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Background: The metabolic syndrome characterized in part by obesity, hyperinsulinemia, and insulin resistance is associated with increased risk of breast cancer. However there remains a need to establish a circulating biomarker metabolic profile indicative of increased risk of breast cancer. In the current study, we performed a comprehensive metabolomics screen to identify biomarkers indicative of increased risk of breast cancer. Methods: Unbiased metabolomics profiling was conducted on an initial Development Set of plasmas collected from 353 newly-diagnosed breast cancer cases and 141 controls. A deep learning neural network with 3 layers each containing 32 nodes based on 11 individual lipids corresponding to discrete lipid subclasses was built for risk prediction of breast cancer. The model was validated in an independent Test Set consisting of 79 breast cancer cases and 163 controls. Using a nested case:control matched design, we evaluated the performance of the model among body mass index (BMI) strata (≥ 30 or < 30kg/m2). Results: An 11-marker lipid biomarker panel encompassing lipid subclasses with known pro-inflammatory and tumor promoting roles yielded an AUC of 0.75 (95% CI: 0.70-0.79) for distinguishing breast cancer cases from controls in the Development Set. Predictive performance of the lipid panel was comparable when stratifying cases into hormone-receptor (HR) positive, HER2-positive/HR negative, and triple-negative breast cancer subtypes. The biomarker panel had an AUC of 0.74 (95% CI: 0.68-0.81) in the independent Test Set. The predictive performance of the panel was most pronounced among obese subjects (BMI ≥ 30) with an AUC of 0.81 (95% CI: 0.71-0.91) in the Test Set. Conclusions: The lipid-based biomarker panel has utility for identifying women with ‘metabolic obesity’ who are at increased risk of breast cancer and would benefit from tailored screening. Citation Format: Johannes Fahrmann, Ehsan Irajizad, Jody Vykoukal, Angelica Gutierrez Barrera, Jennifer Dennison, Ranran Wu, Banu K. Arun, Abenaa Brewster, Samir Hanash. A blood-based lipid panel for personalized risk assessment of breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-05-26.

Cancer heterogeneities hold the key to a deeper understanding of cancer etiology and progression and the discovery of more precise cancer therapy. Modern pathological and molecular technologies offer a powerful set of tools to profile tumor heterogeneities at multiple levels in large patient populations, from DNA to RNA, protein and epigenetics, and from tumor tissues to tumor microenvironment and liquid biopsy. When coupled with well-validated epidemiologic methodology and well-characterized epidemiologic resources, the rich tumor pathological and molecular tumor information provide new research opportunities at an unprecedented breadth and depth. This is the research space where Molecular Pathological Epidemiology (MPE) emerged over a decade ago and has been thriving since then. As a truly multidisciplinary field, MPE embraces collaborations from diverse fields including epidemiology, pathology, immunology, genetics, biostatistics, bioinformatics, and data science. Since first convened in 2013, the International MPE Meeting series has grown into a dynamic and dedicated platform for experts from these disciplines to communicate novel findings, discuss new research opportunities and challenges, build professional networks, and educate the next-generation scientists. Herein, we share the proceedings of the Fifth International MPE meeting, held virtually online, on May 24 and 25, 2021. The meeting consisted of 21 presentations organized into the three main themes, which were recent integrative MPE studies, novel cancer profiling technologies, and new statistical and data science approaches. Looking forward to the near future, the meeting attendees anticipated continuous expansion and fruition of MPE research in many research fronts, particularly immune-epidemiology, mutational signatures, liquid biopsy, and health disparities.

PDF file, 5465KB, Quantitation tables for proteins identified and quantified.

Receiver operator curves for the glycan combination marker panel stratified by time to diagnosis.

