bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessing the prognostic value of transcriptomic data in multiple myeloma through machine learning methodologies.
Authors: Xu Xuan , Raza Shahzad , Ramachandran Remya Ampadi , Anwer Faiz , Tiger Yun Kyoung Ryu , Riviere Jim , Jaberi-Douraki Majid 
Published: June 2024

7513 Background: Multiple myeloma (MM) is a hematological malignancy characterized by the clonal proliferation of plasma cells in the bone marrow. Identifying the characteristics within transcriptomic data of MM patients could be imperative for elucidating long-term cancer prognosis. Prediction and bioinformatic evidence could be valuable for therapeutic intervention. Methods: To discover a precise collection of biomarkers, a large-scale disproportionality analysis of MM patients using the FDA Spontaneous Reporting System database is conducted. This analysis aims to provide pharmacovigilance evidence regarding severe adverse events (AEs) due to medications. Additionally, pharmacogenomic insights from OMIM and MM drug targets are integrated into the analysis to carefully select 84 essential biomarkers. Seven different machine learning (ML) algorithms, namely AdaBoost, K-Nearest Neighbors (KNN), Decision Tree (DT), Gaussian Naïve Bayes (GNB), Support Vector Machine (SVM), Random Forest (RF), and Multilayer Perceptron (MLP) were then employed to assess the classification of disease outcomes. Specifically, the focus was on distinguishing deceased patients (due to MM) from surviving patients in the MM Research Foundation (MMRF) dataset based on the identified essential biomarker. Results: The classification involved a total of 787 patients from MMRF (624 alive and 163 deceased). Four sampling strategies (original raw data, undersampling, oversampling, and simultaneous sampling) were included in the training and testing process on ML models to alleviate the influence of imbalanced data. SVM achieved the highest accuracy while RF achieved highest mean across four sampling methods (Table). We then performed the Kaplan-Meier survival analysis of the prognostic values of essential genes. The expression of two genes (BRAC1 and CTLA4) from MMRF data when comparing deceased to survival patients was associated with patient outcomes. Additionally, seven genes were potentially prognostic factors in MM (Positive prognostic: ATM, CYBA, NR3C1, PIK3CA, and PIK3CG; Negative prognostic: IFNG and NTRK2). Conclusions: This study successfully showcased a computational methodology that holds potential for optimizing future clinical trials in MM. The approach includes identifying the most suitable patient population for a particular MM regimen, thereby contributing to improved patient care. It also offers strategies for reducing AEs, lowering mortality rates, and ensuring the optimal allocation of healthcare resources. [Table: see text]
Access to palliative care in patients with multiple myeloma in the USA: A National Cancer Database (NCDB) analysis of years 2004-2020.
Authors: Saba Ludovic , Fu Chieh Lin , Sarna Kaylee , Chaulagain Chakra Pani 
Published: June 2024

7563 Background: Palliative care (PC) can improve the quality of life for patients with multiple myeloma (MM). Despite its recognized importance, research gaps persist in understanding factors influencing PC accessibility and utilization, particularly in the diverse MM patient landscape in the real-world setting. This study explores potential associations between race, ethnicity, socioeconomic factors, and PC access for MM patients in the USA. Methods: Utilizing the NCDB, we identified 202,949 MM patients diagnosed from 2004 to 2020. Multivariate logistic regression analysis was conducted with SAS version 9.4, to determine independent factors predicting PC access. Results: Analysis of 202,949 MM patients revealed notable disparities for access to PC. Female patients exhibited lower PC odds (OR: 0.94, p < 0.0001) compared to male patients, as did Black patients (OR: 0.80, p < 0.0001) and other racial groups (OR: 0.84, p < 0.0001) compared to White patients. Hispanic ethnicity was associated with lower PC utilization odds (OR: 0.84, p < 0.0001) compared to non-Hispanics. Earlier years (2004-2007) showed higher PC odds (OR: 1.12, p < 0.0001), while subsequent years (2008-2011, 2012-2015) exhibited lower odds (OR: 0.93, p = 0.0009; OR: 0.87, p < 0.0001, respectively) compared to the most recent years (2016-2020). Academic settings had lower PC odds than non-academic facilities (OR: 0.78, p < 0.0001). Insurance status varied, with no insurance (OR: 1.40, p < 0.0001), Medicaid (OR: 1.17, p < 0.0001), and other government insurance (OR: 1.38, p < 0.0001) linked to higher PC odds compared to private insurance. Lower annual household incomes (<$38,000, $38,000-$47,999, $48,000-$62,999) had increased PC odds compared to higher incomes (>$63,000) (all p < 0.0001). Areas with a higher percentage of individuals without a high school degree exhibited lower PC odds, notably 21% (OR: 0.83, p < 0.0001) and 13.0%-20.9% (OR: 0.94, p = 0.0222) compared to <7.0% without a high school degree. Greater distance (>30 miles) reduced PC odds compared to shorter distance (<10 miles) to treatment facility (OR: 0.83, p < 0.0001). A Charlson-Deyo score of 1 increased PC odds compared to a score of 0 (OR: 1.12, p < 0.0001). Conclusions: Our real-world analysis reveals substantial disparities in MM patients’ PC access and utilization. Female patients and minority groups (Blacks and Hispanics) face significant challenges. The dynamic trends over time and socioeconomic impacts underscore the need for integrating PC and targeted interventions for overall MM patient well-being. Our findings pave grounds for future prospective studies.
