Fourteen engagement areas

Problem

Molecular strategy decisions are made early in a programme and are expensive to revisit once trials are running.

How collaboration works

Independent scientific review of biomarker rationale, patient-selection strategy and the molecular assumptions underlying a development plan.

Typical engagement

Advisory sessions, written scientific review, or standing advisory involvement across a programme.

Potential deliverables

• Written scientific assessment
• Biomarker strategy review
• Documented risk and assumption register

Problem

Early-stage companies often hold strong biology and limited computational or clinical translation capacity.

How collaboration works

Collaborative work on molecular and computational elements of a development programme, from target context through to translational design.

Typical engagement

Project-based collaboration or continuing scientific partnership.

Potential deliverables

• Computational analysis
• Translational study design input
• Scientific documentation

Problem

A diagnostic can be analytically sound and still fail to answer a question a clinician actually asks.

How collaboration works

Review of intended use, clinical positioning, reporting design and the decision the assay is meant to inform.

Typical engagement

Advisory or project-based.

Potential deliverables

• Intended-use assessment
• Report design review
• Clinical utility framing

Problem

Companion diagnostic development has to move in step with the therapeutic programme, and frequently does not.

How collaboration works

Scientific input on marker definition, cut-off rationale, assay-to-clinical-endpoint alignment and evidence generation.

Typical engagement

Programme-level advisory.

Potential deliverables

• Marker definition review
• Evidence generation plan input
• Scientific documentation

Problem

Most candidate biomarkers do not survive the move from a discovery cohort to an independent one.

How collaboration works

Analytical design for discovery and validation, with explicit attention to overfitting, cohort composition and replication.

Typical engagement

Project-based analytical collaboration.

Potential deliverables

• Analysis plan
• Discovery and validation analysis
• Reproducible pipelines

Problem

A molecularly-defined eligibility criterion can look reasonable on paper and be unrecruitable in practice.

How collaboration works

Assessment of molecular prevalence, testing availability and realistic identification pathways in target populations.

Typical engagement

Study-level assessment.

Potential deliverables

• Feasibility assessment
• Prevalence and testing landscape analysis

Problem

Eligible patients frequently exist but are not identified in time.

How collaboration works

Design of molecular and clinical identification pathways, including how molecular data already held could be used.

Typical engagement

Project-based or programme-level.

Potential deliverables

• Identification pathway design
• Screening logic specification

Problem

Investigator-initiated studies often carry good clinical questions with limited analytical design support.

How collaboration works

Scientific and analytical collaboration on study design, endpoints and molecular analysis plans.

Typical engagement

Collaborative or co-investigator role.

Potential deliverables

• Study design input
• Statistical and molecular analysis plan
• Analysis and manuscript contribution

Problem

Routine clinical and molecular data is abundant, inconsistent, and difficult to analyse credibly.

How collaboration works

Design and execution of real-world analyses with explicit handling of missingness, confounding and selection.

Typical engagement

Project-based analytical work.

Potential deliverables

• Analysis plan
• Reproducible analysis
• Interpretation with stated limitations

Problem

Clinical AI programmes often optimise a benchmark metric that no clinical decision depends on.

How collaboration works

Review of problem definition, endpoint selection, evaluation design, calibration and clinical integration.

Typical engagement

Advisory or programme-level.

Potential deliverables

• Evaluation framework
• Clinical integration assessment
• Scientific review

Problem

Analytical infrastructure built ad hoc becomes difficult to audit and impossible to reproduce.

How collaboration works

Design of reproducible pipelines, versioned reference management and quality-control frameworks.

Typical engagement

Project-based or embedded technical collaboration.

Potential deliverables

• Pipeline architecture
• QC framework
• Technical documentation

Problem

Interpretation logic embedded in a product determines what its users conclude, and is often undocumented.

How collaboration works

Design and review of variant annotation, tiering and actionability logic, with the evidence basis made explicit.

Typical engagement

Project-based.

Potential deliverables

• Interpretation logic specification
• Evidence tiering framework
• Review documentation

Problem

Clinical text carries negation, hedging and temporality that naive extraction discards.

How collaboration works

Design and evaluation of extraction pipelines that preserve those qualifications, with realistic error analysis.

Typical engagement

Project-based technical collaboration.

Potential deliverables

• Extraction pipeline design
• Annotation schema
• Evaluation report

Problem

Stratification schemes that look clean in a discovery cohort often fail to reproduce.

How collaboration works

Analytical design for stratification with pre-specified validation and honest assessment of stability.

Typical engagement

Project-based analytical work.

Potential deliverables

• Stratification analysis
• Validation assessment
• Documented method