Neoadjuvant Chemotherapy Alone or Combined with EGFR-Directed Targeted Therapy or Anti-PD-1 Immunotherapy for Locally Advanced Lacrimal Sac and Nasolacrimal Duct Carcinomas

BACKGROUND/AIMS

We describe our findings in patients with locally advanced lacrimal sac and nasolacrimal duct (NLD) carcinoma who received neoadjuvant systemic therapy.

METHODS

We identified patients with locally advanced primary lacrimal sac/NLD carcinoma treated with neoadjuvant systemic intravenous therapy at our institution during 2017-2019.

RESULTS

The study included seven patients, four men and three women; the mean age was 60.4 years (range: 43-76). All patients had locally advanced disease with significant orbital soft tissue invasion with or without skull base invasion making eye-sparing surgery not feasible as an initial step. Three patients had poorly differentiated squamous cell carcinoma; two, invasive carcinoma with basaloid and squamous features; one, high-grade carcinoma with features suggestive of sebaceous differentiation; and one, undifferentiated carcinoma. The neoadjuvant regimens were cisplatin and docetaxel (n = 1); carboplatin and docetaxel (n = 1); paclitaxel and cetuximab (n = 1); carboplatin, paclitaxel, and cetuximab (EGFR inhibitor) (n = 2); cisplatin, docetaxel, and pembrolizumab (anti-PD-1 immunotherapy) (n = 1); and carboplatin, paclitaxel, and pembrolizumab (n = 1). All patients had radiologic disease regression, and one patient had radiologic near-complete response. After neoadjuvant therapy, all patients underwent wide local excision and adjuvant concurrent chemoradiation. Two patients had a complete pathologic response. At a median follow-up period of 13 months after chemoradiation (range, 8-54 months), all patients were alive without evidence of disease. One patient had nodal metastasis treated with lymph node dissection and adjuvant chemoradiation.

CONCLUSIONS

Neoadjuvant systemic therapy can shrink tumors in patients with locally advanced primary lacrimal sac/NLD carcinoma with orbital or skull base invasion.

Abstract 3431: Molecular determinants of KRAS p.G12C inhibitor efficacy in advanced NSCLC

Background: Irreversible allosteric KRAS p.G12C inhibitors (KG12Ci) such as sotorasib and adagrasib have revolutionized the therapeutic landscape of advanced KG12C-mutant NSCLC, however individual responses are heterogeneous and curtailed by innate and adaptive/acquired resistance. Molecular determinants of KG12Ci efficacy in NSCLC are poorly defined. We dissected the impact of major KG12C co-mutations and explored the effects of less prevalent co-alterations on the clinical activity of KG12Ci in the largest treated cohort to date of patients (pts) with advanced NSCLC. Key findings were validated in preclinical KG12C NSCLC models.

Methods: Baseline clinico-genomic features and clinical outcome data from pts with stage IV KG12C NSCLC (ECOG PS 0-2) treated with single-agent KG12Ci were collected retrospectively from 20 centers in the US and Europe. The Kaplan-Meier method was used to estimate PFS and OS and differences were assessed with the log-rank test. Hazard ratios (HR) and their 95% CI were estimated using a Cox proportional hazards model stratified for clinical co-variates. The impact of selected co-alterations on sotorasib efficacy was assessed in syngeneic (C57BL/6) KG12C NSCLC models.

Results: 411 eligible pts were included in the study. Median age was 68 years, 77% of pts had received both platinum-based chemotherapy and PD-(L)1 inhibitors and 35% had brain metastases. 83% of pts received sotorasib. ORR with KG12Ci was 32.4% (95% CI, 27.9-37.1), PFS was 5.1m (95% CI, 4.5-5.6) and OS was 10.2m (95% CI, 8.4-12.1). Co-alterations in KEAP1, SMARCA4 and CDKN2A/B were each associated with significantly shorter PFS (KEAP1: 2.8m vs 5.5m, HR 2.50, P<0.001; SMARCA4: 1.7m vs 5.5m, HR 2.64, P=0.001; CDKN2A/B: 2.3m vs 5.3m, HR 2.57, P<0.001) and OS with KG12Ci even after adjustment for clinical covariates. STK11 co-mutations without concurrent KEAP1 alterations did not impact clinical outcomes with KG12Ci. In an exploratory analysis, co-mutations in DNA damage repair (DDR) genes and genes encoding components of the ATRX/DAXX/EZH2 pathway were associated with improved KG12Ci efficacy, whereas PI3K/AKT/MTOR/PTEN alterations and missense ROS1/ALK/BRAF/NTRK1-3 mutations resulted in inferior outcomes. The impact of SMARCA4 and DDR gene inactivation was validated in isogenic syngeneic KG12CNSCLC models; additional co-alterations are under evaluation. Integration of KEAP1/SMARCA4/CDKN2A/B co-mutations identified a subgroup (KSCMUT, 37.6% of all pts) with significantly shorter PFS (2.7m vs 6.2m, P<0.001) and OS (6.3m vs 14.6m, P<0.001) that accounted for 57.3% of pts with primary refractory (PFS≤3m) disease.

