Cancer-specific molecules and methods of use thereof

Abstract

Disclosed herein are systems and methods for identifying candidate targets that can be used for therapeutic agent development. The systems and methods may include receiving and analyzing biologically relevant data to identify information or events, such as splicing events that may be statistically significant or unique to certain diseases, disorders, or conditions. Systems and methods for modulating statistically significant events using compositions or molecules (including small molecule compounds, oligonucleotides, proteins, or cells) are also provided.

Description

[0001] Cross-references

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 62 / 831,604, filed April 9, 2019, U.S. Provisional Patent Application No. 62 / 889,217, filed August 20, 2019, U.S. Provisional Patent Application No. 62 / 944,913, filed December 6, 2019, and U.S. Provisional Patent Application No. 62 / 980,900, filed February 24, 2020, each of which is hereby incorporated by reference in its entirety.

[0003] Sequence Listing

[0004] This application contains a sequence listing, which is submitted electronically in ASCII format and is hereby incorporated by reference in its entirety. The ASCII copy was created on November 18, 2021, is named 53560-703_301_SL.txt, and is 1,311,989 bytes in size.

[0005] background

[0006] Cancer and genetic diseases affect millions of people in the United States. Deregulation of splicing can be a major hallmark of cancer and genetic diseases, influencing progression, metastasis, and treatment resistance. RNA splicing is the process by which introns (non-protein-coding regions of DNA) are removed from nascent precursor messenger RNA (pre-mRNA) and exons (protein-coding regions of DNA) are joined together to form mature messenger RNA (mRNA). Errors in RNA splicing can result in the spliced ​​RNA being unable to produce functional proteins, leading to genetic diseases, including many types of cancer.

[0007] Overview

[0008] RNA splicing is a form of RNA processing in which newly made precursor messenger RNA (pre-mRNA) transcripts are converted into mature messenger RNA (mRNA). During splicing, introns (non-coding regions within a gene) are removed and exons (coding regions) are joined together. RNA splicing not only provides functional mRNA, but may also be responsible for generating additional diversity (i.e., alternative splicing, alternative RNA splicing, or differential splicing). Alternative splicing may result in the production of different mRNAs from the same gene. mRNAs representing isoforms produced from a single gene may differ by using alternative exons or retaining an exon that interrupts two exons. This process often results in different protein products that may have related or significantly different, or even opposing, cellular functions. Alternative splicing can include one or more of exon skipping or cassette exons, mutually exclusive exons, alternative donor sites, alternative acceptor sites, intron retention, and any combination or variant thereof.

[0009] Multiple studies have demonstrated the oncogenic activity of specific splicing events and splicing factors (such as SRSF1, SRSF2, ESRP1, and RBFOX1) in humans and animal models. Therefore, cancer-associated splicing deregulation may represent a new source of clinically applicable biomarkers and therapeutic targets.

[0010] Provided herein are systems and methods for identifying therapeutic targets (e.g., disease-specific splicing events) in various diseases or conditions such as cancer. Non-limiting examples of cancer include prostate cancer, barrier tissue cancer (i.e., colorectal cancer, lung cancer), and other cancers rich in TGFb sites (e.g., ovary), AML / hematological disorders, respiratory cancers, hepatocellular carcinoma (liver cancer) that can include both an environment rich in TGFb and long-term potential dietary-induced inflammation, thoracic cancer, gastric cancer, kidney cancer, pancreatic cancer, skin cancer, or a combination thereof. Examples of some other diseases may include, but are not limited to, autism (i.e., ST7 (ENV19)), lymphatic diseases such as syndromic lymphedema - a genetic disease and milory disease, degenerative eye diseases, brain diseases - peripheral neuropathies, neurometabolic diseases, rare inherited neurological diseases - hereditary motor neuron diseases, psychiatric disorders, chronic inflammatory / autoimmune diseases including IBD, Crohn's disease and similar gastrointestinal disorders, inflammation in pathogenic obesity, including genetic, childhood, leptin and non-leptin dependent diseases, hypertension and / or other comorbidities associated with a Western diet.

[0011] The methods of the present disclosure may also include detecting one or more biomarkers, for example, detecting the presence or absence of a specific DNA sequence, mRNA, and / or protein isoform. The presence or absence of one or more biomarkers can be used to diagnose a disease and / or track disease progression.

[0012] Also provided herein are methods for regulating therapeutic targets (e.g., disease-specific splicing events) using various types of modalities such as oligonucleotides, small molecules, antibodies, or any combination thereof. In some instances, modalities can convert pathogenic RNA isoforms to non-pathogenic RNA isoforms. In some instances, modalities are oligonucleotides that can knock down specific isoforms, including splice conversion oligonucleotides (SSOs), antisense oligonucleotides (ASOs), small interfering ribonucleic acids (siRNAs), conjugated oligonucleotides, or combinations thereof. In some other instances, modalities are antibodies or cell-based (e.g., CAR-T) that can specifically recognize protein isoforms of RNA that can be spliced ​​alternatively, and / or specifically target isoforms to obtain therapeutic compounds (including ASOs, small molecules, or biologics) for treating benefits.

[0013] Further provided in the present disclosure are methods and systems for treating diseases or conditions such as cancer. The systems and methods can include administering an effective amount of a modality to a subject having a disease or condition. The modality can be an oligonucleotide, a small molecule, an antibody, or any combination thereof designed to achieve disease-specific targeting. For example, some identified disease-specific splicing events can open a groove for small molecule binding. In such cases, small molecules can be designed to target the region resulting from the splicing change. In another example, splicing changes in membrane-bound proteins can display altered surface epitopes that can be specifically targeted with antibodies.

[0014] One aspect of the present disclosure provides a method of modulating splicing in pre-mRNA in a biological sample, comprising: contacting the biological sample with an antisense compound comprising one or more oligonucleotides having at least 90% sequence identity to an oligonucleotide selected from Tables 2-33.

[0015] In some embodiments, the biological sample includes a cell, tissue, or blood sample. In some embodiments, the biological sample is in vitro. In some embodiments, the biological sample is a cell. In some embodiments, the method further includes measuring the viability of the cell. In some embodiments, the measurement is performed within a predetermined time period. In some embodiments, the method further includes monitoring the viability of the cell within a predetermined time period. In some embodiments, the method further includes reducing or increasing the concentration of the antisense compound based on the viability of the cell. In some embodiments, the method further includes reducing or increasing the concentration of the antisense compound when the viability of the cell is below a cutoff value. In some embodiments, the cutoff value is about 80%. In some embodiments, the cutoff value is about 90%. In some embodiments, the antisense compound includes one or more oligonucleotides with a concentration greater than or equal to about 300nM. In some embodiments, the antisense compound includes one or more oligonucleotides with a concentration less than or equal to about 450nM. In some embodiments, the one or more oligonucleotides include deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or a combination thereof. In some embodiments, the RNA includes small interfering ribonucleic acid (siRNA). In some embodiments, the one or more oligonucleotides include single-stranded oligonucleotides, double-stranded oligonucleotides, or a combination thereof. In some embodiments, the biological sample is from a subject with or suspected of having a disease or condition. In some embodiments, the disease or condition includes a genetic disease, a CNS disease, an inflammatory disease, a neurodegenerative disease, a cardiovascular disease, an autoimmune disease, or a cancer. In some embodiments, the disease is cancer. In some embodiments, the cancer includes lung cancer, kidney cancer, or breast cancer. In one embodiment, the breast cancer is triple-negative breast cancer. In some embodiments, one or more nucleotides include oligonucleotides selected from Tables 2-33. In some embodiments, one or more oligonucleotides include modified oligonucleotides. In some embodiments, the modified oligonucleotide includes at least one modified internucleoside bond. In some embodiments, at least one modified internucleoside bond is a phosphorothioate bond. In some embodiments, the modified oligonucleotide includes one or more modified nucleotides. In some embodiments, the modified oligonucleotide includes one or more modified nucleosides. In some embodiments, the modified nucleosides of one or more modified nucleosides include modified sugar moieties. In some embodiments, the modified sugar moieties are 2'-substituted sugar moieties. In some embodiments, the 2'-substituted sugar moiety comprises a modification selected from the group consisting of 2'-O-methoxyethyl, 2'-fluoro, 2'-dimethylaminooxyethoxy, 2'-dimethylaminoethoxyethoxy, 2'-guanidino, 2'-O-guanidinoethyl, 2'-carbamate, 2'-aminooxy, 2'-acetamido, and locked nucleic acid. In some embodiments, the modified oligonucleotide comprises more than one modified nucleoside, the modified nucleoside comprising a modified sugar moiety.In some embodiments, at least a subset of more than one modified nucleosides are different from each other. In some embodiments, regulation includes inducing or enhancing exon skipping. In some embodiments, regulation includes inducing or enhancing exon inclusion. In some embodiments, regulation includes promoting splice switching. In some embodiments, regulation includes downregulation or upregulation of splicing. In some embodiments, the antisense compound specifically binds to a segment of a pre-mRNA encoded by a gene including: NEDD4L, MAP3K7, NFYA, ESYT2, MARK2, ST7, ARVCF, SYTL2, R3HDM1, COL4A3BP, TANGO2, SEPT9, ROBO1, FAM122B, CD47, LSR, PBX1, EPB41, ADAM15, EPB41L1, ABI1, FLNB, CTNND1, GPR160, ITGB3BP, INCENP, DENND1B, or CA12.

[0016] Another aspect of the present disclosure provides a pharmaceutical composition comprising (i) an antisense compound comprising one or more oligonucleotides having at least 90% sequence identity with an oligonucleotide selected from Tables 2-33, and (ii) a pharmaceutically acceptable diluent or carrier.

