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A Tumor-Associated Splice-Isoform of MAP2K7 Drives Dedifferentiation in MBNL1-Low Cancers Via JNK Activation

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A Tumor-Associated Splice-Isoform of MAP2K7 Drives Dedifferentiation in MBNL1-Low Cancers Via JNK Activation A tumor-associated splice-isoform of MAP2K7 drives dedifferentiation in MBNL1-low cancers via JNK activation Debleena Raya,1, Yu Chye Yuna, Muhammad Idrisa, Shanshan Chenga,b, Arnoud Boota,b, Tan Bee Huat Iainc, Steven G. Rozena,b, Patrick Tana, and David M. Epsteina,1 aCancer and Stem Cell Biology Program, Duke-NUS Medical School, 165897 Singapore, Singapore; bCentre for Computational Biology, Duke-NUS Medical School, 165897 Singapore, Singapore; and cDivision of Medical Oncology, National Cancer Centre, 169610 Singapore, Singapore Edited by James L. Manley, Columbia University, New York, NY, and approved May 28, 2020 (received for review February 11, 2020) Master splicing regulator MBNL1 shapes large transcriptomic changes Muscleblind-like 1 (MBNL1) is a C3H zinc-finger RNA-binding that drive cellular differentiation during development. Here we protein that is involved in multiple RNA-processing steps during demonstrate that MBNL1 is a suppressor of tumor dedifferentiation. development (13–16). MBNL1Δexon7 isoform functions as a tumor We surveyed MBNL1 expression in matched tumor/normal pairs suppressor in prostate cancer (17) by regulating splicing and tran- MBNL1 across The Cancer Genome Atlas and found that was script abundance of genes involved in DNA repair, cell cycle, and down-regulated in several common cancers. Down-regulation of MBNL1 migration. It also suppresses metastasis in breast (18) and colon predicted poor overall survival in breast, lung, and stomach cancer (19) by regulating transcript abundance of DBNL, TACC1, adenocarcinomas and increased relapse and distant metastasis in and SNAIL transcripts, respectively. MBNL1 has also been pre- triple-negative breast cancer. Down-regulation of MBNL1 led to in- creased tumorigenic and stem/progenitor-like properties in vitro and dicted to control AS patterns of undifferentiated cells across solid in vivo. A discrete set of alternative splicing events (ASEs) are shared tumors (20), a finding that suggests that MBNL1-driven AS events between MBNL1-low cancers and embryonic stem cells including a might underlie emergence of TICs and tumor relapse. The extent MAP2K7Δexon2 splice variant that leads to increased stem/progen- to which MBNL1 functions to drive dedifferentiation and acquisi- itor-like properties via JNK activation. Accordingly, JNK inhibition is tion of stemness properties for the emergence of TICs as well as CELL BIOLOGY capable of reversing MAP2K7Δexon2-driven tumor dedifferentiation underlying biological mechanisms governing this dedifferentiation in MBNL1-low cancer cells. Our work elucidates an alternative- remains unknown. splicing mechanism that drives tumor dedifferentiation and identifies Here we report that low MBNL1 expression is a phenotype of biomarkers that predict enhanced susceptibility to JNK inhibition. many common solid cancers and that it is correlated with reduced overall survival, increased relapse, and distant metastasis. We MBNL1 | MAP2K7 | JNK inhibitors | alternative splicing | tumor cell demonstrate that MBNL1 drives cellular dedifferentiation in cancer dedifferentiation by regulating the skipping of exon2 of MAP2K7 via JNK activation. Importantly, our data show that MBNL1 and MAP2K7Δexon2 lternative splicing (AS) is a posttranscriptional mechanism expression are biomarkers for increased cancer stemness and Athat increases eukaryotic protein diversity during develop- ment (1). Consequently, AS results in acquisition of cellular and Significance organ identity conferred by differential isoform profiles (2). As developmental growth signaling pathways are often coopted in cancer, aberrant AS is a hallmark of cancer progression and me- Targeting stem-like cells in cancer is critical to overcoming re- tastasis (3, 4). Specific tumor initiating cell (TIC) or cancer stem cell sistance and relapse post chemotherapy or immunotherapy. (CSC) populations are also known for specific splicing alterations in We elucidate an alternative-splicing driven mechanism of can- CD44 or alpha6 integrin in breast (5, 6) and DCLK1 in kidney cancer cer dedifferentiation and define a molecular context wherein (7). Recently, TICs were reported to acquire resistance to immu- stem-like tumor cells show enhanced susceptibility to JNK in- MAP2K7Δ notherapy in skin cancer models, placing TICs as the root cause of hibition. MBNL1 and exon2 can prognosticate pa- tumor relapse and an important therapeutic target (8). tients for JNK inhibition that can render stem-like tumor cells At the cellular level, tumorigenic dedifferentiation results in susceptible to therapy. acquisition of cellular plasticity, or stemness, that has many Author contributions: D.R. designed research; D.R., Y.C.Y., M.I., and S.C. performed re- similarities with the pluripotent states of embryonic stem cells search; T.B.H.I., S.G.R., P.T., and D.M.E. contributed new reagents/analytic tools; D.R., M.I., (ESCs) and induced pluripotent