Alternative RNA Splicing as a Potential Major …...2019/02/12 · trans-acting splicing factors...

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Alternative RNA Splicing as a Potential Major Source of Untapped Molecular

Targets in Precision Oncology and Cancer Disparities

Timothy J. Robinson1,, Jennifer A. Freedman2,3,, Muthana Al Abo3, April E. Deveaux2,

Bonnie LaCroix2, Brendon M. Patierno2, Daniel J. George2,3 and Steven R. Patierno2,3,*

1Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, 33612, USA

2Department of Medicine, Division of Medical Oncology, Duke University Medical

Center, Durham, NC, 27710, USA

3Duke Cancer Institute, Duke University Medical Center, Durham, NC, 27710, USA

Running title: RNA splicing in precision oncology and cancer disparities

Keywords: alternative RNA splicing, biomarkers, therapeutic agents, oncology, cancer

disparities

Financial support: This work was partially supported by a RSNA Resident Research

Grant to TJR PI and SRP Mentor, a DoD Prostate Cancer Research Program Health

Disparity Research Award PC131972 to SRP PI and JAF Co-I, a NIH Feasibility Studies

to Build Collaborative Partnerships in Cancer Research P20 Award 1P20-CA202925-

01A1 to SRP Overall PI and JAF PI of Pilot Project One, and a NIH Basic Research in

Cancer Health Disparities R01 Award R01CA220314 to SRP PI and JAF Co-I.

Research. on June 11, 2020. © 2019 American Association for Cancerclincancerres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on February 12, 2019; DOI: 10.1158/1078-0432.CCR-18-2445

http://clincancerres.aacrjournals.org/

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*Corresponding Author:

Steven R. Patierno

10 Bryan Searle Drive

Durham, NC 27710

Phone: +1 919 613 5093

Fax: +1 919 681 7385

Email: [email protected]

The authors declare no potential conflicts of interest.

Word count: 2,354

Total number of figures and tables: 1 figure

These authors contributed equally to this work.



mailto:[email protected]


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Statement of Translational Relevance

The path forward for translational cancer research and clinical practice in oncology is

promising, as drivers of tumor biological diversity remain underexplored. One of the

underexplored mechanisms, for which there is emerging evidence that it plays a critical

role in cancer heterogeneity, aggressiveness, and therapeutic response, is Alternative

RNA Splicing (ARS). There is also emerging evidence for agents to target and exploit

ARS for therapeutic application. Despite the indications that ARS plays such critical

roles in cancer, most translational and clinical cancer research focuses on mutation and

aggregate gene expression. Increasing awareness of the significance of ARS to cancer

and coalescence of ARS bioinformatics and cancer biology have the potential to

increase incorporation of ARS into biomarker and drug development in oncology.

Ultimately, this has the potential to lead to new precision medicine interventions that are

likely to improve outcomes for cancer patients and mitigate cancer disparities among

racial groups.




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Abstract

Studies of Alternative RNA Splicing (ARS) have the potential to provide an abundance

of novel targets for development of new biomarkers and therapeutics in oncology, which

will be necessary to improve outcomes for cancer patients and mitigate cancer

disparities. ARS, a key step in gene expression enabling individual genes to encode

multiple proteins, is emerging as a major driver of abnormal phenotypic heterogeneity.

Recent studies have begun to identify RNA splicing-related genetic and genomic

variation in tumors, oncogenes dysregulated by ARS, RNA splice variants driving race-

related cancer aggressiveness and drug response, spliceosome-dependent

transformation, and RNA splicing-related immunogenic epitopes in cancer. In addition,

recent studies have begun to identify and test, pre-clinically and clinically, approaches

to modulate and exploit ARS for therapeutic application, including splice-switching

oligonucleotides, small molecules targeting RNA splicing or RNA splice variants, and

combination regimens with immunotherapies. Although ARS data holds such promise

for precision oncology, inclusion of studies of ARS in translational and clinical cancer

research remains limited. Technologic developments in sequencing and bioinformatics

are being routinely incorporated into clinical oncology that permit investigation of

clinically relevant ARS events, yet ARS remains largely overlooked either because of a

lack of awareness within the clinical oncology community or perceived barriers to the

technical complexity of analyzing ARS. This perspective aims to increase such

awareness, propose immediate opportunities to improve identification and analysis of

ARS, and call for bioinformaticians and cancer researchers to work together to address

the urgent need to incorporate ARS into cancer biology and precision oncology.




