Title: Proteomic patterns associated with response to breast cancer neoadjuvant treatment
Abstract: Article22 September 2020Open Access Transparent process Proteomic patterns associated with response to breast cancer neoadjuvant treatment Anjana Shenoy Anjana Shenoy orcid.org/0000-0001-5279-9211 Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel Search for more papers by this author Nishanth Belugali Nataraj Nishanth Belugali Nataraj orcid.org/0000-0003-2970-6739 Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Gili Perry Gili Perry Sheba Medical Center, Cancer Research Center, Tel-Hashomer, Israel Search for more papers by this author Fabricio Loayza Puch Fabricio Loayza Puch Netherlands Cancer Institute, Amsterdam, Netherlands Search for more papers by this author Remco Nagel Remco Nagel Netherlands Cancer Institute, Amsterdam, Netherlands Search for more papers by this author Irina Marin Irina Marin Sheba Medical Center, Pathology Institute, Tel-Hashomer, Israel Search for more papers by this author Nora Balint Nora Balint Sheba Medical Center, Pathology Institute, Tel-Hashomer, Israel Search for more papers by this author Noa Bossel Noa Bossel Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Anya Pavlovsky Anya Pavlovsky Sheba Medical Center, Pathology Institute, Tel-Hashomer, Israel Search for more papers by this author Iris Barshack Iris Barshack Sheba Medical Center, Pathology Institute, Tel-Hashomer, Israel Search for more papers by this author Bella Kaufman Bella Kaufman Sheba Medical Center, Oncology Institute, Tel-Hashomer, Israel Search for more papers by this author Reuven Agami Reuven Agami orcid.org/0000-0002-2848-2473 Netherlands Cancer Institute, Amsterdam, Netherlands Search for more papers by this author Yosef Yarden Yosef Yarden orcid.org/0000-0001-8578-0250 Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Maya Dadiani Maya Dadiani Sheba Medical Center, Cancer Research Center, Tel-Hashomer, Israel Search for more papers by this author Tamar Geiger Corresponding Author Tamar Geiger [email protected] orcid.org/0000-0002-9526-197X Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel Search for more papers by this author Anjana Shenoy Anjana Shenoy orcid.org/0000-0001-5279-9211 Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel Search for more papers by this author Nishanth Belugali Nataraj Nishanth Belugali Nataraj orcid.org/0000-0003-2970-6739 Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Gili Perry Gili Perry Sheba Medical Center, Cancer Research Center, Tel-Hashomer, Israel Search for more papers by this author Fabricio Loayza Puch Fabricio Loayza Puch Netherlands Cancer Institute, Amsterdam, Netherlands Search for more papers by this author Remco Nagel Remco Nagel Netherlands Cancer Institute, Amsterdam, Netherlands Search for more papers by this author Irina Marin Irina Marin Sheba Medical Center, Pathology Institute, Tel-Hashomer, Israel Search for more papers by this author Nora Balint Nora Balint Sheba Medical Center, Pathology Institute, Tel-Hashomer, Israel Search for more papers by this author Noa Bossel Noa Bossel Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Anya Pavlovsky Anya Pavlovsky Sheba Medical Center, Pathology Institute, Tel-Hashomer, Israel Search for more papers by this author Iris Barshack Iris Barshack Sheba Medical Center, Pathology Institute, Tel-Hashomer, Israel Search for more papers by this author Bella Kaufman Bella Kaufman Sheba Medical Center, Oncology Institute, Tel-Hashomer, Israel Search for more papers by this author Reuven Agami Reuven Agami orcid.org/0000-0002-2848-2473 Netherlands Cancer Institute, Amsterdam, Netherlands Search for more papers by this author Yosef Yarden Yosef Yarden orcid.org/0000-0001-8578-0250 Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Maya Dadiani Maya Dadiani Sheba Medical Center, Cancer Research Center, Tel-Hashomer, Israel Search for more papers by this author Tamar Geiger Corresponding Author Tamar Geiger [email protected] orcid.org/0000-0002-9526-197X Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel Search for more papers by this author Author Information Anjana Shenoy1, Nishanth Belugali Nataraj2, Gili Perry3, Fabricio Loayza Puch4, Remco Nagel4, Irina Marin5, Nora Balint5, Noa Bossel2, Anya Pavlovsky5, Iris Barshack5, Bella Kaufman6, Reuven Agami4, Yosef Yarden2, Maya Dadiani3 and Tamar Geiger *,1 1Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel 2Weizmann Institute of Science, Rehovot, Israel 3Sheba Medical Center, Cancer Research Center, Tel-Hashomer, Israel 4Netherlands Cancer Institute, Amsterdam, Netherlands 5Sheba Medical Center, Pathology Institute, Tel-Hashomer, Israel 6Sheba Medical Center, Oncology Institute, Tel-Hashomer, Israel *Corresponding author. Tel: +972 52 8611611; E-mail: [email protected] Molecular Systems Biology (2020)16:e9443https://doi.org/10.15252/msb.20209443 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Tumor relapse as a consequence of chemotherapy resistance is a major clinical challenge in advanced stage breast tumors. To identify processes associated with poor clinical outcome, we took a mass spectrometry-based proteomic approach and analyzed a breast cancer cohort of 113 formalin-fixed paraffin-embedded samples. Proteomic profiling of matched tumors before and after chemotherapy, and tumor-adjacent normal tissue, all from the same patients, allowed us to define eight patterns of protein level changes, two of which correlate to better chemotherapy response. Supervised analysis identified two proteins of proline biosynthesis pathway, PYCR1 and ALDH18A1, that were significantly associated with resistance to treatment based on pattern dominance. Weighted gene correlation network analysis of post-treatment samples revealed that these proteins are associated with tumor relapse and affect patient survival. Functional analysis showed that knockdown of PYCR1 reduced invasion and migration capabilities of breast cancer cell lines. PYCR1 knockout significantly reduced tumor burden and increased drug sensitivity of orthotopically injected ER-positive tumor in vivo, thus emphasizing the role of PYCR1 in resistance to chemotherapy. Synopsis Proteomic profiling of matched tumor and normal samples, associates distinct proteomic patterns with patient prognosis in breast cancer. Functional studies in vivo support the effectiveness of PYCR1 suppression in combination with chemotherapeutics in clinical settings. Deep proteomic profiling of matched pre-treatment, post-treatment and tumor adjacent normal samples is performed. Pattern analysis identifies metabolic pathways that are significantly altered in cancer and are not affected by neoadjuvant treatment in patients with worse prognosis. Supervised analysis and unsupervised WGCNA identify a role for PYCR1 in drug response and tumor recurrence. The proline biosynthesis gene PYCR1 affects tumor growth and response to chemotherapy in vivo. Introduction Breast cancer is the leading cause of female cancer-related death. While survival rates are very high when diagnosed early, treatment of large tumors and metastatic disease is more challenging. Treatment decisions are based on the breast cancer subtype, and the tumor stage at diagnosis. Hormone-positive subtypes that express the estrogen receptor (ER) and/or the progesterone receptor (PR) can be treated with hormonal therapy (e.g., tamoxifen). Her2-positive subtype can be targeted by herceptin. The main available option to treat triple-negative breast cancer (TNBC), which do not express any of these receptors, is chemotherapy and surgery (Guarneri & Conte, 2009). These clinical subtypes are linked to molecular classification based on gene expression analysis. Four main subtypes include the following: luminal A (ER+ and/or PR+, HER2−, Ki67low), luminal B (ER+ and/or PR+, HER2+, Ki67high), basal-like (ER−, PR−, HER2−), and Her2 overexpressing (ER−, PR−, and HER2+; Perou et al, 2000; Sorlie et al, 2001, 2003). The subtypes were reproducibly identified in several studies and also reflect clinical outcomes, with basal-like subtype having the worst, and luminal A subtype having the best prognosis (Hodgkinson et al, 2010; The Cancer Genome Atlas Network, 2012). Beyond subtype-specific treatment decisions, tumor TNM staging dictates the therapeutic approach. Locally advanced invasive tumors and tumors that are difficult to operate are considered for neoadjuvant treatment (NAT), which is given before surgery (Saleh et al, 2014). Although NAT is administered to shrink large tumors, it is increasingly used to obtain prognostic information about tumor chemo-sensitivity by evaluation of pathological complete response (pCR). pCR is defined as the complete eradication of malignant disease in the breast and related axillary lymph nodes on completion of NAT and is often associated with improved survival rates (Perou et al, 2000; Goldstein et al, 2007; Cortazar et al, 2014). The lowest pCR rate is found among luminal tumors (6.4–22%) and highest among Her2 overexpressing and triple-negative subtypes (27–32%) (Cortazar et al, 2014; Broglio et al, 2016). The extent of residual disease in partial responders after NAT is variable and is an important prognostic factor to predict risk of relapse and overall survival (OS; Denkert et al, 2011; Symmans et al, 2017). Consequently, there is a major clinical need to understand the underlying resistance mechanisms that eventually lead to tumor recurrence. Previous breast cancer studies analyzed tumor tissues utilizing cDNA microarray and sequencing and identified markers of drug resistance such as CSNK2B, DDB1, ABL, PRKDC, and DUSP4 that were differentially expressed between responders and non-responders to NAT (Chang et al, 2003; Balko et al, 2012). Using reverse-phase protein arrays of 76 proteins, Sohn et al identified AKT, IGFBP2, LKB1, S6, and Stathmin as predictors of recurrence-free survival (RFS) in triple-negative breast cancer (TNBC; Sohn et al, 2013). These studies highlighted the importance of cancer signaling in eliciting resistance. MS-based clinical proteomics of breast cancer has focused in recent years on cancer classification, showing protein networks associated with each subtype, with driver mutations and was able to challenge the RNA-based classification (Mertins et al, 2016; Tyanova et al, 2016a; Yanovich et al, 2018). Recently, proteogenomic analysis of breast cancer treatment response showed proteins associated with Herceptin resistance in a small patient cohort (Satpathy et al, 2020). We hypothesized that an untargeted, proteomic approach has the potential to unravel novel pathways of neoadjuvant chemotherapy response. In this study, we performed LC-MS/MS proteomic analysis of formalin-fixed paraffin-embedded (FFPE) tissues to understand response to NAT in breast cancer. We analyzed matched tumors before and after NAT, with matched tumor-adjacent normal samples from 35 patients with partial response to chemotherapy. We measured the response to chemotherapy using the previously described Miller & Payne pathological response score (M&P score; Ogston et al, 2003), and examined the factors associated with tumor recurrence. The longitudinal analysis highlighted protein expression patterns associated with pathological response and recurrence, revealing two proteins in the proline biosynthesis pathway. Finally, functional analysis in vivo showed that abundance of PYCR1, a mitochondrial metabolic protein, was associated with drug resistance to chemotherapy, thus stressing the role of this protein in breast cancer. Results Proteomic analysis of response to neoadjuvant treatment To identify the proteomic alterations that result in poor response to NAT, we assembled 108 FFPE tissue samples from a cohort of 35 partial responders to NAT. One patient had bilateral invasive cancer, and samples from both breasts were analyzed separately. We obtained matched pre-treatment biopsy, post-treatment residual carcinoma, and tumor-adjacent normal samples from each of the 35 patients. To ensure that the tumor-adjacent normal samples are suitable as controls, we compared them to breast ducts from five breast reduction surgeries of healthy women bringing us to 113 samples (Appendix Fig S1A). The tumor specimens were pathologically classified as triple-positive (ER, PR, and HER2 positive; n = 9), triple-negative (ER, PR, and HER2 negative; n = 5), Her2 overexpressing (n = 1), and hormone-positive (ER/PR positive; n = 21) subtypes. The average follow-up time in the cohort was five years, and seven patients showed relapse post-treatment (Fig 1A, Dataset EV1). For every patient in the cohort, we compared the reduction in tumor cellularity before and after treatment and calculated the Miller & Payne (M&P) pathological response score (MP1: no reduction in tumor cellularity/poor responders, n = 6; MP2: minimum reduction in cellularity/partial responders, n = 13; MP3: partial reduction in cellularity/better responders, n = 16; MP4: significant reduction in cellularity/better responders, n = 1, grouped with MP3 as a better responder). A higher M&P score (MP5) represents full responders with more than 90% reduction in cellularity and was not included in this study since these have no post-treatment specimens. Patients assigned to the three M&P groups showed significantly different relapse rates, and relapse-free survival time (RFS; Fig 1B and C). Figure 1. Unsupervised analysis of proteomics of neoadjuvant treatment Clinical parameters of 35 patients. Subtype at diagnosis (HP: ER+ and/or PR+, TP: ER+ and/or PR+, HER2+, TN: ER−, PR−, HER2− and Her2 overexpressing: ER−, PR−, and HER2+, H: healthy breast ducts, N: tumor adjacent normal); tumor grade at diagnosis (1, 2, or 3); Miller & Payne score(1: no reduction in tumor cellularity, 2: minimum reduction in cellularity, 3: partial reduction in cellularity); relapse (yes/no); death (yes/no); Ki67 intensity measured by immunohistochemistry at diagnosis (1–3); tumor size at diagnosis (1: < 2 cm, 2:2–5 cm, 3:> 5 cm, 4 indicates that tumor has grown into chest wall or skin); age (years); co-clustering of matched pre- and post-treatment samples after hierarchical clustering (yes/no, P < 0.05); relapse-free survival time (years); overall survival time (years). Association between patient pathological response and tumor relapse in our cohort. Significance was determined using chi-square test. Kaplan–Meier survival curve for Miller and Payne pathological response score. Global log rank P value and corrected P value for pairwise comparison are indicated. Dendrogram colors indicate