Analysis of prostate adenocarcinoma.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import and set options\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import matplotlib.style\n",
    "import matplotlib as mpl\n",
    "mpl.style.use('classic')\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import glob\n",
    "import os\n",
    "import scipy.stats as stats\n",
    "import matplotlib as mpl\n",
    "import sklearn\n",
    "import utils\n",
    "mpl.rcParams['pdf.fonttype'] = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of Prostate Adenocarcinoma\n",
    "\n",
    "Here, we perform a detailed analysis of 1,013 prostate adenocarcinoma samples from [Armenia et al](https://www.ncbi.nlm.nih.gov/pubmed/?term=29610475). This allows us to assess whether the expected trajectory of discovery for prostate adenocarcinoma (see [Sub-sampling analysis](Sub-sampling analysis.ipynb)) from The Cancer Genome Atlas (n=477) matches what is observed from more than 1,000 samples. Using only the TCGA, sub-sampling analysis suggested linear increases in the number of unique driver missense mutations and only gradual increases in the prevalence with perhaps diminishing returns. The TCGA data yielded an estimate of 0.31 driver missense mutations per sample. Raw results and mutations from Armenia et al are in the `CHASMplus/data/armenia_et_al_2018_prostate` folder.\n",
    "\n",
    "Additionally, since Armenia et al only performed gene-level analysis, this will also be informative in providing greater resolution to our understanding of cancer drivers at the level of individual mutations in prostate adenocarcinoma.\n",
    "\n",
    "### Data processing \n",
    "\n",
    "The first step is to read in the CHASMplus predictions and the mutations from Armenia et al."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in CHASMplus predictions\n",
    "df = utils.read_result('Armenia_et_al_PRAD', \n",
    "                       base_dir='CHASMplus/data/armenia_et_al_2018_prostate/', \n",
    "                       change_col_name=False)\n",
    "df['CODE'] = 'PRAD'\n",
    "df['Protein_position'] = df['HGVSp_Short'].str[3:-1]\n",
    "df = df.rename(columns={'CHASM2_genome_qval': 'gwCHASMplus q-value'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in mutations from Armenia et al and merge in CHASMplus results\n",
    "mut_df = pd.read_table('CHASMplus/data/armenia_et_al_2018_prostate/Armenia_et_al_PRAD.maf').rename(columns={'Protein_Change': 'HGVSp_Short'})\n",
    "mut_df = pd.merge(mut_df, df, \n",
    "                  on=['Hugo_Symbol', 'HGVSp_Short'],\n",
    "                  how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison of unique driver missense mutations\n",
    "\n",
    "In the TCGA analysis we noted that Prostate Adenocarcinoma, like other cancer types, linearly increased in terms of the number unique driver mutations. The original TCGA analysis found 94 unique driver missense mutations at a false discovery rate of 1%. A strictly linear increase would expect 200 unique driver missense mutations for 1,013 samples (=94 mutations / 477 samples * 1,013 samples)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique driver missense mutations: 203\n"
     ]
    }
   ],
   "source": [
    "# count the number of unique significant mutations\n",
    "is_signif = df['gwCHASMplus q-value']<=.01\n",
    "num_unique_drivers = len(df[is_signif])\n",
    "print('Number of unique driver missense mutations: {0}'.format(num_unique_drivers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TP53      65\n",
       "SPOP      27\n",
       "CTNNB1    19\n",
       "PTEN      11\n",
       "KDM6A     11\n",
       "PIK3CA     9\n",
       "CDK12      7\n",
       "KRAS       5\n",
       "AR         5\n",
       "BRAF       5\n",
       "MED12      4\n",
       "PIK3CB     4\n",
       "FOXA1      3\n",
       "IDH1       3\n",
       "ATM        3\n",
       "CUL3       3\n",
       "APC        3\n",
       "HRAS       2\n",
       "USP48      2\n",
       "RAG1       2\n",
       "ITSN1      2\n",
       "CNOT3      2\n",
       "AKT1       2\n",
       "CDC23      1\n",
       "ERF        1\n",
       "RNF43      1\n",
       "RB1        1\n",
