Identification and Characterization of Immune Checkpoint Inhibitor-Induced Toxicities From Electronic Health Records Using Natural Language Processing.

PURPOSE: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, yet their use is associated with immune-related adverse events (irAEs). Estimating the prevalence and patient impact of these irAEs in the real-world data setting is critical for characterizing the benefit/risk profile of ICI therapies beyond the clinical trial population. Diagnosis codes, such as International Classification of Diseases codes, do not comprehensively illustrate a patient's care journey and offer no insight into drug-irAE causality. This study aims to capture the relationship between ICIs and irAEs more accurately by using augmented curation (AC), a natural language processing-based innovation, on unstructured data in electronic health records. METHODS: In a cohort of 9,290 patients treated with ICIs at Mayo Clinic from 2005 to 2021, we compared the prevalence of irAEs using diagnosis codes and AC models, which classify drug-irAE pairs in clinical notes with implied textual causality. Four illustrative irAEs with high patient impact-myocarditis, encephalitis, pneumonitis, and severe cutaneous adverse reactions, abbreviated as MEPS-were analyzed using corticosteroid administration and ICI discontinuation as proxies of severity. RESULTS: For MEPS, only 70% (n = 118) of patients found by AC were also identified by diagnosis codes. Using AC models, patients with MEPS received corticosteroids for their respective irAE 82% of the time and permanently discontinued the ICI because of the irAE 35.9% (n = 115) of the time. CONCLUSION: Overall, AC models enabled more accurate identification and assessment of patient impact of ICI-induced irAEs not found using diagnosis codes, demonstrating a novel and more efficient strategy to assess real-world clinical outcomes in patients treated with ICIs.

Whole genome sequencing across clinical trials identifies rare coding variants in GPR68 associated with chemotherapy-induced peripheral neuropathy.

BACKGROUND: Dose-limiting toxicities significantly impact the benefit/risk profile of many drugs. Whole genome sequencing (WGS) in patients receiving drugs with dose-limiting toxicities can identify therapeutic hypotheses to prevent these toxicities. Chemotherapy-induced peripheral neuropathy (CIPN) is a common dose-limiting neurological toxicity of chemotherapies with no effective approach for prevention. METHODS: We conducted a genetic study of time-to-first peripheral neuropathy event using 30× germline WGS data from whole blood samples from 4900 European-ancestry cancer patients in 14 randomized controlled trials. A substantial number of patients in these trials received taxane and platinum-based chemotherapies as part of their treatment regimen, either standard of care or in combination with the PD-L1 inhibitor atezolizumab. The trials spanned several cancers including renal cell carcinoma, triple negative breast cancer, non-small cell lung cancer, small cell lung cancer, bladder cancer, ovarian cancer, and melanoma. RESULTS: We identified a locus consisting of low-frequency variants in intron 13 of GRID2 associated with time-to-onset of first peripheral neuropathy (PN) indexed by rs17020773 (p = 2.03 × 10-8, all patients, p = 6.36 × 10-9, taxane treated). Gene-level burden analysis identified rare coding variants associated with increased PN risk in the C-terminus of GPR68 (p = 1.59 × 10-6, all patients, p = 3.47 × 10-8, taxane treated), a pH-sensitive G-protein coupled receptor (GPCR). The variants driving this signal were found to alter predicted arrestin binding motifs in the C-terminus of GPR68. Analysis of snRNA-seq from human dorsal root ganglia (DRG) indicated that expression of GPR68 was highest in mechano-thermo-sensitive nociceptors. CONCLUSIONS: Our genetic study provides insight into the impact of low-frequency and rare coding genetic variation on PN risk and suggests that further study of GPR68 in sensory neurons may yield a therapeutic hypothesis for prevention of CIPN.

Baseline risk factors associated with immune related adverse events and atezolizumab.

