Precision Cancer Medicine Research Articles

Copy number alteration features in pan-cancer homologous recombination deficiency prediction and biology

Homologous recombination deficiency (HRD) renders cancer cells vulnerable to unrepaired double-strand breaks and is an important therapeutic target as exemplified by the clinical efficacy of poly ADP-ribose polymerase (PARP) inhibitors as well as the platinum chemotherapy drugs applied to HRD patients. However, it remains a challenge to predict HRD status precisely and economically. Copy number alteration (CNA), as a pervasive trait of human cancers, can be extracted from a variety of data sources, including whole genome sequencing (WGS), SNP array, and panel sequencing, and thus can be easily applied clinically. Here we systematically evaluate the predictive performance of various CNA features and signatures in HRD prediction and build a gradient boosting machine model (HRDCNA) for pan-cancer HRD prediction based on these CNA features. CNA features BP10MB[1] (The number of breakpoints per 10MB of DNA is 1) and SS[ > 7 & <=8] (The log10-based size of segments is greater than 7 and less than or equal to 8) are identified as the most important features in HRD prediction. HRDCNA suggests the biallelic inactivation of BRCA1, BRCA2, PALB2, RAD51C, RAD51D, and BARD1 as the major genetic basis for human HRD, and may also be applied to effectively validate the pathogenicity of BRCA1/2 variants of uncertain significance (VUS). Together, this study provides a robust tool for cost-effective HRD prediction and also demonstrates the applicability of CNA features and signatures in cancer precision medicine.

Genomic landscape and its prognostic significance in stage III colorectal cancer: JCOG1506A1, an ancillary of JCOG0910.

Large-scale genomic sequencing of colorectal cancers has been reported mainly for Western populations. Differences by stage and ethnicity in the genomic landscape and their prognostic impact remain poorly understood. We investigated 534 Japanese stage III colorectal cancer samples from the Phase III trial, JCOG0910. Targeted-capture sequencing of 171 potentially colorectal cancer-associated genes was performed, and somatic single-nucleotide variants and insertion-deletions were determined. Hypermutated tumors were defined as tumors with MSIsensor score >7 and ultra-mutated tumors with POLE mutations. Genes with alterations associated with relapse-free survival were analyzed using multivariable Cox regression models. In all patients (184 right-sided, 350 left-sided), mutation frequencies were TP53, 75.3%; APC, 75.1%; KRAS, 43.6%; PIK3CA, 19.7%; FBXW7, 18.5%; SOX9, 11.8%; COL6A3, 8.2%; NOTCH3, 4.5%; NRAS, 4.1%; and RNF43, 3.7%. Thirty-one tumors were hypermutated (5.8%; 14.1% right-sided, 1.4% left-sided). Modest associations were observed: poorer relapse-free survival was seen with mutant KRAS (hazard ratio 1.66; p = 0.011) and mutant RNF43 (2.17; p = 0.055), whereas better relapse-free survival was seen with mutant COL6A3 (0.35; p = 0.040) and mutant NOTCH3 (0.18; p = 0.093). Relapse-free survival tended to be better for hypermutated tumors (0.53; p = 0.229). In conclusion, the overall spectrum of mutations in our Japanese stage III colorectal cancer cohort was similar to that in Western populations, but the frequencies of mutation for TP53, SOX9, and FBXW7 were higher, and the proportion of hypermutated tumors was lower. Multiple gene mutations appeared to impact relapse-free survival, suggesting that tumor genomic profiling can support precision medicine for colorectal cancer.

Patient-derived models facilitate precision medicine in liver cancer by remodeling cell-matrix interaction.

