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> Disorders > Neoplastic Process > Hematologic Neoplasms

Hematologic Neoplasms

Hematologic Neoplasms are a diverse group of malignancies affecting the blood, bone marrow, and lymphatic system.
This broad category encompasses a wide range of cancers, including leukemias, lymphomas, myelomas, and myeloproliferative disorders.
These neoplasms can disrupt normal blood cell production and function, leading to complications such as anemia, infection, and bleeding.
Effective research and treatment of hematologic neoplasms require a deep understanding of the underlying molecular pathways and genetic drivers.
PubCompare.ai's AI-driven protocol comparison platform can help researchers streamline their work, effortlessly locating relevant protocols from literature, preprints, and patents, and leveraging cutting-edge AI tools to identify the most promising approaches.
By optimising research with PubCompare.ai's innovative tools, scientists can accelerate progress in the fight against these complex and challenging blood cancers.

Most cited protocols related to «Hematologic Neoplasms»

1
Removing Germline Variants Using Normal Samples
To further reduce false positives and miscalled germline events, we employ a panel of normal samples as a filter. To create this filter we run MuTect on a set of normals as if they were tumors without a matched normal in STD mode. From this data, a VCF file is created for the sites that were identified as variant by MuTect in more than one normal.
This VCF is then supplied to the caller, which rejects these sites. However, if the site was present in the supplied VCF of known mutations (--cosmic) it is retained because these sites could represent known recurrent somatic mutations which have been detected in the panel of normal when the normal are from adjacent tissue or have some contamination tumor DNA.
The more normal samples used to construct this panel, the higher the power will be to detect and remove rare artifacts. Therefore, we typically we use all the normal samples readily available. The results presented here were obtained by using a panel of whole genome sequencing data from blood normals of 125 solid tumor cancer patients. The samples used as part of the virtual tumor approach were not included in this panel.
Cibulskis K., Lawrence M.S., Carter S.L., Sivachenko A., Jaffe D., Sougnez C., Gabriel S., Meyerson M., Lander E.S, & Getz G. (2013). Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nature biotechnology, 31(3), 213-219.
Publication 2013
Cosmic composite resin Diploid Cell DNA, Neoplasm Germ Line Hematologic Neoplasms Malignant Neoplasms Mutation Neoplasms Patients Tissues
2
Removing Germline Variants Using Normal Samples
To further reduce false positives and miscalled germline events, we employ a panel of normal samples as a filter. To create this filter we run MuTect on a set of normals as if they were tumors without a matched normal in STD mode. From this data, a VCF file is created for the sites that were identified as variant by MuTect in more than one normal.
This VCF is then supplied to the caller, which rejects these sites. However, if the site was present in the supplied VCF of known mutations (--cosmic) it is retained because these sites could represent known recurrent somatic mutations which have been detected in the panel of normal when the normal are from adjacent tissue or have some contamination tumor DNA.
The more normal samples used to construct this panel, the higher the power will be to detect and remove rare artifacts. Therefore, we typically we use all the normal samples readily available. The results presented here were obtained by using a panel of whole genome sequencing data from blood normals of 125 solid tumor cancer patients. The samples used as part of the virtual tumor approach were not included in this panel.
Cibulskis K., Lawrence M.S., Carter S.L., Sivachenko A., Jaffe D., Sougnez C., Gabriel S., Meyerson M., Lander E.S, & Getz G. (2013). Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nature biotechnology, 31(3), 213-219.
Publication 2013
Cosmic composite resin Diploid Cell DNA, Neoplasm Germ Line Hematologic Neoplasms Malignant Neoplasms Mutation Neoplasms Patients Tissues
3
Comprehensive Cell Line RNA Isolation
Thirty-four neuroblastoma cell lines were grown to subconfluency according to standard culture conditions. RNA was isolated using the RNeasy Midi Kit (Qiagen) according to the manufacturer's instructions. Nine RNA samples from pooled normal human tissues (heart, brain, fetal brain, lung, trachea, kidney, mammary gland, small intestine and uterus) were obtained from Clontech. Blood and fibroblast biopsies were obtained from different normal healthy individuals. Thirteen leukocyte samples were isolated from 5 ml fresh blood using Qiagen's erythrocyte lysis buffer. Fibroblast cells from 20 upper-arm skin biopsies were cultured for a short time (3-4 passages) and harvested at subconfluency as described [22 (link)]. Bone marrow samples were obtained from nine patients with no hematological malignancy. Total RNA of leukocyte, fibroblast and bone marrow samples was extracted using Trizol (Invitrogen), according to the manufacturer's instructions.
Vandesompele J., De Preter K., Pattyn F., Poppe B., Van Roy N., De Paepe A, & Speleman F. (2002). Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology, 3(7), research0034.1-research0034.11.
Publication 2002
Arm, Upper Biopsy BLOOD Bone Marrow Brain Buffers Cell Lines Erythrocytes Fetus Fibroblasts Heart Hematologic Neoplasms Homo sapiens Intestines, Small Kidney Leukocytes Lung Mammary Gland Neuroblastoma Patients Skin Tissues Trachea trizol Uterus
4
EuroFlow Immunophenotypic Protocol
For immunophenotypic studies, all samples were systematically processed in parallel with the EuroFlow protocol versus the local routine procedures. Accordingly, the EuroFlow standard operating procedures (SOP) for instrument setup, instrument calibration, sample preparation, immunostaining and data acquisition16 were used at individual centers in parallel to the corresponding local protocols and techniques used for routine diagnosis and classification of hematological malignancies according to the WHO criteria. For data analysis, the Infinicyt software (Cytognos SL, Salamanca, Spain) was used in parallel to the local data analysis software programs and procedures.
For multivariate analysis of samples measured with the EuroFlow SOP and antibody panels, the Infinicyt software was used. For this purpose, the merge and calculation functions were applied for multi-tube panels prior to the analysis, as described elsewhere.31 (link), 32 (link) Briefly, prior to multivariate analyses, the populations of interest were selected and stored each in a distinct data file. Data files corresponding to the same cell population from an individual sample but stained with a different antibody tube of a multi-tube panel were merged into a single data file containing all information measured for that specific cell population. In a second step, ‘missing' data in one tube about markers only stained in the other tubes were calculated using previously described algorithms and tools implemented in the Infinicyt software.32 (link) Consequently, the generated final data file contained data about each parameter measured in the multi-tube panel for each of the events composing the cell population in that data file (Figure 2). This data file was further merged with the data files of other samples either to create a reference pool of a population of normal, reactive or malignant cells or to compare it with one or more of such reference pool data files, through multivariate analysis, for example, principal component analysis (PCA).31 (link)
van Dongen J.J., Lhermitte L., Böttcher S., Almeida J., van der Velden V.H., Flores-Montero J., Rawstron A., Asnafi V., Lécrevisse Q., Lucio P., Mejstrikova E., Szczepański T., Kalina T., de Tute R., Brüggemann M., Sedek L., Cullen M., Langerak A.W., Mendonça A., Macintyre E., Martin-Ayuso M., Hrusak O., Vidriales M.B, & Orfao A. (2012). EuroFlow antibody panels for standardized n-dimensional flow cytometric immunophenotyping of normal, reactive and malignant leukocytes. Leukemia, 26(9), 1908-1975.
Publication 2012
Cells Diagnosis Hematologic Neoplasms Immunoglobulins Immunophenotyping Standard Preparations
5
Comprehensive Hematological Sampling for Research
Multiple types of biological samples were collected from healthy volunteers and patients with suspicion or diagnosis of different types of hematological malignancies and other non-clonal hematological and non-hematological disorders, as specified later in each section of this paper. The collected samples concerned peripheral blood (PB), bone marrow (BM), fine needle aspirates (FNA), biopsies from lymphoid and non-lymphoid tissues, cerebrospinal fluid (CSF) and vitreous samples. For all patients with a hematological malignancy, the diagnosis was established according to the WHO criteria.3 Informed consent procedures and forms were proposed and approved at the first EuroFlow meeting (see the Editorial in this Leukemia issue). Informed consent was given by donors or their guardians (for example, parents) in case of children according to the guidelines of the local Medical Ethics Committees and in line with the Declaration of Helsinki Protocol. All participants obtained approval or no-objection from the local Medical Ethics Committees for secondary use of remaining diagnostic material for the EuroFlow studies, which also allows the inclusion of anonymized flow cytometric results into a central (public) database to define reference values for normal, reactive, regenerating and malignant cell samples.
van Dongen J.J., Lhermitte L., Böttcher S., Almeida J., van der Velden V.H., Flores-Montero J., Rawstron A., Asnafi V., Lécrevisse Q., Lucio P., Mejstrikova E., Szczepański T., Kalina T., de Tute R., Brüggemann M., Sedek L., Cullen M., Langerak A.W., Mendonça A., Macintyre E., Martin-Ayuso M., Hrusak O., Vidriales M.B, & Orfao A. (2012). EuroFlow antibody panels for standardized n-dimensional flow cytometric immunophenotyping of normal, reactive and malignant leukocytes. Leukemia, 26(9), 1908-1975.
Publication 2012
Aspiration Biopsy, Fine-Needle Biopharmaceuticals Biopsy BLOOD Bone Marrow Cells Cerebrospinal Fluid Child Clone Cells Diagnosis Donors Flow Cytometry Healthy Volunteers Hematological Disease Hematologic Neoplasms Legal Guardians Leukemia Lymph Lymphoid Tissue Parent Patients Regional Ethics Committees Specimen Collection

