bluuebunny/crossref_metadata_2025_split huggingface dataset:
Efficacy and safety of surufatinib combined with EP regimen and serplulimab in first-line treatment of NEC.
Authors: Zhang Tao , Lin Zhenyu , Zhao Lei , Ma Hong , Zhang Dejun , Yu Dandan , Jin Min , Wang Jing , Liu Hongli 
Published: June 2024

e15123 Background: Neuroendocrine carcinomas (NEC) are more challenging than neuroendocrine tumors (NET) due to poor differentiation, aggressive invasion, and early diagnosis difficulties, often marked by local and distant metastasis at diagnosis, leading to poor outcomes. The ORR for NEC as first-line treatment with EP or EC regimens is approximately 30%, highlighting a substantial treatment gap. Surufatinib, a targeted small molecule kinase inhibitor, acts on vascular endothelial growth factor receptors (VEGFR-1/2/3), fibroblast growth factor receptor (FGFR-1), and colony-stimulating factor-1 receptor (CSF-1R). Its efficacy focuses on anti-angiogenesis and vascular normalization, alongside its role in modulating tumor-associated macrophages (TAMs) and boosting the effectiveness of immunotherapy. Methods: This single-arm, open-label, multicenter clinical study comprised a safety run-in (SR) phase and a dose-expansion (DE) phase. In the SR phase, six patients (pts) were planned to be enrolled to receive treatment with surufatinib (250 mg, QD, PO, Q3W) combined with the EP regimen (etoposide injection: 100mg/m2, d1-3, IV, Q3W; cisplatin: 25mg/m2, d1-3, IV, Q3W) and serplulimab (300mg, d1, IV, Q3W) for a maximum of six cycles, followed by a maintenance phase of surufatinib plus serplulimab until disease progression or other criteria as specified in the protocol. The dose of surufatinib in the DE phase was based on results from the SR phase. The primary endpoints were recommended Phase 2 dose (RP2D) and PFS, while secondary endpoints included ORR, DCR, OS, and safety. Results: As of December 27, 2023, this reports results from the SR phase. Six pts, median age 61.5 years (range 38-70), 66.7% male, all with ECOG performance status 1, were enrolled and completed their first treatment cycle. A majority (83.3%) had gastrointestinal-origin tumors with a median Ki-67% index of 70% (range 30%-90%). During the SR phase, one patient experienced Grade 4 hematologic toxicity (neutropenia and leukopenia) as a dose-limiting toxicity (DLT). Consequently, RP2D of surufatinib was established at 250mg for further research in the DE phase. The most common adverse events were neutropenia (50%), decreased white blood cell count (33.3%), fatigue (33.3%), and diarrhea (33.3%). Among those enrolled in the SR phase, 5 pts underwent at least one assessment of efficacy, yielding an objective response rate (ORR) of 60% and a disease control rate (DCR) of 100%, with the median progression-free survival (mPFS) not yet mature. Conclusions: Surufatinib combined with the EP regimen and serplulimab for NEC as first-line treatment showed notable efficacy and safety. The study established a phase II recommended surufatinib dose of 250mg, alongside immunotherapy and chemotherapy. Clinical trial information: NCT05747729 .
Prognostication of ß-catenin (CTNNB1) in patients with HCC treated with first line immunotherapy.
Authors: Shah Priyanshi M. , Peersen Anina , Jatoi Aminah , McWilliams Robert R. , Jin Zhaohui , Torbenson Michael S. , Halfdanarson Thorvardur Ragnar , Babiker Hani M. , Conboy Caitlin , Kankeu Fonkoua Lionel Aurelien , Washburn Leslie , Roberts Lewis R. , Gores Gregory J. , Bekaii-Saab Tanios S. , Borad Mitesh J. , Ou Fang-Shu , Tran Nguyen H. 