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The widespread adoption of genomic profiling of human tumors is now providing

information to researchers, patients, and providers, and influencing translational

research and clinical practice.1,2 However, studies to date have largely focused on

actionable mutations and aggregate gene expression and have predominantly included

patients of European ancestry. 3 4 As a result, these efforts may have missed drivers of

cancer biological and clinical heterogeneity among patients of different ancestries that

have the potential to aid in the development of new diagnostic and therapeutic

interventions.

As our understanding of the molecular etiology of cancer has evolved past the

“initiation-promotion” paradigm, we are increasingly appreciating the importance of

transcriptional reprogramming in early- and late-stage tumor evolution.5 For cancers

with a long developmental history, such as breast, colorectal, and prostate cancer, the

mutation burden reflects mostly late accumulation events, raising the question as to

whether or not mutations or other genetic alterations are the early oncogenic drivers.

Interestingly, it is for these same cancers for which some of the most striking disparities

in incidence and outcome among patients of different ancestries have been repeatedly

demonstrated. Here we draw attention to the emergence of novel aspects of another

level of clinically relevant genomic complexity that has the potential to explain more

clearly the dynamic diversity in human tumor biology: Alternative RNA Splicing

(ARS)(Recently reviewed by Urbanski et al. 7)

ARS is a key step in gene expression in higher eukaryotes. Humans share 99%

similarity with chimpanzees by DNA sequence, but less than 60% by alternatively

spliced exons.8 The current theory as to how such striking diversity can exist so late in




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evolution is the unique ability of ARS to provide a modular, low-risk mechanism of

protein diversification in risk-averse higher organisms.9 Given the importance of ARS to

evolutionary biological diversity, it could also be reasonably speculated that ARS likely

drives tumor-related biological diversity. Indeed, oncogenes dysregulated by ARS, but

not by mutation, have been identified (e.g. BARD1).10

ARS is the physiological process that creates different RNA variants from the

same sequence of DNA.11 It is regulated by cis-acting splicing elements (nucleotide

sequences or motifs) that recruit trans-acting splicing factors (proteins or RNAs) that

enhance or silence the use of splice sites. Variation in cis-acting splicing elements,

differential expression of trans-acting splicing factors or mutation in genes encoding

components of the RNA splicing machinery can all alter ARS and result in disease,

including cancer.12 In addition, non-canonical RNA splicing events can result in aberrant

RNAs (i.e. not normally expressed in healthy tissues or cells) in pathophysiologic

states.13

Analyses of tumors highlight the magnitude of putative actionable ARS

alterations that have yet to undergo characterization in patients, as half of such tumors

harbor ARS-altering single nucleotide variants.14 The frequency of these alterations

raises the question of whether “mutations of unknown significance” might drive changes

in ARS. Several examples of the role of ARS in tumor biology have been recently

reviewed 7. We have shown that discrepant probe set changes within the same gene,

thought to be “noise” on microarrays intended to measure aggregate gene expression,

is often a signal of changes in ARS.15,16 The ability to detect isoform-specific mRNA

changes within expression data suggests that any physiologic state, characterized by




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significant differences in gene expression is likely to exhibit comparable changes in

more nuanced metrics of alternative mRNA processing and pre-mRNA splicing.

Work from our laboratories and others has begun to highlight the importance of

ARS in cancer biology and cancer disparities19,20 and demonstrate that dysregulation of

ARS may be a principal feature differentiating cancers from their host tissues of origin.21

In prostate cancer, a role of ARS is emerging in association with local22 (for example,

SRPK1, which regulates ARS of VEGF, associates with local prostate cancer stage and

invasion) and distant23 (for example, transcriptome-wide changes in ARS associate with

metastatic colonization) disease progression. Our team participated in a multi-

institutional study demonstrating differences in expression of RNA splice variants

between prostate cancer in African American and white patients. Approximately one-

third of the variants enriched in prostate cancer in African American patients were

likewise present in patient-matched normal prostate specimens, indicating germline

origin and potential clinical significance as biomarkers.19 The number of differentially

expressed, ancestry-related RNA splice variants far exceeded the aggregate gene

expression differences in the same tissues. Ancestry-specific prostate cancer cell lines

and xenografts were used to demonstrate the functional significance of these RNA

splice variants to driving ancestry-related prostate cancer aggressiveness and

influencing drug responses to targeted therapeutics. As one example of the power of

this comparative spliceomics24 approach, Phosphatidylinositol-4,5-bisphosphate 3-

