Early Relapse Following ASCT for Patients with MM: Identification of Predictor Factors in the Era of Novel Agents
Bibliographic record
Abstract
Abstract Introduction Auto-SCT, still remains as the standard therapy for patients with MM deemed to be eligible for this approach. Unfortunately, even when most patients will respond to auto-SCT, 10-20% of cases will progress within a year. Over the last few years, a dramatic improvement on clinical outcomes has been made by using novel agents in the treatment of MM. Based on the above mentioned, we aimed to assess the incidence of Early Relapse (ER) for patients undergoing auto-SCT treated with novel-agents induction combinations at our center and to explore possible predictor factors. Methods All consecutive patients who underwent single auto-SCT at Tom Baker Cancer Center (TBCC) from 01/2006 to March/2016 were evaluated. ER was defined as per recent publications (<12 months from auto-SCT). Two-sided Fisher exact test was used to test for differences between categorical variables. A p value of <0.05 was considered significant. Survival curves were constructed according to the Kaplan-Meier method and compared using the log rank test. All statistical analyses were performed by using the SPSS 24.0 software. Results 232 consecutive patients with MM underwent single auto-SCT at our Institution over the defined period. Clinical characteristics are shown in Table 1. At the time of analysis, 172 patients are still alive and 112 have already progressed. Among these cases, 35 patients have relapsed in <12 months (ER) from auto-SCT (15.1%). 16 out of 35 patients with ER had HRC (high-risk cytogenetics) (45.7%). ER was seen in 25% of cases with HRC and 11% of patients with Standard Risk (SRC) (p=0.01). Patient with 0.5) was associated to a higher rate of ER. Median OS was shorter for the ER group (17.8 months) compared to an estimated 93 months for those patients relapsing >12 months. (p=0.0001) In conclusion, patients with ER after auto-SCT remain to be a challenge. Even with the advent of novel agents, patients with ER had poor outcomes. ER seems to be associated to HRC and low degree of response. Patients with these features should be considered for novel alternatives, aiming to achieve and sustain the deepest possible response. More biological insights on ER cases are needed to further improve survival outcomes. PFS according to level of response at day-100 post ASCT PFS according to level of response at day-100 post ASCT Figure 1 Overall survival according to the pattern of relapse Figure 1. Overall survival according to the pattern of relapse Disclosures Jimenez-Zepeda: Amgen: Honoraria; Takeda: Honoraria; Janssen: Honoraria; Celgene, Janssen, Amgen, Onyx: Honoraria. Neri:Celgene and Jannsen: Consultancy, Honoraria. Bahlis:Onyx: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Other: Travel Expenses, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Other: Travel Expenses, Research Funding, Speakers Bureau; BMS: 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".