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PDCM Finder (www.cancermodels.org) is a cancer research platform that aggregates clinical, genomic and functional data from patient-derived xenografts, organoids and cell lines. It was launched in April 2022 as a successor of the PDX Finder portal, which focused solely on patient-derived xenograft models. Currently the portal has over 6200 models across 13 cancer types, including rare paediatric models (17%) and models from minority ethnic backgrounds (33%), making it the largest free to consumer and open access resource of this kind. The PDCM Finder standardises, harmonises and integrates the complex and diverse data associated with PDCMs for the cancer community and displays over 90 million data points across a variety of data types (clinical metadata, molecular and treatment-based). PDCM data is FAIR and underpins the generation and testing of new hypotheses in cancer mechanisms and personalised medicine development.

1.AbstractBrain metastases (BM) remain a significant cause of morbidity and mortality in breast cancer (BC) patients. Specific factors promoting the process of BM and predilection for selected neuro-anatomical regions remain unknown, yet may have major implications for prevention or treatment. Anatomical spatial distributions of BM from BC suggest a predominance of metastases in the hindbrain and cerebellum. Systematic approaches to quantifying BM location or location-based analyses based on molecular subtypes, however, remain largely unavailable. We analyzed stereotactic Cartesian coordinates derived from 134 patients undergoing gamma-knife radiosurgery (GKRS) for treatment of 407 breast cancer BMs to quantitatively study BM spatial distribution along principal component axes and by intrinsic molecular subtype (ER,PR,Herceptin). We corroborated that BC BMs show a consistent propensity to arise posteriorly and caudally, and that Her2+ tumors are relatively more likely to arise medially rather than laterally. To compare the distributions among varying BC molecular subtypes, we used the notion of mutual information, which revealed that the ER-PR-Her2+ and ER-PR-Her2-subtypes showed the smallest amount of mutual information and were most molecularly distinct. Using kernel density estimators, we found a propensity for triple negative BC to arise in more superiorly or cranially situated BMs. BM location maps according to vascular and anatomical distributions using cartesian coordinates to aid in systematic classification of tumor locations were additionally developed. Further characterization of these patterns may have major impacts on treatment or management of cancer patients.SignificanceThe quantitative spatial distribution of breast cancer metastases to the brain, and the effects of breast cancer molecular subtype on distribution frequencies remain poorly understood. We present a novel and shareable workflow for characterizing and comparing spatial distributions of BM which may aid in identifying therapeutic or diagnostic targets and interactions with the tumor microenvironment.

Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models. Cancer Res; 77(21); e62–66. ©2017 AACR.

A figure showing the process of generating and using PDX models.

PDF file - 232K, The supporting material describes in more detail the Markov chain model, and includes a full Table and two additional figures. Figure S1. Convergence plot for the lung cancer matrix. Figure S2. Metastatic tumor distributions after two-steps, compared with steady-state. Table S1. Comparative table of top two-step metastatic pathways of all types from Lung.

Background Bladder cancer (BCa), the sixth commonest cancer in the USA, is highly lethal when metastatic. Spatial and temporal patterns of patient-specific metastatic spread are deemed random and unpredictable. Whether BCa metastatic patterns can be quantified and predicted more accurately is unknown. Objective To develop a web-based calculator for forecasting metastatic progression in individual BCa patients. Design, setting, and participants We used a prospectively collected longitudinal dataset of 3503 BCa patients who underwent a radical cystectomy following diagnosis and were enrolled continuously. We subdivided patients by their pathologic subgroup stages of organ confined (OC), extravesical (EV), and node positive (N+). We illustrated metastatic pathway progression using color-coded, circular, tree ring diagrams. We created a dynamical, data-visualization, web-based platform that displays temporal, spatial, and Markov modeling figures with predictive capability. Outcome measurements and statistical analysis Patients underwent history and physical examination, serum studies, and liver function tests. Surveillance follow-up included computed tomography scans, chest x-rays, and radiographic evaluation of the reservoir and upper tracts, with bone scans performed only if clinically indicated. Outcomes were measured by time to clinical recurrence and overall or progression-free survival. Results and limitations Metastases developed in 29% of patients (n = 812; median follow-up 15.3 yr), with 5-yr overall survival of 20.2%, compared with 78.6% in those without metastases (n = 1983; median follow-up 10.9 yr). The three commonest sites of spread at the time of first progression were bone (n = 214; 26.4%), pelvis (n = 194; 23.9%), and lung (n = 194; 23.9%). The order and frequency of these sites vary when divided by pathologic subgroup stages of OC (lung [n = 65; 25.1%], urethra [n = 45; 17.4%], and bone [n = 29; 11.2%]), EV (pelvis [n = 63; 33.0%], bone [n = 45; 23.6%], and lung [n = 29; 15.2%]), and N+ (bone [n = 111; 30.7%], retroperitoneum [n = 70; 19.3%], and pelvis [n = 60; 16.6%]). Markov chain modeling indicated a higher probability of spread from bladder to bone (15.5%), pelvis (14.7%), and lung (14.2%). Conclusions Our web-based calculator allows real-time analyses in the clinic based on individual patient-specific demographic and cancer data elements. For contrasting subgroups, the models indicated differences in Markov transition probabilities. Spatiotemporal patterns of BCa metastasis and sites of spread indicated underlying organotropic mechanisms in the prediction of response. This recognition opens the possibility of organ site–specific therapeutic targeting in the oligometastatic BCa setting. In the precision medicine era, visualization of complex, time-resolved clinical data will enhance management of postoperative metastatic BCa patients. Patient summary We developed a web-based calculator to forecast metastatic progression for individual bladder cancer (BCa) patients, based on the clinical and demographic information obtained at diagnosis. This can help in predicting disease status and survival, and improving management in postoperative metastatic BCa patients. Take Home Message Future pathways of metastatic progression for individual bladder cancer patients can be determined based on currently available clinical and demographic information obtained at diagnosis. In focused subgroups of patients, these metastatic spread patterns can also portend disease status and survival.