Published: June 2024

e15146 Background: Wnt/ß-catenin signaling dysregulation is found in approximately 25% of hepatocellular carcinoma (HCC) patients. The impact of B-catenin activation on HCC overall survival has remained controversial. Prior studies suggest activating CTNNB1 mutations may predict for immunotherapy (IO) resistance while others did not corroborate this finding. Here we describe the clinical outcomes of advanced HCC patients (pts) with CTNNB1 (+) and without CTNNB1 (-) mutations treated with novel first line IO/anti-VEGF therapies. Methods: We conducted a multicenter, retrospective analysis of pts with HCC who had molecular analysis performed between 2019 and 2023 at Mayo Clinic in Minnesota, Arizona, and Florida. The study was reviewed and approved by the IRB. Clinical and molecular features were compared descriptively. The primary outcomes were time to treatment failure (TTF), defined as time from first-line treatment initiation to off treatment or death, and overall survival (OS), defined as time from first-line treatment initiation to death, compared between the two cohorts using the Kaplan-Meier method. Results: We identified 29 CTNNB1+ and 62 CTNNB1- (wt) pts with HCC. Median age at diagnosis was 66 (range 30-86), 80 (88%) were Caucasian, 68 (75%) were male gender, and 22 (24%) had etiology as hepatitis B/hepatitis C, 82 (90%) were Child-Pugh A and 77 (85%) were Barcelona Clinic Liver Clinic (BCLC)=C at diagnosis. Eighteen (90%, n=20) CTNNB 1+ pts received first-line IO (Atezolizumab/bevacizumab [AB]) vs 39 (72%, n=54) in CTNNB1- pts. All pts were microsatellite stable (MSS). CTNNB1+ had a similar median tumor mutational burden (TMB), higher rate of TERT co-mutation, and lower rate of TP53 co-mutation (table) compared to CTNNB-. Of 57 AB pts, 5 (27%) of CTNNB1 + had an objective response, compared to 6 (16.7%) CTNNB1- pts. Time to treatment failure with first-line AB and overall survival were similar between the two cohorts (Table). Conclusions: Our study suggests CTNNB1 status is not prognostic in HCC pts receiving IO based therapy. Prospective study is warranted to further investigate predictive biomarkers for HCC sensitivity to immune checkpoint inhibitors and VEGF-targeted therapy. [Table: see text]
A multi-modal artificial intelligence model trained on H&E tissue images and clinical patient data for prediction of BRAF mutations in colorectal cancer.
Authors: Paepper Marc , Frey Patrick , Mulder David , Daifalla Khalid , Faber Felix , Lang Tobias 
Published: June 2024

e15161 Background: Analyzing genetic mutations is essential for personalized therapy decisions in oncology and beyond. In colorectal cancer (CRC), mutations in BRAF ( BRAFmut) render patients eligible for targeted treatment with BRAF inhibitors and immunotherapy. BRAFmut is routinely analyzed via genomic sequencing, which, however, is costly and does not provide diagnostic results immediately. Reliable detection of BRAFmut status from histological images using artificial intelligence (AI) could provide a more accessible and faster diagnostic alternative. Previous approaches to image-based BRAFmut analysis, however, failed to reach a critical level of accuracy on independent test cohorts, with reported area under the receiver operating characteristic curve (AUROC) values typically below 0.8. Methods: We hypothesized that combining tissue image analysis with clinical patient data could improve diagnostic accuracy for BRAFmut. Following our hypothesis, we developed a multi-modal AI model on n = 426 CRC cases from the TCGA database using BRAFmut data, H&E-stained tissue images, and information on patient parameters that are readily available in clinical practice and that are known to be associated with BRAFmut frequency. The model was based on a vision transformer architecture incorporating pathology-specific information using a pretrained embedding model for encoding tissue image features. Model performance was validated on an independent hold-out TCGA cohort of n = 107 samples and an additional external cohort of n = 104 CRC samples from the CPTAC database. Results: Our multi-modal AI model reached an AUROC of 0.85 ± 0.02 on the TCGA hold-out test set. Notably, on the second, external CPTAC dataset the predictive accuracy level was similar (AUROC of 0.83 ± 0.02) indicating the robustness of our approach to generalize to different datasets. Additional analyses showed that the significant performance boost over previous approaches was gained by the multi-modal analysis setup, as predicting BRAFmut based on either tissue, or clinical data alone led to lower accuracy values. Explainability analysis of AI results confirmed that relevant biological features on the histological slides, mainly tumor areas, were considered by the model for decision making. Conclusions: The combination of H&E images with clinically readily available patient parameters enabled us to develop a model predicting BRAFmut in CRC that surpasses previous approaches in diagnostic accuracy on independent evaluation cohorts. This indicates that combining histological tissue information with clinical parameters can increase the ability of AI models to predict genetic variants from tissue and more generally highlights the potential of multi-modal deep learning approaches to enhance predictive AI models in digital pathology.
