Steroid Refractoriness, but Not Rapidity of Response, to Induction Therapy Predicts for Outcome Post-Autologous Stem Cell Transplant (ASCT) in Patients (Pts) with Multiple Myeloma (MM)
Bibliographic record
Abstract
Abstract Introduction: Goals of induction therapy in younger, newly diagnosed MM pts are to ameliorate symptoms, improve performance status, and initiate disease control enabling the move toward ASCT. It is controversial whether more intensive induction regimens which lead to greater depth or more rapid onset of response translate to improved survival outcomes post-transplant. As a result, conservative steroid-based regimens such as high-dose dexamethasone (HDD) or VAD are still frequently used as standard induction therapy despite response rates of only 50–60%. Patients who do not respond appropriately to these conservative regimens can typically be salvaged successfully with the addition of novel agents such as thalidomide and proceed to ASCT. This approach allows for efficient use of novel agents that in many parts of the world are not easily accessed for first-line use. To validate this approach, we performed a retrospective review of 183 MM patients who received conservative induction therapy (HDD, VAD) and correlated induction parameters (rapidity of response, use of salvage induction therapy) with transplant outcomes. Methods: 183 MM patients who received HDD or VAD induction prior to proceeding to ASCT over a 7 year period (Feb 2000–Oct 2006) were reviewed. No pts received tandem transplants. HDD (40mg daily PO days 1–4, 9–12, 17–20 on a 28 day cycle) was used in 42 pts (33%) and VAD (vincristine, adriamycin, HDD) in 141 pts (77%). At our institution, a 50% or greater decrease in the monoclonal protein (PR) with induction therapy is targeted in order to proceed to transplant. Results: Median age for all 183 pts at time of transplant was 59 years (33–75); 60% male; 174 pts (95%) Salmon-Durie stage II–III. MM subtypes included: IgG 132(72%), IgA 39(21%), light chains only 7(4%). Median hemoglobin at diagnosis was 104 g/L, serum creatinine 89μmol/L, β2-microglobulin 248 nmol/L (normal <200) and albumin 35 g/L. Response to induction therapy was assessed with each cycle to evaluate the rapidity of response. Response (PR or CR) by cycle: cycle 1 – 65 pts (35.5%), cycle 2 – 33 pts (18%), cycle 3 – 28 pts (15.3%), cycle 4 – 17 pts (9.3%) and over 4 cycles – 40 pts (22%). Half of the pts (98 pts; 54%) achieved at least a PR by cycle 2 (early responders) with 46% of pts responding after 2 cycles (late responders). Rapidity of induction response did not translate into a significant difference in post-transplant PFS (median PFS from transplant 20.7 vs 20.0 mos for early vs. late responders, P=0.56; median OS from transplant: 64.4 vs. 51.3 mos respectively, P=0.37). 46 pts (25%) failed their first induction regimen: primary nonresponders in 33 pts (18%); responsive but progressed before ASCT in 13 pts (7%). However, all responded subsequently to a salvage induction regimen (most commonly VAD, thalidomide+HDD, cyclophosphamide+prednisone) and proceeded to ASCT. Pts requiring salvage induction had inferior survival outcomes post-transplant compared to pts requiring only one induction regimen: median PFS from transplant 16.3 vs. 21.8 mos, P=0.02; median OS from transplant 46.2 vs. 69.4 mos, P=0.0005. Pts requiring more than one induction regimen had delayed time from induction to ASCT (9.4 vs 6.7 mos; p=0.01). Median follow-up from ASCT was 33 mos. For all pts, the median PFS was 20.3 mos (95% CI: 17.0–24.4); median OS was 63.9 mos (95% CI: 50.3–90.5) from transplant. Pts requiring more than one induction regimen had significantly lower serum albumin levels than those responding to first induction, but no other differences in baseline prognostic factors were identified (Salmon-Durie stage, serum creatinine, B2-microglobulin, LDH, presence of cytopenias). Conclusions: Early response to dexamethasone-based induction therapy does not correlate with PFS or OS post-ASCT. Patients who fail initial dexamethasone-based induction therapy have worse post-transplant outcomes than initial responders, despite ultimately achieving a successful response pre-transplant. Whether the poor outcomes are due to delayed time to transplant, identification of biologically aggressive disease or intrinsic “steroid-refractoriness” remain to be clarified. Our data support the use of novel induction regimens able to achieve high response rates in order to avoid the need for salvage induction before proceeding to transplant.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".