Arid1a is a prognostic marker for aggressive lymphoma transformation and presents a vulnerability to pharmacological inhibition of smarca4 / 2

Abstract

Present disclosure relates to compositions and methods for diagnosing subjects afflicted with follicular lymphoma (FL) that are at risk for transforming into diffuse large B- cell lymphoma (DLBCL), methods of predicting responses of subjects with FL or DLBCL to the pharmacological inhibition of SMARCA4 / 2, as well as methods of preventing and / or treating FL and / or DLBCL, based on the genotype, level, and / or activity of ARID 1 A.

Description

[0001] Arid la is a prognostic marker for aggressive lymphoma transformation and presents a vulnerability to pharmacological inhibition of SMARCA4 / 2

[0002] Statement of Rights

[0003] This invention was made with government support under CA246080, and CA220499 awarded by the National Institutes of Health. The government has certain rights in the invention.

[0004] Cross-Reference to Related Applications

[0005] This application claims the benefit of priority to U.S. Provisional Application Serial No. 63 / 546,844, filed on November 1, 2023; the entire content of said application is incorporated herein in its entirety by this reference.

[0006] Background of the Invention

[0007] The BAF chromatin remodeling complex (BRGl / BRM-associated factors) of the SWI / SNF (Switch / Sucrose Non-Fermentable) family of proteins govern the accessibility of DNA by positioning, ejecting, or reconfiguring nucleosomes, thereby regulating gene expression (Delmas et al., 1993; Kassabov et al., 2003; Kwon et al., 1994; Shen et al., 2000; Tamkun et al., 1992; Tsukiyama et al., 1995; Whitehouse et al., 1999). The biological function of the BAF complexes and their role in facilitating DNA binding of a distinct ensemble of transcription factors, is predominantly determined by the specific cell type in which they operate (Centore et al., 2020). The versatility of their function is derived from their composition, encompassing more than a dozen subunits that have several isoforms, with each isoform selectively integrated to create a variety of distinct complexes (He et al., 2020; Ho et al., 2009; Mashtalir et al., 2018, 2020; W. Wang et al., 1996). This variation equips BAF complexes with an extraordinary ability to adapt and execute a wide range of biological functions (Raab et al., 2015). However, even minor alterations in any subunit can significantly impact the complex function, and when dysregulated contribute to various types of diseases, including cancer (Centore et al., 2020; Kadoch et al., 2013).

[0008] In mammalian cells, there are three distinct BAF complexes including the canonical BAF (cBAF), the polybromo-associated BAF (PBAF), and the non-canonical BAF (ncBAF). Recent studies indicate that nearly 20% of all human malignancies contain mutations in one or more components of the different BAF complexes (Kadoch et al., 2013). Among the subunits of the BAF complex, ARID 1 A, critical for complex targeting to chromatin, stands out as the most frequently mutated subunit across various cancer types (Centore et al., 2020). These mutations often result in a reduction or loss of ARID 1 A expression, an event that has been linked with tumorigenesis as recent reports show (Bailey et al., 2018; Bliimli et al., 2021; J. Li et al., 2020; Mathur et al., 2016; Sun et al., 2017; Z. Wang et al., 2020). Despite the established role of ARID 1 A in the emergence and progression of cancers, the precise chromatin mechanisms underlying the transformative potential of ARID 1 A mutations remain elusive, making it a crucial focus of ongoing oncology research (Xu & Tang, 2021).

[0009] In lymphomas, including follicular lymphoma (FL) and diffuse large B cell lymphomas (DLBCL), AR1D1A mutations are especially prevalent (Green et al., 2015; Morin et al., 2011; Okosun et al., 2014). DLBCLs are a group of diverse and aggressive tumors that can be classified into several genetically-defined subtypes, each having a unique pattern of mutations (Chapuy et al., 2018; Schmitz et al., 2018; Wright et al., 2020). DLBCLs in the EZB / C3 cluster are the most common and are specifically characterized by numerous somatic mutations in genes encoding chromatin regulatory proteins, such as KMTD2, CREBBP, and EZH2 (Mlynarczyk et al., 2019). Notably, among various DLBCL subtypes, the EZB / C3 cluster shows the highest enrichment for ARID1A mutations (Wright et al., 2020). Even though FLs typically begin as indolent tumors, patients are at a high risk for transformation into a more aggressive, and often untreatable type of DLBCL-like lymphoma. Our current understanding of the disease does not allow us to predict which patients will undergo this transformation, and the underlying mechanisms of transformation remain unclear. Interestingly, FLs and EZB / C3 DLBCLs share similar genetic mutation patterns, particularly in genes responsible for chromatin regulation (Mlynarczyk et al., 2019). Both display GCB- like transcriptional characteristics and almost universally carry the t( 14; 18) translocation, resulting in abnormal BCL2 expression.

[0010] Both FLs and DLBCLs emerge from mature B-cells that are actively participating in the humoral immune response within secondary lymphoid tissues (Mlynarczyk et al., 2019). During this response, B-cells establish temporary structures known as germinal centers (GCs). GC B-cells experience extensive and rapid shifts in their chromatin and transcriptional programs as they cycle back and forth between proliferative bursting and T- cell directed selection and subsequent cell fate decisions (Mlynarczyk et al., 2019). Proliferative bursting coincides with periods of immunoglobulin gene somatic hypermutation, which eventually leads to selection of B-cells encoding high affinity antibodies (Mesin et al., 2016). Regrettably, this process also inadvertently introduces numerous off-target mutations, which can contribute to the development of malignancies (Mlynarczyk et al., 2019). These rapid and varied cellular transformations are dictated by constantly evolving chromatin patterns, likely overseen by specific transcription factors (TFs) (Mesin et al., 2016). Despite this understanding, our knowledge remains limited regarding the interplay between TFs and chromatin remodeling complexes in effecting the successive phases of this chromatin reorganization. We also lack insight into the exact roles of chromatin remodelers in instructing the diverse GC B-cell fates, and how they may contribute to the development of lymphomas.

[0011] There is currently no biomarker for the transformation from FLs to more aggressive types of DLBCL-like lymphomas, and no way to target specifically these tumors.

[0012] Summary of the Invention

[0013] ARID 1 A, a subunit of the canonical BAF nucleosome remodeling complex, is commonly mutated in lymphomas. The present disclosure is based, at least in part, on the discovery that ARID 1 A orchestrates B-cell fate during the germinal center (GC) response, facilitating cooperative and sequential binding of PU.l and NF-kB at crucial genes for cytokine and CD40 signaling. The absence of ARID 1 A tilts GC cell-fate towards immature IgM+CD80-PDL2- memory B-cells, known for their potential to re-enter new GCs. When combined with BCL2 oncogene, AR1D1A haploin sufficiency hastens the progression of aggressive follicular lymphomas in mice. Remarkably, follicular lymphoma patients with ARID JA-inactivating mutations preferentially display an immature memory B-cell-like state with increased transformation risk to aggressive disease. These observations offer mechanistic understanding into the emergence of both indolent and aggressive lymphomas in ARIDlA-mutant patients through formation of immature memory-like clonal precursors. Lastly, the present disclosure demonstrates that ARID 1 A mutation induces synthetic lethality to SMARCA2 / 4 inhibition, paving the way for potential precision therapy for high-risk patients.

[0014] Accordingly, in some aspects, provided herein is a method of assessing whether a subject afflicted with follicular lymphoma (FL) is at risk for transforming into diffuse large B-cell lymphoma (DLBCL), the method comprising determining:

[0015] (1) the presence or absence of a loss-of-function mutation of ARID 1 A in a biological sample from the subject; and / or (2) the level and / or activity of ARID 1 A in a biological sample from the subject, wherein the presence of a loss-of-function mutation of ARID 1 A in the biological sample, and / or a significantly decreased level and / or activity of ARID 1 A in the biological sample relative to a control indicates that the subject afflicted with FL is at risk for transforming into DLBCL, and wherein the absence of a loss-of-function mutation of ARID 1 A in the biological sample, and / or a significantly increased level and / or activity of ARID 1 A in the biological sample relative to a control indicates that the subject afflicted with FL is not at risk for transforming into DLBCL.

[0016] In some embodiments, the method comprises obtaining the biological sample from the subject for the determination step.

[0017] In some embodiments, the method comprises determining the presence or absence of a loss-of-function mutation of ARID 1 A in a biological sample from the subject.

[0018] In some embodiments, the loss-of-function mutation of ARID 1 A results in a reduction or loss of ARID 1 A expression and / or activity.

[0019] In some embodiments, the loss-of-function mutation of ARID 1 A is a homozygous mutation or a heterozygous mutation.

[0020] In some embodiments, the loss-of-function mutation of ARID 1 A is a deletion, an insertion, or a substitution.

[0021] In some embodiments, the loss-of-function mutation of ARID 1 A is a nonsense mutation or a missense mutation.

[0022] In some embodiments, the loss-of-function mutation of ARID1A is Q474*.

[0023] In some embodiments, the subject further comprises overexpression of translocation of BCL2.

[0024] In some embodiments, the genomic DNA isolated from the biological sample is used for the determination of the presence of absence of the loss-of-function mutation in ARID 1 A.

[0025] In some embodiments, the loss-of-function mutation of ARID 1 A is detected by a genotyping method.

[0026] In some embodiments, the method comprises determining the level and / or activity of ARID 1 A in a biological sample from the subject and comparing it to a control.

[0027] In some embodiments, the control is a reference value.

[0028] In some embodiments, the control is a level and / or activity determined from a control sample. In some embodiments, the control sample is a non-tumor sample obtained from the subject, a sample from a subject without FL, a sample from a subject without DLBCL, or a sample from a subject with FL but not at risk of transforming into DLBCL.

[0029] In some embodiments, the level of ARID 1 A is detected by western blot or flow cytometry.

[0030] In some embodiments, the western blot or flow cytometry uses a commercially available ARID 1 A antibody.

[0031] In some embodiments, the biological sample is a tumor cell or tissue.

[0032] In some embodiments, the method further comprises administering to the subject an inhibitor of SMARCA4 and / or SMARCA2 if the subject afflicted with FL is at risk for transforming into DLBCL.

[0033] In some embodiments, wherein the inhibitor of SMARCA4 and / or SMARCA2 is FHD-286 or AU-15330.

[0034] In some aspects, provided herein is a method of treating a subject afflicted with DLBLC and having a loss-of-function mutation of ARID 1 A, comprising administering to the subject a therapeutically effective amount of an inhibitor of SMARCA4 and / or SMARCA2.

[0035] In some aspects, provided herein is a method of preventing a subject afflicted with FL and having a loss-of-function mutation of ARID 1 A from transforming into DLBCL, comprising administering to the subject a therapeutically effective amount of an inhibitor of SMARCA4 and / or SMARCA2.

[0036] In some embodiments, the loss-of-function mutation of ARID 1 A results in a reduction or loss of ARID 1 A expression and / or activity.

[0037] In some embodiments, the loss-of-function mutation of ARID 1 A is a homozygous mutation or a heterozygous mutation.

[0038] In some embodiments, the loss-of-function mutation of ARID 1 A is a deletion, an insertion, or a substitution.

[0039] In some embodiments, the loss-of-function mutation of ARID 1 A is a nonsense mutation or a missense mutation.

[0040] In some embodiments, the loss-of-function mutation of ARID1A is Q474*.

[0041] In some embodiments, the subject further comprises overexpression of translocation of BCL2.

[0042] In some embodiments, the inhibitor of SMARCA4 and / or SMARCA2 is FHD-286 or

[0043] AU-15330. In some embodiments, the method further comprises identifying the subject afflicted with FL or DLBCL that has a loss-of-function mutation of ARID 1 A prior to the treatment.

[0044] In some aspects, provided herein is a method of identifying the likelihood of a FL or a DLBCL in a subject to respond to an inhibitor of SMARCA 4 and / or SMARCA2, the method comprising determining:

[0045] (1) the presence or absence of a loss-of-function mutation of ARID 1 A in a biological sample from the subject; and / or

[0046] (2) the level and / or activity of ARID 1 A in a biological sample from the subject, wherein the presence of a loss-of-function mutation of ARID 1 A in the biological sample, and / or a significantly decreased level and / or activity of ARID 1 A in the biological sample relative to the control identifies the FL or DLBCL as being more likely to respond to the inhibitor of SMARCA 4 and / or SMARCA2, and wherein the absence of a loss-of-function mutation of ARID 1 A in the biological sample, and / or a significantly increased level and / or activity of ARID 1 A in the biological sample relative to a control identifies the FL or DLBCL as being less likely to respond to the inhibitor of SMARCA 4 and / or SMARCA2.

[0047] In some embodiments, the method comprises obtaining the biological sample from the subject for the determination step.

[0048] In some embodiments, the method comprises determining the presence or absence of a loss-of-function mutation of ARID 1 A in a biological sample from the subject.

[0049] In some embodiments, the loss-of-function mutation of ARID 1 A results in a reduction or loss of ARID 1 A expression and / or activity.

[0050] In some embodiments, the loss-of-function mutation of ARID 1 A is a homozygous mutation or a heterozygous mutation.

[0051] In some embodiments, the loss-of-function mutation of ARID 1 A is a deletion, an insertion, or a substitution.

[0052] In some embodiments, the loss-of-function mutation of ARID 1 A is a nonsense mutation or a missense mutation.

[0053] In some embodiments, the loss-of-function mutation of ARID1A is Q474*.

[0054] In some embodiments, the subject further comprises overexpression of translocation of BCL2.

[0055] In some embodiments, the genomic DNA isolated from the biological sample is used for the determination of the presence of absence of the loss-of-function mutation in ARID 1 A. In some embodiments, the loss-of-function mutation of ARID 1 A is detected by a genotyping method.

[0056] In some embodiments, the method comprises determining the level and / or activity of ARID 1 A in a biological sample from the subject and comparing it to a control.

[0057] In some embodiments, the control is a reference value.

[0058] In some embodiments, the control is a level and / or activity determined from a control sample.

[0059] In some embodiments, the control sample is a non-tumor sample obtained from the subject, a sample from a subject without FL, a sample from a subject without DLBCL, a sample from a subject with FL but not at risk of transforming into DLBCL, or a sample from a subject with FL or DLBCL and a known outcome of the treatment of the inhibitor of SMARCA4 and / or SMARCA2.

[0060] In some embodiments, the level of ARID 1 A is detected by western blot or flow cytometry.

[0061] In some embodiments, the western blot or flow cytometry uses a commercially available ARID 1 A antibody.

[0062] In some embodiments, the biological sample is a tumor cell or tissue.

[0063] In some embodiments, the method further comprises administering to the subject the inhibitor of SMARCA4 and / or SMARCA2 if the subject afflicted with FL or DLBCL is likely to respond to the inhibitor of SMARCA4 and / or SMARCA2.

[0064] In some embodiments, wherein the inhibitor of SMARCA4 and / or SMARCA2 is FHD-286 or AU-15330.

[0065] In some embodiments, the subject is a mammal.

[0066] In some embodiments, the mammal is a mouse or a human.

[0067] In some embodiments, the mammal is a human.

[0068] In some aspects, provided herein is a method of killing or inhibiting proliferation of a lymphoma cell having a loss-of-function mutation of ARID 1 A, comprising contacting the lymphoma cell with an inhibitor of SMARCA4 and / or SMARCA2.

[0069] In some embodiments, the inhibitor of SMARCA4 and / or SMARCA2 is FHD-286 or AU-15330.

[0070] In some embodiments, the method kills or inhibits proliferation of the lymphoma cell in vitro or in vivo.

[0071] In some embodiments, the lymphoma cell is from a B-cell lymphoma. In some embodiments, the B-cell lymphoma is selected from FL, DLBCL, or Burkitt lymphoma.

[0072] Brief Description of the Drawings

[0073] FIG. 1A shows BAF complex mutations in FL and GCB-DLBCL ranked by frequency (Green et al., 2015; Ma et al., 2021; Schmitz et al., 2018; Wright et al., 2020).

[0074] FIG. IB shows mutational burden of AR1D1A in DLBCL patients. Majority of mutations are truncating mutations that deplete critical BAF interaction domain (BAF250_C- domain) (Ma et al., 2021).

[0075] FIG. 1C shows graphical representation of the germinal center dynamics and exit.

[0076] FIG. ID shows assay schematic, representative flow cytometry (FC) analysis and quantification of splenic B cells. Graph represents pooled mice from three independent experiments, each with n>3 mice.

[0077] FIG. IE shows the same as FIG. ID for GC B cells.

[0078] FIG. 2A is a heatmap showing log2 fold change (log2FC) in mice heterozygous (cKO / WT) or Homozygous (cKO / cKO) for Aridla loss compared to WT / WT controls in Centroblasts (CB) or Centrocytes (CC). ATAC-seq peaks shown are union of WT / WT, cKO / WT and cKO / cKO for CB and CC, respectively. Peaks were ordered by log2 foldchange (log2FC), with individual peak log2FC summarized as a histogram on the right. llog2FCI>1.2 colored as orange or red for cKO / WT and cKO / cKO respectively.

[0079] FIG. 2B is a summary plot of all peaks in FIG. 2A showing pseudo-median reads shown as FPKM in CB and CC.

[0080] FIG. 2C shows the average log2FC of promoter (+ / - 2kb TSS) and non-promoter peaks in cKO / WT or cKO / cKO mice compared to WT / WT in CB and CC. Promoter peaks were compared with non-promoter peaks using a Wilcoxon rank sum test; all comparisons show p-values < l.Oe'300.

[0081] FIG. 2D is a schematic showing RIVA cell lines generated.

[0082] FIG. 2E is a heatmap showing log2FC in Q474* / WT or sh-induced (KD) cells compared to CRISPR-corrected WT / WT controls. ATAC-seq peaks shown are union of Q474* / WT, WT / WT and KD across all samples. Peaks were ordered based on log2FC, with individual peak log2FC summarized as a histogram on the right. llog2FCI>1.2 colored as orange or red for Q474* / WT and sh-KD respectively. FIG. 2F is a summary plot of peaks in FIG. 2E showing Pseudo-median reads normalized by FPKM in Q474* / WT vs. corrected WT / WT.

[0083] FIG. 2G shows the average log2FC of promoter (+ / - 2kb TSS) and non-promoter peaks in Q474* / WT cells compared to corrected WT / WT. Promoter peaks were compared to non-promoter peaks using Wilcoxon rank sum test.

[0084] FIG. 2H shows the electrophoresis of MNase digested nuclei DNA in Q474* / WT or WT / WT with either 3U or 5U enzyme.

[0085] FIG. 21 shows the normalized MNase Signal centered on AT AC peak summit.

[0086] FIG. 2J is a genome browser track showing Q474* / WT and WT / WT MNase tracks alongside AT AC peaks for different genotypes.

[0087] FIG. 2K is direct comparison of Q474* / WT vs. WT / WT nucleosome positions showing loci with significant fuzziness score (p.adj < 0.05). Grey box indicates nucleosomes highly positioned in WT / WT that gain fuzziness in Q474* / WT.

[0088] FIG. 2L shows ranked loci based on Q474* / WT vs. WT / WT fuzziness log2FC.

[0089] FIG. 2M shows CUT&RUN for H3K27me3 in RIVA DLBCL cells. Heatmaps show + / - lOkb of ATAC peak summits that are either significantly (q < 0.05, llog2FCI > 0) gaining (top) or losing (bottom) signal upon Aridla deletion. Summary plots below each heatmap show median reads normalized by FPKM.

[0090] FIG. 2N shows CUT&RUN for H3.3 in RIVA DLBCL cells. Heatmaps show + / - lOkb of ATAC peak summits that are either significantly (q < 0.05, llog2FCI > 0) gaining (top) or losing (bottom). Summary plots below each heatmap show median reads normalized by FPKM.

[0091] FIG. 3A shows mouse Aridla WT / WT, cKO / WT and cKO / cKO ATAC-seq Manhattan Distance of sorted CB or CC cells based on peaks with a standard deviation (SD) > 0.5.

[0092] FIG. 3B shows PCA of mouse WT / WT, cKO / WT and cKO / cKO sorted CB or CC cells based on ATAC-seq peaks with SD > 0.5.

[0093] FIG. 3C shows ATAC-seq log2FC of peaks within lOkb of a gene body categorized by differential gene expression (q < 0.05, llog2FCI > 0) in cKO / WT vs. WT / WT CB (top) or CC (bottom). P-value for accessibility across groups calculated using ANOVA.

[0094] FIG. 3D shows the regulatory potential showing top transcriptional factors that have increased motif accessibility in WT / WT CC vs. CB ATAC-seq, colored by multivariate adjusted p-value (p-adj). FIG. 3E shows the regulatory potential showing top transcriptional factors that have decreased motif accessibility in cKO / WT vs. WT / WT (multivariate with cell type regressed out), colored by multivariate p-adj.

[0095] FIG. 3F shows supervised k-means clustering of differentially accessible peaks between WT / WT, cKO / WT and cKO / cKO CC, normalized to WT / WT CB accessibility.

[0096] FIG. 3G shows peaks from each category shown in F were linked to the nearest gene within lOkb. PAGE analysis was then performed for enrichment of germinal center exit signaling pathways in these linked genes.

[0097] FIG. 3H shows Fisher’s exact test for presence of the indicated transcription factor motif enrichment in each differential accessibility cluster from FIG. 3E.

[0098] FIG. 31 shows TOBIAS footprint binding score analysis for PU.l (left) and NF-kB2 (right). P-values generated by TOBIAS BINDetect function.

[0099] FIG. 3J shows GSEA Normalized Enrichment Score (NES) of PU.l gene signatures in WT / WT vs. cKO / WT (left) or cKO / cKO (right) mouse CC cells.

[0100] FIG. 4A shows DLBCL cell line (RIVA / RI-1) Manhattan distance of ATAC-seq based on peaks with a SD > 0.5.

[0101] FIG. 4B shows ATAC-seq log2FC of peaks within lOkb to gene body grouped by differential gene expression (q < 0.01, llog2FCI > 0.58) between WT / WT and Q474* / WT direction.

[0102] FIG. 4C shows differentially (q < 0.01, llog2FCI > 0.58) opening or closing peaks were linked to the nearest gene body within lOkb. PAGE analysis was then performed for enrichment of germinal center exit signaling pathways in these linked genes (hypergeometric q < 0.05).

[0103] FIG. 4D shows regulatory potential of top transcription factor motifs closing in Q474* / WT vs. WT / WT (top) or KD vs. WT / WT (bottom), colored by multivariate p-adj.

[0104] FIG. 4E shows TOBIAS footprint binding score analysis of PU.l (top) or NF-kB2 (bottom) in Q474* / WT, WT / WT and KD cells. P-values generated by TOBIAS BINDetect function.

[0105] FIG. 4F shows overlap of ChlP-seq (PBRM1 and SMARCA4) and CUT&RUN (ARID1A and BRD9) peaks for BAF complex subunits in WT / WT RIVA / RI-1 cells.