[0017] In some embodiments, the antisense compound includes one or more oligonucleotides at a concentration greater than or equal to about 300nM. In some embodiments, the antisense compound includes one or more oligonucleotides at a concentration less than or equal to about 450nM. In some embodiments, the one or more oligonucleotides include deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or a combination thereof. In some embodiments, RNA includes small interfering ribonucleic acid (siRNA). In some embodiments, the one or more oligonucleotides include single-stranded oligonucleotides, double-stranded oligonucleotides, or a combination thereof. In some embodiments, the pharmaceutical composition is used to treat or alleviate a disease or condition. In some embodiments, the disease includes a genetic disease, a CNS disease, an inflammatory disease, a neurodegenerative disease, a cardiovascular disease, an autoimmune disease, or a cancer. In some embodiments, the disease or condition is cancer. In some embodiments, cancer includes lung cancer, renal cancer, or breast cancer. In one embodiment, breast cancer is triple-negative breast cancer. In some embodiments, the one or more nucleotides include oligonucleotides selected from Tables 2-33. In some embodiments, the one or more oligonucleotides include modified oligonucleotides. In some embodiments, the modified oligonucleotides include at least one modified internucleoside bond. In some embodiments, at least one modified internucleoside bond is a phosphorothioate bond. In some embodiments, the modified oligonucleotide comprises one or more modified nucleotides. In some embodiments, the modified oligonucleotide comprises one or more modified nucleosides. In some embodiments, the modified nucleosides of one or more modified nucleosides comprise modified sugar moieties. In some embodiments, the modified sugar moieties are 2'-substituted sugar moieties. In some embodiments, the 2'-substituted sugar moieties comprise modifications selected from the group consisting of 2'-O-methoxyethyl, 2'-fluoro, 2'-dimethylaminooxyethoxy, 2'-dimethylaminoethoxyethoxy, 2'-guanidino, 2'-O-guanidinoethyl, 2'-carbamate, 2'-aminooxy, 2'-acetamido and locked nucleic acid. In some embodiments, the modified oligonucleotide comprises more than one modified nucleosides, and each of the modified nucleosides comprises a modified sugar moiety. In some embodiments, at least one subset of more than one modified nucleosides is different from each other. In some embodiments, the pharmaceutical composition is used to modulate the splicing of pre-mRNA encoded by genes including: NEDD4L, MAP3K7, NFYA, ESYT2, MARK2, ST7, ARVCF, SYTL2, R3HDM1, COL4A3BP, TANGO2, SEPT9, ROBO1, FAM122B, CD47, LSR, PBX1, EPB41, ADAM15, EPB41L1, ABI1, FLNB, CTNND1, GPR160, ITGB3BP, INCENP, DENND1B or CA12.In some embodiments, modulation comprises inducing or enhancing exon skipping. In some embodiments, modulation comprises inducing or enhancing exon inclusion. In some embodiments, modulation comprises promoting splice switching. In some embodiments, modulation comprises downregulation or upregulation of splicing.

[0018] Another aspect of the present disclosure provides antisense compounds for use in the preparation of a medicament for treating a disease or condition, the antisense compounds comprising one or more oligonucleotides having at least 90% sequence identity to an oligonucleotide selected from Tables 2-33.

[0019] In some embodiments, the antisense compound includes one or more oligonucleotides at a concentration greater than or equal to about 300nM. In some embodiments, the antisense compound includes one or more oligonucleotides at a concentration less than or equal to about 450nM. In some embodiments, the one or more oligonucleotides include deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or a combination thereof. In some embodiments, RNA includes small interfering ribonucleic acid (siRNA). In some embodiments, the one or more oligonucleotides include single-stranded oligonucleotides, double-stranded oligonucleotides, or a combination thereof. In some embodiments, the disease or condition includes a genetic disease, a CNS disease, an inflammatory disease, a neurodegenerative disease, a cardiovascular disease, an autoimmune disease, or a cancer. In some embodiments, the disease is cancer. In some embodiments, cancer includes lung cancer, renal cancer, or breast cancer. In one embodiment, breast cancer is triple-negative breast cancer. In some embodiments, the one or more nucleotides include oligonucleotides selected from Tables 2-33. In some embodiments, the one or more oligonucleotides include modified oligonucleotides. In some embodiments, the modified oligonucleotides include at least one modified internucleoside bond. In some embodiments, at least one modified internucleoside bond is a phosphorothioate bond. In some embodiments, the oligonucleotide of modification comprises one or more modified nucleotides. In some embodiments, the oligonucleotide of modification comprises one or more modified nucleosides. In some embodiments, the modified nucleosides of one or more modified nucleosides comprise modified sugar moieties. In some embodiments, the modified sugar moieties are 2'-substituted sugar moieties. In some embodiments, 2'-substituted sugar moieties comprise modifications selected from 2'-O-methoxyethyl, 2'-fluoro, 2'-dimethylaminooxyethoxy, 2'-dimethylaminoethoxyethoxy, 2'-guanidine, 2'-O-guanidine ethyl, 2'-carbamate, 2'-aminooxy, 2'-acetamide and locked nucleic acid. In some embodiments, the oligonucleotide of modification comprises more than one modified nucleosides, and each of the modified nucleosides comprises modified sugar moieties. In some embodiments, at least one subset of more than one modified nucleosides is different from each other. In some embodiments, the treatment comprises modulating the splicing of pre-mRNA encoded by a gene comprising: NEDD4L, MAP3K7, NFYA, ESYT2, MARK2, ST7, ARVCF, SYTL2, R3HDM1, COL4A3BP, TANGO2, SEPT9, ROBO1, FAM122B, CD47, LSR, PBX1, EPB41, ADAM15, EPB41L1, ABI1, FLNB, CTNND1, GPR160, ITGB3BP, INCENP, DENND1B, or CA12. In some embodiments, the modulation comprises inducing or enhancing exon skipping.In some embodiments, modulation comprises inducing or enhancing exon inclusion. In some embodiments, modulation comprises promoting splicing switching. In some embodiments, modulation comprises downregulation or upregulation of splicing.

[0020] Another aspect of the present disclosure provides a method for modulating splicing in a pre-mRNA in a biological sample, comprising contacting the biological sample with a composition that specifically binds to a segment of a pre-mRNA encoded by a gene selected from the group consisting of NEDD4L, MAP3K7, NFYA, ESYT2, MARK2, ST7, ARVCF, SYTL2, R3HDM1, COL4A3BP, TANGO2, SEPT9, ROBO1, FAM122B, CD47, LSR, PBX1, EPB41, ADAM15, EPB41L1, ABI1, FLNB, CTNND1, GPR160, ITGB3BP, INCENP, DENND1B, and CA12.

[0021] In some embodiments, the segment length of the pre-mRNA is 9-150 nucleotides. In some embodiments, the composition includes an oligonucleotide. In some embodiments, the oligonucleotide is fully complementary to the segment of the pre-mRNA. In some embodiments, the oligonucleotide has at least 80% sequence identity with the segment of the pre-mRNA. In some embodiments, the oligonucleotide has at least 90% sequence identity with the segment of the pre-mRNA. In some embodiments, the oligonucleotide comprises 10-50 nucleotides. In some embodiments, the oligonucleotide comprises 15-30 nucleotides. In some embodiments, the composition includes a small molecule, a nucleic acid molecule, an engineered cell, a protein, or a combination or modification thereof. In some embodiments, the composition includes a chimeric molecule. In some embodiments, the chimeric molecule comprises a nucleic acid molecule and a protein. In some embodiments, the nucleic acid molecule comprises DNA, RNA, PNA, or a combination or hybrid thereof. In some embodiments, the composition induces or enhances exon skipping in the pre-mRNA. In some embodiments, the composition induces or enhances exon inclusion in the pre-mRNA. In some embodiments, the composition promotes splicing switching in the pre-mRNA. In some embodiments, the composition downregulates or upregulates splicing in pre-mRNA. In some embodiments, the composition prevents splicing in pre-mRNA.

[0022] Another aspect of the present disclosure provides a method for treating a disease or condition in a subject in need of such treatment, the method comprising administering to the subject an effective amount of a composition that specifically binds to a segment of a pre-mRNA encoded by a gene selected from the group consisting of NEDD4L, MAP3K7, NFYA, ESYT2, MARK2, ST7, ARVCF, SYTL2, R3HDM1, COL4A3BP, TANGO2, SEPT9, ROBO1, FAM122B, CD47, LSR, PBX1, EPB41, ADAM15, EPB41L1, ABI1, FLNB, CTNND1, GPR160, ITGB3BP, INCENP, DENND1B, and CA12, thereby modulating splicing in the pre-mRNA.

[0023] In some embodiments, the segment of the pre-mRNA is 9-150 nucleotides in length. In some embodiments, the composition comprises an oligonucleotide. In some embodiments, the oligonucleotide is fully complementary to the segment of the pre-mRNA. In some embodiments, the oligonucleotide has at least 80% sequence identity with the segment of the pre-mRNA. In some embodiments, the oligonucleotide has at least 90% sequence identity with the segment of the pre-mRNA. In some embodiments, the oligonucleotide comprises 10-50 nucleotides. In some embodiments, the oligonucleotide comprises 15-30 nucleotides. In some embodiments, the composition comprises a small molecule, a nucleic acid molecule, an engineered cell, a protein, or a combination or modification thereof. In some embodiments, the composition comprises a chimeric molecule. In some embodiments, the chimeric molecule comprises a nucleic acid molecule and a protein. In some embodiments, the nucleic acid molecule comprises DNA, RNA, PNA, or a combination or hybrid thereof. In some embodiments, the composition induces or enhances exon skipping in the pre-mRNA. In some embodiments, the composition induces or enhances exon inclusion in the pre-mRNA. In some embodiments, the composition promotes splicing switching in the pre-mRNA. In some embodiments, the composition downregulates or upregulates splicing in pre-mRNA. In some embodiments, the composition prevents splicing in pre-mRNA. In some embodiments, the effective amount comprises at least 300 nM of the composition. In some embodiments, the effective amount comprises at most 500 nM of the composition. In some embodiments, the disease or condition comprises a genetic disease, a CNS disease, an inflammatory disease, a neurodegenerative disease, a cardiovascular disease, an autoimmune disease, or a cancer. In some embodiments, the disease or condition is cancer. In some embodiments, the cancer comprises lung cancer, kidney cancer, or breast cancer. In one embodiment, the breast cancer is triple-negative breast cancer.

[0024] Another aspect of the present disclosure provides a method for screening, diagnosing, or prognosing a disease or condition in a subject, the method comprising: (a) analyzing a biological sample from the subject to detect the expression level of a protein isoform, wherein the protein isoform is encoded by a gene selected from the group consisting of: NEDD4L, MAP3K7, NFYA, ESYT2, MARK2, ST7, ARVCF, SYTL2, R3HDM1, COL4A3BP, TANGO2, SEPT9, ROBO1, FAM122B, CD47, LSR, PBX1, EPB41, ADAM15, EPB41L1, ABI1, FLNB, CTNND1, GPR160, ITGB3BP, INCENP, DENND1B, and Cal2; and (b) determining a difference in the expression level of the protein isoform in the biological sample relative to the expression level of the protein isoform in a control biological sample, wherein the difference is indicative of or prognostic of the disease or condition.

