Ying Ni Laboratory

Research

Overview

Why do individuals carrying the same inherited cancer-predisposition variant develop very different diseases, at different ages, or sometimes no cancer at all?

The Ni Lab investigates the molecular factors that shape this variability and translates those discoveries into strategies for precision cancer prevention and treatment. Our research sits at the intersection of hereditary cancer genetics, immunology, metabolism, multi-omics, computational biology, and clinical data science.

We integrate deeply phenotyped patient cohorts and biospecimens with germline and tumor genomics, transcriptomics, metabolomics, immune profiling, single-cell and spatial technologies, and longitudinal clinical data. In addition to studying each molecular layer in isolation, we use systems-level and computational approaches to identify interactions among inherited variation, metabolism, immune state, tumor biology, and environmental or clinical factors.

Our long-term goal is to move hereditary cancer care beyond gene-based risk estimates toward individualized prediction: identifying who is at greatest risk, when that risk emerges, which biological processes drive it, and how those processes can be targeted for prevention, early detection, or treatment.

Hereditary Cancer Predisposition & Precision Prevention

Inherited pathogenic variants establish cancer susceptibility, but genotype alone often does not explain the substantial variation in cancer penetrance, age of onset, tumor spectrum, or treatment outcome observed among carriers.

Our laboratory investigates genetic and molecular modifiers of hereditary cancer risk using family-based cohorts, population-scale genomic resources, electronic health records, tumor sequencing, and multi-omic profiling. Our work has identified and characterized susceptibility genes and modifiers across Cowden and Cowden-like syndromes, PTEN-related disorders, thyroid cancer, melanoma, and broader cancer-predisposition populations.

We are particularly interested in converting these discoveries into precision prevention. Instead of applying uniform surveillance to everyone with a particular germline variant, we aim to develop integrated models incorporating genomic, metabolic, immune, and clinical information to identify individuals at highest risk and ultimately inform the timing and intensity of screening and preventive interventions.

Population-scale resources, including All of Us, complement our clinically ascertained hereditary cancer cohorts and enable us to study penetrance, phenotype spectrum, underdiagnosis, ancestry, and modifier effects in less selected populations.

PTEN Hamartoma Tumor Syndrome: A Model of Phenotypic Divergence

PTEN hamartoma tumor syndrome (PHTS) is a central focus of our research program and provides a unique model for understanding how a single germline cancer-predisposition gene can produce remarkably heterogeneous outcomes.

Individuals with germline PTEN variants may develop breast, thyroid, endometrial, renal, colorectal, and other cancers, while others develop neurodevelopmental phenotypes or remain cancer-free for many years. Our laboratory asks why these divergent clinical trajectories occur.

We integrate whole-genome sequencing, transcriptomics, metabolomics, immune profiling, cell-free DNA, clinical phenotypes, and computational modeling to identify modifiers of cancer and neurodevelopmental outcomes. Our studies have demonstrated roles for additional germline variation, metabolic states, mitochondrial biology, copy-number variation, and circulating molecular features in shaping PHTS phenotypes.

This work builds toward an integrated PTEN precision-risk framework linking: Germline genotype → molecular modifiers → immune and metabolic state → clinical phenotype → individualized surveillance and intervention.

Immunometabolism & Tumor-Immune Biology

Metabolic reprogramming and immune regulation are tightly interconnected processes in cancer. Our laboratory studies this relationship from both hereditary cancer and treatment-response perspectives.

Our early work identified germline alterations in succinate dehydrogenase genes as modifiers of cancer susceptibility and demonstrated mechanistic links among mitochondrial metabolism, altered FAD/NAD balance, oxidative stress, p53 regulation, and tumorigenesis. Subsequent studies in PTEN-associated disease identified distinct metabolic profiles associated with different clinical phenotypes.

We now extend this work to tumor immunology and immunotherapy. By integrating transcriptomics, metabolomics, immune profiling, tumor genomics, and clinical outcomes, we study the biological states associated with response and resistance to immune checkpoint inhibition. Our research has identified immune and TGF-β-associated signatures of immunotherapy outcome and has applied genome-scale metabolic modeling to infer metabolic pathways associated with treatment response.

We are also interested in interactions among the tumor microbiome, metabolism, and anti-tumor immunity. These studies reflect a broader goal of identifying metabolic and immune processes that function both as biomarkers and as potential therapeutic vulnerabilities.

Multi-Omics & Computational Systems Biology

Complex diseases such as cancer emerge from interactions among genes, cells, metabolic pathways, immune responses, tissues, and clinical exposures. No single molecular measurement captures this complexity.

