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Cancer Innovation Aug 2024The current standard of care for advanced human epidermal growth factor receptor 2 (HER2)-positive breast cancer is pertuzumab plus trastuzumab and docetaxel as...
BACKGROUND
The current standard of care for advanced human epidermal growth factor receptor 2 (HER2)-positive breast cancer is pertuzumab plus trastuzumab and docetaxel as first-line therapy. However, with the development of newer treatment regimens, there is a lack of evidence regarding which is the optimal treatment strategy. The aim of this network meta-analysis was to evaluate the efficacy and safety of first-line regimens for advanced HER2-positive breast cancer by indirect comparisons.
METHODS
A systematic review and Bayesian network meta-analysis were conducted. The PubMed, EMBASE, and Cochrane Library databases were searched for relevant articles published through to December 2023. The hazard ratio (HR) and 95% credible interval (CrI) were used to compare progression-free survival (PFS) between treatments, and the odds ratio and 95% CrI were used to compare the objective response rate (ORR) and safety.
RESULTS
Twenty randomized clinical trials that included 15 regimens and 7094 patients were analyzed. Compared with the traditional trastuzumab and docetaxel regimen, PFS was longer on the pyrotinib and trastuzumab plus docetaxel regimen (HR: 0.41, 95% CrI: 0.22-0.75) and the pertuzumab and trastuzumab plus docetaxel regimen (HR: 0.65, 95% CrI: 0.43-0.98). Consistent with the results for PFS, the ORR was better on the pyrotinib and trastuzumab plus docetaxel regimen and the pertuzumab and trastuzumab plus docetaxel regimen than on the traditional trastuzumab and docetaxel regimen. The surface under the cumulative ranking curve indicated that the pyrotinib and trastuzumab plus docetaxel regimen was most likely to rank first in achieving the best PFS and ORR. Comparable results were found for grade ≥3 AE rates of ≥10%.
CONCLUSIONS
Our results suggest that the pyrotinib and trastuzumab plus docetaxel regimen is most likely to be the optimal first-line therapy for patients with HER2-positive breast cancer.
PubMed: 38948247
DOI: 10.1002/cai2.126 -
Cancer Innovation Aug 2024The rate at which the anticancer drug paclitaxel is cleared from the body markedly impacts its dosage and chemotherapy effectiveness. Importantly, paclitaxel clearance...
Novel progressive deep learning algorithm for uncovering multiple single nucleotide polymorphism interactions to predict paclitaxel clearance in patients with nonsmall cell lung cancer.
BACKGROUND
The rate at which the anticancer drug paclitaxel is cleared from the body markedly impacts its dosage and chemotherapy effectiveness. Importantly, paclitaxel clearance varies among individuals, primarily because of genetic polymorphisms. This metabolic variability arises from a nonlinear process that is influenced by multiple single nucleotide polymorphisms (SNPs). Conventional bioinformatics methods struggle to accurately analyze this complex process and, currently, there is no established efficient algorithm for investigating SNP interactions.
METHODS
We developed a novel machine-learning approach called GEP-CSIs data mining algorithm. This algorithm, an advanced version of GEP, uses linear algebra computations to handle discrete variables. The GEP-CSI algorithm calculates a fitness function score based on paclitaxel clearance data and genetic polymorphisms in patients with nonsmall cell lung cancer. The data were divided into a primary set and a validation set for the analysis.
RESULTS
We identified and validated 1184 three-SNP combinations that had the highest fitness function values. Notably, , and were found to indirectly influence paclitaxel clearance by coordinating the activity of genes previously reported to be significant in paclitaxel clearance. Particularly intriguing was the discovery of a combination of three SNPs in genes , and . These SNPs-related proteins were confirmed to interact with each other in the protein-protein interaction network, which formed the basis for further exploration of their functional roles and mechanisms.
CONCLUSION
We successfully developed an effective deep-learning algorithm tailored for the nuanced mining of SNP interactions, leveraging data on paclitaxel clearance and individual genetic polymorphisms.
PubMed: 38948246
DOI: 10.1002/cai2.110 -
PeerJ 2024TIN-X (Target Importance and Novelty eXplorer) is an interactive visualization tool for illuminating associations between diseases and potential drug targets and is...
