| Literature DB >> 31108964 |
Arik Bernard Schulze1, Georg Evers2, Andrea Kerkhoff3, Michael Mohr4, Christoph Schliemann5, Wolfgang E Berdel6, Lars Henning Schmidt7.
Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide. With a focus on histology, there are two major subtypes: Non-small cell lung cancer (NSCLC) (the more frequent subtype), and small cell lung cancer (SCLC) (the more aggressive one). Even though SCLC, in general, is a chemosensitive malignancy, relapses following induction therapy are frequent. The standard of care treatment of SCLC consists of platinum-based chemotherapy in combination with etoposide that is subsequently enhanced by PD-L1-inhibiting atezolizumab in the extensive-stage disease, as the addition of immune-checkpoint inhibition yielded improved overall survival. Although there are promising molecular pathways with potential therapeutic impacts, targeted therapies are still not an integral part of routine treatment. Against this background, we evaluated current literature for potential new molecular candidates such as surface markers (e.g., DLL3, TROP-2 or CD56), apoptotic factors (e.g., BCL-2, BET), genetic alterations (e.g., CREBBP, NOTCH or PTEN) or vascular markers (e.g., VEGF, FGFR1 or CD13). Apart from these factors, the application of so-called 'poly-(ADP)-ribose polymerases' (PARP) inhibitors can influence tumor repair mechanisms and thus offer new perspectives for future treatment. Another promising therapeutic concept is the inhibition of 'enhancer of zeste homolog 2' (EZH2) in the loss of function of tumor suppressors or amplification of (proto-) oncogenes. Considering the poor prognosis of SCLC patients, new molecular pathways require further investigation to augment our therapeutic armamentarium in the future.Entities:
Keywords: SCLC; anti-angiogenesis; apoptosis; epigenetics; targeted therapy
Year: 2019 PMID: 31108964 PMCID: PMC6562929 DOI: 10.3390/cancers11050690
Source DB: PubMed Journal: Cancers (Basel) ISSN: 2072-6694 Impact factor: 6.639
Frequent mutations and protein expression patterns in small cell lung cancer (SCLC).
| Mutation/ Protein expression | Function in SCLC | Frequency | Reference | Possible Targeted Therapies | |
|---|---|---|---|---|---|
|
| Inactivation | Tumor suppressor | 84.9% (ntot = 272) | [ | Curaxins |
|
| Inactivation | Tumor suppressor | 57.4% (ntot = 272) | [ | AURKi |
|
| Inactivation | Tumor suppressor | 14.5% (ntot = 110) | [ | Curaxins |
|
| Inactivation | Histone modifier | 12.7% (ntot = 110) | [ | |
|
| Inactivation | Histone modifier | 14.5% (ntot = 110) | [ | HDACi |
|
| Inactivation | Histone modifier | 8.0% (ntot = 40) | [ | |
|
| Inactivation | AKT inhibitor | 7.4% (ntot = 272) | [ | mTORi/PIK3CAi |
|
| Inactivation | Tumor suppressor | 12.7% (ntot = 110) | [ | |
|
| Inactivation | 36.8% (ntot = 272) | [ | ||
|
| Amplification | Oncogene | 6.9% (ntot = 87) | [ | BETi |
|
| Amplification | Oncogene | 4.6% (ntot = 87) | [ | BETi + BCL2i |
|
| Amplification | Oncogene | 6.9% (ntot = 87) | [ | AURKi |
|
| Amplification | RTK | 5.6% (ntot = 251) | [ | FGFRi |
|
| Amplification | Oncogene | 26.8% (ntot = 56) | [ | Arsenic trioxide |
|
| Expression | Oncogene | 62.5% (ntot = 40) | [ | EZH2i |
|
| Expression | Cell adhesion | 95.3% (ntot = 107) | [ | CAR T cells |
|
| Expression | Anti-apoptosis | 97.6% (ntot = 82) | [ | BCL-2i |
|
| Expression | NOTCH inactivator | 82.5% (ntot = 63) | [ | Rova-T |
|
| 31.7% (ntot = 63) | ||||
|
| |||||
Molecular principles for targeted therapies [66,67,68,69,70].
