Faculty Bibliography

Background: Pembro (programmed cell death protein-1 [PD-1] inhibitor) +/- platinum (Pt)-based induction chemotherapy (ICT), with pembro maintained until disease progression (PD), is a standard 1L treatment for advanced/metastatic NSCLC; long-term benefits are limited to a small subset of patients. Nira, a poly (ADP-ribose) polymerase inhibitor (PARPi), promotes PARP trapping, activates the STING pathway, recruits T cells, and upregulates programmed death-ligand 1 (PD-L1), making it a promising partner for PD-1 inhibitors. Nira crosses the blood-brain barrier in animal models with 34-fold higher brain tissue exposure than other PARPis, suggesting it may reduce risk/progression of brain metastasis (BM). Nira + pembro has shownanti-tumour activity and acceptable safety in triple-negative breast and Pt-resistant ovarian cancer (TOPACIO/KEYNOTE-162), and as 1L therapy in advanced/metastatic NSCLC (JASPER). Trial design: ZEAL-1L (NCT04475939) is a phase 3, randomised, double-blind trial comparing efficacy/safety of 1L maintenance therapy with oral nira (200/300 mg/day) + intravenous pembro (200 mg on Day 1 of each 21-day cycle; maximum 35 cycles from 1L ICT initiation) versus placebo + pembro in adults with histologically/cytologically confirmed Stage IIIB-IV NSCLC without known driver mutations and stable disease or partial/complete response to 4-6 cycles of 1L Pt-based ICT + pembro. Patients with asymptomatic BM (off corticosteroids and anticonvulsants for >=7 days) are permitted. Approximately 650 patients will be randomised (1:1), with stratification by histology, PD-L1 status and response to 1L therapy. Treatment will continue until PD, unacceptable toxicity, death, or loss to follow-up. Imaging occurs every 6 weeks (Q6W) for 48 weeks/until PD, and Q12W for patients on treatment thereafter. Primary endpoints are progression-free survival (PFS) and overall survival (OS). Time to central nervous system progression is a key secondary endpoint; others include investigator-assessed PFS, PFS and OS by PD-L1 status, quality of life, safety, and pharmacokinetics. Exploratory analyses are planned. Enrolment began November 2020

Substantial progress has been made in understanding how tumors escape immune surveillance. However, few measures to counteract tumor immune evasion have been developed. Suppression of tumor antigen expression is a common adaptive mechanism that cancers use to evade detection and destruction by the immune system. Epigenetic modifications play a critical role in various aspects of immune invasion, including the regulation of tumor antigen expression. To identify epigenetic regulators of tumor antigen expression, we established a transplantable syngeneic tumor model of immune escape with silenced antigen expression and used this system as a platform for a CRISPR-Cas9 suppressor screen for genes encoding epigenetic modifiers. We found that disruption of the genes encoding either of the chromatin modifiers activating transcription factor 7-interacting protein (Atf7ip) or its interacting partner SET domain bifurcated histone lysine methyltransferase 1 (Setdb1) in tumor cells restored tumor antigen expression. This resulted in augmented tumor immunogenicity concomitant with elevated endogenous retroviral (ERV) antigens and mRNA intron retention. ERV disinhibition was associated with a robust type I interferon response and increased T-cell infiltration, leading to rejection of cells lacking intact Atf7ip or Setdb1. ATF7IP or SETDB1 expression inversely correlated with antigen processing and presentation pathways, interferon signaling, and T-cell infiltration and cytotoxicity in human cancers. Our results provide a rationale for targeting Atf7ip or Setdb1 in cancer immunotherapy.

