Summary: Nascent form of random copolymers of propylene with ethylene, 1-butene, 1-hexene, 1-octene, and 4-methyl-1-pentene was studied by Raman spectroscopy. The most significant spectral alterations with a change in propylene content were observed in two lines at 809 and 841 cm À1. The first line corresponds to vibrations of polypropylene helical chains in the crystalline phase, while the second one is associated with vibrations of polypropylene helical chains having isomeric defects. Raman data confirm that conformational composition and phase state of copolymer macromolecules strongly depend on the comonomer content as well as on the size of the comonomer units.
Although immune checkpoint inhibitors (ICIs) are increasingly used as second-line treatments for urothelial cancer (UC), only a small proportion of patients respond. Therefore, understanding the mechanisms of response to ICIs is critical to improve clinical outcomes for UC patients. The tumor microenvironment (TME) is recognized as a key player in tumor progression and the response to certain anti-cancer treatments. This study aims to investigate the mechanism of response using integrated genomic and transcriptomic profiling of a UC patient who was part of the KEYNOTE-045 trial and showed an exceptional response to pembrolizumab. Diagnosed in 2014 and receiving first-line chemotherapy without success, the patient took part in the KEYNOTE-045 trial for 2 years. She showed dramatic improvement and has now been free of disease for over 6 years. Recently described by Bagaev et al., the Molecular Functional (MF) Portrait was utilized to dissect genomic and transcriptomic features of the patient’s tumor and TME. The patient’s tumor was characterized as Immune Desert, which is suggestive of a non-inflamed microenvironment. Integrated whole-exome sequencing (WES) and RNA sequencing (RNA-seq) analysis identified an ATM mutation and high TMB level (33.9 mut/mb), which are both positive biomarkers for ICI response. Analysis further revealed the presence of the APOBEC complex, indicating the potential for use of APOBEC signatures as predictive biomarkers for immunotherapy response. Overall, comprehensive characterization of the patient’s tumor and TME with the MF Portrait revealed important insights that could potentially be hypothesis generating to identify clinically useful biomarkers and improve treatment for UC patients.
579 Background: Invasive lobular carcinoma (ILC) is more aggressive than hormone receptor (HR)-positive invasive ductal carcinoma (IDC). However, in practice, ILC and IDC are often treated in a similar fashion with endocrine therapy and chemotherapy. Identifying novel biomarkers, genetic alterations, transcriptomic features, and tumor microenvironment (TME) variations could initiate the development of personalized treatment plans for patients with ILC. Methods: We collected ILC and luminal (non-basal/non-HER2) IDC samples from four datasets (TCGA, METABRIC, RATHER PMC4700448, and UQCCR PMC31263747) and performed differential expression and gene set enrichment analyses, revealing novel genomic, transcriptomic, and TME differences. Using methods from Bagaev et al., we quantified the activity of 29 functional gene expression signatures with single sample gene set enrichment analysis before clustering the samples into five TME subtypes; statistical significance was measured with the Mann-Whitney U test. Differential expression analysis of RNA-Seq data was completed using DESeq2. Further, we analyzed the frequency of specific biomarkers to identify potential therapeutic options. Mutations and biomarker enrichment were assessed using the chi-squared test. Results: We analyzed 1,735 samples (1,442 luminal IDCs and 293 ILCs). CDH1 mutations were more prevalent in ILC samples (56%) compared to IDC samples (6%). Of the 44% of ILC samples with wild-type CDH1, 90% had low CDH1 expression. Inference models showed differences in transcription factors expression between ILC and IDC. ILC had significantly higher expression of TFAP2B, SOCS2, NOSTRIN, THBS4, SCUBE2, and GDF9 and lower expression of CDCA4, PSMG1, LMOD1, and SLC7A5 (adj p < 0.0001 for all genes). Analysis of the TME showed that 44% of ILC samples were immune enriched with high PDL1, CTLA4, and LAG3 expression. In comparison, approximately 30% of ILC samples contained enhanced vascularization and expressed high VEGFA, PDGFRA, and PDGFRB. Finally, compared to luminal IDC, ILC tend to have a statistically significant higher TROP2 expression, similar to that seen in basal subtype. Conclusions: ILC and IDC expressed distinct genomic alterations, gene expression, transcriptomic features, TMEs, and biomarkers. These differences can be used as a blueprint to tailor ILC phenotype-specific interventional clinical trials.
