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TMEM30A loss-of-function mutations drive lymphomagenesis and confer therapeutically exploitable vulnerability in B-cell lymphoma

Nature Medicine volume 26, pages 577–588 (2020)Cite this article

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Abstract

Transmembrane protein 30A (TMEM30A) maintains the asymmetric distribution of phosphatidylserine, an integral component of the cell membrane and ‘eat-me’ signal recognized by macrophages. Integrative genomic and transcriptomic analysis of diffuse large B-cell lymphoma (DLBCL) from the British Columbia population-based registry uncovered recurrent biallelic TMEM30A loss-of-function mutations, which were associated with a favorable outcome and uniquely observed in DLBCL. Using TMEM30A-knockout systems, increased accumulation of chemotherapy drugs was observed in TMEM30A-knockout cell lines and TMEM30A-mutated primary cells, explaining the improved treatment outcome. Furthermore, we found increased tumor-associated macrophages and an enhanced effect of anti-CD47 blockade limiting tumor growth in TMEM30A-knockout models. By contrast, we show that TMEM30A loss-of-function increases B-cell signaling following antigen stimulation—a mechanism conferring selective advantage during B-cell lymphoma development. Our data highlight a multifaceted role for TMEM30A in B-cell lymphomagenesis, and characterize intrinsic and extrinsic vulnerabilities of cancer cells that can be therapeutically exploited.

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Fig. 1: Impact of genetic alterations on prognosis and gene expression in DLBCL.
Fig. 2: Genetic characterization of TMEM30A.
Fig. 3: Prevalence and clinical impact of TMEM30A mutation.
Fig. 4: BCR mobility and B-cell signaling is increased in B-cell lymphoma lines with TMEM30A loss-of-function mutation.
Fig. 5: TMEM30A loss of function mediated increased drug uptake in DLBCL.
Fig. 6: TMEM30A loss of function enhanced survival with treatment and increased macrophage infiltration in mouse models.

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Data availability

Full data, including patient characteristics, mutation data and experimental data, are provided as Supplementary Tables 2, 3, 8 and 9) and in our previous paper49. The raw sequencing data have been deposited in the European Genome-phenome Archive (EGA) under accession number EGAS00001002657. Source data for Fig. 5b and Extended Data Figs. 3a, 6g and 7f are presented with the paper.

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Acknowledgements

This study was supported by a Program Project Grant from the Terry Fox Research Institute (R.D.G, grant no. 1023; C.S., grant no. 1061), a Large Scale Applied Research Project (LSARP) from Genome Canada (C.S., grant no. 13124), Genome British Columbia (C.S., 271LYM), Canadian Institutes of Health Research (CIHR) (C.S., GP1-155873) and the British Columbia Cancer Foundation (BCCF). D.E. was supported by fellowships from the Michael Smith Foundation for Health Research (MSFHR), Canadian Institutes of Health Research (CIHR) and Japanese Society for The Promotion of Science. D.W.S. is supported by the British Columbia Cancer Foundation (BCCF) and the Michael Smith Foundation for Health Research (MSFHR). J.M.C. was the Clinical Director of the British Columbia Cancer Agency Centre for Lymphoid Cancer and received research funding support from the Terry Fox Research Institute, Genome Canada, Genome British Columbia, CIHR and the BCCF. C.H. was supported by the Alfred Benzon foundation. A.M. is supported by fellowships from the Mildred-Scheel-Cancer-Foundation (German Cancer Aid), the MSFHR and Lymphoma Canada. E.V. is supported by a fellowship from the Michael Smith Foundation for Health Research. G.V.C.F is supported by an NSERC Discovery grant. R.D.M. is funded by a CIHR New Investigator Award. M.A.M. is UBC Canada Research Chair in Genome Science and is pleased to acknowledge support from CIHR (FDN-143288) and the Terry Fox Research Institute. S.P.S. holds the Canada Research Chair in Computational Cancer Genomics, is a MSFHR scholar, holds a CIHR Foundation grant and acknowledges support from the BCCF. T.A. was supported by the Japanese Society for the Promotion of Science and the Uehara Memorial Foundation. T.A. received research funding support from The Kanae Foundation for the Promotion of Medical Science. L.A., as well as the experiments carried out by L.A., were supported by CIHR grant PJT-152946 (to M.R.G.). R.S.M holds a Canada Research Chair in Vision and Macular Degeneration and acknowledges support from CIHR (PJT-148649). K.T. was supported by fellowships from the Uehara Memorial Foundation. We thank E. Toombs for assistance in performing research, analyzing and interpreting data.