Background Immune checkpoint inhibitors (ICI) have revolutionized the management of lung tumors decreasing mortality rates. However, the response rates to these ICI drugs are limited, and identifying those patients who are most likely to benefit remains a clinical challenge. Due to the complex nature of the host immune response, tissue-based biomarker development for immunotherapy (IO) is challenging. Consequently, there is a critical unmet need to develop accurate, validated imaging biomarkers to predict which Non-Small Cell Lung Cancer (NSCLC) patients will benefit from IO. Airway deformations such as central airway obstruction can be considered an important manifestation of cancer aggressiveness or metastatic disease and may have a significant impact on therapeutic refractoriness. In this study, we sought to evaluate whether quantitative measurements of lung airway morphology (QuaLM) on baseline CT scans are associated with response and overall survival in NSCLC patients treated with ICI. Methods In this retrospective study, 80 cases who underwent 2-3 cycles of PD1/PD-L1 ICI therapy (nivolumab/pembrolizumab/ atezolizumab) were included. RECIST v1.1 was used to define 'responders' and 'non-responders'. Patients were randomly divided into a training (n=40) and a test set (n=40). A region growing algorithm is applied to the trachea, identified by Hough transform, to segment bronchi and bronchioles (figure 1a). 14 QuaLM features were extracted from segmented airway on CT scans. Wilcoxson ranksum test is used to identify the predictive QuaLM features. The top 4 QuaLM features in conjunction with a linear discriminant machine learning classifier were used to predict the response to IO. We also built a QuaLM risk score using the least absolute shrinkage and selection operator (LASSO) Cox regression model to predict overall survival (OS). Results The response prediction model trained with top QuaLM features (table 1) predicts responders to ICI with an area under research operating characteristic curve (ROC AUC) of 0.67+/-0.08 (figure 1.b) in the training (St) and AUC=0.63 in the test set (Sv). The airway radiomics risk-score was found to be significantly associated with OS in St (HR=2.34, 95% CI:[1.08-5.07], P=0.008) and Sv (HR=2.55, 95% CI:[0.8- 8.1], P=0.034) (figure 1.c). Conclusions QuaLM features were able to distinguish responders from non-responders and also were found to be associated with OS for NSCLC patients treated with ICI. With additional validation, QuaLM could potentially serve as an imaging biomarker of ICI response assessment for NSCLC patients. This could allow the selection of NSCLC patients who will benefit from IO and help design more rational clinical trials with a combination of IO

BACKGROUND:Immune checkpoint inhibitors (ICIs) can cause life-threatening cardiovascular adverse events (CVAEs) that may not be attributed to therapy. The outcomes of clinical trials may underestimate treatment-related adverse events due to restrictive eligibility, limited sample size, and failure to anticipate selected toxicities. We evaluated the incidence and clinical determinants of CVAEs in real-world population on ICI therapy. PATIENTS AND METHODS:Among 2 687 301 patients diagnosed with cancer from 2011 to 2018, 16 574 received ICIs for any cancer. Patients in the ICI and non-ICI cohorts were matched in a 1 : 1 ratio according to age, sex, National Cancer Institute comorbidity score, and primary cancer. The non-ICI cohort was stratified into patients who received chemotherapy (N = 2875) or targeted agents (N = 4611). All CVAEs, non-cardiac immune-related adverse events occurring after treatment initiation, baseline comorbidities, and treatment details were identified and analyzed using diagnosis and billing codes. RESULTS:Median age was 61 and 65 years in the ICI and non-ICI cohorts, respectively (P < 0.001). ICI patients were predominantly male (P < 0.001). Lung cancer (43.1%), melanoma (30.4%), and renal cell carcinoma (9.9%) were the most common cancer types. CVAE diagnoses in our dataset by incidence proportion (ICI cohort) were stroke (4.6%), heart failure (3.5%), atrial fibrillation (2.1%), conduction disorders (1.5%), myocardial infarction (0.9%), myocarditis (0.05%), vasculitis (0.05%), and pericarditis (0.2%). Anti-cytotoxic T-lymphocyte-associated protein 4 increased the risk of heart failure [versus anti-programmed cell death protein 1; hazard ratio (HR), 1.9; 95% confidence interval (CI) 1.27-2.84] and stroke (HR, 1.7; 95% CI 1.3-2.22). Pneumonitis was associated with heart failure (HR, 2.61; 95% CI 1.23-5.52) and encephalitis with conduction disorders (HR, 4.35; 95% CI 1.6-11.87) in patients on ICIs. Advanced age, primary cancer, nephritis, and anti-cytotoxic T-lymphocyte-associated protein 4 therapy were commonly associated with CVAEs in the adjusted Cox proportional hazards model. CONCLUSIONS:Our findings underscore the importance of risk stratification and cardiovascular monitoring for patients on ICI therapy.

Introduction: KRAS is a frequent oncogenic driver in solid tumors, including Non-Small Cell Lung Cancer (NSCLC). KRAS is involved in various signaling pathways that could allow for targeting of KRAS by targeting downstream key transcription factors that mediate oncogene signaling. At the same time, co-occurrence of other mutations alters the signaling pathways and the key transcription factors involved in the disease network. The convergence of these dysregulated pathways to activate key kinases and transcription factors defines master regulators, forming the regulatory logic (i.e. oncotecture) of the tumor cell that maintains the malignant phenotype, its hallmark behaviors, and homeostasis in the face of treatment, while also providing a new set of potential therapeutic targets. In this study, we describe the impact of KRAS and a variety of co-mutations on the tumor's oncotecture.
Method(s): 248 NSCLC patients with KRAS mutations were selected from TCGA: 110 had only KRAS mutations, 138 had co-occurrence of other mutations: EGFR 55, MET 44, and PIK3CA 39. Mutation and CNV from each case served as input for the Cellworks Computational Omics Biology Model (CBM) to generate a patient-specific protein network map from PubMed and other resources. Disease-biomarkers unique to each patient were identified within protein network maps. The CBM identified the top 25 master regulators for each patient. We calculated the frequency of occurrence of each of the selected master regulators for KRAS alone and KRAS with other mutation co-occurrences.
