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Mutational burden and chromosomal aneuploidy synergistically predict survival from radiotherapy in non-small cell lung cancer.

ABSTRACT: Therapeutic radiation can result in substantially different survival outcomes for patients with non-small cell lung cancer (NSCLC). Measures for identification of patients who can benefit most throughout radiotherapy remain limited. In this retrospective study, survival analysis was performed based on a discovery cohort from TCGA and a validation cohort from three independent hospitals. Tumor mutational burden (TMB) and chromosomal aneuploidy (ANE) were derived from the whole exome sequencing (WES) data from treatment-naïve tumors. Integrated risk scores were derived from TMB and ANE by a multivariate Cox proportional hazards model. TCGA reveal that TMB and ANE are associated positively and negatively, respectively, with survival throughout radiotherapy. Additionally, the synergistically predictive significance of these two genomic alterations, in differing responders and non-responders to radiotherapy is identified. These biomarkers may have clinical potential to improve personalized treatment management by rationally identifying highly likely responders to therapeutic radiation in patients with NSCLC.

SUBMITTER: Jia Q

PROVIDER: S-EPMC7846582 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Mutational burden and chromosomal aneuploidy synergistically predict survival from radiotherapy in non-small cell lung cancer.

Jia Qingzhu Q Chu Qian Q Zhang Anmei A Yu Jing J Liu Fangfang F Qian Kaiyu K Xiao Yu Y Wang Xue X Yang Ying Y Zhao Yi Y He Ji J Li Guanghui G Wan Yisong Y YY Xie Conghua C Zhu Bo B

Communications biology 20210129 1

Therapeutic radiation can result in substantially different survival outcomes for patients with non-small cell lung cancer (NSCLC). Measures for identification of patients who can benefit most throughout radiotherapy remain limited. In this retrospective study, survival analysis was performed based on a discovery cohort from TCGA and a validation cohort from three independent hospitals. Tumor mutational burden (TMB) and chromosomal aneuploidy (ANE) were derived from the whole exome sequencing (W ...[more]

PMID: 33514859

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Project description:BackgroundLung cancer is the leading cause of cancer death worldwide. With the growing number of targeted therapies and the introduction of immuno-oncology (IO), personalized medicine has become standard of care in patients with metastatic disease. The development of predictive and prognostic biomarkers is of great importance. Mutational signatures harbor potential clinical value as predictors of therapy response in cancer. Here we set out to investigate particular mutational processes by assessing mutational signatures and associations with clinical features, tumor mutational burden (TMB) and targetable mutations.MethodsIn this retrospective study, we studied tumor DNA from patients with non-small cell lung cancer (NSCLC) irrespective of stage. The samples were sequenced using a 2 megabase (Mb) gene panel. On each sample TMB was determined and defined as the total number of single nucleotide mutations per Mb (mut/Mb) including non-synonymous mutations. Mutational signature profiling was performed on tumor samples in which at least 30 somatic single base substitutions (SBS) were detected.ResultsIn total 195 samples were sequenced. Median total TMB was 10.3 mut/Mb (range 0-109.3). Mutational signatures were evaluated in 76 tumor samples (39%; median TMB 15.2 mut/Mb). SBS signature 4 (SBS4), associated with tobacco smoking, was prominently present in 25 of 76 samples (33%). SBS2 and/or SBS13, both associated with activity of the AID/APOBEC family of cytidine deaminases, were observed in 11 of 76 samples (14%). SBS4 was significantly more present in early stages (I and II) versus advanced stages (III and IV; P = .005).ConclusionIn a large proportion of NSCLC patients tissue panel sequencing with a 2 Mb panel can be used to determine the mutational signatures. In general, mutational signature SBS4 was more often found in early versus advanced stages of NSCLC. Further studies are needed to determine the clinical utility of mutational signature analyses.

Project description:Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography/computed tomography (PET/CT) images have predictive power for NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neural networks (CNN) are rapidly emerging as a new method for cancer image analysis, with significantly enhanced predictive power compared to hand-crafted radiomics features. Here we show that CNNs trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value. In a retrospective study on pre-treatment PET-CT images of 96 NSCLC patients before stereotactic-body radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET and CT images, contained features having strong correlation with 2- and 5-year overall and disease-specific survivals. The U-Net algorithm has not seen any other clinical information (e.g. survival, age, smoking history, etc.) than the images and the corresponding tumor contours provided by physicians. In addition, we observed the same trend by validating the U-Net features against an extramural data set provided by Stanford Cancer Institute. Furthermore, through visualization of the U-Net, we also found convincing evidence that the regions of metastasis and recurrence appear to match with the regions where the U-Net features identified patterns that predicted higher likelihoods of death. We anticipate our findings will be a starting point for more sophisticated non-intrusive patient specific cancer prognosis determination. For example, the deep learned PET/CT features can not only predict survival but also visualize high-risk regions within or adjacent to the primary tumor and hence potentially impact therapeutic outcomes by optimal selection of therapeutic strategy or first-line therapy adjustment.

Dataset Information

Mutational burden and chromosomal aneuploidy synergistically predict survival from radiotherapy in non-small cell lung cancer.

Publications

Mutational burden and chromosomal aneuploidy synergistically predict survival from radiotherapy in non-small cell lung cancer.

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