Non-small Cell Lung Cancer Histology Research Articles

Proteogenomic analysis reveals non-small cell lung cancer subtypes predicting chromosome instability, and tumor microenvironment

Non-small cell lung cancer (NSCLC) is histologically classified into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LSCC). However, some tumors are histologically ambiguous and other pathophysiological features or microenvironmental factors may be more prominent. Here we report integrative multiomics analyses using data for 229 patients from a Korean NSCLC cohort and 462 patients from previous multiomics studies. Histological examination reveals five molecular subtypes, one of which is a NSCLC subtype with PI3K-Akt pathway upregulation, showing a high proportion of metastasis and poor survival outcomes regardless of any specific NSCLC histology. Proliferative subtypes are present in LUAD and LSCC, which show strong associations with whole genome doubling (WGD) events. Comprehensive characterization of the immune microenvironment reveals various immune cell compositions and neoantigen loads across molecular subtypes, which predicting different prognoses. Immunological subtypes exhibit a hot tumor-enriched state and a higher efficacy of adjuvant therapy.

Validation of Non-Small Cell Lung Cancer Clinical Insights Using a Generalized Oncology Natural Language Processing Model.

Limited studies have used natural language processing (NLP) in the context of non-small cell lung cancer (NSCLC). This study aimed to validate the application of an NLP model to an NSCLC cohort by extracting NSCLC concepts from free-text medical notes and converting them to structured, interpretable data. Patients with a lung neoplasm, NSCLC histology, and treatment information in their notes were selected from a repository of over 27 million patients. From these, 200 were randomly selected for this study with the longest and the most recent note included for each patient. An NLP model developed and validated on a large solid and blood cancer oncology cohort was applied to this NSCLC cohort. Two certified tumor registrars and a curator abstracted concepts from the notes: neoplasm, histology, stage, TNM values, and metastasis sites. This manually abstracted gold standard was compared with the NLP model output. Precision and recall scores were calculated. The NLP model extracted the NSCLC concepts with excellent precision and recall with the following scores, respectively: Lung neoplasm 100% and 100%, NSCLC histology 99% and 88%, histology correctly linked to neoplasm 98% and 79%, stage value 98.8% and 92%, stage TNM value 93% and 98%, and metastasis site 97% and 89%. High precision is related to a low number of false positives, and therefore, extracted concepts are likely accurate. High recall indicates that the model captured most of the desired concepts. This study validates that Optum's oncology NLP model has high precision and recall with clinical real-world data and is a reliable model to support research studies and clinical trials. This validation study shows that our nonspecific solid tumor and blood cancer oncology model is generalizable to successfully extract clinical information from specific cancer cohorts.

PD-L1 expression in pulmonary sarcomatoid carcinoma and response to immunotherapy.

8638 Background: Pulmonary sarcomatoid carcinoma (PSC) is a rare subtype of poorly differentiated non-small cell lung cancer (NSCLC) with poor prognosis. Outcomes to immune checkpoint inhibitors (ICIs) in patients with PSC have been incompletely examined, nor have they been compared to other NSCLC histologies. Methods: This retrospective study included two cohorts from MDACC and DFCI with metastatic NSCLC treated with ICI alone or with chemotherapy. Patients were categorized by histology as PSC, lung adenocarcinoma (LUAD), and lung squamous cell carcinoma (LUSC). We compared clinical and demographic characteristics between groups. Clinical progression-free survival (PFS), overall survival (OS), and best of response (BOR) were evaluated. BOR was further divided into responders (including partial and complete response) and non-responders (including stable and progressive disease). Results: A total of 1,515 patients from MDACC were included: 65 (4.3%) PSC, 1,138 (75.1%) LUAD, and 312 (20.6%) LUSC. PSC patients were more likely to be older and present with metastatic disease at diagnosis. PD-L1 expression was higher in PSC, with 71.7% having PD-L1 ≥50%. 19 out of 50 PSC patients (38%) treated with ICIs were responders, compared to 26.4% in LUAD and 21.8% in LUSC. In ICI-treated patients, median PFS (p=0.033) was 5.83 months (95%CI:2.9-22.67) in PSC, 7.5 (95%CI:6.97-8.17) in LUAD, and 6.13 (95%CI:5.23-7.23) in LUSC; median OS (p<0.001) was 33.4 (months, 95%CI:15.4-NA) in PSC, 27.6 (95%CI:25.3-30.3) in LUAD, and 18.2 (95%CI:14.6-21.9) in LUSC. Subgroup analysis showed no significant difference among histologies for patients with PD-L1 high expression in either PFS (p=0.16) or OS (p=0.23). In PSC patients, chemotherapy treatment alone had worse outcomes than treatment with ICIs; when added to ICIs, however, there was no difference between ICI-mono and ICI-chemotherapy. The DFCI cohort included 1,405 patients treated with ICI-mono or ICI-chemotherapy: 41 (2.9%) PSC, 1,296 (92.2%) LUAD, and 68 (4.8%) LUSC. As above, PSC patients had higher PD-L1 expression (65.9% PD-L1≥50%) and higher response rate (45.6%). In PSC patients, median PFS was 5.4 (months, 95%CI:3.5-14.37) and median OS was 11.7 (months, 95%CI:7.7-35.9), without significant difference compared to LUAD and LUSC. Across cohorts, PSC tumors were enriched for KRAS, MET, and TP53 mutations. There was no significant difference across histologies in tumor mutational burden or tumor-infiltrating lymphocytes (TIL) in the DFCI cohort, where this data was available. Conclusions: PSC is associated with higher PD-L1 expression and higher proportion of responders when treated with immunotherapy, with similar PFS and OS compared to LUAD and LUSC. Further investigation of the immune profile in PSC is needed to explore predictive factors of immunotherapy to optimize patient selection in clinical practice.

