tbuckley/cabot-search huggingface dataset:
A double-blind, randomized, two-arm, single-dose, parallel-group study in healthy participants to compare the pharmacokinetics, pharmacodynamics, and immunogenicity of FKS518 proposed biosimilar to denosumab with the originator (LUMIADE-1 study).
Authors: Jouliana Sadek, Anna Dryja, Peter Szeles, Anna Lucia Buccarello, Joelle Monnet
Journal: Journal of Clinical Oncology (2024)

e15097 Background: Denosumab is a RANKL inhibitor used in patients with bone metastases from solid tumors. FKS518 is a proposed biosimilar to denosumab originator product. Biosimilars offer similar efficacy, quality, safety, and favorable economic impact. Once the analytical and functional similarity of a biosimilar has been shown, a comparative human pharmacokinetics (PK) study is conducted to demonstrate that there are no clinically meaningful differences between the biosimilar and the reference product. The present study assessed the PK equivalence of FKS518 to the originator product and provided data on the similarity of the pharmacodynamic, safety and immunogenicity profiles. Methods: A randomized, controlled, double-blind, parallel-group study in healthy volunteers was performed to compare denosumab biosimilar candidate FKS518 versus the originator product. Subjects received a single subcutaneous denosumab 60 mg injection and were followed up for 40 weeks. Primary endpoints PK parameters (Cmax, AUC0-last and AUC0-inf) were natural log transformed and analyzed using analysis of covariance methods; results were then transformed back to the original scale. Safety data were collected throughout the study and analyzed descriptively. Results: 213 male subjects 28-55 years of age, with a baseline BMI between 18.2 and 31.7 kg/m2, were randomized to the originator denosumab (n = 106) or FK518 (n = 107). Results of the primary PK analysis demonstrated bioequivalence between FKS518 and the originator denosumab product since the 90% confidence intervals (CIs) for the geometric least squares mean (LSM) ratios for the 3 primary PK parameters were completely included within the predefined 80.00% to 125.00% equivalence margin (point estimate [90%CI for the ratio]): Cmax: 104.79% [97.04%,113.15%], AUC0-last: 112.29% [104.17%,121.04%] and AUC0-inf : 112.65% [104.27%,121.70%]. Secondary PK parameters were similar as well between FKS518 and the originator: tmax (median: 217.45 hrs. vs. 216.03 hrs. respectively), t1/2 (median: 235 hrs. vs 337 hrs. respectively). The nature, frequency, severity, or resolution of adverse events were comparable across treatments. For immunogenicity, there was no difference between the 2 treatment groups in ADA and Nab incidence at each timepoint. Conclusions: PK equivalence of FKS518 and denosumab originator product was demonstrated with similar PD, safety and immunogenicity profile for the two products. This study adds to the totality of evidence supporting FKS518 as a proposed biosimilar to denosumab.
The microbiome landscape of early-onset colorectal cancer: A systematic review.
Authors: Daniel Hong, Ruiyi Tian, Hyun Kim, Michelle Doering, Yin Cao
Journal: Journal of Clinical Oncology (2024)

e15646 Background: The rise of early-onset colorectal cancer (EOCRC, age &lt; 50 years old) necessitates the examination of potential risk factors to enhance prevention and control strategies. Microbial dysbiosis has been hypothesized as a potential contributor, yet studies are limited. To guide future research, we systematically reviewed studies of the EOCRC microbiome, emphasizing methodological heterogeneities. Methods: We systematically reviewed available literature for microbiome studies involving EOCRC and synthesized findings according to comparison groups (healthy/later-onset CRC [LOCRC]), sample type (fecal /tumor), and analyses (taxonomic/metabolomic). Results: Collection, storage, extraction, and analytical methods varied across the 10 studies included out of 2073 studies. There were 377 unique EOCRC patients, of which most originated from China (193 EOCRC cases), the United States (52 EOCRC cases), and Japan (42 EOCRC cases). Six studies analyzed 224 unique EOCRC fecal samples (4 performed 16S, 5 performed WGSS, 4 performed metabolomic); five compared EOCRC against healthy individuals and five to LOCRC. Four studies utilized 153 unique EOCRC tumor samples (3 performed 16S, 1 performed WGSS, 1 performed metabolomic); two compared with tumor-adjacent normal tissue, one to colonic tissue of healthy individuals, and four to LOCRC. Among studies comparing fecal samples of EOCRC to healthy individuals, EOCRC exhibited increased levels of Fusobacterium and Escherichia-Shigella, known for metabolizing 5-Fluorouracil, while butyrate-producing taxa like Faecalibacterium, protective against CRC, were diminished; EOCRC also showed decreased lung-associated pathogens and Clostridium difficile, but increased oral pathogens. Among studies comparing fecal samples of EOCRC to LOCRC, EOCRC fecal samples were more abundant in oral/gastrointestinal pathogens, with varying findings on Fusobacterium nucleatum. Faecalibacterium prausnitzii was more abundant in EOCRC than LOCRC, whereas Escherichia-Shigella was less abundant. Fecal metabolomic studies displayed increased LPS-related pathways and bile acids in EOCRC compared to healthy individuals. Compared to tumor-adjacent normal tissues or colonic tissues of healthy individuals, EOCRC tumors were characterized by higher levels of Fusobacterium, Actinomyces, and Escherichia coli ( E. coli), fewer butyrate producers, and more Bacteroides fragilis. Conclusions: While data is limited and methodologies varied, emerging evidence suggests that EOCRC may have unique microbial signatures compared to both healthy individuals and LOCRC. This may signal unique pathogenesis for EOCRC and present potential for prevention, early detection, and intervention. It is essential to harmonize study methods and analytical approaches to advance understanding of the role of microbiome in EOCRC.
