learning_cases.md

Guided Learning Cases

K R&D Lab — Cancer Research Suite · Learning Sandbox Author: Oksana Kolisnyk | kosatiks-group.pp.ua Repo: github.com/TEZv/K-RnD-Lab-PHYLO-03_2026 Generated: 2026-03-07

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These 5 cases are designed for use with the 📚 Learning Sandbox tab group. All sandbox results are ⚠️ SIMULATED. Use the 🔬 Real Data Tools tabs to validate findings with live data.

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Case 1 — miRNA-Mediated Silencing of TP53 in Pan-Cancer Context

Scientific Question: Which miRNAs are predicted to suppress TP53 expression in cancer, and how does their expression change in TP53-mutant tumors?

Protocol

Step	Tab	Action
1	B1 — miRNA Explorer	Select gene: TP53. Run simulation. Note the top miRNA by binding energy (most negative kcal/mol).
2	B1 — miRNA Explorer	In the expression chart, identify which miRNAs are upregulated (positive log2FC) in TP53-mutant tumors — these are candidate oncogenic miRNAs.
3	A4 — Literature Gap Finder	Search: cancer type = GBM, keyword = miR-34a TP53. Check if the literature trend shows a gap in recent years.
4	A2 — Understudied Target Finder	Select GBM. Check if TP53 appears in the gap index table — compare its paper count vs. essentiality.
5	📓 Lab Journal	Record: top miRNA, its binding energy, expression direction, and whether a literature gap exists for this miRNA in GBM.

Expected Result

• miR-34a-5p should show the strongest binding energy (≈ −19 kcal/mol) and be downregulated (negative log2FC) in TP53-mutant tumors — consistent with miR-34a being a direct p53 transcriptional target that is lost when p53 is mutated.
• miR-25-3p and miR-504-5p should be upregulated, acting as oncogenic suppressors of wild-type p53.
• Literature gap search may reveal sparse recent publications on miR-34a in GBM specifically (vs. breast/lung cancer).

Real PubMed PMID to Read

PMID: 17554337 — He L et al. "A microRNA component of the p53 tumour suppressor network." Nature 2007. Direct link: https://pubmed.ncbi.nlm.nih.gov/17554337/

What to Write in Lab Notebook

Date: [today]
Case: miRNA-TP53 silencing
Gene: TP53
Top suppressive miRNA: miR-34a-5p (binding energy: -19.2 kcal/mol, seed: 8mer)
Top oncogenic miRNA: miR-25-3p (log2FC: +2.0 in TP53-mutant)
Literature gap: [yes/no] for miR-34a in GBM (from Tab A4)
Hypothesis: miR-34a restoration therapy may be viable in GBM with WT TP53
Next step: Check Tab A5 (Druggable Orphans) for TP53-pathway targets in GBM

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Case 2 — LNP Formulation Optimization for Brain Delivery via ApoE Corona

Scientific Question: How does PEG mol% and ionizable lipid content affect ApoE enrichment in the protein corona, and what formulation maximizes predicted brain targeting?

Protocol

Step	Tab	Action
1	B3 — LNP Corona	Set baseline: PEG = 1.5 mol%, ionizable = 50%, helper = 10%, cholesterol = 38%, size = 100 nm, serum = 10%. Run simulation. Record ApoE fraction.
2	B3 — LNP Corona	Increase PEG to 4.0 mol% (all else equal). Run again. Observe ApoE fraction change.
3	B3 — LNP Corona	Return PEG to 1.5 mol%. Increase particle size to 200 nm. Run. Observe fibrinogen fraction change.
4	B4 — Flow Corona	Set kon_ApoE = 0.05, koff_ApoE = 0.01 (tight binding). Run Vroman kinetics for 60 min. Note crossover time.
5	📓 Lab Journal	Record all three formulation conditions and their ApoE fractions. Identify the optimal formulation for brain targeting.

Expected Result

• Baseline (1.5% PEG, 100 nm): ApoE ~30–35% → good brain targeting potential via LRP1
• High PEG (4.0%): ApoE drops to ~10–15% → PEG shields corona formation, reducing receptor-mediated uptake
• Large particles (200 nm): Fibrinogen fraction increases → larger particles recruit more coagulation proteins, increasing lung/macrophage clearance
• Vroman kinetics: Albumin dominates first ~5–10 min, then ApoE displaces it; crossover at ~15–25 min

Real PubMed PMID to Read

PMID: 32251383 — Cheng Q et al. "Selective organ targeting (SORT) nanoparticles for tissue-specific mRNA delivery and CRISPR–Cas gene editing." Nature Nanotechnology 2020. Direct link: https://pubmed.ncbi.nlm.nih.gov/32251383/

