Targeting NFE2L2/KEAP1 Mutations in Advanced NSCLC With the TORC1/2 Inhibitor TAK-228

DataXflow: Synergizing data-driven modeling with best parameter fit and optimal control - An efficient data analysis for cancer research

Building data-driven models is an effective strategy for information extraction from empirical data. Adapting model parameters specifically to data with a best fitting approach encodes the relevant information into a mathematical model. Subsequently, an optimal control framework extracts the most efficient targets to steer the model into desired changes via external stimuli. The DataXflow software framework integrates three software pipelines, D2D for model fitting, a framework solving optimal control problems including external stimuli and JimenaE providing graphical user interfaces to employ the other frameworks lowering the barriers for the need of programming skills, and simultaneously automating reoccurring modeling tasks. Such tasks include equation generation from a graph and script generation allowing also to approach systems with many agents, like complex gene regulatory networks. A desired state of the model is defined, and therapeutic interventions are modeled as external stimuli. The optimal control framework purposefully exploits the model-encoded information by providing those external stimuli that effect the desired changes most efficiently. The implementation of DataXflow is available under https://github.com/MarvelousHopefull/DataXflow. We showcase its application by detecting specific drug targets for a therapy of lung cancer from measurement data to lower proliferation and increase apoptosis. By an iterative modeling process refining the topology of the model, the regulatory network of the tumor is generated from the data. An application of the optimal control framework in our example reveals the inhibition of AURKA and the activation of CDH1 as the most efficient drug target combination. DataXflow paves the way to an agile interplay between data generation and its analysis potentially accelerating cancer research by an efficient drug target identification, even in complex networks.

Immune checkpoint blockade resistance in lung cancer: emerging mechanisms and therapeutic opportunities

Immune checkpoint blockade (ICB) therapy works by inhibiting suppressive checkpoints that become upregulated after T cell activation, like PD-1/PD-L1 and CTLA-4. While the initial FDA approvals of ICB have revolutionized cancer therapies and fueled a burgeoning immuno-oncology field, more recent clinical development of new agents has been slow. Here, focusing on lung cancer, we review the latest research uncovering tumor cell intrinsic and extrinsic ICB resistance mechanisms as major hurdles to treatment efficacy and clinical progress. These include genomic and non-genomic tumor cell alterations, along with host and microenvironmental factors like the microbiome, metabolite accumulation, and hypoxia. Together, these factors can cooperate to promote immunosuppression and ICB resistance. Opportunities to prevent resistance are constantly evolving in this rapidly expanding field, with the goal of moving toward personalized immunotherapeutic regimens.

Dual mTOR1/2 Inhibitor Sapanisertib (FTH-003/TAK-228) in Combination With Weekly Paclitaxel in Patients With Previously Treated Metastatic Urothelial Carcinoma: A Phase II Open-Label Study

BACKGROUND

The PI3K/AKT/mTOR pathway is frequently altered at genomic level in metastatic urothelial carcinoma (mUC). Since mTOR is the last protein in the PI3K signaling cascade, it may have the largest impact on the pathway and has been a focus of targeted therapies. Sapanisertib (FTH-003/TAK-228) is an oral highly selective mTOR1 and mTOR2 inhibitor. NFE2L2 mutations have been described as predictive biomarkers of response in patients with advanced squamous cell lung cancer treated with sapanisertib.

PATIENTS AND METHODS

This was an open-label, investigator-initiated phase II study evaluating safety and efficacy of sapanisertib plus paclitaxel in patients with mUC who had progressed to prior platinum therapy, and the correlation with NFE2L2 mutations in responders. Primary endpoint was objective response rate (ORR). Secondary endpoints included progression-free survival (PFS), overall survival (OS) and safety. Patients were treated with weekly paclitaxel at dose of 80 mg/m2 on days 1, 8, and 15 in combination with sapanisertib 4 mg administered orally 3 days per week on days 2-4, 9-11, 16-18, and 23-25 of a 28-day cycle. NFE2L2 mutations were analyzed by Sanger sequencing in responders.