Cumulative incidence and risk factors of brain metastases in metastatic non-small cell lung cancer without baseline brain metastasis: Pooled analysis of individualized patient data from IMpower130, IMpower131, and IMpower150.

The objective of this study was to explore the abilities of atezolizumab plus chemotherapy in preventing brain metastases (BMs) among metastatic non-small cell lung cancer (NSCLC) without initial BMs, as well as the risk factors of BMs. Individual patient data from three trials involving first-line atezolizumab for metastatic NSCLC (IMpower130, IMpower131, and IMpower150) were pooled. Among patients without baseline BMs and without epidermal growth factor receptor(EGFR) and/or anaplastic lymphoma kinase (ALK) mutations, those receiving atezolizumab+chemotherapy±bevacizumab were classified as the atezolizumabplus chemotherapy group and those receiving placebo+chemotherapy±bevacizumab were classified as the chemotherapy group. The cumulative incidences of BM (CI-BMs) between the two groups were compared. Other factors associated with the CI-BM were analyzed by Cox regression analyses. With a median follow-up of 17.6 months (range, 0.03-33.64 months), 74 (3.1%) of the 2380 enrolled patients developed BMs, including 50 (3.1%) and 24 (3.0%) in the atezolizumab plus chemotherapy group (n=1589) and the chemotherapy group (n=791), respectively. The CI-BMs at 6, 12, and 24months were 1.7%, 2.8%, and 3.3%, respectively. After taking competing risk events into account, there was no significant difference in the CI-BMs between the two groups (p=.888). Nevertheless, the use of bevacizumab and the histology of nonsquamous NSCLC were found to be independently associated with the risk of BMs. In patients with metastatic EGFR/ALK wild-type NSCLC without baseline BMs, adding atezolizumab in the first-line treatment might not reduce the CI-BM. However, the administration of bevacizumab may reduce the risk of BMs.

On-cloud decision-support system for non-small cell lung cancer histology characterization from thorax computed tomography scans

Non-Small Cell Lung Cancer (NSCLC) accounts for about 85% of all lung cancers. Developing non-invasive techniques for NSCLC histology characterization may not only help clinicians to make targeted therapeutic treatments but also prevent subjects from undergoing lung biopsy, which is challenging and could lead to clinical implications. The motivation behind the study presented here is to develop an advanced on-cloud decision-support system, named LUCY, for non-small cell LUng Cancer histologY characterization directly from thorax Computed Tomography (CT) scans. This aim was pursued by selecting thorax CT scans of 182 LUng ADenocarcinoma (LUAD) and 186 LUng Squamous Cell carcinoma (LUSC) subjects from four openly accessible data collections (NSCLC-Radiomics, NSCLC-Radiogenomics, NSCLC-Radiomics-Genomics and TCGA-LUAD), in addition to the implementation and comparison of two end-to-end neural networks (the core layer of whom is a convolutional long short-term memory layer), the performance evaluation on test dataset (NSCLC-Radiomics-Genomics) from a subject-level perspective in relation to NSCLC histological subtype location and grade, and the dynamic visual interpretation of the achieved results by producing and analyzing one heatmap video for each scan. LUCY reached test Area Under the receiver operating characteristic Curve (AUC) values above 77% in all NSCLC histological subtype location and grade groups, and a best AUC value of 97% on the entire dataset reserved for testing, proving high generalizability to heterogeneous data and robustness. Thus, LUCY is a clinically-useful decision-support system able to timely, non-invasively and reliably provide visually-understandable predictions on LUAD and LUSC subjects in relation to clinically-relevant information.

The incidence and risk factors predictive of bone metastases at initial diagnosis of malignancy.

