bluuebunny/crossref_metadata_2025_split huggingface dataset:
A single institution experience with droplet digital polymerase chain reaction (dd-PCR) liquid biopsy (LB) for therapeutic decision in advanced solid tumors.
Authors: Murad Andre Marcio , Carneiro Juliana Garcia , Casali-da-Rocha José Claudio 
Published: June 2021

3038 Background: Droplet digital polymerase chain reaction (ddPCR) is a promising method for analyzing minor amounts of cell-free circulating free nucleic acid (DNA and RNA) due to its high sensitivity, low cost, and fast reading, since it dispenses bioinformatics, making it an appropriate alternative to new generation sequencing (NGS) for the detection of biomarkers to guide molecularly targeted cancer therapy. The assay covers main actionable hotspot alterations across many actionable genes: EGFR(mutations), ALK (fusion, mutations), ROS1(fusion), BRAF(mutations), KRAS (mutations), NRAS(mutations), PIK3CA (mutations), ERBB2(CNV- copy number variation), ESR1(mutations), KIT(CNV) and PDGFRA(CNV). Therefore, our genes can be used in panels, both in therapeutically applied genotyping and in detecting molecular responses as well as in secondary resistance to tumors such as NSCLC, breast, cervix. rectal, GIST, melanoma and pancreas. Methods: dd-PCR was performed using the QX200 system (BIO-RAD, Hercules). The extraction of DNA and RNA was done using the magnetic beads technique of Thermofisher's MagMAX Kit. All samples were tested in duplicate. Up to now, 108 metastatic cancer patients were tested: NSCLC - 28 (26%), breast - 22 (20%), colorectal - 22(20%), melanoma - 18(17%), pancreas - 8(7%), ovarian - 6(6%), salivary glands - 2 (4%) and GIST 2(4%). Results: Significant genomic alterations were detected in 38(35%) patients: 10 (9%) mutations in the KRAS G12V gene (all in colorectal cancer), 10 (9%) ERBB2 amplification (breast cancer), 4 (3.65%) EGFR L858R mutations (NSCLC), 4 (3.5%) EGFR del19 mutations (NSLC), 4 (3.5%) EGFR T790M mutations (NSCLC), 4 (3.5%) BRAF-V600E mutations (colon and melanoma), 2 (1.8%) ALK-EML4 fusion (NSCLC). The MAF (Mutant Allele Fraction) ranged from 0.9% to 24%. In all cases, the results were decisive for the indication or change of a targeted therapy. Median turnaround time was 36 hours and the average cost of the panels was around 500 USD (median of 4 genes per panel). Conclusions: Our results suggest that dd-PCR is a highly sensitive method and could be used for a routine laboratory detection of the important genomic variations to determine the targeted therapy in patients with varied advanced solid tumors.
Addition of selpercatinib to overcome osimertinib resistance in non-small cell lung cancer (NSCLC) with acquired RET fusion detected in ctDNA at very low allele frequency.
Authors: Kim Leeseul , Chae Young Kwang , Jung Chan Mi , Lee Alice Daeun , Yu Emma 
Published: June 2021

3046 Background: Osimertinib, a highly selective third generation EGFR tyrosine kinase inhibitor (TKI) became the standard front-line therapy for EGFR-mutant NSCLC. However, therapeutic options are limited for TKI resistance which commonly occurs. Therefore, overcoming acquired resistance to osimertinib remains an important high unmet need in the field of precision oncology. Herein, we present the first case of advanced adenocarcinoma of the lung that showed notable response with the addition of selpercatinib after acquired resistance to osimertinib monotherapy. Methods: Case presentation. Results: A 37-year-old woman with stage IVB adenocarcinoma of lung with osseous, hepatic and brain metastases initially received one cycle of carboplatin, pemetrexed and pembrolizumab. Based on the EGFR exon19 deletion detected from ctDNA NGS assay (Guardant 360) [variant allele frequency (VAF) 62.7%], the treatment regimen was changed to osimertinib monotherapy (80mg PO daily). Bevacizumab was empirically added given CNS involvement. She maintained overall stable disease for 10 months before subsequent CT showed disease progression. The treatment regimen was switched to atezolizumab, bevacizumab, paclitaxel and carboplatin combination therapy. She tolerated 6 cycles of the regimen in 4 month before a subsequent brain MRI revealed progression of the metastatic brain disease with new leptomeningeal disease. Whole brain radiotherapy was performed and decision was made to start combination TKI treatment of selpercatinib (120mg BID) added to the osimertinib (80mg daily) monotherapy based on her repeat ctDNA NGS assay result showing concurrent acquired CCDC6RET fusion (VAF 0.05%) and EGFR exon 19 deletion (VAF 10.0%). The 6 week follow-up CT demonstrated significant decrease in the largest lung mass (33.95*24.22mm->32.50*16.07mm). Repeat ctDNA NGS assay at one week after selpercatinib use showed disappearance of RET fusion and significant decrease in EGFR clone (VAF 10.0% to 0.05%). Conclusions: It has been reported that co-occurring RET fusions in NSCLC patients with EGFR mutations may contribute to acquired resistance to EGFR inhibitors. Several successful cases of cabozantinib, a non-selective RET inhibitor, or pralsetinib, a selective RET inhibitor combined with EGFR inhibitor, have been reported to aid in overcoming the acquired resistance to EGFR inhibitors. To date, there has been no report of clinical benefit in adding a RET inhibitor based on ctDNA detection of RET fusion with minute variant allele frequency. We for the first time report the case of overcoming acquired resistance to osimertinib by adding selpercatinib, a selective RET inhibitor in NSCLC patients with acquired RET fusion detected in ctDNA at VAF of 0.05%.
