568 Background: Detection of circulating tumor DNA (ctDNA) is strongly associated with recurrence across a variety of cancers. The use of ctDNA testing to measure molecular residual disease (MRD) is still in its infancy, and understanding sample characteristics may help set expectations for oncologists and their patients. Methods: For the first thousand patients with MRD testing in Exact Sciences’ commercial laboratory, the tumor and first plasma sample were used to record tumor type, tumor sample type (biopsy/surgical), tumor stage, MRD assay panel size (up to 200 variants), cfDNA levels, and ctDNA status (qualitative and quantitative), and, when data were available, presence of targetable alterations (TAs) were recorded. Results: A total of 1088 samples with valid results were included. Patients were mostly female (N=703, 64.6%) with a median age of 64 (range 17–97) years, and most had either breast (N=380, 34.9%) or colorectal (N=335, 30.8%) cancer. Most (N=632, 58.1%) tumor samples were surgical, and average number of variants for MRD assays from 86 in ovarian cancer to 189 in melanoma. Plasma cfDNA yield ranged from 15 ng (the minimum required) to 10,709 ng and was significantly higher in 412 ctDNA+ (median 86.1 ng, mean 239.7 ng) compared to ctDNA– (median 73.5 ng, mean 114.6 ng) samples (p<0.001). In 13 cancers with at least 10 patient samples, only breast cancer showed a difference in the average number of identified variants between sample types (biopsy: 129.1 versus surgical: 101.5), stage IV cancers had more cfDNA than stage I, range of ctDNA+ samples was from 27% in melanoma to 65% in gastroesophageal cancers, and ctDNA+ proportion tended to increase with stage: 26 (14.2%) of 183 stage I, 84 (32.1%) of 262 stage II, 140 (38.9%) of 360 stage III, and 140 (65.1%) of 215 stage IV. ctDNA+ samples had levels between 2.6 and ≥352,000 parts per million (PPM), with 27 (6.6%) samples below 15 PPM, 40 (9.7%) between 15 and 50 PPM, and 345 (83.7%) above 50 PPM. Genomic profiling was performed on 158 samples, most from breast (N=50, 31.6%) and colorectal (N=17, 10.8%) cancer patients. The mean number of TAs per sample was 4.6, with little difference between the 114 ctDNA+ patients (mean = 4.4, range 0–24) and 44 ctDNA– patients (mean = 5.1, range 1–29). TAs were present in 83 (72.8%) and 36 (81.8%) tumors in ctDNA+ and ctDNA– patients, respectively. Of note, both high microsatellite instability and high tumor mutational burden tumors had numerically higher proportion in ctDNA– (15.9% and 9.1%, respectively) compared to ctDNA+ (5.3% in both) patients. Conclusions: ctDNA+ frequency varied by cancer type and cfDNA yields and ctDNA+ proportion increased numerically with stage, though where patients were in their treatment cycles was unknown. Of patients with profiling results, a large proportion of patients had TAs regardless of ctDNA status, indicating a potential for immediate consideration of matched therapies in ctDNA+ patients.

Supplementary Figure 1: Schema of the hierarchical classifier development platform Supplementary Figure 2: Process for the simultaneous refinement of training class labels and classifier Supplementary Table 1: Association of Patient Characteristics with Test Classification Supplementary Table 2: Association of Test Classification with Immune Adverse Events Supplementary Table 3: Points in m/Z used to align the raster spectra Supplementary Table 4: m/Z regions used for coarse normalization Supplementary Table 5: m/Z regions used for finer normalization Supplementary Table 6: Points in m/Z used to align the spectra Supplementary Table 7: m/Z regions used for final normalization Supplementary Table 8: Lists of proteins/peptides included in each of the significantly associated biological function protein sets

Supplementary Tables 1-4 from Detection of Tumor Epidermal Growth Factor Receptor Pathway Dependence by Serum Mass Spectrometry in Cancer Patients

