Supplementary figures and figure legends

Abstract The metastatic cascade is a critical component of cancer evolution. Understanding the rewired metabolism in organ-specific metastasis in breast cancer could help identify strategies to improve the treatment and prevention of metastatic disease. Here, we used a systems biology approach to assess the evolution of metabolic fluxes from parental breast cancer cells to their brain- and lung-homing metastatic derivatives. We found that divergent lineages had distinct, heritable metabolic fluxes. Lung-homing cells maintained adaptations for high glycolytic flux despite low levels of glycolytic intermediates, constitutively activating a pathway sink into lactate. This exacerbated Warburg effect –– stronger than the Warburg effect in the primary tumor –– was associated with a high ratio of lactate dehydrogenase (LDH) to pyruvate dehydrogenase (PDH) expression, a signature which correlated with lung metastasis in patients with breast cancer. While feature classification models trained on clinical characteristics alone were unable to predict tropism, the LDH/PDH ratio was a significant predictor of a patient's future metastasis to the lung but not to other organs, independent of other transcriptomic signatures. Lung- and brain-homing lineages had differential selection patterns in acidic metabolic microenvironments. High lactate efflux was also a trait in lung-homing metastatic pancreatic cancer cells, suggesting that lactate production may be a convergent phenotype in lung metastasis. Together, these analyses highlight the essential role that metabolic evolution plays in organ-specific cancer metastasis and identify a putative biomarker for predicting lung metastasis in breast cancer patients. Citation Format: Deepti Mathur, Chen Liao, Wendy Lin, Alessandro La Ferlita, Salvatore Alaimo, Samuel Taylor, Yi Zhong, Christine Iacobuzio-Donahue, Alfredo Ferro, Joao Xavier. Metabolic evolution in breast cancer predicts organ-specific metastasis [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A039.

Abstract Pancreatic ductal adenocarcinoma (PDAC) is among the most lethal of all cancer types. A key — yet poorly understood — facet in the disease state is cachexia, a multi-organ pathological state characterized by physical wasting and tissue catabolism. It occurs in 80% of PDAC patients, and to date there are no preventative or early detection methods. Cachexia leads to limited tolerance to anti-cancer therapy and contributes to disease lethality. Here, we present the first-of-its-kind systemic metabolomic analysis across cachectic stages to better understand disease progression. We used the well-established mutant KRASG12D(LSL/+) mutant p53 inducible mouse model of PDAC. Model physiology faithfully recaptures human cachexia: we observe progressive overall weight loss as well as loss of skeletal muscle and adipose tissue. As with human disease, weight loss occurs prior to loss of appetite in the animals, suggesting physiological changes independent of nutrition. Importantly, our model captures the period prior to weight loss (pre-CAC), as well as early-CAC (<10% weight loss) and late-CAC (>10% weight loss). We profiled the metabolomes of the pancreas, interstitial fluid, plasma, liver, 3 different adipose tissues, and 3 skeletal muscles, from male and female mice, control and tumor-bearing, across all 3 stages of cachexia. Each tissue has a unique metabolic trajectory across cachexia stages; pathway and correlation analyses showed a particular emphasis on lipid alterations in peripheral tissues. Strikingly, we find systemic metabolic changes prior to tissue wasting, including rewiring in the liver before the presence of metastases. This indicates systemic alterations as early as pre-CAC. We therefore used computational modeling to identify metabolites that may be participating in cross-tissue networks. A key finding, confirmed by 13C tracing, was the circulation of lactate and glucose and their uptake by the liver and pancreas. Network maps also suggest lipid rewiring in distal tissues, changes in abundances of these same lipid species in plasma, and their consequent alterations in the tumor and liver. A fundamental question remains: can we predict cachexia development before the manifestation of symptoms? We used feature selection algorithms based on statistical learning, training the models only on a subset of data. Specific sphingolipids and triglycerides were the key lipid species that distinguished control and pre-CAC. We find that the model — trained only on pre-CAC — is able to determine early-CAC with an 85% accuracy and late-CAC with a 90% accuracy. Thus, a limited set of metabolites that are altered in pre-CAC could have predictive value for future cachexia development. Overall, our work provides a resource for the field and advances our understanding of systemic metabolism in PDAC cachexia. We hope this will lay the foundation for cachexia prevention and treatment. Citation Format: Deepti Mathur, Blanca Majem, Courtney Beaulieu, Lucas Dailey, Sarah Jeanfavre, Joao Xavier, Clary Clish, Nada Kalaany. Metabolic patterns of pancreatic cancer cachexia: Cross-tissue lipid networks predict cachexia progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6610.

