Photo by Rhoda Baer
Epigenomic heterogeneity at diagnosis may predict relapse in diffuse large B-cell lymphoma (DLBCL), according to research published in Nature Communications.
Investigators made this connection by reviewing biopsies taken from DLBCL patients before and after treatment.
The epigenome in these patients’ cancer cells changed greatly after treatment, and the global epigenome of pretreatment biopsies was substantially different in patients who relapsed and those who did not. There was more cell-to-cell heterogeneity in patients who relapsed.
“This is the first study I know of in cancer that looks at changes in the epigenome before and after treatment, and what we found could ultimately make traditional treatments much more effective,” said study author Olivier Elemento, PhD, of Weill Cornell Medical College in New York, New York.
To uncover the role of epigenetic involvement in DLBCL, Dr Elemento and his colleagues analyzed banked biopsies from patients. In each sample set, the investigators looked at sites in the epigenome where a methyl group was added or removed after DLBCL recurred.
They found a change in methylation that occurred between 39,808 and 1,035,960 specific methylation sites, depending on the sample. In addition, they identified between 78 and 13,162 differently methylated regions in the epigenome in relapsed disease.
“These are massive changes, given that the epigenome has 20 million methylation sites,” Dr Elemento said. “Our study shows that, in some cases, up to one-twentieth of the entire epigenome is changed after treatment. There are many more epigenetic changes than there are altered genes in DLBCL.”
“Once you have changes in methylation, the end result is an imbalanced expression of proteins,” added Giorgio Inghirami, MD, also of Weill Cornell.
“The tumor after chemotherapy is not the same as the tumor before treatment. This why it is so critical to have biopsies before any treatment of [primary or relapsed] lesions.”
The investigators hope this work will ultimately allow clinicians and researchers to predict treatment resistance in individual patients.
