bluuebunny/crossref_metadata_2025_split huggingface dataset:
Aggregated analysis of 1,000 patients with cancer to assess the benefits of integrated whole exome and whole transcriptome sequencing.
Authors: Chernyshov Konstantin , Love Anna , Senichkin Viacheslav , Sagaradze Georgy , Rumyantsev Konstantin , Kushnarev Vladimir , Postovalova Ekaterina , Kotlov Nikita , Ogloblina Anna , Bagaev Alexander , Fowler Nathan 
Published: June 2023

3076 Background: As the field of precision medicine rapidly expands, clinical oncologists are turning to integrated whole transcriptome (WTS) and whole exome (WES) sequencing to identify the most effective treatment options. We performed a retrospective cohort analysis of WES and WTS data from 1,000 patients with 15 major cancer types to define meaningful relationships between transcriptomic-based tumor microenvironment (TME) subtypes and pan-cancer genomic biomarkers, such as tumor mutational burden (TMB) and microsatellite instability (MSI) status, that are prognostic for outcome and predictive for checkpoint inhibitor response. Methods: We retrospectively analyzed WES/WTS data from cancer patients for biomarkers using our internal bioinformatics workflow. Using transcriptome-based methods described by Bagaev et al., samples were classified into one of four TME subtypes prognostic of patient outcome ( Cancer Cell, 2021). Statistical analysis consisted of Spearman’s rank correlation. Results: Comprehensive WES and WTS analysis provided by the BostonGene Tumor Portrait Test identified biomarkers based on accepted guidelines specific to the patient's cancer type in 22.5% of cases. In addition, we revealed a correlation between WTS and IHC for cancer-relevant genes, including CDX2 (R = 0.68), KRT7 (R = 0.85), KRT20 (R = 0.86), NECTIN4 (R = 0.81), and TACSTD2 (R = 0.81), indicating WTS may also be beneficial for estimating protein expression levels for cancer-specific biomarkers. Analysis of the TME showed that Fibrotic (F) and Immune Desert (D) were the most common subtypes. The F and D subtypes were linked to poor prognosis and were especially prevalent in liver and pancreatic cancers, respectively. TMB-high and MSI status were more prevalent in the immune-enriched subtypes, Immune-Enriched, Fibrotic (IE/F) and Immune-Enriched, Non-fibrotic (IE), over the less favorable F and D subtypes. The frequency of TMB-high and MSI status also differed in both IE subtypes associated with favorable prognosis, suggesting that TME subtyping provides additional prognostic power independent of TMB-high and MSI status. Conclusions: A deep interrogation of our integrated approach to genomic and transcriptomic profiling confirmed the advantages of a unified molecular analysis of tumor traits. We identified clinically significant biomarkers based on molecular features, demonstrated additional capabilities of TME analysis, and illustrated significant correlations between WTS and IHC data. These findings indicate that comprehensive WTS and WES analysis can assist clinicians in optimizing treatment plans as the landscape of personalized therapy continues to grow. [Table: see text]
Alternative allele frequency recallibration after NGS data analysis.
Authors: Ivanov Maxim , Yakushina Valentina , Fedyanin Mikhail 
Published: June 2023

3077 Background: With the spread of NGS in routine clinical practice data analysis methods actively evolve to increase and refine information yield. Not much attention is given to variant allele fraction (VAF) estimation though it impacts clinical insignificance of subclonal somatic variants and identification of variant origin. Methods: Real-world sequencing data of 2379 samples (including 781 tumor-only and 1598 blood-only sequencing datasets) obtained via amplicon-based sequencing was used for the retrospective analysis of variant allele frequency estimation of identified genetic variants employing different methods including ITVC, SiNVICT, Mutect2 and SGA. Original method (AODvAF) utilizing alignment-free algorithm based on wild-type and alternative genome subsequence abundance estimation was developed and compared with the original output from NGS data analysis software. Anticipated VAF estimation was based on analyte type and database annotation. Results: Across 3447 identified potentially germline variations (PGV) VAF estimations provided by ITVC, SiNVICT and SGA were highly correlated (Pearson correlation coefficient [PCC] 1.00), while Mutect2 and AODvAF demonstrated discordant results (Mutect2/AODvAF vs ITVC PCC 0.87/0.82). VAF miscalculation defined by absolute difference between estimated and anticipated VAF of 10% and more was noted for 25%, 26%, 22%, 15% and 7% for ITVC, SiNVICT, SGA, Mutect2 and AODvAF respectively. Across 308 pathogenic BRCA1, BRCA2, PALB2 mutations identified in blood-only sequencing 25, 15, 16, 10 and 5 were characterized by VAF of 30% and lower based on ITVC, SiNVICT, SGA, Mutect2 and AODvAF estimations respectively (54, 49, 33, 51 and 9 for VAF 40% and lower), which may lead to misinterpretation of these variants as post-zygotic. Automatic in-silico annotation of variant origin (germline vs somatic) based on ISOWN was correct for 89%, 91%, 94%, 93% and 98% of these variants. Variant origin for 55 pathogenic variants of genes associated with hereditary cancer syndromes identified in tumor-only sequencing was verified by Sanger sequencing. Cohen's kappa coefficient for variant origin discrimination (Sanger vs in-silico prediction) was 0.91, 0.90, 0.91, 0.94 and 0.97 based on VAF provided by ITVC, SiNVICT, SGA, Mutect2 and AODvAF respectively. Conclusions: Accurate VAF calculation allows precise variant origin identification and impacts interpretation of variants. Developed method allows correction of VAF after NGS data core analysis.
