Biomarkers of Rapid Progression in Advanced Non-Small Cell Lung Cancer

Abstract

Methods and kits for identifying rapidly progressing lung cancer in a subject are provided. The method includes obtaining a biological sample from the subject and assaying a level of a biomarker in a biomarker panel in the biological sample where the panel includes at least one biomarker selected from Table I or Table II. The method further includes determining with the subject is treatment naïve or has received at least one treatment; and comparing the level of the biomarker in the subject's sample to a cutoff value listed in Table I for treatment naïve subjects or Table II for previously treated subjects. The method further includes determining whether the subject's level is above or below the cutoff value to determine whether the subject has rapidly progressing lung cancer.

Description

RELATED APPLICATIONS

This application is a division of U.S. application Ser. No. 15/102,647, filed Dec. 8, 2014, which claims the benefit under 35 U.S.C. § 371 of International Application No. PCT/US2014/069009, filed Dec. 8, 2014, which claims the benefit of U.S. Provisional Application No. 61/913,740, filed Dec. 9, 2013, which is incorporated herein in their entirety.

TECHNICAL FIELD

The present invention relates to methods and kits for identifying patients with rapidly progressing disease, and in particular to methods and kits for identifying patients with rapidly progressing non-small cell lung cancer and for determining optimal treatment plans for patients with rapidly progressing disease and for monitoring treatments.

BACKGROUND

Lung cancer is leading cause of cancer-related mortality worldwide, with a projected 159,480 patients succumbing to the disease in the US in 2014.(1) Lung cancer is typically characterized as being quite aggressive with poor clinical outcomes that stem from the very rapid proliferation rates, high metastatic potential, and general insensitivity to available treatment strategies. Non-small cell lung cancer (NSCLC) presents unique challenges to health care providers because of its common late stage of presentation and the poor median overall survival of advanced disease.(2, 3) Patients often become too ill to receive second line treatment as noted by a recent phase III clinical trial where only 37% of the patients randomized to docetaxel at disease progression received treatment.(4)

An objective of this study was reveal circulating biomarkers to identify patients with rapidly progressing NSCLC. This study examined 76 biomarkers that are surrogates for several pathophysiological processes associated with aggressive disease in both frontline (chemo naïve) and second-line and greater (pretreated) patients. A total of 186 patient serum specimens were evaluated. Processes evaluated include angiogenesis, phenotypic transdifferentiation (i.e. EMT, cancer stem cells), cancer cachexia, chronic inflammation, and immune system response.

Identification of patients with rapidly-progressing disease who are insensitive to standard platinum double-based chemotherapy will provide clinical implications.

There is a need in the art for screening methods and kits that identify patients with rapidly progressing disease in patients that are treatment naïve and in patients that have received a treatment.

BRIEF SUMMARY

Methods and kits for identifying rapidly progressing lung cancer in a subject are provided. The method includes obtaining a biological sample from the subject and assaying a level of a biomarker in a biomarker panel in the biological sample where the panel includes at least one biomarker selected from Table I or Table II. The method is dependent on whether the subject is treatment naïve or has received at least one treatment; and comparing the level of the biomarker in the subject's sample to a cutoff value listed in Table I for treatment naïve subjects or Table II for previously treated subjects. The method further includes determining whether the subject's level is above or below the cutoff value to determine whether the subject has rapidly progressing lung cancer.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1F illustrate representative ‘Box and Whisker’ plots indicating distribution of biomarker levels in the frontline chemotherapy cohort, separated based on a 90 day cutoff for rapid disease progression. Shown are TNF-α (panel 1A), sTNFRII (panel 1B), IL-6 (panel 1C), sVEGFR3 (panel 1D), betacellulin (panel 1E) and Total PSA (panel 1F). All biomarker levels are provided in pg/mL.

FIGS. 2A-2F illustrate representative ‘Box and Whisker’ plots indicating distribution of biomarker levels in the treated chemotherapy cohort, separated based on a 45 day cutoff for rapid disease progression. Shown are sEGFR (panel 2A), sTNFRI (panel 2B), TRAIL (panel 2C), IGFBP-1 (panel 2D), IGFBP-2 (panel 2E) and HGF (panel 2F). All biomarker levels are provided in pg/mL.

DETAILED DESCRIPTION

The present invention will utilize at least one biomarker measured in a biological sample obtained from a subject to identify rapidly progressing lung cancer, and in some embodiments in subjects having rapidly progressing NSCLC. In some embodiments, the at least one biomarker may be selected from a panel of biomarkers. In some embodiments, one or more biomarkers from a panel of biomarkers are used to identify subjects having rapidly progressing NSCLC in subjects that are treatment naïve or that have been previously treated.

The term “biomarker” as used herein, refers to any biological compound that can be measured as an indicator of the physiological status of a biological system. A biomarker may comprise an amino acid sequence, a nucleic acid sequence and fragments thereof. Exemplary biomarkers include, but are not limited to cytokines, chemokines, growth and angiogenic factors, metastasis related molecules, cancer antigens, apoptosis related proteins, proteases, adhesion molecules, cell signaling molecules and hormones.

“Measuring” or “measurement” means assessing the presence, absence, quantity or amount (which can be an effective amount) of a given substance within a sample, including the derivation of qualitative or quantitative concentration levels of such substances, or otherwise evaluating the values or categorization of a subject's clinical parameters. Alternatively, the term “detecting” or “detection” may be used and is understood to cover all measuring or measurement as described herein.

