Abstract 469: A multi-scale analysis and visualization platform for cancer data - deriving tumor microenvironment behavior from pathology and transcriptomics

Heterogeneity is a hallmark of cancer and perhaps one of the most important features associated with resistance to therapies and likelihood of recurrence and/or metastasis. Genomic instability contributes to genetic diversity, which leads to high levels of intratumoural heterogeneity. While genetic diversity is one contributor to phenotypic heterogeneity in tumors, the spatial variation in the tumor microenvironment (TME) also drives emergent phenotypic heterogeneity and intrinsic variation in resistance to drugs and the immune system. Ultimately, heterogeneity leads to higher likelihood of metastasis. Traditionally, pathology allowed for the assessment of heterogeneity.

However, this was limited to gross assessment of the TME, through measurement of mitotic levels and severity of cancer invasion. We present SimBioSys PhenoScope, a multi-scale analysis and visualization platform that integrates cancer data across scales to extract cross modality trends that drive cancer invasion. As a demonstrated use case for the platform, we present a vignette of using the platform to analyze pathology slides at three scales. Three convolutional neural networks (CNN) are developed and validated. The outputs of these networks were combined with 2D simulations of the metabolic behavior and growth of cells within the TME. Two CNNs were developed and one implemented: one that identifies cells undergoing mitosis, one that segments individual cells and classifies their type, and one that segments five tissues from pathology slides. Additionally, transcriptional data was used to generate patient specific metabolic models. Each CNN was developed using training images and validated on the test images. The mitosis detection CNN was found to have an accuracy of 76.2% (precision=83%, recall=76%) in the test set. The classification CNN was found to have a Dice Similarity Coefficient (DSC) of 0.821 for segmenting cells and an F1 score for classifying cells ranging from 0.559 (F1i) to 0.756 (F1d). The segmentation CNN was found to have an accuracy of 78.1% with DSC ranging from 0.66 and 0.86 depending on tissue. The segmentations were input into a proprietary simulation framework along with patient-specific metabolic models to predict the spatial gradients of nutrients, and spatial organization of growth and metabolism. Simulations show multiple behaviors such as regions of high lactate production or consumption by cancer, and regions that differ by lactate production and alanine uptake in cancers. These behaviors were correlated with local cell mitoses and invasion of Tumor Infiltrating Lymphocytes.

Tools to examine cancers across scales and within the TME are currently lacking. We demonstrate a proof-of-principle approach of combining data across scales in a fashion that allows for novel predictions of TME behavior.

Citation Format: Joseph R. Peterson, Matthew Shin, Patricia Carrigan, Michael J. Hallock, Snehal Patel, The SimBioSys Team. A multi-scale analysis and visualization platform for cancer data - deriving tumor microenvironment behavior from pathology and transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 469.

Abstract 2714: Systems medicine based metabolic profiling of metastatic melanoma reveals insights into tumor biology, prognosis, and new therapeutic strategies

Background: Tumor cells are able to reprogram their metabolism in order to sustain continuous growth and proliferation. Although, this has led to the development of metabolism-based therapies, until now there are no systematic ways to identify key metabolic targets for therapy. This area of research deserves more attention considering that targeting tumor metabolism has been shown to increase the efficacy of standard chemotherapy in pre-clinical studies. For tumors with poor prognosis, like late-stage metastatic melanoma, this untapped area for therapeutic discovery could lead to the development of novel metabolism-based therapies.

Method: To address this challenge we designed a systems medicine-based technique to understand tumor metabolism in individual patients at a level of detail not previously achievable. In concrete, we statistically integrated metabolic network modeling with RNA-sequencing data (RNA-seq). This allows us to integrate molecular data about individual tumors (from RNA-seq) with a curated knowledge base of how these molecules interact within a patient’s tumor (using metabolic network models). This results in a mathematical description, or model, of a specific tumor’s metabolism that is able to be interrogated (i.e., it is high-dimensional like RNA-seq data) and able to be simulated (i.e., we can “drug” the model and investigate downstream effects). We applied this technique to 325 metastatic melanoma tumor RNA-seq profiles downloaded from The Cancer Genome Atlas to characterize metabolic differences across patients.

