tbuckley/cabot-search huggingface dataset:
Neoadjuvant immunotherapy-driven bladder preservation for muscle-invasive bladder cancer: A multicenter, propensity score-matched cohort study.
Authors: Luzhe Yan, Jinhui Liu, Jinbo Chen, Zhenyu Ou, Minfeng Chen, Xiongbing Zu, Jiao Hu
Journal: Journal of Clinical Oncology (2024)

115 Background: Neoadjuvant immunotherapy-driven bladder preservation (Neoimmu-CMT), despite its prominence, is constrained in application due to the absence of robust clinical evidence. Currently, there is a lack of comparative studies on the efficacy of Neoimmu-CMT versus the traditional trimodal therapy (TMT) or neoadjuvant chemotherapy- driven bladder preservation (NAC-CMT). To address this gap, our study (ChiCTR2300069303) aims to assess Neoimmu-CMT's viability and identify suitable candidates through a multicenter, propensity score-matched analysis. Methods: The study included 163 patients from 14 hospitals, categorized into Neoimmu-CMT (n=97), TMT (n=30), and NAC-CMT (n=36) subgroups. PSM was utilized to mitigate baseline variability. Primary outcomes were disease-free survival (DFS), bladder-intact DFS (BI-DFS), and overall survival (OS). Univariate and multivariate Cox analyses were used to identify potential prognostic factors. Biomarker assessment comprised immunohistochemistry and single-cell RNA sequencing. Results: Post-PSM, Neoimmu-CMT significantly outperformed NAC-CMT in DFS (HR: 2.112, 95% CI: 1.247-3.576, P=0.005) and BI-DFS (HR: 2.329, 95% CI: 1.138-4.770, P=0.021), with a 2-year BI-DFS rate of 90.28% compared to NAC-CMT's 71.59%. However, Neoimmu-CMT and TMT showed no significant difference in DFS and BI-DFS, with Neoimmu-CMT marginally surpassing TMT in 2-year BI-DFS rates (86.42% vs. 80.89%). Clinical complete response to neoadjuvant treatment and lower clinical T stage were positive prognostic factors for Neoimmu-CMT. Biomarker analysis showed the tumor microenvironment immune phenotype closely relates to bladder preservation outcomes. Conclusions: Neoimmu-CMT is a promising bladder preservation strategy, comparable to TMT and superior to NAC-CMT. Its advancement could significantly broaden bladder preservation treatment options. [Table: see text]
Efficacy assessment of a novel pan-RAS inhibitor in KRAS-mutant and wild type colorectal 3D bioprinted organoid tumor tissue.
Authors: Parmanand Ahirwar, Areesha A. Charania, Dana Zuaiter, Logan C. Eiler, Alyssa Nizamuddin, Chelsea L. Crawford, Yulia Y. Maxuitenko, Gary Piazza, Karim I. Budhwani
Journal: Journal of Clinical Oncology (2024)

91 Background: Colorectal cancer (CRC) is the third leading type of cancer worldwide, with ~150,000 new cases in the US annually and a grim 14% 5-year survival for patients diagnosed at a late stage. A lack of treatment options leads to persistently poor prognosis for patients with advanced stage disease. KRAS mutations are well known drivers of CRC and other GI cancers. Multiple KRAS mutations occur in CRC, including G12D (34%), G12V (21%), G13D (20%), G12C (8%), and others (18%). Existing KRAS-targeted therapies have limited use in CRC, underscoring the need for pan-RAS inhibitors in treating CRC and other RAS driven cancers. Objective: Assess activity of ADT-007, our pan-RAS inhibitor, on wild-type (WT) and KRAS-mutant 3D bioprinted organoid tumor (BOT) tissue using our high-throughput ex vivo platform. Methods: Using previously established bioprinting protocols, WT and mutant BOTs were printed with HT29 and HCT116 cells, respectively. HT29 is an established human WT CRC cell line with known sensitivity to proteosome and survivin inhibitors. HCT116 is a KRAS G13D mutant human CRC cell line. 3 sets of BOTs were generated and acclimated for 24h. One set was treated for 72h with proteosome inhibitor Bortezomib, another with survivin inhibitor YM155, and the third with our novel pan-RAS inhibitor ADT-007. Dose response curves were generated from both conventional ATP luminescence readouts and high-content imaging. Results: BOT tissue microarchitecture was validated and &gt;200 µm diffusion in BOTs was confirmed using high-content imaging. Differential response was quantified using Cell TiterGlo endpoint assay as well as advanced image processing of high-content live/dead nuclear stained images captured at multiple z-plains. ADT-007 IC 50 was found to be substantially lower for mutant HCT116 compared to that for WT HT29 cell line BOTs, which was consistent with separately conducted in vitro and in vivo studies. Conclusions: A pan-RAS inhibitor, such as ADT-007 with high selectivity for cancer cells with activated RAS that is not limited to a specific KRAS mutant allele or RAS isozyme, could have broader use for CRC and other RAS-driven cancers. Further, due to their potential to replicate biophysical characteristics of a tumor and its microenvironment, BOT based precision and personalized medicine platforms can provide more accurate drug efficacy readout compared to in vitro cancer models.
New approaches to active monitoring: Patient-reported outcomes and wearables utilisation in Waldenstrom macroglobulinemia.
Authors: Kim Summers, Orlando Agrippa, Shirley D'Sa
Journal: Journal of Clinical Oncology (2024)