Liver cancer is an aggressive tumor originating in the liver with a dismal prognosis. Current evidence suggests that liver cancer is the fifth most prevalent cancer worldwide and the second most deadly type of malignancy. Tumor heterogeneity accounts for the differences in drug responses among patients, emphasizing the importance of precision medicine. Patient-derived models of cancer are widely used preclinical models to study precision medicine since they preserve tumor heterogeneity ex vivo in the study of many cancers. Patient-derived models preserving cell-cell and cell-matrix interactions better recapitulate in vivo conditions, including patient-derived xenografts (PDXs), induced pluripotent stem cells (iPSCs), precision-cut liver slices (PCLSs), patient-derived organoids (PDOs), and patient-derived tumor spheroids (PDTSs). In this review, we provide a comprehensive overview of the different modalities used to establish preclinical models for precision medicine in liver cancer.

Hope in the era of precision oncology: a qualitative study of informal caregivers’ experiences

ObjectivesTo explore informal caregivers’ perspectives on precision medicine in cancer care.DesignSemi-structured interviews with the informal caregivers of people living with cancer and receiving targeted/immunotherapies. Interview transcripts were analysed thematically using...

Liquid biopsy in pancreatic cancer - Current perspective and future outlook.

Pancreatic cancer is a lethal condition with a rising incidence and often presents at an advanced stage, contributing to abysmal five-year survival rates. Unspecific symptoms and the current lack of biomarkers and screening tools hamper early diagnosis. New technologies for liquid biopsies and their respective evaluation in pancreatic cancer patients have emerged over recent years. The term liquid biopsy summarizes the sampling and analysis of circulating tumor cells (CTCs), small extracellular vesicles (sEVs), and tumor DNA (ctDNA) from body fluids. The major advantages of liquid biopsies rely on their minimal invasiveness and repeatability, allowing serial sampling for dynamic insights to aid diagnosis, particularly early detection, risk stratification, and precision medicine in pancreatic cancer. However, liquid biopsies have not yet developed into a new pillar for clinicians' routine armamentarium. Here, we summarize recent findings on the use of liquid biopsy in pancreatic cancer patients. We discuss current challenges and future perspectives of this potentially powerful alternative to conventional tissue biopsies.

Targeting Oncometabolites in Peritoneal Cancers: Preclinical Insights and Therapeutic Strategies.

Peritoneal cancers present significant clinical challenges with poor prognosis. Understanding the role of cancer cell metabolism and cancer-promoting metabolites in peritoneal cancers can provide new insights into the mechanisms that drive tumor progression and can identify novel therapeutic targets and biomarkers for early detection, prognosis, and treatment response. Cancer cells dynamically reprogram their metabolism to facilitate tumor growth and overcome metabolic stress, with cancer-promoting metabolites such as kynurenines, lactate, and sphingosine-1-phosphate promoting cell proliferation, angiogenesis, and immune evasion. Targeting cancer-promoting metabolites could also lead to the development of effective combinatorial and adjuvant therapies involving metabolic inhibitors for the treatment of peritoneal cancers. With the observed metabolomic heterogeneity in cancer patients, defining peritoneal cancer metabolome and cancer-promoting metabolites holds great promise for improving outcomes for patients with peritoneal tumors and advancing the field of precision cancer medicine. This review provides an overview of the metabolic signatures of peritoneal cancer cells, explores the role of cancer-promoting metabolites as potential therapeutic targets, and discusses the implications for advancing precision cancer medicine in peritoneal cancers.

Tumor Microenvironment‐Responsive Nanocarrier Based on VOx Nanozyme Amplify Oxidative Stress for Tumor Therapy

AbstractThe construction of a novel nanocarrier that can break the redox balance in tumor cell is a promising anti‐tumor strategy. Herein, a tumor microenvironment (TME)‐responsive nanocarrier VC@Lipo is rationally designed by embedding ultrasmall VOx nanozyme and photosensitizer chlorin e6 (Ce6) into liposomes. The size of VC@Lipo nanocarrier is ≈35 nm and can be degraded in the weakly acidic environment of TME. The VOx nanozyme exhibits peroxidase‐like activity and generates highly toxic hydroxyl radical ∙OH through Fenton‐like reaction and 1O2 in the presence of H2O2 independent of light, and more 1O2 can be generated by the photodynamic effect of Ce6. In addition, the VOx nanozyme can effectively deplete intracellular overexpressed glutathione (GSH) through redox reactions. In vivo experiments demonstrate that the nanocarrier shows excellent biocompatibility, presents the largest enrichment at the tumor site after 6 h of intravenous injection into mice with the highest tumor inhibition rate of 54.18% after laser irradiation. Compared with the single treatment mode, VC@Lipo shows the best synergistic effect of chemodynamic‐photodynamic therapy. This work provides a new paradigm for nanocatalytic therapy of cancer and is expected to provide new ideas for precision medicine in cancer.

