Articles tagged with Tumor Samples
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Researchers are expanding options for patients with hard-to-treat cancers using a personalized approach that identifies effective therapies by testing large libraries of drugs on living tumor cells. The approach has shown stronger responses than initial findings, suggesting it can reveal opportunities missed by traditional methods.
A new study by Mass General Brigham researchers has found a link between genetic mutations and toxin exposure in firefighters' brain tumors. The study identified a unique pattern of genetic mutations, known as a mutational signature, in many firefighter samples, especially those who had spent more years firefighting.
Researchers create microfluidic device to sort tumor cells based on adhesive strength, revealing potential method to predict aggressive cancer behavior. The study's findings offer a promising approach for personalizing treatment plans and identifying high-risk patients.
Researchers found a biomarker, RNA Polymerase II (RNAPII), associated with tumor aggressiveness and recurrence in meningioma and breast cancers. The study developed a novel profiling technology, Cleavage Under Targeted Accessible Chromatin (CUTAC), to measure gene transcription activity from DNA, which predicted cancer outcomes.
A new study has mapped genetic differences across children with Wilms tumour, a type of childhood kidney cancer, to understand how it develops and responds to treatment. The research suggests that inherited genetic changes predetermine tumour development, growth, and response to treatments.
The Cancer Cooperative Group System is being re-engineered to bring new money to correlative science research through public-private partnerships, aiming to remove barriers for researchers to access biological samples. This will facilitate the integration of innovative science into cancer clinical trials.
MicroDicer and MicroGrater tools enable efficient creation of uniform organoids from tumor samples, allowing researchers to test multiple cancer therapies and improve data quality. The precision cutting tools facilitate bespoke, personalized cancer therapy by standardizing process flow and organoid sizes.
A research team has identified novel biomarkers in cervical mucus samples that show high diagnostic power for detecting cervical cancer. The findings suggest that analyzing local expression levels may offer a superior diagnostic strategy, potentially reducing the need for invasive procedures.
A new study has identified specific RNAs that may help identify stage II colon cancer patients who are likely to benefit from chemotherapy following surgery. The researchers found that certain housekeeping RNAs were up-regulated in patients with recurrence, suggesting their potential role in the metastatic process.
A recent study published in Journal of Hepatology found that over 40% of liver cancer samples harbor more than one molecular subtype, leading to a
Researchers developed a rapid genotyping test for patients with central nervous system lesions, detecting key mutations associated with brain cancers in samples taken during a lumbar puncture. The test eliminated the need for surgical brain biopsies in seven cases and significantly accelerated time to treatment, from an average of 12 d...
Researchers developed DiFC, a two-color diffuse flow cytometry system that detects rare cancer cells in the bloodstream without invasive methods. The technology provides insights into cancer progression and response to treatments by studying different subpopulations of cancer cells simultaneously.
A new surgical platform using mass spectrometry identifies key gene mutations in brain cancer, including IDH mutations, during surgery. This allows for rapid diagnosis, prognosis, and tumor resection to improve patient outcomes.
Researchers have developed a new imaging technique that rapidly and accurately identifies cancerous tissues in breast samples. The method uses machine learning algorithms trained on hyperspectral dark-field microscopy data to pinpoint regions of invasive ductal carcinoma and invasive mucinous carcinoma.
Researchers have identified an inflammatory protein profile associated with poor survival in aggressive B-cell lymphoma patients. Protein profiles from blood samples can help classify subtypes of the disease and monitor patient response to treatment.
A study by the University of Gothenburg found that women with triple-negative breast cancer and high immune cell levels have a lower relapse risk after surgery. This suggests that these patients may not need chemotherapy, despite having faster-growing tumors and higher relapse rates than other breast cancers.
A groundbreaking treatment approach that combines genetic testing with individualized drug testing on tumor samples has shown promising results in treating relapsed pediatric cancer patients. The study achieved an impressive 83% improvement rate, including Logan Jenner's successful treatment after his leukemia relapse.
NeXT Personal assay detects up to ~1,800 somatic variants specific to the patient's tumor with a detection threshold of 1.67 PPM and 99.9% specificity. The assay showed linearity over a range of 0.8 to 300,000 PPM.
A new AI method combines satellite imaging and ecological analysis techniques to interpret large amounts of data from tumor tissue, providing insights into how cancer works. This approach aims to tailor cancer treatments to individual needs and avoid unnecessary side effects.
Researchers developed a reliable and reproducible real-time live tissue sensitivity assay (RT-LTSA) using fresh tumor samples to predict patients' clinical response to chemotherapy. The study showed improved disease-free survival and reduced recurrence rates in patients who received RT-LTSA sensitive adjuvant regimens.