168 Background: Waldenstrom macroglobulinemia (WM) is a rare haematological malignancy that has seen an emergence of novel targeted therapies. With a range of clinical features impacting quality of life (QoL), there is a need for disease-specific patient-reported outcomes (PRO), outside of existing measures with limited validity in WM. The aim of this work was to enrich understanding of the patient experience and identify potential WM-specific metrics, through wearable-captured physiological metrics and electronic-PROs (ePROs). Methods: Informed consent was provided by 79 patients with WM for the analysis of data from an FDA-cleared wearable smartwatch capturing activity, sleep, and heart rate, and a mobile app for patients to input daily ePROs. These ePROs included treatments taken, EQ-5D-5L, and disease symptoms (fatigue, weakness, numbness/tingling, breathlessness, difficulty concentrating/confusion, vision, rash, night sweats, dizziness/light-headedness, constipation, loss of appetite, diarrhoea, and vomiting). Data was integrated within a digital platform, and anonymised extracts analysed as a cohort and by treatment. Results: Patients had a mean±SD (range) age of 65±10 (43-88) years. 53% were female. As of the most recent self-reported treatment entry, 22% (17/79) reported Bruton tyrosine kinase inhibitor (BTKi) use. Completion rates over a 142-day snapshot for daily PROs was 60%, and 85% across wearable activity and sleep metrics (devices synced=58/79). A cohort mean EQ-5D-5L Score of 0.760±0.181 and Health State (0-100) of 74±18 was reported. The highest symptom severity scores (1-5) were ‘fatigue’ (1.9/5), ‘weakness’ (1.7/5), and ‘numbness/tingling’ (1.5/5), all other symptoms reporting a mean score of 1.4 or less. Increasing activity levels significantly correlated with decreasing symptom severity and increasing QoL scores (p&lt;0.05). Lower symptom severity also correlated with higher total sleep durations, contrasting higher ‘difficulty concentrating/confusion’, ‘dizziness/light-headedness’, and ‘usual activity impairment’ scores with increasing levels of deep sleep. Breathing disturbance intensities (36.8 vs. 17.6, p=0.008) and nightly wakeup counts (1.9 vs. 1.6, p=0.038), tracked by wearables, were significantly higher in patients reporting BTKi treatment compared to the remaining cohort. Conclusions: Our data support the enrichment of knowledge around baseline physiological and QoL metrics for patients with WM, demonstrating the feasibility of a digital ecosystem of wearable-captured and ePRO metrics at a high data completeness level. Further work will ensure the generation of more population-representative outputs, through the expansion of the patient cohort, and a greater depth of insight into the impact of health events, disease complications, and therapeutic intervention on these remotely-tracked metrics.
Effect of tumor-immune microenvironment (TIME) on disparate ascending (A) and descending (D) subtypes of nasopharyngeal carcinoma (NPC).
Authors: Eugenia Li Ling Yeo, Enya Ong, Boon Hao Hong, Darren Wan-Teck Lim, Joe Yeong, Sze Huey Tan, Melvin L.K. Chua, Jacqueline Siok Gek Hwang, Tianzhu Lu
Journal: Journal of Clinical Oncology (2024)

