Research group guide

Michelle Monje Lab

Michelle Monje

Research at the intersection of neurodevelopment, glioma biology, neural activity, tumor microenvironment signaling, cancer neuroscience, and mechanism-guided therapies.

Official research website ↗

This independent educational primer introduces scientific concepts relevant to research themes in the Michelle Monje Lab at Stanford University. It is not an official Stanford University or Michelle Monje Lab course and does not imply endorsement or affiliation.

Questions behind the work

Research questions

01

How does neuronal activity influence glioma growth?

02

How do developing neural and glial programs become hijacked by malignant cells?

03

Can glioma cells integrate functionally into neural circuits?

04

How do different neurotransmitter systems regulate brain tumors?

05

Can neural-circuit mechanisms reveal therapeutic vulnerabilities?

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Learn this research

Key concepts

Activity-dependent glioma growth and NLGN3

Interpret activity-dependent growth and conditional NLGN3 dependence.

Cancer as clonal evolution

Explain tumor change through clonal variation and selection.

Cancer-neuroscience translation and treatment-related neural effects

Evaluate preclinical findings, retrospective associations, and neural treatment effects.

Correlation, perturbation, and causal mechanism

Combine controlled perturbations and recordings to strengthen causal claims.

Diffuse midline glioma and developmental cell-state biology

Relate diffuse midline glioma to developmental cell states.

Enabling/emerging characteristics in modern Hallmarks taxonomy

Keep enabling characteristics and proposed new dimensions distinct from core capabilities.

Experimental methods for cancer neuroscience

Match experimental methods to structure, activity, and cell-state questions.

Functional neuron-to-glioma synapses

Separate functional synaptic evidence from cell identity.

Glutamatergic, GABAergic, cholinergic, and electrical tumor signaling

Distinguish transmitter mechanisms and tumor-specific electrical responses.

H3K27 alteration, anatomy, and treatment difficulty

Distinguish H3K27-altered midline tumors and the pontine DIPG clinical label.

Hallmark capabilities governing proliferation and survival

Distinguish hallmark capabilities from individual genes.

Immune, stromal, vascular, neural, and extracellular components

Distinguish general carcinoma stroma from the brain tumor microenvironment.

Invasion, metabolism, immune evasion, and plasticity

Relate invasion, metabolic change, immune escape, and plasticity to cancer behavior.

Mechanism-guided therapy and GD2 CAR T

Distinguish early GD2 CAR T activity from established clinical efficacy.

NLGN3-CSPG4/mechanotransduction and developmental-state hijacking

Explain NLGN3-CSPG4 mechanotransduction within tested models.

Oncogenes, tumor suppressors, and tumor heterogeneity

Distinguish oncogenes, tumor suppressors, and heterogeneous cell states.

Replicative immortality and tumor vascularization

Explain telomere maintenance and inducing or accessing blood vessels.

Tumor microenvironment as an ecosystem

Describe a tumor as interacting malignant and nonmalignant populations.

Important papers

Cancer Discov · 2022

Hallmarks of Cancer: New Dimensions

Hanahan D

Why this matters: Distinguishes eight core capabilities from proposed plasticity and other new dimensions in the 2022 framework.

DOI: 10.1158/2159-8290.CD-21-1059

Independent project ideas inspired by this research

Projects you could do

Educational ideas using public or synthetic data. These projects are not offered or supervised by the lab or research group.

Intermediate

Analyze public glioma single-cell states

Computational simulation or public-data/literature analysis

Compare OPC-like and other expression programs in a public dataset without inferring universal origins.

Background
Complete the relevant primer lessons, Basic Python and data-analysis skills
Data
{"Public data accompanying Filbin 2018; no private patient records."}
Output
A cell-state analysis with donor-aware validation and limitations.

Independent learning idea, not offered or supervised by the lab. Use public de-identified data or synthetic simulations only; no animal, human, or wet-lab intervention protocols and no clinical recommendations.

Intermediate

Map hallmark capabilities to glioma expression signatures

Computational simulation or public-data/literature analysis

Build a version-labeled literature and expression map distinguishing core, enabling, and proposed dimensions.

Background
Complete the relevant primer lessons, Basic Python and data-analysis skills
Data
{"Hanahan 2022 framework and public glioma transcriptomic datasets."}
Output
A taxonomy matrix and expression analysis that does not treat RNA as functional proof.

Independent learning idea, not offered or supervised by the lab. Use public de-identified data or synthetic simulations only; no animal, human, or wet-lab intervention protocols and no clinical recommendations.

Intermediate

Model neuron-to-glioma excitatory signaling

Computational simulation or public-data/literature analysis

Use a toy receptor-current model to compare transient synaptic and slower depolarizing inputs.

Background
Complete the relevant primer lessons, Basic Python and data-analysis skills
Data
{"Venkatesh 2019 and Taylor 2023 source documentation; synthetic signals."}
Output
Parameter-sensitivity simulations with explicit nonclinical scope.

Independent learning idea, not offered or supervised by the lab. Use public de-identified data or synthetic simulations only; no animal, human, or wet-lab intervention protocols and no clinical recommendations.

Intermediate

Compare glutamatergic and GABAergic tumor signaling

Computational simulation or public-data/literature analysis

Use literature and a chloride-gradient toy model to explain cell-dependent current direction.

Background
Complete the relevant primer lessons, Basic Python and data-analysis skills
Data
{"Venkatesh 2019 and Barron 2025 papers; synthetic ion gradients."}
Output
A mechanistic comparison of evidence, current direction, and tumor context.

Independent learning idea, not offered or supervised by the lab. Use public de-identified data or synthetic simulations only; no animal, human, or wet-lab intervention protocols and no clinical recommendations.

Intermediate

Analyze public tumor-microenvironment cell types

Computational simulation or public-data/literature analysis

Compare annotated public tumor datasets while separating neural, immune, vascular, and stromal states.

Background
Complete the relevant primer lessons, Basic Python and data-analysis skills
Data
{"Public single-cell datasets and the approved m1-4 source map."}
Output
A cell-composition report distinguishing brain tumors from general carcinomas.

Independent learning idea, not offered or supervised by the lab. Use public de-identified data or synthetic simulations only; no animal, human, or wet-lab intervention protocols and no clinical recommendations.

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