Kinase delta (PI3K) was identified as a novel driver of prostate cancer aggressiveness

and RNA splice variants of PI3K were discovered with distinct functions that serve as

biomarkers of drug response. Studies in metastatic prostate cancer suggest that




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aberrant RNA splicing may play roles in progression25 and studies have identified high-

frequency tumor-associated differences in ARS in breast, liver and lung cancer.26

Furthermore, the Androgen Receptor (AR), a driver of prostate cancer progression and

treatment target, undergoes aberrant RNA splicing with predictive and prognostic

treatment implications in castration-resistant disease.27 Additional examples of the role

of ARS in cancer are emerging in the dysregulation of tumor suppressor genes and

oncogenes, including TP53, BARD1, AR and BCL210, and oncogenes, including MYC,

appear to rely on the spliceosome to drive transformation.28 In fact, ARS has been

causally demonstrated across all of the hallmarks of cancer.20 Recently, the plastic

nature of ARS and the bridge between ARS and therapeutic effect has been

demonstrated with the discovery that ionizing radiation induces senescence through

ARS of TP53.29, and that hypoxia, a fundamental driver of both chemotherapy and

radiation resistance, regulates ARS of genes involved in the hallmarks of cancer in

breast cancer cells.30

Germline or somatic genetic variation in cis-acting splicing elements has also

been found to associate with cancer risk and prognosis. We have identified associations

between germline single nucleotide polymorphisms predicted to regulate RNA splicing

of stemness genes and disparities in prostate cancer risk and prostate cancer

survival.31,32 Work focusing on somatic mutations in BRCA1 has shown that African

American women have 24% of mutations associated with cis-acting splicing elements,

greater than in women of other ancestries.33 In addition, others have observed higher

rates of germline “variants of uncertain significance” in African Americans as compared

to whites with early onset breast cancer34, suggesting that ARS might be relevant to




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disease as a function of ancestry. Somatic mutations in genes encoding core units of

the spliceosome have been identified in cancers.35 Dysregulated trans-acting splicing

factors have also been identified, with roles in genomic stability (via inhibition of

destabilizing RNA:DNA complexes)36 and are overexpressed in breast, colon and lung

tumors.37 In breast cancer, an appreciation of trans-acting splicing factors as drivers of

progression is emerging, with such factors being differentially expressed during

progression.38

Therapeutic approaches to manipulate ARS, correct aberrant RNA splicing or

produce novel RNA splice variants are being developed and tested in human clinical

trials. Splice-switching oligonucleotides (SSOs) can modulate pre-mRNA splicing by

binding to target pre-mRNAs and blocking access of the RNA splicing machinery to a

particular splice site.39 Thus, SSOs can simultaneously limit production of pathogenic

variants and induce expression of variants with therapeutic value, as reported in spinal

muscular atrophy40, leading to the first FDA-approved splicing-targeted therapy

(Spinraza) in December 2016. Additional SSOs exhibit therapeutic potential in mouse

models of disease, including cancer.41 These successes dovetail with advances in RNA

therapeutic delivery.42 In addition to SSO-based approaches, studies have used

phenotypic screens and splicing-specific reporters to conduct high throughput screens

of small molecules and have identified modulators of RNA splicing, including those with

activity in cancer cells.43,44 A small molecule modulator of RNA splicing is in clinical trials

for spinal muscular atrophy.45 Despite such proofs of principle, relatively limited effort

focuses on adopting these technologies in cancer drug development. Much as in

current targeted therapy approaches, it is likely that the ultimate efficacy of any




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proposed “splice targeted” therapy will strongly depend on the hallmark of cancer5 and

gene-specific splicing profile under consideration.

