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Reliable information on building rooftops is crucial for utilizing limited urban space effectively. In recent decades, the demand for accurate and up-to-date data on the areas of rooftops on a large-scale is increasing. However, obtaining these data is challenging due to the limited capability of conventional computer vision methods and the high cost of 3D modeling involving aerial photogrammetry. In this study, a geospatial artificial intelligence framework is presented to obtain data for rooftops using high-resolution open-access remote sensing imagery. This framework is used to generate vectorized data for rooftops in 90 cities in China. The data was validated on test samples of 180 km2 across different regions with spatial resolution, overall accuracy, and F1 score of 1 m, 97.95%, and 83.11%, respectively. In addition, the generated rooftop area conforms to the urban morphological characteristics and reflects urbanization level. These results demonstrate that the generated dataset can be used for data support and decision-making that can facilitate sustainable urban development effectively.

Microsatellite instability (MSI) is a phenotypic manifestation of mismatch repair (MMR) deficiency and has been used as a biomarker through NGS-based assays to aid clinical cancer diagnosis and prognosis, as being advantageous over traditional electrophoresis-based tests and immunohistochemistry. Critical to detection accuracy but seldomly systematically discussed and poorly resolved by currently available methods are topics such as influences of changing experimental conditions, MSI-specific duplication removal and tradeoffs associated with loci selection strategies. In seeking solution to these questions, we have developed b/tMSI-CAST (blood/tissue MSI Caller Adjusted with Sequence duplicaTes), an integrated method consisting of a set of loci selection principles, an MSI-specific duplication removal strategy and a two-mode calling algorithm targeting both blood and tissue samples, equipped with a baseline construction method based on duplication distributions. We benchmarked b/tMSI-CAST against two established tools, mSINGS and MSIsensor-pro and found b/tMSI-CAST showed matched performance as MSIsensor-pro and outperformed mSINGS. We further applied it to a retrospective cohort of 18084 clinical samples to present a comprehensive landscape view on MSI prevalence across 26 cancer types, the largest of its kind in Chinese population so far, and demonstrated it on presurgical cfDNA samples of patients with early stage cancers.

Aphelinus mali (Haldeman) (Hymenoptera: Aphelinidae) in China is comprised of two clades (Shandong and Liaoning). In order to clarify the genetic relationship of them, we compared and analyzed the morphological characteristics and the mitochondrial genome of each, and ran a hybridization experiment between these clades. Morphological results showed that both males and females of the Liaoning clade were larger than Shandong clade, in terms of whole body, abdominal lengths and antennal lengths, but there were no significant differences between clades for total lengths of the middle and hind foot of females. The arrangement of the mitochondrial genomes was same in both clades, and each contained 31 genes, including 13 protein-encoded genes, 16 tRNA genes, and 2 rRNA genes. The highest AT levels among the 13 protein-coding genes for the two clades were the same gene (atp8) (Shandong clade, 91.52%; Liaoning clade, 90.91%). By hybridization and backcrossing, we found there was no cross incompatibility between these two clades of A. mali. Our results indicate that the historic geographical isolation between these two A. mali clades has not yet caused reproductive isolation of these two geographically separated populations, and they belong to the same species.

Ying Hu,1,* Yawei Chen,2,* Hao Guo,2 Jianing Yu,2 Yanhui Chen,3 Yang Liu,2 Ling Lan,4 Jian Li,5 Huaqing Wang,6 Henghui Zhang3 1Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, People’s Republic of China; 2Genecast Precision Medicine Technology Institute, Beijing 100000, People’s Republic of China; 3Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing Key Laboratory of Emerging Infectious Diseases, Beijing 100015, People’s Republic of China; 4Department of Gastroenterology, Zhengzhou University People’s Hospital, Zhengzhou 450003, People’s Republic of China; 5Department of General Surgery, The Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou 450000, People’s Republic of China; 6Department of Medical Oncology, Tianjin Union Medical Center, The Affiliated Hospital of Nankai University, Tianjin 300000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Huaqing Wang; Henghui Zhang Email huaqingw@163.com; zhhbao@ccmu.edu.cnPurpose: The aim of this study was to evaluate the clinical value of plasma cell-free DNA (cfDNA) in the diagnosis of colorectal cancer (CRC).Patients and Methods: The plasma cfDNA and tissue DNA mutation profiles were investigated in 77 patients (9 colon polyps, 18 colon adenoma, 26 colon cancer, and 24 rectal cancer) by a cancer gene-targeted NGS panel.Results: During the progression from adenoma to carcinoma, mutations occur in genes such as RAS, Wnt, Hippo, Nrf2, TGFβ, PI3K, Notch, and P53, as well as in those encoding cell cycle pathway components. The somatic mutation burden and plasma cfDNA concentration were significantly higher in the colon carcinoma group than in the adenoma and colon polyp groups. The combination of plasma cfDNA concentration, CEA, and cfDNA had a significantly greater area under the curve than cfDNA or CEA alone. Right-sided colon cancer tissues showed a greater distribution of somatic mutations among more genes than left-sided colon cancer tissues. In addition, tissue tumor mutational burden (TMB) was higher in the right-sided colon cancer group than in the rectal cancer or left-sided colon cancer group (P< 0.05).Conclusion: These results may indicate that somatic mutations in plasma cfDNA are potential biomarkers for the diagnosis of CRC. In addition, somatic mutations may be distributed in more genes and pathways in right-sided colon cancer than in left-side colon cancer.Keywords: molecular alterations, cell-free DNA, diagnosis, colorectal adenoma, colorectal cancer