Neoadjuvant and perioperative immunotherapy in resectable non-small cell lung cancer (NSCLC): A systematic review and extracted individual patient data meta-analysis.
Authors: Trinconi Cunha Mateus Trinconi , Sperandio Rubens Copia , Chan Kelvin K. , Menjak Ines B. 
Published: June 2024

8045 Background: Neoadjuvant and perioperative immunotherapy are emerging approaches for resectable NSCLC. However, survival outcomes are still immature and subgroup analyses are underpowered. We report extracted individual patient (pt) data (eIPD) and trial-level (TL) meta-analyses of phase II and III randomized controlled trials (RCTs) in this setting. Methods: The systematic review included Cochrane, Embase, and major oncology conferences (ASCO annual meetings, ESMO meetings, WCLC meetings). Primary objectives were eIPD event-free survival (EFS) and overall survival (OS). Secondary objectives included eIPD and TL meta-analysis of subgroups. Kaplan-Meier plots of time-to-event outcomes were reconstructed with WebPlotDigitizer (v4.6, 2022), and eIPD was estimated with IPDfromKM (v0.1.10, 2020) stratified by study. Comparisons between arms were made in a 1-stage model with a Cox Proportional Hazards model stratified by study. The difference of restricted mean survival time (D-RMST), a quantification of the postponement of an event during a specified interval, was used to compare survival when the proportional hazard assumption (PHA) was violated, tau being the shortest follow-up time of the available trials. TL meta-analysis was performed with a random effects model. This meta-analysis was registered in the PROSPERO database under CRD42024502150. Results: Seven RCTs (AEGEAN, CM816, CM77T, KN671, NADIM II, NeoTorch, and Lei et al. [ESMO IOTECH 2022, #56O]) were identified, comprising 2995 pts. EFS analysis included all pts, with HR of 0.58 (0.52-0.65; p<0.01). OS analysis (1645 evaluable pts from 4 trials) showed a D-RMST of 5.17 mo. (p<0.01) due to PHA violation. Full eIPD analysis in the table. TL meta-analysis showed benefit in all subgroups, including PD-L1 expression, histology, stage, smoking status, sex, and age. TL EFS and OS results were comparable to results obtained from eIPD analysis, with low heterogeneity measures. Conclusions: This meta-analysis provides robust and nuanced insights into the positive impact of immunotherapy in resectable NSCLC. While evidence supports its efficacy, uncertainty surrounding the benefit in stage < III disease highlights the need for additional research and more mature results to guide clinical decision-making effectively. [Table: see text]
Annotations-free survival prediction with WSIs using graph convolutional neural networks.