Conclusions: Co-mutations in KEAP1, SMARCA4 and CDKN2A/2B define subgroups of KG12C NSCLC pts with markedly distinct outcomes with KG12Ci monotherapy. Tailoring of KG12C inhibitor-anchored therapeutic strategies and patient stratification should take into account the co-mutation status of individual tumors.

Citation Format: Marcelo V. Negrao, Haniel A. Araujo, Giuseppe Lamberti, Alissa J. Cooper, Teng Zhou, Neal Akhave, Lukas Delasos, J Kevin Hicks, Mihaela Aldea, Gabriele Minuti, Jacobi Hines, Jacqueline V. Aredo, Michael J. Dennis, Turja Chakrabarti, Susan Scott, Paolo Bironzo, Matthias Scheffler, Petros Christopoulos, So Yeon Kim, Sarah Goldberg, Ying Ni, Blerina Resuli, Lorenza Landi, Shu-Chi Tseng, Mizuki Nishino, Dwight Owen, Collin Blakely, Giannis Mountzios, Catherine A. Shu, Christine Bestvina, Marina Garassino, Kristen Marrone, Jhanelle Gray, Sandip Pravin Patel, Amy L. Cummings, Heather A. Wakelee, Jurgen Wolf, Giorgio V. Scagliotti, Federico Cappuzzo, Fabrice Barlesi, Pradnya Patil, Don L. Gibbons, Funda Meric-Bernstam, J Jack Lee, John V. Heymach, David S. Hong, Rebecca S. Heist, Mark M. Awad, Ferdinandos Skoulidis. Molecular determinants of KRAS p.G12C inhibitor efficacy in advanced NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3431.

Immunogenomic profiling of lung adenocarcinoma reveals poorly differentiated tumors are associated with an immunogenic tumor microenvironment

OBJECTIVES

Pathologists have routinely observed distinct histologic patterns of growth in early-stage lung adenocarcinoma (LUAD), which have been suggested to be associated with prognosis. Herein, we investigated the relationship between LUAD patterns of growth, as defined by the updated international association for the study of lung cancer (IASLC) grading criteria, and differences in the tumor immune microenvironment to identify predictors of response to immunotherapy.

METHODS

174 resected stage I-III LUAD tumors were classified by histologic pattern of growth (i.e. solid, micropapillary, acinar, papillary, and lepidic) and then grouped as well differentiated, moderately differentiated, and poorly differentiated. Comprehensive multiplatform analysis including whole exome sequencing, gene expression profiling, immunohistochemistry, CIBERSORT, and T-cell receptor sequencing was performed and groups were compared for differences in genomic drivers, immune cell infiltrate, clonality, and survival. Finally, multivariate analysis was performed adjusting for pathologic stage and smoking status.

RESULTS

Poorly differentiated tumors demonstrated a strong association with smoking relative to moderately differentiated or well differentiated tumors. However, unlike in prior reports, poorly differentiated tumors were not associated with a worse survival after curative-intent resection. Genomic analysis revealed that poorly differentiated tumors are associated with high tumor mutation burden but showed no association with oncogenic drivers. Immune analyses revealed that poorly differentiated tumors are associated with increased T-cell clonality, expression of PD-L1, and infiltration by cytotoxic CD8 T-cells, activated CD4 T-cells, and pro-inflammatory (M1) macrophages. Finally, multivariate analysis controlling for stage and smoking status confirmed independence of immune differences between IASLC grade groups.

CONCLUSIONS

Poorly differentiated tumors, as defined by the updated IASLC grading criteria, are associated with a distinct immunogenic tumor microenvironment that predicts for therapeutic response to immune agents, including checkpoint inhibitors, and should be included in the clinical trial design of immunotherapy studies in early-stage lung adenocarcinoma.

Shared Nearest Neighbors Approach and Interactive Browser for Network Analysis of a Comprehensive Non-Small-Cell Lung Cancer Data Set

PURPOSE

Advances in biological measurement technologies are enabling large-scale studies of patient cohorts across multiple omics platforms. Holistic analysis of these data can generate actionable insights for translational research and necessitate new approaches for data integration and mining.

METHODS

We present a novel approach for integrating data across platforms on the basis of the shared nearest neighbors algorithm and use it to create a network of multiplatform data from the immunogenomic profiling of non-small-cell lung cancer project.