The terms “sample” or “biological sample” as used herein, refers to a sample of biological fluid, tissue, or cells, in a healthy and/or pathological state obtained from a subject. Such samples include, but are not limited to, blood, bronchial lavage fluid, sputum, saliva, urine, amniotic fluid, lymph fluid, tissue or fine needle biopsy samples, peritoneal fluid, cerebrospinal fluid, and includes supernatant from cell lysates, lysed cells, cellular extracts, and nuclear extracts. In some embodiments, the whole blood sample is further processed into serum or plasma samples. In some embodiments, the sample includes blood spotting tests.

The term “subject” or “patient” as used herein, refers to a mammal, preferably a human.

The term “rapid progression” or “rapidly progressing” as used herein, refers to cases of disease that were observed to not respond to chemotherapy or targeted agents and advance (evidence of nascent metastases, increasing tumor volume, etc.) within a defined time interval. Thresholds for rapid progression were set to 90 days after the first treatment for the treatment naïve patients and 45 days after the second or subsequent treatment for the previously treated patients. Circulating levels of 27 biomarkers were found to be significantly associated (p≤0.05) with progression within 90 days of treatment initiation in treatment naive patients. Circulating levels of 34 biomarkers were found to be significantly associated (p≤0.05) with progression within 45 days of treatment initiation in previously treated patients.

Biomarkers

Biomarkers that may be used include but are not limited to cytokines, chemokines, growth and angiogenic factors, metastasis related molecules, cancer antigens, apoptosis related proteins, proteases, adhesion molecules, cell signaling molecules and hormones. In some embodiments, the biomarkers may be proteins that are circulating in the subject that may be detected from a fluid sample obtained from the subject. In some embodiments, the fluid sample may be serum or plasma. In some embodiments, one or more biomarkers from a panel of biomarkers may be used.

In some embodiments, one or more biomarkers may be measured in a biomarker panel. The biomarker panel may include a plurality of biomarkers. In some embodiments, the biomarker panel may include ten or fewer biomarkers. In yet other embodiments, the biomarker panel may include 1, 2, 3, 4, 5, 6 or 7 biomarkers. In some embodiments, the biomarker panel may be optimized from a candidate pool of biomarkers. By way of non-limiting example, the biomarker or biomarker panel may be configured for determining whether a treatment naïve subject is likely to have rapidly progressing disease. In some embodiments, the biomarker or biomarker panel may be configured for determining whether a previously treated subject is likely to have rapidly progressing disease.

In some embodiments, the biomarker panel may include biomarkers from several biological pathways. By way of non-limiting example, the biomarkers may be associated the tumor necrosis factor (TNF) family, the epidermal growth factor (EGF) family, the vascular endothelial growth factor (VEGF) family, the Insulin-like growth factor (IGF) family and/or associated with angiogenesis. In some embodiments, the TNF family may include, but is not limited to TNF-RI, TNF-RII, TNF-α and TRAIL. In some embodiments, the EGF family may include but is not limited to betacellulin, amphiregulin, and soluble-EGFR. In some embodiments, the VEGF family may include but is not limited to VEGF-A, VEGF-C, and soluble-VEGFR3. In some embodiments, the IGF family may include but is not limited to IGF-I, IGF-II, IGFBPs-2, -3, and -7. In some embodiments, the biomarkers associated with angiogenesis may include follistatin, IL-6, endoglin, PDGF-BB, IGF-1, and endothelin-1, PLGF, IL-8, MMP-2, HGF, sVEGFR2, VEGF-A, leptin, PDGF-AA, and others. In some embodiments, the biomarker panel may include one or more biomarkers from a panel of biomarkers. In some embodiments, the one or more biomarkers may be selected from the list of biomarkers in Table I. In some embodiments, the one or more biomarkers may be selected from the list of biomarkers in Table II. In some embodiments, other biomarkers may be used and may be combined with the biomarkers listed in Tables I and II.

In some embodiments, patients with rapid disease progression in a treatment naïve group may be identified using one or more biomarkers selected from a panel of biomarkers listed Table I. In some embodiments, the one or more biomarkers may be selected from the group of biomarkers identified in Table I as having a p-value of 0.01 or less. In some embodiments, the one or more biomarkers may include at least one biomarker from Table I having a p-value of 0.01 or less and at least one biomarker from Table I having a p-value of 0.05 or less. In some embodiments, the one or more biomarkers may include biomarkers selected from the group consisting of sTNFRI, sTNFRII, CA 19-9, Follistatin, Total PSA, TNF-α and IL-6. In some embodiments, the biomarkers may include 3, 4, 5, 6 or 7 biomarkers selected from the group consisting of sTNFRI, sTNFRII, CA 19-9, Follistatin, Total PSA, TNF-α and IL-6 and may also include additional biomarkers. In some embodiments, patients with rapid disease progression in a treatment naïve group may be identified using a panel of one or more biomarkers selected from Table I where the panel may include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26 or 27 biomarkers.

In some embodiments, patients with rapid disease progression in a previously treated group may be identified using one or more biomarkers selected from a panel of biomarkers selected from Table II. In some embodiments, the one or more biomarkers may be selected from the group of biomarkers identified in Table II as having a p-value of 0.01 or less. In some embodiments, the one or more biomarkers may include at least one biomarker from Table II having a p-value of 0.01 or less and at least one biomarker form Table II having a p-value of 0.05 or less. In some embodiments, the one or more biomarkers may be selected from the group consisting of TRAIL, sTNFRI, IGFBP-1, sEGFR, IGF-1, TGF-β, HGF, MMP-7, MMP-2, α-fetoprotein, Osteopontin, sVEGFR2 and IL-6. In some embodiments, the one or more biomarkers may include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or 13 biomarkers selected from the group consisting of TRAIL, sTNFRI, IGFBP-1, sEGFR, IGF-1, TGF-β, HGF, MMP-7, MMP-2, α-fetoprotein, Osteopontin, sVEGFR2 and IL-6 and may also include additional biomarkers. In some embodiments, patients with rapid disease progression in a previously treated group may be identified using one or more biomarkers selected from Table II where the panel may include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33 or 34 biomarkers.

