Citations

Abstract Human chromosomal anomalies, notably trisomies, disrupt gene expression, leading to diverse cellular and organ phenotypes. Increased cellular senescence (SEN) and oxidative stress in trisomies have gained recent attention. We assessed SEN, SEN-associated secretory phenotype (SASP), and oxidative stress on trisomy 13 (T13), T18, and T21 human fetal lung tissues and isolated primary human fetal lung fibroblasts. Telomerase-associated foci staining showed DNA damage primarily within T21 and T18 lungs. These results were confirmed by real-time quantitative PCR showing an increase of the SEN marker CDKN2B and SASP markers IL-6 and CXCL8. In contrast, lung tissues from T13 showed an upregulation of CDKN2A, whereas no significant changes in SASP marker genes were observed. γ-H2AX (H2A histone family member X) was upregulated in each genotype, particularly in T21. Isolated fibroblasts demonstrated a strong association between T21 and several SEN markers. An increase of γ-H2AX–positive cells were observed in fibroblasts from T21, T18, and T13, but only T21 exhibited an increase in P21 expression. Only T21 fibroblasts displayed a significant increase in reactive oxygen species levels, as indicated by MitoSOX and CellROX. This study provides the first evidence of a link between SEN and trisomy anomalies during prenatal human lung development, particularly in T21.

Purpose: Palliative care focuses on providing relief from the stress and symptoms of serious illness. Obstetrician gynecologists (OBGYNs) manage a variety of complex clinical situations ranging from delivering a cancer diagnosis to caring for a neonatal demise to symptom management, all of which fall within the realm of palliative care. Palliative care is relevant to the practice of OBGYNs, yet residents rarely receive formal primary palliative care training. We sought to design, implement, and evaluate a dedicated curriculum on palliative care for OBGYN residents. Methods: We performed a needs assessment of an OBGYN residency at a single institution. This information was used to develop a multidisciplinary palliative care curriculum. Post-curriculum surveys were distributed and analyzed. Descriptive statistics were utilized. Results: Our needs assessment identified a lack of knowledge and competency in palliative care concepts and skills. Using this information, we created a multidisciplinary palliative care curriculum, including clinical experiences, chalk talks, and online didactics. Post-curriculum surveys revealed a marked improvement in both knowledge base and perceived competence. Feedback was overwhelmingly positive; residents indicated that the curriculum was both a valuable use of time and would impact their provision of care in the future. Conclusion: A dedicated palliative care curriculum is an impactful addition to OBGYN training.