| Monoclonal Antibody (mAb) | Small Molecule Inhibitor (SMI) | |
|---|---|---|
|
| Protein | Molecule (<1 kDa) |
|
| a) whole antibody*-mab | a) tyrosine kinase inhibitor |
|
| a) cell surface markers, | a) cellular signaling |
|
| Intravenous (i.v.), subcutaneous (s.c.) | Orally (p.o.), intravenous (i.v.), subcutaneous (s.c.) |
Figure 1Targeted therapies in the vascular system of SCLC. [97,98,99]. (a) Antiangiogenesis: There is an autocrine stimulation of SCLC by the secretion of ‘basic fibroblast growth factor’ (bFGF) and ‘vascular endothelial growth factor’ (VEGF). The latter activates the receptors FGFR and VEGFR and thus induces angiogenesis. Binding of bFGF and VEGF by monoclonal antibodies (mAb) interferes with the signaling cascade. Its receptors may be influenced by monoclonal antibodies or small molecule inhibitors (SMI) such as erdafitinib or TAS-120 for FGFR and apatinib or chiauranib for VEGFR. (b) Vascular infarction: The truncated tissue factor-NGR fusion protein binds CD13 on tumor endothelial cells and induces the extrinsic coagulation cascade by activating factor X. (c) Vascular disruption: bavituximab binds the phosphatidylserine of tumor vasculature and induces cellular inflammation.
Figure 2Curaxins’ mechanism of action [145]. (A) Without curaxin interaction, Tp53 is degraded by mouse double minute 2 homolog (Mdm2) and the NFκB gene region is transcripted and translated. (B) By interaction with curaxin, FACT binds to the DNA double strand and inhibits the transcription of the NFκB gene region. Additionally, casein kinase 2 (CK2) phosphorylates Tp53 and therefore, reduces its degradation.
Figure 3Targeting surface markers and TROP-2 and DLL3 mechanisms of action in SCLC [179,180,181]. Two SCLC cells interact. In SCLC, Notch1 inhibits proliferation and survival (1.). Upon DLL3-binding to Notch (2.), Notch is cleaved and the ‘Notch intracellular domain’ (NICD) detaches and activates target genes. Moreover, the extracellular Notch compartment is internalized into the DLL3-carrying cell. TROP2 acts via calcium-mediated Cyclin D and E dependent cell cycle control, as well as by activating the ‘mitogen-activated protein kinase’ (MAPK), ‘extracellular signal–regulated kinases’ (ERK), and ‘activator protein 1’ (AP-1) transcription factor signaling cascade.
Figure 4Pro-apoptotic and anti-apoptotic pathways in cancerogenesis. Both extracellular and intracellular pathways lead to caspase activation and thus induce cell death. Distinct factors such as pp32/PHAPI sensitize cells to apoptotic stimuli.
(Part I). Clinical trials evaluating targeted therapies in SCLC.