In silico approaches to patient selection: Credentialing elraglusib as a novel treatment in metastatic melanoma resistant to checkpoint inhibitors.
Authors: McDermott Joseph , Weiskittel Taylor , Borden Brittany , Cavalcante Ludimila , Giles Francis J. , Carneiro Benedito A. , Li Hu , Mazar Andrew Paul 
Published: June 2023

3079 Background: Elraglusib (9-ING-41), a novel inhibitor of GSK-3β, has been evaluated in >230 patients (pts) with advanced malignancies including metastatic melanoma in the 1801 phase 1/2 trial (NCT03678883). Pts with melanoma treated with single agent elraglusib demonstrated clinical benefit including a durable complete response despite multiple prior lines of therapy including checkpoint inhibitors (CPI). Methods: We collected data from numerous -omic modalities in the 1801 trial to characterize markers of elraglusib response with bioinformatics and machine learning (ML). Genomic panels were collected on 105 pts. The most frequent tumor types included colorectal cancer (n=35), pancreas (n=31), lung (n=15) and metastatic melanoma (n=12). Pts were treated with elraglusib as a single agent or in combination with chemotherapy. Pathway based features were generated by quantifying the percent of each Reactome pathway that was mutated. These pathway features were iteratively evaluated for their performance in shallow neural networks predicting patient RECIST with each model using 5-15 randomly selected features at a time. Publicly available genomic data from an anti-PD-1 trial in patients with metastatic melanoma (Hugo et al., 2017) was transformed into pathway-based features and input into the final model to predict elraglusib response in this population. Results: Our final pathway based genomic model for elraglusib response performed with a training accuracy of 93% and a test accuracy of 88% on the 1801 patients. We then tested the model on metastatic melanoma patients who were treated with anti-PD-1 therapy. We found that patients from Hugo et al. were predicted to respond to elraglusib (22/26). In addition, most patients with tumors resistant to anti-PD-1 therapy were also predicted to respond to elraglusib (10/12). Finally, we tested the model on metastatic melanoma patients treated with elraglusib in the 1801 study that had failed CPI and for whom -omic data was available. The model correctly identified 5/5 patients that demonstrated clinical benefit (1 CR, ongoing>1400 days; 4 prolonged OS of 107, 256, 357, and 556 days). Conclusions: We applied robust in-silico modeling developed using 1801 trial data to develop a rationale for the development of elraglusib in the treatment of metastatic melanoma including patients that failed or were refractory to anti-PD-1 treatment. This model correctly identified metastatic melanoma patients in 1801 with good clinical outcomes that had failed prior CPI treatment. Further, we present this in silico approach as a robust way to repurpose prior clinical study genomics and ML modeling for population enrichment during the clinical development of novel drugs. Clinical trial information: NCT03678883 .
Intrapatient comparative efficacy of selective RET inhibitors using growth modulation index in patients with RET aberrant cancers.
