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Author

Arkajyoti Bhattacharya

Other affiliations: University of Groningen
Bio: Arkajyoti Bhattacharya is an academic researcher from University Medical Center Groningen. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 4, co-authored 9 publications receiving 58 citations. Previous affiliations of Arkajyoti Bhattacharya include University of Groningen.
Topics: Medicine, Biology, Gene, Cancer, Innate immune system

Papers
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Journal ArticleDOI
TL;DR: The authors present transcriptional adaptation to CNA (TACNA) profiling; a tool to extract the transcriptional effect of CNAs from expression data without requiring paired CNA profiles, and provide a novel tool to gain insight into how CNAs drive tumor behavior via altered expression levels.
Abstract: Copy number alterations (CNAs) can promote tumor progression by altering gene expression levels. Due to transcriptional adaptive mechanisms, however, CNAs do not always translate proportionally into altered expression levels. By reanalyzing >34,000 gene expression profiles, we reveal the degree of transcriptional adaptation to CNAs in a genome-wide fashion, which strongly associate with distinct biological processes. We then develop a platform-independent method-transcriptional adaptation to CNA profiling (TACNA profiling)-that extracts the transcriptional effects of CNAs from gene expression profiles without requiring paired CNA profiles. By applying TACNA profiling to >28,000 patient-derived tumor samples we define the landscape of transcriptional effects of CNAs. The utility of this landscape is demonstrated by the identification of four genes that are predicted to be involved in tumor immune evasion when transcriptionally affected by CNAs. In conclusion, we provide a novel tool to gain insight into how CNAs drive tumor behavior via altered expression levels.

50 citations

Journal ArticleDOI
07 Oct 2020
TL;DR: The data suggest that Cyclin E1 overexpression can be used to select patients for treatment with replication checkpoint inhibitors and point to mitotic catastrophe as an underlying mechanism.
Abstract: Oncogene-induced replication stress, for instance as a result of Cyclin E1 overexpression, causes genomic instability and has been linked to tumorigenesis. To survive high levels of replication stress, tumors depend on pathways to deal with these DNA lesions, which represent a therapeutically actionable vulnerability. We aimed to uncover the consequences of Cyclin E1 or Cdc25A overexpression on replication kinetics, mitotic progression, and the sensitivity to inhibitors of the WEE1 and ATR replication checkpoint kinases. We modeled oncogene-induced replication stress using inducible expression of Cyclin E1 or Cdc25A in non-transformed RPE-1 cells, either in a TP53 wild-type or TP53-mutant background. DNA fiber analysis showed Cyclin E1 or Cdc25A overexpression to slow replication speed. The resulting replication-derived DNA lesions were transmitted into mitosis causing chromosome segregation defects. Single cell sequencing revealed that replication stress and mitotic defects upon Cyclin E1 or Cdc25A overexpression resulted in genomic instability. ATR or WEE1 inhibition exacerbated the mitotic aberrancies induced by Cyclin E1 or Cdc25A overexpression, and caused cytotoxicity. Both these phenotypes were exacerbated upon p53 inactivation. Conversely, downregulation of Cyclin E1 rescued both replication kinetics, as well as sensitivity to ATR and WEE1 inhibitors. Taken together, Cyclin E1 or Cdc25A-induced replication stress leads to mitotic segregation defects and genomic instability. These mitotic defects are exacerbated by inhibition of ATR or WEE1 and therefore point to mitotic catastrophe as an underlying mechanism. Importantly, our data suggest that Cyclin E1 overexpression can be used to select patients for treatment with replication checkpoint inhibitors.

34 citations

Journal ArticleDOI
TL;DR: In this article, the authors use a consensus independent component analysis and guilt-by-association approach to predict over 23,000 functional groups comprised of over 55,000 coding and non-coding transcripts using publicly available transcriptomic profiles.
Abstract: The interpretation of high throughput sequencing data is limited by our incomplete functional understanding of coding and non-coding transcripts. Reliably predicting the function of such transcripts can overcome this limitation. Here we report the use of a consensus independent component analysis and guilt-by-association approach to predict over 23,000 functional groups comprised of over 55,000 coding and non-coding transcripts using publicly available transcriptomic profiles. We show that, compared to using Principal Component Analysis, Independent Component Analysis-derived transcriptional components enable more confident functionality predictions, improve predictions when new members are added to the gene sets, and are less affected by gene multi-functionality. Predictions generated using human or mouse transcriptomic data are made available for exploration in a publicly available web portal. Our understanding of the function of many transcripts is still incomplete, limiting the interpretability of transcriptomic data. Here the authors use consensus-independent component analysis, together with a guilt-by-association approach, to improve the prediction of gene function.