e22515 Background: Given peripheral blood cells (PBCs) matched cell-free DNA (cfDNA), accurate mutation calling in next generation sequencing (NGS)-based assays relies on discriminating artifacts and clonal hematopoiesis mutations from tissue originated somatic mutations. Although clonal hematopoiesis has been considered in previous overall error modeling, it has not been adapted to PBCs without using unique molecular identifiers (UMIs). Moreover, previous studies on background error profiling were mainly based on healthy controls without matched PBC gDNA, which may lead to potential overestimation of the error rates on those sites with clonal hematopoiesis mutations. Additionally, the fraction of tissue cells is also an important influencing factor but is usually ignored. Methods: We performed UMI-assisted capture-based DNA assays on cfDNA samples, matched PBCs, and oral epithelium cells from 150 healthy donors. A site-specific and subtype-specific background error model was first built for PBCs using the SNVs called from PBCs with matched oral epithelium cells to exclude potential clonal hematopoiesis mutations. Then a similar background model was established for cfDNA with the SNVs inferred from cfDNA to exclude clonal hematopoiesis. The SNVs identified in cfDNA and matched PBCs were separately filtered with the cfDNA and PBC background error models. In this study, we used the ultrasensitive liquid biopsy approach to evaluate paired with tissue and blood samples from 56 early-stage NSCLC patients. All samples are sequenced using NGS target-capture panels covering 29 genes. Results: The mutations were detected in 91.1% of tissue and 67.9% were discovered in plasma. The coincidence rate between tissue and plasma of the 56 early-stage NSCLC patients was 66.1%. Conclusions: We have developed a novel method tailored for UMI-assisted capture-based targeted DNA assays, which outperforms currently available methods in terms of modeling background errors and filtering clonal hematopoietic mutations.

10544 Background: Screening the biomarkers from the cell-free DNA (cfDNA) of peripheral blood is a non-invasive and promising method for cancer diagnosis. Among diverse types of biomarkers, epigenetic biomarkers have been reported to be one of the most promising ones. Epigenetic modifications are widespread on the human genome and generally have strong signals due to the similar methylation patterns shared by adjacent CpG sites. Although some epigenetic diagnostic methods have been developed based on cfDNAs, few of them could be applied to pan-cancer and their sensitivities are barely satisfactory for early cancer detection. Methods: Targeted methylation sequencing was performed using our in-house-designed panel targeting regions with abundant cancer-specific methylation CpGs. The cfDNA samples from 80 healthy individuals and 549 cancer patients of 14 cancer types were separately sequenced. The dataset was randomly split into one discovery dataset and one validation dataset. Moreover, cfDNA samples from four cancer patients were diluted with the healthy cfDNAs to generate 12 in vitro simulated samples with low circulating tumor DNA (ctDNA) fraction. Additionally, DNAs extracted from 130 unmatched tumor formalin fixation and paraffin embedding (FFPE) samples of 10 cancer types were sequenced to screen the diagnostic biomarkers. Adjacent CpG sites were first merged into methylation-correlated blocks (MCB) according to their correlations of methylation levels in tumor DNAs. The MCBs with higher methylation levels in tumor DNAs than that of healthy cfDNAs (from the discovery dataset) were defined as our hypermethylation biomarkers. For each cfDNA sample, a hypermethylation score (HM-score) was computed to measure the overall methylation level difference of selected biomarkers. The performance of our method was evaluated with the real-world dataset, while the limit of detection was estimated using the simulated low-ctDNA samples. Results: Our model based on 37 hypermethylation MCB biomarkers achieved an area under the curve (AUC) of 0.89 and 0.86 in the real-world pan-cancer discovery and validation cfDNA datasets, respectively. Furthermore, the overall specificity and sensitivity are 100% and 76.19% in the discovery dataset, and 96.67% and 72.86% in the validation dataset. In the validation dataset, 28/40 (70%) of early-stage colorectal cancer patients and 10/20 (50%) of non-small-cell lung cancer patients were successfully diagnosed. Additionally, all the simulated samples with theoretical ctDNA factions over 0.5% were predicted as diseased, demonstrating the ability of our method to detect tumor signals at early stages. Conclusions: Our cfDNA-based epigenetic method outperforms currently available methods in various cancer types, and is promising to be applied to early-stage cancer detection and samples with low ctDNA fractions.