Author information

Author notes

  1. These authors contributed equally: Daisuke Ennishi, Shannon Healy, Ali Bashashati.

  2. These authors jointly supervised this work: Sohrab P. Shah, Randy D. Gascoyne, David W. Scott, Christian Steidl.

Authors and Affiliations

  1. Centre for Lymphoid Cancer, British Columbia Cancer, Vancouver, British Columbia, Canada

    Daisuke Ennishi, Shannon Healy, Christoffer Hother, Anja Mottok, Fong Chun Chan, Lauren Chong, Robert Kridel, Merrill Boyle, Barbara Meissner, Tomohiro Aoki, Katsuyoshi Takata, Bruce W. Woolcock, Elena Viganò, Adele Telenius, Michael Y. Li, Gerben Duns, Abigail Baticados, Angel Madero, Brianna N. Bristow, Pedro Farinha, Graham W. Slack, Susana Ben-Neriah, Hennady P. Shulha, Alina S. Gerrie, Diego Villa, Laurie H. Sehn, Kerry J. Savage, Joseph M. Connors, Randy D. Gascoyne, David W. Scott & Christian Steidl

  2. Department of Hematology and Oncology, Okayama University Hospital, Okayama, Japan

    Daisuke Ennishi

  3. Molecular Oncology, British Columbia Cancer, Vancouver, British Columbia, Canada

    Ali Bashashati, Saeed Saberi, Daniel Lai, Allen W. Zhang, Sohrab Salehi & Sohrab P. Shah

  4. Institute of Human Genetics, Ulm University and Ulm University Medical Center, Ulm, Germany

    Anja Mottok

  5. Bioinformatics Graduate Program, University of British Columbia, Vancouver, British Columbia, Canada

    Fong Chun Chan

  6. Department of Microbiology and Immunology, University of British Columbia, Vancouver, British Columbia, Canada

    Libin Abraham & Michael Gold

  7. Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, British Columbia, Canada

    Laurie L. Molday & Robert S. Molday

  8. Department of Experimental Therapeutics, British Columbia Cancer, Vancouver, British Columbia, Canada

    Nicole Wretham, Nancy Dos Santos & Marcel B. Bally

  9. Trillium Therapeutics Inc., Mississauga, Ontario, Canada

    Mark Wong, Natasja N. Viller & Robert A. Uger

  10. Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada

    Derek S. Chiu, Sara Mostafavi & Gabriela V. Cohen Freue

  11. Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada

    Sara Mostafavi & Marco A. Marra

  12. Lymphoid Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    Da Wei Huang & Louis M. Staudt

  13. Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, British Columbia, Canada

    Christopher Rushton & Ryan D. Morin

  14. Canada’s Michael Smith Genome Sciences Centre, British Columbia Cancer, Vancouver, British Columbia, Canada

    Andrew J. Mungall, Ryan D. Morin, Marco A. Marra & Sohrab P. Shah

  15. Terry Fox Laboratory, British Columbia Cancer, Vancouver, British Columbia, Canada

    Andrew P. Weng

  16. Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada

    Sohrab P. Shah, Randy D. Gascoyne & Christian Steidl

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  1. Daisuke Ennishi
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Contributions

D.E., S.H., A.B., R.D.G., S.P.S., D.W.S. and C.S. designed and performed the research, analyzed and interpreted data and wrote the paper; D.E. and A.J.M. performed library construction and DNA- and RNA-seq; S. Saberi analyzed and interpreted the genomic and transcriptome data and also contributed to some of the figures; C.H., F.C.C., L.C., D.L., A.W.Z., S. Salehi, G.D., C.R., R.D.M. and H.P.S. performed and supervised genomic and transcriptome data analysis; A.B., M.A.M. and R.D.M. provided bioinformatics assistance; S.H., T.A., N.W., N.D.S., R.S.M., L.L.M., M.Y.L. and K.T. performed in vitro and in vivo studies under the supervision of A.P.W. and M.B.B.; L.A. performed single-particle tracking studies under the supervision of M.G; M.W., N.N.V. and R.A.U. performed and helped SIRPα experiments; A.B., B.N.B., B.W.W., A.M. E.V. and A.T. designed, performed and validated experimental material; D.E., B.M., M.B. and S.B.-N. performed RNA and DNA extractions and FISH analysis; L.L.M. and R.S.M. performed flippase immunoprecipitations and activity assays; A.M., P.F., G.W.S. and R.D.G. performed pathological review of samples; A.M., P.F. and R.D.G. stained and scored IHC work; D.S.C., G.D., S.M. and G.V.C.F. analyzed clinical data and developed the prognostic model; D.W.H. and L.M.S. analyzed publicly available datasets; R.K., A.S.G., D.V., L.H.S., K.J.S. and J.M.C. assembled and interpreted clinical data; S.P.S., D.W.S., C.S. and R.D.G. supervised the study.