Identifying the genetic determinants of pain is a scientific imperative given the magnitude of the global health burden that pain causes. Here, we report a genetic screen for nociception, performed under the auspices of the International Mouse Phenotyping Consortium. A biased set of 110 single-gene knockout mouse strains was screened for 1 or more nociception and hypersensitivity assays, including chemical nociception (formalin) and mechanical and thermal nociception (von Frey filaments and Hargreaves tests, respectively), with or without an inflammatory agent (complete Freund's adjuvant). We identified 13 single-gene knockout strains with altered nocifensive behavior in 1 or more assays. All these novel mouse models are openly available to the scientific community to study gene function. Two of the 13 genes (Gria1 and Htr3a) have been previously reported with nociception-related phenotypes in genetically engineered mouse strains and represent useful benchmarking standards. One of the 13 genes (Cnrip1) is known from human studies to play a role in pain modulation and the knockout mouse reported herein can be used to explore this function further. The remaining 10 genes (Abhd13, Alg6, BC048562, Cgnl1, Cp, Mmp16, Oxa1l, Tecpr2, Trim14, and Trim2) reveal novel pathways involved in nociception and may provide new knowledge to better understand genetic mechanisms of inflammatory pain and to serve as models for therapeutic target validation and drug development.

B-cell maturation antigen (BCMA), a key regulator of B-cell proliferation and survival, is highly expressed in almost all cases of plasma cell neoplasms and B-lymphoproliferative malignancies. BCMA is a robust biomarker of plasma cells and a therapeutic target with substantial clinical significance. However, the expression of BCMA in circulating tumor cells of patients with hematological malignancies has not been validated for the detection of circulating plasma and B cells. The application of BCMA as a biomarker in single-cell detection and profiling of circulating tumor cells in patients’ blood could enable early disease profiling and therapy response monitoring. Here, we report the development and validation of a slide-based immunofluorescence assay (i.e., CD138, BCMA, CD45, DAPI) for enrichment-free detection, quantification, and morphogenomic characterization of BCMA-expressing cells in patients (N = 9) with plasma cell neoplasms. Varying morphological subtypes of circulating BCMA-expressing cells were detected across the CD138(+/−) and CD45(+/−) compartments, representing candidate clonotypic post-germinal center B cells, plasmablasts, and both normal and malignant plasma cells. Genomic analysis by single-cell sequencing and correlation to clinical FISH cytogenetics provides validation, with data showing that patients across the different neoplastic states carry both normal and altered BCMA-expressing cells. Furthermore, altered cells harbor cytogenetic events detected by clinical FISH. The reported enrichment-free liquid biopsy approach has potential applications as a single-cell methodology for the early detection of BCMA+ B-lymphoid malignancies and in monitoring therapy response for patients undergoing anti-BCMA treatments.

Metastatic colorectal cancer (mCRC) is characterized by its extensive disease heterogeneity, suggesting that individualized analysis could be vital to improving patient outcomes. As a minimally invasive approach, the liquid biopsy has the potential to longitudinally monitor heterogeneous analytes. Current platforms primarily utilize enrichment-based approaches for epithelial-derived circulating tumor cells (CTC), but this subtype is infrequent in the peripheral blood (PB) of mCRC patients, leading to the liquid biopsy’s relative disuse in this cancer type. In this study, we evaluated 18 PB samples from 10 mCRC patients using the unbiased high-definition single-cell assay (HDSCA). We first employed a rare-event (Landscape) immunofluorescence (IF) protocol, which captured a heterogenous CTC and oncosome population, the likes of which was not observed across 50 normal donor (ND) samples. Subsequent analysis was conducted using a colorectal-targeted IF protocol to assess the frequency of CDX2-expressing CTCs and oncosomes. A multi-assay clustering analysis isolated morphologically distinct subtypes across the two IF stains, demonstrating the value of applying an unbiased single-cell approach to multiple assays in tandem. Rare-event enumerations at a single timepoint and the variation of these events over time correlated with progression-free survival. This study supports the clinical utility of an unbiased approach to interrogating the liquid biopsy in mCRC, representing the heterogeneity within the CTC classification and warranting the further molecular characterization of the rare-event analytes with clinical promise.