4591 Background: UC is associated with high recurrence rates, progression, and resistance to platinum-based therapy. Checkpoint inhibitors (CPIs) are often used for treating UC, but predictive biomarkers that characterize response are lacking in the majority of patients. Defining the TME is essential to understanding patient response to CPIs. Employing a transcriptome-based classification platform, we sought to identify predictive and prognostic subtypes of UC using malignant cell and TME features. Methods: We collected a metacohort of 2,418 UC samples from 14 publicly available datasets, one of which had atezolizumab response data (IMvigor210). Using the methodology described in Bagaev et al. 2021, we selected 28 signatures composed of specific gene sets reflecting distinct cellular processes. Analysis of signature expression and unsupervised clustering was performed. Results: We identified 7 recurring novel UC subtypes (Table 1) with unique genomic and molecular characteristics. The subtypes and key findings include: an immune desert (D) subtype characterized by genomic instability high HER2 expression; an immune desert, FGFR-altered (D-FGFR) subtype with FGFR alterations; an immune enriched (IE) subtype with an enriched TME and high CPI response; a fibrotic (F) subtype with a mesenchymal TME, and strong TGFβ signaling; an immune enriched, fibrotic (IE-F) subtype with a mixed TME and a high CPI response rate; a fibrotic, basal (F-B) subtype with a mesenchymal TME and minimal genomic targets, and a neuroendocrine-like (NE-L) subtype with a high CPI response rate. Conclusions: UC can be classified into 7 subtypes with distinct prognoses, CPI response rates, and druggable targets using malignant cell and TME profiling. Patients with IE, IE-F, and NE-L UCs may be good candidates for CPIs. D UCs may benefit from HER2-i and PARP-i, while FGFR-i might be more suitable for D-FGFR UCs. TGFβi and PARPi may be effective for F UCs, but F-B UCs have no targetable findings. These findings warrant additional investigation for clinical translation. [Table: see text]
1039 Background: GATA3 expression is broadly used as a biomarker for diagnosing cancer of breast origin and its expression is strongly associated with estrogen receptor (ER)-positive luminal phenotype. Although GATA3 mutations are observed in 12-18% of breast cancers (BC), they are poorly characterized. Recently, the pharmacological inhibition of MDM2 has been shown to significantly impair tumor growth in GATA3-deficient models in vitro, in vivo, and patient-derived xenograft harboring GATA3 somatic mutations. Therefore, given the potential targetability of GATA3-mutated BC cells, it is important to better understand and characterize the mutational landscape of GATA3 to help guide future prospective clinical studies. Methods: We accessed a cohort of BC samples profiled genomically and transcriptomically from two open-source datasets: TCGA (n=961) and METABRIC (n=1866). We used the chi-squared test to analyze the frequency of GATA3 mutations across PAM50 subtypes (Basal, HER2, Luminal A, Luminal B, Normal-like) and histological BC subtypes (invasive ductal carcinoma [IDC] and invasive lobular carcinoma [ILC]). Mutations affecting other genes in the GATA3-mutated samples and the GATA3 wild-type (WT) samples were compared using the chi-squared test. We analyzed the mutational landscape of the GATA3 gene and correlated different sites of mutations with GATA3 expression, MDM2 amplification, other co-occurring mutations, and clinical behavior. GATA3 expression levels were compared using the Mann-Whitney test. Results: Of the analyzed 2,827 BC samples, GATA3 mutations were unevenly distributed in the five PAM50 subtypes, as Luminal A and B subtypes had the highest frequency (16% and 17%, respectively) and the basal subtype had no observed GATA3 mutations (p <0.0001). Further analysis showed that GATA3 mutations were more common in luminal IDC than ILC (17% vs 8.7%; p <0.0001). While mutations in CBFB, AKT1, TBX3 and ARID1A were more frequently co-occurring with GATA3 mutations, mutations in TP53, CDH1 and PIK3CA were mutually exclusive with GATA3 mutations (adjusted p <0.0001 for all genes). Analysis of the GATA3 mutational landscape identified two types of mutations in GATA3: a group of truncating mutations occurring at the end of the GATA3 gene (after the 307 position including the splice site at 308) and a group occurring before the 307 position. The first group was associated with increased GATA3 expression, MDM2 amplification, and was mutually exclusive with TP53 mutations. The second group had little to no effect on GATA3 expression and behaved similarly to GATA3 WT. Conclusions: A subtype of GATA3 mutations are mutually exclusive with TP53 mutations and are associated with increased MDM2 amplification, making them an ideal target for clinical trials involving MDM2 inhibitors.
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]
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