A Transcriptome-Based Precision Oncology Platform for Patient-Therapy Alignment in a Diverse Set of Treatment-Resistant Malignancies.

Complementary precision cancer medicine paradigms are needed to broaden the clinical benefit realized through genetic profiling and immunotherapy. In this first-in-class application, we introduce two transcriptome-based tumor-agnostic systems biology tools to predict drug response in vivo. OncoTarget and OncoTreat are scalable for the design of basket and umbrella clinical trials. This article is highlighted in the In This Issue feature, p. 1275.

Abstract LB163: A microfluidic platform for developing circulating tumor cells (CTCs) organoids for precision medicine in pancreatic cancer

Abstract Introduction Pancreatic ductal adenocarcinoma (PDAC) is predicted to be the second leading cause of cancer death in the US by 2030. Besides the late detection and lack of effective treatments, the greatest treatment hurdle to PDAC is the ineffectiveness of patient-derived tumor models. Precision medicine-based treatment decisions require the generation of consistent and trackable complex organoid models that is not achievable by tissue biopsies. Liquid biopsies such as blood, instead, contain circulating biomarkers released by the tumor, among which especially interested in for this study are circulating tumor cells (CTCs). This work develops microfluidic 3D organoids from CTCs to be used as a precision medicine tool to predict and guide the drug response of early stage I-IIB (PDAC) patients. Experimental Methods CTCs are isolated from PDAC patient blood samples using microfluidic Labyrinth following removal of red blood cells with 6% dextran. Collected cells post-isolation are immunofluorescence (IF) stained with DAPI (nuclei), CD45 (leukocyte), pancytokeratin (type I/II cytokeratin 1-8, 10, 14- 16 and 19), Vimentin, and Epithelial cell adhesion molecule (EpCAM) to identify CTCs and characterize their phenotypes. The mold of the microfluidic platform for CTC organoids is fabricated with the standard SU-8 photolithography. The PDMS-based device is tested with a low seeding number of a pancreatic cancer CTC cell line labeled fluorescently. Decellularization of cancer associated fibroblasts (CAFs) generates the in vitro matrix that supplies amino-acid precursors for PDAC metabolism, promoting CTC organoid formation. Serial images of cell growth are obtained throughout the course using a Ti-20 microscope. After each 7-day culture, organoids are dissociated from the device and IF stained using a similar panel as above (with Ki67 marker instead of CD45 for cell proliferation). Preliminary gemcitabine dosage-performance analysis is performed on-chip. Results CTC count from the tested patient panel (n=16) averages 17.0±8.4/mL of blood. CAFs are successfully decellularized on-chip and the embedded pancreatic CTC cell line growth curve is generated. IF staining of the dissociated cells from the organoids confirms the proliferation of the loaded pseudo-CTCs. Conclusion The work above demonstrates the successful isolation of CTCs from PDAC patient blood samples and an engineered microfluidic platform to expand low abundance of cells into organoids. It has a potential to be tested and employed to expand CTCs from PDAC into organoids for drug screening. Citation Format: Yu Zhang, Scott Smith, Minal Nenwani, Valerie Gunchick, Vaibhav Sahai, Sunitha Nagrath, Deepak Nagrath. A microfluidic platform for developing circulating tumor cells (CTCs) organoids for precision medicine in pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB163.

