Bioinformatics and Computational Biology Lab, Department of Computer Engineering, Sharif University of Technology, 11365, Tehran, Iran.
Center for Computational and Integrative Biology (CCIB), Rutgers University, Camden, NJ, 08102, USA.
BMC Bioinformatics. 2022 Apr 19;23(1):138. doi: 10.1186/s12859-022-04652-8.
Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. Recent studies have observed causative mutations in susceptible genes related to colorectal cancer in 10 to 15% of the patients. This highlights the importance of identifying mutations for early detection of this cancer for more effective treatments among high risk individuals. Mutation is considered as the key point in cancer research. Many studies have performed cancer subtyping based on the type of frequently mutated genes, or the proportion of mutational processes. However, to the best of our knowledge, combination of these features has never been used together for this task. This highlights the potential to introduce better and more inclusive subtype classification approaches using wider range of related features to enable biomarker discovery and thus inform drug development for CRC.
In this study, we develop a new pipeline based on a novel concept called 'gene-motif', which merges mutated gene information with tri-nucleotide motif of mutated sites, for colorectal cancer subtype identification. We apply our pipeline to the International Cancer Genome Consortium (ICGC) CRC samples and identify, for the first time, 3131 gene-motif combinations that are significantly mutated in 536 ICGC colorectal cancer samples. Using these features, we identify seven CRC subtypes with distinguishable phenotypes and biomarkers, including unique cancer related signaling pathways, in which for most of them targeted treatment options are currently available. Interestingly, we also identify several genes that are mutated in multiple subtypes but with unique sequence contexts.
Our results highlight the importance of considering both the mutation type and mutated genes in identification of cancer subtypes and cancer biomarkers. The new CRC subtypes presented in this study demonstrates distinguished phenotypic properties which can be effectively used to develop new treatments. By knowing the genes and phenotypes associated with the subtypes, a personalized treatment plan can be developed that considers the specific phenotypes associated with their genomic lesion.
结直肠癌（CRC）是全球癌症相关死亡的主要原因之一。最近的研究观察到，在 10%至 15%的患者中，与结直肠癌相关的易感基因存在因果突变。这突出表明，对于高危人群，识别突变对于更有效地治疗这种癌症，进行早期检测非常重要。突变被认为是癌症研究的关键点。许多研究已经根据经常发生突变的基因类型或突变过程的比例对癌症进行了亚型分类。然而，据我们所知，从未将这些特征结合起来用于这项任务。这突出了使用更广泛的相关特征引入更好、更具包容性的亚型分类方法的潜力，从而发现生物标志物，并为 CRC 的药物开发提供信息。
在这项研究中，我们开发了一种基于称为“基因基序”的新概念的新管道，该管道将突变基因信息与突变位点的三核苷酸基序融合在一起，用于结直肠癌亚型识别。我们将我们的管道应用于国际癌症基因组联盟（ICGC）CRC 样本，并首次在 536 个 ICGC 结直肠癌样本中鉴定出 3131 个显著突变的基因-基序组合。使用这些特征，我们确定了七种具有可区分表型和生物标志物的 CRC 亚型，包括独特的癌症相关信号通路，其中大多数都有针对性的治疗方案。有趣的是，我们还鉴定出了几个在多个亚型中发生突变但具有独特序列背景的基因。
我们的研究结果强调了在识别癌症亚型和癌症生物标志物时同时考虑突变类型和突变基因的重要性。本研究提出的新 CRC 亚型表现出明显的表型特征，可以有效地用于开发新的治疗方法。通过了解与亚型相关的基因和表型，可以制定考虑与基因组损伤相关的特定表型的个性化治疗计划。