Authors: Gouda Mohamed Alaa , Ahmed Jibran , Nelson Blessie Elizabeth , Nardo Mirella , Roszik Jason , Cabanillas Maria E. , Hu Mimi I-Nan , Busaidy Naifa Lamki , Sherman Steven I. , Dadu Ramona , Naing Aung , Karp Daniel D. , Rodon Ahnert Jordi , Hong David S. , Elamin Yasir Y , Blumenschein George R. , Heymach John , Meric-Bernstam Funda , Subbiah Vivek 
Published: June 2023

3086 Background: Selective RET inhibitors (RETi) have changed the paradigm for treatment of RET altered cancers. Current evidence on their efficacy comes from single-arm basket trials. In this study, we performed intrapatient efficacy comparison between approved selective RETi (selpercatinib and pralsetinib) and prior and/or subsequent therapies. We used growth modulation index (GMI) as a tool to compare efficacy, where a value of >1.33 is presumed a cutoff to establish superior efficacy. Methods: We included patients with RET-positive tumors who were treated at our institution as part of RETi clinical trials. We excluded patients with no other systemic therapy received before or after the RETi of interest. We also excluded patients who received both drugs to avoid overlapping efficacy bias, and patients who were treated for ≤ 30 days or had non-evaluable disease. Since some patients have been treated beyond progression or discontinued RETi due to intolerance, we used ratios for both time to treatment takeoff (TTT) and time to progression (TTP) to compare efficacy of RETi (at the RETi treatment line (n)) to prior therapy (n-1) and to subsequent therapy (n+1). GMI was defined as the ratio between TTT/TTT (n±1) or TTP to TTP (n±1). Results: We included 66 patients who received RETi and met our inclusion criteria [39 received selpercatinib and 27 received pralsetinib]. Most patients had GMI>1.33 using either TTT or TTP (61% (n=40) and 58% (n=38), respectively). The median GMI based on pre-RETi therapy was 2.1 and 1.6 (using TTT and TTP, respectively); while the median GMI based on post-RETi therapy was 4.9 and 4.4 (using TTT and TTP, respectively). Patients with GMI>1.33 were more likely to have PR as best response compared to patients with GMI<1.33 (85% vs 53%, p=0.005 using TTT; and 87% vs 54%, p=0.003 using TTP). GMI using TTT (n/n-1) was higher in patients with RET fusions compared to patients with RET mutations (3.7 vs 1.4, p=0.048). GMI using TTT and GMI using TTP(n/n+1) were lower in patients with WBCs <8 at baseline (2.6 vs 13.8, p=0.014; 2.6 vs 11.1, p=0.014; for TTT and TTP) and GI cancer diagnosis (0.4 vs 9.3, p=0.042; 0.4 vs 10.4, p=0.042; for TTT and TTP). Conclusions: Intrapatient efficacy comparisons are feasible using GMI calculations and provide a proof of concept on the favorability of selective RETi compared to other systemic therapies. [Table: see text]
Proposition of a drug score to predict the clinical usefulness of therapeutic dose monitoring of oral molecularly targeted therapies.
Authors: Geraud Arthur , Combarel David , Funck-Brentano Christian , Beaulien Quentin , Zahr Noel , Broutin Sophie , Massard Christophe , Besse Benjamin , Gougis Paul 
Published: June 2023

3091 Background: Therapeutic drug monitoring (TDM) consists in measuring and interpreting drug concentrations in biological fluids to personalize drug dosage. This method is widely used for drugs with a narrow therapeutic index ( e.g., immunosuppressants or neuroleptics agents). In onco-hematology, current recommendations of TDM for oral molecularly targeted therapies (oMTT) differ among molecules, making it difficult to identify good TDM candidates. This study aims to propose a quantitative approach with a score to predict the clinical usefulness of TDM of oMTT. Methods: We selected the four key parameters needed for an oMTT to be a good candidate for TDM (e.g. interpatient pharmacokinetic variability, feasible dose-adaptation strategy, exposure-efficacy relationship, exposure-safety relationship). We collected each drug’s pharmacokinetic (PK) and pharmacodynamics variables from drug labels, Center for Drug Evaluation and Research reports and early trials data. The score was defined from oMTT’s exposition variability (variability score: coefficient of variation of drug exposition, the impact of food and proton pump inhibitors), technical complexity (PK linearity, active metabolite, half-life), efficacy (Exposure-response relationship (ERR)) and safety (maximum tolerated dose, Exposure-safety relationship (ESR)). The global score ranged from 0 to 100. To assess the relevance of the score, we evaluated molecules from other therapeutic classes with known strong recommendations of TDM (clozapine, everolimus, voriconazole). Results: In September 2021, we collected data from sixty-seven oMTTs. Scores were normally distributed (mean 48.3, standard deviation 15.6) and varies from 15 for acalabrutinib to 80 for sunitinib. Scores of drugs with strong recommendation of TDM were 68, 68 and 64 for clozapine, everolimus as an immunosuppressant, and voriconazole, respectively. We hypothesized that a score above 60 might be associated with relevant indications of TDM. Fifteen molecules had a score above 60 (sunitinib (80), cabozantinib (76.5), nilotinib (76), abemaciclib (76), axitinib (75), sorafenib (69.5) gefitinib (69.5), trametinib (67.5), talazoparib (67), crizotinib (66), pazopanib (65), sonidegib (63.5), neratinib (62.5), panobinostat (62) and imatinib (60.5)). Conclusions: We defined a score to identify good candidates for TDM in onco-hematology. Usefulness of TDM in oncology should be confirmed prospectively prioritizing these molecules.
