Soft tissue sarcomas in elderly patients beyond the age of 65 years account for almost 50 % of the sarcomas of the total patient population in this rare tumor entity. Distinct clinical and biological characteristics of the different sarcoma subtypes have to be taken into account in the various age groups. Old age should not per se be a contraindication to chemotherapy administration or to undergo surgery; however, particular aspects regarding safety, comorbidities, toxicity, and quality of life have to be considered. The use of a geriatric assessment and especially the use of predictive tools for estimating the expected toxicity may be helpful in this challenging situation. On the other side, osteosarcomas in the elderly patient population represent an extreme rarity. The adequate primary surgical care of these patients seems crucial in terms of prognosis as the addition of chemotherapy does not significantly improve patients' overall survival.

The challenge of treating elderly patients with advanced bone and soft tissue sarcomas.

BackgroundThe aim of this study was to evaluate the landscape of gene mutations and the clinical significance of tumor mutation burden (TMB) in patients with soft tissue sarcoma who underwent surgi...

https://journals.sagepub.com/doi/pdf/10.1177/1724600820925095

The landscape of gene mutations and clinical significance of tumor mutation burden in patients with soft tissue sarcoma who underwent surgical resection and received conventional adjuvant therapy.

Simple Summary Older patients ≥ 55 years represent more than 50% of all sarcoma patients and have a worse prognosis compared to younger patients. Age alone should not be a reason to deprive patients of standard treatment with chemotherapy and surgery, provided they are functionally fit enough and willing to take the risk of adverse events. The E-TRAB study evaluated the feasibility and prognostic value of a comprehensive geriatric assessment representing different domains such as activities of daily living, co-morbidities and Patient-Reported Outcomes (PROs) for quality of life (QoL) and treatment tolerability. The study included 69 patients with soft tissue sarcoma (STS) between 55 and 88 years who were unsuited for treatment with anthracyclines and ifosfamide and received trabectedin as first-line therapy. Results show evidence that specific geriatric screening instruments could help predict or limit adverse treatment effects and, thereby, optimize treatment strategies in older STS patients. Abstract E-TRAB was a non-interventional, prospective trial investigating the feasibility and predictive value of geriatric assessments (GA) in older STS patients treated with trabectedin as first-line therapy. Primary endpoints were overall survival (OS), quality of life and individual clinical benefit assessed by the patient-reported outcome measures QLQ-C30 and PRO-CTCAE. Further, several GA tools were applied and correlated with clinical outcomes and treatment-related toxicities. The final analyses included 69 patients from 12 German-speaking sites. The median age of patients was 78 years (range: 55 to 88). Baseline data on PROs and GA identified a diverse population of older patients with respect to their global health status, although a large proportion of them suffered from limitations, required geriatric help and had a high risk of morbidity. The Cancer and Age Research Group (CARG) score classified 38%, 29% and 23% of the patients with low, intermediate and high risks for therapy-related side effects, respectively. Median OS was 11.2 months [95%CI: 5.6; 19.4]. The study confirmed that trabectedin as first-line treatment in older patients with STS has an acceptable and manageable safety profile. Potential prognostic factors for clinical outcome and therapy-related toxicity were identified among the GA tools. Long Timed Up and Go (TUG) showed a significant correlation to OS and early death, whereas a high CARG score (>9) was associated with an increase in unplanned hospitalizations and the incidence of toxicities grade ≥ 3.

Geriatric Assessment of Older Patients Receiving Trabectedin in First-Line Treatment for Advanced Soft Tissue Sarcomas: The E-TRAB Study from The German Interdisciplinary Sarcoma Group (GISG-13)

With the expected rise in the geriatric patient population, delivery of healthcare for elderly patients will require more resources in the next several decades. The development of cancer is more common in the geriatric population since two-thirds of cancers are typically discovered in the elderly. The elderly often have associated medical comorbidities. Treatment of the geriatric patient population will encompass more personalized approaches based on their functional status. Surgical management of elderly cancer patients is complex. Not only does it require incorporation of multimodality care, the treating physician needs to balance risks and benefits from options available due to patient medical comorbidities leading to posttreatment debilitation and side effect profiles. Furthermore, there are also insufficient studies in the elderly population in terms of long-term oncologic follow-up as many studies frequently exclude the elderly in their study designs. With the development of newer modalities of treatment such as ablative procedures, minimally invasive approaches, and immunotherapy, the selection of treatment methods will need to balance optimal oncologic outcome with expected patient survival.