Most recents protocols related to «Hematologic Neoplasms»

1
Biodistribution of Imaging Agent in Oncomice
Not available on PMC !

Example 10

Oncomice®, obtained through an in-house breeding program, were anesthetized intramuscularly with 0.1 mL of ketamine/acepromazine (1.8 mL saline, 1.0 mL ketamine, and 0.2 mL acepromazine) prior to dosing and tissue sampling. Individual mice were then injected via the tail vein with an imaging agent of the present invention (0.5-2.0 mCi/kg in 0.1 mL). Mice were euthanized and biodistribution performed at 1 h post-injection. Selected tissues were removed, weighed, and counted on a gamma counter. Results are expressed as the percentage of injected dose per gram tissue (mean±SEM; Table 3).

TABLE 3
Summary of imaging agent distribution in the Oncomouse ®
Imaging Agent Distribution
(% ID/g)
tissue246
blood1.07 ± 0.0600.41 ± 0.0990.88 ± 0.061
heart0.95 ± 0.0650.36 ± 0.0640.69 ± 0.073
lung0.97 ± 0.1210.45 ± 0.0711.69 ± 0.382
liver13.1 ± 2.1723.6 ± 5.1911.3 ± 1.73
spleen0.69 ± 0.0850.34 ± 0.0570.81 ± 0.021
kidney20.6 ± 3.2514.8 ± 1.796.66 ± 1.46
bone2.02 ± 0.3201.28 ± 0.2002.86 ± 0.124
muscle0.50 ± 0.0730.17 ± 0.0430.44 ± 0.049
urine71.87.67 ± 5.007.21 ± 6.71
tumor0.95 ± 0.1031.12 ± 0.2040.73 ± 0.026