NTRK positivity and conversion to appropriate personalized treatment.
Authors: Slifko Bethany Michelle , McLeod Howard L. , Moore Wendy , Abrunhosa Amaral Inês , Munksted Susanne 
Published: June 2024

e15163 Background: The field of precision medicine continues to grow in size and complexity. In oncology alone, the FDA has approved companion diagnostics for 53 unique biomarkers across 28 different indications linking to 59 separate drugs to treat cancer patients beyond traditional options (FDA 21 December 2023). Two of these drugs, entrectinib and larotrectinib, are approved across all tumor types for NTRK fusions, which while rare in common cancer indications are more common in understudied, rare cancers (Cocco et al Nature 2018). These treatment options have significant outcomes for patients, however, there is still a large barrier for patients to access the right treatment (Sadik et al JCO 2022). We hypothesized not all patients with identified NTRK alterations are being prescribed these treatments and assessed this in real-world data for US oncology patients. Methods: Diaceutics proprietary DXRX database contains real-world claims, specialty pharmacy and laboratory data that enables the identification of patients positive for biomarker alterations and resultant treatment pathways in the US. The result-level laboratory data was filtered to solid tumor patients who were positive for NTRK alterations in the 2-year window between 2022 and 2023. The identified NTRK positive patients were then tokenized and joined to our medical claims and pharmacy data to assess which treatment combinations these patients received. Results: A total of 4,251 cancer patients were identified as having tumors with NTRK alterations. Of the patients that could be linked to treatment (800 patients), 1.5% (n = 12) patients went on to receive a targeted treatment for their NTRK alteration, either entrectinib (n = 8), larotrectinib (n = 3), or both entrectinib and larotrectinib (n = 1). When not prescribed either of these, 43.25% (n = 346) patients received different targeted treatment, possibly because of other alterations identified. There were still 55.25% (n = 442) receiving no targeted treatment, either being prescribed chemotherapy (chemo) alone (28.25%, n = 226), immunotherapy (IO) alone (21.1%, n = 169), or an IO/chemo combination (5.9%, n = 47). Conclusions: Despite the established significant disease control from targeted NTRK treatments, most patients with these alterationsdo not have an opportunity to benefit from the most effective personalized treatment for their cancer. These findings may suggest that targeted therapy for rarer and/or tissue agnostic alterations may get deprioritized or overlooked. Therefore, we advocate that a new, different approach for bringing targeted treatments for rare biomarkers to patients is still needed. Different economic models and an optimized approach to engage and inform key stakeholders including oncologists of the presence and benefit of targeted treatments is critical and needs further investigation.
Utilizing comprehensive next-generation sequencing to determine the clinical significance of copy number variation in advanced/metastatic non-small cell lung cancer.
