363 Dose of the molecularly targeted agents (MTA) in Phase 1 trials correlates with clinical benefit

11018 Background: Genomic analyses have revealed many potentially actionable mutations across sarcoma subtypes. Whether sarcoma patients enrolled on genomically matched early phase trials have improved clinical outcomes over patients enrolled on non-genomically matched trials remains unclear. Methods: We analyzed clinical and next gen sequencing data from sarcoma patients on phase 1 trials at MD Anderson Cancer Center (MDACC) and performed logistic and Cox proportional hazards regression analyses to compare response rate (RR), median time to progression (mTTP), clinical benefit rate (CBR = CR, PR, or SD > 6 months), and median overall survival (mOS) between patients treated on genomically-matched and non-genomically matched trials. Results: Among the 406 patients with advanced sarcomas (321 soft tissue sarcoma [STS], 85 bone sarcomas) treated on phase 1 trials at MDACC from May 2006 to May 2018, median age was 53 (range 11-84), 48% were female, and patients had a median 3 prior lines of therapy (range 0-9). The most commonly treated STS subtypes were leiomyosarcoma (n = 66; 16%), liposarcoma (n = 52; 13%), GIST (n = 44; 11%), and synovial sarcoma (n = 11; 3%) and most commonly treated bone sarcomas were osteosarcoma (n = 34; 8%), chondrosarcoma (n = 28; 7%), and Ewing's sarcoma (n = 25; 6%). 23% (n = 93) of sarcoma patients treated on phase 1 trials were treated on genomically-matched trials. RR on non-genomically matched trials was 6% compared to 11% on genomically-matched trials, OR 1.97 (95% CI 0.88, 4.44), p = 0.10. Responses on genomically-matched trials were seen with novel agents targeting TRK, LRRC15, cMET, mTOR, VEGF, MDM2, KIT/PDGFRA, and FGFR. mTTP on non-genomically matched trials was 2.7 months compared to 3.7 months on genomically-matched trials, HR 0.72 (95% CI 0.57, 0.91), p = 0.0048. CBR on non-genomically matched trials was 19% compared to 41% on genomically-matched trials, OR 2.91 (95% CI 1.77, 4.80), p < 0.0001. mOS on non-genomically matched trials was 15.5 months compared to 22.1 months on genomically-matched trials, HR 0.70 (95% CI 0.50,0.98), p = 0.031. Conclusions: Enrollment on genomically-matched phase 1 trials is associated with an improvement in clinical benefit rate, time to progression, and overall survival in heavily pretreated, metastatic sarcoma patients. While RR remained low, we report the mutations associated with responses on genomically-matched trials. A prospective, biomarker-driven genomically-matched basket trial for these alterations is warranted in advanced sarcomas.

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Patient survival with immune checkpoint inhibitors and targeted agents in phase 1 trials: A propensity score weighted analysis.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.2580 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. 2580-2580

Author(s):

Itziar Gardeazabal ◽

Ignacio Matos ◽

Cinta Hierro ◽

Analia Azaro ◽

Cristina Viaplana ◽

...

Keyword(s):

Prognostic Factors ◽

Propensity Score ◽

Positive Impact ◽

Checkpoint Inhibitors ◽

Phase 1 ◽

Univariate Analysis ◽

Significant Heterogeneity ◽

Tumor Type ◽

Targeted Agents ◽

Phase 1 Trials

2580 Background: There have been important changes in early drug development units with an unprecedented increase of immune-oncology (IO) trials. Currently at the Vall d’Hebron Institute Oncology (VHIO) close to 50% of our Phase 1 trials (Ph1t) portfolio includes IO drugs, while from 2011 to 2015 more than 80% of our trials assessed targeted agents (TA). We wanted to investigate whether this swift had a positive impact on patient (pts) outcome. Methods: We performed a retrospective analysis of the pts treated with IO and TA at VHIO Ph1t Unit from Jun’11 to May’18. Only patients treated with IO in ≥ 2nd line were included (and without an approved IO therapy as per standard-of-care) and those with TA classified as tiers II-III-IV by the ESMO scale for clinical actionability of molecular targets ESCAT (which also represents unapproved indications). The aim of this study was to compare overall survival (OS) for the two cohorts. Given the non-randomized nature of the study a propensity score weighting (PSW) was used to control for selection bias in treatment effect estimation. Results: Out of 545 eligible pts, 281 (51.5%) received TA and 264 (48.5%) IO, with unadjusted median OS (mOS) of 7.7 months (m) and 9.2m, respectively. In univariate analysis, OS was associated with tumor type, number of previous treatment lines, regimen (monotherapy vs combination), and clinical-laboratory prognostic factors (Vioscore: albumin < 3.5 g/dl; LDH > upper limit of normal; neutrophil/[leukocytes minus neutrophils] ratio (dNLR) > 3; more than 2 sites of metastasis; and presence of liver metastasis) (p < 0.05). After adjusting for these factors in a PSW model, the IO group showed statistically significant longer OS with HR = 0.75 (CI95% 0.65 – 0.86, p < 0.0001). The In a stratified analysis by tumor type we found no significant heterogeneity in the relative benefit of IO over TA. Conclusions: In real world data from our Ph1t population, treatment with IO was associated with longer OS than treatment with TA, even after adjusting for known prognostic factors and treatment selection biases. These results suggest that the likelihood of patient benefit with IO therapies in Ph1t is increasing.

