The structure of KRASG12C bound to divarasib highlights features of potent switch-II pocket engagement

<i>PIK3CA</i> mutations in nasopharyngeal carcinoma

Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome

Canine cancer patients represent an important translational model for human cancer research with their spontaneous tumor pathogenesis and the many histological, genetic, molecular and clinical similarities between canine and human tumors. Canine tumors provide an opportunity to investigate shared biomarkers and their response to therapeutic interventions over an accelerated timeline compared to human cancers due to differences in expected lifespan and other factors. Dogs, like humans, have three related RAS genes, HRAS, KRAS, and NRAS, with extremely similar genetic sequence and resultantly high protein homology. These proteins have many key molecular functions including roles in cell proliferation and survival. Across multiple human cancer types, recurrent gain-of-function missense mutations in the RAS-family genes occur primarily at codons 12, 13, and 61. These mutations result in constitutively active RAS proteins. The frequencies of mutations across the three RAS genes and codons have been hypothesized to lead to distinct biological behaviors. Mutations are most common in KRAS, then NRAS followed by HRAS. We investigated RAS-family genetic mutations in a dataset of 213 canine patients with hemangiosarcoma (HSA), a common, aggressive malignancy derived from endothelial cells for which treatment with both surgery and traditional chemotherapies often fail to prolong survival beyond six months. Recent publications show that genomic profiling of canine HSA tumors can help group tumors into subtypes with potential for guiding therapy selection and improving outcomes. All 213 dogs had tumor tissue sequenced via the FidoCure® Precision Medicine Platform targeted sequencing panel and were followed until time of death or were censored to the date patients were last known to be living. All SNVs and Indels detected were missense mutations. Unlike human pan-cancer data, somatic mutations in NRAS (n=32) were most common with only 1 dog with a somatic KRAS mutation and no dogs with somatic HRAS mutations. Similar to human RAS mutations, affected codons were primarily codon 61 (n=28), codon 13 (n=5), and codon 12 (n=1). Pairwise survival comparisons between dogs with missense mutations in NRAS codon 61 showed that this mutation was associated with better prognosis (n=28, MST 251 [CI 213;327]) compared to dogs with missense mutations at other codons in the NRAS and KRAS genes (n=5, MST 66 [CI 64;NA], p=1.58E-05) and compared to dogs without missense mutations in the RAS family of genes (n=180, MST 123 [CI 100;149], p=0.036). These preliminary findings of similarities between canine and human tumors and associations between mutated codons and prognosis in our growing dataset create an important opportunity to accelerate cancer research, including clinical and biopharmaceutical studies, with great potential benefit to both dogs and humans. Citation Format: Michelle E. White, Garrett Harvey, Lucas Rodrigues, Chase Schwalbach, Dorothy Girimonte, Aubrey Miller, Abigail Hull, Lindsay Lambert, Christina Lopes, Gerald Post. Canine hemangiosarcoma as a model for RAS-mutated human cancers: Preliminary data [abstract]. In: Proceedings of the AACR Special Conference: Targeting RAS; 2023 Mar 5-8; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Res 2023;21(5_Suppl):Abstract nr A005.

1553 Background: Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. Methods: We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient’s response to a variety of therapies in multiple cancer types, importantly, without training on previous treatment response data. Consequently, in addition to its ability to predict patients' response to approved and well-studied therapies, ENLIGHT can predict the response to new treatments in early development, even before clinical data has accumulated. Accordingly, we study ENLIGHT in two translationally relevant scenarios: Personalized Oncology (PO), aimed at prioritizing approved treatments to a given patient, and Clinical Trial Design (CTD), selecting the subset of most likely responders in a patient cohort. Results: Evaluating ENLIGHT’s performance on 21 blinded clinical trial datasets spanning 11 indications and 15 different drugs in the PO setting, we show that it can effectively predict a patient’s treatment response across multiple therapies and cancer types, with an overall odds ratio of 2.59 ( p=3.41e-8), and a 36% increase in response rate over the baseline ( p=3.30e-13). Its prediction accuracy is better than other state-of-the-art transcriptomics-based signatures. Unlike most signatures that are prognostic or provide insights for only very few, specific treatments, ENLIGHT provides matching scores to a broad range of treatments. Quite strikingly, its performance is comparable to that of supervised predictors developed for specific indications and drugs. In combination with the IFN-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, our results show that by excluding non-responders ENLIGHT can enhance clinical trial success for immunotherapies and other monoclonal antibodies, achieving &gt; 90% of the response rate attainable under an optimal exclusion strategy. Conclusions: ENLIGHT is a powerful transcriptomics-based precision oncology pipeline developed by Pangea Biomed that broadly predicts response to both extant and novel targeted and immune therapies, going beyond context-specific biomarkers.

