Thyroid Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
Overview
Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 5 different clustering approaches and 6 clinical features across 177 patients, 13 significant findings detected with P value < 0.05.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death',  'AGE', and 'HISTOLOGICAL.TYPE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'AGE' and 'HISTOLOGICAL.TYPE'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'AGE',  'GENDER', and 'HISTOLOGICAL.TYPE'.

  • CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death' and 'HISTOLOGICAL.TYPE'.

  • Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death',  'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 5 different clustering approaches and 6 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 13 significant findings detected.

Clinical
Features 		Statistical
Tests 		METHLYATION
CNMF 		RNAseq
CNMF
subtypes 		RNAseq
cHierClus
subtypes 		MIRseq
CNMF
subtypes 		MIRseq
cHierClus
subtypes
Time to Death logrank test 0.0253 0.157 0.157 0.0455 0.0455
AGE ANOVA 0.00306 0.0347 0.00234 0.444 0.47
GENDER Fisher's exact test 0.938 0.133 0.000857 0.356 0.444
HISTOLOGICAL TYPE Chi-square test 6.57e-11 1.53e-08 1.54e-10 1.05e-14 4.66e-13
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 0.102 0.0801 0.215 0.124 0.00996
NEOADJUVANT THERAPY Fisher's exact test 1 1 0.586 0.513 0.389
Clustering Approach #1: 'METHLYATION CNMF'

Table S1.  Get Full Table Description of clustering approach #1: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 43 23 91
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0253 (logrank test)

Table S2.  Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 157 1 0.1 - 66.1 (9.0)
subtype1 43 1 0.2 - 65.9 (7.7)
subtype2 23 0 0.1 - 66.1 (6.8)
subtype3 91 0 0.2 - 66.1 (10.4)

Figure S1.  Get High-res Image Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00306 (ANOVA)

Table S3.  Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 157 46.7 (15.9)
subtype1 43 53.5 (16.8)
subtype2 23 42.0 (14.1)
subtype3 91 44.7 (15.0)

Figure S2.  Get High-res Image Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.938 (Fisher's exact test)

Table S4.  Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 113 44
subtype1 30 13
subtype2 17 6
subtype3 66 25

Figure S3.  Get High-res Image Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 6.57e-11 (Chi-square test)

Table S5.  Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients OTHER THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL 4 91 43 19
subtype1 4 9 28 2
subtype2 0 15 5 3
subtype3 0 67 10 14

Figure S4.  Get High-res Image Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.102 (Fisher's exact test)

Table S6.  Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 14 143
subtype1 1 42
subtype2 1 22
subtype3 12 79

Figure S5.  Get High-res Image Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S7.  Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 3 154
subtype1 1 42
subtype2 0 23
subtype3 2 89

Figure S6.  Get High-res Image Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #2: 'RNAseq CNMF subtypes'

Table S8.  Get Full Table Description of clustering approach #2: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 34 27 42 13
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.157 (logrank test)

Table S9.  Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 116 1 0.1 - 65.9 (8.9)
subtype1 34 1 0.3 - 65.9 (7.4)
subtype2 27 0 0.5 - 65.7 (9.6)
subtype3 42 0 1.0 - 65.9 (10.2)
subtype4 13 0 0.1 - 65.9 (9.2)

Figure S7.  Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0347 (ANOVA)

Table S10.  Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 116 47.7 (16.3)
subtype1 34 54.2 (17.3)
subtype2 27 42.9 (13.5)
subtype3 42 46.6 (16.2)
subtype4 13 44.4 (15.4)

Figure S8.  Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.133 (Fisher's exact test)

Table S11.  Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 81 35
subtype1 23 11
subtype2 23 4
subtype3 25 17
subtype4 10 3

Figure S9.  Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.53e-08 (Chi-square test)

Table S12.  Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients OTHER THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL 3 65 32 16
subtype1 3 7 23 1
subtype2 0 21 4 2
subtype3 0 29 3 10
subtype4 0 8 2 3

Figure S10.  Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0801 (Fisher's exact test)

Table S13.  Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 11 105
subtype1 1 33
subtype2 1 26
subtype3 8 34
subtype4 1 12

Figure S11.  Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S14.  Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 3 113
subtype1 1 33
subtype2 1 26
subtype3 1 41
subtype4 0 13

