Tumor Drug Response Research Articles

Role of PRDM1 in Tumor Immunity and Drug Response: A Pan-Cancer Analysis.

Background: PR domain zinc finger protein 1 (PRDM1) is a regulator of both B cell and T cell differentiation and plays a critical role in immunosuppression. Its role in tumor immunity and correlation with drug response remain unknown. Methods: This work comprehensively analyzed the transcriptional expression pattern of the PRDM1 among 33 types of malignancies from The Cancer Genome Atlas and the Genotype-Tissue Expression projects. Besides, correlation of the PRDM1 with cancer prognosis, immune infiltrates, checkpoint markers, cancer stemness and drug response were explored. Results: High expression level of PRDM1 were observed in ACC, COAD, LAML, LGG, LUAD, OV, PAAD, STAD, TGCT. Cox regression model showed high expression of PRDM1 in tumor samples correlates with poor prognosis in LGG, PAAD, UVM while favorable prognosis in KIRC, SKCM and THCA. PRDM1 expression positively correlates with the expression of LAG3, CTLA4, PDCD1 (PD-1), CD274 (PD-L1), PDCD1LG2 (PD-L2), TIGIT in the majority of 33 cancer types. PRDM1 positively correlated with TNFRSF14 in LGG and UVM among cancers with unfavorable prognosis; this correlation were weak or even negative in cancers with favorable prognosis. The top negatively enriched KEGG terms in high PRDM1 subgroup were B cell receptor signaling, T cell receptor signaling, and the top negatively enriched HALLMARK terms included IL-2-STAT5 signaling and allograft rejection. The expression of PRDM1 was found positively correlated with cancer stemness in CHOL, KIRP, TGCT, THYM and UVM. A series of targeted drugs and small-molecule drugs with promising efficacy predicted by PRDM1 level were identified. Conclusion: The clinical significance and biological impact of high transcriptional expression of PRDM1 differs across different cancers. Inhibiting the PRDM1-dependent signaling could be a novel and promising strategy of immunotherapy in cancers including LGG, PAAD and UVM.

Three-Dimensional Spheroids as In Vitro Preclinical Models for Cancer Research.

Most cancer biologists still rely on conventional two-dimensional (2D) monolayer culture techniques to test in vitro anti-tumor drugs prior to in vivo testing. However, the vast majority of promising preclinical drugs have no or weak efficacy in real patients with tumors, thereby delaying the discovery of successful therapeutics. This is because 2D culture lacks cell–cell contacts and natural tumor microenvironment, important in tumor signaling and drug response, thereby resulting in a reduced malignant phenotype compared to the real tumor. In this sense, three-dimensional (3D) cultures of cancer cells that better recapitulate in vivo cell environments emerged as scientifically accurate and low cost cancer models for preclinical screening and testing of new drug candidates before moving to expensive and time-consuming animal models. Here, we provide a comprehensive overview of 3D tumor systems and highlight the strategies for spheroid construction and evaluation tools of targeted therapies, focusing on their applicability in cancer research. Examples of the applicability of 3D culture for the evaluation of the therapeutic efficacy of nanomedicines are discussed.

A Primer on Human Brain Organoids for the Neurosurgeon.

Human brain organoids emerged in 2013 as a technology that, unlike prior in Vitro neural models, recapitulates brain development with a high degree of spatial and temporal fidelity. As the platform matured with more accurate reproduction of cerebral architecture, brain organoids became increasingly valuable for studying both normal cortical neurogenesis and a variety of congenital human brain disorders. While the majority of research utilizing human brain organoids has been in the realm of basic science, clinical applications are forthcoming. These present and future translational efforts have the potential to make a considerable impact on the field of neurosurgery. For example, glioma organoids are already being used to study tumor biology and drug responses, and adaptation for the investigation of other neurosurgery-relevant diseases is underway. Moreover, organoids are being explored as a structured neural substrate for repairing brain circuitry. Thus, we believe it is important for our field to be aware and have an accurate understanding of this emerging technology. In this review, we describe the key characteristics of human brain organoids, review their relevant translational applications, and discuss the ethical implications of their use through a neurosurgical lens.

Abstract 2782: Neuroendocrine tumor spheroid growth and drug response assessment using optical metabolic imaging