Results: We found considerable differences in metabolic function across metastatic melanoma patient tumors. One of the main drivers of patient-to-patient metabolic variability appeared to be BRAF mutations, with BRAF mutated tumors showing higher levels of glutathione metabolism, lower levels of glutamate metabolism, and lower levels of oxidative phosphorylation. In addition to these differences, we found that several metabolic pathways were associated with poor overall survival prognosis regardless of BRAF mutations status including high specific growth rate, high cholesterol metabolism, and high serotonin and melatonin biosynthesis. Furthermore, simulating knockouts of enzymes involved in these pathways resulted in a set of actionable targets that could enhance chemotherapy for metastatic melanoma patients.

Conclusion: Using systems medicine, we have characterized metabolism in metastatic melanoma at high resolution in 325 patient tumors. This characterization revealed the variability in metastatic melanoma across patients and how this variability is associated with clinical outcome. Furthermore, our approach suggests additional therapeutic targets to enhance patient care.

Citation Format: Daniel Cook, Dorys A. Lopez-Ramos, Patricia Carrigan, Joseph R. Peterson, The SimBioSys Team. Systems medicine based metabolic profiling of metastatic melanoma reveals insights into tumor biology, prognosis, and new therapeutic strategies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2714.

Abstract 1916: Using systems medicine for comprehensive metabolic profiling of tumors: how tumor metabolism shapes prognosis and response to chemotherapy

Background: Metabolic disfunction is one of the hallmarks of cancer, and metabolism-based therapies are a key class of chemotherapeutics. Despite this, there has previously been no systematic way to identify metabolism-based targets for therapeutic intervention. This is especially concerning when considering that targeting tumor metabolism has been shown to increase the efficacy of standard chemotherapy in pre-clinical studies. We therefore developed a systems medicine approach to interrogate tumor metabolism systematically, identify the effects of metabolic heterogeneity on overall survival prognosis, and gain insights into new metabolism-associated targets or therapies to complement standard of care for cancer patients. Method: Our systems medicine framework statistically integrates metabolic network modeling with RNA-sequencing data (RNA-seq). This allows us to integrate molecular data about individual tumors (from RNA-seq) with a curated knowledge base of how these molecules interact within a patient’s tumor (using metabolic network models). This results in a mathematical description, or model, of a specific tumor’s metabolism that is able to be interrogated (i.e., it is high-dimensional like RNA-seq data) and able to be simulated (i.e., we can “drug” the model and investigate downstream effects). Results: We applied this systems medicine approach across a range of tumor types, including lung adenocarcinoma, pancreatic adenocarcinoma, metastatic melanoma, clear cell renal cell carcinoma, and salivary cystic adenoid carcinoma. Within each cancer type, we found significant patient-to-patient metabolic heterogeneity. Across cancer types, a consistent pattern that emerged was that the metabolic systems with the most patient-to-patient heterogeneity were also the systems most associated with patient overall survival. This result suggests that metabolic variability is a key driver of tumor response to therapy and overall patient survival. Furthermore, targeting metabolic pathways in a patient-specific manner could enhance chemotherapy in tumors with metabolic profiles associated with poor prognosis. Conclusion: This study shows that the specific metabolic profiles of tumors are key drivers of overall survival. We therefore believe that metabolic tumor characterization and intervention could be a useful strategy to enhance chemotherapy efficacy and overall survival in patients across tumor types.

Citation Format: Daniel Cook, John A. Cole, Patricia Carrigan, Joseph R. Peterson, The SimBiosys Team. Using systems medicine for comprehensive metabolic profiling of tumors: how tumor metabolism shapes prognosis and response to chemotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1916.

Abstract 4936: Development of NTRK reference materials for global assay standardization

Tropomyosin receptor kinases (TRK) are a family of transmembrane proteins (TRKA, B, and C) encoded by NTRK1, NTRK2 and NTRK3 genes. Fusions involving NTRK genes result in uncontrolled TRK signaling and oncogenesis. Cancers driven by TRK fusions are rare but occur in a broad range of tumor types in both pediatric and adult patients. Accurate testing for NTRK fusions has become critically important because of precision biopharmaceutical treatments, such as Larotrectinib. This drug, which was granted Breakthrough Therapy Designation by the FDA and is currently undergoing review, is a promising treatment option for patients with locally advanced or metastatic solid tumors harboring NTRK fusions.