In some embodiments, the biomarker panel may be selected using a reference profile that can be made in conjunction with a statistical algorithm used with a computer to implement the statistical algorithm to sort the subject into a group. In some embodiments, the statistical algorithm is a learning statistical classifier system. The learning statistical classifier system can be selected from the following list of non-limiting examples, including Random Forest (RF), Classification and Regression Tree (CART), boosted tree, neural network (NN), support vector machine (SVM), general chi-squared automatic interaction detector model, interactive tree, multiadaptive regression spline, machine learning classifier, and combinations thereof. By way of non-limiting example, exemplary tools for selecting a biomarker panel may be found in WO 2012/054732 and U.S. Provisional Application No. 61/792,710 which are incorporated by reference herein.

Biomarker Measurement

Measurement of a biomarker generally relates to a quantitative measurement of an expression product, which is typically a protein or polypeptide. In some embodiments, the measurement of a biomarker may relate to a quantitative or qualitative measurement of nucleic acids, such as DNA or RNA. The measurement of the biomarker of the subject detects expression levels of one or more biomarkers in subjects having lung cancer and in some embodiments, compares the expression level of each biomarker measured to a cutoff value listed in Table I or in Table II.

Expression of the biomarkers may be measured using any method known to one skilled in the art. Methods for measuring protein expression include, but are not limited to Western blot, immunoprecipitation, immunohistochemistry, Enzyme-linked immunosorbent assay (ELISA), Radio Immuno Assay (RIA), radioreceptor assay, proteomics methods, mass-spectrometry based detection (SRM or MRM) or quantitative immunostaining methods. Methods for measuring nucleic acid expression or levels may be any techniques known to one skilled in the art. Expression levels from the one or more biomarkers are measured in the subject and compared to the levels of the one or more biomarkers obtained from a cohort of subjects described below.

In some embodiments, MILLIPLEX® MAP multiplex assays may be used to determine the expression levels of the one or more biomarkers in a panel of biomarkers. (EMD Millipore, Billlerica, Mass.) In some embodiments, Luminex-based xMAP® multiplexed immunoassays may be used to determine the expression levels of the panel of biomarkers. (Luminex Corp.; Austin, Tex.) In some embodiments, biomarker concentrations may be calculated based on 7-point standard curves using a five-parametric fit algorithm in xPONENT v4.0.3 (Luminex Corp.) Other measurement systems and techniques may also be used.

In some embodiments, a kit may be provided with reagents to measure at least one biomarker. In some embodiments, the kit may be provided with reagents to measures at least two biomarkers in a panel of biomarkers. The panel of biomarkers to be measured with the kit may include two or more biomarkers from Table I or Table II. The kit may include reagents to measure a panel of biomarkers for subjects that are treatment naïve. The kit may include reagents to measure a panel of biomarkers for subjects that have been previously treated.

Analysis of Biomarker Measurements

In some embodiments, methods for determining whether a subject has rapidly progressing lung cancer may be based upon the biomarker measurements from the subject compared to a reference cutoff level for each biomarker measured. The reference cutoff level for a plurality of biomarkers is listed in Tables I-IV.

Treatment Stratification

In some embodiments, the analysis of the biomarker panel may be used to determine a treatment regime for the subject. In some embodiments, the measurement of one or more biomarkers in the panel may be used to determine whether to begin a treatment, to continue the same treatment or to modify the treatment regime for a subject. The treatment may be modified by changing the drug administered to the subject or to add an additional drug to the existing drug treatment regime, to change the dosage or other changes. In some embodiments, other types of treatment regimes may be used such as radiation. In some embodiments, the identification of patients with rapidly progressing disease who are insensitive to standard platinum doublet-based chemotherapy may have multiple clinical implications. The identification of patients with rapidly progressing disease using the biomarker level may place the patient in a specific treatment, a different treatment or an earlier treatment in the overall treatment strategy. In some embodiments, a specific targeted chemotherapeutic agent may be selected based on the identification of rapidly progressing disease. In some embodiments, the specific chemotherapeutic agent may be changed based in the biomarker level measured relative to the cutoff value. By way of non-limiting example, VEGF-A levels in patients taking bevacizumab may be monitored and the treatment regime may be changed or not changed based on the level of VEGF-A measured and compared to the cutoff level in either Table I for treatment naïve patients or Table II for previously treated patients.

Patient Cohorts

Between 2004 and 2011, 186 patients at Rush University Medical Center (Chicago, Ill.) were enrolled and divided into the following cohorts: patients with advanced lung adenocarcinoma naïve to previous chemotherapy (n=76) and patients with advanced lung adenocarcinoma that have failed at least 1 line of chemotherapy (n=110). All stage classifications were determined according to the American Joint Committee on Cancer (AJCC) seventh edition criteria and confirmed by pathological evaluation (5, 6). All patient data was obtained after informed consent was given by the patient. The study was conducted in absolute compliance with the Institutional Review Board at Rush University Medical Center.