RESULTS

22 patients were enrolled from May 2018 to April 2020; the trial was halted early due to slow accrual and the COVID-19 pandemic. ORR was 18.2% (n = 4). Disease control rate was 50% (7 SD and 4 PR). Median PFS was 3.4 months (95% CI: 1.8-6.1) and median OS was 6.1 months (95% CI: 1.8-13.4). Adverse events (AE) of grade 3-4 were seen in 86% of patients, but no patients discontinued treatment due to AEs. NFE2L2 mutations were not found in responders.

CONCLUSIONS

Although the primary endpoint was no met, sapanisertib and paclitaxel combination demonstrated clinical activity in a heavily pretreated population of mUC. This trial generates insight for future combination of sapaniserib with immunotherapy and/or antibody drug conjugates.

AURKA emerges as a vulnerable target for KEAP1-deficient non-small cell lung cancer by activation of asparagine synthesis

AURKA is an established target for cancer therapy; however, the efficacy of its inhibitors in clinical trials is hindered by differential response rates across different tumor subtypes. In this study, we demonstrate AURKA regulates amino acid synthesis, rendering it a vulnerable target in KEAP1-deficient non-small cell lung cancer (NSCLC). Through CRISPR metabolic screens, we identified that KEAP1-knockdown cells showed the highest sensitivity to the AURKA inhibitor MLN8237. Subsequent investigations confirmed that KEAP1 deficiency heightens the susceptibility of NSCLC cells to AURKA inhibition both in vitro and in vivo, with the response depending on NRF2 activation. Mechanistically, AURKA interacts with the eIF2α kinase GCN2 and maintains its phosphorylation to regulate eIF2α-ATF4-mediated amino acid biosynthesis. AURKA inhibition restrains the expression of asparagine synthetase (ASNS), making KEAP1-deficient NSCLC cells vulnerable to AURKA inhibitors, in which ASNS is highly expressed. Our study unveils the pivotal role of AURKA in amino acid metabolism and identifies a specific metabolic indication for AURKA inhibitors. These findings also provide a novel clinical therapeutic target for KEAP1-mutant/deficient NSCLC, which is characterized by resistance to radiotherapy, chemotherapy, and targeted therapy.

The Clinical, Genomic, and Transcriptomic Landscape of BRAF Mutant Cancers

BACKGROUND

BRAF mutations are classified into four molecularly distinct groups, and Class 1 (V600) mutant tumors are treated with targeted therapies. Effective treatment has not been established for Class 2/3 or BRAF Fusions. We investigated whether BRAF mutation class differed according to clinical, genomic, and transcriptomic variables in cancer patients.

METHODS

Using the AACR GENIE (v.12) cancer database, the distribution of BRAF mutation class in adult cancer patients was analyzed according to sex, age, primary race, and tumor type. Genomic alteration data and transcriptomic analysis was performed using The Cancer Genome Atlas.

RESULTS

BRAF mutations were identified in 9515 (6.2%) samples among 153,834, with melanoma (31%), CRC (20.7%), and NSCLC (13.9%) being the most frequent cancer types. Class 1 harbored co-mutations outside of the MAPK pathway (TERT, RFN43) vs. Class 2/3 mutations (RAS, NF1). Across all tumor types, Class 2/3 were enriched for alterations in genes involved in UV response and WNT/β-catenin. Pathway analysis revealed enrichment of WNT/β-catenin and Hedgehog signaling in non-V600 mutated CRC. Males had a higher proportion of Class 3 mutations vs. females (17.4% vs. 12.3% q = 0.003). Non-V600 mutations were generally more common in older patients (aged 60+) vs. younger (38% vs. 15% p < 0.0001), except in CRC (15% vs. 30% q = 0.0001). Black race was associated with non-V600 BRAF alterations (OR: 1.58; p < 0.0001).

