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Polyak, K.

Publications and source records attributed to Polyak, K..

4 recordsLinked to original sources

Design of full-length agonist and antagonist antibodies for hard targets

Therapeutic antibodies have transformed modern medicine, yet conventional discovery relies on lengthy experimental workflows, offers limited control over how antibodies engage their targets, and struggles to deliver candidates across many important target classes. Despite recent advances in computational protein design, generating full-length antibodies with desired functional activity and favorable developability profiles remains challenging. Here we present an end-to-end computational design pipeline built around Pythia-1, an all-atom generative model that reasons jointly over amino acid sequence and three-dimensional atomic structure under explicit design constraints. We generated candidates in full-length human immunoglobulin G (IgG) format across 22 therapeutic targets spanning receptor ectodomains, cytokines, growth factors, and G protein-coupled receptors (GPCRs), a particularly challenging target class for antibody discovery. Following a single computational design round, experimental screening of at most 48 candidates per target identified antibodies with single-digit nanomolar affinities for soluble proteins and single-digit nanomolar cell-binding EC50 values for GPCRs. The pipeline's ability to target specified epitopes and conformational states enabled the design of antibodies with agonist and antagonist activity. In vitro characterization demonstrated target-specific engagement and potent functional modulation, with single-digit nanomolar inhibitory potency in functional assays for a range of targets, including multiple GPCRs. To our knowledge, this study provides the first demonstration of both GPCR agonism and antagonism by de novo designed full-length IgG antibodies. We incorporated key developability and manufacturability requirements into the initial designs by conditioning generation on human germline frameworks and applying sequence constraints to limit known liabilities. The resulting antibody designs exhibited biophysical profiles comparable to or better than those of clinical-stage therapeutic controls. We provide the complete sequences of all lead candidates to support independent experimental validation and further development. Together, these findings demonstrate that our technology can deliver antibodies with programmable function and key properties expected of lead candidates, opening opportunities for first-in-class therapeutics against hard-to-drug targets.

molecular biology↗

EstroGene2.0: A multi-omic database of response to estrogens, ER-modulators, and resistance to endocrine therapies in breast cancer

Endocrine therapies targeting the estrogen receptor (ER/ESR1) are the cornerstone to treat ER-positive breast cancers patients, but resistance often limits their effectiveness. Understanding the molecular mechanisms is thus key to optimize the existing drugs and to develop new ER-modulators. Notable progress has been made although the fragmented way data is reported has reduced their potential impact. Here, we introduce EstroGene2.0, an expanded database of its precursor 1.0 version. EstroGene2.0 focusses on response and resistance to endocrine therapies in breast cancer models. Incorporating multi-omic profiling of 361 experiments from 212 studies across 28 cell lines, a user-friendly browser offers comprehensive data visualization and metadata mining capabilities (https://estrogeneii.web.app/). Taking advantage of the harmonized data collection, our follow-up meta-analysis revealed substantial diversity in response to different classes of ER-modulators including SERMs, SERDs, SERCA and LDD/PROTAC. Notably, endocrine resistant models exhibit a spectrum of transcriptomic alterations including a contra-directional shift in ER and interferon signaling, which is recapitulated clinically. Furthermore, dissecting multiple ESR1-mutant cell models revealed the different clinical relevance of genome-edited versus ectopic overexpression model engineering and identified high-confidence mutant-ER targets, such as NPY1R. These examples demonstrate how EstroGene2.0 helps investigate breast cancers response to endocrine therapies and explore resistance mechanisms.

cancer biology↗

A non-canonical EZH2 function sensitizes solid tumors to genotoxic stress

Drugs that block the activity of the methyltransferase EZH2 are in clinical development for the treatment of non-Hodgkin lymphomas harboring gain-of-function EZH2 mutations that enhance its polycomb repressive function. In contrast, in castration-resistant prostate cancer (CRPC) we have previously reported that EZH2 plays a non-canonical role as a transcriptional activator. In this setting, we now show that EZH2 inhibitors can also block the non-canonical activity of EZH2 and inhibit the growth of CRPC cells. Gene expression and epigenomic profiling of cells treated with EZH2 inhibitors demonstrated that rather than de-repressing tumor suppressor genes silenced by PRC2, EZH2 inhibitors downregulate a set of DNA repair genes that are directly regulated by EZH2. In addition, genome-wide CRISPR/Cas9-mediated loss-of-function screens in the presence of EZH2 inhibitors identified these DNA repair genes to underlie the growth-inhibitory function of these compounds. Interrogation of public data from diverse solid tumor types expressing wild-type EZH2 showed that expression of DNA damage repair genes is significantly correlated with cellular sensitivity to EZH2 inhibitors. Consistent with these findings, treatment of CRPC cells with EZH2 inhibitors dramatically enhanced their sensitivity to genotoxic stress. These studies reveal a previously unappreciated mechanism of action of EZH2 inhibitors and provide a mechanistic basis for potential new combination cancer therapies.

cancer biology↗

Intra-tumor heterogeneity defines treatment-resistant HER2+ breast tumors

Targeted therapy for patients with HER2 positive (HER2+) breast cancer has improved the overall survival, but many patients still suffer relapse and death of the disease. Intra-tumor heterogeneity of both estrogen receptor (ER) and HER2 expression has been proposed to play a key role in treatment failure, but little work has been done to comprehensively study this heterogeneity at the single-cell level.\n\nIn this study, we explored the clinical impact of intra-tumor heterogeneity of ER protein expression, HER2 protein expression, and HER2 gene copy number alterations. Using combined immunofluorescence and in situ hybridization on tissue sections followed by a validated computational approach, we analyzed more than 13,000 single tumor cells across 37 HER2+ breast tumors. The samples were taken both before and after neoadjuvant chemotherapy plus HER2-targeted treatment, enabling us to study tumor evolution as well.\n\nWe found that intra-tumor heterogeneity for HER2 copy number varied substantially between patient samples. Highly heterogeneous tumors were associated with significantly shorter disease-free survival and fewer long-term survivors. Patients for which HER2 characteristics did not change during treatment had a significantly worse outcome.\n\nThis work shows the impact of intra-tumor heterogeneity in molecular diagnostics for treatment selection in HER2+ breast cancer patients and the power of computational scoring methods to evaluate in situ molecular markers in tissue biopsies.

cancer biology↗