[0106] FIG. 4G is a Venn diagram showing the overlap of ARID 1 A WT / WT CUT&RUN peaks and significantly (q < 0.05) opening and closing ATAC peaks in Q474* / WT vs.

[0107] WT / WT. FIG. 4H shows heatmaps showing log2FC for ARID1A, PRBM1 or IgG control at differentially closing AT AC peaks shown in order of decreasing ARID 1 A log2FC. Summary plots below each heatmap show mean FPKM of Q474* / WT and WT / WT binding signal.

[0108] FIG. 41 is a heatmap showing log2FC of CUT&RUN reads for PU.l at differentially closing AT AC peaks with SPI1 motif shown in the order of PU.l log2FC. Summary plot below show mean FPKM of Q474* / WT and WT / WT CUT&RUN signal.

[0109] FIG. 4J shows PAGE Analysis of genes closest to IgG or PU.l peaks for GC exit signatures.

[0110] FIG. 4K shows a genome browser track showing PU.l, ARID1A, H3K27me3 and H3.3 in WT / WT and Q474* / WT.

[0111] FIG. 4L shows unsupervised clustering of peaks based on WT / WT and Q474* / WT ATAC-seq, H3K27Ac and H3K27me3 signal. WT / WT log2 FPKM of accessibility, H3K27Ac and H3K27me3 show in column one. Difference in ATAC, H3K27Ac and H3K27me3 between Q474* / WT and WT / WT shown in in column two. Difference in ATAC between KD and WT / WT show in column three. Delta indicates Q474* / WT vs. WT / WT unless stated otherwise.

[0112] FIG. 4M shows summaries of region size and peak number for each peak cluster.

[0113] FIG. 4N shows a change in RNA signal for genes closest to respective peaks in each cluster.

[0114] FIG. 40 shows a difference in log2FC between genotypes for the indicated BAF subunit and cluster.

[0115] FIG. 4P shows peak cluster annotation percentage for promoter, enhancer or superenhancer.

[0116] FIG. 4Q shows peak cluster percentage containing motifs from SPI1 / SPIB, NF-kB or both.

[0117] FIG. 5 A is a Venn diagram showing the number of all ATAC peaks across all genotypes that have motifs for SPI1, NF-kB, or both within the ATAC dataset (top). GSEA enrichment scores for each of these categories (middle), based on ATAC log2FC ranking of Q474* / WT VS. WT / WT (bottom). All p-values < 0.001.

[0118] FIG. 5B shows enrichment of gene signatures based on hypergeometric analysis of genes within lOkb of peaks grouped by motif presence.

[0119] FIG. 5C shows NF-kB luciferase reporter assay in Raji cells WT / WT and Q474* / WT. FIG. 5D shows NF-kB luciferase reporter assay in Raji cells with and without CRISPR targeted to the PU.l motif in the NF-kB reporter 75bp away. Luciferase was measured 48h post-CRISPR treatment.

[0120] FIG. 5E is a heatmap of ATAC-seq peaks binned by distance of TF motif (SPI1 and NF-kB) to the peak summit, colored by log2FC of AT AC signal. Only peaks that contain both motifs are shown.

[0121] FIG. 5F shows the log2FC of peaks in (F) divided into two bins: a motif present within 150bp of the AT AC summit, or a motif situated more than 150bp away from the summit. Wilcoxon rank sum was performed between different bins.

[0122] FIG. 5G is a proposed model of PU.l, cBAF and NF-kB chromatin remodeling.

[0123] FIG. 6A shows Uniform Manifold Approximation and Projection (UMAP) based on Multiome RNA and ATAC-seq with cell types labels transferred from previously published scRNA reference dataset based on RNA expression. Gene signatures used to confirm cell type assignment in Figure 16.

[0124] FIG. 6B shows UMAP of cells split and colored by genotype.

[0125] FIG. 6C shows projection of indicated gene expression level Klf2, Cd38, Ccr6) onto the UMAP.

[0126] FIG. 6D shows cell type density along Slingshot pseudotime based on RNA expression for each cell type in the lineage, anchored at CB.

[0127] FIG. 6E shows difference in density between cKO / WT and WT / WT along pseudotime. Wilcoxon Rank Sum p-value of cKO / WT vs. WT / WT pseudotime < 2.2e'16.

[0128] FIG. 6F shows expression of prememory genes (Laidlaw, GSE89897) across pseudotime. Grey area represents 99% confidence interval.

[0129] FIG. 6G shows difference in prememory gene expression across pseudotime between cKO / WT and WT / WT. Pseudotime values were broken into 10 deciles and Wilcoxon rank sum was performed between genotypes. Each significant decile is colored red with the minimum p < 1.82e'45.

[0130] FIG. 6H shows motif accessibility across pseudotime of the indicated transcription factor, with grey areas representing 95% confidence interval.

[0131] FIG. 61 shows difference in motif accessibility across pseudotime of the indicated transcription factor between genotypes (bottom). Pseudotime values were broken into 10 deciles and Wilcoxon rank sum was performed between cKO / WT and WT / WT. Each significant decile is colored red with minimum PU.l p < 3.83e'4, and minimum significant NF-KB p<1.84e'3. Grey deciles are non-significant.

[0132] FIG. 6J shows TPM log2FC RNA-seq expression of Memory B cell genes for each replicate of RIVA / RI-1 cells Q474* / WT and WT / WT. Genes ranked on average log2FC. CXCR4 is not implied in MB regulation, it is shown to highlight similarity between mouse CB expansion phenotype with transcriptional regulation of human DLBCL cells upon ARID 1 A deletion.

[0133] FIG. 6K shows RT-PCR relative mRNA abundance of individual memory B cell genes normalized to GAPDH, each dot represents mean of three technical replicates, each bar represents a different CRISPR clone.

[0134] FIG. 6L shows TOBIAS motif footprinting score of KLF2 and BCL6 based on ATAC-seq from Q474* / WT, WT / WT and KD cells. P-values generated by TOBIAS BINDetect function.

[0135] FIG. 6M shows GSVA of Memory B cell signature in Q474* / WT or WT / WT cells.

[0136] FIG. 6N shows transcriptional regulation map of GC to MB fate.

[0137] FIG. 7A shows an experimental scheme and timeline for (FIG. 7C)-(FIG. 7E).

[0138] FIG. 7B shows representative FC plots and gating strategy of total splenocytes in mixed-bone marrow chimera animals (n=3 per group).

[0139] FIG. 7C shows relative GC B cell proportions compare to total naive B cells as defined in (FIG. 7B).

[0140] FIG. 7D shows relative MB cell proportions compare to total naive B cells as defined in (FIG. 7B).

[0141] FIG. 7E shows relative MB cell proportions compare to total GC B cells as defined in (FIG. 7B).

[0142] FIG. 7F shows experimental scheme and timeline for (FIG. 7G) and (FIG. 7H).

[0143] FIG. 7G shows time-course FC analysis and quantification of cell type composition of antigen- specific MB cells.

[0144] FIG. 7H shows day 50 FC analysis and quantification of cell type composition of antigen- specific MB cells.

[0145] FIG. 8A shows an experimental scheme and lymphomagenesis timeline for (FIG. 7B)-( FIG. 7F).

[0146] FIG. 8B shows Representative FC plots and quantification of total B cells at the early lymphomagenesis time-point (n=4 per group). FIG. 8C shows representative FC plots and quantification of GC B cells at the early lymphomagenesis time-point (n=4 per group).

[0147] FIG. 8D shows survival curves for CY-l-Cre;AridlacKO / WT, CY-l-Cre;AridlaWT / WTVavP-Bcl2;CY-l-Cre;AridlacKO / WTand VavP-Bcl2;CY-l-Cre;AridlaWT / WTmice.

[0148] FIG. 8E shows spleen size quantification at time of necropsy. Graph shows spleen to body weight ratios for mice from the entire cohort.

[0149] FIG. 8F shows H&E of spleen sections from animals in (FIG. 8A) late time-point. Scale 100 pm (far left). Arrow indicates a mitotic figure in Bcl2;CY-l-Cre;AridlacKO / WTanimal.

[0150] FIG. 8G shows human ortholog GSVA scores of the mouse RNA signature comparing cKO / WT vs WT / WT for Aridla in patients from the NCI-DLBCL cohort. Patients stratified by ARID 1 A mutation status.

[0151] FIG. 8H shows dimensional reduction UMAP plots of CyTOF B cell data obtained from the combined analysis of 36 reactive lymph node (rLN) and 155 follicular lymphoma (FL) patient samples. Each dot in the plot represents a single cell, with cells colored according to their assigned cluster. The normal B cell populations in reactive lymph node (rLN) samples were manually identified and annotated based on the expression patterns of reference marker proteins as per (X. Wang et al., 2022). The contour lines on the plot represent the density of cells in the combined dataset.

[0152] FIG. 81 shows UMAP as in (I) but with cells colored by patients (only patients with ARID 1 A mutations shown).

[0153] FIG. 8J shows UMAP as in (I) but with cells colored by cluster (only memory-like B and D clusters are shown).

[0154] FIG. 8K shows number of cases with (ARID 1 A*) or without (WT) ARID1A mutations categorized and colored based on their cluster identity.

[0155] FIG. 8L shows Kaplan-Meier curve showing risk of DLBCL transformation for patients in clusters B or D.

[0156] FIG. 9A shows viability measurement over time for Q474* / WT and WT / WT cells treated with lOnM SMARCA4 / 2 inhibitor (FHD-286). Black dots indicate DMSO controls. Two independent experiment were performed, each with n=2. 2-way ANOVA was used.

[0157] FIG. 9B shows the same as in (FIG. 9A) except lOOnM FHD-286 was used.

[0158] FIG. 9C shows apoptosis analysis using Annexin V and 7-AAD for cells treated with lOnM FHD-286. FIG. 9D shows DepMap Chronos score categorized by tissue and ranked by ARID1A dependency.

[0159] FIG. 9E shows the same as (FIG. 9A) but for cell lines with lymphoid tissue annotation, including RIVA / RI-1 and Raji cell lines used in this study.

[0160] FIG. 9F shows Q474* / WT and WT / WT (either DMSO or FHD-286 treated) ATAC- seq PCA based on peaks with a standard deviation (SD) > 0.5.

[0161] FIG. 9G shows the same as (FIG. 9G) but shown as Manhattan Distance.

[0162] FIG. 9H shows heatmaps showing log2FC of differentially closing ATAC-seq loci upon FHD-286 treatment (WT-FHD-286 and HET-FHD-286 vs. WT DMSO).

[0163] FIG. 91 shows psuedo-median FPKM ATAC-seq of ATAC-seq WT DMSO, WT- FHD-286 and HET-FHD-286.

[0164] FIG. 9J shows genome browser ATAC-seq track showing Q474* / WT and WT / WT both DMSO or FHD-286 treated, we well as non-treated PU.l CUT&RUN in Q474* / WT and WT / WT. IRF4 enhancer has been functionally verified previously (Wood et al., 2018).

[0165] FIG. 9K shows regulatory potential showing top transcriptional factors that show decreased motif accessibility in upon FHD-286 treatment compared to DMSO colored by multivariate p.adj. Top; WT / WT. Bottom: Q474* / WT.

[0166] FIG. 9L shows viability measurement over time for WT and PU.l-KD cells treated with lOOnM SMARCA4 / 2 inhibitor (FHD-286). n=3. 2-way ANOVA was used.

[0167] FIG. 9M shows the same as in (FIG. 9L), except lOnM FHD-286 was used. n=3. 2- way ANOVA was used.

[0168] FIG. 10 is a schematic summarizing the findings.

[0169] FIG. 11A show a heatmap showing log2FC of Nucleosome-free ATAC-seq Peaks (peaks <120bp) in mice heterozygous (cKO / WT) or Homozygous (cKO / cKO) for Aridla loss compared to WT / WT controls in Centroblasts (CB) or Centrocytes (CC). ATAC-seq peaks shown are union of WT / WT, cKO / WT and cKO / cKO for CB and CC, respectively. Peaks were ordered by log2FC of the Nucleosome-free ATAC-seq peaks.

[0170] FIG. 11B shows sanger-seq validation of RIVA / RI-1 CRISPR ARID1A Q747* mutation correction. Two separate clones are shown.

[0171] FIG. 11C shows genotyping of mouse GCB cells Aridla exon deletion post Cre- activation.

[0172] FIG. 11D shows genotyping of mouse tail Aridla exon deletion pre Cre-activation. FIG. HE shows Representative FC analysis and quantification of GFP in sh- ARID1A (KD) cells of both KD-induced (left) and uninduced (right).

[0173] FIG. HF shows western blot of ARID1A protein Ctrl (uninduced) and KD-induced (sh-1 and sh-2).

[0174] FIG. 12A shows mouse TPM of Spil RNA in Aridla WT / WT, cKO / WT and cKO / cKO CB and CC.

[0175] FIG. 12 B shows mouse TPM of Nfkbl, Nfkb2, Rel and Rela RNA respectively in Aridla WT / WT, cKO / WT and cKO / cKO CB and CC.

[0176] FIG. 13A shows a full model with all 16 peak clusters. Clusters are ordered by hierarchical clustering.

[0177] FIG. 13B shows a fraction of PU.l CUT&RUN peaks with SPI1 or SPIB motifs for each replicate and the union of peaks.

[0178] FIG. 14A is an immunoblot showing Raji ARID 1 A CRISPR clone protein expression for ARID 1 A.

[0179] FIG. 14B shows a sanger-seq of example molecules in the CRISPR pool upon PU.l DNA motif CRISPR editing showing the mutations are constrained to the region with the PU.l motif.

[0180] FIG. 15A shows gene expression signatures used to identify and confirm populations present in the Multiome UMAP.

[0181] FIG. 15B shows an Emsa plot of pseudotime with Prememory cells removed (top) and difference in density plot of Aridla HET - WT (bottom). P-value calculated using Wilcoxon Rank Sum.

[0182] FIG. 16A shows an immunophenotyping assay schematic.

[0183] FIG. 16B shows representative FC analysis and quantification of MB cells in WT / WT (top) and cKO / WT mice.

[0184] FIG. 16C shows quantification of day 10 MB cells in relation to all B220+IgD- cells (top) and GC cells (bottom). P-value calculated using unpaired t-test.

[0185] FIG. 16D shows quantification of IgM+ MB cells. P-value calculated using unpaired t-test.

[0186] FIG. 16E shows memory B cell compartment percentage of IgD- cells over time. Two-way ANOVA test was used showing the main effect of time and genotype interacting p[cKO / WT vs. WT : time]=0.0113 and the main effect of genotype p[cKO / WT vs. WT]=0.007. FIG. 16F shows IgM+ percentage of MB cells over time. Two-way ANOVA test was used showing the main effect of time and genotype interacting p[cKO / WT vs. WT : time]=0.0002 and the main effect of genotype p[cKO / WT vs. WT]=0.0159.

[0187] FIG. 16G shows assay schematic and representative FC analysis and quantification of MB cells upon NP-KLH immunization. Ratio of MB to GC cells at days 5, 11 and 50.

[0188] FIG. 16H shows bone marrow-derived long live plasm cells (LLPC) at day 50 post NP-KLH immunization.

[0189] FIG. 17 shows H&E of spleen sections from BCL2 and BCL2+HET animals at late time-point. Scale, 100 pm (left), 50 pm (center) or 20pm (right).

[0190] FIG. 18A shows representative FC analysis and quantification of apoptosis stage in lOnM FHD-286 treated WT / WT (left) and Q474* / WT (right) cells.

[0191] FIG. 18B shows representative FC analysis and quantification of GFP in sh-PU.l cells of both KD-induced (left) and uninduced (right).

[0192] FIG. 18C shows viability of PU.l-KD or non-targeting control cells after lOOnM FHD-286 treatment. 2- way ANOVA was used.

[0193] FIGs. 19A-19D show IC50 for both the FHD-286 inhibitor and AU-15330 degrader, showing vulnerability of ARID 1 A mutated lymphoma cells lines to both.

[0194] FIG. 20 shows in vivo data showing higher tumor growth inhibition of ARID 1 A mutated tumors compared to ARID 1 A WT tumors.

[0195] FIG. 21 shows in vivo data showing that animals with ARID 1 A mutation have a better prognosis after treatment with FHD-286 compared to ARID1A WT animals.

[0196] FIG. 22 shows drug treatment (FHD-286) of a different lymphoma cell line (Burkitt lymphoma), showing that the same vulnerability of ARID 1 A mutated tumor cells to treatment is observed in other tumor types.

[0197] FIGs. 23A-23C show BAF multi-subunit complex remodels nucleosomes (figures are adapted from Mashtalir et al. 2020 Cell).

[0198] FIGs. 24A-24B show BAF is highly mutated in cancer (figures are adapted from Kadoch et al. 2013 Nat Genetics; Wright et al. 2020 Cancer Cell).

[0199] FIG. 25 shows ARID1A loss increases H3K27me3 and decreases H3.3.

[0200] Detailed Description of the Invention The present disclosure is based, at least in part, on a discovery of ARID 1 A mutation and / or loss as a novel biomarker for FL to DLBCL transformation and, in some aspects, provides a pharmacological way to target specifically ARID 1 A mutated cells.

[0201] FHD-286 (SMARCA4 / 2 inhibitor) used in this study is currently in clinical trials for AML (https: / / foghomtx.com / pipeline / ). However, whether this is applicable to lymphomas is not known. The results provided in the present disclosure indicate that this drug would target specifically ARIDlA-mutated cells in FL and DLBCL patients. More importantly, currently FL patients at diagnosis are not being treated because of the indolent nature of the disease. However, these patients are at high risk of transformation to DLBLC during their lifetime. ARID 1 A can be a biomarker to determine if these FL patients should be treated to prevent the onset of transformation to DLBCL, something that is currently not done. It have also been demonstrated herein the same effect with another drug, AU-15330, a degrader of SMARCA4 / 2, further proving that the vulnerability of ARIDlA-mutated tumors is due to BAF complex loss.

[0202] The present discosure shows that: (1) ARIDlA-mutated patients are found within the FL group of patients with higher risk of transformation to DLBCL; (2) ARIDlA-deletion mice develop aggressive form of FL (DLBCL-like) and have shortened survival; (3) ARIDlA-loss leads to formation of immature memory B-cells that have the potential to undergo somatic hypermutation at a higher rate than WT cells; and (4) inhibition of SMARCA4 / 2 (BAF complex subunits) specifically kills ARIDlA-mutated but now WT cells.

[0203] I. Definitions

[0204] The articles “a” and “an” are used herein to refer to one or to more than one (z.e. to at least one) of the grammatical object of the article. By way of example, “an element” means one element or more than one element.

[0205] The term “administering” is intended to include routes of administration which allow an agent (such as the compositions described herein) to perform its intended function. Examples of routes of administration for treatment of a body which can be used include injection (subcutaneous, intravenous, parenterally, intraperitoneally, intrathecal, etc.), oral, inhalation, and transdermal routes. The injection can be bolus injections or can be continuous infusion. Depending on the route of administration, the agent can be coated with or disposed in a selected material to protect it from natural conditions which may detrimentally affect its ability to perform its intended function. The agent may be administered alone, or in conjunction with a pharmaceutically acceptable carrier. The agent also may be administered as a prodrug, which is converted to its active form in vivo. In some embodiments, the agent is orally administered. In other embodiments, the agent is administered through anal and / or colorectal route.

[0206] “About” and “approximately” shall generally mean an acceptable degree of error for the quantity measured given the nature or precision of the measurements. Typically, exemplary degrees of error are within 20%, preferably within 10%, and more preferably within 5% of a given value or range of values. Alternatively, and particularly in biological systems, the terms “about” and “approximately” may mean values that are within an order of magnitude, preferably within 5-fold and more preferably within 2-fold of a given value. Numerical quantities given herein are approximate unless stated otherwise, meaning that the term “about” or “approximately” can be inferred when not expressly stated.

[0207] The amount of a biomarker (e.g., one or more metabolites described herein) in a subject is “significantly” higher or lower than the normal amount of the biomarker, if the amount of the biomarker is greater or less, respectively, than the control level by an amount greater than the standard error of the assay employed to assess amount, and preferably at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 150%, 200%, 300%, 350%, 400%, 500%, 600%, 700%, 800%, 900%, 1000% or than that amount. Alternately, the amount of the biomarker in the subject can be considered “significantly” higher or lower than the control amount if the amount is at least about two, and preferably at least about three, four, or five times, higher or lower, respectively, than the control amount of the biomarker. Such “significance” can also be applied to any other measured parameter described herein, such as for expression, inhibition, activity, and the like.

[0208] The term “biomarker” refers to a measurable parameter of the present disclosure that has been determined to be predictive of (1) a subject at risk for developing a specific condition (e.g., transforming into diffuse large B-cell lymphoma (DLBCL)), or (2) of the effects of an agent or therapy described herein, either alone or in combination with at least one other therapies, on a target disease or disorder (e.g., DLBLC or FL). Biomarkers can include, without limitation, a loss-of-mutation of ARID1A, level and / or activity of ARID1A, and clinical characteristics of a subject, including those shown in the Tables, the Examples, the Figures, and otherwise described herein.

[0209] The term “body fluid” refers to fluids that are excreted or secreted from the body as well as fluids that are normally not (e.g. amniotic fluid, aqueous humor, bile, blood and blood plasma, cerebrospinal fluid, cerumen and earwax, cowper’s fluid or pre-ejaculatory fluid, chyle, chyme, stool, female ejaculate, interstitial fluid, intracellular fluid, lymph, menses, breast milk, mucus, pleural fluid, pus, saliva, sebum, semen, serum, sweat, synovial fluid, tears, urine, vaginal lubrication, vitreous humor, vomit). For example, any body fluid may be taken to detect and / or measure at least one biomarker described herein.

[0210] The term “control” refers to any reference standard suitable to provide a comparison to the biomarkers / products in the test sample. In certain embodiments, the control comprises obtaining a “control sample” from which product or biomarker levels are detected and compared to the product or biomarker levels from the test sample. Such a control sample may comprise any suitable sample, including but not limited to a sample from a control subject (can be stored sample or previous sample measurement) with a known outcome; cultured primary cells / tissues isolated from a control subject, a tissue or cell sample isolated from a control subject, or a primary cells / tissues obtained from a depository. In other preferred embodiments, the control may comprise a reference standard expression product or biomarker level from any suitable source, including but not limited to housekeeping genes, an expression product level range from normal tissue (or other previously analyzed control sample), a previously determined expression product level range within a test sample from a group of patients, or a set of patients with a certain outcome or receiving a certain treatment. It will be understood by those of skill in the art that such control samples and reference standard product or biomarker levels can be used in combination as controls in the methods of the present disclosure. In the former case, the specific product or biomarker level of each patient can be assigned to a percentile level of expression, or expressed as either higher or lower than the mean or average of the reference standard expression level. In other embodiments, the control may also comprise a measured value for example, average level of expression of a particular gene in a population compared to the level of expression of a housekeeping gene in the same population.

