Table 2.

Coefficients of linear discriminant functions (LDF) derived from LDA for all possible combination of antibodies

Antibody combination		Sens	Spec	COO	Constant	Antibody
						1	2	3	4	5
						CD10	BCL6	FOXP1	GCET1	MUM1
As in Nyman	3,5	0.81	0.74	GCB	− 0.57			2.47		1.11
				Non-GCB	− 3.29			4.38		5.06
As in Hans and	1,2,5	0.86	0.88	GCB	− 4.21	5.01	7.00			1.05
As in Colomo				Non-GCB	− 3.09	0.20	4.99			5.69
As in Hans*	1,5	0.77	0.91	GCB	− 2.29	6.53				2.11
				Non-GCB	− 2.12	1.28				6.45
As in Choi	1,2,3,4,5	0.87	0.91	GCB	− 4.80	4.59	6.31	0.71	3.28	1.02
				Non-GCB	− 3.98	− 0.41	3.80	3.75	1.24	4.73
As in Choi*	1,3,4,5	0.86	0.91	GCB	− 3.34	5.67		1.78	4.03	1.67
				Non-GCB	− 3.46	0.24		4.39	1.69	5.12
As in VY3	1,2,3	0.87	0.91	GCB	− 4.18	4.86	6.99	0.64
				Non-GCB	− 2.90	− 0.74	4.58	4.61
As in VY4	1,2,3,4	0.84	0.90	GCB	− 4.75	4.49	6.44	0.91	3.26
				Non-GCB	− 2.97	− 0.86	4.40	4.70	1.12
–	1,4,5	0.81	0.92	GCB	− 3.14	6.01			3.93	2.22
				Non-GCB	− 2.23	1.09			1.44	6.49

Columns (Antibody) show the coefficient associated with each antibody for GCB classification (Sensibility performance) and non-GCB classification (Specificity performance). The first column remarks the combinations of antibodies as were used by IHC-decision trees. BCL6 and CD10 in the LDF are strongly related to the classification of GCB cases, whereas the inclusion of MUM1 in any algorithm is related to non-GCB detection, getting the most considerable value in the LDF

Numeric Tags 1 = CD10, 2 = BCL6, 3 = FOXP1, 4 = GCTE1, and 5 = MUM1