Abstract Gastroenteropancreatic neuroendocrine tumors (GEP-NET) are the second most common form of gastrointestinal cancer, and account for roughly 60% of all neuroendocrine tumors. Unfortunately, no animal models or cell lines exist that reflect the in vivo characteristics of human GEP-NETs. Specifically, human GEP-NETs have low proliferation rates that animal models and cell lines fail to capture. Therefore, drug testing for GEP-NETs often begins in patients without rigorous pre-clinical validation. There is a need to predict the most effective treatments prior to clinical trials for GEP-NET patients, to increase treatment efficacy and reduce toxicities. Here we show that a three-dimensional spheroid model of primary GEP-NET patient tumors is representative of the original patient tumors including histological features and markers of proliferation (Ki67). Furthermore, this study uses the spheroid model to test a novel drug combination on GEP-NET spheroids, specifically ABT263, a Bcl-2 family inhibitor, and everolimus, a standard GEP-NET treatment that inhibits mTOR. Previous studies have shown that ABT263 induces apoptosis in cancer cells when used in combination with everolimus, making this a promising drug combination for GEP-NET patients. Treatment response to ABT263, everolimus, and the combination, was tested in three-dimensional spheroids derived from seven primary GEP-NET patients, using two-photon optical metabolic imaging (OMI). OMI captures the fluorescence lifetime (τm) and intensity of metabolic cofactors NAD(P)H and FAD. These metrics can be combined into a single number, the OMI index, which was developed to separate responsive and non-responsive cells. The OMI index is the linear combination of the norm-centered redox ratio (NAD(P)H intensity / FAD intensity) + NAD(P)H τm - FAD τm. The OMI index also provides single cell response information from intact spheroids, so that heterogeneity in response can be quantified. Altogether, two-photon OMI in primary patient GEP-NET spheroids provides a novel tool to measure metabolic response to drug treatment. At 72 hours post-treatment, all patients except two showed a decrease in OMI index, corresponding with a response, to the combination treatment. Heterogeneous response was resolved with two distinct subpopulations of cell metabolism in one of the nonresponsive patients. No changes in spheroid diameter were observed with any treatment, due to the slow growth rate of the GEP-NET tumors and spheroids, further highlighting a need for molecular measures of drug response in these samples. We have shown that representative GEP-NET models can be generated in patient-derived tumor spheroids. Additionally, ABT263 plus everolimus is a promising combination treatment for GEP-NETs. Overall, this work shows that patient-derived tumor spheroids can be established from GEP-NETs, and that OMI provides single-cell metabolic measurements of drug response to guide drug development for patients. Citation Format: Amani Gillette, Christopher Babiarz, Cheri Pasch, Dustin Deming, Melissa Skala. Neuroendocrine tumor spheroid growth and drug response assessment using optical metabolic imaging [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2782.

Abstract 1118: Absence of mouse-specific tumor evolution in patient-derived cancer xenografts

Abstract Patient-Derived Xenografts (PDXs) are preclinical models largely used to study tumor biology and drug response. Recent literature highlighted the possibility that growth of human tumors in a mouse microenvironment imposes a selection driving mouse-specific genetic evolution of PDXs, which may compromise their reliability as human cancer models. Conversely, independent studies observed a conservation of the genomic landscape during PDX engraftment and passaging. We noticed that PDX genetic evolution was particularly evident in studies based on copy number aberration (CNA) inferred from gene expression data, while it was negligible when DNA-based CNA profiles were employed. Therefore, in a joint international effort of the EurOPDX and PDXNet consortia, we assembled a dataset of 37 hepatocellular and 54 gastric carcinoma tumor or PDX samples with matched RNA-based and DNA-based CNA profiles. We found that DNA-based CNA profiles invariably yield higher concordance between patient's tumor and derived PDXs than those inferred from RNA. RNA-based profiles displayed poor concordance with matched DNA-based profiles, and much lower resolution, so that they missed many focal copy number events detected by DNA-based methods. These results revealed that CNA measurements cannot be accurately estimated by expression data and that a systematic reassessment of CNA dynamics in PDXs based on DNA data is required. To this aim, we generated CNA profiles by low-pass whole genome sequencing (WGS) of 87 colorectal and 43 breast cancer triplets, each composed of matched patient's tumor (PT) and PDX at early (PDX-early) and later (PDX-late) passage. In this way, for each tumor type, we generated three perfectly matched PT, PDX-early and PDX-late cohorts and performed CNA recurrence analysis by GISTIC in each cohort. The hypothesis was that if the mouse host induces a selective pressure capable of shaping the CNA landscape during PDX engraftment and propagation, GISTIC analysis would highlight systematic and progressive changes, from the PT to the PDX-early cohort, and then to the PDX-late cohort. Notably instead, the CNA profiles of the PT and PDX-early/late cohorts were virtually indistinguishable, with no progressive accumulation or loss of CNA during PDX passage and only minor changes not functionally related or associated to cancer-driver or actionable genes. These results were not consequence of insufficient capture of the CNA repertoire, since the GISTIC profiles recapitulated those generated by TCGA for colorectal and breast cancer. In summary, our analyses highlighted that while RNA-based CNA inferences have inadequate resolution and accuracy to study genomic evolution in PDXs, DNA-based CNA profiles confirm retention of CNAs in PTs and PDXs, excluding a systematic mouse driven selection via copy number changes. Ultimately, these results support the robustness of PDXs as preclinical models for predicting drug response. Citation Format: Jessica Giordano, Xing Yi Woo, Anuj Srivastava, Zi-Ming Zhao, Michael W. Lloyd, Roebi de Bruijn, Yun-Suhk Suh, Francesco Galimi, Andrea Bertotti, Adam Lafferty, Alice C. O'Farrell, Elodie Modave, Diether Lambrechts, Petra ter Brugge, Violeta Serra, Elisabetta Marangoni, Rania El Botty, Jong-Il Kim, Han-Kwang Yang, Charles Lee, Dennis A. Dean, Brandi Davis-Dusenbery, Yvonne A. Evrard, James H. Doroshow, Alana L. Welm, Bryan E. Welm, Michael T. Lewis, Bingliang Fang, Jack Roth, Funda Meric-Bernstam, Meenhard Herlyn, Michael Davies, Li Ding, Shunqiang Li, Ramaswamy Govindan, Jeffrey A. Moscow, Carol J. Bult, Claudio Isella, Livio Trusolino, Annette T. Byrne, Jos Jonkers, Jeffrey H. Chuang, Enzo Medico, EurOPDX consortium & PDXNET consortium. Absence of mouse-specific tumor evolution in patient-derived cancer xenografts [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1118.

