Q (sonnet-5.5): show me EGFR expression across cancer types

▶ read_guide
{
 "uri": "cbioportal://gene-expression-guide"
}

◀ result
# Gene Expression Analysis Guide

This guide covers continuous-value genomic data: gene **expression**, **copy number** values, **methylation**, and related profile types. Mutation/CNA/SV *frequency* analysis lives in `cbioportal://mutation-frequency-guide`.

## Where this data lives

Continuous per-sample-per-gene values are stored in `genetic_alteration_derived`:

| Column | Description |
|---|---|
| `sample_unique_id` | `<study>_<sample_stable_id>` |
| `cancer_study_identifier` | study scope |
| `hugo_gene_symbol` | gene |
| `profile_type` | which assay/normalization (see below) |
| `alteration_value` | the actual value — stored as Nullable(String); cast with `toFloat64OrNull` |

`alteration_value` is a string because the same column hosts many different value scales. The `''` and `'NA'` sentinels mean "missing"; always filter them out and use `toFloat64OrNull(alteration_value) IS NOT NULL` for downstream math.

## Discovering profile types for a study

Different studies expose different profile types depending on what assays were run and how the data was normalized. Always check what a specific study supports before picking one:

```sql
SELECT DISTINCT profile_type
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'brca_metabric'
ORDER BY profile_type;
```

Common values across the public portal:

| Family | Profile types |
|---|---|
| mRNA expression | `rna_seq_v2_mrna`, `rna_seq_v2_mrna_median_Zscores`, `rna_seq_v2_mrna_median_all_sample_Zscores` (TCGA PanCancer Atlas), `mrna`, `mrna_median_Zscores`, `mrna_seq_v2_rsem`, `mrna_seq_v2_rsem_Zscores`, `mrna_seq_cpm`, `mrna_seq_fpkm`, `mrna_U133`, `mrna_outliers` |
| Copy number (continuous) | `cna`, `linear_CNA`, `log2CNA`, `cna_consensus`, `cna_rae`, `gistic` |
| Methylation | `methylation_hm27`, `methylation_hm450`, `methylation_epic`, `methylation_promoters_rrbs` |
| miRNA | `mirna`, `mirna_median_Zscores` |
| Protein | `protein_quantification`, `protein_level`, `RPPA` |

**Z-score vs raw choice.** When the user asks "is X correlated with Y", either works for Spearman (rank-based) — Pearson would care. Default to the non-Z-score variant if both exist, and call out which one in the response.

## Canonical recipe — Spearman correlation between two genes

```sql
SELECT *
FROM gene_pair_coexpression(
    study        = 'brca_metabric',
    gene_a       = 'TP53',
    gene_b       = 'MYC',
    profile_type = 'mrna'
);
```

Returns one row: `(gene_a, gene_b, profile_type, spearman_correlation, num_samples)`.

- `spearman_correlation` in [−1, 1]; `NULL` when fewer than 3 valid paired samples.
- Mirrors cbioportal-backend's `ClickhouseCoExpressionMapper.getCoExpressions`, simplified to a pair lookup (the backend computes one ref gene vs ALL other genes for the coexpression page; here the agent asks about a specific pair).

### Verified examples

| Study | gene_a | gene_b | profile_type | spearman | n |
|---|---|---|---|---|---|
| `brca_metabric` | TP53 | MYC | `mrna` | 0.118 | 1980 |
|  … (4931 more chars)

▶ clickhouse_run_select_query
{
 "query": "WITH cohort AS (
    SELECT cancer_study_identifier FROM cancer_study_query_preferences WHERE preference_name = 'pan_cancer_tcga'
),
ct AS (
    SELECT sample_unique_id, attribute_value AS cancer_type
    FROM clinical_data_derived
    WHERE attribute_name = 'CANCER_TYPE' AND cancer_study_identifier IN (SELECT cancer_study_identifier FROM cohort)
)
SELECT ct.cancer_type,
       count() AS samples,
       round(median(log2(toFloat64OrNull(g.alteration_value) + 1)), 2) AS median_log2_rsem,
       round(quantile(0.9)(log2(toFloat64OrNull(g.alteration_value) + 1)), 2) AS p90_log2_rsem
FROM genetic_alteration_derived g
JOIN ct USING (sample_unique_id)
WHERE g.cancer_study_identifier IN (SELECT cancer_study_identifier FROM cohort)
  AND g.hugo_gene_symbol = 'EGFR'
  AND g.profile_type = 'rna_seq_v2_mrna'
  AND g.alteration_value NOT IN ('', 'NA')
  AND toFloat64OrNull(g.alteration_value) IS NOT NULL
GROUP BY ct.cancer_type
ORDER BY p90_log2_rsem DESC;",
 "max_rows": 100
}