[0211] The term “comprise” or variations such as “comprises” or “comprising” will be understood to imply the inclusion of a stated integer (or components) or group of integers (or components), but not the exclusion of any other integer (or components) or group of integers (or components).

[0212] The term “increased / decreased amount” or “increased / decreased level” refers to increased or decreased absolute and / or relative amount and / or value of a biomarker (e.g., one or more metabolites described herein) in a subject, as compared to the amount and / or value of the same biomarker in the same subject in a prior time and / or in a normal and / or control subject, or a normal / control level representative of such subjects in general.

[0213] A “kit” is any manufacture (e.g., a package or container) comprising at least one reagent, e.g. a probe or small molecule, for specifically detecting and / or affecting the expression of a marker of the present disclosure. The kit may be promoted, distributed, or sold as a unit for performing the methods of the present disclosure. The kit may comprise one or more reagents necessary to express a composition useful in the methods of the present disclosure. In certain embodiments, the kit may further comprise a reference standard. One skilled in the art can envision many such controls, including, but not limited to, common molecules. Reagents in the kit may be provided in individual containers or as mixtures of two or more reagents in a single container. In addition, instructional materials which describe the use of the compositions within the kit can be included.

[0214] An “over-expression” or “significantly higher level of expression” of a biomarker refers to an expression level in a test sample that is greater than the standard error of the assay employed to assess expression, and is preferably at least 10%, and more preferably 1.2, 1.3,

[0215] 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.5, 4, 4.5, 5,

[0216] 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10, 10.5, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 times or more higher than the expression activity or level of the biomarker in a control sample and preferably, the average expression level of the biomarker in several control samples. A “significantly lower level of expression” of a biomarker refers to an expression level in a test sample that is at least 10%, and more preferably 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10,

[0217] 10.5, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 times or more lower than the expression level of the biomarker in a control sample and preferably, the average expression level of the biomarker in several control samples. The same determination can be made to determine overactivity or underactivity.

[0218] In some embodiments, levels of one or more biomarkers (e.g., one or more metabolites described herein) are measured and compared at different time points to assess the progression of a disease or to assess the efficacy of an agent for treating a disease. Therefore, in some embodiments, a “significantly higher level” or “significantly increased level” of a biomarker refers to an expression level, amount and / or activity level in a subject sample at one point in time that is greater than the standard error of the assay employed to assess the expression level, amount and / or activity level, and is preferably at least 10%, and more preferably 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10, 10.5, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 times or more higher than the expression level, amount or activity level of the biomarker in a subject sample at another point in time. In some embodiments, a “significantly lower level” or “significantly decreased level” of a biomarker refers to an expression level, amount and / or activity level in a subject sample at one point in time that is at least 10%, and more preferably 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10, 10.5, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 times or more lower than the expression level, amount or activity level of the biomarker in a subject sample at another point in time.

[0219] The term “pre-determined” biomarker amount and / or activity measurement(s) may be a biomarker amount and / or activity measurement(s) used to, by way of example only, evaluate a subject that may be selected for a particular treatment, evaluate a response to a treatment such as using a composition described herein, alone or in combination with other therapy. A pre-determined biomarker amount and / or activity measurement(s) may be determined in populations of patients with or without a disease. The pre-determined biomarker amount and / or activity measurement(s) can be a single number, equally applicable to every patient, or the pre-determined biomarker amount and / or activity measurement(s) can vary to reflect differences among specific subpopulations of patients. Age, weight, height, and other factors of a subject may affect the pre-determined biomarker amount and / or activity measurement(s) of the individual. Furthermore, the pre-determined biomarker amount and / or activity can be determined for each subject individually. In certain embodiments, the amounts determined and / or compared in a method described herein are based on absolute measurements. In other embodiments, the amounts determined and / or compared in a method described herein are based on relative measurements, such as ratios (e.g., biomarker normalized to the expression of housekeeping or otherwise generally constant biomarker). The pre-determined biomarker amount and / or activity measurement(s) can be any suitable standard. For example, the pre-determined biomarker amount and / or activity measurement(s) can be obtained from the same or a different subject for whom a subject selection is being assessed. In some embodiments, the pre-determined biomarker amount and / or activity measurement(s) can be obtained from a previous assessment of the same subject. In such a manner, the progress of the selection of the patient can be monitored over time. In addition, the control can be obtained from an assessment of another subject or multiple subjects, e.g., selected groups of subjects. In such a manner, the extent of the selection of the subject for whom selection is being assessed can be compared to suitable other subjects, e.g., other subjects who are in a similar situation to the human of interest, such as those suffering from similar or the same condition(s) and / or of the same ethnic group.

[0220] As used herein, a therapeutic that “prevents” a condition refers to a composition that, when administered to a statistical sample prior to the onset of the disorder or condition, reduces the occurrence of the disorder or condition in the treated sample relative to an untreated control sample, or delays the onset or reduces the severity of one or more symptoms of the disorder or condition relative to the untreated control sample.

[0221] The term “sample” used for detecting or determining the presence or level of at least one biomarker is typically brain tissue, cerebrospinal fluid, whole blood, plasma, serum, saliva, urine, stool (e.g., feces), tears, and any other bodily fluid (e.g., as described above under the definition of “body fluids”), or a tissue sample (e.g., biopsy) such as a small intestine, colon sample, or surgical resection tissue. In certain instances, the method of the present disclosure further comprises obtaining the sample from the individual prior to detecting or determining the presence or level of at least one biomarker in the sample.

[0222] The terms “subject” refer to either a human or a non-human animal. This term includes mammals such as humans, primates, livestock animals (e.g., bovines, porcines), companion animals (e.g., canines, felines) and rodents (e.g., mice, rabbits and rats).

[0223] “Treating” a disease in a subject or “treating” a subject having a disease refers to subjecting the subject to a pharmaceutical treatment, e.g., the administration of a drug, such that at least one symptom of the disease is decreased or prevented from worsening.

[0224] The term “therapeutic effect” refers to a local or systemic effect in animals, particularly mammals, and more particularly humans, caused by a pharmacologically active substance. The term thus means any substance intended for use in the diagnosis, cure, mitigation, treatment or prevention of disease or in the enhancement of desirable physical or mental development and conditions in an animal or human. The phrase “therapeutically- effective amount” means that amount of such a substance that produces some desired local or systemic effect at a reasonable benefit / risk ratio applicable to any treatment. In certain embodiments, a therapeutically effective amount of a compound will depend on its therapeutic index, solubility, and the like. For example, certain compounds discovered by the methods of the present disclosure may be administered in a sufficient amount to produce a reasonable benefit / risk ratio applicable to such treatment. Unless otherwise defined herein, scientific and technical terms used in this application shall have the meanings that are commonly understood by those of ordinary skill in the art. Generally, nomenclature and techniques relating to chemistry, molecular biology, cell and cancer biology, immunology, microbiology, pharmacology, and protein and nucleic acid chemistry, described herein, are those well-known and commonly used in the art.

[0225] II. Subjects

[0226] In certain embodiments, the subject suitable for the compositions and methods disclosed herein is a mammal (e.g., mouse, rat, primate, non-human mammal, domestic animal, such as a dog, cat, cow, horse, and the like), and is preferably a human. In other embodiments, the subject is an animal model of FL or DLBCL.

[0227] In other embodiments of the methods of the present disclosure, the subject has not undergone treatment for FL or DLBCL. In still other embodiments, the subject has undergone treatment for FL or DLBCL.

[0228] The methods of the present disclosure can be used to treat FL or DLBCL such as those described herein, and / or determine the responsiveness to a composition described herein, alone or in combination with other therapies.

[0229] III. Uses and Methods of the Present disclosure

[0230] In some aspects, provided herein are a variety of diagnostic, prognostic, and therapeutic methods. In any method described herein, such as a diagnostic method, prognostic method, therapeutic method, or combination thereof, all steps of the method can be performed by a single actor or, alternatively, by more than one actor. For example, diagnosis can be performed directly by the actor providing therapeutic treatment. Alternatively, a person providing a therapeutic agent can request that a diagnostic assay be performed. The diagnostician and / or the therapeutic interventionist can interpret the diagnostic assay results to determine a therapeutic strategy. Similarly, such alternative processes can apply to other assays, such as prognostic assays.

[0231] (1) Predictive Medicine

[0232] The present disclosure can pertain to the field of predictive medicine in which diagnostic assays, prognostic assays, and monitoring clinical trials are used for prognostic (predictive) purposes to thereby treat an individual prophy tactically. Accordingly, one aspect of the present disclosure relates to diagnostic assays for determining the amount and / or activity level of a biomarker described herein in the context of a biological sample (e.g., blood, serum, cells, stool, or tissue) to thereby determine whether an individual afflicted with follicular lymphoma (FL) is at risk for transforming into diffuse large B-cell lymphoma (DLBCL), or whether a FL or a DLBCL in a subject is likely to respond to an inhibitor of SMARCA 4 and / or SMARCA2. Such assays can be used for prognostic or predictive purpose alone, or can be coupled with a therapeutic intervention to thereby prophylactically treat an individual prior to the onset or after recurrence of a disorder characterized by or associated with biomarker level or activity. The skilled artisan will appreciate that any method can use one or more (e.g., combinations) of biomarkers described herein, such as those in the tables, figures, examples, and otherwise described in the specification.

[0233] (2) Diagnostic Assays

[0234] The present disclosure provides, in part, methods, systems, and code for accurately classifying whether a biological sample (e.g., from a subject) or a subject afflicted with follicular lymphoma (FL) is at risk for transforming into diffuse large B-cell lymphoma (DLBCL). In some embodiments, the present disclosure is useful for classifying a sample (e.g., from a subject afflicted with FL) or a subject afflicted with FL as at risk for transforming into DLBCL as disclosed herein using a statistical algorithm and / or empirical data (e.g., the amount or activity of a biomarker (e.g., ARID1A) described herein, such as in the tables, figures, examples, and otherwise described in the specification).

[0235] An exemplary method for detecting the amount or activity of a biomarker described herein, and thus useful for classifying whether a sample or a subject afflicted with follicular lymphoma (FL) is at risk for transforming into diffuse large B-cell lymphoma (DLBCL) involves obtaining a biological sample from a test subject and contacting the biological sample with an agent, such as a protein-binding agent like an antibody or antigen-binding fragment thereof, or a nucleic acid-binding agent like an oligonucleotide, capable of detecting the amount or activity of the biomarker (e.g., ARID 1 A) in the biological sample. In some embodiments, at least one antibody or antigen-binding fragment thereof is used, wherein two, three, four, five, six, seven, eight, nine, ten, or more such antibodies or antibody fragments can be used in combination (e.g., in sandwich ELISAs) or in series. In other embodiments, the presence or absence of a loss-of-function mutation of the biomarker (e.g., ARID1A) in the biological sample is measured by standard methods used to detect a mutation, including but not limited to, sequencing. In certain instances, the statistical algorithm is a single learning statistical classifier system. For example, a single learning statistical classifier system can be used to classify a sample as a based upon a prediction or probability value and the presence or level of the biomarker. The use of a single learning statistical classifier system typically classifies the sample as, for example, a likely therapy responder or progressor sample with a sensitivity, specificity, positive predictive value, negative predictive value, and / or overall accuracy of at least about 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0236] Other suitable statistical algorithms are well-known to those of skill in the art. For example, learning statistical classifier systems include a machine learning algorithmic technique capable of adapting to complex data sets (e.g., panel of markers of interest) and making decisions based upon such data sets. In some embodiments, a single learning statistical classifier system such as a classification tree (e.g., random forest) is used. In other embodiments, a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, or more learning statistical classifier systems are used, preferably in tandem. Examples of learning statistical classifier systems include, but are not limited to, those using inductive learning (e.g., decision / classification trees such as random forests, classification and regression trees (C&RT), boosted trees, etc.), Probably Approximately Correct (PAC) learning, connectionist learning (e.g., neural networks (NN), artificial neural networks (ANN), neuro fuzzy networks (NFN), network structures, perceptrons such as multi-layer perceptrons, multi-layer feed-forward networks, applications of neural networks, Bayesian learning in belief networks, etc.), reinforcement learning (e.g., passive learning in a known environment such as naive learning, adaptive dynamic learning, and temporal difference learning, passive learning in an unknown environment, active learning in an unknown environment, learning action- value functions, applications of reinforcement learning, etc.), and genetic algorithms and evolutionary programming. Other learning statistical classifier systems include support vector machines (e.g., Kernel methods), multivariate adaptive regression splines (MARS), Levenberg- Marquardt algorithms, Gauss-Newton algorithms, mixtures of Gaussians, gradient descent algorithms, and learning vector quantization (LVQ). In certain embodiments, the method of the present disclosure further comprises sending the sample classification results to a clinician, e.g., an oncologist.

[0237] In other embodiments, the diagnosis of a subject is followed by administering to the individual a therapeutically effective amount of a defined treatment based upon the diagnosis. In some embodiments, the methods further involve obtaining a control biological sample (e.g., biological sample from a subject who does not have FL or DLBCL, or from a subject who is afflicted with FL but is not at risk for transforming into DLBCL), a biological sample from the subject during remission, or a biological sample from the subject during treatment for developing DLBCL with FL progressing.

[0238] (3) Prognostic Assays

[0239] The diagnostic methods described herein can furthermore be utilized to identify subjects having FL or DLBCL or at risk of transforming into DLBCL that is likely or unlikely to be responsive to a composition as disclosed herein. The assays described herein, such as the preceding diagnostic assays or the following assays, can be utilized to identify a subject having or at risk of developing a disorder associated with a misregulation of the amount or activity of at least one biomarker (e.g., ARID1A) described herein. Alternatively, the prognostic assays can be utilized to identify a subject having or at risk for developing a disorder associated with a misregulation of the at least one biomarker (e.g., ARID1A) described herein. Furthermore, the prognostic assays described herein can be used to determine whether a subject can be administered a composition as disclosed herein and / or an additional therapeutic regimen to treat a disease or disorder associated with the aberrant biomarker (e.g., ARID1A) expression or activity.

[0240] An “isolated” or “purified” biomarker (e.g., ARID 1 A) is substantially free of cellular material or other contaminating proteins from the cell or tissue source from which the protein is derived, or substantially free of chemical precursors or other chemicals when chemically synthesized. The language “substantially free of cellular material” includes preparations of protein in which the protein is separated from cellular components of the cells from which it is isolated or recombinantly produced. Thus, protein that is substantially free of cellular material includes preparations of protein having less than about 30%, 20%, 10%, or 5% (by dry weight) of heterologous protein (also referred to herein as a “contaminating protein”). When the protein or biologically active portion thereof is recombinantly produced, it is also preferably substantially free of culture medium, z.e., culture medium represents less than about 20%, 10%, or 5% of the volume of the protein preparation. When the protein is produced by chemical synthesis, it is preferably substantially free of chemical precursors or other chemicals, z.e., it is separated from chemical precursors or other chemicals which are involved in the synthesis of the protein. Accordingly, such preparations of the protein have

[0241] T1 less than about 30%, 20%, 10%, 5% (by dry weight) of chemical precursors or compounds other than the polypeptide of interest.

[0242] In some embodiments, agents that specifically bind to a biomarker protein other than antibodies are used, such as peptides. Peptides that specifically bind to a biomarker protein can be identified by any means known in the art. For example, specific peptide binders of a biomarker protein can be screened for using peptide phage display libraries.

[0243] (4) Sampling Methods

[0244] In some embodiments, biomarker amount and / or activity measurement(s) in a sample from a subject is compared to a predetermined control (standard) sample. The control sample can be from the same subject or from a different subject. The control sample is typically a normal, non-diseased sample. However, in some embodiments, such as for staging of disease or for evaluating the efficacy of treatment, the control sample can be from a diseased tissue. The control sample can be a combination of samples from several different subjects. In some embodiments, the biomarker amount and / or activity measurement(s) from a subject is compared to a pre-determined level. This pre-determined level can be, for example, obtained from normal samples, or from control samples with known outcome. As described herein, a “pre-determined” biomarker amount and / or activity measurement(s) may be a biomarker amount and / or activity measurement(s) used to, by way of example only, evaluate a subject that may be selected for treatment, evaluate a response to a composition as disclosed herein, alone or in combination with one or more additional therapies. A pre-determined biomarker amount and / or activity measurement(s) may be determined in populations of patients with or without FL or DLBCL, or with FL but not at risk of transforming into DLBCL. The predetermined biomarker amount and / or activity measurement(s) can be a single number, equally applicable to every patient, or the pre-determined biomarker amount and / or activity measurement(s) can vary according to specific subpopulations of patients. Age, weight, height, and other factors of a subject may affect the pre-determined biomarker amount and / or activity measurement(s) of the individual. Furthermore, the pre-determined biomarker amount and / or activity can be determined for each subject individually. In some embodiments, the amounts determined and / or compared in a method described herein are based on absolute measurements.

[0245] In other embodiments, the amounts determined and / or compared in a method described herein are based on relative measurements, such as ratios (e.g., biomarker copy numbers, level, and / or activity before a treatment vs. after a treatment, such biomarker measurements relative to a spiked or man-made control, such biomarker measurements relative to the expression of a housekeeping gene, and the like). For example, the relative analysis can be based on the ratio of pre-treatment biomarker measurement as compared to post-treatment biomarker measurement. Pre-treatment biomarker measurement can be made at any time prior to initiation of a treatment, e.g., a treatment with an inhibitor of SMARCA4 and / or SMARCA2. Post- treatment biomarker measurement can be made at any time after initiation of the treatment. In some embodiments, post-treatment biomarker measurements are made 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 weeks or more after initiation of the treatment, and even longer toward indefinitely for continued monitoring. Treatment can comprise, e.g., a therapeutic regimen comprising a composition as disclosed herein, or further in combination with other agents.

[0246] The pre-determined biomarker amount and / or activity measurement(s) can be any suitable standard. For example, the pre-determined biomarker amount and / or activity measurement(s) can be obtained from the same or a different human for whom a patient selection is being assessed. In some embodiments, the pre-determined biomarker amount and / or activity measurement(s) can be obtained from a previous assessment of the same patient. In such a manner, the progress of the selection of the patient can be monitored over time. In addition, the control can be obtained from an assessment of another human or multiple humans, e.g., selected groups of humans, if the subject is a human. In such a manner, the extent of the selection of the human for whom selection is being assessed can be compared to suitable other humans, e.g., other humans who are in a similar situation to the human of interest, such as those suffering from similar or the same condition(s) and / or of the same ethnic group.

[0247] In some embodiments of the present disclosure, the change of biomarker amount and / or activity measurement(s) from the pre-determined level is about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, or 5.0-fold or greater, or any range in between, inclusive. Such cutoff values apply equally when the measurement is based on relative changes, such as based on the ratio of pre-treatment biomarker measurement as compared to post-treatment biomarker measurement.

[0248] Biological samples can be collected from a variety of sources from a patient including a body fluid sample, cell sample, or a tissue sample comprising nucleic acids and / or proteins. “Body fluids” refer to fluids that are excreted or secreted from the body as well as fluids that are normally not (e.g., amniotic fluid, aqueous humor, bile, blood and blood plasma, cerebrospinal fluid, cerumen and earwax, cowper’s fluid or pre-ejaculatory fluid, chyle, chyme, stool, female ejaculate, interstitial fluid, intracellular fluid, lymph, menses, breast milk, mucus, pleural fluid, pus, saliva, sebum, semen, serum, sweat, synovial fluid, tears, urine, vaginal lubrication, vitreous humor, vomit). In preferred embodiments, the subject and / or control sample is selected from the group consisting of cells, cell lines, histological slides, paraffin embedded tissues, biopsies, whole blood, nipple aspirate, serum, plasma, buccal scrape, saliva, cerebrospinal fluid, urine, stool, and bone marrow. In some embodiments, the sample is serum, plasma, urine, or stool. In other embodiments, the sample is stool.

[0249] The samples can be collected from individuals repeatedly over a longitudinal period of time (e.g., once or more on the order of days, weeks, months, annually, biannually, etc.). Obtaining numerous samples from an individual over a period of time can be used to verify results from earlier detections and / or to identify an alteration in biological pattern as a result of, for example, disease progression, drug treatment, etc. For example, subject samples can be taken and monitored every month, every two months, or combinations of one, two, or three month intervals according to the present disclosure. In addition, the biomarker amount and / or activity measurements of the subject obtained over time can be conveniently compared with each other, as well as with those of controls during the monitoring period, thereby providing the subject’s own values, as an internal, or personal, control for long-term monitoring.

[0250] Sample preparation and separation can involve any of the procedures, depending on the type of sample collected and / or analysis of biomarker measurement(s). Such procedures include, by way of example only, concentration, dilution, adjustment of pH, removal of high abundance polypeptides (e.g., albumin, gamma globulin, and transferrin, etc.), addition of preservatives and calibrants, addition of protease inhibitors, addition of denaturants, desalting of samples, concentration of sample proteins, extraction and purification of lipids.

[0251] The sample preparation can also isolate molecules that are bound in non-covalent complexes to other protein (e.g., carrier proteins). This process may isolate those molecules bound to a specific carrier protein (e.g., albumin), or use a more general process, such as the release of bound molecules from all carrier proteins via protein denaturation, for example using an acid, followed by removal of the carrier proteins.

[0252] Removal of undesired proteins (e.g., high abundance, uninformative, or undetectable proteins) from a sample can be achieved using high affinity reagents, high molecular weight filters, ultracentrifugation and / or electrodialysis. High affinity reagents include antibodies or other reagents (e.g., aptamers) that selectively bind to high abundance proteins. Sample preparation could also include ion exchange chromatography, metal ion affinity chromatography, gel filtration, hydrophobic chromatography, chromatofocusing, adsorption chromatography, isoelectric focusing and related techniques. Molecular weight filters include membranes that separate molecules on the basis of size and molecular weight. Such filters may further employ reverse osmosis, nanofiltration, ultrafiltration and microfiltration.

[0253] Ultracentrifugation is a method for removing undesired polypeptides from a sample. Ultracentrifugation is the centrifugation of a sample at about 15,000-60,000 rpm while monitoring with an optical system the sedimentation (or lack thereof) of particles. Electrodialysis is a procedure which uses an electro-membrane or semipermeable membrane in a process in which ions are transported through semi-permeable membranes from one solution to another under the influence of a potential gradient. Since the membranes used in electrodialysis may have the ability to selectively transport ions having positive or negative charge, reject ions of the opposite charge, or to allow species to migrate through a semipermeable membrane based on size and charge, it renders electrodialysis useful for concentration, removal, or separation of electrolytes.