Abstract 5054: Synergistic anti-cancer activity of palbociclib in combination with sunitinib in patient-derived xenograft (PDX) models of various human cancers types

Abstract Introduction: Treatment for cancer, particularly in later lines, is often inadequate. Combinational therapies targeting several signaling pathways simultaneously are considered a promising approach, with limited clinical evidence. Effectiveness of such combinations can be explored in patient-derived xenograft (PDX) tumor models, which more accurately represent human tumor biology, drug responsiveness, and treatment-related toxicity than standard xenograft models derived from established human cancer cell lines. Objectives: To evaluate, in PDX models, the anti-tumor effect and safety of combinational therapy including a CDK4/6 inhibitor (palbociclib) plus a multi-targeted receptor tyrosine kinase inhibitor (mtRTKI; sunitinib). Methods: Overall, 20 PDX models were developed from patients with various solid tumors. For each, tumor was implanted subcutaneously in immune-deficient mice (NRG or NSG). The mice were randomized to 4 treatment groups: vehicle as control, palbociclib (100 mg/kg), sunitinib (50 mg/kg), or combination thereof. Each treatment group included 5-6 mice (18/20 models). Drugs were administered orally, 5 days per week. Tumor volume was monitored twice a week with digital caliper. Mice body weight and clinical signs were examined twice a week to evaluate toxicity. Results: The mean experiment time (all experiments) was 66 days (SEM, 10 days; range, 15-170 days). In 14 of the 20 evaluated models (70%), including those for cholangiocarcinoma, breast, lung, pancreas, colon, stomach, and ovarian cancer, combinational therapy with palbociclib and sunitinib had significantly superior anti-cancer activity compared to either drug alone, without any unexpected toxicity. Mean duration of response in these 14 models was 72 days (SEM, 12 days). The combination of palbociclib and sunitinib prevented tumor growth, and in 3 models reduced tumor size. In the 2 PDX models developed from patients with palbociclib-resistant disease, the combination overcame tumor resistance to palbociclib. In 5 of the models which demonstrated synergy between palbociclib and sunitinib, the effect of administrating 2-fold lower doses of both drugs was evaluated. The lower doses were shown to suppress tumor growth in a dose-response manner. Conclusions: These PDX results suggest that the combination of palbociclib and sunitinib exert a synergistic anti-tumor effect on multiple tumor types without adding unexpected toxicity. Citation Format: Neta Moskovits, Idit Peretz, Eva Chausky, Raisa Meerson, Ella Itzhaki, Noga Marsiano, Nofar Shmuel, Ranny Yaffe, Natalia Edison, Tal Goldman, Salomon Stemmer. Synergistic anti-cancer activity of palbociclib in combination with sunitinib in patient-derived xenograft (PDX) models of various human cancers types [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5054.

Abstract A24: Establishment of a panel of patient-derived tumor xenograft models recapitulating molecular heterogeneity and drug response of muscle-invasive bladder tumors

