Lung cancer is the chief cause of cancer death. The new standard-of-care (SOC) in operable lung cancer combines chemotherapy and an immune-stimulating drug before the surgery (neoadjuvant approach). This results in a large increase in complete cancer clearance rates compared to chemotherapy alone (±30% with combination vs ±4% with chemotherapy alone), leading to much better long-term survival and probably many more cures. However, most still don't achieve complete clearance, and a few have increases in, or spread of, their tumors while on treatment. Therefore, we need to understand why some patients benefit (responders) and others don't benefit (non-responders) on an immunotherapy-based treatment. Also, some patients unpredictably develop severe immune-type side effects related to the immunotherapy drug, although such side effects may be associated with improved anti-cancer effects. In short, the same treatment can result in complete cancer clearance in one patient, and in a worst-case scenario may result in severe toxicity or fail to control spread/growth thus precluding surgery. The immune system obviously plays a key role in both benefit and harm, yet most of the research in this field has focused only on the cancer. We plan an in-depth study in 60 patients, focusing on the cancer as well as the patient's immune system, pre-surgery. This will enable us to identify factors predicting complete cancer clearance, and the occurrence of immune-type side effects. Using highly sophisticated resources available to us here in London, we will develop predictive models enabling better patient management (including possible avoidance of surgery), and identification of key biological differences between major responders and non-responders, to highlight important new targets for the development of even newer and better therapies.
London, Ontario N6A 5W9, Canada
1. Participants with histologically confirmed Stage IB (≥ 4 cm), II, IIIA (N2) NSCLC (as per the 8th American Joint Committee on Cancer (AJCC)) who are considered to have resectable disease.
2. Measurable disease according to Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1).
3. Participants must have tumor tissue available for PD-L1 immunohistochemical (IHC) testing.
4. Eastern Cooperative Group (ECOG) Performance Status 0-2. 5. Able to give informed consent.
1. Presence of locally advanced, unresectable, or metastatic disease. 2. Participants with known EGFR mutations, ALK or ROS1 translocation. 3. Subjects with active, known, or suspected autoimmune disease (except subjects with type I diabetes mellitus, residual hypothyroidism due to autoimmune thyroiditis only requiring hormone replacement, skin disorders (such as vitiligo, psoriasis, or alopecia) not requiring systemic treatment).
4. Subjects with a condition requiring systemic treatment with either corticosteroids (10 mg daily prednisone or equivalent) or other immunosuppressive medications within 14 days of study drug administration. Inhaled or topical steroids are permitted in the absence of active autoimmune disease.
5. Subjects with previous malignancies are excluded unless a complete remission was achieved at least 5 years prior to study entry and no additional therapy is required or anticipated to be required during the study (non-melanoma skin cancer and other indolent malignancies not requiring any treatment and that are unlikely to affect blood-based biomarkers are allowed).
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Model to Predict pCR and IrAEs in Early Stage Non-small Cell Lung Cancer 0 reviewsWrite Your Review
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We propose a prospective single cohort study to analyze the predictors of pCR in a similar-sized cohort of our pts on the same neo-adjuvant chemo/nivo but employing a range of parameters that are both broader and, in some cases, more sophisticated. We intend to make use of parameters that reflect both the tumor itself as well as the integrity of immune system of the host, obviously a critical determinant of immune-mediated efficacy, yet strangely neglected in the literature. We will then seek to develop an initial predictive model for pCR with a good sensitivity/specificity, as a prelude to refining/testing the model in future work, with a much larger sample.
Noting that immune-related adverse events (IrAE's) are strongly associated with efficacy with immunotherapy, we will not only be including the emergence of early on-treatment IrAE's in our pCR modeling, but also develop an additional model as a subsidiary aim to predict IrAE's themselves. Note the incidence of ≥ grade3 AE's was 33.5% in CM816, and there is currently no available way to predict their occurrence. This is not to suggest such a model, if successful, should be used to deny such pts immune checkpoint inhibitors, but it would allow a more informed consent process, as well as more intensive pro-active monitoring to avoid the worst outcomes of serious IrAE's (which are occasionally fatal) by early intervention.