Abstract Cachexia or body wasting is a significant contributing factor to the morbidity and mortality of pancreatic cancer. Despite an unmet need, its metabolic underpinnings remain poorly understood. Here, we characterize an inducible mouse model of pancreatic cancer cachexia that recaptures human disease progression. We present longitudinal systemic metabolic profiling analyses through pre-, early and late cachexia stages in male and female pancreas, its interstitial fluid (IF), plasma, liver, adipose and skeletal muscle. We find that each tissue has a unique metabolome and trajectory across stages, and reveal metabolic signatures with marked lipid enrichment in the pancreatic IF. Using mathematical modeling, we identify metabolites participating in cross-tissue networks and in vivo validate a lactate-hexose connection. We find systemic metabolic changes prior to weight loss, and use feature selection algorithms to identify potential predictive markers of cachexia progression. Our study provides a resource for system-wide metabolic network evolution of pancreatic cancer cachexia, highlighting the potential for prevention and therapy. Citation Format: Blanca Majem, Min-Sik Lee, Kyung Cheul Shin, Insia Naqvi, Courtney Dennis, Lucas Dailey, Sarah Jeanfavre, Mari Mino-Kenudson, Joao B Xavier, Clary B Clish, Deepti Mathur, Nada Y Kalaany. Spatiotemporal metabolic networks in pancreatic cancer and associated cachexia [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr IA-09.

Abstract Cachexia is a multi-organ pathological state characterized by physical wasting and tissue catabolism. It occurs in 80% of pancreatic cancer patients, and to date there are no preventative or early detection methods. Cachexia leads to limited tolerance to anti-cancer therapy and is a lethal disease. Here, we have characterized a mouse model of cachexia in pancreatic ductal adenocarcinoma (PDAC) in order to better understand disease progression: the physiology across stages of cachexia in the model recaptures well the progressive disease in humans. We present the first-of-its-kind systemic metabolomic analysis across cachectic stages in the tumor, interstitial fluid, liver, fats, muscles, and blood plasma. The primary source of variation in the data is the tissue type, indicating that each tissue has a unique metabolome and trajectory across cachexia stages. We use mathematical modeling to identify metabolites that may be participating in cross-tissue networks in pre-, early-, and late-stage cachexia. Pathway analysis shows a particular emphasis on lipid alterations. Strikingly, we find systemic metabolic changes prior to weight loss or other disease symptoms, and use feature selection algorithms to identify potential predictive markers of cachexia progression. Overall, we hope our work is a valuable resource for the field and will lay the foundation for metabolic insights into pancreatic cancer cachexia and its prevention and treatment. Citation Format: Deepti Mathur, Blanca Majem, Nada Kalaany. Metabolic patterns in pancreatic cancer cachexia [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr A060.

Supplementary Figure S6. ATR signaling defects contribute to synthetic lethality.

Supplementary Figure from Loss of PBRM1 Alters Promoter Histone Modifications and Activates ALDH1A1 to Drive Renal Cell Carcinoma

Supplementary Figure S4. DHODH is a target of sensitivity in PTEN deficient cells.

Supplementary Figure S2. Pyrimidine synthesis is upregulated in Pten-/- MEFs.

Supplementary Figure S5. Leflunomide inhibits PTEN mutant tumors.

Abstract Understanding the rewired metabolism underlying organ-specific metastasis in breast cancer could help identify strategies to improve the treatment and prevention of metastatic disease. Here, we used a systems biology approach to compare metabolic fluxes used by parental breast cancer cells and their brain- and lung-homing derivatives. Divergent lineages had distinct, heritable metabolic fluxes. Lung-homing cells maintained high glycolytic flux despite low levels of glycolytic intermediates, constitutively activating a pathway sink into lactate. This strong Warburg effect was associated with a high ratio of lactate dehydrogenase (LDH) to pyruvate dehydrogenase (PDH) expression, which correlated with lung metastasis in patients with breast cancer. Although feature classification models trained on clinical characteristics alone were unable to predict tropism, the LDH/PDH ratio was a significant predictor of metastasis to the lung but not to other organs, independent of other transcriptomic signatures. High lactate efflux was also a trait in lung-homing metastatic pancreatic cancer cells, suggesting that lactate production may be a convergent phenotype in lung metastasis. Together, these analyses highlight the essential role that metabolism plays in organ-specific cancer metastasis and identify a putative biomarker for predicting lung metastasis in patients with breast cancer. Significance: Lung-homing metastatic breast cancer cells express an elevated ratio of lactate dehydrogenase to pyruvate dehydrogenase, indicating that ratios of specific metabolic gene transcripts have potential as metabolic biomarkers for predicting organ-specific metastasis.