▶ clickhouse_run_select_query
{
 "query": "SELECT cancer_study_identifier FROM cancer_study_query_preferences WHERE preference_name = 'pan_cancer_tcga' ORDER BY cancer_study_identifier"
}

◀ result
{"rows":[{"cancer_type":"Glioblastoma","samples":160,"median_log2_rsem":11.93,"p90_log2_rsem":15},{"cancer_type":"Glioma","samples":514,"median_log2_rsem":11.24,"p90_log2_rsem":13.05},{"cancer_type":"Head and Neck Cancer","samples":515,"median_log2_rsem":11.5,"p90_log2_rsem":12.98},{"cancer_type":"Renal Clear Cell Carcinoma","samples":510,"median_log2_rsem":11.28,"p90_log2_rsem":12.54},{"cancer_type":"Non-Small Cell Lung Cancer","samples":994,"median_log2_rsem":10.49,"p90_log2_rsem":12.05},{"cancer_type":"Esophagogastric Cancer","samples":593,"median_log2_rsem":9.6,"p90_log2_rsem":11.7},{"cancer_type":"Bladder Cancer","samples":407,"median_log2_rsem":9.27,"p90_log2_rsem":11.45},{"cancer_type":"Pleural Mesothelioma","samples":87,"median_log2_rsem":9.75,"p90_log2_rsem":11.33},{"cancer_type":"Cervical Cancer","samples":294,"median_log2_rsem":9.71,"p90_log2_rsem":11.32},{"cancer_type":"Hepatobiliary Cancer","samples":366,"median_log2_rsem":9.67,"p90_log2_rsem":11.22},{"cancer_type":"Renal Non-Clear Cell Carcinoma","samples":348,"median_log2_rsem":9.41,"p90_log2_rsem":11.03},{"cancer_type":"Thyroid Cancer","samples":498,"median_log2_rsem":9.61,"p90_log2_rsem":10.96},{"cancer_type":"Thymic Epithelial Tumor","samples":119,"median_log2_rsem":8.58,"p90_log2_rsem":10.93},{"cancer_type":"Cholangiocarcinoma","samples":36,"median_log2_rsem":9.47,"p90_log2_rsem":10.76},{"cancer_type":"Prostate Cancer","samples":493,"median_log2_rsem":9.5,"p90_log2_rsem":10.62},{"cancer_type":"Sarcoma","samples":253,"median_log2_rsem":8.62,"p90_log2_rsem":10.54},{"cancer_type":"Pancreatic Cancer","samples":177,"median_log2_rsem":9.36,"p90_log2_rsem":10.35},{"cancer_type":"Colorectal Cancer","samples":592,"median_log2_rsem":9.05,"p90_log2_rsem":9.95},{"cancer_type":"Breast Cancer","samples":1082,"median_log2_rsem":7.39,"p90_log2_rsem":9.76},{"cancer_type":"Endometrial Cancer","samples":584,"median_log2_rsem":8.11,"p90_log2_rsem":9.62},{"cancer_type":"Adrenocortical Carcinoma","samples":78,"median_log2_rsem":7.66,"p90_log2_rsem":9.59},{"cancer_type":"Non-Seminomatous Germ Cell Tumor","samples":86,"median_log2_rsem":7.24,"p90_log2_rsem":9.54},{"cancer_type":"Ovarian Epithelial Tumor","samples":300,"median_log2_rsem":7.76,"p90_log2_rsem":9.02},{"cancer_type":"Melanoma","samples":443,"median_log2_rsem":4.85,"p90_log2_rsem":7.96},{"cancer_type":"Pheochromocytoma","samples":147,"median_log2_rsem":4.9,"p90_log2_rsem":7.82},{"cancer_type":"Seminoma","samples":63,"median_log2_rsem":6.28,"p90_log2_rsem":7.74},{"cancer_type":"Miscellaneous Neuroepithelial Tumor","samples":31,"median_log2_rsem":5.43,"p90_log2_rsem":7.67},{"cancer_type":"Mature B-Cell Neoplasms","samples":48,"median_log2_rsem":4.25,"p90_log2_rsem":6.74},{"cancer_type":"Ocular Melanoma","samples":80,"median_log2_rsem":4.94,"p90_log2_rsem":6.69},{"cancer_type":"Leukemia","samples":173,"median_log2_rsem":-0.1,"p90_log2_rsem":2.67}]}

