Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms.

Most human Transcription factors (TFs) genes encode multiple protein isoforms differing in DNA binding domains, effector domains, or other protein regions. The global extent to which this results in functional differences between isoforms remains unknown. Here, we systematically compared 693 isoforms of 246 TF genes, assessing DNA binding, protein binding, transcriptional activation, subcellular localization, and condensate formation. Relative to reference isoforms, two-thirds of alternative TF isoforms exhibit differences in one or more molecular activities, which often could not be predicted from sequence. We observed two primary categories of alternative TF isoforms: "rewirers" and "negative regulators", both of which were associated with differentiation and cancer. Our results support a model wherein the relative expression levels of, and interactions involving, TF isoforms add an understudied layer of complexity to gene regulatory networks, demonstrating the importance of isoform-aware characterization of TF functions and providing a rich resource for further studies.

Splicing neoantigen discovery with SNAF reveals shared targets for cancer immunotherapy.

Immunotherapy has emerged as a crucial strategy to combat cancer by "reprogramming" a patient's own immune system. Although immunotherapy is typically reserved for patients with a high mutational burden, neoantigens produced from posttranscriptional regulation may provide an untapped reservoir of common immunogenic targets for new targeted therapies. To comprehensively define tumor-specific and likely immunogenic neoantigens from patient RNA-Seq, we developed Splicing Neo Antigen Finder (SNAF), an easy-to-use and open-source computational workflow to predict splicing-derived immunogenic MHC-bound peptides (T cell antigen) and unannotated transmembrane proteins with altered extracellular epitopes (B cell antigen). This workflow uses a highly accurate deep learning strategy for immunogenicity prediction (DeepImmuno) in conjunction with new algorithms to rank the tumor specificity of neoantigens (BayesTS) and to predict regulators of mis-splicing (RNA-SPRINT). T cell antigens from SNAF were frequently evidenced as HLA-presented peptides from mass spectrometry (MS) and predict response to immunotherapy in melanoma. Splicing neoantigen burden was attributed to coordinated splicing factor dysregulation. Shared splicing neoantigens were found in up to 90% of patients with melanoma, correlated to overall survival in multiple cancer cohorts, induced T cell reactivity, and were characterized by distinct cells of origin and amino acid preferences. In addition to T cell neoantigens, our B cell focused pipeline (SNAF-B) identified a new class of tumor-specific extracellular neoepitopes, which we termed ExNeoEpitopes. ExNeoEpitope full-length mRNA predictions were tumor specific and were validated using long-read isoform sequencing and in vitro transmembrane localization assays. Therefore, our systematic identification of splicing neoantigens revealed potential shared targets for therapy in heterogeneous cancers.

Quantifying tumor specificity using Bayesian probabilistic modeling for drug target discovery and prioritization.

In diseases such as cancer, the design of new therapeutic strategies requires extensive, costly, and unfortunately sometimes deadly testing to reveal life threatening "off target" effects. A crucial first step in predicting toxicity are analyses of normal RNA and protein tissue expression, which are now possible using comprehensive molecular tissue atlases. However, no standardized approaches exist for target prioritization, which instead rely on ad-hoc thresholds and manual inspection. Such issues are compounded, given that genomic and proteomic data detection sensitivity and accuracy are often problematic. Thus, quantifiable probabilistic scores for tumor specificity that address these challenges could enable the creation of new predictive models for combinatorial drug design and correlative analyses. Here, we propose a Bayesian Tumor Specificity (BayesTS) score that can naturally account for multiple independent forms of molecular evidence derving from both RNA-Seq and protein expression while preserving the uncertainty of the inference. We applied BayesTS to 24,905 human protein-coding genes across 3,644 normal samples (GTEx and TCGA) spanning 63 tissues. These analyses demonstrate the ability of BayesTS to accurately incorporate protein, RNA and tissue distribution evidence, while effectively capturing the uncertainty of these inferences. This approach prioritized well-established drug targets, while deemphasizing those which were later found to induce toxicity. BayesTS allows for the adjustment of tissue importance weights for tissues of interest, such as reproductive and physiologically dispensable tissues (e.g., tonsil, appendix), enabling clinically translatable prioritizations. Our results show that BayesTS can facilitate novel drug target discovery and can be easily generalized to unconventional molecular targets, such as splicing neoantigens. We provide the code and inferred tumor specificity predictions as a database available online (https://github.com/frankligy/BayesTS). We envision that the widespread adoption of BayesTS will facilitate improved target prioritization for oncology drug development, ultimately leading to the discovery of more effective and safer drugs.

