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Cox et al. J Transl Genet Genom 2021;5:80-8 https://dx.doi.org/10.20517/jtgg.2021.06 Page 86

GENETICS OF THE TUMOR AS A MECHANISM OF RELAPSE AND RESISTANCE
Tumor evolution and escape are major mechanisms of resistance to chemotherapy or to checkpoint
blockade. Analysis of mutational burden has uncovered a novel mechanism of resistance to checkpoint
blockade and led to the development of a FDA-approved test to predict response . A predominant
[38]
[39]
mechanism of relapse enacted by tumors in CART cell therapy is antigen escape . The disease is able to
escape detection by the CART19 because the proliferating leukemic cells no longer express CD19 and are
not detected by the CAR. The initial complete response rate is 90%; however, 60%-70% of responders
eventually relapse with a variant of CD19 that is undetectable by the CAR [3,40] . This phenomenon was seen in
the first published phase I clinical trial of the CD19-directed 4-1BB-ζ CART cell therapy at the Children’s
[40]
Hospital of Philadelphia . The pre-treatment leukemic cells were analyzed and compared to the CD19-
negative relapsed cells by performing whole genome sequencing and RNA sequencing . These studies
[41]
revealed that the primary mechanism of antigen escape occurs in exon 2 of the CD19 gene . While
[41]
frameshift mutations in exon 2 were observed in some of these cases, it did not appear to be the primary
mechanism of the mutation . This led the team to look at gene regulation. The AVISPA algorithm was
[41]
used to predict which RNA-binding proteins are associated with a particular intron-exon site . This
[42]
algorithm revealed possible candidates for the regulation of CD19 exon 2, and regulation by SRSF3 splice
factor was confirmed by immunoprecipitation and SRSF3 knockdown experiments .
[41]

A genetic signature for mutated death receptor pathways was recently identified as a novel mechanism of
primary resistance to CART cell therapy in patients with ALL. In this study, a CRISPR/Cas9 library was
used to generate a pool of single-gene-knockout cells in the ALL cell line Nalm6 . These were then co-
[43]
cultured with CART19 cells and sgRNA sequencing was performed, which revealed gene enrichment for
[43]
death receptor pathways . This finding was corroborated by samples from two clinical trials in which the
leukemic cells in non-responders had significantly lower death receptor signature expression compared to
[43]
responders . These data were used to create a predictive multivariate logistic regression model to predict
treatment response based on death receptor signature , again confirming these findings.
[43]
CONCLUSION
The use of gene sequencing tools, the amount of genetics data available, and the bioinformatics tools to
analyze these data have exploded in recent years. Over the next decade, we expect to see genomic
sequencing incorporated into routine care. Non-invasive methods such as liquid biopsies to determine the
risk of cancer development, the presence of cancer, or the efficacy of therapy is the future of oncology and
precision medicine. Classification tools such as the one reviewed in this paper could help determine which
treatment or dose of treatment will be effective to better inform clinical decisions. We would expect see such
automated screening tools using genetic data in the not-so-distant future. As sequencing efficiency and cost
continue to improve and more productive computational methods and pipelines are developed,
personalized genomics will become a more attainable goal. The methods reviewed here have demonstrated
to be useful in the field of CART cell therapy to investigate the genomic factors involved in therapeutic
resistance initiated by the tumor, TME, or patient’s T cells. These studies will lead the way to future omics
investigations and targeted therapies to overcome the current challenges in CART cell therapy.

DECLARATIONS
Authors’ contributions
Designed the content of this review, wrote the manuscript, and approved the final version: Cox MJ,
Kenderian SS