There are various subtypes of T cells, each with a specific function. T cells control other T cells as well as other immune cells. T cells are located in various places in the body such as the lymph system, blood, mucous membranes such as the small intestine, inflammatory sites including cancer and sites of infection.

Dr. Hiroshi Kagamu, a professor at Saitama Medical University International Medical Center found that when he was at Niigata University, some of a certain subset of CD4 T cells (with low expression of biomarker CD62L, described as CD62Llow) have an anti-cancer effect and some of another type of CD4 T cells (regulatory T cells) can suppress that anti-cancer effect. He also discovered that in patients with small cell lung cancer, the ratio of the numbers of these two types of CD4 T cells in the blood relates to the type of lung cancer between limited and advanced types. Thus, also in humans, it can be considered that CD62Llow CD4 T cells and regulatory CD4 T cells can affect anti-cancer effects.

Furthermore, it is supposed that the effect of anti-PD-1 antibody, which is an immune checkpoint inhibitor, is determined by the immune status of patients, that is, the status of CD4 T cells in the blood. He examined the frequency of CD4 T cells in blood and the effect of anti-PD-1 antibody. Results of the clinical studies showed that when the frequency of CD62Llow CD4 T cells in blood is X and the frequency of regulatory CD4 T cells in blood is Y, the value of X2/Y can predict the effect of anti-PD-1 antibody (in press, Caner Immunology Research) (patent applied).

A patent that includes this effect prediction method as a part of its content has been applied for by Saitama Medical University, and ImmuniT Research Inc. has obtained an exclusive license for it. To commercialize this method, we have licensed out to Sysmex Corporation which is developing a diagnostic agent.

Direct anti-tumor effects are mainly performed by CD8 T cells which can recognize specifically cancer cells and exhibit cytotoxic activity. However, although anti-PD-1 antibody has been described as interfering with the interaction between PD-1 on CD8 T cells and PD-L1 on cancer cells, recent publications showed that the action of anti-PD-1 antibody may require PD-L1 expression on dendritic cells and other antigen presenting cells and that CD4 T cells are required for anti-PD-antibody function. Therefore, some important factors other than the interaction between CD8 T cells and cancer cells may be important. There should still be many unclear points about the effects of PD-1 inhibitors. An example of another immune checkpoint inhibitor of anti-CTLA-4 antibody not directly working on cancer cells is available, since this antibody works in the lymphatic system to release the brake on T cell immunity.

Research is currently being conducted at Saitama Medical University to determine whether the above X2/Y value can apply to the effects on other cancers or of other immune checkpoint inhibitors.

It has also been shown that clarifying the type of T cells in the blood is not limited to predicting the effects of PD-1 antibodies. Prof. Kagamu indicated that calculating the same X2/Y value can predict patients with long-term survival (5 years or more) after administration with anti-PD-1 antibody (the above paper and patent pending). Other effects of anti-PD-1 antibody administration can also be predicted (patent pending). Furthermore, it was found from the X2/Y values that the patients who did not expect the effect of anti-PD-1 antibody could be changed to a type that was expected to be effective after a certain treatment (patent pending).

As described above, by determining the value of X2/Y, it is possible to determine the treatment policy according to the X2/Y for each patient, Thus, we believe that precision medicine for PD-1 inhibitor therapy can be realized.

We know many other T cells in the blood besides those corresponding to X and Y above, and we believe that a comprehensive count of these numbers of each T cell types in the blood can reveal various findings. In addition to counting T cells as well as counting the number of many other types of cells at the same time, it can be expected that more insights will be obtained.

Thus, we are aiming to reveal all relationships of immune cell patterns with patient conditions by monitoring immune cells in the blood. For this purpose, we will:

  • ■ collect many blood samples
  • ■ create a system of processing and measuring many samples efficiently and perform them
  • ■ create a system of acquiring, storing and analyzing big data and perform them
  • ■ obtain insights from measured data by logical and mathematical procedures

and then we will develop an immune monitoring system.