US11865195B2. Evaluation of presence of and vulnerability to atrial fibrillation and other indications using matrix metalloproteinase-based imaging (2024-01-09). Lantheus Medical Imaging, Inc. [US], Yale University [US]. Inventors: Albert J. Sinusas [US], Joseph G. Akar [US], Richard R. Cesati [US], Heike S. Radeke [US], Stephen B. Haber [US].
Patent 2024
Acepromazine Bones Gamma Rays Hematologic Neoplasms Hematuria Ketamine Kidney Liver Lung Mus Myocardium Neoplasms Saline Solution Spleen Tail Tissues Urine Veins
2
VRE Colonization in Hematology Patients
This study was conducted in the Hematology ward, comprising 46 beds at the University Hospital “Dr. George Stranski” in Pleven, Bulgaria, a tertiary care hospital with 950 beds.
The Hematology ward is a specialized unit for treatment of patients with hematological and hemato-oncological diseases, but without transplant recipients. All patients admitted between May 1st, 2018 and January 31st, 2019, who had hematologic malignancy without a prior history of hospitalization/intervention in the last 30 days and duration of hospitalization of more than 48 h, were enrolled in the study. Written consent was obtained from each patient in accordance with the rules of Hospital Ethics Committee.
The data collected from patients and their medical records during the entire hospital stay included: demographic characteristics (sex and age), clinical information (primary hematologic disease, length of stay in the Hematology ward, co-morbidities, obesity, corticosteroid use, chemotherapy, use of medications prior the hospital admission) and information about all antimicrobials used within the previous 30 days before hospital admission and during the current hospital stay. Epidemiological data related to housing, environmental or work-related factors was collected for all colonized patients.
To determine the intestinal colonization with VRE, fecal samples were taken within the first 48 h of hospital stay, on the 5th day after admission and then on a weekly basis until discharge or death. VRE colonization in each patient without a prior history of hospitalization/intervention in the last 30 days and with a positive initial (in the first 48 h) fecal culture was considered to be community-acquired. All patients with a positive VRE stool culture and without VRE infection or other infections were defined as VRE colonized group. The non-VRE colonized group included patients without detected VRE colonization or infections.
Hristova P.M., Nankov V.M., Hristov I.G., Trifonov S.V., Alexandrova A.S, & Hitkova H.Y. (2023). Gut colonization with vancomicyn-resistant enterococci among patients with hematologic malignancies. Gut Pathogens, 15, 12.
Publication 2023
Adrenal Cortex Hormones Ethics Committees, Clinical Feces Hematological Disease Hematologic Neoplasms Hospitalization Infection Intestines Microbicides Neoplasms Obesity Patient Discharge Patients Pharmaceutical Preparations Pharmacotherapy Transplant Recipients
3
Chronic GVHD Severity and Outcomes
All analyzes were performed based on patients who survived ≥6 months post-HSCT (index date). This was selected as it was more unlikely to include acute GVHD patients, however it may exclude some patients with true cGVHD who died early. Additionally, it may have included some patients who received donor lymphocyte infusion ≥ 6 months post-HSCT and developed acute GVHD. During the first 6 months post-HSCT, 193 patients died and were excluded from further analysis.
Chi-square and Kruskal–Wallis tests were used to compare the demographic and baseline differences between the three cGVHD groups. Overall survival (OS) rates were estimated for each group using the Kaplan-Meier method and the difference among the groups was analyzed by log-rank/Mantel–Haenszel test. The time-dependent hazard ratios (HRs) were estimated using a multivariate Cox-regression model to assess the relative effect of cGVHD on death rates, adjusting for age, sex, calendar year of HSCT, donor and stem cell source. Relapse-related mortality (RRM) and transplant-related mortality (TRM) were determined using data from the Cause of Death Register. When a haematological malignancy was stated as the main cause of a patient’s death, this case was considered for RRM. All other causes of death were considered for TRM.
Morbidities were classified using the International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10) codes. Morbidity crude incidence rates with 95% confidence intervals (CIs) were derived from a generalized linear Poisson univariate model for each complication. The adjusted incidence rate ratios (IRRs) and 95% CIs were obtained from a multivariate negative binomial model, adjusting for age, sex, and follow-up year with person-time at risk as offset.
Rate of healthcare resource utilization for each follow-up year was calculated as the total number of days in inpatient/outpatient care reported in the Patient Register for all causes, divided by the total days contributed to follow-up time for that follow-up year.
Data cleaning and statistical analyzes were performed using the statistical software R version 3.5.1 with the aid of R statistical package survival.
