Follicular Lymphoma In Young Adults: Clinical Characteristics and Early Treatment Outcomes
Bibliographic record
Abstract
Abstract Introduction Adolescents and young adults with hematologic malignancies have distinct tumor biology, treatment outcomes and psychosocial consequences from cancer diagnosis and its treatment. Follicular lymphoma (FL) is generally considered to be a disease of the elderly, with a median age at diagnosis of 67 years and 65% of patients 60 years or older. FL is rare among young adults (age<40, YA), and the clinical features, natural history and treatment outcomes have not been well defined in this vulnerable patient population, who have unique life challenges while facing what is often considered an incurable lymphoma. We describe the characteristics and outcomes of a large group of YA with newly diagnosed FL treated at our center and compare them to older patients Methods The Princess Margaret Cancer Centre lymphoma database was interrogated for patients registered with FL between 1995 and 2009. Database and retrospective chart review was undertaken to collect data on FLIPI score at diagnosis, time to first and subsequent second treatments, histologic transformation and overall survival (OS). Outcomes were compared between YA and older patients (age 40-65). The older age group was limited to< 65 years to ensure comparable therapies, and because patients in this age range may be potentially eligible for intensive therapy including stem cell transplantation. Results 410 patients with newly diagnosed FL were identified. Stage at diagnosis: I:34.1%; II: 18.3%, III: 21%, IV: 26.7%; FLIPI score: 0 22.9%, 1: 25.8%, 2: 32.8%, 3: 15%, 4: 2.6%, 5: 1%. Fifty five patients (13.4%) were age< 40 at diagnosis. Median age in the YA cohort was 36 years, and 53 years in the older adults. Chi-square testing showed no difference in sex, stage or FLIPI score at diagnosis between YA and older adults. Initial treatment consisted of observation in 105 patients (25.7%); 11 of those managed expectantly were YA. With median follow-up in the observed group of 7.3 years (range 0.68-14.64), 52.3% required therapy (radiation, chemotherapy or combined modality), with median time to therapy from diagnosis of 22 months. Among all patients eventually treated, intial treatment included chemotherapy alone in 37.4%, radiotherapy in 33.8% and combined modality therapy in 25.6%. The most common chemotherapy regimens were CHOP and CVP, alone or with rituximab. Probability of requiring second treatment following initial radiation or chemotherapy was 54.5% at 2 years. FLIPI score strongly predicted time to second treatment (0=0.0047). Time to second treatment was similar for those initially observed compared to those receiving treatment at time of diagnosis, and for young compared to older adults. For the entire cohort, with median follow-up of 8.1 years (range 0.34-17.75), 5-year OS was 86.9%. On univariable analysis there was a significant difference in survival between YA and older patients: 10-year OS 89.3% v 74.2%, p=0.04. On analysis of lymphoma-specific death, there was lower probability of death in YA at 10 years: 4% compared to 15.3% for older adults, although this was not statistically significant (p=0.088). There was no significant difference in survival for patients initially observed compared to those receiving therapy at diagnosis (10-year OS 82.9% v 74%, respectively, p=0.18). Multivariable analysis demonstrated that FLIPI score at diagnosis and age<40 were significant independent variables predictive of OS. Furthermore, despite lack of significance on univariable analysis, in this model patients managed initially with observation had significantly improved OS compared to patients treated at diagnosis with radiation, chemotherapy or both (HR 2.05, p=0.0223). Histologic transformation occurred in 53 patients, 8 in YA (14.5% of all YA) and 45 in the older cohort (12.7% of all older patients). Conclusions YA with follicular lymphoma present with similar clinical characteristics to older patients, and comparable proportions of patients requiring therapy at diagnosis for symptomatic disease. YA demonstrate improved OS independent of FLIPI score compared to older adults. Whether this reflects competing mortality risks or age-related differences in lymphoma biology warrants further investigation. Disclosures: Kuruvilla: Roche: Honoraria. Kukreti:Millennium Pharmaceuticals: Research Funding; Onyx: Research Funding. Tiedemann:Celgene: Honoraria; Janssen: Honoraria. Crump:Roche: Honoraria; Jansen-Ortho: Honoraria; Celgene: Honoraria; Lundbeck: Honoraria; Novartis: Research Funding; Seattle Genetics: Honoraria.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".