def assess_itx_resistance(job, gene_expression, univ_options, reports_options):
    """
    Assess the prevalence of the various genes in various cancer pathways and return a report in the txt
    format.

    :param toil.fileStore.FileID gene_expression: fsID for the rsem gene expression file
    :param dict univ_options: Dict of universal options used by almost all tools
    :param dict reports_options: Options specific to reporting modules
    :return: The fsID for the itx resistance report file
    :rtype: toil.fileStore.FileID
    """

    work_dir = os.getcwd()
    tumor_type = univ_options['tumor_type']

    # Get the input files
    input_files = {
        'rsem_quant.tsv': gene_expression,
        'itx_resistance.tsv.tar.gz': reports_options['itx_resistance_file'],
        'immune_resistance_pathways.json.tar.gz': reports_options['immune_resistance_pathways_file']}

    input_files = get_files_from_filestore(job, input_files, work_dir, docker=False)

    input_files['itx_resistance.tsv'] = untargz(input_files['itx_resistance.tsv.tar.gz'], work_dir)
    input_files['immune_resistance_pathways.json'] = untargz(input_files['immune_resistance_pathways.json.tar.gz'], work_dir)

    full_data = pd.read_table(input_files['itx_resistance.tsv'], index_col=0)
    # Read pathways descriptions and cancer pathway data
    with open(input_files['immune_resistance_pathways.json']) as json_file:
        json_data = json.load(json_file)

    # Read patient file
    patient_df = pd.read_csv('rsem_quant.tsv', sep=' ', delimiter='\t', header='infer', index_col=0)
    patient_df.index = (patient_df.index).str.replace('\\..*$', '')

    with open('immunotherapy_resistance_report.txt', 'w') as report_file:
        # Check if data exsits for specified tumor type
        try:
            pathways = json_data['Cancer_to_pathway'][tumor_type]
        except KeyError:
            print('Data not available for ' + tumor_type, file=report_file)
        else:
            # If data exists, write a report
            for pathway in pathways:
                up_is_good = json_data['Pathways'][pathway]['up_is_good']

                if up_is_good:
                    comp_fn = lambda x, y: x >= y
                else:
                    comp_fn = lambda x, y: x < y

                # Describe pathway and genes for it
                print('Pathway: ' + pathway + '\n', file=report_file)
                print ('Papers: ' + json_data['Pathways'][pathway]['paper'], file=report_file)
                description = json_data['Pathways'][pathway]['description']
                print('Description of pathway:\n' + textwrap.fill(description, width=100),
                      file=report_file)
                print('Pathway genes: ', file=report_file)
                print('\t{:10}{:<20}{:<20}{:<12}'.format('Gene', 'GTEX Median',
                                                         'TCGA N Median', 'Observed'),
                                                         file=report_file)
                status = []
                # Write TCGA, GTEX, and observed values
                for gene in json_data['Pathways'][pathway]['genes']:
                    gtex = '{0:.2f}'.format(
                            float(full_data.loc[gene, TCGAToGTEx[tumor_type]])) \
                            if gene in full_data.index else 'NA'
                    tcga = '{0:.2f}'.format(
                            float(full_data.loc[gene, tumor_type + ' normal'])) \
                            if gene in full_data.index else 'NA'
                    tpm_value = '{0:.2f}'.format(float(patient_df.loc[gene, 'TPM'])) \
                                if gene in patient_df.index else 'NA'
                    ensg = json_data['Pathways'][pathway]['genes'][gene]
                    print('\t{:10}{:<20}{:<20}{:<12}'.format(ensg, gtex, tcga, tpm_value),
                          file=report_file)
                    if gtex != 'NA' and tpm_value != 'NA':
                        tcga_bool = comp_fn(float(tpm_value), float(tcga))
                        gtex_bool = comp_fn(float(tpm_value), float(gtex))
                        status.append(tcga_bool and gtex_bool)
                    else:
                        status.append(False)

                # Based on the number of genes with expression values above normal, assess the status
                print ('Status: ' + json_data['Pathways'][pathway]['status'][
                    str(sum(status) >= 0.75 * len(status))] + '\n', file=report_file)

    output_file = job.fileStore.writeGlobalFile(report_file.name)
    export_results(job, output_file, report_file.name, univ_options, subfolder='reports')
    job.fileStore.logToMaster('Ran create immunotherapy resistance report on %s successfully'
                              % univ_options['patient'])
    return output_file