Hepatocellular carcinoma (HCC) is one of the fastest growing causes of cancer-related death. Guidelines recommend obtaining a screening ultrasound with or without alpha-fetoprotein (AFP) every 6 months in at-risk adults. AFP as a screening biomarker is plagued by low sensitivity/specificity, prompting interest in discovering alternatives. Mass spectrometry-based techniques are promising in their ability to identify potential biomarkers. This study aimed to use machine learning utilizing spectral data and AFP to create a model for early detection. Serum samples were collected from three separate cohorts, and data were compiled to make Development, Internal Validation, and Independent Validation sets. AFP levels were measured, and Deep MALDI® analysis was used to generate mass spectra. Spectral data were input into the VeriStrat® classification algorithm. Machine learning techniques then classified each sample as “Cancer” or “No Cancer”. Sensitivity and specificity of the test were >80% to detect HCC. High specificity of the test was independent of cause and severity of underlying disease. When compared to AFP, there was improved cancer detection for all tumor sizes, especially small lesions. Overall, a machine learning algorithm incorporating mass spectral data and AFP values from serum samples offers a novel approach to diagnose HCC. Given the small sample size of the Independent Validation set, a further independent, prospective study is warranted.

The remarkable success of immune checkpoint inhibitors (ICIs) has given hope of cure for some patients with advanced cancer; however, the fraction of responding patients is 15–35%, depending on tumor type, and the proportion of durable responses is even smaller. Identification of biomarkers with strong predictive potential remains a priority. Until now most of the efforts were focused on biomarkers associated with the assumed mechanism of action of ICIs, such as levels of expression of programmed death-ligand 1 (PD-L1) and mutation load in tumor tissue, as a proxy of immunogenicity; however, their performance is unsatisfactory. Several assays designed to capture the complexity of the disease by measuring the immune response in tumor microenvironment show promise but still need validation in independent studies. The circulating proteome contains an additional layer of information characterizing tumor–host interactions that can be integrated into multivariate tests using modern machine learning techniques. Here we describe several validated serum-based proteomic tests and their utility in the context of ICIs. We discuss test performances, demonstrate their independence from currently used biomarkers, and discuss various aspects of associated biological mechanisms. We propose that serum-based multivariate proteomic tests add a missing piece to the puzzle of predicting benefit from ICIs.

Abstract Background: Understanding of biological processes associated with irAEs for patients treated with immune checkpoint inhibitors (ICI) is limited. We used serum-based, proteomic scores at baseline and after treatment initiation to explore mechanisms of irAEs for patients with non-small cell lung cancer (NSCLC) treated with ICI. Methods: Under an ongoing clinical protocol, 43 patients with advanced NSCLC were consented and serum samples were prospectively collected at two timepoints: baseline and approximately 3 weeks after treatment initiation with ICI (median 22 [IQR, 21 - 26] days). Samples were analyzed, blinded to clinical data, using MALDI-ToF mass spectrometry. Protein Set Enrichment Analysis (PSEA) approach applied to mass-spectral data was used to assign biological scores characterizing activation of 10 processes of interest (e.g., Type 1 immunity, complement, interferon (IFN)-gamma). irAEs after initiation of ICI with or without chemotherapy were classified per standard definitions. Patients were classified into two groups based on irAEs of any grade: irAE positive and negative. Results: Of the 43 participants, 28 received ICI with chemotherapy and 15 received monotherapy. 18 of 43 patients (42%) were determined to have irAEs. These included the following: 9 pneumonitis, 3 thyroiditis, 3 adrenal insufficiency, 1 arthritis, 1 flare of pre-existing psoriasis, 1 mucositis, 1 colitis, 1 myocarditis, and 1 hepatitis (2 patients had both thyroiditis and adrenal insufficiency, 1 patient had both mucositis and pneumonitis). The median timeframe between treatment initiation and development of irAEs was 105 days [IQR, 42 - 169 days]. PSEA scores measured at 3 weeks after initiation of systemic therapy showed significant differences between irAE positive and negative groups in the following processes: extracellular matrix remodeling, complement activation, IFN-gamma signaling, and immune tolerance (P<0.05 for PSEA scores of each pathway identified). These processes did not show any significant differences in PSEA scores at baseline. However, the changes in PSEA scores of all processes analyzed from baseline to 3 weeks after treatment initiation were not significantly different between the two groups. Conclusions: Our findings demonstrate that serum-based, proteomic scores can provide insight into understanding early mechanisms for the development of irAEs in patients treated with ICI. In particular, we identified several mechanisms associated with the development of irAEs, including extracellular matrix remodeling, complement activation, IFN-gamma signaling, and immune tolerance. These associations were not present at baseline and were only observed after treatment initiation, suggesting that early changes in the blood may provide insight into prediction of irAEs. Citation Format: Andrew A. Davis, Jonghanne Park, Leeseul Kim, Gahyun Gim, Wade T. Iams, Michael S. Oh, Robert W. Lentz, Heinrich Roder, Joanna Roder, Senait Asmellash, Lelia Net, Julia Grigorieva, Nisha Mohindra, Victoria Villaflor, Young Kwang Chae. Serum proteomic scores for understanding the mechanisms of immune-related adverse events (irAEs) in non-small cell lung cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5527.