Abstract Objective The fast accumulation of vast pharmacogenomics data of cancer cell lines provide unprecedented opportunities for drug sensitivity prediction (DSP), a crucial prerequisite for the advancement of precision oncology. Recently, Generative Large Language Models (LLM) have demonstrated performance and generalization prowess across diverse tasks in the field of natural language processing (NLP). However, the structured format of the pharmacogenomics data poses challenge for the utility of LLM in DSP. Therefore, the objective of this study is multi-fold: to adapt prompt engineering for structured pharmacogenomics data toward optimizing LLM’s DSP performance, to evaluate LLM’s generalization in real-world DSP scenarios, and to compare LLM’s DSP performance against that of state-of-the-science baselines. Methods We systematically investigated the capability of the Generative Pre-trained Transformer (GPT) as a DSP model on four publicly available benchmark pharmacogenomics datasets, which are stratified by five cancer tissue types of cell lines and encompass both oncology and non-oncology drugs. Essentially, the predictive landscape of GPT is assessed for effectiveness on the DSP task via four learning paradigms: zero-shot learning, few-shot learning, fine-tuning and clustering pretrained embeddings. To facilitate GPT in seamlessly processing the structured pharmacogenomics data, domain-specific novel prompt engineering is employed by implementing three prompt templates (i.e., Instruction, Instruction-Prefix, Cloze) and integrating pharmacogenomics-related features into the prompt. We validated GPT’s performance in diverse real-world DSP scenarios: cross-tissue generalization, blind tests, and analyses of drug-pathway associations and top sensitive/resistant cell lines. Furthermore, we conducted a comparative evaluation of GPT against multiple Transformer-based pretrained models and existing DSP baselines. Results Extensive experiments on the pharmacogenomics datasets across the five tissue cohorts demonstrate that fine-tuning GPT yields the best DSP performance (28% F1 increase, p-value= 0.0003) followed by clustering pretrained GPT embeddings (26% F1 increase, p-value= 0.0005), outperforming GPT in-context learning (i.e., few-shot). However, GPT in the zero-shot setting had a big F1 gap, resulting in the worst performance. Within the scope of prompt engineering, performance enhancement was achieved by directly instructing GPT about the DSP task and resorting to a concise context format (i.e., instruction-prefix), leading to F1 performance gain of 22% (p-value=0.02); while incorporation of drug-cell line prompt context derived from genomics and/or molecular features further boosted F1 score by 2%. Compared to state-of-the-science DSP baselines, GPT significantly asserted superior mean F1 performance (16% gain, p-value<0.05) on the GDSC dataset. In the crosstissue analysis, GPT showcased comparable generalizability to the within-tissue performances on the GDSC and PRISM datasets, while statistically significant F1 performance improvements on the CCLE (8%, p-value=0.001) and DrugComb (19%, p-value=0.009) datasets. Evaluation on the challenging blind tests suggests GPT’s competitiveness on the CCLE and DrugComb datasets compared to random splitting. Furthermore, analyses of the drug-pathway associations and log probabilities provided valuable insights that align with previous DSP findings. Conclusion The diverse experiment setups and in-depth analysis underscore the importance of generative LLM, such as GPT, as a viable in silico approach to guide precision oncology. Availability https://github.com/bioIKEA/SensitiveCancerGPT

Alcohol use disorder (AUD) is the most prevalent substance use disorder. Excessive alcohol consumption leads to a range of health issues. We set out to identify inflammatory markers linked to alcohol consumption, which might ultimately offer novel insight into genetic underpinnings and have implications for alcohol-associated disease. Alcohol consumption and blood-based multi-omics data were collected by The Mayo Clinic Center for Individualized Treatment of Alcohol Dependence study. Plasma samples from patients with AUD were used for proteomics analysis using the OLINK “Explore Inflammation” panel (n=410). Liver enzymes were also measured. A genome-wide association study (GWAS) was performed to explore the relationship between genetic variants and plasma TREM2 levels. Our findings show thatplasma triggering receptor expressed on myeloid cells 2 (TREM2), a key gene associated with neurodegenerative disease, was the most significant signal correlated with alcohol consumption, and has also been associated with liver enzyme levels in patients with AUD. We identified the rs7232 single nucleotide polymorphism (SNP) in MS4A6A as a key genetic variant associated with plasma TREM2 levels, with the minor allele (A) linked to higher TREM2 levels and increased alcohol consumption, particularly in men. Furthermore, MA4A6A is an ethanol-responsive gene in a SNP-dependent manner, and the variant genotype of the rs7232 SNP was associated with lower expression for MA4A6A due to proteasome-mediated protein degradation. In summary, this study provides insight into the relationship between plasma TREM2 levels, alcohol consumption, and liver function in AUD patients, shedding light on genetic factors underlying alcohol-related diseases.