[0254] Separation and purification in the present disclosure may include any procedure known in the art, such as capillary electrophoresis (e.g., in capillary or on-chip) or chromatography (e.g., in capillary, column or on a chip). Electrophoresis is a method which can be used to separate ionic molecules under the influence of an electric field. Electrophoresis can be conducted in a gel, capillary, or in a microchannel on a chip. Examples of gels used for electrophoresis include starch, acrylamide, polyethylene oxides, agarose, or combinations thereof. A gel can be modified by its cross-linking, addition of detergents, or denaturants, immobilization of enzymes or antibodies (affinity electrophoresis) or substrates (zymography) and incorporation of a pH gradient. Examples of capillaries used for electrophoresis include capillaries that interface with an electrospray.

[0255] Capillary electrophoresis (CE) is preferred for separating complex hydrophilic molecules and highly charged solutes. CE technology can also be implemented on microfluidic chips. Depending on the types of capillary and buffers used, CE can be further segmented into separation techniques such as capillary zone electrophoresis (CZE), capillary isoelectric focusing (CIEF), capillary isotachophoresis (cITP) and capillary electrochromatography (CEC). CE techniques can be coupled to electrospray ionization through the use of volatile solutions, for example, aqueous mixtures containing a volatile acid and / or base and an organic such as an alcohol or acetonitrile.

[0256] Capillary isotachophoresis (cITP) is a technique in which the analytes move through the capillary at a constant speed but are nevertheless separated by their respective mobilities. Capillary zone electrophoresis (CZE), also known as free-solution CE (FSCE), is based on differences in the electrophoretic mobility of the species, determined by the charge on the molecule, and the frictional resistance the molecule encounters during migration which is often directly proportional to the size of the molecule. Capillary isoelectric focusing (CIEF) allows weakly-ionizable amphoteric molecules, to be separated by electrophoresis in a pH gradient. CEC is a hybrid technique between traditional high performance liquid chromatography (HPLC) and CE.

[0257] Separation and purification techniques used in the present disclosure include any chromatography procedures known in the art. Chromatography can be based on the differential adsorption and elution of certain analytes or partitioning of analytes between mobile and stationary phases. Different examples of chromatography include, but not limited to, liquid chromatography (LC), gas chromatography (GC), high performance liquid chromatography (HPLC), etc.

[0258] (5) Treatment Methods

[0259] In some aspects, provided herein are methods of treating a subject afflicted with DLBLC and having a loss-of-function mutation of ARID 1 A, comprising administering to the subject a therapeutically effective amount of an inhibitor of SMARCA4 and / or SMARCA2. In some aspects, provided herein are methods of preventing a subject afflicted with FL and having a loss-of-function mutation of ARID 1 A from transforming into DLBCL, comprising administering to the subject a therapeutically effective amount of an inhibitor of SMARCA4 and / or SMARCA2. In some embodiments, provided herein is a method of killing or inhibiting proliferation of a lymphoma cell having a loss-of-function mutation of ARID 1 A, comprising contacting the lymphoma cell with an inhibitor of SMARCA4 and / or SMARCA2. The inhibitor of SMARCA4 and / or SMARCA2 includes but is not limited to, for example, FHD-286 or AU-15330.

[0260] IV. Pharmaceutical Compositions The present disclosure provides pharmaceutically acceptable compositions of the agents disclosed herein. As described in detail below, the pharmaceutical compositions of the present disclosure may be specially formulated for administration in solid or liquid form, including those adapted for the following: (1) oral administration, for example, drenches (aqueous or non-aqueous solutions or suspensions), tablets, boluses, powders, granules, pastes; (2) parenteral administration, for example, by subcutaneous, intramuscular or intravenous injection as, for example, a sterile solution or suspension; (3) topical application, for example, as a cream, ointment or spray applied to the skin; (4) intravaginally or intrarectally, for example, as a pessary, cream or foam; or (5) aerosol, for example, as an aqueous aerosol, liposomal preparation or solid particles.

[0261] The phrase “pharmaceutically acceptable” is employed herein to refer to those agents, materials, compositions, and / or dosage forms which are, within the scope of sound medical judgment, suitable for use in contact with the tissues of human beings and animals without excessive toxicity, irritation, allergic response, or other problem or complication, commensurate with a reasonable benefit / risk ratio.

[0262] The phrase “pharmaceutically-acceptable carrier” as used herein means a pharmaceutically-acceptable material, composition or vehicle, such as a liquid or solid filler, diluent, excipient, solvent or encapsulating material, involved in carrying or transporting the subject chemical from one organ, or portion of the body, to another organ, or portion of the body. Each carrier must be “acceptable” in the sense of being compatible with the other ingredients of the formulation and not injurious to the subject. Some examples of materials which can serve as pharmaceutically-acceptable carriers include: (1) sugars, such as lactose, glucose and sucrose; (2) starches, such as com starch and potato starch; (3) cellulose, and its derivatives, such as sodium carboxymethyl cellulose, ethyl cellulose and cellulose acetate; (4) powdered tragacanth; (5) malt; (6) gelatin; (7) talc; (8) excipients, such as cocoa butter and suppository waxes; (9) oils, such as peanut oil, cottonseed oil, safflower oil, sesame oil, olive oil, com oil and soybean oil; (10) glycols, such as propylene glycol; (11) polyols, such as glycerin, sorbitol, mannitol and polyethylene glycol; (12) esters, such as ethyl oleate and ethyl laurate; (13) agar; (14) buffering agents, such as magnesium hydroxide and aluminum hydroxide; (15) alginic acid; (16) pyrogen-free water; (17) isotonic saline; (18) Ringer's solution; (19) ethyl alcohol; (20) phosphate buffer solutions; and (21) other non-toxic compatible substances employed in pharmaceutical formulations. Formulations suitable for oral administration may be in the form of capsules, cachets, pills, tablets, lozenges (using a flavored basis, usually sucrose and acacia or tragacanth), powders, granules, or as a solution or a suspension in an aqueous or non-aqueous liquid, or as an oil-in-water or water-in-oil liquid emulsion, or as an elixir or syrup, or as pastilles (using an inert base, such as gelatin and glycerin, or sucrose and acacia) and / or as mouth washes and the like, each containing a predetermined amount of one or more bacterial strains as disclosed herein.

[0263] The present disclosure also encompasses kits for detecting and / or modulating biomarkers described herein. A kit of the present disclosure may also include instructional materials disclosing or describing the use of the kit or an antibody of the disclosed invention in a method of the disclosed invention as provided herein. A kit may also include additional components to facilitate the particular application for which the kit is designed. For example, a kit may additionally contain means of detecting the label (e.g., enzyme substrates for enzymatic labels, filter sets to detect fluorescent labels, appropriate secondary labels such as a sheep anti-mouse-HRP, etc.) and reagents necessary for controls (e.g., control biological samples or standards). A kit may additionally include buffers and other reagents recognized for use in a method of the disclosed invention. Non-limiting examples include agents to reduce non-specific binding, such as a carrier protein or a detergent.

[0264] Example 1 - Materials and Methods

[0265] Mouse Models

[0266] The experimental procedures involving animals were executed in stringent accordance with the institutional guidelines delineated by Weill Cornell Medicine, as per the Guide for the Care and Use of Laboratory Animals (Guide for the Care and Use of Laboratory Animals, 2011), and standards established by the Association for Assessment and Accreditation of Laboratory Animal Care International. The Research Animal Resource Center and the Institutional Animal Care and Use Committee of Weill Cornell Medicine, having vetted all procedures, duly approved the entirety of the study involving mice under protocol #2011-0031. Experiments were uniformly performed on male and female mice that were age- and sex-matched, with the age bracket ranging from 8 to 16 weeks, barring any exceptions explicitly stated otherwise. All experimental groups incorporated both sexes, except for instances involving bone marrow transplantations, where donors were sex- matched, but the recipient mice were exclusively female. No evidence of sex-based influences or biases was observed. The following strains were obtained from The Jackson Laboratory: C57BL / 6J (CD45.2, strain 000664), B6.SJL-PtprcaPepcb / BoyJ (CD45.1, SJL+ / +, strain 002014), Cdl9Cre (strain 006785), CglCre (strain 010611), AridlaCre (strain 027717) The VavP-Bcl2 (Ogilvy et al., 1999) model was developed by J. M. Adams (Walter and Eliza Hall Institute of Medical Research, Australia).

[0267] Cell Culture

[0268] The Raji cells were cultured with Roswell Park Memorial Institute (RPMI) medium supplemented with 20% Fetal Bovine Serum (FBS), 2 mmol / E E-glutamine, and 10 mmol / E HEPES. The RIVA (RL1) cell line was propagated in Iscove's Modified Dulbecco's medium that was supplemented with 20% FBS and 2 mmol / L L-glutamine. In the case of the 293T cell line for virus production, it was cultured in Dulbecco's Modified Eagle Medium (DMEM) with 10% FBS. Cell lines were grown under the presence of 1% penicillin G and streptomycin, maintained at 37 °C within a humidified environment comprising 5% carbon dioxide. The RIVA cells were procured from Jose A. Martinez-Climent, (Universidad de Navarra, Pamplona, Spain). Routine examinations are performed within the laboratory to check for any potential mycoplasma contamination in the cell lines.

[0269] RNA Extraction and qPCR

[0270] Mouse cells were harvested from their culture and subsequently re-suspended in TRIzol (Invitrogen 15596018). Alternatively, cells were sorted directly into a more concentrated version of TRIzol reagent, TRIzol LS (Invitrogen 10296028), facilitating total RNA extraction. RNeasy Mini Kit (Qiagen #74104) was additionally harnessed to extract RNA from RIVA and Raji cell lines. Following extraction, the purified RNA was resuspended in molecular-grade RNase-free water. RNA concentration was determined using the Qubit RNA High Sensitivity Kit (Thermo Fisher Q32855) and Nanodrop (#FS-0057491) and RNA integrity was assessed using RNA 6000 Pico Kit (Agilent 5067-1513) on Agilent 2100 Bioanalyzer. Synthesis of cDNA was executed with consistent quantities of total RNA across all samples within each experiment, using the Verso cDNA Synthesis kit (Thermo Fisher AB1453B). Subsequent RT-qPCR was conducted on an equal volume of cDNA across all samples using a QuantStudio6 Flex Real-Time PCR System (#278860403), coupled with Fast SYBR Green Master Mix (Thermo Fisher 4385614). The qPCR program has cycling stage; 95C for 30 sec, [95C for 3 sec, 60C for 30 sec] for 40 cycles and a melting curve stage; 95C for 3 sec, 60C for 30 sec, and 95C for 10 sec. The qPCR signal for every gene was adjusted to align with those of Gapdh by employing the delta-Ct method. The results were then portrayed as fold expression in comparison to wild type, inclusive of the standard deviation calculated from 3-4 biological replicates.

[0271] Virus Production and Infection

[0272] For the generation of NF-kB-RE luciferase reporter lentivirus particles, HEK293T cells were plated and transfected with a suspension containing the packaging plasmids psPax2 (Addgene 12260) and psMD2.G (Addgene 12259), alongside with the NF-kB reporter plasmid (Xia et al., 2022). This was performed at a ratio of 4:3:1 in serum-free media. The supernatant, laden with viral particles, was collected at 48h and 72h hours posttransfection and pooled, followed by filtration through a 0.45-pm filter, and subsequent concentration using PEG-it in accordance with the manufacturer's protocol (LV825A-1 System Biosciences). The viral suspension was reconstituted with PBS incorporating 25 pmol / L HEPES, and subsequently introduced to RIVA cell lines for infection. A selection was conducted 1 day post-transfection, involving the addition of puromycin for a minimum period of 7 days. For RNAi experiments, lentiviruses were generated in 293T cells by cotransfection of short hairpin RNA (shRNA) sequences. The shRNAs were designed using the SplashRNA algorithm (Pelossof et al., 2017) and cloned into LT3GEPIR (Adgene: 111177) Tet-ON miR-E (miR-30 variant)-based RNAi vector. Antisense guide sequence used: ARID1A-7170: TATGTTTACAGTATCAGGTGTA, ARID1A-6964: TTACCATTAAAAACAGTTATGA, SPI1-1378: TTGAGAATAACTTTACTTGTTT, SPI1- 1315: TCTGATTTACATACGGACTTGA, SPI1-1371: TAACTTTACTTGTTTTTTGGGA, SPI1-876: TAGGTCATCTTCTTGCGGTTGC, SPI1-1367: TTTACTTGTTTTTTGGGAGGAG. Raji cell line carrying NF-kB-RE cassette was infected with ARID 1 A knockdown constructs. Subsequently, knockdown was induced by the addition of doxycycline (1:1,000, Img / ml stock solution) for 24 hours and the GFP positive cells were sorted and transferred back to culture in media without doxycycline. In total four clones per cell line were kept based on their GFP positivity and frozen down to be used for further experiments.

[0273] CRISPR / Cas9 Gene Editing

[0274] In order to generate the ARID 1 A deletion model in lymphoma cell lines, we utilized Raji and RIVA (RI-1) lines. Exome sequencing revealed the presence of an AR1D1A mutation, precisely Q474*, in the RIVA cell lines, which resulted in a premature stop codon on one allele. We confirmed this mutation via PCR amplicon sequencing on Illumina MiSeq of the mutated region, determining that Q474* was present at a rate of 50%, thereby affirming the heterozygous status of this mutation. We then employed targeted genome editing to revert the mutation to its wild type status. To accomplish this, the cells were electroporated with a ribonucleoprotein (RNP) complex, which contained the Alt-R recombinant S.p. HiFi Cas9 Nuclease (IDT 1081061), Alt-R CRISPR-Cas9 tracrRNA (IDT 1072534), and Alt-R CRISPR-Cas9 crRNA, the latter targeting the precise Q474* mutation region (GGACTTTACTGGTTGTAATA), as well as the WT DNA template (*A*C*AAGGCCCCAGCGGGTATGGTCAACAGGGCCAGACTCCATATTACAACCA GCAAAGTCCTCACCCTCAGCAGCAGCAGCCACCCTACTCCCAGCAA*C*C*A) (star sign indicates phosphorothioate nucleotides that were modified to ensure DNA stability). Electroporation was carried out in accordance with the manufacturer's protocol using the SF Cell Line 96-well Nucleofector Kit (V4SC-2096). Subsequently, single cells were seeded into 96-well plates by single-cell fluorescence-activated cell sorting. Clones were screened and verified using both Sanger sequencing of PCR amplicons and Western Blot. Aridla Q474* mutation was introduced in the Raji cell line by the same method, with the exception of using Alt-R CRISPR-Cas9 crRNA targeting WT allele (GGACTTTGCTGGTTGTAATA) and Q474* DNA template was used to introduce the mutation

[0275] (ACAAGGCCCCAGCGGGTATGGTCAACAGGGCCAGACTCCATAcTACAAtCAGtAA AGTCCTCACCCTCAGCAGCAGCAGCCACCCTACTCCCAGCAACCA). Two silent mutations were introduced in the Q474* DNA template to ensure the CRISPR / Cas9 targeting was substantially reduced once the template is integrated into the locus (codon usage frequency was considered when designing silent mutations). For the introduction of a mutation into the PU.l binding motif at the NF-kB luciferase reporter gene (NF-kB-RE Luc), the cells were electroporated with a RNP complex that was composed of the Alt-R recombinant S.p. Cas9 Nuclease V3 (IDT 1081059), Alt-R CRISPR-Cas9 tracrRNA (IDT 1073190), and two distinct Alt-R CRISPR-Cas9 crRNAs CD.Cas9.VPFX5778.AA (5’- UGGAAGCUCGACUUCAAGCUGUUUUAGAGCUAUGCU-3 ’ ) or CD.Cas9.VPFX5778.AC (5’- AUUAUAUACCCUCUAGUGUCGUUUUAGAGCUAUGCU-3’).

[0276] Immunoblot

[0277] Immunoblotting was conducted as follows: cells were lysed in RIPA buffer (20 mM HEPES KOH pH 7.5, 50 mM b-glycerophosphate, 1 mM EDTA pH 8.0, 1 mM EGTA pH 8.0, 0.5 mM Na3VO4, 100 mM KC1, 10% glycerol, and 1% Triton X-100, supplemented with complete protease inhibitor cocktail; Roche #05056489001) and the lysates were subjected to resolution through SDS-PAGE, then transferred to a PVDF membrane overnight. This membrane was subsequently blocked with 5% milk (Biorad 1706404) and probed using primary antibodies. The membrane was then incubated with a corresponding peroxidase- conjugated secondary antibody (1:10,000 dilution Cell Signaling Technology, 7074 & 7076), and detected with chemiluminescence (WBKLS0500, Millipore & VWR #PI34095, Thermo Scientific) on a ChemiDoc Touch imaging system, ChemiDoc MP Imaging System (BioRad). Densitometry values were subsequently gathered using Fiji software and the Image Lab software v6.1.0 (Bio-Rad). Antibodies used: Aridla:12354S (CST) 1:1,000 or Aridla:HPA005456 (Sigma Aldrich) 1:1,000, Tubulin: T0198-25UL (Sigma) 1:5,000, NF- kB2: 37359S (CST) 1:1,000, PU.l 2266S (CST), 1:100.

[0278] Bulk ATAC-seq

[0279] ATAC-seq was prepared from FACS-sorted cell suspensions as per the Omni- AT AC protocol (Ref). In brief, 50,000 freshly sorted live cells were rinsed in cold PBS and lysed in 50 pL of cold lysis buffer (comprising 10 mM Tris-HCl at pH 7.5, 10 mM NaCl, 3 mM MgC12, 0.1% NP-40, 0.1% Tween-20, and 0.01% Digitonin) for 3 minutes on ice. This was followed by adding 1 mF wash buffer (comprising 10 mM Tris-HCl at pH 7.5, 10 mM NaCl, 3 mM MgC12, and 0.1% Tween-20), and centrifugation to pellet nuclei. The nuclei were then resuspended in a transposition reaction mix that was prepared using the Illumina Tagment DNA TDE1 Enzyme and Buffer Kits (Illumina 20034197) and incubated at 37 °C for 30 minutes on a thermomixer set at 1,000 rpm. Tagged DNA fragments were purified using the Clean & Concentrator-5 Kit (Zymo D4014) or AMPure XP beads (Beckman Coulter A63881) with a 2:1 bead-to-DNA ratio, and then subjected to PCR amplification to generate sequencing libraries. PCR amplicons were purified using AMPure XP beads at a 2:1 bead-to- DNA ratio. The size distribution and quality of the library were evaluated using the High Sensitivity DNA Kit (Agilent 5067-4626) and ran on the Agilent 2100 Bioanalyzer. The libraries were sequenced on Illumina HiSeq X and NovaSeq 6000 platforms using the HiSeq X Ten Reagent Kit (FC-501-2501) and PE50 SP Kit (Illumina 20028401), respectively.

[0280] CUT&RUN

[0281] For the Epicypher CUT ANA Cut&Run protocol, 0.5M cell suspension was used for each sample. Post centrifugation, cells were reintroduced to PBS, then fixed for a minute by adding 16% PFA directly into the tube to achieve a 0.1% PFA working concentration. This was followed by a strict 1 -minute incubation at room temperature, after which the fixation was terminated with the addition of l / 20th glycine. After 5 minutes of chilling and subsequent centrifugation, the cells were permeabilized via Triton-X (0.1%) in Wash Buffer (20 mM HEPES, pH 7.5, 150 mM NaCl, 0.5 mM Spermidine (#S25O1-1G), lx Roche Complete Protease Inhibitor-mini (CPI-mini) EDTA-free (0505648900). The samples were then transferred to individual eppendorf tubes and chilled. After introducing Wash Buffer and centrifugation, the pellet was reconstituted in Wash Buffer and Concanavalin A beads (#NC 1526856) that were activated in Bead Activation Buffer (20 mM HEPES, pH 7.9 10 mM KC1, 1 mM CaC12,l mM MnC12), and this cell-bead mixture was chilled for 10 minutes. The samples were then allocated into 8-well strip tubes and placed on a magnetic stand for supernatant removal. The beads were then incubated in the Antibody Buffer (Digitonin Buffer + 2 mM EDT) by adding the respective antibody in each tube (antibody added in 1:100 ratio), chilled, resuspended, and left to incubate overnight on a nutator. The next day, the supernatant was removed, and the beads were washed with Digitonin Buffer twice (Wash Buffer + 0.01% Digitonin 300410-250MG). Then, a pAG-MNase (SKU 15-1116) solution was introduced and samples were left at room temperature for 10 minutes. Post another supernatant removal, the beads were washed again twice with Digitonin Buffer, then treated withlOOmM CaC12 (E506-100ML) in the same buffer. Following a 2-hour incubation at 4°C, a Stop Buffer (340 mM NaCl, 20 mM EDTA, 4 mM EGTA, 50 pg / ml RNase A, 50 pg / ml Glycogen) was added and samples were left at 37°C for 10 minutes. After placing the 8-strip tubes on the magnetic stand, the supernatant was transferred to fresh tubes, and crosslinks were reversed by the addition of 0.09% SDS (V6551) and Proteinase K (Life Technologies 25530049). This mixture was then left to incubate overnight at 55 °C in a PCR thermocycler and libraries were prepared following DNA purification. Antibodies used; ARID 1 A (1235S), IgG (ABIN101961), BRD9 (137245), PU.l (2266S), H3.3 (91191), H3K27me3 (13027S).

[0282] ChlP-seq

[0283] ChlP-seq was carried out as previously described (Weber et al., 2007) with slight modifications. Changes to the protocol were following; (1) crosslinking was performed at 37C for lOmin., (2) chromatin was sonicated for -30 cycles of 30 sec. using a Diagenode Bioruptor, with 45 sec. breaks in between cycles, (3) protein A / G magnetic Dynabeads Magnetic beads (Thermo Fisher Scientific) were used for both pre-clearing and IP, (4) DNA was purified using on-column purification instead of chloroform / phenol extraction (Qiagen PCR Purification Kit). Immunoprecipitated DNA and input DNA were submitted to library preparation (NEBNext Ultra II DNA Library Prep Kit, Illumina). Antibodies used; PBRM1 (A301-591A), IgG (ABIN101961), SMARCA4 (A300-813A), PU.l (2266S), H3K27ac (39133).

[0284] NEB Libraries

[0285] For the generation of libraries for next-generation sequencing (CUT&RUN, ChlP-seq, Amplicon-PCR) the NEBNext Ultra II DNA Library Prep Kit for Illumina (New England Biolabs #E7645L) was utilized. The fragmented DNA was subjected to end repair reaction followed by adaptor ligation. Concerning the latter step the NEBNext Adaptor for Illumina was diluted using 1:25 ratio with nuclease-free water. Following the adaptor ligation reaction, 1.1X of AMpure beads (Thermo Fischer Scientific #NC9933872) was added to the ligated samples to be purified. Finally, DNA was eluted in 0.1X TE. NEBNext Ultra II Q5 was used for PCR together with single (New England Biolabs #E6609S) or dual-indexes (New England Biolabs #E6440S). For CUT&RUN, PCR reaction was run according to the Epicypher CUT & RUN-specific PCR cycling parameters; 45 sec at 98C, [15 sec at 98C & 10 sec at 60C] 14x, 1 min at 72C. To purify PCR reaction products 1.1X Ampure beads was used.

