Fig 1: Personalized predictive cancer models were created for MM cell line MM.1S (ATCC CRL-2974) and MM cell line U266B1 (ATCC TIB-196). Single cell and multi-cell computational models became ‘personalized’ when cell line-specific genomic data were annotated into MM computational models and validated when predicted responses were compared to chemokine, cytokine, and cell-associated biomarker responses from the same cell lines grown alone or with DC in multi-cell cultures. (A–D) There were differences in the predicted and observed CD47, FASL, and PD-L1 responses and the IL6, IL-10 TGFB1, and VEGFA responses between MM.1S and U266B1. (E) When ‘personalized,’ U266B1 was predicted to have higher concentrations of IL-10, IL6, VEGFA, and PD-L1 than MM.1S and these predictions were validated by measuring the IL-10, IL6, VEGFA, and PD-L1 concentrations from MM.1S and U266B1 grown in single cell cultures. When the MM.1S vs. U266B1 responses were compared, the U266B1 > MM.1S matched 75% (3/4). (F) In multi-cell computational models with DC, U266B1 and MM.1S were predicted to inhibit DC CD80, CD86, IL2, IFNG, and IL12B responses. The percent change with respect to control was greatest with U266B1 > MM.1S. (G) Production of DC markers CD80, CD86, IL2, IFNG, and IL12B were lower when DC were cultured in multi-cell cultures with U266B1 or MM.1S: U266B1 attenuated DC marker production more than MM.1S and these responses matched 100% (6/6).
Fig 2: Correlation between CD56 expression on the surface of plasma cells, SOX4 expression and inflammation state in bone marrow niche. Transforming growth factor beta 1 (TGF|31; 5 ng/mL for 12 hours in whole RMPI medium) upregulates SOX4 and CD56 expression in commercial U266 (ATCC® TIB-196™, Manassas, VA, USA) and RPMI-8226 (ATCC® CCL-155™, Manassas, VA, USA) cell lines. (A) SOX4 (lower panels) and CD56 (upper panels) were analyzed using IDT PrimeTime® Predesigned qPCR Assays (Hs.PT.58.24974948.g for SOX4 and Hs. PT.58.39694135 for CD56; IDT, San Diego, CA, USA). The histogram values represent the mean of three independent experiments. Bars represent the standard deviation. The statistical analysis was performed by a Student’s t test for unpaired data. *P<0.05, **P<0.01. (B) CD56 expression on the surfaces of U266 and RPMI cells, as evaluated by fluorescence-activated cell sorting (FACS) analysis at 0 and 12 hours after incubation with TGFβ1 (NCAM1/CD56-APC BD Biosciences, #341027). (C) There was a significant, direct correlation (R2=0.49, P=0.02) between SOX4 (x axis) and CD56 (y axis) expression, as determined by IDT PrimeTime® Predesigned qPCR Assays (Hs. PT58.24974948.g for SOX4 and Hs.PT.58.39694135 for CD56; IDT, San Diego, CA, USA). Data are shown as fold change values in CD138-positive cells from 20 patients’ samples using actin B as the housekeeping gene. Data were analyzed by standard regression analysis. (D, E) TGFβ1 and progranulin (PGRN) concentrations in bone marrow CD56-negative (N=12) and CD56-positive (N=20) samples. The presence of CD56 was detected by FACS assay (NCAM1/CD56-APC BD Biosciences, #341027), intended for the diagnosis and follow-up of multiple myeloma. The concentrations of active TGFβ1 (D) and PRGN (E) in bone marrow plasma collected from enrolled patients were determined using a Quantikine ELISA Immunoassay (R&D systems, Bio-Techne Ltd., UK Cat. N. DY240) and human PGRN ELISA kit (AdipoGen Inc. Cat. N. AG-45A-0018YEK-KI01) according to the manufacturers’ instructions. The optical density of each sample was determined using a microplate reader (Tecan INFINITE M200) set at 450 nm, with wavelength correction set at 540 nm. The concentrations of TGFβ1 and PGRN were calculated using standard curves, considering the dilution factors, and healthy donor peripheral serum samples were used as control. The solid line within the box plot represents the mean value, the x denotes the median value. The statistical analysis was performed by a Student’s t test for unpaired data. *P<0.05.
Supplier Page from ATCC for U266B1 [U266]