Operative Surgical Oncology in the Elderly: Epidemiology, Opportunity, Outcome, and Ethical Issues

Soft Tissue Sarcomas in Octogenarian Patients: Are Treatment Options and Oncological Outcomes Different? A SEER Retrospective Study.

A key issue when designing clinical trials is the estimation of the number of subjects required. Assuming for multicenter trials or biomarker-stratified designs that the effect size between treatment arms is the same among the whole study population might be inappropriate. Limited work is available for properly determining the sample size for such trials. However, we need to account for both, the heterogeneity of the baseline hazards over clusters or strata but also the heterogeneity of the treatment effects, otherwise sample size estimates might be biased. Most existing methods account for either heterogeneous baseline hazards or treatment effects but they dot not allow to simultaneously account for both sources of variations. This article proposes an approach to calculate sample size formula for clustered or stratified survival data relying on frailty models. Both theoretical derivations and simulation results show the proposed approach can guarantee the desired power in worst case scenarios and is often much more efficient than existing approaches. Application to a real clinical trial designs is also illustrated.

Sample size estimation for cancer randomized trials in the presence of heterogeneous populations.

Introduction L’objectif des essais cliniques de phase II est de confirmer l’activite clinique preliminaire d’un nouveau traitement. Contrairement aux schemas frequentistes classiques utilises pour ce type d’essai (Fleming, Simon, Gehan), S Zohar [1] a introduit une approche bayesienne permettant de gerer les inclusions de facon dynamique. A chaque analyse intermediaire fixee, une probabilite predictive cumulee a posteriori d’arret pour efficacite ou inefficacite du traitement est estimee a partir des observations recueillies et du modele beta-binomial utilise. Nous presentons ici les analyses des caracteristiques operationnelles (risque α, puissance 1-β, taille d’echantillon n) qui nous ont permis de fixer les composantes du modele beta-binomial. Methodes Nous nous appuierons sur quatre essais de phase II evaluant l’activite anti-tumorale d’une combinaison « therapie ciblee-immunotherapie » pour des tumeurs solides. Le critere principal utilise evalue le taux de reponses objectives sous traitement. Celui-ci pouvant etre decompose de maniere deterministe par un nombre de succes et d’echecs (les parametres a et b respectifs de la loi beta), un modele beta-binomial est applique. Dans un contexte bayesien, la premiere etape de la mise en œuvre d’un essai consiste a definir la distribution beta a priori de la probabilite de reponse, le nombre minimal et maximal de patients a inclure, le nombre d’analyses a realiser et les regles d’arret de l’etude. Puis, au fur et a mesure des inclusions, la probabilite predictive cumulee a posteriori est estimee et soumise aux regles d’arret pour decider de la poursuite ou non des inclusions. Enfin, des analyses de simulation approfondies ont ete realisees afin d’evaluer les caracteristiques operationnelles du modele choisi. Resultats La distribution a priori non-informative B(1 ;1) donne des proprietes statistiques acceptables (α=1,9 %, 1-β=70,9 %, n = 33 patients) et meilleures que celles de la distribution a priori sceptique B(0,19 ;3,56) (α=10,9 %, 1-β=62,9 %, n = 33 patients) ou celles de la distribution a priori enthousiaste B(3 ;12) (α=1,4 %, 1-β=63,7 %, n = 38 patients). Au regard des caracteristiques operationnelles, et en l’absence d’hypothese forte sur le taux de reponses objectives, le prior non-informatif B(1 ;1) a ete choisi pour la suite de l’etude. Puis, le seuil des regles d’arret a ete fixe a 80 % car les caracteristiques operationnelles sont meilleures et la taille d’echantillon moyenne est plus faible que s’il etait fixe a des proportions plus elevees. Conclusion L’approche bayesienne est une alternative interessante par rapport aux schemas standards. En reevaluant la distribution a posteriori du taux de reponse tout au long de l’essai et en reduisant le nombre d’inclusions par rapport a la limite maximale (n = 50), elle permet de maintenir une certaine flexibilite sur les donnees collectees. Le modele beta-binomial est pertinent pour des essais evaluant des taux de reponses, un critere de jugement frequemment utilise dans les phases II. En contrepartie, cette approche impose une reactivite forte pour disposer des donnees (monitoring, data management) et une collaboration etroite des differents acteurs de l’essai. Par la suite, un tutoriel de la mise en œuvre du schema sera developpe et les premiers resultats seront presentes.

