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Acute myeloid leukemia (AML) is an aggressive hematological malignancy arising from hematopoietic stem and progenitor cells. This neoplasm is characterized by the rapid proliferation of tumor cells...

Bleeding in patients with acute myeloid leukemia (AML) receiving intensive induction chemotherapy is multifactorial and contributes to early death. We sought to define incidence and risk factors of grade 4 bleeding to support strategies for risk mitigation. Bleeding events were assessed according to the WHO bleeding assessment scale, which includes grade 4 bleeding as fatal, life-threatening, retinal with visual impairment, or involving the central nervous system. Using multivariable competing-risk regression analysis with grade 4 bleeding as the primary outcome, we identified risk factors in the development cohort (n=341), which were tested in an independent cohort (n=143). Grade 4 bleeding occurred in 5.9% and 9.8% of patients in the development and validation cohort, respectively. Risk factors that were independently associated with grade 4 bleeding included baseline platelet count ≤40 ×109/L compared with >40 ×109/L, and baseline PT-INR >1.5 or >1.3-1.5 compared with ≤1.3. These variables were allocated points, which allowed for stratification of patients with low- and high-risk for grade 4 bleeding. Cumulative incidence of grade 4 bleeding at day+60 was significantly higher among patients with high- versus low-risk (development: 31±7% vs. 2±1%, PP=0.008). In both cohorts, high bleeding risk was associated with disseminated intravascular coagulation (DIC) and proliferative disease. We developed and validated a simple risk model for grade 4 bleeding, which enables development of rational risk mitigation strategies to improve early mortality of intensive induction treatment.KEY POINTSRisk factors predicting grade 4 bleeding were consistent with DIC-like coagulopathy, including prolonged PT-INR and thrombocytopenia.The grade 4 bleeding score was externally validated and allows for preventive strategies to improve early mortality in high-risk patients.

Bleeding in patients with acute myeloid leukemia (AML) receiving intensive induction chemotherapy is multifactorial and contributes to early death. We sought to define the incidence and risk factors of grade 4 bleeding to support strategies for risk mitigation. Bleeding events were retrospectively assessed between day-14 and day +60 of induction treatment according to the World Health Organization (WHO) bleeding assessment scale, which includes grade 4 bleeding as fatal, life-threatening, retinal with visual impairment, or involving the central nervous system. Predictors were considered pretreatment or prior to grade 4 bleeding. Using multivariable competing-risk regression analysis with grade 4 bleeding as the primary outcome, we identified risk factors in the development cohort (n = 341), which were tested in an independent cohort (n = 143). Grade 4 bleeding occurred in 5.9% and 9.8% of patients in the development and validation cohort, respectively. Risk factors that were independently associated with grade 4 bleeding included baseline platelet count ≤40 × 109/L compared with >40 × 109/L, and baseline international normalized ratio of prothrombin time (PT-INR) >1.5 or 1.3 > 1.5 compared with ≤1.3. These variables were allocated points, which allowed for stratification of patients with low- and high-risk for grade 4 bleeding. Cumulative incidence of grade 4 bleeding at day+60 was significantly higher among patients with high- vs low-risk (development: 31 ± 7% vs 2 ± 1%; P < .001; validation: 25 ± 9% vs 7 ± 2%; P = .008). In both cohorts, high bleeding risk was associated with disseminated intravascular coagulation (DIC) and proliferative disease. We developed and validated a simple risk model for grade 4 bleeding, which enables the development of rational risk mitigation strategies to improve early mortality of intensive induction treatment.

Myeloid derived suppressor cells (MDSCs) are a diverse collection of immune cells that suppress anti-tumor immune responses. Decreasing MDSCs accumulation in the tumor microenvironment could improve the anti-tumor immune response and improve immunotherapy. Here, we examine the impact of physiologically relevant thermal treatments on the accumulation of MDSCs in tumors in mice. We found that different temperature-based protocols, including 1) weekly whole-body hyperthermia, 2) housing mice at their thermoneutral temperature (TT, ~30 °C), and 3) housing mice at a subthermoneutral temperature (ST,~22 °C) while providing a localized heat source, each resulted in a reduction in MDSC accumulation and improved tumor growth control compared to control mice housed at ST, which is the standard, mandated housing temperature for laboratory mice. Additionally, we found that low dose β-adrenergic receptor blocker (propranolol) therapy reduced MDSC accumulation and improved tumor growth control to a similar degree as the models that relieved cold stress. These results show that thermal treatments can decrease MDSC accumulation and tumor growth comparable to propranolol therapy.