What to Write in Lab Notebook

Date: [today]
Case: LNP corona optimization for brain delivery
Condition 1 (baseline): PEG 1.5%, 100nm → ApoE = [X]%
Condition 2 (high PEG): PEG 4.0%, 100nm → ApoE = [X]%
Condition 3 (large): PEG 1.5%, 200nm → ApoE = [X]%, Fibrinogen = [X]%
Vroman crossover time: ~[X] min
Conclusion: [optimal formulation] maximizes ApoE for brain targeting
Caveat: High PEG reduces corona but triggers anti-PEG immunity on repeat dosing (see PMID 34880493)

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Case 3 — KRAS G12C Variant Classification and Clinical Significance

Scientific Question: How is the KRAS G12C somatic mutation classified in ClinVar, what is its population frequency in gnomAD, and does it represent a research gap in PDAC vs. LUAD?

Protocol

Step	Tab	Action
1	A3 — Real Variant Lookup	Enter HGVS: NM_004985.5:c.34G>T (KRAS G12C). Run lookup. Record ClinVar classification and gnomAD AF.
2	B5 — Variant Concepts	Select Pathogenic. Read the ACMG criteria. Identify which codes apply to a known cancer hotspot like KRAS G12C.
3	A4 — Literature Gap Finder	Search: cancer type = PDAC, keyword = KRAS G12C. Compare trend to LUAD (repeat with SCLC).
4	A2 — Understudied Target Finder	Select PDAC. Check if KRAS appears and what its gap index is.
5	📓 Lab Journal	Record classification, AF, literature trend comparison, and gap index.

Expected Result

• ClinVar: KRAS G12C classified as Pathogenic (somatic) — PS1 (same amino acid change as established pathogenic), PM2 (absent from healthy population), PS3 (functional studies confirm oncogenicity)
• gnomAD AF: Should be extremely rare or absent in germline population (AF < 0.0001) — somatic mutations are not in gnomAD germline
• Literature trend: LUAD shows rapid growth post-2021 (sotorasib approval); PDAC shows lower but growing activity; SCLC shows near-zero publications → SCLC is the true gap
• Gap index: KRAS in PDAC may have moderate gap index despite high essentiality, due to growing literature

Real PubMed PMID to Read

PMID: 31820981 — Lanman BA et al. "Discovery of a Covalent Inhibitor of KRAS(G12C) (AMG 510) for the Treatment of Solid Tumors." J Med Chem 2020. Direct link: https://pubmed.ncbi.nlm.nih.gov/31820981/

What to Write in Lab Notebook

Date: [today]
Case: KRAS G12C variant analysis
HGVS: NM_004985.5:c.34G>T
ClinVar classification: [result from Tab A3]
gnomAD germline AF: [result — expected: not found / ultra-rare]
ACMG codes (simulated, B5): PS1, PM2, PS3
Literature gap: SCLC shows lowest KRAS G12C publications
PDAC gap index (Tab A2): [value]
Clinical note: Sotorasib/adagrasib approved for LUAD; PDAC trials ongoing; SCLC = unexplored

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Case 4 — siRNA Delivery Feasibility for KRAS-Driven Cancers

Scientific Question: Which KRAS-driven cancer type (LUAD, BRCA, COAD) has the most favorable siRNA target profile, and what are the key delivery barriers?

Protocol

Step	Tab	Action
1	B2 — siRNA Targets	Select LUAD. Note KRAS G12C efficacy score and delivery challenge rating.
2	B2 — siRNA Targets	Select COAD. Note KRAS G12D efficacy and delivery challenge. Compare to LUAD.
3	B3 — LNP Corona	Set formulation for tumor delivery: PEG = 1.5%, ionizable = 50%, size = 80 nm, serum = 50% (mimicking tumor microenvironment). Run corona simulation.
4	A5 — Druggable Orphans	Select PDAC. Check if KRAS appears as an orphan (no approved drug, no trial).
5	📓 Lab Journal	Compare all three cancer types. Identify which has the best siRNA opportunity and why.

