Breathomics as Predictive Biomarker for Checkpoint Inhibitor Response

1014 Background: TAPUR is a phase II basket study evaluating the anti-tumor activity of commercially available targeted agents in pts with advanced cancers with specific genomic alterations. P is an immune checkpoint inhibitor and HTMB is an emerging predictive biomarker for checkpoint inhibitor therapy. Results in a cohort of MBC pts with HTMB treated with P are reported. Methods: Eligible pts had advanced cancer, no standard treatment options, ECOG PS 0-1, measurable disease and acceptable organ function. Genomic testing was performed using commercially available tests selected by sites. Pts matched to P had HTMB defined as ≥9 mutations/megabase (Muts/Mb) by a FoundationOne test (n=20) or approved by the TAPUR Molecular Tumor Board for other tests (n=8). A Simon two-stage design was used to test a null rate of 15% vs. 35% (power = 0.85; α = 0.10). If ≥2 of 10 pts in stage 1 have disease control (DC) (objective response (OR) or stable disease at 16 weeks (wks) (SD16+)), an additional 18 pts are enrolled. If ≥7 of 28 pts have DC, the drug is considered worthy of further study. Secondary endpoints are progression-free survival (PFS), overall survival (OS) and safety. Results: Twenty-eight female MBC pts were enrolled from October 2016 to July 2018. Pts received P at 2 mg/kg (n=8) or 200 mg (n=20) IV over 30 minutes, every 3 wks. HTMB ranged from 9 to 37 Muts/Mb. Demographics and outcomes are summarized in Table (N=28). No relationship was observed between #Muts/Mb and PFS or OS. Two grade 3 AEs (weight loss and hypoalbuminemia) and 1 grade 2 SAE (urinary tract infection) were reported as at least possibly related to P. Conclusions: P demonstrated anti-tumor activity in heavily pre-treated MBC pts with HTMB . Additional study of P is warranted in MBC pts with HTMB. Clinical trial information: NCT02693535. [Table: see text]

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Deep learning-based predictive biomarker for immune checkpoint inhibitor response in metastatic non-small cell lung cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.9094 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. 9094-9094

Author(s):

Sehhoon Park ◽

Chang Ho Ahn ◽

Geunyoung Jung ◽

Sarah Lee ◽

Kyunghyun Paeng ◽

...

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Immune Checkpoint ◽

Immune Checkpoint Inhibitor ◽

Checkpoint Inhibitor ◽

Predictive Biomarker ◽

Small Cell ◽

Small Cell Lung

9094 Background: In the era of immunotherapy, immune checkpoint inhibitor (ICI) has changed the treatment paradigm in metastatic non-small cell lung cancer (NSCLC). Along with clinical trials, there is an ongoing investigation to discover the predictive biomarker of ICI which so far has unsatisfactory reliability. As an effort to enhance the predictive value of ICI treatment, we applied deep learning and developed artificial intelligent (AI) score (range from 0 to 1) to analyze the specific context of immune-tumor microenvironment (TME) extracted by scanned images from H&E slides. Methods: As a ground work, deep learning-based H&E image analyzer, Lunit SCOPE, has been trained with H&E images (n = 1824) from ICI naive NSCLC samples. For the calculation of AI score, training was conducted using responder/non-responder labeled ICI treated samples from the exploratory cohort. The ICI responder was defined as the patient with a best overall response of partial or complete response and stable disease for more than 6 months. The positivity of PD-L1 immunohistochemistry (IHC) was assessed manually by pathologists. Results: The exploratory cohort is composed of NSCLC patients treated with ICI (n = 189) in Samsung Medical Center, and response to ICI was observed in 72 (38.1%) patients. Median follow-up duration was 6.8 months (6.6~8.2). Samples with PD-L1 IHC positive, defined by ≥ 1%, was observed in 138 (73.0%) patients. AI score was significant higher in the responder group (median: 0.391 vs 0.205, P = 6.14e-5), and the patients with AI score above the cut-off (0.337) showed a better response to ICI (odds ratio [OR] 3.47 P = 7.34e-5) which is higher than patients with PD-L1 ≥ 1% (OR 1.92, P = 0.069). High AI score group (n = 83) showed significantly favorable PFS compared to low AI score group (n = 106, median PFS: 5.1m vs 1.9m, hazard ratio [HR] 0.51, P = 9.6e-5) and this outcome was independent with PD-L1 status (P = 6.0e-5). In subgroup analysis, PFS of PD-L1 high / AI score high group (n = 63) had longer median PFS (6.7m) compared to both PD-L1 high / AI score low group (n = 70, 4.0m, P = 0.001) and PD-L1 low/AI score low group (n = 35, 1.9m, P = 4.0e-6). Tumor infiltrating lymphocyte (TIL) density of cancer epithelium was significantly correlated with AI score (Pearson’s r = 0.310, P = 1.43e-5), which suggests that AI score may partly reflect TME represented by TIL. Conclusions: The AI score by machine-learned information, extracted from H&E images without additional IHC stain, could predict responsiveness and PFS of ICI treatment independent of PD-L1 IHC positivity.