e18816 Background: Bone metastases are associated with increased morbidity and decreased quality of life in patients with solid tumors. We report here the incidence and predictive factors for bone metastases at initial diagnosis in a large population-based dataset. Methods: The Surveillance, Epidemiology, and End Results (SEER) database was used to identify patients 18 years-old or older and with solid cancers of all stages diagnosed from 2010 to 2019. We calculated the incidence and predictive factors for bone metastases according to demographic and tumor characteristics. Results: Among the 1,132,154 patients identified, 1,075,069 (94.96%) had known bone metastasis status and were eligible for the study. Bone metastases were detected in 55,903 (4.9%) patients. Among patients with bone metastases, the most common primary tumors were lung (44.4%), prostate (19.3%), breast (12.4%), kidney (4.0%), colorectal (2.24%) and pancreas (2.23%). The percentage of bone metastases in patients with non-small cell lung cancer (NSCLC), prostate adenocarcinoma, infiltrating ductal carcinoma of the breast (IDC), and colorectal adenocarcinoma (CRC) was 18.04% (18,123 out of 100,447), 5.92% (9,071 out of 153,325), 2.9% (4,490 out of 154,626), and 1.17% (1,043 out of 89,510) respectively. In addition to T and N stage, independent risk factors by multivariable analysis for bone metastases included female sex and non-squamous histology in NSCLC, black race, hormonal and HER2 status in breast cancer, Gleason score and PSA in prostate cancer, male sex, black race, rectal location, liver metastases and lung metastases in CRC. In univariate analysis, the strongest predictors for bone metastases were N2/3 (28.3%) and T3/4 (24.9%) in NSCLC, T3/4 (15.8%), N2/3 (10.6%), N1 (5.9%) and HER2 positive (4.9%) in IDC, N1 (38.1%), Gleason score 9/10 (22.8%), PSA 98 or higher (19.5%) and PSA 20-97 (16.8%) in prostate cancer, and rectal or rectosigmoid location (1.43% vs 1.05% in colon cancer, p < 0.001) in CRC. Conclusions: Approximately 5% of patients with solid tumors have bone metastases at presentation. In addition to T and N stage, there are several risk factors for bone metastases according to the primary tumor site.

LATIFY: Phase 3 study of ceralasertib + durvalumab vs docetaxel in patients with locally advanced or metastatic non-small-cell lung cancer that progressed on or after anti-PD-(L)1 and platinum-based therapy.

TPS9161 Background: Following progression on first- or second-line immunotherapy ± platinum-based chemotherapy (CT), the prognosis for patients (pts) with metastatic non-small-cell lung cancer (NSCLC) is poor with limited alternative options to docetaxel. Ceralasertib is a selective inhibitor of Ataxia Telangiectasia and Rad3-related (ATR) protein kinase, which is activated in response to DNA damage. Clinical data suggest that ceralasertib may sensitize tumors to immunotherapy by biasing T cells to an immune-effective phenotype. Durvalumab is a selective, high-affinity human IgG1 monoclonal antibody that binds programmed cell death ligand-1 (PD-L1) and inhibits PD-L1-mediated suppression of T-cell activation. Combining ceralasertib with durvalumab may amplify the antitumor immune response, and potentially lead to durable tumor control. In the ongoing phase 2 HUDSON study in pts with locally advanced or metastatic NSCLC who progressed on anti-PD-(L)1 therapy and platinum-doublet regimen, ceralasertib + durvalumab showed promising efficacy with median progression-free survival (PFS) of 6.0 mos (80% CI, 4.6–7.5) and median overall survival (OS) of 15.9 mos (80% CI, 14.1–20.3). Methods: LATIFY is a phase 3, open-label, randomized, multicenter study in pts with NSCLC (NCT05450692). Key inclusion criteria are age ≥18 years; ECOG performance status 0–1; documented EGFR and ALK wild-type, and tumor cell PD-L1 status; and adequate organ and bone marrow function. Stable brain metastases are allowed. Pts should be eligible for second- or third-line therapy, and must have received an anti-PD-(L)1 therapy and a platinum-containing doublet regimen for locally advanced or metastatic NSCLC either separately or in combination, but no other prior therapies. Key exclusion criteria include mixed small-cell lung and NSCLC histology, unresolved toxicities of Grade ≥2 (NCI CTCAE v5.0) from prior therapy, active or prior autoimmune or inflammatory disorders, > 1 line of prior anti-PD-(L)1 therapy (alone or in combination), and > 1 line of platinum-based CT in a metastatic setting. Pts are randomized 1:1 to receive either oral ceralasertib 240 mg twice daily on Days (D) 1–7 with IV durvalumab 1500 mg on D8 (28-day cycle) or IV docetaxel 75 mg/m2 (D1, 21-day cycle). The primary objective is to assess efficacy by OS. Secondary objectives include evaluating efficacy by PFS, objective response rate and disease control rate by RECIST v1.1; duration of, and time to, response; time to second progression or death; OS at 12 mos; time to deterioration of health-related quality of life and physical function; determining the pharmacokinetics of ceralasertib; and assessing safety. Approximately 580 pts will be recruited from around 21 countries in the Americas, Europe, and Asia Pacific. Clinical trial information: NCT05450692 .

Role of Radiomics Features and Machine Learning for the Histological Classification of Stage I and Stage II NSCLC at [18F]FDG PET/CT: A Comparison between Two PET/CT Scanners.

The aim of this study was to compare two different PET/CT tomographs for the evaluation of the role of radiomics features (RaF) and machine learning (ML) in the prediction of the histological classification of stage I and II non-small-cell lung cancer (NSCLC) at baseline [18F]FDG PET/CT. A total of 227 patients were retrospectively included and, after volumetric segmentation, RaF were extracted. All of the features were tested for significant differences between the two scanners and considering both the scanners together, and their performances in predicting the histology of NSCLC were analyzed by testing of different ML approaches: Logistic Regressor (LR), k-Nearest Neighbors (kNN), Decision Tree (DT) and Random Forest (RF). In general, the models with best performances for all the scanners were kNN and LR and moreover the kNN model had better performances compared to the other. The impact of the PET/CT scanner used for the acquisition of the scans on the performances of RaF was evident: mean area under the curve (AUC) values for scanner 2 were lower compared to scanner 1 and both the scanner considered together. In conclusion, our study enabled the selection of some [18F]FDG PET/CT RaF and ML models that are able to predict with good performances the histological subtype of NSCLC. Furthermore, the type of PET/CT scanner may influence these performances.

