ALBERT

Description: Background: Multiple myeloma (MM) is characterized by the invasion of malignant plasma cells into the bone marrow. While first line treatment options result in significant clinical benefit to patients, spatiotemporal clonal evolution results in disease relapse and mortality. Advances in genomics have armed clinicians with unprecedented insight into the molecular architecture of MM cells, however, the clinical benefit derived by genomics-guided intervention has been limited. We present a novel computational biology modelling (CBM) tool, which takes into account the combined effect of individual mutations, gene copy number abnormalities and large scale chromosomal changes in order to predict the salient molecular pathways utilized by the MM cell for survival. By reverse-engineering MM cell architecture in silico, the CBM tool is able to predict drug response and resistance mechanisms. Thus, our aim was to determine the accuracy of the CBM tool in predicting treatment response of relapsed/refractory MM patients for future management of their disease, in a more individualized manner. Methods: Cytogenetics and somatic mutations (by targeted NGS) for 15 MM patients were input into the CBM model to predict responses to different therapeutic combinations. All patients were relapsed to prior treatment. CBM uses PubMed and other online resources to generate patient-specific protein network maps of activated and inactivated disease pathways. We simulated the specific combinations of the drugs per patient and measured the quantitative drug effect on a composite MM disease inhibition score (i.e., cell proliferation, viability, apoptosis and paraproteins). The actual clinical outcome of the treatments was compared with predicted outcomes. Results: Fifteen patients were analysed using CBM for prediction of treatment response after NGS was performed. 13/15 were clinically evaluable, of which 1 was a responder and 12 were non-responder. 6/13 patients were treated on clinical trial and 7/13 were on drug combinations per physician decision. CBM correctly predicted 1 responder and 11 non-responder with a PPV of 50%, NPV 100%, specificity 91.67%, sensitivity 100%. The accuracy of CBM prediction was 92.30%. CBM also predicted the response of prior drug therapies for its non-response at relapse. For prior drug treatment options, 14 patients were evaluable. All the 14 patients were clinically non-responders and CBM correctly predicted for 13 patients with NPV 100%, Specificity 92.85% and overall accuracy of 92.85%. The majority of patients did not respond to therapies recommended at relapse. As an example, the operative molecular pathways from 2 patients who did not respond to combination treatment, either pre-NGS or post-NGS profiling, are shown in Fig. 1 and Table 1. CBM identified amplification (AMP) of chromosome (chr) 1 (WNT3A, IL6R, CKS1B, MCL1, PIK3C2B, USF1), chr 3 (HES1, PIK3CA, CTNNB1, WNT7A, FANCD2), chr 5 (IL6ST, IRF1, GLRX, SKP2), chr 7 (CDK5, EZH2, IL6, CAV1, ABCB1), chr 9 (NOTCH1, HSPA5, FANCC, FANCG), chr 15 (DLL4, FANCI, ALDH1A2), chr 19 (ERCC1, ERCC2, USF2); deletion(DEL) of chr 13 (CUL4A) , chr 16 (AXIN1, CDH1) and TP53 mutation in different combinations, which confer resistance to therapies at relapse. Conclusions: The CBM technology represents a potential means to identify therapeutic options for MM patients based on the patients individual tumor-genome profile and which can also be deployed for uncovering drug resistance mechanisms. This tool may aid clinicians in decision making for recommending the most appropriate therapy based on standard of care agents or clinical trials; thus improving patient outcomes and reducing unnecessary costs or drug-related toxicities. Disclosures Singh: Cellworks Research India Private Limited: Employment. Sauban:Cellworks Research India Private Limited: Employment. Husain:Cellworks Research India Private Limited: Employment. Kumar:Cellworks Research India Private Limited: Employment. Kumari:Cellworks Research India Private Limited: Employment. Tyagi:Cellworks Research India Private Limited: Employment. Abbasi:Cell Works Group Inc.: Employment. Vali:Cell Works Group Inc.: Employment. Ailawadhi:Pharmacyclics: Research Funding; Takeda: Consultancy; Celgene: Consultancy; Amgen: Consultancy; Janssen: Consultancy.

