Prostate-VarBench: A Benchmark with Interpretable TabNet Framework for Prostate Cancer Variant Classification

Abraham Francisco Arellano Tavara, Umesh Kumar, Jathurshan Pradeepkumar, Jimeng Sun
Proceedings of the Fifth Machine Learning for Health Symposium, PMLR 297:886-897, 2026.

Abstract

Variants of Uncertain Significance ({VUS}) limit the clinical utility of prostate cancer genomics by delaying diagnosis and therapy when evidence for pathogenicity or benignity is incomplete. Progress is further limited by inconsistent annotations across sources and the absence of a prostate-specific benchmark for fair comparison. We introduce Prostate-VarBench, a curated pipeline for creating prostate-specific benchmarks that integrates {COSMIC} (somatic cancer mutations), ClinVar (expert-curated clinical variants), and {TCGA}-{PRAD} (prostate tumor genomics from The Cancer Genome Atlas) into a harmonized dataset of 193,278 variants supporting patient- or gene-aware splits to prevent data leakage. To ensure data integrity, we corrected a Variant Effect Predictor ({VEP}) issue that merged multiple transcript records, introducing ambiguity in clinical significance fields. We then standardized 56 interpretable features across eight clinically relevant tiers, including population frequency, variant type, and clinical context. AlphaMissense pathogenicity scores were incorporated to enhance missense variant classification and reduce {VUS} uncertainty. Building on this resource, we trained an interpretable TabNet model to classify variant pathogenicity, whose step-wise sparse masks provide per-case rationales consistent with molecular tumor board review practices. On the held-out test set, the model achieved 89.9% accuracy with balanced class metrics and the {VEP} correction yields an 6.5% absolute reduction in {VUS}.

Cite this Paper

BibTeX
@InProceedings{pmlr-v297-arellano-tavara26a, title = {Prostate-VarBench: A Benchmark with Interpretable TabNet Framework for Prostate Cancer Variant Classification}, author = {Arellano Tavara, Abraham Francisco and Kumar, Umesh and Pradeepkumar, Jathurshan and Sun, Jimeng}, booktitle = {Proceedings of the Fifth Machine Learning for Health Symposium}, pages = {886--897}, year = {2026}, editor = {Argaw, Peniel and Zhang, Haoran and Jabbour, Sarah and Chandak, Payal and Ji, Jerry and Mukherjee, Sumit and Salaudeen, Olawale and Chang, Trenton and Healey, Elizabeth and Gröger, Fabian and Adibi, Amin and Hegselmann, Stefan and Wild, Benjamin and Noori, Ayush}, volume = {297}, series = {Proceedings of Machine Learning Research}, month = {13--14 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v297/main/assets/arellano-tavara26a/arellano-tavara26a.pdf}, url = {https://proceedings.mlr.press/v297/arellano-tavara26a.html}, abstract = {Variants of Uncertain Significance ({VUS}) limit the clinical utility of prostate cancer genomics by delaying diagnosis and therapy when evidence for pathogenicity or benignity is incomplete. Progress is further limited by inconsistent annotations across sources and the absence of a prostate-specific benchmark for fair comparison. We introduce Prostate-VarBench, a curated pipeline for creating prostate-specific benchmarks that integrates {COSMIC} (somatic cancer mutations), ClinVar (expert-curated clinical variants), and {TCGA}-{PRAD} (prostate tumor genomics from The Cancer Genome Atlas) into a harmonized dataset of 193,278 variants supporting patient- or gene-aware splits to prevent data leakage. To ensure data integrity, we corrected a Variant Effect Predictor ({VEP}) issue that merged multiple transcript records, introducing ambiguity in clinical significance fields. We then standardized 56 interpretable features across eight clinically relevant tiers, including population frequency, variant type, and clinical context. AlphaMissense pathogenicity scores were incorporated to enhance missense variant classification and reduce {VUS} uncertainty. Building on this resource, we trained an interpretable TabNet model to classify variant pathogenicity, whose step-wise sparse masks provide per-case rationales consistent with molecular tumor board review practices. On the held-out test set, the model achieved 89.9% accuracy with balanced class metrics and the {VEP} correction yields an 6.5% absolute reduction in {VUS}.} }
Endnote
%0 Conference Paper %T Prostate-VarBench: A Benchmark with Interpretable TabNet Framework for Prostate Cancer Variant Classification %A Abraham Francisco Arellano Tavara %A Umesh Kumar %A Jathurshan Pradeepkumar %A Jimeng Sun %B Proceedings of the Fifth Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2026 %E Peniel Argaw %E Haoran Zhang %E Sarah Jabbour %E Payal Chandak %E Jerry Ji %E Sumit Mukherjee %E Olawale Salaudeen %E Trenton Chang %E Elizabeth Healey %E Fabian Gröger %E Amin Adibi %E Stefan Hegselmann %E Benjamin Wild %E Ayush Noori %F pmlr-v297-arellano-tavara26a %I PMLR %P 886--897 %U https://proceedings.mlr.press/v297/arellano-tavara26a.html %V 297 %X Variants of Uncertain Significance ({VUS}) limit the clinical utility of prostate cancer genomics by delaying diagnosis and therapy when evidence for pathogenicity or benignity is incomplete. Progress is further limited by inconsistent annotations across sources and the absence of a prostate-specific benchmark for fair comparison. We introduce Prostate-VarBench, a curated pipeline for creating prostate-specific benchmarks that integrates {COSMIC} (somatic cancer mutations), ClinVar (expert-curated clinical variants), and {TCGA}-{PRAD} (prostate tumor genomics from The Cancer Genome Atlas) into a harmonized dataset of 193,278 variants supporting patient- or gene-aware splits to prevent data leakage. To ensure data integrity, we corrected a Variant Effect Predictor ({VEP}) issue that merged multiple transcript records, introducing ambiguity in clinical significance fields. We then standardized 56 interpretable features across eight clinically relevant tiers, including population frequency, variant type, and clinical context. AlphaMissense pathogenicity scores were incorporated to enhance missense variant classification and reduce {VUS} uncertainty. Building on this resource, we trained an interpretable TabNet model to classify variant pathogenicity, whose step-wise sparse masks provide per-case rationales consistent with molecular tumor board review practices. On the held-out test set, the model achieved 89.9% accuracy with balanced class metrics and the {VEP} correction yields an 6.5% absolute reduction in {VUS}.
APA
Arellano Tavara, A.F., Kumar, U., Pradeepkumar, J. & Sun, J.. (2026). Prostate-VarBench: A Benchmark with Interpretable TabNet Framework for Prostate Cancer Variant Classification. Proceedings of the Fifth Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 297:886-897 Available from https://proceedings.mlr.press/v297/arellano-tavara26a.html.

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