BACKGROUND:Tusamitamab ravtansine demonstrated antitumor activity in the Phase 1/1b study of advanced non-squamous non-small cell lung cancer with high (HE, ≥2+ intensity in ≥50 % of tumor cells) or moderate (ME, ≥2+ intensity in ≥1 % to <50 % of tumor cells) carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) expression. Tumor CEACAM5 expression, biomarker associations and whether biomarkers predict objective response rate (ORR) were explored. METHODS:We assessed CEACAM5, circulating CEACAM5 (cCEACAM5) and CEA (cCEA). Enrollment was according to immunohistochemistry (IHC) CEACAM5 membrane expression: HE (n=64) and ME (n=28). Patients received tusamitamab ravtansine 100 mg/m2 intravenously every 2 weeks. RESULTS:cCEA and cCEACAM5 were strongly associated (Spearman ρ, 0.99), with moderate associations between IHC CEACAM5 and cCEA or cCEACAM5 (Spearman ρ, 0.43 and 0.38). In patients with baseline cCEA data, 40.3 % (25/62) of HE and 25 % (7/28) of ME had cCEA ≥100 µg/L (median: 71.6 µg/L [1-8809] versus 12.4 µg/L [0.5-684]). Among response-evaluable patients in HE, ORR for high cCEA (≥100 µg/L) was 41.7 % (10/24) versus 8.1 % (3/37) for low cCEA, and in ME, ORR was 0/7 versus 10 % (2/20). Elevated CEACAM5 mRNA was observed in HE versus ME (P = 0.0027). EGFR and KRAS alterations were present in 44.8 % and 65.5 % of HE and in 21.4 % and 78.6 % of ME patients, respectively. CONCLUSIONS:In CEACAM5 HE, the ORR was greater with high versus low cCEA. Associations were observed between cCEA and cCEACAM5; IHC CEACAM5, cCEA, and cCEACAM5; IHC CEACAM5 and CEACAM5 mRNA, but not between IHC CEACAM5 and oncogenic drivers. CLINICAL TRIAL REGISTRATION:NCT02187848.

Background Tusamitamab ravtansine is an antibody-drug conjugate of a humanized carcinoembryonic antigen (CEA)-related cell adhesion molecule 5 (CEACAM5)-specific monoclonal antibody linked to DM4. A Phase 1/2 study (NCT02187848) showed tusamitamab ravtansine antitumor activity in pretreated patients (pts) with advanced nonsquamous NSCLC and high CEACAM5 expression. Here, we explore biomarker associations with tumor CEACAM5 expression by immunohistochemistry (IHC), and whether biomarkers predict objective response rate (ORR). Methods We assessed CEACAM5 expression by IHC, RNA sequencing, and whole exome sequencing (WES) on latest archival tumor samples; and circulating CEACAM5 (cCEACAM5) and CEA (cCEA). We enrolled 2 cohorts of pts with IHC CEACAM5 membrane expression at ≥2+ intensity: in ≥50% of tumor cells (high expressors, HEs, n = 64); and in ≥1% to <50% of tumor cells (moderate expressors, MEs, n = 28). Pts received tusamitamab ravtansine 100 mg/m2 IV every 2 weeks. Results cCEA and cCEACAM5 were strongly associated (Spearman rho, 0.9), with weak associations between IHC CEACAM5 and cCEA or cCEACAM5 (Spearman rho, 0.3 and 0.4, respectively). Higher levels of CEACAM5 mRNA were observed in CEACAM5 HEs vs MEs (P=0.0027). EGFR and KRAS genetic alterations by WES were present in 44.8% and 65.5% of CEACAM5 HEs, respectively, and 21.4% and 78.6% of CEACAM5 MEs, respectively. Confirmed partial responses were seen in 13/64 HEs (ORR 20.3%) and 2/28 MEs (ORR 7.1%). In CEACAM5 HEs with available baseline (BL) cCEA data, 25/62 (40.3%) had a cCEA level ≥100 µg/L, with a median value of 71.6 µg/L (range 1-8809); corresponding values in CEACAM5 MEs were 7/28 (25.0%) and 12.4 µg/L (range 0.5-684). In response evaluable CEACAM5 HEs with available BL cCEA data (n = 61), ORR was 10/24 (41.7%) in pts with high cCEA (≥100 µg/L) and 3/37 (8.1%) in pts with low cCEA (<100 µg/L); corresponding ORRs in CEACAM5 MEs were 0/7 and 2/21 (9.5%). Conclusions In CEACAM5 HEs, high cCEA was associated with numerically greater ORR vs low cCEA (41.7% vs 8.1%). Associations were also observed between: cCEA and cCEACAM5; IHC CEACAM5, cCEA, and cCEACAM5; and IHC CEACAM5 and CEACAM5 tumor mRNA levels, but not between IHC CEACAM5 and actionable oncogenic drivers. Clinical Trials Registration: ClinicalTrials.gov NCT02187848 Citation Format: Anas Gazzah, Joon Sang Lee, Emma Wang, Nils Ternès, Hong Wang, Eric Boitier, Aude Lartigau, Mustapha Chadjaa, Colette Dib, Gaëlle Muzard, Sandrine Valence, Anne Remaury, Cintia C. Palu, Anne-Laure Bauchet. Biomarker analysis from Phase 1/2 study of tusamitamab ravtansine (SAR408701) in patients with advanced non-small cell lung cancer (NSCLC) [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 CT213.