Risk of pneumonitis in non-small cell lung cancer patients with preexisting interstitial lung diseases treated with immune checkpoint inhibitors: a nationwide retrospective cohort study.

Chemotherapy for non-small cell lung cancer (NSCLC) patients with preexisting interstitial lung diseases (ILDs) increases the risk of developing pneumonitis. However, the association between pneumonitis and immune checkpoint inhibitors (ICIs) and related factors remains unclear. We conducted a retrospective cohort study using a nationwide inpatient database. We included patients (aged ≥ 20years) newly diagnosed with ILD and NSCLC and who started chemotherapy (ICIs or conventional chemotherapy) between January 2016 and December 2019. The primary endpoint was the onset of pneumonitis. We estimated the cumulative incidence function of pneumonitis and compared it with patients taking ICIs and patients receiving conventional chemotherapy using Gray's test. We calculated the subdistribution hazard ratios (HRs) and 95% confidence intervals (CIs) for the incidence of pneumonitis using Fine and Gray's model to adjust for sex, age, smoking status, histology of NSCLC, surgical history, and medical histories, considering death as the competing risk. We identified 1177 patients (mean age 72years, 13.8% female), of which 328 and 849 were in the ICI and conventional chemotherapy groups, respectively. There was no significant difference in the cumulative incidence function of pneumonitis between the two groups (p = 0.868). The adjusted subdistribution HR for the incidence of pneumonitis was 1.08 (95% CI: 0.74-1.57). Age (≥ 65years) (HR: 1.86, 95% CI: 1.11-3.10) and smoking history (HR: 2.04, 95% CI: 1.02-4.11) were associated with developing pneumonitis. The risk of developing pneumonitis with ICIs for NSCLC patients with preexisting ILD was similar to that with conventional chemotherapy.

Cloud-YLung for Non-Small Cell Lung Cancer Histology Classification from 3D Computed Tomography Whole-Lung Scans.

Non-Small Cell Lung Cancer (NSCLC) represents up to 85% of all malignant lung nodules. Adenocarcinoma and squamous cell carcinoma account for 90% of all NSCLC histotypes. The standard diagnostic procedure for NSCLC histotype characterization implies cooperation of 3D Computed Tomography (CT), especially in the form of low-dose CT, and lung biopsy. Since lung biopsy is invasive and challenging (especially for deeply-located lung cancers and for those close to blood vessels or airways), there is the necessity to develop non-invasive procedures for NSCLC histology classification. Thus, this study aims to propose Cloud-YLung for NSCLC histology classification directly from 3D CT whole-lung scans. With this aim, data were selected from the openly-accessible NSCLC-Radiomics dataset and a modular pipeline was designed. Automatic feature extraction and classification were accomplished by means of a Convolutional Long Short-Term Memory (ConvLSTM)-based neural network trained from scratch on a scalable GPU cloud service to ensure a machine-independent reproducibility of the entire framework. Results show that Cloud- YLung performs well in discriminating both NSCLC histotypes, achieving a test accuracy of 75% and AUC of 84%. Cloud-YLung is not only lung nodule segmentation free but also the first that makes use of a ConvLSTM-based neural network to automatically extract high-throughput features from 3D CT whole-lung scans and classify them. Clinical relevance- Cloud-YLung is a promising framework to non-invasively classify NSCLC histotypes. Preserving the lung anatomy, its application could be extended to other pulmonary pathologies using 3D CT whole-lung scans.

Proteomic analysis of plasma exosomes in patients with non-small cell lung cancer.

BackgroundCurrently, the prognosis of patients with non-small cell lung cancer (NSCLC) remains unsatisfactory. This current study evaluated the relationship between histology of NSCLC and protein expression of exosomes in the plasma from NSCLC patients, and furthermore investigate the impact of the exosome profile on the tumor, node, metastasis (TNM) classification.MethodsPlasma samples were collected from 26 NSCLC patients before surgery. The exosomes were extracted from the plasma and liquid chromatography-mass spectrometry (LC/MS) was used to evaluate the expression of the proteins in the exosomes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the Cytoscape 3.8.2 software. Multivariate logistic regression and receiver operating characteristic (ROC) curves were used to identify proteins which could effectively distinguish between lung adenocarcinoma and lung squamous cell carcinoma. The relationship between protein expression and the TNM stage was calculated using Spearman rank correlation.ResultsThe expression levels of ZSWIM9, FYB1, SERPINF1, C1orf68, MASP2, and IGHV3-72 were higher in patients with lung adenocarcinoma compared to patients with lung squamous cell carcinoma. MFGE8 was associated with the occurrence of squamous cell carcinoma. CORO1A was positively correlated with the TNM stage of the patients, and COL4A2 was negatively correlated with TNM stage. GO and KEGG analyses revealed that cholesterol metabolism was important in NSCLC development.ConclusionsLung adenocarcinoma may be distinguished from squamous cell carcinoma by the molecular profile of exosomes in the plasma samples. And, proteomics analysis suggested that cholesterol metabolism may play an important role of cancer progress in NSCLC.

