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Baseline and on-treatment CNV landscapes of patients treated with pictilisib. Somatic variants significantly increased in EOT compared to baseline.

Purpose:We developed a method to monitor copy number variations (CNV) in plasma cell-free DNA (cfDNA) from patients with metastatic squamous non–small cell lung cancer (NSCLC). We aimed to explore the association between tumor-derived cfDNA and clinical outcomes, and sought CNVs that may suggest potential resistance mechanisms.Experimental Design:Sensitivity and specificity of low-pass whole-genome sequencing (LP-WGS) were first determined using cell line DNA and cfDNA. LP-WGS was performed on baseline and longitudinal cfDNA of 152 patients with squamous NSCLC treated with chemotherapy, or in combination with pictilisib, a pan-PI3K inhibitor. cfDNA tumor fraction and detected CNVs were analyzed in association with clinical outcomes.Results:LP-WGS successfully detected CNVs in cfDNA with tumor fraction ≥10%, which represented approximately 30% of the first-line NSCLC patients in this study. The most frequent CNVs were gains in chromosome 3q, which harbors the PIK3CA and SOX2 oncogenes. The CNV landscape in cfDNA with a high tumor fraction generally matched that of corresponding tumor tissue. Tumor fraction in cfDNA was dynamic during treatment, and increases in tumor fraction and corresponding CNVs could be detected before radiographic progression in 7 of 12 patients. Recurrent CNVs, such as MYC amplification, were enriched in cfDNA from posttreatment samples compared with the baseline, suggesting a potential resistance mechanism to pictilisib.Conclusions:LP-WGS offers an unbiased and high-throughput way to investigate CNVs and tumor fraction in cfDNA of patients with cancer. It may also be valuable for monitoring treatment response, detecting disease progression early, and identifying emergent clones associated with therapeutic resistance.

Purpose:We recently reported the development of a cell-free DNA (cfDNA) targeted methylation (TM)-based sequencing approach for a multi-cancer early detection (MCED) test that includes cancer signal origin prediction. Here, we evaluated the prognostic significance of cancer detection by the MCED test using longitudinal follow-up data.Experimental Design:As part of a Circulating Cell-free Genome Atlas (CCGA) substudy, plasma cfDNA samples were sequenced using a TM approach, and machine learning classifiers predicted cancer status and cancer signal origin. Overall survival (OS) of cancer participants in the first 3 years of follow-up was evaluated in relation to cancer detection by the MCED test and clinical characteristics.Results:Cancers not detected by the MCED test had significantly better OS (P < 0.0001) than cancers detected, even after accounting for other covariates, including clinical stage and method of clinical diagnosis (i.e., standard-of-care screening or clinical presentation with signs/symptoms). Additionally, cancers not detected by the MCED test had better OS than was expected when data were adjusted for age, stage, and cancer type from the Surveillance, Epidemiology, and End Results (SEER) program. In cancers with current screening options, the MCED test also differentiated more aggressive cancers from less aggressive cancers (P < 0.0001).Conclusions:Cancer detection by the MCED test was prognostic beyond clinical stage and method of diagnosis. Cancers not detected by the MCED test had better prognosis than cancers detected and SEER-based expected survival. Cancer detection and prognosis may be linked by the underlying biological factor of tumor fraction in cfDNA.

Supplementary Table 1 from Prognostic Significance of Blood-Based Multi-cancer Detection in Plasma Cell-Free DNA

Supplementary Table 3 from Prognostic Significance of Blood-Based Multi-cancer Detection in Plasma Cell-Free DNA

Supplementary Table 2 from Prognostic Significance of Blood-Based Multi-cancer Detection in Plasma Cell-Free DNA

Supplementary Figures from Prognostic Significance of Blood-Based Multi-cancer Detection in Plasma Cell-Free DNA

The Anopheles gambiae 1000 Genomes (Ag1000G) Consortium utilized deep sequencing methods to catalogue genetic diversity across African Anopheles gambiae populations. We analyzed the complete datasets of 1,142 individually sequenced mosquitoes through Microsoft Premonition’s Bayesian mixture model based(BMM) metagenomics pipeline. All samples were confirmed as either An. gambiae sensu stricto (s.s.) or An. coluzzii with a high degree of confidence (>98% identity to reference). Homo sapiens DNA was identified in all specimens indicating contamination may have occurred either at the time of sample collection, preparation and/or sequencing. We found evidence of vertebrate hosts in 162 specimens. 59 specimens contained validated Plasmodium falciparumreads. Non-mosquito borne, human hepatitis B and primate erythroparvovirus-1 viral sequences were identified in fifteen and three specimens, respectively. 478 of the 1,142 specimens were found to contain bacterial reads and bacteriophage-related contigs were detected in 27 samples. This analysis demonstrates the capacity of metagenomic approaches to elucidate important vector-host-pathogen interactions of epidemiological significance.

Background Early cancer detection could identify tumors at a time when outcomes are superior and treatment is less morbid. This prospective case-control sub-study (from NCT02889978 and NCT03085888) assessed the performance of targeted methylation analysis of circulating cell-free DNA (cfDNA) to detect and localize multiple cancer types across all stages at high specificity. Participants and methods The 6689 participants [2482 cancer (>50 cancer types), 4207 non-cancer] were divided into training and validation sets. Plasma cfDNA underwent bisulfite sequencing targeting a panel of >100 000 informative methylation regions. A classifier was developed and validated for cancer detection and tissue of origin (TOO) localization. Results Performance was consistent in training and validation sets. In validation, specificity was 99.3% [95% confidence interval (CI): 98.3% to 99.8%; 0.7% false-positive rate (FPR)]. Stage I–III sensitivity was 67.3% (CI: 60.7% to 73.3%) in a pre-specified set of 12 cancer types (anus, bladder, colon/rectum, esophagus, head and neck, liver/bile-duct, lung, lymphoma, ovary, pancreas, plasma cell neoplasm, stomach), which account for ∼63% of US cancer deaths annually, and was 43.9% (CI: 39.4% to 48.5%) in all cancer types. Detection increased with increasing stage: in the pre-specified cancer types sensitivity was 39% (CI: 27% to 52%) in stage I, 69% (CI: 56% to 80%) in stage II, 83% (CI: 75% to 90%) in stage III, and 92% (CI: 86% to 96%) in stage IV. In all cancer types sensitivity was 18% (CI: 13% to 25%) in stage I, 43% (CI: 35% to 51%) in stage II, 81% (CI: 73% to 87%) in stage III, and 93% (CI: 87% to 96%) in stage IV. TOO was predicted in 96% of samples with cancer-like signal; of those, the TOO localization was accurate in 93%. Conclusions cfDNA sequencing leveraging informative methylation patterns detected more than 50 cancer types across stages. Considering the potential value of early detection in deadly malignancies, further evaluation of this test is justified in prospective population-level studies.