Urothelial carcinomas (UCs) are a broad and heterogeneous group of malignancies, with the prevalence of upper tract urothelial carcinoma (UTUC) being rare, accounting for only 5–10% of total malignancies. There is a need for additional toolsets to assist the current clinical paradigm of care for patients with UTUC. As a non-invasive tool for the discovery of cancer-related biomarkers, the liquid biopsy has the potential to represent the complex process of tumorigenesis and metastasis. Herein, we show the efficacy of the liquid biopsy as a source of biomarkers for detecting UTUC. Using the third-generation high-definition single-cell assay (HDSCA3.0) workflow, we investigate liquid biopsy samples collected from patients with UTUC and normal donors (NDs) to provide critical information regarding the molecular and morphological characteristics of circulating rare events. We document several important findings from the liquid biopsy analysis of patients diagnosed with UTUC prior to surgery: (1) Large extracellular vesicles (LEVs) and circulating tumor cells (CTCs) are detectable in the peripheral blood. (2) The rare-event profile is highly heterogeneous. (3) Clinical data elements correlate with liquid biopsy analytes. Overall, this study provides evidence for the efficacy of the liquid biopsy in understanding the biology of UTUC with the future intent of informing clinical decision making, ultimately improving patient outcomes.

Sex differences in the lifetime risk and expression of disease are well-known. Preclinical research targeted at improving treatment, increasing health span, and reducing the financial burden of health care, has mostly been conducted on male animals and cells. The extent to which sex differences in phenotypic traits are explained by sex differences in body weight remains unclear. We quantify sex differences in the allometric relationship between trait value and body weight for 375 phenotypic traits in male and female mice, recorded in >2.1 million measurements from the International Mouse Phenotyping Consortium. We find sex differences in allometric parameters (slope, intercept, residual SD) are common (76% traits). Body weight differences do not explain all sex differences in trait values but scaling by weight may be useful for some traits. Our results support a trait-specific patterning of sex differences in phenotypic traits, promoting case-specific approaches to drug dosage scaled by body weight.

PDCM Finder (www.cancermodels.org) is a cancer research platform that aggregates clinical, genomic and functional data from patient-derived xenografts, organoids and cell lines. Currently, the portal has over 6300 models across 13 cancer types, including rare pediatric models (17%) and models from minority ethnic backgrounds (33%), making it the largest free to consumer and open access resource of this kind. The PDCM Finder standardizes, harmonizes and integrates the complex and diverse data associated with PDCMs for the cancer community and displays over 90 million data points across a variety of data types (clinical metadata, molecular and treatment-based). PDCM Finder was launched in April 2022 and has been recently enhanced with new functionality and an updated user interface. Users can search for models of interest by either (1) exploring molecular data summaries for models of specific cancer types, or (2) using the intuitive search and faceted filtering options of the web user interface or (3) accessing resource database via REST API to run their own analysis. The data includes gene expression, gene mutation, copy number alteration, cytogenetics, patient treatment and drug dosing studies. We link external resources like publication platforms and cancer-specific annotation tools enabling exploration and prioritization of PDCM variation data (COSMIC, CIViC, OncoMX, OpenCRAVAT). PDCM Finder builds on the success of the PDX Finder resource (PMID:30535239). Critical PDCM attributes, such as diagnosis, drug names and genes, are harmonized and integrated into a cohesive ontological model based on the PDX Minimal information standard (PDX MI, PMID: 29092942). PDX MI has become established in the community for data exchange, adopted by the PDX providers, consortia and informatics tools integrating PDX data. We are driving the development of and promoting the use of descriptive standards to facilitate data interoperability and promote global sharing of models. PDCM Finder data is FAIR and underpins the generation and testing of new hypotheses in cancer mechanisms and personalized medicine development. We provide expertise and software components to support several worldwide consortia including PDXNet, PDMR and EurOPDX. PDCM Finder is freely available under an Apache 2.0 license (https://github.com/PDCMFinder). This work is supported by NCI U24 CA204781 01, U24 CA253539, and R01 CA089713. We welcome feedback on the resource and are looking for participants for usability studies - please get in touch if interested. Citation Format: Zinaida Perova, Mauricio Martinez, Tushar Mandloi, Federico Lopez Gomez, Csaba Halmagyi, Alex Follette, Jeremy Mason, Steven Neuhauser, Dale A. Begley, Debra M. Krupke, Carol Bult, Helen Parkinson, Tudor Groza, Marcelo P. Rios Almanza. PDCM Finder: an open global research platform for patient-derived cancer models. [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 4682.