TIN-X (Target Importance and Novelty eXplorer) is an interactive visualization tool for illuminating associations between diseases and potential drug targets and is publicly available at newdrugtargets.org. TIN-X uses natural language processing to identify disease and protein mentions within PubMed content using previously published tools for named entity recognition (NER) of gene/protein and disease names. Target data is obtained from the Target Central Resource Database (TCRD). Two important metrics, novelty and importance, are computed from this data and when plotted as log(importance) . log(novelty), aid the user in visually exploring the novelty of drug targets and their associated importance to diseases. TIN-X Version 3.0 has been significantly improved with an expanded dataset, modernized architecture including a REST API, and an improved user interface (UI). The dataset has been expanded to include not only PubMed publication titles and abstracts, but also full-text articles when available. This results in approximately 9-fold more target/disease associations compared to previous versions of TIN-X. Additionally, the TIN-X database containing this expanded dataset is now hosted in the cloud Amazon RDS. Recent enhancements to the UI focuses on making it more intuitive for users to find diseases or drug targets of interest while providing a new, sortable table-view mode to accompany the existing plot-view mode. UI improvements also help the user browse the associated PubMed publications to explore and understand the basis of TIN-X's predicted association between a specific disease and a target of interest. While implementing these upgrades, computational resources are balanced between the webserver and the user's web browser to achieve adequate performance while accommodating the expanded dataset. Together, these advances aim to extend the duration that users can benefit from TIN-X while providing both an expanded dataset and new features that researchers can use to better illuminate understudied proteins.
Topics: Humans; User-Computer Interface; Natural Language Processing; PubMed; Software
PubMed: 38948230
DOI: 10.7717/peerj.17470 -
PeerJ 2024Gastric cancer (GC), one of the highest venous thromboembolism (VTE) incidence rates in cancer, contributes to considerable morbidity, mortality, and, prominently, extra...
OBJECTIVE
Gastric cancer (GC), one of the highest venous thromboembolism (VTE) incidence rates in cancer, contributes to considerable morbidity, mortality, and, prominently, extra cost. However, up to now, there is not a high-quality VTE model to steadily predict the risk for VTE in China. Consequently, setting up a prediction model to predict the VTE risk is imperative.
METHODS
Data from 3,092 patients from December 15, 2017, to December 31, 2022, were retrospectively analyzed. Multiple logistic regression analysis was performed to assess risk factors for GC, and a nomogram was constructed based on screened risk factors. A receiver operating curve (ROC) and calibration plot was created to evaluate the accuracy of the nomogram.
RESULTS
The risk factors of suffering from VTE were older age (OR = 1.02, 95% CI [1.00-1.04]), Karnofsky Performance Status (KPS) ≥ 70 (OR = 0.45, 95% CI [0.25-0.83]), Blood transfusion (OR = 2.37, 95% CI [1.47-3.84]), advanced clinical stage (OR = 3.98, 95% CI [1.59-9.99]), central venous catheterization (CVC) (OR = 4.27, 95% CI [2.03-8.99]), operation (OR = 2.72, 95% CI [1.55-4.77]), fibrinogen degradation product (FDP) >5 µg/mL (OR = 1.92, 95% CI [1.13-3.25]), and D-dimer > 0.5 mg/L (OR = 2.50, 95% CI [1.19-5.28]). The area under the ROC curve (AUC) was 0.82 in the training set and 0.85 in the validation set.
CONCLUSION
Our prediction model can accurately predict the risk of the appearance of VTE in gastric cancer patients and can be used as a robust and efficient tool for evaluating the possibility of VTE.
Topics: Humans; Nomograms; Venous Thromboembolism; Stomach Neoplasms; Retrospective Studies; Male; Female; Middle Aged; Risk Factors; Aged; China; Risk Assessment; ROC Curve; Fibrin Fibrinogen Degradation Products; Adult
PubMed: 38948205
DOI: 10.7717/peerj.17527 -
Npj Imaging 2024Patient-derived tumor organoids have emerged as a crucial tool for assessing the efficacy of chemotherapy and conducting preclinical drug screenings. However, the...