◀ result
{"rows":[{"cancer_study_identifier":"acc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"blca_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"brca_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"cesc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"chol_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"coadread_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"dlbc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"esca_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"gbm_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"hnsc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"kich_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"kirc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"kirp_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"laml_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"lgg_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"lihc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"luad_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"lusc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"meso_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"ov_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"paad_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"pcpg_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"prad_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"sarc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"skcm_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"stad_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"tgct_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"thca_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"thym_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"ucec_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"ucs_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"uvm_tcga_pan_can_atlas_2018"}]}

▶ resolve_and_route
{
 "studyIds": [
  "acc_tcga_pan_can_atlas_2018",
  "blca_tcga_pan_can_atlas_2018",
  "brca_tcga_pan_can_atlas_2018",
  "cesc_tcga_pan_can_atlas_2018",
  "chol_tcga_pan_can_atlas_2018",
  "coadread_tcga_pan_can_atlas_2018",
  "dlbc_tcga_pan_can_atlas_2018",
  "esca_tcga_pan_can_atlas_2018",
  "gbm_tcga_pan_can_atlas_2018",
  "hnsc_tcga_pan_can_atlas_2018",
  "kich_tcga_pan_can_atlas_2018",
  "kirc_tcga_pan_can_atlas_2018",
  "kirp_tcga_pan_can_atlas_2018",
  "laml_tcga_pan_can_atlas_2018",
  "lgg_tcga_pan_can_atlas_2018",
  "lihc_tcga_pan_can_atlas_2018",
  "luad_tcga_pan_can_atlas_2018",
  "lusc_tcga_pan_can_atlas_2018",
  "meso_tcga_pan_can_atlas_2018",
  "ov_tcga_pan_can_atlas_2018",
  "paad_tcga_pan_can_atlas_2018",
  "pcpg_tcga_pan_can_atlas_2018",
  "prad_tcga_pan_can_atlas_2018",
  "sarc_tcga_pan_can_atlas_2018",
  "skcm_tcga_pan_can_atlas_2018",
  "stad_tcga_pan_can_atlas_2018",
  "tgct_tcga_pan_can_atlas_2018",
  "thca_tcga_pan_can_atlas_2018",
  "thym_tcga_pan_can_atlas_2018",
  "ucec_tcga_pan_can_atlas_2018",
  "ucs_tcga_pan_can_atlas_2018",
  "uvm_tcga_pan_can_atlas_2018"
 ]
}

◀ result
Error: result (81,891 characters across 1 line) exceeds maximum allowed tokens. Output has been saved to /home/ino/.claude-cbioportal-team/projects/-tmp-mcp-qa-claude-pqyf1b1k/6382f21a-c5cd-480c-bad7-d284adfc6347/tool-results/mcp-navigator-resolve_and_route-1790657193513.txt.
Format: Plain text
- For targeted searches (find a string): use grep on the file directly.
- For analysis or summarization that requires reading the full content: the file's lines are too long for Read's offset/limit. Slice by character range via Bash instead — e.g. python3 -c 'print(open("/home/ino/.claude-cbioportal-team/projects/-tmp-mcp-qa-claude-pqyf1b1k/6382f21a-c5cd-480c-bad7-d284adfc6347/tool-results/mcp-navigator-resolve_and_route-1790657193513.txt").read()[A:B])' in ~80,000-char spans until you have read 100% of it.
- If the Agent tool is available, do this inside a subagent so the full output stays out of your main context. Give it the instruction above verbatim, and be explicit about what it must return — e.g. "Slice /home/ino/.claude-cbioportal-team/projects/-tmp-mcp-qa-claude-pqyf1b1k/6382f21a-c5cd-480c-bad7-d284adfc6347/tool-results/mcp-navigator-resolve_and_route-1790657193513.txt in ~80,000-char spans via python (read()[A:B]) until you have read all 81,891 characters, then summarize and quote any key findings verbatim." A vague "summarize this" may lose detail.


