Liquid biopsy: a non-invasive approach for Hodgkin lymphoma genotyping.
Miguel AlcocebaMaría García-ÁlvarezM Carmen ChillónCristina JiménezAlejandro MedinaAlicia AntónOscar BlancoLuis G DíazPilar TamayoVerónica González-CalleMaría Jesús VidalRebeca CuelloFrancisco Javier Díaz GálvezJosé Antonio QueizánAlejandro MartínMarcos GonzálezMiriam SanteroMaría Eugenia SarasquetePublished in: British journal of haematology (2021)
The Hodgkin lymphoma (HL) genomic landscape is hardly known due to the scarcity of tumour cells in the tissue. Liquid biopsy employing circulating tumour DNA (ctDNA) can emerge as an alternative tool for non-invasive genotyping. By using a custom next generation sequencing (NGS) panel in combination with unique molecule identifiers, we aimed to identify somatic variants in the ctDNA of 60 HL at diagnosis. A total of 277 variants were detected in 36 of the 49 samples (73·5%) with a good quality ctDNA sample. The median number of variants detected per patient was five (range 1-23) with a median variant allele frequency of 4·2% (0·84-28%). Genotyping revealed somatic variants in the following genes: SOCS1 (28%), IGLL5 (26%), TNFAIP3 (23%), GNA13 (23%), STAT6 (21%) and B2M (19%). Moreover, several poor prognosis features (high LDH, low serum albumin, B-symptoms, IPI ≥ 3 or at an advanced stage) were related to significantly higher amounts of ctDNA. Variant detection in ctDNA by NGS is a feasible approach to depict the genetic features of HL patients at diagnosis. Our data favour the implementation of liquid biopsy genotyping for the routine evaluation of HL patients.
Keyphrases
- copy number
- circulating tumor
- genome wide
- hodgkin lymphoma
- poor prognosis
- end stage renal disease
- high throughput
- primary care
- prognostic factors
- healthcare
- long non coding rna
- ultrasound guided
- quality improvement
- ionic liquid
- induced apoptosis
- signaling pathway
- gene expression
- oxidative stress
- physical activity
- single molecule
- fine needle aspiration
- case report
- patient reported outcomes
- deep learning
- quantum dots
- depressive symptoms