Objective: Iodine deficiency disorders (IDDs) remain a major public health concern in most parts of the world but are extremely rare in North America. We describe a case of goiter in a young male with dietary history and findings suggestive of IDD. Methods: Laboratory and imaging procedures including thyroid function tests, autoantibodies, urine iodine, thyroid ultrasound, and radioactive iodine (RAI) uptake scan were performed. Results: On initial presentation, thyroid-stimulating hormone (TSH) was 24.4 mIU/L (normal range is 0.4 to 5.0 mIU/L), free thyroxine was

Lower back pain has been one among the difficulties faced in general, occurring in prevalent due to natural intervertebral disc failures in the lumbar spine region. Studies are required to examine natural disc failures due to day to day activities. A model of human lumbar spine section L4-L5 was generated to check for stability of natural disc under various loading conditions using finite element analysis. Computer tomography scan images were compiled together to generate a three-dimensional model of lumbar spine section L4-L5 by segmentation technique. Further, the model was prepared along with the generation of cortical bone, annulus pulposus, nucleus pulpous, vertebral endplates and corresponding ligaments. The material properties for each of the components were incorporated into the model from the literature available and meshed for analysis. The L4–L5 lumbar spine section model created, upon being checked for different motions of spine showed satisfying results yielding normal human motion preservation. Natural disc deformation parameters were observed under different loading conditions for daily activities.

INTRODUCTION Hypoglycemia in malignancy is well known with several etiologies; impaired liver function, insulin receptor autoantibodies, production of insulin-like substance by malignant cells or large tumor burden. Another possible mechanism is the Warburg effect where metabolism shifts towards glycolytic pathways over oxidative phosphorylation even under aerobic conditions leading to excess lactate production. This leads to glucose consumption due to shunting of glucose away from normal cells to cancer cells. Very few cases of lactic acidosis and severe hypoglycemia in Non-Hodgkin’s Lymphoma have been described in the literature. We report such an unusual case here. CASE DISCUSSION A 52-year-old, African-American male was admitted for severe malnutrition with 80 lb. weight loss, severe hypoglycemia and progressively increasing right chest wall mass. On arrival, he had no classic symptoms of hypoglycemia. Physical exam revealed persistent tachycardia, white patches on oral mucosa and posterior tongue, and an indurated mass on right chest wall with extensive swelling of right upper limb. Lab work was significant for blood glucose (BG) of 41 mg/dL (60–100), lactate 16 mmol/L (0.5 to 2), anion gap 26 mEq/L (3–10), albumin 2.3 g/dL (3.4 to 5.4) and normal renal and liver function tests. Fingerstick sugar readings were persistently in the 20s with no response to multiple boluses of dextrose and glucagon. He was started on dextrose 5% (D5) drip and intravenous solumedrol. Solumedrol was weaned off and D5 titrated down to investigate causes of hypoglycemia. BG dropped to 39 and corresponding labs showed insulin level of < 2 mcUnit/mL (2–20), C-peptide of 0.2 ng/mL (0.8–6.0) and ketone level of 0.2 mmol/L ( CONCLUSION In our case; suppressed insulin, low C-peptide and IGF-2 levels indicate non-insulin mediated hypoglycemia due to rapid glucose utilization by cancer cells. Severe hypoglycemia with lack of neuroglycopenic symptoms suggests use of lactate (rather than glucose) as an alternative metabolic fuel for brain, thus preserving its function. Our patient presented with an exaggerated Warburg effect (hyper-Warburgism) as evident by extreme glucose consumption, severe lactic acidosis and large tumor burden on FDG/PET. Chemotherapy must be instituted timely to correct these abnormalities.

Paraneoplastic neurological syndromes are a rare manifestation of non-Hodgkin lymphoma and can make treatment of these patients more challenging. We report the case of a 67-year-old man with high grade diffuse large B-cell lymphoma who presented with severe paraneoplastic Guillain-Barre syndrome. He was treated with intravenous immunoglobulin therapy and definitive chemoimmunotherapy, and achieved a full neurological recovery. In this report, we discuss various mechanisms of neurological dysfunction seen in lymphomas. Prompt oncologic treatment and immunotherapy for Guillain-Barre syndrome if instituted concurrently and early in the course of the disease can be associated with the best outcomes.