Most circulating tumor DNA (ctDNA)-based molecular residual disease (MRD) detection methods leverage a limited genomic footprint, restricting detection sensitivity to 10-4 ~ 10-5 tumor fraction and thus their utility in many clinical settings. For example, early-stage, low tumor mutational burden (TMB) cancers may lack sufficient variants in these limited footprints to produce detectable signals. Further, insights into tumor evolution, including actionable mutations may be missed.Here we report a performance update of the NeXT Personal™️ platform. Utilizing whole genome sequencing of tumor and normal DNA to guide design of bespoke MRD assays, we select up to 1800 high signal, low noise MRD targets and up to 400 exonic variants. Along with proprietary algorithms, this achieves high sensitivity with a limit of detection of 1 ~ 3 parts per million, and targets high specificity (99.99%). Specificity was demonstrated with healthy donor plasma samples and > 200 cancer patient panels from a broad range of solid tumors. Sensitivity and linearity of MRD measurements were determined using dilution series from cell lines and clinical samples, which were orthogonally confirmed (R2 = 0.909, PPA = 100%, NPA = 100%) by digital droplet PCR (ddPCR).In addition, NeXT Personal simultaneously surveys tumor-agnostic content in the same workflow, providing detection of actionable mutations, markers of drug resistance, and mechanisms of tumor evolution from a curated set of variants in 90 clinically-relevant genes. Patient plasma (1 ~ 8 mL; 2 ~ 50 ng of cfDNA) can be rapidly queried to provide mutation-level information about tumor biology, longitudinal trajectory, and clinical actionability. The specificity of NeXT Personal variant detection is > 99.99% with 100% PPV, while individual variant content demonstrates high sensitivity at allele fractions of 0.1% and above, with high accuracy and signal linearity as confirmed by ddPCR (R2 = 0.998).To explore the utility of NeXT Personal in a clinical setting, a retrospective analysis was undertaken in an advanced liver cancer (low TMB) cohort of 11 patients undergoing immunotherapy. Our results demonstrated that changes in ctDNA levels during therapy correlated highly with disease status (6wk vs. baseline, p = 0.017; 9wk vs. baseline, p = 0.004), and were detected prior to clinical response confirmation by RECIST 1.1. Examination of the clinical content provided useful insights into the potential of the assay.Our data demonstrate high analytical performance of the NeXT Personal platform in both MRD and individual variant detection. The assay is sufficiently sensitive for early stage, low shedding, and low TMB cancers, early time points or samples with limited input. The addition of clinically relevant tumor-agnostic actionable content makes NeXT Personal unique in its ability to detect MRD and to ultimately help guide clinicians. Citation Format: Rui Chen, Gábor Bartha, John Lyle, Jason Harris, Sean M. Boyle, Josette Northcott, Dan Norton, Rachel M. Pyke, Fábio C. Navarro, Charles W. Abbott, Christian Haudenschild, Rose Santiago, Darren Nichols, Stephanie Huang, Christopher Nelson, Manju Chinnappa, Yi Chen, Yuker Wang, Laurie Goodman, Qi Zhang, Manqing Hong, Xiaoji Chen, Erin Ayash, Nitin Udar, Sebastian Saldivar, Jian Yan, John West, Richard O. Chen. Analytical performance of an ultra-sensitive, tumor-informed liquid biopsy platform for molecular residual disease detection and clinical guidance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3381.

Purpose: We developed a method to monitor copy number variations (CNV) in plasma cell-free DNA (cfDNA) from patients with metastatic squamous non–small cell lung cancer (NSCLC). We aimed to explore the association between tumor-derived cfDNA and clinical outcomes, and sought CNVs that may suggest potential resistance mechanisms. Experimental Design: Sensitivity and specificity of low-pass whole-genome sequencing (LP-WGS) were first determined using cell line DNA and cfDNA. LP-WGS was performed on baseline and longitudinal cfDNA of 152 patients with squamous NSCLC treated with chemotherapy, or in combination with pictilisib, a pan-PI3K inhibitor. cfDNA tumor fraction and detected CNVs were analyzed in association with clinical outcomes. Results: LP-WGS successfully detected CNVs in cfDNA with tumor fraction ≥10%, which represented approximately 30% of the first-line NSCLC patients in this study. The most frequent CNVs were gains in chromosome 3q, which harbors the PIK3CA and SOX2 oncogenes. The CNV landscape in cfDNA with a high tumor fraction generally matched that of corresponding tumor tissue. Tumor fraction in cfDNA was dynamic during treatment, and increases in tumor fraction and corresponding CNVs could be detected before radiographic progression in 7 of 12 patients. Recurrent CNVs, such as MYC amplification, were enriched in cfDNA from posttreatment samples compared with the baseline, suggesting a potential resistance mechanism to pictilisib. Conclusions: LP-WGS offers an unbiased and high-throughput way to investigate CNVs and tumor fraction in cfDNA of patients with cancer. It may also be valuable for monitoring treatment response, detecting disease progression early, and identifying emergent clones associated with therapeutic resistance.