co-clustering of matched pre- and post-treatment tumor samples coming from the same patient. Association between co-clustering of matched pre- and post-treatment tumor samples with relapse. Significance was determined using chi-square test. Association between co-clustering of matched pre- and post-treatment tumor samples with M&P score. Significance was determined using chi-square test. Download figure Download PowerPoint We performed LC-MS/MS-based proteomic analysis on each sample, with a common super-SILAC mix as a heavy-labeled reference sample for accurate quantification. Due to limited sample amount (from biopsies and from post-treatment samples), we reached a partial proteome coverage of 7,600 identified and 7,180 quantified proteins in total, with 3,200–3,900 proteins in each sample group (Appendix Fig S1B, Dataset EV2). Principal component analysis (PCA) showed a clear separation between tumor-adjacent normal samples and tumor samples (pre- and post-treatment) in the first two components (Appendix Fig S1C). Healthy samples from breast reduction surgery (non-transformed normal samples) and the tumor-adjacent normal samples were not separated, and had no significant differences between them (Student's t-test FDR < 5%), thus confirming the longitudinal analysis of matched adjacent normal samples as a normal breast reference. In agreement with the PCA analysis, hierarchical clustering of sample correlations separated between normal and cancer samples (Fig 1D). Notably, the matched pre-treatment and post-treatment tumor samples from 19 patients co-clustered significantly (P < 0.05). Interestingly, we found that pre- and post-treatment co-clustering, representing higher proteome correlations and reduced treatment effect, also showed significantly higher relapse (P = 0.051) and poor pathological response (P = 0.035) compared to patients that did not show co-clustering of tumor samples (Fig 1E and F). Spearman rank correlations of all samples ranged between 0.18 and 0.82 (Appendix Fig S1D). Average correlation between matched pre- and post-treatment samples was 0.58, and the correlations with matched tumor-adjacent normal were markedly lower (post-treatment: tumor-adjacent normal (R = 0.43) and pre-treatment: tumor-adjacent normal (R = 0.39; Appendix Fig S1E). Pattern analysis links pattern 3 to poor survival Pathological response is reflected in the change that occurs between two tumor states (pre- and post-treatment), rather than a snapshot of one of them. Therefore, in order to identify resistance mechanisms from the protein abundance data, we took advantage of the matched nature of our cohort. For each patient, we divided the proteins into eight profiles of protein level changes between normal, pre-treatment tumors, and post-treatment tumor samples (Dadiani et al, 2016). For example, pattern 1 consists of all the proteins upregulated in the pre-treatment sample compared to the tumor-adjacent normal sample and downregulated post-treatment (Fig 2A). We first performed the analysis using three paired Student's t-tests, each between two groups with an FDR cutoff of 5% across all patients (henceforth referred to as global pattern analysis). Significantly changing proteins (904 proteins) followed patterns 1, 2, 3, 4, and 6, across the 36 matched samples, and no significant proteins followed patterns 5, 7, and 8 (Appendix Fig S1F, Dataset EV3). We postulated that proteins dominated by global patterns 1 and 2, which revert to normal levels upon treatment, are associated with good prognosis, while proteins that mostly follow patterns 3, 4, 7, and 8, which remain significantly different from the tumor-adjacent normal even after treatment, may be associated with poor response. Pattern 3, which includes proteins significantly upregulated in cancer, that are not affected by treatment, was the most dominant pattern, with 736 proteins. Centrality analysis of pattern 3 protein network highlighted several oncogenes among the top 10% most central proteins, including AKT, MTOR, and STAT1, suggesting a role for these proteins in forming specific patterns of protein level changes and treatment resistance (Fig 2B and C). Figure 2. Pattern analysis associates proteins with patient prognosis Eight patterns of protein level changes between the three clinical groups: adjacent normal, pre-treatment tumor, and post-treatment tumor. Pattern 3 protein network based on global pattern analysis: Node size and color are based on betweenness centrality score of each node in the network. Top 10% of the most central nodes are indicated Profile plot shows z-scored protein pattern of selected significantly changing oncogenes in pattern 3. Each line represents a patient and colored based on the distance to the median profile. Radial bar plot with percentages of proteins following each pattern (patient-wise pattern) in 35 patients