Paper - Review

DOI: 10.1038/ng.2764

Abstract

TCGA
→ (profiled & analyzed) large number of human tumors
→ to discover (molecular aberrations) at the (DNA & RNA & protein & epigenetic levels)

Pan-Cancer
→ compares the 12 tumor types profiled by TCGA
→ extend therapies effective in one cancer types with a similar genomic profile

Introduction

Cancer
→ can take different forms (← depending on the location & origin & spectrum)

Molecular profiling of single tumor types

Cancer is fundamentally a genomic disease

✒ Oncogenes were identified ← using functional assays on genetic material
✒ Tumor Suppressor genes was identified ← by analyzing (loss of heterozygosity)

Analysis across tumor types

Increased↑ (← tumor sample data sets) → enhance↑ to (detect & analyze) molecular defects
Large cohorts → uncover a list (← of recurrent genomic aberrations)

(Set of molecular aberrations) integrates → into known (biological pathways)

Use (← of large cohorts) enables DNA sequencing → to uncover (a list of recurrent genomic aberrations)
e.g. 1⃣ mutations 2⃣ amplications 3⃣ deletions 4⃣ translocations 5⃣ fusions 6⃣ structural variants

TCGA samples have alterations (← which are not shared with other cohorts)
Molecular aberration → integrates into known (biological pathways)
∴ Identification (← of more driver aberrations) → boost personalized care

Cancers (← from the same organ) are often quite distinct

❗ Similarity (← among tumor subtypes from different organs)
e.g. TP53 → ovarian & endo-metrial & basal-like brease carcinomas
e.g. ERBB2-HER2 is (mutated & amplified) → glioblastoma & gastric & endometrial & bladder & lung cancer
e.g. BRCA1-BRCA2 pathway → serious ovarian & basal-like breast cancer

The Pan-Cancer project

TCGA → launched the Pan-Cancer analysis project

Pan-Cancer project
→ to gain analytical breadth
→ to assemble coherent & consistent TCGA data → across tumor types
→ to analyze & interpret these data

Functional validation (← of aberrations) in individual genes

Increases (← in statistical power) help
→ to distinguish new (driver mutations)
→ (Assembled Pan-Cancer data) enabled the identification (← of new patterns of genomic drivers)
→ enable the identification (← of frequently mutated genes)

❓ What tissue associations underlie the major genomic structural changes in cancer?

Analysis ← of structural variation
→ identify (genomic & epigenetic) regulators in multiple peak regions
∴ Tissue-associated patterns is established ← for the (rate & timing) of whole-genome duplication events

❗Emerging (← as cancer drivers) identified
1⃣ Collecting (less frequent events) across tumor types
2⃣ Integrating events type: e.g. mutations & copy number changes & epigenetic silencing
3⃣ Combining (multiple algorithms) → to identify (predicted drivers)
4⃣ Aggregating genes ← using (gene networks) & pathways

❓ Can (an increase ← in the number of samples) enhance ↑ analysis of the co-occurrence & mutual exclusivity of (gene aberration)
❓Can (an increase ← in the number of samples) improve ↑ ability (← to distinguish driver aberrations)

❓Can (molecular subtypes) be delineated → to disentangle tissue-specific (← from tissue-independent component)
→ Analyses (← of the epigenome & transcriptome & proteome) show strong 💪 influence of tissue

❓ Which events (← actionable in one tumor lineage) are also actionable in another tumor lineage?
❓ Which events (← actionable in one tumor lineage) are potentially increasing (the range of indications) for targeted therapeutics?

Limitation of analysis across tumor types

❗A key challenge: the integration of data (← from different platforms)
→ to assess (systematic & platform)-specific biases

❗ (Nature & quality) of available clinical data vary widely by cancer type
→ These differences limit (the ability) → to establish one-size-fits-all norms → for comparison of (demographic information & histopathologic characterization & behavioral context & clinical outcomes)

Pan-Cancer analysis (← of the infectious etiologies of other cancers) NOT be conducted at present
∵ Infection status was recorded for only (some tumors)

Tumor (stage & grade) are NOT easily comparable (← across different tumor types)

Ensure (← that the increased sample size achieved) by cross-cancer comparison
→ does NOT lead to increased false-negative rates for discovery

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