Patient-derived tumor organoids have emerged as a crucial tool for assessing the efficacy of chemotherapy and conducting preclinical drug screenings. However, the conventional histological investigation of these organoids necessitates their devitalization through fixation and slicing, limiting their utility to a single-time analysis. Here, we use stimulated Raman histology (SRH) to demonstrate non-destructive, label-free virtual staining of 3D organoids, while preserving their viability and growth. This novel approach provides contrast similar to conventional staining methods, allowing for the continuous monitoring of organoids over time. Our results demonstrate that SRH transforms organoids from one-time use products into repeatable models, facilitating the efficient selection of effective drug combinations. This advancement holds promise for personalized cancer treatment, allowing for the dynamic assessment and optimization of chemotherapy treatments in patient-specific contexts.
PubMed: 38948153
DOI: 10.1038/s44303-024-00019-1 -
Npj Imaging 2024Label-free autofluorescence lifetime is a unique feature of the inherent fluorescence signals emitted by natural fluorophores in biological samples. Fluorescence...
Label-free autofluorescence lifetime is a unique feature of the inherent fluorescence signals emitted by natural fluorophores in biological samples. Fluorescence lifetime imaging microscopy (FLIM) can capture these signals enabling comprehensive analyses of biological samples. Despite the fundamental importance and wide application of FLIM in biomedical and clinical sciences, existing methods for analysing FLIM images often struggle to provide rapid and precise interpretations without reliable references, such as histology images, which are usually unavailable alongside FLIM images. To address this issue, we propose a deep learning (DL)-based approach for generating virtual Hematoxylin and Eosin (H&E) staining. By combining an advanced DL model with a contemporary image quality metric, we can generate clinical-grade virtual H&E-stained images from label-free FLIM images acquired on unstained tissue samples. Our experiments also show that the inclusion of lifetime information, an extra dimension beyond intensity, results in more accurate reconstructions of virtual staining when compared to using intensity-only images. This advancement allows for the instant and accurate interpretation of FLIM images at the cellular level without the complexities associated with co-registering FLIM and histology images. Consequently, we are able to identify distinct lifetime signatures of seven different cell types commonly found in the tumour microenvironment, opening up new opportunities towards biomarker-free tissue histology using FLIM across multiple cancer types.
PubMed: 38948152
DOI: 10.1038/s44303-024-00021-7 -
Theranostics 2024Sorafenib is the standard treatment for advanced hepatocellular carcinoma (HCC), but acquired resistance during the treatment greatly limits its clinical efficiency....
Sorafenib is the standard treatment for advanced hepatocellular carcinoma (HCC), but acquired resistance during the treatment greatly limits its clinical efficiency. Lipid metabolic disorder plays an important role in hepatocarcinogenesis. However, whether and how lipid metabolic reprogramming regulates sorafenib resistance of HCC cells remains vague. Sorafenib resistant HCC cells were established by continuous induction. UHPLC-MS/MS, proteomics, and flow cytometry were used to assess the lipid metabolism. ChIP and western blot were used to reflect the interaction of signal transducer and activator of transcription 3 (STAT3) with glycerol-3-phosphate acyltransferase 3 (GPAT3). Gain- and loss-of function studies were applied to explore the mechanism driving sorafenib resistance of HCC. Flow cytometry and CCK8 in and tumor size in were used to evaluate the sorafenib sensitivity of HCC cells. Our metabolome data revealed a significant enrichment of triglycerides in sorafenib-resistant HCC cells. Further analysis using proteomics and genomics techniques demonstrated a significant increase in the expression of GPAT3 in the sorafenib-resistant groups, which was found to be dependent on the activation of STAT3. The restoration of GPAT3 resensitized HCC cells to sorafenib, while overexpression of GPAT3 led to insensitivity to sorafenib. Mechanistically, GPAT3 upregulation increased triglyceride synthesis, which in turn stimulated the NF-κB/Bcl2 signaling pathway, resulting in apoptosis tolerance upon sorafenib treatment. Furthermore, our and studies revealed that pan-GPAT inhibitors effectively reversed sorafenib resistance in HCC cells. Our data demonstrate that GPAT3 elevation in HCC cells reprograms triglyceride metabolism which contributes to acquired resistance to sorafenib, which suggests GPAT3 as a potential target for enhancing the sensitivity of HCC to sorafenib.