Scatter plots show Spearman rank correlations between percent of proteins following each pattern and relapse-free survival (RFS), based on patient-wise pattern analysis. Significance was determined using t-test, and P values were corrected using Benjamini–Hochberg FDR correction. Download figure Download PowerPoint To directly associate the pattern of protein changes to treatment response in individual patients, we defined a patient-wise protein pattern for every protein in each patient, using a fold change cutoff of 1.5 for protein level changes between matched samples (Dataset EV4). Percentage of each patient-wise pattern in the different patients shows dominance of patterns 1–4 in all patients (Fig 2D). In support of our hypothesis, percentage of pattern 3 proteins in patients was associated with shorter relapse-free survival (R = −0.45, q = 0.03; Fig 2E). Next, to identify the proteins and pathways associated with patient response and relapse, we divided the patients into two groups based on their M&P score (better responders-M&P = 3,4 and worse responders-M&P = 1,2) and performed a paired Student's t-test between matched pre-treatment and post-treatment samples. We identified 316 significantly altered proteins in better responders (q < 0.05); however, 93% of these proteins remained unaltered after treatment in poor responders (Fig 3A). Essentially, significantly downregulated proteins follow pattern 1 in responders and pattern 3 in non-responders, while significantly upregulated proteins follow pattern 2 in responders and pattern 4 in non-responders. Similarly, separate analyses of the relapse groups identified 223 proteins that were differentially expressed in patients with no relapse, while these were unaffected by treatment in patients who showed relapse (Fig 3B). Interestingly, 150 proteins were associated with both drug response and relapse in our cohort. Combined network of upregulated proteins upon treatment in better responders showed a significant enrichment of amino acid biosynthesis pathway, pentose phosphate pathway, inflammatory response, and glycolysis/gluconeogenesis (Fisher exact test, q < 0.05, Fig EV1A). Combined network of downregulated proteins upon treatment in better responders showed a significant enrichment of TCA cycle, oxidative phosphorylation, PPAR signaling, and proline biosynthesis pathway (Fig EV1B). Figure 3. Supervised analysis and unsupervised WGCNA identifies protein modules associated with neoadjuvant treatment response Volcano plot shows significantly altered proteins in better responders before and after treatment. Samples are compared by paired Student's t-test, FDR 5%. These proteins are not significantly altered in poor responders. Same as (a) but comparing patients with and without relapse Heat map shows 19 eigengene modules that show significant Pearson correlation (P < 0.05) to at least one clinical parameter in post-treatment samples. Age (years); M&P score (1:no reduction in tumor cellularity, 2: minimum reduction in cellularity, 3: partial reduction in cellularity); relapse-free survival time (years); overall survival time (years); tumor grade at diagnosis (1, 2 or 3); relapse (yes/no); death (yes/no); tumor size at diagnosis (1: < 2 cm, 2:2–5 cm, 3: > 5 cm, 4 indicates that tumor has grown into chest wall or skin); Ki67 intensity measured by immunohistochemistry at diagnosis (1–3); co-clustering of matched pre- and post-treatment samples after hierarchical clustering (yes/no, P < 0.05). Selected gene ontology processes, KEGG pathways, and gene sets from MSigDB enriched in these modules are indicated (Fisher's exact test, FDR 2%). Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Supervised analysis. Related to Fig 3 Networks of all proteins that are significantly upregulated in patients with good prognosis and unaltered in patients with poor prognosis. Networks were constructed using the STRING database, and connected nodes were visualized in Cytoscape. Node color is based on the association to clinical feature. Selected significantly enriched GO and KEGG biological pathways are indicated by node border colors (Fisher exact test, FDR 5%). Same as (A) but for all proteins that are significantly downregulated in patients with good prognosis and unaltered in patients with poor prognosis. Download figure Download PowerPoint Weighted gene correlation network analysis (WGCNA) identifies protein modules associated with neoadjuvant treatment response To further examine the association of the proteomics data with clinical parameters, we performed unsupervised WGCNA (Storey, 2004; Zhang & Horvath, 2005; Langfelder & Horvath, 2008) on the post-treatment tumor samples, which best reflect tumor response (Appendix Fig S2A, Dataset EV5). Analysis resulted in 39 modules, which were subjected to correlation analysis with clinical features, including age, Ki67 