▶ navigate_to_results_view
{
 "studyIds": [
  "acc_tcga_pan_can_atlas_2018",
  "blca_tcga_pan_can_atlas_2018",
  "brca_tcga_pan_can_atlas_2018",
  "cesc_tcga_pan_can_atlas_2018",
  "chol_tcga_pan_can_atlas_2018",
  "coadread_tcga_pan_can_atlas_2018",
  "dlbc_tcga_pan_can_atlas_2018",
  "esca_tcga_pan_can_atlas_2018",
  "gbm_tcga_pan_can_atlas_2018",
  "hnsc_tcga_pan_can_atlas_2018",
  "kich_tcga_pan_can_atlas_2018",
  "kirc_tcga_pan_can_atlas_2018",
  "kirp_tcga_pan_can_atlas_2018",
  "laml_tcga_pan_can_atlas_2018",
  "lgg_tcga_pan_can_atlas_2018",
  "lihc_tcga_pan_can_atlas_2018",
  "luad_tcga_pan_can_atlas_2018",
  "lusc_tcga_pan_can_atlas_2018",
  "meso_tcga_pan_can_atlas_2018",
  "ov_tcga_pan_can_atlas_2018",
  "paad_tcga_pan_can_atlas_2018",
  "pcpg_tcga_pan_can_atlas_2018",
  "prad_tcga_pan_can_atlas_2018",
  "sarc_tcga_pan_can_atlas_2018",
  "skcm_tcga_pan_can_atlas_2018",
  "stad_tcga_pan_can_atlas_2018",
  "tgct_tcga_pan_can_atlas_2018",
  "thca_tcga_pan_can_atlas_2018",
  "thym_tcga_pan_can_atlas_2018",
  "ucec_tcga_pan_can_atlas_2018",
  "ucs_tcga_pan_can_atlas_2018",
  "uvm_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "EGFR"
 ],
 "tab": "plots",
 "plotsHorzSelection": {
  "dataType": "clinical_attribute",
  "selectedGeneOption": "EGFR"
 },
 "plotsVertSelection": {
  "dataType": "MRNA_EXPRESSION",
  "selectedGeneOption": "EGFR",
  "selectedDataSourceOption": "rna_seq_v2_mrna"
 }
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/plots?cancer_study_list=acc_tcga_pan_can_atlas_2018%2Cblca_tcga_pan_can_atlas_2018%2Cbrca_tcga_pan_can_atlas_2018%2Ccesc_tcga_pan_can_atlas_2018%2Cchol_tcga_pan_can_atlas_2018%2Ccoadread_tcga_pan_can_atlas_2018%2Cdlbc_tcga_pan_can_atlas_2018%2Cesca_tcga_pan_can_atlas_2018%2Cgbm_tcga_pan_can_atlas_2018%2Chnsc_tcga_pan_can_atlas_2018%2Ckich_tcga_pan_can_atlas_2018%2Ckirc_tcga_pan_can_atlas_2018%2Ckirp_tcga_pan_can_atlas_2018%2Claml_tcga_pan_can_atlas_2018%2Clgg_tcga_pan_can_atlas_2018%2Clihc_tcga_pan_can_atlas_2018%2Cluad_tcga_pan_can_atlas_2018%2Clusc_tcga_pan_can_atlas_2018%2Cmeso_tcga_pan_can_atlas_2018%2Cov_tcga_pan_can_atlas_2018%2Cpaad_tcga_pan_can_atlas_2018%2Cpcpg_tcga_pan_can_atlas_2018%2Cprad_tcga_pan_can_atlas_2018