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Additional file 4: Fig. S1. RNA expression of TYMP within individual tissues. Data were extracted from the GTex Portal ( https://gtexportal.org ), a web resource studying human gene expression and regulation, and sorted by decreasing expression rate. The V8 Release used for this analysis was based on data from 17382 samples, 54 tissues, and 948 donors. Tissues with a TPM value under 40 were not presented. TPM: transcripts per million. Fig. S2. Assessment of CRISPR-Cas9 editing efficiency with gRNA targeting TYMP exon 5 in control and edited ASC. (A) Sanger sequencing of the target region (exon 5) confirmed high level of recombination. The expected break site is indicated by a vertical dotted line and the gRNA sequence is underlined. (B) Determination of CRISPR indel pattern in control and edited ASC by analyzing Sanger sequencing data with the Synthego software ( https://ice.synthego.com ). Left panel: The discordance plot details the rate of sequence alignment per base between the control and edited samples in the inference window (i.e., the region around the recombination site). Before the gRNA target site, the green line (edited sample) and the orange line (control sample) are close together. After the gRNA target site, a jump is observed, corresponding to a high level of sequence misalignment. Right panel: The graph displays the frequency of indels in relationship with the indel size. The editing efficiency corresponding to the recombination rate was evaluated at 91%. Fig. S3. Quantification of osteogenic markers in control and edited ASC. Data were obtained in ASC, ASC with a CRISPR-Cas9-mediated TP-knockout (KO), and ASC transduced with a Cas9/scramble gRNA plasmid corresponding to control (CTL) cells. The Western blots of osteoblast markers related to Fig. 3G were quantified using FIJI software and normalized to the value of CTL cells at D14. p-values were determined by analysis of variance (ANOVA) with Bonferroni’s post hoc multiple comparison test. n.s., non-significant. Fig. S4. Quantification of adipocyte markers in control and edited ASC. Data were obtained in ASC, ASC with a CRISPR-Cas9-mediated TP-knockout (KO), and ASC transduced with a Cas9/scramble gRNA plasmid corresponding to control (CTL) cells. The Western blots of adipocyte markers related to Fig. 4A were quantified using FIJI software and normalized to the value of CTL cells at D20. p-values were determined by analysis of variance (ANOVA) with Bonferroni’s post hoc multiple comparison test. *p

Additional file 3. Uncropped WBs: Uncropped and unedited Western blots seen in the different figures.

Additional file 1: Table S1. Prediction of pathogenicity of variants identified in TYMP. Bioinformatic tools used to predict the pathogenicity of splice site and missense variants are not the same. CADD: Combined Annotation Dependent Depletion; SIFT: Sorting Intolerant From Tolerant; SPiP: Splicing Prediction Pipeline.

Additional file 2: Table S2. List of predicted off-target sequences of the CRISPR/Cas9 editing strategy, with mismatch position and genomic location. The CRISPOR web tool ( http://crispor.tefor.net/ ) is well recognized to predict the risk of off-target sequences by providing a cutting frequency determination (CFD) specificity score ranging from 1 to 100. The higher the number, the lower the risk of off-target effects. It is based on the accurate CFD off-target model from Doench JG et al. (Nat Biotechnol 2016 Feb;34(2):184-196), which recommends guides with a CFD specificity score > 50. The gRNA used herein to target TYMP exon 5 has a CFD score of 84. This gRNA did not match perfectly any other genomic region. The table below provides a list of potential off-target sequences with up to three mismatches with the gRNA used (CAGAGATGTGACAGCCACCG). Notably, off-targets are considered if they are flanked by an NGG motif, which corresponds to the PAM sequence allowing the Cas9 to cut DNA.

Additional file 7: Table S6. Correlation between serum sCD26/DPP4 titer variation (%) and tumor volume change (%) or PFS (days) in 12 MM cases with Q2W administration by PPMC or SRDC analysis.

Additional file 2: Table S2. Detailed information about 26 evaluable cases

Additional file 6: Table S5. Correlation between serum sCD26/DPP4 titer variation (%) and tumor volume change (%) or PFS (days) in 19 MM cases by PPMC or SRDC analysis.

Additional file 8: Table S7. Correlation between serum sCD26/DPP4 titer variation (%) and tumor volume change (%) or PFS (days) in 9 MM, male cases with Q2W administration by PPMC or SRDC analysis.

Additional file 3: Figure S1. Correlation between serum soluble CD26 level and DPP4 enzyme activity

Additional file 9: Figure S2. Cell surface protein expression of CD26 on the human tumor and non-tumor cells.

Additional file 1: Table S1. Demographic chart of 26 evaluable cases on administration frequency and dosage, gender, PFS, age, BMI, RECIST evaluation, tumor volume change and serum sCD26/DPP4 titer change

Additional file 5: Table S4. Correlation between serum sCD26/DPP4 titer variation (%) and tumor volume change (%) or PFS (days) in 14 male cases with Q2W administration by PPMC or SRDC analysis

Additional file 4: Table S3. Correlation between serum sCD26/DPP4 titer variation (%) and tumor volume change (%) or PFS (days) in 18 cases with Q2W administration by PPMC or SRDC analysis

Supplementary methods. supplementary data on subject recruiting, blood collection, DNA extraction, genotyping and imputation. (DOCX 26 kb)

Additional file 1: The questionnaire used. More information on methods. More results from the interviews. Figure S1 explaining the MCA and HCA process. Figure S2 showing the standard deviation around intakes per patient (in hours), estimated from the MEMs data. Table S1 results of the content analysis of the interviews.