Authors: Kong Qianqian , Li Ruilei , Zhang Jiaran , Li Ke , Ge Chunlei , Yan Xieqiao , Yao Hong , Guo Jun , LI Chen 
Published: June 2024

e16501 Background: Survival prediction of cancer patients has always been an challenging problem.Tumor microenvironment (TME) Analyzation based on whole-slide-images (WSIs) has provide an effective perspective for survival prediction. However, most existing TME analyzation based on cell segmentation or classification relies heavily on labor-intensive cell-level annotations of pathologists. Furthermore, except for each individual cell or local pathological feature, survival prediction also involves local-level pathological features interactions in tumor microenvironments. This requires context-awareness based on histological features to fully infer the patient's survival risk. Therefore, we explored a model based on graph convolutional neural networks (GCNN) to perform survival prediction of cancer patients using WSIs. Methods: We utilized WSIs collected from The Cancer Genome Atlas (TCGA) to develop a graph convolutional neural network for survival prediction of cancer patients. The model leverages the advantages of graph structures to autonomously learn the histopathological contextual features in WSIs, and therefore can incorporate additional and crucial tumor microenvironment interaction information while avoiding the labor-intensive annotations. WSIs of two different cancers, KIRC and LUAD, were randomly divided into training, validation, and testing sets in a ratio of 7:1:2. The performance of the constructed model is evaluated using the test set and the results are compared with other state-of-art methods. Results: Our work is compared with other state-of-art weakly supervised learning methods for survival prediction in computational pathology. Abundant experimental results shown that our method outperformed previous methods on these two cancer types (achieving a 2.9% improvement compared to Multiple Instance Learning (MIL) and a 2.6% improvement compared to Attention MIL), with an overall c-index of 0.646. Additionally, we evaluated the interpretability of our model through attention heatmaps of low-risk and high-risk patients. Conclusions: We have developed a GCNN based model, combined with attention mechanisms, to learn features of heterogeneous tumor microenvironments and their contextual information from memory-efficient representations of highly correlated image patches for cancer patients survival prediction. This model is applicable to any weakly supervised learning task in computational pathology that involves slide-level or patient-level labels, making it an effective supplementary diagnostic tool for oncologists and pathologists.
Prediction of immune checkpoint inhibitor (ICI) response in patients with urothelial cancer (UC) by a novel RNA-based ICI response signature (ICI-PRS).
Authors: Shepherd Jonathan , Mayhew Gregory , Guo John , Beebe Kirk , Eisner Joel Robert , Milburn Michael Vance 
Published: June 2024

e16502 Background: Immune checkpoint inhibition (ICI) shows great promise, including approval of pembrolizumab, nivolumab and avelumab in urothelial cancer (UC). However, the overall percentage of UC patients with clinical benefit is relatively small, despite biomarker use to select patients (e.g., PD-1 expression and TMB). Recently, several accelerated approvals (atezolizumab and durvalumab) were subsequently withdrawn following failure to show improved survival compared to standard of care (SOC) treatment in Phase 3 +/- biomarker use. To address the need for better ICI biomarkers, we developed an RNA-based predictive response signature (ICI-PRS) with the primary objective of being prognostic for ICI-treated patients, but not for SOC treatment (Shepherd et al, 2024 ASCO GU). ICI-PRS uses a novel ICI-specific survival signature with tumor-derived and immune associated features to evaluate the likelihood of responding and having longer survival with ICI therapy independent of non-ICI therapy. Herein we report the initial clinical validation of the ICI-PRS using independent datasets of patients with UC treated with ICI. Methods: RNA expression and clinical data were retrospectively evaluated in two independent cohorts of patients with UC treated with ICI: Bladder Cancer Advocacy Network (BCAN) UC-GENOME cohort (dbGaP phs003066.v1.p1 and cBioPortal (n=105) [PMID36333289]) and BACI cohort (Gene Expression Omnibus (GEO;GSE176307 (n=84 for patients treated with >1 cycle) [PMID: 34294892])). ICI-PRS was applied and an optimal cutoff value to designate binary response prediction (complete response + partial response (PR/CR) vs. stable disease + progressive disease (SD/PD)) was derived with Youdin’s method using the receiver operator characteristic (ROC) curve of the original IMVigor210 training set. Results: Samples predicted to be ICI-PRS positive show consistent associations with overall survival (OS) when receiving ICI treatment. A lower hazard ratio (HR) was found for both BACI [HR (95% CI): 0.455 (0.24-0.86); and BCAN 0.540 (0.30-0.98)]. Response to ICI therapy was also associated with ICI-PRS scores, as patients achieving either CR or PR had significantly lower ICI-scores than those with SD or PD in both BACI (p=0.001) and BCAN (p=0.012). Conclusions: Herein we describe initial clinical validation of ICI-PRS in two independent cohorts of UC patients treated with ICI. It provides ICI-treatment specific prognostic and predictive value for patients with UC and may have application alone or in combination with other signatures when determining treatment sequencing or rational combination strategies. These results support further evaluation of the ICI-PRS and potential development as a diagnostic test for selecting UC patients most likely to benefit from ICI therapy either alone or in combination with non-ICI therapy.
