Molecular modifiers of KRAS G12C inhibitor (KRAS G12Ci) efficacy in advanced KRAS G12C-mutant NSCLC are poorly defined. In a large unbiased clinico-genomic analysis of 424 NSCLC patients, we identified and validated co-alterations in KEAP1, SMARCA4 and CDKN2A as major independent determinants of inferior clinical outcomes with KRAS G12Ci monotherapy. Collectively, co-mutations in these three tumor suppressor genes segregated patients into distinct prognostic subgroups and captured ~50% of those with early disease progression (PFS≤3 months) with KRAS G12Ci. Pathway-level integration of less prevalent co-alterations in functionally related genes nominated PI3K/AKT/MTOR pathway and additional baseline RAS gene alterations, including amplifications, as candidate drivers of inferior outcomes with KRAS G12Ci, and revealed a possible association between defective DNA damage response/repair and improved KRAS G12Ci efficacy. Our findings propose a framework for patient stratification and clinical outcome prediction in KRAS G12C-mutant NSCLC that can inform rational selection and appropriate tailoring of emerging combination therapies.

Methylthioadenosine phosphorylase, an essential enzyme for the adenine salvage pathway, is often deficient (MTAPdef) in tumors with 9p21 loss and hypothetically renders tumors susceptible to synthetic lethality by antifolates targeting de novo purine synthesis. Here we report our single arm phase II trial (NCT02693717) that assesses pemetrexed in MTAPdef urothelial carcinoma (UC) with the primary endpoint of overall response rate (ORR). Three of 7 enrolled MTAPdef patients show response to pemetrexed (ORR 43%). Furthermore, a historic cohort shows 4 of 4 MTAPdef patients respond to pemetrexed as compared to 1 of 10 MTAP-proficient patients. In vitro and in vivo preclinical data using UC cell lines demonstrate increased sensitivity to pemetrexed by inducing DNA damage, and distorting nucleotide pools. In addition, MTAP-knockdown increases sensitivity to pemetrexed. Furthermore, in a lung adenocarcinoma retrospective cohort (N = 72) from the published BATTLE2 clinical trial (NCT01248247), MTAPdef associates with an improved response rate to pemetrexed. Our data demonstrate a synthetic lethal interaction between MTAPdef and de novo purine inhibition, which represents a promising therapeutic strategy for larger prospective trials.

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The role of combination chemotherapy with immune checkpoint inhibitors (ICI) (ICI-chemo) over ICI monotherapy (ICI-mono) in non-small cell lung cancer (NSCLC) remains underexplored. In this retrospective study of 1133 NSCLC patients, treatment with ICI-mono vs ICI-chemo associate with higher rates of early progression, but similar long-term progression-free and overall survival. Sequential vs concurrent ICI and chemotherapy have similar long-term survival, suggesting no synergism from combination therapy. Integrative modeling identified PD-L1, disease burden (Stage IVb; liver metastases), and STK11 and JAK2 alterations as features associate with a higher likelihood of early progression on ICI-mono. CDKN2A alterations associate with worse long-term outcomes in ICI-chemo patients. These results are validated in independent external (n = 89) and internal (n = 393) cohorts. This real-world study suggests that ICI-chemo may protect against early progression but does not influence overall survival, and nominates features that identify those patients at risk for early progression who may maximally benefit from ICI-chemo.

Abstract-This paper addresses two main challenges for clustering which require extensive human effort: selecting appropriate parameters for an arbitrary clustering algorithm and identifying alternative clusters. We propose an architecture and a concrete system MR-CLEVER for multi-run clustering that integrates active learning with clustering algorithms. The key hypothesis of this work is that better clustering results can be obtained by combining clusters that originate from multiple runs of clustering algorithms. By defining states that represent parameter settings of a clustering algorithm, the proposed architecture actively learns a state utility function. The utility of a parameter setting is assessed based on clustering run-time, quality and novelty of the obtained clusters. Furthermore, the utility function plays an important role in guiding the clustering algorithm to seek novel solutions. Cluster novelty measures are introduced for this purpose. Finally, we also contribute a cluster summarization algorithm that assembles a final clustering as a combination of high-quality clusters originating from multiple runs. Merits of our proposed system are that it is generic and therefore can be used in conjunction with different clustering algorithms, and it reduces human effort for selecting the parameters, for comparing clustering results and for assembling clustering results. We evaluate the proposed system in conjunction with a representative based clustering algorithm namely CLEVER for a challenging data mining task involving an earthquake dataset. The obtained results demonstrate that, in comparison to the best single-run clustering, multi-run clustering discovers solutions of higher quality.