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OR19-5 The Impact Of High Dose Glucocorticoids On The Outcome Of Immune Checkpoint Inhibitor-related Thyroid Disorders And The Baseline TSH As A Predictive Biomarker

Journal of the Endocrine Society ◽

10.1210/js.2019-or19-5 ◽

2019 ◽

Vol 3 (Supplement_1) ◽

Author(s):

Le Min ◽

Chanjuan Ma ◽

F. Stephen Hodi ◽

Anita Giobbie-Hurder ◽

Romualdo Barroso-Sousa ◽

...

Keyword(s):

Immune Checkpoint ◽

Immune Checkpoint Inhibitor ◽

Checkpoint Inhibitor ◽

Predictive Biomarker ◽

High Dose ◽

Thyroid Disorders ◽

The Impact

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Ratio of the interferon-γ signature to the immunosuppression signature predicts anti-PD-1 therapy response in melanoma

npj Genomic Medicine ◽

10.1038/s41525-021-00169-w ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Chuanliang Cui ◽

Canqiang Xu ◽

Wenxian Yang ◽

Zhihong Chi ◽

Xinan Sheng ◽

...

Keyword(s):

Immune Checkpoint Inhibitor ◽

Validation Cohort ◽

Checkpoint Inhibitor ◽

Therapy Response ◽

Predictive Biomarkers ◽

Predictive Biomarker ◽

Interferon Γ ◽

Therapy Outcome ◽

Sequencing Data ◽

Internal Validation

AbstractImmune checkpoint inhibitor (ICI) treatments produce clinical benefit in many patients. However, better pretreatment predictive biomarkers for ICI are still needed to help match individual patients to the treatment most likely to be of benefit. Existing gene expression profiling (GEP)-based biomarkers for ICI are primarily focused on measuring a T cell-inflamed tumor microenvironment that contributes positively to the response to ICI. Here, we identified an immunosuppression signature (IMS) through analyzing RNA sequencing data from a combined discovery cohort (n = 120) consisting of three publicly available melanoma datasets. Using the ratio of an established IFN-γ signature and IMS led to consistently better prediction of the ICI therapy outcome compared to a collection of nine published GEP signatures from the literature on a newly generated internal validation cohort (n = 55) and three published datasets of metastatic melanoma treated with anti-PD-1 (n = 54) and anti-CTLA-4 (n = 42), as well as in patients with gastric cancer treated with anti-PD-1 (n = 45), demonstrating the potential utility of IMS as a predictive biomarker that complements existing GEP signatures for immunotherapy.

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Peripheral Blood Autoantibodies Against to Tumor-Associated Antigen Predict Clinical Outcome to Immune Checkpoint Inhibitor-Based Treatment in Advanced Non-Small Cell Lung Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.625578 ◽

2021 ◽

Vol 11 ◽

Author(s):

Juan Zhou ◽

Jing Zhao ◽

Qingzhu Jia ◽

Qian Chu ◽

Fei Zhou ◽

...

