SERS-based differential diagnosis between multiple solid malignancies: breast, colorectal, lung, ovarian and oral cancer

Figures & data

Table S1 Clinical characteristics of breast cancer group patients.

Variables	Number of patients (n=42)
GENDER
M	0
F	42
AGE
≤60	31
>60  N/A	8 3
HISTOLOGIC GRADE (Nottingham score)
I	6
II	17
III	18
N/A	1
RECEPTOR STATUS
ER
+	27
-	9
N/A	6
PR
+	27
-	9
N/A	6
HER
0  1+  2+  3+  N/A	17 5 6 8 6

Table S2 Clinical characteristics of colorectal cancer group patients.

VARIABLES	NUMBER OF PATIENTS (N=109)
GENDER
M	55
F	43
N/A	11
AGE
≤60	45
>60	53
N/A	11
TNM CLASSIFICATION
T
1	5
2	13
3	55
4  N/A	22 14
N
0	54
+	41
N1	26
N2	15
N/A	14
M
0	79
+  N/A	16 14
LOCALIZATION
ASCENDING COLON	12
TRANSVERSE COLON	4
DESCENDING COLON	3
SIGMOID COLON	19
CECUM	9
RECTOSIGMOID JUNCTION	6
SUPERIOR RECTUM	7
MID RECTUM	13
UPPER AND MID RECTUM	1
INFERIOR RECTUM	10
SIGMOID COLON AND UPPER RECTUM	5
HEPATIC FLEXURE  SPLENIC FLEXURE  N/A	6 2 12

Table S3 Clinical characteristics of lung cancer group patients.

VARIABLES	NUMBER OF PATIENTS (N=33)
GENDER
M	26
F	7
AGE
≤60	14
>60	19
TNM CLASSIFICATION
T
1	0
2	6
3	10
4  N/A	14 3
N
0	2
+	28
N1	1
N2	21
N3  N/A	6 3
M
0	22
+	8
M1	7
M1A  N/A	1 3
LOCALIZATION
LSS	3
LSD	8
LM	3
BPD	2
LID	5
LIS	5
BLID	1
TRACHEA, BPS	1
TRACHEA, LSD	1
TRACHEA, BPD	1
LM, LID	1
MEDIASTINAL	1
N/A	1

Table S4 Clinical characteristics of ovarian cancer group patients.

Variables	Number of patients (n=13)
AGE
≤60	10
>60	3
PATHOLOGY
Endometriosis	2
Serous borderline tumor	3
Mucinous borderline tumor	2
Mucinous carcinoma	1
Endometrioid carcinoma	2
Serous high grade	1
Clear cells	1
Serous high grade/clear cells	1
Stage
IA	1
IC2	1
IIA	1
IIIC	3

Table S5 Clinical characteristics of oral cancer group patients

VARIABLES	NUMBER OF PATIENTS (N=17)
GENDER
M	14
F	3
AGE
≤60	6
>60	11
TNM CLASSIFICATION
T
1	3
2	6
3	2
4  N/A	5 1
N
0	9
+	7
N1  N2  N/A	6 1 1
M
0	16
+  N/A	0 1
LOCALIZATION
FLOOR OF MOUTH	6
BUCCAL MUCOSA	2
TONGUE	4
LOWER GINGIVAE	2
HARD PALATE	1
FLOOR OF MOUTH AND TONGUE	2

Table S6 Demographic information of healthy controls.

VARIABLES	NUMBER OF CONTROLS (N=39)
GENDER
M	25
F	14
AGE
50–60	37
>60	2

Figure 1 The mean surface-enhanced Raman scattering (SERS) spectra of serum from controls and breast, colorectal, lung, ovarian and oral cancer samples (all types combined) and their spectral difference. All SERS spectra were mean normalized and for each spectrum, two measurements were averaged.

Figure 2 The results provided by the five principal component analysis–linear discriminant analysis (PCA-LDA) models, which compared control samples versus breast, colorectal, lung, ovarian and oral cancer samples. PPV denotes the positive predictive value, while NPV refers to the negative predictive value. The number of principal components (PCs) was chosen such that the sensitivities and specificities for predicting on the validation sets were similar to the ones of the resubstitution analysis.

Figure 3 The results of the principal component analysis–linear discriminant analysis (PCA-LDA) for all cancer types combined (breast, colorectal, lung, ovarian and oral cancer). PPV denotes the positive predictive value, while NPV refers to the negative predictive value. The number of principal components (PCs) was chosen such that the sensitivities and specificities for predicting on the validation sets were similar to the ones of the resubstitution analysis.

Figure 4 The results of the principal component analysis–linear discriminant analysis (PCA-LDA) that assessed the differential diagnosis between controls and breast, colorectal, lung, ovarian and oral cancer. PPV denotes the positive predictive value, while NPV refers to the negative predictive value. The number of principal components (PCs) was chosen such that the sensitivities and specificities for predicting on the validation sets were similar to the ones of the resubstitution analysis. The figures of merit represent the accuracy to distinguish between control samples and all types of cancer combined.

Figure S1 The physical properties of silver nanoparticles used for acquiring surface-enhanced Raman scattering (SERS) spectra of serum samples. The UV-Vis spectrum (A) and a representative transmission electron microscopy image (B) of the silver nanoparticles used for SERS

Figure S2 The mean surface-enhanced Raman scattering (SERS) spectra of serum from controls and breast cancer patients and their difference. The SERS spectra were acquired by focusing a 532 nm laser (10 mW) on the samples for 40 s. The SERS spectra were mean normalized and for each spectrum, two measurements were averaged

Figure S3 The mean surface-enhanced Raman scattering (SERS) spectra of serum from controls and colorectal cancer patients and their difference. The SERS spectra were acquired by focusing a 532 nm laser (10 mW) on the samples for 40 s. The SERS spectra were mean normalized and for each spectrum, two measurements were averaged

Figure S4 The mean surface-enhanced Raman scattering (SERS) spectra of serum from controls and lung cancer patients and their difference. The SERS spectra were acquired by focusing a 532 nm laser (10 mW) on the samples for 40 s. The SERS spectra were mean normalized and for each spectrum, two measurements were averaged

Figure S5 The mean surface-enhanced Raman scattering (SERS) spectra of serum from controls and ovarian cancer patients and their difference. The SERS spectra were acquired by focusing a 532 nm laser (10 mW) on the samples for 40 s. The SERS spectra were mean normalized and for each spectrum, two measurements were averaged

Figure S6 The mean surface-enhanced Raman scattering (SERS) spectra of serum from controls and oral cancer patients and their difference. The SERS spectra were acquired by focusing a 532 nm laser (10 mW) on the samples for 40 s. The SERS spectra were mean normalized and for each spectrum, two measurements were averaged