       "Name: Hugo_Symbol, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[is_signif][['Hugo_Symbol', 'HGVSp_Short']]['Hugo_Symbol'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed, we find 203 unique driver missense mutations from the data in Armenia et al, strikingly close to expectations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison of driver prevalence\n",
    "\n",
    "First, we examine the prevalence of driver missense mutations with this expanded data set compared to that from the TCGA. To compare the two, we use the average number of driver missense mutations per sample. Like in the TCGA analysis, a 1% false discovery rate threshold is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The driver prevalence is 0.40 mutations per sample compared with 0.31 mutations per sample from the TCGA data\n"
     ]
    }
   ],
   "source": [
    "# Calculate the number of significant mutations\n",
    "is_signif = mut_df['gwCHASMplus q-value']<=.01\n",
    "num_muts = len(mut_df[is_signif])\n",
    "\n",
    "# print out the comparison\n",
    "print('The driver prevalence is {0:.2f} mutations per sample compared with 0.31 mutations per sample from the TCGA data'.format(num_muts / 1013))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like expected, driver prevalence increased gradually but with a rate showing diminishing returns. A strictly linear increase would have yielded 0.66 mutations per sample. Next, we looked at mutations that were marginally significant at a false discovery rate of 5%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At 5% FDR, the driver prevalence is 0.48 mutations per sample\n"
     ]
    }
   ],
   "source": [
    "# Calculate the number of significant mutations at 5% FDR\n",
    "is_signif = mut_df['gwCHASMplus q-value']<=.05\n",
    "num_muts = len(mut_df[is_signif])\n",
    "\n",
    "print('At 5% FDR, the driver prevalence is {0:.2f} mutations per sample'.format(num_muts / 1013))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Despite the diminishing returns for discovery, there are a reasonable prevalence of driver missense mutations that are marginally significant, suggesting that discovery is not saturated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the fraction of samples containing an identified driver\n",
    "mutated_samps = mut_df[is_signif].groupby(['CODE'])['Tumor_Sample_Barcode'].nunique()\n",
    "num_muts = len(mut_df[is_signif])\n",
    "total_samps = mut_df['Tumor_Sample_Barcode'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison of driver genes with Armenia et al\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "armenia_gene_list = [\n",
    "    'TP53', 'SPOP', 'FOXA1', 'APC', 'PTEN', 'CDK12', 'KMT2D', 'PIK3CA', 'ZFHX3', 'KMT2C',\n",
    "    'ATM', 'CDKN1B', 'KDM6A', 'CTNNB1', 'RNF43', 'USP28', 'RB1', 'BRCA2', 'PIK3R1', 'NDST2',\n",
    "    'ZNF292', 'TAF1L', 'NCOR1', 'ERF', 'CHD1', 'AKT1', 'MED12', 'ITSN1', 'USP7', 'IDH1',\n",
    "    'MGA', 'CHD3', 'ETV3', 'CDC27', 'IL6ST', 'CUL3', 'CHD7', 'DHX30', 'COL15A1', 'PAX6',\n",
    "    'CNOT3', 'TMPRSS2', 'KRAS', 'SETDB1', 'RNF31', 'AR', 'MRE11A', 'ARID4A', 'JADE2', 'PTPRC',\n",
    "    'ZMYM3', 'IGF2R', 'EHHADH', 'BRAF', 'COL5A3', 'RPRD2', 'SMARCAD1', 'BRPF1', 'SLC4A2', 'SPATA18',\n",
    "    'UNC13D', 'PIK3R2', 'PPL', 'TBC1D2', 'CDH1', 'U2AF1', 'HRAS', 'ADAMTS8', 'SMARCA1', 'MET',\n",
    "    'SPEN', 'ITSN2', 'COL5A1', 'PYHIN1', 'JAK1', 'MATN4', 'PIK3CB', 'RAG1', 'TAP1', 'CHD6',\n",
    "    'TRPM4', 'WASF3', 'SF3B1', 'PMS1', 'MYBBP1A', 'PDS5A', 'SAMD9', 'PALB2', 'STAB2', 'MBD1',\n",
    "    'NOX3', 'XPO1', 'KEAP1', 'KMT2A', 'ARID1A', 'SETD2', 'ARID2'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the unique genes at a FDR of 1% and 5%\n",
    "chasmplus_list_01 = df[df['gwCHASMplus q-value']<=0.01]['Hugo_Symbol'].unique()\n",
    "chasmplus_list_05 = df[df['gwCHASMplus q-value']<=0.05]['Hugo_Symbol'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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LzJfU8qiFU1MnKlapKHdoRU6SJCLuRXDjzA3uhdyjcsXKjB81nnbt2qGlpSV3eKWGSEKl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\nhRAhxK4ohAghdkUhRAixq38DKJ8Kz+S08fcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109ed6940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib_venn import venn2, venn2_circles\n",
