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Lung cancer is a malignant tumor with the highest incidence and mortality in China.
Early detection, early diagnosis and early treatment of lung cancer are essential
to prolong patients' survival, reduce economic burden and improve quality of life.
At present, low-dose spiral telemetry (LDCT) is the preferred method for early screening of lung cancer, but LDCT carries radiation risks and a high false-positive rate, which may lead to overtreatment
.
Early tumor screening technology based on liquid biopsy has the advantages of accuracy, non-invasiveness and high efficiency, and has become a new direction
for early screening of lung cancer in recent years.
However, it is known that some liquid biopsy techniques are still weak in the detection of lung cancer, especially early lung cancer [1][2].
More sensitive and stable models are still needed to accurately interpret
early lung cancer signals.
On November 8, 2022, the team of Xu Lin and Yin Rong of Nanjing Medical University Cancer Hospital published a title in the American Journal of Respiratory and Critical Care Medicine Multi-dimensional cell-free DNA fragmentomic assay for detection of early-stage lung cancer [3].
。 Based on liquid biopsy early screening technology, the study was modeled by the omics characteristics of cfDNA (cell-free DNA) fragments, which showed excellent and stable performance
in lung cancer screening prediction.
In this study, a total of 542 patients with non-small cell lung cancer and healthy volunteers (312 cases of non-small cell lung cancer and 230 cases of healthy people) were randomly divided into three cohorts: model training cohort, validation cohort I, and validation cohort II
。 Validation Cohort I was used to evaluate model performance and determine the threshold (cutoff) for tumor judgment of Cancer Score, which was further evaluated
in Validation Cohort II.
In addition, 240 independently validated cohorts (120 non-small cell lung cancer and 120 healthy people)
were included.
Low-depth whole genome sequencing (WGS) was performed on the cfDNA of the enrolled population, and the fragment size distribution (FSD), fragment size ratio (FSR), end sequence (EDM), break point sequence (BPM), and copy number change (CNV) characteristics of genomic cfDNA were extracted.
An ensemble model using a combination of five machine learning methods to identify healthy people from cancer patients
.
Wang Siwei, Department of Thoracic Surgery, Jiangsu Cancer Hospital, Master Meng Fanchen, Master of the Fourth Clinical Medical College of Nanjing Medical University, and Professor Li Ming, Director of the Second Department of Thoracic Surgery, Jiangsu Cancer Hospital, were the joint first authors
.
This research was strongly supported
by Nanjing Shihe Genomics and the School of Public Health of Nanjing Medical University.
References
Early detection, early diagnosis and early treatment of lung cancer are essential
to prolong patients' survival, reduce economic burden and improve quality of life.
At present, low-dose spiral telemetry (LDCT) is the preferred method for early screening of lung cancer, but LDCT carries radiation risks and a high false-positive rate, which may lead to overtreatment
.
Early tumor screening technology based on liquid biopsy has the advantages of accuracy, non-invasiveness and high efficiency, and has become a new direction
for early screening of lung cancer in recent years.
However, it is known that some liquid biopsy techniques are still weak in the detection of lung cancer, especially early lung cancer [1][2].
More sensitive and stable models are still needed to accurately interpret
early lung cancer signals.
On November 8, 2022, the team of Xu Lin and Yin Rong of Nanjing Medical University Cancer Hospital published a title in the American Journal of Respiratory and Critical Care Medicine Multi-dimensional cell-free DNA fragmentomic assay for detection of early-stage lung cancer [3].
。 Based on liquid biopsy early screening technology, the study was modeled by the omics characteristics of cfDNA (cell-free DNA) fragments, which showed excellent and stable performance
in lung cancer screening prediction.
In this study, a total of 542 patients with non-small cell lung cancer and healthy volunteers (312 cases of non-small cell lung cancer and 230 cases of healthy people) were randomly divided into three cohorts: model training cohort, validation cohort I, and validation cohort II
。 Validation Cohort I was used to evaluate model performance and determine the threshold (cutoff) for tumor judgment of Cancer Score, which was further evaluated
in Validation Cohort II.
In addition, 240 independently validated cohorts (120 non-small cell lung cancer and 120 healthy people)
were included.
Low-depth whole genome sequencing (WGS) was performed on the cfDNA of the enrolled population, and the fragment size distribution (FSD), fragment size ratio (FSR), end sequence (EDM), break point sequence (BPM), and copy number change (CNV) characteristics of genomic cfDNA were extracted.
An ensemble model using a combination of five machine learning methods to identify healthy people from cancer patients
.
Research highlights
1.
Large sample size study (n=782, 432 cases of non-small cell lung cancer, 350 cases of healthy people); Among them, more than 75% of lung cancer samples were from early-stage patients, 53.
7% of stage I patients and 23.
1% of stage II patients.
2.
Using machine learning methods to decode and identify the fragmentomic characteristics of tumors, the established model showed excellent performance in the training set, verification cohort and independent verification cohort, and the AUC exceeded 97%;
3.