e22509 Background: Genomic instability is a typical characteristic of the majority of cancers. Early non-invasive detection of cancer is the most effective way of improving the success of treatment and prognosis at present. Traditional tumor screening methods have limitations in terms of selection methods, sensitivity, specificity, cost, and comfortability. Furthermore, although traditional tumor screening is useful for common cancers, there is no available screening test for rare cancers. Here we developed a novel method for cancer detection with Low-Pass Whole Genome Sequencing (WGS) of cell-free DNA (cfDNA). Methods: The cfDNA samples from 52 healthy donors were first used to establish a blacklist of bins. Then the baseline was established to calculate the chromosomal instability score (CINscore). We optimized the parameters of our model using the following discovery datasets: healthy controls (n = 50), breast invasive carcinoma (BRCA) (n = 44), ovarian serous cystadenocarcinoma (OV) (n = 25), colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma (COREAD) (n = 52), hepatocellular carcinoma (HCC) (n = 43), Gastric adenocarcinoma (GAC) (n = 31); pancreatic adenocarcinoma (PAC) (n = 38), non-small cell lung cancer (NSCLC) (n = 45). Results: We further evaluated the performance of our method using the confirmation datasets of healthy controls (n = 30), BRCA (n = 20), prostate adenocarcinoma (PRAD) (n = 6), OV (n = 7), head and Neck squamous cell carcinoma (HNSC) (n = 10), Uterine Corpus Endometrial Carcinoma (UCEC) (n = 6), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) (n = 5), COREAD (n = 75), HCC (n = 50), GAC (n = 31), cholangiocarcinoma (CHOL) (n = 8), PAC (n = 10), NSCLC (n = 98), esophageal carcinoma (ESCA) (n = 10), and nasopharyngeal carcinoma (NAC) (n = 4). Overall, the area under the curve (AUC) for pan-cancer is 88.7%, and for BRCA, COREAD, HCC, NSCLC, and GAC are 91.7%, 90.0%, 94.5%, 83.4%, and 88.6%, respectively. Moreover, our approach achieved good performance on the the early stage samples of pan-cancer (AUC = 84.3%) as well as COREAD (AUC = 89.3%) and NSCLC (AUC = 79.0%). Conclusions: Collectively, we show that the CINscore inferred from low-pass WGS could be applied in early non-invasive detection of different cancers with high accuracy. Our approach may aid the improvement of the cancer diagnosis.

e22510 Background: Recent advances in circulating cell-free DNA (cfDNA) of plasma have shown that tumor diagnosis based on tumor-specific genetic and epigenetic changes (e.g., somatic mutations, copy number variations, and DNA methylation) is a promising non-invasive method. However, the number of tumor-specific genomic variants identified by whole-genome sequencing (WGS) in early cancer patients is very limited. Moreover, the mutations generated by clonal hematopoiesis in cfDNA can further confound the detection of cancer-specific mutations. It has been shown that ctDNA and cfDNA fragments have differences in length distribution. Compared with a limited number of genomic mutations, cfDNA fragment size index (FSI) is more abundant and easier to be detected. Methods: We designed a novel method for fragment detection of plasma cfDNA based on low-coverage WGS. The fragment length differences between healthy individuals and tumor patients were systematically analyzed. The training dataset includes 50 healthy individuals and 354 patients from eight different cancers. After the data preprocessing, we calculated the weight of fragmental bins and built a model for distinguishing healthy individuals from cancer patients. An independent dataset involving 22 healthy controls and 340 cancer patients was used to validate the model. The performance of our method was measured by the area under the curve (AUC) using the one-versus-all approach. Results: In our analysis, a total of 504 markers were selected from the dataset for model construction. Our model performed well for all cancer types on both training (AUC = 0.804) and validation (AUC = 0.837) datasets. Conclusions: The good performance of our model in large-scale plasma samples demonstrates the potential clinical application of cfDNA fragment analysis in early cancer detection based on low-coverage WGS.