Somatic KRAS mutations are the most common oncogenic variants in lung cancer and are associated with poor prognosis. Using a Kras-induced lung cancer mouse model, CC-LR, we previously showed a role for inflammation in lung tumorigenesis through activation of the NF-κB pathway, along with induction of interleukin 6 (IL6) and an IL17-producing CD4+ T-helper cell response. IL22 is an effector molecule secreted by CD4+ and γδ T cells that we previously found to be expressed in CC-LR mice. IL22 mostly signals through the STAT3 pathway and is thought to act exclusively on nonhematopoietic cells with basal IL22 receptor (IL22R) expression on epithelial cells. Here, we found that higher expression of IL22R1 in patients with KRAS-mutant lung adenocarcinoma was an independent indicator of poor recurrence-free survival. We then showed that genetic ablation of Il22 in CC-LR mice (CC-LR/IL22KO mice) caused a significant reduction in tumor number and size. This was accompanied by significantly lower tumor cell proliferation, angiogenesis, and STAT3 activation. Il22 ablation was also associated with significant reduction in lung-infiltrating inflammatory cells and expression of protumor inflammatory cytokines. Conversely, this was accompanied with increased antitumor Th1 and cytotoxic CD8+ T-cell responses, while suppressing the protumor immunosuppressive T regulatory cell response. In CC-LR/IL22KO mice, we found significantly reduced expression of core stemness genes and the number of prototypical SPC+CCSP+ stem cells. Thus, we conclude that IL22 promotes Kras-mutant lung tumorigenesis by driving a protumor inflammatory microenvironment with proliferative, angiogenic, and stemness contextual cues in epithelial/tumor cells. Cancer Immunol Res; 6(7); 788–97. ©2018 AACR.

Glycans that were identified to be significantly different between NSCLC cases and controls in the discovery set.

PDF file, 3.66MB, Proteomic data table. Tabular list of proteomic data from the 2 hr and 16 hour gefitinib treated experiments. Whole cell lysate (WCL), cell-surface (SUR), and SPEG/GC.

Supplementary Figure S1. Lung cancer cell lines do not express NE and P3 gene transcripts. Supplementary Figure S2. CTL proliferate in the presence of NE-exposed lung cancer cells. Supplementary Figure S3. HLA class I presentation of mutant neoantigens is enhanced by NE and P3. Supplementary Figure S4. Immunoproteasome expression by lung cancer cells is unaltered by serine proteases.

PDF file, 47KB, Protein Identification for Solid Phase Extraction of Glycoproteins (SPEG)with 16 hours of gefitinib treatment.

Formulas used to calculate glycan feature data.

Predictive values of risk factors, pro-SFTPB and the glycan marker panel in the discovery set.

Variables associated to the glycan variables in the combination marker model, as locked down from the discovery set.

Bar graphs of grouped glycan intensities showing that the sample preparation method at the individual level yields comparable results to a plate-based method.

Detailed explanation of the procedures used for glycan release and analysis.

Typical chromatogram of N-glycans obtained from human serum.

Previous studies have suggested occurrence of altered serum glycan profiles in patients with lung cancer. Here, we aimed to determine the predictive value of serum glycans to distinguish non–small cell lung cancer (NSCLC) cases from controls in prediagnostic samples using a previously validated predictive protein marker pro-SFTPB, as anchor. Blinded prediagnostic serum samples were obtained from the Carotene and Retinol Efficacy Trial (CARET), and included a discovery set of 100 NSCLC cases and 199 healthy controls. A second test set consisted of 108 cases and 216 controls. Cases and controls were matched for age at baseline (5-year groups), sex, smoking status (current vs. former), study enrollment cohort, and date of blood draw. Serum glycan profiles were determined by mass spectrometry. Twelve glycan variables were identified to have significant discriminatory power between cases and controls in the discovery set (AUC > 0.6). Of these, four were confirmed in the independent validation set. A combination marker yielded AUCs of 0.74 and 0.64 in the discovery and test set, respectively. Four glycan variables exhibited significant incremental value when combined with pro-SFTPB compared with pro-SFTPB alone with AUCs of 0.73, 0.72, 0.72, and 0.72 in the test set, indicating that serum glycan signatures have relevance to risk assessment for NSCLC. Cancer Prev Res; 9(4); 317–23. ©2016 AACR.

PDF file, 321KB, Protein levels changes associated with EGFR targeted and non-EGFR targeted drugs.

