Figure 4
Figure 4. iPEPs at baseline and after 3 and 9 cycles of induction therapy. iPEPs of CD4 and CD8 T lymphocytes and CD56dim and CD56bright NK cells in PB samples from high-risk patients with SMM (n = 31) studied at baseline and after 3 and 9 cycles of induction therapy with LenDex. (A) Each of the 63 phenotypic parameters evaluated is distributed per individual columns, indicated at the bottom as “expression of the marker/immune cell population,” and represented by color bars depicting normalized intensity values against those observed in heathy individuals aged >60 years (n = 10), ranging from low (dark green) to high (dark red) expression levels. (B) PCA graphical view of patient iPEPs. In the 2-dimensional PCA representation (left), each patient is represented by a single dot colored according to the sample time point: baseline (orange) and cycles 3 (blue), and 9 (green) of LenDex, whereas in the 3-dimensional PCA representation (right), all patient samples were grouped according to their respective time point. PCA, principal component analysis.

iPEPs at baseline and after 3 and 9 cycles of induction therapy. iPEPs of CD4 and CD8 T lymphocytes and CD56dim and CD56bright NK cells in PB samples from high-risk patients with SMM (n = 31) studied at baseline and after 3 and 9 cycles of induction therapy with LenDex. (A) Each of the 63 phenotypic parameters evaluated is distributed per individual columns, indicated at the bottom as “expression of the marker/immune cell population,” and represented by color bars depicting normalized intensity values against those observed in heathy individuals aged >60 years (n = 10), ranging from low (dark green) to high (dark red) expression levels. (B) PCA graphical view of patient iPEPs. In the 2-dimensional PCA representation (left), each patient is represented by a single dot colored according to the sample time point: baseline (orange) and cycles 3 (blue), and 9 (green) of LenDex, whereas in the 3-dimensional PCA representation (right), all patient samples were grouped according to their respective time point. PCA, principal component analysis.

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