Abstract 5200: The incidence and risk factors of hyperprogressive disease(HPD) in non-small cell lung cancer(NSCLC) treated with immune checkpoint inhibitors(ICI)

Abstract Background: HPD is an unexpected rapid acceleration of tumor growth reported for patients treated with ICI. The definition of HPD is not fully established and risk factors are uncertain. Our aim in this study was to find the incidence of HPD as well as its significant clinical and genetic risk factors. Method: A total of 203 patients with NSCLC who received ICI at Northwestern Memorial Hospital during 2008-2020 were included. Tumor growth kinetics (TGK) on immunotherapy and TGK pre-immunotherapy were collected and TGK ratio (TGKR) was calculated. HPD was defined as TGKR≥2. Result: HPD was observed in 16% (n=33) of patients. HPD was significantly associated with high ECOG (3-4 vs 1-2) [OR = 4.52, 95% CI = 1.31 to 16.29, P = 0.01], presence of bone metastasis [OR = 2.51, 95% CI = 1.10 to 5.28, P = 0.01], neutrophils/lymphocyte ratio(NLR) ≥5 [OR = 2.68, 95% CI = 1.24 to 5.81 P = 0.01], thrombocytosis (platelet count >450k) [OR = 5.31, 95% CI = 2.07 to 14.70, P=0.0003], and treatment with PD-L1 inhibitor vs PD-1 inhibitor [OR = 2.14, 95% CI= 0.97 to 4.68, P= 0.068]. HPD was inversely associated with positive PD-L1 expression [OR = 0.34, 95% CI = 0.13 to 0.86, P = 0.01], ICI combined with chemotherapy vs ICI monotherapy [OR = 0.24, 95% CI = 0.055 to 0.97, P=0.04], and positive TTF-1 expression [OR = 0.46, 95% CI = 0.21 to 1.02, P=0.057]. HPD was not associated with histology of NSCLC (adenocarcinoma, squamous cell carcinoma, large cell carcinoma, and large cell neuroendocrine carcinoma), age(<70, ≥70), sex, smoking history, number of metastatic lesions (<3, ≥3), brain metastasis, liver metastasis, brain metastasis before treatment initiation, EGFR mutation, TP53 mutation, BRAF mutation, and TNM staging. Conclusion: The incidence of HPD in NSCLC treated with ICI was consistent with current literature. High NLR and platelet count are suggestive of an immunosuppressive environment that could contribute to the rapid growth of tumors. To our knowledge, this is the first study to report an inverse correlation of PD-L1 expression and TTF-1 expression with HPD. The strength of our study lies in a large number of patients. Further studies on validating risk factors of HPD and exploring their associated mechanisms are warranted. Citation Format: Hyeonseon Kim, Yeun Ho Lee, Chiwoo Song, Leeseul Kim, Min Jeong Kim, Horyun Choi, Jinah Kim, Young Kwang Chae. The incidence and risk factors of hyperprogressive disease(HPD) in non-small cell lung cancer(NSCLC) treated with immune checkpoint inhibitors(ICI) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5200.

Lung cancer treatment patterns in patients with diabetes.

e18723 Background: Lung cancer is the leading cause of cancer death in the United States, with > 85% classified as non-small cell lung cancer (NSCLC). Diabetes mellitus (DM) is a common comorbidity in patients with NSCLC. While surgery is the standard of care for early-stage NSCLC, patients who have DM with end organ damage are considered medically inoperable according to treatment guidelines and whether this influences NSCLC treatment and outcomes is unclear. This study aimed to investigate treatment patterns and outcomes among patients with early-stage NSCLC and DM. Methods: Using the Surveillance, Epidemiology, and End Results database linked to Medicare (2000-2016), we identified patients ≥65 years old with Stage I-IIIA NSCLC treated with lobectomy, limited resection (wedge resection and segmentectomy), or no surgery. DM and complications at the time of NSCLC diagnosis were ascertained through published claims-based algorithms. Patients were categorized as having no DM, DM without severe complications (DM-c), or DM with ≥1 severe complication (i.e., end-organ damage, DM+c). We used multinomial logistic regression to assess if DM was associated with treatment. Association of DM with overall survival (OS) and lung cancer-specific survival (LCSS) was analyzed with Cox regression stratified by treatment type. These analyses controlled for demographics, comorbidities, and NSCLC histology and stage. Results: Of 60,300 patients analyzed, 45,270 (75%) had no DM, 6,873 (12%) had DM-c and 7,887 (13%) had DM+c. More patients with DM+c (N = 4,508[57%]), did not receive surgery vs. patients with DM-c (N = 3,771[55%]) and without DM (N = 23,289[51%]). DM was associated with lower odds of receiving lobectomy vs. no surgery in adjusted analysis (odds ratio [OR]: 0.88; 95% confidence interval [CI]: 0.83-0.93 for DM-c, and OR: 0.91; 95% CI: 0.86-0.97 for DM+c vs. no DM), but not for limited resection vs. no surgery (OR: 0.92; 95% CI: 0.83-1.02 for DM-c and OR: 0.91; 95% CI: 0.92-1.11 for DM+c vs. no DM). Cox regression showed that in patients with lobectomy and limited resection, compared to no DM, DM+c was associated with worse OS (hazard ratio [HR]: 1.21; 95% CI: 1.15 to 1.27 [lobectomy]; HR: 1.17; 95% CI: 1.07-1.28 [limited resection]), but not LCSS (HR: 1.06; 95% CI: 0.99-1.14 [lobectomy]; HR: 1.02; 95% CI: 0.90-1.17 [limited resection]). Among patients who received no surgery, DM+c patients had both worse LCSS (HR: 1.05; 95% CI: 1.00-1.09 and OS (HR: 1.12; 95% CI: 1.08-1.16) vs. no DM. DM-c was not associated with worse LCSS or OS for all treatment categories. Conclusions: Patients with Stage I-IIIA NSCLC and DM+c were less likely to undergo surgery and had worse OS but not LCSS if they underwent full or limited resection, while they had worse OS and LCSS if they did not have surgery. These findings suggest that patients with DM with end-organ damage benefit from more aggressive NSCLC treatment, but research is needed to determine optimal treatments in these patients.