[0286] Bone Marrow Transplantation

[0287] Bone marrow cells were collected from the femur and tibia of donor mice aged between 8 and 12 weeks, followed by treatment with a red blood cell lysis solution (Qiagen 158904). Lethally irradiated C57BL / 6J host mice, who received two doses of 450 RAD via the Rad Source Technologies RS 2000 Biological Research X-ray Irradiator, were then injected with one million of these cells through the retro-orbital sinus. These transplanted mice were incorporated into experiments 8 to 14 weeks post-transplantation. Excluding the mice euthanized at predetermined intervals, all rodents participating in lymphomagenesis research were observed until they exhibited one or more conditions warranting euthanasia. These conditions included significant lethargy and body weight loss as well as discernible splenomegaly. This procedure aligns with the guidelines of our animal protocol approved by the Weill Cornell Medicine Institutional Animal Care and Use Committee (protocol #2011- 0031).

[0288] Immunization

[0289] To induce germinal center (GC) formation in spleens, mice were immunized by intraperitoneal (i.p.) injection of 500 pL of 2% sheep red blood cells (SRBC) resuspended in sterile IX DPBS from a solution of sheep blood in Alsever's (Cocalico Biologicals 20- 1334A), or by i.p. injection of 80 pg NP(30-32)-KLH (Biosearch Technologies N-5060) absorbed in alum adjuvant (ThermoFisher 77161) at a 1:1 ratio of alum:immunogen prior to injection.

[0290] H&E Staining and Immunohistochemistry

[0291] Mouse organs were fixed in 10% neutral buffered formalin (Sigma) for 24-48h and transferred to 70% ethanol. Tissues were then embedded in paraffin, processed and stained at the Laboratory of Comparative Pathology (WCM / MSKCC). Briefly, 5 pm sections were deparaffinized and stained with hematoxylin and eosin (H&E) or processed for immunohistochemistry by heat antigen-retrieval in citrate buffer at pH 6.4, followed by treatment with 3% hydrogen peroxide in methanol to block endogenous peroxidase (HRP) activity. Indirect immunohistochemistry was then performed using biotinylated peanut agglutinin (PNA, Vector Laboratories B1075) or anti-species-specific biotinylated secondary antibodies, followed by avidin-horseradish peroxidase and development with DAB substrate (Vector Laboratories). Sections were counterstained with hematoxylin. The following primary antibodies were used: anti-B220 (BD 550286) and anti-Ki67 (Cell Signaling Technology 12202).

[0292] Flow Cytometry

[0293] Spleens were mashed, splenocytes were filtered through a 40 pm cell strainer and mononuclear cells enriched by density gradient centrifugation using Eicoll-Paque Premium (Cytiva 17-5442-02). Resulting single cell suspensions were resuspended in PBE with 0.5 pg / mL rat anti-mouse CD16 / CD32 (clone 2.4G2, BD) and incubated for >5 min on ice to block Ec receptors. Without wash, cells were stained for 20 min on ice, in a mix of fluorochrome-conjugated anti-mouse antibodies diluted either in PBE, or in a 50:50 mixture of Brilliant Stain Buffer (BD 566349) and PBS with 0.5% BSA but without EDTA when the mix contained >2 BV- or BUV-conjugated antibodies, to avoid staining artifacts due to interaction between these fluorescent dyes. All cells were then washed twice before analysis. DAPI (ThermoFisher D1306) was added just prior to acquisition at final 0.5 pg / mL for the exclusion of dead cells. Data was acquired on BD Symphony A3 and analyzed using Flow Jo software package (TreeStar).

[0294] Antibodies used:

[0295] B220-BV786; clone RA3-6B2, BD 563894, dilution 1:300 CD138-BUV737; clone 281-2, BD 564430, dilution 1:500 CD38-APC; clone 90, eBioscience 17-0381-81, dilution 1:500 CD38-BUV395; clone 90, BD 740245, dilution 1:500 CD38-BUV563; clone 90, BD 741271, dilution 1:400

[0296] CD80-BV421; clone 16-10A1, BioLegend 104725, dilution 1:300

[0297] CD86-BV605; clone GL-1, BioLegend 105037, dilution 1:200

[0298] CXCR4-biotin; clone 2B11, BD 551968, dilution 1:200

[0299] FAS-BUV805; clone Jo2, BD 741968 , dilution 1:100

[0300] FAS-PE-Cy7; clone Jo2, BD 557653, dilution 1:500

[0301] GL7-FITC; clone GL7, BD 553666, dilution 1:500

[0302] GL7-PerCPCy5.5; clone GL7, BioLegend 144610, dilution 1:500

[0303] IgD-BV510; clone l l-26c.2a, BD 563110, dilution 1:500

[0304] IgGl-BV650; clone RMG1-1, BioLegend 406629, dilution 1:500

[0305] IgM-BUV661; clone 11 / 41, BD 750660, dilution 1:300

[0306] IgM-BUV805; clone 11 / 41, BD 749307, dilution 1:500

[0307] IgM-FITC; clone 11 / 41, BD 553437, dilution 1:300 kappa- APC-Cy7 ; clone RMK-45, BioLegend 409503, dilution 1:200 PD-L2-BUV395; clone TY25, BD 565102, dilution 1:75

[0308] Secondary:

[0309] SA-APC-Cy7; BioLegend 405208, dilution 1:100

[0310] Other:

[0311] NP-PE; N-5070-1, diluted to 5pg / mL

[0312] Luciferase Assay

[0313] The Dual-Luciferase Reporter 1000 Assay System (Promega #E1980) was used to determine Luciferase activity in the RIVA and Raji cell lines. Specifically, 125,000 cells from each sample were gathered and spun down at 300xg for 5 minutes. Each of these cell pellets was then reconstituted in 200ul of media that contained either DMSO (Sigma Aldrich D8418-500ML) or PMA (Santa cruz Biotechnology SC-3576A) and ionomycin (Santa cruz Biotechnology I0634-1MG), serving to stimulate the cells. The resuspended samples were then transferred to a 96- well U-bottom plate and incubated at 37 °C for an hour.

[0314] Following this incubation period, the plate was centrifuged again at 300xg for 5 minutes. The supernatant was carefully discarded, and the cell pellets were rinsed with 200ul of IX DPBS. This centrifugation process was then repeated, after which the cells were lysed using 50ul of PLB lysis Buffer (Promega E194A). The plate was then subjected to shaking for 15 minutes at RT. Once the incubation was complete, a 20ul aliquot from each sample was transferred to a white flat-bottom 96-well plate. This plate was then loaded into the Biotek plate reader to determine the background luminescence without the addition of any substrate. To quantify the Firefly Luciferase signal, 50ul of reconstituted Luciferase Assay Reagent II (Substrate E151C reconstituted in Buffer E195C) was added to each well, and the luminescence indicative of the firefly luciferase signal was measured. Following this, an additional 50ul of the Stop & Gio Reagent (50X Stop & Gio Substrate #E640B in Stop & Gio Buffer #E641B to achieve IX final concentration for the substrate) was added to each well to quench the firefly luminescence and simultaneously activate the Renilla luciferase, whose signal was then measured.

[0315] MNase-seq

[0316] 5.5x106 cells were resuspended in ImL of buffer 1 (0.3M Sucrose, 15mM Tris pH 7.5, 60mM KC1, 15mM NaCl, 5mM MgC12, 2mM EDTA, 0.5mM DTT, lx PIC, 0.2mM spermine, ImM spermidine) with 0.02% NP-40 and incubated on ice for 5 minutes. Samples were then spun down at 300xg for 5 min at 4C. The pellets were resuspended in ImL of buffer 2 (0.3M Sucrose, 15mM Tris pH 7.5, 60mM KC1, 15mM NaCl, 5mM MgC12, 0.5mM DTT, lx PIC, 0.2mM spermine, ImM spermidine) and spun down at 300xg for 5 min at 4C. Nuclei integrity was checked before continuing with protocol and only samples that had nuclei recovery >85% were processed. Pellets were resuspended in 400ul MNase buffer (0.3M Sucrose, 50mM Tris ph 7.5, 4mM MgC12, ImM CaC12, lx PIC) and 3-5U of MNase S7 micrococcal nuclease (Roche) was added. Samples were incubated at 37C for 30 minutes. EDTA (5mM) was added to stop the reaction. SDS (1% final concentration) and Proteinase K (200ug / mL) were added and samples were incubated for 1 hour at 55C with shaking. DNA was purified using the Qiagen PCR Purification Kit and digestion efficiency was assessed using an Agilent Bioanalyzer. The mononucleasomal fraction was purified using Ampure XP beads (1.1X) and libraries were prepared using NEBNext Ultra II Library Preparation Kit, using 8 PCR cycles and lOOng of starting DNA.

[0317] Bulk RNA-seq

[0318] Library preparation, sequencing and post-processing of raw data was performed at the Genomics Core at Weill Cornell Medicine. Library preparation using the Illumina TruSeq Stranded mRNA Library prep kit (Illumina, 20020594) and sequenced with PE50 paired endsequencing, performed on an Illumina HiSeq4000 sequencer.

[0319] Multiome Single Cell RNA and ATAC Sequencing

[0320] Single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) libraries were prepared employing the 10X Genomics Single Cell Multiome assay kit (10X Genomics; 1000230, 1000283, 1000494, 1000215, 1000212) strictly following the manufacturer's guidelines. Briefly, GC cells from splenocytes were sorted using antibody cocktails described above (Flow Cytometry), rinsed with 0.04% BSA PBS, and 100,000 cells underwent the manufacturer's nuclei preparation procedure. Then, about 20,000 nuclei were placed into a lOx Chromium X instrument. The remainder of the library preparation steps were conducted in accordance with the manufacturer's protocol. Libraries were evaluated using an Agilent Bioanalyzer, quantified utilizing a KAPA Library Quantification Kit, and sequenced on an Illumina Novaseq platform.

[0321] Targeted Sequencing

[0322] Targeted sequencing of FL patients was performed as described in X. Wang et al., 2022. Mainly, we used the TruSeq Custom Amplicon assay (TSCA; mean coverage: 767; range: 128-2,039; SD: 180) to identify variants within the protein coding regions of 59 genes commonly mutated in human B-cell lymphomas that includes ARID 1 A. TSCA variants were validated with the Fluidigm Access Array system which achieved a 97% validation rate. Discrepancies between TSCA and Fluidigm results were further validated by Sanger sequencing.

[0323] Targeted Sequencing Data Analysis

[0324] Reads were mapped with BWA (v0.7.5a). SNVs and indels were predicted with Mutascope (vl.02). SAMtools (vO.1.19) was used to create pileup files and dbSNP (vl37) for SNP annotation. All variants with an allele frequency of >5% at loci covered at least 50-fold were retained.

[0325] Bulk ATAC-seq Analysis

[0326] Raw reads were checked by FastQC (Andrews, 2010). Sequence adapters were trimmed by Trim Galore! and the resulting reads were aligned to the mm 10 or hg38 genome reference with BWA (Krueger, 2016; H. Li & Durbin, 2009). Duplicate reads were marked by Picard (Broad Institute, 2016). After the alignment, mitochondrial reads, duplicate reads, reads in blacklisted regions, secondary alignments and multimapping reads, reads with insert size greater than 2kb or wrong orientation of the pairs, or orphan reads were filtered. Peaks were called using MACS2 narrow peak calling with shift of -75 bp and extension of 150 bp (Gaspar, 2018). ATAC-seq quality was performed by checking the fragment length distribution, FRIP scores, TSS enrichment and high quality autosomal reads using Picard, ataqv and MultiQC (Ewels et al., 2016; Orchard et al., 2020). Consensus peaks were called by taking the overlap of regions covered in at least 2 of the samples. Reads within peaks were quantified using featureCounts (Liao et al., 2014). Variance stabilizing transform (vst) was used for normalizing the data for visualizations and unsupervised clustering (Love et al., 2014).

[0327] The differential peak accessibility was called by negative binomial model using DESeq2 using multivariate models and filtering for an absolute log2-fold-change value of log2(1.5) (0.58) and FDR adjusted p-value of 0.01 (Love et al., 2014). The peaks were annotated and collapsed to genes by taking the maximally changing peak with respect to p- value within 5kb of each transcription start site and assigning that peak to each gene by their promoter, or alternatively putative proximal enhancers in their regulatory regions defined by GREAT (McLean et al., 2010). The genes were ranked by the log2-fold-change value or the Wald statistic and fed into GSEA using MSigDB database and the curated gene signatures from the RNA-seq experiments as well as literature. An adjusted p-value of 0.05 was used to filter the resulting gene sets (Liberzon et al., 2011; Subramanian et al., 2005).

[0328] Regulatory potential of transcription factors was calculated by comparing all the mammalian motifs in JASPAR within the sequences of accessible peaks (Castro-Mondragon et al., 2022; Doane et al., 2021). The log2 fold-changes of peak accessibility were modeled against the presence of such motifs in a multivariate manner while correcting for GC content, i.e. log2FC ~ pO + pi* xl + P2*%2+ ... + piV*GC , where xi denotes a Boolean variable for the presence of a TF motif in a peak as a binary variable and GC is the GC-content of the peak. The effect sizes and p-values for each term are taken after multivariate linear modeling using ordinary least squares regression with robust standard errors. As an orthogonal method, TF binding sites, binding scores, and differential binding between conditions were assessed by TOBIAS by footprinting analysis (Bentsen et al., 2020). Pseduomedians and 95% confidence intervals were determined using the wilcox.test function from the R framework (v3.4.3). Read density heatmaps were generated from normalized bigwigs using the deepTools suite (v3.5.1).

[0329] Bulk RNA-seq Analysis

[0330] Reads were trimmed with TrimGalore!, and mapped to mm 10 or hg38 genome reference using STAR and Gencode gene annotations (Dobin et al., 2013; Harrow et al., 2012; Krueger, 2016) . Counts were assigned into transcripts and genes using featureCounts (Liao et al., 2014). Differential gene expression was calculated using a negative binomial model with DESeq2, using univariate models for RIVA / RL1, and multivariate models correcting for cell type and genotype in the mouse models. Genes were considered differentially expressed with an absolute fold change greater than log2(1.5) and adjusted p- value of less than 0.01 (Love et al., 2014). The correlation between RNA-seq and ATAC-seq was performed by taking all the ATAC-seq peaks within lOkb of the transcription start site of every gene and binning the genes into differentially opening or closing and non-differential categories, and plotting the log2-fold-change of expression differences.

[0331] CUT&RUN Analysis

[0332] Sequencing QC and adapter trimming were performed by FastQC and Trimmomatic (Andrews, 2010; Bolger et al., 2014). Trimmed sequences were aligned to the hg38 genome for RIVA samples by Bowtie2, and filtered for mapping reads with proper pairs and duplicate marked using samtools (Langmead & Salzberg, 2013; H. Li et al., 2009). All read fragments were utilized during the analysis of histone marks and BAF complex proteins. For all the other transcription factors the sequences were filtered to fragments with insert sizes less than 120 base pairs. Resulting alignments were converted into track files by RPKM normalization using deepTools (Ramirez et al., 2016). Peaks were called using seacr with stringent method and 1% threshold (Meers et al., 2019). Consensus peaks between different replicates and conditions were merged by collapsing all peaks by any amount of overlap, then filtering to merged peaks that have at least 3 replicates as called peaks. Reads within the peaks were quantified using featureCounts and normalized using variance-stabilizing transform in DESeq2 for visualization purposes, such as heatmaps, unsupervised clustering, t-SNE and PCA(Liao et al., 2014; Love et al., 2014; Van Der Maaten & Hinton, 2008). Differential peak binding was calculated with a negative binomial model and TMM normalization using DESeq2.

[0333] ChlP-seq Analysis

[0334] Raw read QC and adapter trimming were performed by FastQC and TrimGalore!, and reads were aligned to the hg38 genome reference using BWA (Andrews, 2010; Krueger, 2016; H. Li & Durbin, 2009). Duplicate reads were marked by Picard (Broad Institute, 2016). After the alignment, duplicate reads, reads in blacklisted regions, secondary alignments and multimapping reads, reads with insert size greater than 2kb or wrong orientation of the pairs, or orphan reads were filtered. Peaks were called for each IP against the control samples using MACS2 (Gaspar, 2018; Liu, 2014).

[0335] MNase-seq Analysis

[0336] MNase-seq data was processed and filtered in a similar manner to ChlPseq, utilizing FastQC, Trim Galore!, BWA alignment, Picard and samtools filtering (Andrews, 2010; Broad Institute, 2016; Krueger, 2016; H. Li et al., 2009; H. Li & Durbin, 2009). After alignment and filtering, DANPOS3 was used for nucleosome occupancy prediction, nucleosome fuzziness, and differential nucleosome binding (Chen et al., 2013, 2015). Smoothed predicted nucleosome occupancy tracks were quantile normalized for each condition. To create the meta-plots either the TSSs of canonical isoforms of genes, or the summits of ATACseq peaks were taken. Occupancy scores were calculated at lObp bins, and the median of those values were taken summarizing across the flanking regions of TSSs or peak summits. The meta profiles were normalized by dividing the profile of each replicate by its median and multiplying by the median of all the conditions in a 2kb window.

[0337] Chromatin Segmentation and Clustering Analysis

[0338] The workflow for segmenting chromatin into regions with distinct epigenomic marks comprised four main steps: 1- Defining regions via peak clustering, 2- Quantifying these regions, 3- Clustering these regions, and 4- Matching the regions with read-out values from other epigenomic marks.

[0339] In the peak clustering step, peak calls from every mark relevant for genome segmentation (e.g., ATACseq, H3K27ac, H3K27me3) were extended to at least 500bp. A graph, which represented the combined data from all replicates of all marks, was created. Each peak was treated as a node, and nodes were connected with a vertex only if the corresponding peaks overlapped by 50% in both directions. As a result, two peaks situated roughly in the same location and having similar sizes shared an edge. However, a broad peak encompassing a much smaller peak did not have an edge with the smaller one if the latter was less than 50% the size of the broad peak. Connected components within the graph were identified, and the members of each component were collapsed into a single region by extending their start and end coordinates to their furthest extents within the component. Unconnected peak nodes retained their original coordinates. The start and end coordinates of this refined peak set served as boundaries, segmenting the chromatin into disjoint regions. This new set of regions was cross-referenced with the initial peak calls across different marks and replicates. Any region with fewer than two separate overlaps was discarded. Following this, immediately adjacent regions showing similar peak presence or absence patterns across various replicates, as defined by a Hamming distance of less than 2, were combined to generate the final set of segments. Subsequently, featureCounts was employed to quantify reads within each segment. The counts were then normalized to log(FPKM + 1). For each segment, the median log FPKM value was calculated for all replicates of a specific mark and genotype. For each mark, the log2 fold-change for Het vs WT and KD vs WT was calculated based on the difference between the log FPKM values. This yielded baseline WT logFPKM values and Het Vs WT log2 fold-change values for ATACseq, H3K27ac, and H3K27me3 marks, and an additional KD vs WT value for ATACseq. These seven metrics were used to cluster all regions into 16 groups using k- means clustering. Marks that were not considered during peak definition or clustering were used as read-outs. The quantification and normalization of these marks were done similarly, using featureCounts for quantification, log FPKM for normalization, and the calculation of Het vs WT log2 fold-change. Additionally, differential gene expression data from RNAseq experiments were used as an independent read-out, by associating each region with the gene having the closest transcription start site (TSS).

[0340] To summarize, these clusters were represented as either the median signal across all regions within a cluster or as violin plots. External annotations from Human GCB enhancers and predicted transcription factor binding sites (TFBS) from TOBIAS were also included in the summary plot (Doane et al., 2021).

[0341] Multiome Analysis

[0342] Multiome fastq files were aligned and processed with CellRanger Arc version 2.0.0 (lOx Genomics). CellRanger outputs were then further processed individually with Signac version 1.5.0 (Stuart et al., 2021). Peaks were called with MACS2 using default settings (Zhang et al., 2008). Samples were combined together, and a combined peak set was generated from all samples. Peaks larger than lOkb and less than 20 bases were filtered from this combined peak set. Harmony version 0.1.0 was used on PCA based on RNA and LSI based on ATAC to correct batch effect between experiments (Korsunsky et al., 2019). Signac FindMultiModalNeighbors was used on these Harmony corrected values to generate multimodal nearest neighbors upon which the UMAP is based. Cell type labels were assigned based on RNA expression using the Seurat TransferLabel function and reference datasets (Rivas et al., 2021). Gene signature module scores for expression were calculated using the AddModuleScore function, with a control value of 5. Slingshot version 2.8.0 was used to generate Pseudotime based on the first and second Harmony corrected PCA of expression, with the starting anchor being Centroblasts. Density and difference in density of cells for WT and ARID 1 A HET along this pseudotime was caluclated, with significance being tested using wilcoxon rank sum of this pseudotime. ChromVAR scores for the JASPAR2020 core, vertebrates motif list were calculated with Signac’s AddMotif and RunChromVAR functions on the filtered, combined peak set. The indicated gene signature or ChromVAR accessibility of the indicated motif were plotted against pseudotime, which was broken into 10 deciles. Expression of these gene signatures or ChromVAR accessibility for each decile was then tested for significance between WT and ARID 1 A HET using wilcoxon rank sum. The difference in splines were plotted and colored according to decile significance. Pre-plasma cells were defined as cycling BCL6-\ow non-pre-memory cells. Code for multiome processing can be found at https: / / github.com / crchin / multiome_process.

[0343] GEO Accession Number

[0344] GEO super series code: GSE237990.

[0345] Example 2 - Aridla deletion disrupts germinal center dynamics and progression

[0346] Chromatin accessibility profiling on different GC B-cell populations have revealed significant changes in chromatin accessibility states (Doane et al., 2021). These findings suggest a crucial role of BAF complexes in shaping GC cell-fate and phenotypes. Given the frequent occurrence of ARID 1 A mutations in lymphomas, these considerations have led us to explore the molecular, chromatin and biological impacts of ARID 1 A in these cells, as well as its role in the onset and progression of lymphomas.

[0347] Mutations in the SWI / SNF chromatin remodeling complex BAF are a recurring feature in many cancers, with a high prevalence in follicular lymphoma (FL; >19%) and diffuse large B-cell lymphoma (DLBCL; >34%). Despite this, the mechanism that links mutations in ARID 1 A, most frequently mutated BAF subunit, to the development of lymphoma is still not understood.