Real-world survival outcomes associated with immunotherapy followed with anti-vascular targeted therapy for metastatic clear cell renal cell carcinoma.
Authors: Zhang Hui , Hou Zhiwei , Lai Long , Wu Huaguo 
Published: June 2024

e16507 Background: The Food and Drug Administration (FDA) has approved a variety of drugs for the treatment of advanced metastatic renal cell carcinoma, including anti-vascular tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs). Options for initial therapy range from monotherapy to combination therapy. However, the challenge of choosing the most effective first- and second-line treatments to enhance overall survival persists. Methods: In this retrospective, propensity-matched cohort study, we included 135 patients with metastatic clear cell renal cell carcinoma (mRCC) treated at Shandong Cancer Hospital between January 1, 2017, and December 31, 2022. These patients received ICIs as part of their first or second line of therapy. Statistical analysis was conducted from June 1, 2023, to August 1, 2023. The primary endpoint was OS, measured from the date of diagnosis to death or the last follow-up. PFS was assessed during treatment. Survival analysis was performed using Cox proportional hazards regression and Kaplan-Meier estimates. Results: The final cohort comprised 135 patients, of whom 84 received first-line therapy with ICIs. The remaining 51 patients were treated with ICIs as second-line therapy following an initial regime of TKIs. Patients were initially categorized based on whether ICIs were used in the first-line treatment. It was observed that the OS for patients receiving first-line targeted therapy was higher than for those receiving first-line immunotherapy, with median OS figures of 33 months versus 15 months. To investigate this outcome, we further refined the patient groups based on the sequence of ICIs and TKIs in the treatment regimen. We discovered that the median PFS following first-line anti-vascular targeted therapy combined with immunotherapy was 3.5 months, compared to 14.5 months when immunotherapy followed first-line anti-vascular targeted therapy (p=0.0092). The median PFS for second-line treatments was 6.5 months versus 15 months (p=0.0014). Similarly, the median OS figures were 16.66 months and 31.88 months, respectively (p=0.008). Conclusions: This study suggests that administering immunotherapy after the progression of anti-vascular therapy significantly improves both OS and PFS compared to other sequences of immunotherapy. These findings offer valuable insights and strong empirical support for clinical decision-making regarding the sequencing of treatments for mRCC.
Phase II clinical study of the safety and efficacy of sintilimab in combination with axitinib and stereotactic radiotherapy in the treatment of local-regional recurrent renal cell cancer.
Authors: Liu Ruiqi , Liu Yang , Zhang Zhiling , Gao Jianming , Zhang Xinyue , Pan Qiwen , Li Yilin , Cai Lingling , Guo Shengjie , Han Hui , Zhou Fangjian , Dong Pei , He Liru 
Published: June 2024

e16511 Background: Currently, targeted agents and immunotherapy are applied to local-regional recurrent renal cell carcinoma (LRRCC). The combination with systemic therapy and stereotactic radiotherapy (SBRT) is worth exploring. In the present study, we aimed to investigate the efficacy and safety profile of sintilimab plus axitinib combined with SBRT in the treatment for LRRCC. Methods: This was a single-arm, phase II study enrolled LRRCC patients (pts), for whom the SBRT can be safety applied for all the recurrent lesions visible on imaging. Eligible pts received sintilimab (200mg, day 1) and axitinib (5mg, bid, day 1-21) in a 21d cycle. SBRT (35-45Gy/5 fractions) was performed before the third administration of sintilimab. The primary endpoint was progression-free survival (PFS). Results: From Oct 2021 to Jan 2024, 19 pts with a median age of 58 (range: 33-74) were enrolled. Among them, 9 (47.37%) pts had clear cell carcinoma. Nine (47.37%) pts were IMDC intermediate (7 pts) or high risk, and 8 (42.11%) pts recurred in less than 1 year after surgery. Seven(36.84%) pts received at least one prior systemic therapy. Four (21.05%) pts had recurrence in the renal fossa, while 3(15.79%) pts in retroperitoneal lymph node, 2(10.53%) pts in retroperitoneal lymph node with renal fossa and 10(52.63%) pts in intra-abdominal soft tissue. With a median follow-up of 9.5 months (range: 3.0-27.1m), 18 pts were evaluable for response. The median tumor volume was 37.47cc (range: 2.44-493.19cc). The overall response rate was 55.56% with 4 complete responses and 6 partial responses per RECIST v1.1. The median time-to-response was 5 months. The local control rate and the disease control rate were 100% and 94.44%, respectively. The median PFS was not reached, and the 9-month PFS rate was 94.44%. All 19 pts were evaluable for toxicity. Any-grade treatment-related adverse events (TRAEs) occurred in 16(84.21%) pts. The most common TRAEs were albuminuria (8/19; 42.11%), nausea (8/19; 42.11%), hypertension (7/19; 36.84%), and diarrhea (7/19; 36.84%). Grade 3 or higher TRAEs occurred in 6/19(31.58%). Conclusions: Sintilimab plus axitinib combined with SBRT showed promising antitumor activity and manageable toxicity in pts with LRRCC. Clinical trial information: ChiCTR2100049523 .