Aim: Patients receiving checkpoint inhibitors (CPI) are frequently on other medications for co-morbidities. We explored the impact of concomitant medication use on outcomes. Materials & methods: 210 metastatic cancer patients on CPI were identified and association between concomitant medication use and immune-related adverse events with clinical outcomes was determined. Results: Aspirin, metformin, β-blockers and statins were not shown to have any statistically significant difference on clinical benefit. 26.3% patients with clinical benefit developed rash versus 11.8% without clinical benefit (p

Immune checkpoints are cell surface molecules that initiate regulatory pathways which have powerful control of CD8(+) cytolytic T cell activity. Antagonistic and agonistic antibodies engaging these molecules have demonstrated profound impact on immune activation and have entered clinical use for the treatment of a variety of diseases. Over the past decade, antagonistic antibodies known as immune checkpoint inhibitors have become a new pillar of cancer treatment and have reshaped the therapeutic landscape in oncology. These agents differ in their mechanism of action and toxicity profiles compared to more traditional systemic cancer treatments such as chemo‐ and targeted therapies. This article reviews the pharmacology of this new class of agents.

In 1996, Jim Allison demonstrated that blocking the immune regulatory molecule CTLA-4 with anit-CTLA4 antibody led to enhance tumor responses in mice. It would take an additional 15 years for human studies to confirm the potency and clinical efficacy of anti-CTLA4, ultimately leading to US FDA approval of the first checkpoint inhibitor, ipilimumab. Now with a plethora of immune-modulating agents demonstrating single agent safety and benefit across many tumor types, investigation on the optimal combination of immune-based therapies has begun in earnest. While there are many challenges, a central one is how to select which combination for which patient is the best. Here we review the current approaches that a practitioner can use to achieve this therapeutic goal.

Immune checkpoint inhibitors (ICI) have revolutionized the management of cancer over the last decade. Instead of targeting the cancer cell directly these agents work by augmenting the immune response towards tumor. Although they are associated with improved responses compared to traditional treatments in several malignancies, a majority of the patients don't respond to ICIs even when used in the frontline setting. In patients who do respond, a significant number eventually develop resistance. We will review ICI mechanisms of action and resistance. We will also discuss new therapeutic options and combinations with other agents that are currently being evaluated to overcome resistance to ICI.

