Study of the Chromosomal Abnormalities and Associated Complex Karyotypes in Hematological Cancer in the Population of West Bengal: A Prospective Observational Study

Abstract Introduction Chromosomal instability is an important feature of hematological cancer. The pathogenesis is complex and it involves genetic and epigenetic factors. As a genetic factor, chromosomal instability may play a key role in leukemogenesis. Accumulation of genetic alteration is mainly responsible for numerical and structural chromosomal rearrangement or clonal evaluation. But disease progression is often driven by chromosomal translocation, hyper- or hypodiploidy with structural abnormalities, and complex karyotypes. Objective This research aimed to study the different types of chromosomal abnormalities in clinically suspected hematological cancer patients. Materials and Methods Cytogenetic analysis was performed based on phytohemaglutinin stimulated peripheral blood lymphocyte cultures and bone marrow culture, without mitogen, of the respective patients of West Bengal from March 2016 to February 2018. All clinically suspected hematological cancer patients referred for karyotyping to the institutional genetics department have been included without any biasness of sex and age. Karyotypes were described according to the International System for Cytogenetic Nomenclature (ISCN 2005). Results In the present study, 56 clinically suspected hematological cancer cases were observed and 41 cases of chromosomal rearrangement were found which clearly show chromosomal instability as the main driving force for hematological cancer transformation. Presence of variant Philadelphia chromosomes with classical translocation, mosaic complex karyotypes, variable numerical, and structural chromosomal abnormality, along with severe-to-moderate hypo- and hyperdiploidy, and presence of marker chromosomes were the main findings of this study. Conclusion The result shows that the detection of chromosomal instability was important for preliminary diagnosis, treatment, prognosis, and further management. So the present study provided additional information about chromosomal instability in hematological cancer at Kolkata and adjoining regions.

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Chromosomal Aberrations in Breast Cancer Patients in West Bengal, India

International Journal of Human Genetics ◽

10.31901/24566330.2013/13.01.01 ◽

2013 ◽

Vol 13 (01) ◽

Author(s):

Hinglaj Saha

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Chromosomal Aberrations ◽

West Bengal ◽

Breast Cancer Patients

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Faculty Opinions recommendation of Additional information from chromosomal microarray analysis (CMA) over conventional karyotyping when diagnosing chromosomal abnormalities in miscarriage: a systematic review and meta-analysis.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718040723.793524394 ◽

2016 ◽

Author(s):

Ignatia Van den Veyver

Keyword(s):

Systematic Review ◽

Microarray Analysis ◽

Chromosomal Abnormalities ◽

Meta Analysis ◽

Chromosomal Microarray ◽

Chromosomal Microarray Analysis ◽

Additional Information

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Association between thiamine decrease and neuropsychiatric symptoms in gastrointestinal and hematological cancer patients receiving chemotherapy

Biomedicine & Pharmacotherapy ◽

10.1016/j.biopha.2021.111929 ◽

2021 ◽

Vol 141 ◽

pp. 111929

Author(s):

Yohei Iimura ◽

Tomohiro Kurokawa ◽

Shohei Andoh ◽

Yoshiaki Kanemoto ◽

Toyotaka Kawamata ◽

...

Keyword(s):

Cancer Patients ◽

Neuropsychiatric Symptoms ◽

Hematological Cancer

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Comparison of Cancer Patients to Non-Cancer Patients among COVID-19 Inpatients at a National Level

Cancers ◽

10.3390/cancers13061436 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1436

Author(s):

Alain Bernard ◽

Jonathan Cottenet ◽

Philippe Bonniaud ◽

Lionel Piroth ◽

Patrick Arveux ◽

...

Keyword(s):

Colorectal Cancer ◽

Cancer Patients ◽

Mortality Risk ◽

Hospital Admissions ◽

National Level ◽

Administrative Database ◽

Hospital Death ◽

Solid Cancer ◽

Hematological Cancer ◽

Risk Of Death

(1) Background: Several smaller studies have shown that COVID-19 patients with cancer are at a significantly higher risk of death. Our objective was to compare patients hospitalized for COVID-19 with cancer to those without cancer using national data and to study the effect of cancer on the risk of hospital death and intensive care unit (ICU) admission. (2) Methods: All patients hospitalized in France for COVID-19 in March–April 2020 were included from the French national administrative database, which contains discharge summaries for all hospital admissions in France. Cancer patients were identified within this population. The effect of cancer was estimated with logistic regression, adjusting for age, sex and comorbidities. (3) Results: Among the 89,530 COVID-19 patients, we identified 6201 cancer patients (6.9%). These patients were older and were more likely to be men and to have complications (acute respiratory and kidney failure, venous thrombosis, atrial fibrillation) than those without cancer. In patients with hematological cancer, admission to ICU was significantly more frequent (24.8%) than patients without cancer (16.4%) (p < 0.01). Solid cancer patients without metastasis had a significantly higher mortality risk than patients without cancer (aOR = 1.4 [1.3–1.5]), and the difference was even more marked for metastatic solid cancer patients (aOR = 3.6 [3.2–4.0]). Compared to patients with colorectal cancer, patients with lung cancer, digestive cancer (excluding colorectal cancer) and hematological cancer had a higher mortality risk (aOR = 2.0 [1.6–2.6], 1.6 [1.3–2.1] and 1.4 [1.1–1.8], respectively). (4) Conclusions: This study shows that, in France, patients with COVID-19 and cancer have a two-fold risk of death when compared to COVID-19 patients without cancer. We suggest the need to reorganize facilities to prevent the contamination of patients being treated for cancer, similar to what is already being done in some countries.