A study on the value of ctDNA-based molecular residual disease (MRD) in dynamic monitoring in the clinical treatment of advanced/metastatic renal cell carcinoma.
Authors: Xue Wei , Chen Qiong , Wang Xun , Kong Wen , Zhang Jin , Chen Yonghui , Teng Jiaman , Cheng Shaochen , Huang Jiwei 
Published: June 2024

e16522 Background: While the options for renal cell therapy continue to expand, the traditional methods pose significant challenges in accurately treating and evaluating the effectiveness of interventions for advanced or metastatic renal cell carcinoma (RCC) because of the high tumor heterogeneity. Consequently, there is an escalating demand for precision therapy in clinical settings. The use of molecular markers such as circulating tumor DNA (ctDNA) and ctDNA-based Molecular Residual Disease (MRD) detection through ultra-deep sequencing are emerging as potent biomarkers that can assist clinical treatment decision making. Methods: Patients were recruited with advanced/metastatic Renal Cell Carcinoma would subjected to immunotherapy. Tumor tissues were collected by biopsy or surgery before the initiation of treatment, and plasma samples were drawn at baseline and approximately every three months during treatment. Tissue samples were subjected to Whole Exome Sequencing (WES) at 1000x coverage to obtain individual mutation information, and up to 40 loci were selected to construct a bespoke MRD assay panel. Blood samples underwent 100,000x ultra-deep sequencing to evaluate the tumor molecular signals in peripheral blood. Results: 22 RCC patients (17M:5F) were recruited from 4 to 12, 2023 at a median age of 65 (24 to 82). 21 were diagnosed as clear cell Renal Cell Carcinoma (ccRCC) with 18 metastatic cases, and one is transcription factor E3-Renal Cell Carcinoma (TEF3-RCC). WES resulted in a total of 7,854 mutations across 3,724 genes, including 761 insertions/deletions (InDel), 7,093 single nucleotide variants (SNV), and 3 gene fusions. The highest mutation frequency was observed in VHL (73.68%), followed by PBRM1 (63.16%), ASPA (52.63%), SETD2 (42.11%), TTN (42.11%), and THADA (42.11%). The MRD assay was conducted on 14 patients using a bespoke panel. CtDNA was detected in plasma of 92.9% (13/14) cases at baseline. The concordance with the surgical/biopsy tissue samples was 69.7% (p=0.019, Spearman correlation analysis). For ctDNA-MRD dynamic monitoring, results from 5 PR, 7 SD, and 1 PD patients were available. ctDNA concentrations decreased in 5 PR and 5 SD patients, showing a 100% (5 out of 5) and 71.4% (5 out of 7) concordance with clinical outcomes respectively. Meanwhile, ctDNA levels slightly increased in 2 SD patients and significantly increased in 1 PD patient. Six patients (2 PR, 4 SD) reached MRD-negative levels. Conclusions: The findings from this study illustrate the potential of using circulating tumor DNA (ctDNA) for dynamic monitoring in advanced/metastatic renal cell carcinoma. Furthermore, the levels of ctDNA-Minimal Residual Disease (MRD) may be useful in evaluating the effectiveness of immunotherapy. ctDNA could also serve as a radiological surrogate for assessing the progression of metastatic renal cell carcinoma (mRCC).