Result(s): Comparative networks analyses of KRAS alone and KRAS with another mutation were performed. We identified that FOXM1, IKBKB, PLK1, PAK1, MTOR, AURKA, PIK3CA, CSNK2A1 function as master regulators in more than 70% of KRAS-only mutated cancers (Table 1). When co-mutations were present, the master regulator network was upregulated, e.g.: (1) MET: RPS6KA3, STAT3, NEK2 (2) EGFR: NFKB1, CEBPA, NEK2 (3) PIK3CA: SRPK1, GLI2, AKT [Formula presented]
Conclusion(s): The Cellworks CBM can reveal transcription factor addiction by identifying the convergence points of numerous upstream dysregulated pathways. These master regulators reveal another set of potential treatment vulnerabilities or Achilles heels in the network that can inform specific treatment options. This study identifies the key transcriptional mediators of KRAS mutations and how they are shuffled by the presence of co-mutations in other common oncogenes. The CBM biosimulation platform identifies the regulatory network in the cancer laying the foundation for new therapeutic strategies targeting key master regulators. Keywords: Personalized Cancer Therapy, Cancer Therapy Biosimulation, Multi-omics Therapy Biosimulation
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Introduction: Sotorasib is a first-in-class small molecule that specifically and irreversibly inhibits KRAS^G12C. The phase 1/2 CodeBreaK 100 trial evaluated sotorasib in patients with pretreated advanced non-small cell lung cancer (NSCLC) harboring KRAS p.G12C. In the registrational phase 2 part, sotorasib showed an objective response rate (ORR) of 37.1% and a median progression-free survival (PFS) of 6.8 months. Here, we report on the activity of sotorasib in patients with treated brain metastases (BM).
Method(s): Patients from the phase 1/2 CodeBreaK 100 trial receiving 960mg dose were included. Patients with active untreated BM were excluded. Patients who had BM resected or had received radiation therapy ending >=4 weeks prior to the trial were eligible. Systemic response was assessed by independent central review per RECIST 1.1. The presence of neurologically stable/asymptomatic BM at baseline was determined by investigators. CNS response was retrospectively evaluated by central neuroradiologic review, using the response assessment in neuro-oncology BM (RANO-BM) criteria, in patients with >=1 target CNS lesions (>= 10mm) and/or non-target CNS lesions. For non-target lesions, stable disease (SD) refers to response that is neither complete response (CR) nor progressive disease (PD).
Result(s): 174 patients were included: 40 had stable BM (23.0%) while 134 (77.0%) had no BM at baseline. In the BM group, 65% had received prior radiotherapy, and 20% had received prior brain surgery. Systemic efficacy of sotorasib per RECIST 1.1 is shown in the Table. Per central RANO-BM review, 16 patients had baseline and >=1 on-treatment evaluable scans: 3 had target and 13 had non-target CNS lesions. 9 patients had 1 lesion, 2 had 4 lesions, and 5 had >=5 lesions. Of 13 patients with non-target CNS lesions, 2 had CR, 11 had SD. Of 3 patients with target lesions, 1 had SD, and 2 had PD. Overall, intracranial disease control was achieved in 14 of 16 patients (87.5%) with evaluable BM. Safety in the BM group was consistent with previous reports. [Formula presented]
Conclusion(s): Sotorasib demonstrated systemic durable anticancer activity, with a median PFS and OS of 5.3 and 8.3 months in NSCLC patients with stable BM previously treated with radiation or surgery. Intracranial complete responses were observed, with continued intracranial stabilization observed in the majority of patients with evaluable BM. Additional studies are ongoing to evaluate sotorasib in patients with active untreated BM (NCT04185883). Keywords: brain metastases, KRAS p.G12C, sotorasib (AMG 510)
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Introduction: Gemcitabine and carboplatin/cisplatin ("platinum")-based combinations are used to treat a wide variety of malignancies including gynecologic, breast, lung, and occult primary cancers. In Non-Small Cell Lung Cancer (NSCLC), these combinations led to a substantial improvement in overall survival. Nevertheless, a large proportion of patients do not respond. An optimal cytotoxic strategy for managing NSCLC and the discovery of predictive biomarkers for cytotoxic chemotherapy to guide treatment selection remain unmet needs in the clinic. The Cellworks Computational Omics Biology Model (CBM) platform identified a unique chromosomal signature which permits a stratification of patients that are most likely to respond to gemcitabine and platinum treatments.