Background: Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. Methods: We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient’s response to a variety of therapies in multiple cancer types, importantly, without training on previous treatment response data. Consequently, in addition to its ability to predict patients' response to approved and well-studied therapies, ENLIGHT can predict the response to new treatments in early development, even before clinical data has accumulated. Accordingly, we study ENLIGHT in two translationally relevant scenarios: Personalized Oncology (PO), aimed at prioritizing approved treatments to a given patient, and Clinical Trial Design (CTD), selecting the subset of most likely responders in a patient cohort. Results: Evaluating ENLIGHT’s performance on 21 blinded clinical trial datasets spanning 11 indications and 15 different drugs in the PO setting, we show that it can effectively predict a patient’s treatment response across multiple therapies and cancer types, with an overall odds ratio of 2.59 (p=3.41e-8), and a 36% increase in response rate over the baseline (p=3.30e-13). Its prediction accuracy is better than other state-of-the-art transcriptomics-based signatures. Unlike most signatures that are prognostic or provide insights for only very few, specific treatments, ENLIGHT provides matching scores to a broad range of treatments. Quite strikingly, its performance is comparable to that of supervised predictors developed for specific indications and drugs. In combination with the IFN-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, our results show that by excluding non-responders ENLIGHT can enhance clinical trial success for immunotherapies and other monoclonal antibodies, achieving &amp;gt; 90% of the response rate attainable under an optimal exclusion strategy. Conclusion: ENLIGHT is a powerful transcriptomics-based precision oncology pipeline developed by Pangea Biomed that broadly predicts response to both extant and novel targeted and immune therapies, going beyond context-specific biomarkers. Citation Format: Gal Dinstag, Eldad D. Shulman, Efrat Elis, Doreen S. Ben-Zvi, Omer Tirosh, Eden Maimon, Isaac Meilijson, Emmanuel Elalouf, Boris Temkin, Philipp Vitkovsky, Eyal Schiff, Danh-Tai Hoang, Sanju Sinha, Nishanth Ulhas Nair, Joo Sang-Lee, Alejandro A. Schäffer, Ze'ev Ronai, Dejan Juric, Andrea B. Apolo, William L. Dahut, Stanley Lipkowitz, Raanan Berger, Razelle Kurzrock, Antonios Papanicolau-Sengos, Fatima Karzai, Mark R. Gilbert, Kenneth Aldape, Padma S. Rajagopal, Tuvik Beker, Eytan Ruppin, Ranit Aharonov. Prediction of patient response to targeted and immunotherapies from the tumor transcriptome in a wide set of indications and clinical trials [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 957.

Protein tyrosine phosphatase non-receptor type 1 (PTPN1), a member of the protein tyrosine phosphatase superfamily, has been identified as an oncogene and therapeutic target in various cancers. However, its precise role in determining the prognosis of human cancer and immunological responses remains elusive. This study investigated the relationship between PTPN1 expression and clinical outcomes, immune infiltration, and drug sensitivity in human cancers, which will improve understanding regarding its prognostic value and immunological role in pan-cancer. The PTPN1 expression profile was obtained from The Cancer Genome Atlas and Cancer Cell Line Encyclopedia databases. Kaplan-Meier, univariate Cox regression, and time-dependent receiver operating characteristic curve analyses were utilized to clarify the relationship between PTPN1 expression and the prognosis of pan-cancer patients. The relationships between PTPN1 expression and the presence of tumor-infiltrated immune cells were analyzed using Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data and Tumor Immune Estimation Resource. The cell counting kit-8 (CCK-8) assay was performed to examine the effects of PTPN1 level on the sensitivity of breast cancer cells to paclitaxel. Immunohistochemistry and immunoblotting were used to investigate the relationship between PTPN1 expression, immune cell infiltration, and immune checkpoint gene expression in human breast cancer tissues and a mouse xenograft model. The pan-cancer analysis revealed that PTPN1 was frequently up-regulated in various cancers. High PTPN1 expression was associated with poor prognosis in most cancers. Furthermore, PTPN1 expression correlated highly with the presence of tumor-infiltrating immune cells and the expression of immune checkpoint pathway marker genes in different cancers. Furthermore, PTPN1 significantly predicted the prognosis for patients undergoing immunotherapy. The results of the CCK-8 viability assay revealed that PTPN1 knockdown increased the sensitivity of MDA-MB-231 and MCF-7 cells to paclitaxel. Finally, our results demonstrated that PTPN1 was associated with immune infiltration and immune checkpoint gene expression in breast cancer. PTPN1 was overexpressed in multiple cancer types and correlated with the clinical outcome and tumor immunity, suggesting it could be a valuable potential prognostic and immunological biomarker for pan-cancer.