RESULTS

Benchmarking demonstrates that the shared nearest neighbors-based network approach outperforms a traditional gene-gene network in capturing established interactions while providing new ones on the basis of the interplay between measurements from different platforms. When used to examine patient characteristics of interest, our approach provided signatures associated with and new leads related to recurrence and TP53 oncogenotype.

CONCLUSION

The network developed offers an unprecedented, holistic view into immunogenomic profiling of non-small-cell lung cancer, which can be explored through the accompanying interactive browser that we built.

Abstract P009: A shared nearest neighbors approach for integrated, multi-platform networks and its application to the exploration of multiomics data from early-stage non-small cell lung cancers

Background: The ImmunogenomiC prOfiling of Non-small cell lung cancer (NSCLC) Project (ICON) represents an ambitious undertaking to comprehensively characterize immuno-genomic diversity in NSCLC across diverse platforms. The depth and breadth of this cohort presented a unique opportunity to develop a specialized method for multi-platform data integration and exploration, which can be broadly applied to forthcoming large-scale patient profiling studies. Such a holistic approach can unlock insights for therapeutic targets, biomarkers, and treatment plans by providing a more complete view of phenomena driving disease pathogenesis and evolution. Purpose: We developed a novel shared nearest neighbors (SNN) approach to create an integrated network of ICON’s multi-platform data and identified collections of closely related measurements within the resulting network tied to noteworthy patient characteristics, including recurrence and oncogenotype. Methods: The ICON dataset is derived from tumor and normal lung tissue samples collected from 150 patients at time of resection as well as blood samples collected then and at intervals during the year following. Tissue samples underwent RNA-sequencing (RNA-seq), whole exome sequencing, T-cell receptor sequencing, multiplex immunofluorescence for immune cells, and reverse phase protein array profiling; flow cytometry for immune cells was performed on tissue and blood samples. From these data, the ICON data network was built using an integrative approach based on the SNN algorithm in which genes were linked on the basis of their shared top correlates in orthogonal datasets. Results: The ICON data network currently includes over 20,000 genes linked by over 500,000 connections derived from correlations between RNA-seq and orthogonal platforms. We captured established associations between cancer-related genes and examined these along with new ones in the network. To do so, we used the InfoMap algorithm to extract more interpretable sub-networks, termed modules, from the ICON data network. Single sample gene set enrichment scores for each module were used in multivariate analysis to highlight modules linked to clinical characteristics of interest. As an example, we found modules significantly tied to disease recurrence. The most notable of these was strongly associated with metabolic pathways, and other modules associated with platelets and ion channels were also identified. The metabolic pathway module is being explored as a prognostic biomarker, underscoring the opportunites enabled by mining the network. Conclusions: Through the framework developed, we identified modules in the ICON data network significantly associated with important patient characteristics like recurrence and oncogenotype. We are validating the gene sets identified as potential biomarkers and are developing an interactive application to facilitate further mining of the network. Taken together, our SNN network-building approach enables the integration and exploration of patient data from diverse platforms.

Citation Format: Stephanie T. Schmidt, Neal Akhave, Alexandre Reuben, Tina Cascone, Jianhua Zhang, Jun Li, Junya Fujimoto, Lauren A. Byers, Beatriz Sanchez-Espiridion, Lixia Diao, Jing Wang, Lorenzo Federico, Marie-Andree Forget, Daniel J McGrail, Annikka Weissferdt, Shiaw-Yih Lin, Younghee Lee, Natalie Vokes, Carmen Behrens, Ignacio I. Wistuba, Andrew Futreal, Ara Vaporciyan, Boris Sepesi, John V. Heymach, Chantale Bernatchez, Cara Haymaker, Jianjun Zhang, Christopher A. Bristow, Timothy P. Heffernan, Marcelo V. Negrao, Don L. Gibbons. A shared nearest neighbors approach for integrated, multi-platform networks and its application to the exploration of multiomics data from early-stage non-small cell lung cancers [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P009.

46 Proximity between cytotoxic antigen-experienced T cells and tumor cells is associated with improved clinical outcomes in early-stage NSCLC

Background

While the development of immunotherapies has improved the treatment of non-small cell lung cancer (NSCLC), most patients still fail to respond. Immune cell densities have been utilized to predict clinical responses but have largely failed to do so. However, the spatial distribution and interaction of these cells at the tissue level have been less studied. Here, we performed spatial analysis of the cells within the tumor immune microenvironment in order to evaluate their relationship with clinical outcomes in early-stage NSCLC.