Multiple myeloma (MM), a disease caused by the clonal proliferation of plasma cells (PCs), is the second leading hematologic malignancy in the world, with a global incidence rate increase of 126% and mortality rate increasing by 94% in the past decade. Recently, B cell maturation antigen (BCMA) has emerged as a promising biomarker and therapeutic target for MM due to its selective high expression on malignant PCs, improving the treatment landscape. However, the current diagnostic workup relies on bone marrow aspirates (BMA) which are highly invasive and do not allow for the detection of BCMA levels on circulating PCs, severely limiting the options for repeatable assessments and treatment-monitoring. In our study, we report the utility of a blood-based liquid biopsy, using enrichment-free high definition single cell assay (HDSCA) for the detection, quantification, and morphological characterization of circulating PCs and other BCMA+ cells. We applied a previously validated 4-color immunofluorescence assay using DAPI, CD138, BCMA, and CD45 to examine matched peripheral blood and BMAs from 68 patients across the MM disease spectrum (11 monoclonal gammopathy of undetermined significance [MGUS], 21 smoldering MM [SMM], 18 newly diagnosed MM, and 18 relapsed refractory MM [RRMM]) and peripheral blood from 23 normal donors as controls. Our analysis revealed diverse subtypes of circulating CD138(+/-), BCMA(+/-), and CD45(+/-) cells across the patients, representing both normal and abnormal PCs. We detected significantly higher circulating CD138+ BCMA(+/-) cells in MM precursor conditions (MGUS, SMM), compared to the normal donors. Additionally, we were able to detect BCMA+ oncosomes (identified by the lack of a nuclear structure) in the circulation of patients from both precursor and overt disease conditions. Our findings establish the utility of HDSCA blood-based liquid biopsy for early disease detection and anti-BCMA therapy response monitoring in MM and other B-cell malignancies expressing BCMA. Citation Format: Sonia Maryam Setayesh, Stephanie Shishido, Libere Ndacayisaba, Amin Naghdloo, Jeremy Mason, Dean Tessone, Carli Kaleta, Robert Z. Orlowski, Elisabet E. Manasanch, James Hicks, Peter Kuhn. Single cell characterization of circulating plasma cells and BCMA levels in patients with multiple myeloma. [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 5579.

The liquid biopsy presents potential as a non-invasive cancer screening tool, and its role in diagnosing and monitoring the progression of primary upper tract urothelial carcinoma (UTUC) to reveal the tumor’s kinetics remains to be fully understood. This study includes 22 patients diagnosed with UTUC, from which we collected 1 peripheral blood (PB) sample prior to surgery (pre-op; n = 22) and 1 sample at follow-up (post-op; n = 11). Each patient’s sample was analyzed via our enrichment-free third generation comprehensive High-Definition Single Cell Assay (HDSCA3.0) workflow to detect rare events in the PB. Rare events include circulating tumor cells (CTCs) and large extracellular vesicles (LEVs) and are identified via a 4-channel immunofluorescence assay that captures the following morphometric expressions: pan cytokeratin (CK), vimentin (V), CD45/CD31 (CD), and cellular nucleation (which is absent in LEVs) through DAPI (D). Through a matched sample analysis approach via a Wilcoxon signed-rank test, pre-op and post-op liquid biopsy profiles were compared to each other as well as to samples from 50 normal donors (NDs) who had no known pathology. The HDSCA3.0 analysis discovered a highly heterogeneous spread of rare events in PB samples collected from patients with UTUC in both pre-op and post-op time periods and in ND samples. For pre-op vs ND samples, significant differences were found in total rare events, total rare cells, events expressing D|V, D|CK|V|CD, D|V|CD, total LEVs, total CK+ cells, CK LEVs, and events expressing D and D|CK. For post-op vs ND samples, significant differences were in total rare events, total rare cells, events expressing D|V, D|CK|V|CD, total CK+ cells, and events expressing D|CK|V. For pre-op vs post-op samples, significant differences manifested in total LEVs, CK LEVs, and CK|CD LEVs. At follow-up, 3 patients had an increase in rare events, and 8 patients had a decrease in rare events, 4 of the 8 presenting with ND rare event levels. This study has the potential to inform how primary UTUC evolves in patients over time as well as elucidate if rare event expressions may correlate with specific disease states. By analyzing circulating rare events through channel-type classifications, different frequencies at various time points compared to ND samples can assist in clinical decision making for treating UTUC. Citation Format: Salmaan Sayeed, George Courcoubetis, Alireza Ghoreifi, Amy Huang, Jeremy Mason, Inderbir S. Gill, Peter Kuhn, Hooman Djaladat, Stephanie N. Shishido. Circulating rare events inform disease progression in patients with upper tract urothelial carcinoma undergoing surgery [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 2203.

Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease with a five-year survival rate of around 7% due to its late diagnosis, rapid metastasis, and chemotherapy resistance. For a small proportion (10-20%) of early-stage patients however, surgical resection of the pancreatic tumor offers the best chance for survival (5-year rate of 20%). Given the clinical challenges and the need for personalized care strategies to maximize patient survival, we propose the utility of the minimally invasive liquid biopsy to identify circulating biomarkers in patient blood to guide prognosis and monitor treatment. This study used the non-enriching third generation High-Definition Single Cell Assay (HDSCA3.0) workflow to investigate the clinical significance of a heterogeneous circulating rare cell population in both the peripheral and portal vein blood of early-stage PDAC patients (n = 20) at four different time points—pre-, during, and post- surgical resection, as well as at one-week follow-up. Compared to normal donor samples (n = 50), PDAC patients had a significantly greater incidence of circulating tumor cells (CTCs: cytokeratin-positive (CK+) and CD35/45-negative). There was no significant difference in rare cell incidence between peripheral and portal vein samples. However, specific phenotypes of rare cells were observed at different frequencies based on time of sample collection. Patient samples collected pre-surgery had a significantly higher incidence of CTCs than samples collected during surgery, while post-surgery samples had a significantly higher incidence of total CK+ rare cells and mesenchymal CTCs (Vimentin-positive, CK+) than during-surgery samples. While post-surgical samples also had a higher total CK+ rare cell and CTC incidence than pre-surgical samples, the difference was not statistically significant. Additionally, patients who received neoadjuvant therapy with surgical resection had lower average incidence of rare cells. Overall, the data presented here reveal that 1) liquid biopsy analytes are detected at a higher incidence in localized PDAC than normal donors; 2) the time point of sample collection in relation to surgery leads to a statistically significant different in frequency of detectable analytes; and 3) anatomical location of blood draw is not associated with difference in rare cell incidence. This study demonstrates the liquid biopsy’s utility in early-stage PDAC detection at the time of surgical resection. Citation Format: Emmeline Y. Lin, George Courcoubetis, Divya Suresh, Jeremy Mason, Stephen J. Pandol, Simon Lo, Nicholas Nissen, Srinivas Gaddam, Peter Kuhn, Stephanie N. Shishido. Liquid biopsy to detect biomarkers in early-stage surgically-resected pancreatic cancer [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 3320.

Breast cancer (BC) affects 1 in every 8 women in the United States and is currently the most prevalent cancer worldwide. Precise staging at diagnosis and prognosis are essential components for the clinical management of BC patients. The liquid biopsy (LBx) has promising applications in cancer screening and early diagnosis ultimately leading to better survival results and less disease burden. In this study, we evaluated the feasibility of the high-definition single cell assay (HDSCA) LBx platform to stratify disease states (early- and late-stage BC) and normal donors using peripheral blood samples. In the HDSCA3.0 workflow, both common white blood cells (WBCs) and rare cells, including circulating tumor cells (CTCs), EMT and platelet-coated cells, plus acellular structures are identified and classified computationally from scanned immunofluorescent images. This comprehensive LBx approach provides a quantitative landscape view of each individual case. In a striking example of the insight provided by HDSCA, we compared LBx results for early-stage and late-stage BC patients with two independent cohorts of normal donors to show the utility of a blood draw as a source of biomarkers for early-stage cancer detection. As expected, CTCs were detected at a higher level in late-stage patients, compared to either the early-stage or normal donors. Surprisingly, however, we observed a significantly higher incidence of tumor-associated large extracellular vesicles (LEVs) in the early-stage patients, compared to the other two groups. A patient-level classification model was able to correctly classifying LBx profiles as normal, early, or late with LEV enumeration as the strongest predictor, followed by epi.CTC enumeration. We will present a reproducible LBx profile of rare cells and LEVs of early-stage disease compared to late-stage BC and normal donors with high accuracy, allowing for robust stratification. Our findings illustrate the feasibility of the LBx to assess early disease states prior to clinically defined metastasis, stratified from normal donors, highlighting the general consideration of the liquid biopsy for the diagnostic work-up and potentially screening. Citation Format: Stephanie Shishido, Peter Kuhn, Jeremy Mason, James Hicks, Rafael Nevarez, Nicholas Matsumoto, Anand Kolatkar, Carmen Ruiz Velasco, Amanda Anderson, Michael Kidd, Kathy Wilkinson, E Shelley Hwang, Janice Lu, Jorge Nieva, Nikki Higa, Amin Naghdloo, Olivia Hart, Sonia Maryam Setayesh. Multianalyte liquid biopsy to aid the diagnostic workup 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 P5-05-09.

The evolution of interfaces is intrinsic to many physical processes ranging from cavitation in fluids to recrystallization in solids. Computational modeling of interface motion entails a number of challenges, many of which are related to the range of topological transitions that can occur over the course of the simulation. Microstructure evolution in a polycrystalline material that involves grain boundary motion is a particularly complex example due to the extreme variety, heterogeneity, and anisotropy of grain boundary properties. Accurately modeling this process is essential to determining processing-structure-property relationships in polycrystalline materials though. Simulations of microstructure evolution in such materials often use diffuse interface methods like the phase field method that are advantageous for their versatility and ease of handling complex geometries but can be prohibitively expensive due to the need for high interface resolution. Discrete interface methods require fewer grid points and can consequently exhibit better performance but have received comparatively little attention, perhaps due to the difficulties of maintaining the mesh and consistently implementing topological transitions on the grain boundary network. This work explicitly compares a recently-developed discrete interface method to a multiphase field method on several classical problems relating to microstructure evolution in polcrystalline materials: a shrinking spherical grain, the steady-state triple junction dihedral angle, and the steady-state quadruple point dihedral angle. In each case, the discrete method is found to meet or outperform the multiphase field method with respect to accuracy for comparable levels of refinement, demonstrating its potential efficacy as a numerical approach for microstructure evolution in polycrystalline materials., Comment: 25 pages, 12 figures