Purpose: We recently reported the development of a cell-free DNA (cfDNA) targeted methylation (TM)-based sequencing approach for a multi-cancer early detection (MCED) test that includes cancer signal origin prediction. Here, we evaluated the prognostic significance of cancer detection by the MCED test using longitudinal follow-up data. Experimental Design: As part of a Circulating Cell-free Genome Atlas (CCGA) substudy, plasma cfDNA samples were sequenced using a TM approach, and machine learning classifiers predicted cancer status and cancer signal origin. Overall survival (OS) of cancer participants in the first 3 years of follow-up was evaluated in relation to cancer detection by the MCED test and clinical characteristics. Results: Cancers not detected by the MCED test had significantly better OS (P < 0.0001) than cancers detected, even after accounting for other covariates, including clinical stage and method of clinical diagnosis (i.e., standard-of-care screening or clinical presentation with signs/symptoms). Additionally, cancers not detected by the MCED test had better OS than was expected when data were adjusted for age, stage, and cancer type from the Surveillance, Epidemiology, and End Results (SEER) program. In cancers with current screening options, the MCED test also differentiated more aggressive cancers from less aggressive cancers (P < 0.0001). Conclusions: Cancer detection by the MCED test was prognostic beyond clinical stage and method of diagnosis. Cancers not detected by the MCED test had better prognosis than cancers detected and SEER-based expected survival. Cancer detection and prognosis may be linked by the underlying biological factor of tumor fraction in cfDNA.

In recent years, the multiobjective evolutionary algorithm based on decomposition (MOEA/D) has shown superior performance in solving multiobjective optimization problems (MOPs). In MOEA/D, the adaptive replacement strategy (ARS) plays a key role in balancing convergence and diversity. However, existing ARSs do not effectively balance convergence and diversity. To overcome this disadvantage, we propose a mechanism for adapting neighborhood and global replacement. This mechanism determines whether a neighborhood or global replacement strategy should be employed in the search process. Furthermore, we design an offspring generation strategy to generate high-quality solutions. We call this new algorithm framework MOEA/D-ARS. The experimental results suggest that the proposed algorithm performs better than certain state-of-the-art MOEAs.

In multiobjective evolutionary algorithms based on decomposition (MOEA/D), how to generate offspring significantly influences the convergence speed and the population diversity. In this paper, we propose an offspring generation strategy for MOEA/D. In this strategy, we firstly generate multiple temporary solutions for each subproblem in MOEA/D, secondly employ the whole parent population to construct surrogate model, thirdly, employ surrogate model to sort and identify the optimal temporary solution, and finally, generate offspring by employing differential evolution (DE) operator with polynomial mutation on the optimal temporary solution. We conduct extensive experiments and compare our method with some state-of-the-art multiobjective evolutionary algorithms. The experimental results show that our method obtains the best performance.

Introduction: A test that detects cancer signal across multiple cancer types and predicts signal (tissue) origin (SO) could aid in more efficient diagnostic workup and shorten time to cancer diagnosis in individuals with signs and symptoms.Methods: The Circulating Cell-free Genome Atlas (CCGA; NCT02889978) study is a prospective, longitudinal, multicenter, case-control study to develop and validate a multi-cancer detection test. The 2nd CCGA substudy utilized a targeted methylation-based cell-free DNA assay and machine learning algorithm and included assessment of test performance (sensitivity and SO prediction accuracy) in a subgroup of participants with clinically presenting cancers (CPCs) that were undiagnosed prior to blood draw. Specificity was assessed in the noncancer group and subgroups with confounding (nonmalignant) conditions (CCs; eg, cirrhosis) and noncancer participants enrolled in hematology clinics (HCs).Results: Specificity was 99.5% (95% confidence interval: 98.2-99.9%; 396/398), 93.8% (71.7-99.7%; 15/16), and 99.3% (96.0-100.0%; 136/137) for the noncancer group, CCs subgroup, and HCs subgroup, respectively. Overall sensitivity among those with CPCs was 66.4% (62.2-70.3%; 344/518). Sensitivity of cancer signal detection increased with increasing clinical stage (Table). SO prediction accuracy was 91.7% (88.3-94.3%; 300/327) among CPC participants with cancers detected, excluding those with multiple or unknown primaries. The test demonstrated prognostic value as detected cancer participants had worse survival probability than those not detected. Conclusions: This multi-cancer detection test detected cancer signals and predicted SO in individuals with CPCs with high specificity. These findings support further clinical development of this multi-cancer detection test that could accelerate the diagnostic resolution of symptomatic cancers. Table. Sensitivity by Clinical Stage Across Cancer Type in Clinically Presenting CancersClinical StagePositive Test/Total Cancer; Sensitivity (95% CI)All*344/518; 66.4% (62.2-70.3%)I33/122; 27.0% (20.0-35.5%)II60/102; 58.8% (49.1-67.9%)III103/121; 85.1% (77.7-90.4%)IV136/147; 92.5% (87.1-95.8%)Not expected to be staged9/21; 42.9% (24.5-63.5%)Non-informative2/4; 50.0% (15.0-85.0%)CI, confidence interval.*One participant who had a positive test result had multiple primaries with clinical stage I and not-expected-to-be-staged. Citation Format: Alan H. Bryce, Minetta C. Liu, Michael V. Seiden, David D. Thiel, Donald Richards, Carlos Becerra, Kathryn N. Kurtzman, Xiaoji Chen, Tony Wu, Quan Zhang, Jingjing Gao, Nan Zhang, Earl Hubbell, Arash Jamshidi, Eric T. Fung, Eric A. Klein. Performance of a cell-free DNA-based multi-cancer detection test as a tool for diagnostic resolution of symptomatic cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB058.

