Neoadjuvant nivolumab or nivolumab plus LAG-3 inhibitor relatlimab in resectable esophageal/gastroesophageal junction cancer: a phase Ib trial and ctDNA analyses

Gastroesophageal cancer dynamics and drivers of clinical responses with immune checkpoint inhibitors (ICI) remain poorly understood. Potential synergistic activity of dual programmed cell death protein 1 (PD-1) and lymphocyte-activation gene 3 (LAG-3) inhibition may help improve immunotherapy responses for these tumors. We report a phase Ib trial that evaluated neoadjuvant nivolumab (Arm A, n = 16) or nivolumab-relatlimab (Arm B, n = 16) in combination with chemoradiotherapy in 32 patients with resectable stage II/stage III gastroesophageal cancer together with an in-depth evaluation of pathological, molecular and functional immune responses. Primary endpoint was safety; the secondary endpoint was feasibility; exploratory endpoints included pathological complete (pCR) and major pathological response (MPR), recurrence-free survival (RFS) and overall survival (OS). The study met its primary safety endpoint in Arm A, although Arm B required modification to mitigate toxicity. pCR and MPR rates were 40% and 53.5% for Arm A and 21.4% and 57.1% for Arm B. Most common adverse events were fatigue, nausea, thrombocytopenia and dermatitis. Overall, 2-year RFS and OS rates were 72.5% and 82.6%, respectively. Higher baseline programmed cell death ligand 1 (PD-L1) and LAG-3 expression were associated with deeper pathological responses. Exploratory analyses of circulating tumor DNA (ctDNA) showed that patients with undetectable ctDNA post-ICI induction, preoperatively and postoperatively had a significantly longer RFS and OS; ctDNA clearance was reflective of neoantigen-specific T cell responses. Our findings provide insights into the safety profile of combined PD-1 and LAG-3 blockade in gastroesophageal cancer and highlight the potential of ctDNA analysis to dynamically assess systemic tumor burden during neoadjuvant ICI that may open a therapeutic window for future intervention. ClinicalTrials.gov registration: NCT03044613 .

Dynamics of Sequence and Structural Cell-Free DNA Landscapes in Small-Cell Lung Cancer

PURPOSE

Patients with small-cell lung cancer (SCLC) have an exceptionally poor prognosis, calling for improved real-time noninvasive biomarkers of therapeutic response.

EXPERIMENTAL DESIGN

We performed targeted error-correction sequencing on 171 serial plasmas and matched white blood cell (WBC) DNA from 33 patients with metastatic SCLC who received treatment with chemotherapy (n = 16) or immunotherapy-containing (n = 17) regimens. Tumor-derived sequence alterations and plasma aneuploidy were evaluated serially and combined to assess changes in total cell-free tumor load (cfTL). Longitudinal dynamic changes in cfTL were monitored to determine circulating cell-free tumor DNA (ctDNA) molecular response during therapy.

RESULTS

Combined tiered analyses of tumor-derived sequence alterations and plasma aneuploidy allowed for the assessment of ctDNA molecular response in all patients. Patients classified as molecular responders (n = 9) displayed sustained elimination of cfTL to undetectable levels. For 14 patients, we observed initial molecular responses, followed by ctDNA recrudescence. A subset of patients (n = 10) displayed a clear pattern of molecular progression, with persistence of cfTL across all time points. Molecular responses captured the therapeutic effect and long-term clinical outcomes in a more accurate and rapid manner compared with radiographic imaging. Patients with sustained molecular responses had longer overall (log-rank P = 0.0006) and progression-free (log-rank P < 0.0001) survival, with molecular responses detected on average 4 weeks earlier than imaging.

CONCLUSIONS

ctDNA analyses provide a precise approach for the assessment of early on-therapy molecular responses and have important implications for the management of patients with SCLC, including the development of improved strategies for real-time tumor burden monitoring. See related commentary by Pellini and Chaudhuri, p. 2176.

Abstract 3366: A machine learning approach to determine the cellular origin of variants in liquid biopsies

Introduction: Targeted next-generation sequencing (NGS) of cell-free DNA in plasma, referred to as liquid biopsy, has become a valuable diagnostic tool in clinical oncology. However, detection of variants related to clonal hematopoiesis (CH) is a major confounder that significantly impairs the clinical utility of liquid biopsies. Here we developed a machine-learning model to determine tumor versus CH origin of variants identified in plasma-only NGS.