ARS is also likely a mechanism generating immunogenic epitopes in cancer and

a predictive indicator of immunogenic diversity. Examples of ARS driving immunogenic

potential date back 20 years, but further pursuit has not occurred in the immune

checkpoint therapy era.46 Molecular analyses of melanoma support the potential for

ARS to affect immunotherapy; for example, melanomas that have mutations in the RNA

splicing regulator RNA Binding Motif protein, X-linked Like 1 (RBMXL1) may have

corresponding widespread ARS,47 although the prevalence of mutated RBMXL1 may be

low (~8%). 48 It has been confirmed that novel alternatively spliced gene fusion products

may provide novel immunogenic epitopes.49 50 Further, interventions to drive ARS may

synergize with immune checkpoint inhibitors. For example, small molecule and drug

screens have identified both new and existing RNA splicing modulators, e.g. digoxin,51

although the efficacy of such agents in combination with immunotherapies remain

untested.

Despite the significance of ARS to cancer, clinically-oriented reviews of cancer

biomarkers, therapeutics, and profiling of tumor heterogeneity often fail to mention or

only peripherally reference RNA splicing52,53,54, suggesting that this aspect of genomic

regulation has remained outside the mainstream of discussions of clinical cancer

genomics. We are only now starting to appreciate the translational importance of ARS in

cancer; for example, patients having exon 14 splice site alterations in MET exhibit

positive clinical response to MET inhibitors.55 These examples of missed “hits” suggest

that many RNA splice variants with potential as targets in precision oncology have yet to




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be discovered. ARS can yield targets relevant to all aspects of precision oncology. As

described herein and shown in Figure 1, RNA splice variants can pre-exist in normal

cells and persist following transformation or can be expressed de novo in cancer cells.

Such RNA splice variants and variation in cis-acting splicing elements can serve as

biomarkers. RNA splice variants can serve as targets for RNA-targeted therapeutics,

including SSOs and RNA-targeted small molecules. The proteins encoded by RNA

splice variants and trans-acting splicing factors can serve as targets for protein-targeted

therapeutics, including protein-targeted small molecules. RNA splice variants and their

encoded proteins can also serve as neoantigens.

There are likely reasons that ARS has not risen to the forefront of translational

research, despite its enormous potential. ARS is complex and related analyses must

specify details of the structures of the events and reference this information with respect

to the relative abundance of one RNA variant to another within the same gene. Exon-

level annotation is highly variable by data source. Definitions of RNA splice variant

ratios or other non-standardized metrics must be used to quantify ARS. Lastly, the

distinction between RNA splice variant-specific expression versus overall expression is

not always made and may in some circumstances be more accurately described by

mRNA transcript-specific changes in abundance.

Technical limitations and analyses of ARS are not trivial. Standardized

computational approaches to analyzing these data do not exist. Sequence-based

approaches are typically described as structural or count-based.56 Count-based

approaches require selecting a database to provide the coordinates or “bins” with which

to quantify exon abundance, and can produce variable results depending on bin




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definition. Thus, the same software, using a different reference genome or alignment,

can produce different results. Liu et al. compared the ability of current RNA-seq based

methods to detect ARS within a heat shock dataset in plants.56 The study did not detect

a single gene as alternatively spliced by the seven programs included in the analysis,

underscoring the need to understand the relative strengths and limitations of various

ARS analysis methods. The application of novel bioinformatics techniques to existing

data with an ARS focus is resulting in substantial advances in understanding tumor

genomic heterogeneity,57 58 and efforts are underway to better understand how ARS

interrelates to other genomic phenomena including long non-coding RNAs, miRNAs,

and protein translation.59 Although we focused on the role of ARS of mRNAs, it is

important to note that long non-coding RNAs have been demonstrated to undergo, as

well as regulate, ARS. 60,61 Lastly, it should be noted that there are emerging

technologies such as single-molecule real-time (SMRT) isoform sequencing (Iso-Seq)

that are used in conjunction with the commercial RNA-seq platforms (i.e. “third

generation sequencing). This technology and companion software permit

comprehensive analysis of entire molecules and variants of RNA (messenger, non-

coding, circular, etc).62 This technology holds much potential for the future of ARS

analyses, however its present utility in clinical oncology remains limited, given that it is

not incorporated in clinically used genomic assays in oncology and its analytic

performance in this setting remains to be confirmed.