Methods

Multiplex immunofluorescence was performed on 123 early-stage NSCLC patients from the ICON (Immunogenomic profiling of non-small cell lung cancer) cohort including Cytokeratin (CK), CD3, CD8, CD45RO, FoxP3, CD68, CD20, CD57, Granzyme B (GzmB), PD-1, and PD-L1. Area under the curve (AUC) was calculated using Ripley’s L function, which evaluates the degree of spatial proximity of two cell populations, with a high AUC indicating clustering and low AUC indicating scattering. Findings were integrated with clinical parameters.

Results

Adenocarcinomas demonstrated CD3+PD1+ T cells were closer to CK+ tumor cells (n=60, p=0.035), and B cells were closer to cytotoxic T cells (n=43, p=0.03) than in squamous cell carcinoma. Higher AUC was observed between CD3+PD1+ T cells (n=56, p=0.035), with cytotoxic antigen-experienced T cells (CD45RO+GzmB+) closer to tumor cells (n=35, p=0.017) in stage I and II compared to stage III tumors. Untreated patient tumors exhibited higher proximity between CD20+ B cells and CD57+ NK cells (n=59, p=0.012), CD3+ T cells and PD-L1+ tumor cells (n=56, p=0.027), and CD68+ macrophages and PD-L1+ tumor cells (n=52, p=0.016) than neoadjuvant chemotherapy-treated patients. Patients with no recurrence presented higher AUC in antigen-experienced CD45RO+GzmB+ T cells and tumor cells (n=36, p=0.006), while those with improved survival demonstrated greater proximity between CD68+ macrophages and PD-L1+ tumors (n=52, p=0.016), CD20+ B cells and GzmB+ cells (n=49, p=0.03), and antigen-experienced CD45RO+GzmB+ T cells and tumor cells (n=36, p=0.047). Lastly, patients with improved survival also displayed greater proximity between CD3+CD8+ cytotoxic T cells and PD-L1- epithelial cells (n=76, p=0.04) in tumors versus matched adjacent lungs.

Conclusions

Overall, our findings shed light on some of the potential cell interactions at play in the tumor microenvironment of early-stage NSCLC patients and suggest cell distributions could be utilized to predict clinical outcomes in early-stage NSCLC patients.

Evaluation of an oncology diagnostic and screening clinic at a large safety net hospital

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Background: Increasingly, dedicated clinics have been established to expedite patients with suspicion of malignancy into cancer care. The Lyndon B. Johnson General Hospital (LBJGH) is part of the Harris County Hospital District (HCHD), serving the third largest county in the United States. A 2012 study at our institution found long mean ambulatory wait times for the diagnosis of breast, colon, and lung cancers (77.1 days, 65.4 days, and 70.8 days, respectively). The LBJGH Oncology Diagnostic and Screening clinic (DSC) was subsequently established to reduce ambulatory diagnostic times, decrease frequency of admissions, and improve patient outcomes. Our present study sought to establish the effectiveness of the DSC in bringing patients into care. Methods: The charts of 100 new patients seen in the DSC between August 2018 and November 2020 were reviewed. Demographic data, insurance status, date of referral to the DSC, date of first DSC visit, date biopsy or outside pathology ordered, date biopsy performed or outside pathology obtained, total number of visits to the DSC, and total time from initial DSC visit to initial oncology clinic visit were collected, as well as cancer stage upon arrival to the oncology clinic. The project was approved by the MD Anderson Quality Improvement Assessment Board and the Harris Health Quality Improvement Committee. Results: 57% of patients seen in the DSC were referred by the emergency department, and 23% by a PCP. The median time from referral to initial visit in the DSC was 19 days. 26% of patients had a known cancer diagnosis at the time of referral. The median time from biopsy ordered to performed was 39 days, and the median time from pathology requested to obtained was 23 days. The median total number of visits per patient to the DSC was 2. 48% of patients seen in the DSC were ultimately diagnosed with cancer, 29% had a benign condition, and 23% were lost to follow up. Of those patients with confirmed malignancy, 46% of patients had stage IV disease at their first oncology clinic visit. The average time from initial DSC visit to initial visit at the oncology clinic was 53 days. Conclusions: Establishment of an oncology DSC has improved the ambulatory wait time for entry into oncologic care from an average of approximately 70 days to an average of 53 days. However, almost half of patients referred from the DSC had stage IV disease at their initial oncology visit, indicating an urgent need to further expedite entry of these patients into oncologic care. Avenues for future quality improvement efforts include an expedited process to obtain outside pathology results, implementation of an “e-consult” option for PCPs to avoid unnecessary referrals, and a close analysis of insurance and financial barriers to entry into care. Reference: Mougalian SS, Wang J, Zarzour M et al. Feasibility and savings of a suspicion of cancer clinic at a large county hospital. JCO 2012; 30 (34 supplement): 104.