Experimental Design (cc-study-design)

When to trigger

• The mechanism rests on a single system (e.g., cell lines only)
• Reviewers will ask "is this true in vivo?" or "does it hold in patients?"
• Controls, replicates, or sample-size logic are unclear
• Planning mouse / PDX / organoid experiments and unsure about rigor elements

Orthogonal validation ladder

Cancer Cell expects a mechanism corroborated across independent, complementary systems. Build the strongest ladder the biology allows:

A Cancer Cell paper typically spans in vitro + in vivo + a human anchor. Decide early which layers carry the causal claim and which provide corroboration.

Controls (the parts reviewers attack)

• Genetic perturbation: include rescue / add-back; use ≥2 independent sgRNAs or shRNAs (or rule out off-target); non-targeting / scramble control matched to delivery.
• Pharmacology: vehicle control, dose-response, on-target genetic phenocopy, and where possible a resistant mutant or analog.
• In vivo: isotype/vehicle controls, littermate or co-housed controls for GEMMs, tumor-volume and endpoint pre-defined.
• Antibody / staining: isotype, KO/KD-validated, single-stain compensation for flow.

Replicates: biological vs technical

• Biological replicates = independent biological units (separate mice, independent cell passages/cultures, distinct patients). These define n.
• Technical replicates (duplicate wells, repeat measurements) reduce measurement noise but do not increase n.
• Report both clearly; never inflate n with technical replicates (see cc-statistics).

Sample size, randomization, blinding (especially animals)

• Power / sample size: justify mouse n (effect size + variance from pilot or literature); state the basis even if informal.
• Randomization: allocate animals to arms randomly (e.g., when tumors reach a set volume), not by cage convenience.
• Blinding: blind outcome assessment (tumor measurement, histology scoring, imaging quantification) wherever feasible.
• Inclusion/exclusion: pre-define humane endpoints and exclusion criteria; report all animals/samples and any exclusions.

Human-sample design

• Define cohort, inclusion/exclusion, and how samples link to outcomes.
• Power survival / association analyses; pre-specify primary comparison.
• Note IRB/consent (route to cc-ethics-registration).

Checklist

• Mechanism validated across ≥2 orthogonal systems; causal layer identified
• In vivo evidence present (GEMM / xenograft / PDX / orthotopic) where claims require it
• Genetic perturbations include rescue and ≥2 independent reagents
• Matched controls defined for every perturbation and treatment
• Biological vs technical replicates distinguished; n = biological units
• Animal sample size justified; randomization and blinding specified
• Inclusion/exclusion criteria and humane endpoints pre-defined
• Human-sample cohort and primary comparison pre-specified

Anti-patterns

• Conclusions from a single cell line or a single system
• shRNA/CRISPR phenotype with no rescue (off-target not excluded)
• "n=3" that is three wells of one experiment (pseudo-replication)
• Mouse experiments with no randomization, no blinding, no power basis
• Therapeutic efficacy claimed without an in vivo tumor model
• Selecting samples post hoc to fit the hypothesis

Output format

【Causal claim layer】in vitro / in vivo / human
【Orthogonal systems planned】...
【Controls per perturbation】rescue + 2 reagents? vehicle/isotype?
【Replicates】biological n = ... ; technical handled separately
【Animal rigor】power basis / randomization / blinding / endpoints
【Human anchor】cohort + primary comparison
【Gaps to close before submission】...
【Next step】cc-reporting-standards (rigor reporting) or cc-statistics