Researchers created the Cancer Atlas of Metabolic Profiles (CAMP) to analyze gene activity and metabolite levels in tumor samples. The atlas reveals two broad classes of gene-metabolite connections, pointing to mechanisms at work across cancer types.
Researchers developed a new tool called Orion that integrates histologic and molecular information to improve cancer diagnosis and treatment. The tool uses a digital imaging platform to analyze tumor samples, identifying biomarkers that predict patient outcomes.
Researchers found that formalin fixation does not significantly alter the polarimetric properties of brain tissue, making it suitable for training machine-learning models. The study suggests that formalin-fixed brain tissue specimens can provide high-quality data for rapid and accurate diagnostic imaging in surgery.
Researchers analyzed outcomes for 144 Colorado patients who had lymph node sampling at the time of surgery for kidney tumors between 2005 and 2019. They found no statistically significant difference in clinically significant complications between patients who underwent more extensive lymph node sampling than those who didn’t.
Researchers developed a gene signature called CisSig to predict cancer patients' response to cisplatin. The approach aims to overcome the obstacle of interpreting gene signatures in the human body and has been validated in muscle-invasive bladder cancer patients.
Acute myeloid leukemia is a cancer that affects blood cells and can lead to infection, anemia, and easy bleeding. The Georgia Cancer Center has received a $2.3 million grant to study how cancer cells resist treatment and propose new options to improve patient survival.
A team of scientists at Max Planck Institute for the Science of Light developed a method to quickly and accurately diagnose cancer using artificial intelligence and real-time deformability cytometry. The method reduces analysis time from hours to under 30 minutes, enabling faster decision-making during surgery.
A study led by Tokyo University of Science researchers identified Dectin-1's role in promoting colorectal cancer by enhancing PGE2 production and suppressing IL-22BP expression. The study used mouse models and clinical samples to validate the findings, which have immediate clinical implications for CRC patients.
A team of scientists created a new, fast, and precise method for analyzing cells in tissue samples from cancer patients without the need for a trained pathologist. The method uses artificial intelligence to evaluate data produced by a technique called real-time deformability cytometry.
Researchers at Georgia Institute of Technology developed a synthetic tumor model to understand the impact of microenvironment on targeted therapies for Activated B Cell-like Diffuse Large B cell lymphoma. The model showed promise in demonstrating how combining therapeutics can overcome tumor resistance to inhibitors.
Researchers have identified markers associated with an increased risk of metastasis in patients with metastatic pheochromocytoma, allowing for personalized clinical management. The study also identifies patients who could benefit from immunotherapy treatments, paving the way for potential new therapies.
Researchers found that certain gene signaling pathways, such as interferon γ and beta-catenin, can lead to tumor hyperprogression after immunotherapy. Targeting these pathways may prevent hyperprogression in preclinical models.
Researchers developed a new technique to analyze frozen tumor specimens, enabling the study of rare cancers and unique patient histories. This breakthrough increases the number and variety of tumor samples available for scientific analysis.
A new AI tool developed by Brigham and Women's Hospital improves the accuracy of time-critical pathology diagnostics during surgery. The tool, which leverages deep-learning technology, translates frozen tissue samples into high-quality images, increasing diagnostic accuracy and reducing the need for lengthy laboratory tests.
Researchers analyzed COX-2 levels and segmental chromosome aberrations in pediatric neuroblastoma tumor samples. Positive correlations between pre-CT Ch 7q gain and COX-2 expression were found, as well as negative correlations between Ch 7q gain and Ch 11q deletion.
Researchers identify CDK4 and filamin proteins as predictors of response to paclitaxel in HER2-negative breast cancer patients. High levels of these proteins are associated with a positive response rate in 90% of cases.
A new study explores the value of 'trash data' from cancer genome sequencing, identifying new strategies to uncover previously unexplored information. The researchers found that genomic and transcriptomic data contain relevant information that can help elucidate carcinogenesis and discover putative biomarkers with clinical applications.
Researchers identified genes associated with aggressive clear cell renal carcinoma, a subtype of kidney cancer. Anticoagulant therapies may enhance cancer treatment efficacy. Specific genes involved in blood clotting and insulin transport were found to be linked to disease progression.
A retrospective study found that tumor hyaluronan levels are associated with improved time to progression in non-small cell lung cancer patients. HA-high tumors showed a trend towards improved clinical benefit, suggesting its potential as a prognostic biomarker and therapeutic target.
Researchers developed a new taxonomy of bowel cancer using gene interaction networks, grouping tumors into six distinct clinical and molecular subtypes. This classification system could aid in precision medicine by tailoring treatments to individual patients.
The study analyzed DNA methylation in skin tumor samples, revealing correlations between changes in proteins regulating the organism and prognosis. The results identified potential markers of patient survival and suggested a new route for understanding melanoma subtypes.
