Genesee Community College

BackgroundCell-free DNA (cfDNA) fragmentomics represents a transformative approach for early breast cancer detection, offering significant potential to improve patient survival through timely intervention. Despite this promise, existing cfDNA-based methods demonstrate inadequate sensitivity for clinical implementation, particularly in early-stage malignancies. There remains an urgent need to develop robust, cost-effective diagnostic strategies integrating cfDNA fragmentomic profiling with advanced machine learning algorithms.MethodsThis research involved a total of 191 participants who did not have cancer and 204 participants diagnosed with breast cancer. The plasma cfDNA samples from the participants underwent profiling through whole-genome sequencing. A variety of cfDNA characteristics and machine learning models were assessed within the training cohort to attain the best model. The evaluation of model performance took place in a separate validation cohort.ResultsAn assembled ensemble model that combines three cfDNA characteristics with six machine learning algorithms, developed in the training cohort (cancer: 119; healthy: 112), outperformed all models created from individual feature-algorithm pairs. This composite model demonstrated enhanced sensitivities of 93.3% at a specificity of 94.6% for the training cohort (area under the curve [AUC], 0.983) and 96.5% at 93.7% specificity for the validation cohort (AUC, 0.989) (cancer: 85; healthy: 79). Additionally, our model exhibited sensitivity across various stages, distinct pathological types, and diverse molecular classifications.ConclusionWe have established a stacked ensemble model using cfDNA fragmentomics features and achieved superior sensitivity for detecting early-stage breast cancer, which could promote early diagnosis and benefit more patients.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13058-025-02190-8.

GLI1 gene alterations including fusions and amplifications compromise a subset of malignant mesenchymal tumors exhibiting characteristic monomorphic nested morphology and frequent S100 positivity, which mimic glomus tumors or well differentiated neuroendocrine tumors. We report four high-grade uterine endometrial stromal sarcomas (ESS) harboring GLI1 and MDM2/CDK4 co-amplifications with a median age of 51.5 years (range 43 ~ 72 years). Histologically, tumors showed a heterogenous morphology, including ovoid to spindle cells, showing nested/nodular arrangement (4/4). Myxoid background was observed at least partially in 4 tumors with prominent capillary networks. Mitoses index was 2 to 20/10 HPF (median 9.5/10 HPF). Immunochemically, tumors showed diffuse staining of CD10 (3/4) with frequently positive CyclinD1(2/4 tested) and mostly negative S100 protein (3/4). Next-generationsequencing (NGS) studies revealed GLI1 and MDM2/CDK4 co-amplification in all cases (4/4) and GLI1 fusion in 1 case (1/4), which were validated by fluorescence in situ hybridization (FISH) analysis. BCOR fusions were firstly identified with GLI1 and MDM2/CDK4 co-amplification in 2 cases (2/4). Copy number (CN) segmentation data showed GLI1 co-amplified cases present generally a single peak at the 12q13.3-15 locus. Follow-up (range:3 to 112 months; median 37.5 months) showed recurrence and/or metastasis in all cases (4/4), in which 1 patient developed lungs and liver metastasis. Relapse-free survival (RFS) analysis showed similar median RFS between GLI1 co-amplified HGESS and GLI1 non-amplified HGESS groups, which were shorter than LGESS group. Unusual clinicopathologic features of these HGESS with GLI1 and MDM2/CDK4 co-amplification mimicked other neoplasms, which caused significant diagnostic challenge and pitfalls. However, identification of GLI1 alterations in these tumors is beneficial for diagnosis and potential use of targeted GLI1 inhibitors.