    "\n",
    "# figure out the overlap\n",
    "len_intersect = len(set(chasmplus_list_01) & set(armenia_gene_list))\n",
    "len_chasmplus = len(chasmplus_list_01)\n",
    "len_armenia = len(armenia_gene_list)\n",
    "overlap = (len_chasmplus - len_intersect, len_armenia - len_intersect, len_intersect)\n",
    "\n",
    "# plot venn diagram\n",
    "with sns.plotting_context('paper', font_scale=1.4):\n",
    "    venn2(subsets=overlap, set_labels=('CHASMplus\\n(FDR<=0.01)', 'Armenia et al.'))\n",
    "    venn2_circles(subsets=overlap, linestyle='solid', linewidth=.75)\n",
    "    plt.title('Overlap of genes', size=16)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('CHASMplus/figures/figure_S7b.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fn1raD7xxlab1ndXStpA327JWeDpU5NqRK3KXIgDODZ3Zsk2+Wz4KRRDN/+MP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\nKLujhYiiKLv7P5JgN+4dE70GAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a15a13d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# figure out the overlap\n",
    "len_intersect = len(set(chasmplus_list_05) & set(armenia_gene_list))\n",
    "len_chasmplus = len(chasmplus_list_05)\n",
    "len_armenia = len(armenia_gene_list)\n",
    "overlap = (len_chasmplus - len_intersect, len_armenia - len_intersect, len_intersect)\n",
    "\n",
    "# plot venn diagram\n",
    "with sns.plotting_context('paper', font_scale=1.4):\n",
    "    venn2(subsets=overlap, set_labels=('CHASMplus\\n(FDR<=0.05)', 'Armenia et al.'))\n",
    "    venn2_circles(subsets=overlap, linestyle='solid', linewidth=.75)\n",
    "    plt.title('Overlap of genes', size=16)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('CHASMplus/figures/figure_S7c.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that there are three reasons why there is a larger number of genes reported in Armenia et al: 1) they refelct genes driven by mutations other than missense mutations; 2) They selectively rescued previously known cancer genes seen in other types of cancers; and 3) They used a considerably laxer threshold at a False discovery rate of 25%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "twentyTwentyPlus = pd.read_table('CHASMplus/data/armenia_et_al_2018_prostate/2020plus_results.txt')\n",
    "is_driver = twentyTwentyPlus['driver q-value']<=0.1\n",
    "is_og = twentyTwentyPlus['oncogene q-value']<=0.1\n",
    "is_tsg = twentyTwentyPlus['tsg q-value']<=0.1\n",
    "twentyTwentyPlus_genes = twentyTwentyPlus[is_og | is_tsg | is_driver]['gene'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HJpNhamXKjRs3lBtXqdHewbL58wlQcp2UN4lLTUVbVxv9clLdlM9FU1OGb2NX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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a133182e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# figure out the overlap\n",
    "len_intersect = len(set(twentyTwentyPlus_genes) & set(armenia_gene_list))\n",
    "len_ttplus = len(twentyTwentyPlus_genes)\n",
    "len_armenia = len(armenia_gene_list)\n",
    "overlap = (len_ttplus - len_intersect, len_armenia - len_intersect, len_intersect)\n",
    "\n",
    "# plot venn diagram\n",
    "with sns.plotting_context('paper', font_scale=1.4):\n",
    "    venn2(subsets=overlap, set_labels=('20/20+\\n(FDR<=0.1)', 'Armenia et al.'))\n",
    "    venn2_circles(subsets=overlap, linestyle='solid', linewidth=.75)\n",
    "    plt.title('Overlap of genes', size=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Format supplementary tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "frac_samples = mut_df[is_signif].groupby(['Hugo_Symbol', 'Protein_position']).size() / total_samps\n",
    "frequency_df = frac_samples.reset_index(name='fraction of samples')\n",
    "supp_table = pd.merge(df, frequency_df, on=['Hugo_Symbol', 'Protein_position'], how='left')\n",
    "supp_table = supp_table[supp_table['gwCHASMplus q-value']<=0.05]\n",
    "supp_table['Note'] = 'Marginally significant'\n",
    "supp_table.loc[supp_table['gwCHASMplus q-value']<=0.01, 'Note'] = 'Significant'\n",
    "cols = ['Hugo_Symbol', 'HGVSp_Short', 'fraction of samples', 'gwCHASMplus q-value', 'Note']\n",
    "supp_table[cols].to_csv('CHASMplus/results/table_S6.txt', sep='\\t', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}