Our laboratory develops and applies computational approaches for integrating:

• Whole-genome and exome sequencing
• Germline and somatic variation
• Bulk transcriptomics
• Single-cell and single-nucleus transcriptomics
• Spatial transcriptomics
• Proteomics
• Metabolomics
• Cell-free DNA
• Immune profiling
• Electronic health record and clinical outcome data

A major methodological interest is genome-scale metabolic modeling. Using transcriptome-constrained metabolic networks and flux balance analysis, we infer pathway-level metabolic activity from high-dimensional molecular data. This enables metabolic phenotyping of large patient cohorts even when direct metabolomic measurements are unavailable.

We combine these approaches with statistical genetics, machine learning, unsupervised molecular subtyping, and predictive modeling to discover biomarkers and generate clinically testable hypotheses.

Across these studies, computation is not simply an analytic endpoint. It is a bridge between molecular mechanism and clinical translation.

Precision Oncology & Cancer Data Science

Cancer genomic testing has generated an extraordinary volume of molecular information, but translating that information into treatment decisions remains challenging.

The Ni Lab works at the interface of cancer genomics and clinical data science to develop scalable approaches for precision oncology. We integrate tumor sequencing with longitudinal clinical data, treatment history, outcomes, biomarkers, and external knowledge resources to support translational research and clinical decision-making.

Our work spans cancer genomic data infrastructure, cBioPortal-based exploration, clinical cohort discovery, trial matching, computational biomarker development, and AI-supported precision oncology. These efforts allow us to study treatment response in real-world patient populations while also developing tools that make complex genomic information more accessible to clinicians and investigators.

By connecting molecular discovery with clinical informatics, our goal is to develop precision oncology approaches that can ultimately help match the right prevention, surveillance, or treatment strategy to the right patient.

Selected Publications

View publications for Ying Ni, PhD
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1. Ni Y, Zbuk KM, Sadler T, Patocs A, Lobo G, Edelman E, et al. Germline mutations and variants in the succinate dehydrogenase genes in Cowden and Cowden-like syndromes. Am J Hum Genet. 2008;83(2):261-268.

2. Ni Y, He X, Chen J, Moline J, Mester J, Orloff MS, Ringel MD, Eng C. Germline SDHx variants modify breast and thyroid cancer risks in Cowden and Cowden-like syndrome via FAD/NAD-dependent destabilization of p53. Hum Mol Genet. 2012;21(2):300-310.

3. Yehia L*, Ni Y*, Niazi F, Sesock K, Chen J, Eng C. Unexpected cancer-predisposition gene variants in Cowden syndrome and Bannayan-Riley-Ruvalcaba syndrome patients without underlying germline PTEN mutations. PLoS Genet. 2018;14(4).

4. Yehia L*, Ni Y*, Feng F, Seyfi M, Sadler T, Frazier TW, Eng C. Distinct alterations in tricarboxylic acid cycle metabolites are associated with cancer and autism phenotypes in Cowden syndrome and Bannayan-Riley-Ruvalcaba syndrome. Am J Hum Genet. 2019;105(4):813-821.

5. Ni Y, Soliman A, Joehlin-Price A, Rose PG, Vlad A, Edwards R, Mahdi H. High TGF-β signature predicts immunotherapy resistance in gynecologic cancer patients treated with immune checkpoint inhibition. NPJ Precis Oncol. 2021;5(1):101.

6. Liu D, Yehia L, Dhawan A, Ni Y, Eng C. Cell-free DNA fragmentomics and second malignant neoplasm risk in patients with PTEN hamartoma tumor syndrome. Cell Rep Med. 2024;5(2):101384.

7. Yehia L, Plitt G, Tushar AM, Liu D, Joo J, Ni Y, Patil S, Eng C. Extended spectrum of cancers in PTEN hamartoma tumor syndrome. NPJ Precis Oncol. 2025;9(1):61.

8. Idumah G*, Newell D*, Hadrys M, Ribaudo I, Ni Y#, Arbesman J#. Pathogenic germline variants in cancer susceptibility genes. JAMA. 2025;334(19):1765-1768.

9. Idumah G, Li L, Yehia L, Mahdi H, Ni Y#. Metabolic signatures of immune checkpoint inhibitor response in gynecologic cancers: insights from flux balance analysis. Comput Biol Med. 2026;200:111366.

10. Yehia L*, Li L*, Idumah G, Frazier TW, Makarov V, Bose A, et al, Ni Y#. Genomic modifiers of malignant and neurodevelopmental phenotypes in individuals with PTEN hamartoma tumor syndrome. NPJ Genom Med. 2026;11(1):25.

    *Co-first author. #Corresponding/co-corresponding author.