Introduction: Hematologic (heme) malignancies and their precursor conditions are highly prevalent. They are also diverse in biology, clinical presentation, and outcomes, underlining the importance of differentiating them. Previously, we demonstrated that a blood-based targeted methylation assay detected multiple cancer types across stages. Here, we examined test performance on various heme cancers, identifying specific methylation signatures. Methods: From the second substudy (training set) of the Circulating Cell-free Genome Atlas (CCGA) study (NCT02889978), we evaluated 325 participants from 17 different heme disease subtypes and 3,211 non-cancer controls enrolled without a cancer diagnosis. A cross-validated mutual information-based algorithm was used to identify features that discriminated heme subtypes. The resulting feature distribution was visualized using uniform manifold approximation and projection (UMAP) dimensionality reduction on held-out data. In cross validation with feature selection, we then trained a multinomial classifier to distinguish from among the major heme cancers and non-cancer and correlated the model's class probabilities to positions in feature space. Results: Dimensionality reduction and visualization of input features demonstrated that heme malignancies separated into five major clusters reflecting developmental lineages and disease ontogeny: myeloid, circulating lymphomas, hodgkin lymphomas, non-hodgkin lymphomas, and plasma cell neoplasm. The position of samples within each heme cluster correlated with the cancer signal strength. At 99.4% specificity [95% CI: 99.1, 99.7], heme cancer detection was 74.5% [69.4, 79.1] overall, 67.7% [41.1, 87.8] for myeloid, 77.9% [66.3, 86.9] for circulating lymphomas, 90.7% [73.2, 98.4] for hodgkin lymphomas, 68.6% [60.4, 76.1] for other non-hodgkin lymphomas, and 78.8% [67.0, 87.9] for plasma cell neoplasms. Of 18 non-cancer participants who were classified as having heme cancers, 4 were predicted as myeloid, 6 as circulating lymphoid, and 8 as other non-hodgkin lymphoid neoplasms ( Conclusion: Methylation features of cfDNA in patients with heme malignancies delineated five major clusters that reflected disease ontogeny and heme lineage. Lineage-specific signals followed a gradient suggestive of variation in disease-related methylation or tumor DNA shedding. These findings contribute to the understanding of biological signals that arise from various heme conditions. Since in general, most cfDNA arises from blood lineages, this knowledge will guide further efforts towards removing interfering biological signals from cfDNA-based cancer detection assays and achieving even more sensitive detection of multiple cancer types. Citation Format: Qinwen Liu, Rita Shaknovich, Xiaoji Chen, Zhao Dong, M. C. Maher, Samuel Gross, Alexander P. Fields, Jan Schellenberger, Kathryn N. Kurtzman, Eric T. Fung, Anne-Renee Hartman, Earl Hubbell, Arash Jamshidi, Alexander M. Aravanis, Oliver Venn. cfDNA methylation profiling distinguishes lineage-specific hematologic malignancies [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 139.

Background: The Circulating Cell-free Genome Atlas study (NCT02889978) is a multi-center, case-control, observational study with longitudinal follow-up (n=15,254; 56% cancer, 44% non-cancer) to support development of a cell-free DNA (cfDNA) multi-cancer early detection test. Previously, we reported that a targeted methylation assay detected and localized >20 cancer types at >99% specificity in individuals with cancer.1,2 Here, we report prediction of cancer (presence/absence) and tissue of origin (TOO) in individuals enrolled with clinical suspicion of cancer but without pathologic diagnosis or treatment at time of enrollment. Methods: Plasma cfDNA from blood samples collected prior to clinical diagnosis was subjected to targeted methylation sequencing. Samples were divided into a training set and an independent validation set to train and validate a machine learning classifier to assess cancer and predict TOO. Performance was assessed in a subset of participants enrolled with suspicion of cancer; subsequently, cancer was confirmed by evaluating a pathologic specimen. Results: Participants being evaluated for suspicion of cancer were classified as confirmed cancer (>20 cancer types; n=164 in training, n=75 in validation) or confirmed non-cancer (n=49 training, n=15 validation). In the confirmed non-cancer group, all training and validation samples were correctly predicted as non-cancer (100% specificity). In the confirmed cancer group, cancer detection across all stages was 40.2% (66/164; 95% confidence interval [CI], 32.7-48.2%) in training and 46.7% (35/75; 95% CI, 35.1-58.6%) in validation. Excluding stage I renal cancers (where detection/tumor fraction is low in plasma and which comprised 20% of participants in this subset) detection across stages was 50.4% (66/131; 95% CI, 41.5-59.2%) and 59.3% (35/59; 95% CI, 45.7-71.9%), respectively. In stages II and above, detection was 70.7% (58/82; 95% CI, 59.6-80.3%) and 78.9% (30/38; 95% CI, 62.7-90.4%), respectively. For detected cancers, TOO was predicted in 93.9% (62/66) samples in training and 100% (35/35) in validation. Of those with a TOO call, accuracy was 85.5% (53/62; 95% CI, 74.2-93.1%) and 97.1% (34/35; 95% CI, 85.1-99.9%), respectively. Conclusion: A cfDNA multi-cancer detection test has shown the potential to predict cancer and TOO in individuals with suspicion of cancer ahead of histologic diagnosis with performance comparable to those with confirmed cancer at the time of blood collection. This was achieved with high specificity and TOO accuracy. The high specificity suggests that the false positive rate could be comparable in populations with average versus higher risk (suspicion) of cancer. These findings suggest that a cfDNA multi-cancer detection test could accelerate the diagnostic resolution of suspicion of cancer. References: 1. Oxnard GR, et al. ASCO Breakthrough Meeting 2019; Abstract 44. 2. Oxnard GR, et al. ESMO Annual Meeting 2019; Abstract 5639. Citation Format: David D. Thiel, Xiaoji Chen, Kathryn N. Kurtzman, Jessica Yecies, Tony Wu, Quan Zhang, Hai Liu, Nan Zhang, Eric T. Fung, Michael V. Seiden, Minetta C. Liu, Geoffrey R. Oxnard, Earl Hubbell, Alexander M. Aravanis, Anne-Renee Hartman, Eric A. Klein. Prediction of cancer and tissue of origin in individuals with suspicion of cancer using a cell-free DNA multi-cancer early detection test [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr CT021.

