Dataset: Identification and validation of a multigene predictor of recurrence in primary laryngeal cancer.

Background: Local recurrence is the major manifestation of treatment failure in patients with operable laryngeal carcinoma. Established...

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Background: Local recurrence is the major manifestation of treatment failure in patients with operable laryngeal carcinoma. Established clinicopathological factors cannot sufficiently predict patients that are likely to recur after treatment. Additional tools are therefore required to accurately identify patients at high risk for recurrence. Methods: Using Affymetrix U133A Genechips, we profiled fresh-frozen tumor tissues from 59 patients with operable laryngeal cancer. All patients were treated locally with surgery, with or without radiation therapy. We performed Cox regression proportional hazards modeling to identify multigene predictors of recurrence. The end-point of our analysis was disease-free survival (DFS). Gene models were directly validated in a separate, similarly treated cohort of 50 patients using Affymetrix chips. In an attempt to further validate our results, we profiled 12 selected genes of our model in formalin-fixed tumor tissues from an independent cohort of 75 patients, using quantitative real time-polymerase chain reaction (qRT-PCR). Results: We focused on genes univariately associated with DFS (p<0.05) in the training set. Among several gene models comprising different numbers of genes, a 30-gene model demonstrated optimal performance (log-rank, p<0.001). We directly applied these gene models to the validation set, after adjusting for non-biological experimental variability, and observed similar results. Specifically, median DFS, as predicted by the 30-gene model, was 34 and 80 months for high- and low-risk patients, respectively (p=0.01). Hazard Ratio (HR) for recurrence for the high-risk group was 3.87 (95% CI 1.28-11.73, p=0.017). Furthermore, unsupervised hierarchical clustering of the 75 patients, based on the qRT-PCR 12-gene profile, yielded two groups, which differed significantly in DFS (log-rank, p=0.027). HR= for recurrence was 2.26, (95% CI 1.08-4.76, p=0.031). Conclusion: We have established and validated gene models that can successfully stratify patients with laryngeal cancer, based on their risk for recurrence. Thus, patients with unfavorable prognosis, when accurately identified, could be ideal candidates for the application of more aggressive treatment modalities. Training set comprises 59 samples and validation set 50 samples

Species:: human
Samples:: 109
Source:: E-GEOD-27020
PubMed:: 23950933
Updated:: Dec.12, 2014
Registered:: Jun.18, 2014

Factors: (via ArrayExpress)

Sample	DFS STATUS 1 = RECURRED	GRADE	GROUP	DFS MONTHS	AGE
GSM665652	1	2	validation set	94	41
GSM66565	1	1	validation set	71	74
GSM665650	1	1	validation set	13	68
GSM665649	1	2	validation set	23	82
GSM665648	1	1	validation set	24	53
GSM665647	1	1	validation set	42	70
GSM665646	1	2	validation set	9	61
GSM665645	1	1	validation set	12	74
GSM665644	1	1	validation set	80	62
GSM665643	1	1	validation set	16	63
GSM665642	0	3	validation set	20	61
GSM66564	1	1	validation set	2	55
GSM665640	1	3	validation set	15	47
GSM665639	0	2	validation set	21	60
GSM665638	0	1	validation set	21	82
GSM665637	1	2	validation set	5	69
GSM665636	0	3	validation set	18	72
GSM665635	0	2	validation set	23	57
GSM665634	0	2	validation set	18	78
GSM665633	0	2	validation set	12	63
GSM665632	0	2	validation set	24	56
GSM66563	0	2	validation set	24	73
GSM665630	0	3	validation set	27	60
GSM665629	0	2	validation set	27	76
GSM665628	1	1	validation set	22	77
GSM665627	0	2	validation set	27	64
GSM665626	0	1	validation set	28	50
GSM665625	1	1	validation set	3	69
GSM665624	1	2	validation set	20	60
GSM665623	0	1	validation set	28	48
GSM665622	0	1	validation set	30	70
GSM66562	0	1	validation set	30	53
GSM665620	0	3	validation set	20	63
GSM665619	0	1	validation set	30	56
GSM665618	0	2	validation set	31	67
GSM665617	0	1	validation set	31	67
GSM665616	1	1	validation set	8	79
GSM665615	0	2	validation set	33	60
GSM665614	1	2	validation set	6	59
GSM665613	0	1	validation set	33	53
GSM665612	0	2	validation set	34	63
GSM6656	0	1	validation set	34	69
GSM665610	0	2	validation set	35	64
GSM665609	1	2	validation set	10	72
GSM665608	0	2	validation set	36	66
GSM665607	1	2	validation set	11	67
GSM665606	1	3	validation set	34	72
GSM665605	0	1	validation set	37	67
GSM665604	0	1	validation set	38	81
GSM665603	0	1	validation set	40	54
GSM665602	0	2	training set	41	67
GSM66560	0	3	training set	42	81
GSM665600	1	2	training set	13	68
GSM665599	0	1	training set	44	41
GSM665598	0	2	training set	42	62
GSM665597	0	1	training set	43	54
GSM665596	0	1	training set	43	49
GSM665595	0	2	training set	43	55
GSM665594	0	2	training set	45	80
GSM665593	0	2	training set	44	65
GSM665592	0	1	training set	46	66
GSM66559	0	2	training set	46	67
GSM665590	0	1	training set	47	49
GSM665589	0	3	training set	48	65
GSM665588	1	2	training set	8	64
GSM665587	0	2	training set	49	68
GSM665586	0	1	training set	50	79
GSM665585	0	2	training set	51	61
GSM665584	0	2	training set	52	69
GSM665583	0	2	training set	92	60
GSM665582	1	1	training set	50	60
GSM66558	0	2	training set	52	67
GSM665580	0	1	training set	54	68
GSM665579	0	1	training set	54	59
GSM665578	1	3	training set	14	61
GSM665577	0	1	training set	1	74
GSM665576	0	2	training set	37	71
GSM665575	0	2	training set	55	60
GSM665574	0	3	training set	56	74
GSM665573	1	2	training set	20	50
GSM665572	0	3	training set	42	55
GSM66557	0	3	training set	58	69
GSM665570	1	2	training set	13	55
GSM665569	0	3	training set	57	72
GSM665568	0	3	training set	41	70
GSM665567	1	1	training set	29	46
GSM665566	0	2	training set	36	58
GSM665565	1	2	training set	6	65
GSM665564	0	1	training set	61	49
GSM665563	0	2	training set	61	48
GSM665562	0	1	training set	62	50
GSM66556	0	1	training set	43	72
GSM665560	0	2	training set	46	74
GSM665559	1	2	training set	8	62
GSM665558	0	1	training set	62	51
GSM665557	0	2	training set	43	43
GSM665556	0	2	training set	64	45
GSM665555	0	not specified	training set	63	68
GSM665554	0	2	training set	65	48
GSM665553	0	2	training set	64	64
GSM665552	1	2	training set	14	88
GSM66555	0	2	training set	65	82
GSM665550	1	1	training set	16	58
GSM665549	0	1	training set	64	70
GSM665548	1	3	training set	7	58
GSM665547	0	1	training set	68	57
GSM665546	1	3	training set	8	69
GSM665545	0	not specified	training set	70	60
GSM665544	0	1	training set	45	54