[0025] In some embodiments, the biological sample is a cell, tissue, or blood sample. In some embodiments, (a) comprises quantitatively detecting the amount of protein isoforms in the biological sample. In some embodiments, the difference comprises an increase or decrease in the expression level of the protein isoform in the biological sample relative to the expression level of the protein isoform in a control biological sample. In some embodiments, an increase in the expression level of the protein isoform in the biological sample relative to the expression level of the protein isoform in the control biological sample indicates or predicts a disease or condition. In some embodiments, a decrease in the expression level of the protein isoform in the biological sample relative to the expression level of the protein isoform in the control biological sample indicates or predicts a disease or condition. In some embodiments, the protein isoform comprises an alternatively spliced ​​protein isoform. In some embodiments, the alternatively spliced ​​protein isoform is formed by alternative splicing of a gene. In some embodiments, alternative splicing comprises exon skipping, exon inclusion, intron retention, competing 5' splice sites, competing 3' splice sites, multiple promoter sites, multiple poly(A) sites, or a combination thereof. In some embodiments, the method further comprises detecting the expression level of the gene in the biological sample. In some embodiments, the method further comprises detecting the presence or absence of a difference in the expression level of a gene in a biological sample of the subject relative to the expression level of the gene in a biological sample of a control. In some embodiments, the presence or absence of a difference in the expression level of a gene further indicates or predicts a disease or condition. In some embodiments, the presence of a difference comprises an increase or decrease in the expression level of a gene in a biological sample of the subject relative to the expression level of the gene in a biological sample of a control. In some embodiments, the method further comprises monitoring the progression of the disease or condition in the subject. In some embodiments, monitoring comprises repeating (a) multiple times within a predetermined time period. In some embodiments, the method further comprises providing treatment to the subject after the disease or condition is diagnosed in the subject. In some embodiments, treatment comprises administering to the subject an effective amount of a composition that modulates the expression level of a protein isoform. In some embodiments, treatment comprises administering to the subject an effective amount of a composition that modulates the splicing of a gene encoding a protein isoform. In some embodiments, the disease or condition comprises a genetic disease, a CNS disease, an inflammatory disease, a neurodegenerative disease, a cardiovascular disease, an autoimmune disease, or cancer. In some embodiments, the disease or condition is cancer. In some embodiments, cancer comprises lung cancer, kidney cancer, or breast cancer. In some embodiments, breast cancer is triple-negative breast cancer.

[0026] Other aspects and advantages of the present disclosure will become apparent to those skilled in the art from the following detailed description, which merely illustrates and describes an illustrative embodiment of the present disclosure. As will be appreciated, the present disclosure can have other and different embodiments, and its several details can be modified in various obvious aspects, all without departing from the present disclosure. Accordingly, the drawings and descriptions are to be considered illustrative and non-restrictive in nature.

[0027] Incorporated by Reference

[0028] All publications, patents, and patent applications mentioned in this specification are incorporated herein by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent that publications, patents, or patent applications incorporated by reference contradict the disclosure contained in this specification, the specification is intended to supersede and / or take precedence over any such contradictory material. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The novel features of the present invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by referring to the following detailed description and the accompanying drawings (also referred to herein as "Figures 5 and 6), which set forth illustrative embodiments utilizing the principles of the present invention, wherein:

[0031] This patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

[0032] Figure 1 Shown are exemplary oncoprint summaries of recurrent genomic aberrations and transcriptional changes for splicing-related RNA-binding proteins in triple-negative breast cancer (TNBC) patient samples.

[0033] Figure 2 Examples of data analysis and target selection for luminal versus TNBC RNA-seq are schematically illustrated.

[0034] Figure 3 Example graphs of splicing changes in The Cancer Genome Atlas (TCGA) and cell lines are shown, showing independent and overlapping events.

[0035] Figure 4 Shown are exemplary reverse transcription polymerase chain reaction (RT-PCR) images demonstrating differential splicing of selected candidates in luminal versus TNBC cell lines.

[0036] Figure 5 Shown are exemplary Western blots of protein lysates from luminal and basal cell lines demonstrating isoform expression of different genes at the protein level.

[0037] Figure 6 Survival analysis of breast cancer patients expressing NEDD4L containing isoforms versus skipping isoforms is shown, demonstrating that patients with skipping isoforms had poorer overall survival.

[0038] Figure 7 Comparison of splicing differences and global gene expression differences between multiple breast cancer subtypes is shown.

[0039] Figure 8 The SpliceLearn scores and corresponding eCLIP peaks near the NEDD4L exon trios are shown. The scores can be used to design oligonucleotide sequences and the figure below shows the results of a splice switching experiment, in which high-scoring ASOs (GTGGGTTTCAGGGATTCTGA (SEQ ID NO: 1), CCCTGATTCAGACAGCAGGG (SEQ ID NO: 2) were significantly switched to contain isoforms in MDA-MB-231 cells.

[0040] Figure 9 Shown are exemplary experimental validation and quantification of NEDD4L shut-off in MCF7 and MDA-Mb-231 cells treated with 400 nM specific oligonucleotides and controls treated with Lipofectamine or PBS. Radioactive RT-PCR is shown above and quantification of the results is shown below.

[0041] Figure 10A An exemplary dose-response curve for the inclusion-promoting SSO targeting NEDD4L (GTGGGTTTCAGGGATTCTGA (SEQ ID NO: 1)) is shown. SSO treatment resulted in a dose-dependent loss of viability in MDA-Mb-231 cells compared to MCF7 cells. The optimal LC50 value was approximately 370 nM.

[0042] Figure 10B An exemplary dose-response curve for the inclusion-promoting SSO targeting NEDD4L (CCCTGATTCAGACAGCAGGG (SEQ ID NO: 2)) is shown. SSO treatment resulted in a dose-dependent loss of viability in MDA-Mb-231 cells compared to MCF7 cells. The optimal LC50 value was approximately 420 nM.

[0043] Figure 11 Exemplary PCR validation of candidate genes is shown.

[0044] Figure 12 Exemplary PCR validation of candidate genes is shown.

[0045] Figure 13 Exemplary PCR validation of candidate genes is shown.

[0046] Figure 14 Exemplary PCR validation of candidate genes is shown.

[0047] Figure 15 An exemplary survival analysis of breast cancer patients expressing long and short isoforms of the candidate gene is shown.

[0048] Figure 16 An exemplary survival analysis of breast cancer patients expressing the long and short isoforms of the candidate gene is shown.

[0049] Figure 17 A table of selection criteria for the example is shown.

[0050] Figure 18 It is shown how the use of selection criteria can identify more target genes than using splicing alone.

[0051] Detailed description

[0052] Although various embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many variations, modifications, and substitutions may occur to those skilled in the art without departing from the present invention. It should be understood that a variety of alternatives to the embodiments of the present invention described herein may be employed.

[0053] Whenever the term "at least," "greater than," or "greater than or equal to" precedes the first value in a series of two or more values, the term "at least," "greater than," or "greater than or equal to" applies to every value in the series. For example, greater than or equal to 1, 2, or 3 is equivalent to greater than or equal to 1, greater than or equal to 2, or greater than or equal to 3.

[0054] Whenever the term "not more than," "less than," or "less than or equal to" precedes the first value in a series of two or more numerical values, the term "not more than," "less than," or "less than or equal to" applies to every value in the series. For example, less than or equal to 3, 2, or 1 is equivalent to less than or equal to 3, less than or equal to 2, or less than or equal to 1.

[0055] Identifying candidate targets for therapeutic development

[0056] In some aspects of the present disclosure, methods and systems are provided for detecting or identifying candidate drug targets. The candidate drug target can be associated with a specific disease, disorder, or condition. The disease, disorder, or condition can include cancer. Non-limiting examples of diseases, disorders or conditions can include, but are not limited to, ductal carcinoma, medullary carcinoma, colloid carcinoma, tubular carcinoma, breast cancer or subtypes thereof in breast duct tissue; ovarian cancer, including epithelial ovarian tumors such as adenocarcinomas in the ovaries and adenocarcinomas that have migrated from the ovaries into the abdominal cavity, uterine cancer, cervical cancer, such as adenocarcinomas in the cervical epithelium (including squamous cell carcinomas and adenocarcinomas); prostate cancer, such as a prostate cancer selected from the group consisting of adenocarcinomas or adenocarcinomas that have migrated to the bone; pancreatic cancer, such as epithelioid carcinomas in pancreatic duct tissue and adenocarcinomas in the pancreatic ducts; bladder cancer, such as transitional cell carcinomas in the bladder, urothelial carcinomas (transitional cell carcinomas), tumors in the epithelial cells that line the bladder, squamous cell carcinomas, adenocarcinomas and small cell carcinomas; leukemias, such as acute myeloid leukemia (AML), acute lymphocytic leukemia, chronic lymphocytic leukemia, chronic myeloid leukemia, hairy cell leukemia, myelodysplasia, myeloproliferative diseases (myeloproliferative diseases disorders), acute myeloid leukemia (AML), chronic myeloid leukemia (CML), mastocytosis, chronic lymphocytic leukemia (CLL), multiple myeloma (MM), and myelodysplastic syndrome (MDS); bone cancer; lung cancer, such as non-small cell lung cancer (NSCLC), which is divided into squamous cell carcinoma, adenocarcinoma, and large cell undifferentiated carcinoma, and small cell lung cancer; skin cancers, such as basal cell carcinoma, melanoma, squamous cell carcinoma, and actinic keratosis (a skin condition that sometimes develops into squamous cell carcinoma); retinoblastoma of the eye; skin or intraocular (eye) melanoma; primary liver cancer (cancer that begins in the liver); kidney cancer; thyroid cancer, such as papillary, follicular, medullary, and anaplastic carcinomas; AIDS-related lymphomas, such as diffuse large B-cell lymphoma, B-cell immunoblastic lymphoma, and small non-lytic cell lymphoma; Kaposi's sarcoma Sarcoma); viral-induced cancers, including hepatitis B virus (HBV), hepatitis C virus (HCV), and hepatocellular carcinoma; human lymphotropic virus type 1 (HTLV-1) and adult T-cell leukemia / lymphoma; and human papillomavirus (HPV) and cervical cancer; central nervous system cancers (CNS), such as primary brain tumors, including gliomas (astrocytomas, anaplastic astrocytomas, or glioblastoma multiforme), oligodendrogliomas, ependymomas, meningiomas, lymphomas, schwannomas, and medulloblastomas;Peripheral nervous system (PNS) cancers, such as acoustic neuroma and malignant peripheral nerve sheath tumors (MPNST), including neurofibromas and schwannomas, malignant fibrous cell tumors, malignant fibrous histiocytomas, malignant meningiomas, malignant mesotheliomas, and mixed Müllerian tumors; oral and oropharyngeal cancers, such as hypopharyngeal cancer, laryngeal cancer, nasopharyngeal cancer, and oropharyngeal cancer; gastric cancers, such as lymphomas, gastric stromal tumors, and carcinoid tumors; testicular cancers, such as germ cell tumors (GCTs), including seminoma and non-seminoma, and gonadal stromal tumors, including Leydig cell tumors and Sertoli cell tumors; thymus cancers, such as thymomas, thymic carcinomas, Hodgkin disease, non-Hodgkin lymphoma carcinoids, or carcinoid tumors; rectal cancer; and colon cancer. In some embodiments, the pharmaceutical composition is used to treat a non-cancerous hyperproliferative disease, such as benign hyperplasia of the skin (e.g., psoriasis), restenosis, or benign hyperplasia of the prostate (e.g., benign prostatic hypertrophy (BPH).