Tutoriel d’un schéma adaptatif de phase II à bras unique pour les réponses binaires (approche bayésienne) – Application dans le cadre d’une thérapie ciblée associée à une immunothérapie sur les tumeurs solides

Background Soft-tissue sarcomas (STSs) are a group of rare cancers that can occur at any age. Prognostic outcomes of patients with STS are usually established at the time of the patient's initial disease presentation. Conditional survival affords a dynamic prediction of prognosis for patients surviving a given period after diagnosis. Estimates of conditional survival can provide crucial prognostic information for patients and caregivers, guide subsequent cancer follow-up schedules, and impact decisions regarding management. This study aims to estimate conditional survival and prognostic factors in patients with STS according to age at diagnosis (≤75 years and ≥75 years). Subjects, materials, and methods A total of 6,043 patients with nonmetastatic STS at first diagnosis who underwent complete surgical resection (R0 or R1) were assessed. Cox proportional hazards regression was used to establish prognostic factors of conditional metastasis-free survival and overall survival at 1, 2, and 5 years after diagnosis. Results Elderly patients have more adverse prognostic features at presentation and tend to receive less aggressive treatment than do younger patients. However, at baseline as well as at each conditional survival time point, the 5-year estimated probability of metastatic relapse decreases in both young and elderly patients and is almost identical in both groups at 2 years and 5 years after initial diagnosis. Prognostic factors for metastatic relapse and death change as patient survival time increases in both young and elderly patients. Grade, the strongest prognostic factor for metastatic relapse and death at baseline, is no longer predictive of metastatic relapse in patients surviving 5 years after initial diagnosis. Leiomyosarcoma is the histological subtype associated with the highest risk of metastatic relapse and death in young patients surviving 5 years after initial diagnosis. The positive impact on the outcome of peri-operative treatments tends to decrease and disappears in patients surviving 5 years after initial diagnosis. Conclusion Conditional survival estimates show clinically relevant variations according to time since first diagnosis in both young and elderly patients with STS. These results can help STS survivors adjust their view of the future and STS care providers plan patient follow-up. Implications for practice For patients with sarcoma who are followed up years after being treated for their disease, a common scenario is for the patient and caregivers to ask practitioners what the longer-term prognosis may be. The question posed to practitioners may be, "Doc, am I now cured? It's been 5 years since we finished treatment." Survival probability changes for patients who survive a given period of time after diagnosis, and their prognosis is more accurately described using conditional survival. By analyzing more than 6,000 sarcoma patients, an overall improvement was found in the risk of relapse as patients conditionally survive. Prognostic factors for metastatic relapse and death change as patient survival time increases in both young and elderly patients.

Outcome of Patients with Soft-Tissue Sarcomas: An Age-Specific Conditional Survival Analysis.

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Sample size estimation for cancer randomized trials in the presence of heterogeneous populations.

Tutoriel d’un schéma adaptatif de phase II à bras unique pour les réponses binaires (approche bayésienne) – Application dans le cadre d’une thérapie ciblée associée à une immunothérapie sur les tumeurs solides

Outcome of Patients with Soft-Tissue Sarcomas: An Age-Specific Conditional Survival Analysis.