Description: Background: Multiple myeloma (MM) is an incurable and heterogeneous haematological malignancy in which immune suppression and complex biology affect the disease and its response to treatment. Several new treatments have been approved for MM in recent years providing numerous options for patients with relapsed/refractory disease. However, there is no validated method for selecting the best treatment combination for each patient, making patient management difficult. The ability to predict treatment response based on disease characteristics could improve clinically outcomes. Aim: This was a validation of a genomics-informed response prediction using computational biology modelling (CBM) in patients with relapsed/refractory MM. Methods: Input data from fluorescence in-situ hybridization (FISH), karyotype, and a MM specific next generation sequencing capture array were analysed using CBM. This was a retrospective review of patients which were treated with different combinations based on patient/physician choice. The CBM uses PubMed and other online resources to generate patient-specific protein network maps of activated and inactivated pathways. The specific drug combination for each patient was simulated and the quantitative drug effect was measured on a composite MM disease inhibition score (i.e., cell proliferation, viability, apoptosis and paraproteins). The predicted outcomes were then compared to the clinical response (≥PR or 〈 PR per IMWG) to assess the accuracy of this CBM predictive approach. Results: 27 patients were selected for the study; 3 failed CBM due to missing inputs and in 3 clinical response was not able to be assessed, leaving 21 eligible for the analysis. The median age at presentation was 57 years (range 37-76) and 52% were male. The median prior lines of MM therapy was 5 (range 1-15). 38% were refractory to bortezomib, 62% to lenalidomide, 52% to carfilzomib, 57% to pomalidomide, and 43% to daratumumab. 81% had a prior autologous stem cell transplant. The treatments modelled included IMiD-based regimens (n = 9), PI-based regimens (n = 6), chemo-based regimens (n = 3), selinexor (n = 2), PI/IMiD combination regimens (n = 1). Sixteen were clinical responders and 5 were non-responders. CBM predictions matched for 17 of 21 treatments overall, 15 of 16 clinical responders and 2 of 5 non-responders. The statistics of prediction accuracy against clinical outcome are presented in Table 1. Interestingly, the CBM identified drugs within the combination regimens which may not have impacted efficacy. For example, the CBM predicted that one patient treated with bortezomib, venetoclax, and dexamethasone would have had similar response if venetoclax had been omitted from the regimen. Conclusion: We have demonstrated that a CBM approach, which incorporates genomics, can help predict response in patients with relapsed or refractory MM. Prospective studies using the CBM as part of treatment decision-making will help determine its application into clinical settings. Disclosures Vij: Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Jazz Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharma: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Jansson: Honoraria, Membership on an entity's Board of Directors or advisory committees. Singh:Cellworks Research India Private Limited: Employment. Sauban:Cellworks Research India Private Limited: Employment. Husain:Cellworks Research India Private Limited: Employment. Lakshminarayana:Cellworks Research India Private Limited: Employment. Talawdekar:Cellworks Research India Private Limited: Employment. Mitra:Cellworks Research India Private Limited: Employment. Abbasi:Cell Works Group Inc.: Employment. Vali:Cell Works Group Inc.: Employment.

Description: The growing disenchantment with state management of natural resources has led to increasing reliance on co-management. This involves devolution of the rights to manage and control access to the resource from the state to the resource appropriators. Co-management has been introduced in many Third World countries with varying success. Co-management programmes have typically assumed that the resource community wants to conserve the resource and is prevented from doing so by their inability to form a collective choice arena. Hence such programmes have attempted to provide a collective choice arena. However, these attempts overlook the need to change the attitudes of resource users and create a demand for the resource regime. In this paper we have presented two case studies of Joint Forest Management in India to illustrate this point.