Biology subjects

Sears, T. J.

Publications and source records attributed to Sears, T. J..

3 recordsLinked to original sources

bioRxiv2025

Translating clinical gene sequencing into a foundational representation of tumor subtype

While gene sequencing is routine in cancer care, translating sequences into treatment decisions remains a challenge. Here we introduce MutationProjector, an AI foundation model that transforms tumor mutation profiles into a compact representation of cancer subtype, with broad implications for diagnosis and therapy. MutationProjector is pre-trained by integrating genomic alterations from >30,000 tumors with extensive molecular knowledge, yielding a model that accurately reconstructs held-out genetic profiles (demonstrating strong generalization) and determines subtype representations from altered molecular pathways (enabling model interpretability). We evaluate MutationProjector in independent tasks related to prediction of immunotherapy response, prediction of chemotherapy response, and classification of metastasis, recording leading performance in all areas. Each task identifies key biomarkers of interest, including KMT2A and KRAS-STK11 alterations which govern immunotherapy response.

systems biology↗

bioRxiv2025

NeoPrecis: Enhancing Immunotherapy Response Prediction through Integration of Qualified Immunogenicity and Clonality-Aware Neoantigen Landscapes

Despite the transformative impact of cancer immunotherapy, the need for improved patient stratification remains critical due to suboptimal response rates. While neoantigens are central to anti-tumor immunity, current metrics like tumor mutation burden are limited by their neglect of immunogenicity and tumor heterogeneity. We present NeoPrecis, a computational framework designed to refine neoantigen characterization across MHC-I and MHC-II pathways and integrate tumor clonality to improve immunotherapy response prediction. NeoPrecis features an interpretable T-cell recognition model that reveals the critical influence of MHC molecules on TCR recognition beyond mere antigen presentation. Benefit HLA alleles identified through model-driven contribution analysis exhibit significant predictive power for patient outcomes in immune checkpoint inhibitor treatment (melanoma: p-value = 0.04; NSCLC: p-value = 0.01). Applying NeoPrecis to immunotherapy-treated tumors, we show the clonality-aware neoantigen landscape improves response prediction in melanoma and heterogeneous NSCLC, achieving 11% and 20% improvement of AUROC compared to TMB respectively. Heterogeneous NSCLCs, more common among never smokers, retain more subclonal neoantigens due to lower immunoediting pressure, where NeoPrecis better captures the varying prevalence of neoantigens. We propose NeoPrecis as a more comprehensive evaluative framework for neoantigen assessment by incorporating both immunogenicity and tumor clonality, offering insights into the link between collective quality of neoantigen landscapes and immunotherapy response.

bioinformatics↗

bioRxiv2024

Integrated germline and somatic features reveal divergent immune pathways driving ICB response

Immune Checkpoint Blockade (ICB) has revolutionized cancer treatment, however mechanisms determining patient response remain poorly understood. Here we used machine learning to predict ICB response from germline and somatic biomarkers and interpreted the learned model to uncover putative mechanisms driving superior outcomes. Patients with higher T follicular helper infiltrates were robust to defects in the class-I Major Histocompatibility Complex (MHC-I). Further investigation uncovered different ICB responses in MHC-I versus MHC-II neoantigen reliant tumors across patients. Despite similar response rates, MHC-II reliant responses were associated with significantly longer durable clinical benefit (Discovery: Median OS=63.6 vs. 34.5 months P=0.0074; Validation: Median OS=37.5 vs. 33.1 months, P=0.040). Characteristics of the tumor immune microenvironment reflected MHC neoantigen reliance, and analysis of immune checkpoints revealed LAG3 as a potential target in MHC-II but not MHC-I reliant responses. This study highlights the value of interpretable machine learning models in elucidating the biological basis of therapy responses. Statement of SignificanceImmune checkpoint blockade works only in a fraction of patients for reasons that are still not fully understood. Our study reveals heterogeneity in the immune responses of ICB responders that correlates with characteristics of the neoantigen landscape. This heterogeneity is accompanied by differences in the duration of clinical benefit as well as by differences as to which immune checkpoint gene serves as a biomarker of ICB response. These findings suggest possible new strategies for improving ICB responses. HighlightsO_LIWe used machine learning to study ICB response across 708 patients from 8 studies across 3 tumor types (melanoma, RCC, and NSCLC). C_LIO_LICombining germline and somatic features improves prediction of ICB response C_LIO_LIInteractions between germline and somatic features reveal mechanisms contributing to ICB sensitivity. C_LIO_LIMHC-I vs. MHC-II reliance implicates LAG3 as a prognostic biomarker in the context of CD4 T cell driven responses. C_LIO_LIMHC-II neoantigen reliant responses provide superior durable clinical benefit in response to ICB. C_LI

cancer biology↗