| Reference | Study | Target | Drug | Phase | Setting | Treatment Arms | eval. n | ORR | OS | PFS/TTP/FFS | Result | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [ | Tiseo et al., 2017 | VEGF | Bevacizumab | III | ES-SCLC 1st | Cis/Eto + Placebo | 103 | 8.9 m | PFS 5.7 m | ||||
| Cis/Eto + Bevacizumab | 101 | 9.8 m | PFS 6.7 m | - | |||||||||
| [ | Han et al., 2016 | VEGF-R, FGFR, PDGF-R | Nintedanib | II | R/R SCLC | Nintedanib mono | 22 | 5% | 9.8 m | PFS 1.0 m | - | ||
| [ | Ready et al., 2015 | PDGF-R, VEGF-R, RET, c-KIT, FLT3 | Sunitinib | II | ES-SCLC 1st | Platin/Eto4-6, Placebo maint.Platin/Eto4-6, Sunitinib maint. | 4649 | 6.9 m | PFS 2.1 m | +/- | |||
| [ | Sanborn et al., 2017 | VEGF-R, EGFR, RET | Vandetanib | II | ES-SCLC 1st | Platin/Eto + Placebo | 33 | 65.4% | 9.23 m | TTP 5.62 m | |||
| Platin/Eto + Vandetanib | 33 | 50.0% | 13.24 m | TTP 5.68 m | - | ||||||||
| [ | Abdelraouf et al., 2016 | PDGF-R, VEGF-R, | Sunitinib | II | ES-SCLC 1st/ | Sunitinib mono | 9 | 11.0% | |||||
| RET, c-KIT, FLT3 | relapsed SCLC | - | |||||||||||
| [ | Pujol et al., 2007 | VEGF-R, FGFR | Thalidomide | III | ES-SCLC 1stadd on | PDCE2; Placebo + PDCE4 | 43 | 84% | 8.7 m | PFS 6.4 m | |||
| PDCE2; Thalidomide + PDCE4 | 49 | 87% | 11.7 m | PFS 6.6 m | - | ||||||||
| [ | Lee et al., 2009 | VEGF-R, FGFR | Thalidomide | III | LS/ES-SCLC 1st | Carbo/Eto + Placebo | 359 | 10.5 m | PFS 7.6 m | ||||
| Carbo/Eto + Thalidomide | 365 | 10.1 m | PFS 7.6 m | -- | |||||||||
| [ | Ellis et al., 2013 | VEGF-R, FGFR | Pomalidomide | I | ES-SCLC | Cis/Eto + Pomalidomide | 22 | 31.8% | 49.6 w | PFS 12.4 w | - | ||
| [ | Daniel et al., 2017 | Notch2, Notch3 | Tarextumab | Ib/ II | ES-SCLC 1st | Platin/Eto + Placebo | 1371:1 | 10.3 m | PFS 5.5 m | ||||
| Platin/Eto + Tarextumab | 9.3 m | PFS 5.5 m | - | ||||||||||
| [ | Melichar et al., 2015 | AURK-A | Alisertib | II | R/R SCLC | Alisertib mono | 48 | 21% | PFS 2.1 m | + | |||
| [ | Owonikoko et al., 2017 | AURK-A | Alisertib | II | R/R SCLC | Paclitaxel + Placebo | 89 | 18% | 165 d | PFS 66 d | |||
| Paclitaxel + Alisertib | 89 | 22% | 186 d | PFS 101 d | +/- | ||||||||
| [ | Pietanza et al., 2016 | SMO | Sonidegib | I | ES-SCLC 1st | Cis/Eto + Sonidegib | 15 | 79% | 19.7 m | PFS 5.5 m | - | ||
| [ | Belani et al., 2016 | SMO | Vismodegib | Cis/Eto + Placebo | 53 | 48% | 8.8 m | PFS 4.4 m | |||||
| II | ES-SCLC 1st | Cis/Eto + Vismodegib | 53 | 56% | 9.8 m | PFS 4.4 m | - | ||||||
| Cis/Eto + Cixutumumab | 52 | 50% | 10.1 m | PFS 4.6 m | - | ||||||||
| [ | Tarhini et al., 2010 | mTOR | Everolimus | II | R/R SCLC | Everolimus mono | 35 | 3% | 6.7 m | TTP 1.3 m | - |
(Part II). Clinical trials evaluating targeted therapies in SCLC.