Circulating genetically abnormal cells combined with artificial intelligence for accurate and non-invasive early detection on NSCLC.
Authors: Yang Han , Chen Hongjie , Ni Ran , Zhang Guorui , Huang Yongjie , Ye Xin , Fan Xianjun , Kuang Yinglan , Zhang Juncheng , Huang Chuoji , Liu Hong 
Published: June 2021

3056 Background: Non-small-cell carcinoma (NCSLC) is the most common type of early lung cancer. Early detection of NSCLC is still a diagnostic challenge. Current clinical management of patients with pulmonary nodule is inefficient and may lead to misclassification, thus increasing healthcare expenses. Further, a few previous studies showed liquid biopsy and artificial intelligence (AI) platform on computed tomography (CT) imaging contributes to early NSCLC detection. Still, there is something lacking in a precise and authorized lung nodule classifier to minimize discomfort of patients. This study aims to evaluate the possibility and effectiveness of combining circulating genetically abnormal cells (CACs) with an AI platform on CT imaging to improve the diagnostic route for NSCLC. Methods: A prospective cohort of 101 in-patients was enrolled from Sep. 1, 2020 to Jan. 15, 2021, with non-calcified pulmonary nodules, ranging from 0.5 to 3 cm in diameter, indicated by CT. The participants' pulmonary nodules will be assessed by two evaluation tools: CAC detection on full blood and AI platform on CT imaging. The diagnostic performances of the two tools were evaluated in a blinded validation study respectively and combined in an open-label retrospective analysis. Results: 68 of enrolled patients were confirmed as NSCLC by pathology. The diagnostic performance of CACs for NSCLC detection was 80.9% for sensitivity and 87.3% for positive predictive value (PPV) while overall accuracy reaches 79.2%. AI platform showed a slight disadvantage as 78.6% for PPV and 73.5% for accuracy. 9 false-negative patients on CAC results could be reversed with a combination of AI platform on CT imaging, while sensitivity rises to 94.1%. However, of 33 benign nodules patients, 8 wrong diagnoses by CAC detection could decrease to 2 when combined with the results from the AI platform, which may avoid unnecessary biopsies. Conclusions: Coupling CAC with an AI platform on CT imaging could be a useful strategy to improve the diagnostic route for NSCLC and avoid unnecessary biopsies.[Table: see text]
Machine learning models to quantify HER2 for real-time tissue image analysis in prospective clinical trials.