Expected Result

• LUAD KRAS G12C: Efficacy ~0.82, delivery challenge = High (lung delivery requires inhalation or IV LNP)
• COAD KRAS G12D: Efficacy ~0.79, delivery challenge = High (colorectal delivery requires oral or local administration)
• Corona at 50% serum: Higher albumin and IgG fractions → more immune recognition; ApoE still present but diluted
• PDAC orphan check: KRAS may appear as orphan or near-orphan — KRAS G12D has no approved covalent inhibitor as of 2026
• Best opportunity: LUAD KRAS G12C has highest efficacy + existing clinical precedent (sotorasib); COAD KRAS G12D is the true unmet need

Real PubMed PMID to Read

PMID: 33208369 — Rosenblum D et al. "CRISPR-Cas9 genome editing using targeted lipid nanoparticles for cancer therapy." Science Advances 2020. Direct link: https://pubmed.ncbi.nlm.nih.gov/33208369/

What to Write in Lab Notebook

Date: [today]
Case: siRNA delivery for KRAS cancers
LUAD KRAS G12C: efficacy = [X], delivery = High, off-target = Medium
COAD KRAS G12D: efficacy = [X], delivery = High, off-target = Medium
Corona at 50% serum: ApoE = [X]%, Albumin = [X]%
PDAC orphan status (Tab A5): [result]
Conclusion: [best cancer type for siRNA KRAS targeting]
Key barrier: Endosomal escape efficiency <2% for siRNA-LNPs (literature)
Next step: Design LNP formulation screen using Tab B3 to maximize ApoE for tumor targeting

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Case 5 — Identifying a Novel Research Gray Zone in a Rare Cancer

Scientific Question: In uveal melanoma (UVM), which biological processes are most underexplored, and is there an essential gene with no drug and no trial that could be targeted via a novel mechanism?

Protocol

Step	Tab	Action
1	A1 — Gray Zones Explorer	Select UVM. Run. Identify the top 3 processes with lowest paper counts (red/white cells in heatmap).
2	A4 — Literature Gap Finder	Search: cancer type = UVM, keyword = the top gap process from Step 1 (e.g. ferroptosis or phase separation). Confirm the gap with the year-by-year chart.
3	A2 — Understudied Target Finder	Select UVM. Find the gene with the highest gap index. Note its essentiality and paper count.
4	A5 — Druggable Orphans	Select UVM. Check if the gene from Step 3 appears as an orphan target.
5	🤖 Research Assistant (A6)	Ask: "What is known about LNP delivery to uveal melanoma or ocular tumors?" Note the confidence flag.
6	📓 Lab Journal	Synthesize all findings into a 3-sentence research hypothesis.

Expected Result

• Gray zones in UVM: Likely top gaps = phase separation, liquid-liquid phase separation, cryptic splicing, protein corona — these are emerging fields with minimal UVM-specific literature
• Literature gap: Year-by-year chart should show 0–2 papers/year for the top gap process in UVM
• Understudied target: A gene with high OT association score, low paper count, and no drug (e.g. GNA11, GNAQ pathway effectors)
• Orphan status: GNA11/GNAQ are mutated in >90% of UVM but have no approved targeted therapy
• RAG chatbot: Will likely return MEDIUM or SPECULATIVE confidence for UVM-specific LNP delivery (not in indexed papers) — demonstrating the system's honesty about knowledge limits

Real PubMed PMID to Read

PMID: 27328919 — Bouaoun L et al. "TP53 Variations in Human Cancers: New Lessons from the IARC TP53 Database and Genomics Data." Human Mutation 2016. (For variant landscape context) Also: Search PubMed for "uveal melanoma" AND "GNA11" AND "treatment" to find the most recent therapeutic approaches.

What to Write in Lab Notebook

Date: [today]
Case: UVM gray zone discovery
Top 3 gray zones (Tab A1): [process 1], [process 2], [process 3]
Literature gap confirmed (Tab A4): [process] — [X] papers/year average
Top understudied target (Tab A2): [gene], gap index = [X]
Orphan status (Tab A5): [yes/no drug, yes/no trial]
RAG chatbot confidence for UVM LNP: [HIGH/MEDIUM/SPECULATIVE]
Research hypothesis: "[Gene] is an essential driver in UVM with no approved therapy.
  The [process] pathway is underexplored in UVM. LNP-mediated delivery of
  [siRNA/mRNA] targeting [gene] via [process] mechanism represents a novel
  therapeutic strategy warranting in vitro validation."

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Quick Reference: Tab-to-Question Mapping

Research Question Type	Primary Tab	Validation Tab
What is understudied in cancer X?	A1 Gray Zones	A4 Literature Gap
Which gene should I target?	A2 Target Finder	A5 Druggable Orphans
Is this variant real/classified?	A3 Variant Lookup	B5 Variant Concepts
How does my LNP formulation behave?	B3 LNP Corona	B4 Flow Corona
What do the papers say?	A6 Research Assistant	A4 Literature Gap
How does miRNA regulate my gene?	B1 miRNA Explorer	A4 Literature Gap
Which cancer is best for siRNA?	B2 siRNA Targets	A5 Druggable Orphans

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Learning Cases generated by K R&D Lab Cancer Research Suite | 2026-03-07