Analyses of single extracellular vesicles from non-small lung cancer cells to reveal effects of epidermal growth factor receptor inhibitor treatments

Precision cancer medicine has changed the treatment landscape of non-small cell lung cancer (NSCLC) as illustrated by the introduction of tyrosine kinase inhibitors (TKIs) towards mutated epidermal growth factor receptor (EGFR). However, as responses to EGFR-TKIs are heterogenous among NSCLC patients, there is a need for ways to early monitor changes in treatment response in a non-invasive way e.g., in patient's blood samples. Recently, extracellular vesicles (EVs) have been identified as a source of tumor biomarkers which could improve on non-invasive liquid biopsy-based diagnosis of cancer. However, the heterogeneity in EVs is high. Putative biomarker candidates may be hidden in the differential expression of membrane proteins in a subset of EVs hard to identify using bulk techniques. Using a fluorescence-based approach, we demonstrate that a single-EV technique can detect alterations in EV surface protein profiles. We analyzed EVs isolated from an EGFR-mutant NSCLC cell line, which is refractory to EGFR-TKIs erlotinib and responsive to osimertinib, before and after treatment with these drugs and after cisplatin chemotherapy. We studied expression level of five proteins; two tetraspanins (CD9, CD81), and three markers of interest in lung cancer (EGFR, programmed death-ligand 1 (PD-L1), human epidermal growth factor receptor 2 (HER2)). The data reveal alterations induced by the osimertinib treatment compared to the other two treatments. These include the growth of the PD-L1/HER2-positive EV population, with the largest increase in vesicles exclusively expressing one of the two proteins. The expression level per EV decreased for these markers. On the other hand, both the TKIs had a similar effect on the EGFR-positive EV population.

Combinatorial drug screening on 3D Ewing sarcoma spheroids using droplet-based microfluidics

Culturing and screening cells in microfluidics, particularly in three-dimensional formats, has the potential to impact diverse areas from fundamental biology to cancer precision medicine. Here, we use a platform based on anchored droplets for drug screening. The response of spheroids of Ewing sarcoma (EwS) A673 cells to simultaneous or sequential combinations of etoposide and cisplatin was evaluated. This was done by culturing spheroids of EwS cells inside 500nL droplets then merging them with secondary droplets containing fluorescent-barcoded drugs at different concentrations. Differences in EwS spheroid growth and viability were measured by microscopy. After drug exposure such measurements enabled estimation of their IC50 values, which were in agreement with values obtained in standard multiwell plates. Then, synergistic drug combination was evaluated. Sequential combination treatment of EwS with etoposide applied 24h before cisplatin resulted in amplified synergistic effect. As such, droplet-based microfluidics offers the modularity required for evaluation of drug combinations.

Precision Medicine in Head and Neck Cancer

Precision Medicine in Head and Neck Cancer

Computational pathology to improve biomarker testing in breast cancer: how close are we?

The recent advancements in breast cancer precision medicine have highlighted the urgency for the precise and reproducible characterization of clinically actionable biomarkers. Despite numerous standardization efforts, biomarker testing by conventional methodologies is challenged by several issues such as high inter-observer variabilities, the spatial heterogeneity of biomarkers expression, and technological heterogeneity. In this respect, artificial intelligence-based digital pathology approaches are being increasingly recognized as promising methods for biomarker testing and subsequently improved clinical management. Here, we provide an overview on the most recent advances for artificial intelligence-assisted biomarkers testing in breast cancer, with a particular focus on tumor-infiltrating lymphocytes, programmed death-ligand 1, phosphatidylinositol-3 kinase catalytic alpha, and estrogen receptor 1. Challenges and solutions for this integrative analysis in pathology laboratories are also provided.

Integrated analysis of circulating cell free nucleic acids for cancer genotyping and immune phenotyping of tumor microenvironment.