[0057] Candidate drug targets can include one or more differentially expressed genes, differentially spliced ​​exons (e.g., exon duos or exon trios), or a combination thereof. The methods and systems can be exon-centric and highly sensitive in detecting low-abundance aberrant mRNA isoforms.

[0058] In addition, artificial intelligence (AI) can be utilized by the methods and systems provided herein. Artificial intelligence can include the use of machine learning algorithms, non-limiting examples of which can include supervised (or predictive) learning, semi-supervised learning, active learning, unsupervised machine learning or reinforcement learning, support vector machines (SVM), linear, logical, tree, random forest, xgboost, neural networks, deep neural networks, enhancement techniques, bootstrapping techniques, ensemble techniques, or combinations thereof.

[0059] As provided herein, the system or method can include receiving data from a database. The database can be a public database (e.g., TCGA, GTEX, dbGAP), a private database, or a combination thereof. The database can include public data, proprietary data, or a combination thereof. The database can include clinical or biological data. The database can include RNA-seq data. ,000, 175,000, 200,000 samples or more. At least one subset of the samples can be obtained from different subjects with the same or different diseases, disorders or conditions.

[0060] The database can include data extracted or obtained from a cell line from certain diseases, disorders or conditions and / or from a sample of a subject with certain different diseases, disorders or conditions. In some cases, the disease, disorder or condition include breast cancer or a subtype thereof, for example, luminal A type, luminal B type, Her2+ type, TNBC type. Non-limiting examples of cell lines can include BT483, CAMA1, EFM19, HCC1428, HCC712, IBEP2, KPL1, LY2, MCF7, MDAMB134, MDAMB134VI, MDAMB157, MDAMB175, MDAMB175VII, MDAMB231, MDAMB330, MDAMB361, MDAMB415, MDA MB435, MDAMB436, MDAMB453, MDAMB468, T47D, ZR751, ZR75B, BSMZ, BT474, EFM192A, IBEP1, IBE P3, UACC812, ZR7527, ZR7530, 21MT1, 21MT2, 21NT, 21PT, AU565, HCC1008, HCC1569, HCC1954, HC C202, HCC2218, HH315, HH375, KPL-4, OCUB-F, SKBR3, SKBR5, SUM190PT, SUM225CWN, UACC893, B T20, CAL148, DU4475, EMG3, HCC38, HCC1143, HCC1187, HCC1395, HCC1599, HCC1739, HCC1806, HC C1937, HCC2157, HCC3153, HCC70, HMT3522, KPL-3C, MA11, MFM223, SUM185PE, SUM229PE, BT549, CAL120, CAL851, HDQ-P1, Hs578T, SKBR7, SUM102PT, SUM1315M02, SUM149PT, SUM159PT, or any combination thereof.

[0061] Data can be through one or more analysis or processing steps.Data can be analyzed and / or quantized to identify information or event, such as splicing event.The information or event identified can be statistically significant or unique for one or more diseases, illness or situation.Data analysis or processing can comprise that data are mapped to genome, transcriptome or its combination.Data can be processed to remove any information that may be irrelevant to genome, transcriptome or its combination.Data can be based on one or more predetermined parameters or standard (comprising type or hypotype such as disease, illness or situation) processing or analysis.

[0062] In the case where the database includes data associated with different types or subtypes of a disease, disorder, or condition, the data associated with each type or subtype can be analyzed or processed separately to identify information or events that are statistically significant or unique to each type or subtype. The identified information or events can be grouped together and compared with one or more controls to determine one or more candidate targets. Alternatively, the information or events identified for each subtype or type can be compared to each other to generate a list of candidate targets. In some cases, a candidate target includes information or events identified or shared by at least two different types or subtypes.

[0063] The list of candidate targets can include any number of candidate targets, for example, greater than or equal to about 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, 200, 220, 240, 260, 280, 300, 350, 400, 450, 500 or more. In some cases, the list can include a number falling between any two of the above values, for example, about 275 candidate targets.

[0064] At least a portion (e.g., at least about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or more) of the generated candidate targets can be subjected to further data analysis or processing. The candidate targets can be ranked in a certain order based on one or more parameters or criteria. The candidate targets can be selected based on one or more parameters or criteria to generate a refined list of candidate targets. Non-limiting examples of parameters that can be used to rank candidate targets include splicing index, disease index, splice switching oligonucleotide (SSO) druggability index, or any combination thereof.

[0065] The splicing index can be determined at least in part based on factors including, for example, splicing changes in the sample (or data) analyzed compared to a control, consistency and / or reproducibility of a given information or event (e.g., in a patient dataset), repeated occurrence of a given information or event in multiple disease datasets, absence of a given information or event in a normal or control dataset. Each factor can be assigned the same or different weight in the determination and based on the determination, a score can be generated.

[0066] The disease index can be determined at least in part based on factors including, for example, the effect of a given information or event (such as a splicing change) on the function of an expression product (such as a protein), the degree of association with a disease, disorder, or condition, pathway analysis (such as pathways listed in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database), literature evidence, or any combination thereof. Each factor can be assigned the same or different weight in the determination and based on the determination, a score can be generated.

[0067] The SSO druggability index can be determined at least in part based on factors including, for example, the ability to identify unique and / or specific splicing correction molecules (e.g., oligonucleotide sequences) using AI (e.g., machine learning algorithms), the presence or absence of strong enhanced cross-linking and immunoprecipitation (eCLIP) peaks mapping to the identified target (e.g., exon), the presence or absence of disease-specific expression of isoforms compared to genotype-tissue expression (GTEx) normal data, or any combination thereof. Each factor can be assigned the same or different weight in the determination and a score can be generated based on the determination.

[0068] In the case of generating a refined list of candidate targets, the candidate targets included in the list can undergo additional analysis or processing steps. For example, splicing events associated with at least a subset of candidate targets can undergo an evaluation process. The evaluation process can evaluate expression at different levels, for example, at the RNA level, at the protein level, or both. The evaluation process can confirm differential isoform expression in samples from different diseases (or their subtypes), disorders, or conditions. After confirmation, the candidate targets can be selected and used for further development of therapeutic agents. In some cases, the selected targets include one or more genes. Non-limiting examples of the one or more genes may include NEDD4L (ENV2), MAP3K7 (ENV3), NFYA (ENV11), ESYT2 (ENV21), MARK2 (ENV18), ST7 (ENV19), ARVCF (ENV22), SYTL2 (ENV17), R3HDM1 (ENV23), COL4A3BP (ENV9), TANGO2 (ENV6), SEPT9 (ENV15), ROBO1 (ENV4), FAM122B (ENV5), CD47 (ENV13), LSR (ENV20), PBX1 (ENV16), EPB41 (ENV14), ADAM15 (ENV7), EPB41L1 (ENV8), ABI1 (ENV10), FLNB (ENV1), CTNND1 (ENV12), GPR160 (ENV24), ITGB3BP (ENV25), INCENP (ENV26), DENND1B (ENV27), CA12 (ENV28), or any combination thereof.

[0069] Adjusting targets using various modalities

[0070] In some aspects of the present disclosure, methods and systems for regulating splicing targets are provided. Regulation can include regulating splicing events associated with a target (e.g., a gene). Regulation can include promoting or prompting splicing switching. For example, regulation can include converting a pathogenic isoform to a non-pathogenic isoform.

[0071] Modulation can include the use of one or more compositions or molecules that can specifically interact (e.g., hybridize) with a target, thereby controlling or altering the splicing of the target or regulating the expression of the target at the RNA level, the protein level, or both. The composition or molecule can target any element or combination of elements that regulate splicing (e.g., one or more genomic regions within the target), including, for example, 3' splice sites, 5' splice sites, branch points, polypyrimidine tracts, exonic splicing enhancers, exonic splicing silencers, intronic splicing enhancers, intronic splicing silencers, or any combination thereof.

[0072] The composition or molecule may include, for example, a small molecule, a polymer (natural or synthetic), a nucleotide sequence (such as an oligonucleotide or RNA), a therapeutic agent, a cell (such as a CAR-T cell), a protein (such as an antibody), or any combination thereof.

[0073] The composition or molecule can be mixed, encapsulated, conjugated or otherwise associated with other molecules, molecular structures or mixtures of compounds (e.g., liposomes, receptor targeting molecules, oral formulations, rectal formulations, topical formulations or other formulations) to aid in uptake, distribution and / or absorption.

[0074] Composition or molecule can be used in vivo or in vitro.In order to realize target specificity or disease specific targeting, composition or molecule can be added with certain concentration.For example, composition or molecule can have less than or equal to about 5 micromoles / liter (μM), 4 μM, 3 μM, 2 μM, 1 μM, 900 nanomoles / liter (nM), 800nM, 700nM, 650nM, 600nM, 550nM, 500nM, 450nM, 400nM, 350nM, 300nM, 250nM, 200nM, 150nM, 100nM, 50nM, 10nM or less concentration. The concentration can be greater than or equal to about 1 nM, 10 nM, 50 nM, 100 nM, 200 nM, 300 nM, 400 nM, 500 nM, 600 nM, 700 nM, 800 nM, 900 nM or more. In some cases, the concentration can fall between any two values ​​discussed above, for example, about 370 nM or 420 nM.