Abstract Background: Understanding of biological processes associated with response and resistance to immune checkpoint inhibitors (ICI) with or without chemotherapy is limited. We used serum-based, proteomic scores at baseline and after treatment initiation to explore mechanisms of early resistance for patients with non-small cell lung cancer (NSCLC) treated with ICI. Methods: Under an ongoing clinical protocol, 43 patients with advanced NSCLC were consented and serum samples were prospectively collected at two timepoints: baseline and approximately 3 weeks after treatment initiation with ICI (median 22 [IQR, 21 - 26] days). Samples were analyzed, blinded to clinical data, using MALDI-ToF mass spectrometry. Protein Set Enrichment Analysis (PSEA) approach applied to mass-spectral data was used to assign biological scores characterizing activation of 10 processes of interest (e.g., Type 1 immunity (Th1), complement, interferon (IFN)-gamma). Statistical associations with clinical response data using RECIST, progression-free survival (PFS), and overall survival (OS) were examined. The distribution of each PSEA score at baseline and 3 weeks was compared for patients with progression of disease (PD) as best response or PFS <6 months classified as “Early PD" vs. patients with best response of SD, PR or CR and PFS ≥6 months as “no Early PD” using mixed effect models, with no adjustments for multiple comparisons. Results: Of the 43 participants, 28 received ICI with chemotherapy and 15 received as monotherapy. 31 of 43 patients (72%) were treatment naïve at baseline blood collection. PSEA scores measured at 3 weeks after initiation of systemic therapy showed significant differences between the Early PD (N=25) and no Early PD (N=18) groups in complement activation, IFN-gamma, Th1, and immune tolerance. In contrast, no differences in PSEA scores were observed in baseline measurements. For three biological processes (complement, IFN-gamma, immune tolerance), the differences in PSEA scores between the Early and no Early PD groups were more prominent with measurements at 3 weeks (Pinteraction < 0.05). Conclusions: Collectively, these data demonstrate the potential utility of serum-based, proteomic scores to provide insight into mechanisms for early disease progression for patients treated with ICI. We identified several resistance mechanisms including complement activation, IFN-gamma signaling, and immune tolerance. The observed associations were more prominent after one cycle of treatment, suggesting that for a subset of patients early changes in the blood after treatment initiation may provide insight into mechanisms of resistance to ICI. Citation Format: Andrew A. Davis, Jonghanne Park, Wade T. Iams, Michael S. Oh, Robert W. Lentz, Heinrich Roder, Joanna Roder, Senait Asmellash, Lelia Net, Julia Grigorieva, Nisha Mohindra, Victoria Villaflor, Young Kwang Chae. Serum proteomic scores for understanding response and mechanisms of resistance to immune checkpoint inhibitors in non-small cell lung cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5526.

IntroductionMost diseases involve a complex interplay between multiple biological processes at the cellular, tissue, organ, and systemic levels. Clinical tests and biomarkers based on the measurement of a single or few analytes may not be able to capture the complexity of a patient’s disease. Novel approaches for comprehensively assessing biological processes from easily obtained samples could help in the monitoring, treatment, and understanding of many conditions.ObjectivesWe propose a method of creating scores associated with specific biological processes from mass spectral analysis of serum samples.MethodsA score for a process of interest is created by: (i) identifying mass spectral features associated with the process using set enrichment analysis methods, and (ii) combining these features into a score using a principal component analysis-based approach. We investigate the creation of scores using cohorts of patients with non-small cell lung cancer, melanoma, and ovarian cancer. Since the circulating proteome is amenable to the study of immune responses, which play a critical role in cancer development and progression, we focus on functions related to the host response to disease.ResultsWe demonstrate the feasibility of generating scores, their reproducibility, and their associations with clinical outcomes. Once the scores are constructed, only 3 µL of serum is required for the assessment of multiple biological functions from the circulating proteome.ConclusionThese mass spectrometry-based scores could be useful for future multivariate biomarker or test development studies for informing treatment, disease monitoring and improving understanding of the roles of various biological functions in multiple disease settings.