MEN1, which encodes the nuclear protein menin, acts as a tumor suppressor in lung cancer and is often inactivated in human primary lung adenocarcinoma. Here, we show that the inactivation of MEN1 is associated with increased DNA methylation at the MEN1 promoter by K-Ras. On one hand, the activated K-Ras up-regulates the expression of DNA methyltransferases and enhances the binding of DNA methyltransferase 1 to the MEN1 promoter, leading to increased DNA methylation at the MEN1 gene in lung cancer cells; on the other hand, menin reduces the level of active Ras-GTP at least partly by preventing GRB2 and SOS1 from binding to Ras, without affecting the expression of GRB2 and SOS1. In human lung adenocarcinoma samples, we further demonstrate that reduced menin expression is associated with the enhanced expression of Ras (p < 0.05). Finally, excision of the Men1 gene markedly accelerates the K-Ras(G12D)-induced tumor formation in the Men1(f/f);K-Ras(G12D/+);Cre ER mouse model. Together, these findings uncover a previously unknown link between activated K-Ras and menin, an important interplay governing tumor activation and suppression in the development of lung cancer.

PD-0025 Evaluation of Codon 12 and 13 KRAS Mutations as Biomarkers of Response to Panitumumab in Patients with Metastatic Colorectal Cancer

A Two-in-One Antibody against HER3 and EGFR Has Superior Inhibitory Activity Compared with Monospecific Antibodies

Genomic regions undergoing frequent alteration in human cancers often point to genes that play causal roles in oncogenesis. We have performed high-resolution analyses of copy-number alterations from 3131 cancer specimens across multiple histological types. We identified 158 regions of that are significantly altered by focal copy-number changes. Among these, 122 regions cannot be explained by the presence of a known cancer gene. Several gene families are enriched among these regions, including the BCL2 family of apoptosis regulators and the NF-κB pathway. Cell lines with amplifications of the anti-apoptotic genes MCL1 and BCL2L1 depend upon these genes for survival. Finally, we find that the majority of copy-number changes identified in individual cancer types are present across multiple cancer types. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 5759.