NGS based assays have the potential to accurately detect NTRK fusions; however, the majority of tests currently available are laboratory-developed tests, which vary substantially in their workflow, chemistry, sensitivity, and bioinformatics analyses. In order to achieve precision diagnostics for personalized therapy, testing standardization is needed worldwide. Towards this end, we developed Seraseq® FFPE NTRK Fusion RNA Reference Material for standardization of NTRK fusion testing by targeted NGS panels.

The reference material contains 15 clinically-relevant NTRK fusions, selected based on prevalence data, in a single reference sample. The five fusion RNAs each for NTRK1, NTRK2, and NTRK3 are all incorporated in the well characterized GM24385 human reference cell line. The reference material challenges detection across multiple break points for each NTRK gene, as well as 12 different amino terminal fusion partners. Each fusion is quantified by digital PCR with concentrations ranging from 150 - 600 fusion copies per nanogram of total extractable RNA.

The reference material was prepared in a formalin fixed paraffin embedded (FFPE) format, which allows evaluation of the full technical workflow. In our laboratory, one 10-micron FFPE curl yields more than 400 ng of extractable RNA when using the Agencourt Formapure® extraction kit. NGS analysis on the RNA-based ArcherDx FusionPlex Solid Tumor assay correctly identified all 15 fusions, demonstrating compatibility with a leading commercial assay.

Highly characterized, patient-like reference materials help standardize testing across clinical trial sites and can speed test adoption, training and validation at clinical labs. The Seraseq FFPE NTRK Fusion RNA Reference Material is designed to aid standardization of NTRK fusion testing to identify patients that will benefit from new precision therapeutics.

Note: This abstract was not presented at the meeting.

Citation Format: Sebastian Bender, Patricia Carrigan, Kara O’Brien, Catherine Huang, Rajeswari Vemula, Praveena Kamineni, Omo Clement, Russell Garlick, Bharathi Anekella. Development of NTRK reference materials for global assay standardization [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 4936.

Influence of a CYP1A2 polymorphism on post-exercise heart rate variability in response to caffeine intake: a double-blind, placebo-controlled trial

BACKGROUND

Proposed differences in caffeine metabolism due to the CYP1A2*1F polymorphism have been linked to variations in cardiovascular disease risk.

AIMS

We examined the influence of a CYP1A2*1F polymorphism on post-exercise heart rate variability (HRV) in response to caffeine intake.

METHODS

Volunteers were identified as A/A homozygotes (A/A; 4 females and 7 males; age: 25.3 ± 4.1 years; BMI: 25.9 ± 4.4 kg/m²) or C allele carriers (C allele; 3 females and 6 males; age: 25.5 ± 2.8 years; BMI: 26.6 ± 5.0 kg/m²) for participation in a repeated measures, counterbalanced, double-blind, placebo-controlled trial. Participants chewed three pieces of gum containing either caffeine (CAF) (100 mg/piece) or placebo for 5 min. Thereafter, participants cycled for 15 min at 75 % of their peak oxygen consumption. Eight HRV indices computed during 5 min at baseline (BASE), 0-5 min after exercise (POST1), and 5-10 min after exercise (POST2) were used for analysis.

RESULTS

No significant group differences were detected in HRV indices at BASE, POST1, or POST2 during both trials (p > 0.05). Rate of recovery (POST2-POST1) for the square root of the mean of squared differences between successive RR intervals (RMSSD) was significantly different between A/A (6.0 ± 2.5 ms) and C allele (3.6 ± 2.5 ms) groups during the CAF trial (p = 0.048).

CONCLUSIONS

Rate of RMSSD recovery was the only variable influenced by the CYP1A2*IF polymorphism during post-exercise in response to caffeine intake. Thus, the CYP1A2*1F polymorphism did not overtly influence the effects of caffeine intake on post-exercise HRV.