The circulating cell-free nucleic acids (ccfNAs) consist of a heterogenous cocktail of both single (ssNA) and double-stranded (dsNA) nucleic acids. These ccfNAs are secreted into the blood circulation by both healthy and malignant cells via various mechanisms including apoptosis, necrosis, and active secretion. The major source of ccfNAs are the cells of hematopoietic system under healthy conditions. These ccfNAs include fragmented circulating cell free DNA (ccfDNA), coding or messenger RNA (mRNA), long non-coding RNA (lncRNA), microRNA (miRNA), and mitochondrial DNA/RNA (mtDNA and mtRNA), that serve as prospective biomarkers in assessment of various clinical conditions. For, e.g., free fetal DNA and RNA migrate into the maternal plasma, whereas circulating tumor DNA (ctDNA) has clinical relevance in diagnostic, prognostic, therapeutic targeting, and disease progression monitoring to improve precision medicine in cancer. The epigenetic modifications of ccfDNA as well as circulating cell-free RNA (ccfRNA) such as miRNA and lncRNA show disease-related variations and hold potential as epigenetic biomarkers. The messenger RNA present in the circulation or the circulating cell free mRNA (ccf-mRNA) and long non-coding RNA (ccf-lncRNA) have gradually become substantial in liquid biopsy by acting as effective biomarkers to assess various aspects of disease diagnosis and prognosis. Conversely, the simultaneous characterization of coding and non-coding RNAs in human biofluids still poses a significant hurdle. Moreover, a comprehensive assessment of ccfRNA that may reflect the tumor microenvironment is being explored. In this review, we focus on the novel approaches for exploring ccfDNA and ccfRNAs, specifically ccf-mRNA as biomarkers in clinical diagnosis and prognosis of cancer. Integrating the detection of circulating tumor DNA (ctDNA) for cancer genotyping in conjunction with ccfRNA both quantitatively and qualitatively, may potentially hold immense promise towards precision medicine. The current challenges and future directions in deciphering the complexity of cancer networks based on the dynamic state of ccfNAs will be discussed.

Abstract 6612: Data-driven hallmarks of cancer as a new paradigm for precision medicine: multi-omics and functional profiling in acute myeloid leukemia

Abstract The cancer hallmark concept defines biological properties that play a key role in cancer development and progression. The hallmarks reflect generic properties across all cancer types, and are not directly quantifiable nor applicable in cancer diagnostics. Here, we applied a data-driven approach to identify quantifiable hallmarks to be used as a basis for precision cancer medicine (PCM) in Acute Myeloid Leukemia (AML). We applied deep exome, transcriptome, DNA methylation and proteome profiling as well as ex-vivo functional testing of 525 drugs to 118 AML patient samples. Unsupervised multi-omic dimensionality reduction defined 11 independent axes of biological variability that we interpreted to reflect data-driven hallmarks (DDHM) of AML. Each DDHM integrates and ranks different data types and features, pinpointing those molecular features that were most informative for each hallmark. We calculated values for the 11 DDHM for each patient, constructing a DDHM-based precision medicine approach for AML diagnosis and therapy assignment. Most DDHMs were driven by other data types than genomics. For several DDHMs, different cytogenetic and mutational drivers converge on the same hallmarks and specific drug vulnerabilities. We also see how DDHM predictors of poor prognosis and high-risk AML are distinct from those that dictate specific drug response vulnerabilities. DDHM 2 reflected the cell differentiation path leading towards resistance to BH3 mimetics, ACK-inhibitors, and anthracyclines, and sensitivity to MEK inhibitors and TLR8 agonists. DDHM 2 derived a strong impact from protein expression of GATA2, MLLT11, DNMT3B and from RNA expression of WT1 and SOX4 as well as from multiple markers of different monocytic subtypes. DDHM 5 depicted cell cycle regulation and the Megakaryocytic-erythrocyte progenitor cell state that was enriched in AML patients with antecedent hematological disease. Interestingly, DDHM5 was linked to the sensitivity to purine analogs and vinca alkaloids. Moreover, DDHMs 1 and 8 captured clinical risk groups, prognosis, and responsiveness to hypomethylating agents. These DDHMs also revealed the phenotypic and functional biology that distinguished patients with high versus low FLT3-ITD mutant allele frequency. Validation of the DDHMs is realized through profiling and analysis of prospective AML samples, AML cell lines and previously published datasets. In summary, we present a data-driven approach for defining hallmarks in AML. The application of the DDHMs in AML provides a new paradigm for PCM and an opportunity for combinatorial therapeutic targeting. Each patient is characterized by a combination of independent and potentially druggable hallmarks, as opposed to traditional stratification in PCM, where each patient is assigned to one specific subgroup defined by genetic or other biomarkers. Citation Format: Tom Erkers, Nona Struyf, Tojo James, Francesco Marabita, Mattias Vesterlund, Nghia Vu, Cornelia Arnroth, Albin Österroos, Anna Bohlin, Sofia Bengtzén, Matthias Stahl, Rozbeh Jafari, Lukas Orre, Yudi Pawitan, Brinton Seashore-Ludlow, Janne Lehtiö, Sören Lehmann, Päivi Östling, Olli Kallioniemi. Data-driven hallmarks of cancer as a new paradigm for precision medicine: multi-omics and functional profiling in acute myeloid leukemia. [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 6612.