We suggest that key factors that have limited incorporation of ARS in genome-

wide studies within the clinical oncology community are lack of awareness, cost, and

technical complexity and interpretation. We hope that this Perspective and ongoing




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research will increase awareness. Fortunately, cost of such analyses continues to

decrease. The largest barrier is technical complexity and interpretation. We call for

attention to spliceomics, and the need for increased collaboration between

bioinformaticians and cancer biologists to develop improved methods to identify and

analyze ARS. Of particular value would be the expansion of RNA-Seq software to

include analyses of ARS in parallel to standard gene expression pipelines, which would

greatly remove current time and technical barriers to investigator examination of RNA

splicing. Such software should also provide pathway analysis, analysis of factors that

regulate ARS, and be accessible without sophisticated bioinformatics expertise.63

Lastly, there are immediate opportunities to standardize variant names, exon

descriptions and numbering and the approaches that report RNA splicing events.

In summary, ARS is a principal driver of biological diversity and plays a role in

every hallmark of cancer, yet is rarely examined in profiling of tumors and is largely

overlooked in biomarker and drug development in oncology. We believe the primary

barrier to taking advantage of this plethora of potentially actionable data is the difficulty

of analyzing ARS data and call for a partnership between bioinformaticians and cancer

researchers to address this need. Although the time and learning curve associated with

these analyses is steep, such efforts are likely to solve unmet challenges in cancer

biology, including cancer disparities, and patient care.




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22. Bullock N, Potts J, Simpkin AJ, et al: Serine-arginine protein kinase 1 (SRPK1), a determinant of angiogenesis, is upregulated in prostate cancer and correlates with disease stage and invasion. J Clin Pathol, 2015 23. Lu ZX, Huang Q, Park JW, et al: Transcriptome-wide landscape of pre-mRNA alternative splicing associated with metastatic colonization. Mol Cancer Res 13:305-18, 2015 24. Rao AR, Sahu TK, Singh N: Spliceomics: The OMICS of RNA Splicing, in Barh D, Zambare V, Azevedo V (eds): OMICS Applications in Biomedical, Agricultural, and Environmental Sciences (ed 1st Edition). Boca Raton, Taylor & Francis Group, 2013 25. Sowalsky AG, Xia Z, Wang L, et al: Whole transcriptome sequencing reveals extensive unspliced mRNA in metastatic castration-resistant prostate cancer. Mol Cancer Res 13:98-106, 2015 26. Tsai YS, Dominguez D, Gomez SM, et al: Transcriptome-wide identification and study of cancer-specific splicing events across multiple tumors. Oncotarget 6:6825-39, 2015 27. Antonarakis ES, Lu C, Wang H, et al: AR-V7 and resistance to enzalutamide and abiraterone in prostate cancer. N Engl J Med 371:1028-38, 2014 28. Anczukow O, Krainer AR: The spliceosome, a potential Achilles heel of MYC-driven tumors. Genome Med 7:107, 2015 29. Chen J, Crutchley J, Zhang D, et al: Identification of a DNA Damage-Induced Alternative Splicing Pathway That Regulates p53 and Cellular Senescence Markers. Cancer Discov 7:766-781, 2017 30. Han J, Li J, Ho JC, et al: Hypoxia is a Key Driver of Alternative Splicing in Human Breast Cancer Cells. Sci Rep 7:4108, 2017 31. Wang Y, Freedman JA, Liu H, et al: Associations between RNA splicing regulatory variants of stemness-related genes and racial disparities in susceptibility to prostate cancer. Int J Cancer, 2017 32. Freedman JA, Wang Y, Li X, et al: Single Nucleotide Polymorphisms of Stemness Genes Predicted to Regulate RNA Splicing, microRNA and Oncogenic Signaling are Associated with Prostate Cancer Survival. Carcinogenesis, 2018 33. Rebbeck TR, Mitra N, Wan F, et al: Association of type and location of BRCA1 and BRCA2 mutations with risk of breast and ovarian cancer. JAMA 313:1347-61, 2015 34. Haffty BG, Silber A, Matloff E, et al: Racial differences in the incidence of BRCA1 and BRCA2 mutations in a cohort of early onset breast cancer patients: African American compared to white women. J Med Genet 43:133-7, 2006 35. Bejar R: Splicing Factor Mutations in Cancer. Adv Exp Med Biol 907:215-28, 2016 36. Li X, Manley JL: Inactivation of the SR protein splicing factor ASF/SF2 results in genomic instability. Cell 122:365-78, 2005 37. Karni R, de Stanchina E, Lowe SW, et al: The gene encoding the splicing factor SF2/ASF is a proto-oncogene. Nat Struct Mol Biol 14:185-93, 2007 38. Silipo M, Gautrey H, Tyson-Capper A: Deregulation of splicing factors and breast cancer development. J Mol Cell Biol, 2015 39. Bauman J, Jearawiriyapaisarn N, Kole R: Therapeutic potential of splice-switching oligonucleotides. Oligonucleotides 19:1-13, 2009 40. Zanetta C, Nizzardo M, Simone C, et al: Molecular therapeutic strategies for spinal muscular atrophies: current and future clinical trials. Clin Ther 36:128-40, 2014 41. Zammarchi F, de Stanchina E, Bournazou E, et al: Antitumorigenic potential of STAT3 alternative splicing modulation. Proc Natl Acad Sci U S A 108:17779-84, 2011 42. Hong D, Kurzrock R, Kim Y, et al: AZD9150, a next-generation antisense oligonucleotide inhibitor of STAT3 with early evidence of clinical activity in lymphoma and lung cancer. Sci Transl Med 7:314ra185, 2015




