Lymphoma

Selected articles obtained using Internet search tools, including PubMed and syllabi from meetings (e.g., Clinical PET and PET/CT syllabus, Radiological Society of North America, 2007), were identified. Publications resulting from database searches and including the main search terms RECIST, positron, FDG, ROI (region of interest), cancer, lymphoma, PET, WHO, and treatment response were included. The search strategy for relevant ¹⁸F-FDG PET studies articulated by Mijnhout et al. was also applied (34 (link),35 (link)). These were augmented by key references from those studies, as well as the authors' own experience with PET assessments of treatment response, informal discussions with experts on PET treatment response assessment, and pilot evaluations of clinical data from the authors' clinical practice. Limitations and strengths of the anatomic and functional methods to assess treatment response were evaluated with special attention to studies that had applied qualitative or quantitative imaging metrics, had determined the precision of the method, and had histologic correlate or outcome data available. On the basis of these data, proposed treatment response criteria including PET were formulated, drawing from both prior anatomic models (notably WHO, RECIST, and RECIST 1.1) and the EORTC PET response draft criteria (36 (link)). These conclusions were based on a consensus approach among the 4 authors. Thus, a systematic review and a limited Delphilike approach augmented by key data were undertaken to reach consensus in a small group. For demonstration purposes, ¹⁸F-FDG PET scans obtained at our institution on 1 of 2 GE Healthcare PET/CT scanners were analyzed with several tools, including a tool for response assessment.

If mutation frequency, corrected for mutation context, gene length, and other parameters, cannot reliably identify modestly mutated driver genes, what can? In our experience, the best way to identify Mut-driver genes is through their pattern of mutation rather than through their mutation frequency. The patterns of mutations in well-studied oncogenes and tumor suppressor genes are highly characteristic and nonrandom. Oncogenes are recurrently mutated at the same amino acid positions, whereas tumor suppressor genes are mutated through protein-truncating alterations throughout their length (Fig. 4 and table S2A).
On the basis of these mutation patterns rather than frequencies, we can determine which of the 18,306 mutated genes containing a total of 404,863 subtle mutations that have been recorded in the Catalogue of Somatic Mutations in Cancer (COSMIC) database (30 (link)) are Mut-driver genes and whether they are likely to function as oncogenes or tumor suppressor genes. To be classified as an oncogene, we simply require that >20% of the recorded mutations in the gene are at recurrent positions and are missense (see legend to table S2A). To be classified as a tumor suppressor gene, we analogously require that >20% of the recorded mutations in the gene are inactivating. This “20/20 rule” is lenient in that all well-documented cancer genes far surpass these criteria (table S2A).
The following examples illustrate the value of the 20/20 rule. When IDH1 mutations were first identified in brain tumors, their role in tumorigenesis was unknown (2 (link), 31 (link)). Initial functional studies suggested that IDH1 was a tumor suppressor gene and that mutations inactivated this gene (32 (link)). However, nearly all of the mutations in IDH1 were at the identical amino acid, codon 132 (Fig. 4). As assessed by the 20/20 rule, this distribution unambiguously indicated that IDH1 was an oncogene rather than a tumor suppressor gene, and this conclusion was eventually supported by biochemical experiments (33 (link), 34 (link)). Another example is provided by mutations in NOTCH1. In this case, some functional studies suggested that NOTCH1 was an oncogene, whereas others suggested it was a tumor suppressor gene (35 (link), 36 (link)). The situation could be clarified through the application of the 20/20 rule to NOTCH1 mutations in cancers. In “liquid tumors” such as lymphomas and leukemias, the mutations were often recurrent and did not truncate the predicted protein (37 (link)). In squamous cell carcinomas, the mutations were not recurrent and were usually inactivating (38 (link)–40 (link)). Thus, the genetic data clearly indicated that NOTCH1 functions differently in different tumor types. The idea that the same gene can function in completely opposite ways in different cell types is important for understanding cell signaling pathways.

All patient samples in this study were reviewed and approved by the Stanford Institutional Review Board in accordance with the Declaration of Helsinki. Tonsils were collected as part of routine tonsilectomy procedures at Lucile Packard Children’s Hospital at Stanford University with informed consent for research use, and then mechanically disaggregated before cell suspensions were cryopreserved (Supplementary Fig. 3b). Peripheral blood mononuclear cells (PBMCs) were isolated from specimens taken before and immediately following four weekly doses of infusional rituximab (375 mg m⁻²) monotherapy for extranodal marginal zone lymphoma (EMZL) in a subject without measurable circulating disease (patient 1 in Supplementary Fig. 4c). PBMCs were respectively isolated from specimens taken immediately following four cycles and six cycles of RCHOP immunochemotherapy for treatment of DLBCL (patients 2 and 3 in Supplementary Fig. 4c). PBMCs were also isolated from a subject following four cycles of Rituximab for treatment of FL (patient 4 in Supplementary Fig. 4c); this subject had ~2% circulating lymphoma cells at diagnosis, which were undetectable by CIBERSORT and flow cytometry following four Rituximab infusions. Specimens of adjacent normal lung tissue were obtained during surgical resection of early stage non-small cell lung tumors (Fig. 2h). Surgical tissue biopsies were obtained from untreated FL patients enrolled in a Phase III clinical trial (NCT00017290^{18 (link)}) (Fig. 2i and Fig. 3c). Lastly, PBMCs were obtained from 20 adults of varying ages receiving influenza immunization (NCT01827462) (Fig. 3a), and from seven adults consisting of patient 4 in Supplementary Fig. 4c and six healthy subjects (Fig. 3b, which includes patient 4).