific gene mutations derived from machine learning with survival in lung adenocarcinoma

Cho

Lee

et al. 2018

PLoS ONE

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Lung cancer is the second most common cancer in the United States and the leading cause of mortality in cancer patients. Biomarkers predicting survival of patients with lung cancer have a profound effect on patient prognosis and treatment. However, predictive biomarkers for survival and their relevance for lung cancer are not been well known yet. The objective of this study was to perform machine learning with data from The Cancer Genome Atlas of patients with lung adenocarcinoma (LUAD) to find survival-specific gene mutations that could be used as survival-predicting biomarkers. To identify survival-specific mutations according to various clinical factors, four feature selection methods (information gain, chi-squared test, minimum redundancy maximum relevance, and correlation) were used. Extracted survival-specific mutations of LUAD were applied individually or as a group for Kaplan-Meier survival analysis. Mutations in MMRN2 and GMPPA were significantly associated with patient mortality while those in ZNF560 and SETX were associated with patient survival. Mutations in DNAJC2 and MMRN2 showed significant negative association with overall survival while mutations in ZNF560 showed significant positive association with overall survival. Mutations in MMRN2 showed significant negative association with disease-free survival while mutations in DRD3 and ZNF560 showed positive associated with disease-free survival. Mutations in DRD3, SETX, and ZNF560 showed significant positive association with survival in patients with LUAD while the opposite was true for mutations in DNAJC2, GMPPA, and MMRN2. These gene mutations were also found in other cohorts of LUAD, lung squamous cell carcinoma, and small cell lung cancer. In LUAD of Pan-Lung Cancer cohort, mutations in GMPPA, DNAJC2, and MMRN2 showed significant negative associations with survival of patients while mutations in DRD3 and SETX showed significant positive association with survival. In this study, machine learning was conducted to obtain information necessary to discover specific gene mutations associated with the survival of patients with LUAD. Mutations in the above six genes could predict survival rate and disease-free survival rate in patients with LUAD. Thus, they are important biomarker candidates for prognosis.

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Improvement in prediction of prostate cancer prognosis with somatic mutational signatures

Cited by 23 publications

References 24 publications

Application of a novel machine learning framework for predicting non-metastatic prostate cancer-specific mortality in men using the Surveillance, Epidemiology, and End Results (SEER) database

Application of a novel machine learning framework for predicting non-metastatic prostate cancer-specific mortality in men using the Surveillance, Epidemiology, and End Results (SEER) database

A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features

Association of specific gene mutations derived from machine learning with survival in lung adenocarcinoma

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