Box plots representing the summed integrals of one glycan per batch, before and after batch corrections.

PDF file, 299KB, Protein level changes associated with gefitinib treatment in NSCLC tissue.

Clinical oncology is hampered by lack of tools to accurately assess a patient's response to pathway-targeted therapies. Serum and tumor cell surface proteins whose abundance, or change in abundance in response to therapy, differentiates patients responding to a therapy from patients not responding to a therapy could be usefully incorporated into tools for monitoring response. Here, we posit and then verify that proteomic discovery in in vitro tissue culture models can identify proteins with concordant in vivo behavior and further, can be a valuable approach for identifying tumor-derived serum proteins. In this study, we use stable isotope labeling of amino acids in culture (SILAC) with proteomic technologies to quantitatively analyze the gefitinib-related protein changes in a model system for sensitivity to EGF receptor (EGFR)-targeted tyrosine kinase inhibitors. We identified 3,707 intracellular proteins, 1,276 cell surface proteins, and 879 shed proteins. More than 75% of the proteins identified had quantitative information, and a subset consisting of 400 proteins showed a statistically significant change in abundance following gefitinib treatment. We validated the change in expression profile in vitro and screened our panel of response markers in an in vivo isogenic resistant model and showed that these were markers of gefitinib response and not simply markers of phospho-EGFR downregulation. In doing so, we also were able to identify which proteins might be useful as markers for monitoring response and which proteins might be useful as markers for a priori prediction of response. Mol Cancer Ther; 11(5); 1071–81. ©2012 AACR.

Table 1. List of Q-RT-PCR primers. Supplementary methods and supplementary figure legends.

PDF file - 48K, Peptide sequences from proteins identified in immune complexes.

Supplementary Figure 2. (A) Heatmap representing IL-6/STAT3 pathway genes expression status. (B-C) Mass spectrometric quantification of IL-6 and STAT3 in lung cancer cell lines. (D) Siltuximab and Tocilizumab decreased STAT3 activation in NSCLC cell lines.

Supplementary Figure 5. Relative expression of Arg1 mRNA normalized by Cd45 expression in lungs of CC-LR mice.

We assessed the autoantibody repertoire of a mouse model engineered to develop breast cancer and the repertoire of autoantibodies in human plasmas collected at a preclinical time point and at the time of clinical diagnosis of breast cancer. In seeking to identify common pathways, networks, and protein families associated with the humoral response, we elucidated the dynamic nature of tumor antigens and autoantibody interactions. Lysate proteins from an immortalized cell line from a MMTV-neu mouse model and from MCF7 human breast cancers were spotted onto nitrocellulose microarrays and hybridized with mouse and human plasma samples, respectively. Immunoglobulin-based plasma immunoreactivity against glycolysis and spliceosome proteins was a predominant feature observed both in tumor-bearing mice and in prediagnostic human samples. Interestingly, autoantibody reactivity was more pronounced further away than closer to diagnosis. We provide evidence for dynamic changes in autoantibody reactivity with tumor development and progression that may depend, in part, on the extent of antigen–antibody interactions. Cancer Res; 73(5); 1502–13. ©2012 AACR.

Supplementary Figure 3. Increased levels of IL-6 in bronchoalveolar lavage fluid (BALF) of CC-LR mice during tumor progression.

Supplementary Figure 1. Gating method for FACS analysis and sorting.

Supplementary Figure 6. Increased levels of IL-6 in bronchoalveolar lavage fluid (BALF) of CC-LR mice after NTHi exposure.

Supplementary Figure 4. Lung lesion distribution in lungs of CC-LR mice.

PDF file - 54K, Coefficients from linear regression models used for ROC analysis.