Breast cancer (BC) affects 1 in every 8 women in the United States and is currently the most prevalent cancer worldwide. Precise staging at diagnosis and prognosis are essential components for the clinical management of BC patients. In this study, we set out to evaluate the feasibility of the high-definition single cell (HDSCA) liquid biopsy (LBx) platform to stratify late-stage BC, early-stage BC, and normal donors using peripheral blood samples. Utilizing 5 biomarkers, we identified rare circulating events with epithelial, mesenchymal, endothelial and hematological origin. We detected a higher level of CTCs in late-stage patients, compared to the early-stage and normal donors. Additionally, we observed more tumor-associated large extracellular vesicles (LEVs) in the early-stage, compared to late-stage and the normal donor groups. Overall, we were able to detect reproducible patterns in the enumeration of rare cells and LEVs of cancer vs. normal donors and early-stage vs. late-stage BC with high accuracy, allowing for robust stratification. Our findings illustrate the feasibility of the LBx assay to provide robust detection of rare circulating events in peripheral blood draws and to stratify late-stage BC, early-stage BC, and normal donor samples.

Background While it has been suspected that different primary cancers have varying predilections for metastasis in certain brain regions, recent advances in neuroimaging and spatial modeling analytics have facilitated further exploration into this field. Methods A systematic electronic database search for studies analyzing the distribution of brain metastases (BMs) from any primary systematic cancer published between January 1990 and July 2020 was conducted using PRISMA guidelines. Results Two authors independently reviewed 1957 abstracts, 46 of which underwent full-text analysis. A third author arbitrated both lists; 13 studies met inclusion/exclusion criteria. All were retrospective single- or multi-institution database reviews analyzing over 8227 BMs from 2599 patients with breast (8 studies), lung (7 studies), melanoma (5 studies), gastrointestinal (4 studies), renal (3 studies), and prostate (1 study) cancers. Breast, lung, and colorectal cancers tended to metastasize to more posterior/caudal topographic and vascular neuroanatomical regions, particularly the cerebellum, with notable differences based on subtype and receptor expression. HER-2-positive breast cancers were less likely to arise in the frontal lobes or subcortical region, while ER-positive and PR-positive breast metastases were less likely to arise in the occipital lobe or cerebellum. BM from lung adenocarcinoma tended to arise in the frontal lobes and squamous cell carcinoma in the cerebellum. Melanoma metastasized more to the frontal and temporal lobes. Conclusion The observed topographical distribution of BM likely develops based on primary cancer type, molecular subtype, and genetic profile. Further studies analyzing this association and relationships to vascular distribution are merited to potentially improve patient treatment and outcomes.

Background: Three cyclin dependent kinase 4/6 inhibitors (CDKIs) are approved by the Food and Drug Administration (FDA) for the treatment of patients with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative advanced or metastatic breast cancer in combination with hormonal therapy. However, the choice of when to initiate CDKI therapy (in the first- or second-line setting, or with aromatase inhibitor or fulvestrant) can be clinically challenging as studies have not directly compared these two approaches, nor have studies determined the benefit of continuation of CDKI therapy with changes in hormonal therapy backbone. Methods: Patient-level data from eight randomized controlled pivotal trials submitted to the FDA for new or supplemental marketing applications were pooled. We performed exploratory analyses of the predictive power of the baseline patient and disease characteristic data to forecast outcomes based on a specific treatment pathway. Variables included were: age, race, ethnicity, country and continent of origin, height, weight, body mass index (BMI), menopause status, estrogen receptor (ER) status, progesterone receptor (PR) status, ECOG performance score, histological type, histological grade, and initial disease stage. Additionally, baseline levels at the time of enrollment of alanine aminotransferase (ALT), albumin, alkaline phosphatase, aspartate aminotransferase (AST), bilirubin, blood urea nitrogen (BUN), calcium, creatinine, hemoglobin, magnesium, potassium, sodium, lymphocytes, neutrophils, platelets, and white blood cells. Statistical and machine learning algorithms were used to build predictive models based on the clinical trial results. Results: The pooled studies included 4580 breast cancer patients. Baseline random survival forest models were constructed for patients that received a CDKI plus hormonal therapy and for patients that received hormonal therapy alone. Preliminary findings for these models produced prediction accuracies of 69.2% for the CDKI model and 70.6% for the hormonal therapy alone model. Median predicted survival times for patients were calculated in both treatment scenarios and used to identify those that were more likely to benefit from initial use of CDKIs and those less likely to benefit. Through further analysis of the patients in each group, we identified patient characteristics that are prognostic of survival. Conclusions: This exploratory analysis utilized clinical trial data to create a model where clinical and disease characteristics are entered to come up with an efficacy prediction. Future efforts will extend this model to predict adverse event development. Citation Format: Jeremy Mason, Yutao Gong, Laleh Amiri-Kordestani, Suparna Wedam, Jennifer J Gao, Harpreet Singh, Richard Pazdur, Peter Kuhn, Gideon Blumenthal, Julia A Beaver. Prediction of CDK inhibitor efficacy in ER+/HER2- breast cancer using machine learning algorithms [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD2-07.