Corresponding authors

Correspondence to David W. Scott or Christian Steidl.

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Competing interests

D.W.S., J.M.C. and R.D.G. are inventors on a patent held by the National Cancer Institute that has been licensed by NanoString Technologies. C.S. has performed consultancy for Seattle Genetics, Curis Inc., Roche, AbbVie, Juno Therapeutics and Bayer, and has received research funding from Bristol-Myers Squibb and Trillium Therapeutics Inc. D.W.S. has performed consultancy for Janssen and Celgene and has received research funding from Roche/Genentech, Janssen and NanoString Technologies. R.D.G. has performed consultancy for Celgene. R.K. received travel support from Roche. D.V., A.S.G, K.J.S., J.M.C., L.H.S. and D.W.S. received institutional research funding from Roche. L.H.S. has performed consultancy and received honoraria from Amgen, Abbvie, Apobiologix, Celgene, Lundbeck, Janssen, Karyopharm, TG Therapeutics, Roche/Genentech, Teva and Takeda. K.J.S. received honoraria and provided consultancy to Bristol-Myers Squibb, Merck, Takeda, Verastem and Servier. A.S.G. received honoraria and provided consultancy to Janssen, AbbVie and Seattle Genetics. D.V. received honoraria from and participated in advisory boards for: Roche, Abbvie, Celgene, Seattle Genetics, Lundbeck, AstraZeneca, Nanostring, Janssen and Gilead.

Additional information

Peer review information Javier Carmona was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Recurrent mutation and copy number alterations (CNAs) of DLBCL.

a, Frequencies and type of mutations identified by deep targeted sequencing in 57 recurrently mutated genes in 347 DLBCL tumors. Genes significantly enriched in ABC-DLBCL and GCB-DLBCL are highlighted in blue and orange, respectively. Colored asterisks represent the pathways to which mutations belong. b, Significant (q-value<0.25) focal amplifications (red) and deletions (blue) identified by GISTIC 2.0 analysis (n = 338). Representative genes (functionally known in DLBCL, as well as other cancers) within significant GISTIC regions are annotated. Genes significantly enriched in ABC-DLBCL and GCB-DLBCL are highlighted in blue and orange, respectively. Colored asterisks represent the pathways to which CNAs belong.

Extended Data Fig. 2 Frequencies and prognostic effects of individual genetic alterations.

Mutated genes (frequency > 5%) and representative CNAs within GISTIC regions were evaluated. Frequencies of genetic alterations are depicted according to COO subtype. Genes significantly enriched (adjusted p < 0.10, two-sided fisher’s exact test) in ABC-DLBCL (blue) and GCB-DLBCL (orange) are highlighted (left panel). *P < 0.05, **P < 0.01, ***P < 0.001 (adjusted by multiple test). Forest plot summarizes the results of univariate analyses (time-to progression (TTP)) for each genomic alteration, in all DLBCL (grey), in ABC-DLBCL (light blue) and in GCB-DLBCL (light-orange). Hazard ratios and 95% confidence intervals are shown. Yellow circles represent significant hazard ratios (p < 0.05) (right panel). HETD, heterozygous deletion; HOMD, homozygous deletion; AMP, amplification.

Extended Data Fig. 3 ATPase activity of ATP8A2-TMEM30A variants, and association of recurrent genetic alterations with TMEM30A mutation in DLBCL.