Introduction: Immunotherapy has recently emerged as the cornerstone of metastatic melanoma treatment. Monotherapy with PD1 inhibitors results in response rate (RR) ~30%, while RR with combination PD1 + CTLA-4 inhibitors is ~60% but associated with high-grade toxicities in 50% patients (pts). We have shown in a mouse model that adrenergic receptor signaling blockade with propranolol (PRO) enhances anti-tumor activity of PD1 inhibition. Thus, we designed a phase Ib/II clinical trial to improve the activity of pembrolizumab (P) by combination with PRO at different doses recruiting 3 pts at each dose (10, 20 & 30 mg BID). Methods: 3 pts with metastatic melanoma were treated with P 200 mg every 3 weeks + PRO 10 mg BID. Biomarkers analysis were planned to identify PRO dose which should be tested in phase II. Blood samples were collected at baseline, day 8 & prior to each cycle. MDSC & its subtypes, Treg, CD8+ & CD4+ T cells & 29 cytokine/chemokines were measured at each time point. Ratios of CD8+/m-MDSC, CD8+/e-MDSC, CD8+/PMN-MDSC & CD8+/Treg were analyzed. Results: Trend toward an increase in CD8+T cell & MDSC subsets with decrease in above ratios was observed. There was a significant time-effect of CD8+/ PMN-MDSC (p=0.039). The 6 and 30 wk levels were significantly lower than baseline (p=0.046; 0.008). The 30 wk value of CD8+T cell/m-MDSC was also significantly lower compared to baseline (p=0.037). There was a significant time-effect for IP-10 (p=0.007) where the expression increases from baseline, peaking around 3 wk (Table 1). Conclusion: We see a decrease in CD8+ T cell/MDSC (all subtypes) & increase in IP-10 over time. The inverse relationship of high IP-10 with decreasing CD8+/MDSC & CD8+/Treg populations would suggest potential role of IFN-γ & trafficking of CD8+ T-cells into tumor/tissue. We plan to recruit more pts in the study to analyze these trends & confirm our findings. Table 1VariablesBaseline1 week3 weeks6 weeks9 weeks12 weeks30 weeksp-valueCD8+T cell%/Eosinophilic(e)-MDSC%31.1±19.215.9±7.145.8±37.113.9±4.619.3±6.813.9±213±1.50.532CD8+T cell %/monocytic(m)-MDSC%290.4±287.25.8±2.18±3.37.2±4.24±1.56±2.92.5±1.10.170CD8+T cell%/Polymorphonuclear(PMN)-MDSC%3544.2±2900.3208.3±77.7485.6±368.7110.3±76.1231.9±116.4168.3±76.339.5±14.90.039CD8+ T cell%/Treg%8.3±3.25.8±1.76.8±2.46.9±1.97±26.5±1.97.1±0.20.65Interferon gamma induced protein-10(IP-10) pg/ml576.1±83.91009±295.11865±593.51464.7±400.31353.4±445.91307.2±357.8934.2±227.30.007 Citation Format: Shipra Gandhi, Manu Pandey, Kristopher M. Attwood, Cheryl Allen, Joseph Tario, Shin La Shu, Maria Levis, Suzanne M. Stack, Paul Wallace, Paul Bogner, Igor Puzanov, Mark J. Bucsek, Elizabeth Repasky, Marc S. Ernstoff. Measurement of myeloid derived suppressor cells (MDSC), T regulatory cells (Treg), CD8+ and CD4+T-cells, and cytokines/chemokines in patients with metastatic melanoma treated with pembrolizumab and propranolol [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4057.