Abstract Background: Muscle-invasive bladder cancers (MIBCs) constitute a heterogeneous group of tumors with poor outcome. Recently, MIBC molecular subtyping efforts from an international consortium led to the identification of six subtypes, improving prediction of clinical outcomes and treatment responses. FGFR3 alterations (mutations and translocations), observed in 20% of MIBCs, are found mainly in the luminal papillary subtype that respond poorly to chemo- and immunotherapy. Basal tumors represent 35% of MIBCs and were shown to be better responders to chemotherapy. Here, we describe the development and characterization of patient-derived primary MIBC xenografts (PDX) belonging to these main subtypes. Methods: Bladder tumors were obtained from patients at surgery. Tumor fragments were subcutaneously engrafted into immune-compromised mice. Primary tumors and matched PDX tumors at multiple passages were analyzed regarding growth characteristics, histopathology (H&E staining, CK5/6, FOXA1, and GATA3 immunohistochemistry), gene expression (Affymetrix U133 plus 2.0 microarray), and genetic stability (STR profiling). Hotspot oncogenic mutations for FGFR3, PIK3CA, HRAS, KRAS, NRAS, PPARG, and RXRA were also assessed. Additionally, pharmacologic responses to standard-of-care and targeted therapies were characterized. Findings: From 152 MIBC tumors at all stages and grades, 32 PDX models were successfully established (21.1% success rate). This take rate did not seem correlated to any classical tumor characteristics. Importantly, transcriptomic analysis allowed us to identify PDX models belonging to different molecular subtypes, notably the basal-like and luminal papillary subtypes, including PDXs with FGFR3 mutations. All histologic, genetic, and molecular features validated the stability of the PDX models compared to the parental tumors. Histologic analyses correlated with the molecular classification. These models reproduced the response to cisplatin-based therapies observed in the clinic. Basal models, except one harboring a FGFR3 mutation, were sensitive to anti-EGFR therapies but to a lesser extent than to chemotherapy. FGFR3-mutated PDX models, including a basal model, were highly responsive to FGFR3 inhibitors and less responsive to chemotherapy. Conclusion: We have developed and characterized highly relevant preclinical models for MIBCs, including basal and FGFR3-mutated tumors, recapitulating molecular heterogeneity and drug responses as observed in patients with MIBCs. They represent essential tools for developing new, efficient therapies against this deadly disease. Citation Format: Claire Béraud, Hervé Lang, Myriam Lassalle, Véronique Lindner, Aurélie Kamoun, Michel Soulié, Elodie Guillon, Clémentine Krucker, Xavier Gamé, Pascal Rischmann, Aurélien De Reynies, Yves Allory, François Radvanyi, Philippe Lluel, Thierry Massfelder, Isabelle Bernard-Pierrot. Establishment of a panel of patient-derived tumor xenograft models recapitulating molecular heterogeneity and drug response of muscle-invasive bladder tumors [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2019 May 18-21; Denver, CO. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(15_Suppl):Abstract nr A24.

Bladder cancer hunting: A microfluidic paper-based analytical device.

Bladder cancer is the fourth most common cancer in men, and it is becoming a prevalent malignancy. Most of the regular clinical examinations are prompt evaluations with cystoscopy, renal function testing, which require high-precision instrument, well-trained operators, and high cost. In this study, a microfluidic paper-based analytical device (μPAD) was fabricated to detect nuclear matrix protein 22 (NMP22) and bladder cancer antigen (BTA) from the urine samples. Urine samples were collected from 11 bladder cancer patients and 10 well-beings as experiment and control groups, respectively, to verify the working efficiency of μPAD. A remarkable checkout efficiency of up to 90.91% was found from the results. Meanwhile, this method is feasible for home-based self-detection from urine samples within 10 min for the total process, which provides a new way for quick, economical, and convenient tumor diagnosis, prognosis evaluation, and drug response.

Assessment of mutation burden after clinical intervention in pancreatic ductal adenocarcinomas (PDAC), and biliary tract cancers (BTC) via profiling circulating tumor DNA (ctDNA).

e15529 Background: PDAC and BTC have very poor prognosis with limited treatment options. Blood-based monitoring of ctDNA may have great potential to improve early detection of relapse or failure to chemotherapy and to possibly identify actionable genetic alterations. Methods: We performed targeted sequencing using NGS with Ion S5 Oncomine PanCancer gene panel to investigate mutations in 52 genes in 42 PDAC and 24 BTC patients. Blood samples were obtained from PDAC and BTC cases before the first intervention (baseline) as well as 2-4 weeks later to evaluate cfDNA mutations after surgery or chemotherapy. Actionable alterations with potential therapeutic relevance was defined as those with possible treatments within OncoKB system. We also compared the amount of cfDNA and mutant allele detection rates in pre-, intra- and post-operative samples. Results: Overall detection of ctDNA alterations in baseline samples was 74% (31/42) in PDAC cases. Alterations including SNVs and CNVs were detected among 15 genes; mutations in TP53 (53%), KRAS (41%) and SMAD4 (10%) were most commonly identified. Overall detection rate in BTC patients was 83% (20/24) comprising 9 genes; frequent mutations were observed in TP53 (58%) and KRAS (33%). Decrease in MAF (mutant allele frequency) was observed in 64% (18/28) of PDAC cases after the treatment and that of CA19-9 was observed in 50% (14/28) of the cases. In BTC pts, reduction of MAF was observed in 60% of the cases after surgery. Potentially actionable mutations were observed in 38% of PDAC samples and in 25% of BTC samples. Interestingly, the amounts of cfDNA were 4-fold higher in intraoperative samples compared to pre- and post-operative samples indicating release of cfDNA during surgery, possibly by tissue damage. Conclusions: We have shown the feasibility to assess minimum residual tumor cells or drug response by liquid biopsy in both PDAC and BTC which may improve the clinical management.

Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer.

Preclinical models that faithfully recapitulate tumor heterogeneity and therapeutic response are critical for translational breast cancer research. Immortalized cell lines are easy to grow and genetically modify to study molecular mechanisms, yet the selective pressure from cell culture often leads to genetic and epigenetic alterations over time. Patient-derived xenograft (PDX) models faithfully recapitulate the heterogeneity and drug response of human breast tumors. PDX models exhibit a relatively short latency after orthotopic transplantation that facilitates the investigation of breast tumor biology and drug response. The transplantable genetically engineered mouse models allow the study of breast tumor immunity. The current protocol describes the method to orthotopically transplant breast tumor fragments into the mammary fat pad followed by drug treatments. These preclinical models provide valuable approaches to investigate breast tumor biology, drug response, biomarker discovery and mechanisms of drug resistance.

Abstract A24: New mouse models with KRASG12D or KRASG12V mutation in Amhr2-Cre mice develop different gynecologic tumors

Abstract Low-grade ovarian serous carcinoma (LGSC) accounts for a relatively large percentage of epithelial ovarian cancers in young women, and approximately 30% of LGSC carries a KRAS mutation. Chemotherapy and hormonal therapy are relatively ineffective and have generally failed to reduce high morbidity and mortality. Importantly, although LGSC is considered to be an indolent cancer, with an estimated median overall survival of 80 months, an updated analysis of 350 women with LGSC from our Low-Grade Serous Tumor Database indicated that over 85% developed recurrent disease, and almost all ultimately died of the disease. The overall response rate of recurrent LGSC to various chemotherapy drugs is dismal—less than 4%. Our previous study by correlating the KRAS mutation status of 503 cancer cell lines and their sensitivity to MEK inhibitor from the Cancer Cell Line Encyclopedia (CCLE) database suggests that KRASG12V-mutated cell line might be more sensitive to MEK inhibitor selumetinib. In addition, we found that patients with ovarian serous borderline tumor that progressed as LGSC with KRASG12V mutation appear to have shorter survival than that with KRASG12D mutation or wild-type gene. To address whether tumors initiated with KRASG12D or KRASG12V have any difference on tumor initiation and drug response, we have generated two mouse models with conditionally expressed KRASG12DS(+/-) or KRASG12V mutation using Amhr2-Cre mice. The Amhr2-Cre mice express Cre recombinase in tissues that develop into the fallopian tubes, uterus and ovary. In this presentation, we will describe the differences in the gynecologic tumors formed by these mice. Mice with the genotype of Ahmr2-Cre Pten(-/-) KRASG12D/+ had abnormal follicle structure and developed low-grade ovarian serous carcinomas with 100 penetrance within 18 weeks. Surprisingly, mice with the genotype of Ahmr2-Cre Pten(-/-) KRASG12V/+ had normal follicle structure but developed uterine tumors with diverse histology resembling that of leiomyoma, and leiomyosarcoma. Some mice even developed uterine tumors with leiomyoma region, leiomyosarcoma-like region as well as low-grade carcinoma. Multinodular and highly vascular leiomyosarcoma was also observed in some of these mice. Therefore, these models will be useful for studying the difference in signaling pathways driven by KRASG12D or KRASG12V mutation for developing targeted therapies for specific KRAS mutant variants. Currently, we are investigating whether KRASG12D or KRASG12V mutation has any impact on the estrogen receptor pathway in the uterine tissue. The leiomyoma model driven by KRASG12V mutation in this study will also be useful to decipher the malignant progression of leiomyoma to leiomyosarcoma. Citation Format: Kwong-Kwok Wong, Yvonne T. Tsang, Wei Bao, Sophia Lin, Eucharist Kun, Yi A. Ren, JoAnne S. Richards, David M. Gershenson. New mouse models with KRASG12D or KRASG12V mutation in Amhr2-Cre mice develop different gynecologic tumors [abstract]. In: Proceedings of the AACR Special Conference on Targeting RAS-Driven Cancers; 2018 Dec 9-12; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2020;18(5_Suppl):Abstract nr A24.

Comparison of Cell and Organoid-Level Analysis of Patient-Derived 3D Organoids to Evaluate Tumor Cell Growth Dynamics and Drug Response.