Mass spectral data from multiple samples are suitable for a hypothesis-free development of clinically useful multivariate tests using modern machine learning techniques. However, the transition from discovery to adoption of proteomic tests has proved challenging. Slow adoption of these tests in clinical practice is, in part, related to insufficient understanding of the biological mechanisms underlying multivariate tests developed based on correlative studies. While identification of individual proteins may provide important insights, elucidation of concerted relationships of sets of proteins with biological pathways can better reflect complex phenomena, such as cancerogenesis and response to treatment. Protein set enrichment analysis (PSEA) allows identification of associations of mass spectral features or test classifications with biological function by looking for consistent correlations across a group of proteins. We evaluated the utility of PSEA for exploring the biological information content of mass spectra, using a sample set with mass spectral data and matched protein expression information. This made it possible to detect significant biological associations with mass spectral peaks without identifying their protein constituents. We demonstrated that the method produces reproducible associations and can be used for elucidation of the mechanisms of action associated with two previously developed multivariate mass spectrometry-based tests. Significant correlations with several host immune response-related processes were found on the level of individual mass spectral features and with test classifications. The results illustrate the utility of the PSEA approach applied to mass spectral data as a method for elucidating biological mechanisms underlying phenotypes related to different physiological states of the organism. (C) 2019 The Author(s). Published by Elsevier B.V. on behalf of The Association for Mass Spectrometry: Applications to the Clinical Lab (MSACL).

Objective Timely assessment of patient-specific prognosis is critical to oncology care involving a shared decision-making approach, but clinical prognostic factors traditionally used in NSCLC have limitations. We examine a proteomic test to address these limitations. Methods This study examines the prognostic performance of the VeriStrat blood-based proteomic test that measures the inflammatory disease state of patients with advanced NSCLC. A systematic literature review (SLR) was performed, yielding cohorts in which the hazard ratio (HR) was reported for overall survival (OS) of patients with VeriStrat Poor (VSPoor) test results versus VeriStrat Good (VSGood). A study-level meta-analysis of OS HRs was performed in subgroups defined by lines of therapy and treatment regimens. Results Twenty-four cohorts met SLR criteria. Meta-analyses in five subgroups (first-line platinum-based chemotherapy, second-line single-agent chemotherapy, first-line EGFR-tyrosine kinase inhibitor (TKI) therapy, and second- and higher-line TKI therapy, and best supportive care) resulted in statistically significant (p <= .001) summary effect sizes for OS HRs of 0.42, 0.54, 0.41, 0.52, and 0.50, respectively, indicating increased OS by about two-fold for patients who test VSGood. No significant heterogeneity was seen in any subgroup (p > .05). Conclusions Advanced NSCLC patients classified VSGood have significantly longer OS than those classified VSPoor. The summary effect size for OS HRs around 0.4-0.5 indicates that the expected median survival of those with a VSGood classification is approximately 2-2.5 times as long as those with VSPoor. The robust prognostic performance of the VeriStrat test across various lines of therapy and treatment regimens has clinical implications for treatment shared decision-making and potential for novel treatment strategies.