Early detection of colorectal cancer (CRC) is crucial for improving patient survival. This innovative multi-center study aims to develop a non-invasive blood-based assay using cell-free DNA (cfDNA) fragmentomics to differentiate CRC from advanced colorectal adenomas and non-cancerous colorectal and other digestive diseases. A total of 167 CRC patients and 227 with benign colorectal conditions were divided into training and validation cohorts (1:1 ratio). Plasma cfDNA underwent Low-depth whole-genome sequencing to profile three fragmentomics features, which were integrated into a stacked ensemble model. The model was validated on 69 CRC patients and 96 benign controls, with an additional cohort of 31 advanced adenoma patients included to assess its performance in differentiating advanced adenomas from benign cases. The model achieved an AUC of 0.926, with sensitivity of 91.3% and specificity of 82.3% in validation. Sensitivities were consistently high across CRC stages (I: 94.4%, II: 86.4%, III: 91.3%, IV: 100%). Notably, the model demonstrated exceptional accuracy in distinguishing advanced adenomas from benign cases, achieving an AUC of 0.846 and sensitivity of 67.7%, outperforming traditional blood tests. This multi-center study underscores a significant advancement in liquid biopsy technology, offering a highly accurate and non-invasive approach for early CRC detection and differentiation of advanced colorectal adenomas.

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis and limited first-line treatments. This phase Ib/II randomized trial (NCT05218889) investigated the efficacy and safety of surufatinib plus camrelizumab and nab-paclitaxel/S-1 (NASCA) versus nab-paclitaxel and gemcitabine in patients with locally advanced or metastatic PDAC. The primary endpoints were dose-limiting toxicities and the recommended phase II dose (RP2D) of surufatinib in phase Ib, and the objective response rate (ORR) in phase II. Phase Ib used a 3 + 3 dose-escalation design to determine the RP2D of surufatinib in six patients, which was established at 200 mg. In phase II, patients were randomized 1:1 to receive the NASCA (45 patients) or nab-paclitaxel and gemcitabine (45 patients). NASCA group showed an ORR of 51.1% (23/45) versus 24.4% (11/45) in the nab-paclitaxel and gemcitabine group (odds ratio 3.2, 95% CI 1.3–8.2, p = 0.01). The median progression-free survival (PFS) was 7.9 vs. 5.3 months (HR 0.63, 95% CI 0.40–0.99, p = 0.045). The median overall survival was 13.0 vs. 11.0 months (HR 0.77, 95% CI 0.47–1.28, p = 0.318). The most common Grade ≥3 treatment-related adverse event was decreased neutrophil count (33.3% vs. 35.6%). In the NASCA group, enrichment of CD8+ and CD8+PD-1+ cells, a high baseline M1/M2 macrophage ratio, and a reduction in CA19-9 levels at weeks 6 and 12 were associated with improved PFS compared to patients without these features. The NASCA regimen showed promising efficacy with tolerable safety relative to nab-paclitaxel and gemcitabine for locally advanced or metastatic PDAC.

BackgroundNo combined antiangiogenic and PD-1/PD-L1 blockade therapy has been investigated as a chemo-free first-line treatment for advanced esophageal squamous cell carcinoma (ESCC). This study evaluates the efficacy and safety of anlotinib combined with benmelstobart as a chemo-free treatment in previously untreated advanced ESCC, and identifies potential predictive biomarkers using next-generation sequencing (NGS).MethodsALTER-E-003, a single-arm, open-label phase II trial, enrolled patients with advanced ESCC across five Chinese centers. Patients received oral anlotinib 12 mg daily on days 1–14 per three-week cycle, with benmelstobart 1200 mg infused on day 1 of each cycle for up to 24 months. Thereafter, patients received anlotinib maintenance therapy. Primary endpoint was objective response rate (ORR). Secondary endpoints included progression-free survival (PFS), overall survival (OS), disease control rate (DCR), duration of response (DOR), and safety. NGS and fluorescent multiplex immunohistochemistry (mIHC) were performed on tumor specimens.ResultsOf 53 screened patients, 46 completed the study. The confirmed ORR was 56.5% (95% CI 41.1–71.1), and DCR was 91.3% (95% CI 79.2–97.6). Median PFS was 15.74 months (95% CI 9.03–21.91). Treatment-related adverse events occurred in 93.5% of patients, with 28.3% experiencing grade 3 or higher events. NGS revealed a novel predictive mutational signature (TP53+/FAT1+/NOTCH3-) that was associated with better ORR (65.6% versus 11.1%, P < 0.001), longer median PFS (17.91 versus 5.32 months, P = 0.005) and improved OS (P = 0.006).ConclusionFirst-line anlotinib-benmelstobart combination demonstrated durable responses and acceptable safety in ESCC patients. Exploratory biomarker analyses identified a TP53+/FAT1+/NOTCH3- mutational signature potentially associated with improved outcomes, though further validation in randomized trials is warranted.Trial registrationNCT05038813.