A new method using machine learning corrects damaged DNA and unveils true mutation processes in tumour samples, helping early cancer detection and accurate diagnosis. The tool predicted over 90% of developing cancer processes, offering a significant advancement in cancer patient care.
Researchers at TIBI developed a minimally invasive method for targeted delivery of immunotherapeutic treatments, resulting in slower tumor growth and higher activation of T-cells. The injectable gelatin biomaterial containing silicate nanoplatelets showed sustained drug release and controlled ICI delivery.
Researchers developed a mathematical technique to measure total tumor-specific mRNA levels from bulk tumor sequencing data, associating higher mRNA levels with reduced patient survival. The study suggests this approach could serve as a prognostic biomarker for various cancers, guiding treatment selection.
Researchers developed a computational approach called CellTrek to combine parallel gene-expression profiling methods, creating spatial maps at single-cell resolution. The tool provides detailed information on individual cell types' location within tissues, enabling unique biological insights.
Researchers at UVA Cancer Center have made a groundbreaking discovery about the EP300 gene and its role in small-cell lung cancer. The study found that the gene makes a protein with properties that can both foster and prevent tumor formation, providing a new potential target for treatment.
Researchers at Children's Hospital of Philadelphia define a 3-tiered molecular classification system for pediatric differentiated thyroid cancer, where fusion oncogenes are associated with more invasive disease and lower remission rates. The study contrasts findings from adults, who have a two-tiered system based on BRAF mutations.
A new bilateral tumor model was demonstrated to be useful for investigating the relationship between T-cell repertoire and cancer immunotherapy's therapeutic effects. The study found that T-cell profiles of both tumors were almost identical, indicating a similar anti-tumor response in a single mouse.
Researchers have developed a simple, postal urine test that can detect signs of urothelial cancer in Lynch Syndrome (LS) patients, who are at high risk of developing tumors. The test uses cell-free DNA shed into the urine to identify DNA from tumor cells with characteristic microsatellite instability.
A pilot study reveals that whole genome sequencing can refine diagnoses and provide new treatment options for children with cancer. The study found genetic variants that could change prognosis or suggest hereditary causes of the cancers, as well as treatments that may be effective but were not previously considered.
Researchers found immune cell patterns within tumours that can predict if patients with kidney cancer will respond to immunotherapy. The study identified a specific 'clonal' T cell receptor pattern linked to a greater chance of positive immunotherapy response.
Researchers at Weill Cornell Medicine have profiled individual cells from patients' brain tumors in unprecedented detail, revealing distinct states and programming marks that could be targeted with future drugs. The study offers insights into glioma dynamics and may lead to better detection, staging, monitoring, and treatment methods.
Researchers at Tel Aviv University successfully printed the first entirely active and viable glioblastoma tumor using a 3D printer. The 3D-bioprinted model includes functional blood vessels that simulate a real tumor, making it a promising tool for predicting treatment efficacy and drug development.
A team of Beckman researchers developed software to boost infrared imaging-based cancer diagnosis, enhancing image resolution and accelerating recording speeds. The software integrates data analysis and reduces limitations associated with IR imaging, making it faster and more accurate.
Scientists have developed a procedure, SCENITH, that identifies the energy status of immune and cancer cells within tumors to personalize treatment. This method uses protein synthesis as an indicator of cell activity, enabling clinicians to predict patient response and improve therapy efficacy.
The Childhood Solid Tumor Network data portal on St. Jude Cloud provides comprehensive data for studying pediatric solid tumors, accelerating discoveries and novel therapies. The portal offers interactive tools for analyzing genomic sequences, epigenetic data, and preclinical pharmacokinetic reports.
A new noninvasive diagnostic method using circulating tumor DNA can detect colorectal cancer in at-risk patients with high accuracy. The technology, developed by Huiyan Luo et al., uses methylation markers to identify patients with a sensitivity and specificity of 87.5% and 89.9%, respectively.
A community-based program enables patients to donate tumor tissue and blood samples for research, allowing researchers to study genetic and molecular makeup of tumors after treatment failure. Analysis reveals similar DNA mutations and varying protein biomarker expression among samples from the same patient.
Researchers developed an automated platform using exclusive liquid repellency microdrops for lossless single-cell isolation, identification and retrieval. The system combines a robotic liquid handler, microscopic imaging system and real-time image-processing software to enable rapid hands-free isolation of rare cellular samples.
Researchers at the University of Georgia have developed a microfluidic device that captures nearly every circulating tumor cell (CTC) in a blood sample, over 99%. This technology has the potential to provide key information on CTCs and aid in the early detection of cancer relapse and disease progression.