Background The Circulating Cell-free Genome Atlas (CCGA; NCT02889978) study is a prospective, multi-center, observational, case-control study with longitudinal follow-up to support the development of a plasma cfDNA-based multi-cancer early detection assay. We previously reported that the fraction of tumor-derived cfDNA fragments in plasma (estimated tumor fraction [TF]) is associated with detection of multiple cancer types using a prototype whole-genome bisulfite sequencing (WGBS) assay in a pre-specified substudy. In addition, detectability of breast cancer varied by hormone-receptor (HR) status: HR negative (HR-) breast cancer had a higher detection rate than the overall breast cancer cohort. Here, we performed a planned in-depth analysis of the breast cancer cohort to understand the main clinical and biological determinants of detectability using our WGBS assay. Methods Blood samples were prospectively collected from participants (pts) with newly-diagnosed, untreated breast cancer. In a pre-specified CCGA substudy, 511 (31.4%) of 1,628 pts had a clinicopathologic diagnosis of breast cancer (any stage, excluding stage 0) and a WGBS result. Pre-specified biological and clinical factors were assessed for correlation with TF: clinical stage, pathologic N stage, lesion size (by imaging), T stage, histologic grade, hormone receptor (HR) status, and proliferative rate (Ki-67 by IHC [MIB-1 clone]) via univariate and multivariate analyses. Ki-67 was obtained from pathology reports provided by enrollment sites and also assessed centrally (Mayo Clinic [Rochester, MN]). Stage-specific differences in detection rates between HR positive (HR+) and HR- breast cancers due to biological differences such as histologic grade and/or proliferative rate was also assessed. Results Of 511 pts, 94 (18.4%; 48.9% HR- and 12.3% in HR+) had WGBS-detected cancer. Higher TF was significantly associated with cancer detection (p Conclusions Using a prototype methylation-based plasma cfDNA cancer detection assay, TF was found to be associated with detection, and with clinical and biological features of breast cancer. Features routinely used to assess clinical aggressiveness (eg, HR- status and histologic grade) also demonstrated stage-specific associations with TF. Given the known relationship between TF and detection, these data suggest that clinical and biological cancer features may also provide insight into the variability of cancer detection using plasma-based cfDNA tests. Citation Format: Minetta C. Liu, Jodi M. Carter, Daniel W. Visscher, Karla Kopp, Rita Shaknovich, Xiaoji Chen, Kathryn N. Kurtzman, Shilpen Patel, Jacqueline D. Brooks, Carlo R. Cosenza, Jafi A. Lipson, Donald A. Richards, Fergus J. Couch, Zhao Dong, Hai Liu, Oliver Venn, Joerg Bredno, Eric T. Fung, Anne-Renee Hartman. Blood-based cancer detection in plasma cell-free DNA (cfDNA): Evaluating clinical and pathologic tumor characteristics in participants with breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-01-01.

243 Background: Circulating Cell-free Genome Atlas (CCGA; NCT02889978) is a multi-center, case-control, observational study with longitudinal follow-up to develop a cfDNA assay in which classifiers were trained on whole-genome bisulfite sequencing (WGBS) and targeted methylation (TM) sequencing data for detection of multiple cancer types. Previously, we showed that the fraction of ctDNA fragments (TF) was a stronger predictor of cancer detection than clinical stage and an equivalent predictor for survival. Given that CRC tumors can be described via surface area (TSA) and microscopic tumor extent (microinvasion), CRC was used as a model to examine the biophysical determinants of TF. Methods: Detection of multiple cancers with WGBS at 98% and TM at > 99% specificity, and methods for determining TF, were previously reported. A model to predict the presence of detectable cfDNA fragments for CRC adenocarcinomas of stages I, II, and III included TSA and microinvasion beyond the subserosa. Predictors were combined assuming a linear increase of cfDNA shedding with tumor size, with scaling factors depending on microinvasion. Model parameters were determined for 27 participants (7, 11, 9 for stages I, II, III, resp.) with WGBS and applied to 40 participants (12, 15, 13 for I, II, III, resp.) with TM assay and information on tumor size and microinvasion. Results: CRC detection at stages I/II/III was 33/46, 61/73, 57/74% for WGBS/TM. TF predicted detection with AUC = 97.6. The model predicted TF as TSA multiplied by 3.81*10−6 / mm2 for tumors that invaded beyond the subserosa (p < 0.001). This was 4.4x higher than estimates for tumors below the subserosa. The model trained on the WGBS assay predicted CRC detection in the TM assay with an AUC of 0.844. Conclusions: This model used TSA (number of tumor cells) and microinvasion (bloodstream access) to predict the fraction of CRC ctDNA fragments in blood without needing to account for stage. Tumors not penetrating the subserosa had low ctDNA shedding that likely limited detection. These findings may generalize to other cancer types, providing principles to predict ctDNA shedding and thus cancer detectability based on microinvasion and surface area. Clinical trial information: NCT02889978.

283 Background: Cancers of the esophagus, stomach, pancreas, gallbladder, liver, bile duct, colon and rectum will account for 17% of incident cancer diagnoses and 26% of cancer-related deaths in the US in 2019. We developed a methylation-based cfDNA early multi-cancer detection test that also can predict the tissue of origin (TOO) of these and other cancers types; performance of this test for gastrointestinal (GI) tract cancers is reported here. Methods: The Circulating Cell-free Genome Atlas (CCGA; NCT02889978) study is a prospective, multi-center, observational, case-control study with longitudinal follow-up, enrolling individuals with cancer ( > 20 cancers, all stages, newly diagnosed) and without cancer. Plasma cfDNA was subjected to a cross-validated targeted methylation (TM) sequencing assay. Methylation fragments were combined across targeted genomic regions and assigned a probability of cancer and a predicted TOO. GI cancer classes were upper GI (esophagus/stomach, n = 67), pancreas/gallbladder/extrahepatic bile duct (n = 95), liver/intrahepatic bile duct (n = 29), and colon/rectum (n = 121). Results: Detection across all GI cancers was 82% (95% CI 77-86) at a > 99% pre-set specificity. Overall predicted TOO accuracy was 92% (88-95) among the samples for which TOO was predicted (6/255 had indeterminate predicted TOO). The table shows performance by GI cancer type. Conclusions: Simultaneous detection at high specificity ( > 99%) of multiple cancer types, including GI cancers across stages at high sensitivity (82%), was shown using TM analysis of cfDNA. Accurate (92%) localization of cancers to specific regions of the GI tract was also achieved. Detection of multiple GI cancers from a single noninvasive blood test could be a practical method for detecting GI and other cancers, and may facilitate diagnostic work-ups. Clinical trial information: NCT02889978. [Table: see text]

In multiobjective evolutionary algorithms, how to select the optimal solutions from the offspring candidate set significantly affects the optimization process. Usually, the selection process is largely based on the real objective values or surrogate model estimating objective values. However, these selection processes are very time consuming sometimes, especially for some real optimization problems. Recently, some researches began to employ supervised classification to assist offspring selection, but these works are difficult to prepare the exact positive and negative samples or time consuming of parameter tuning problems. In order to solve these disadvantages, we propose a decomposition based multiobjective evolutionary algorithm with semi-supervised classification. This approach using random sampling and non-dominated sorting to construct semi supervised classifier. In each generation, a set of candidate solutions are generated for each subproblem and only good solutions are reserved by classifier. If there is more than one good solutions, we calculate each of good solutions by real objective function and choose the best one as the offspring solution. Based on the typical decomposition based multiobjective evolutionary algorithm MOEA/D, we design algorithm framework through integrating the novel offspring selection process based on semi-supervised classification. Experiments show that the proposed algorithm performs best in most test cases and improves the performance of MOEA/D.