Introduction: Tumor heterogeneity has been well documented for mutational analysis in virtually all types of tumors and is accepted as a true finding. Heterogeneity of the tumor microenvironment (TME) in the context of response to checkpoint inhibitors has not been well studied; the belief is that variation will be identified across multiple tumors from the same patient. The expectation is that multiple tumors from a single patient would demonstrate extensive TME heterogeneity driven by the neoplasm. Methods: We validated and utilized a targeted RNA-seq immune panel of >350 genes to interrogate the TME of 49 different tumors from 17 unique patients. These samples for one patient represented primary and metastatic tumors that were separated by multiple years. Prior to this study we built a reference database of RNA-seq immune results for this panel of 167 samples. An in-depth analysis of genes associated with checkpoint inhibition (CPI) and tumor infiltrating lymphocytes (TILs) were the focus of the comparative analysis. Unsupervised analysis and gene rank by RNA-seq were the primary modes of comparison. Results: For more than one-half of these patients the different tumors for a single patient separated by multiple years more closely resembled the other tumors from that patient than the reference population by unsupervised clustering. When ranked by LOW, MODERATE, or HIGH expression of genes associated with TILs or CPI the results for the majority of patients were highly concordant: LOW TILs / LOW CPI associated gene expression. Conclusion: Our results support a paradigm shift in the influence of the host on TME heterogeneity with evidence that the host and not the neoplastic cells are the primary determining factor. TME heterogeneity is not identified across multiple tumors of the same histology collected from different sites across time points from the same patient. This study does not evaluate multiple primary tumors from the same patient, but is an additional study we have planned. Subject IDM-PathT-PathPMRSpecimen YearTILs ScoreTILs InterpretationCPI ScoreCPI Interpretation1MelanomaLymph node NOSMetastatic200945LOW13LOW1MelanomaSubcutaneous tissue NOSMetastatic20108LOW2LOW2Bladder cancerUrinary bladder NOSPrimary201237LOW16LOW2Bladder cancerUrinary bladder NOSPrimary201310LOW11LOW2Bladder cancerSoft TissueMetastatic201414LOW23LOW3MelanomaBrain NOSMetastatic201058MODERATE37LOW3MelanomaDuodenum NOSMetastatic201251MODERATE41LOW4MelanomaSoft Tissue NOSMetastatic200598HIGH88HIGH4MelanomaSoft Tissue NOSMetastatic200522LOW3LOW5MelanomaMesentery NOSMetastatic201245LOW35LOW5MelanomaPeritoneum NOSMetastatic201487HIGH86HIGH6Bladder cancerUrinary bladder NOSPrimary20095LOW4LOW6Bladder cancerLung NOSMetastatic201243LOW41LOW7MelanomaSkin of Trunk NOSMetastatic20114LOW4LOW7MelanomaChest wall NOSMetastatic20122LOW1LOW7MelanomaSoft TissueMetastatic201225LOW25LOW8Lung adenocarcinomaLung right upper lobe of, NOSPrimary200999HIGH88HIGH8Lung adenocarcinomaLung right upper lobe of, NOSPrimary201688HIGH80HIGH9MelanomaLymph node NOSMetastatic20092LOW5LOW9MelanomaSmall intestine NOSMetastatic20110LOW1LOW10MelanomaSkin of Lower Extremity NOSMetastatic201216LOW8LOW10MelanomaLymph node NOSMetastatic201243LOW31LOW10MelanomaSkin of Lower Leg NOSMetastatic20134LOW13LOW11MelanomaLung left lower lobe of, NOSMetastatic201214LOW5LOW11MelanomaSkin of Scalp NOSPrimary201022LOW20LOW12Lung adenocarcinomaLung NOSPrimary201089HIGH77HIGH12Lung adenocarcinomaBrain NOSMetastatic201441LOW13LOW13MelanomaLiver NOSMetastatic201166MODERATE51MODERATE13MelanomaLymph node NOSMetastatic201147LOW13LOW14Renal cell carcinomaLung right lower lobe of, NOSMetastatic201395HIGH87HIGH14Renal cell carcinomaAdrenal gland NOSMetastatic201465MODERATE71MODERATE15Lung adenocarcinomaLung right middle lobe of, NOSPrimary201488HIGH75HIGH15Lung adenocarcinomaBrain NOSMetastatic201526LOW1LOW16MelanomaTonsil NOSMetastatic201462MODERATE53MODERATE16MelanomaSoft TissueMetastatic201513LOW1LOW17MelanomaDura Mater NOSMetastatic201553MODERATE16LOW17MelanomaLymph node NOSMetastatic201599HIGH88HIGH Citation Format: Carl D. Morrison, Jeffrey Conroy, Sean Glenn, Blake Burgher, Sarabjot Pabla, Maochun Qin, Antonios Papanicolau-Sengos, Jon Andreas, Vincent Giamo, Mary Nesline, Shipra Gandhi, Manu Pandey, Nischala Ammannagari, Kunle Odunsi, Marc Ernstoff, Mark Gardner. Tumor microenvironment heterogeneity is not identified across multiple histologically similar tumors from the same patient [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 620. doi:10.1158/1538-7445.AM2017-620

11565 Background: Predicting response to checkpoint inhibitors (CPIs) using biological knowledge-based decision processes with machine learning (ML) has a great potential to predict rapid progression in patients treated with checkpoint inhibitors (CPIs) (hyperprogressive disease (HPD)) as well as responders. ML models risk overfitting data and do not always evaluate the underlying biology, thus performing well in the initial training cohort but lack generalizability when extended to other cohorts. Biology-based decision may not perform as well initially due to limited understanding and a simplified rule set, but often perform equally well when extended to larger similar cohorts of patients. Methods: A custom NGS cancer immune gene expression assay compared 87 patients treated with CPIs classified as CR, PR, or SD versus 12 HPD. A ML-based polynomial regression model based on 54 immune-related genes combined with mutational burden was optimized for prediction of response. A biological 4-gene decision tree model was constructed independently based on ML. A second biological decision tree incorporated the weighted average relative rank of the expression of multiple genes in 4 different immune functions including immune cell infiltration, regulation, activation, and cytokine signaling. Bayesian model average (BMA) incorporated all three models’ results into the final prediction. Results: For87 patients classified as CR, PR, or SD the PPV >96% for responders and a NPV >90% for non-responders was achieved with the regression model, however with response indeterminate for 24% of the population. While the two biological decision tree models’ PPV were in the 70% range, they accurately revealed the critical genes’ roles in immune response with strong literature support. BMA process integrated these three models resulted in a PPV >96% and a NPV >90% and eliminated the indeterminate group. For HPD a unique biology related to priming of short term memory T-cells was identified. Conclusion: Prediction of response to CPIs is best attained by combining ML with biological knowledge. Decision tree models using a large panel of immune related genes in the context of archival samples from patients treated with CPIs can be used to better understand the biology of responders versus non-responders and provides new insights into HPD.