| Reference | Study | Target | Drug | Phase | Setting | Treatment Arms | eval. n | ORR | OS | PFS/TTP/FFS | Result | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [ | Sun et al., 2013 | mTOR | Everolimus | Ib | R/R SCLC | Paclitaxel + Everolimus | 18 | 28% | |||||
| [ | Cis/ Eto + d Everol. - GCSF | 40.0% | |||||||||||
| Besse et al., 2014 | mTOR | Everolimus | Ib | ES-SCLC 1st | Cis/ Eto + w Everol. - GCSF | 40 | 61.1% | − | |||||
| Cis/ Eto + d Everol. + GCSF | 58.3% | PFS 35.1 w | +/− | ||||||||||
| [ | Chiappori et al., 2016 | IGF-R1 | Linsitinib | II | R/R SCLC | Topotecan mono | 15 | 6.7% | 5.3 m | PFS 3.0 m | p < 0.001 | ||
| Linsitinib mono | 29 | 0.0% | 3.4 m | PFS 1.2 m | − | ||||||||
| [ | Socinski et al., 2017 | CD56 | Lorvotuzumabmertansine | I/ II | ES-SCLC 1st | Carbo/ Eto | 47 | 59% | 10.1 m | PFS 6.7 m | p = n.s. | ||
| Carbo/ Eto + Lorvot. mertans. | 94 | 67% | 11.0 m | PFS 6.2 m | − | ||||||||
| [ | Rudin et al., 2017 | DLL3 | Rovalpitu-zumabtesirine | I | R/R SCLC | Rova-T mono overall | 60 | 18% | PFS 2.8 m | ||||
| Rova-T mono - DLL3+ | 26 | 38% | PFS 4.3 m | ||||||||||
| Rova-T mono - DLL3- | 8 | 0% | PFS 2.2 m | + | |||||||||
| [ | Gray et al., 2017 | TROP-2 | Sacituzumabgovitecan | II | R/R ES-SCLC | Sacituzumab govitecan mono | 50 | 14% | 7.5 m | PFS 3.7 m | + | ||
| [ | Gladkov et al., 2015 | EpCAM | Tucotuzumab | II | ES-SCLC 1stmaintenance | Platin based CTx + BSC | 44 | 14.1 m | PFS 1.4 m | p = n.s. | |||
| Platin based CTx + Cyc + Tuco. | 64 | 12.3 m | PFS 1.5 m | − | |||||||||
| [ | Rudin et al., 2008 | BCL-2 | Oblimersen | II | ES-SCLC 1st | Carbo/ Eto + Placebo | 15 | 60% | 10.6 m | FFS 7.6 m | p = 0.070 | ||
| Carbo/ Eto + Oblimersen | 41 | 61% | 8.6 m | FFS 6.0 m | − | ||||||||
| [ | Rudin et al., 2012 | BCL-2 | Navitoclax | IIa | R/R SCLC | Navitoclax mono | 39 | 2.6% | 3.2 m | PFS 1.5 m | − | ||
| [ | Atrafi et al., 2018 | PARP1 | Veliparib | I | ES-SCLC 1st | Carbo/Eto + Veliparib overall | 25 | 64% | PFS 5.3 m | ||||
| Carbo/Eto + Veliparib 240 mg | 6 | 83% | PFS 5.6 m | + | |||||||||
| [ | Owonikoko et al., 2018 | PARP1 | Veliparib | II | ES-SCLC 1st | Cis/Eto + Placebo | 64 | 65.6% | 8.9 m | PFS 5.5. m | p = 0.010 | ||
| Cis/Eto + Veliparib | 64 | 71.9% | 10.3 m | PFS 6.1 m | + | ||||||||
| [ | Pietanza et al., 2018 | PARP1 | Veliparib | II | R/R SCLC | Temozolomid + Placebo | 52 | 14% | 7.0 m | PFS 2.0 m | p = 0.390 | ||
| Temozolomid + Veliparib | 52 | 39% | 8.2 m | PFS 3.8 m | +/− | ||||||||
| Tem. + Placebo - SLFN11+ | 11 | 7.5 m | PFS 3.6 m | p = 0.009 | |||||||||
| Tem. + Veliparib - SLFN11+ | 12 | 12.2 m | PFS 5.7 m | + |