The Prognosis Of Locally Advanced Cervical Cancer Research Based On Targeted CfDNA Sequencing

Background: Cervical cancer is one of the most common cancers in the world,with nearly 600,000 new cases and 300,000 deaths every year.More than 85% of cervical cancer patients and deaths occurred in developing countries.Due to the large population and uneven economic development in our country,new cases of cervical cancer patients account for more than 20% of the world.With the iteration and popularization of cervical cancer screening methods,the incidence of cervical cancer can be effectively controlled.Among patients with cervical cancer,more than half are patients with locally advanced cervical cancer,for which primary surgical operations cannot produce effective treatment effects.Therefore,for locally advanced cervical cancer,concurrent chemotherapy or neoadjuvant chemotherapy is often used as the first-line treatment.However,locally advanced cervical cancer has the characteristics of easy metastasis,difficult control,and poor prognosis.At the same time,there are still controversies about the best treatment plan,and various treatment plans have certain limitations.Therefore,timely evaluation of treatment efficacy and prediction of patient prognosis are very important.Method: In this study,we applied NGS sequencing methods to detect genomic mutations in cervical cancer patients by designing a sequencing panel of cancer-related genes.We collected samples from 82 patients at different stages of disease development for DNA extraction and DNA library construction.We use next-generation sequencing to sequence the target fragments to detect the genetic information of tumor cells contained in ct DNA enriched in plasma free DNA,instead of traditional tumor biopsy methods,to achieve long-term real-time dynamic monitoring of patient disease progression.During the research,we used Duplex multiplex sequencing technology to improve the accuracy of sequencing results.We use Fast QC to perform strict quality control on the genomic sequence information obtained by sequencing to ensure the accuracy and authenticity of the data obtained.Use SAMTools,BWA and other software to compare and analyze the genome information,filter out the sub-optimal comparison reads,retain the optimal comparison,and remove the results of multiple hit reads in the genome during the comparison process.Use Mu Tect2 to compare and analyze cfDNA and matched white blood cell samples to remove background noise caused by clonal hematopoiesis and germline mutations.Result: Through bioinformatics analysis methods,we have identified the genome mutation landscape of cervical cancer patients,and accurately detected the genetic mutation characteristics of cervical cancer patients.In the study,we grouped samples according to whether patients had relapse and metastasis,and performed statistical analysis on two different patient cohorts.We found that in patients with locally advanced cervical cancer that are prone to metastasis,there are genomic features with PIK3 CA as the key mutated gene,and four synergistic genes that interact with it have been identified,which are defined as relapse and metastasis feature mutation genes(MSG).In the process of using the KM algorithm to estimate the survival curve of patients,it was found that patients who tested positive for MSG had worse prognostic performance.In addition,we use the MATH algorithm to quantitatively analyze the level of intratumoral heterogeneity in patients,and introduce the absolute median difference to measure the heterogeneity information revealed by the deviation of allele mutation frequency.The results of the study found that patients with high intratumoral heterogeneity scores have shorter survival times.After obtaining the above effective results,in order to evaluate the efficacy of patients with recurrence and metastasis,we compared the trend of changes in the samples at two time points,post-treatment and baseline.It was found that the abundance of mutations and the number of relapsing and metastatic characteristic mutations decreased in patients with good therapeutic response,while the opposite trend was observed in patients with disease progression.In addition,heterogeneity scores within the tumor showed a similar trend,but it was not statistically significant.Conclusion: This study used tumor liquid biopsy technology combined with NGS high-depth sequencing to explore the genome mutation characteristics of patients with locally advanced cervical cancer,revealing the difference between patients with relapse and metastasis and patients with stable disease.At the same time,mutation detection based on plasma cfDNA reduces the burden and pain of patients,and realizes rapid and accurate monitoring and evaluation.The identifiable characteristics of genomic mutations discovered in the study are closely related to patient prognosis and treatment effects,and have potential clinical practical significance.While there are still some controversies about the various treatment options for cervical cancer,the discovery of long-term dynamic monitoring methods based on blood samples will play an important role in the treatment of patients.