stem cells (iPSCs) (9). Profound S.C., and A.B. analyzed data; D.R. wrote the paper; and D.M.E. provided overall splicing alterations occur during differentiation of stem and supervision. progenitor cells (10) as well as during somatic reprogramming of Competing interest statement: D.R is an inventor on a patent “Stratification of Cancer differentiated cells into iPSCs by the Yamanaka factors (11). In Patients by MBNL1 and MAP2K7Δexon2 Expression for Susceptibility to JNK Inhibition” an effort to understand how AS profiles drive such dramatic cell filed in the Singapore patent office, Provisional Application No. 10201910208U. D.M.E is the founder, director, and CEO of Black Diamond Therapeutics and a consultant to Engine fate changes, Han et al. compared AS patterns in ESCs and Bioscience in areas unrelated to this manuscript. iPSCs with those of differentiated mouse and human cell types This article is a PNAS Direct Submission. (12). Remarkably, they found that levels of muscleblind-like This open access article is distributed under Creative Commons Attribution-NonCommercial- proteins (MBNL1 and MBNL2), implicated in myotonic dys- NoDerivatives License 4.0 (CC BY-NC-ND). trophy, not only differed between pluripotent (low) and differ- Data deposition: All RNA-seq data generated are available via European Nucleotide Ar- entiated (high) cells, but also controlled differentiation such that chive (ENA) under accession no. PRJEB32567. reducing MBNL1 and MBNL2 expression in differentiated cells 1To whom correspondence may be addressed. Email: [email protected] or led to a switch toward an ESC-like AS pattern and vice versa. [email protected]. This work demonstrated that MBNL proteins function as master This article contains supporting information online at https://www.pnas.org/lookup/suppl/ splicing regulators capable of shaping large transcriptomic doi:10.1073/pnas.2002499117/-/DCSupplemental. changes that can drive cellular differentiation. First published June 29, 2020. www.pnas.org/cgi/doi/10.1073/pnas.2002499117 PNAS | July 14, 2020 | vol. 117 | no. 28 | 16391–16400 Downloaded by guest on October 1, 2021 increased JNK activity. MBNL1–JNK-driven cancer stemness cancer (Fig. 1A and Dataset S1, Table S1). In total, 312 (86%) can be reversed by JNK inhibition. tumors out of 360 analyzed tumor/normal pairs across the “MBNL1-low cancers” show lower expression of MBNL1 (Fig. 1B). Results As MBNL1 and MBNL2 share structural and functional similarities Down-Regulation of MBNL1 Is Correlated with Poor Prognosis in (21), we analyzed the expression of MBNL2 using the same TCGA Cancer. To survey MBNL1 expression across different forms of RNA-seq data. MBNL2 was down-regulated in all MBNL1-low cancer, we took advantage of The Cancer Genome Atlas (TCGA) cancers except for colon adenocarcinoma (COAD) (SI Appendix, from which RNA-sequencing (RNA-seq) data in 16 cancer types Fig. S1A and Dataset S1, Table S1), indicating a complementary with matching tumor and normal samples were available. We found role for MBNL1 and 2 in most MBNL1-low cancers. that MBNL1 was significantly down-regulated in 8 cancer types— We next performed immunohistochemistry (IHC) in an in- bladder, breast, colon, lung adenocarcinoma (LUAD), lung squa- dependent cohort of matched stomach adenocarcinoma clinical mous cell carcinoma (LUSC), prostate, stomach, and uterine samples and found that MBNL1 was down-regulated in 36% of cancers—which we hereafter refer to as MBNL1-low cancers. those samples (9/25) (Fig. 1C, SI Appendix, Fig. S1 B and C, and MBNL1 was also up-regulated in three subtypes of renal Dataset S1, Table S2). MBNL1 showed both nuclear and A MBNL1 Expression 2 1 0 −1 VST Counts (Tumor − Normal) VST Counts (Tumor −2 Liver (LIHC) Lung Lung (LUAD) (LUSC) (KIRP) Colon (KICH) (KIRC) Breast Renal Renal Renal Head & Neck Uterine Thyroid Bladder Prostate Bile duct Stomach BCDEsophageal MBNL1 expression (VST counts) Normal Tumor Higher in Lower in %Tumor showing Cancer Type Tumor (N) Tumor (N) down-regulation #32456 Bladder 2 17 89 (BLCA) Breast 13 99 88 (BRCA) Colon #32810 0 26 100 (COAD) Normal Poorly differentiated Moderately differentiated differentiated Well Lung 10 48 83 (LUAD) Lung #33510 5 46 90 AGS (LUSC) GES1 HGC27 TMK1 IM95 MKN1 MKN7 HFE-145 MKN45 NUGC3 Prostate 45 87 (PRAD) 7 MBNL1 Stomach 9 72 #34138 (STAD) 23 Uterine GAPDH 8 80 (UCEC) 2 E Overall Survival Gastric Adenocarcinoma (n=882) Breast Adenocarcinoma (n=1402) Lung Adenocarcinoma (n=720) 1.0 1.0 1.0 HR = 0.56 (0.47 − 0.67) HR= 0.66(0.53-0.81) HR= 0.53 (0.42-0.68) logrank P = 3.5x10 −11 logrank P=0.00012 logrank P= 1.4x10-07 0.8 0.8 0.8 0.6 0.6 0.6 0.4 0.4 0.4 Probability 0.2 Expression 0.2 Expression 0.2 Expression low low low 0 high 0 high 0 high 0 50 100 150 0 50 100 150 200 250 300 0 50 100 150 200 F Time(months) G Time(months) Time(months) Relapse-Free Survival Distant-metastasis Free Survival Triple Negative Breast Cancer (n=255) Triple Negative Breast Cancer (n=43) 1.0 HR=0.38(0.21-0.69) 1.0 logrank P= 0.00089 HR=0.12(0.01-0.96) 0.8 0.8 logrank P= 0.017 0.6 0.6 0.4 0.4 Probability 0.2 Expression 0.2 Expression low low 0 high 0 high 050100 150 200 0 20406080 Time(months) Time(months) Fig.
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