[0075] In some cases, compositions or molecules include oligonucleotides. Oligonucleotides can include any number of nucleotides or nucleotide residues, for example, more than or equal to about 5, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 55, 60 nucleotides or nucleotide residues, or more. In some cases, oligonucleotides can include less than or equal to about 50, 45, 40, 35, 30, 29, 27, 25, 24, 23, 22, 21, 19, 18, 17, 16, 13, 10, 8, 6 nucleotides or nucleotide residues, or less. In some cases, the number of nucleotides or nucleotide residues contained in the oligonucleotide can fall between any of the values ​​described above, for example, about 16 (16-mer), 17 (17-mer), 18 (18-mer), 19 (19-mer), 20 (20-mer), 21 (16-mer) or 22 (22-mer). In some cases, the oligonucleotide comprises a DNA molecule, an RNA molecule or a combination thereof.

[0076] In some cases, the oligonucleotide comprises an antisense oligonucleotide.Antisense oligonucleotides can be DNA and / or RNA oligonucleotides that are complementary to a given sequence, which can be a region within a target gene.

[0077] Oligonucleotides can be prepared using various techniques (such as solid phase synthesis, phosphorothioate and / or alkylated derivatives). The nucleotides or nucleotide residues contained in the oligonucleotides can include natural, unmodified nucleotides (e.g., cytosine, guanine, adenine, uracil or thymine), modified nucleotides or any combination thereof. In some cases, modified nucleotides or bases are used. Modification can be designed to enhance binding affinity. Modification can include chemical modification. Modification can include backbone modification, sugar ring modification or a combination thereof. Sugar ring modification can include 2'-sugar modification. For example, the oligonucleotides of the present disclosure can include a phosphothioate-modified backbone and / or a ribose modified to contain methoxyethane (2'MOE) at the 2'-position. Oligonucleotides comprising modified nucleotides or bases may not activate RNase H. In some cases, one or more (e.g., at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 35, 40, 45 or more) of the phosphate residues of the internucleotide bridge is a modified phosphate, such as methylphosphonates, methylphosphonothioates, phosphoromorpholidates, phosphoropiperazidates, phosphoroamidates, or any combination thereof. In some cases, one or more (e.g., at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 35, 40, 45 or more) of the nucleotides or bases comprises a 2'-alkyl moiety (e.g., a C1-C4 alkyl, linear or branched, saturated or unsaturated, including, for example, methyl, ethyl, vinyl, propyl, 1-propenyl, 2-propenyl, isopropyl, or combinations or derivatives thereof).

[0078] In some cases, the composition or molecule comprises a small molecule. Small molecules may include a wide range of compounds that can convert isoforms of the above-mentioned targets at the pre-mRNA level or the protein level. These compounds can be identified by high-throughput screening methods using chemical libraries, where the addition of a compound or combination of compounds may induce isoform conversion or modulate (e.g., inhibit or enhance) the biological activity of a specific isoform of one or more genes (e.g., genes mentioned above or described elsewhere herein) at the RNA or protein or both levels.

[0079] application

[0080] The systems and methods of the present disclosure can be used to determine or identify new splicing events or targets (e.g., genes) associated with the new splicing events. New splicing events can be statistically significant or unique to one or more given types or subtypes of diseases, disorders, or conditions. In order to identify new splicing events, the methods and systems of the present disclosure can receive data from one or more public and / or private databases, which can include biologically relevant data about the type or subtype of the disease, disorder, or condition being investigated. The data can be analyzed, processed, or annotated. Data analysis, processing, and / or annotation can be performed using a machine learning algorithm. Machine learning can be supervised learning, unsupervised learning, or a combination thereof. The algorithm can be a trained algorithm. The algorithm can be trained using a training set. The training set can include training samples. The training samples can be cell lines from certain diseases, disorders, or conditions; samples obtained from subjects with certain diseases, disorders, or conditions; controls, including positive and / or negative controls; or any combination thereof.

[0081] Data analysis, processing and / or annotation can generate a list of candidate targets that can potentially be used for therapeutic agent development. The candidate targets included in the list can undergo further screening processes or analysis. The splicing of the candidate targets can be evaluated or verified. The evaluation or verification of the candidate targets can produce a refined list of targets that can undergo further therapeutic agent development.

[0082] Compositions or molecules can be used to modify the splicing of individual targets included in the refinement list. Splicing can be modulated to promote the conversion of pathogenic isoforms to non-pathogenic isoforms. Compositions or molecules can be designed to target selected regions or combinations of selected regions within a target to achieve disease-specific targeting. In some cases, compositions or molecules are designed to modify the splicing of two or more (e.g., at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or more) different targets. Compositions or molecules targeting different targets can be used sequentially or simultaneously.

[0083] In some aspects of the present disclosure, methods and systems for providing treatment to a subject having or suspecting a disease, illness or condition are provided. The methods and systems may include obtaining a sample from a subject having or suspecting a disease, illness or condition. Biologically relevant data (e.g., DNA, RNA-seq data) may be obtained or extracted from a sample. Biologically relevant data may be screened or processed to remove any data unrelated to a genome or transcriptome. Processed data may undergo one or more data analysis, processing or annotation processes that identify one or more statistically significant or unique new splicing events for a subject having or suspecting a disease, illness or condition. The identified splicing events may be further analyzed or filtered using one or more parameters or filtering criteria, which may generate a final list of splicing events and associated targets for treatment.

[0084] After target identification, the system and method can also include administering a therapeutically effective amount of a composition or molecule to a subject having or suspecting the disease, illness or condition. Administration can be carried out within a given time period. The subject can be monitored, and the amount of the composition or molecule administered to the subject can be adjusted according to the monitoring results. In addition, monitoring can include obtaining one or more samples from the subject when the subject receives treatment. One or more samples can be analyzed or tested to determine whether treatment is effective. If treatment is determined to be invalid, treatment can be stopped and / or different treatments (e.g., administering different types of compositions or molecules) can be provided. Example

[0085] Example 1 - Identification of alternatively spliced ​​transcripts in triple-negative breast cancer (TNBC) and treatment to correct splicing changes therapeutic agents

[0086] Breast cancer is the most commonly diagnosed cancer and the second leading cause of cancer mortality in women, with nearly 30% of initial disease diagnoses resulting in metastatic breast cancer. One of the challenges in treating breast cancer is overcoming its enormous heterogeneity and unique cancer subtypes that may require different treatment approaches, including chemotherapy, hormone therapy, and human epidermal growth factor receptor 2 (Her2-) targeted therapy (depending on the subtype). However, a significant number of patients may develop resistance to current standard of care therapies. This emphasizes the need to identify new targets and develop alternative therapies for complete disease remission.

[0087] Splicing errors may be a source of coding variation in breast cancer. Due to dysregulated splicing factor expression in breast cancer, aberrant splicing of several genes may occur without DNA mutations or epigenetic changes. Several studies have shown an oncogenic role for core splicing factors (such as SRSF1, SRSF2, ESRP1, RBFOX1, etc.) in breast cancer. For example, overexpression of SRSF1 can promote the transformation of non-cancerous breast epithelial cells. In addition, it has been suggested that spliceosomal components may be particularly important factors in TNBC subtypes, and TNBC tumors sometimes show a dependence on these factors. For example, Figure 1 Shown is an oncoprint summary of recurrent genomic aberrations and transcriptional changes targeting splicing-related RNA-binding proteins in patient samples from The Cancer Genome Atlas (TCGA) breast cancer subtype TNBC.

[0088] In addition, splice site mutations that cause expression of alternative variants have been reported in key breast cancer genes such as ESR1 (encoding estrogen receptor α and conferring resistance to first-line drugs such as tamoxifen in ER-positive breast cancer). Dysregulation of splicing factors has been shown to contribute to the epithelial to mesenchymal transition through the generation of mesenchymal isoforms of key genes such as CD44 and FGFR2, thereby promoting tumor progression and metastasis. Given the suboptimal treatment options available for TNBC patients and the widespread splicing errors observed in TNBC patient RNA-seq data, disease-specific alternative splicing may be a source of viable candidates for therapeutic targeting.

[0089] The system of the present disclosure can be used to discover recurring splicing changes in RNA-seq of TNBC patients and to design modalities such as antisense oligonucleotides targeting specific isoforms to promote splicing switching to achieve therapeutic benefits. As discussed above or elsewhere herein, the system can include the system described in International Patent Application No. WO2019 / 226804. To discover splicing changes occurring in TNBC subtypes, we used The software platform compared RNA-seq data from luminal subtype patients and TNBC subtype patients from the TCGA breast cancer dataset. In addition, RNA sequencing was performed in triplicate on breast cancer cell lines. Two representative luminal cell lines (i.e., MCF7, T47D) as well as two representative TNBC B subtype cell lines (i.e., HS578T, BT549) and one TNBC A subtype cell line (i.e., MDA-MB 468) were used. Analysis flow chart in Figure 2 Instructions.

[0090] Comparison of splicing changes between luminal and basal breast cancers from TCGA and cell lines may independently contribute to splicing changes that differ from TCGA (12,860 changes) and splicing changes that differ from cell lines (944 changes). Figure 3 ). Of these changes, approximately 274 splicing changes were identified in both TCGA and cell lines ( Figure 3 ). These 274 splicing changes are considered candidates for target selection and can undergo further analysis. The analysis can be prioritized according to a number of parameters. The parameters (or buckets) used for candidate selection can serve as a variety of target selection criteria, which can include, for example, splicing index, disease index, therapeutic index, functional index, splice switching oligonucleotide (SSO) druggability index or any combination thereof. Example selection criteria and results are in Figure 17 This figure shows a representative illustration of the candidate selection scoring matrix based on the different parameters described above.

[0091] The splicing index can score the splicing changes (dPSI) in a given case and control group, the consistency and reproducibility of a given event in the patient dataset, and the repeated occurrence of a given event in multiple disease datasets and the absence of a given event in the normal dataset. The disease index can score the effect of splicing changes on protein function (i.e., "SpliceImpact TM ”), disease association (through open target scoring integration), pathway analysis (KEGG) and literature evidence. The SSO druggability index can be used to score drugs using machine learning (i.e., “SpliceLearn TM The genomic DNA was scored based on its ability to identify unique and specific splice correction oligonucleotide (oligo) sequences (eCLIP score), the presence of strong eCLIP peaks mapping to the identified exons, and the disease-specific expression of isoforms (compared to GTex normal data).