Novitzky-Basso I., Schain F., Batyrbekova N., Webb T., Remberger M., Keating A, & Mattsson J. (2023). Population-based real-world registry study to evaluate clinical outcomes of chronic graft-versus-host disease. PLOS ONE, 18(3), e0282753.
Publication 2023
Care, Ambulatory Donors Grafts Hematologic Neoplasms Hospitalization Inpatient Lymphocyte Outpatients Patients Relapse Stem Cells
4
Classifying Chronic Graft-Versus-Host Disease
All patients with a record of an allogeneic HSCT between 2006 and 2015 were identified in the Patient Register (n = 2147). This cohort and methods used for identification of cGVHD have been described previously [14 (link)]. Briefly, the following exclusion criteria were applied: absence of a haematological malignancy prior to the HSCT; reused proxy identification numbers; age <18 or >75; and death within 6 months post-HSCT (S1 Fig). cGVHD is commonly defined as occurring onwards of 6 months post-HSCT [16 (link), 17 (link)]. For patients who survived ≥6 months post-HSCT (index date), the 0–6-month follow-up period was therefore 6–12 months post-HSCT. End of follow-up was Dec 31, 2016 (Cancer Register and Patient Register) and Dec 31, 2017 (Prescribed Drug Register and Cause of Death Register). Patient register records were reviewed, and patients were classified as having non-, mild, or moderate-severe cGVHD based on timing and extent of treatments commonly used for cGVHD using criteria developed by the authors (Fig 1) [14 (link)].
Patients were classified as non-cGVHD if, after taper of post-HSCT GvHD prophylaxis immunosuppression, they received neither systemic corticosteroids nor systemic immunosuppressive treatment (including everolimus, cyclosporine, methotrexate, mycophenolate, sirolimus, and tacrolimus) during the entire observation period. In Sweden, GvHD prophylaxis post-HSCT is discontinued by 3 months for matched sibling donors, and by 5 months for matched unrelated donors.
Patients with low-level cGVHD require less intensive immunosuppressive treatment. Based on this rationale, mild cGVHD was defined as patients receiving either corticosteroids or immunosuppressants alone. The following four mild cGVHD groups were defined: 1) patients who received systemic corticosteroid treatment >3 months alone, 2) patients whose last date of systemic corticosteroid treatment ended <3 months before censoring, 3) patients whose last date of systemic corticosteroid treatment ended <6 months before death, and 4) patients who received immunosuppressive treatment only (Fig 1).
Treatment modalities are similar for patients with moderate and severe cGVHD, which meant that it was not possible to separate these two groups. Patients with moderate-severe cGVHD require more intensive treatment than those with mild cGVHD. Based on this rationale, the following three moderate-severe cGVHD groups were defined: 1) patients who received corticosteroid treatment (irrespective of duration) and immunosuppressive treatment, 2) patients who received corticosteroid treatment (irrespective of duration) and extracorporeal photopheresis (ECP), and 3) patients who only received ECP.
Patients were assigned retrospectively to respective groups based on treatment (or not) received.
Novitzky-Basso I., Schain F., Batyrbekova N., Webb T., Remberger M., Keating A, & Mattsson J. (2023). Population-based real-world registry study to evaluate clinical outcomes of chronic graft-versus-host disease. PLOS ONE, 18(3), e0282753.
Publication 2023
Adrenal Cortex Hormones Cyclosporine Donors Everolimus Hematologic Neoplasms Immunosuppression Immunosuppressive Agents Malignant Neoplasms Methotrexate Patients Pharmaceutical Preparations Photopheresis Sirolimus Tacrolimus Unrelated Donors
5
Systematic Review of PD1/PD-L1 and Anti-Angiogenic Therapy
The inclusion criteria were as follows: retrospective studies and prospective studies (including single-arm studies, cohort studies, and randomized control trials); patients who were pathologically diagnosed with any type of solid cancer; patients treated with PD1/PD-L1 inhibitors combined with anti-angiogenic drugs and RT; and studies that reported efficacy endpoints, including objective response rate (ORR), complete response rate (CRR), disease control rate (DCR), mortality rate (MR), and AEs.
The exclusion criteria were as follows: experiments performed in vitro or in vivo, but not based on patients; incomplete data for the targeted outcomes; patients with hematologic tumors, including leukemia, multiple myeloma, and malignant lymphoma; and studies published as conference abstracts, reviews, comments, case reports, and letters.
Two researchers independently reviewed the titles and abstracts of the studies and submitted eligible studies for full-text analysis to confirm whether they should be included in the meta-analysis. After each selection stage, the 2 researchers compared their findings. Inconsistencies were resolved and discussed by a third researcher.
Xian F., Wu J., Zhong L, & Xu G. (2023). Efficacy and safety of PD1/PDL1 inhibitors combined with radiotherapy and anti-angiogenic therapy for solid tumors: A systematic review and meta-analysis. Medicine, 102(10), e33204.
Publication 2023
Angiogenesis Inhibitors Conferences Hematologic Neoplasms Leukemia Lymphoma Malignant Neoplasms Multiple Myeloma Patients PD-L1 Inhibitors

