scMSI: Accurately inferring the sub-clonal Micro-Satellite status by an integrated deconvolution model on length spectrum

Microsatellite instability (MSI) is an important genomic biomarker for cancer diagnosis and treatment, and sequencing-based approaches are often applied to identify MSI because of its fastness and efficiency. These approaches, however, may fail to identify MSI on one or more sub-clones for certain cancers with a high degree of heterogeneity, leading to erroneous diagnoses and unsuitable treatments. Besides, the computational cost of identifying sub-clonal MSI can be exponentially increased when multiple sub-clones with different length distributions share MSI status. Herein, this paper proposes “scMSI”, an accurate and efficient estimation of sub-clonal MSI to identify the microsatellite status. scMSI is an integrative Bayesian method to deconvolute the mixed-length distribution of sub-clones by a novel alternating iterative optimization procedure based on a subtle generative model. During the process of deconvolution, the optimized division of each sub-clone is attained by a heuristic algorithm, aligning with clone proportions that adhere optimally to the sample’s clonal structure. To evaluate the performance, 16 patients diagnosed with endometrial cancer, exhibiting positive responses to the treatment despite having negative MSI status based on sequencing-based approaches, were considered. Excitingly, scMSI reported MSI on sub-clones successfully, and the findings matched the conclusions on immunohistochemistry. In addition, testing results on a series of experiments with simulation datasets concerning a variety of impact factors demonstrated the effectiveness and superiority of scMSI in detecting MSI on sub-clones over existing approaches. scMSI provides a new way of detecting MSI for cancers with a high degree of heterogeneity.Author summary: Microsatellites are short, repetitive sequences of DNA, and their instability (MSI) is an important marker for cancer diagnosis and treatment. However, tumors often consist of diverse groups of cells, or sub-clones, and existing sequencing methods often fail to detect MSI that occurs only in some sub-clones. This can lead to incorrect diagnoses and prevent patients from receiving the most effective therapies. To solve this problem, we developed a new computational method named as scMSI to accurately identify MSI of sub-clones within a tumor. scMSI utilizes advanced statistical techniques to deconvolute the complex mixture of genetic mutations. As a result, we can use scMSI to detect sub-clonal MSI that other methods might miss. In the testing, we examined scMSI on samples from 16 patients with endometrial cancer, who had been incorrectly labeled as MSI-negative by existing methods. Our method successfully identified MSI in sub-clones, showing that scMSI outperforms existing tools. Additionally, simulation experiments under various conditions further confirmed the effectiveness of scMSI in detecting sub-clonal MSI. By improving the detection of MSI in cancers with a high degree of heterogeneity, scMSI can enhance cancer diagnosis and treatments more effectively.