Abstract 6088: Charting new cancer dependencies with patient derived organoids

Abstract The number of patients benefiting from precision cancer medicine (PCM) is very limited. The Cancer Dependency Map Initiative aims to dramatically accelerate PCM by systematically identifying the landscape of cancer vulnerabilities across all tumors. Genome-wide knockout screens have been performed in over 1000 cancer cell lines, but these have been sourced from historically derived models, leaving many cancers underrepresented. Advances have been made in model derivation by growing cells in 3D formats, such as organoids or spheroids. These models present an opportunity to screen new cancer types, but come with challenges that require a new pipeline. Here, we present a set of screens using a genome wide CAS12 library in organoid tumor models representing novel subtypes of pancreatic, esophageal, and gastric cancer, along with treatment-resistant breast, ovarian, and prostate cancer organoids. We show that genome-wide screening of these models is feasible and can be achieved with quality comparable to 2D cell lines. Our work uncovered a set of novel dependencies associated with cell-ECM interactions that have not previously been identified in 2D models. Our methods provide a framework for screening future patient derived 3D culture models and discovering vulnerabilities in new tumor types. Citation Format: James V. Neiswender, Lisa Brenan, Tate Bertea, Jimmy Guo, Ashley Anderson, Megan Wong, Zoe Posner, Kevin Kapner, Connor Hennessey, Sarah Wie, Isabella Boyle, Barbara De Kegel, Joshua Dempster, Yuen-Yi (Moony) Tseng, David Root, Andrew Aguirre, Francisca Vazquez. Charting new cancer dependencies with patient derived organoids [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 6088.

Abstract 5432: Validation of spatial gene expression patterns predicted by deep convolutional neural networks from breast cancer histopathology images

Abstract Molecular profiling is central in cancer precision medicine but remains costly and is only based on tumor average profiles. Morphologic patterns observable in histopathology sections from tumors are determined by the underlying molecular phenotype and therefore have the potential to be exploited for the prediction of molecular phenotypes. Transcriptome-wide expression morphology (EMO) analysis with deep convolutional neural networks (CNN) enables the prediction of mRNA expression and proliferation markers from routine histopathology whole slide images in breast cancer. The NanoString GeoMx® Digital Spatial Profiler (DSP) platform has capabilities of associating crucial spatial information with intratumor variabilities of gene expression, serving as an ideal tool to characterize and validate the prediction of intratumor heterogeneity. The GeoMx platform also provides functionalities of overlaying and aligning hematoxylin and eosin (H&amp;E) whole slide scans on serial tissue sections with morphology marker staining to facilitate region of interest (ROI) selection to match relevant image tiles from the model input, which is a key step to ensure accuracy for the evaluation of the model predictions. The GeoMx RNA Immune Pathways Panel contains 84 gene targets including key genes involved in immune pathways and tumorigenesis and was used to validate the prediction of mRNA expression output from deep CNN models. Citation Format: Kathy Ton, Yinxi Wang, Liuliu Pan, Kimmo Kartasalo, Balazs Acs, Philippe Weitz, Liang Zhang, Yan Liang, Johan Hartman, Masi Volkonen, Christer Larsson, Pekka Ruusuvuori, Joseph Beechem, Mattias Rantalainen. Validation of spatial gene expression patterns predicted by deep convolutional neural networks from breast cancer histopathology images. [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 5432.