[0348] To this aim, we crossed Arz / a-floxed mice with CyJ-Cre mice to yield offspring with a conditional (cKO) Aridla deletion in germinal center (GC) B cells, FL and DLBCL cell-of-origin. We immunized WT / WT (Arzt Ja+ / +;CyJCre / +) and cKO / WT (Arz Ja+ / -;CyJCre / +)mice with sheep red blood cells and analyzed 10 days later at the peak of the GC reaction. Upon Aridla deletion, we found a decrease in the total number of GC B cells relative to total B cells (WT / WT vs. cKO / WT; p<0.001). However, we observed skewing of GC polarity manifesting as increased proportions of proliferating centroblasts (CB) vs. centrocytes(CC) (WT / WT vs. cKO / WT; p=0.004). These observations imply a substantial dysfunction at the CC stage, which could account for the overall reduced number of GC B-cells. To investigate this, we performed a detailed molecular characterization of these cells. We performed the ATAC-seq assay on sorted CB and CC from our mouse model and observed an extensive loss of chromatin accessibility. Changes in chromatin accessibility are thought to be dependent on the recruitment of the BAF complex by specific transcription factors. We discovered strong enrichment for DNA motifs of PU.l and NF-kB factors in closing chromatin (p-value <0.001), indicating their dependence on the BAF complex. Furthermore, we noted that this chromatin closing occurred in proximity to canonical GC exit programs, including genes induced by the CD40, NF-kB signaling, IRF4,STAT3, IL2, IL4, IL6 and Notch pathways (hypergeometric p-value <0.001), indicating GC-exit perturbation.

[0349] Expanding upon our findings, we leveraged a combined single-cell RNA and ATAC assay(Multiome) to scrutinize subtle shifts within GC populations. Determining the chromatin accessibility within the pseudo-time trajectory of GC transitions, we found that PU.l and NF-kB factors are chronologically dependent on ARID 1 A with PU.l accessibility initially compromised, followed by a subsequent decrease at binding sites for NF-kB factors. This sequence reveals ARIDlA's unrecognized function as a regulator of temporal dynamics of these key transcription factors. Strikingly, our Multiome data revealed expansion of the pre-memory B cells accompanied by a decrease in the proportion of pre -plasma cells upon Aridla deletion (p-value <0.001), suggesting that Aridla loss favors GC exit towards the memory cell fate. To validate our findings, we performed immunophenotyping and observed an increase in memory B cells in cKO / WT mice (p=0.019), and a decrease in long-lived plasma cells (p=0.012) upon NP-KLH immunization. Furthermore, we observed that the absence of ARID 1 A tilts GC cell-fate towards immature IgM+CD80-PDL2- memory B cells (p=0.0016), known for their potential to re-enter new GCs.

[0350] Linking this critical role of ARID 1 A in chromatin regulation to lymphomagenesis, we observed a reduction in overall survival for mice carrying VavP-Bcl2 Aridla+l- allele (compared to VavP-Bcl2 Aridla+l+', p=0.0087). Remarkably, we further show that FL patients with ARZD JA-inactivating mutations display an immature memory B-cell-like state with increased transformation risk to aggressive disease (p=0.0391). These observations offer mechanistic understanding into the emergence of both indolent and aggressive lymphomas in ARIDlA-mutant patients through formation of immature memory-like clonal precursors.

[0351] Based on publicly available datasets, we found BAE complex genes to be mutated at a high frequency in both EL and DLBCL (FL; >19%, DLBCL; >34%), with ARID1A mutations occurring in 15% of FLs and 12% of GCB-DLBCLs (Figure 1A). These mutations were predominantly truncating, resulting in a truncated protein lacking a critical BAF- interaction C-terminal domain, indicative of loss-of-function (Figure IB). FL and DLBCL originate from GC B cells, and we therefore investigated the effect of Aridla loss in GC formation during the humoral immune response (Figure 1C). Arz / a-floxed mice were crossed with CyJ-Cre mice to yield offspring with a conditional (cKO) Aridla deletion in GC B cells. To investigate the GC response, we immunized WT / WT (Arzz Ja+ / +;CyJCre / +), cKO / WT (Arid] a+l- ;Cy 1 Cre / +) and cKO / cKO (Arid! a-l -;C / Crc / +) animals with sheep red blood cells (SRBCs) and analyzed spleens 10 days later at the peak of the GC reaction (Figure ID). We found a gene dosage-dependent decrease in the total number of GC B cells relative to total B cells, a surprising result considering mutations in many DLBCL-tumor suppressor genes result in GC expansion. In contrast, total B cell populations and architecture were not perturbed in GCs from either genotype. GCs are composed of several major subpopulations of cells. The most abundant of these are called centroblasts (CBs) which experience proliferative bursting and somatic hypermutation. These are complemented by post-replicative centrocytes (CCs), which compete interact with T follicular helper cells for positive selection based on their B-cell receptor affinity for antigen (Mesin et al., 2016). We sought to examine the effects of Aridla deletion on CB / CC proportions and signals that trigger the GC exit. We observed skewing of GC polarity (compared to the normal 60 / 30 ratio of CB / CC) manifesting as increased proportions of proliferating CB vs. CC, in an Aridla dose-dependent manner (Figure IE). These observations imply a substantial dysfunction especially at the CC stage, which could potentially account for the overall reduced number of GC B-cells. We hypothesized that this reduction could be attributed to an accelerated GC exit and differentiation. To investigate this, we performed a more detailed molecular characterization of these cells.

[0352] Example 3 - ARID 1 A loss leads to widespread chromatin repression and decrease in active nucleosome turnover

[0353] GC cells are characterized by high chromatin plasticity and an open chromatin structure, which allow for their high proliferation rate and rapid cell-fate transitions (Doane et al., 2021). We therefore aimed to determine if this plasticity is regulated by the BAF complex. We performed the assay for transposase-accessible chromatin with sequencing (ATAC-seq) on sorted mouse CB and CC from our mouse model (WT / WT, cKO / WT, cKO / cKO) 10 days post-SRBC immunization. Ranking all ATAC peaks (Figure 2A) showed extensive loss of chromatin accessibility, with cKO / WT, cKO / cKO cells showing dose- dependent chromatin closing (Figure 2A and 2B). The upper section of Figure 2A shows gain of signal flanking ATAC peak midpoints, with midpoint accessibility being mostly unchanged. We posited that these signals reflect gain of positioned nucleosomes. Indeed, this signal was no longer observed when plotting only those ATAC fragments shorter than 120bp, thus excluding nucleosomal length reads, but still conserving many shorter reads that correspond to TF binding sites at peak midpoints (Figure 12A). Importantly, heterozygous (cKO / WT) profiles yielded a partial reduction of accessibility at the same regions affected (more severely) by homozygous loss of Aridla (cKO / cKO), indicating loss of function in cells heterozygous for Aridla. Previous research has demonstrated the functionality of cBAF complex preferentially at distal regions (enhancers), while the PBAF complex tends to exhibits its function at promoters (Pan et al., 2019). We found that distal regions were more affected than promoter regions, consistent with the cBAF function (Figure 2C).

[0354] To determine potential relevance for human disease we profiled human AR1D1A- deleted lymphoma cells. We identified a DLBCL cell line (RIVA / RI-1) derived from a patient with a nonsense heterozygous Q474* ARID 1 A mutation, which we verified via PCR amplicon sequencing. A knock-in CRISPR approach was used to repair the AR1D1A mutation, and we selected clones with the mutation repaired (WT / WT) as well as control clones that underwent clonal selection but did not repair the allele (Q474* / WT; Figure 12B). Notably, ARID 1 A protein expression was rescued upon CRISPR-editing in WT / WT cells compared to parental Q474* / WT cells as shown via immunoblot (Figure 12B). In conjunction with our Q474* / WT and WT / WT, an inducible short hairpin (sh) knockdown construct within the miR-30 backbone, carrying the GFP reporter, was integrated into the Q474* / WT cells (sh-KD) to further suppress ARID 1 A levels beyond Q474* / WT levels (Figure 2D). Efficiency of the knockdown was robust, exhibiting high inducibility (>90%) and negligible cassette expression pre-induction (<0.5%: Figure 12E and 12F). Concurrent ATAC-seq on human cell line cells (Q474* / WT, WT / WT, sh-KD) showed a gene-dose dependent decrease in chromatin accessibility upon ARID1A loss (Figure 2E and 2F). Consistent with the findings in mice, we observed a higher degree of chromatin compaction at non-promoter regions compared to promoters int the ARID 1 A loss-of-function lymphoma cells (Figure 2G).

[0355] Considering the substrate for the BAF complex is a nucleosome, we aimed to investigate the impact of ARID 1 A deficiency on nucleosome occupancy and positioning. Given the requirement for a large number of cells in nucleosome profiling methods, we carried out this procedure using our lymphoma cell line model. Nuclei from Q474* / WT DLBCL cells exhibited increased resistance to Micrococcal nuclease digestion (MNase; treatment with 3U of enzyme), indicating a state of highly compact chromatin upon ARID 1 A inactivation (Figure 2H). We subsequently examined whether nucleosome occupancy was altered at ATAC peaks. We observed increased nucleosome occupancy over the summit of ATAC peaks, indicating invasion of nucleosomes over the binding site upon loss of ARID1A (Figure 21 and J). Importantly, when we used higher nuclease concentration (5U) to match the WT / WT digestion levels (Figure 2H), the increase in nucleosome occupancy was still evident, suggesting that chromatin compaction occurs even on the local genomic level (Figure 21 and J).

[0356] Chromatin remodelers are recognized for their role in regulating well-positioned nucleosome placement adjacent to DNA binding motifs. To ascertain whether the increase in nucleosome occupancy was accompanied by a decrease in well-defined nucleosome positions, we assessed nucleosome fuzziness on a genome-wide scale (Chen et al., 2013). Nucleosome fuzziness refers to the variation in nucleosome positions (within each nucleosome peak) from the most preferred nucleosome position. As a measure of this variation, we utilized the standard deviation of the positions of reads in each peak. We observed that Q474* / WT cells showed an increase in nucleosome fuzziness score specifically at nucleosomes that were highly positioned (low fuzziness score) in WT / WT (Figure 2K). Furthermore, we observed an increase in the overall number of nucleosomes that show statistically significant increase in fuzziness (n=l,093,285 for Q474* / WT and WT / WT n=678,040; Figure 2L), indicating loss of well-defined nucleosomes upon AR1D1A deletion.

[0357] The BAF complex has been documented to displace the PRC2 complex from chromatin with corresponding reduction in H3K27me3 histone mark (Kadoch et al., 2017; Wilson et al., 2010). We aimed to determine whether loss of ARID1A would lead to an increase in Polycomb activity on chromatin. To this aim, we profiled the genomic distribution of H3K27me3 and observed an increase in the enrichment of this mark at locations exhibiting decreased accessibility following AR1D1A deletion (Figure 2M), suggesting active chromatin repression. Given that the H3.3 histone variant is a known hallmark of active nucleosome turnover (Ahmad & Henikoflf, 2002; Dion et al., 2007; Tagami et al., 2004) we aimed to discern if AR1D1A deletion prevents incorporation of nucleosomes into chromatin. By profiling H3.3 across the genome, we observed a marked decrease in nucleosome turnover at locations that lose accessibility upon AR1D1A deletion (Figure 2N). These results show the importance of ARID 1 A in maintaining chromatin accessibility and proper nucleosome patterning in B cells, with its loss leading to chromatin closing, disruptions in nucleosome positioning and loss of active nucleosome turnover in B-cells.

[0358] Example 4 - ARID1A enables chromatin opening for PU.l and NF-kB factors during germinal center B-cell transitions

[0359] Aridla loss of function caused skewing of the GC reaction towards CBs and away from CCs (Figure ID). This led us to determine whether a primary role of ARID 1 A is to enable the GC transition (CB to CC) through its effect on chromatin accessibility. Unsupervised analysis of CB and CC ATAC-seq profiles revealed that their primary difference is driven by genotypes, followed by cell types (Manhattan Distance SD>0.5; Figure 3A). Similarly, Principal Component Analysis (PCA) showed the strongest separation to be based on genotypes (PCI, 38% variance) and then on cell type (PC2, 18%; Figure 3B), suggesting that the loss of Aridla induces even more substantial chromatin reorganization than the CB to CC transition. We next performed RNA-seq on the same sorted CBs and CCs from all three genotypes to investigate the relationship between chromatin accessibility and gene expression. We observed a highly significant correlation between changes in chromatin accessibility and expression of nearby genes (nearest gene + / - 5kB), linking the effect of ARID1A on chromatin to gene expression (Figure 3C).

[0360] Changes in chromatin accessibility are thought to be dependent on the recruitment of the BAF complex by specific transcription factors. We first aimed to identify transcription factors that have a role in opening chromatin during the GC transition (CB to CC). Using a TF motif accessibility regulatory potential model controlling for genomic covariates (Doane et al., 2021), we discovered strong enrichment for DNA motifs of known BAF-interacting TFs (Centore et al., 2020), notably PU.l and NF-kB (Figure 3D), indicating a critical role of BAF in GC chromatin dynamics. To determine whether A rid 1 a deficiency impairs accessibility at these sites, we examined the accessibility regulatory potential in Aridla WT / cKO vs. WT / WT GC B-cells, among which PU.l was the most perturbed, followed by NF-kB factors and other TFs (Figure 3E). Importantly, there was no discernible change in expression of the Spil (PU.l) gene when comparing cKO / WT to WT / WT in either CBs or CCs (Figure 13A). This was also true for NF-kB (including NFkBl, NFkB2, Rel, and Rela) (Figure 13B). We therefore concluded that the loss of accessibility at binding sites for PU.l and NF-kB is likely due to the decrease in chromatin binding capacity in the absence of chromatin remodeling, rather than being a consequence of regulation of gene expression.

[0361] In order to further investigate the impact of Aridla haploinsufficiency on chromatin dynamics during the transition from CB to CC, we classified differential peaks upon Aridla deletion based on whether they showed opening or closing in the WT CB to CC transition. This categorization revealed four distinct clusters that change accessibility in relation to Aridla loss (Figure 3F). The highest proportion of peaks that closed upon Aridla loss (cluster 4) were those expected to open in CCs. Additional closing peaks corresponded to peaks that typically exhibit stable accessibility in CCs (cluster 2), or peaks that were already undergoing closing in CCs (cluster 3). Notably, opening accessibility peaks upon Aridla deletion do not have a function in the CB to CC transition (cluster 1 ; not changing in WT from CB to CC). A large number of genes that showed increased accessibility in WT CCs are known to play a critical role in the exit from the GC reaction (Ise et al., 2018). We therefore performed hypergeometric enrichment analysis to determine functions of genes linked to each cluster. We noted that cluster 4 was most consistently linked to canonical CC / GC exit programs, including genes induced by the CD40, NF-kB signaling, STAT3, IL2, IL4, IL6 and Notch pathways, as well as IRF4 and other plasma cell differentiation signatures (as compared to the other clusters; Figure 3G).

[0362] We directed our further analyses towards the cKO / WT condition to align with the heterozygous genetic alteration observed in human FL and DLBCLs. We performed motif analysis to identify enriched TFs within the four clusters, providing insights into the potential mechanisms underlying Aridl a haploinsufficient chromatin changes. Notably, motifs for PU.l (and the closely related SPIB) were enriched across all cKO / WT closing peaks (Figure 3H). Conversely, NF-kB motifs exhibited enrichment specifically in cluster 4, which corresponds to peaks expected to be opening in CCs but that fail to do so in cKO / WT mice. Other TFs such as API and STAT factors also showed this pattern (Figure 3H). This pointed to a potential hierarchy of TFs controlled by ARID1A. Since PU.l and NF-kB were consistent hits in our data, we used the TOBIAS footprinting analysis (Bentsen et al., 2020) to directly link chromatin accessibility loss to the change in DNA binding. We observed significant reduction in binding score for both factors, indicating loss of both chromatin accessibility and protein binding (Figure 31). Accordingly, there was significant enrichment for the PU.l knockout gene expression signature derived from CD40L-activated PU.l-cKO B cells (Willis et al., 2017) in our Aridla heterozygous GC B cells (Figure 3J). Thus, the binding sites of PU.l seemed to be perturbed across all Aridla WT / cKO closing peaks, however, the disruption of NF-kB motifs was primarily observed among those specific peaks that usually open in CCs but fail to do so in the Aridla haplo-insufficient state. This implies a potential hierarchy among Arid la-associated TFs in GC B-cells.

[0363] Example 5 - PU.l and NF-kB factors require ARID1A for binding and transcriptional activation of cytokine-induced genes in lymphoma cells

[0364] To extend the scope of our discoveries to human lymphoma cells and further investigate the impact of ARID 1 A deletion on TF binding, we analyzed chromatin features of our RIVA / RI-1 DLBCL cells, which serve as an isogenic model. Unsupervised hierarchical clustering of AT AC profiles showed clear separation between samples that was consistent with their genotypes (Figure 4A). Similar to the observations in mouse GC cells, RNA-seq analysis performed in DLBCL cells corroborated the association between differential gene expression and chromatin accessibility (Figure 4B). Genes associated with closing peaks were enriched for CD40, NF-kB, IRF4, interleukin / STAT induced genes, among others, similar to what we observed in mouse GC B cells (Figure 4C). Examining TF regulatory potential, we observed PU.l and NF-kB motifs as the most perturbed TF motifs, which occurred in a dose dependent manner in Q474* / WT and sh-KD cells (Figure 4D). TOBIAS analysis likewise showed a reduction in PU.l and NF-kB footprinting score suggesting loss of DNA binding for these factors (Figure 4E).

[0365] Having confirmed remarkably consistent results between human DLBCL cells and mouse GC B cells, we next evaluated whether sites losing accessibility were direct targets of the canonical BAF complex. For this we first profiled genomic binding of ARID1A (cBAF), PBRM1 (PBAF), SMARCA4 (cBAF, PBAF and ncBAF), and BRD9 (ncBAF) in isogenic Q474* / WT and WT / WT DLBCL cells. As expected, the majority of ARID 1 A peaks were a subset of SMARCA4 peaks, as were PBRM1 and BRD9 peaks (Figure 4F). This is consistent with the known overlapping and distinct distributions of BAF complexes and BAF sub-complexes bound to chromatin (Mashtalir et al., 2018; Pan et al., 2019) and defines which of these regions are bound by cBAF. 47.0% of sites with significant reduction in chromatin accessibility in Q474* / WT DLBCL cells were direct ARID 1 A target genes, in contrast to 14.2% among sites gaining accessibility (Figure 4G). Moreover, we observed reduced ARID 1 A binding across AT AC peaks closing in Q474* / WT, but no discernible changes in PBRM1, consistent with these effects being cBAF-specific (Figure 4H). No detectable signal was observed from the IgG control profile (Figure 4H).

[0366] To test whether loss of accessibility at PU.l motifs corresponds to actual loss of PU.l occupancy in Q474* / WT compared to WT / WT cells, we profiled genomic binding of PU.l (CUT&RUN) in cells with these genotypes. 50% of the PU.l specific peaks contained canonical PU.l / SpiB motifs (Figure 14B). Notably, we observed significant loss of PU.l binding at sites with impaired accessibility in Q474* / WT cells (Figure 41), further demonstrating that PU.l requires ARID 1 A for binding. Among PU.l binding sites in DLBCL cells we found a significant enrichment for PU.l direct binding sites in human primary GCB cells (Figure 4J). Furthermore, we observed enrichment for CD40 response and IRF4 (both linked to NF-kB), as well as cytokine / interleukin target gene sets (Figure 4J). Consistent with results shown earlier (Figure 2M), regions with loss of ARID 1 A and PU.l tended to gain nearby H3K27me3 and lose H3.3 incorporation (Figure 4K).

[0367] To characterize and integrate the functional impact of ARID 1 A on different chromatin states in an unbiased manner, we generated computational models that cluster genomic regions based on chromatin accessibility, H3K27me3, H3K27ac patterns, as well as their variability between genotypes. This approach yielded 16 clusters with substantial numbers of peaks and specific chromatin features (histone marks and size of regions; Figures 4L, 4M and 14A). We focused on those involving non-promoter regions since the bulk of ARID 1 A effects are at non-promoter elements (Figure 2C and 2G).

[0368] Clusters showing a reduction in both chromatin accessibility and H3K27ac (Cl-3, 8, 11) displayed transcriptional downregulation (Figure 4N) and the most significant ARID1A binding loss (Figure 4L and 40). To a lesser extent, these regions showed reduced loading of pan-BAF subunits SMARCA4 and SMARCC1, consistent with the requirement for ARID 1 A in formation of the functional cBAF complex (Mashtalir et al., 2018) and its correct genomic targeting (Pan et al., 2019) (Figure 40). Consistent with the notion that our phenotype is driven by loss of cBAF, we observed no reduction in PBAF (PBRM1) loading at these loci. Interestingly, we observed a reduction in ncBAF (BRD9) (Figure 40). Considering that ncBAF predominantly binds at cBAF sites in our DLBCL cells (Figure 4F), this suggests a likely dependency of ncBAF activity on cBAF, corroborating their known coregulation of enhancers in embryonic stem cells (Gatchalian et al., 2018). Concurrent with cBAF loss of binding, we observed reduction of nucleosome turnover (as measured by H3.3 abundance) and PU.l loading (Figure 40). Among annotated DNA elements, clusters with loss of AT AC and cBAF were primarily linked to GC B cell enhancers and super-enhancers (Figure 4P). Moreover, these clusters also contained peaks with either PU.l and NF-kB motifs, as well as regions that contained both motifs together (Figure 4Q). Taken together, we showed that ARID 1 A is required for cBAF binding, nucleosome remodeling and transcriptional activation at GC B cell enhancers and super-enhancers, most specifically at sites containing PU1, NF-kB or both PU.l and NF-kB motifs.

[0369] Example 6 - Co-dependency of PU.l and NF-kB marks the regulation of accessibility at germinal center cell-fate genes

[0370] We further aimed to elucidate the relative roles and requirement of both NF-kB and PU.l for the activation of genes implicated in GC exit. Using our RIVA / RI-1 ATAC-seq, we discovered a substantial co-occurrence of chromatin accessibility peaks with both NF-kB and PU.l DNA motifs in our RIVA / RI-1 DLBCL cells (Figure 5A). Remarkably, ranking differential ATAC-seq peaks based on their chromatin accessibility upon AR1D1A deletion showed that the most pronounced chromatin closing occurs at peaks containing both PU.1 and NF-kB motifs, vs. peaks containing either factor alone (Figure 5A). The reduction in chromatin accessibility of peaks with both motifs was greater than for NF-kB-only peaks, with NES scores of -2.0 and -1.32, respectively (GSEA scores for both sets is significant with p<0.001). This suggests that PU.l presence at these loci might facilitate binding of NF-kB factors.