First-in-human study of HMPL-295, an ERK1/2 inhibitor, in patients with advanced solid tumors: Dose-escalation results of monotherapy.
Authors: Xianjun Yu, Jian Zhang, Yuping Sun, Yanhong Deng, Shuangshuang Guo, Wei Hong, Si Shi, Rujiao Liu, Shuiping Gao, Qi Dang, Jianwei Zhang, Weiguo Su, Michael Shi, Songhua Fan, Bo Zhang, Beiqing Pan, Xiaoyun Jia, Jian Wang, Chen Zhong, Luguang Ma
Journal: Journal of Clinical Oncology (2024)

e15112 Background: HMPL-295 (’295) is an extracellular signal-regulated kinase 1 and 2 (ERK1/2) inhibitor with potent activity in non-clinical studies. This is a first-in-human, dose escalation study of ’295 in patients (pts) with advanced solid tumors. Here, we report the results of the mono dose escalation stage. Methods: Pts with advanced solid tumors who have failed standard therapy were enrolled to receive ’295 once daily (QD) in 28-day cycles or ’295 QD 2 weeks on/1 week off (2w on/1w off) in 21-day cycles. The mTPI-2 design was used for dose escalation, having explored from 5 to 75 mg QD in 6 cohorts, as well as 75 mg and 100 mg QD 2w on/1w off. The study aims to determine the maximum tolerated dose (MTD)/recommended phase 2 dose (RP2D), safety, tolerability, pharmacokinetics/pharmacodynamics (PK/PD), and preliminary efficacy (best of response, BOR). Results: As of Dec 11, 2023, 47 pts with advanced solid tumors were enrolled (n=1, 1, 3, 6, 12, 5, 13, 6 in 5, 10, 20, 40, 50, 75 mg QD cohorts, 75, 100 mg QD 2w on/1w off cohorts, respectively), with a median age of 58 years, 23 (48.9%) male pts. During the dose escalation from 5 to 75 mg QD cohort, 1 pt given 40 mg QD experienced a dose-limiting toxicity (DLT) of grade 3 dermatitis acneiform and 2 pts given 75 mg QD experienced DLTs of grade 3 rash and acute kidney injury (AKI). As for intermittent-administration cohorts, 2 pts given 100 mg QD 2w on/1w off experienced DLTs of grade 3 AKI and rash maculo-papular. After multiple-dose oral administration, ’295 plasma concentrations reach peak at 1.0-2.0 hours post dose and the exposure (C max and AUC) displayed dose dependent increase. The accumulation ratio (AUC) was 2.1–3.4 after repeat dosing, reaching steady-state around day 15. ’295 inhibited ribosomal S6 kinase (RSK) phosphorylation which is a downstream signaling molecule regulated by ERK1/2 and stimulated by phorbol 12-myristate 13-acetate (PMA). The BOR was partial response each in 1 pt with duodenal adenocarcinoma in 40 mg QD cohort,1 pt with endometrial cancer in 50 mg QD cohort and 1 pt with non-small cell lung cancer (NSCLC) in 75 mg QD 2w on/1w off cohort. Stable disease with tumor shrinkage was also observed in 2 pts with NSCLC on 50 mg QD and 75 mg QD 2w on/1w off cohort respectively, and 1 pt with pancreatic ductal adenocarcinoma in 100 mg QD 2w on/1w off cohort. 25 (53.2%) pts reported grade ≥3 TEAEs. The most common (≥10.0%) ≥ grade 3 TEAEs were rash (10.6%) and anemia (10.6%). Conclusions: Considering safety, tolerability, PK/PD and preliminary efficacy results, RP2D was determined to be 50 mg QD. Clinical trial information: NCT04908046 .
