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Scalera S, Ricciuti B, Mazzotta M, Calonaci N, Alessi JV, Cipriani L, Bon G, Messina B, Lamberti G, Di Federico A, Pecci F, Milite S, Krasniqi E, Barba M, Vici P, Vecchione A, De Nicola F, Ciuffreda L, Goeman F, Fanciulli M, Buglioni S, Pescarmona E, Sharma B, Felt KD, Lindsay J, Rodig SJ, De Maria R, Caravagna G, Cappuzzo F, Ciliberto G, Awad MM, Maugeri-Saccà M. Clonal KEAP1 mutations with loss of heterozygosity share reduced immunotherapy efficacy and low immune cell infiltration in lung adenocarcinoma. Ann Oncol 2023; 34:275-288. [PMID: 36526124 DOI: 10.1016/j.annonc.2022.12.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 11/26/2022] [Accepted: 12/06/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND KEAP1 mutations have been associated with reduced survival in lung adenocarcinoma (LUAD) patients treated with immune checkpoint inhibitors (ICIs), particularly in the presence of STK11/KRAS alterations. We hypothesized that, beyond co-occurring genomic events, clonality prediction may help identify deleterious KEAP1 mutations and their counterparts with retained sensitivity to ICIs. PATIENTS AND METHODS Beta-binomial modelling of sequencing read counts was used to infer KEAP1 clonal inactivation by combined somatic mutation and loss of heterozygosity (KEAP1 C-LOH) versus partial inactivation [KEAP1 clonal diploid-subclonal (KEAP1 CD-SC)] in the Memorial Sloan Kettering Cancer Center (MSK) MetTropism cohort (N = 2550). Clonality/LOH prediction was compared to a streamlined clinical classifier that relies on variant allele frequencies (VAFs) and tumor purity (TP) (VAF/TP ratio). The impact of this classification on survival outcomes was tested in two independent cohorts of LUAD patients treated with immunotherapy (MSK/Rome N = 237; DFCI N = 461). Immune-related features were studied by exploiting RNA-sequencing data (TCGA) and multiplexed immunofluorescence (DFCI mIF cohort). RESULTS Clonality/LOH inference in the MSK MetTropism cohort overlapped with a clinical classification model defined by the VAF/TP ratio. In the ICI-treated MSK/Rome discovery cohort, predicted KEAP1 C-LOH mutations were associated with shorter progression-free survival (PFS) and overall survival (OS) compared to KEAP1 wild-type cases (PFS log-rank P = 0.001; OS log-rank P < 0.001). Similar results were obtained in the DFCI validation cohort (PFS log-rank P = 0.006; OS log-rank P = 0.014). In both cohorts, we did not observe any significant difference in survival outcomes when comparing KEAP1 CD-SC and wild-type tumors. Immune deconvolution and multiplexed immunofluorescence revealed that KEAP1 C-LOH and KEAP1 CD-SC differed for immune-related features. CONCLUSIONS KEAP1 C-LOH mutations are associated with an immune-excluded phenotype and worse clinical outcomes among advanced LUAD patients treated with ICIs. By contrast, survival outcomes of patients whose tumors harbored KEAP1 CD-SC mutations were similar to those with KEAP1 wild-type LUADs.
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Affiliation(s)
- S Scalera
- SAFU Laboratory, Department of Research, Advanced Diagnostic, and Technological Innovation, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - B Ricciuti
- Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA
| | - M Mazzotta
- Medical Oncology Unit, Sandro Pertini Hospital, Rome, Italy
| | - N Calonaci
- Department of Mathematics and Geosciences, University of Trieste, Trieste, Italy
| | - J V Alessi
- Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA
| | - L Cipriani
- SAFU Laboratory, Department of Research, Advanced Diagnostic, and Technological Innovation, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - G Bon
- Cellular Network and Molecular Therapeutic Target Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - B Messina
- Clinical Trial Center, Biostatistics and Bioinformatics Division, IRCCS Regina Elena National Cancer Institute, Roma, Italy
| | - G Lamberti
- Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA
| | - A Di Federico
- Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA
| | - F Pecci
- Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA
| | - S Milite
- Department of Mathematics and Geosciences, University of Trieste, Trieste, Italy
| | - E Krasniqi
- Division of Medical Oncology 2, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - M Barba
- Division of Medical Oncology 2, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - P Vici
- UOSD Phase IV Studies, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - A Vecchione
- Department of Clinical and Molecular Medicine, Pathology Unit, Sant'Andrea Hospital, Sapienza University, Rome, Italy
| | - F De Nicola
- SAFU Laboratory, Department of Research, Advanced Diagnostic, and Technological Innovation, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - L Ciuffreda
- SAFU Laboratory, Department of Research, Advanced Diagnostic, and Technological Innovation, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - F Goeman
- SAFU Laboratory, Department of Research, Advanced Diagnostic, and Technological Innovation, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - M Fanciulli
- SAFU Laboratory, Department of Research, Advanced Diagnostic, and Technological Innovation, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - S Buglioni