Human ATP8A2 was expressed with or without human TMEM30A or missense mutants in HEK293T cells. a, The expressed complex was purified on an ATP8A2 immunoaffinity column and the input and eluted samples were subjected to SDS gel electrophoresis and western blots were labeled for ATP8A2 with the Atp6C11 and Cdc50-7F4 antibodies. Cropped images from the same Western blot are shown. Tubulin was detected as a loading control for the input lanes. Independent experiments were repeated twice with similar results. Source Data Extended Fig. 3. b, ATPase activity is measured as nmols of ATP hydrolyzed per 30 min at 37 °C. ATPase activities of the isolated complexes were measured in either 100% dioleylphosphatidylcholine (PC) or a mixture of 80% dioleoylphosphatidylcholine: 20% dioleylphosphatidylserine (PS). Samples are: ATP8A2 - expression in the absence of expressed TMEM30A; ATP8A2/WT - co-expression of ATP8A2 with WT- TMEM30A; and ATP8A2/ W41L, ATP8A2/C94R, ATP8A2/D181Y. Graphs represent mean values ± s.d. (n = 3). c, Distributions of genetic alterations of TNFAIP3, PRDM1 and EPHA7 in the patients with TMEM30A mutation and deletion. The header includes status of TMEM30A mutation and heterozygous deletion. MT; mutation, CN; copy number. d, Distributions of recurrent mutation, COO subtypes and IPI groups in TMEM30A mutated and wild type DLBCL patients. The header includes the status of TMEM30A mutation and CNAs.

Source data

Extended Data Fig. 4 Prevalence and clinical impact of TMEM30A mutation.

a, b, Deep targeted sequencing data of transformed FL samples with TMEM30A mutation detected at the time of diagnosis or transformation (A). The affected positions of the TMEM30A are visualized in an integrative genomic viewer. Variant allelic frequencies are visualized in the pie charts per case (B). Those data demonstrate a fraction of variants at the time of diagnosis (upper row) and transformation (lower row). The number in the pie charts represents the coverage of sequencing. FL1179 exhibited the opposite pattern (T1-positive/T2-negative), however the variant was a missense mutation supported by relatively low coverage (five reads out of 81 (VAF=6%). (c–e) Subtype analysis of survival according to TMEM30A genetic alteration. Kaplan Meier curves represent TTP (upper) and OS (bottom) according to TMEM30A mutation in ABC-DLBCL (C), in GCB-DLBCL (D) and in IPI-low/low-intermediate group (E). P values were derived from two-sided log-rank test.

Extended Data Fig. 5 mRNA expression of TMEM30A, related flippases, scramblases and P4-ATPases in in vitro TMEM30A knockout cells.

a, A screenshot of a representative sequence trace file of a CRISPR-generated TMEM30A frameshift mutation in exon 1 (DOHH-2, MUT1). b, TMEM30A mRNA expression is reduced in 3 unique bi-allelic frame-shift mutations in Karpas422, compared with parental and single cell enriched controls. mRNA expression is normalized to GAPDH expression. Graphs represent mean values ± s.d. (n = 3). (c) mRNA expression of TMEM30A is ‘rescued’ in one unique bi-allelic TMEM30A mutant, by ectopically expressing a stable insertion of a TMEM30A cDNA. Graphs represent mean values ± s.d. (n = 3). d, mRNA levels of different scramblases (PLSCR1-5; ANO1-10, XKR1-9), flippases (TMEM30A-C) and P4-ATPases in Karpas422 (left panel) and NU-DUL-1 (right panel) native (WT, CON) and TMEM30A−/− (MUT) cell lines. Protein family members are categorized, and the heat map values show log-normalized expression by RNA-seq. e, XKR8 and ANO6 mRNA expression is reduced in 3 unique bi-allelic (XKR8) and mono-allelic (ANO6) frame-shift mutations in TMEM30A−/− and wildtype controls. mRNA expression is normalized to GAPDH expression. Graphs represent mean values ± s.d. (n = 3). All P values are based on a paired two-tailed Student’s t test.