Authors: EL Helali Aya , Ko Elaine Yee-Ling , Wong Charlene Hoi Lam , Shih David Jen-Hao , Wong Jason , Chiu Matthew , Lee Victor Ho-Fun 
Published: June 2024

e15174 Background: Large population-based comprehensive tumor genomic profiling using next-generation sequencing (NGS) technology offers insight into the detection of copy number variation (CNV) in advanced/metastatic solid cancers. However, the significance and role of CNV in advanced-stage non-small cell lung cancer (NSCLC) is limited. In this analysis, we investigated the incidence and impact of CNV on patient outcomes. Methods: Patients were recruited as part of an ongoing real-world multicentre prospective NSCLC Precision Oncology Programme in Hong Kong. All patients underwent tissue and liquid NGS using Foundation One CDx and Foundation One Liquid CDx, respectively. Patients were classified into two groups based on CNV status: group 1 (CNV+) was defined as tumors with CNV reported; group 2 (CNV-) was defined as tumors with no CNV reported. The association of clinical factors with CNV was reported using Chi-square tests. The overall survival (OS) was calculated from the date of diagnosis of advanced-stage cancer to the date of death or the last follow-up date. Survival analysis was estimated using the Kaplan-Meier method with the log-rank test. Results: 353 patients were included in this analysis. NGS was performed in 216 (61%) tumor tissues, and 137 (39%) liquid specimens. 37% (n = 129) were female, and 50% (n = 175) were ever-smoker. 63% (n = 223) of our patient cohort harbored druggable mutations. The detection rate of CNV was 74% in tissue and 14% in liquid specimens. Patients with brain, liver, or bone metastasis (p = 0.018) and/or TMB-high (TMB-H) (p = 0.0014) were associated with CNV+. Next, we assessed the impact of CNV on treatment outcomes. Among patients who received immunotherapy ± chemotherapy, CNV+ was associated with a median OS of 20.4 months compared to 31.4 months in the CNV- group (HR:2.09, 95% CI: 1.05-4.16, p = 0.034). In contrast, there was no statistically significant difference in OS among the CNV+ and CNV- patient groups treated with chemotherapy alone. Although not significant, there was a trend towards poorer OS to immunotherapy ± chemotherapy in the TMB-H CNV+ cohort (23.6 months versus 31.4 months; p = 0.082). Conclusions: We demonstrate the significance of CNV as a prognostic tool for the prediction of survival outcomes among lung cancer patients. Our data suggests that CNV represents a distinct entity in advanced/metastatic NSCLC, associated with poorer survival outcomes to immunotherapy.
Reasons for biomarker testing in NSCLC: An analysis of reasoning between 2021-2023 in early- vs. late-stage patients.
Authors: Levent Ayse , De Richter Pieter 
Published: June 2024

e15175 Background: Given the number of targeted therapies that are approved for non-small lung cancer (NSCLC), timely testing for biomarkers is critical to ensure eligible patients benefit from precision medicines. This study uses real-world data to explore how factors associated with routine and reflex testing in the US are influencing the decision to test for biomarkers. Methods: Ipsos’ MDx Monitor is a multi-stakeholder, physician-reported syndicated patient record database. Cancer-treating physicians in the US (2021 = 56, 2022 = 64, 2023 = 44) were screened for seniority and caseload and submitted data on 414 NSCLC patients (aggregate) who had been tested for biomarkers. Data were collected online in September-October of each year. Results: Comparing sampled physician-cited reasons for testing over time, standard procedure was mentioned more frequently in the reported 2023 cohort (87% in 2023 vs. 62% in 2021) as was lab performs this test as reflex (18% in 2023 vs. 2% in 2021). Testing because of patient histology or to inform treatment decision declined from 2021 to 2023. Reasons that were mentioned consistently more frequently among the reported early-stage patient cohorts vis-à-vis metastatic patients included histology, to inform treatment decision and, notably, patient request. The top cited reasons are shown in Table 1. Conclusions: Results from this study indicate that physicians’ approach to biomarker testing is evolving towards more routine procedures, rather than testing to inform a specific treatment decision, marked by a recorded increase in reflex and broader panel-based testing. Although guidelines focus on assessing metastatic patients’ eligibility for targeted therapies, early-stage patients also stand to benefit from more automated testing processes. Further investigation on time between diagnosis and implementation of biomarker testing, along with subsequent treatment outcomes, is warranted. [Table: see text]
INSIHGT: A scalable and homogeneous 3D multiplexed molecular mapping platform for spatial biology and precision medicine.