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Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.
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The QIAamp DNA Mini Kit is a laboratory equipment product designed for the purification of genomic DNA from a variety of sample types. It utilizes a silica-membrane-based technology to efficiently capture and purify DNA, which can then be used for various downstream applications.
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TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.
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More about "Hematologic Neoplasms"

Hematologic Neoplasms, also known as blood cancers or hematological malignancies, are a diverse group of cancerous disorders that affect the blood, bone marrow, and lymphatic system.
This broad category encompasses a wide range of malignancies, including leukemias, lymphomas, myelomas, and myeloproliferative disorders.
These neoplasms can disrupt the normal production and function of blood cells, leading to complications such as anemia, infection, and bleeding.
Effective research and treatment of hematologic neoplasms require a deep understanding of the underlying molecular pathways and genetic drivers.
Researchers can leverage cutting-edge tools like PubCompare.ai's AI-driven protocol comparison platform to streamline their work and accelerate progress in the fight against these complex and challenging blood cancers.
PubCompare.ai's innovative platform allows researchers to effortlessly locate relevant protocols from literature, preprints, and patents, and utilize advanced AI tools to identify the most promising approaches.
By optimizing their research with PubCompare.ai's tools, scientists can explore a wide range of techniques, including those involving FBS, QIAamp DNA Mini Kit, Penicillin/streptomycin, RPMI 1640, L-glutamine, TRIzol reagent, RPMI 1640 medium, and Histopaque-1077, among others.
Furthermore, the HiSeq 4000 sequencing platform can be leveraged to conduct in-depth genetic analysis and uncover the key molecular drivers of hematologic neoplasms, paving the way for more targeted and effective therapies.
By embracing the power of AI-driven protocol comparison and cutting-edge research tools, scientists can make significant strides in understanding and treating these challenging blood cancers.