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Potential utility of a longitudinal relative dose intensity of molecularly targeted agents in phase 1 dose-finding trials

Cancer Science ◽

10.1111/cas.13436 ◽

2017 ◽

Vol 109 (1) ◽

pp. 207-214 ◽

Cited By ~ 6

Author(s):

Akihiro Hirakawa ◽

Kan Yonemori ◽

Fumie Kinoshita ◽

Yumiko Kobayashi ◽

Hitomi S. Okuma ◽

...

Keyword(s):

Phase 1 ◽

Dose Intensity ◽

Dose Finding ◽

Relative Dose Intensity ◽

Potential Utility ◽

Targeted Agents ◽

Molecularly Targeted Agents

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Molecularly profiled trials: toward a framework of actions for the “nil actionables”

British Journal of Cancer ◽

10.1038/s41416-021-01423-9 ◽

2021 ◽

Author(s):

Allan Michael Jordan

Keyword(s):

Holistic Approach ◽

Step Change ◽

Additional Patient ◽

Targeted Agents ◽

Patient Benefit ◽

Blood Samples ◽

Left Behind ◽

Clinical Community ◽

Molecularly Targeted Agents ◽

Early Drug

AbstractThe sequencing of tumour or blood samples is increasingly used to stratify patients into clinical trials of molecularly targeted agents, and this approach has frequently demonstrated clinical benefit for those who are deemed eligible. But what of those who have no clear and evident molecular driver? What of those deemed to have “nil actionable” mutations? How might we deliver better therapeutic opportunities for those left behind in the clamour toward stratified therapeutics? And what significant learnings lie hidden in the data we amass but do not interrogate and understand? This Perspective article suggests a holistic approach to the future treatment of such patients, and sets a framework through which significant additional patient benefit might be achieved. In order to deliver upon this framework, it encourages and invites the clinical community to engage more enthusiastically and share learnings with colleagues in the early drug discovery community, in order to deliver a step change in patient care.

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Assessing the efficacy of molecularly targeted agents by using Kalman filter

2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) ◽

10.1109/gensips.2011.6169439 ◽

2011 ◽

Cited By ~ 1

Author(s):

Xiangfang Li ◽

Lijun Qian ◽

Michael L. Bittner ◽

Edward R. Dougherty

Keyword(s):

Kalman Filter ◽

Targeted Agents ◽

Molecularly Targeted Agents

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A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial

npj Precision Oncology ◽

10.1038/s41698-021-00191-2 ◽

2021 ◽

Vol 5 (1) ◽

Author(s):

Istvan Petak ◽

Maud Kamal ◽

Anna Dirner ◽

Ivan Bieche ◽

Robert Doczi ◽

...

Keyword(s):

Clinical Benefit ◽

Progressive Disease ◽

Genetic Alterations ◽

Outcome Data ◽

Computational Method ◽

Molecular Profile ◽

Precision Oncology ◽

Driver Genes ◽

Oncology Clinical Trial ◽

Molecularly Targeted Agents

AbstractPrecision oncology is currently based on pairing molecularly targeted agents (MTA) to predefined single driver genes or biomarkers. Each tumor harbors a combination of a large number of potential genetic alterations of multiple driver genes in a complex system that limits the potential of this approach. We have developed an artificial intelligence (AI)-assisted computational method, the digital drug-assignment (DDA) system, to prioritize potential MTAs for each cancer patient based on the complex individual molecular profile of their tumor. We analyzed the clinical benefit of the DDA system on the molecular and clinical outcome data of patients treated in the SHIVA01 precision oncology clinical trial with MTAs matched to individual genetic alterations or biomarkers of their tumor. We found that the DDA score assigned to MTAs was significantly higher in patients experiencing disease control than in patients with progressive disease (1523 versus 580, P = 0.037). The median PFS was also significantly longer in patients receiving MTAs with high (1000+ <) than with low (<0) DDA scores (3.95 versus 1.95 months, P = 0.044). Our results indicate that AI-based systems, like DDA, are promising new tools for oncologists to improve the clinical benefit of precision oncology.

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