Supplementary Figure from Optimal Strategy and Benefit of Pulsed Therapy Depend On Tumor Heterogeneity and Aggressiveness at Time of Treatment Initiation

Supplementary Figure Legends, Methods

Supplementary Figure S1. Loss of Pten increases proliferation.

For decades, mathematical models have influenced how we schedule chemotherapeutics. More recently, mathematical models have leveraged lessons from ecology, evolution, and game theory to advance predictions of optimal treatment schedules, often in a personalized medicine manner. We discuss both established and emerging therapeutic strategies that deviate from canonical standard-of-care regimens, and how mathematical models have contributed to the design of such schedules. We first examine scheduling options for single therapies and review the advantages and disadvantages of various treatment plans. We then consider the challenge of scheduling multiple therapies, and review the mathematical and clinical support for various conflicting treatment schedules. Finally, we propose how a consilience of mathematical and clinical knowledge can best determine the optimal treatment schedules for patients.

Abstract Subunits of SWI/SNF chromatin remodeling complexes are frequently mutated in human malignancies. The PBAF complex is composed of multiple subunits, including the tumor-suppressor protein PBRM1 (BAF180), as well as ARID2 (BAF200), that are unique to this SWI/SNF complex. PBRM1 is mutated in various cancers, with a high mutation frequency in clear cell renal cell carcinoma (ccRCC). Here, we integrate RNA-seq, histone modification ChIP-seq, and ATAC-seq data to show that loss of PBRM1 results in de novo gains in H3K4me3 peaks throughout the epigenome, including activation of a retinoic acid biosynthesis and signaling gene signature. We show that one such target gene, ALDH1A1, which regulates a key step in retinoic acid biosynthesis, is consistently upregulated with PBRM1 loss in ccRCC cell lines and primary tumors, as well as non-malignant cells. We further find that ALDH1A1 increases the tumorigenic potential of ccRCC cells. Using biochemical methods, we show that ARID2 remains bound to other PBAF subunits after loss of PBRM1 and is essential for increased ALDH1A1 after loss of PBRM1, whereas other core SWI/SNF components are dispensable, including the ATPase subunit BRG1. In total, this study uses global epigenomic approaches to uncover novel mechanisms of PBRM1 tumor suppression in ccRCC. Implications: This study implicates the SWI/SNF subunit and tumor-suppressor PBRM1 in the regulation of promoter histone modifications and retinoic acid biosynthesis and signaling pathways in ccRCC and functionally validates one such target gene, the aldehyde dehydrogenase ALDH1A1.

Abstract Therapeutic resistance is a fundamental obstacle in cancer treatment. Tumors that initially respond to treatment may have a preexisting resistant subclone or acquire resistance during treatment, making relapse theoretically inevitable. Here, we investigate treatment strategies that may delay relapse using mathematical modeling. We find that for a single-drug therapy, pulse treatment—short, elevated doses followed by a complete break from treatment—delays relapse compared with continuous treatment with the same total dose over a length of time. For tumors treated with more than one drug, continuous combination treatment is only sometimes better than sequential treatment, while pulsed combination treatment or simply alternating between the two therapies at defined intervals delays relapse the longest. These results are independent of the fitness cost or benefit of resistance, and are robust to noise. Machine-learning analysis of simulations shows that the initial tumor response and heterogeneity at the start of treatment suffice to determine the benefit of pulsed or alternating treatment strategies over continuous treatment. Analysis of eight tumor burden trajectories of breast cancer patients treated at Memorial Sloan Kettering Cancer Center shows the model can predict time to resistance using initial responses to treatment and estimated preexisting resistant populations. The model calculated that pulse treatment would delay relapse in all eight cases. Overall, our results support that pulsed treatments optimized by mathematical models could delay therapeutic resistance.