Biology subjects

Paramasivam, N.

Publications and source records attributed to Paramasivam, N..

4 recordsLinked to original sources

bioRxiv2023

Proteomic profiling identifies Serpin B9 as mediator of resistance to CAR T-cell and bispecific antibody treatment in B-cell lymphoma

Although T-cell-engaging therapies are highly effective in patients with relapsed and/or refractory B-cell non-Hodgkin lymphoma (B-NHL), responses are often not durable. To identify tumor-intrinsic drivers of resistance, we quantified in-vitro response to CD19-directed chimeric antigen receptor T-cells (CD19-CAR) and bispecific antibodies (BsAb) across 46 B-NHL cell lines and measured their proteomic profiles at baseline. Among the proteins associated with poor in-vitro response was Serpin B9, an endogenous granzyme B inhibitor. Knock-out of SERPINB9 in cell lines with high intrinsic expression rendered them more susceptible to CD19-CAR and CD19-BsAb. Overexpression in cell lines with low intrinsic expression attenuated responses. Polatuzumab, vorinostat, lenalidomide, or checkpoint inhibitors improved response to CD19-CAR, although independently of Serpin B9 expression. Besides providing an important resource of therapy response and proteomic profiles, this study refines our understanding of resistance in T-cell engaging therapies, and suggests clinically relevant combination regimes.

cancer biology↗

bioRxiv2023

ZygosityPredictor

SummaryZygosityPredictor provides functionality to evaluate how many copies of a gene are affected by mutations in next generation sequencing data. In cancer samples, the tool processes both somatic and germline mutations. In particular, ZygosityPredictor computes the number of affected copies for single nucleotide variants and small insertions and deletions (Indels). In addition, the tool integrates information at gene level via phasing of several variants and subsequent logic to derive how strongly a gene is affected by mutations and provides a measure of confidence. This information is of particular interest in precision oncology, e.g. when assessing whether unmutated copies of tumor-suppressor genes remain. Availability and implementationZygosityPredictor was implemented as an R-package and is available via Bioconductor at https://bioconductor.org/packages/ZygosityPredictor. Detailed documentation is provided in the vignette including application to an example genome.

bioinformatics↗

bioRxiv2020

A novel germline mutation in the POT1 gene predisposes to familial non-medullary thyroid cancer

Non-medullary thyroid cancer (NMTC) is a common endocrine malignancy with a genetic basis that has yet to be unequivocally established. In a recent whole genome sequencing study of five families with recurrence of NMTCs, we shortlisted promising variants with the help of bioinformatics tools. Here, we report in silico analyses and in vitro experiments on a novel germline variant (p.V29L) in the highly conserved oligonucleotide/oligosaccharide binding domain of the Protection of Telomeres 1 (POT1) gene in one of the families. The results showed that the variant demonstrates a reduction in telomere-bound POT1 levels in the mutant protein as compared to its wild-type counterpart. HEK293Tcells carrying POT1V29L showed increased telomere length in comparison to wild type cells, strongly suggesting that the mutation causes telomere dysfunction and may play a role in predisposition to NMTC in this family. This study reports the first germline POT1 mutation in a family with a predominance of thyroid cancer, thereby expanding the spectrum of cancers associated with mutations in the shelterin complex.

genomics↗

bioRxiv2017

Identification and prioritisation of causal variants in human genetic disorders from exome or whole genome sequencing data

With genome sequencing entering the clinics as diagnostic tool to study genetic disorders, there is an increasing need for bioinformatics solutions that enable precise causal variant identification in a timely manner.\n\nBackgroundWorkflows for the identification of candidate disease-causing variants perform usually the following tasks: i) identification of variants; ii) filtering of variants to remove polymorphisms and technical artifacts; and iii) prioritization of the remaining variants to provide a small set of candidates for further analysis.\n\nMethodsHere, we present a pipeline designed to identify variants and prioritize the variants and genes from trio sequencing or pedigree-based sequencing data into different tiers.\n\nResultsWe show how this pipeline was applied in a study of patients with neurodevelopmental disorders of unknown cause, where it helped to identify the causal variants in more than 35% of the cases.\n\nConclusionsClassification and prioritization of variants into different tiers helps to select a small set of variants for downstream analysis.

bioinformatics↗