Topics: Sorafenib; Carcinoma, Hepatocellular; Liver Neoplasms; Humans; Drug Resistance, Neoplasm; Cell Line, Tumor; Animals; STAT3 Transcription Factor; Mice; Antineoplastic Agents; Mice, Nude; Xenograft Model Antitumor Assays; Lipid Metabolism; Apoptosis; Gene Expression Regulation, Neoplastic; Signal Transduction
PubMed: 38948063
DOI: 10.7150/thno.92646 -
Theranostics 2024Immune checkpoint inhibitors (ICI) are routinely used in advanced clear cell renal cell carcinoma (ccRCC). However, a substantial group of patients does not respond to...
Immune checkpoint inhibitors (ICI) are routinely used in advanced clear cell renal cell carcinoma (ccRCC). However, a substantial group of patients does not respond to ICI therapy. Radiation is a promising approach to increase ICI response rates since it can generate anti-tumor immunity. Targeted radionuclide therapy (TRT) is a systemic radiation treatment, ideally suited for precision irradiation of metastasized cancer. Therefore, the aim of this study is to explore the potential of combined TRT, targeting carbonic anhydrase IX (CAIX) which is overexpressed in ccRCC, using [Lu]Lu-DOTA-hG250, and ICI for the treatment of ccRCC. In this study, we evaluated the therapeutic and immunological action of [Lu]Lu-DOTA-hG250 combined with aPD-1/a-CTLA-4 ICI. First, the biodistribution of [Lu]Lu-DOTA-hG250 was investigated in BALB/cAnNRj mice bearing Renca-CAIX or CT26-CAIX tumors. Renca-CAIX and CT26-CAIX tumors are characterized by poor versus extensive T-cell infiltration and homogeneous versus heterogeneous PD-L1 expression, respectively. Tumor-absorbed radiation doses were estimated through dosimetry. Subsequently, [Lu]Lu-DOTA-hG250 TRT efficacy with and without ICI was evaluated by monitoring tumor growth and survival. Therapy-induced changes in the tumor microenvironment were studied by collection of tumor tissue before and 5 or 8 days after treatment and analyzed by immunohistochemistry, flow cytometry, and RNA profiling. Biodistribution studies showed high tumor uptake of [Lu]Lu-DOTA-hG250 in both tumor models. Dose escalation therapy studies in Renca-CAIX tumor-bearing mice demonstrated dose-dependent anti-tumor efficacy of [Lu]Lu-DOTA-hG250 and remarkable therapeutic synergy including complete remissions when a presumed subtherapeutic TRT dose (4 MBq, which had no significant efficacy as monotherapy) was combined with aPD-1+aCTLA-4. Similar results were obtained in the CT26-CAIX model for 4 MBq [Lu]Lu-DOTA-hG250 + a-PD1. analyses of treated tumors revealed DNA damage, T-cell infiltration, and modulated immune signaling pathways in the TME after combination treatment. Subtherapeutic [Lu]Lu-DOTA-hG250 combined with ICI showed superior therapeutic outcome and significantly altered the TME. Our results underline the importance of investigating this combination treatment for patients with advanced ccRCC in a clinical setting. Further investigations should focus on how the combination therapy should be optimally applied in the future.
Topics: Animals; Carcinoma, Renal Cell; Mice; Immune Checkpoint Inhibitors; Kidney Neoplasms; Carbonic Anhydrase IX; Humans; Cell Line, Tumor; Radioisotopes; Lutetium; Female; Antigens, Neoplasm; Tissue Distribution; Tumor Microenvironment; Tumor Protein, Translationally-Controlled 1; Xenograft Model Antitumor Assays; Combined Modality Therapy; Mice, Inbred BALB C; Antibodies, Monoclonal
PubMed: 38948062
DOI: 10.7150/thno.96944 -
Theranostics 2024Trophoblast cell surface antigen 2 (Trop2) is overexpressed in a range of solid tumors and participants in multiple oncogenic signaling pathways, making it an attractive... (Review)
Review
Trophoblast cell surface antigen 2 (Trop2) is overexpressed in a range of solid tumors and participants in multiple oncogenic signaling pathways, making it an attractive therapeutic target. In the past decade, the rapid development of various Trop2-targeted therapies, notably marked by the advent of the antibody-drug conjugate (ADC), revolutionized the outcome for patients facing Trop2-positive tumors with limited treatment opinions, such as triple-negative breast cancer (TNBC). This review provides a comprehensive summary of advances in Trop2-targeted therapies, including ADCs, antibodies, multispecific agents, immunotherapy, cancer vaccines, and small molecular inhibitors, along with in-depth discussions on their designs, mechanisms of action (MOAs), and limitations. Additionally, we emphasize the clinical research progress of these emerging Trop2-targeted agents, focusing on their clinical application and therapeutic efficacy against tumors. Furthermore, we propose directions for future research, such as enhancing our understanding of Trop2's structure and biology, exploring the best combination strategies, and tailoring precision treatment based on Trop2 testing methodologies.