Major Aim: Development of a model predicting pCR after neo-adjuvant chemo/nivo in pts with resectable NSCLC.
Hypothesis: That an initial model, combining predictive variables from baseline tumor characteristics, baseline factors likely to be associated with host immunity, as well as treatment-emergent events, in pts with resectable NSCLC on neoadjuvant chemo/nivo, can predict a pCR with an area under the Receiver Operator Characteristic (ROC) curve of at least 0.8.
Objectives: Primary Objectives: (1) To explore the feasibility of acquiring a combination of baseline and treatment-emergent potentially predictive variables in pts with early-stage NSCLC subject to neoadjuvant chemo/nivo; (2) To curate these variables and by uni-and multi-variate analyses, to identify those independently useful in predicting pCR in these pts; (3) To combine these independently predictive factors into a model with adequate sensitivity and specificity.
Secondary Objectives: (1) To assess outcomes in patients with resectable NSCLC treated with neoadjuvant chemo/immuno; (2) To evaluate exploratory and potential biomarkers for predicting pCR, major pathological responses (MPR), overall response rate (ORR), event-free survival (EFS), overall survival (OS) and IrAE's; (3) To assess if a post-treatment (but pre-surgery) metabolic response by F18-Fluorodeoxyglucose(FDG)-Positron emission tomography(PET)/CT and a blood-only molecular residual disease assay (ctDNA clearance) can accurately predict a pCR added to or instead of a more complex model.
Endpoints: Primary: (1) To determine the predictive power (sensitivity, specificity, ROC characteristic curves) of a model, combining variables, in predicting pCR; (2) To determine whether a similar model can be constructed to predict IrAE's
Secondary Endpoints: (1) Assess pCR, MPR, clinical objective response rates (ORR), EFS and OS; and IrAE's; (2) Exploratory the feasibility of acquiring novel potential biomarkers - e.g., Lymphocyte-activation gene 3 (LAG3), Lysine-specific histone demethylase1A (LSD1), Leukemia inhibitory factor (LIF), Interleukin 6 (IL6), Interleukin 7 (IL7).
Study Population: Participants with histologically confirmed Stage IB-IIIA NSCLC who are considered to have a resectable disease; with available baseline tumor tissue for immunohistochemistry (IHC) and next-generation sequencing (NGS) except those with known epidermal growth factor receptor (EGFR) mutations, anaplastic lymphoma kinase (ALK) or receptor tyrosine kinase (ROS1) translocation; or active, known, or suspected autoimmune disease.
Study Design: A prospective single cohort (N=60) study.
Treatment Details: Patients with resectable stage Ib-IIIa NSCLC will be treated with new SoC, 3 cycles of neoadjuvant nivolumab immunotherapy plus platinum doublet chemo.
Sample Size: Our planned accrual of 60 patients will result in approximately 15 pCRs, assuming an event rate of 25%. This will allow us to explore our potential predictors and their univariable associations with pCR and/or no-pCR outcomes. The top predictors identified in univariable analyses could be used as candidate predictors in a multivariable prediction model. This framework would allow for an exploratory multivariable model predicting no-pCR with up to 9 of the top candidate predictors identified in univariable analyses, and/or up to 3- 4 of the top candidates predicting pCR. This assumes a two-sided type I error rate of 0.0516.
Feasibility: Our centre treats an average 60 patients per year with resectable NSCLC who are eligible for neoadjuvant therapy. As such, we should be able to meet our enrollment goal in approximately 12-14 months.
Significance: A model that can predict pCR could help in identifying pts most likely to benefit from neo-adjuvant chemo/nivo. If subsequently validated such a model would have several applications including pt selection for neo-adjuvant chemo/immuno; potential avoidance of surgery in reliably-predicted pCR pts of morbid thoracotomy; identification of pts needing additional treatments (e.g. CTLA4 inhibitors, or radiotherapy); acquisition of important insights into the biological underpinnings of resistance; and most crucially, identification of novel targets or strategies to overcome resistance.