145 Background: Endemic NPC is invariably linked to Epstein-Barr virus infection, and often presents as locoregionally-advanced (LA-) disease. Nonetheless, it is acknowledged that LA-NPC is clinically heterogeneous, with disparate clinical presentations of ascending (A) and descending (D) subtypes that are characterized by T3-4N0-1 and T1-2N2-3 disease, respectively. Here, we investigated the TIME between these distinct NPC phenotypes using whole transcriptome sequencing and digital pathology. Methods: We utilized a cohort of 111 patients with NPC from 2 institutions – NCCS (n=68) and JXCH (n=43) as test and validation datasets, respectively. Whole transcriptome profiling using RNAseq (Illumina, CA) was performed in the NCCS cohort, while spatial analyses of hematoxylin and eosin-stained whole slide images (WSIs) were undertaken in the NCCS cohort (35 A- and 33 D-subtypes), and validated in the JXCH cohort (26 A- and 17 D-subtypes). A customized pipeline was developed to analyze the spatial relationships between tumor-rich and immune-rich regions (tiles) within the WSIs. Mean distances between each tumor-rich and its nearest immune-rich tiles were computed for each WSI. Highest and lowest quartiles of the mean distances in the test cohort were used as thresholds to classify NPC tumors into 3 immune classes of differing immune cell density. Results: Transcriptomic profiling revealed an enrichment of immune-related genesets and differentially expressed genes in D- relative to A-subtype tumors. This corresponded to observations of higher overall immune, T and B cell scores in D- than A-subtypes by deconvolution analyses (ESTIMATE and CIBERSORT). Through spatial analyses of our WSIs, we defined three immune classes – immune-dense, -sparse, and -desert, which corresponded to mean distances of &lt;88 μm, 88-208 μm, and &gt;208 μm between tumour- to immune-rich tiles, respectively. D-subtype NPC had significantly more immune-rich regions compared with A-subtypes in both NCCS and JXCH cohorts ( P=0.02 [NCCS]; P=0.01 [JXCH]). This corresponded to shorter median distances between tumor-rich to immune-rich tiles in D- than A-subtypes (103 μm vs 200 μm, P=0.01 [NCCS]; 123 μm vs250 μm, P=0.01 [JXCH]). Based on our novel spatial-based classification, we observed a higher proportion of immune dense tumors in D- than A-subtypes in the NCCS cohort, with only 3/33 (9%) D-subtypes being classified as immune desert. Using the same distance thresholds, we derived a consistent trend in the JXCH cohort, where a higher proportion of D-subtypes were classified as immune dense and sparse than A-subtypes (59% vs 31%, P=0.01). Conclusions: Our results support a model where the TIME delineates the biology underpinning the disparate clinical presentations of distinct A- and D-subtypes of LA-NPC.
An artificial intelligence system for early prediction of breast cancer regression pattern during neoadjuvant chemotherapy.
Authors: Yuhong Huang, Teng Zhu, Kun Wang
Journal: Journal of Clinical Oncology (2024)

3 Background: Breast cancer exhibits diverse tumor regression patterns (TRP) following neoadjuvant chemotherapy (NAC). We aims to develop an artificial intelligence (AI) system utilizing longitudinal magnetic resonance imaging (MRI) for precise TRP prediction. Methods: This retrospective study involved 2249 breast cancer patients from 12 institutions. The dataset comprised a training cohort (n=1006) from institutions I and II, and an external validation cohort (n=1243) from institutions III-XII. We utilized a 3D U-Net model for automated tumor delineation, incorporating spatial habitat analysis and 3D ResNet-50 model for extracting imaging featur, to develop an AI system. Results: The 3D U-Net model showed significant accuracy, with Dice coefficients of 0.875 in the validation cohort. The AI system achieved areas under the curve of 0.912. It demonstrated robust performance across diverse molecular subtypes (accuracies: 80·68% to 87·87%) and tumor stages (accuracies: 80·16% to 84·98%) in the validation cohorts. Conclusions: Our study provides a noninvasive AI system for early TRP prediction in breast cancer, potentially assisting clinicians in adjusting NAC regimens and planning breast-conserving surgery.
The single cell transcriptional landscape of fibrosarcoma.
Authors: Juan Wang, Zhenfeng Li, Ning Zhang
Journal: Journal of Clinical Oncology (2024)