Analysis of actionable genetic alterations in lung carcinoma from the VA National Precision Oncology Program

Lung cancer is the leading cause of cancer mortality in men and women. Genomic sequencing of non-small cell lung cancer (NSCLC) is critical for the optimal treatment of NSCLC. In this study we sought to describe the frequencies of highly actionable driver mutations in lung adenocarcinoma (LUAD), squamous cell (LUSQ) and other NSCLC histologies (LUOT) in Veterans tested through the VA's National Precision Oncology Program (NPOP) and compare these frequencies to other published datasets from highly specialized academic cancer centers. The NPOP cohort included 3,376 unique Veterans with a diagnosis of lung carcinoma tested between February 2019 and January 2021 including 1892 with LUAD, 940 with LUSQ, and 549 with LUOT. Among patients with LUAD, 27.5% had highly actionable genetic variants. The frequency of targetable mutations was as follows: ALK rearrangement 0.8%, BRAF V600E 2.1%, EGFR exon 20 insertion mutation 0.48%, EGFR sensitizing mutations 6.6%, ERBB2 small variants 1.2%, KRAS G12C 14.0%, MET exon 14 skipping mutation 1.5%, NTRK rearrangement 0.1%, RET rearrangement 0.4%, and ROS1 rearrangement 0.3%. The frequency of EGFR mutations, RET rearrangement, MET exon 14 and ERBB2 small variants frequencies were significantly lower in NPOP compared to other published reports while MET amplification was more common in NPOP. Combined rates of highly actionable genetic variants were 2.7% and 13.4% in LUSQ and LUOT, respectively. In this study, 27.5% of Veterans with lung adenocarcinoma have actionable genetic alterations eligible for FDA approved targeted therapies, a frequency only slightly lower than other published datasets despite higher smoking rates in Veterans. Genomic sequencing should be performed in all Veterans with advanced LUAD and LUOT.

Integrative analysis of non-small cell lung cancer patient-derived xenografts identifies distinct proteotypes associated with patient outcomes

Non-small cell lung cancer (NSCLC) is the leading cause of cancer deaths worldwide. Only a fraction of NSCLC harbor actionable driver mutations and there is an urgent need for patient-derived model systems that will enable the development of new targeted therapies. NSCLC and other cancers display profound proteome remodeling compared to normal tissue that is not predicted by DNA or RNA analyses. Here, we generate 137 NSCLC patient-derived xenografts (PDXs) that recapitulate the histology and molecular features of primary NSCLC. Proteome analysis of the PDX models reveals 3 adenocarcinoma and 2 squamous cell carcinoma proteotypes that are associated with different patient outcomes, protein-phosphotyrosine profiles, signatures of activated pathways and candidate targets, and in adenocarcinoma, stromal immune features. These findings portend proteome-based NSCLC classification and treatment and support the PDX resource as a viable model for the development of new targeted therapies.

A quantitative evaluation of the histological type dependence of the programmed death-ligand 1 expression in non-small cell lung cancer including various adenocarcinoma subtypes: a cross-sectional study.