Background: Early detection is critical to improve outcome in hepatocellular carcinoma (HCC). Despite inadequate sensitivity (SS) and specificity (SP), abdominal ultrasound and alpha-fetoprotein (AFP) are considered methods of choice for HCC surveillance. As less than 30% of patients (pts) are diagnosed early enough for resection or transplantation, a test with improved SS and SP is needed. A test to detect HCC in a high-risk population from 10 uL serum, combining MALDI mass spectrometry and AFP data was developed using a dropout-regularized hierarchical machine learning approach designed to minimize overfitting in small development sets. It was previously validated in 293 high risk pts (158 HCC, 135 non-HCC) with SS/SP of 83%/84% in development and 81%/79% in validation across various etiologies and Child-Pugh classification [1]. Methods: The test was applied to an independent validation cohort of 156 pts (97 HCC, 59 non-HCC healthy controls), blinded to clinical data, the performance was assessed by SS, SP. Sub-group analyses were performed by grade and stage. Performance was compared with that of AFP by area under the curve (AUC), using the test output prior to the predefined thresholding which yields the final binary cancer/benign test result. Results: Table 1.HCC patient demographics in the independent validation setClinical characteristicsIndependent validationPatients with HCC (N=97)Median age (range), years62 (38 - 89)Median AFP (range), ng/mL4.0 (less than 1.5 - 10,000)Gender, male / female85% / 16%Hepatitis B / C / No virus / not available (NA)3% / 27% / 20% / 51%Grade I / II / III / NA17% / 22% / 12% / 50%Stage I / II / III / IV / NA12% / 14% / 33% / 26% / 14%Previous liver directed or systemic therapy yes/no44% / 54%ECOG PS 0 / 1 / 2 / 3 / NA13% / 20% / 11% / 5% / 51% In independent validation, AUC for the test output prior to thresholding was 0.979, significantly better than AFP AUC 0.915 (P<0.001). SS and SP were 88% and 100%. SS in grade I, II, and III was 75%, 76%, and 92% and in stage I, II, III, and IV was 75%, 86%, 94%, and 92%. Conclusion: Results in the independent cohort confirm performance of the test, with better SS and SP than AFP and historical ultrasound. The SS was also high in low grade and early stage disease, indicating the test’s potential to improve early detection of HCC when curative approaches are feasible. Data and samples from Data Bank and BioRepository, Roswell Park; funding by the NCI grant P30CA016056 [1] D Mahalingam et al. Hepatology 62(S1): 1135A (2015) Citation Format: Sunyoung S. Lee, Kristopher Attwood, Heinrich Roder, Senait Asmellash, Krista Meyer, Stylianos Kakolyris, Carlos Oliveira, Joanna Roder, Julia Grigorieva, Leonidas Chelis, Renuka Iyer, Devalingam Mahalingam. An independent validation of a screening test using mass spectrometry for detection of hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4530.

The therapeutic landscape in metastatic melanoma has changed dramatically in the last decade, with the success of immune checkpoint inhibitors resulting in durable responses for a large number of patients. For patients with BRAF mutations, combinations of BRAF and MEK inhibitors demonstrated response rates and benefit comparable to those from immune checkpoint inhibitors, providing the rationale for sequential treatment with targeted and immunotherapies and raising the question of optimal treatment sequencing. Biomarkers for the selection of anti-PD-1 therapy in BRAF wild type (BRAF WT) and in BRAF mutated (BRAF MUT) patients help development of alternative treatments for patients unlikely to benefit, and might lead to better understanding of the interaction of checkpoint inhibition and targeted therapy. In this paper we evaluate the performance of a previously developed serum proteomic test, BDX008, in metastatic melanoma patients treated with anti-PD-1 agents and investigate the role of BRAF mutation status. BDX008, a pre-treatment proteomic test associated with acute phase reactants, wound healing and complement activation, stratifies patients into two groups, BDX008+ and BDX008-, with better and worse outcomes on immunotherapy. Serum samples were available from 71 patients treated with anti-PD1 inhibitors; 25 patients had BRAF mutations, 39 were wild type. Overall, BDX008+ patients had significantly better overall survival (OS) (HR = 0.50, P= 0.016) and a trend for better progression-free survival (PFS) (HR = 0.61, P = 0.060) than BDX008- patients. BDX008 classification was statistically significant in the analyses adjusted for mutation status, LDH, and line of treatment (P = 0.009 for OS and 0.031 for PFS). BRAF WT BDX008+ patients had markedly long median OS of 32.5 months and 53% landmark 2 years survival, with statistically significantly superior OS as compared to BDX008- patients (HR = 0.41, P = 0.032). The difference between BDX008+ and BDX008- in PFS in BRAF WT patients and in OS and PFS in BRAF MUT patients did not reach statistical significance, though numerically was consistent with overall results. The test demonstrated significant interaction with neutrophil-to-lymphocyte ratio (NLR) (PFS P = 0.041, OS P = 0.004). BDX008 as a biomarker selecting for benefit from immune checkpoint blockade, especially in patients with wild type BRAF and in subgroups with low NLR, warrants further evaluation.