235 Background: Fibrosarcoma (FS) is a relatively uncommon subtype of soft tissue sarcoma, which predominantly occurs in the extremities and girdles of elderly individuals. FS are famous for its high recurrence and metastasis rate. However, the current treatment regimens have shown poor efficacy in treating these progressive lesions. Therefore, a deep understanding of the tumor microenvironment characteristics of fibrosarcoma is needed to develop more effective drugs.Therefore, a deeper understanding of the tumor microenvironment characteristics of fibrosarcoma is needed for further development of therapeutic drugs. Methods: We performed single-cell transcriptome sequencing for the first time on 6 patients bearing with FS. Bulk-seq data of soft tissue sarcoma was downloaded from public database including TCGA and GEO for prognostic analysis. What' s more, a cohort of 54 FS patients was retrospectively collected for protein detection. Results: The tumor microenvironment landscape was characterized by the presence of malignant fibroblast, diverse macrophages, limited T-cell presence, and high levels of vascular infiltration. The expression of siglec15 was significantly upregulated in the malignant cells. And the prognostic significance of siglec15 was confirmed by the TCGA public database. What’s more, the high expression of siglec15 protein was negatively related to overall survival of fibrosarcoma patients from our institution. We also explored the interactions among different cell subtypes, especially the interactions between malignant cells and endothelial cells, as well as macrophages and endothelial cells, which not only promote tumor progression but also inhibit T-cell infiltration and function. Conclusions: In total, our work has uncovered the low T cell infiltration landscape of FS. The primary reason of insufficient immune infiltration may be due to the blockage of T cell extravasation caused by the interaction between endothelial cells, malignant cells and macrophages.
Efficacy and safety of low-dose versus standard-dose immune checkpoint inhibitor as monotherapy in patients with solid tumors: A meta-analysis and systematic review.
Authors: Yu Qian, Yuan Wu, Jianing Qin, Xin Jin
Journal: Journal of Clinical Oncology (2024)

188 Background: Immunotherapy is a novel anti-cancer therapy with different mechanisms compared with chemotherapy. Several studies have reported that low dose of immune checkpoint inhibitors (ICIs) is effective. However, it is unknown whether the efficacy of low-dose immunotherapy is inferior to that of standard dose immunotherapy. We aimed to compare the existing randomized clinical trials on the efficacy of immunotherapy at low dose and standard dose as a single agent for patients with solid tumors. Methods: We searched PubMed, EMBASE, and the Cochrane Library for randomized clinical trials with the clinical outcomes of low dose and standard dose of programmed cell death-1 (PD-1)/programmed cell death ligand 1 (PD-L1) inhibitor as monotherapy until November 30, 2023. The low dose is defined as lower than the dose approved by FDA. The hazard ratio (HR) and 95% confidence interval (CI) of overall survival (OS) and disease control rate (DCR) were collected. The Stata 15.0 software was used to analyze the data. Results: Twenty-two studies with 3226 patients with five ICIs for 22 solid tumors were included. There is no difference in OS (HR 1.12, 95% CI = 0.97-1.31; p &gt; 0.05), ORR (Odds ratio (OR) 0.92, 95% CI = 0.76-1.11; p &gt; 0.05) and DCR (OR 0.83, 95% CI = 0.65-1.06; p &gt; 0.05) for patients with low dose and standard dose of PD-1/PD-L1 inhibitors. Conclusions: Our results suggested that PD-1/PD-L1 inhibitor as monotherapy with a low dose may be as effective as standard dose for patients with solid tumors. The regular dose-response relationship may not be appropriate for ICIs. Further work is needed to explore the optimal dose of ICIs based on the comprehensive consideration of efficacy, safety, and economic assessment.
Chimeric antigen receptor T-cell therapy associated hemophagocytic lymphohistiocytosis syndrome: clinical presentation, outcomes, and management
Authors: Arushi Khurana, Allison C. Rosenthal, Razan Mohty, Mamatha Gaddam, Radhika Bansal, Matthew A. Hathcock, Adrienne N. Nedved, Urshila Durani, Madiha Iqbal, Yucai Wang, Jonas Paludo, J. C. Villasboas, David Dingli, Taxiarchis Kourelis, Nelson Leung, Hassan Alkhateeb, Michael W. Ruff, Alice Gallo de Moraes, Paschalis Vergidis, Joerg Herrmann, Saad S. Kenderian, N. Nora Bennani, Patrick B. Johnston, Stephen M. Ansell, Yi Lin
Journal: Blood Cancer Journal (2024)
Lenvatinib and pembrolizumab in patients with metastatic papillary renal cell carcinoma: A phase 2 pilot study.
Authors: Ilya Tsimafeyeu, Alexander Sultanbaev, Sergey Vilkov
Journal: Journal of Clinical Oncology (2024)