Authors: Glass Benjamin , Vandenberghe Michel Erminio , Chavali Surya Teja , Javed Syed Ashar , Rebelatto Marlon , Sridharan Shamira , Elliott Hunter , Rao Sudha , Montalto Michael , Resnick Murray , Wapinski Ilan , Beck Andrew , Barker Craig 
Published: June 2021

3061 Background: Patient eligibility for HER2-targeting treatments is commonly informed by testing tumor HER2 expression using immunohistochemistry. As HER2 expression is visually assessed by pathologists, inter- and intra-rater variability might affect treatment decisions. Here, we report the development of an automated machine learning (ML)-based algorithm to quantify HER2 cell membrane expression across a diversity of breast cancer phenotypes as a clinical tool for monitoring HER2 testing quality. Methods: A total of 689 breast cancer tissue samples were either procured (Avaden Biosciences) or were anonymized samples from the AstraZeneca biobank comprising tissues from primary and metastatic tumors, core needle biopsies and surgical resections, lobular and ductal carcinomas, across tumor grades and HER2 expression levels. Samples were stained for HER2 detection (Ventana HER2 (4B5) Assay) and digitized (Leica Biosystems) across 5 laboratories in the US. Whole-slide images (WSIs) were stratified into training (n = 407), validation (n = 110), and test sets (n = 172). Multiple convolutional neural network based ML models (PathAI, Boston, MA) were trained using 190,000 manual annotations provided by 30 board-certified pathologists to identify artifacts, invasive tumor, identify individual cancer cells and measure tumor cell membrane HER2 expression as partial or complete, and negative, weak-or-moderate, or intense. Cell-level scores were validated against a consensus of manual cell counts from 5 independent pathologists in 320 representative regions of test set WSIs. HER2 scores were generated by automatically applying rules derived from 2018 ASCO/CAP guidelines and then compared in the test set with consensus scores from 3 independent pathologists. Results: Cell counts provided by the ML model were strongly consistent with cell counts obtained by pathologist consensus in all cell-types except for faintly positive HER2 cells where ML-based quantification identified more cells on average. Automatically generated ML-ASCO/CAP HER2 scores using WSI showed substantial consistency across IHC categories with the consensus of pathologists (ICC 0.88, 95%CI 0.82-0.92) in the test set and improved further when ML models were trained to agree with pathologists by adjusting cut offs (ICC 0.91, 95%CI 0.89-0.94). The ML-based model was deployed through the PathAI cloud platform to calculate HER2 testing quality control metrics in real-time in multicentric clinical trials. Conclusions: Automated image analysis of HER2-stained breast cancer tissues using ML-based models is consistent with pathologist consensus across breast cancer tissue types. The results support evidence that ML-based algorithms can help pathologists assess HER2 testing reproducibility in clinical trials.
Patient-derived micro-organospheres recapitulate tumor microenvironment and heterogeneity for precision oncology.
Authors: Ding Shengli , Wang Zhaohui , Obando Marcos Negrete , Palomino Grecia rivera , Rotstein Tomer , Williamson Ian , Hsu Shiaowen David , Shen Xiling 
Published: June 2021

3076 Background: Preclinical models that can recapitulate patients’ intra-tumoral heterogeneity and microenvironment are crucial for tumor biology research and drug discovery. In particular, the ability to retain immune and other stromal cells in the microenvironment is vital for the development of immuno-oncology assays. However, current patient-derived organoid (PDO) models are largely devoid of immune components. Methods: We first developed an automated microfluidic and membrane platform that can generate tens of thousands of micro-organospheres from resected or biopsied clinical tumor specimens within an hour. We next characterized growth rate and drug response of micro-organospheres. Finally, extensive single-cell RNA-seq profiling were performed on both micro-organospheres and original tumor samples from lung, ovarian, kidney, and breast cancer patients. Results: Micro-organospheres derived from clinical tumor samples preserved all original tumor and stromal cells, including fibroblasts and all immune cell types. Single-cell analysis revealed that unsupervised clustering of tumor and non-tumor cells were identical between original tumors and the derived micro-organospheres. Quantification showed similar cell composition and percentages for all cell types and also preserved functional intra-tumoral heterogeneity.. An automated, end-to-end, high-throughput drug screening pipeline demonstrated that matched peripheral blood mononuclear cells (PBMCs) from the same patient added to micro-organospheres can be used to assess the efficacy of immunotherapy moieties. Conclusions: Micro-organospheres are a rapid and scalable platform to preserve patient tumor microenvironment and heterogeneity. This platform will be useful for precision oncology, drug discovery, and immunotherapy development. Funding sources: NIH U01 CA217514, U01 CA214300, Duke Woo Center for Big Data and Precision Health
Does oxaliplatin pharmacokinetics (PKs) explain associations between body composition and chemotherapy toxicity risk in older adults with gastrointestinal (GI) cancers?