The visualization of large networks is a challenging problem due to the limitation of human visual perception. Sampling technology is an essential strategy in most of large network visualization and analysis. Moreover, when we consider large heterogeneous networks consisting various types of nodes and links, which has attracted much attention from multiple disciplines, it is much more difficult for users to explore and mine some useful information in such complex networks. Thus to design sampling methods for large heterogeneous network visualization has become a challenge. However, most of existing sampling methods ignore the types of nodes and links, hence they could not be used for heterogeneous network directly. In this paper, we propose an eigenvector centrality based sampling method considering both multiple types of nodes and links, supporting sampling the heterogeneous networks directly and efficiently. Furthermore, we develop HeteVis, a prototype visualization system with the proposed sampling algorithm built-in. The parameters of HeteVis can be dynamically personalized through user interactions and such system can help users to explore real large heterogeneous networks easily and flexibly. Extensive case study using the UCI KDD Movie network demonstrates how users can apply HeteVis to explore the heterogeneous networks.

1545 Background: Screening tests for early cancer detection are often criticized due to risk of overdiagnosis—detection of good prognosis cancers which may not require immediate treatment. We recently reported development of cfDNA sequencing approaches for cancer detection; longitudinal follow-up (F/U) data were utilized here to evaluate prognostic significance of cancer detection using cfDNA. Methods: Plasma cfDNA samples were subjected to whole-genome bisulfite sequencing (WGBS, 30X) as part of a previously-reported Circulating Cell-free Genome Atlas (CCGA; NCT02889978) substudy. This exploratory analysis evaluated the overall survival (OS) of training and test set participants (pts) with cancer (20 cancer types, any stage I-IV). Combining train and test set pts, univariate and multivariate analyses (Cox proportional hazards) assessed OS association with WGBS result (cancer detected vs not detected, set at 98% specificity), clinical stage (IV vs I-III), diagnostic method (symptom- vs screen-detected), sex, age, and histologic grade. Results: Of 827 pts from the training set with F/U (median 12.2 mo), 334 (40.4%) had WGBS-detected cancer. Among 127 (15.4%) pts with cancer that died during F/U, cancer was detected in 104 (81.9%). Results were similar in the test set. In univariate analyses all variables were associated with prognosis, including WGBS result (HR 7.7 p

Gastric cancer (GC) is the fifth most common cancer diagnosis with roughly 60% of patients with metastatic disease. Tumor heterogeneity represents a major challenge to personalized cancer therapy. Intratumor heterogeneity can result in missing critical DNA genomic alterations using conventional tumor biopsies. Circulating cell-free DNA (cfDNA) in the peripheral blood has the potential to capture DNA alterations in a more dynamic manner in particular types of advanced tumors where feasibility of repeat tissue biopsies is limited. Using low-pass whole-genome sequencing (LP-WGS) data from 329 blood plasma samples collected from 63 1L metastatic (mGC) patients enrolled in phase II trial NCT01590719, receiving either onartuzumab or placebo in combination with mFOLFOX6, we evaluated the global percent genome change (%GC) and copy number (CN) gain at baseline and throughout the course of treatment. cfDNA samples were isolated with the automated QIAsymphony system and analyzed on the Illumina NextSeq 500 system with a novel LP-WGS protocol at 0.5x coverage. Seven chromosomes (chr1, 7, 8, 14, 15, 20, and 21) showed the most frequent CN gains of evaluated patients (≥15%). Of these seven chromosomes, chr8 and 20 featured the largest number of genes at baseline with CN gains with 30.2% (19/63) at chr8 and 25.4% (16/63) at chr20. 81% of patients (13/16) with chr20 CN gain also showed an increase in CN at chr8. Patients with gains at chr8 and/or 20 have higher %GC compared to patients with normal copy status. Recurrent CN gains at chr8 and 20 in tumor tissues were previously reported in GC patients and correlated with tumor progression. Copy number gains at chr8 and 20 in cfDNA from mGC patients, reported here for the first time, suggests a potential utility for early detection of disease progression. Moreover, patients with CN gains at chr8 and/or 20 had higher circulating tumor cells detected at baseline (p=0.003) and also showed the most decrease in %GC post-treatment. 75% (9/12) of the patients with CN gains at chr8 and/or 20 were responders and showed a mean 3-fold decrease (range of 1.6-4.7 fold change) in tumor size post-treatment. This suggests CN gain at chr8 or 20 in cfDNA could be used as a noninvasive measure to identify a novel subset of mGC patients sensitive to chemo-based treatment. When comparing %GC pre- and post-treatment, the %GC decreased significantly at C2D1 (p=0.006). This decrease in %GC correlates with a decrease in tumor burden in patients with response, while %GC and tumor burden remain unchanged or increased in nonresponders. Monitoring %GC pre- and post-treatment demonstrates the ability to assess the depth of the treatment and early detection of disease progression. Findings from this study suggest that plasma genomic abnormality monitoring may be used for treatment decision-making in mGC and should be evaluated further in clinical trials. Citation Format: An D. Do, Charles D. Baudo, Samir Seshadri, Xiaoji Chen, Rebecca Suttmann, David S. Shames, Mark R. Lackner, Shih-Min A. Huang, Teiko Sumiyoshi. Changes in plasma genomic abnormalities at chromosome 8 and 20 predict treatment response and monitor disease progression in advanced gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5571.