Abstract 5723: An in vivo pharmacogenomics platform replicates and extends biomarkers of therapy response identified via causal inference analysis of clinical data

Abstract Recent development of therapies targeting oncogenes have dramatically improved cancer care for subsets of patients and generated a wave of interest in cancer precision medicine. However, effective targeted therapies are scarce as we have a limited understanding of how drug responses are modulated by tumor genotype. Here, we utilize both human data and in vivo models to identify genetic drivers of therapy response in KRAS-driven non-small cell lung cancer patients treated with chemotherapy. We first present an integrative causal inference analysis of three clinical data resources: (1) the recently released Genomics Evidence Neoplasia Information Exchange, Biopharma Collaboration dataset (GENIE-BPC; n=197), (2) a selected set of patients from the Tempus Clinico-genomic Database (n=330), and (3) an additional cohort from Memorial Sloan Kettering Cancer Center (n=218). Each dataset was first analyzed separately using a causal inference pipeline. Doubly robust estimators for each variant were inferred within a counting process survival analysis model accounting for time-varying treatments and immortal time bias. Meta-analysis of the results from all three cohorts identified three commonly mutated and highly replicable genes with a significant effect on overall survival: KEAP1, SMARCA4, and CDKN2A. As further validation, we used our murine in vivo pharmacogenomics (PGx) platform that can quantify the effects of therapies across thousands of tumors of diverse genotypes. These tumors are initiated de novo in the native microenvironmental context in mice with an intact adaptive immune system. We tested a chemotherapy combination of carboplatin and pemetrexed in mice with KrasG12D-driven lung tumors and inactivation of each of 60 putative tumor suppressors. Treatment led to &amp;gt;75% reduction in tumor sizes relative to tumor suppressor inactivated (matched) vehicle-treated controls. These models identified causal effects for two out of the three candidates above: KEAP1 (resistance) and CDKN2A (sensitive). Moreover, our PGx platform identified additional candidate genes beyond those found using the clinical data, which had insufficient sample size for these rarely mutated genes. Together, we demonstrate how leveraging our PGx platform together with human data within a causal inference framework may improve the stratification of patients by their clinical outcomes, profoundly advancing the promise of precision medicine. Citation Format: David Amar, Erick Scott, Ian P. Winters, Gregory D. Wall, Dmitri A. Petrov, Monte M. Winslow, Joseph Juan, Ian K. Lai, Lafia Sebastian, Edwin A. Apilado, Gabriel Grenot, Vy B. Tran, Charles Rudin, Michael J. Rosen. An in vivo pharmacogenomics platform replicates and extends biomarkers of therapy response identified via causal inference analysis of clinical data. [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 5723.

Abstract 2151: Identification of transcript adenosine fingerprint to enrich for A2AR and PD-1 inhibition responders