Performance of Single Cell RNA sequencing (scRNA-seq) experiments depends on multiple factors including the number of cells loaded, cell recovery rates, and sequencing coverage. We optimized such factors in 10x Genomics gene expression profiling and compared targeted panels to whole transcriptome sequencing using Human donor PBMC samples. We expect that our findings will apply to scRNA-seq studies of other sample types as well. Optimizing cell recovery in scRNA-seq experiments is essential in order to ensure that changes in low-abundance cell populations (e.g. Treg) can be accurately quantified. Statistical analysis indicated that at least 100 cells are needed to assess cell-type specific state changes. We expect to recover 40-65% of cells loaded into the 10x chip. We found that loading 20k-30k cells/lane combined with cell hashtags for improved doublet detection improved cell recovery over the recommended loading of 10K-16K cells. We also surveyed internal single cell experiments and observed variable cell recovery across different sample types. The amount of ambient RNA in the library was correlated with lower performance in single cell studies, suggesting that cell death or damage was causing lower recovery. We found that using PBS for cell resuspension (which is less likely to cause cell rupture) performs as well as the nuclease-free water suggested by 10x. We also assessed the impact of sequencing depth and protocol on scRNA-seq data quality. Sequencing depth impacts both experiment cost and data quality due to dropouts. We performed computational simulations of lower depth sequencing by subsampling various number of reads from PBMC experiments to obtain coverages of 10K, 20K, 40K and 80K reads per cell. We observed that 40K reads per cell, yielding about 70% sequencing saturation, provided a good balance between cost and sequencing depth. Finally, we compared dropout rates and cell type annotation for two targeted panels, the Human Gene Signature and Human Immunology panels to whole transcriptome scRNAseq. On average we detected only 200 genes out of over 1000 represented in each panel. Dropout rates for targeted panels were only reduced at low coverage; at read depths higher than 40K reads per cell the whole transcriptome and targeted panels had similar dropout rates. Both methods detected major immune cell types, but targeted sequencing could not accurately identify some subtypes due to low number of detected genes. We conclude that for immune cell profiling whole-transcriptome analysis at coverage of 40K reads per cell or higher with inputs of 20k-30k cells and use of sample hashtag antibodies provides the best balance of experiment cost, cell recovery and transcriptome coverage. Although these guidelines were established for Human PBMCs we expect similar outcomes with other complex cell mixtures such as dissociated tissues. Citation Format: Amir Bayegan, Julien Tessier, Emma Wang, Adalis Maisonet, Shu Yan, Shannon McGrath, Donald G. Jackson, Jack Pollard. Practical guidelines for the design of single cell sequencing studies [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 762.

