NEW YORK (GenomeWeb) – A team led by investigators at Brigham and Women's Hospital, Harvard Medical School, and the Broad Institute has uncovered evidence that checkpoint blockade immunotherapy response may involve shifts in the activity of tumor microenvironment T cells that do not express receptors for the immune checkpoint proteins that such therapies target.
"Our study broadens the focus of what checkpoint blockade therapy may be doing and how it mediates its effects," co-corresponding and co-senior author Ana Anderson, a BWH researcher, said in a statement.
Using bulk and single-cell RNA sequencing, she and her colleagues followed CD8+ tumor-infiltrating lymphocyte (TIL) dynamics in response to anti-PD-1 and anti-Tim-3 therapy in preclinical mouse models of colon carcinoma that did or did not respond to the checkpoint blockade immunotherapy. The findings, published online today in the journal Immunity, revealed widespread transcriptional changes in TILs that do not express PD-1 or other immune checkpoint receptor proteins, including TILs resembling naïve-, effector- and memory precursor-like CD8+ T cells.
"[We] were surprised and puzzled to find that even T cells that don't express checkpoint inhibitors showed significant changes at the genetic level," Anderson said, noting that these cells "have largely been ignored before."
The team noted that a particular expansion and proliferation of the effector- and memory precursor-like TILs following checkpoint blockade treatment, indirectly influencing response to the immunotherapy, though the activity of the memory precursor-like CD8+ T cells appeared to be at the mercy of a transcriptional regulator called Tcf7. In the absence of Tcf7, however, the investigators documented decreased immunotherapy response.
Direct interactions between immunotherapy antibodies and immune checkpoint receptor proteins such as PD-1 are most often credited with producing successful checkpoint blockade treatment responses, Anderson and her colleagues noted, since these interactions seem to liberate the CD8+ T cells to tackle tumors. But the broader tumor microenvironment was suspected of influencing this process as well, prompting the team's new analyses.
To that end, the researchers focused on PD-1-negative, Tim-3-negative, CD8+ T cells previously shown to have enhanced effector functional potential relative to CD8+ T cells that do express PD-1 and Tim-3 but showed strong "dysfunction signatures" in prior gene expression analyses.
Using antibody-based immune cell isolation, array-based gene expression profiling on bulk T cell samples, and RNA sequencing on individual TILs, the team characterized the activity of such T cells in anti-Tim-3-, anti-PD-1-treated mice bearing tumors corresponding to the MC38 colon carcinoma cell line. The treatment prompted transcriptional shifts in TILs that overlapped to some extent, regardless of checkpoint protein expression, particularly when it came to effector gene expression.
And in their subsequent experiments, the researchers saw treatment-related expansions of several TIL populations that lacked PD-1 — shifts that appeared to resemble those in human patients with good checkpoint blockade outcomes, based on available gene expression data for immune cells from dozens of individuals with non-small cell lung cancer, melanoma, or other cancers.
"[T]he identification of PD-1- CD8+ memory precursor-like TILs that share features with human CD8+ T cells associated with good prognosis and response to therapy has important clinical implications for the identification of biomarkers of therapeutic response," the authors concluded, "as well as of targets that can be modulated in T cells used for adoptive cell therapies to ensure sustained and durable responses."
Anderson further noted in the statement that the study "helps define an important immune cell population that responds to checkpoint blockade immunotherapy across different cancers and points to a critical factor in this therapy’s success," adding that further research may allow the team to "define biomarkers to predict a patient's response to therapy and identify which cells are most important to target with immunotherapy approaches."