Circulating cell-free DNA (cfDNA) in plasma offers a non-invasive approach to monitor tumor molecular profiling in real-time at multiple time-points, detection of emerging genomic alterations associated with drug resistance and clarifying cancer prognosis and diagnosis of cancer recurrence or progression. Therefore, it is crucial to improve our current NGS technology to detect low frequency alleles in cfDNA. There are many technical challenges in NGS technology, including base errors generated from library preparation and sequencing, making it hard to distinguish real mutations from false positives produced from amplification sequencing and base-calling processes. We have adopted a more sensitive approach to overcome this challenge. We used short, random DNA sequences called unique molecular tags (UMTs) during library preparation to label input DNA. This technique can remove background errors, false positives during data analysis, providing high confident mutation calls with better sensitivity down to 0.1% (depends on DNA input amount). We will present data showing validation of this NGS platform using commercially available DNA control, archival FFPE tissue and cfDNA matched samples and demonstrate robust sensitivity and specificity by using the off-the-shelf Archer Reveal ctDNA panel for Illumina covers 28 oncogenes. This molecular-tagged NGS panel can detect mutations down to 0.1% allelic frequency. This robust NGS platform is being used in our lab to analyze cfDNA samples, enabling the development of a non-invasive method to overcome existing challenges to provide molecular understanding of patient’s tumor evolution in real time, and aid in the development of personalized therapies for cancer patients. Citation Format: Nga Wan Rachel Tam, Irmina Diala, Xiaoji Chen, Walter Darbonne. Ultrasensitive mutational analysis in cell-freeDNA by targeted next-generation sequencing with molecular tags [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5704. doi:10.1158/1538-7445.AM2017-5704

Circulating tumor DNA (ctDNA) is released from necrotic/apoptotic tumor cells into the bloodstream. Recent studies have demonstrated the value of using ctDNA as biomarkers in cancer diagnosis, prognosis, and drug resistance. Unlike local tissue biopsy, ctDNA collection and analysis is non-invasive, allows continuous monitoring of clonal evolution, and provides an overview of tumor heterogeneity. Copy number variations (CNVs) play an important role in cancer biology. However, traditional CNV analyses of ctDNA using droplet digital PCR (ddPCR) and SNP arrays can only assess a small number of genes due to the low abundance of ctDNA in the majority of patient samples. Next generation sequencing (NGS) offers a more efficient and high-throughput way to study CNVs in ctDNA. Here, we evaluated the use of low-pass whole genome sequencing (WGS) in determining CNVs in ctDNA. In this work, cell-free DNA was isolated from 1-5 ml of plasma from phase II clinical trial patients with metastatic breast cancer and non-small cell lung cancer (NSCLC) using the QIAamp Circulating Nucleic Acid Kit (QIAGEN). DNA yield was determined by ddPCR, with a range from 2-4000 ng. Sequencing libraries were prepared using 2-10 ng DNA by the ThruPLEX Plasma-Seq Kit (Rubicon Genomics). WGS at 0.1x, 0.25x, 0.5x, and 1x coverage was performed on Illumina NextSeq, and data was analyzed using Nexus Copy Number software (BioDiscovery). As a reference, we sequenced DNA from peripheral blood mononuclear cells (PBMCs) of 20 healthy donors at the same coverage. Low-pass WGS was also performed on a subset of matched tumor tissue samples as comparisons. Our results showed that WGS of ctDNA at 0.5x coverage was efficient to identify CNVs. CNVs were detected in ctDNA from about half of the patients analyzed, and in general CNVs identified in ctDNA matched the ones found in tumor tissue from the same patient. We also found that CNV patterns from different time points of the same patients clustered together. With this promising system, we will present CNV analysis in ctDNA from breast cancer and NSCLC patients enrolled in phase II clinical trials of the PI3K inhibitor pictilisib. We will evaluate the ability of the method to classify patients into different subgroups, monitor tumor progression, and identify drug resistance mechanisms. Citation Format: Xiaoji Chen, Jill M. Spoerke, Kathryn Yoh, Walter C. Darbonne, Ling-Yuh Huw, Steven Gendreau, Shih-Min A. Huang, Mark R. Lackner. Low-pass whole genome sequencing detects copy number variations in circulating tumor DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2739. doi:10.1158/1538-7445.AM2017-2739

G9a and GLP are conserved protein methyltransferases that play key roles during mammalian development through mono- and dimethylation of histone H3 Lys 9 (H3K9me1/2), modifications associated with transcriptional repression. During embryogenesis, large H3K9me2 chromatin territories arise that have been proposed to reinforce lineage choice by affecting high-order chromatin structure. Here we report that in adult human hematopoietic stem and progenitor cells (HSPCs), H3K9me2 chromatin territories are absent in primitive cells and are formed de novo during lineage commitment. In committed HSPCs, G9a/GLP activity nucleates H3K9me2 marks at CpG islands and other genomic sites within genic regions, which then spread across most genic regions during differentiation. Immunofluorescence assays revealed the emergence of H3K9me2 nuclear speckles in committed HSPCs, consistent with progressive marking. Moreover, gene expression analysis indicated that G9a/GLP activity suppresses promiscuous transcription of lineage-affiliated genes and certain gene clusters, suggestive of regulation of HSPC chromatin structure. Remarkably, HSPCs continuously treated with UNC0638, a G9a/GLP small molecular inhibitor, better retain stem cell-like phenotypes and function during in vitro expansion. These results suggest that G9a/GLP activity promotes progressive H3K9me2 patterning during HSPC lineage specification and that its inhibition delays HSPC lineage commitment. They also inform clinical manipulation of donor-derived HSPCs.

Protein kinase signaling regulates human hematopoietic stem/progenitor cell (HSPC) fate, yet little is known about critical pathway substrates. To address this, we have developed and applied a large-scale, empirically-optimized phosphopeptide affinity enrichment strategy with high-throughput 2D LC-MS/MS screening to evaluate the phosphoproteome of an isolated human CD34+ HSPC population. We first used hydrophilic interaction chromatography (HILIC) as a first dimension separation to separate and simplify protein digest mixtures into discrete fractions. Phosphopeptides were then enriched offline using TiO2-coated magnetic beads and subsequently detected online by C18 reverse phase nanoflow HPLC using data-dependent MS/MS High-Energy Collision-activated Dissociation (HCD) fragmentation on a high performance Orbitrap hybrid tandem mass spectrometer. We identified 15533 unique phosphopeptides in 3574 putative phosphoproteins. Systematic computational analysis revealed biological pathways and phosphopeptides motifs enriched in CD34+ HSPC that are markedly different from those observed in an analogous parallel analysis of isolated human T cells, pointing to the possible involvement of specific kinase-substrate relationships within activated cascades driving hematopoietic renewal, commitment and differentiation.