Abstract Accurate classification of cells in a population is important for most immune phenotyping applications. Surface protein marker expression measured with multiplexed platforms like Flow Cytometry and sequential gating strategies are typically employed to classify cells. However, new single-cell RNA sequencing (scRNA-seq) methods that measure RNA expression alone or Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) methods that concurrently measure both surface protein and RNA expression in single cells are providing new data and opportunities to develop methods both to classify cells and to assess their states of activation. Traditional manual gating methods using biaxial plots, where one designates a threshold separating a bimodal distribution through visual approximation, are subjective and can vary widely between individuals. Instead of manual, sequential gating for cell classification, we have implemented a probabilistic Gaussian Mixture Model (GMM) and K-means clustering to separate cell populations based on a library of marker thresholds for a variety of cell types present in human peripheral blood mononuclear cells (PBMCs) using a CITE-seq ADT expression matrix that has been preprocessed with centered-log-ratio (CLR) normalization. Furthermore, we coupled this GMM method with a hierarchical approach for cell typing. We have developed a Cell ID Matrix that outlines over 40 different immune cell types using consensus markers applicable to immune cell types across various studies. In this typing method, major cell types are identified following positive and negative marker expression as described in the Cell ID Matrix. Hierarchical gating of cells can then be used to identify subpopulations in a principled manner. The information in the Cell ID matrix is input as a binary matrix for each hierarchy level and is matched to the binarized expression values for classifying the cells. This approach can also be extended to CITE-seq datasets from disassociated tumor cells (DTCs). We conclude that automated cell gating with CITE-seq data may enhance the classification of numerous cell types in addition to significantly decreasing the amount of human intervention and time allocated for gating cells. Identifying cell states is a work in progress and will supplement this technique in the future. Citation Format: Lila Fakharzadeh, Julien Tessier, Shannon McGrath, Emma Wang, Angelique Biancotto, Alexei Protopopov, Jack Pollard, Joon Sang Lee. Improving cell type and state classification in CITE-seq using probabilistic models and hierarchical cell ID matrix [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2279.

Immune checkpoint blockade elicits durable anti-cancer responses in the clinic, however a large proportion of patients do not benefit from treatment. Several mechanisms of innate and acquired resistance to checkpoint blockade have been defined and include mutations of MHC I and IFNγ signaling pathways. However, such mutations occur in a low frequency of patients and additional mechanisms have yet to be elucidated. In an effort to better understand acquired resistance to checkpoint blockade, we generated a mouse tumor model exhibiting in vivo resistance to anti-PD-1 antibody treatment. MC38 tumors acquired resistance to PD-1 blockade following serial in vivo passaging. Lack of sensitivity to PD-1 blockade was not attributed to dysregulation of PD-L1 or β2M expression, as both were expressed at similar levels in parental and resistant cells. Similarly, IFNγ signaling and antigen processing and presentation pathways were functional in both parental and resistant cell lines. Unbiased gene expression analysis was used to further characterize potential resistance mechanisms. RNA-sequencing revealed substantial differences in global gene expression, with tumors resistant to anti-PD-1 displaying a marked reduction in expression of immune-related genes relative to parental MC38 tumors. Indeed, resistant tumors exhibited reduced immune infiltration across multiple cell types, including T and NK cells. Pathway analysis revealed activation of TGFβ and Notch signaling in anti-PD-1 resistant tumors, and activation of these pathways was associated with poorer survival in human cancer patients. While pharmacological inhibition of TGFβ and Notch in combination with PD-1 blockade decelerated tumor growth, a local mRNA-based immunotherapy potently induced regression of resistant tumors, resulting in complete tumor remission, and resensitized tumors to treatment with anti-PD-1. Overall, this study describes a novel anti-PD-1 resistant mouse tumor model and underscores the role of two well-defined signaling pathways in response to immune checkpoint blockade. Furthermore, our data highlights the potential of intratumoral mRNA therapy in overcoming acquired resistance to PD-1 blockade.

Hormone or endocrine therapy is the primary treatment for estrogen receptor-positive (ER+) breast cancer. Selective Estrogen Receptor Modulators (SERMs) have been used for women with ER+ invasive cancer in the adjuvant setting. However, they do not fully inhibit ER transcriptional activity. Therefore, selective estrogen receptor degraders (SERDs) such as fulvestrant have been developed to overcome the partial modulation of ER transcriptional activity by SERMs. However, the clinical benefit of fulvestrant is limited by its pharmaceutical properties, and burden of intramuscular administration. We developed amcenestrant (SAR439859), a novel, orally bioavailable SERD to improve drug properties and is under clinical investigation.To assess the relative ER modulating activity, we developed a gene signature by transcriptional profiling of multiple ER+ cell lines. We identified 87 genes that were either up- or down-regulated by estradiol and reversed by SERM and SERD compounds or differentially expressed between a SERM/SERD compound and estradiol. We assessed ER activity scores by applying Gene Set Variation Analysis (GSVA) method to the ER gene signature and evaluated the effect of 6 SERD compounds. Amcenestrant and fulvestrant illuminated a deeper inhibition of ER activity compared to the other compounds. In addition, these ER activity scores could potentially be utilized in clinics as pharmacodynamic (PD) biomarker to assess target engagement. We applied this ER signature to RNA-seq data from paired pre- and post-treatment tumor biopsies from patients of NCT03284957 study (AMEERA-1), an open-label, phase 1/2 study of amcenestrant in postmenopausal women with ER+/HER2- metastatic breast cancer. The results of our analysis showed that ER activity is down-regulated by amcenestrant (oral SERD) for 3 out of 5 patients, in parallel with clinical benefit. Validation in a larger patient cohort is needed. Citation Format: Joon Sang Lee, Maysoun Shomali, Monsif Bouaboula, Emma Wang, Wilson Dos-Santos-Bele, Colette Dib, Eric Boitier, Vasiliki Pelekanou, Nils Ternes, Alice Gosselin, Patrick Cohen, Marina Celanovic, Christopher Soria, Alexei Protopopov, Jack Pollard. Development of a gene signature assessing ER modulation by SERMs and SERDs as a target engagement biomarker for endocrine therapy in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 406.