Authors: Williams Grant Richard , Al-Obaidi Mustafa , Rower Joseph , Harmon Christian , Dai Chen , Acosta Edward , Giri Smith , Zamboni William , Lucas Andrew T. , Shachar Shlomit Strulov , Gbolahan Olumide B. , Meyerhardt Jeffrey A. , Caan Bette Jane , Bhatia Smita 
Published: June 2021

3095 Background: Considerable inter-individual variability in oxaliplatin toxicity exists in older adults with GI cancers. Low lean body mass (LBM), commonly known as sarcopenia, influences toxicity and is not incorporated in standard body surface area-based dosing, which may affect oxaliplatin PK and tolerability, but has not been examined systematically. Methods: We examined oxaliplatin PK in 26 older adults (103 concentrations) with GI cancers (NCT03998202). Using the transverse section at L3, skeletal muscle area (SMA) and total adipose tissue (TAT) were quantified (Slice-O-Matic software) and LBM was calculated (LBM = 0.30 × SMA + 6.06). Noncompartmental methods (WinNonlin 7.0) were used for PK estimates and a one compartment population PK model (PopPK) was developed. Covariates included age, sex, LBM, TAT, weight, BMI, creatinine clearance, BSA, serum albumin, and body composition phenotypes (i.e. low LBM-high TAT, etc.). Results: Median age was 68yrs, 69% male, 88% white, and mostly colorectal (62%) and pancreatic (27%) cancers. There was wide variability in oxaliplatin volume of distribution (Vd: 12.5-259L), peak concentrations (Cmax: 404-3642ng/mL), and clearance (CL: 26.7-270L/hr). Participants with lower LBM had lower Vd (r = 0.51, p< 0.01); those with higher TAT had higher Cmax (r = 0.53, p< 0.01). Higher albumin was associated with lower Cmax (r = -0.49, p= 0.01) and higher CL (r = 0.47, p= 0.01). The phenotype of low LBM + high TAT had the lowest Vd (Relative Risk [RR] 0.32, p= 0.01), lowest CL (RR 0.39, p< 0.01), and highest Cmax (RR 3.3, 95% CI 1.7-6.5, p< 0.01). Eleven patients (44%) had grade 3-5 chemotoxicity. Vd (r = -0.46, p= 0.02) and Cmax (r = 0.44, p= 0.03) were associated with grade 3-5 chemotoxicity. The phenotype of low LBM + high TAT was associated with a 45% higher risk of grade 3-5 chemotoxicity (RR = 1.45, 95% CI 1.1-2.1, p= 0.04), while BSA was not (r = -0.04, p= 0.9). In the popPK model, body composition was associated with PK (TAT with Vd [p = 0.006] and CL [p < 0.001]), as was albumin (Vd p = 0.004; CL p = 0.002), while BSA was not (Vd p = 0.08; CL p = 0.2). Compared to BSA, an additional 11-17% in oxaliplatin PK variability was explained by LBM (11%), TAT (14%), and albumin (17%). Conclusions: Relationships between body composition, oxaliplatin PK, and severe chemotoxicity suggest the need for novel dosing strategies that incorporate body composition to reduce chemotoxicity and improve outcomes. Clinical trial information: NCT03998202.
A phase Ib study of the PI3Kδ inhibitor linperlisib in patients with advanced solid tumors.
Authors: Li Jin , Xu Nong , Liu Tianshu , Huang Jianjin , Yin Yongmei , Mou Haibo , Zhang Jun , Wu Lihua , Liu Zong liang , Bao Hanying , Xu Zusheng 
Published: June 2021

3099 Background: Phosphatidylinositol-3 kinase (PI3K) pathways are important elements of tumor survival and progression, and PIK3C genes are often mutated or overexpressed in many cancers. Additionally, PIK3D (PI3Kδ) modulates immune cell functions in tumors, elaborating another PI3Kδ inhibition feature with a potential clinical benefit. Linperlisib, an oral and highly selective PI3Kδ inhibitor, demonstrated potent anti-tumor activity in syngeneic animals from previous research. In this Phase 1b study, the safety, tolerability, and efficacy of linperlisib is under investigation for patients with advanced solid tumors. Methods: Linperlisib was given orally once daily (QD) in 28-day cycle until disease progression, unacceptable toxicity, or withdrawal from the study. Adverse events (AEs) were graded by NCI-CTCAE v5.0. Efficacy was assessed according to RECIST1.1 criteria. Results: As of December 28, 2020, 70 patients were enrolled in the Phase1b study, with advanced cancers, including colorectal (n = 22), breast (n = 8), lung (n = 8), kidney (n = 5), liver (n = 4), ovarian (n = 1), head and neck (n = 5), and esophageal (n = 1) cancers; sarcomas, (n = 4), small intestinal stromal tumor (n = 3), thymic (n = 2), gallbladder (n = 2), gastric (n = 4), and pancreatic (n = 1) carcinomas. The patients were heavily pretreated with an average of 4 previous lines of therapy. Among the 70 patients, the most common nonhematologic TEAEs (all grades/grade≥3) were proteinuria (37.14%/0%), elevated aspartate aminotransferase (20%/0%), nausea (20%/0%), oral mucositis (2.8%/2.8%), diarrhea (2.8%/2.8%). The most common hematological TEAEs were leukopenia (24.28%/0%) and neutropenia (17.14%/4.28%). There were no unexpected toxicities in this study. Of 42 patients evaluable for response, the overall response rate was 2.38%. Notably, the disease control rate (DCR) was 45.24% from monotherapy treatment. One patient with thymic carcinoma obtained a partial response (80.8% reduction of the target lesion), with a duration of response of more than 6 cycles. The treatment of this subject is continuing. A lung adenocarcinoma subject reached radiological stable disease associated with 13.7% reduction in the target lesion and disease control for approximately 6 months. Conclusions: In this study, the PI3K inhibitor, linperlisib exhibited a favorable safety profile as was previously seen in lymphoma patients. Monotherapy treatment with linperlisib was observed to impart a high DCR in advanced solid cancers of many types. Available data from linperlisib and other PI3K inhibitors suggests that linperlisib may limit tumor growth directly, but also by affecting the tumor immune microenvironment. With these promising indications of clinical tolerability and activity, further investigation of linperlisib alone or in key therapeutic combinations is warranted. Clinical trial information: NCT04049929.