CONCLUSIONS

Class 2/3 BRAFs are more present in Black male patients with co-mutations outside of the MAPK pathway, likely requiring additional oncogenic input for tumorigenesis. Improving access to NGS and trial enrollment will help the development of targeted therapies for non-V600 BRAF mutations.

Pan-cancer analysis of NFE2L2 mutations identifies a subset of lung cancers with distinct genomic and improved immunotherapy outcomes

BACKGROUND

Mutations in the KEAP1-NFE2L2 signaling pathway were linked to increased tumorigenesis and aggressiveness. Interestingly, not all hotspot mutations on NFE2L2 were damaging; some even were activating. However, there was conflicting evidence about the association between NFE2L2 mutation and Nrf2-activating mutation and responsiveness to immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC) and other multiple cancers.

METHODS

The study with the largest sample size (n = 49,533) explored the landscape of NFE2L2 mutations and their impact response/resistance to ICIs using public cohorts. In addition, the in-house WXPH cohort was used to validate the efficacy of immunotherapy in the NFE2L2 mutated patients with NSCLC.

RESULTS

In two pan-cancer cohorts, Nrf2-activating mutation was associated with higher TMB value compared to wild-type. We identified a significant association between Nrf2-activating mutation and shorter overall survival in pan-cancer patients and NSCLC patients but not in those undergoing ICIs treatment. Similar findings were obtained in cancer patients carrying the NFE2L2 mutation. Furthermore, in NSCLC and other cancer cohorts, patients with NFE2L2 mutation demonstrated more objective responses to ICIs than patients with wild type. Our in-house WXPH cohort further confirmed the efficacy of immunotherapy in the NFE2L2 mutated patients with NSCLC. Lastly, decreased inflammatory signaling pathways and immune-depleted immunological microenvironments were enriched in Nrf2-activating mutation patients with NSCLC.

CONCLUSIONS

Our study found that patients with Nrf2-activating mutation had improved immunotherapy outcomes than patients with wild type in NSCLC and other tumor cohorts, implying that Nrf2-activating mutation defined a distinct subset of pan-cancers and might have implications as a biomarker for guiding ICI treatment, especially NSCLC.

Emerging Targeted Therapies in Advanced Non-Small-Cell Lung Cancer

Lung cancer remains the leading cause of cancer-related mortality worldwide. Non-small-cell lung cancer (NSCLC) is the most common type and is still incurable for most patients at the advanced stage. Targeted therapy is an effective treatment that has significantly improved survival in NSCLC patients with actionable mutations. However, therapy resistance occurs widely among patients leading to disease progression. In addition, many oncogenic driver mutations in NSCLC still lack targeted agents. New drugs are being developed and tested in clinical trials to overcome these challenges. This review aims to summarize emerging targeted therapy that have been conducted or initiated through first-in-human clinical trials in the past year.

Recent progress in targeted therapy for non-small cell lung cancer

The high morbidity and mortality of non-small cell lung cancer (NSCLC) have always been major threats to people's health. With the identification of carcinogenic drivers in non-small cell lung cancer and the clinical application of targeted drugs, the prognosis of non-small cell lung cancer patients has greatly improved. However, in a large number of non-small cell lung cancer cases, the carcinogenic driver is unknown. Identifying genetic alterations is critical for effective individualized therapy in NSCLC. Moreover, targeted drugs are difficult to apply in the clinic. Cancer drug resistance is an unavoidable obstacle limiting the efficacy and application of targeted drugs. This review describes the mechanisms of targeted-drug resistance and newly identified non-small cell lung cancer targets (e.g., KRAS G12C, NGRs, DDRs, CLIP1-LTK, PELP1, STK11/LKB1, NFE2L2/KEAP1, RICTOR, PTEN, RASGRF1, LINE-1, and SphK1). Research into these mechanisms and targets will drive individualized treatment of non-small cell lung cancer to generate better outcomes.