Abstract KRas mutations are frequently seen in solid tumors such as lung (~25%), colorectal (~40%) and pancreatic (~90%) cancers. The G12C mutation accounts for ~50% of all KRas mutations in lung cancer and to a lower percentage in other tumor types. Recently, selective inhibitors targeting the KRas-G12C mutation through covalent link to the cysteine of the mutated protein have been advanced to early clinical development and exhibited encouraging response in patients with lung cancer. Here, we present a novel small molecule compound that specifically inhibits KRas-G12C in vitro (cell lines) and in vivo (cell line-derived xenograft tumor models). The anti-tumor activity of D-1553 is evaluated across a panel of cancer cell lines including lung, pancreatic and colorectal cancers and shown to be active only in cancer cells with KRas-G12C mutation. In addition, D-1553 is highly potent in vivo in various cell line-derived xenograft tumor models with KRas-G12C mutation as a single agent. When D-1553 is combined with MEK inhibitor, SHP2 inhibitor or cytotoxic agents, further tumor growth inhibition or regression was observed in tumor xenograft models. These data suggest that D-1553 has anti-tumor activity in a broad spectrum of cancers and is suitable for tissue-agnostic clinical development targeting cancers with KRas-G12C mutation. D-1553 is currently in a Phase 1/2 clinical trial evaluating safety, tolerability, PK and efficacy in patients with advanced solid tumors harboring KRasG12C mutation (NCT04585035). Citation Format: Zhe Shi, Jifang Weng, Xiaochong Fan, Emma Wang, Qingqing Zhu, Liangshan Tao, Zixing Han, Zhenwu Wang, Haotao Niu, Yueheng Jiang, Ling Zhang, Xing Dai, Yaolin Wang. Discovery of D-1553, a novel and selective KRas-G12C Inhibitor with potent anti-tumor activity in a broad spectrum of tumor cell lines and xenograft models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 932.

Abstract Single cell characterization of protein epitopes is usually associated with fluorescent Flow Cytometry, which can be both costly and time consuming to optimize for multiple epitopes. To overcome the limitations of this method, we introduce Genomic Cytometry that utilizes cellular indexing of transcriptomes and protein epitopes by next-generation sequencing (CITE-seq) technology instead of flow. Genomic Cytometry is based on staining cells with antibodies conjugated to unique 15-bp DNA barcodes instead of fluorophores. After sequencing and data analysis the barcode read number can be linked to protein abundance. We utilized Genomic Cytometry to simultaneously analyze over 50 different protein biomarkers in a single scalable workflow. The application is ideal for characterization of heterogeneous cell populations such as tumors including their immune content. In addition to profiling a number cell surface protein markers that exceeds capabilities of traditional flow cytometry the technology allows for assessment of single cell gene expression data to link protein and genomic data sets. Corresponding pre-analytical and 10X Genomics-based analytical workflows were established for human PBMC samples, as well as for solid tissue including tumor specimens derived from mouse syngeneic tumor models. Data analysis pipelines were developed and validated that includes custom scripts. We ran a number of cross-platform validation studies (mouse and human cells) and found that the Genomic Cytometry is highly correlative to fluorescent flow. Multiplex methods were implemented to allow pooling of samples to reduce biases related to sample manipulation. The method enables large-scale single-cell sequencing experiments. In proof of concept experiments, a number of preclinical models, such as MC38 and H22, were deeply analyzed using CITE-seq approach. Genomic Cytometry enables rapid and cost effective characterization of both immune-oncology (I-O) models and modes of action of new I-O molecules in order to develop more differentiated therapeutics. Citation Format: Joon Sang Lee, Shannon McGrath, Emma Wang, Maximilian Rogers-Grazado, Yu-an Zhang, Natalia Malkova, Jack Pollard, Alexei Protopopov. Genomic cytometry characterization of preclinical models for development of immune-oncology therapeutics [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 174.