Measurement of Serum Biomarker Concentrations

All peripheral blood was collected pre-treatment and processed into serum using standard phlebotomy protocols. Serum was archived at −80° C. in aliquots; and no evaluable specimen were subjected to more than two freeze-thaw cycles (7-10). Serum was evaluated using the following biomarker panels: the MILLIPLEX® MAP Human Angiogenesis/Growth Factor Panel (EMD Millipore, Billerica, Mass.) and included the following assays: epidermal growth factor (EGF), angiopoietin-2, granulocyte colony-stimulating factor (G-CSF), bone morphogenic protein 9 (BMP-9), endoglin, endothelin-1, leptin, fibroblast growth factor-1 (FGF-1), FGF-2, follistatin, interleukin-8 (IL-8), hepatocyte growth factor (HGF), heparin-binding epidermal growth factor (HB-EGF), placental growth factor (PLGF), vascular endothelial growth factor-A (VEGF-A), VEGF-C and VEGF-D; the MILLIPLEX® MAP Human Soluble Cytokine Receptor Panel which includes sVEGFR1, sVEGFR2, sVEGFR3, sIL-6R, sgp130, sTNFRI, and sTNFRII; the MILLIPLEX® MAP Human Circulating Cancer Biomarker Panel 1 which includes sFasL, IL-6, prolactin, SCF, TGF-α, and TNF-α; the MILLIPLEX® MAP Human MMP1 and MMP2 panels which combine to provide MMPs-1, -2, -3, -7, -9, and -10; and the MILLIPLEX® MAP Human Cytokine/Chemokine Panel II, which includes SDF-1 (α+β). All assays were performed according to the manufacturer's recommended protocols and in a blinded fashion. All data was collected on a Luminex FlexMAP 3D system with concentrations calculated based on 7-point standard curves using a five-parametric fit algorithm in xPONENT v4.0.3 (Luminex Corp., Austin, Tex.).

Statistical Methods

One endpoint of the investigation was to evaluate associations of the circulating biomarkers tested with clinical outcome measures for patients determined to have rapidly progressing disease. Progression status values were classified as ‘slow’ or ‘rapid’ based on chosen clinically-relevant cutoff (45 days for frontline and 90 days for those second-line and above) value. Association of the slow/rapid progression state was then accomplished with low and high values of a biomarker based on cutoff values obtained from a grid search for an optimal cutoff within the potential range of the biomarker values that maximizes the p-value for disease progression via Fisher's exact test. Additionally, an adjusted p-value, which adjusts for the grid search, is also obtained. These analyses were performed regardless of regimen type. All statistical analyses were completed using the R Statistical Package.

Results

Frontline Treatment for Advanced NSCLC

A total of 27 biomarkers were identified for identifying advanced stage NSCLC patients that were chemotherapy naïve with rapidly progressing disease. The specific cutoff values, number of patients in each arm, and optimal p-values are all provided in Table I, with the distribution of these classifications for select representative biomarkers provided in FIG. 1. A complete account for all 76 biomarkers is provided in Table III. Included in the findings were biomarkers with optimal p-value ≤0.05 representing the following processes: angiogenesis (sTNFRI, sTNFRI I, Follistatin, TNF-α, Betacellulin, sVEGFR3, VEGF-A, Endoglin, MMP-10, PDGF-BB, VEGF-C, IGF-I, IGFBP-3, IGFBP-5, Endothelin-1, and Amphiregulin), cancer cachexia (TNF-α, sTNFRI, sTNFRII, IGF-I, IGFBP-3, IGFBP-5, IL-6, IL-6R), and phenotypic transdifferentiation (betacellulin, IGF-I, IGFBP-3, IGFBP-5, sEGFR, and prolactin).

TABLE I
Treatment Naïve Cohort
	Cutoff
	Value	Cases ≤	Cases >		Adjusted
Biomarker	(pg/mL)	cutoff	Cutoff	p-Value	p-value
sTNFRI	1879.8	48	12	0.0003	0.001
sTNFRII	8493.9	49	11	0.0011	0.006
CA 19-9	20.9	62	13	0.0029	0.018
Follistatin	990.0	48	15	0.0033	0.007
Total PSA	67.1	47	28	0.0042	0.027
TNF-α	0.61	12	45	0.0050	0.029
IL-6	13.1	48	10	0.0100	0.021
TGF β1	30665.9	64	11	0.0132	0.083
Betacellulin	14.9	25	28	0.0162	0.058
CA 15.3	55.0	62	13	0.0203	0.077
sCD30	101.5	40	20	0.0219	0.111
sVEGFR3	2930.8	48	12	0.0221	0.094
VEGF-A	727.1	49	27	0.0222	0.092
Endoglin	993.8	48	15	0.0228	0.084
MMP-10	272.6	43	32	0.0244	0.122
sRAGE	84.2	39	21	0.0244	0.121
PDGF-BB	13336.6	12	40	0.0248	0.075
VEGF-C	32.9	10	53	0.0255	0.105
IGF-I	31266.8	46	30	0.0264	0.133
IGFBP-3	1452.8	53	23	0.0308	0.168
sEGFR	25073.2	29	43	0.0392	0.177
IGFBP-5	151.1	48	28	0.0428	0.192
Endothelin-1	8.0	39	24	0.0430	0.124
Amphiregulin	38.7	16	36	0.0445	0.222
GRO	3960.3	47	28	0.0453	0.181
sIL-6R	10666.7	20	40	0.0493	0.203
Prolactin	5353.7	30	28	0.0497	0.152