3D cell culture models have been developed to better mimic the physiological environments that exist in human diseases. As such, these models are advantageous over traditional 2D cultures for screening drug compounds. However, the practicalities of transitioning from 2D to 3D drug treatment studies pose challenges with respect to analysis methods. Patient-derived tumor organoids (PDTOs) possess unique features given their heterogeneity in size, shape, and growth patterns. A detailed assessment of the length scale at which PDTOs should be evaluated (i.e., individual cell or organoid-level analysis) has not been done to our knowledge. Therefore, using dynamic confocal live cell imaging and data analysis methods we examined tumor cell growth rates and drug response behaviors in colorectal cancer (CRC) PDTOs. High-resolution imaging of H2B-GFP-labeled organoids with DRAQ7 vital dye permitted tracking of cellular changes, such as cell birth and death events, in individual organoids. From these same images, we measured morphological features of the 3D objects, including volume, sphericity, and ellipticity. Sphericity and ellipticity were used to evaluate intra- and interpatient tumor organoid heterogeneity. We found a strong correlation between organoid live cell number and volume. Linear growth rate calculations based on volume or live cell counts were used to determine differential responses to therapeutic interventions. We showed that this approach can detect different types of drug effects (cytotoxic vs cytostatic) in PDTO cultures. Overall, our imaging-based quantification workflow results in multiple parameters that can provide patient- and drug-specific information for screening applications.

Pharmacogenomic Testing to Guide Personalized Cancer Medicine Decisions in Private Oncology Practice: A Case Study.

Innovative tumor profiling methodologies are utilized to elucidate the pharmacogenomic landscape of tumor cells in order to support the molecularly guided delivery of therapeutics. Indeed, improved clinical outcomes are achieved in oncology practice by providing the physicians with expert-guided, standardized, and easily interpretable knowledge, translated from molecular profiling analysis to support clinical decision-making. However, there is still limited utilization of the technology especially in small private oncology practices. In this work, we analyzed how molecularly guided interventions in 17 consented cancer patients led to an overall improvement of disease response rates in a private oncology center. The precision medicine strategy was based on the OncoDEEP™ profiling solutions and focused on finding clinically actionable relationships between tumor biomarkers and drug responses. The obtained data support the notion that (a) following the pharmacogenomic-derived recommendations favorably impacted cancer therapy progression, and (b) the earlier profiling followed by the delivery of molecularly targeted therapy led to more durable and improved pharmacological response rates. Moreover, we report the example of a patient with metastatic gastric adenocarcinoma who, based on the molecular profiling data, received an off-label therapy that resulted in a complete response and a current cancer-free maintenance status. Overall, our data provide a paradigm on how molecular tumor profiling can improve decision-making in the routine private oncology practice.

Diverse BRAF Gene Fusions Confer Resistance to EGFR-Targeted Therapy via Differential Modulation of BRAF Activity.

Fusion genes can be oncogenic drivers in a variety of cancer types and represent potential targets for targeted therapy. The BRAF gene is frequently involved in oncogenic gene fusions, with fusion frequencies of 0.2%-3% throughout different cancers. However, BRAF fusions rarely occur in the same gene configuration, potentially challenging personalized therapy design. In particular, the impact of the wide variety of fusion partners on the oncogenic role of BRAF during tumor growth and drug response is unknown. Here, we used patient-derived colorectal cancer organoids to functionally characterize and cross-compare BRAF fusions containing various partner genes (AGAP3, DLG1, and TRIM24) with respect to cellular behavior, downstream signaling activation, and response to targeted therapies. We demonstrate that 5' fusion partners mainly promote canonical oncogenic BRAF activity by replacing the auto-inhibitory N-terminal region. In addition, the 5' partner of BRAF fusions influences their subcellular localization and intracellular signaling capacity, revealing distinct subsets of affected signaling pathways and altered gene expression. Presence of the different BRAF fusions resulted in varying sensitivities to combinatorial inhibition of MEK and the EGF receptor family. However, all BRAF fusions conveyed resistance to targeted monotherapy against the EGF receptor family, suggesting that BRAF fusions should be screened alongside other MAPK pathway alterations to identify patients with metastatic colorectal cancer to exclude from anti-EGFR-targeted treatment. IMPLICATIONS: Although intracellular signaling and sensitivity to targeted therapies of BRAF fusion genes are influenced by their 5' fusion partner, we show that all investigated BRAF fusions confer resistance to clinically relevant EGFR inhibition.

Chromosome arm aneuploidies shape tumour evolution and drug response

Chromosome arm aneuploidies (CAAs) are pervasive in cancers. However, how they affect cancer development, prognosis and treatment remains largely unknown. Here, we analyse CAA profiles of 23,427 tumours, identifying aspects of tumour evolution including probable orders in which CAAs occur and CAAs predicting tissue-specific metastasis. Both haematological and solid cancers initially gain chromosome arms, while only solid cancers subsequently preferentially lose multiple arms. 72 CAAs and 88 synergistically co-occurring CAA pairs multivariately predict good or poor survival for 58% of 6977 patients, with negligible impact of whole-genome doubling. Additionally, machine learning identifies 31 CAAs that robustly alter response to 56 chemotherapeutic drugs across cell lines representing 17 cancer types. We also uncover 1024 potential synthetic lethal pharmacogenomic interactions. Notably, in predicting drug response, CAAs substantially outperform mutations and focal deletions/amplifications combined. Thus, CAAs predict cancer prognosis, shape tumour evolution, metastasis and drug response, and may advance precision oncology.