Single-cell and bulk sequencing analysis to evaluate the oncogenic role of PDE3A in pan-cancer and validation in breast cancer.
Authors: Shengyu Pu, Bohong Chen, Huimin Zhang, Jianjun He, Na Hao
Journal: Journal of Clinical Oncology (2024)

e15128 Background: PDE3A is involved in critical physiological processes through the hydrolysis of cycle AMP (cAMP) and GMP (cGMP). Its role in the occurrence and progression of cancer has recently attracted attention, but carcinogenic mechanism in pan-cancer has not been fully elucidated. Methods: We explored the expression, prognostic and diagnostic value, mutation, methylation, immune infiltration, and signaling pathway enrichment analysis of PDE3A in pan-cancer by combining multiple bioinformatics databases and single-cell sequencing analysis. Furthermore, we verified the protein and mRNA expression levels of PDE3A and investigated its oncogenic role in the progression of breast cancer (BRCA). Results: PDE3A was expressed at lower levels than normal tissues in most cancers and showed a wonderful prognostic value. PDE3A was strongly correlated with immune cells, especially cancer-associated fibroblast (CAF), and was involved in various vital signaling pathways. Furthermore, PDE3A could facilitate BRCA progression by promoting cells invasion and migration, and it may be associated with the efficacy of endocrine therapy in ER+ BRCA. Conclusions: We provided new insights into the carcinogenic effect of PDE3A in pan-cancer. PDE3A may be a potential biomarker for cancer diagnosis and prognosis, and it is also a promising target for cancer immunotherapy.
Clinical validation of Histotype Px Colorectal in patients in a U.S. colon cancer cohort.
Authors: Vidya Arole, Sepp De Raedt, Jessica Maupin, Sandra Boutros, Anne M. Noonan, Giovanni Lujan, Eric David Miller
Journal: Journal of Clinical Oncology (2024)

3622 Background: According to current guidelines, most patients with high-risk stage II and stage III colon cancer should receive adjuvant chemotherapy (ACT). However, risk determination is controversial and most patients do not benefit from ACT. Histotype Px Colorectal is a novel artificial intelligence-based biomarker validated for R0 stage II-III colorectal adenocarcinoma that analyzes digitized routine H&amp;E-stained FFPE tumor resections. Combined with clinical parameters, patients are stratified into distinct low, intermediate, and high-risk groups. This pilot study seeks to validate the applicability of Histotype Px Colorectal in an independent cohort from the United States. Methods: This was a retrospective analysis of patients diagnosed with pathological stage II-III colon adenocarcinoma at Ohio State University from 2016-2020. Anonymized slides were collected and digitized using the Aperio AT2 scanner. Clinical parameters and outcome data were extracted from patient records. Histotype Px Colorectal biomarker was blindly applied to each scan and subsequently linked to clinical outcomes. Statistical analysis was performed using Cox proportional hazards regression analysis. The pre-specified primary outcome was cancer-specific survival with a secondary outcome of time-to-recurrence. Results: Baseline characteristics of the 159 eligible patients included median age of 63 years (range 22-91), 52% female, 52% stage III, 64% right-sided tumors, and 31% MSI. 18% of stage II and 70% of stage III patients received ACT, respectively, with FOLFOX in 61%. Median follow-up for all patients was 54.2 months. For stage II patients, 21% were classified as intermediate-risk and 79% as low-risk. Only 6 (8%) of the 76 stage II patients had a cancer-related death with 3 of those patients classified as intermediate-risk. For stage III patients, 26% were classified as high-risk, 22% as intermediate-risk, and 52% as low-risk. 18 (22%) of the 83 stage III patients had a cancer-related death with 14 of those patients classified as high or intermediate-risk. Overall, Histotype Px Colorectal was a significant predictor of cancer-specific survival with HR=7.80 (95% CI 2.96-20.56, p&lt;0.001) for high vs. low-risk patients and 2.81 (95% CI 0.98-8.04, p=0.05) for intermediate vs. low-risk patients. In addition, it was found to be a significant predictor for time-to-recurrence with HR=7.59 (3.56-16.20, p&lt;0.001) for high vs. low-risk patients and 2.67 (1.20-5.96, p=0.02) for intermediate vs. low-risk patients. Conclusions: The findings from this study highlight the potential utility of this innovative biomarker in guiding clinical decisions regarding ACT. Further research involving a larger and more diverse patient cohort and subsequent clinical studies are planned to solidify these initial findings and to enable personalized treatment strategies based on individual risk assessments.