Methods: We assembled a training cohort of 352 variants identified by targeted deep plasma sequencing from 199 patients with stage I-IV breast, colorectal, esophageal, lung, and ovarian cancer, coupled with matched white blood cell (WBC) and tumor tissue NGS to allow determination of the reference origin for each plasma variant. We employed Extreme Gradient Boosting (XGBoost) to integrate fragment, variant, gene, and patient level features to predict tumor versus CH plasma variant origin, evaluating the performance of this approach within the training cohort using 10-fold cross-validation. We applied the fixed model to two independent validation cohorts: a small cell lung cancer (SCLC) cohort comprising of 74 variants from targeted plasma NGS from 26 patients and a multi-cancer cohort of 409 variants detected using the MSK-Impact panel from 74 patients with breast, colorectal, and prostate cancer.

Results: Variant allele frequencies (VAF) did not differentiate tumor from CH variants, as the VAFs between tumor (median VAF 0.53%) and CH (median VAF 0.409%) variants in the training cohort were largely overlapping (area under the ROC curve-AUC 0.54, 95% confidence interval-CI 0.48-0.61). Similarly, individual fragmentomic features (mutant fragment length, cut points, and endpoint motifs) had limited ability to distinguish tumor from CH variants (AUC range 0.51-0.76). Using serial plasma samples, we identified stable statistical measures of differences in fragment feature distributions between mutant and wild type fragments; these were subsequently incorporated into an XGBoost machine-learning model along with variant, gene and patient features to predict tumor versus CH variant origin. Our model predicted variant origin with an AUC of 0.95 (95% CI 0.87-1) from 10-fold cross validation in the training cohort. The performance of the model was tested in independent SCLC and multi-cancer validation cohorts; the fixed model predicted plasma variant origin with an AUC of 0.87 (95% CI 0.73-1) and 0.89 (95% CI 0.86-0.92) respectively.

Conclusion: We developed a machine-learning model that integrates patient, gene, variant and fragment features to predict tumor versus CH origin of plasma variants across solid tumors and NGS sequencing platforms. The ability to identify bona fide tumor variants in plasma-only sequencing fills a critical need in the clinical implementation of liquid biopsy-guided cancer therapy by reducing misinterpretation due to CH contamination.

Citation Format: Jenna V. Canzoniero, Archana Balan, Jillian Phallen, Blair V. Landon, Lavanya Sivapalan, Benjamin Green, Zineb Belcaid, Susan C. Scott, Gavin Pereira, Vincent K. Lam, Ali H. Zaidi, Ronan J. Kelly, Christine L. Hann, Wade T. Iams, Christine M. Lovly, Patrick M. Forde, Gerrit A. Meijer, Geraldine R. Vink, Remond J. Fijneman, The MEDOCC Group, Victor E. Velculescu, Robert B. Scharpf, Valsamo Anagnostou. A machine learning approach to determine the cellular origin of variants in liquid biopsies [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 3366.

Abstract 3374: Circulating cell-free tumor DNA dynamics capture minimal residual disease with neoadjuvant immune checkpoint blockade plus chemoradiotherapy for patients with operable esophageal/gastroesophageal junction cancer

Introduction: There is a critical need to incorporate molecular assessments of minimal residual disease (MRD) during neoadjuvant immunotherapy, in order to identify individuals at high risk for disease recurrence based on analyses of circulating cell-free tumor DNA (ctDNA) landscapes. Here we employed longitudinal liquid biopsies to dynamically assess clinical outcomes with neoadjuvant immuno-chemoradiotherapy in patients with esophageal/gastroesophageal junction (E/GEJ) cancer.

Methods: We utilized targeted error-correction sequencing to perform high-depth ctDNA next-generation sequencing for 141 serial plasma and 32 matched white blood cell (WBC) DNA samples from 32 patients with operable stage II/III E/GEJ cancer that received neoadjuvant immune checkpoint blockade (ICB) with chemoradiotherapy prior to surgery (NCT03044613). ctDNA analyses were performed at baseline, post-ICB induction, after completion of chemoradiotherapy (pre-op), and post-operatively (post-op). Using a tumor-agnostic WBC DNA-informed panel NGS approach we determined the cellular origin of plasma variants, filtering out germline and clonal hematopoiesis (CH) variants and evaluated ctDNA clonal dynamics over time. Molecular MRD was evaluated post-ICB, pre-op and post-op and correlated with recurrence-free (RFS) and overall survival (OS).