Previous Treatment for Advanced NSCLC

A total of 34 biomarkers were identified for identifying advanced stage NSCLC patients that failed frontline chemotherapy with rapidly progressing disease. The specific cutoff values, number of patients in each arm, and optimal p-values are all provided in Table II, with the distribution of these classifications for select representative biomarkers provided in FIG. 2. A complete account for all 76 biomarkers are provided in Table IV. Included in the findings were biomarkers with optimal p-value D105 representing the following processes: angiogenesis (sVEGFR2, follistatin, IGF-I, IGF-II, IGFBPs-1, -2, -3, -5, -7, IL-8, MMP-2, MMP-7, PLGF, Leptin, PDGF-AA, TNF-α, sTNFRI, sTNFRII, and VEGF-A), cancer cachexia (HGF, IGF-I, IGF-II, IGFBPs-1, -2, -3, -5, -7, IL-6, Leptin, TRAIL, sCD30, TNF-α, sTNFRI, and sTNFRII), phenotypic transdifferentiation (beta-HCG, α-fetoprotein, HE4, HGF, IGF-I, IGF-II, IGFBPs-1, -2, -3, -5, -7, osteopontin, PLGF, TGF-β, and sEGFR), and inflammation or immune response (CYFRA 21-1, GRO, IL-6, IL-8, sIL-2Rα, sIL-4R, TRAIL, sCD30, TNF-α, sTNFRI, and sTNFRII).

TABLE II
Post Treatment Cohort
	Cutoff
	Value	Cases ≤	Cases >		Adjusted
Biomarker	(pg/mL)	cutoff	Cutoff	p-Value	p-value
TRAIL	60.3	35	76	0.0005	0.003
sTNFRI	1506.6	62	34	0.0007	0.005
IGFBP-1	9.2	88	22	0.0009	0.005
sEGFR	50565.2	36	68	0.0012	0.007
IGF-I	7807.7	32	78	0.0021	0.008
TGF-β1	9073.7	10	77	0.0028	0.016
HGF	239.7	81	30	0.0035	0.034
MMP-7	8326.9	68	43	0.0043	0.024
MMP-2	33782.5	31	80	0.0044	0.027
α-fetoprotein	964.1	63	40	0.0050	0.031
Osteopontin	34067.2	49	54	0.0058	0.034
sVEGFR2	10846.5	13	83	0.0073	0.049
IL-6	8.0	66	37	0.0098	0.035
CYFRA21.1	768.7	51	52	0.0119	0.03
IGF-II	507.7	37	68	0.0129	0.083
sTNFRII	7588.9	65	31	0.0151	0.093
CA 15.3	34.5	83	28	0.0156	0.056
sCD30	129.7	73	23	0.0177	0.081
sIL-4R	2096.7	14	82	0.0201	0.103
sIL-2Ralpha	836.6	55	41	0.0208	0.118
IGFBP-7	53.7	18	92	0.0211	0.137
TNF-α	91.1	93	17	0.0213	0.114
VEGF-A	1297.7	98	14	0.0227	0.05
Follistatin	968.9	11	10	0.0237	0.013
Leptin-1	11840.0	55	48	0.0239	0.154
IGFBP-3	1486.8	90	20	0.0263	0.161
PLGF	86.8	69	10	0.0268	0.127
IGFBP-5	193.5	44	66	0.0296	0.166
GRO	4254.6	80	30	0.0333	0.162
IL-8	63.9	96	15	0.0334	0.178
HE4	2993.4	88	23	0.0343	0.201
IGFBP-2	22.9	64	46	0.0344	0.169
PDGF-AA	10851.4	12	98	0.0375	0.218
B-HCG	0.16	26	85	0.0446	0.239