Background Patient-derived xenografts (PDX) mice models play an important role in preclinical trials and personalized medicine. Sharing data on the models is highly valuable for numerous reasons – ethical, economical, research cross validation etc. The EurOPDX Consortium was established 8 years ago to share such information and avoid duplicating efforts in developing new PDX mice models and unify approaches to support preclinical research. EurOPDX Data Portal is the unified data sharing platform adopted by the Consortium. Main body In this paper we describe the main features of the EurOPDX Data Portal (https://dataportal.europdx.eu/), its architecture and possible utilization by researchers who look for PDX mice models for their research. The Portal offers a catalogue of European models accessible on a cooperative basis. The models are searchable by metadata, and a detailed view provides molecular profiles (gene expression, mutation, copy number alteration) and treatment studies. The Portal displays the data in multiple tools (PDX Finder, cBioPortal, and GenomeCruzer in future), which are populated from a common database displaying strictly mutually consistent views. (Short) Conclusion EurOPDX Data Portal is an entry point to the EurOPDX Research Infrastructure offering PDX mice models for collaborative research, (meta)data describing their features and deep molecular data analysis according to users’ interests.

PURPOSE Three cyclin-dependent kinase 4/6 inhibitors (CDKIs) are approved by the US Food and Drug Administration for the treatment of patients with hormone receptor–positive, human epidermal growth factor receptor 2–negative advanced or metastatic breast cancer in combination with hormonal therapy (HT). We hypothesized that on an individual basis, efficacy outcomes and adverse event (AE) development can be predicted using baseline patient and tumor characteristics. METHODS Individual-level data from seven randomized controlled trials submitted to the US Food and Drug Administration for new or supplemental marketing applications of CDKIs were pooled. Progression-free survival (PFS), overall survival (OS), and AE prediction models were developed for specific treatment regimens (HT v HT plus CDKI). An individual's characteristics were used in all models simultaneously to create a group of predicted outcomes that are comparable across treatment settings. RESULTS Accuracy of the PFS and OS prediction models for HT were 66% and 64%, respectively, with the strongest predictors being menopausal status and therapy line. The corresponding AE prediction models resulted in an average area under the curve of 0.613. Accuracy of the PFS and OS prediction models for HT plus CDKI were 62% and 63%, respectively, with the strongest predictors being histologic grade for both. The corresponding AE prediction models resulted in an average area under the curve of 0.639. CONCLUSION This exploratory analysis demonstrated that models of efficacy outcomes and AE development can be developed using baseline patient and tumor characteristics. Comparison of paired models can inform treatment selection for individuals on the basis of the patient's personalized goals and concerns. Although use of CDKIs is standard of care in the first- or second-line setting, this model provides prognostic information that may inform individual treatment decisions.

Most current biomedical and protein research focuses only on a small proportion of genes, which results in a lost opportunity to identify new gene-disease associations and explore new opportunities for therapeutic intervention. The International Mouse Phenotyping Consortium (IMPC) focuses on elucidating gene function at scale for poorly characterized and/or under-studied genes. A key component of the IMPC initiative is the implementation of a broad phenotyping pipeline, which is facilitating the discovery of pleiotropy. Characterizing pleiotropy is essential to identify gene-disease associations, and it is of particular importance when elucidating the genetic causes of syndromic disorders. Here we show how the IMPC is effectively uncovering pleiotropy and how the new mouse models and gene function hypotheses generated by the IMPC are increasing our understanding of the mammalian genome, forming the basis of new research and identifying new gene-disease associations.

A gram-scale synthesis of iboxamycin, an antibiotic candidate bearing a fused bicyclic amino acid residue, is presented. A pivotal transformation in the route involves an intramolecular hydrosilylation–oxidation sequence to set the ring-fusion stereocenters of the bicyclic scaffold. Other notable features of the synthesis include a high-yielding, highly diastereoselective alkylation of a pseudoephenamine amide, a convergent sp3–sp2 Negishi coupling, and a one-pot transacetalization–reduction reaction to form the target compound’s oxepane ring. Implementation of this synthetic strategy has provided ample quantities of iboxamycin to allow for its in vivo profiling in murine models of infection.