17 citations

Journal ArticleDOI
01 May 2018-Ejso
TL;DR: The updated models resulted in more accurate ALN metastasis prediction and could be useful preoperative tools in selecting low-risk patients for omission of axillary surgery.
Abstract: Purpose This study aimed to validate and update a model for predicting the risk of axillary lymph node (ALN) metastasis for assisting clinical decision-making. Methods We included breast cancer patients diagnosed at six Dutch hospitals between 2011 and 2015 to validate the original model which includes six variables: clinical tumor size, tumor grade, estrogen receptor status, lymph node longest axis, cortical thickness and hilum status as detected by ultrasonography. Subsequently, we updated the original model using generalized linear model (GLM) tree analysis and by adjusting its intercept and slope. The area under the receiver operator characteristic curve (AUC) and calibration curve were used to assess the original and updated models. Clinical usefulness of the model was evaluated by false-negative rates (FNRs) at different cut-off points for the predictive probability. Results Data from 1416 patients were analyzed. The AUC for the original model was 0.774. Patients were classified into four risk groups by GLM analysis, for which four updated models were created. The AUC for the updated models was 0.812. The calibration curves showed that the updated model predictions were better in agreement with actual observations than the original model predictions. FNRs of the updated models were lower than the preset 10% at all cut-off points when the predictive probability was less than 12.0%. Conclusions The original model showed good performance in the Dutch validation population. The updated models resulted in more accurate ALN metastasis prediction and could be useful preoperative tools in selecting low-risk patients for omission of axillary surgery.

13 citations


Cited by
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01 Jan 2001
TL;DR: Genetical genomics as discussed by the authors combines the power of genomics and genetics in a way that is likely to become instrumental in the further unravelling of metabolic, regulatory and developmental pathways.
Abstract: The recent successes of genome-wide expression profiling in biology tend to overlook the power of genetics. We here propose a merger of genomics and genetics into ‘genetical genomics’. This involves expression profiling and marker-based fingerprinting of each individual of a segregating population, and exploits all the statistical tools used in the analysis of quantitative trait loci. Genetical genomics will combine the power of two different worlds in a way that is likely to become instrumental in the further unravelling of metabolic, regulatory and developmental pathways.

952 citations

01 Jan 1988

249 citations

Journal ArticleDOI
TL;DR: Radiomics methods based on the fat-suppressed T2 sequence and the nomogram are helpful for preoperative accurate predicting ALN metastasis in breast carcinoma.

39 citations

Journal ArticleDOI
TL;DR: In this article, the origins of the mutational landscape associated with BRCAness were explored by exploring the molecular biology of alternative DNA repair pathways, which may represent actionable therapeutic targets in in these cells.
Abstract: Tumours with mutations in the BRCA1/BRCA2 genes have impaired double-stranded DNA break repair, compromised replication fork protection and increased sensitivity to replication blocking agents, a phenotype collectively known as ‘BRCAness’. Tumours with a BRCAness phenotype become dependent on alternative repair pathways that are error-prone and introduce specific patterns of somatic mutations across the genome. The increasing availability of next-generation sequencing data of tumour samples has enabled identification of distinct mutational signatures associated with BRCAness. These signatures reveal that alternative repair pathways, including Polymerase θ-mediated alternative end-joining and RAD52-mediated single strand annealing are active in BRCA1/2-deficient tumours, pointing towards potential therapeutic targets in these tumours. Additionally, insight into the mutations and consequences of unrepaired DNA lesions may also aid in the identification of BRCA-like tumours lacking BRCA1/BRCA2 gene inactivation. This is clinically relevant, as these tumours respond favourably to treatment with DNA-damaging agents, including PARP inhibitors or cisplatin, which have been successfully used to treat patients with BRCA1/2-defective tumours. In this review, we aim to provide insight in the origins of the mutational landscape associated with BRCAness by exploring the molecular biology of alternative DNA repair pathways, which may represent actionable therapeutic targets in in these cells.

33 citations