A phase I study of an IDO inhibitor (SHR9146) plus camrelizumab and in combination with/without apatinib in patients with advanced solid tumors: Safety and efficacy analysis.
Authors: Cheng Ying , Liu Ying , Xu Jinhua , Zhu Jing , Wang Ying , Xin Ying , Wang Yanping , Wu Chunjiao , Cui Hongxia , Liu Xianhong , Dai Jixin , Li Zili , Zhang XiangHua , Zhang Jing , Zhang Ben 
Published: June 2021

3101 Background: IDO is an enzyme of interest in immuno-oncology because of the immunosuppressive effects that result from its role in tryptophan catabolism. Clinical trials of IDO inhibitors with immunotherapy are under active investigation. The addition of angiogenesis inhibitor may further enhance the anti-tumor immune responses. Here we report the safety and efficacy results of SHR9146 (IDO inhibitor) plus camrelizumab (PD-1 antibody) with/without apatinib (VEGFR-2 inhibitor) in patients (pts) with advanced solid cancers who failed standard antitumor therapies. Methods: This was an open-label, phase I study. Eligible puts would receive SHR9146 (escalated dose) plus camrelizumab (200 mg IV, q2w) alone (Cohort A) or in combination with apatinib (250 mg p.o. qd) (Cohort B). Each cohort was conducted according to a 3+3 dose escalation design. The starting dose of SHR9146 was 100mg bid, followed by 200, 400, 600 mg bid. The two primary endpoints were Dose-limiting Toxicity (DLT) and Maximum Tolerated Dose (MDT). The secondary objective was to analysis the incidence of Adverse Events (AEs) and efficacy. Results: As of Oct 31, 2020, 23 pts have been enrolled (Cohort A:14, Cohort B: 9; median age: 54 years; median prior therapies: 2 lines;). Cohort A was escalating at 600mg, and Cohort B was escalating at 400mg. Two pts experienced DLTs: one DLT (G4 hypercalcemia) was observed at 600mg in Cohort A; the other DLT (G3 rash) was observed at 400mg in Cohort B. MDT was not reached and the study was still ongoing. In Cohort A, ORR and DCR in evaluable pts were 21.4% (3/14, all confirmed) and 42.9% (6/14). Partial response was observed in 3 pts with liver cancer (1/3), renal cancer (1/3), and cervix cancer (1/3). In Cohort B, ORR and DCR in evaluable pts were 33.3%(3/9, all confirmed) and 77.8%(7/9). Partial response was observed in 3 pts with SCLC (1/3), prostate cancer (1/3) and renal cancer (1/3). The incidence of pts with TRAEs and grade>=3 TRAEs were 91.3% (21/23) and 39.1% (9/23) respectively. The most common grade>=3 TRAEs were hypercalcemia (26.1%, 6/23), fatigue (17.4%, 4/23) and nausea (13.0%, 3/23). No fatal AEs were observed. G3 nausea, G3 lipase increased and G2 GGT increased resulted in SHR9146 dose reduction in 3 pts (Cohort A). Conclusions: SHR9146 plus camrelizumab in combination with/without apatinib demonstrated promising anti-tumor activity with acceptable safety in pts with advanced solid tumors. Further study is needed to validate the efficacy and safety. Clinical trial information: NCT03491631.