Impact of Biomarkers on Personalized Medicine

The field of personalized medicine that involves the use of measuring biomarkers in clinical samples is an area of high interest and one that has tremendous impact on drug development. With the emergence of more sensitive and specific technologies that are now able to be run in clinical settings and the ability to accurately measure biomarkers, there is a need to understand how biomarkers are defined, how they are used in clinical trials, and most importantly how they are used in conjunction with drug treatment. Biomarker approaches have entered into early clinical trials and are increasingly being used to develop new diagnostics that help to differentiate or stratify the likely outcomes of therapeutic intervention. Tremendous efforts have been made to date to discover novel biomarkers for use in clinical practice. Still, the number of markers that make it into clinical practice is rather low. In the next following chapters, we will explain the various classifications of biomarkers, how they are applied, measured, and used in personalized medicine specifically focusing on how they are used in de-risking the 10 plus years drug development process and lastly how they are validated and transformed into companion diagnostic assays.

Early phase bronchoconstriction in the mouse requires allergen-specific IgG

Allergen provocation of allergic asthma patients is often characterized by an initial period of bronchoconstriction, or early phase reaction (EPR), that leads to maximal airway narrowing within 15-30 min, followed by a recovery period returning airway function to baseline within 1-2 h. In this study, we used a defined OVA provocation model and mice deficient for specific leukocyte populations to investigate the cellular/molecular origins of the EPR. OVA-sensitized/challenged wild-type (C57BL/6J) mice displayed an EPR following OVA provocation. However, this response was absent in gene knockout animals deficient of either B or T cells. Moreover, transfer of OVA-specific IgG, but not IgE, before the OVA provocation, was capable of inducing the EPR in both strains of lymphocyte-deficient mice. Interestingly, an EPR was also observed in sensitized/challenged mast cell-deficient mice following an OVA provocation. These data show that the EPR in the mouse is an immunologically based pathophysiological response that requires allergen-specific IgG but occurs independent of mast cell activities. Thus, in the mouse the initial period of bronchoconstriction following allergen exposure may involve neither mast cells nor IgE-mediated events.

Mouse eosinophil-associated ribonucleases: a unique subfamily expressed during hematopoiesis

A unique family of ribonucleases was identified by exhaustive screening of genomic and cDNA libraries using a probe derived from a gene encoding a ribonuclease stored in the mouse eosinophil secondary granule. This family contains at least 13 genes, which encode ribonucleases, and two potential pseudogenes. The conserved sequence identity among these genes (approximately 70%), as well as the isolation/purification of these ribonucleases from eosinophil secondary granules, has led us to conclude that these genes form a unique clade in the mouse that we have identified as the Ear (Eosinophil-associated ribonuclease) gene family. Analyses of the nucleotide substitutions that have occurred among these ribonuclease genes reveal that duplication events within this family have been episodic, occurring within three unique periods during the past 18 x 10(6) years. Moreover, comparisons of non-synonymous (K(a)) vs. synonymous (K(s)) rates of nucleotide substitution show that although these genes conserve residues necessary for RNase activity, selective evolutionary pressure(s) exist such that acquired amino acid changes appear to be advantageous. The selective advantage of these amino acid changes is currently unclear, but the occurrence of this phenomenon in both the mouse and the human highlights the importance of these changes for Ear and, therefore, eosinophil effector function(s).

Stimulus definition in conditional discriminations

With a customary arrangement of three horizontally aligned stimulus/response keys, two rhesus monkeys learned conditional hue and line discriminations--an "identity-matching" procedure. First, sample stimuli were presented on the center key, and comparison stimuli were presented on the two side keys. Next, the sample was allowed to appear on any one of the three keys, with the comparisons on the remaining two. The change from fixed to variable sample and comparison locations caused the horizontal and vertical lines to lose control over the animals' responses; the conditional hue discrimination remained intact. Accurate description of controlling stimuli in a matching-to-sample procedure may therefore require that their spatial location be specified.

A search for symmetry in the conditional discriminations of rhesus monkeys, baboons, and children

Procedures for generating arbitrary matching-to-sample performances may generate only conditional discriminations. Rational grounds for this distinction are proposed, based on the properties that any equivalence relation must possess. Empirical tests are described for determining whether subjects trained on conditional discriminations are also engaged in true matching to sample. A series of studies than leads to the conclusion that proof of true matching to sample by monkeys, pigeons, or baboons is yet to be provided. Whether the absence of such proof reflects experiential factors or species-defined limitations is not presently clear.