[0092] Figure 18 The present invention reveals the identification of biologically relevant targets for leukemia treatment described in this paper. Approximately 1178 RNA-seq datasets from acute myeloid leukemia (AML) patients obtained from the Leucegene consortium were analyzed to identify potential therapeutic targets. Different methods were used to analyze alternative splicing events, including variance assessment (selecting the strongest splicing changes above a cutoff value), reproducibility (selecting splicing changes that were repeatedly observed in biological replicates), cross-validation (splicing changes confirmed in independent datasets), and Splicing (a software platform described in International Patent Application No. WO2019 / 226804, which is incorporated herein by reference in its entirety). For each method, the top 30 gene candidates were selected, and gene candidates that were also known to be linked to AML pathogenesis were selected using records from OpenTargets (a public-private collaboration supported by GSK, Sanofi, Biogen, Takeda, Celgene, EMBL-EBI and the Sanger Institute to identify drug targets using genomics data). Figure 18 As shown in Figure 2, 23 of the top 30 candidates identified by SpliceCore were known to be linked to AML. Other methods identified a smaller number of candidates with known links to AML: the variance method identified 10 targets; the reproducibility method identified 8 targets; and the cross-validation method identified 8 targets.

[0093] exist Figure 17 In an exemplary application of the selection criteria, an alternative splicing index is determined by observing one or more alternative splicing events / changes in in-house cell lines and public BRCATCGA RNA-seq data; a therapeutic index is determined by confirming that one or more alternative splicing events / changes are disease-specific and not found in normal breast tissue using public GTEx RNA-seq; a functional index is determined by noting that the scores of one or more alternative splicing events / changes generated by SpliceImpact (a software platform described in International Patent Application No. WO2019 / 226804, which is incorporated herein by reference in its entirety) have significant damaging effects; and a druggability index is determined by predicting that one or more alternative splicing events / changes are drug targets using SpliceLearn (a software platform described in International Patent Application No. WO2019 / 226804, which is incorporated herein by reference in its entirety). In some cases, the drug target is an SSO regulatory target.

[0094] exist Figure 17In another exemplary application of the selection criteria, an alternative splicing index is determined by observing one or more alternative splicing events / changes in internal organoids and public BRCA TCGA RNA-seq from the Metabrick dataset; a therapeutic index is determined by confirming that one or more alternative splicing events / changes are disease-specific and are not found in various postmortem tissues (including liver, heart, muscle, and / or kidney) using public GTEx RNA-seq; a functional index is determined by noting that one or more alternative splicing events / changes occur in one or more genes with high BRCA association scores (estimated using OpenTargets); and a druggability index is determined by confirming that one or more oncogenic splicing factors bind to one or more target genes with one or more alternative splicing events / changes using CLIP-seq data. In some cases, one or more target genes may be blocked by an ASO analyzed based on the selection criteria.

[0095] exist Figure 17 In another exemplary application of selection criteria, an alternative splicing index is determined by observing one or more alternative splicing events / variations in in-house cell lines and licensed RNA-seq from a partner; a therapeutic index is determined by confirming that one or more alternative splicing events / variations are disease-specific and not found in normal tissues from a partner's RNA-seq; a functional index is determined by confirming that one or more alternative splicing events / variations are functionally associated with breast cancer using published literature; and a druggability index is determined by confirming that one or more alternative splicing events / variations occur in one or more genes known to be targets of small molecule proteins.

[0096] Based on one or more of the above-mentioned filtering criteria or parameters, a total of 28 candidates with potentially significant splicing changes in TNBC subtypes (Table 1 - List of the highest-scoring splicing events specific to TNBC and their corresponding gene names) were selected. The splicing events of these candidates were then evaluated for expression at the RNA level by PCR in experimental models (such as cell lines, primary cells, tissues, organoids, PDX tumors, patient tissue materials, body fluids, etc.). Where antibodies are available, protein expression of splicing isoforms can also be assessed. Hybridization methods such as RNA-FISH can also be used to verify the expression of specific isoforms in tumor tissue sections.

[0097] Table 1

[0098] TXDBID Gene (HUGO) ENV code CA-18-58335477-58335537.2267.0 NEDD4L ENV2 CA-6-90544551-90544632.1 MAP3K7 ENV3 CA-6-41080810-41080897.1 NFYA ENV11 CA-7-158752780-158752843.1 ESYT2 ENV21 CA-11-63903985-63904147.1 MARK2 ENV18 CA-7-117134123-117134192.1 ST7 ENV19 CA-22-19971215-19971335.1 ARVCF ENV22 CA-11-85717482-85717530.1 SYTL2 ENV17 CA-2-135641535-135641604.2 R3HDM1 ENV23 CA-5-75399309-75399387.1 COL4A3BP ENV9 CA-22-20053436-20053551.3 TANGO2 ENV6 CA-17-77307140-77307197.5 SEPT9 ENV15 CA-3-78647628-78647655.1 ROBO1 ENV4 CA-X-134772142-134772283.1 FAM122B ENV5 CA-3-58141857-58141929.1 FLNB ENV1 CA-3-108050577-108050602.2 CD47 ENV13 CA-1-29058588-29058645.2 EPB41 ENV14 CA-1-164820071-164820184.1 PBX1 ENV16 CA-19-35262545-35262692.1 LSR ENV20 CA-1-155061417-155061489.4 ADAM15 ENV7 CA-20-36209487-36209898.2 EPB41L1 ENV8 CA-10-26755654-26755741.1 ABI1 ENV10 CA-11-57791493-57791673.2 CTNND1 ENV12 CA-3-170082616-170082758.1 GPR160 ENV24 CA-1-63447564-63447614.1 ITGB3BP ENV25 CA-11-62141499-62141511.1 INCENP ENV26 CA-1-197647054-197647114.1 DENND1B ENV27 CA-15-63328097-63328130.1 CA12 ENV28

[0099] First, reverse transcription polymerase chain reaction (RT-PCR) was performed on selected candidates from the list in Table 1 to confirm differential isoform expression in luminal versus basal cell lines. Representative PCR results were obtained in Figure 4 In the Figure 2 , the specific expression of the long isoform or the short isoform was enriched in TNBC cell lines versus luminal cell lines. Further, for the selected group of candidates, Western blot analysis was also performed to verify protein expression, and representative Western blot images of these candidates are shown in Figure 2 . Figure 5 , which also shows differential isoform expression in protein lysates extracted from luminal and basal cell lines.

[0100] Next, we focused on testing specific candidates to see if they could serve as good therapeutic targets for the development of therapeutic compounds. NEDD4L was recommended as one of the top candidates by the SpliceCore software platform and showed the strongest dPSI changes and very high reproducibility in TCGA data as well as known cancer associations in key signaling pathways (i.e., TGFβ). By studying the impact of the observed splicing changes on protein function, we determined that the skipping isoforms (short isoforms) enriched in the TNBC subtype lacked a short loop region near the WW domain that may be responsible for protein-protein interactions. This loop contains a threonine residue that may undergo post-translational modification by kinases that can phosphorylate NEDD4L to maintain homeostasis of TGFβ signaling. Loss of the TNBC cancer-specific loop through alternative splicing can deregulate the signaling cascade, leading to tumor progression. In addition, breast cancer patients expressing NEDD4L containing isoforms were observed to have better overall survival than breast cancer patients with expression of the skipping isoforms ( Figure 6 Further analysis of subtype-stratified data from TCGA has shown that NEDD4L skipping isoforms are significantly enriched in the TNBC subtype compared to normal breast or luminal or HER2 subtypes of breast cancer. This difference was only observed at the level of alternative splicing and there was only a modest difference in total RNA expression of NEDD4L among these subtypes ( Figure 7 ).

[0101] By using The software platform and a module called SpliceLearn, based on machine learning (ML) methods, are used to predict nucleotide sequences that have a high probability of promoting splicing switching if blocked using molecules (e.g., oligonucleotides). These sequences are scored and ranked for each three-exon group on the candidate list. Additional scoring criteria can include RBP binding peaks, which can be obtained from ENCODE eCLIP-seq data and / or internally generated eCLIP-seq data for splicing regulatory proteins (including but not limited to RBFOX2, TDP-43, HNRNPL).

[0102] Next, a list of k-mer sequences spanning high-scoring regions within the three exomes can be generated and further filtered based on SSO specificity, repetitive motifs and off-target effects, and secondary structure ( Figure 8 The first five oligonucleotides are chemically synthesized and may contain a phosphorothioate modified backbone and / or a ribose sugar that is uniformly modified to contain methoxyethane (2'MOE) at the 2' position. Purified oligonucleotides can undergo functional analysis in breast cancer cell lines.

[0103] For NEDD4L, a total of 5 different sequences were tested (GCTGGCTTTGTCTGGATAGG (SEQ ID NO:3), GTGGGTTTCAGGGATTCTGA (SEQ ID NO:1), TCTCACGTCACCTGCCTTAC (SEQ ID NO:4), AGCGCTGCCACAGCAGTGGG (SEQ ID NO:5), CCCTGATTCAGACAGCAGGG (SEQ ID NO:2)), and 2 of these sequences were found to significantly promote inclusion of the middle exon. GTGGGTTTCAGGGATTCTGA (SEQ ID NO:1) (SSO2-2) promoted inclusion by an average of 40% in 3 of 4 experiments, while (SSO2-5) CCCTGATTCAGACAGCAGGG (SEQ ID NO:2) promoted inclusion by an average of 30% in 4 of 4 experiments. Figure 9 ).

[0104] Dose response experiments were performed to evaluate the LC50 values ​​of SSO compounds in two breast cancer cell lines, namely MCF7 (luminal) and MDA-MB-231 (TNBC) cell lines. Transfection of the SSO compounds showed a dose-responsive loss of viability in MDA-MB-231 cells but not in MCF7 cells. The optimal dose concentration in the cell lines was found to be between 370nM-420nM, at which concentration >50% cell death was observed. Cell viability can be assessed by measuring mitochondrial ATP flux using the Celltitre luminescence assay. After normalization to control non-transfected cells, the mean luminescence can be converted to the percentage of viable cells. Dose response curves for the two different cell lines to the two SSOs are shown in Figure 2. Figures 10A-10B Displayed in.