Murine models for familial pancreatic cancer: Histopathology, latency and drug sensitivity among cancers of Palb2, Brca1 and Brca2 mutant mouse strains.

Alterations of the PALB2 tumor suppressor gene have been identified in familial breast, ovarian and pancreatic cancer cases. PALB2 cooperates with BRCA1/2 proteins through physical interaction in initiation of homologous recombination, in maintenance of genome integrity following DNA double-strand breaks. To determine if the role of PALB2 as a linker between BRCA1 and BRCA2 is critical for BRCA1/2-mediated tumor suppression, we generated Palb2 mouse pancreatic cancer models and compared tumor latencies, phenotypes and drug responses with previously generated Brca1/2 pancreatic cancer models. For development of Palb2 pancreatic cancer, we crossed conditional Palb2 null mouse with mice carrying the KrasG12D; p53R270H; Pdx1-Cre (KPC) constructs, and these animals were observed for pancreatic tumor development. Individual deletion of Palb2, Brca1 or Brca2 genes in pancreas per se using Pdx1-Cre was insufficient to cause tumors, but it reduced pancreata size. Concurrent expression of mutant KrasG12D and p53R270H, with tumor suppressor inactivated strains in Palb2-KPC, Brca1-KPC or Brca2-KPC, accelerated pancreatic ductal adenocarcinoma (PDAC) development. Moreover, most Brca1-KPC and some Palb2-KPC animals developed mucinous cystic neoplasms with PDAC, while Brca2-KPC and KPC animals did not. 26% of Palb2-KPC mice developed MCNs in pancreata, which resemble closely the Brca1 deficient tumors. However, the remaining 74% of Palb2-KPC animals developed PDACs without any cysts like Brca2 deficient tumors. In addition, the number of ADM lesions and immune cells infiltrations (CD3+ and F/480+) were significantly increased in Brca1-KPC tumors, but not in Brca2-KPC tumors. Interestingly, the level of ADM lesions and infiltration of CD3+ or F/480+ cells in Palb2-KPC tumors were intermediate between Brca1-KPC and Brca2-KPC tumors. As expected, disruption of Palb2 and Brca1/2 sensitized tumor cells to DNA damaging agents in vitro and in vivo. Altogether, Palb2-KPC PDAC exhibited features observed in both Brca1-KPC and Brca2-KPC tumors, which could be due to its role, as a linker between Brca1 and Brca2.

Pharmacokinetic/Pharmacodynamics Modeling of Drug-Loaded PLGA Nanoparticles Targeting Heterogeneously Vascularized Tumor Tissue.

Nanoparticle-mediated drug delivery and efficacy for cancer applications depends on systemic as well as local microenvironment characteristics. Here, a novel coupling of a nanoparticle (NP) kinetic model with a drug pharmacokinetic/pharmacodynamics model evaluates efficacy of cisplatin-loaded poly lactic-co-glycolic acid (PLGA) NPs in heterogeneously vascularized tumor tissue. Tumor lesions are modeled with various levels of vascular heterogeneity, as would be encountered with different types of tumors. The magnitude of the extracellular to cytosolic NP transport is varied to assess tumor-dependent cellular uptake. NP aggregation is simulated to evaluate its effects on drug distribution and tumor response. Cisplatin-loaded PLGA NPs are most effective in decreasing tumor size in the case of high vascular-induced heterogeneity, a high NP cytosolic transfer coefficient, and no NP aggregation. Depending on the level of tissue heterogeneity, NP cytosolic transfer and drug half-life, NP aggregation yielding only extracellular drug release could be more effective than unaggregated NPs uptaken by cells and releasing drug both extra- and intra-cellularly. Model-based customization of PLGA NP and drug design parameters, including cellular uptake and aggregation, tailored to patient tumor tissue characteristics such as proportion of viable tissue and vascular heterogeneity, could help optimize the NP-mediated tumor drug response.