Extended Data Fig. 6 BCR activation, mobility and clustering in NU-DUL-1 and DOHH-2 as measured by single particle tracking analysis.

a, Single-state diffusion coefficients and b, confinement-diameters are shown for both cell lines, and c, BCR tracks and d, transition rates are measured for DOHH-2. The dots indicate median values; lines indicate 95% confidence intervals ((n=3, DOHH-2) (n = 2, NU-DUL-1). (E-F) TMEM30A mutation does not alter CD19 diffusion and confinement diameters. Cells were labelled with anti-CD19 Fab-Cy3, settled onto poly-l-lysine coated coverslips and imaged for 10 s at 33 Hz. e, Single-state diffusion coefficients were calculated for all tracks and cumulative frequency curves are shown. The dots on the curves indicate the median values and are reported in brackets. Total number of tracks analyzed in each condition is shown above the plot. (f) The cumulative frequency curves of the confinement-diameters with the median values (dots on the curves) indicated in brackets. g, BCR activation of DLBCL cell lines following incubation with F(ab)2 fragments does not lead to an increase in apoptotic signaling through caspase 3 activation, measurable by western blot analysis. Cells (Karpas422) pretreated with vincristine were included as a positive control. Cropped images from the same Western blot are shown. B-actin is used as a loading control. Experiments were repeated independently three times with similar results. Source Data Extended Fig. 6. h, Ca2+ mobilization was measured in wild type (WT) and TMEM30A−/− NU-DUL-1, DOHH-2 and Karpas422 cells, following HBSS, F(ab)2 addition (10 and 40 μg/ml) and ionomycin. Graphs represent mean values ± s.d. (n = 3). Significance is evaluated using one-way ANOVA.

Source data

Extended Data Fig. 7 B-cell signalling following ibrutinib treatment in B cell lymphoma lines with TMEM30A loss-of-function mutation.

a, DOHH-2 toxicity of ibrutinib after 24 hours incubation. Toxicity between wildtype (red line) and TMEM30A−/− (blue line) was measured by WST1 colormetric assays (mean of triplicate is shown). P value is based on an unpaired two-tailed Student’s t test. b, Comparisons of CRISPR screen score (CSS) between ibrutinib-sensitive vs -resistance cell lines in publicly available dataset. CSS was defined as the number of standard deviations away from the average effect of inactivating a gene. The differences of CSS in TMEM30A-tageting was not observed, while there are strong differences of CSS in other genes known to be involved in the determination of ibrutinib-sensitivity (for example MYD88, MEF2B etc) c, Ca2+ mobilization was measured in wildtype (WT) and TMEM30A−/− DOHH-2 cells, following HBSS, F(ab)2 and ionomycin, with or without (UT) overnight pretreatment with ibrutinib. Graphs represent mean values ± s.d. (n = 3 (HBSS), n = 6 (F(ab)2), n = 3 (iono)). d, Effects of ibrutinib pretreatment on F(ab)2 fragment PS exposure in DOHH-2 TMEM30A−/− and native control, measured by annexin V-APC binding. Addition of ibrutinib to F(ab)2 fragment stimulated TMEM30A−/− cells shows a significant reduction in PS exposure. Graphs represent mean values ± s.d. (n = 4). P value is based on a two-way ANOVA test. e, Induction of CD25 (left panel) and CD23 (right panel) following BCR activation in Karpas422 cell lines with (MUT) or without (CON) a biallelic loss-of function in TMEM30A. Surface marker expression was measured by flow cytometry, using protein specific antibodies. Graphs represent mean values ± s.d. (n = 3 (CD25), n = 5 (CD23)). P value is based on an unpaired two-tailed Student’s t test. f, Activation by phosphorylation of AKT and ERK1/2 in Karpas422 and NU-DUL-1 native and TMEM30A−/− cells, detected by Western blot analysis, following overnight F(ab)2 fragment stimulation. Cropped images from the same Western blot are shown. Unphosphorylated AKT or ERK1/2 is used as a loading control. Experiments were repeated independently three times with similar results. Source Data Extended Fig. 7.

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Extended Data Fig. 8 TMEM30A loss-of-function enhanced survival and increased macrophage engulfment following vincristine and CD47 blockade treatment.