e16287 Background: Bile duct cancer (BDC) remains a challenging malignancy with high recurrence rates after surgery, thus minimal residual disease (MRD) detection is critical for predicting outcomes and guiding postoperative management. Methods: This study prospectively enrolled 79 patients with BDC (intrahepatic, n = 19; perihilar, n = 15; distal, n = 42; mixed, n = 3) undergoing curative-intent surgeries (30 recurrence and 49 non-recurrence), of whom pre-surgery, 1-, 3-, and 6-month post surgery plasma cell-free DNA (cfDNA), surgical tumor tissue, and adjacent normal tissue samples were collected. Plasma cfDNA samples of 54 non-cancer individuals were additionally collected. We developed a comprehensive fragmentomic tumor score (FTS) algorithm for cfDNA MRD assessment, which integrated a copy number variant (CNV) score through whole-genome sequencing performed on non-BDC and pre-surgery BDC cfDNA, a methylation (Methy) score based on applying fragmentomics-based methylation analysis on BDC-specific differentially methylated regions, and a fragmentomic transcription start site (TSS) score representing the TSS coverage of differentially expressed genes identified by RNA-sequencing applied to tumor and adjacent tissues. FTS performance in MRD detection was evaluated, and its utility in impending progression prediction was investigated in post-surgery cfDNA samples. Results: The CNV, Methy, and TSS scores achieved an area under the curve (AUC) of 0.904, 0.779, and 0.891, respectively, when distinguishing cancer from non-cancer samples. The combined FTS demonstrated superior performance, achieving an AUC of 0.950 with 77.8% sensitivity at 98.0% specificity. Furthermore, patients with MRD-positive 1-month post-surgery cfDNA samples, whose FTS were over the threshold fixed at 98.0% specificity, had inferior disease-free survival [DFS; median, 7.89 months vs. not reached (NR); hazard ratio (HR), 7.64; 95% confidence interval (CI), 2.85–20.45; p &lt; 0.001] and overall survival (OS; median, 13.3 months vs. NR; HR: 9.62; 95% CI, 2.21–41.79; p = 0.003) than those with MRD-negative samples. Similar trends towards worse DFS in MRD-positive patients were observed at 3- (median, 9.99 months vs. NR; HR: 7.59; 95% CI, 1.54–37.37; p = 0.013), and 6-month (median, 11.5 months vs. NR; HR: 12.76; 95% CI, 1.25–130.00; p = 0.032) after surgery. Conclusions: A novel MRD assessment algorithm was established by integrating fragmentomic CNV, methylation, and transcription features, offering a promising tool for monitoring disease progression and prognostic prediction.