[0105] The criticality of alternative splicing events to TNMC tumors is also Figure 11 、 Figure 12 、 Figure 13 and Figure 14The alternative splicing events can be associated with one or more genes as described above or elsewhere herein, for example, genes including: NEDD4L (ENV2), MAP3K7 (ENV3), NFYA (ENV11), ESYT2 (ENV21), MARK2 (ENV18), ST7 (ENV19), ARVCF (ENV22), SYTL2 (ENV17), R3HDM1 (ENV23), COL4A3BP (ENV9), TANGO2 (ENV6), SEPT9 (ENV15), ROBO1 (ENV26), VEGF (ENV27), VEGF (ENV28), VEGF (ENV29), VEGF (ENV30), VEGF (ENV31), VEGF (ENV32), VEGF (ENV33), VEGF (ENV34), VEGF (ENV35), VEGF (ENV36), VEGF (ENV37), VEGF (ENV38), VEGF (ENV39), VEGF (ENV40), VEGF (ENV41), VEGF (ENV42), VEGF (ENV43), VEGF (ENV44), VEGF (ENV45), VEGF (ENV46), VEGF (ENV47), VEGF (ENV48), VEGF (ENV49), VEGF (ENV50), VEGF (ENV51), VEGF (ENV52), VEGF (ENV53), VEGF (ENV54), VEGF (ENV55), VEGF (ENV56), VEGF (ENV57), VEGF (ENV58), VEGF (ENV59), VEGF (ENV61), VEGF (ENV62), VEGF (ENV63), VEGF (ENV64), VEGF (ENV65), VEGF (ENV66), VEGF (ENV ENV4), FAM122B (ENV5), CD47 (ENV13), LSR (ENV20), PBX1 (ENV16), EPB41 (ENV14), ADAM15 (ENV7), EPB41L1 (ENV8), ABI1 (ENV10), FLNB (ENV1), CTNND1 (ENV12), GPR160 (ENV24), ITGB3BP (ENV25), INCENP (ENV26), DENND1B (ENV27), CA12 (ENV28), or a combination thereof. Survival analysis of TCGA BRCA patients harboring candidate long and short isoforms was performed, and the results were 15 and 18, respectively. Figure 16 The candidate may be one or more genes as described above or elsewhere herein, for example, genes including: NEDD4L (ENV2), MAP3K7 (ENV3), NFYA (ENV11), ESYT2 (ENV21), MARK2 (ENV18), ST7 (ENV19), ARVCF (ENV22), SYTL2 (ENV17), R3HDM1 (ENV23), COL4A3BP (ENV9), TANGO2 (ENV6), SEPT9 (ENV15), ROBO1 (ENV21), VEGF (ENV22), VEGF (ENV23), VEGF (ENV24), VEGF (ENV25), VEGF (ENV26), VEGF (ENV27), VEGF (ENV28), VEGF (ENV29), VEGF (ENV30), VEGF (ENV31), VEGF (ENV32), VEGF (ENV33), VEGF (ENV34), VEGF (ENV35), VEGF (ENV36), VEGF (ENV37), VEGF (ENV38), VEGF (ENV39), VEGF (ENV40), VEGF (ENV41), VEGF (ENV42), VEGF (ENV43), VEGF (ENV44), VEGF (ENV45), VEGF (ENV46), VEGF (ENV47), VEGF (ENV48), VEGF (ENV49), VEGF (ENV50), VEGF (ENV51), VEGF (ENV52), VEGF (ENV53), VEGF (ENV54), VEGF (ENV55), VEGF (ENV56), VEGF (ENV57), VEGF (ENV58), VEGF (ENV59), VEGF (ENV61), VEGF (ENV62), VEGF V4), FAM122B (ENV5), CD47 (ENV13), LSR (ENV20), PBX1 (ENV16), EPB41 (ENV14), ADAM15 (ENV7), EPB41L1 (ENV8), ABI1 (ENV10), FLNB (ENV1), CTNND1 (ENV12), GPR160 (ENV24), ITGB3BP (ENV25), INCENP (ENV26), DENND1B (ENV27), CA12 (ENV28), or a combination thereof. The analysis showed a significant difference in overall survival in patients expressing any one isoform.

[0106] Therefore, the above results show that TNBC subtypes may be vulnerable to splicing changes. Using the software platform of the present disclosure, reproducible and high-impact splicing changes can be identified and verified in RNA-seq data sets from patient samples. The listed candidates are likely to serve as therapeutic targets for TNBC breast cancer. The platform also designs and specifies oligonucleotide sequences that can promote splicing conversion when targeted using antisense oligonucleotides. For NEDD4L, the oligonucleotides designed by the platform were experimentally verified using uniformly modified 2'MOE oligonucleotide chemistry. NEDD4L variable splicing is a splicing event specific to the TNBC subtype of breast cancer. Modal targeting of NEDD4L isoform conversion using antisense oligonucleotides specifically exhibits selective viability loss in TNBC cells in a dose-responsive manner. Therefore, NEDD4L splicing can be used as a viable event to develop therapeutic agents for treating TNBC patients.

[0107] It should be understood that although the above examples relate to TNBC RNA-seq datasets, NEDD4L and other candidate splicing events discussed above or elsewhere herein may also be important in other solid or hematological malignancies, as well as neurological or metabolic diseases. One or more of the splicing changes may be responsible for the pathogenesis or progression of such diseases, disorders, or conditions, and the therapeutic targets of the present disclosure may be applicable to such diseases, disorders, or conditions.

[0108] Alternatively or additionally, splicing targets can be regulated using modalities including but not limited to small molecules, GAPMER oligonucleotides, siRNA, CAR-T cells, or any combination thereof to achieve disease-specific targeting. For example, some identified disease-specific splicing events can open grooves for small molecule binding. In such cases, small molecules can be designed to target regions resulting from splicing changes. In another example, splicing changes of membrane-bound proteins can display altered surface epitopes that can be specifically targeted with antibodies. The SpliceCore software platform and methods as described above and elsewhere herein can be used to analyze, design, and develop multimodal therapeutic targets for a wide range of disease indications.

[0109] Example 2 – Target-specific antisense oligonucleotides (ASO)

[0110] As provided above and elsewhere herein, ASOs can be identified based on exon location and SpliceLearn TMFeatures (such as RNA binding proteins) are identified. ASO can be used for splicing conversion experiments to promote the exon inclusion in the target candidate provided herein. In some cases, the ASO of chemical modification can be synthesized based on the ASO identified. For example, one or more chemical modifications are introduced into one or more ASOs. Chemical modification can include phosphorothioate backbone modification and / or 2'-O-(2-methoxyethyl) ribose modification (2'MOE) (modification of ribose). The sequence of ASO can be used for one or more in vitro experiments for breast cancer cell lines to determine the impact of ASO on the splicing conversion of target gene candidates.

[0111] The ASO of the selected group can be used for further in vitro and in vivo experiments and preclinical studies. In some cases, specific splicing switching events can be identified as being closely associated with triple negative breast cancer (TNBC). In some embodiments, antisense oligonucleotides can have a powerful splicing switching effect and less toxicity. In some cases, ASO can be used for preclinical studies and / or the development of therapeutic agents for cancer-specific gene targeting in patients. For example, by inducing splicing switching that may have anti-tumor effects, ASO can be used for the development of therapeutic agents for targeting TNBC breast cancer patients.

[0112] Tables 2-8 list examples of target-specific oligonucleotide sequences with variable sequence lengths that can be used to induce isoform switching or modulate (e.g., inhibit or enhance) the biological activity of a specific isoform of one or more of the genes described above or elsewhere herein (e.g., genes including one or more of the following: NEDD4L (ENV2), MAP3K7 (ENV3), NFYA (ENV11), ESYT2 (ENV21), MARK2 (ENV18), ST7 (ENV19), ARVCF (ENV22), SYTL2 (ENV17), R3HDM1 (ENV23), COL4A3BP (ENV9) , TANGO2(ENV6), SEPT9(ENV15), ROBO1(ENV4), FAM122B(ENV5), CD47(ENV13), LSR(ENV20), PBX1(ENV16), EPB41(ENV14), ADAM15(ENV7), EPB4 1L1(ENV8), ABI1(ENV10), FLNB(ENV1), CTNND1(ENV12), GPR160(ENV24), ITGB3BP(ENV25), INCENP(ENV26), DENND1B(ENV27), CA12(ENV28)).

[0113] In some cases, the oligonucleotide sequences included in the table are specific for a single target. In some cases, the oligonucleotide sequences included in the table are specific for more than one target. In some cases, the oligonucleotide sequences included in more than one table are specific for a single target. For example, the oligonucleotide sequences included in Tables 2-8 can be specific for a single target. The target can be a gene selected from the genes described above or elsewhere herein.

[0114] Table 2: 16-mer target-specific ASOs

[0115]

[0116]

[0117]

[0118]

[0119]

[0120]

[0121] Table 3: 17-mer target-specific ASOs

[0122]

[0123]

[0124]

[0125]

[0126]

[0127]

[0128] Table 4: 18-mer target-specific ASOs

[0129]

[0130]

[0131]

[0132]

[0133]

[0134] Table 5: 19-mer target-specific ASOs

[0135]

[0136]

[0137]

[0138]

[0139]

[0140]

[0141] Table 6: 20-mer target-specific ASOs

[0142]

[0143]

[0144]

[0145]

[0146]

[0147]

[0148] Table 7: 21-mer target-specific ASOs

[0149]

[0150]

[0151]

[0152]

[0153]

[0154]

[0155] Table 8: 22-mer target-specific ASOs

[0156]

[0157]

[0158]

[0159]

[0160]

[0161]

[0162] Tables 9-15 include some additional examples of oligonucleotide sequences with variable sequence lengths that can be used to induce isoform switching or modulate (e.g., inhibit or enhance) the biological activity of a particular isoform of one or several genes described above or elsewhere herein (e.g., genes include one or more of the following: NEDD4L (ENV2), MAP3K7 (ENV3), NFYA (ENV11), ESYT2 (ENV21), MARK2 (ENV18), ST7 (ENV19), ARVCF (ENV22), SYTL2 (ENV17), R3HDM1 (ENV23), COL4A3BP (ENV9) , TANGO2(ENV6), SEPT9(ENV15), ROBO1(ENV4), FAM122B(ENV5), CD47(ENV13), LSR(ENV20), PBX1(ENV16), EPB41(ENV14), ADAM15(ENV7), EPB4 1L1(ENV8), ABI1(ENV10), FLNB(ENV1), CTNND1(ENV12), GPR160(ENV24), ITGB3BP(ENV25), INCENP(ENV26), DENND1B(ENV27), CA12(ENV28)).