Retinoid X receptor promotes hematopoietic stem cell fitness and quiescence and preserves hematopoietic homeostasis.

Hematopoietic stem cells (HSCs) balance self-renewal and differentiation to maintain hematopoietic fitness throughout life. In steady-state conditions, HSC exhaustion is prevented by the maintenance of most HSCs in a quiescent state, with cells entering the cell cycle only occasionally. HSC quiescence is regulated by retinoid and fatty-acid ligands of transcriptional factors of the nuclear retinoid X receptor (RXR) family. Herein, we show that dual deficiency for hematopoietic RXRα and RXRß induces HSC exhaustion, myeloid cell/megakaryocyte differentiation, and myeloproliferative-like disease. RXRα and RXRß maintain HSC quiescence, survival, and chromatin compaction; moreover, transcriptome changes in RXRα;RXRß-deficient HSCs include premature acquisition of an aging-like HSC signature, MYC pathway upregulation, and RNA intron retention. Fitness loss and associated RNA transcriptome and splicing alterations in RXRα;RXRß-deficient HSCs are prevented by Myc haploinsufficiency. Our study reveals the critical importance of RXRs for the maintenance of HSC fitness and their protection from premature aging.

Prevalence of Homologous Recombination Pathway Gene Mutations in Melanoma: Rationale for a New Targeted Therapeutic Approach.

Homologous recombination DNA damage repair (HR-DDR) deficient patients with various solid tumors have been treated with PARP inhibitors. However, the clinical characteristics of patients with melanoma who have HR-DDR gene mutations and the consequences of PARP inhibition are poorly understood. We compared the commercially available next-generation sequencing data from 84 patients with melanomas from our institution with a dataset of 1,986 patients as well as 1,088 patients profiled in cBioportal. In total, 21.4% of patients had ≥1 functional HR-DDR mutation, most commonly involving BRCA1, ARID1A, ATM, ATR, and FANCA. Concurrent NF1, BRAF, and NRAS mutations were found in 39%, 39%, and 22% of cases, respectively. HR-DDR gene mutation was associated with high tumor mutational burden and clinical response to checkpoint blockade. A higher prevalence of HR-DDR mutations was observed in the datasets from Foundation Medicine (Cambridge, CA) and those from the Cancer Genome Atlas. Treatment of HR-DDR‒mutated patient-derived xenograft models of melanoma with PARP inhibitor produced significant antitumor activity in vivo and was associated with increased apoptotic activity. RNA sequencing analysis of PARP inhibitor-treated tumors indicated alterations in the pathways involving extracellular matrix remodeling, cell adhesion, and cell-cycle progression. Melanomas with HR-DDR mutations represent a unique subset, which is more likely to benefit from checkpoint blockade and may be targeted with PARP inhibitor.

CTC1-STN1 terminates telomerase while STN1-TEN1 enables C-strand synthesis during telomere replication in colon cancer cells.

Telomerase elongates the telomeric G-strand to prevent telomere shortening through conventional DNA replication. However, synthesis of the complementary C-strand by DNA polymerase α is also required to maintain telomere length. Polymerase α cannot perform this role without the ssDNA binding complex CST (CTC1-STN1-TEN1). Here we describe the roles of individual CST subunits in telomerase regulation and G-overhang maturation in human colon cancer cells. We show that CTC1-STN1 limits telomerase action to prevent G-overhang overextension. CTC1-/- cells exhibit telomeric DNA damage and growth arrest due to overhang elongation whereas TEN1-/- cells do not. However, TEN1 is essential for C-strand synthesis and TEN1-/- cells exhibit progressive telomere shortening. DNA binding analysis indicates that CTC1-STN1 retains affinity for ssDNA but TEN1 stabilizes binding. We propose CTC1-STN1 binding is sufficient to terminate telomerase action but altered DNA binding dynamics renders CTC1-STN1 unable to properly engage polymerase α on the overhang for C-strand synthesis.