Method(s): Twenty patients treated with gemcitabine and platinum were identified from a TCGA dataset and analyzed. The mutation and copy number aberrations from individual cases served as input into the CBM to generate a patient-specific protein network map from PubMed and other online resources. Disease-biomarkers unique to each patient were identified within patient-specific protein network maps. Digital drug biosimulations were conducted by measuring the effect of gemcitabine and platinum on a cell growth score comprised of a composite of cell proliferation, viability, apoptosis, metastasis, and other cancer hallmarks. Drug biosimulations were conducted by mapping the drug combination to the patient genome along with a rational mechanism of action and validated based on the patient's genomic profile and biological consequences.
Result(s): Of the 20 patients treated with gemcitabine and platinum, 12 had clinical responses while 8 were non-responders. The CBM correctly predicted response in 17/20 patients with 85% accuracy, 63% specificity and 100% sensitivity. The CBM identified that novel amplified segments of Chromosome 6p were associated with non-responsiveness to gemcitabine and platinum therapy. Key genes on these segments include E2F3, MDC1, TAP1 and TNF. Amplification of E2F3 leads to activation of MSH2/6, which enhances mismatch repair thereby causing resistance. Amplification of MDC1 leads to activation of CHECK2, BRCA1, ATM, and NBN_RAD51_MRE1 Complex which stimulates homologous recombination repair. Amplification of TAP1 reduces gemcitabine transport. Besides 6p amplification, PRMT7 deletion was also associated with gemcitabine resistance. Notably, BRCA2-del, RB1-del, NPM1-del, LIG4-del, XRCC4-del, RAD50-del, ATRX-Del, RBBP8-del, XRCC6-del, and FBXW7-del were also prevalent among gemcitabine non-responders. Interestingly, these aberrations also happen to be key criteria for predicting response to etoposide. Therefore, etoposide and platinum combinations might have provided better disease control for these patients.
Conclusion(s): Amplification of chromosome 6p appears to be an important cause of treatment failure for patients receiving gemcitabine-platinum combinations. In this small patient group, the Cellworks CBM was especially useful for identifying non-responders. Biosimulation can identify novel patient subgroups for therapy response prediction and has promise to help select more effective therapies. Keywords: Multi-omics Therapy Biosimulation, Personalized Cancer Therapy, Cancer Therapy Biosimulation
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Introduction: PD-L1 is an immune checkpoint protein that mediates immune evasion. In Non-Small Cell Lung Cancer (NSCLC), its expression is used to predict the outcome of treatment targeting PD-1/L1. However, clinical benefits do not occur uniformly, and new approaches are needed to assist in selecting patients for immunotherapy.
Method(s): 26 patients with known clinical response to pembrolizumab were selected from publicly available data (PMID:25765070) (Table 1). Mutation and copy number aberrations from individual cases served as input into the Cellworks Omics Biology Model (CBM) to generate a patient-specific protein network map from PubMed and other online resources. Disease-biomarkers unique to each patient were identified within protein network maps. Digital drug biosimulations were conducted by measuring the effect of pembrolizumab on a cell growth score comprised of a composite of cell proliferation, viability, apoptosis, metastasis, and other cancer hallmarks. Drug biosimulations were conducted to identify and evaluate therapeutic efficacy.
Result(s): Among 26 patients treated with pembrolizumab, 14 were clinical responders, defined as stable disease or partial response lasting longer than 6 months, and 12 non-responders. Notably, 9/12 non-responders were PD-L1 positive (Table 1). Cellworks biosimulation predicted response with 84.6% accuracy, 75% specificity, and 92.86% sensitivity. Positive predictive value was 81.25% and negative predictive value was 90%. CBM identified that response was influenced by pathways that impacted the tumor microenvironment (TME). Deletions of adenosine pathway genes were observed in responders, whereas CNVs for these genes were enriched in non-responders (Table 1). Loss of ENPP1, and INSIG1 and SENP2 CNV aberrations, all of which regulate the STING pathway, could play a significant role in governing immune checkpoint blockade (ICB) response. Although STK11 loss appears to be a biomarker for poor ICB response, it was equally enriched in responders (n=7) and non-responders (n=7) in this dataset. Notably, responders had STK11 mutations and chromosome 6 loss, whereas non-responders had STK11 loss with chromosome 6 wild type or gain. Finally, frameshift mutations (FSM) enhance neoepitope formation and were higher in responders (average FSM = 48) vs non-responders (average FSM = 19). [Formula presented]
Conclusion(s): Alterations of the adenosine and STING pathways play key roles in determining benefit from PD-1/L1 targeting and highlight therapeutic possibilities for improving outcome in specific patient subgroups based on PD-L1 expression. The Cellworks CBM captures a holistic picture of the TME using tumor omics and improves response prediction beyond PD-L1 testing. Keywords: Multi-omics Therapy Biosimulation, Personalized Cancer Therapy, Immunotherapy Biosimulation
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