Results: Twenty out of 32 patients had detectable ctDNA at any timepoint. Of the 12 patients with undetectable ctDNA, 9 had only CH- and/or germline-derived variants, while 3 patients had no detectable variants of any origin. ctDNA clearance post-ICB was correlated with tumor regression &gt;80% at the time of resection (Fischer’s exact p=0.04). The subset of patients that did not attain complete pathologic response was heterogeneous with respect to ctDNA dynamics; such that ctDNA clearance pre-op identified patients with longer OS despite residual tumor of &gt;0% at the time of resection (log rank p=0.06). Patients with undetectable ctDNA or ctDNA clearance pre-op had a longer RFS (log rank p=0.007) and OS (log rank p=0.03). Molecular MRD was associated with RFS and OS such that patients with ctDNA clearance post-op had longer RFS (log-rank p=0.007) and OS (log-rank p=0.017).

Conclusion: ctDNA clearance post-ICB, pre-op and post-op reflects differential clinical outcomes for patients with E/GEJ cancer receiving neoadjuvant immuno-chemoradiotherapy. Understanding ctDNA dynamics and their relationship with pathological response and long-term outcomes can help identify patients at higher risk for recurrence and open a therapeutic window for future intervention.

Citation Format: Blair V. Landon, Ronan J. Kelly, Ali H. Zaidi, Archana Balan, Jenna V. Canzoniero, Gavin Pereira, Zineb Belcaid, Russell K. Hales, K Ranh Voong, Richard J. Battafarano, Blair A. Jobe, Stephen C. Yang, Stephen Broderick, Jinny Ha, Kellie N. Smith, Elizabeth Thompson, Fyza Y. Shaikh, James R. White, Cynthia L. Sears, Eun J. Shin, Ali I. Amjad, Benny Weksler, Josephine L. Feliciano, Chen Hu, Vincent K. Lam, Valsamo Anagnostou. Circulating cell-free tumor DNA dynamics capture minimal residual disease with neoadjuvant immune checkpoint blockade plus chemoradiotherapy for patients with operable esophageal/gastroesophageal junction cancer [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 3374.

Translating the evolving molecular landscape of tumors to biomarkers of response for cancer immunotherapy

Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapeutics, triggering studies to understand the molecular and cellular wiring of response and resistance. Our increased understanding of the underlying biology of response to ICI has enabled the investigation of tumor-intrinsic and -extrinsic features that may predict therapeutic outcomes. In parallel, liquid biopsy measurements of circulating tumor DNA (ctDNA) can be used to assess real-time molecular responses and guide clinical decisions during ICI. The combination of these approaches provides a deeper understanding of cancer biology, immunoediting, and evolution during ICI and promise to extend the utility of immunotherapies for patients with cancer.

Genomic Landscapes and Hallmarks of Mutant RAS in Human Cancers

The RAS family of small GTPases represents the most commonly activated oncogenes in human cancers. To better understand the prevalence of somatic RAS mutations and the compendium of genes that are coaltered in RAS-mutant tumors, we analyzed targeted next-generation sequencing data of 607,863 mutations from 66,372 tumors in 51 cancer types in the AACR Project GENIE Registry. Bayesian hierarchical models were implemented to estimate the cancer-specific prevalence of RAS and non-RAS somatic mutations, to evaluate co-occurrence and mutual exclusivity, and to model the effects of tumor mutation burden and mutational signatures on comutation patterns. These analyses revealed differential RAS prevalence and comutations with non-RAS genes in a cancer lineage-dependent and context-dependent manner, with differences across age, sex, and ethnic groups. Allele-specific RAS co-mutational patterns included an enrichment in NTRK3 and chromatin-regulating gene mutations in KRAS G12C-mutant non-small cell lung cancer. Integrated multiomic analyses of 10,217 tumors from The Cancer Genome Atlas (TCGA) revealed distinct genotype-driven gene expression programs pointing to differential recruitment of cancer hallmarks as well as phenotypic differences and immune surveillance states in the tumor microenvironment of RAS-mutant tumors. The distinct genomic tracks discovered in RAS-mutant tumors reflected differential clinical outcomes in TCGA cohort and in an independent cohort of patients with KRAS G12C-mutant non-small cell lung cancer that received immunotherapy-containing regimens. The RAS genetic architecture points to cancer lineage-specific therapeutic vulnerabilities that can be leveraged for rationally combining RAS-mutant allele-directed therapies with targeted therapies and immunotherapy.

SIGNIFICANCE

The complex genomic landscape of RAS-mutant tumors is reflective of selection processes in a cancer lineage-specific and context-dependent manner, highlighting differential therapeutic vulnerabilities that can be clinically translated.