Keyword(s):

Clinical Outcome ◽

Peripheral Blood ◽

Immune Checkpoint Inhibitor ◽

Advanced Nsclc ◽

Validation Cohort ◽

Checkpoint Inhibitor ◽

Predictive Biomarker ◽

Line Treatment ◽

Tumor Associated Antigen ◽

Nsclc Patients

BackgroundPeripheral blood biomarkers to immunotherapy have attracted more and more attentions owing to noninvasive nature. This study was designed to identify a panel of tumor associated autoantibodies (TAAbs) in plasma to predict the clinical outcome of ICIs-based treatment in advanced NSCLC patients and correlation between TAAbs and checkpoint inhibitor pneumonitis (CIP) would also be investigated.Materials and MethodsBaseline plasma was collected from patients with advanced NSCLC before receiving ICIs-based treatment. ELISA was used to detect concentration of autoantibodies. Clinical efficacy was evaluated according to RECIST v1.1.ResultsWe have identified a panel of five-TAAbs to predict responses of ICIs-based treatment in a discovery cohort (n = 37), and confirmed its predictive value in a validation cohort (n = 129). In the validation cohort, the positivity of this 5-TAAbs panel was significantly associated with better response (ORR: 44.4% vs. 13.6%, P < 0.001) and longer PFS (7.6 vs. 3.3m, P < 0.001). This significant association was remained in subgroup of patients treated with combination therapy (ORR: 43.8% vs. 13.7%, P = 0.004,PFS: 6.7 vs. 3.7m, P = 0 .017). Furthermore, this 5-TAAs panel worked better in patients who received subsequent-line treatment (ORR: 42.4% vs. 7.7%, P = 0.001, PFS: 6.2 vs. 3.0m, P = 0.004) than those received first-line treatment (ORR: 46.7% vs. 35.7%, P = 0.345, PFS: NR vs. 10.48m, P = 0.146). In addition, the CIP incidence in patients with 5-TAAbs positive was significantly higher comparing to negative patients (20.4% vs. 5.9%, P = 0.015).ConclusionOur 5-TAAbs panel is a potential predictive biomarker for responses and toxicities to ICIs-based treatment in patients with advanced NSCLC.

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MSI-low is an intermediate type between MSI-high and MSS in esophagogastric junction adenocarcinoma.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.4_suppl.44 ◽

2019 ◽

Vol 37 (4_suppl) ◽

pp. 44-44

Author(s):

Yu Imamura ◽

Tasuku Toihata ◽

Manabu Takamatsu ◽

Norio Tanaka ◽

Shinji Mine ◽

...

Keyword(s):

Immune Checkpoint ◽

Esophagogastric Junction ◽

Immune Checkpoint Inhibitor ◽

Checkpoint Inhibitor ◽

Predictive Biomarker ◽

Intermediate Type ◽

Immune Microenvironment ◽

Ki 67 ◽

Barr Virus ◽

Epigenetic Alteration

44 Background: High level of microsatellite instability (MSI-high) is an actionable molecular status in oncology, informing tumor response to immune checkpoint inhibitor. However, little is known about MSI-low. The aim of this study is to unveil the characteristics of MSI-low tumor in esophagogastric junction (EGJ) adenocarcinoma. Methods: Using 372 cases with chemo-naive EGJ adenocarcinoma tissue, MSI testing and Epstein Barr Virus (EBV)-DNA detection were performed by DNA fragment analysis (BAT25, BAT26, BAT40, D2S123, D5S346, and D17S250) and real-time PCR, respectively. MSI-high was defined as having two or more unstable markers, MSI-low as one, and microsatellite stable (MSS) as none. MSI status was compared with clinicopathological and molecular status including epigenetic alteration in MLH-1, LINE-1, and CpG methylator phenotype (CIMP), TP53 status (sequencing and immunohistological expression), Ki-67 index, intra-/peri-tumoral lymphocyte counts (CD8+ or FOXP3+), combined positive score (CPS) of PD-L1 expression. Results: MSI-low was detected in 29, MSI-high in 28, and EBV (+) in 9 cases. Three cases with MSI-low showed EBV (+), but MSI-high and EBV (+) were mutually exclusive. In 363 EBV (-) cases, MSI-low was associated with younger age (MSI-high 71.7 ± 11.3, MSI-low 61.3 ± 12.5, and MSS, 65.0 ± 12.3 years old, P = 0.003). Notably, MSI-low exhibited an intermediate type between MSI-high and MSS, in terms of no. of lymph node metastasis, and tumor immune microenvironment [no. of intra-/peri-tumoral CD8+ cells, no. of intra-/peri-tumoral FOXP3+ cells, and CPS of PD-L1 (all P among the 3 groups < 0.05)]. In contrast to MSI-high tumors, MSI-low tumor was not associated with epigenetic alteration in any of MLH-1, LINE-1, or CIMP status. In survival analysis, 3-year disease-specific survival rate of MSI-low was better than that of MSS tumors, and similar to MSI-high (92.2% for MSI-low, 92.2% for MSI-high, and 69.6% for MSI-low). This trend was also observed in time to recurrence (P = 0.01). Conclusions: MSI-low cases exhibited an intermediate immune microenvironment between MSI-high and MSS, and favorable outcome. Our results may implicate MSI-low as a predictive biomarker for immune checkpoint inhibitor.