Figure S12.  Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'RNAseq cHierClus subtypes'

Table S15.  Get Full Table Description of clustering approach #3: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 31 64 21
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.157 (logrank test)

Table S16.  Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 116 1 0.1 - 65.9 (8.9)
subtype1 31 1 0.3 - 65.9 (7.6)
subtype2 64 0 0.1 - 65.9 (9.4)
subtype3 21 0 0.5 - 65.7 (9.0)

Figure S13.  Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00234 (ANOVA)

Table S17.  Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 116 47.7 (16.3)
subtype1 31 56.1 (16.2)
subtype2 64 45.3 (15.7)
subtype3 21 42.5 (14.3)

Figure S14.  Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.000857 (Fisher's exact test)

Table S18.  Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 81 35
subtype1 20 11
subtype2 40 24
subtype3 21 0

Figure S15.  Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.54e-10 (Chi-square test)

Table S19.  Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients OTHER THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL 3 65 32 16
subtype1 3 5 22 1
subtype2 0 45 5 14
subtype3 0 15 5 1

Figure S16.  Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.215 (Fisher's exact test)

Table S20.  Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 11 105
subtype1 1 30
subtype2 9 55
subtype3 1 20

Figure S17.  Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.586 (Fisher's exact test)

Table S21.  Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 3 113
subtype1 1 30
subtype2 1 63
subtype3 1 20

Figure S18.  Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'MIRseq CNMF subtypes'

Table S22.  Get Full Table Description of clustering approach #4: 'MIRseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 47 56 47
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.0455 (logrank test)

Table S23.  Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 150 1 0.1 - 66.1 (8.1)
subtype1 47 1 0.3 - 65.9 (7.2)
subtype2 56 0 0.2 - 66.1 (11.1)
subtype3 47 0 0.1 - 66.1 (8.0)

Figure S19.  Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.444 (ANOVA)

Table S24.  Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 150 46.6 (15.7)
subtype1 47 48.8 (17.1)
subtype2 56 44.8 (15.2)
subtype3 47 46.6 (14.9)

Figure S20.  Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.356 (Fisher's exact test)

Table S25.  Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 108 42
subtype1 31 16
subtype2 44 12
subtype3 33 14

Figure S21.  Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.05e-14 (Chi-square test)

Table S26.  Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients OTHER THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL 6 78 50 16
subtype1 5 5 36 1
subtype2 0 42 9 5
subtype3 1 31 5 10

Figure S22.  Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.124 (Fisher's exact test)

Table S27.  Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 14 136
subtype1 2 45
subtype2 9 47
subtype3 3 44

Figure S23.  Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.513 (Fisher's exact test)

Table S28.  Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 3 147
subtype1 2 45
subtype2 1 55
subtype3 0 47

Figure S24.  Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'MIRseq cHierClus subtypes'

Table S29.  Get Full Table Description of clustering approach #5: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 43 55 52
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0455 (logrank test)

Table S30.  Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 150 1 0.1 - 66.1 (8.1)
subtype1 43 1 0.3 - 65.9 (7.1)
subtype2 55 0 0.1 - 66.1 (9.2)
subtype3 52 0 0.2 - 66.1 (9.4)

Figure S25.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.47 (ANOVA)

Table S31.  Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 150 46.6 (15.7)
subtype1 43 49.0 (16.6)
subtype2 55 46.1 (15.2)
subtype3 52 45.1 (15.5)

Figure S26.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.444 (Fisher's exact test)

Table S32.  Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 108 42
subtype1 29 14
subtype2 43 12
subtype3 36 16

Figure S27.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 4.66e-13 (Chi-square test)

Table S33.  Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients OTHER THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL 6 78 50 16
subtype1 5 5 32 1
subtype2 0 34 9 12
subtype3 1 39 9 3

Figure S28.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.00996 (Fisher's exact test)

Table S34.  Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 14 136
subtype1 1 42
subtype2 3 52
subtype3 10 42

Figure S29.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.389 (Fisher's exact test)

Table S35.  Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 3 147
subtype1 1 42
subtype2 0 55
subtype3 2 50

Figure S30.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

  • Cluster data file = THCA.mergedcluster.txt

  • Clinical data file = THCA.clin.merged.picked.txt

  • Number of patients = 177

  • Number of clustering approaches = 5

  • Number of selected clinical features = 6

  • Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches
CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
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