Abstract Background: Precision cancer medicine aims to identify the right drug for the right patient, enriching for patients more likely to respond to a particular treatment. This paradigm is gaining importance during the early clinical lifecycle of a new potential drug to improve patient-centric trial designs, drive clinical success and eventually increase approval rates - enlarging the therapeutic arsenal available for oncology patients. To optimize the chance of success with our A2AR-selective antagonist, EXS-21546 (546; NCT04727138, discovered in collaboration with Evotec), we have identified an adenosine-induced immunosuppression biomarker signature (adenosine burden score or ABS) for clinical trial patient selection that also correlates with checkpoint inhibitor (CI) response prediction in ex vivo primary models. Here we present transcriptional and functional data mapping adenosine burden at the single cell level, and investigate subsequent modulation through antagonism of A2AR with 546, combination effects with CI, to prioritize patients for 546+CI therapy. Methods: By leveraging disease-relevant primary human tissues together with matched single cell and bulk transcriptomics, we assess adenosine-induced anticancer immune suppression and show initial biological confirmation of patient selection methodology and combination therapy effects with a translatable high content imaging platform (Kornauth et al 2021 &amp; Snidjer et al 2017). Results: The ABS detects adenosine rich microenvironments with greater specificity and sensitivity than other published signatures. Validating the ABS in TGCA, we found the ABS anti-correlates with a validated predictor of anti-PD-1 therapy success (TIS, Damotte et al 2019), unraveling that high-adenosine/ABS cases are among patients least likely to respond to immunotherapy (low TIS). A2AR antagonism with 546 demonstrated a reduction of the adenosine burden, and restored the CI response potential as addressed by the ABS and TIS, respectively. Further immune reactivation was seen with antagonism of adenosine signaling by ‘546/CI combination ex vivo in primary tissues pre-selected with our ABS signature. Conclusions: Combining deep learning of single cell functional and multi-omics profiling data of disease relevant primary model systems, we model the association of the immune response potential to A2AR antagonism in cancer to define a biomarker signature to predict patients likely to benefit from A2AR antagonism and CI. This will be confirmed and validated retrospectively in an ongoing clinical study of 546 in two cancer indications. Citation Format: Isabella Alt, Robert Sehlke, Anna Lobley, Claudia Baumgaertler, Maja Stulic, Klaus Hackner, Lucia Dzurillova, Edgar Petru, Laudia Hadjari, Judith Lafleur, Josef Singer, Nikolaus Krall, Jozef Šufliarsky, Lukas Hefler, Thorsten Füreder, Christina Taubert, Andrew Payne, Christophe Boudesco, Gregory Ian Vladimer. Identification of transcript adenosine fingerprint to enrich for A2AR and PD-1 inhibition responders [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 2151.

Abstract 4347: A pivotal clinical trial of cResponse, a functional assay for cancer precision medicine

Abstract Precision cancer therapy has the potential to revolutionize treatment outcome. While genomic analysis has become central to cancer personalized medicine, recent studies have not shown that it drastically improves the patient’s survival as compared to standard drug selection. Additionally, genomic mutations may suggest several treatment protocols without elucidating which approach will yield the best clinical response. To advance cancer precision guidance, we have developed cResponse, a combined genomic-functional drug sensitivity platform to determine individualized patient treatment regimens. Fresh patient cancer samples are taken by biopsy or resection and sectioned into 250uM slices which when cultured in cResponse platform demonstrate similar architecture and tissue proliferation to those found in vivo. An initial clinical study showed that cResponse can preserve human cancer tissue in 3D culture together with its microenvironment, including endothelial and immune cells, at a high viability (&amp;gt;90%) with continued cell division for more than 7 days. On a cohort of 34 patients treated with neoadjuvant therapy or systemic therapy for metastatic disease, the assay was able to predict patient response to drug treatment with a sensitivity of 96% and a specificity of 77.7%. To further validate the capacity of the cResponse platform to predict patient response to cancer therapy, a follow up pivotal clinical study was established at 7 major cancer centers located in the UK with the goal of recruiting a total of 170 patients to provide a large statistical validation of the previous results. Here we report on the outcome of the first 50 patients recruited to the pivotal trial and describe the predictive results correlated to patient response. Citation Format: Seth Salpeter, Vered Bar, Guy Neev, Adi Zundelevich, Gil Rosen, Sandra Hanks, Naoise Costelloe, Jonathan Krell, Ravid Straussman. A pivotal clinical trial of cResponse, a functional assay for cancer precision medicine. [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 4347.