The nuclear factor erythroid 2-related factor 2 (NRF2) pathway is frequently activated in various cancer types. Aberrant activation of NRF2 in cancer is attributed to gain-of-function mutations in the NRF2-encoding gene NFE2L2 or a loss of function of its suppressor, Kelch-like ECH-associated protein 1 (KEAP1). NRF2 activation exerts pro-tumoral effects in part by altering cancer cell metabolism. Previously, we reported a novel mechanism of NRF2 tumoral immune suppression through the selective upregulation of the tryptophan-metabolizing enzyme kynureninase (KYNU) in lung adenocarcinoma. In the current study, we explored the relevance of NRF2-mediated KYNU upregulation across multiple cancer types. Specifically, using a gene expression dataset for 9801 tumors representing 32 cancer types from The Cancer Genome Atlas (TCGA), we demonstrated that elevated KYNU parallels increased gene-based signatures of NRF2-activation and that elevated tumoral KYNU mRNA expression is strongly associated with an immunosuppressive tumor microenvironment, marked by high expression of gene-based signatures of Tregs as well as the immune checkpoint blockade-related genes CD274 (PDL-1), PDCD1 (PD-1), and CTLA4, regardless of the cancer type. Cox proportional hazard models further revealed that increased tumoral KYNU gene expression was prognostic for poor overall survival in several cancer types, including thymoma, acute myeloid leukemia, low-grade glioma, kidney renal papillary cell carcinoma, stomach adenocarcinoma, and pancreatic ductal adenocarcinoma (PDAC). Using PDAC as a model system, we confirmed that siRNA-mediated knockdown of NRF2 reduced KYNU mRNA expression, whereas activation of NFE2L2 (the coding gene for NRF2) through either small-molecule agonists or siRNA-mediated knockdown of KEAP1 upregulated KYNU in PDAC cells. Metabolomic analyses of the conditioned medium from PDAC cell lines revealed elevated levels of KYNU-derived anthranilate, confirming that KYNU was enzymatically functional. Collectively, our study highlights the activation of the NRF2–KYNU axis as a multi-cancer phenomenon and supports the relevance of tumoral KYNU as a marker of tumor immunosuppression and as a prognostic marker for poor overall survival.

BACKGROUND: In East Asia, the number of patients with adenocarcinoma, especially those presenting with ground-glass nodules (GGNs), is gradually increasing. Family aggregation of pulmonary GGNs is not uncommon; however, genetic predisposition in these patients remains poorly understood and identification of genes involved in the cause of these early-stage lung cancers might contribute to understanding of the underlying mechanisms and potential prevention strategies. METHODS: Fifty patients with early-stage lung adenocarcinoma (LUAD) presenting as GGNs and a first-degree family history of lung cancer (FHLC) from 34 independent families were enrolled into this study. Germline mutations of these patients were analyzed with whole exome sequencing (WES) and compared with age- and sex-matched 39 patients with sporadic lung cancer and 689 local healthy people. We used a stepwise variant filtering strategy, gene-based burden testing, and enrichment analysis to investigate rare but potentially pathogenic heritable mutations. Somatic tumor mutations were analyzed to consolidate germline findings. RESULTS: In total, 1,571 single nucleotide variants (SNVs) and 238 frameshifts with a minor allele frequency (MAF)