Phase 1 study of ERK inhibitor JSI-1187 for patients with advanced solid tumors with MAPK pathway mutations.
Authors: Yuqin Xi, Zhenghang Wang, Lin Shen, Lin Wu, Yu Chen, Meiyu Fang, Mei Li, Feng Ye, Linda J. Paradiso, Thomas J. Myers, Junqiang He, Jintao Zhang, Wen Xu, Lei Zhang, Ao Li
Journal: Journal of Clinical Oncology (2024)

e15111 Background: Mitogen-activated protein kinase (MAPK) pathway is a key regulator of cellular proliferation and survival, it is highly correlated with the occurrence of many cancers, JSI-1187 is a potent and reversible small molecule inhibitor of ERK1/2. This study is designed to assess the safety and efficacy of ERK Inhibitor JSI-1187 for advanced MAPK-mutant tumors patients. Methods: This is a phase 1 study included dose escalation and expansion (NCT06239623). The dose escalation study was conducted using a 3+3 escalation design to assess JSI-1187 16, 24, 32, 48,72, 80 and 96 mg BID, and 80, 128 and 152mg QD, repeated q 28 days. Dose limiting toxicities (DLTs) were predefined in the protocol. MAPK-mutant tumor patients whose disease progressed after existing standard treatment were treated. RAS and BRAF-mutated solid malignant tumors were explored in the dose-escalation phase. Results: As of December 23, 2023, 26 patients were enrolled, including 23 from the BID group and 3 from the 80mg QD group. No DLT was observed in QD group, 1 DLT (1 macular edema) and 2 DLTs (1 macular edema and 1 pneumonia) were observed at 96 and 80 mg BID, respectively. The most common JSI-1187 16-96mg BID related AEs were hypoalbuminemia (69.6%), rash (62.5%), and anemia (43.5%), while the most common JSI-1187 80mg QD related AEs were rash (100%), leukopenia (100%) and neutropenia (66.7%). The most common Grade 3 JSI-1187 16-96mg BID treatment-related AEs were anemia (17.4%) and thrombocytopenia (8.7%). Tumor response was evaluated in 18 of the total 26 patients. SD was noted as best response at doses of 16 – 96 mg BID and 80 mg QD including 29.8% tumor shrinkage in 1 NRAS-mutant melanoma patient treated at 32mg BID and 21.6% tumor shrinkage in 1 MEK1 K57N-mutant lung cancer patient treated at 72mg BID. Conclusions: JSI-1187 monotherapy is well-tolerated in advanced solid tumor patients and demonstrated preliminary antitumor effect. Current evidence supports future exploration of JSI-1187 as monotherapy, and in combination, in patients with MAPK-mutant tumors. Research Sponsor: JS Innopharm Clinical trial information: NCT06239623 .
Experience of personalizing cancer treatment in economically-constrained and resource-restricted community oncology practice in India through cost-effective broad spectrum NGS and survival outcomes: An observational study of 3 years.
