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Introduction: Genetic variants could aid in predicting antidiabetic drug response by associating them with markers of glucose control, such as glycated hemoglobin (HbA1c). However, pharmacogenetic implementation for antidiabetics is still under development, as the list of actionable markers is being populated and validated. This study explores potential associations between genetic variants and plasma levels of HbA1c in 100 patients under treatment with metformin. Methods: HbA1c was measured in a clinical chemistry analyzer (Roche), genotyping was performed in an Illumina-GSA array and data were analyzed using PLINK. Association and prediction models were developed using R and a 10-fold cross-validation approach. Results: We identified genetic variants on SLC47A1, SLC28A1, ABCG2, TBC1D4, and ARID5B that can explain up to 55% of the interindividual variability of HbA1c plasma levels in diabetic patients under treatment. Variants on SLC47A1, SLC28A1, and ABCG2 likely impact the pharmacokinetics (PK) of metformin, while the role of the two latter can be related to insulin resistance and regulation of adipogenesis. Conclusions: Our results confirm previous genetic associations and point to previously unassociated gene variants for metformin PK and glucose control.

Cancer metastasis is the most lethal attribute of cancer, frequently caused by insufficient therapies and limited therapy options. Novel compounds able to interfere with metastasis formation are therefore of tremendous interest. Colorectal Cancer (CRC) is the third most prevalent and second most lethal cancer worldwide. In CRC, metastasis formation is linked to poor patient survival and treatment failure. Up to 90% of CRC related deaths are attributed to metastasis formation. Identification of causative drivers of metastasis represents the basis for effective anti-metastatic therapy. Our lab newly identified the gene MACC1 (Metastasis Associated in Colon Cancer 1). MACC1 is a key causal molecule for tumor progression and metastasis formation. It is a stage independent prognostic marker, predicting the onset of metastasis in stages I, II and III, based on tumor tissue analyses or blood-based tests. MACC1 promotes several cancer hallmark capabilities, providing cells with a malignant phenotype. Further, MACC1 has been established as a prognostic and predictive biomarker for metastasis in CRC and more than 20 other solid cancer entities. We therefore searched for novel compounds targeting MACC1 transcription. A high-throughput screen with more than 118,500 compounds (EMBL, Heidelberg), employing CRC cells stably transfected with a MACC1 promoter-luciferase reporter construct, revealed a Tetrazolo-pyridazine based compound as a promising lead for effective inhibition of MACC1 expression. We demonstrated that several SAR and MedChem-generated analogues of our lead compound effectively inhibit MACC1 gene expression and MACC1 driven cancer cell motility in vitro in CRC and cross entity cell lines. Further, they inhibit MACC1-induced tumor progression and metastasis in vivo in CRC xenograft models in mice. Moreover, ADMET studies were conducted, confirming our compounds are likely to be orally active drug with high stability in human plasma, low plasma protein binding and great permeability with neglectable efflux in MDR1-MDCKII assay. Through RNA-sequencing and subsequent gene set enrichment analysis a hypothesis on the mode of action was formed. An immune pathway has been identified as the most promising signaling pathway targeted by these compounds, which is currently explored through knock down and signaling studies. Taken together, this novel class of small molecules represents promising candidates for anti-metastatic therapy in CRC and other solid cancer patients in a personalized medicine setting. Citation Format: Yan Shixian, Paul Schöpe, Joe Lewis, Kerstin Putzker, Ulrike Uhrig, Edgar Specker, Jens von Kries, Hector E. Sanchez-Ibarra, Anke Unger, Mia Zischinsky, Bert Klebl, Peter Lindemann, Wolfgang Walther, Marc Nazaré, Dennis Kobelt, Ulrike Stein. Novel Tetrazolo-pyridazine based MACC1 inhibitors are promising for anti-metastatic therapy [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 2471.

The leading cause of cancer related deaths is the formation of metastasis, frequently caused by insufficient therapies and limited therapy options. Novel compounds able to interfere with metastasis formation are therefore of tremendous interest. Colorectal Cancer (CRC) is the third most prevalent and second most lethal cancer worldwide. In CRC, metastasis formation is linked to poor patient survival and treatment failure. Up to 90% of CRC related deaths are attributed to metastasis formation. Identification of causative drivers of metastasis represents the basis for effective anti-metastatic therapy. Our lab newly identified the gene MACC1 (Metastasis Associated in Colon Cancer 1) in 2009. Since then, MACC1 has been established as a key causal molecule for tumor progression and metastasis formation. It was shown that MACC1 can function as a stage independent prognostic marker, predicting the onset of metastasis in stages I, II and III, based on tumor tissue analyses or through a blood based test with an accuracy of up to 85%. MACC1 promotes several cancer hallmark capabilitites, providing cells with a malignant phenotype. Further, MACC1 has been established as a prognostic and predictive biomarker for metastasis in CRC and more than 20 other solid cancer entities. We therefore searched for novel compounds targeting MACC1 transcription. A high-throughput screen employing HCT116 cells stably transfected with a MACC1 promoter-luciferase reporter construct with more than 118,500 compounds was conducted at the EMBL in Heidelberg. The screen revealed a Tetrazolo-pyridazine based compound as a promising lead for effective inhibition of MACC1 expression. We demonstrated that several SAR and Medchem-generated analogues of our lead compound effectively inhibit MACC1 gene expression and MACC1 driven cancer cell motility in vitro in CRC and cross entity cell lines. Further, they inhibit MACC1-induced tumor progression and metastasis in vivo in a CRC xenograft model in mice. Moreover, ADMET studies were conducted, confirming our compounds are likely to be orally active drugs with high stability in human plasma, low plasma protein binding and great permeability with neglectable efflux in MDR1-MDCKII assay. Through RNA-sequencing and subsequent gene set enrichment analysis a first hypothesis on the mode of action was shaped. An immune pathway has been identified as the most promising signaling pathway targeted by these compounds and is currently explored through knock down and signaling studies. Taken together, this novel class of small molecules represents promising candidates for anti-metastatic therapy in CRC and other solid cancer patients in a personalized medicine setting. Funding: This study is adviced and financed by the SPARK BIH Program Citation Format: Paul Curtis Schöpe, Shixian Yan, Dennis Kobelt, Joe Lewis, Kerstin Putzker, Ulrike Uhrig, Edgar Specker, Jens Peter von Kries, Mathias Dahlmann, Hector E Sanchez-Ibarra, Anke Unger, Mia-Lisa Zischinsky, Bert Klebl, Peter Lindemann, Wolfgang Walther, Marc Nazaré, Ulrike Stein. Novel tetrazolo-pyridazine based MACC1 transcriptional inhibitors as promising anti-metastatic therapy [abstract]. In: Proceedings of the AACR Special Conference: Cancer Metastasis; 2022 Nov 14-17; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;83(2 Suppl_2):Abstract nr A039.