TABLE III
Treatment Naïve Cohort
		Prop. ≤	No. ≤	Prop. >	No. >	Total	Optimal	Adjusted
Biomarker	Cutoff	cutoff	Cutoff	cutoff	Cutoff	No.	p-value	p-value
sTNFRI	1879.789	0.75	48	0.166667	12	60	0.000344	0.001
sTNFRII	8493.872	0.734694	49	0.181818	11	60	0.001094	0.006
CA.19.9	20.91498	0.758065	62	0.307692	13	75	0.00293	0.018
Follistatin	990.0075	0.625	48	1	15	63	0.003303	0.007
Total.PSA	67.1	0.808511	47	0.464286	28	75	0.004239	0.027
TNF.alpha	0.614	1	12	0.577778	45	57	0.005026	0.029
IL.6	13.09553	0.75	48	0.3	10	58	0.009969	0.021
TGF.beta1	30665.91	0.625	64	1	11	75	0.013211	0.083
Betacellulin	14.915	0.88	25	0.571429	28	53	0.016219	0.058
CA.15.3	55.03582	0.741935	62	0.384615	13	75	0.020338	0.077
sCD30	101.452	0.525	40	0.85	20	60	0.021891	0.111
sVEGFR3	2930.841	0.708333	48	0.333333	12	60	0.022146	0.094
VEGF	727.1049	0.591837	49	0.851852	27	76	0.02221	0.092
Endoglin	993.8066	0.791667	48	0.466667	15	63	0.022804	0.084
MMP.10	272.6212	0.790698	43	0.53125	32	75	0.024406	0.122
SRAGE	84.19616	0.74359	39	0.428571	21	60	0.024413	0.121
PDGF.BB	13336.56	0.416667	12	0.8	40	52	0.024825	0.075
VEGF.C	32.93148	0.4	10	0.773585	53	63	0.025532	0.105
IGF.1	31266.81	0.586957	46	0.833333	30	76	0.026375	0.133
IGFBP.3	1452.848	0.603774	53	0.869565	23	76	0.030829	0.168
sEGFR	25073.18	0.827586	29	0.581395	43	72	0.039227	0.177
IGFBP.5	151.1263	0.770833	48	0.535714	28	76	0.042771	0.192
Endothelin.1	8.012	0.615385	39	0.875	24	63	0.043036	0.124
Amphiregulin	38.666	0.5	16	0.805556	36	52	0.044476	0.222
GRO	3960.296	0.765957	47	0.535714	28	75	0.045349	0.181
sIL.6R	10666.72	0.45	20	0.725	40	60	0.049324	0.203
Prolactin	5353.744	0.8	30	0.535714	28	58	0.049721	0.152
sIL.2Ralpha	1390.342	0.702128	47	0.384615	13	60	0.051904	0.177
FGF.2.1	77.31547	0.744186	43	0.466667	15	58	0.061694	0.192
VEGF.D	102.6913	0.782609	46	0.529412	17	63	0.063445	0.242
PDGF.AA	47558.59	0.607843	51	0.833333	24	75	0.065221	0.315
Tenascin.C	1130.558	0.84	25	0.592593	27	52	0.068469	0.207
RANTES	38837	0.821429	28	0.595745	47	75	0.071934	0.311
IGFBP.4	19.09024	0.821429	28	0.604167	48	76	0.072878	0.299
sIL.1RI	61.50253	0.704545	44	0.4375	16	60	0.073962	0.299
MIF	192.023	0.875	16	0.627119	59	75	0.074126	0.225
IGF.II	304.3222	0.5	18	0.745455	55	73	0.078394	0.348
Angiopoietin.2	2037.563	0.8	40	0.565217	23	63	0.080545	0.237
VEGF.A	383.2805	0.785714	42	0.571429	21	63	0.086763	0.279
MMP.7	8619.594	0.595238	42	0.787879	33	75	0.087197	0.313
CYFRA.21.1	1129.805	0.78125	32	0.538462	26	58	0.09016	0.356
C.Peptide	3468.038	0.640625	64	0.909091	11	75	0.093476	0.27
CEA	24090.46	0.725806	62	0.461538	13	75	0.099668	0.212
sIL.1RII	7636.054	0.6875	48	0.416667	12	60	0.102237	0.289
sVEGFR1	112.065	0.416667	12	0.6875	48	60	0.102237	0.421
HGF	594.8866	0.74026	77	0.5	14	91	0.108812	0.452
OPN	41149.3	0.738095	42	0.5	16	58	0.118998	0.34
FP	1001.211	0.738095	42	0.5	16	58	0.118998	0.451
IGFBP.7	88.26316	0.725806	62	0.5	14	76	0.119713	0.423
MMP.2	29712.45	0.555556	27	0.75	48	75	0.121477	0.46
Beta.HCG	0.227662	0.866667	15	0.633333	60	75	0.122652	0.372
EGF.1	214.3822	0.590909	22	0.806452	31	53	0.123603	0.319
HB.EGF	566.2644	0.746835	79	0.5	10	89	0.135974	0.491
CA125	27.12589	0.74359	39	0.526316	19	58	0.137596	0.297
EGF	7.196994	0.578947	19	0.772727	44	63	0.138297	0.407
Leptin.1	24785.75	0.625	48	0.9	10	58	0.142086	0.412
sgp130	247102.2	0.68	50	0.4	10	60	0.149211	0.483
SCF	44.52682	0.833333	18	0.631579	57	75	0.15047	0.568
BMP.9	175.4831	0.641026	39	0.833333	24	63	0.151565	0.529
Leptin	25030.11	0.666667	51	0.916667	12	63	0.153203	0.471
MMP.9	60960.72	0.9	10	0.646154	65	75	0.153985	0.527
MMP.1	1716.111	0.454545	11	0.71875	64	75	0.158025	0.535
TNF.Alpha	60.611	0.71875	64	0.454545	11	75	0.158025	0.471
G.CSF	29.014	0.76	50	0.538462	13	63	0.167521	0.572
TRAIL	43.58466	0.809524	21	0.62963	54	75	0.173113	0.63
IGFBP.2	4.596466	0.5	12	0.71875	64	76	0.178147	0.6
sIL.4R	2621.552	0.724138	29	0.548387	31	60	0.188122	0.579
IGFBP.6	93.69005	0.576923	26	0.74	50	76	0.194708	0.64
PDGF.AB.BB	53802.61	0.756757	37	0.605263	38	75	0.2169	0.759
Epiregulin	27.317	0.793103	29	0.625	24	53	0.226799	0.655
sVEGFR2	15112.92	0.682927	41	0.526316	19	60	0.263893	0.675
FGF.1	22.43221	0.74359	78	0.545455	11	89	0.27982	0.558
HE4	2394.042	0.738095	42	0.606061	33	75	0.318789	0.847
IGFBP.1	4.214632	0.742857	35	0.634146	41	76	0.33443	0.836
TGF.alpha	25.20851	0.655172	58	0.777778	18	76	0.396001	0.889
IL.8	8.220301	0.6	20	0.714286	56	76	0.405188	0.812
FGF.2	43.838	0.735849	53	0.6	10	63	0.452219	0.778
PLGF	19.93105	0.75	56	0.666667	33	89	0.466906	0.959