The dearth of new medicines effective against antibiotic-resistant bacteria presents a growing global public health concern. For more than five decades, the search for new antibiotics has relied heavily upon the chemical modification of natural products (semi-synthesis), a method ill-equipped to combat rapidly evolving resistance threats. Semi-synthetic modifications are typically of limited scope within polyfunctional antibiotics, usually increase molecular weight, and seldom permit modifications of the underlying scaffold. When properly designed, fully synthetic routes can easily address these shortcomings. Here we report the structure-guided design and component-based synthesis of a rigid oxepanoproline scaffold which, when linked to the aminooctose residue of clindamycin, produces an antibiotic of exceptional potency and spectrum of activity, here named iboxamycin. Iboxamycin is effective in strains expressing Erm and Cfr rRNA methyltransferase enzymes, products of genes that confer resistance to all clinically relevant antibiotics targeting the large ribosomal subunit, namely macrolides, lincosamides, phenicols, oxazolidinones, pleuromutilins, and streptogramins. X-ray crystallographic studies of iboxamycin in complex with the native 70S bacterial ribosome, as well as the Erm-methylated 70S ribosome, uncover the structural basis for this enhanced activity, including an unforeseen and unprecedented displacement of upon antibiotic binding. In mice, iboxamycin is orally bioavailable, safe, and effective in treating bacterial infections, testifying to the capacity for chemical synthesis to provide new antibiotics in an era of rising resistance.

PDCM Finder (https://cancermodels.org) is a new portal that aggregates patient-derived models (xenografts, cell lines and organoids) from 27 academic and commercial providers, enables users to search and compare over 6000 models and associated molecular data, and connects users with model providers to facilitate collaboration among researchers. Users can search for models of interest by either (1) exploring molecular data summaries for models of specific cancer types, or (2) using the intuitive search and faceted filtering options of the web user interface or (3) access resource database via REST API to run their own analysis. The data includes gene expression, gene mutation, copy number alteration, cytogenetics, patient treatment and drug dosing studies. We link external resources like publication platforms and cancer specific annotation tools enabling exploration and prioritization of PDCM variation data (COSMIC, CIViC, OncoMX, OpenCRAVAT). In addition to exploring PDCM metadata and data, the portal enables users to validate their PDX models against PDX MI standard, get a FAIRness score of the model of interest, explore originating resource data processing protocols, training materials and contact the model supplier/provider. PDCM Finder builds on the success of the PDX Finder resource (PMID:30535239). Critical PDCM attributes, such as diagnosis, drug names and genes, are harmonized and integrated into a cohesive ontological model based on the PDX Minimal information standard (PDX MI, PMID: 29092942). PDX MI has become established in the community for data exchange, adopted by the PDX providers, consortia and informatics tools integrating PDX data. We are working with the community on the PDCM Minimal Information standard in an effort to make PDCM datasets adhere to the FAIR data principles. We are driving the development of and promoting the use of descriptive standards to facilitate data interoperability and promote global sharing of models. We provide expertise and software components to support several worldwide consortia including PDXNet, PDMR and EurOPDX. PDCM Finder is freely available under an Apache 2.0 license (https://github.com/PDCMFinder). This work is supported by NCI U24 CA204781 01, U24 CA253539, and R01 CA089713. We welcome feedback on the resource and are looking for participants for usability studies - please get in touch if interested. Citation Format: Zinaida Perova, Csaba Halmagyi, Alex Follette, Mauricio Martinez, Federico Lopez-Gomez, Jeremy Mason, Abayomi Mosaku, Nathalie Conte, Ross Thorne, Steven Neuhauser, Dale Begley, Debra Krupke, Terrence Meehan, Carol Bult, Helen Parkinson. PDCM Finder: An open global cancer research platform for patient-derived cancer models [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 3105.

The genetic landscape of diseases associated with changes in bone mineral density (BMD), such as osteoporosis, is only partially understood. Here, we explored data from 3,823 mutant mouse strains for BMD, a measure that is frequently altered in a range of bone pathologies, including osteoporosis. A total of 200 genes were found to significantly affect BMD. This pool of BMD genes comprised 141 genes with previously unknown functions in bone biology and was complementary to pools derived from recent human studies. Nineteen of the 141 genes also caused skeletal abnormalities. Examination of the BMD genes in osteoclasts and osteoblasts underscored BMD pathways, including vesicle transport, in these cells and together with in silico bone turnover studies resulted in the prioritization of candidate genes for further investigation. Overall, the results add novel pathophysiological and molecular insight into bone health and disease., Author summary Patients affected by osteoporosis frequently present with decreased BMD and increased fracture risk. Genes are known to control the onset and progression of bone diseases such as osteoporosis. Therefore, we aimed to identify osteoporosis-related genes using BMD measures obtained from a large pool of mutant mice genetically modified for deletion of individual genes (knockout mice). In a collaborative endeavor involving several research sites world-wide, we generated and phenotyped 3,823 knockout mice and identified 200 genes which regulated BMD. Of the 200 BMD genes, 141 genes were previously not known to affect BMD. The discovery and study of novel BMD genes will help to better understand the causes and therapeutic options for patients with low BMD. In the long run, this will improve the clinical management of osteoporosis.