- Department of Pathology, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - E Pescarmona
- Department of Pathology, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - B Sharma
- ImmunoProfile, Brigham & Women's Hospital and Dana-Farber Cancer Institute, Boston, USA
| | - K D Felt
- ImmunoProfile, Brigham & Women's Hospital and Dana-Farber Cancer Institute, Boston, USA
| | - J Lindsay
- Knowledge Systems Group, Dana-Farber Cancer Institute, Boston, USA
| | - S J Rodig
- ImmunoProfile, Brigham & Women's Hospital and Dana-Farber Cancer Institute, Boston, USA; Department of Pathology, Brigham and Women's Hospital, Boston, USA
| | - R De Maria
- Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Rome, Italy; Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - G Caravagna
- Department of Mathematics and Geosciences, University of Trieste, Trieste, Italy
| | - F Cappuzzo
- Division of Medical Oncology 2, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - G Ciliberto
- Scientific Direction, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - M M Awad
- Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA
| | - M Maugeri-Saccà
- Clinical Trial Center, Biostatistics and Bioinformatics Division, IRCCS Regina Elena National Cancer Institute, Roma, Italy; Division of Medical Oncology 2, IRCCS Regina Elena National Cancer Institute, Rome, Italy.
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Spiteri I, Caravagna G, Cresswell GD, Vatsiou A, Nichol D, Acar A, Ermini L, Chkhaidze K, Werner B, Mair R, Brognaro E, Verhaak RGW, Sanguinetti G, Piccirillo SGM, Watts C, Sottoriva A. Evolutionary dynamics of residual disease in human glioblastoma. Ann Oncol 2019; 30:456-463. [PMID: 30452544 PMCID: PMC6442656 DOI: 10.1093/annonc/mdy506] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Glioblastoma is the most common and aggressive adult brain malignancy against which conventional surgery and chemoradiation provide limited benefit. Even when a good treatment response is obtained, recurrence inevitably occurs either locally (∼80%) or distally (∼20%), driven by cancer clones that are often genomically distinct from those in the primary tumour. Glioblastoma cells display a characteristic infiltrative phenotype, invading the surrounding tissue and often spreading across the whole brain. Cancer cells responsible for relapse can reside in two compartments of residual disease that are left behind after treatment: the infiltrated normal brain parenchyma and the sub-ventricular zone. However, these two sources of residual disease in glioblastoma are understudied because of the difficulty in sampling these regions during surgery. PATIENT AND METHODS Here, we present the results of whole-exome sequencing of 69 multi-region samples collected using fluorescence-guided resection from 11 patients, including the infiltrating tumour margin and the sub-ventricular zone for each patient, as well as matched blood. We used a phylogenomic approach to dissect the spatio-temporal evolution of each tumour and unveil the relation between residual disease and the main tumour mass. We also analysed two patients with paired primary-recurrence samples with matched residual disease. RESULTS Our results suggest that infiltrative subclones can arise early during tumour growth in a subset of patients. After treatment, the infiltrative subclones may seed the growth of a recurrent tumour, thus representing the 'missing link' between the primary tumour and recurrent disease. CONCLUSIONS These results are consistent with recognised clinical phenotypic behaviour and suggest that more specific therapeutic targeting of cells in the infiltrated brain parenchyma may improve patient's outcome.
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Affiliation(s)
- I Spiteri
- Evolutionary Genomics & Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London
| | - G Caravagna
- Evolutionary Genomics & Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London
| | - G D Cresswell
- Evolutionary Genomics & Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London
| | - A Vatsiou
- Evolutionary Genomics & Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London
| | - D Nichol
- Evolutionary Genomics & Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London
| | - A Acar
- Evolutionary Genomics & Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London
| | - L Ermini
- Evolutionary Genomics & Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London; Centre for Evolution and Cancer, The Institute of Cancer Research, London
| | - K Chkhaidze
- Evolutionary Genomics & Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London
| | - B Werner
- Evolutionary Genomics & Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London
| | - R Mair
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - E Brognaro
- Department of Neurosurgery, S. Maria Della Misericordia Hospital, Rovigo, Italy
| | - R G W Verhaak
- Jackson Laboratory for Genomic Medicine, Farmington, USA
| | - G Sanguinetti
- School of Informatics, University of Edinburgh, Edinburgh, UK
| | - S G M Piccirillo
- Division of Hematology and Oncolog, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, USA.
| | - C Watts
- Institute of Cancer Genome Sciences, University of Birmingham, Birmingham, UK.
| | - A Sottoriva
- Evolutionary Genomics & Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London.
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