TABLE IV
Post Treatment Cohort
		Prop. ≤	No. ≤	Prop. >	No >	Total	Optimal	Adjusted
Biomarker	Cutoff	cutoff	Cutoff	Cutoff	Cutoff	No.	p-value	p-value
TRAIL	60.26394	0.485714	35	0.828947	76	111	0.00046	0.003
sTNFRI	1506.637	0.83871	62	0.5	34	96	0.000738	0.005
IGFBP.1	9.170897	0.795455	88	0.409091	22	110	0.000918	0.005
sEGFR	50565.21	0.5	36	0.823529	68	104	0.001168	0.007
IGF.1	7807.722	0.5	32	0.807692	78	110	0.002077	0.008
TGF.beta1	9073.747	0.3	10	0.792208	77	87	0.002814	0.016
HGF	239.72	0.802469	81	0.5	30	111	0.003543	0.034
MMP.7	8326.919	0.823529	68	0.55814	43	111	0.004292	0.024
MMP.2	33782.48	0.516129	31	0.8	80	111	0.004435	0.027
FP	964.1461	0.650794	63	0.9	40	103	0.005027	0.031
OPN	34067.17	0.877551	49	0.62963	54	103	0.005844	0.034
sVEGFR2	10846.5	0.384615	13	0.771084	83	96	0.007303	0.049
IL.6	8.036509	0.833333	66	0.594595	37	103	0.009841	0.035
CYFRA.21.1	768.7162	0.862745	51	0.634615	52	103	0.01185	0.03
IGF.II	507.7223	0.540541	37	0.794118	68	105	0.01287	0.083
sTNFRII	7588.941	0.8	65	0.548387	31	96	0.015052	0.093
CA.15.3	34.52987	0.783133	83	0.535714	28	111	0.015644	0.056
sCD30	129.7465	0.657534	73	0.913043	23	96	0.017669	0.081
sIL.4R	2096.678	0.428571	14	0.768293	82	96	0.020119	0.103
sIL.2Ralpha	836.577	0.818182	55	0.585366	41	96	0.020777	0.118
IGFBP.7	53.66313	0.944444	18	0.673913	92	110	0.021058	0.137
TNF.Alpha	91.0655	0.666667	93	0.941176	17	110	0.021294	0.114
VEGF	1297.73	0.755102	98	0.428571	14	112	0.022711	0.05
Follistatin	968.871	0.363636	11	0.9	10	21	0.023736	0.013
Leptin.1	11840.03	0.654545	55	0.854167	48	103	0.023887	0.154
IGFBP.3	1486.814	0.766667	90	0.5	20	110	0.026339	0.161
PLGF	86.76473	0.710145	69	0.3	10	79	0.026822	0.127
IGFBP.5	193.509	0.840909	44	0.636364	66	110	0.029601	0.166
GRO	4254.61	0.65	80	0.866667	30	110	0.033305	0.162
IL.8	63.90276	0.75	96	0.466667	15	111	0.033355	0.178
HE4	2993.413	0.772727	88	0.521739	23	111	0.034259	0.201
IGFBP.2	22.89657	0.796875	64	0.608696	46	110	0.034418	0.169
PDGF.AA	10851.41	0.416667	12	0.744898	98	110	0.03748	0.218
Beta.HCG	0.1585	0.884615	26	0.670588	85	111	0.044598	0.239
RANTES	112016.4	0.677083	96	0.928571	14	110	0.062467	0.353
Amphiregulin	18.45182	0.818182	22	0.588235	51	73	0.066034	0.236
FGF.2.1	66.75736	0.677966	59	0.840909	44	103	0.069766	0.338
sgp130	242843.7	0.771429	70	0.576923	26	96	0.075576	0.348
C.Peptide	3348.411	0.734694	98	0.454545	11	109	0.078178	0.289
CA.19.9	9.618456	0.615385	39	0.777778	72	111	0.079573	0.34
Endoglin	662.959	0.4	10	0.818182	11	21	0.080495	0.042
Total.PSA	528.9822	0.764706	85	0.576923	26	111	0.080808	0.25
Tenascin.C	3835.28	0.698413	63	0.4	10	73	0.081497	0.309
PDGF.AB.BB	66331.14	0.673913	92	0.888889	18	110	0.089174	0.436
sVEGFR1	360.3484	0.763158	76	0.55	20	96	0.091301	0.412
TGF.alpha	9.548921	0.783333	60	0.634615	52	112	0.096262	0.408
HB.EGF	311.0071	0.542857	35	0.733333	45	80	0.099623	0.442
sVEGFR3	2158.519	0.66129	62	0.823529	34	96	0.102868	0.428
IGFBP.4	79.29117	0.752809	89	0.571429	21	110	0.111044	0.546
MMP.1	6532.742	0.766234	77	0.617647	34	111	0.115508	0.467
sIL.1RI	65.47095	0.753086	81	0.533333	15	96	0.116428	0.506
CA125	56.64133	0.777778	81	0.636364	22	103	0.180181	0.453
MIF	303.2853	0.671875	64	0.787234	47	111	0.204763	0.685
MMP.9	55018.82	0.833333	24	0.689655	87	111	0.204937	0.752
FGF.1	10.48625	0.6	50	0.758621	29	79	0.218824	0.596
SCF	77.97	0.741935	93	0.611111	18	111	0.264052	0.827
sIL.1RII	9586.476	0.697674	86	0.9	10	96	0.273533	0.817
PDGF.BB	28385.66	0.68254	63	0.5	10	73	0.295399	0.791
Betacellulin	17.683	0.717949	39	0.588235	34	73	0.323903	0.877
Prolactin	15306.05	0.764045	89	0.642857	14	103	0.335635	0.848
EGF.1	31.14602	0.785714	14	0.627119	59	73	0.354469	0.865
sIL.6R	16360.28	0.683333	60	0.777778	36	96	0.357432	0.938
MMP.10	290.6754	0.75	72	0.666667	39	111	0.380985	0.877
G.CSF	4.271	0.727273	11	0.5	10	21	0.386997	0.252
BMP.9	167.918	0.5	10	0.727273	11	21	0.386997	0.228
Leptin	10637.02	0.5	10	0.727273	11	21	0.386997	0.236
VEGF.C	75.486	0.5	10	0.727273	11	21	0.386997	0.225
VEGF.A	228.021	0.727273	11	0.5	10	21	0.386997	0.232
CEA	24257.18	0.702128	94	0.823529	17	111	0.388648	0.739
TNF.alpha	2.975929	0.666667	21	0.768293	82	103	0.400402	0.966
SRAGE	54.18307	0.8	20	0.697368	76	96	0.417848	0.922
Epiregulin	71.12777	0.678571	56	0.588235	17	73	0.564203	0.636
IGFBP.6	150.9202	0.704545	88	0.772727	22	110	0.605457	1.00
EGF	5.662	0.7	10	0.545455	11	21	0.659443	0.542
Angiopoietin.2	1761.646	0.545455	11	0.7	10	21	0.659443	0.524
Endothelin.1	3.598	0.7	10	0.545455	11	21	0.659443	0.392
VEGF.D	49.626	0.545455	11	0.7	10	21	0.659443	0.571