espanolAntecedentes: la presencia de mutaciones clinicamente relevantes en losgenes KRAS y NRAS determina la respuesta de la terapia con anticuerpos anti-receptor del factor de crecimiento epidermico para el cancer colorrectal metastasico (mCRC). Las unicas pruebas de diagnostico basadas en la reaccion en cadena de la polimerasa cuantitativa (qPCR) aprobadas por la Administracion de Alimentos y Medicamentos (FDA) evaluan simplemente las mutaciones en los codones 12 y 13 de KRAS . Objetivo: El objetivo del estudio fue estudiar la frecuencia de mutaciones clinicamente relevantes en losgenes KRAS y NRAS que no estan incluidas en las pruebas qPCR aprobadas por la FDA. Metodos: Se analizaron muestras tumorales incluidas en parafina fijadas con formalina de 1113 pacientes mexicanos con CCRm de diferentes instituciones de salud del pais mediante secuenciacion de Sanger para lasmutaciones de KRAS en los exones 2, 3 y 4. Ademas, se analizaron 83 en los exones 2, 3 y 4 de NRAS . Resultados: De las muestras analizadas para KRAS , el 33,69% albergaba una mutacion. De estos, el 71,77% estaban en el codon 12 y el 27,69% en el codon 13 (ambos ubicados en el exon 2). Los codones 59 (exon 3) y 146 (exon 4) representaron el 0,54% restante. De las 83 muestras, en las quese analizo NRAS , se encontraron tres mutaciones en el codon 12 (3,61%). Aproximadamente el 6% de las muestras mutadas de RAS se habrian informado falsamente como RAS de tipo salvaje si se hubiera utilizado una prueba de diagnostico de qPCR aprobada por la FDA. Conclusiones: Si bien estos kits basados en qPCR pueden ser muy practicos y altamente sensibles, su cobertura de mutacion ignora las mutaciones de poblaciones mal caracterizadas geneticamente. EnglishBackground: The presence of clinically relevant mutations in KRAS and NRAS genes determines the response of anti-epidermal growth factor receptor antibody therapy for metastatic colorectal cancer (mCRC). The only quantitative polymerase chain reaction (qPCR)-based diagnostic tests approved by the Food and Drug Administration (FDA) screen merely for mutations in codons 12 and 13 of KRAS. Objective: The objective of the study was to study the frequency of clinically relevant mutations in KRAS and NRAS genes that are not included in FDA-approved qPCR tests. Methods: Formalin-fixed paraffin-embedded tumor specimens from 1113 mCRC Mexican patients from different health institutions across the country were analyzed by Sanger sequencing for KRAS mutations in exons 2, 3, and 4. Furthermore, 83 were analyzed in exons 2, 3, and 4 of NRAS. Results: From the specimens tested for KRAS, 33.69% harbored a mutation. From these, 71.77% were in codon 12 and 27.69% in codon 13 (both located in exon 2). Codons 59 (exon 3) and 146 (exon 4) accounted for the remaining 0.54%. From the 83 specimens, in which NRAS was analyzed, three mutations were found in codon 12 (3.61%). Approximately 6% of RAS mutated specimens would have been falsely reported as RAS wild type if an FDA-approved qPCR diagnostic test had been used. Conclusions: While these kits based on qPCR can be very practical and highly sensitive, their mutation coverage ignores mutations from poorly genetically characterized populations.

Mutations in KRAS, NRAS, and BRAF (RAS/BRAF) genes are the main predictive biomarkers for the response to anti-EGFR monoclonal antibodies (MAbs) targeted therapy in metastatic colorectal cancer (mCRC). This retrospective study aimed to report the mutational status prevalence of these genes, explore their possible associations with clinicopathological features, and build and validate a predictive model. To achieve these objectives, 500 mCRC Mexican patients were screened for clinically relevant mutations in RAS/BRAF genes. Fifty-two percent of these specimens harbored clinically relevant mutations in at least one screened gene. Among these, 86% had a mutation in KRAS, 7% in NRAS, 6% in BRAF, and 2% in both NRAS and BRAF. Only tumor location in the proximal colon exhibited a significant correlation with KRAS and BRAF mutational status (p-value = 0.0414 and 0.0065, respectively). Further t-SNE analyses were made to 191 specimens to reveal patterns among patients with clinical parameters and KRAS mutational status. Then, directed by the results from classical statistical tests and t-SNE analysis, neural network models utilized entity embeddings to learn patterns and build predictive models using a minimal number of trainable parameters. This study could be the first step in the prediction for RAS/BRAF mutational status from tumoral features and could lead the way to a more detailed and more diverse dataset that could benefit from machine learning methods.