[0371] Next, we aimed to identify the pathways enriched in proximity to ARIDlA-dependent chromatin accessibility peaks with both PU.l and NF-kB motifs. We found that CD40 signaling, NF-kB target genes (in B cells) were most significantly enriched among peaks encompassing both NF-kB and PU.l DNA motifs, compared with peaks containing either PU.l or NF-kB motifs exclusively (Figure 5B). Furthermore, our findings unveiled that certain pathways traditionally associated with NF-kB signaling (such as AKT1 and IGF1) were solely dependent on NF-kB DNA motifs, with no evident reliance on PU.l, suggesting that the functional requirement for both factors is not universal. In contrast, cytokine signaling pathways known to be linked to PU.l function (Batista et al., 2017; DeKoter et al., 2002) including IL2 and IL4, were specifically enriched for PU.l motifs (Figure 5B). These results suggest that ARID 1 A is required for cooperative effects between NF-kB and PU.l in the activation of specific subset of genes involved in transcriptional programming of GC exit, and that functional requirement for both factors is not universal. Example 7 - PU.l is required for ARIDlA-dependent NF-kB -mediated transcriptional activation

[0372] To assess the requirement of ARID 1 A for the PU.l and NF-kB-driven gene expression, we took advantage of a previously characterized Raji lymphoma cell lines containing a stably integrated (and hence chromatinized) NF-kB-response element reporter (Xia et al., 2022). We used CRISPR to knock-in the Q474* / WT mutation (the mutation found in RIVA / RI-1 cells) into the ARID1A locus (Figure 15A). Notably, Q474* / WT manifested a significant (average of 5-fold) reduction in luciferase expressed by the NF-KB reporter as compared to WT / WT cells (Figure 5C).

[0373] The reporter also contained PU.1 DNA motifs in proximity to the NF-kB response element in our reporter. To determine the extent to which PU.l binding was necessary for NF-kB activity we used CRISPR to disrupt the strongest PU.l motif, which was separated from the NF-kB response element by half of nucleosomal length (75bp). We ensured that the CRISPR-mediated deletions of the PU.1 motif were confined to the region of the motif and did not affect the NF-kB response element (Figure 15B). 48h after CRISPR modification, we observed a significant >3-fold reduction in luciferase expression (Figure 5D), showing requirement of PU.1 DNA binding motif for NF-kB -mediated gene activation.

[0374] Given that ARID 1 A deficiency impaired opening of NF-kB target genes in CC in vivo (Figure 3G) we tested whether in an analogous manner, signaling induced activation of the NF-KB reporter was similarly impaired. Stimulating the same Raji cells with PMA / IO (phorbol 12-myristate 13-acetate / ionomycin) induced the expected increase in NF-kB reporter activity. However, this effect was strongly and significantly impaired in Raji cells with PU.l-motif disruption (>4-fold decrease; Figure 5D). These findings provide evidence for the involvement of PU.l in chromatin opening of NF-kB elements through the recruitment of ARID 1 A, facilitating nucleosome remodeling and NF-kB binding.

[0375] If this mode of co-regulation is mediated by nucleosome remodeling, it would require the presence of both PU.1 and NF-kB motifs in close proximity within the genome. To investigate this, we examined chromatin accessibility peaks containing both PU.l and NF-kB motifs (Figure 5E). When peaks were arranged according to the distance separating the two motifs from the AT AC peak summit, we noted a substantial and significant decrease in accessibility at peaks where motifs were within 75 base pairs of one another, compared to peaks that featured a greater distance between motifs (Figure 5E and 5F). Collectively these data suggest that ARID 1 A nucleosome remodeling is especially required at closely positioned PU.l and NF-kB binding sites associated with genes activated in CC involved in controlling GC exit. Moreover, we demonstrate that the activation of NF-kB target genes relies on PU.l, but not the other way around, indicating that the ability of PU.l to open chromatin might precede and facilitate the subsequent binding of NF-kB (Figure 5G).

[0376] Example 8 - ARID1A deficiency drives transcriptional programming and cell-fate decision toward a pre-memory B-cell state

[0377] To determine the functional impact of ARID 1 A loss in CC transcriptional programming, we performed combined single-cell RNA and AT AC (Multiome) on sorted GC B cells from cKO / WT, cKO / cKO or WT / WT mice, 10 days post-SRBC immunization. We applied the Uniform Manifold Approximation and Projection (UMAP) method for reducing dimensionality and identified cell subpopulations through Graph-based clustering and weighted nearest neighbor cells, taking both RNA expression and chromatin accessibility into account. We further annotated GC B cell clusters as CB, transitioning (from CB to CC), CC, recycling (CC to CB), pre-memory, and pre-plasma cells by transferring labels from previously published datasets (Rivas et al., 2021) (Figure 6A and Figure 16A). Using this approach, we were able to distinguish between sub-populations of GC cells that are predisposed to exit either as plasma (pre-plasma) or memory (pre-memory) B cells. Examining the distribution of cells of each genotype separately within UMAP plots suggested expansion of the pre-memory B cell populations accompanied by a decrease in the proportion of pre-plasma cells (Figure 6B). The pre-memory cluster expressed MB-associated genes and signatures (such as Cd38, Klf2 and pre-memory signature; Figure 6C).

[0378] To further explore how Aridla loss of function might alter the timely progression of transcriptional programs during the GC reaction, we generated pseudo-time trajectories using Slingshot (Street et al., 2018), focusing on the pseudo-time trajectory that leads from CB to the pre-memory B cell fate (Figure 6D). Plotting cell density for each genotype along the pseudo-time axis, we noted a considerable and significant (p<2.2xl0‘16) shift in distribution favoring pre-memory B cells in cKO / WT vs. WT / WT mice (Figure 6E). Of note, a shift towards pre-memory transcriptional programming was noted across all GC B cell populations, consistent with the ATAC-seq data shown earlier where Aridla deletion resulted in similar chromatin accessibility profiles in CBs and CCs (Figures 6F and 6G). Furthermore, eliminating the pre-memory population from the pseudo-time trajectory analysis and focusing on CB to CC transition allowed us to confirm the skew towards CB in cKO / WT cells, consistent with our phenotype (Figure 16B). These data suggest that Aridla loss of function favors GC exit towards the MB cell fate at the expense of the plasma cell fate.

[0379] Example 9 - ARID1A enables sequential and cooperative function of PU.l and NF-kB in germinal center B cells

[0380] To determine if these shifts in population are linked to impaired PU.1 and NF-kB function on chromatin, we assigned single cell ATAC-seq profiles to populations characterized by single cell RNA-seq using shared barcodes from our Multiome profiling. To determine the activity of transcription factors in populations we identified, we applied ChromVAR (Schep et al., 2017), an algorithm that determines the variation in chromatin accessibility across different sets of genomic regions in single cell ATAC data and correlates this variation to TF motifs. Next, we calculated single-cell motif accessibility scores for these transcription factors along the pseudo-time axis in both Aridla cKO / WT and WT / WT cells. These calculations helped determine the progression paths of the cell populations, as depicted in Figure 6D. This analysis revealed significant and sustained loss of PU.l motif accessibility starting from the early CB stage in cKO / WT mice (Figure 6H and 61). In contrast, significant loss of accessibility at NF-kB motifs developed later in the trajectory as cells began to transition towards the CC state. Collectively these data suggest a hierarchy between these factors whereby loss of PU.1 binding leads to subsequent impaired NF-kB function, especially at sites where motifs for these TFs are clustered together. This is consistent with our proposed model (Figure 5G). This sequence suggests a previously unrecognized role for ARID 1 A as a regulator of temporal dynamics of these key GC B cell transcription factors.

[0381] Example 10 - ARID1A deletion promotes memory B-cell transcriptional programming in lymphoma cells

[0382] To determine whether ARID 1 A effects on pre-memory B cell programming also occurs in DLBCL cells, we determined differential expression of canonical memory B cell genes in our isogenic DLBCL cell lines. This analysis revealed robust and significant upregulation of canonical memory B cell genes, such as CD27, CD38. FCRL5. KLF2. and CEACAM21 , including BACH2 and FFFFEX, master regulators of the MB transcriptional program (Figure 6J and 6K) (Laidlaw & Cyster, 2021). We also noted upregulation of MAP2K5, which induces expression of KLF2, a transcription factor linked to the activation of the MB program (Bhattacharya et al., 2007; Laidlaw et al., 2020). Reciprocally, we observed reduced expression of the canonical GC B cell TF BCL6, another hallmark of MB formation (Ise et al., 2018; Kuo et al., 2007; Laidlaw et al., 2017). These findings were further substantiated by our observation of reduced TOBIAS BCL6 footprinting indicating loss of BCL6 binding to chromatin and increased KLF2 footprinting indicating gain of binding (Figure 6L). Finally, a GSVA analysis showed that correction of Q474* / WT to WT / WT restored GC transcriptional program, back from the MB signature (Figure 6M). The fact that the pre-memory B cell program remains enriched in ARID 1 A mutant DLBCL cells is suggestive of its relevance to the process of malignant transformation.

[0383] Example 11 - Aridla deletion enhances germinal center production of immature memory B cells

[0384] Considering our data suggested that ARID 1 A deficiency reprograms GC B cells towards the pre-memory B cell fate, we next investigated the biological implications of these findings in vivo. It has been proposed that FLs develop from continuous re-entries of prelymphoma cells (potentially MB cells) into new GCs, leading to successive rounds of mutation (Milpied et al., 2021). To further explore this, we isolated spleens from mice at 10, 16, and 21 days post- immunization, representing both GC peak activity and resolution period of GC (Figure 17A). As noted earlier, GC B cells (B220+CD138-FAS+CD38-) were reduced in abundance in cKO / WT mice (data not shown). In contrast, we observed an increase in the cell abundance of MB-containing population (B220+CD138-FAS- CD38+IgD-) throughout the entire time course (Figure 17B, 17C and 17F). This significant increase persisted even when measured relative to the GC B cells, which are presumed to be their origin (Figure 17C). A significant number of B cells entering the GC reaction, which typically begin as IgM+, undergo class switch recombination, a mechanism that involves selecting specific immunoglobulin heavy chain genes to structure their B-cell receptors (BCRs; Laidlaw & Cyster, 2021). Intriguingly, Aridla haploinsufficient mice produced a higher proportion of IgM+ MB cells, representing non class-switched, potentially less mature MB cells (Figure 17D).

[0385] In order to determine whether cKO / WT GC B cells form MB cells more readily than their WT counterparts, we conducted mixed chimera experiments, wherein Cd45.2;Aridla+ / ' (cKO) or Cd45.1 / 2;Aridla+ / +(WT) bone marrow cells were transplanted into lethally irradiated syngeneic recipients. We performed transplants using several cKO / WT : WT / WT cell ratios, specifically at 3:1, 1:1, and 1:3 (Figure 7A). To control for variability in starting cell numbers, we normalized the cell counts to the respective naive B cell compartment for each mouse (B220+CD138-IgD+CD45.1+ / CD45.2+; Figure 7B). In accordance with our previous observations, cKO / WT GC B cells displayed a significant GC disadvantage compared to competing WT / WT GC B cells, irrespective of the initial ratio of transferred cells (Figure 7C). In marked contrast, we witnessed a competitive advantage of cKO / WT mice in forming MB cells, a trend that persisted regardless of the initial ratio of transferred cells (Figure 7D). The magnitude of this cKO / WT MB cell advantage was further accentuated when directly compared to their respective GC B cell populations (Figure 7E), suggesting that reduction in GC B cells may largely be due to preferential exit to the MB cell fate.

[0386] MB cells can arise both from GCs as well as extrafollicular populations (Elsner & Shlomchik, 2020). To more precisely characterize the MB cell compartment and determine whether the observed MB cell competitive advantage was directly a consequence of exiting GC B cells, we immunized mice with NP-KLH. This antigen allows tracking emergence of MB cells that have undergone affinity maturation for this antigen in the GC reaction. We examined NP-specific MB cell formation at d5, dl l, d50 (Figure 7F). The longer timepoints was included to determine whether the memory compartment remained altered in cKO / WT mice, in the absence of ongoing GC formation. First, we confirmed that GC B cells were less abundant, whereas the MB -containing B cell compartment was increased (B220+CD138-IgD-FAS+CD38-) in cKO / WT animals compared to WT / WT, at all timepoints (Figure 17G). We next specifically measure NP-specific post-GC MB cell populations, which have developed high affinity for the NP antigen used for immunization. Among the NP+MB cells, we distinguished between those that were IgGl+vs. IgM+, since previous reports suggested that IgM+cells are less mature and could re-enter new GCs (I. Dogan et al., 2009; Pape et al., 2011; Zuccarino-Catania et al., 2014). These analyses revealed a remarkable and dynamic distinction between genotypes. Specifically, cKO / WT mice formed more NP+IgGl+MBs at the earliest time point, but preferentially and significantly generated more NP+IgM+MBs at the mid-GC, and more importantly in the long-term MB cells (Figure 7G). These data suggests that cKO / WT mice may generate more long-lived MBs competent to re-enter new, subsequent GC reactions. Along these lines, MB cells are now known to be quite phenotypically and biologically heterogeneous. For example, MB cells with a CD8CFPDL2- (double negative) immunophenotype are generally IgM+and are prone to re-enter GC reactions (Zuccarino- Catania et al., 2014). In contrast, CD80+PDL2+(double positive) MB cells are generally IgG+, cannot re-enter GCs, and tend to differentiate into plasma cells upon antigenic recall. In this context it was notable that cKO / WT antigen experienced GC-derived MB cells (B220+CD138-IgD-FAS+CD38-NP+) featured significantly increased abundance of long- lived CD80-PDL2- MBs as compared to WT cells (Figure 7H). Furthermore, we observed a corresponding decrease in CD80+PDL2+ MBs, aligning with the hypothesis that ARID 1 A deletion yields MBs with a predisposition to repopulate GC upon recall. Notably, we observed a decrease in bone marrow long lived plasma cells (LLPC) at this late time point in cKO / WT as compared to WT / WT mice (Figure 17H), indicating that Aridla deletion specifically drives GC exit towards long-lived MB cells, to the detriment of the long-lived PC compartment.

[0387] Example 12 - ARID1A is a haplo-insufficient tumor-suppressor in mouse germinal center B cells

[0388] To determine whether ARID 1 A loss of function contributes to lymphomagenesis we crossed our Aridla conditional knockout mice with Be 12 transgenic animals (Egle et al., 2004). This scenario reflects the common co-occurrence of AR1D1A mutations and BCL2 translocations in FL and GCB-DLBCL (Wright et al., 2020). We generated cohorts of WT / WT ( / Crc), cKO / WT (Aridlaflox / +; C / Cre), Bcl2;AridlWT / WT(VavP-Bcl2). and Bcl2;AridlacKO / WT(Aridlaflox / +; CylCrsVavP-Bcl2) mice (n=30 mice per genotype), which were immunized once with SRBC to initiate GCs and monitored for disease onset (Figure 8A). Four mice per cohort were sacrificed at an early timepoint prior to malignant transformation to assess B cell phenotypes by flow cytometry in splenic tissues. These studies showed a significantly increased abundance of GC B cells in Bcl2;AridlacKO / WTmice vs. Bcl2;AridlWT / WT, the latter of which are known for exhibiting substantial GC expansion (Figure 8B). Haploinsufficiency of Aridla further altered the phenotype, leading to a more pronounced bias of GC B cells towards the CB population (Figure 8C). This finding aligns with our prior observations in the context of an Aridla-only deletion (in the absence of BCL2; Figure IE). The remaining mice were followed to assess onset of fatal disease. Long-term followup of these mice revealed significantly shorter survival for Bcl2;AridlaWT / WTvs. Bcl2;AridlacKO / WT(p=0.0087 median survival 273d in Bcl2;AridlacKO / WTcompared to 324d in BCL2;AridlaWT / WT) as well as vs. WT / WT and cKO / WT; Figure 8D). Bcl2;AridlacKO / WTmice also manifested greater spleen size vs. Bcl2;AridlaWT / WT(Figure 8E), indicative of greater disease burden. Histologic analysis revealed a more advanced lymphoma phenotype in Bcl2;AridlacKO / WTmice. Bcl2;AridlacKO / WTlymphomas featured large and dysplastic immunoblasts, with large, irregular nuclei and presence of mitotic figures (Figure 8F and 18A), which are similar traits as human DLBCLs. Notably, these aberrant cells were frequently found outside the follicular regions leading to marked disruption of the splenic architecture. In contrast, Bcl2;AridlaWT / WTmice displayed monotonous populations of smaller but still irregular lymphocytes, and better preservation of follicular architecture consistent with a more FL-like phenotype (Figure 8F and 18A). Collectively, these findings suggest that Aridla functions as a haploinsufficient tumor suppressor that accelerates malignant transformation in combination with BCL2 to manifest a more aggressive and proliferative lymphoma phenotype.

[0389] Example 13 - ARID1A mutations are associated with the FL memory B cell subgroup prone to transformation to DLBCL

[0390] To determine to what extent the ARID 1 A deficient phenotypes in mice are reflected in human patients, we next examined whether the ARID 1 A cKO / WT CC transcriptional signature was enriched in the RNA-seq profiles of 138 GCB-DLBCL patients with either truncating (n=13) or missense (n=4) mutations vs. 121 patients with no mutations in any BAF subunit (Schmitz et al., 2018). Using GSVA we noted that the Aridla CC signature was significantly enriched among ARID 1 A truncating (but not ARID 1 A missense) mutant cases, vs. those without BAF complex mutations (Figure 8G). This was evident despite the notable transcriptional heterogeneity of DLBCLs in humans, and the fact that other GCB-DLBCL mutations also display GC-exit blockade signatures (Mlynarczyk et al., 2019).

[0391] To investigate whether the phenotypes observed upon Aridla mutation, especially the expansion of IgM+ MB cell populations, were also evident in primary human patients, we analyzed the CyTOF profiles from a pre-existing cohort of 155 newly diagnosed FL patients (X. Wang et al., 2022). CyTOF profiles of these cases were previously shown to yield two recurring immunophenotypes: those resembling IgG+ GCB-like cells and those resembling IgM+ MB-like cells. Notably, the FLs corresponding to the IgM+ MB-type (B cluster) showed increased risk of transformation to DLBCL. Given the MB phenotype of Aridla deficient murine B cells, we aimed to determine the ARID 1 A mutation status in these patients. We determined mutation profiles of patients from this cohort with available DNA and obtained mutational profile for 83 patients in total. We identified 13 instances of ARID 1 A mutations (Figure 8H and I), nine of which were nonsense / frame- shift mutations, and four missense mutations. Two of the missense mutations (L2238Q, K1772E) were located within the BAF250_C-domain, a region crucial for interactions with the BAF complex, and one (L1080R) within the ARID DNA-binding domain, implying a detrimental effect on these mutation on the function of ARID 1 A. The analysis of these CyTOF profiles identified clusters (B and D) that we found were immediately proximal and largely overlapping with each other and were composed of FLs with MB-like signatures (Figure 8J). Notably, we observed that FLs from patients carrying AR1D1A mutations were significantly associated with the MB-like B and D clusters (Figure 8K), most of which are IgM+ (X. Wang et al., 2022). Furthermore, patients in the B / D clusters presented a significantly greater risk of FL transformation into DLBCL (Figure 8L). These observations link the IgM+ MB phenotype of Aridla deletion in murine cells and the MB phenotype in human FLs with ARID 1 A mutations. We speculate that the heightened propensity of IgM+ MB cells to re-engage in proliferative bursts within new GCs may predispose these types of FLs to develop into DLBCL.

[0392] Example 14 - ARID1A -mutant lymphoma cells show enhanced vulnerability to BAF complex inhibition

[0393] Considering that AR1D1A loss-of-function significantly impairs the function of the BAF complex, we hypothesized that the survival of ARID 1 A mutant lymphoma cells might be more dependent on the continued functionality of the remaining BAF complexes (cBAF or other). To test this, we subjected our isogenic Q474* / WT and WT / WT DLBCL cells to FHD- 286, a highly potent and selective dual SMARCA4 and SMARCA2 allosteric ATPase inhibitor that is currently in clinical trials for other tumor types (NCT04879017 and NCT04891757) and which could disrupt all residual BAF nucleosome remodeling activity in these cells. Remarkably, when our cells were treated with lOnM FHD-286, Q474* / WT were almost eliminated, while the wild-type counterparts remained largely unaffected after a period of 6 days (main effect of treatment p[Q474* vs. WT]=0.003, main effect of time and treatment interaction p[Q474* vs. WT : time]<0.0001, Figure 9A). At a ten-fold higher dose of lOOnM there was still greater effect against ARID 1 A mutant cells (main effect of treatment p[Q474* vs. WT]=0.0285, main effect of time and treatment interaction p[Q474* vs. WT : time] =0.0094), although WT cells also were eventually affected (Figure 9B). The impact of FHD-289 was primarily driven by apoptosis, which occurred at a significantly higher rate in the ARID1A mutant isogenic DLBCL cells (Figure 9C and 19A). This evidence implies that inhibitors of SMARCA4 / 2 may show significant efficacy in the treatment of DLBCL patients carrying AR1D1A mutations.

[0394] Further justification for translation of BAF inhibitors for use in B cell lymphomas was afforded by examining the effect of ARID 1 A CRISPR knockout screen in 1145 Cancer cell lines annotated in the DepMap database. Notably, lymphoid neoplasms manifested the greatest dependency on ARID 1 A (i.e. canonical BAF complex) among all tumor types (Figure 9D). Moreover, among lymphoid neoplasms, non-Hodgkin lymphomas were the most dependent subset (Figure 9E); consistent with this notion, we were unable to generate DLBCL cell lines with homozygous CRISPR knockout of ARID1 A (RIVA / RL1, OCI-LY1, OCLLY7; 438 clones screened).