The practice of the present invention will employ, unless otherwise indicated, conventional methods for measuring the level of the biomarker within the skill of the art. The techniques may include, but are not limited to, molecular biology and immunology. Such techniques are explained fully in the literature. See, e.g., Sambrook, et al. Molecular Cloning: A Laboratory Manual (Current Edition, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y.); Current Protocols in Molecular Biology (Eds. A Ausubel et al., NY: John Wiley & Sons, Current Edition); DNA Cloning: A Practical Approach, vol. I & II (D. Glover, ed.); Oligonucleotide Synthesis (N. Gait, ed., Current Edition); Nucleic Acid Hybridization (B. Hames & S. Higgins, eds., Current Edition); Transcription and Translation (B. Hames & S. Higgins, eds., Current Edition).

The above Figures and disclosure are intended to be illustrative and not exhaustive. This description will suggest many variations and alternatives to one of ordinary skill in the art. All such variations and alternatives are intended to be encompassed within the scope of the attached claims. Those familiar with the art may recognize other equivalents to the specific embodiments described herein which equivalents are also intended to be encompassed by the attached claims.

REFERENCES

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Claims

1. A method for identifying rapidly progressing lung cancer in a subject, the method comprising:

obtaining a biological sample from the subject;

assaying a level of a biomarker in a biomarker panel in the biological sample, the panel comprising at least one biomarker selected from Table I or Table II;

determining whether the subject is treatment naïve or has received at least one treatment;

comparing the level of the biomarker in the subject's sample to a cutoff value listed in Table I for treatment naïve subjects or Table II for previously treated subjects;

determining whether the subject's level is above or below the cutoff value to determine whether the subject has rapidly progressing lung cancer.

2. The method according to claim 1, wherein the at least one biomarker assayed has a p-value p≤0.05.

3. The method according to claim 1, wherein the at least one biomarker assayed has a p-value where p≤0.01.

4. The method according to claim 1, comprising determining the level of at least two biomarkers selected from Table I for treatment naïve subjects or from Table II for previously treated subjects.

5. The method according to claim 1, comprising determining the level of the biomarker for the panel of biomarkers wherein the at least one biomarker in the panel is selected from sTNFRI, sTNFRII, CA 19-9, Follistatin, Total PSA, TNF-α and IL-6 for treatment naïve subjects.

6. The method according to claim 1, comprising determining the level of the biomarker for the panel of biomarkers wherein the at least one biomarker in the panel comprises TRAIL, sTNFRI, IGFBP-1, sEGFR, IGF-1, TGF-β, HGF, MMP-7, MMP-2, α-fetoprotein, Osteopontin, sVEGFR2 and IL-6 for the pretreated subjects.

7. The method according to claim 1, wherein the lung cancer is non-small cell lung cancer.

8. The method according to claim 1, wherein the biological sample comprises plasma sample or serum sample.

9. The method according to claim 1, further comprising modifying a treatment regime for the subject when the comparison indicates that the subject has rapidly progressing lung cancer.

10. A kit for performing the measurement of the level of the biomarker of the subject in claim 1, wherein the kit comprises reagents for measuring the at least one biomarker.

11. The kit according to claim 10, wherein the kit comprises reagents for measuring serum or plasma.

12. The kit according to claim 10, wherein the kit comprises reagents for measuring the at least one biomarker in the panel wherein the at least one biomarker has a p-value where p≤0.05.

13. The kit according to claim 10, wherein the kit comprises reagents for measuring the at least one biomarker in the panel wherein the at least one biomarker is selected from sTNFRI, sTNFRII, CA 19-9, Follistatin, Total PSA, TNF-α and IL-6 for treatment naïve subjects.

14. The kit according to claim 10, wherein the kit comprises reagents for measuring the at least one biomarker, wherein the at least one biomarker is selected from TRAIL, sTNFRI, IGFBP-1, sEGFR, IGF-1, TGF-β, HGF, MMP-7, MMP-2, α-fetoprotein, Osteopontin, sVEGFR2 and IL-6 for the pretreated subjects.