Abstract Purpose: Germline alterations in homologous recombination repair (gHRR) genes affect the pathogenesis, treatment options, and survival of patients with cancer. However, distinct gHRR gene alterations may differentially affect treatment response and oncogenic signaling. In this study, we interrogated genomic and transcriptomic data and assessed clinical outcomes of patients with gHRR mutations across four BRCA-associated cancers (breast, ovarian, pancreatic, and prostate cancers) to identify therapeutic vulnerabilities. Experimental Design: We assessed 24,309 patients undergoing matched tumor/normal next-generation DNA and RNA sequencing. Annotated gHRR gene variants [germline BRCA1, germline BRCA2, germline PALB2, germline ATM (gATM), and germline CHEK2] were analyzed. HRs were used to assess survival outcomes comparing germline versus sporadic groups. Somatic alterations and their frequencies were compared across gHRR-altered groups. Differential gene expression and gene set enrichment analysis were used to compare transcriptomic profiles. Results: Somatic TP53 mutations were depleted in gATM carriers (P &lt; 0.05) across all four BRCA-associated cancers by up to 2.5-fold. Tumors with germline BRCA1/2 mutations were associated with improved survival in patients with ovarian cancer and had consistent enrichment of TP53 mutations in all four cancers. gATM mutations displayed elevated p53 transcriptional activity in all four cancers, with significance reached in breast and prostate cancers (P &lt; 0.01). In breast, ovarian, and prostate cancers, gATM tumors demonstrated significantly increased inflammatory pathways (P &lt; 0.001). Finally, using gene dependency data, we found that cell lines that were highly dependent on ATM were co-dependent on canonical p53 function. Conclusions: gATM-associated cancers seem to require intact p53 activity and this synthetic essentiality may be used to guide targeted therapies that perturb canonical TP53 function.

Abstract Overall survival (OS) and quality of life (QoL) are clinically relevant outcomes/end points during examination of therapies. However, with median OS approaching 20 years for patients with follicular lymphoma (FL), it is often not feasible to use OS as a primary end point in clinical studies. Although validated tools exist for assessing QoL in patients with lymphoma, QoL data are rarely the sole basis for drug approvals in FL. Therefore, other survival end points, surrogates, and intermediate clinical end points have all been used to measure outcomes in FL trials. In this review, we discuss the strengths and limitations of these commonly used traditional end points in FL trials and examine the current gaps, including delayed availability of results and suboptimal sensitivity in distinguishing difference in therapeutic effects. To help address the identified gaps, well-validated surrogates, such as complete remission 30 months after starting frontline immunochemotherapy, may be used as the primary or co-primary end point in confirmatory randomized trials. Emerging intermediate end points like minimal residual disease may be useful in early phase trials and in guiding accelerated approvals after appropriate validation. As patient preference plays a crucial role in treatment decisions in FL, it is critical to include QoL as an important secondary trial end point and to measure nonmedical burdens, including time and financial toxicities. End points that are clinically relevant, timely, and patient-centered may identify new drugs that help patients with FL live longer and better lives.

Background: Rates of early-onset colorectal cancer (eoCRC), defined as disease diagnosed at <50 years of age, are increasing. The incidence and spectrum of somatic and pathogenic germline variants (PGV) in this population are not well understood. Methods: This cross-sectional study leveraged Tempus’ clinicogenomic database, including de-identified records of patients diagnosed with CRC between 2000–2022, to analyze and compare eoCRC and average-onset colorectal cancer (aoCRC, disease diagnosed ≥50 years of age) patients. The frequency and spectrum of somatic mutations and PGVs in patients with eoCRC and aoCRC were evaluated and compared. Results: Among 11,006 participants in this study, 57% were male, 76% were white, and 80% had stage 4 disease. Within the total cohort, 2379 had eoCRC and 8627 had aoCRC. Among patients with eoCRC, 4.2% had a tumor with high microsatellite instability and/or deficient mismatch repair (MSI-H/dMMR) and 6.8% with aoCRC had an MSI-H/dMMR tumor (p < 0.001). The most frequent somatic mutations involved TP53, APC, and KRAS, with the most significant difference in BRAF, which was more frequently mutated in aoCRC (9.8% vs. 4.7%, p < 0.0001). In total, 1413 (59.4%) eoCRC and 4898 (56.8%) aoCRC patients had matched normal specimen (blood or saliva) sequencing and a PGV was identified in 6.9% of eoCRC and 5.0% of aoCRC patients. Conclusions: Somatic and germline mutation profiles were similar for eoCRC and aoCRC patients and may not adequately explain differences in tumor behavior and age of disease onset.