a, Proliferation rates of native (blue line) and TMEM30A−/− (red line) DOHH-2 cells, measured over 3 days by WST1 absorbance. Cell numbers were extrapolated from a WST1 standard curve, using known quantities of DOHH-2. P value is based on an unpaired two-tailed Student’s t test. b, Xenotransplantation models of DOHH-2 cells with parental or TMEM30A−/− subcutaneously injected into NSG mice (5 per group). Vincristine treatment (0.1 mg/kg) or saline control (untreated) was administered when tumor volumes per group reached 100 mm3. Line graphs show tumor growth (mm3) over time (days). Graphs represent mean values ± s.d. P values are based on a paired two-tailed Student’s t test. *P<0.05. c, The comparison of macrophage counts in tumor tissue from untreated DOHH-2 xenotransplantation models, visualized by CD68 antibody with H&E counter staining, per tissue within randomly selected 400x high power field regions per tumor from 3 separate mice per group. Graphs represent mean values ± s.d. (n = 12 (control), n = 18 (MT)). P values are based on a paired two-tailed Student’s t test. d, Xenotransplantation models of DOHH-2 cells with parental or TMEM30A−/− subcutaneously injected into NSG mice (10 per group). TTI-621 treatment (10 mg/kg) or TTI-402 (6.67 mg/kg) was initiated when tumor volumes per group reached 100 mm3. Normalized mean tumor growth (mm3) over time (days) starting at injection time. Graphs represent mean values ± s.d. P values are based on a paired two-tailed Student’s t test. *P<0.05, **P<0.01.

Extended Data Fig. 9 In vitro and in vivo macrophage association and infiltration of A20 Tmem30A loss-of-function in Balb/c.

a, Syngeneic mouse modeling in Balb/c mice with Tmem30a−/− or wildtype (Tmem30a+/+) A20 cells. Proliferation rates of Tmem30a−/− (red line) and wildtype (blue line) A20 cells, measurable over 4 days by WST1 absorbance. P values are based on an unpaired two-tailed Student’s t test (n = 6). b, Comparison of PS exposure in Tmem30a−/− or wildtype A20 cells, measured by annexin V-APC binding. Graphs represent mean values ± s.d. (n = 8) P values are based on an unpaired two-tailed Student’s t test. c, Syngeneic mouse models of A20 cells with wildtype or Tmem30a−/− cells intravenously injected into Balb/c mice (5 per group). Kaplan-Meier plots show overall survival of Balb/c inoculated mice. P value is based on a Mantel-Cox test. d, Organ weight of livers from Balb/c mice intravenously injected with Tmem30a−/− or wildtype cells. All mice were sacked at the first presentation of abdominal distention in the control group (‘early sac’). Graphs represent mean values ± s.d. (n = 5). P values are based on an unpaired two-tailed Student’s t test. (E-F) Observation of tumor infiltrate in mice injected with Tmem30a−/− or wildtype cells at early sac. e, Blue or red filled column indicates positive tumor infiltration in wildtype or Tmem30a−/− cells, with blank indicating no tumor infiltration. f, H&E staining of each organ at early sac (liver, spleen, lung, and bone marrow) with inset of B220 IHC in the lung specimen. N = 5 mice per group. g, The comparison of macrophage counts in tumor tissue from subcutaneous injected A20 Tmem30a−/− and wildtype cells, visualized by F4/80 antibody with H&E counter staining, per tissue within 3 randomly selected 400x high power field regions per tumor from 3 separate mice per group. Graphs represent mean values ± s.d. P values are based on an unpaired two-tailed Student’s t test. h, i, Comparison of gene expression between tumor tissue from Tmem30a−/− and control injection groups. (N = 3 RNA samples from each mouse group). To generate P values (padj/FDR), we used a Wald test of the coefficients fit with a negative binomial GLM. The P values are two-sided and have all been adjusted for multiple comparisons using the Benjamini-Hochberg method. (H) Volcano plot of difference in gene expression between Tmem30a−/− and control groups (x-axis; log2 fold change of difference) and significance (y-axis). Red dots show the genes with significant difference (FDR < 0.05). (I) Dot plots show gene expression (FPKM) of Il4i1, Tmem30a and Xkr5 between Tmem30a−/− and control groups.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2.

Reporting Summary

Supplementary Table

Supplementary Tables 1–9.

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Source Data Fig. 5

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Source Data Extended Data Fig. 3

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Source Data Extended Data Fig. 6

Unprocessed Western Blots

Source Data Extended Data Fig. 7

Unprocessed Western Blots

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Ennishi, D., Healy, S., Bashashati, A. et al. TMEM30A loss-of-function mutations drive lymphomagenesis and confer therapeutically exploitable vulnerability in B-cell lymphoma. Nat Med 26, 577–588 (2020). https://doi.org/10.1038/s41591-020-0757-z

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