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43. Younis I, Berg M, Kaida D, et al: Rapid-response splicing reporter screens identify differential regulators of constitutive and alternative splicing. Mol Cell Biol 30:1718-28, 2010 44. Pawellek A, McElroy S, Samatov T, et al: Identification of small molecule inhibitors of pre-mRNA splicing. J Biol Chem 289:34683-98, 2014 45. ClinicalTrials.gov: An Open Label Study of LMI070 (Branaplam) in Type 1 Spinal Muscular Atrophy (SMA), 2014 46. Scanlan MJ, Chen YT, Williamson B, et al: Characterization of human colon cancer antigens recognized by autologous antibodies. Int J Cancer 76:652-8, 1998 47. Cifola I, Pietrelli A, Consolandi C, et al: Comprehensive genomic characterization of cutaneous malignant melanoma cell lines derived from metastatic lesions by whole-exome sequencing and SNP array profiling. PLoS One 8:e63597, 2013 48. Berger MF, Hodis E, Heffernan TP, et al: Melanoma genome sequencing reveals frequent PREX2 mutations. Nature 485:502-6, 2012 49. Volpe G, Cignetti A, Panuzzo C, et al: Alternative BCR/ABL splice variants in Philadelphia chromosome-positive leukemias result in novel tumor-specific fusion proteins that may represent potential targets for immunotherapy approaches. Cancer Res 67:5300-7, 2007 50. Smart AC, Margolis CA, Pimentel H, et al: Intron retention is a source of neoepitopes in cancer. Nat Biotechnol, 2018 51. Stoilov P, Lin CH, Damoiseaux R, et al: A high-throughput screening strategy identifies cardiotonic steroids as alternative splicing modulators. Proc Natl Acad Sci U S A 105:11218-23, 2008 52. Zardavas D, Irrthum A, Swanton C, et al: Clinical management of breast cancer heterogeneity. Nat Rev Clin Oncol 12:381-94, 2015 53. Jamal-Hanjani M, Wilson GA, McGranahan N, et al: Tracking the Evolution of Non-Small-Cell Lung Cancer. N Engl J Med 376:2109-2121, 2017 54. Garman B, Anastopoulos IN, Krepler C, et al: Genetic and genomic characterization of 462 melanoma patient-derived xenografts, tumor biopsies and cell lines. Cell Rep 21:1936-52, 2017 55. Frampton GM, Ali SM, Rosenzweig M, et al: Activation of MET via diverse exon 14 splicing alterations occurs in multiple tumor types and confers clinical sensitivity to MET inhibitors. Cancer Discov, 2015 56. Liu R, Loraine AE, Dickerson JA: Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems. BMC Bioinformatics 15:364, 2014 57. Jayasinghe RG, Cao S, Gao Q, et al: Systematic Analysis of Splice-Site-Creating Mutations in Cancer. Cell Rep 23:270-281 e3, 2018 58. Jian X, Boerwinkle E, Liu X: In silico tools for splicing defect prediction: a survey from the viewpoint of end users. Genet Med 16:497-503, 2014 59. Soreq L, Guffanti A, Salomonis N, et al: Long non-coding RNA and alternative splicing modulations in Parkinson's leukocytes identified by RNA sequencing. PLoS Comput Biol 10:e1003517, 2014 60. Ernst C, Morton CC: Identification and function of long non-coding RNA. Front Cell Neurosci 7:168, 2013 61. Niland CN, Merry CR, Khalil AM: Emerging Roles for Long Non-Coding RNAs in Cancer and Neurological Disorders. Front Genet 3:25, 2012 62. Gao Y, Wang H, Zhang H, et al: PRAPI: post-transcriptional regulation analysis pipeline for Iso-Seq. Bioinformatics 34:1580-1582, 2018 63. Han S, Kim D, Kim Y, et al: CAS-viewer: web-based tool for splicing-guided integrative analysis of multi-omics cancer data. BMC Med Genomics 11:25, 2018