AbstractA mass spectrometry analysis was performed using serum from patients receiving checkpoint inhibitors to define baseline protein signatures associated with outcome in metastatic melanoma. Pretreatment serum was obtained from a development set of 119 melanoma patients on a trial of nivolumab with or without a multipeptide vaccine and from patients receiving pembrolizumab, nivolumab, ipilimumab, or both nivolumab and ipilimumab. Spectra were obtained using matrix-assisted laser desorption/ionization time of flight mass spectrometry. These data combined with clinical data identified patients with better or worse outcomes. The test was applied to five independent patient cohorts treated with checkpoint inhibitors and its biology investigated using enrichment analyses. A signature consisting of 209 proteins or peptides was associated with progression-free and overall survival in a multivariate analysis. The test performance across validation cohorts was consistent with the development set results. A pooled analysis, stratified by set, demonstrated a significantly better overall survival for “sensitive” relative to “resistant” patients, HR = 0.15 (95% confidence interval: 0.06–0.40, P < 0.001). The test was also associated with survival in a cohort of ipilimumab-treated patients. Test classification was found to be associated with acute phase reactant, complement, and wound healing pathways. We conclude that a pretreatment signature of proteins, defined by mass spectrometry analysis and machine learning, predicted survival in patients receiving PD-1 blocking antibodies. This signature of proteins was associated with acute phase reactants and elements of wound healing and the complement cascade. This signature merits further study to determine if it identifies patients who would benefit from PD-1 blockade. Cancer Immunol Res; 6(1); 79–86. ©2017 AACR.

### A1 Intratumoral immunotherapy. B16-F10 murine melanoma model #### J. Ženka1, V. Caisova1, O. Uher1, P. Nedbalova1, K. Kvardova1, K. Masakova1, G. Krejcova1, L. Paďoukova1, I. Jochmanova2, K. I. Wolf3, J. Chmelař1, J. Kopecký1 ##### 1Department of Medical Biology, Faculty of

Abstract A method was developed that allows the evaluation of complex biological processes from mass spectrometry of human serum samples. Applying gene set enrichment analysis ideas to matched protein expression data from a panel of 1129 proteins and deep MALDI® mass spectral data from a set of 49 human serum samples with lung cancer (n=45) or non-cancer (n=4), subsets of mass spectral features associated with selected biological functions were identified. Biological functions included acute response, complement system, and wound healing and the protein members of each function were assigned using the intersection of gene ontologies and the protein panel. Using mass spectral data collected from an independent NSCLC sample cohort (n=85) from patients treated with targeted therapy, principal component analysis was used to derive scoring functions (combinations of the feature subsets) for each biological class. These scoring functions were validated in an additional NSCLC sample set (n=123) treated with chemotherapy. The scoring functions can be applied to mass spectra obtained from any human serum sample to generate scores associated with individual biological processes. We have developed scoring functions for several biological processes. Acute response score was associated with outcome in Cox proportional hazard analysis in several independent patient cohorts across multiple therapies and indications, including lung cancer, as mentioned, and ovarian cancer (n=165) treated with platinum doublets following surgery. Choice of a single cutoff allowed stratification of patients into groups with significantly better or worse outcome. In a cohort of nivolumab treated NSCLC patients (n=67) with available longitudinal samples, outcome was also found to depend on changes in scores during therapy. Interestingly, while the distributions of acute phase scores were quite similar across multiple tumor types, scores for other biological functions, such as wound healing, varied considerably. This may reflect differences in relative importance of individual biological functions between tumor types. A scoring system based on biological functional categories has a wide range of uses that could be tested and applied in a clinical setting. Currently, tests that measure a single biomarker for monitoring a particular disease have questionable utility and limit the usefulness to the clinician and patient treatment decision making. A tool that measures the activity of a complex biological process could be useful in deciding when an intervention is needed (screening), or for monitoring the effects during therapy. Citation Format: Carlos Oliveira, Julia Grigorieva, Krista D. Meyer, Joanna Roder, Heinrich Roder. Development of scores reflective of biological processes underlying human disease states from mass spectrometry of serum [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 210. doi:10.1158/1538-7445.AM2017-210