%2Csarc_tcga_pan_can_atlas_2018%2Cskcm_tcga_pan_can_atlas_2018%2Cstad_tcga_pan_can_atlas_2018%2Ctgct_tcga_pan_can_atlas_2018%2Cthca_tcga_pan_can_atlas_2018%2Cthym_tcga_pan_can_atlas_2018%2Cucec_tcga_pan_can_atlas_2018%2Cucs_tcga_pan_can_atlas_2018%2Cuvm_tcga_pan_can_atlas_2018&gene_list=EGFR&case_set_id=all&plots_horz_selection=%7B%22selectedGeneOption%22%3A%221956%22%2C%22dataType%22%3A%22clinical_attribute%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%221956%22%2C%22dataType%22%3A%22MRNA_EXPRESSION%22%2C%22selectedDataSourceOption%22%3A%22rna_seq_v2_mrna%22%7D","url":"https://www.cbioportal.org/results/plots?cancer_study_list=acc_tcga_pan_can_atlas_2018%2Cblca_tcga_pan_can_atlas_2018%2Cbrca_tcga_pan_can_atlas_2018%2Ccesc_tcga_pan_can_atlas_2018%2Cchol_tcga_pan_can_atlas_2018%2Ccoadread_tcga_pan_can_atlas_2018%2Cdlbc_tcga_pan_can_atlas_2018%2Cesca_tcga_pan_can_atlas_2018%2Cgbm_tcga_pan_can_atlas_2018%2Chnsc_tcga_pan_can_atlas_2018%2Ckich_tcga_pan_can_atlas_2018%2Ckirc_tcga_pan_can_atlas_2018%2Ckirp_tcga_pan_can_atlas_2018%2Claml_tcga_pan_can_atlas_2018%2Clgg_tcga_pan_can_atlas_2018%2Clihc_tcga_pan_can_atlas_2018%2Cluad_tcga_pan_can_atlas_2018%2Clusc_tcga_pan_can_atlas_2018%2Cmeso_tcga_pan_can_atlas_2018%2Cov_tcga_pan_can_atlas_2018%2Cpaad_tcga_pan_can_atlas_2018%2Cpcpg_tcga_pan_can_atlas_2018%2Cprad_tcga_pan_can_atlas_2018%2Csarc_tcga_pan_can_atlas_2018%2Cskcm_tcga_pan_can_atlas_2018%2Cstad_tcga_pan_can_atlas_2018%2Ctgct_tcga_pan_can_atlas_2018%2Cthca_tcga_pan_can_atlas_2018%2Cthym_tcga_pan_can_atlas_2018%2Cucec_tcga_pan_can_atlas_2018%2Cucs_tcga_pan_can_atlas_2018%2Cuvm_tcga_pan_can_atlas_2018&gene_list=EGFR&case_set_id=all&plots_horz_selection=%7B%22selectedGeneOption%22%3A%221956%22%2C%22dataType%22%3A%22clinical_attribute%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%221956%22%2C%22dataType%22%3A%22MRNA_EXPRESSION%22%2C%22selectedDataSourceOption%22%3A%22rna_seq_v2_mrna%22%7D","data":{"studyIds":["acc_tcga_pan_can_atlas_2018","blca_tcga_pan_can_atlas_2018","brca_tcga_pan_can_atlas_2018","cesc_tcga_pan_can_atlas_2018","chol_tcga_pan_can_atlas_2018","coadread_tcga_pan_can_atlas_2018","dlbc_tcga_pan_can_atlas_2018","esca_tcga_p … (4717 more chars)