[0163] As discussed above, the oligonucleotide sequences included in the table can be specific to a single target or more than one target. In some cases, the oligonucleotide sequences included in more than one table are specific to a single target. For example, the oligonucleotide sequences included in Tables 9-15 can be specific to a single target. The target can be the same or different from the target to which the oligonucleotide sequences included in Tables 2-8 are specific. The target can be a gene selected from the genes described above or elsewhere herein.

[0164] Table 9: 16-mer target specificity

[0165]

[0166]

[0167]

[0168]

[0169] Table 10: 17-mer target-specific ASOs

[0170]

[0171]

[0172]

[0173]

[0174] Table 11: 18-mer target-specific ASOs

[0175]

[0176]

[0177]

[0178]

[0179] Table 12: 19-mer target-specific ASOs

[0180]

[0181]

[0182]

[0183] Table 13: 20-mer target-specific ASOs

[0184]

[0185]

[0186]

[0187] Table 14: 21-mer target-specific ASOs

[0188]

[0189]

[0190]

[0191]

[0192] Table 15: 22-mer target-specific ASOs

[0193]

[0194]

[0195]

[0196]

[0197] Additional target-specific ASOs are also provided herein (Tables 16-33) that can be used in the methods and / or systems of the present disclosure. ASOs can be used to induce isoform switching or modulate (e.g., inhibit or enhance) the biological activity of a specific isoform of one or more of the genes described above or elsewhere herein (e.g., genes including one or more of the following: NEDD4L (ENV2), MAP3K7 (ENV3), NFYA (ENV11), ESYT2 (ENV21), MARK2 (ENV18), ST7 (ENV19), ARVCF (ENV22), SYTL2 (ENV17), R3HDM1 (ENV23), OL4A3BP (ENV9), TANGO2 (ENV6), SE PT9(ENV15), ROBO1(ENV4), FAM122B(ENV5), CD47(ENV13), LSR(ENV20), PBX1(ENV16), EPB41(ENV14), ADAM15(ENV7), EPB41L1(ENV8 ), ABI1(ENV10), FLNB(ENV1), CTNND1(ENV12), GPR160(ENV24), ITGB3BP(ENV25), INCENP(ENV26), DENND1B(ENV27), CA12(ENV28)).

[0198] As discussed above, the oligonucleotide sequences included in the table can be specific to a single target or more than one target. In some cases, the oligonucleotide sequences included in more than one table are specific to a single target. For example, the oligonucleotide sequences included in Tables 16-33 can each be specific to a single target. The target can be the same or different from the target to which the oligonucleotide sequences included in Tables 2-15 are specific. The target can be one or more genes described above or elsewhere in this article. In some cases, the ASOs included in Table 16 are specific to NEDD4L (ENV2). In some cases, the ASOs included in Table 17 are specific to MAP3K7 (ENV3). In some cases, the ASOs included in Table 18 are specific to ROBO1 (ENV4). In some cases, the ASOs included in Table 19 are specific to FAM122B (ENV5). In some cases, the ASOs included in Table 20 are specific to TANGO2 (ENV6). In some cases, the ASOs included in Table 21 are specific for ADAM15 (ENV7). In some cases, the ASOs included in Table 22 are specific for EPB41L1 (ENV8). In some cases, the ASOs included in Table 23 are specific for COL4A3BP (ENV9). In some cases, the ASOs included in Table 23 are specific for ABI1 (ENV10). In some cases, the ASOs included in Table 24 are specific for NFYA (ENV11). In some cases, the ASOs included in Table 26 are specific for CTNND1 (ENV12). In some cases, the ASOs included in Table 27 are specific for SEPT9 (ENV15). In some cases, the ASOs included in Table 28 are specific for SYTL2 (ENV17). In some cases, the ASOs included in Table 29 are specific for MARK2 (ENV18). In some cases, the ASOs included in Table 30 are specific for ST7 (ENV19). In some cases, the ASOs included in Table 31 are specific for ESYT2 (ENV21). In some cases, the ASOs included in Table 32 are specific for ARVCF (ENV22). In some cases, the ASOs included in Table 33 are specific for R3HDM1 (ENV23).

[0199] Table 16: 25-mer target-specific ASOs

[0200]

[0201]

[0202]

[0203]

[0204] Table 17: 25-mer target-specific ASOs

[0205]

[0206]

[0207]

[0208]

[0209] Table 18: 25-mer target-specific ASOs

[0210]

[0211]

[0212]

[0213] Table 19: 25-mer target-specific ASOs

[0214]

[0215]

[0216]

[0217]

[0218]

[0219]

[0220] Table 20: 25-mer target-specific ASOs

[0221]

[0222]

[0223]

[0224]

[0225]

[0226]

[0227] Table 21: 25-mer target-specific ASOs

[0228]

[0229]

[0230]

[0231]

[0232] Table 22: 25-mer target-specific ASOs

[0233]

[0234]

[0235]

[0236]

[0237]

[0238]

[0239]

[0240]

[0241]

[0242]

[0243]

[0244]

[0245]

[0246] Table 23: 25-mer target-specific ASOs

[0247]

[0248]

[0249]

[0250]

[0251] Table 24: 25-mer target-specific ASOs

[0252]

[0253]

[0254]

[0255]

[0256]

[0257] Table 25: 25-mer target-specific ASOs

[0258]

[0259]

[0260]

[0261]

[0262]

[0263] Table 26: 25-mer target-specific ASOs

[0264]

[0265]

[0266]

[0267]

[0268]

[0269]

[0270]

[0271] Table 27: 25-mer target-specific ASOs

[0272]

[0273]

[0274]

[0275]

[0276]

[0277] Table 28: 25-mer target-specific ASOs

[0278]

[0279]

[0280]

[0281]

[0282] Table 29: 25-mer target-specific ASOs

[0283]

[0284]

[0285]

[0286]

[0287]

[0288]

[0289] Table 30: 25-mer target-specific ASOs

[0290]

[0291]

[0292]

[0293]

[0294]

[0295] Table 31: 25-mer target-specific ASOs

[0296]

[0297]

[0298]

[0299]

[0300] Table 32: 25-mer target-specific ASOs

[0301]

[0302]

[0303]

[0304]

[0305]

[0306]

[0307] Table 33: 25-mer target-specific ASOs

[0308]

[0309]

[0310]

[0311]

[0312] Although preferred embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the present invention be limited to the specific examples provided in this specification. Although the present invention has been described with reference to the description mentioned above, the description and illustration of the embodiments herein are not intended to be interpreted in a restrictive sense. Those skilled in the art will now appreciate that many variations, changes, and replacements do not depart from the present invention. In addition, it should be understood that all aspects of the present invention are not limited to the specific description, configuration, or relative proportions set forth herein according to various conditions and variables. It should be understood that various alternatives to the embodiments of the present invention described herein can be adopted when practicing the present invention. Therefore, it is contemplated that the present invention also encompasses any such alternatives, modifications, variations, or equivalents. The appended claims are intended to define the scope of the present invention, and thus encompass methods and structures and their equivalents within the scope of these claims.

Claims

1. An antisense oligonucleotide targeting NEDD4L, the antisense oligonucleotide being complementary to a sequence selected from the group consisting of: 5'-GTGGGTTCAGGGATTCTGA-3' (SEQ ID NO: 1), and 5'-CCCTGATTCAGACAGCAGGG-3' (SEQ ID NO: 2).

2. The antisense oligonucleotide according to claim 1, wherein the antisense oligonucleotide is capable of regulating the splicing of NEDD4L mRNA in a cell.

3. The antisense oligonucleotide of claim 2, wherein said modulation of splicing comprises promoting splice switching.

4. The antisense oligonucleotide according to claim 1, wherein the antisense oligonucleotide comprises deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or a combination or hybrid thereof.

5. The antisense oligonucleotide of claim 1, wherein the antisense oligonucleotide comprises one or more modified oligonucleotides.

6. The antisense oligonucleotide of claim 1, wherein the antisense oligonucleotide comprises at least one modified internucleoside linkage.

7. The antisense oligonucleotide of claim 6, wherein all internucleoside bonds are modified.

8. The antisense oligonucleotide of claim 6, wherein the modified internucleoside bond is a phosphorothioate bond.

9. The antisense oligonucleotide of claim 1, wherein the antisense oligonucleotide comprises one or more modified nucleosides selected from the group consisting of 2'-O-methoxyethyl nucleosides, 2'-fluoro nucleosides, 2'-dimethylaminooxyethoxy nucleosides, 2'-dimethylaminoethoxyethoxy nucleosides, 2'-guanidino nucleosides, 2'-O-guanidinoethyl nucleosides, 2'-carbamate nucleosides, 2'-aminooxy nucleosides, 2'-acetamido nucleosides, and locked nucleic acids.

10. A pharmaceutically acceptable composition comprising the antisense oligonucleotide according to any one of claims 1 to 9 and a pharmaceutically acceptable diluent.

11. A pharmaceutically acceptable composition comprising the antisense oligonucleotide according to any one of claims 1 to 9 and a pharmaceutically acceptable carrier.

12. Use of the antisense oligonucleotide according to any one of claims 1 to 9 in the preparation of a medicament for treating triple-negative breast cancer.

13. Use of an antisense oligonucleotide according to any one of claims 1 to 9 for the preparation of a medicament for cancer characterized by disease-specific splicing events of the pre-mRNA encoded by the NEDD4L gene resulting in short pre-mRNA isoforms.

14. The use according to claim 13, wherein the antisense oligonucleotide promotes the inclusion of the excluded middle exon of the three exon group identified in the pre-mRNA.

15. A method for regulating the splicing of NEDD4L precursor mRNA in cells in vitro or in vitro, the method comprising: The cell is contacted with an antisense compound targeting NEDD4L, the antisense compound being complementary to a sequence selected from the group consisting of: 5'-GTGGGTTCAGGGATTCTGA-3' (SEQ ID NO: 1), and 5'-CCCTGATTCAGACAGCAGGG-3'(SEQ ID NO:2), Splicing in the NEDD4L pre-mRNA in the cell is thereby regulated.

16. The method of claim 15, wherein the modulation of splicing comprises promoting a splicing switch in the NEDD4L pre-mRNA.

17. The method of claim 16, wherein the modulation of splicing comprises promoting inclusion of exons in pre-mRNA isoforms.

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