Genomic landscape of Indian DLBCL: Ready for prime time.

Abstract

e23296 Background: While many studies have explored the mutational landscape of DLBCL, the Indian DLBCL remains relatively unexplored. Studies have shown Indian patients diagnosed with DLBCL face poorer outcomes; attributable to a lack of access to early testing and inability to afford treatment. Existing studies have also shown that mutations in certain genes are correlated with varying outcomes, with some mutations amenable to targeted therapy demonstrating better patient outcomes. Methods: Patient data for 61 Indian patients diagnosed with DLBCL-R/R was obtained. NGS using a targeted lymphoma panel (Genes2Me) comprising 75 genes was done. The data was collated and analyzed, with a comparison with studies across different regions of the world. The comparator data was obtained from cbioportal database. Results: The median age of the patients was 53. The most common mutations found in our data set of patients were TP53(29%), KMT2D (19%), CREBBP (12%), MYD88 (10%), SOCS1 (7%), TET2 (7%).In comparison KMT2D was the prevalent gene in the data sets obtained from cbioportal. In addition, no SGK1 mutations was found in our data set of Indian patients, but appeared with moderate frequency (7%) in the others. The lack of this mutation in our subset of population could lead to an overall poorer outcome relative to other populations with this mutation.Fisher’s exact test was used to determine if there was a significant correlation between gender and type of genetic mutation involved in the disease. Co-occurrences revealed values depicted in the table below but due to our small sample size, reader discretion is cautioned. Notably, all cases with ARID1A occurred in conjunction with KMT2D. This pattern can be observed in other studies as well, as data indicates more than 50% of all cases with ARID1A appeared with the KMT2D. Conclusions: This is an initial experience of genomic of DLBCL from India, which has not been highlighted in studies earlier. This clearly depicts differences from the West, which in turn results in differential responses and outcomes to standard of care therapies. This necessitates larger trials and regional policy making for treatment in a resource constrained setting with limited access to newer molecules. A B Neither A Not B B Not A Both Log2 Odds Ratio p-Value q-Value SOCS1 MYC 58 1 0 2 >3 0.003 1 HPSE2 ALK 60 0 0 1 >3 0.024 1 IDH1 ATM 60 0 0 1 >3 0.024 1 IDH1 NBN 60 0 0 1 >3 0.024 1 ATM NBN 60 0 0 1 >3 0.024 1 NEB FAT4 60 0 0 1 >3 0.024 1 IDH2 PMS2 60 0 0 1 >3 0.024 1 TCF3 ARHGEF12 60 0 0 1 >3 0.024 1 EZH2 FERMT1 60 0 0 1 >3 0.024 1 KMT2D ARID1A 53 6 0 2 >3 0.033 1 EGR2 POT1 59 1 0 1 >3 0.048 1 ARID1A POT1 59 1 0 1 >3 0.048 1 ARID1A TCF3 59 1 0 1 >3 0.048 1 Statistically relevant (p<0.05) co occurrence. Columns A and B contain the two genes that co-occur. Following Columns give data on the quantity and combinations in which they do or don’t co-occur. The final 2 columns include p-value and q-value, a value lower than 0.05 is considered statistically significant.