Despite the significant clinical success of the Immuno-Oncology treatment of microsatellite instability (MSI) patients, there remains huge unmet medical need because of mechanisms of resistance. Through an effort to analyze publicly available large-scale shRNA screening and CRISPR screening, we surprisingly found that WRN is a potent synthetic lethality target in the context of MSI. WRN is an enzyme known as the “Werner syndrome ATP-dependent helicase”. WRN is involved in multiple cellular functions, including DNA repair and telomere maintenance. Silencing of WRN by RNAi and CRISPRi in a panel of MSI-H cells lead to tumor cell growth inhibition in vitro and in vivo, thus demonstrated WRN is a novel synthetic lethality target in context of MSI. Our discovery has recently been cross-validated by multiple independent studies1-4. Furthermore, we also report here the first time that silencing of WRN by RNAi and CRISPRi in DLD1, a MSI-H cell, does not lead to tumor cell inhibition. Taken together, our data using independent genetic approach CRISPRi in vitro and in vivo, further highlight fundamental importance of WRN as a synthetic lethality target in some, but not all, MSI context. References:1 Chan E.M. et al. (2019). Nature. 568(7753):551-556. 2 Behan F.M. et al (2019). Nature. 2019 568(7753):511-516. 3 Kategaya L. et al. (2019). iScience. 13:488-497. 4 Lieb S. et al. (2019). Elife. 8. pii: e43333. Citation Format: Zhihu (Jeff) Ding, Jing Zhang, Chandra Sekhar Pedamallu, Steve Rowley, Jane Cheng, Shujia Dai, Bridget Zhou, Malvika Koundinya, Zhuyan Guo, Stephane Poirier, Joern Hopke, Amanda Lennon, Jennifer Buell, May Cindhuchao, Karen Wong, Emma Wang, Alexei Protopopov, Bailin Zhang, Dietmar Hoffmann, Fangxian Sun, Jack Pollard, Laurent Debussche, Monsif Bouaboula. Identification and validation of WRN as a novel synthetic lethality target in context of microsatellite instability [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 1377.

Introduction: Treatment of head and neck squamous cell cancer (HNSCC) frequently includes surgery, radiation, chemotherapy, targeted agents, and increasingly combination of the former with immune checkpoint inhibition. An understanding of the mechanisms mediating response to these therapies as well as biomarkers that can better tailor therapy on an individualized basis and reduce treatment-related toxicity would benefit patients. Methods: To uncover these mechanisms and biomarkers, we analyzed whole transcriptome RNA-Seq data of two different cohorts of head and neck squamous cell cancer patients: pre and post-treatment samples from radiation therapy and pre-treatment samples from anti-PD1 therapy. We evaluated the abundance of eight immune and two stromal cell populations and pathway activity changes by using Gene Set Variation Analysis (GSVA). Results and Conclusions: Following radiation genes involved in epithelial to mesenchymal transition, extracellular matrix remodeling, and wound healing are upregulated. Additionally, there is evidence of tumor microenvironment remodeling post-radiation consistent with increased fibroblast abundance and decreased immune cell infiltration. Furthermore, innately resistant tumors to anti-PD1 also display similar signatures of gene regulation, suggesting a common mechanism mediating response to both types of treatment. Notably, these signatures are strongly correlated to activation of the transforming growth factor (TGF-β) signaling pathway and suggest that attenuating its signaling may improve anti-PD-1 response and also response to radiation treatment in head and neck squamous cell cancer. Citation Format: Joon Sang Lee, Michele Sanicola-Nadel, Alexei Protopopov, Emma Wang, Joachim Theilhaber, Tun Tun Lin, Michael Korrer, Sohini Roy, Young Jun Kim, Jack Pollard. Discovery of biomarkers predicting response to radiation and anti PD1 therapy in head and neck squamous cell cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5140.