OBJECTIVES:VeriStrat® is a blood-based test that utilizes matrix-assisted laser desorption/ionization time-of-flight (MALDI ToF) mass spectrometry to assign a binary classification of VeriStrat Good or VeriStrat Poor that is associated with treatment outcomes in cancer patients. A number of other studies have shown an association between VeriStrat status and clinical outcomes in second and subsequent lines of therapy. The prognostic properties of VeriStrat were demonstrated in the placebo arms of two randomized studies in non-small cell lung cancer (NSCLC): TOPICAL and BR.21; the predictive properties of the test were shown in a prospective randomized phase III study PROSE in the second line treatment of NSCLC with erlotinib versus chemotherapy. Motivated by these observations, we sought to extend the clinical utility of VeriStrat to standard first line chemotherapy and evaluated the performance of the test in a number of clinical studies of patients treated with platinum-based regimens. MATERIALS AND METHODS:We examine the performance of VeriStrat in three independent clinical trials where the test classification was acquired for prospectively collected baseline samples from 481 patients treated with platinum-based chemotherapy in first line. RESULTS:Across these trials, 66-70% of patients were classified as VeriStrat Good; patients classified as VeriStrat Good had significantly longer progression-free survival and overall survival than VeriStrat Poor patients, with hazard ratios ranging from 0.36 to 0.72 and 0.26 to 0.51, respectively. These results demonstrated that VeriStrat is a strong prognostic test in NSCLC patients treated with platinum-based regimens in the first line. CONCLUSION:VeriStrat provides valuable clinical information that may be used to support patient-physician conversations regarding prognosis and treatment options, and to identify a subset of patients who might benefit from other treatment strategies, possibly in the framework of clinical trials.

Methods using mass spectral data analysis and a classification algorithm provide an ability to determine whether a solid epithelial tumor cancer patient is likely to benefit from a therapeutic agent or a combination of therapeutic agents tar geting agonists of the receptors, receptors or proteins involved in MAPK (mitogen-activated protein kinase) path ways or the PKC (protein kinase C) pathway upstream from or at Akt or ERK/JNK/p38 or PKC, such as therapeutic agents targeting EGFR and/or HER2. The methods also provide the ability to determine whether the cancer patient is likely to benefit from the combination of a therapeutic agent targeting EFGR and a therapeutic agent targeting COX2: or whether the cancer patient is likely to benefit from the treatment with an NF-kB inhibitor.

Anti-PD1 inhibitors are becoming the treatment of choice for 2nd line non-small cell lung cancer (NSCLC). While existing testing for PDL-1 expression may correlate with anti-PD1 benefit, current data do not support these tests to be sufficient to guide therapy. We evaluated the utility of a serum-based pre-treatment test first developed to identify patients benefitting from anti-PD1 therapy in metastatic melanoma1 in patients with NSCLC. These results were compared to the data obtained from application of the established VeriStrat2 test to the same samples. 60 advanced NSCLC patients treated with nivolumab in an observational study were included. Pretreatment serum samples were classified using the fully locked mass spectrometry-based multivariate tests BDX008 and VeriStrat. BDX008 generates a classification of positive (BDX008+, good outcomes) or negative (BDX008-, poor outcomes); VeriStrat classifies samples as Good and Poor. The association of test classifications with overall survival (OS), progression-free survival (PFS), and time-to-failure (TTF) were assessed using Kaplan-Meier method and Cox proportional hazards model. 37% of patients were classified as BDX008+ and 63% as BDX008-; 62% were classified as VeriStrat Good and 38% as Poor. Both tests significantly stratified OS (Table), but not PFS or TTF, and remained significant for OS in multivariate analyses (p=0.0167 and 0.0184, for BDX008 and VeriStrat, respectively). Out of 11 patients who died before the first radiological evaluation, 10 were classified as BDX008-, 9 as VeriStrat Poor.Tabled 1TestLog-rank p valueCPH HR [95% CI]Median Survival (months) [95% CI]BDX0008 (+ vs -)0.00260.189 [0.056-0.637]BDX0008+: Not reached BDX0008-: 5.5 [2.6-11.9]VeriStrat (Good vs Poor)0.01860.390 [0.173-0.880]VS Good: Not reached VS Poor: 4.1 [1.0-Undef.] Open table in a new tab BDX008 developed for immunotherapy of patients with melanoma can be applied to NSCLC patients, and shows a significant separation for OS. Clinical utility of BDX008 will need to be further evaluated. VeriStrat is also prognostic for the same patients. 1. J. Weber et al, "Pre-treatment patient selection for nivolumab benefit based on serum mass spectra," SITC 2015. 2. V. Gregorc et al, The Lancet Oncology, p713, 15(7), 2014.