2023 American Society of Clinical Oncology (ASCO) Symposium: Meeting highlights
Bibliographic record
Abstract
The American Society of Clinical Oncology (ASCO) annual meeting, held in Chicago and online from June 2-6, 2023, showcased the latest research in cancer care, with over 200 sessions centered around the theme, "Partnering with patients: The cornerstone of cancer care and research."Following the meeting, on June 8, the Canadian Urological Association (CUA) held an online webinar where Canadian experts highlighted key research findings in kidney, prostate, and bladder cancers.This report provides a summary of the significant advances in genitourinary cancers as presented at ASCO 2023.The full webinar can be accessed on UROpedia Canada, and meeting abstracts are available at the ASCO meeting library. KIDNEY CANCERDr. Naveen Basappa presented an update on the advances in the treatment of kidney cancer.The CheckMate 914 trial investigated the use of adjuvant nivolumab plus ipilimumab (NIVO+IPI) in patients with localized renal cell carcinoma (RCC) who were at high risk of relapse after nephrectomy.Surprisingly, the combination treatment did not show any improvement in disease-free survival (DFS) compared to placebo (PBO). 1 To gain a better understanding of the outcomes, exploratory post-hoc analyses were conducted, focusing on specific patient subsets.The results revealed that patients with grade 4 histology, particularly those with sarcomatoid features, showed a potential improvement in DFS with NIVO+IPI.Similarly, patients with over 1% PD-L1 expression seemed to benefit more from NIVO+IPI compared to those with low or no PD-L1 expression.These findings suggest that sarcomatoid features and % PD-L1 expression could serve as a potential biomarker for predicting outcomes in this context.Moreover, NIVO+IPI treatment did not have any detrimental effects on patient quality of life compared to PBO.Another interesting observation was that patients who received more than six cycles of NIVO+IPI tended to have better DFS; however, patients who received six or fewer cycles and discontinued treatment due to adverse events did not experience any DFS benefit.The limited exposure to NIVO+IPI and discontinuation due to adverse events may have contributed to the lack of observed DFS benefit in the trial.Data on overall survival (OS) is still pending. 2Patient selection is crucial in this treatment setting, and in addition to clinical features, biological characteristics should also be considered.Long-term followup of KEYNOTE-426 was presented at the meeting.This trial assessed the efficacy of pembrolizumab (PEMBRO), a PD-1 inhibitor, in combination with axitinib (AXI), a vascular endothelial growth factor receptor tyrosine kinase inhibitor (VEGFR TKI), as a first-line treatment for advanced clear-cell (cc)RCC.The first interim analysis demonstrated significant improvements in OS, progressionfree survival (PFS), and objective response rate (ORR) with PEMBRO+AXI compared to sunitinib (SUN), the control arm in the study.After a minimum followup of five years, the 60-month OS rates were 41.9% for PEMBRO+AXI and 37.1% for SUN (hazard ratio [HR] 0.84, 95% confidence interval [CI] 0.71-0.99).Similarly, the 60-month PFS rates were 18.3% and 7.3%, respectively.The median duration of response (DOR) was also longer in the PEMBRO+AXI group.Subsequent anticancer treatments influenced OS, but even after adjusting for this effect, PEMBRO+AXI appeared to show a benefit; however, combination
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.134 | 0.094 |
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".