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STK11/LKB1 revisited: A prognostic rather than predictive biomarker for immune checkpoint inhibitor in EGFR/ALKWT nonsquamous non-small cell lung cancer (NSCLC).

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e21548 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e21548-e21548

Author(s):

Hui Zhao ◽

Nan Qi ◽

Dianjun Chen ◽

Donghui Li ◽

Yan Fu ◽

...

Keyword(s):

Immune Checkpoint ◽

Immune Checkpoint Inhibitor ◽

Checkpoint Inhibitor ◽

Progression Free Survival ◽

Predictive Biomarker ◽

Control Group ◽

Predictive Utility ◽

Level Data ◽

Liver Kinase B1 ◽

The Impact

e21548 Background: Previous retrospective studies identified the mutation of serine/threonine-protein kinase (STK11, also known as Liver Kinase B1 [LKB1]) as predictor to the benefit from immune checkpoint inhibitor (ICI) in KRAS-mutant lung adenocarcinoma. This conclusion was drawn from merely the patients receiving ICI, lacking the indispensable control group undergoing chemotherapy. Therefore, we hereby revisit the impact of STK11 mutation in both immunotherapeutic and chemotherapeutic cohorts, in addition to TCGA database as a control cohort representing prognosis. Methods: 6 immunotherapeutic cohorts involving 807 patient-level data (Van Allen, Rizvi-34, Rizvi-240, MSKCC-75, MSKCC-350, and POPLAR/OAK-atezolizumab), 1 chemotherapeutic cohort (POPLAR/OAK-docetaxel, n = 244), and TCGA database (n = 485) were included to comprehensively re-examine the questionably predictive effect of STK11 mutation on the benefit from immunotherapy in patients with EGFR/ALKWT non-squamous NSCLC. Results: In the 6 immunotherapeutic cohorts, STK11 mutation was associated with shorter progression-free survival (PFS, HR = 1.54, 95%CI 1.17-2.03, P = 0.002) and overall survival (OS, HR = 1.57, 95%CI 1.16-2.11, P = 0.003), but no statistically lower overall response rate (ORR, RR = 0.71, 95%CI 0.39-1.28, P = 0.251). Similarly, in the patients receiving docetaxel, worse ORR and PFS at borderline level and significantly reduced OS (HR = 1.82, 95%CI 1.18-2.80, P = 0.006) were observed. Specifically, in the POPLAR/OAK cohort retrieved from two randomized controlled trials comparing atezolizumab and docetaxel, STK11 mutation and treatment choice separately impacted on OS, but the interaction between these two variables was barely profound (HR 1.10, 95%CI 0.60-2.01, P = 0.766), indicating the prognostic, but not predictive utility of STK11 mutation. Furthermore, in TCGA database, STK11 mutation was linked with poorer prognosis, with the similar hazard ratio in immunotherapeutic cohorts. Conclusions: Genomic abberation of STK11 remarkably worsens prognosis among 6 immunotherapeutic cohorts, 1 chemotherapeutic cohort, and TCGA database, demonstrating the prognostic, but not predictive utility of STK11 mutation in patients with EGFR/ALKWT non-squamous NSCLC.

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