e14590 Background: The benefit of CPI is limited to a subgroup of patients. Significant toxicity and financial burden make it imperative to identify biomarkers predictive of clinical benefit. We aimed to analyze if immune related adverse events (irAE), laboratory parameters (besides PD-L1 status), prior chemotherapy and concomitant medication use could predict response to CPI. Methods: 216 consecutive patients treated at RPCI with single/multiple CPI from 2011 to 2016 were identified. Demographics, irAE (CTCAE v4.03), concomitant medication use, response (RECIST v1.1) and laboratory parameters were recorded. Progression free (PFS) and overall survival (OS) were calculated. Clinical benefit (responder) was defined as objective response or stable disease with PFS ≥ 6 months. Statistical analysis was performed using SAS v9.4. Results: 185/216 patients (86%) had clinical outcomes with first CPI available (55.7% melanoma, 28.1% lung, 10.8% kidney, 3.8% bladder, 1.6% others), 14% died before disease assessment. Median age was 61 years; 64% were males. 31% were responders (R); 69% non-responders (NR). Median PFS and OS for R were not reached and for NR were 4.7/7 months (p < 0.001). 26% R developed rash vs. 12% NR (p = 0.02). irAE diarrhea was associated with better OS (p = 0.03); rash with better PFS (p = 0.007) and OS (p = 0.05). Concomitant beta-blocker/NSAID use was noted in 35%/28% R vs 20%/12% NR (p = 0.04; p = 0.01). Higher median absolute eosinophil (AEC) and absolute lymphocyte count (ALC) were noted in R (400 and 2000/µl in R vs. 300 and 1600/µl in NR; p = 0.008 and 0.024). NR were noted to have a higher median lactate dehydrogenase (LDH) of 565 vs. 503 (p < 0.001). AEC > 100/µl and ALC > 1000/ µl were associated with better OS (P = 0.03; P = 0.001). Absolute neutrophil count (ANC) > 7500/µl and leucocyte count > 8750/ µl were associated with inferior PFS (p = 0.005; P = 0.02) and OS (p = 0.002; p = 0.03). Pre-CPI chemotherapy did not correlate with response to CPI. Conclusions: Among irAE, diarrhea and rash predicted improved outcomes. Eosinophilia, lymphocytosis, neutrophilia, leukocytosis and LDH could serve as potential prognostic biomarkers to CPI. Prospective studies are warranted to validate these findings.

Background NSCLC with a PD-L1 tumor proportion score (TPS) by immunohistochemistry (IHC) of greater than 50% has a positive predictive value (PPV) of 42% for response to pembrolizumab. Across all tumor types and different checkpoint inhibitors (CPIs), the evidence supports the inflamed phenotype associated with responders. Currently very little is known about predicting response to CPIs in PD-L1 negative NSCLC. Additionally, the association of the PD-L1 negative NSCLC with the inflamed phenotype has not been well described. Methods PD-L1 (22C3) IHC and a custom NGS cancer immune gene expression assay were used to interrogate 50 NSCLC samples of which 23 were treated with one or more CPIs. PD-L1 TPS >50% was considered positive for IHC. Select over expression of 43 genes related to T-cell activation and 11 genes associated with tumor infiltrating lymphocytes were considered positive for the inflamed phenotype. RECIST v1.1 was used to assess patient response. Results 9 of the 50 samples were PD-L1 IHC positive and showed an inflamed phenotype. An additional 7 samples exhibited an inflamed phenotype that were PD-L1 IHC negative. Of the 23 patients that could be evaluated for response to CPIs, 9 showed an inflamed phenotype while 14 were non-inflamed. Conclusion A subset of PD-L1 IHC negative NSCLC patients with an inflamed phenotype show a favorable response rate to CPIs. No complete or partial responses were identified in patients that were PD-L1 IHC negative with a non-inflamed phenotype.