Characterizing the landscape of RING-type E3 ubiquitin transferase-altered (RNF43 alt) colorectal cancer (CRC) and defining unique subsets with potential therapeutic vulnerabilities in microsatellite instability-high (MSI-H) CRC.
Authors: Naqash Abdul Rafeh , Nassar Amin , Macera Lisa , Mauer Elizabeth , Guinney Justin , Chao Calvin Y. , Adib Elio , Petricoin Emanuel , Mittra Arjun , Takebe Naoko , Cannon Timothy Lewis 
Published: June 2023

3134 Background: RNF43 is a tumor suppressor gene that represses the WNT/β-catenin pathway. Emerging data show that RNF43 alt are commonly present in MSI-H CRC with improved outcomes to immune checkpoint inhibitors (ICI). We evaluated whether genomic and transcriptomic analysis of MSI-H RNF43 alt CRC defines distinct subsets with potential therapeutic vulnerabilities. Methods: We analyzed CRC tumors (any stage/ subtype) sequenced with Tempus xT assay (DNA-seq of 648 genes at 500x coverage, full transcriptome RNA-seq). RNF43 alt were defined as pathogenic/likely pathogenic per OncoKB, or copy number aberrations (CNA); deep deletions, or amplifications (>8x). RNF43 alt vs. wildtype (wt) were compared using Chi-squared/Fisher’s Exact tests or Wilcoxon rank-sum tests to evaluate differences in biomarkers. Gene Set Variation Analysis (GSVA) was applied to gene expression data to derive single-sample pathway enrichment scores. ICI-treated MSI-H CRC from the MSKCC cohort (n = 74), publicly available in AACR GENIE v13, was used to assess the overall survival (OS) using the Kaplan-Meier method. Results: RNF43 alt was identified in 5.2% (635 of 12,243) CRC tumors. CNA accounted for 6% of RNF43 alt CRC. RNF43 alt vs. RNF43 wt CRC was associated with older age (median 69 vs. 59 years, p < 0.001), more prevalent in females (56% vs 43%; p < 0.001). A detailed comparison of RNF43 alt vs. RNF43 wt CRC with p < 0.001 are shown (Table). Of MSI-H CRC (n = 714), 51% were RNF43 alt, with 80% being RNF43 p.GLY659fs. Among MSI-H RNF43 alt CRC, BRAF alt was co-mutated in 59%, with BRAFV600E constituting 99%. Median TMB was higher for MSI-H RNF43 alt vs. MSI-H RNF43 wt (40 vs. 31 mut/MB, p < 0.001). Among MSI-H CRC, GSVA identified significant downregulation of Cetuximab_benefit pathway and upregulation of IGF1R and PI3K/AKT pathways in RNF43 alt vs RNF43 wt. Among ICI-treated MSI-H CRC (n = 74) from the MSKCC cohort, no significant OS difference (HR 0.81 [95%CI: 0.34 – 1.97]; p = 0.65) was noted for RNF43 alt (n = 42) vs. wt (n = 32). Conclusions: RNF43 alt are common in MSI-H CRC and associated with potentially actionable genomic and transcriptomic signatures. Functional characterization of interplay between the WNT signaling and therapeutically relevant pathways (PI3K/AKT, BRAF, IGF1R, Cetuximab benefit) to better define optimal ICI combination approaches in MSI-H RNF43 alt CRC is ongoing. [Table: see text]
Exploring expression levels of HER2, HER3, MET, Claudin18.2, and MUC16 across 16 cancer types using an artificial intelligence-powered immunohistochemistry analyzer.