Authors: Udip Maheshwari, Dr Disha Morzaria, Vashishth Maniar, Ashish Joshi, Chirantan Bose, Kunal Jobanputra, Akshay Shivchhand, Ritu Dave, Pritam Kalaskar, Chandrashekhar Pethe, Kshitij Joshi, Pradip Kendre
Journal: Journal of Clinical Oncology (2024)

e15173 Background: NGS based mutational findings are increasingly used in oncology practice to personalize treatment strategies. We elucidate the impact of comprehensive genomic profiling and the new avenues it opens for personalized precision medicine. Methods: This was a retrospective study evaluating patients who underwent a cost-effective (&lt;US $1000), inhouse developed comprehensive NGS test. Descriptive data was tabulated &amp; mutational distribution, therapeutic strategies and survival outcomes were evaluated. Results: A total of 336 patients were evaluated from a community oncology practice over the last 3 years in Western India. The mean age was 59.2 years; with F:M being 1.04:1 and stage distribution being 3.5%, 5.9%, 9.5% and 80.9% for Stage I, II, III and IV respectively. Comprehensive NGS was performed to evaluate SNVs, CNVs, and indels spanning across over 15000 loci for 1080 tumor specific genes wherein 71.7% were tissue-based, 24.4% were liquid biopsy-based and 3.8% were combined tissue-DNA and ctDNA-based analysis. Majority (69.9%) of the patients were pre-treated while 30% were therapy naïve. The most common cancers evaluated were Lung (18.1%), Pancreatobiliary (15.1%), Breast (11.3%), Gynaec (9.5%), and Lower GI (9.5%). Pathogenic mutations were reported in 83.6% of the cohort while in 16.3% no mutations were detected. Most common mutations observed in this retrospective study were TP53 (38.3%), KRAS (16.6%), PIK3CA (9.5%), EGFR (7.4%) and PTEN (5.6%); and the distribution was largely similar in untreated as well as pre-treated cohorts. Furthermore, genomic alterations for each cancer type are described in Table 1. MSI high and TMB high (&gt;10) were reported in 0.5% and 7.4% respectively. PDL1 positive IHC was reported in 3.2% of tissue samples, whereas HRD high (&gt;50) was reported in 2.3% of the cohort of which the majority (11.5%) were from Upper GI tumors. Targetable genomic alterations were detected in 53.8% (181/336) of which 38.1%(69/181) received the suggested targeted therapy. Clinical response was seen as PR in 14.4% and CR in 2.8% of patients. Of 336 patients 4.76% received modified therapeutic regimens which differed from the standard of care, wherein median PFS was 8 mo [CI 95% (5.3-10.7)] while median OS was 55.1 mo [CI 95% (25.8-NA)]. Conclusions: This observational study outlines the potential benefit of personalizing treatment regimens in the management of solid organ cancers, especially in scenarios where standard-of-care regimens may have failed and the need to incorporate broad-spectrum NGS. [Table: see text]
A multi-modal artificial intelligence model trained on H&amp;E tissue images and clinical patient data for prediction of BRAF mutations in colorectal cancer.
Authors: Marc Paepper, Patrick Frey, David Mulder, Khalid Daifalla, Felix Faber, Tobias Lang
Journal: Journal of Clinical Oncology (2024)

e15161 Background: Analyzing genetic mutations is essential for personalized therapy decisions in oncology and beyond. In colorectal cancer (CRC), mutations in BRAF ( BRAF mut ) render patients eligible for targeted treatment with BRAF inhibitors and immunotherapy. BRAF mut is routinely analyzed via genomic sequencing, which, however, is costly and does not provide diagnostic results immediately. Reliable detection of BRAF mut status from histological images using artificial intelligence (AI) could provide a more accessible and faster diagnostic alternative. Previous approaches to image-based BRAF mut analysis, however, failed to reach a critical level of accuracy on independent test cohorts, with reported area under the receiver operating characteristic curve (AUROC) values typically below 0.8. Methods: We hypothesized that combining tissue image analysis with clinical patient data could improve diagnostic accuracy for BRAF mut . Following our hypothesis, we developed a multi-modal AI model on n = 426 CRC cases from the TCGA database using BRAF mut data, H&amp;E-stained tissue images, and information on patient parameters that are readily available in clinical practice and that are known to be associated with BRAF mut frequency. The model was based on a vision transformer architecture incorporating pathology-specific information using a pretrained embedding model for encoding tissue image features. Model performance was validated on an independent hold-out TCGA cohort of n = 107 samples and an additional external cohort of n = 104 CRC samples from the CPTAC database. Results: Our multi-modal AI model reached an AUROC of 0.85 ± 0.02 on the TCGA hold-out test set. Notably, on the second, external CPTAC dataset the predictive accuracy level was similar (AUROC of 0.83 ± 0.02) indicating the robustness of our approach to generalize to different datasets. Additional analyses showed that the significant performance boost over previous approaches was gained by the multi-modal analysis setup, as predicting BRAF mut based on either tissue, or clinical data alone led to lower accuracy values. Explainability analysis of AI results confirmed that relevant biological features on the histological slides, mainly tumor areas, were considered by the model for decision making. Conclusions: The combination of H&amp;E images with clinically readily available patient parameters enabled us to develop a model predicting BRAF mut in CRC that surpasses previous approaches in diagnostic accuracy on independent evaluation cohorts. This indicates that combining histological tissue information with clinical parameters can increase the ability of AI models to predict genetic variants from tissue and more generally highlights the potential of multi-modal deep learning approaches to enhance predictive AI models in digital pathology.