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