109 Background: Traditionally, metastatic papillary renal cell carcinoma (papillary mRCC) has been treated with tyrosine kinase inhibitors, resulting in a 23% objective response rate and a progression-free survival of 9 months (SWOG 1500 trial). Combinations of immunotargeted therapy have shown improved outcomes in clear-cell mRCC. This prospective pilot study aimed to evaluate the preliminary efficacy of combining lenvatinib with pembrolizumab in patients with papillary mRCC. Methods: At study entry in August 2021, patients with papillary mRCC types 1 or 2, multiple measurable lesions per RECIST 1.1 criteria, and no prior therapy for advanced disease were included. The primary endpoint was the objective response rate. The study employed Simon's two-stage design (Simon, 1989) to test the null hypothesis that the true response rate is 23% against a one-sided alternative. In the first stage, 5 patients were enrolled. If there were 1 or fewer responses among these initial 5 patients, the study would be halted. The null hypothesis would be rejected if 6 or more responses were observed in the entire cohort of 12 patients. This design yields a type I error rate of 0.05 and power of 0.8 when the true response rate is 63%. Results: As planned, 5 patients were enrolled in the stage 1. The median age was 57.4 years. Among them, 4 patients were diagnosed with papillary RCC type 2, and 3 patients had an unresected primary tumor. Additionally, 1 patient presented with brain metastases. The objective response rate was 100%, of which there was 1 complete and 4 partial responses. As of the last follow-up, the status of patients on therapy was as follows: i) three patients remained on therapy for more than 12 months; ii) one patient experienced disease progression after 14.3 months of treatment; iii) one patient who initially had an ongoing response developed grade 3 uncontrolled diarrhea after 5.5 months from the start of therapy. Despite the positive outcomes observed in stage 1 of the study, the enrollment was stopped. This decision was influenced by emerging results from the large phase 2 trial KEYNOTE-B61, as reported by Chung-Han Lee et al. (ASCO 2023). Conclusions: All patients responded to lenvatinib and pembrolizumab therapy. These results suggest promising efficacy of the combination in metastatic papillary RCC. Clinical trial information: KCRB01012021 .
A novel artificial intelligence-enhanced electrochemical method for rapid methylation detection in cfDNA: Advancing multi-cancer early diagnosis.
Authors: Li-Yue Sun, Xin-Xin Zeng, Yu-Ying Jiang, Ju Shen, Ke-Xin Xian, Hua-Gen Li, Rui-Qi Wang, Fang Wang, Sun-Fang Jiang
Journal: Journal of Clinical Oncology (2024)

181 Background: The detection of methylation in circulating free DNA (cfDNA) has become a pivotal approach for the early screening and diagnosis of cancer. Traditional methods, face challenges including high detection limits, inconvenience, and elevated costs. Our prior work developed a rapid, cost-effective, and sensitive electrochemical methylation detection method for multi-cancer early detection (MCED), yet it couldn't identify the tissue of origin (TOO). We aimed to enhance this method by integrating traditional tumor biomarkers with artificial intelligence (AI) algorithms for improved cfDNA methylation detection and rapid TOO determination. Methods: A training cohort of 626 individuals was analyzed, including 173 colorectal cancer (CRC) patients, 49 patients with hematological tumors, 273 patients with other types of tumors, and 131 healthy controls. This analysis utilized electrochemical detection, carcinoembryonic antigen (CEA), and carbohydrate antigen 19-9 (CA19-9). We evaluated the diagnostic performance of these markers using ten types of AI algorithms. The optimal algorithm was selected to construct diagnostic models for CRC and hematological tumors. These models were then validated in an external cohort comprising 100 cancer patients. Results: In the training cohort, the logistic algorithm outperformed others, achieving AUC of 0.915 for CRC, 0.850 for hematological tumors, and 0.860 for other tumors. Based on these results, we opted to utilize the logistic algorithm for the development of diagnostic models for CRC and hematological tumors. The CRC model surpassed the performance of the electrochemical adsorption rate (AUC = 0.817) in CRC, though it showed limited efficacy for hematological tumors (AUC = 0.544) and other tumor types (AUC = 0.806). Conversely, the hematological tumors model achieved its highest performance in hematological tumors (AUC = 0.833), with commendable results for CRC (AUC = 0.831) and other cancers (AUC = 0.799) as well. In the validation cohort, the CRC model accurately identified 32 out of 35 CRC patients, and 17 of these were also detected by the hematological tumors model. Among the hematological tumors patients, 18 out of 20 positive with the hematological tumors model, and 15 were detected by the CRC model. For the remaining 45 patients with various other cancer, 22 tested positive with the CRC model and 28 with the hematological tumors model. Conclusions: Integrating machine learning with electrochemical cfDNA methylation detection and tumor markers provides a powerful approach for accurate cancer detection and TOO determination, promising to improve early diagnosis and personalized treatment strategies.