The model still has high screening stability between different detection batches and low-depth WGS sequencing, which provides support for clinical feasibility;
4.
The model has excellent predictive performance for early tumors, with a sensitivity of 83.
2% for stage I lung cancer and 85%
for tumors less than 1cm.
Wang Siwei, Department of Thoracic Surgery, Jiangsu Cancer Hospital, Master Meng Fanchen, Master of the Fourth Clinical Medical College of Nanjing Medical University, and Professor Li Ming, Director of the Second Department of Thoracic Surgery, Jiangsu Cancer Hospital, were the joint first authors
.
This research was strongly supported
by Nanjing Shihe Genomics and the School of Public Health of Nanjing Medical University.
References
- Schrag et al.
Abstract 903O - A prospective study of a multi-cancer early detection blood test.
Annals of Oncology (2022) 33 (suppl_7): S417-S426. - Shin et al.
Abstract 23P - MRE-seq based cancer screening for lung and colorectal cancer by deep learning analysis of cfDNA methylation pattern cancer screening.
Annals of Oncology (2022) 33 (suppl_7): S4-S18. - Wang et al.
Multi-Dimensional Cell-free DNA Fragmentomic Assay for Detection of Early-Stage Lung Cancer.
Am J Respir Crit Care Med.
2022 Nov 8.
Shihe Genomics is committed to tumor precision medicine, companion diagnosis and clinical translational research, providing one-stop NGS solutions for hospitals; Chinese unicorn enterprise
NGS and liquid biopsy laboratories have obtained CAP/CLIA/ISO15189 triple international accreditation, and have passed the inter-laboratory quality assessment of the National Health Commission Clinical Examination Center 212 times
The company's core technical personnel participated in the publication and the company participated in clinical research projects to publish more than 470 SCI papers, with a total impact factor (IF) of more than 3900 points; 2020 Jiangsu Province Science and Technology Second Prize: "Innovation and Industrialization of High-throughput Sequencing and Detection Technology for Precision Medicine for Lung Cancer" project; It has 24 invention patents (including 22 in China), including 19 invention patents that form the main business income
In 2018, the "EGFR/ALK/ROS1/BRAF/KRAS/HER2 gene mutation detection kit (reversible end termination sequencing)" developed by the company was registered and approved by the State Food and Drug Administration, and was one of the first batch of high-throughput genetic detection products approved for tumor companion diagnosis in China during the same period
The results of CALIBRATE series of perioperative large-scale clinical studies of multiple cancers based on liquid biopsy technologies such as ATG-Seq have been published
one after another.
In May 2021, the results of the prospective multicenter large-scale clinical study of ctDNA MRD after early and intermediate bowel cancer surgery (CALIBRATE-CRC) were published in J Hematol Oncol (IF=23.
2).
In September 2021, the results of a large-scale clinical study (CALIBRATE-RC-nCRT) on the prediction of neoadjuvant efficacy in rectal cancer by liquid biopsy were published in PLoS Med (IF=11.
6).
In November 2021, the results of the prospective large-scale clinical study of ctDNA MRD after surgery for early and intermediate lung cancer (CALIBRATE-NSCLC) were published in Nature Comms (IF=17.
7).
In May 2022, the results of the large clinical study on the efficacy of precision chemoradiotherapy in advanced NSCLC (CALIBRATE-LC/EC-CRT) were published in Mol Cancer (IF=41.
4).
In October 2022, the results of a prospective large-scale study of MRD ctDNA cloning analysis after early and intermediate lung cancer surgery (CALIBRATE-LuCaTH) were published in J Hematol Oncol (IF=23.
2).
IN ADDITION, SEWA IS APPLYING MERCURY TECHNOLOGY TO A NUMBER OF MRD STUDIES
.
In October 2022, the results of a large-scale clinical study (M-CALIBRATE-RC-nCRT) based on MERCURY multiomics technology to predict the efficacy of new adjuvant rectal cancer were published in Clinical Chemistry (IF=12.
2)
THE RESEARCH RESULTS OF THE DECIPHER SERIES OF MULTI-CANCER EARLY SCREENING BASED ON MERCURY MULTIOMICS LIQUID BIOPSY TECHNOLOGY HAVE BEEN PUBLISHED
SUCCESSIVELY.
In October 2021, the results of the large bowel cancer early screening clinical study (DECIPHER-Colon) were published in J Hematol Oncol (IF=23.
2); In December 2021, the results of the large-scale liver cancer early screening clinical study (DECIPHER-Liver) were published in Hepatology (IF=17.
3); In June 2022, the results of the first large-scale pan-cancer early screening study (DECIPHER-Multi) in China were published in Mol Cancer (IF=41.
4); In June 2022, the results of the first large-scale clinical study of early screening of lung cancer based on cfDNA Motif characteristics (DECIPHER-Lung) were published in eBioMedicine (IF=11.
2).
In November 2022, the results of the large lung cancer early screening clinical study (DECIPHER-Lung 2) were published in Am J Respir Crit Care Med (IF=30.
528).