Topics: Humans; Antigens, Neoplasm; Cell Adhesion Molecules; Immunoconjugates; Molecular Targeted Therapy; Neoplasms; Immunotherapy; Animals; Cancer Vaccines
PubMed: 38948057
DOI: 10.7150/thno.98178 -
Theranostics 2024Prostate Specific Membrane Antigen Positron Emission Tomography (PSMA-PET) is routinely used for the staging of patients with prostate cancer, but data on response...
Prostate Specific Membrane Antigen Positron Emission Tomography (PSMA-PET) is routinely used for the staging of patients with prostate cancer, but data on response assessment are sparse and primarily stem from metastatic castration-resistant prostate cancer (mCRPC) patients treated with PSMA radioligand therapy. Still, follow-up PSMA-PET is employed in earlier disease stages in case of clinical suspicion of disease persistence, recurrence or progression to decide if localized or systemic treatment is indicated. Therefore, the prognostic value of PSMA-PET derived tumor volumes in earlier disease stages (i.e., hormone-sensitive prostate cancer (HSPC) and non-[Lu]Lu-PSMA-617 (LuPSMA) therapy castration resistant prostate cancer (CRPC)) are evaluated in this manuscript. A total number of 73 patients (6 primary staging, 42 HSPC, 25 CRPC) underwent two (i.e., baseline and follow-up, median interval: 379 days) whole-body [Ga]Ga-PSMA-11 PET/CT scans between Nov 2014 and Dec 2018. Analysis was restricted to non-LuPSMA therapy patients. PSMA-PETs were retrospectively analyzed and primary tumor, lymph node-, visceral-, and bone metastases were segmented. Body weight-adjusted organ-specific and total tumor volumes (PSMAvol: sum of PET volumes of all lesions) were measured for baseline and follow-up. PSMAvol response was calculated as the absolute difference of whole-body tumor volumes. High metastatic burden (>5 metastases), RECIP 1.0 and PSMA-PET Progression Criteria (PPP) were determined. Survival data were sourced from the cancer registry. The average number of tumor lesions per patient on the initial PET examination was 10.3 (SD 28.4). At baseline, PSMAvol was strongly associated with OS (HR 3.92, p <0.001; n = 73). Likewise, response in PSMAvol was significantly associated with OS (HR 10.48, p < 0.005; n = 73). PPP achieved significance as well (HR 2.19, p <0.05, n = 73). Patients with hormone sensitive disease and poor PSMAvol response (upper quartile of PSMAvol change) in follow-up had shorter outcome (p < 0.05; n = 42). PSMAvol in bones was the most relevant parameter for OS prognostication at baseline and for response assessment (HR 31.11 p < 0.001; HR 32.27, p < 0.001; n = 73). PPP and response in PSMAvol were significantly associated with OS in the present heterogeneous cohort. Bone tumor volume was the relevant miTNM region for OS prognostication. Future prospective evaluation of the performance of organ specific PSMAvol in more homogeneous cohorts seems warranted.
Topics: Humans; Male; Aged; Positron Emission Tomography Computed Tomography; Prostatic Neoplasms, Castration-Resistant; Middle Aged; Follow-Up Studies; Gallium Radioisotopes; Retrospective Studies; Aged, 80 and over; Prostatic Neoplasms; Glutamate Carboxypeptidase II; Radiopharmaceuticals; Antigens, Surface; Gallium Isotopes; Prognosis; Lutetium; Positron-Emission Tomography; Tumor Burden; Heterocyclic Compounds, 1-Ring; Dipeptides
PubMed: 38948055
DOI: 10.7150/thno.96738