═ answer (success)
**EGFR mRNA expression across cancer types (TCGA PanCancer Atlas)**

Values are log2(RSEM+1) from the batch-normalized RNA-seq profile, which is comparable across the TCGA PanCancer Atlas studies. I left out z-scores because they are centered within each study and would hide differences between cancer types. The table is sorted by the 90th percentile (p90), because EGFR amplification raises the upper tail more than the median. Counts are samples.

| Cancer type | Samples | Median | p90 |
|---|---|---|---|
| Glioblastoma | 160 | 11.93 | 15.00 |
| Glioma | 514 | 11.24 | 13.05 |
| Head and Neck Cancer | 515 | 11.50 | 12.98 |
| Renal Clear Cell Carcinoma | 510 | 11.28 | 12.54 |
| Non-Small Cell Lung Cancer | 994 | 10.49 | 12.05 |
| Esophagogastric Cancer | 593 | 9.60 | 11.70 |
| Bladder Cancer | 407 | 9.27 | 11.45 |
| Pleural Mesothelioma | 87 | 9.75 | 11.33 |
| Cervical Cancer | 294 | 9.71 | 11.32 |
| Hepatobiliary Cancer | 366 | 9.67 | 11.22 |
| Renal Non-Clear Cell Carcinoma | 348 | 9.41 | 11.03 |
| Thyroid Cancer | 498 | 9.61 | 10.96 |
| Thymic Epithelial Tumor | 119 | 8.58 | 10.93 |
| Cholangiocarcinoma | 36 | 9.47 | 10.76 |
| Prostate Cancer | 493 | 9.50 | 10.62 |
| Sarcoma | 253 | 8.62 | 10.54 |
| Pancreatic Cancer | 177 | 9.36 | 10.35 |
| Colorectal Cancer | 592 | 9.05 | 9.95 |
| Breast Cancer | 1082 | 7.39 | 9.76 |
| Endometrial Cancer | 584 | 8.11 | 9.62 |
| Adrenocortical Carcinoma | 78 | 7.66 | 9.59 |
| Non-Seminomatous Germ Cell Tumor | 86 | 7.24 | 9.54 |
| Ovarian Epithelial Tumor | 300 | 7.76 | 9.02 |
| Melanoma | 443 | 4.85 | 7.96 |
| Pheochromocytoma | 147 | 4.90 | 7.82 |
| Seminoma | 63 | 6.28 | 7.74 |
| Miscellaneous Neuroepithelial Tumor | 31 | 5.43 | 7.67 |
| Mature B-Cell Neoplasms | 48 | 4.25 | 6.74 |
| Ocular Melanoma | 80 | 4.94 | 6.69 |
| Leukemia | 173 | −0.10 | 2.67 |

- **Highest:** Glioblastoma has the highest median (11.93) and by far the highest p90 (15.00), which fits a subset of tumors with very high EGFR. Glioma, head and neck, and clear cell renal tumors are also high.
- **Lowest:** Leukemia is near zero. Melanoma, pheochromocytoma, B-cell neoplasms and ocular melanoma are also low.
- **Caveats:**
  - The "Glioblastoma" (160) and "Glioma" (514) groups come from the cancer-type labels in the data.
  - The comparison uses TCGA data only, so other cohorts are not comparable.
  - This is descriptive only. I did not run any statistical tests.

[View EGFR mRNA expression by cancer type in the cBioPortal Plots tab](https://www.cbioportal.org/results/plots?cancer_study_list=acc_tcga_pan_can_atlas_2018%2Cblca_tcga_pan_can_atlas_2018%2Cbrca_tcga_pan_can_atlas_2018%2Ccesc_tcga_pan_can_atlas_2018%2Cchol_tcga_pan_can_atlas_2018%2Ccoadread_tcga_pan_can_atlas_2018%2Cdlbc_tcga_pan_can_atlas_2018%2Cesca_tcga_pan_can_atlas_2018%2Cgbm_tcga_pan_can_atlas_2018%2Chnsc_tcga_pan_can_atlas_2018%2Ckich_tcga_pan_can_atlas_2018%2Ckirc_tcga_pan_can_atlas_2018%2Ckirp_tcga_pan_can_atlas_2018%2Claml_tcga_pan_can_atlas_2018%2Clgg_tcga_pan_can_atlas_2018%2Clihc_tcga_pan_can_atlas_2018%2Cluad_tcga_pan_can_atlas_2018%2Clusc_tcga_pan_can_atlas_2018%2Cmeso_tcga_pan_can_atlas_2018%2Cov_tcga_pan_can_atlas_2018%2Cpaad_tcga_pan_can_atlas_2018%2Cpcpg_tcga_pan_can_atlas_2018%2Cprad_tcga_pan_can_atlas_2018%2Csarc_tcga_pan_can_atlas_2018%2Cskcm_tcga_pan_can_atlas_2018%2Cstad_tcga_pan_can_atlas_2018%2Ctgct_tcga_pan_can_atlas_2018%2Cthca_tcga_pan_can_atlas_2018%2Cthym_tcga_pan_can_atlas_2018%2Cucec_tcga_pan_can_atlas_2018%2Cucs_tcga_pan_can_atlas_2018%2Cuvm_tcga_pan_can_atlas_2018&gene_list=EGFR&case_set_id=all&plots_horz_selection=%7B%22selectedGeneOption%22%3A%221956%22%2C%22dataType%22%3A%22clinical_attribute%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%221956%22%2C%22dataType%22%3A%22MRNA_EXPRESSION%22%2C%22selectedDataSourceOption%22%3A%22rna_seq_v2_mrna%22%7D)

The plot opens with EGFR mRNA expression on the y-axis. The x-axis loads as a clinical attribute, so you may need to choose "Cancer Type" there.