Authors: Lai Hei Ming , Chow King Ngai , Hung Tin Shing , Yau Chun Ngo , Chan Shuk Ling 
Published: June 2024

e15180 Background: The dynamic interplay between cancer cells and their microenvironment is a determinant of disease evolution and response to treatment. However, the current state of three-dimensional (3D) tumor microenvironment analysis is hindered by inadequate penetration of antibodies into thick tissue sections, leading to a substantial gap in acquiring unbiased, precise 3D multiplexed immunohistochemistry (IHC) images. Methods: We developed INSIHGT to surmount these limitations by enabling multi-round, multiplexed 3D IHC in standard formalin-fixed tissues. This fully automatable, fast-track method requires a simple series of chemical cocktail exchanges, translating to a tissue-to-image time of 6 days for 1 cm³ samples and as little as 6 hours for biopsy specimens. INSIHGT offers a scalable and efficient solution for detailed 3D spatial mapping of cancerous cells and their interactions within their native context. Results: 3D IHC with INSIHGT shows ideal penetration homogeneity, which quantitatively matches reference signals in 1cm3 samples. We further validated INSIHGT with 309 commercially available primary antibodies and is compatible with simultaneous fluorescent in situ hybridization (FISH) to detect RNA transcripts or DNA chromosomal abnormalities. Notably, the quality of post-INSIHGT traditional histological analyses, such as H&E and standard IHC, was not altered. We demonstrated a 3D mapping of over 20 markers in a 1 mm-thick tumor sample across seven INSIHGT cycles, and an atlas of multiple tumour samples with 3D IHC & H&E. The large datasets seamlessly integrates with single-cell segmentation and analysis workflows, yielding comprehensive maps that elucidate protein expression, cell morphology, spatial positioning, and potential cell-cell interactions. Conclusions: INSIHGT provides an unparalleled level of visualization and analytical detail concerning the spatial dynamics within the tumor microenvironment at a single-cell resolution. As a transformative next generation histopathology platform, it can uncover new structural and molecular details within clinical specimens, accelerating biomarker discovery and novel diagnostic assays development, thereby enriching the field of tumor biology and propelling the progress of personalized oncology treatments.
Using sample reinterrogation to guide high-parameter, spatially resolved tissue biomarker discovery.
Authors: Schwarz J. Spencer , Berigan Benton , Ingalls Matthew , Meshik Xenia , Jackson Charles , Cashion Kayla , Christians Arne , Brix Anke , Boettcher Marcus , Braubach Oliver 
Published: June 2024

e15182 Background: High-parameter, spatially resolved phenotyping can accelerate the discovery of tissue biomarkers as well as reveal emergent disease indicators like cellular neighborhood architecture. These spatial biology techniques hold high potential as diagnostic and prognostic indicators yet can be challenging to deploy due to high cost and lack of standardization. We have developed a powerful spatial biology workflow based on multiplex cyclic immunofluorescent (IF) staining using validated markers and assay kits that allows the researcher to adjust the markers included in the assay with feedback from marker expression in previous rounds. We term this sample re-interrogation. Here we present an illustration of this new workflow. Methods: Three FFPE lung biopsy specimens were repeatedly assayed for biomarker expression using CellScape™ Precise Spatial Multiplexing paired with VistaPlex™ Assay Kits. In this workflow, tissues are mounted in enclosed fluidic chambers. IF Staining and imaging were conducted on the CellScape instrument as iterative rounds. Between each imaging round, we conducted analyses on data from previously collected rounds. Conclusions drawn between staining rounds then guided decisions of whether and how to continue the experiment. Results: Each sample provided evidence for the utility of staged rounds of tissue staining, analysis, and rational assay planning. Two FFPE lung samples from the biopsy were not considered for further iterative staining after initial interrogation for consensus markers resulted in poor expression ( e.g.: CD4, CD20, CD45, pan-cytokeratin). One of these two samples was stained with markers indicative of cell death pathways and fibrosis to provide a positive explanation for initial poor staining. A third lung tissue showed punitive indication of tertiary lymphoid structure (TLS) inclusion. Further rounds of staining were conducted to iteratively explore the nature of these punitive TLS structures. Each round of additional stains was chosen based on data derived from the previous staining series following analysis and deliberation among the biomarker team. Conclusions: The data illustrate that even among tissue sections taken from the same biopsy, a specimen’s unique biomarker expression profile may require adjustments to assay parameters. Two of the FFPE sections proved unacceptable for further investigation after only a handful of stains. This finding highlights the benefit of rational reinterrogation, as the saved resources in not staining these tissues could measure in the thousands of dollars in reagents alone. The third lung tissue specimen offers an example of a tailored spatial biology assay through iterative reinterrogation. All included biomarkers were judged best suited to elucidate the next round of staining, representing an optimized and flexible spatial biology workflow.