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Figure 1. Roles of RNA splicing events and RNA splice variants in precision oncology.

Genetic variation in cis-acting splicing elements in different populations can result in

expression of alternative RNA splice variants, as exemplified by pre-mRNA #1. Some of

these can be oncogenic RNA splice variants that pre-exist in normal cells and persist in

cancer cells, as exemplified by pre-mRNA #1. Alterations that occur during

transformation e.g. differential expression of trans-acting splicing factors can result in

oncogenic RNA splice variants that arise de novo in cancer cells, as exemplified by pre-

mRNA #2. Such RNA splicing events and RNA splice variants can be biomarkers,

therapeutic targets and/or neoantigens. Ultimately, such RNA splicing events and RNA

splice variants can influence cancer aggressiveness and drug response. Solid lines

within pre-mRNAs, RNA splicing patterns. E, exon. I, intron. Joined Es depict RNA

splice variants and schematics below joined Es depict corresponding encoded protein

isoforms. Gray oval, nucleus. Red letters, single nucleotide polymorphism in cis-acting

splicing element. SF, trans-acting splicing factor. SSOs, splice-switching

oligonucleotides.




GTGG

GTGG GTGG

Normal Cell in Ancestral Population 1

E1 E3 E2

SF1 SF2 GTGG

GTAG GTAG

Normal Cell in Ancestral Population 2

E1 E3

E1 E2 E3 E4 I1 I2 I3

pre-mRNA

#1

SF1 SF2

E1 E2 E3 E4 I1 I2 I3

pre-mRNA

#1

E1 E2 E3 E4 I1 I2 I3

pre-mRNA

#2

E1 E3 E2 E4

E1 E2 E3 E4 I1 I2 I3

pre-mRNA

#2

E4 E1 E3 E2 E4 E4

Figure 1




Tumor Cell in Ancestral Population 2

GTGG

GTAG GTAG

E1 E3 E4

E1 E2 E3 E4 I1 I2 I3

pre-mRNA

#1

SF1 SF2

E1

E1 E2 E3 E4 I1 I2 I3

pre-mRNA

#2

SF2

Tumor cells have different aggressiveness and drug response depending

on RNA splice variants/corresponding protein isoforms expressed

RNA-Targeted

Therapeutics

(SSOs, small

molecules)

Tumor Cell in Ancestral Population 1

Protein-

Targeted

Therapeutics

(small

molecules)

GTGG

GTGG GTGG

E1 E3 E2

SF1

SF2

E1 E2 E3 E4 I1 I2 I3

pre-mRNA

#1

E1 E2 E3 E4 I1 I2 I3

pre-mRNA

#2

E1 E2 E4

SF1

CD8+

T Cell

Neoantigens

Biomarkers

Biomarkers

E4 E4

Figure 1




Published OnlineFirst February 12, 2019.Clin Cancer Res Timothy J Robinson, Jennifer A Freedman, Muthana Al Abo, et al. DisparitiesUntapped Molecular Targets in Precision Oncology and Cancer Alternative RNA Splicing as a Potential Major Source of

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