Activation of the nuclear factor erythroid 2-related factor 2 (NRF2) pathway, either through gain-of-function mutations in the NRF2 encoding gene NFE2L2 or through loss-of-function of its suppressor, Kelch-like ECH-associated protein 1 (KEAP1), is a frequent manifestation in various malignancies. NRF2 activation exerts pro-tumoral effects in part by altering cancer cell metabolism. We previously reported a novel mechanism of NRF2 tumoral immune suppression through selective upregulation of the tryptophan metabolizing enzyme kynureninase (KYNU) in lung adenocarcinoma. In the current study, we explored the Pan-Cancer relevance of NRF2-mediated KYNU upregulation. We analyzed the gene expression dataset for 9,801 tumors representing 32 cancer types in The Cancer Genome Atlas (TCGA). Elevated KYNU expression levels paralleled increased gene-based signatures of NRF2-activation and was strongly associated with an immunosuppressive tumor microenvironment, marked by high expression of gene-based signatures of Tregs as well as immune checkpoint blockade-related genes CD274 (PDL-1), PDCD1 (PD-1), and CTLA4, regardless of cancer type. Cox proportional hazard models further revealed that increased tumoral KYNU gene expression was prognostic for poor overall survival in several cancer types, including thymoma, acute myeloid leukemia, low grade glioma, kidney renal papillary cell carcinoma, gastric adenocarcinoma, and pancreatic ductal adenocarcinoma (PDAC). Using PDAC as a model system, we confirmed that siRNA-mediated knockdown of NRF2 reduces KYNU mRNA expression, whereas activation of NFE2L2 (the encoding gene for NRF2) through either small molecule agonists or siRNA-mediated knockdown of KEAP1 upregulated KYNU. Metabolomic analyses of conditioned media from PDAC cell lines revealed elevated levels of KYNU-derived anthranilate, confirming KYNU as enzymatically functional. Collectively, our findings highlight the multi-cancer relevance of the NRF2-KYNU axis and of tumoral KYNU as a prognostic marker of poor overall survival associated with immunosuppression. Citation Format: Ricardo A. Leon Letelier, Ali H. Abdel Sater, Yihui Chen, Ranran Wu, Jennifer B. Dennison, Soyoung Park, Ehsan Irajizad, Hiroyuki Katayama, Jody Vykoukal, Samir Hanash, Edwin J. Ostrin, Johannes F. Fahrmann. KYNU upregulation is a prominent feature of NRF2-activated cancers and is associated with tumor immunosuppression and poor prognosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6031.

Background: The MERIT cohort (Mammography, Early Detection, Risk Assessment, and Imaging Technologies, 2017-present) has enrolled women receiving annual screening mammograms (MG) at MD Anderson with a primary goal to integrate clinical data and imaging data with blood biomarker profiles to determine risk of developing breast and other cancers. Here we report interim results for breast cancers among post-menopausal women in the cohort categorized based on breast density and BMI and differences between participants who underwent MRI/MG screening vs standard annual MG screening. Methods: The study annually collects comprehensive health measurements, questionnaire information, imaging data, and blood specimens. Plasma is processed and frozen within 4 hours of collection (draw-to-freezer, >500,000 aliquots to date) for biomarker research. Part of the cohort also has MRI screening every 6 months alternating with standard mammography (MRI/MG). BI-RADS breast density was determined by radiologist scoring using the baseline mammogram. Self-reported post-menopausal status (12 months without a menstrual period) was used to classify participants. When not available, those participants older than 50 years were classified as post-menopausal. Results: 4,392 of the 6,222 eligible subjects from MERIT were post-menopausal and included in the analyses. The average follow up was 2.4 mammograms per participant. MRI/MG screening was used for 385 (8.8%) participants who were more likely to be younger (59.6 vs 62.1 years, P< 0.01), have lower BMI (27.9 vs 28.6, P = 0.02) and dense breasts (64% vs 50%, P< 0.01). The rates of breast cancer were overall higher for those screened by MRI/MG vs standard MG (13.9 vs 6.9 cases per 1,000 mammograms). A total of 79 breast cancers (7.6 cases per 1,000 mammograms) were diagnosed with the highest rate of breast cancers in high BMI participants with dense breasts (see table). A blood-based biomarker profile for risk of breast cancer with high BMI was developed using matched pre-diagnostic plasma by mass spectrometry metabolomic analyses. Conclusions: The MERIT cohort has a higher-than-average rate of breast cancers, in part explained by a high-risk MRI/MG screening group. High BMI and dense breasts were generally associated with higher rates of breast cancer. The differences in the rates of breast cancer incidence for the high BMI group between non dense and dense breasts is likely understated for the standard mammogram group because of the lower sensitivity of mammography in dense breasts. Interestingly, the rates of breast cancers in the low BMI/non dense breast group were almost equally high as the low BMI/dense breast group, likely a result of reduced sensitivity of mammography for dense breasts. For future work, we will integrate the blood biomarker profiles with the breast density and BMI information to develop a more personalized risk model. MERIT Cohort Breast Cancers Rates of diagnosed breast cancers per 1,000 mammograms for post-menopausal women (N = 79 breast cancers)*‡P Citation Format: Jessica Leung, Olena Weaver, Samir Hanash, Jennifer Dennison. The MERIT Cohort: An MD Anderson Initiative to Integrate Blood and Imaging Biomarkers to Personalize Breast Cancer Risk [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-03-01.