e15077 Background: Homologous recombination deficiency (HRD) is a promising biomarker for poly ADP-ribose polymerase (PARP) inhibitors. In the past, the HRD status was mainly estimated by summing the loss of heterozygosity (LOH), telomeric allelic imbalance (TAI), and large-scale state transition (LST) based on target sequencing. However, the heterozygous loci coverage and probe number are closely correlated to B allele frequency and copy number variations (CNVs). Here we developed a novel method to combine the data from low-pass whole-genome sequencing (WGS) and target sequencing to accurately estimate the genome-wide CNVs and allelic imbalance. Methods: We first performed low-pass WGS and target sequencing on 30 healthy controls, formalin-fixed and paraffin-embedded (FFPE) samples, and matched blood cell samples for 29 patients with breast cancer or ovarian cancer. Two HRD positive samples were diluted to 40%, 30%, 20%, and 10% tumor purity with matched blood cell samples, respectively. Each diluted sample was sequenced 20 times. The baseline for CNVs was built based on the sequencing data of healthy donors. To eliminate the artifacts resulted from the sequencing platform, models for each candidate single nucleotide polymorphism (SNP) in target regions were trained using the data from healthy donors. The mutations of BRCA1/BRCA2 of 29 patients were used to define the threshold of the HRD score. The numbers of LOH, LST, and TAI were evaluated by CNVs and B allele frequency. Results: We defined a cutoff of 42 for the HRD status based on the low-pass WGS and target sequencing data of 29 patients. Specifically, the original HRD scores for two selected HRD positive samples were 68 and 89. The median HRD scores for 40%, 30%, 20%, and 10% tumor purity samples were 69 and 88, 66.5 and 90, 61 and 68, 52 and 34.5, respectively. Conclusions: We developed a novel method for robustly inferring the HRD status using low-pass WGS and target sequencing with a limit of detection of 20%.

e13554 Background: Approximately 3-5% of human cancers are cancer of unknown primary (CUP). Treatment of a cancer patient is largely dependent on the tumor origin. Therefore, identification of the tumor origin can improve the survival of patients with CUP. We developed a multi-class classification model using DNA methylation profile as biomarker to determine the primary site of CUP. Methods: We split 7,082 primary tumor samples of 19 cancers and 679 normal samples of 15 tissues from TCGA into a 75% training set and a 25% testing set to develop the classification model. We started with multiple support vector machine (SVM) models, and then combined them into an optimal multi-class ensemble model. Predictors included tumor-specific markers and tissue-specific markers, which were filtered by comparing between groups. Only the training samples were used for feature selection and model development. A validation dataset consisting of 150 primary tissues, 54 metastasis tissues, 105 plasma samples with known cancer site origins from 12 classes was generated in house by a self-designed panel. Performance was measured by area under the curve (AUC) using the one-vs-all approach. Results: 7,453 tumor-specific and 1,533 tissue-specific markers were selected for model construction. AUCs of all cancer types were high in TCGA training and testing set (AUC≥0.96 for all classes). In our validation tissues, esophageal cancer, pancreatic cancer, colorectal cancer, lung adenocarcinoma, breast cancer and liver cancer achieved high AUC in both primary (0.83, 0.83, 0.82, 0.82, 0.80 and 0.79 respectively) and metastasis (0.74, 0.92, 0.86, 0.61, 0.92 and 0.65 respectively). Lung adenocarcinoma, colorectal cancer, liver cancer, breast cancer and esophageal cancer even achieved high AUC in the plasmas. Conclusions: Performance of our model in tissue and plasma samples indicated the potential clinical application of DNA methylation profile in unknown cancer origin identification.

e13053 Background: Circulating tumor DNA (ctDNA) has been applied and showed potential in cancer early/late-stage detection, tumor genotyping and post-operation recurrence monitoring. The fraction of ctDNA in cell-free DNA (noted as ccf hereby), in addition to standard SNV/INDEL/CNV analysis, has also been showed to associate with the tumor progression and prognosis. In theory, accurate ccf can further be useful in correcting and improving given SNV/INDEL/CNV results. Existing tools capable for calculating ccf (PureCN, FACETS, Sequenza, etc.) use coverage data in targeted regions and SNP allele frequency to calculate the tumor fraction, which fail to give accurate estimation at relatively low ctDNA concentrations. Methods: A Maximum Likelihood model was built to estimate ccf. We first select informative SNPs with significantly different VAF in the case and paired-control samples. The mutation type of an informative SNP is determined by the variant allele frequency (VAF) in the paired samples and the copy number of the case sample. Likelihood of each SNP given a specific ccf was then calculated. After clustering SNPs into clones, the ccf of each clone was estimated using a global likelihood. Results: Performance of the method was validated by ctDNA dilution series analysis. 6 cfDNA from cancer patient was diluted (concentrations: 1/3 - 1/81). Detection limit of the method is ~2%, and correlation between estimated and expected ccf ranged from 0.93 to 0.98. Conclusions: We have developed a novel method to better estimate cancer cell fractions in cell-free DNA. Results showed our method is able to calculate ccf at lower ctDNA concentrations with higher accuracy and stability than benchmarked tools. We describe here a method for target-sequencing data that is more sensible, accurate and stable than currently available tools.