Genomic Analysis Unravels Complexities of the Most Common Form of Lymphoma and Enables Personalized Treatment:
The majority of patients with diffuse large B cell lymphoma (DLBCL) can be treated effectively. However, people whose disease recurs face a shortage of good options, especially because the disease is driven by a complicated mix of genetic alterations. Genomic analysis by scientists at Dana-Farber Cancer Institute and the Broad Institute of MIT and Harvard now offers a better framework for understanding the disease’s many forms, which will help to predict individual patient outcomes and guide personalized treatment.
Analyzing 304 patient samples, the study showed that DLBCL tumors can
be divided into five genetic subtypes, says Margaret Shipp, MD, chief
of Dana-Farber’s Hematologic Neoplasia division and director of the
Lymphoma Program of the Dana-Farber/Harvard Cancer Center.
These
genetic subtypes can help in identifying likely therapeutic targets,
some of which can be inhibited by drugs that already are approved for
other conditions or in clinical trials, says Shipp, who, along with
Broad Institute’s Gad Getz, is co-corresponding author on a
Nature Medicine paper presenting the work.
“These
genetic signatures also clearly suggest that we want to think about
using a combination of targeted agents, because in DLBCL, combinations
of genetic alterations occur together in specific subtypes,” she says.
DLBCL
is the most common form of lymphoma among adults, with roughly 25,000
people diagnosed with the disease each year in the United States. About
60% of patients can be treated successfully with a combination of four
chemotherapies plus a targeted drug that inhibits a B cell surface
protein. “But the other very substantial fraction of patients develops
recurrent disease, and their treatment options are far less successful,”
says Shipp.
Current clinical tests do a relatively good job of
predicting which patients with DLBCL can be treated effectively with
standard treatments, but the tests do not offer insights into how
treatments could be improved for other patients. The Dana-Farber/Broad
collaboration is among several research groups bringing genomic tools to
this task. An earlier effort led by National Cancer Institute (NCI)
scientists established a widely used “cell of origin” classification
scheme for DLBCL, which employed RNA profiling to categorize tumor cells
by stages of normal B-cell development.
Unlike previous DLBCL
research efforts, Shipp says, the Dana-Farber/Broad collaboration sought
to integrate data on three types of genetic alterations that can drive
tumors–mutations to genes, changes in gene copy numbers and chromosomal
rearrangements–and define previously unappreciated disease
substructure.
“Specific genes that were perturbed by mutations
could also be altered by changes in gene copy numbers or by chromosomal
rearrangements, underscoring the importance of evaluating all three
types of genetic alterations,” Shipp notes. “Most importantly, we saw
that there were five discrete types of DLBCL that were distinguished one
from another on the basis of the specific types of genetic alterations
that occurred in combination.”
The investigators followed up to
examine these tumor subtypes by RNA data associated with cell of origin.
They found that each of the two major cell-of-origin subtypes could be
split into separate categories with distinct genetic signatures. An
additional subtype defined by
TP53 gene alterations and
associated genomic instability was unrelated to cell of origin. The team
then went on to discover clear links between given genetic subtypes and
how patients responded to standard treatment.
“We feel this
research opens the door to a whole series of additional investigations
to understand how the combinations of these genetic alterations work
together, and then to use that information to benefit patients with
targeted therapies,” says Shipp.
The study underlined the high
genetic diversity in DLBCL—for instance, the median number of genetic
driver alterations in individual tumors was 17. “That large number of
alterations tells us that we need to understand the complexity of the
genetic signature, because it's unlikely that simply focusing on one
genetic alteration will be enough to target therapeutically,” Shipp
points out. “By understanding the genetic basis of that heterogeneity,
we will be able to apply more specifically targeted agents that have the
highest likelihood of impacting the right pathways in the right
patients.”
She and her collaborators are now working on creating a
clinical tool to identify these genetic signatures in patients.
Studying related biological mechanisms in the lab, the team also is
developing clinical trials that will match patients with given genetic
signatures to treatment that includes appropriate targeted agents.
Dana-Farber’s
Bjoern Chapuy and Broad’s Chip Stewart and Andrew Dunford are co-first
authors on the paper, and Broad’s Gad Getz is co-corresponding author.
Clinical investigators from the German High-Grade Lymphoma Study Group
and the Mayo Clinic also participated. “This was very much a
collaborative effort of a multidisciplinary group of investigators who
approached the problem with complementary skill sets,” Shipp emphasizes.
“This multidisciplinary model, which is increasingly used to
investigate cancers, will bring practical benefits for patients.”
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