Activation of the NRF2 pathway through gain-of-function mutations or loss-of-function of its suppressor KEAP1 is a frequent finding in lung cancer. NRF2 activation has been reported to alter the tumor microenvironment. Here, we demonstrated that NRF2 alters tryptophan metabolism through the kynurenine pathway that is associated with a tumor-promoting, immune suppressed microenvironment. Specifically, proteomic profiles of 47 lung adenocarcinoma (LUAD) cell lines (11 KEAP1 mutant and 36 KEAP1 wild-type) revealed the tryptophan-kynurenine enzyme kynureninase (KYNU) as a top overexpressed protein associated with activated NRF2. The siRNA-mediated knockdown of NFE2L2, the gene encoding for NRF2, or activation of the NRF2 pathway through siRNA-mediated knockdown of KEAP1 or via chemical induction with the NRF2-activator CDDO-Me confirmed that NRF2 is a regulator of KYNU expression in LUAD. Metabolomic analyses confirmed KYNU to be enzymatically functional. Analysis of multiple independent gene expression datasets of LUAD, as well as a LUAD tumor microarray demonstrated that elevated KYNU was associated with immunosuppression, including potent induction of T-regulatory cells, increased levels of PD1 and PD-L1, and resulted in poorer survival. Our findings indicate a novel mechanism of NRF2 tumoral immunosuppression through upregulation of KYNU.

c-MYC and its paralogues MYCN and MYCL are among the most frequently amplified and/or overexpressed oncoproteins in ovarian cancer. c-MYC plays a key role in promoting ovarian cancer initiation and progression. The polyamine pathway is a bona fide target of c-MYC signaling, and polyamine metabolism is strongly intertwined with ovarian malignancy. Targeting of the polyamine pathway via small molecule inhibitors has garnered considerable attention as a therapeutic strategy for ovarian cancer. Herein, we discuss the involvement of c-MYC signaling and that of its paralogues in promoting ovarian cancer tumorigenesis. We highlight the potential of targeting c-MYC-driven polyamine metabolism for the treatment of ovarian cancers and the utility of polyamine signatures in biofluids for early detection applications.

Background: The metabolic syndrome characterized in part by obesity, hyperinsulinemia, and insulin resistance is associated with increased risk of breast cancer. However there remains a need to establish a circulating biomarker metabolic profile indicative of increased risk of breast cancer. In the current study, we performed a comprehensive metabolomics screen to identify biomarkers indicative of increased risk of breast cancer. Methods: Unbiased metabolomics profiling was conducted on an initial Development Set of plasmas collected from 353 newly diagnosed breast cancer cases and 141 controls. A deep learning neural network with 3 layers each containing 32 nodes based on 11 individual lipids corresponding to discrete lipid subclasses was built for risk prediction of breast cancer. The model was validated in an independent Test Set consisting of 79 breast cancer cases and 163 controls. Using a nested case: control matched design, we evaluated the performance of the model among body mass index (BMI) strata (≥ 30 or Citation Format: Johannes F. Fahrmann, Ehsan Irajizad, Jody Vykoukal, Angelica Gutierrez Barrera, Jennifer B. Dennison, Ranran Wu, Banu Arun, Abenaa Brewster, Samir Hanash. A blood-based lipid biomarker panel for personalized risk assessment of breast cancer. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P077.

The potential of blood tests for multi-cancer early detection (MCED) to reduce cancer mortality has elicited both optimism and concern about their generalized applicability. Identifying in the general population individuals at increased risk, through the use of biomarkers together with subject characteristics, has the potential to trigger preventive intervention and to improve effectiveness of early detection through a tailored approach to screening based on a personalized risk profile. The contribution of blood-based biomarkers for personalized lung cancer risk assessment will be highlighted. Citation Format: Samir Hanash. Contribution of biomarker-based risk profiles to cancer early detection. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr IA021.