staining, tumor grade, tumor size, relapse, death, survival time, M&P score, and tumor co-clustering (Appendix Fig S2B). Of these, 19 modules correlated with at least one clinical feature (Fig 3C and D; P < 0.05, Dataset EV5); six eigengene modules correlated with M&P score (Fig EV2A). Three modules, which positively correlated to M&P score, or better responders (pale-turquoise, yellow, and purple modules, collectively referred to as protein cluster A), presented high levels of collagens, integrins, and actin regulators that mediate focal adhesion and cytoskeletal organization (Fig EV2B). In contrast, turquoise, orange, and brown modules (collectively referred to as protein cluster B) negatively correlated with M&P score (poor responders) and was enriched for mRNA processing, components of the ubiquitin-dependent protein catabolic process, spliceosome, fatty acid, and ketone body metabolism (q < 0.05; Fig EV3A). Poor responders also showed upregulation of MHC protein complex, and related proteins, including HLA-A, HLA-B, HLA-C, HLA-DR, TAP2, and STAT1 along with interferon signaling post-treatment, suggesting potential involvement of the immune system (see Discussion). Examination of pattern enrichment within these clusters showed that clusters A and B were significantly enriched for pattern 3 and 4 proteins, respectively (global pattern, Fisher's exact test q < 0.02). In agreement, clustering of the average protein levels in normal, pre-treatment, and post-treatment samples shows that better responders present dominance of patterns 1 and 2, while poorer responders (MP1 and 2) present dominance of patterns 3 and 4 (Fig EV3B). Click here to expand this figure. Figure EV2. Module eigengene profiles associated with M&P score. Related to Fig 3 Module eigengene profiles of protein modules in post-treatment samples that show a significant correlation to M&P score. Each bar represents a patient and is sorted and colored according to M&P score. Protein networks of all cluster A proteins are shown. Networks were constructed using the STRING database, and connected nodes were visualized in Cytoscape. Node size is based on degree of connectivity of each protein to other interacting proteins (minimum 1, maximum 88). Node colors represent different biological pathways as indicated. Download figure Download PowerPoint Click here to expand this figure. Figure EV3. Protein network of WGCNA modules associated with M&P score. Related to Fig 3 Protein networks of all cluster B proteins. Networks were constructed using the STRING database, and connected nodes were visualized in Cytoscape. Node size reflects degree of connectivity of each protein to other interacting proteins. Node colors represent different biological pathways as indicated. Global pattern proteins in cluster A (combined protein list of WGCNA modules, turquoise, orange, and brown), or in cluster B (combined protein list of pale-turquoise, yellow, and purple modules). Patients are separated by M&P response score. Heatmap shows average protein levels in each sample type. Global pattern of protein levels and WGCNA module in each response group is indicated as row annotation. Download figure Download PowerPoint To evaluate proteins associated with tumorigenic phenotypes such as tumor size and relapse, we looked at protein networks of cluster C (combined proteins from module blue, white, sienna3, and yellowgreen) that show a positive correlation to these clinical features, and protein cluster D (proteins of module pale-turquoise and red) that shows a negative correlation to tumor size and relapse. Smaller tumors showed increased oxidative phosphorylation and TCA cycle. As expected, large tumors showed higher levels of proliferation markers such as MKI67, EGFR, and MCM complex proteins and elevated glycolysis. This metabolic shift was also accompanied by upregulated pentose phosphate pathway, serine synthesis, and proline biosynthesis (Appendix Fig S3A and B). PYCR1 level is associated with drug response and relapse All bioinformatic analyses described above associate the proline biosynthesis pathway, specifically, PYCR1 and ALDH18A1 with relapse-free survival (global pattern analysis, Fig 2B); relapse (WGCNA, Fig 4A and B, Appendix Fig S3A and supervised analysis, Fig 3B); and treatment response (supervised analysis, Fig 3A, Appendix Fig S4A). PYCR1 and ALDH18A1 are members of the proline cycle that consists of four enzymes that convert glutamate to proline, and two enzymes that catalyze the reverse reactions. Aldehyde dehydrogenase family 18 member A1 (ALDH18A1), also known as pyrroline-5-carboxylate synthetase (P5CS), converts glutamate to Δ1-pyrroline-5-carboxylate (P5C), an intermediate metabolite. P5C is converted to proline by the mitochondrial enzymes pyrroline-5-carboxylate reductase (PYCR1/2) or cytosolic pyrroline-5-carboxylate reductase-like (PY