The association between non-small cell lung cancer histology and programmed death-ligand 1 expression remains controversial. We retrospectively analyzed histological dependence of the programmed death-ligand 1 expression by a multiple regression analysis of 356 non-small cell lung cancer patients. The programmed death-ligand 1 expression patterns of adenocarcinoma were consistent with a pathological predominant growth pattern as a reference to papillary adenocarcinoma: minimally invasive adenocarcinoma[partial regression coefficient (B), 0.17; 95% confidence interval, 0.05–0.59], lepidic adenocarcinoma (B, 0.46; 95% confidence interval, 0.23–0.90), acinar adenocarcinoma (B, 1.98; 95% confidence interval, 1.05–3.76) and solid adenocarcinoma (B, 5.11; 95% confidence interval, 2.20–11.9). In histology other than adenocarcinoma, the programmed death-ligand 1 expression tended to be high with poor differentiation: adenosquamous carcinoma (B, 4.17; 95% confidence interval, 1.05–16.6), squamous cell carcinoma (B, 4.32; 95% confidence interval, 2.45–7.62) and pleomorphic carcinoma (B, 13.0; 95% confidence interval, 4.43–38.2). We showed quantitatively that the programmed death-ligand 1 expression in non-small cell lung cancer tended to be clearly histology-dependent, with more poorly differentiated histology showing a higher expression.

Plasma complement C7 as a target in non-small cell lung cancer patients to implement 3P medicine strategies.

Programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) immune checkpoint inhibitors (ICIs) significantly affect outcomes in non-small cell lung cancer (NSCLC) patients. However, differences in reactions toward PD-1/PD-L1 ICI among patients impose inefficient treatment. Therefore, developing a reliable biomarker to predict PD-1/PD-L1 ICI reaction is highly necessary for predictive, preventive, and personalized (3P) medicine. We recruited 63 patients from the National Cancer Center (NCC) and classified them into the training and validation sets. Next, 99 patients were recruited for inclusion into the external validation set at the Samsung Medical Center (SMC). Proteomic analysis enabled us to identify plasma C7 levels, which were significantly different among groups classified by their overall response to the RECIST V 1.1-based assessment. Analytical performance was evaluated to predict the PD-1/PD-L1 ICI response for each type of immunotherapy, and NSCLC histology was evaluated by determining the C7 levels via ELISA. Plasma C7 levels were significantly different between patients with and without clinical benefits (PFS ≥ 6 months). Among the groups sorted by histology and PD-1/PD-L1 immunotherapy type, only the predicted accuracy for pembrolizumab-treated patients from both NCC and SMC was greater than 73%. In patients treated with pembrolizumab, C7 levels were superior to those of the companion diagnostics 22C3 (70.3%) and SP263 (62.1%). Moreover, for pembrolizumab-treated patients for whom the PD-L1 tumor proportion score (TPS) was < 50%, the predictive accuracy of C7 was nearly 20% higher than that of 22C3 and SP263. Evaluation of plasma C7 levels shows an accurate prediction of NSCLC patient reactions on pembrolizumab. It demonstrates plasma C7 is an alternative and supportive biomarker to overcome the predictive limitation of previous 22C3 and SP263. Thus, it is clear that clinical use of plasma C7 allows predictive diagnosis on lung cancer patients who have not been successfully treated with current CDx and targeted prevention on metastatic diseases in secondary care caused by a misdiagnosis of current CDx. Reduction of patients' financial burden and increased efficacy of cancer treatment would also enable prediction, prevention, and personalization of medical service on NSCLC patients. In other words, plasma C7 provides efficient medical service and an optimized medical economy followed which finally promotes the prosperity of 3P medicine. The online version contains supplementary material available at 10.1007/s13167-021-00266-x.

Anti-PD1 Therapy Plus Whole-Brain Radiation Therapy May Prolong PFS in Selected Non-Small Cell Lung Cancer Patients with Brain Metastases: A Retrospective Study.

BackgroundWhole-brain radiotherapy (WBRT) remains an essential modality of treatment for brain metastases (BMs) derived from non-small cell lung cancer (NSCLC) patients and anti-PD-1 therapy has demonstrated intracranial responses in these patients. We aimed to evaluate if the combination of the two treatments could yield additive efficacy.MethodsA retrospective review of our institution’s database was carried out to identify NSCLC patients with BMs who had been treated with anti-PD1 therapy and/or WBRT between 2015 and 2020. Patient characteristics, main outcomes, including progression-free survival (PFS) and overall survival (OS), and factors affecting these outcomes were analyzed. SPSS 24 was used for statistical analysis. Appropriate statistical tests were employed according to the type of data.ResultsOverall, 21 NSCLC BM patients were identified that had received WBRT. Of these, ten had been additionally treated with anti-PD1 therapy within 30 days of WBRT initiation. Median PFS was 3 (95% CI 0.8–5.1) months with WBRT alone versus 11 (95% CI 6.3–15.6) months with combined treatment. Risk of disease progression was 71% lower with the combined approach (HR 0.29, 95% CI 0.11–0.80; p=0.016). A trend toward improved OS was also observed with the combined approach (HR 0.33, 95% CI 0.08–1.12; p=0.107). Concurrent treatment (p=0.028) and male sex (p=0.052) were associated with improved PFS, while OS was associated only with age (p=0.02).ConclusionConcurrent WBRT and anti-PD1 therapy may delay progression and improve survival in BM patients with confirmed EGFR- and ALK-negative NSCLC histology. Prospective studies are warranted to validate and elucidate on the additive effect of the two modalities.