Development of specific immunotherapy for colorectal cancer (CRC) will require identification of antigens selectively or exclusively expressed on CRC cells and strategies to induce and enhance immune responses against these antigenic targets. Cancer-testis (C-T) antigens are proving to be excellent targets for immunotherapy of solid tumors such as melanoma, but their clinical utility for treatment of CRC has to date been limited by their infrequent expression in CRC cells. Here we report that the hypomethylating agent 5-aza-2'-deoxycytidine (DAC) induces expression of NY-ESO-1 and other C-T genes in CRC cells both in vitro and in vivo in a dose-dependent manner but has negligible effects on the expression of C-T genes in normal nontransformed cells such as fibroblasts. The induction by DAC of NY-ESO-1 expression in CRC cells persists over 100 days after DAC exposure and is associated with increased levels of NY-ESO-1 protein. CRC cells exposed to DAC at concentrations that can be readily achieved in vivo are rendered susceptible to major histocompatibility complex-restricted recognition by CD8 NY-ESO-1-specific T cells. We also demonstrate that retroviral transduction of polyclonal peripheral blood T cells from a metastatic CRC patient with the T-cell receptor α-chain and β-chain genes encoding a human leukocyte antigen-A2-restricted, NY-ESO-1157-165-specific T-cell receptor can be used to generate both CD8 and CD4 NY-ESO-1157-165-specific T cells that selectively recognize DAC-treated CRC but not nontransformed cells. Collectively, these results suggest that the combination of epigenetic modulation and adoptive transfer of genetically engineered T lymphocytes may enable specific immunotherapy for CRC.

‘In the next century, planet earth will don an electronic skin. It will use the Internet as a scaffold to support and transmit its sensations. This skin is already being stitched together. It consists of millions of embedded electronic measuring devices: thermostats, pressure gauges, pollution detectors, cameras, microphones, glucose sensors, EKGs, electroencephalographs. These will probe and monitor cities and endangered species, the atmosphere, our ships, highways and fleets of trucks, our conversations, our bodies – even our dreams.’ (Gross, 1999) Following this comprehensive vision by Neil Gross (1999), it can be assumed that sensor network deployments will increase dramatically within the coming years, as pervasive sensing has recently become feasible and affordable. This enriches knowledge about our environment with previously uncharted real-time information layers. However, leveraging sensor data in an ad-hoc fashion is not trivial as ubiquitous geo-sensor web applications comprise numerous technologies, such as sensors, communications, massive data manipulation and analysis, data fusion with mathematical modelling, the production of outputs on a variety of scales, the provision of information as both hard data and user-sensitive visualisation, together with appropriate delivery structures. Apart from this, requirements for geo-sensor webs are highly heterogeneous depending on the functional context. This chapter addresses the nature of this supply chain; one overarching aspect is that all elements are currently undergoing both great performance enhancement combined with drastic price reduction (Paulsen & Riegger, 2006). This has led to the deployment of a number of geo-sensor networks. On the positive side the growing establishment of such networks will further decrease prices and improve component performance. This will particularly be so if the environmental regulatory structure moves from a mathematical modelling base to a more pervasive monitoring structure. Of specific interest in this chapter is our concern that most sensor networks are being built up in monolithic and specific application-centred measurement systems. In consequence, there is a clear gap between sensor network research and mostly very heterogeneous end user requirements. Sensor network research is often dedicated to a long-term vision, which tells a compelling story about potential applications. On the contrary, the actual implementation is mostly not more than a very limited demonstration without taking into account well-known issues such as interoperability, sustainable development, portability or the coupling with established data analysis systems.

Human dynamics are inextricably intertwined with the social, geographical and economic environment. The continuous flux of people communicating as well as migrating, commuting, and traveling inevitably spans acquaintances across geographic space that is far from random and exhibits regular patterns. For instance, it has been shown that the probability of being acquainted with someone is closely related to the inverse distance between them. In this paper we investigate aggregated mobile phone call detail records from a large US cellular operator and map them into space to characterize the social radius of influence at two different scales: communication and mobility. We discover that scaling properties with respect to population agglomeration are similar to those discovered for other indicators of cities. We also discover spatial community structures that are divorced from administrative boundaries, and use them to quantify the different social radii of influence discovered from the data.

Software maintenance ensure the normal operation of the basic work, accurate cost estimation ensure the normal maintenance of the necessary software tools. The Article describe the types of software maintenance, analysis the impact of software maintenance costs of technical factors and non-technical factors, give software maintenance of the basic cost estimation model and improvement of cost estimation models for software maintenance, use form lists the cost factors affecting the workload of maintaining weight. Basic cost estimation model to estimate the cost of basic maintenance is multiplied by the annual change in the project development costs of conflict; improve cost estimation model to estimate the maintenance cost is the cost of basic maintenance factors multiplied by each of the weight. It can use appropriate adjustments to affect the cost of software maintenance workload of the weight factors at last, improve the accuracy of software maintenance cost estimation. The Article propose method for software developers or users of the software maintenance cost estimation has a good reference value.

Reasonable choice of logistics center location, has important significance, for optimizing the regional industrial layout, and promoting the development of modern logistics industry. For the problems such as the logistics center location by many influence factors, the decision-making and evaluation of location program are complex, this paper use the rough sets method to study. first, analyze the influence factors of logistics center location, to build the influence factors index system, second, on the basis of properties importance formula which has been given, study the attributes knowledge acquisition algorithm, then study the decision model of program evaluation, including basic evaluating relationship model, fuzzy partial order model and mathematical formula which estimate the total order of evaluation objects, in the end, through logistics center location examples to illustrate the concrete steps of attributes knowledge acquisition and evaluation decision. The results show that, use the rough sets theory, conduct the evaluation decision after the attributes knowledge acquisition, this method is simple and applicable, and is the scientific method of logistics center location.

The exactly software cost and progress estimates is the necessary method to ensure the development and application of e-commerce platform to operate, while in this article we take use of combining the Function Point Analysis and COCOMO II model to estimate. In the procedure of estimation, the first thing is to estimate the function points, which is according to the type and basis for classification, B to C e-commerce platform, functions of the complexity level and other factors point to estimate the unadjusted function points, the adjustment factor calculated by the adjusted function points; then based e-commerce platform development tool in the general level of the developer's estimated development costs of labor productivity; and finally take the use of COCOMO II model to estimate the progress of the development.