Background: Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of cancer patients in the last decade, but immune-related adverse events (irAEs) pose significant clinical challenges. Despite advances in the management of these unique toxicities, there remains an unmet need to further characterize the patient-level drivers of irAEs in order to optimize the benefit/risk balance in patients receiving cancer immunotherapy. Methods: An individual-patient data post-hoc meta-analysis was performed using data from 10,344 patients across 15 Roche sponsored clinical trials with atezolizumab in five different solid tumor types to assess the association between baseline risk factors and the time to onset of irAE. In this study, the overall analysis was conducted by treatment arm, indication, toxicity grade and irAE type, and the study design considered confounder adjustment to assess potential differences in risk factor profiles. Results: This analysis demonstrates that the safety profile of atezolizumab is generally consistent across indications in the 15 studies evaluated. In addition, our findings corroborate with prior reviews which suggest that reported rates of irAEs with PD-(L)1 inhibitors are nominally lower than CTLA-4 inhibitors. In our analysis, there were no remarkable differences in the distribution of toxicity grades between indications, but some indication-specific differences regarding the type of irAE were seen across treatment arms, where pneumonitis mainly occurred in lung cancer, and hypothyroidism and rash had a higher prevalence in advanced renal cell carcinoma compared to all other indications. Results showed consistency of risk factors across indications and by toxicity grade. The strongest and most consistent risk factors were mostly organ-specific such as elevated liver enzymes for hepatitis and thyroid stimulating hormone (TSH) for thyroid toxicities. Another strong but non-organ-specific risk factor was ethnicity, which was associated with rash, hepatitis and pneumonitis. Further understanding the impact of ethnicity on ICI associated irAEs is considered as an area for future research. Conclusions: Overall, this analysis demonstrated that atezolizumab safety profile is consistent across indications, is clinically distinguishable from comparator regimens without checkpoint inhibition, and in line with literature, seems to suggest a nominally lower reported rates of irAEs vs CTLA-4 inhibitors. This analysis demonstrates several risk factors for irAEs by indication, severity and location of irAE, and by patient ethnicity. Additionally, several potential irAE risk factors that have been published to date, such as demographic factors, liver enzymes, TSH and blood cell counts, are assessed in this large-scale meta-analysis, providing a more consistent picture of their relevance. However, given the small effects size, changes to clinical management of irAEs associated with the use of Anti-PDL1 therapy are not warranted.

Genetic variation associated with thyroid autoimmunity shapes the systemic immune response to PD-1 checkpoint blockade.

Activation of systemic immune responses using PD-1 checkpoint inhibitors is an essential approach to cancer therapy. Yet, the extent of benefit relative to risk of immune related adverse events (irAE) varies widely among patients. Here, we study endocrine irAE from 7 clinical trials across 6 cancers where atezolizumab (anti-PD-L1) was combined with chemotherapies and compared to standard of care. We show that atezolizumab-induced thyroid dysfunction is associated with longer survival. We construct a polygenic risk score (PRS) for lifetime risk of hypothyroidism using a GWAS from the UK Biobank and apply this PRS to genetic data collected from 2,616 patients of European ancestry from these trials. Patients with high PRS are at increased risk of atezolizumab-induced thyroid dysfunction and lower risk of death in triple negative breast cancer. Our results indicate that genetic variation associated with thyroid autoimmunity interacts with biological pathways driving the systemic immune response to PD-1 blockade.

Polygenic risk for skin autoimmunity impacts immune checkpoint blockade in bladder cancer.

PD-1 and PD-L1 act to restrict T cell responses in cancer and contribute to self-tolerance. Consistent with this role, PD-1 checkpoint inhibitors have been associated with immune-related adverse events (irAEs), immune toxicities thought to be autoimmune in origin. Analyses of dermatological irAEs have identified an association with improved overall survival (OS) following anti-PD-(L)1 therapy, but the factors that contribute to this relationship are poorly understood. We collected germline whole-genome sequencing data from IMvigor211, a recent phase 3 randomized controlled trial comparing atezolizumab (anti-PD-L1) monotherapy to chemotherapy in bladder cancer. We found that high vitiligo, high psoriasis, and low atopic dermatitis polygenic risk scores (PRSs) were associated with longer OS under anti-PD-L1 monotherapy as compared to chemotherapy, reflecting the Th17 polarization of these diseases. PRSs were not correlated with tumor mutation burden, PD-L1 immunohistochemistry, nor T-effector gene signatures. Shared genetic factors impact risk for dermatological autoimmunity and anti-PD-L1 monotherapy in bladder cancer.

Mechanisms of immune-related adverse events associated with immune checkpoint blockade: using germline genetics to develop a personalized approach.

Personalized care of cancer patients undergoing treatment with immune checkpoint inhibitors will require approaches that can predict their susceptibility to immune-related adverse events. Understanding the role of germline genetic factors in determining individual responses to immunotherapy will deepen our understanding of immune toxicity and, importantly, it may lead to tools for identifying patients who are at risk.

Proteomic profiling of cancer biomarkers.

Early detection and correct diagnosis are essential for effective treatment of cancer and patient survival. Complete sequencing of the human genome, and the genomes of other species, provides valuable tools for discerning the genetic abnormalities in cancer. However, differences between cancerous and normal cells reflect more than variations in genetic sequences and abundance of transcribed RNA. Many cancer biomarkers are manifestation of differences in post-transcriptional splicing and/or post-translational modifications. Thus, proteomic tools are being increasingly utilised in the post-genomic era for discovery of new cancer biomarkers. In this paper we will provide an overview of the biomarker discovery process from the proteomic profiling point of view, with emphasis given to the principles that are involved in the process, including the protein identification strategies, and how surface enhanced laser desorption ionisation mass spectrometry fits into the picture. The aim is to provide a resource for the experimental practitioner seeking awareness of the analytical tools that are now available in contemporary cancer research.