[0395] To better understand the nature of this vulnerability, we performed ATAC-seq in isogenic Q474* / WT and WT / WT DLBCL cells treated with either lOOnM FHD-286 or DMSO for 24h. Hierarchical clustering and PCA distinctly separated samples first according to treatment effect and then by genotype (Figures 9F and 9G), underlying the potent effect of this drug. Notably, we noticed that both Q474* / WT and WT / WT cells exhibited chromatin closure at matching locations following treatment, however, this closure was significantly more pronounced in Q474* / WT cells (Figure 9H and 91). We then identified transcription factors that were overrepresented in closing chromatin upon FHD-286 treatment, both in Q474* / WT and WT / WT treated DLBCL cells. In both cases, PU.l was the most affected TF, followed by NF-kB (Figure 9K and 9 J). However, the effect was more profound in ARID 1 A mutant DLBCL cells, which also featured significant perturbation of additional factors. Finally, to determine whether the impairment of PU.1 might at least partially explain the greater response of ARID 1 A mutant cells to BAF inhibitors, we generated cell lines with stable integration of two different inducible shRNAs against PU.l (PU.l-KD) or a nontargeting shRNA. These cell lines were exposed to lOOnM FHD-286 or DMSO. Flow cytometry confirmed robust induction of the inducible vector and no leakiness (Figure 19B). Strikingly, PU.l-KD, but not the non-targeting control, conferred significantly greater sensitivity to FHD-286 (main effect of treatment p[PU.l-KD vs. WT]=0.0293, main effect of time and treatment interaction p[PU.l-KD vs. WT : time]=0.0296, Figure 9L, 9M and 19C), suggesting that loss of function of PU.1 is a major contributor to the impact of ARID 1 A loss of function and vulnerability to BAF inhibitors. These findings suggest that ARID 1 A mutant lymphoma cells exhibit significant vulnerability to dual SMARCA4 and SMARCA2 ATPase inhibitor, linked at least in part to the disruption of transcription factor PU.1 function, suggesting a promising role for BAF inhibitors in treating cBAF-deficient B cell lymphomas.

[0396] Our study reveals a pivotal role for ARID 1 A in the dynamics of chromatin regulation during the humoral immune response. We suggest a mechanism whereby the loss of ARID 1 A function impairs ability of PU.l to bind to chromatin, thus inhibiting the establishment of chromatin accessibility at adjacent NF-kB motifs. This reprograms GC B cells to adopt a memory-like transcriptional program, while attenuating plasma cell GC exit normally facilitated by robust NF-kB activation. The abnormal programming of ARID JA-deficient GC B cells to transition as MB cells likely diminishes their residency period within the GC reaction (hence the reduced CC abundance). This might consequently reduce their opportunities for class-switching and explain why ARID 1 A deficiency favors the production of IgM+ CD80-PDL2- MB cells, recognized for their augmented potential to re-enter new GC reactions. This paves the way for additional rounds of somatic hypermutation and lymphomagenesis, positioning these cells as putative clonal precursor cells.

[0397] TFs are known to cooperate on chromatin, as many cannot independently occupy their target sites, highlighting their interdependence in gene regulation (Iwafuchi-Doi & Zaret, 2014; Zaret & Carroll, 2011). Of these, pioneering TFs possess the unique ability to bind both nucleosomal DNA and partially exposed DNA motifs, but still require nucleosome remodeling for chromatin opening (Michael et al., 2020; Soufi et al., 2015). It has been shown that certain pioneering factors (OCT4, SOX2, API) require the BAF complex to mediate nearby nucleosome remodeling (Barisic et al., 2019; King & Klose, 2017; Wolf et al., 2023). However, our understanding of how this pioneering function and chromatin opening influences subsequent binding of non-pioneering factors is limited. Our work provides new insights into this process, demonstrating that NF-kB binding to specific target genes depends on PU.1 directed chromatin remodeling by the BAF complex. Furthermore, we show that proximity of NF-kB and PU.l motifs is crucial for this function. Recent studies indicated that PU.l can bind nucleosomal DNA even in the absence of chromatin remodeling (Frederick et al., 2023) but BAF complex is essential for spreading of chromatin accessibility through nucleosome remodeling, supporting our model of nucleosome-mediated transcription factor co-dependency.

[0398] Genome-wide studies show that majority of potential DNA-binding sites for a given TF remain unbound, raising the question of how selective motif binding might occur. We propose that PU.l, by recruiting BAF complex, selectively unmasks specific NF-kB motifs that drive GC B cell exit transcriptional programs, thus explaining how context- specific TF motif occupancy occurs. Along these lines, Barozzi et al. reported variable nucleosome occupancy at PU.l peak summits in macrophages, ranging from nucleosome-depleted PU.l binding sites to sites with tightly positioned nucleosomes flanking the PU.l motifs (Barozzi et al., 2014). They showed that sites with high nucleosome presence were enriched for NF-kB motifs. This observation supports our proposed model that NF-kB binding to these nucleosome-protected sites can occur only following PU.l-directed nucleosome remodeling in the GC B cell context. This sequential process unveils a role of ARID 1 A in controlling the timing and function of transcription factors critical for the function of B cells during the humoral immune response.

[0399] It remains uncertain whether this model holds true for other factors in mammalian systems. The phosphate-regulated yeast PHO5 promoter contains an array of highly positioned nucleosomes when the PHO5 gene is transcriptionally inactive (Svaren & Hdrz, 1997). In low phosphate conditions, nucleosomes are evicted from the promoter to expose binding sites for transcription factors (Bryant et al., 2008) by a complex network of five chromatin remodeling complexes (Musladin et al., 2014). Once the binding site has been exposed, the transcriptional activator Pho4 and subsequently the TATA-box binding protein (TBP) bind the promoter and activate PHO5 expression illustrating how specific nucleosome position and occupancy inhibits transcription factor binding and downstream gene activation in yeast. We proposed that similar regulation might be occurring in highly complex mammalian systems that involve an additional layer of promoter-enhancer interaction, specifically involving pioneering (PU.l) and non-pioneering factors (NF-kB).

[0400] PU.l binds extensively to genes architecturally remodeled and activated in the GC reaction (Bunting et al., 2016). While an early study did not report any defects in GC upon PU.l knockout (Polli et al., 2005), a more recent investigation using a Cre allele activated in mature B cells revealed a decrease in GC B cells, a reduction in IgGl+ cells, and a trend towards a reduction in long-lived post-GC plasma cells (Willis et al., 2017). PU.l deficient B cells were also shown to be less responsive to CD40 and IL4 signaling and showed a downregulation of CD40 along with several other genes enriched in the ARID 1 A signature (Willis et al., 2017) (also refer to Figure 3J). These observations notably mirror our findings, thereby supporting the idea that a compromised PU.l functionality is a key factor in our observed GC Aridla haploinsufficient phenotype. Moreover, we extend these findings by detailing the implications of these alterations on lymphoma progression.

[0401] Among terminally differentiated cells, GC B cells stand out due to their exceptional phenotypic plasticity, which allows them to transform into a diverse range of memory B cells. Moreover, they are even capable of changing lineages to adopt the dramatically different transcriptional, chromatin, and 3D architectural characteristics of plasma cells. Our findings suggest a crucial role for ARID 1 A in steering GC B cells through these decisions by promoting PU.l -dependent, NFkB-driven chromatin and transcriptional programming towards the plasma cell phenotype. In addition to this loss-of-function effect, ARID 1 A haploinsufficiency induces a gain of memory B cell programming state in GC B cells, which could further influence their ultimate trajectories. It is suggested that commitment to the memory B cell pathway primarily occurs among intermediate antigen affinity GC B cells. These cells may experience BCR activation through interaction with follicular dendritic cells (FDCs), but may receive insufficient CD40 co-stimulatory signaling from follicular helper T (TFH) cells to engage in plasma cell differentiation (Figure 8N) (Laidlaw & Cyster, 2021).

[0402] Our hypothesis is substantiated by the marked decrease in CD40 signature gene expression, potentially linked to impaired NF-kB functions. We put forth that a combination of diminished NF-kB-driven plasma cell potential and enhanced MB cell programming contributes to curtailing the expansion of Aridla deficient GCs, by facilitating the early exit of GC B cells towards the MB cell fate. Importantly, MB cells bear high resemblance to naive B cells concerning chromatin accessibility and transcriptional attributes (Doane et al., 2021; Vilarrasa-Blasi et al., 2021). Consequently, the transition from the GC to MB cell fate could be perceived as a partial reversal to the original mature resting B cell condition. Alternatively, the MB gene expression profiles might result from an undiscovered influence of BAF haploinsufficiency on other transcription factors. Lastly, it's worth noting that different BAF complexes might govern distinct GC B cell fates, as the pan-BAF loss of function triggered by SMARCA4 haploinsufficiency has shown a notably distinct phenotype with GC hyperplasia and heightened light zone to dark zone re-cycling (see accompanying article).

[0403] MB cells have been proposed as likely clonal precursor cells for FL and other lymphomas, have been extensively studied (Milpied et al., 2021). IgM+ MB cells in particular were described as potential normal equivalents to various IgM+ lymphoma subtypes (Klein et al., 1997), and quiescent clonal interfollicular memory-like cells were suggested to represent tumor reservoirs linked to FL therapy resistance (A. Dogan et al., 1998). BCL2 translocated t( 14; 18) positive B cells have been identified in normal donors (Limpens et al., 1995), and were shown to derive from GCs and often have an IgM+IgD+CD27+MB-like phenotype (Roulland et al., 2006). In mice, IgM+ and IgG+ memory B cells follow unique paths, with the former exhibiting a lower somatic hypermutation load and the ability to enter new GCs, whereas the latter were more mutated, did not re-enter the GC reaction, and mainly differentiated into plasma cells upon immune challenge (I. Dogan et al., 2009; Pape et al., 2011). Similarly, in humans, memory B cells with a CD80-PDL2- phenotype were found to be less mutated and more likely to re-enter new GC reactions, whereas CD80+PDL2+MBs primarily formed plasma cells (Zuccarino- Catania et al., 2014).

[0404] Finally, Sungalee and colleagues demonstrated through mouse models that B cells overexpressing BCL2, imitating the t( 14; 18) translocation, predominantly displayed an IgM+ phenotype, preferentially underwent GC cyclic re-entry. Additionally, these mice exhibited an incremental expansion and a gradual increase in the somatic hypermutation of both GC and MB populations. These findings collectively support the notion that IgM+ memory B cells could constitute at least one form of clonal precursor pool for follicular lymphoma. Nevertheless, the specific mechanisms by which GCs may preferentially produce such cells remain a significant unresolved question within the field. Our research provides insight into this crucial matter, demonstrating that a partial loss of ARID 1 A function biases GC outcomes towards the production of IgM+CD80-PDL2- memory B cells, and away from IgG+ or CD80+PDL2+ memory B cells.

[0405] Our findings indicate that ARIDlA-deficient MB cells display a competitive advantage over their WT counterparts when placed in the same microenvironment, and are generated at a higher rate. The re-entry of ARIDlA-deficient IgM+CD80-PDL2- MB cells into new GCs would provide an opportunity for clonal precursor cells to accumulate additional oncogenic mutations and initiate malignant transformation. It is also worth noting that these ARIDlA-induced precursor cells differ from the proposed autoimmune B cell clonal precursor cells that contribute to many activated B-cell-like (ABC)-DLBCLs (Venturutti et al., 2023), which could potentially shed light on the underlying biology behind transcriptional and phenotypic differences between GC-like tumors and post-GC tumors. The differences between clonal precursor cells specific to various immunoglobulin isotypes may have clinical significance. Specifically, memory B-cell-like IgM+ follicular lymphomas have been demonstrated to carry a higher risk of transforming into aggressive follicular lymphomas, as opposed to the typically IgG+ germinal center-like follicular lymphomas (X. Wang et al., 2022). Discovering the mechanisms and biomarkers associated with this transformation represents a crucial unmet medical need, as patients identified as high-risk could potentially benefit from more intensive treatments or participation in clinical trials aiming to delay or prevent this often lethal progression.

[0406] In our study, we found a significant association between AR1D1A mutations and MB- cell-like FLs in an expanded cohort, with these lymphomas showing a substantial link to early transformation. Although our study is not designed to independently evaluate the statistical relationship between ARID 1 A mutations and transformation, our mechanistic evidence suggests that the development of high-risk MB -cell-like FLs is likely influenced, at least in part, by mutations in ARID 1 A. We propose that the predisposition of these lymphomas to transform might be related to their potential clonal precursor cells driven by ARID 1 A mutations, which seem more capable of re-entering the proliferative germinal center phenotype than their IgG counterparts. Future research will be needed to validate ARID 1 A as a predictive biomarker requiring early intervention in a prospective manner. Furthermore, our work highlights a potential targeted therapy approach for these high-risk patients, considering the increased sensitivity of ARID 1 A mutant cells to a SMARCA2 / 4 inhibitor, which is currently under investigation in clinical trials for other types of tumors (NCT04879017 and NCT04891757).

[0407] Example 12

[0408] ARID 1 A as a biomarker could be used by determining the mutation status of ARID 1 A gene in FL patients using precision medicine genotyping methods for diagnosis. Furthermore, a Flow cytometry method could be used to determine the intracellular levels of ARID 1 A protein as a diagnostic marker. There are available antibodies to stain for ARID 1 A protein levels that can be developed into a biomarker tool: https: / / www.thermofisher.com / antibody / product / ARIDlA-Antibody-Polyclonal / PA5-52146; https: / / www.thermofisher.com / antibody / product / ARIDlA-Antibody-Polyclonal / PA5-85568; https: / / www.abcam.com / products / primary-antibodies / aridla-antibody-cl3595- ab242377.html. FHD-286 or AU-15330 could be used in a clinical trial for FL patients with ARID1A mutations to prevent the onset of transformation to DLBLC as well as to treat DLBCL patients with ARID 1 A mutation. We also have negative data (not shown here) of treatment with other inhibitors of different BAF complexes that does not show any vulnerability of

[0409] ARID 1 A mutated cells. The inhibitors are:

[0410] Additional disclosure can be found in the attached abstract, slide deck, and manuscript, and data that are also relevant to the present disclosure are included as follows.

[0411] IC50 for both the FHD-286 inhibitor and AU-15330 degrader showing vulnerability of ARID 1 A mutated lymphoma cells lines to both (FIGs. 19A-19D).

[0412] In vivo data showing higher tumor growth inhibition of ARID 1 A mutated tumors compared to ARID 1 A WT tumors (FIG. 20).

[0413] In vivo data showing animals with ARID 1 A mutation have a better prognosis after treatment with FHD-286 compared to ARID 1 A WT animals (FIG. 21).

[0414] Drug treatment (FHD-286) of a different lymphoma cell line (Burkitt lymphoma), showing that the same vulnerability of ARID 1 A mutated tumor cells to treatment is observed in other tumor types (FIG. 22).

[0415] References:

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Claims

What is claimed is:

1. A method of assessing whether a subject afflicted with follicular lymphoma (FL) is at risk for transforming into diffuse large B-cell lymphoma (DLBCL), the method comprising determining:(1) the presence or absence of a loss-of-function mutation of ARID 1 A in a biological sample from the subject; and / or(2) the level and / or activity of ARID 1 A in a biological sample from the subject, wherein the presence of a loss-of-function mutation of ARID 1 A in the biological sample, and / or a significantly decreased level and / or activity of ARID 1 A in the biological sample relative to a control indicates that the subject afflicted with FL is at risk for transforming into DLBCL, and wherein the absence of a loss-of-function mutation of ARID 1 A in the biological sample, and / or a significantly increased level and / or activity of ARID 1 A in the biological sample relative to a control indicates that the subject afflicted with FL is not at risk for transforming into DLBCL.

2. The method of claim 1, comprising obtaining the biological sample from the subject for the determination step.

3. The method of claim 1 or 2, wherein the method comprises determining the presence or absence of a loss-of-function mutation of ARID 1 A in a biological sample from the subject.

4. The method of claim 3, wherein the loss-of-function mutation of ARID 1 A results in a reduction or loss of ARID 1 A expression and / or activity.

5. The method of claim 3 or 4, wherein the loss-of-function mutation of ARID1A is a homozygous mutation or a heterozygous mutation.

6. The method of any one of claims 3-5, wherein the loss-of-function mutation of ARID 1 A is a deletion, an insertion, or a substitution.

7. The method of any one of claims 3-6, wherein the loss-of-function mutation of ARID 1 A is a nonsense mutation or a missense mutation.

8. The method of any one of claims 3-7, wherein the loss-of-function mutation of ARID 1 A is Q474*.

9. The method of any one of claims 3-8, wherein the subject further comprises overexpression of translocation of BCL2.

10. The method of any one of claims 3-9, wherein genomic DNA isolated from the biological sample is used for the determination of the presence of absence of the loss-of- function mutation in ARID 1 A.

11. The method of any one of claims 3-10, wherein the loss-of-function mutation of ARID 1 A is detected by a genotyping method.

12. The method of any one of claims 1-11, wherein the method comprises determining the level and / or activity of ARID 1 A in a biological sample from the subject and comparing it to a control.

13. The method of claim 12, wherein the control is a reference value.

14. The method of claim 12 or 13, wherein the control is a level and / or activity determined from a control sample.

15. The method of claim 14, wherein the control sample is a non-tumor sample obtained from the subject, a sample from a subject without FL, a sample from a subject without DLBCL, or a sample from a subject with FL but not at risk of transforming into DLBCL.

16. The method of any one of claims 12-15, wherein the level of ARID 1 A is detected by western blot or flow cytometry.

17. The method of claim 16, wherein the western blot or flow cytometry uses a commercially available ARID 1 A antibody.

18. The method of any one of claims 1-17, wherein the biological sample is a tumor cell or tissue.

19. The method of any one of claims 1-18, further comprising administering to the subject an inhibitor of SMARCA4 and / or SMARCA2 if the subject afflicted with FL is at risk for transforming into DLBCL.

20. The method of claim 19, wherein the inhibitor of SMARCA4 and / or SMARCA2 is FHD-286 or AU-15330.

21. A method of treating a subject afflicted with DLBLC and having a loss-of-function mutation of ARID 1 A, comprising administering to the subject a therapeutically effective amount of an inhibitor of SMARCA4 and / or SMARCA2.

22. A method of preventing a subject afflicted with FL and having a loss-of-function mutation of ARID 1 A from transforming into DLBCL, comprising administering to the subject a therapeutically effective amount of an inhibitor of SMARCA4 and / or SMARCA2.

23. The method of claim 21 or 22, wherein the loss-of-function mutation of ARID1A results in a reduction or loss of ARID 1 A expression and / or activity.

24. The method of any one of claims 21-23, wherein the loss-of-function mutation of ARID 1 A is a homozygous mutation or a heterozygous mutation.

25. The method of any one of claims 21-24, wherein the loss-of-function mutation of ARID 1 A is a deletion, an insertion, or a substitution.

26. The method of any one of claims 21-25, wherein the loss-of-function mutation of ARID 1 A is a nonsense mutation or a missense mutation.

27. The method of any one of claims 21-26, wherein the loss-of-function mutation of ARID1A is Q474*.

28. The method of any one of claims 21-27, wherein the subject further comprises overexpression of translocation of BCL2.

29. The method of any one of claims 21-28, wherein the inhibitor of SMARCA4 and / or SMARCA2 is FHD-286 or AU- 15330.

30. The method of any one of claims 21-29, further comprising identifying the subject afflicted with FL or DLBCL that has a loss-of-function mutation of ARID 1 A prior to the treatment.

31. A method of identifying the likelihood of a FL or a DLBCL in a subject to respond to an inhibitor of SMARCA 4 and / or SMARCA2, the method comprising determining:(1) the presence or absence of a loss-of-function mutation of ARID 1 A in a biological sample from the subject; and / or(2) the level and / or activity of ARID 1 A in a biological sample from the subject, wherein the presence of a loss-of-function mutation of ARID 1 A in the biological sample, and / or a significantly decreased level and / or activity of ARID 1 A in the biological sample relative to the control identifies the FL or DLBCL as being more likely to respond to the inhibitor of SMARCA 4 and / or SMARCA2, and wherein the absence of a loss-of-function mutation of ARID 1 A in the biological sample, and / or a significantly increased level and / or activity of ARID 1 A in the biological sample relative to a control identifies the FL or DLBCL as being less likely to respond to the inhibitor of SMARCA 4 and / or SMARCA2.

32. The method of claim 31, comprising obtaining the biological sample from the subject for the determination step.

33. The method of claim 31 or 32, wherein the method comprises determining the presence or absence of a loss-of-function mutation of ARID 1 A in a biological sample from the subject.

34. The method of claim 33, wherein the loss-of-function mutation of ARID1A results in a reduction or loss of ARID 1 A expression and / or activity.

35. The method of claim 33 or 34, wherein the loss-of-function mutation of ARID1A is a homozygous mutation or a heterozygous mutation.

36. The method of any one of claims 33-35, wherein the loss-of-function mutation of ARID 1 A is a deletion, an insertion, or a substitution.

37. The method of any one of claims 33-36, wherein the loss-of-function mutation of ARID 1 A is a nonsense mutation or a missense mutation.

38. The method of any one of claims 33-37, wherein the loss-of-function mutation of ARIDlA is Q474*.

39. The method of any one of claims 33-38, wherein the subject further comprises overexpression of translocation of BCL2.

40. The method of any one of claims 33-39, wherein genomic DNA isolated from the biological sample is used for the determination of the presence of absence of the loss-of- function mutation in ARID 1 A.

41. The method of any one of claims 33-40, wherein the loss-of-function mutation of ARID 1 A is detected by a genotyping method.

42. The method of any one of claims 31-41, wherein the method comprises determining the level and / or activity of ARID 1 A in a biological sample from the subject and comparing it to a control.

43. The method of claim 42, wherein the control is a reference value.

44. The method of claim 42 or 43, wherein the control is a level and / or activity determined from a control sample.

45. The method of claim 44, wherein the control sample is a non-tumor sample obtained from the subject, a sample from a subject without FL, a sample from a subject without DLBCL, a sample from a subject with FL but not at risk of transforming into DLBCL, or a sample from a subject with FL or DLBCL and a known outcome of the treatment of the inhibitor of SMARCA4 and / or SMARCA2.

46. The method of any one of claims 42-45, wherein the level of ARID 1 A is detected by western blot or flow cytometry.

47. The method of claim 46, wherein the western blot or flow cytometry uses a commercially available ARID 1 A antibody.

48. The method of any one of claims 31-47, wherein the biological sample is a tumor cell or tissue.

49. The method of any one of claims 31-48, further comprising administering to the subject the inhibitor of SMARCA4 and / or SMARCA2 if the subject afflicted with FL or DLBCL is likely to respond to the inhibitor of SMARCA4 and / or SMARCA2.

50. The method of claim 49, wherein the inhibitor of SMARCA4 and / or SMARCA2 is FHD-286 or AU-15330.

51. The method of any one of claims 1-50, wherein the subject is a mammal.

52. The method of any one of claims 1-51, wherein the mammal is a mouse or a human.

53. The method of any one of claims 1-52, wherein the mammal is a human.

54. A method of killing or inhibiting proliferation of a lymphoma cell having a loss-of- function mutation of ARID 1 A, comprising contacting the lymphoma cell with an inhibitor of SMARCA4 and / or SMARCA2.

55. The method of claim 54, wherein the inhibitor of SMARCA4 and / or SMARCA2 is FHD-286 or AU-15330.

56. The method of claim 54 or 55, wherein the method kills or inhibits proliferation of the lymphoma cell in vitro or in vivo.

57. The method of any one of claims 54-56, wherein the lymphoma cell is from a B-cell lymphoma.

58. The method of claim 57, wherein the B-cell lymphoma is selected from FL, DLBCL, or Burkitt lymphoma.

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