Authors: Shin Seunghwan , Cho Soo Ick , Choi Sangjoon , Lee Taebum , Kim Seulki , Jung Wonkyung , Mostafavi Mohammad , Brattoli Biagio , Ryu Jeongun , Park Seonwook , Pereira Sergio , Nesmith Ken , Park Jiyoung , Seo Melody SeungHui , Kim Huijeong , Kim Seokhwi , Ock Chan-Young 
Published: June 2023

3135 Background: Quantifying tumor-associated antigen (TAA) expression levels on tumor cells (TC) from immunohistochemistry (IHC) stained slides has been widely used to predict response to novel anti-tumor agents. However, obtaining the precise quantification of TAA expression levels from various cancer types is still challenging. To quantify five targets of interest, we applied an artificial intelligence (AI)-powered analyzer to 16 cancer types. Methods: AI-powered whole slide image (WSI) analyzers were developed using IHC positive or negative tumor cells (TC) from PD-L1 22C3 pharmDx and HER2 4B5 datasets, annotated by pathologists. The universal model was trained by IHC-positive or negative TCs from all datasets, while the second model was trained by the intensity of positive (from 1+ to 3+) or negative TCs from the HER2 dataset. Both AI models were applied to the pan-cancer tissue microarray dataset (n = 1,370) with MUC16, CLDN18.2, MET, HER3, and HER2 IHC staining, respectively. The expression levels of those TAAs were calculated with tumor proportion score (TPS) or ASCO/CAP scoring criteria for HER2 in 16 cancer types including biliary tract, breast, colorectum, esophagus, head and neck, kidney, liver, lung, ovary, bladder, pancreas, prostate, stomach, thyroid, cervical, and endometrial cancer. Results: Based on the TPS of 1% or more, the positivity of MUC16 and CLDN18.2 were most frequently observed in ovary (51.6%) and kidney (24.2%), respectively, and MET and HER3 in colorectum (63.8%; 40.5%). The five most highly expressed tumors and their frequency of TPS≥1% are shown. HER2 3+ tumor cells were most highly expressed in the breast, followed by the bladder. Conclusions: This study describes the TAA expression levels in various tumors using an AI-powered IHC analyzer. This method could be useful for exploring target cancer types and predicting response to novel TAA targeted agents. [Table: see text]
Evolution of HER2 scores after neoadjuvant systemic therapy in breast cancer.
Authors: Chong Esther G , Jeon Won Jin , Pham Bryan , Castillo Dan Ran , Mehta Akhil , Pairawan Seyed , Denham Laura , Nagaraj Gayathri 
Published: June 2023

3139 Background: Breast cancer (BC) is a malignancy with a wide array of histopathological characteristics and tumor heterogeneity leading to major clinical implications for management. With the recent approval of antibody-drug conjugates (ADCs) for human epidermal growth factor receptor 2 (HER2)-low advanced BC, there is increased significance for characterizing the spectrum of HER2 expression in the curative setting. Methods: We conducted a single institution retrospective review of patients with BC who received neoadjuvant systemic therapy (NAST) followed by definitive surgical resection from 2008 to 2022. The initial tumor biopsies were tested for estrogen receptor (ER), progesterone receptor (PR), and HER2 status per ASCO/CAP guidelines and repeated on the residual tumor tissue after NAST. HER2 status was categorized into HER2-negative (IHC 0), HER2-low (IHC 1 or IHC 2/ Fluorescence in situ hybridization [FISH] negative), and HER2-positive (IHC 2/ FISH positive or IHC 3). Concordance and discordance with HER2 status in the pre and post NAST tumor samples were analyzed using descriptive statistics. Results: Of 340 patients with BC who received NAST, 55 patients with residual disease had pre and post treatment biomarker data available for analysis. Prevalence of HER2-low was 70.9 % (39/55) in the pretreatment tumors while it was 58.2% (32/55) in the post treatment tumors. 40% of tumors (22/55) had HER2 discordance while 60% (33/55) demonstrated concordance with HER2 expression after NAST. Among the changes in HER2 status, a shift from HER2-low to HER2-negative status occurred most frequently in 18.2% of tumors (10/55). In tumors with HER2 discordance, 36.4% (8/22) were also noted to have changes in ER or PR status while this was seen in 48.5% (16/33) of tumors with HER2 concordance. For rates of recurrence, 22.7% (5/22) of patients in the HER2 discordant group had relapsed compared to 18.2% (6/33) patients in the HER2 concordant group. Conclusions: Dynamics of HER2 expression is increasingly important in the rapidly changing therapeutic landscape of BC. Our study shows high prevalence of HER2-low expression in tumors with residual disease after NAST and frequent conversion of HER2-low to HER2-negative status with NAST. Further studies are warranted to determine the clinical and therapeutic relevance of biomarker evolution and tumor heterogeneity in the curative setting. [Table: see text]