Long non-coding RNAs (lncRNAs) have emerging roles in many aspects of biology. In the context of cancer, it has been reported that lncRNAs can act as cancer oncogenes and are altered in various cancer types. Among them, MALAT1 is a lncRNA highly expressed in multiple cancer types and associated with disease progression in patients. MALAT1 is highly upregulated in metastatic lesions of breast cancer patients. Both genetic and pharmacologic inhibition of MALAT1 suppressed tumor epithelial to mesenchymal transition, resulted in the differentiation of breast cancers and had a dramatic impact on tumor metastasis. MALAT1 is a nuclear retained lncRNA, and as such MALAT1 is highly sensitive to RNaseH1-dependant Antisense Oligonucleotide (ASO) degradation, because RNaseH1 is also predominantly localized to the nucleus. Thus, MALAT1 represents an attractive novel therapeutic target uniquely tractable to ASOs with the potential to impact metastatic cancer progression. Here, we describe the identification and characterization of FTX-001 leading to its selection as a clinical drug candidate for the treatment of human cancers. Preclinical pharmacology, tolerability, and toxicity studies conducted in rodents and non-human primates demonstrated the safety and potency of FTX-001. FTX-001 ASO contains constrained ethyl (cEt) chemistry Gen 2.5 and was identified by screening over 2700 ASOs against MALAT1. FTX-001 showed a consistent potent inhibition of MALAT1 RNA expression in human cancer cell lines in vitro as well as in human xenograft tumor models in vivo upon systemic delivery. Good exposure, tissue distribution and tolerability profile were observed in mice, rats and non-human primates (NHP) after repeated subcutaneous (sc) administrations. Genotoxicity studies including the bacterial reverse mutation assay and in vivo micronucleus assays demonstrated that FTX-001 did not cause mutagenic or chromosome alterations at doses up to 2000 mg/kg in mice. Preliminary results from 6-weeks nonclinical toxicity testing in mice (sc administration up to 45mg/kg) and NHP (intravenous administration up to 24mg/kg) revealed no significant adverse events observed during dosing and recovery phases. No major variation of blood markers or blood count were observed. Ongoing histopathological evaluation will complete the health hazard evaluation of FTX-001. Taken together, these results demonstrate the high potency and good preclinical safety profile of FTX-001 which supports the potential to achieve good target engagement (MALAT1 RNA knock down in human tumors) at well tolerated clinical doses in cancer patients. Citation Format: Marie-Aline C. Neveu, Alexey Ravenko, Tae-Won Kim, Xiaolin Luo, Youngsoo Kim, Joanna Schmidt, Robert A. MacLeod. Preclinical development of FTX-001: first in class inhibitor of the long non-coding RNA MALAT1 [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 450.

Evaluation of MET Non-Exon-14 Mutations as Biomarkers for Immunotherapy Outcomes Across Multiple Cancer Types

383 Background: Pmab is a fully human monoclonal antibody against the epidermal growth factor receptor (EGFR). Significant improvement in progression-free survival (PFS) was demonstrated in pts with wild-type (WT) KRAS mCRC treated with pmab+FOLFOX4 (1st-line; study 20050203), pmab+FOLFIRI (2nd-line; study 20050181), and pmab monotherapy (study 20020408). In mCRC, mutations in KRAS codons 12 and 13 are established biomarkers for lack of clinical benefit to anti-EGFR therapies. We retrospectively examined individual MT KRAS codon 12 and 13 alleles as prognostic and predictive biomarkers of response in three phase 3 studies. Methods: Pts were randomized to receive FOLFOX4, FOLFIRI, or best supportive care +/- pmab 6.0 mg/kg Q2W in studies 20050203, 20050181, and 20020408, respectively. The MT KRAS codon 12 and 13 alleles (G12A, G12C, G12D, G12R, G12S, G12V, G13D) were detected using the Therascreen K-RAS Mutation Kit (Qiagen). Results: MT KRAS codon 12 and 13 alleles were detected in 40% (440/1096), 45% (486/1083), and 43% (184/427) of pts in studies 20050203, 20050181, and 20020408, respectively. MT KRAS allele distribution was conserved across studies and balanced between treatment arms. Baseline demographic and clinical features were comparable between all MT KRAS allele subgroups. Across all studies, no single MT KRAS allele was consistently prognostic for PFS or overall survival (OS) in the control arm-treated pts. Similarly, no single MT KRAS allele was a consistent positive or negative predictive factor for PFS or OS in pmab-treated pts. Only in the pmab+FOLFOX4 arm of study 20050203 were G13D (unfavorably) and G12V (favorably) significantly associated with OS. Response rates were similar between MT KRAS allele groups in the 1st- and 2nd-line mCRC treatment setting. Finally, in analyses of pts pooled from all 3 trials, only the G12A KRAS allele emerged as a significant negative predictive factor for OS. Conclusions: The lack of consistent results across three lines of therapy indicates pts with MT KRAS codon 12 or 13 alleles are unlikely to respond to pmab therapy. Currently, only pts with WT KRAS mCRC should be treated with pmab.