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The possible significance of trisomy 8 in acute myeloid leukemia

International Journal of Research in Medical Sciences ◽

10.18203/2320-6012.ijrms20172464 ◽

2017 ◽

Vol 5 (6) ◽

pp. 2652 ◽

Cited By ~ 2

Author(s):

Salil N. Vaniawala ◽

Monika V. Patel ◽

Pratik D. Chavda ◽

Shivangi H. Zaveri ◽

Pankaj K. Gadhia

Keyword(s):

Acute Myeloid Leukemia ◽

Chromosomal Aberrations ◽

Myeloid Leukemia ◽

Chromosomal Abnormalities ◽

Prognostic Significance ◽

Chromosomal Translocation ◽

Banding Technique ◽

Trisomy 8 ◽

Increased Risk ◽

Acute Myeloid

Background: Acute myeloid leukemia (AML) is a heterogeneous disorder that results from a block in the differentiation of haematopoietic progenitor cells along with uncontrolled proliferation. Trisomy 8 is the most common recurring numerical chromosomal aberrations in acute myeloid leukemia (AML). It occurs either as a sole anomaly or together with other additional chromosomal aberrations. The prognostic significance of trisomy 8 in presence of other additional chromosomal abnormality depends on clonal cytogenetic changes. The patients with trisomy 8 had shorter survival with significantly increased risk with other chromosomal abnormality.Methods: Total 139 patients were screened between January 2016 to November 2016 who were suspected of AML cases. Bone marrow cultures were set up using conventional cytogenetic methods. Chromosomal preparation was made and subjected to GTG banding technique. Banded metaphases were analysed and karyotyped for further analysis.Results: Cytogenetic evaluation of karyotyped of 139 suspected AML patients showed 52 with t(8;21)(q22;q22), 36 with t(15;17)(q22;q12), and 11 with inv(16)(p13;q22). The rest 40 cases found with additional chromosomal abnormalities, of which 16 were sole trisomy 8 and 24 cases were found with other chromosomal abnormalities In addition, only one person found with t(8;21) and trisomy 8, while three person having t(15;17) with trisomy 8.Conclusions: AML is considered to be one of the most important cytogenetic prognostic determinants. Recurrent chromosomal translocation with trisomy 8 varying 1.9% for t(8;21) and 8.3% for t(15;17). In the present study trisomy 8 in AML with known favourable anomalies is very small. Therefore, it cannot be taken as a prognostic marker.

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Using Histopathology Images to Predict Chromosomal Instability in Breast Cancer: A Deep Learning Approach

10.1101/2020.09.23.20200139 ◽

2020 ◽

Author(s):

Zhuoran Xu ◽

Akanksha Verma ◽

Uska Naveed ◽

Samuel Bakhoum ◽

Pegah Khosravi ◽

...

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Spatial Heterogeneity ◽

Cancer Patients ◽

Gold Standard ◽

Chromosomal Instability ◽

Human Cancer ◽

Breast Cancer Subtype ◽

The Cancer Genome Atlas ◽

Test Set

Chromosomal instability (CIN) is a hallmark of human cancer that involves mis-segregation of chromosomes during mitosis, leading to aneuploidy and genomic copy number heterogeneity. CIN is a prognostic marker in a variety of cancers, yet, gold-standard experimental assessment of chromosome mis-segregation is difficult in the routine clinical setting. As a result, CIN status is not readily testable for cancer patients in such setting. On the other hand, the gold-standard for cancer diagnosis and grading, histopathological examinations, are ubiquitously available. In this study, we sought to explore whether CIN status can be predicted using hematoxylin and eosin (H&E) histology in breast cancer patients. Specifically, we examined whether CIN, defined using a genomic aneuploidy burden approach, can be predicted using a deep learning-based model. We applied transfer learning on convolutional neural network (CNN) models to extract histological features and trained a multilayer perceptron (MLP) after aggregating patch features obtained from whole slide images. When applied to a breast cancer cohort of 1,010 patients (Training set: n=858 patients, Test set: n=152 patients) from The Cancer Genome Atlas (TCGA) where 485 patients have high CIN status, our model accurately classified CIN status, achieving an area under the curve (AUC) of 0.822 with 81.2% sensitivity and 68.7% specificity in the test set. Patch-level predictions of CIN status suggested intra-tumor spatial heterogeneity within slides. Moreover, presence of patches with high predicted CIN score within an entire slide was more predictive of clinical outcome than the average CIN score of the slide, thus underscoring the clinical importance of spatial heterogeneity. Overall, we demonstrated the ability of deep learning methods to predict CIN status based on histopathology slide images. Our model is not breast cancer subtype specific and the method can be potentially extended to other cancer types.

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