Abstract Introduction: Minimal Residual Disease (MRD) detection using circulating tumor DNA (ctDNA) has significant clinical value in cancer therapy. However, existing methods primarily focus on mutations, restricting their broader applicability. Personalized panels, while targeted, are limited by prolonged turnaround times (TAT) and reliance on central labs. Fixed-panel approaches provide standardized, efficient solutions with strong potential for in vitro diagnostic kit development. Method: We present ShieldingUltra, a fixed-panel MRD solution targeting hotspot mutations across 2, 365 genes using ultra-deep unique molecular identifier sequencing. Using over 1, 000 clinical plasma samples, cutting-edge AI-driven models were designed to reduce sequencing background error and clonal hematopoiesis interference, and improve sensitivity by integrating mutations, copy number variations, and fragmentomics. ShieldingUltra demonstrates exceptional performance validated across diverse clinical settings. Result: ShieldingUltra demonstrated exceptional performance, achieving over 70% and 90% presurgical plasma positivity in early- and late-stage pan-cancer samples, respectively, with &amp;gt;99% specificity in healthy plasma. We further evaluated ShieldingUltra in a few most challenging clinical scenarios, rarely studied before with poor outcomes, and achieved unparalleled performance. In a cohort of lung cancer patients, ShieldingUltra achieved the most sensitive MRD detection with 1-week postsurgical landmark plasma (61.5%, 16/26), with sensitivity rising to 96% (25/26) using longitudinal plasma. In another challenging and unresolved scenario aimed at further stratifying advanced non-small cell lung cancer (NSCLC) patients who exhibited a partial response upon completion of immunotherapy (IO), as well as identifying those unable to maintain prolonged progression-free survival, ShieldingUltra successfully identified over 50% (18/34) of individuals who quickly relapsed. This highlights ShieldingUltra’s potential to pinpoint patients most likely to ultimately benefit from IO, which marks an innovative frontier in MRD research. Additionally, ShieldingUltra was explored for perioperative MRD tracking and the potential to guide the treatment strategies in an advanced stage ovarian cancer cohort, including evaluating neoadjuvant therapy for surgical decisions, assessing the need for adjuvant therapy post-surgery, and monitoring treatment efficacy and MRD status after surgery. Conclusion: ShieldingUltra combines a fixed panel with multiple genomic features and AI modeling for exceptional MRD detection. Its standardized design minimizes TAT, eliminates custom setup delays, and ensures reliable, cost-effective results, making it ideal for time-sensitive clinical decisions and broad clinical adoption. Citation Format: Hao Zhang, Ningyou Li, Wanxiangfu Tang, Jinfeng Zhang, Xunbiao Liu, Peng He, Baihan Zhu, Shuang Chang, Zilin Wang, Zhili Chang, Dongqin Zhu, Rui Liu, Xiaoxi Chen, Haimeng Tang, Hua Bao, Xue Wu, Yang Shao. ShieldingUltra: A novel approach for enhanced minimal residual disease detection through the integration of mutation, copy number variation, and fragmentomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4561.

B‐cell acute lymphoblastic leukaemia (B‐ALL) is the most prevalent hematologic malignancy in children and a leading cause of mortality. Managing B‐ALL remains challenging due to its heterogeneity and relapse risk. This study aimed to delineate the molecular features of paediatric B‐ALL and explore the clinical utility of circulating tumour DNA (ctDNA). We analysed 146 patients with paediatric B‐ALL who received systemic chemotherapy. The mutational landscape was profiled in bone marrow (BM) and plasma samples using next‐generation sequencing. Minimal residual disease (MRD) testing on day 19 of induction therapy evaluated treatment efficacy. RNA sequencing identified gene fusions in 61% of patients, including 37 novel fusions. Specifically, the KMT2A‐TRIM29 novel fusion was validated in a boy who responded well to initial therapy but relapsed after 1 year. Elevated mutation counts and maximum variant allele frequency in baseline BM were associated with significantly poorer chemotherapy response (p = 0.0012 and 0.028, respectively). MRD‐negative patients exhibited upregulation of immune‐related pathways (p < 0.01) and increased CD8+ T cell infiltration (p = 0.047). Baseline plasma ctDNA exhibited high mutational concordance with the paired BM samples and was significantly associated with chemotherapy efficacy. These findings suggest that ctDNA and BM profiling offer promising prognostic insights for paediatric B‐ALL management.