Abstract GMM-021: UTILIZATION OF PATIENT-DERIVED TUMOR XENOGRAFT (PDX) MODELS IN ONCOLOGY

Abstract BACKGROUND: The 5-year survival rate of patients with recurrent epithelial ovarian cancer is only 30%, in part due to relapse and resistance to first-line platinum-based chemotherapy. Even with marked progress toward understanding ovarian cancer biology, the translation of research findings into new therapies is still an enormous barrier to progress. Recent data suggests a 90% failure rate for new oncology drugs in the clinic. Development and preclinical testing of new cancer therapies has been limited by the availability of clinically relevant models that recapitulate the molecular and phenotypic characteristics of primary ovarian cancers. To overcome this barrier, we have initiated collaborative project between Stephenson Cancer Center (SCC) and Oklahoma Medical Research Foundation (OMRF) with the goal to generate a biobank of patient-derived xenograft (PDX) models derived directly from ovarian cancer patients undergoing therapy. RESULTS: PDX tumor models are generated in the Patient-Derived Xenograft and Preclinical Therapeutics (PDX-PCT) Core at OMRF and used to improve preclinical evaluation of new drugs towards more personalized medicine. We have collected blood samples and tumor tissues from consenting patients having primary debulking surgery at Stephenson Cancer Center since 2015 (>140 unique patients). Fresh tumor tissue is minced into small tumor chunks (~2 mm) and implanted into immunocompromised mice. PDXs are expanded for 3-4 passages and characterized based on tumor type, histology and molecular characteristics. Immunohistochemical and molecular analysis showed that histology of the original patient tumors as well as the expression of commonly used markers for high-grade serous ovarian cancer such as cytokeratin, PAX8 and WT1 are perfectly maintained in the derivative PDX lines. Genotyping of patients' tumors and their corresponding PDXs using Illumina's Exome Array revealed that single nucleotide polymorphism (SNP) profile found in the original tumors is perfectly maintained in PDX lines. One of the PDX limitations is that human tumor stromal and immune cells are being replaced with mouse equivalents upon in vivo propagation of PDXs, which negatively affects quality of genomic or proteomic analyses. To overcome this challenge, we optimized a method to deplete mouse cells from PDX tumors by magnetic cells separation. Our data showed that ovarian cancer PDXs contain around 28-54% of human tumor cells, where the rest are mouse cells. We also optimized protocols to utilize pure population of human tumor cells isolated from PDXs to transduce these with lentiviral vectors expressing GFP and/or luciferase to generate luciferized PDXs. Luciferized ovarian PDXs are excellent models used for non-invasive orthotopic tumor growth and drug response monitoring. CONCLUSIONS: Collection of our PDXs illustrates heterogeneity and diversity of human ovarian tumors. However, each PDX maintains essential molecular features of the original patients' tumor. Our goal is to generate clinically faithful ovarian tumor models that will provide a platform to learn more about cancer biology and to screen these for new better therapies. Citation Format: Magdalena Cybula, Katherine Moxley, Lin Wang, Luyao Wang, Magdalena Bieniasz. UTILIZATION OF PATIENT-DERIVED TUMOR XENOGRAFT (PDX) MODELS IN ONCOLOGY [abstract]. In: Proceedings of the 12th Biennial Ovarian Cancer Research Symposium; Sep 13-15, 2018; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2019;25(22 Suppl):Abstract nr GMM-021.

Advanced technological tools to study multidrug resistance in cancer.

The complexity of cancer biology and its clinical manifestation are driven by genetic, epigenetic, transcriptomic, proteomic and metabolomic alterations, supported by genomic instability as well as by environmental conditions and lifestyle factors. Although novel therapeutic modalities are being introduced, efficacious cancer therapy is not achieved due to the frequent emergence of distinct mechanisms of multidrug resistance (MDR). Advanced technologies with the potential to identify and characterize cancer MDR could aid in selecting the most efficacious therapeutic regimens and prevent inappropriate treatments of cancer patients. Herein, we aim to present technological tools that will enhance our ability to surmount drug resistance in cancer in the upcoming decade. Some of these tools are already in practice such as next-generation sequencing. Identification of genes and different types of RNAs contributing to the MDR phenotype, as well as their molecular targets, are of paramount importance for the development of new therapeutic strategies aimed to enhance drug response in resistant tumors. Other techniques known for many decades are in the process of adaptation and improvement to study cancer cells' characteristics and biological behavior including atomic force microscopy (AFM) and live-cell imaging. AFM can monitor in real-time single molecules or molecular complexes as well as structural alterations occurring in cancer cells induced upon treatment with various antitumor agents. Cell tracking methodologies and software tools recently progressed towards quantitative analysis of the spatio-temporal dynamics of heterogeneous cancer cell populations and enabled direct monitoring of cells and their descendants in 3D cultures. Besides, novel 3D systems with the advanced mimicking of the in vivo tumor microenvironment are applicable to study different cancer biology phenotypes, particularly drug-resistant and aggressive ones. They are also suitable for investigating new anticancer treatment modalities. The ultimate goal of using phenotype-driven 3D cultures for the investigation of patient biopsies as the most appropriate in vivo mimicking model, can be achieved in the near future.

Pharmacogenomics, biomarker network, and allele frequencies in colorectal cancer.

Colorectal cancer is one of the leading causes of cancer death worldwide. Over the last decades, several studies have shown that tumor-related genomic alterations predict tumor prognosis, drug response, and toxicity. These observations have led to the development of several therapies based on individual genomic profiles. As part of these approaches, pharmacogenomics analyses genomic alterations which may predict an efficient therapeutic response. Studying these mutations as biomarkers for predicting drug response is of a great interest to improve precision medicine. We conduct a comprehensive review of the main pharmacogenomics biomarkers and genomic alterations affecting enzyme activity, transporter capacity, channels, and receptors; and therefore the new advances in CRC precision medicine to select the best therapeutic strategy in populations worldwide, with a focus on Latin America.