3581 Background: Pmab is a fully human monoclonal antibody against the epidermal growth factor receptor (EGFR). Significant improvement in progression-free survival (PFS) was demonstrated in pts with wild-type (WT) KRAS mCRC treated with pmab+FOLFOX4 (1st-line; study 20050203), pmab+FOLFIRI (2nd-line; study 20050181), and pmab monotherapy (study 20020408). In mCRC, mutations in KRAS codons 12 and 13 are established biomarkers for lack of clinical benefit to anti-EGFR therapies. We retrospectively examined individual MT KRAS codon 12 and 13 alleles as prognostic and predictive biomarkers of response in three phase 3 studies. Methods: Pts were randomized to receive FOLFOX4, FOLFIRI, or best supportive care +/- pmab 6.0 mg/kg Q2W in trials 20050203, 20050181, and 20020408, respectively. The MT KRAS codon 12 and 13 alleles (G12A, G12C, G12D, G12R, G12S, G12V, G13D) were detected using the Therascreen K-RAS Mutation Kit (Qiagen). Results: MT KRAS codon 12 and 13 alleles were detected in 40% (440/1096), 45% (486/1083), and 43% (184/427) of pts in trials 20050203, 20050181, and 20020408, respectively. MT KRAS allele distribution was conserved across studies and balanced between treatment arms. Baseline demographic and clinical features were comparable between all MT KRAS allele subgroups. There was no consistent evidence that any individual MT KRAS allele, compared to the remaining MT KRAS alleles or the entire MT KRAS group, differentially impacted PFS or overall survival (OS) in control arm-treated or pmab-treated pts. Only in the pmab+FOLFOX4 arm of study 20050203 were G13D (unfavorably) and G12V (favorably) significantly associated with OS. Response rates were similar between MT KRAS allele groups in the 1st- and 2nd-line mCRC treatment setting. Finally, in analyses of pts pooled from all 3 trials, only the G12A KRAS allele emerged as a significant negative predictive factor for OS. Conclusions: The lack of consistent results across three lines of therapy indicates pts whose tumors harbor MT KRAS codon 12 or 13 alleles are unlikely to respond to pmab therapy. Currently, only pts with WT KRAS mCRC should be treated with EGFR antibodies.

Sequences surrounding splice junctions function to recruit splicing elements to promote and suppress the inclusion of an adjacent exon. Mutations disrupting motifs around splice regions and the creation of de novo splice sites have not been functionally characterized genome-wide. Furthermore, many variants that alter the mRNA isoform have been shown to introduce splicing defects, such as premature termination codons (PTCs), which are degraded via nonsense-mediated decay (NMD). Using cancer samples across multiple cancer types, we have the unique opportunity to study the effects of somatic and germline events introduced throughout the genome and with the large dataset provided by The Cancer Genome Atlas (TCGA), we have the power to identify significant events that are influencing mRNA splicing and stability. In this study we aim to characterize splice altering variants (SAVs) and nonsense mutations that directly effect exon inclusion and mRNA degradation, respectively, utilizing the TCGA dataset consisting of both DNA-Seq (whole genome and/or exome sequencing) and RNA-seq from approximately 8,000 tumors representing 24 major cancer types. We have identified 23,615 somatic splice site mutations and 71,009 somatic nonsense mutations in 24 cancer types. For each variant, we have collected the following expression data derived from the RNA-seq: exon, splice junction, isoform, and gene; defining the expression signature of each variant. Expression signatures of known SAVs will facilitate the identification of novel and mis-classified missense and silent mutations affecting splicing. Exploring the distribution of mutations and their correlation with the aforementioned expression data will allow us to establish commonalities and differences that may be indicative of cancer type, molecular subtype, or clinical characteristics. Furthermore our position dependent analysis of nonsense mutations and their correlation with degradation will enhance our ability to determine significant sites influencing mRNA isoform presence. We hypothesize that some intronic/exonic variants outside the canonical splice site will influence exon inclusion and alter the ratio of mRNA isoforms present. Our analysis has identified a number of SAVs, including variants that were mis-classified as missense mutations such as c.190 in BRCA1. The germline coding mutation was found to strengthen a cryptic splice site and discovered to have a higher variant allele fraction in the tumor relative to the normal tissue in two ovarian samples. In conclusion our findings and the continued development of our project will contribute to improving current annotation methods and broaden our understanding of variants that affect splicing and their biological contribution to mRNA isoforms selection in the cell. Citation Format: Reyka G. Jayasinghe, Kuan-lin Huang, Jie Ning, Matthew Wyczalkowski, Charles Lu, Mingchao Xie, Michael Wendl, Michael McLellan, Kai Ye, Li Ding. Pan-Cancer analysis of the effects of splice-altering variants on mRNA splicing and stability. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1929. doi:10.1158/1538-7445.AM2015-1929