Identification of Clinical Predictors of Benefit of Palliative Thoracic Radiotherapy

Palliative thoracic radiotherapy (PTRT) is frequently utilized for relief of pain, obstruction and hemoptysis from lung and bronchial primary tumors and metastatic disease. Historically, the prognosis for these patients has been dismal with radiotherapy (RT) often serving as a last line of therapy. However, with continued advancements in systemic therapies for non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC) and other malignancies, PTRT may provide increased benefit both in relief of symptoms and prolongation of life for select patients. In this study, we sought to determine clinical predictors of benefit of PTRT in a contemporary cohort of patients with the goal of aiding in appropriate patient selection for escalation or de-escalation of RT in patients with locally advanced or metastatic disease.Patients treated with standard palliative courses of RT, including 8 Gy/1 fx, 20 Gy/5 fx, and 30 Gy/10 fx, to the thoracic cavity between 2014 and 2020 were identified. Clinical information was collected, including age, ECOG PS, histology, stage, presence of brain metastases, presence of lung collapse, lines of previous systemic therapy, neutrophil-lymphocyte ratio (NLR), mutational status, and survival outcomes. Kaplan-Meier analyses were used for survival and sensitivity analyses. Cox regression was performed for multivariate analysis.161 evaluable patients were identified who received PTRT for various indications including dyspnea, SVC syndrome, hemoptysis, pain and obstruction/lung collapse. A majority of patients (61%) had a diagnosis of NSCLC, although SCLC (10%) and other histologies (29%) were also included. 94% of patients were metastatic at the time of receipt of PTRT. Multivariate analysis identified female sex, multi-fraction regimens, better baseline ECOG performance status, presence of an actionable mutation, absence of brain mets, and non-lung histologies as positive predictors of survival following PTRT (P < 0.05). Other factors including age, number of lines of systemic therapy and NLR were not identified as independent predictors in this cohort in either multivariate or univariate analyses. Notably, 14 patients failed to complete prescribed therapy all of whom had ECOG PS of 2 or greater at start of RT.Patients with symptomatic lung disease derive differential benefit from PTRT with ECOG 0-1, absence of brain mets and presence of actionable mutations important clinical predictors of longer-term survival following treatment. Single fraction courses should be strongly considered for poor PS patients who may derive less significant survival benefit from PTRT and are at higher risk for early treatment discontinuation.

P40.09 Using Real World Data to Identify a Patient Cohort Who Require Prehabilitation to Improve Treatment Rates for Stage 3 NSCLC

Patients with stage 3 Non Small Cell lung Cancer (NSCLC) are a heterogeneous group. Recent National Lung Cancer Audit (NLCA) indicates a third of patients (36%) received no active treatment. We audited the treatment received by all stage III NSCLC patients treated between April 2014 and April 2017 at the Edinburgh Cancer Centre. Using this data, we hope to delineate patterns in our selection of treatment based on a patient’s performance status and co-morbidities, to better understand which patient groups may be more likely to receive sub-optimal treatment. 374 patients were identified via local cancer registry. Patients were then categorised by treatment modality, ECOG performance status (PS) and Charlson morbidity score (CMS). Radical treatment was defined as chemoradiotherapy, radical radiotherapy or surgery. 374 patients were identified: 190 males (50.8%) and 184 females (49.2%). 222 patients (59.36%) were aged ≥70yrs, with a median age of 71yrs (range 42-93yrs). Median follow-up period was 398 days (range: 0-2445 days). 105 patients (28.07%) had a CMS of ≥1. 118 patients (31.55%) had an adenocarcinoma tumour type, 117 patients (31.28%) had squamous histology, 35 patients (9.36%) had other NSCLC histology and 4 patients (1.07%) had non-lung cancer histology. Histology was unavailable for 100 patients (26.74%). Radical treatment was given to 200 patients - surgery (24 patients, 12%), chemoradiotherapy (99 patients, 49.5%) and radical radiotherapy (77 patients, 38.5%). Of those with a PS 0 (64 patients): 47 (73.44%) received radical treatment; 12 (18.75%) received non-radical treatment, and 5 (7.81%) did not receive any treatment. Of those with PS 1 (178 patients): 115 (64.61%) received radical treatment; 27 (15.17%) received non-radical treatment, and 36 (20.22%) did not receive any treatment. Of those with a PS 2 (112 patients): 37 (33.04%) received radical treatment; 29 (25.89%) received non-radical treatment, and 46 (41.07%) did not receive any treatment. 20 patients did not have a clear documentation of their PS – 18 of these did not receive any treatment. Of those with CMS 0 (269 patients): 152 (56.51%) received radical treatment; 56 (20.82%) received non-radical treatment, and 61 (22.68%) did not receive any treatment. Of those with CMS 1 (61 patients): 34 (55.74%) received radical treatment; 6 (9.84%) received non-radical treatment, and 21 (34.43%) did not receive any treatment. Of those with a CMS ≥2 (44 patients): 14 (31.82%) received radical treatment; 7 (15.91%) received non-radical treatment, and 23 (52.27%) did not receive any treatment. Those with a better PS and CMS were more likely to receive treatment with radical intent. Those with a higher CMS were far more likely to receive no treatment or non-radical treatment. These patients may benefit from prehabilitation prior to treatment, to allow provision of life prolonging therapy to a greater cohort.